From 8ac838bbb0cde157e77d6d3fc6507e9213bec99a Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Thu, 9 Jan 2025 04:56:46 +0000 Subject: [PATCH] Updated datasets 2025-01-09 UTC --- aws_geo_datasets.json | 1485 ++++++++++++++++++----------------- aws_geo_datasets.tsv | 797 +++++++++---------- aws_open_datasets.json | 1228 ++++++++++++++++------------- aws_open_datasets.tsv | 206 ++--- gee_catalog.json | 210 ++--- gee_catalog.tsv | 198 ++--- nasa_cmr_catalog.json | 1686 +++++++++++++++++----------------------- nasa_cmr_catalog.tsv | 636 ++++++++------- 8 files changed, 3176 insertions(+), 3270 deletions(-) diff --git a/aws_geo_datasets.json b/aws_geo_datasets.json index d3f3ff4..fe80bba 100644 --- a/aws_geo_datasets.json +++ b/aws_geo_datasets.json @@ -19,10 +19,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nws-graphcastgfs-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -46,8 +46,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -77,10 +77,10 @@ "mapping", "planetary" ], - "RequesterPays": null, "Explore": [ "[STAC 1.0.0 endpoint](https://api.impactobservatory.com/stac-aws/collections/io-10m-annual-lulc/items)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -108,8 +108,8 @@ "optimization", "routing" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -131,8 +131,8 @@ "satellite imagery", "survey" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": false }, @@ -155,8 +155,8 @@ "cog", "labeled" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -181,8 +181,31 @@ "robotics", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, + "Host": null, + "AccountRequired": null + }, + { + "Name": "AI Weather Prediction (AIWP) Model Reforecasts", + "Description": "AIWP data", + "ARN": "arn:aws:s3:::noaa-oar-mlwp-data", + "Region": "us-east-1", + "Type": "S3 Bucket", + "Documentation": "https://noaa-oar-mlwp-data.s3.amazonaws.com/README.txt", + "Contact": "For questions regarding data availability, content, or quality, contact Dr. Jacob Radford (jacob.radford@noaa.gov). For any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov.
We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov", + "ManagedBy": "Dr. Jacob Radford (jacob.radford@noaa.gov)", + "UpdateFrequency": "2 times a day, every 12 hours starting at midnight UTC", + "License": "Open Data. There are no restrictions on the use of this data.", + "Tags": [ + "environmental", + "meteorological", + "weather" + ], + "Explore": [ + "[Browse Bucket](https://noaa-oar-mlwp-data.s3.amazonaws.com/index.html)" + ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -205,8 +228,8 @@ "model", "solar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -229,8 +252,8 @@ "model", "solar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -253,8 +276,8 @@ "model", "solar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -277,8 +300,8 @@ "model", "solar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -301,8 +324,8 @@ "model", "solar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -325,10 +348,10 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://asf-event-data.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -351,8 +374,8 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -377,8 +400,8 @@ "mining", "cog" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -403,8 +426,8 @@ "mining", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -427,8 +450,8 @@ "machine learning", "life sciences" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -450,8 +473,8 @@ "satellite imagery", "survey" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": false }, @@ -475,8 +498,8 @@ "satellite imagery", "survey" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": false }, @@ -503,11 +526,11 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)", "[stacindex](https://stacindex.org/catalogs/cbers)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -534,8 +557,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -562,8 +585,8 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -590,8 +613,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -618,10 +641,10 @@ "stac", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": [ "[STAC V1.0.0 endpoint](https://scottyhq.github.io/sentinel1-rtc-stac/#/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -648,8 +671,8 @@ "stac", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -675,10 +698,10 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": [ "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics.json)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -704,10 +727,10 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": [ "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/strips.json)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -730,8 +753,8 @@ "robotics", "geospatial" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -753,8 +776,8 @@ "satellite imagery", "NASA SMD AI" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -780,10 +803,10 @@ "model", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://mf-nwp-models.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -813,8 +836,8 @@ "machine learning", "deep learning" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -835,10 +858,10 @@ "environmental", "satellite imagery" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://s3-us-west-2.amazonaws.com/blended-tropomi-gosat-methane/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -867,8 +890,8 @@ "aws-pds", "zarr" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -894,11 +917,11 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)", "[stacindex](https://stacindex.org/catalogs/cbers)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -924,8 +947,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -951,8 +974,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -978,8 +1001,8 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -1005,8 +1028,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1029,10 +1052,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://2016v3platform.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1055,10 +1078,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://2018v2platform.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1081,10 +1104,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://2019platform.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1107,10 +1130,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://2020platform.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1133,10 +1156,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://cmas-amet.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1159,10 +1182,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://cmas-cmaq-modeling-platform-2018.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1185,10 +1208,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://cmas-cmaq.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1211,10 +1234,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://cmas-cmaq-conus2-benchmark.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1237,10 +1260,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://cmaq-release-benchmark-data-for-easy-download.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1263,10 +1286,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://cmas-wwlln-lightning.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1289,10 +1312,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://cmas-equates.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1315,10 +1338,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://mpas-cmaq.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1341,10 +1364,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://cmas-smoke-modeling-platform-2016.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1367,10 +1390,10 @@ "environmental", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://cmas-smoke-testcase.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1397,10 +1420,10 @@ "simulations", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://wrf-cmip6-noversioning.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1425,10 +1448,10 @@ "agriculture", "hydrology" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://s3-us-west-2.amazonaws.com/sissa-forecast-database/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1454,11 +1477,11 @@ "computer vision", "synthetic aperture radar" ], - "RequesterPays": false, "Explore": [ "[STAC Catalog](https://capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)", "[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -1484,8 +1507,8 @@ "computer vision", "synthetic aperture radar" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -1508,8 +1531,8 @@ "satellite imagery", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1532,8 +1555,8 @@ "satellite imagery", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1564,10 +1587,10 @@ "satellite imagery", "zarr" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://chalmerscloudiceclimatology.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1592,8 +1615,8 @@ "lidar", "IMU" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1619,8 +1642,8 @@ "mapping", "life sciences" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1647,8 +1670,8 @@ "cog", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1676,11 +1699,11 @@ "weather", "zarr" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://cadcat.s3.amazonaws.com/index.html)", "[Data Catalog](https://cadcat.s3.amazonaws.com/cae.yaml)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1705,8 +1728,8 @@ "mapping", "survey" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1734,8 +1757,8 @@ "sustainability", "zarr" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1762,10 +1785,10 @@ "aws-pds", "sustainability" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://ncar-cesm2-arise.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1793,8 +1816,8 @@ "sustainability", "zarr" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1819,10 +1842,10 @@ "disaster response", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC V1.0.0 endpoint](https://copernicus-dem-30m-stac.s3.amazonaws.com/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1847,10 +1870,10 @@ "disaster response", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC V1.0.0 endpoint](https://copernicus-dem-90m-stac.s3.amazonaws.com/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1877,11 +1900,11 @@ "simulations", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://esgf-world.s3.amazonaws.com/index.html)", "[Data Catalog](https://cmip6-nc.s3.amazonaws.com/esgf-world.csv.gz)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1908,11 +1931,11 @@ "simulations", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://cmip6-pds.s3.amazonaws.com/index.html#CMIP6/)", "[Data Catalog](https://cmip6-pds.s3.amazonaws.com/pangeo-cmip6.csv)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1939,10 +1962,10 @@ "water", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nws-uwpd-cmip5-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1962,10 +1985,10 @@ "earth observation", "oceans" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-dcdb-bathymetry-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -1985,8 +2008,8 @@ "earth observation", "oceans" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2009,10 +2032,10 @@ "meteorological", "water" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FAtlantic%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2035,10 +2058,10 @@ "meteorological", "water" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FHawaii%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2061,10 +2084,10 @@ "meteorological", "water" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FWest_Coast%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2087,10 +2110,10 @@ "meteorological", "water" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FAtlantic%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2113,10 +2136,10 @@ "meteorological", "water" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FWest_Coast%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2139,10 +2162,10 @@ "meteorological", "water" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2165,10 +2188,10 @@ "meteorological", "water" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2Fvirtual_buoy%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2191,10 +2214,10 @@ "meteorological", "water" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2217,10 +2240,10 @@ "meteorological", "water" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.1%2FAtlantic%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2242,8 +2265,8 @@ "disaster response", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2265,8 +2288,8 @@ "disaster response", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2288,8 +2311,8 @@ "disaster response", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2311,8 +2334,8 @@ "disaster response", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2334,8 +2357,8 @@ "disaster response", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2357,8 +2380,8 @@ "disaster response", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2380,8 +2403,8 @@ "geospatial", "earth observation" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2406,10 +2429,10 @@ "industrial", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dsgrid-2018-efs%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2434,10 +2457,10 @@ "industrial", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-dsgrid%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2462,10 +2485,10 @@ "industrial", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-dsgrid&prefix=tempo%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2489,10 +2512,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=building_synthetic_dataset%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2516,10 +2539,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=butter%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2543,10 +2566,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=buildings-bench)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2570,10 +2593,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2597,10 +2620,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dsgrid-2018-efs%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2624,10 +2647,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dgen%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2651,10 +2674,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=nrel-pds-building-stock%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2678,10 +2701,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=tracking-the-sun%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2705,10 +2728,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=ATB%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2732,10 +2755,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pv-rooftop%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2759,10 +2782,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pv-rooftop-pr%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2786,10 +2809,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=PR100%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2813,10 +2836,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pvdaq%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2840,10 +2863,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=SMART-DS%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2867,10 +2890,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=umcm%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2894,10 +2917,10 @@ "solar", "lidar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=NSO%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2925,10 +2948,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/alos_palsar_mosaic)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -2956,8 +2979,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -2985,8 +3008,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3015,8 +3038,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3045,10 +3068,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/rainfall_chirps_daily)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3077,10 +3100,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/rainfall_chirps_monthly)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3109,8 +3132,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3135,8 +3158,8 @@ "sustainability", "deafrica" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3161,8 +3184,8 @@ "sustainability", "deafrica" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3187,8 +3210,8 @@ "sustainability", "deafrica" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3215,8 +3238,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3243,10 +3266,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/crop_mask)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3273,8 +3296,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3302,8 +3325,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3331,10 +3354,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/fc_ls_summary_annual)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3362,10 +3385,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/fc_ls)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3393,8 +3416,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3422,8 +3445,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3450,8 +3473,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3478,10 +3501,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_ls5_ls7_annual)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3508,10 +3531,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_ls8_annual)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3538,8 +3561,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3566,10 +3589,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_s2_annual)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3596,10 +3619,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_s2_semiannual)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3627,8 +3650,8 @@ "cog", "land cover" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3656,10 +3679,10 @@ "cog", "land cover" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gmw)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3687,8 +3710,8 @@ "cog", "land cover" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3715,8 +3738,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3743,10 +3766,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3773,8 +3796,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3801,8 +3824,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3829,8 +3852,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3857,10 +3880,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/ndvi_anomaly)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -3887,8 +3910,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3915,8 +3938,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3943,8 +3966,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3971,8 +3994,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -3999,10 +4022,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/ndvi_climatology_ls)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -4029,8 +4052,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4058,8 +4081,8 @@ "cog", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4087,8 +4110,8 @@ "cog", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4116,8 +4139,8 @@ "cog", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4145,10 +4168,10 @@ "cog", "synthetic aperture radar" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s1_rtc)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -4175,8 +4198,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4203,8 +4226,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4231,8 +4254,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4259,10 +4282,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s2_l2a)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -4290,8 +4313,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4319,8 +4342,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4348,8 +4371,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4377,10 +4400,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls_summary_alltime)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -4408,10 +4431,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls_summary_annual)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -4439,10 +4462,10 @@ "stac", "cog" ], - "RequesterPays": false, "Explore": [ "[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -4464,10 +4487,10 @@ "us-dc", "disaster response" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://dc-lidar-2018.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4489,10 +4512,10 @@ "us-dc", "disaster response" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://dc-lidar-2015.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4518,10 +4541,10 @@ "aws-pds", "sustainability" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](http://wrf-ak-ar5.s3-website-us-east-1.amazonaws.com/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4547,8 +4570,8 @@ "precipitation", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4606,10 +4629,10 @@ "hydrology", "water" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://epa-edde.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4667,8 +4690,8 @@ "hydrology", "water" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4687,10 +4710,10 @@ "aws-pds", "environmental" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://epa-rsei-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4714,8 +4737,8 @@ "meteorological", "model" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4747,10 +4770,10 @@ "machine learning", "stac" ], - "RequesterPays": false, "Explore": [ "[STAC endpoint](https://services.terrascope.be/stac/)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -4782,11 +4805,11 @@ "machine learning", "stac" ], - "RequesterPays": false, "Explore": [ "[Products Grid](https://esa-worldcover.s3.eu-central-1.amazonaws.com/esa_worldcover_grid_composites.fgb)", "[WorldCover Viewer](https://viewer.esa-worldcover.org/worldcover/?layer=WORLDCOVER_2021_S1_VVVHratio)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -4818,11 +4841,11 @@ "machine learning", "stac" ], - "RequesterPays": false, "Explore": [ "[Products Grid](https://esa-worldcover.s3.eu-central-1.amazonaws.com/esa_worldcover_grid_composites.fgb)", "[WorldCover Viewer](https://viewer.esa-worldcover.org/worldcover/?layer=WORLDCOVER_2021_S2_NDVI)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -4846,8 +4869,8 @@ "geospatial", "zarr" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4873,10 +4896,10 @@ "stac", "aws-pds" ], - "RequesterPays": false, "Explore": [ "[BDC STAC V0.9.0 endpoint](https://bdc-cbers.s3.us-west-2.amazonaws.com/catalog.json)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -4902,10 +4925,10 @@ "stac", "aws-pds" ], - "RequesterPays": false, "Explore": [ "[BDC STAC V0.9.0 endpoint](https://bdc-sentinel-2.s3.us-west-2.amazonaws.com/catalog.json)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -4931,8 +4954,8 @@ "stac", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4958,8 +4981,8 @@ "stac", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -4983,10 +5006,10 @@ "signal processing", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://gnss-ro-data.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5012,10 +5035,10 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": [ "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/earthdem/strips.json)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5043,10 +5066,10 @@ "sustainability", "utilities" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=nrel-pds-building-stock%2Fend-use-load-profiles-for-us-building-stock%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5069,10 +5092,10 @@ "precipitation", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://emearth.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5094,8 +5117,8 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5117,8 +5140,8 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5140,10 +5163,10 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://fmi-opendata-radar-geotiff.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5165,10 +5188,10 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://fmi-opendata-radar-volume-hdf5.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5194,8 +5217,8 @@ "weather", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5221,10 +5244,10 @@ "atmosphere", "model" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://geos-chem.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5250,10 +5273,10 @@ "atmosphere", "model" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://s3.amazonaws.com/gcgrid/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5274,8 +5297,8 @@ "geospatial", "demographics" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5301,11 +5324,11 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": [ "[STAC V1.0.0 endpoint](https://stac.asf.alaska.edu/collections/glo-30-hand)", "[Via STAC Browser](https://radiantearth.github.io/stac-browser/#/external/stac.asf.alaska.edu/collections/glo-30-hand)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5331,8 +5354,8 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5355,8 +5378,8 @@ "conservation", "life sciences" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5379,10 +5402,10 @@ "conservation", "life sciences" ], - "RequesterPays": null, "Explore": [ "[Browse bucket](https://gbif-open-data-af-south-1.s3.af-south-1.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5405,8 +5428,8 @@ "conservation", "life sciences" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5429,10 +5452,10 @@ "conservation", "life sciences" ], - "RequesterPays": null, "Explore": [ "[Browse bucket](https://gbif-open-data-ap-southeast-2.s3.ap-southeast-2.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5455,8 +5478,8 @@ "conservation", "life sciences" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5479,10 +5502,10 @@ "conservation", "life sciences" ], - "RequesterPays": null, "Explore": [ "[Browse bucket](https://gbif-open-data-eu-central-1.s3.eu-central-1.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5505,10 +5528,10 @@ "conservation", "life sciences" ], - "RequesterPays": null, "Explore": [ "[Browse bucket](https://gbif-open-data-sa-east-1.s3.sa-east-1.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5531,8 +5554,8 @@ "conservation", "life sciences" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5555,8 +5578,8 @@ "conservation", "life sciences" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5579,10 +5602,10 @@ "conservation", "life sciences" ], - "RequesterPays": null, "Explore": [ "[Browse bucket](https://gbif-open-data-us-east-1.s3.us-east-1.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5602,8 +5625,8 @@ "events", "disaster response" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5623,8 +5646,8 @@ "events", "disaster response" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5658,8 +5681,8 @@ "synthetic aperture radar", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5693,8 +5716,8 @@ "synthetic aperture radar", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5716,10 +5739,10 @@ "sustainability", "model" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gadal&prefix=gadal_ieee123%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5743,12 +5766,12 @@ "environmental", "aws-pds" ], - "RequesterPays": null, "Explore": [ "[Explore dataset](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647/page/Data-Explorer/)", "[README](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647/page/Project-Information/)", "[Data processing notebook](https://github.com/waterinstitute/avian_data_ingestor/blob/master/doc/Metadata%20for%20DottedImages.ipynb)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5771,8 +5794,8 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5795,8 +5818,8 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5819,10 +5842,10 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://fmi-opendata-rcrhirlam-pressure-grib.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5845,10 +5868,10 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://fmi-opendata-rcrhirlam-surface-grib.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5876,8 +5899,8 @@ "image processing", "geospatial" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5905,8 +5928,8 @@ "image processing", "geospatial" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5934,8 +5957,8 @@ "image processing", "geospatial" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5963,8 +5986,8 @@ "image processing", "geospatial" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -5992,8 +6015,8 @@ "image processing", "geospatial" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6019,10 +6042,10 @@ "aws-pds", "sustainability" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](http://wrf-se-ak-ar5.s3-website-us-west-2.amazonaws.com/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6048,8 +6071,8 @@ "disaster response", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6075,8 +6098,8 @@ "disaster response", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6101,8 +6124,8 @@ "cog", "labeled" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6124,8 +6147,8 @@ "geospatial", "lidar" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -6148,8 +6171,8 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6170,8 +6193,8 @@ "meteorological", "environmental" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6193,8 +6216,8 @@ "satellite imagery", "environmental" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6221,8 +6244,8 @@ "sustainability", "agriculture" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6248,8 +6271,8 @@ "sustainability", "agriculture" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6277,10 +6300,10 @@ "zarr", "stac" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://its-live-data.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6308,8 +6331,8 @@ "zarr", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6331,10 +6354,10 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_monoscopic_uncontrolled_observations)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6356,10 +6379,10 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_spsupport_uncontrolled_observations)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6381,10 +6404,10 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_stereoscopic_uncontrolled_observations)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6409,10 +6432,10 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-himawari8.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6437,10 +6460,10 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-himawari9.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6465,8 +6488,8 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6491,8 +6514,8 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6517,10 +6540,10 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-gk2a-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6545,8 +6568,8 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6569,10 +6592,10 @@ "lidar", "elevation" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -6595,10 +6618,10 @@ "lidar", "elevation" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -6621,10 +6644,10 @@ "lidar", "elevation" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -6647,10 +6670,10 @@ "lidar", "elevation" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/obliques/)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -6673,10 +6696,10 @@ "lidar", "elevation" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/orthos/)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -6699,10 +6722,10 @@ "lidar", "elevation" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/PointCloud/)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -6725,10 +6748,10 @@ "lidar", "elevation" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/DEM/)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -6751,10 +6774,10 @@ "lidar", "elevation" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/SpotElevations/)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -6777,10 +6800,10 @@ "lidar", "elevation" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/KyTopoMapSeries/)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -6803,10 +6826,10 @@ "lidar", "elevation" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/Contours/)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -6842,8 +6865,8 @@ "urban", "water" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6874,8 +6897,8 @@ "logistics", "robotics" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6906,8 +6929,8 @@ "logistics", "robotics" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6938,8 +6961,8 @@ "logistics", "robotics" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -6962,8 +6985,8 @@ "natural resource", "disaster response" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -6986,8 +7009,8 @@ "natural resource", "disaster response" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7010,8 +7033,8 @@ "natural resource", "disaster response" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -7034,8 +7057,8 @@ "natural resource", "disaster response" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -7058,8 +7081,8 @@ "natural resource", "disaster response" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -7082,8 +7105,8 @@ "natural resource", "disaster response" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -7106,8 +7129,8 @@ "natural resource", "disaster response" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -7128,8 +7151,8 @@ "transportation", "graph" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7168,10 +7191,10 @@ "post-processing", "x-ray crystallography" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://materialsproject-build.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7210,10 +7233,10 @@ "post-processing", "x-ray crystallography" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://materialsproject-parsed.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7252,8 +7275,8 @@ "post-processing", "x-ray crystallography" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7277,11 +7300,11 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": [ "[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/maxar-opendata.s3.dualstack.us-west-2.amazonaws.com/events/catalog.json)", "[STAC Catalog](https://stacindex.org/catalogs/maxar-open-data-catalog-ard-format#/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7309,10 +7332,10 @@ "netcdf", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse bucket](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7340,8 +7363,8 @@ "netcdf", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7371,10 +7394,10 @@ "netcdf", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse bucket](https://met-office-global-ensemble-model-data.s3.eu-west-2.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7404,8 +7427,8 @@ "netcdf", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7433,10 +7456,10 @@ "netcdf", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse bucket](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7464,8 +7487,8 @@ "netcdf", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7493,10 +7516,10 @@ "sustainability", "CMIP6" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://met-office-ukesm1-arise.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7522,8 +7545,8 @@ "water", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7545,8 +7568,8 @@ "infrastructure", "schools" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7572,8 +7595,8 @@ "sustainability", "zarr" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7598,8 +7621,8 @@ "regulatory", "cog" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -7624,8 +7647,8 @@ "regulatory", "cog" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -7650,8 +7673,8 @@ "regulatory", "cog" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -7673,10 +7696,10 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_dtms?.language=en)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7698,10 +7721,10 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/mo_themis_controlled_mosaics)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7723,10 +7746,10 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_mosaics)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7748,10 +7771,10 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_observations)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7774,10 +7797,10 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/collections/mro_ctx_controlled_usgs_dtms)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7799,10 +7822,10 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/mro_hirise_socet_dtms)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7824,10 +7847,10 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/collections/mro_hirise_uncontrolled_observations)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7860,10 +7883,10 @@ "NASA Center for Climate Simulation (NCCS)", "cog" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://nex-gddp-cmip6.s3.us-west-2.amazonaws.com/index.html)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -7896,10 +7919,10 @@ "NASA Center for Climate Simulation (NCCS)", "cog" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://nex-gddp-cmip6-cog.s3.us-west-2.amazonaws.com/index.html)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -7923,8 +7946,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7948,8 +7971,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7973,8 +7996,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -7998,8 +8021,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8023,8 +8046,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8048,8 +8071,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8073,8 +8096,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8098,8 +8121,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8123,8 +8146,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8148,8 +8171,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8173,8 +8196,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8198,8 +8221,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8223,8 +8246,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8248,8 +8271,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8273,8 +8296,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8298,8 +8321,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8323,8 +8346,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8348,8 +8371,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8373,8 +8396,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8398,8 +8421,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8423,8 +8446,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8448,8 +8471,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8473,8 +8496,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8498,8 +8521,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8523,8 +8546,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8548,8 +8571,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8573,8 +8596,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8598,8 +8621,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8623,8 +8646,8 @@ "satellite imagery", "x-ray" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8647,8 +8670,8 @@ "imaging", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8671,8 +8694,8 @@ "imaging", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8695,8 +8718,8 @@ "imaging", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8719,8 +8742,8 @@ "imaging", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8773,10 +8796,10 @@ "zarr", "aws-pds" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://power-datastore.s3.us-west-2.amazonaws.com/index.html)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -8829,10 +8852,10 @@ "zarr", "aws-pds" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](https://nasa-power.s3.us-west-2.amazonaws.com/index.html)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -8853,8 +8876,8 @@ "transportation", "life sciences" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8876,8 +8899,8 @@ "satellite imagery", "survey" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": false }, @@ -8901,8 +8924,8 @@ "satellite imagery", "survey" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": false }, @@ -8925,10 +8948,10 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nexrad-level2.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8951,8 +8974,8 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -8975,10 +8998,10 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://unidata-nexrad-level3.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9001,8 +9024,8 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9025,8 +9048,8 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9049,8 +9072,8 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9074,10 +9097,10 @@ "oceans", "environmental" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-oar-hourly-gdp-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9101,8 +9124,8 @@ "oceans", "environmental" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9129,10 +9152,10 @@ "transportation", "oceans" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nos-scuba-icesat2-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9159,8 +9182,8 @@ "transportation", "oceans" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9189,10 +9212,10 @@ "sustainability", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nos-stofs3d-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9221,8 +9244,8 @@ "sustainability", "climate" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9247,10 +9270,10 @@ "agriculture", "transportation" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nws-aorc-v1-1-1km.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9273,10 +9296,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-aerosol-optical-thickness-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9299,10 +9322,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-precip-cmorph-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9325,10 +9348,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-cloud-properties-isccp-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9351,10 +9374,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-cloud-properties-polar-orbiter-nasa-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9377,10 +9400,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-precip-gpcp-daily-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9403,10 +9426,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-precip-gpcp-monthly-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9429,10 +9452,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-hydrological-properties-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9455,10 +9478,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-precip-nexrad-qpe-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9481,10 +9504,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-ocean-heat-content-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9507,10 +9530,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-ocean-heatflux-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9533,10 +9556,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-ocean-nearsurface-atmos-profiles-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9559,10 +9582,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-outgoing-longwave-radiation-daily-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9585,10 +9608,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-outgoing-longwave-radiation-monthly-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9611,10 +9634,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-ozone-esrl-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9637,10 +9660,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-precip-persiann-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9663,10 +9686,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-solar-spectral-irradiance-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9689,10 +9712,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-total-solar-irradiance-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9714,10 +9737,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cfs-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9739,8 +9762,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9766,10 +9789,10 @@ "water", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nodd-kerchunk-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9795,8 +9818,8 @@ "water", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9820,10 +9843,10 @@ "lidar", "stac" ], - "RequesterPays": null, "Explore": [ "[STAC V1.0.0 endpoint](https://noaa-nos-coastal-lidar-pds.s3.us-east-1.amazonaws.com/entwine/stac/catalog.json)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9847,8 +9870,8 @@ "lidar", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9877,10 +9900,10 @@ "RINEX", "survey" ], - "RequesterPays": null, "Explore": [ "[Browse NOAA-NCN Bucket](https://noaa-cors-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9903,10 +9926,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-gridsat-b1-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9929,10 +9952,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-msu-brit-temp-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9955,10 +9978,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-mean-layer-temp-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -9981,10 +10004,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-mean-layer-temp-rss-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10007,10 +10030,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-mean-layer-temp-uah-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10033,10 +10056,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-mean-layer-temp-lower-strat-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10059,10 +10082,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-mean-layer-temp-upper-trop-lower-strat-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10085,10 +10108,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-microwave-brit-temp-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10111,10 +10134,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-microwave-humidity-sounder-brit-temp-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10137,10 +10160,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-microwave-imager-brit-temp-csu-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10163,10 +10186,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-microwave-imager-brit-temp-rss-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10189,10 +10212,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-microwave-temp-sounder-brit-temp-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10215,10 +10238,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-radiances-and-cloud-properties-polar-orbiter-nasa-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10241,10 +10264,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-polar-pathfinder-fcdr-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10267,10 +10290,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-polar-pathfinder-extended-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10293,10 +10316,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-ir-water-vapor-brit-temp-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10321,10 +10344,10 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-goes16.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10349,10 +10372,10 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-goes17.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10377,10 +10400,10 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-goes18.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10405,8 +10428,8 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10431,8 +10454,8 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10457,8 +10480,8 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10482,10 +10505,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ufs-gdas-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10509,8 +10532,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10532,8 +10555,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10555,10 +10578,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-gefs-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10580,10 +10603,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-gefs-retrospective.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10607,10 +10630,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-gfs-warmstart-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10634,10 +10657,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-gfs-bdp-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10661,8 +10684,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10686,8 +10709,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10709,10 +10732,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ghcn-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10734,8 +10757,8 @@ "water", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10757,10 +10780,10 @@ "water", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ghe-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10782,10 +10805,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-gmgsi-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10807,8 +10830,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10835,10 +10858,10 @@ "oceans", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nws-rtofs-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10865,8 +10888,8 @@ "oceans", "climate" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10890,8 +10913,8 @@ "natural resource", "regulatory" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10918,10 +10941,10 @@ "oceans", "climate" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-gestofs-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10948,8 +10971,8 @@ "oceans", "climate" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10972,10 +10995,10 @@ "environmental", "disaster response" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-hrrr-bdp-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -10998,10 +11021,10 @@ "environmental", "disaster response" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://hrrrzarr.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11024,8 +11047,8 @@ "environmental", "disaster response" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11048,10 +11071,10 @@ "geospatial", "survey" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nos-historicalcharts-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11073,10 +11096,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nws-hafs-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11098,8 +11121,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11121,10 +11144,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-global-hourly-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11146,10 +11169,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-isd-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11171,10 +11194,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-jpss.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11196,10 +11219,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nesdis-n20-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11221,10 +11244,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nesdis-n21-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11246,10 +11269,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nesdis-snpp-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11271,15 +11294,15 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, { "Name": "NOAA Joint Polar Satellite System (JPSS) - New data notifications for JPSS data, only Lambda and SQS protocols allowed", "Description": "New data notifications for JPSS data, only Lambda and SQS protocols allowed", - "ARN": "arn:aws:sns:us-east-1:709902155096:NewSNPPObject", + "ARN": "arn:aws:sns:us-east-1:709902155096:NewNOAA21Object", "Region": "us-east-1", "Type": "SNS Topic", "Documentation": "https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS", @@ -11294,15 +11317,15 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, { "Name": "NOAA Joint Polar Satellite System (JPSS) - New data notifications for JPSS data, only Lambda and SQS protocols allowed", "Description": "New data notifications for JPSS data, only Lambda and SQS protocols allowed", - "ARN": "arn:aws:sns:us-east-1:709902155096:NewNOAA20Object", + "ARN": "arn:aws:sns:us-east-1:709902155096:NewSNPPObject", "Region": "us-east-1", "Type": "SNS Topic", "Documentation": "https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS", @@ -11317,15 +11340,15 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, { "Name": "NOAA Joint Polar Satellite System (JPSS) - New data notifications for JPSS data, only Lambda and SQS protocols allowed", "Description": "New data notifications for JPSS data, only Lambda and SQS protocols allowed", - "ARN": "arn:aws:sns:us-east-1:709902155096:NewNOAA21Object", + "ARN": "arn:aws:sns:us-east-1:709902155096:NewNOAA20Object", "Region": "us-east-1", "Type": "SNS Topic", "Documentation": "https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS", @@ -11340,8 +11363,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11363,10 +11386,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-mrms-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11388,8 +11411,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11413,10 +11436,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-oar-myrorss-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11438,10 +11461,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-reanalyses-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11463,8 +11486,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11488,10 +11511,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nws-naqfc-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11515,10 +11538,10 @@ "marine navigation", "oceans" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ocs-nationalbathymetry-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11542,8 +11565,8 @@ "marine navigation", "oceans" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11566,10 +11589,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nbm-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11592,10 +11615,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nbm-grib2-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11618,8 +11641,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11642,8 +11665,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11665,10 +11688,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ndfd-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11690,8 +11713,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11716,10 +11739,10 @@ "agriculture", "transportation" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://nwm-archive.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11744,10 +11767,10 @@ "agriculture", "transportation" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nwm-retro-v2-0-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11772,10 +11795,10 @@ "agriculture", "transportation" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nwm-retrospective-2-1-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11800,10 +11823,10 @@ "agriculture", "transportation" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nwm-retrospective-3-0-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11828,10 +11851,10 @@ "agriculture", "transportation" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nwm-retrospective-2-1-zarr-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11856,10 +11879,10 @@ "agriculture", "transportation" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nwm-retro-v2-zarr-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11884,10 +11907,10 @@ "agriculture", "transportation" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nwm-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11912,10 +11935,10 @@ "agriculture", "transportation" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nodd-kerchunk-pds.s3.amazonaws.com/index.html#nwm/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11940,8 +11963,8 @@ "agriculture", "transportation" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11963,8 +11986,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -11986,10 +12009,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nam-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12013,10 +12036,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-sea-ice-concentration-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12040,10 +12063,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-sea-surface-temp-optimum-interpolation-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12067,10 +12090,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-sea-surface-temp-whoi-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12094,10 +12117,10 @@ "marine navigation", "oceans" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ocs-hydrodata-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12121,8 +12144,8 @@ "marine navigation", "oceans" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12148,10 +12171,10 @@ "water", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nos-ofs-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12177,10 +12200,10 @@ "water", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ofs-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12206,8 +12229,8 @@ "water", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12233,8 +12256,8 @@ "water", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12256,8 +12279,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12279,10 +12302,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-rap-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12304,8 +12327,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12327,10 +12350,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-rrfs-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12352,8 +12375,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12375,8 +12398,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12398,10 +12421,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-rtma-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12423,10 +12446,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-urma-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12448,10 +12471,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-swdi-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12473,8 +12496,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12496,10 +12519,10 @@ "solar", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-swpc-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12521,8 +12544,8 @@ "solar", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12545,10 +12568,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-leaf-area-index-fapar-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12571,10 +12594,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-ndvi-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12597,10 +12620,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-cdr-snow-cover-ext-north-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12622,10 +12645,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nclimgrid-daily-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12647,17 +12670,17 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nclimgrid-monthly-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, { "Name": "NOAA U.S. Climate Gridded Dataset (NClimGrid) - New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowed", "Description": "New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowed", - "ARN": "arn:aws:sns:us-east-1:123901341784:NewNClimGridMonthlyObject", + "ARN": "arn:aws:sns:us-east-1:123901341784:NewNClimGridDailyObject", "Region": "us-east-1", "Type": "SNS Topic", "Documentation": "https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332", @@ -12672,15 +12695,15 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, { "Name": "NOAA U.S. Climate Gridded Dataset (NClimGrid) - New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowed", "Description": "New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowed", - "ARN": "arn:aws:sns:us-east-1:123901341784:NewNClimGridDailyObject", + "ARN": "arn:aws:sns:us-east-1:123901341784:NewNClimGridMonthlyObject", "Region": "us-east-1", "Type": "SNS Topic", "Documentation": "https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332", @@ -12695,8 +12718,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12719,10 +12742,10 @@ "sustainability", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-normals-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12744,8 +12767,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12767,10 +12790,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ufs-gefsv13replay-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12795,10 +12818,10 @@ "weather", "oceans" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ufs-htf-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12823,8 +12846,8 @@ "weather", "oceans" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12846,8 +12869,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12869,10 +12892,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ufs-land-da-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12894,10 +12917,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ufs-rnrmarine-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12919,8 +12942,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12942,10 +12965,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ufs-srw-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12970,8 +12993,8 @@ "weather", "oceans" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -12996,10 +13019,10 @@ "weather", "oceans" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ufs-prototypes-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13021,8 +13044,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13044,10 +13067,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-ufs-regtests-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13069,10 +13092,10 @@ "solar", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-wsa-enlil-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13094,8 +13117,8 @@ "solar", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13120,10 +13143,10 @@ "mapping", "oceans" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-wcsd-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13145,10 +13168,10 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nws-gefswaves-reforecast-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13170,8 +13193,8 @@ "meteorological", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13193,10 +13216,10 @@ "solar", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nws-wam-ipe-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13218,8 +13241,8 @@ "solar", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13245,10 +13268,10 @@ "transportation", "oceans" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nos-cora-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13274,8 +13297,8 @@ "transportation", "oceans" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13297,8 +13320,8 @@ "oceans", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13320,10 +13343,10 @@ "oceans", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-oar-keo-papa-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13346,10 +13369,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fnsrdb%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13372,10 +13395,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=mts1%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13398,10 +13421,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=mts2%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13424,10 +13447,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13450,10 +13473,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=full_disc%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13476,10 +13499,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=conus%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13502,10 +13525,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=himawari%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13528,10 +13551,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=meteosat%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13554,10 +13577,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=india%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13580,10 +13603,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Fpuerto_rico%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13606,10 +13629,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13632,10 +13655,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftdy%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13658,10 +13681,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftgy%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13684,10 +13707,10 @@ "meteorological", "solar" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftmy%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13708,10 +13731,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=bangladesh%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13732,10 +13755,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=bchrrr%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13756,10 +13779,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=indonesia%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13780,10 +13803,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=now23_california%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13804,10 +13827,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=central_asia%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13828,10 +13851,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=eastern_wind%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13852,10 +13875,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=western_wind%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13876,10 +13899,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Great_Lakes%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13900,10 +13923,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=gulf_of_mexico%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13924,10 +13947,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fwtk%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13948,10 +13971,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fwtk-us.h5%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13972,10 +13995,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Hawaii%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -13996,10 +14019,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=india%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -14020,10 +14043,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=kazakhstan%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -14044,10 +14067,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=maine%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -14068,10 +14091,10 @@ "geospatial", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Mid_Atlantic%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -14092,10 +14115,10 @@ "geospatial", "meteorological" ], - 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"RequesterPays": null, "Explore": [ "[Browse Bucket](https://oin-hotosm.s3.amazonaws.com/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -15106,10 +15129,10 @@ "earthquakes", "aws-pds" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://grillo-openeew.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -15131,8 +15154,8 @@ "osm", "disaster response" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -15154,8 +15177,8 @@ "osm", "disaster response" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -15180,8 +15203,8 @@ "simulations", "survey" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": false }, @@ -15206,8 +15229,8 @@ "simulations", "survey" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": false }, @@ -15239,8 +15262,8 @@ "open source software", "signal processing" ], - 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"RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASV%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16202,10 +16225,10 @@ "image processing", "geospatial" ], - "RequesterPays": null, "Explore": [ "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASV%2F)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16230,8 +16253,8 @@ "mapping", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16256,8 +16279,8 @@ "mapping", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16285,8 +16308,8 @@ "energy modeling", "utilities" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16314,8 +16337,8 @@ "metagenomics", "life sciences" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16342,8 +16365,8 @@ "cog", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16366,10 +16389,10 @@ "environmental", "disaster response" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://rapid-nrt-flood-maps.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16395,10 +16418,10 @@ "synthetic aperture radar", "stac" ], - "RequesterPays": null, "Explore": [ "[EODMS STAC for RCM CEOS ARD](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/collections/rcm-ard/items/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16424,8 +16447,8 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16450,8 +16473,8 @@ "aws-pds", "labeled" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16477,10 +16500,10 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": [ "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/mosaics.json)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16506,10 +16529,10 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": [ "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/strips.json)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16536,8 +16559,8 @@ "vcf", "variant annotation" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16560,8 +16583,8 @@ "air quality", "meteorological" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16584,8 +16607,8 @@ "air quality", "meteorological" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16608,10 +16631,10 @@ "air quality", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://fmi-opendata-silam-surface-netcdf.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16634,10 +16657,10 @@ "air quality", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16664,8 +16687,8 @@ "water", "weather" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16687,10 +16710,10 @@ "weather", "meteorological" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://smn-ar-wrf.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16710,8 +16733,8 @@ "environmental", "air quality" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16729,8 +16752,8 @@ "Tags": [ "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16753,10 +16776,10 @@ "geospatial", "radiation" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://safecast-opendata-public-us-east-1.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16779,8 +16802,8 @@ "geospatial", "radiation" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16802,10 +16825,10 @@ "air quality", "health" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://s3.us-west-2.amazonaws.com/v6.pm25.global/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16824,8 +16847,8 @@ "oceans", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16850,8 +16873,8 @@ "cog", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16875,8 +16898,8 @@ "global", "geospatial" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16906,8 +16929,8 @@ "mapping", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16933,10 +16956,10 @@ "synthetic aperture radar", "stac" ], - "RequesterPays": null, "Explore": [ "[EODMS STAC for Sentinel products](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -16961,10 +16984,10 @@ "cog", "synthetic aperture radar" ], - "RequesterPays": true, "Explore": [ "[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)" ], + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -16989,8 +17012,8 @@ "cog", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17015,8 +17038,8 @@ "cog", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17041,8 +17064,8 @@ "sentinel-1", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17067,10 +17090,10 @@ "sentinel-1", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": [ "[AWS S3 Explorer](https://s1-orbits.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17095,8 +17118,8 @@ "environmental", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17120,8 +17143,8 @@ "environmental", "synthetic aperture radar" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17146,13 +17169,13 @@ "disaster response", "stac" ], - "RequesterPays": true, "Explore": [ "[Earth Search STAC L1C Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)", "[Earth Search STAC Browser L1C Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)", "[STAC V1.0.0 endpoint](https://sentinel-s2-l1c-stac.s3.amazonaws.com/)", "[Earth Viewer by Element 84](https://viewer.aws.element84.com/)" ], + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -17177,10 +17200,10 @@ "disaster response", "stac" ], - "RequesterPays": true, "Explore": [ "[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)" ], + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -17205,8 +17228,8 @@ "disaster response", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17231,8 +17254,8 @@ "disaster response", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17257,8 +17280,8 @@ "disaster response", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17283,8 +17306,8 @@ "disaster response", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17309,8 +17332,8 @@ "disaster response", "stac" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -17335,8 +17358,58 @@ "disaster response", "stac" ], + "Explore": null, "RequesterPays": true, + "Host": null, + "AccountRequired": null + }, + { + "Name": "Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States - New scene notification", + "Description": "New scene notification", + "ARN": "arn:aws:sns:us-west-2:242201296900:usgs-wma-sentinel-2-aqr-acolite-dsf-object_created", + "Region": "us-west-2", + "Type": "SNS Topic", + "Documentation": "https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed", + "Contact": "tvking@usgs.gov", + "ManagedBy": "[United States Geological Survey](https://www.usgs.gov)", + "UpdateFrequency": "New scenes are added daily.", + "License": "Contains modified Copernicus Sentinel data, which is available under the Creative Commons CC BY-SA 3.0 IGO license. Please reference King et al., 2024 (doi 10.5066/P904243C) when referring to the aquatic reflectance, and include the statement 'Contains modified Copernicus Sentinel data [Year]' to acknowledge the data originator.", + "Tags": [ + "aws-pds", + "earth observation", + "satellite imagery", + "geospatial", + "natural resource", + "cog", + "water" + ], "Explore": null, + "RequesterPays": null, + "Host": null, + "AccountRequired": null + }, + { + "Name": "Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States - Scenes and metadata", + "Description": "Scenes and metadata", + "ARN": "arn:aws:s3:::usgs-wma-sentinel-2-aqr-acolite-dsf/version_01", + "Region": "us-west-2", + "Type": "S3 Bucket", + "Documentation": "https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed", + "Contact": "tvking@usgs.gov", + "ManagedBy": "[United States Geological Survey](https://www.usgs.gov)", + "UpdateFrequency": "New scenes are added daily.", + "License": "Contains modified Copernicus Sentinel data, which is available under the Creative Commons CC BY-SA 3.0 IGO license. Please reference King et al., 2024 (doi 10.5066/P904243C) when referring to the aquatic reflectance, and include the statement 'Contains modified Copernicus Sentinel data [Year]' to acknowledge the data originator.", + "Tags": [ + "aws-pds", + "earth observation", + "satellite imagery", + "geospatial", + "natural resource", + "cog", + "water" + ], + "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17362,11 +17435,11 @@ "cog", "stac" ], - "RequesterPays": false, "Explore": [ "[Earth Search STAC L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)", "[STAC Browser L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -17392,8 +17465,8 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17419,8 +17492,8 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17445,8 +17518,8 @@ "machine learning", "cog" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -17472,10 +17545,10 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": [ "[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17501,8 +17574,8 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17528,8 +17601,8 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17555,8 +17628,8 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17582,10 +17655,10 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": [ "[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17611,8 +17684,8 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17638,8 +17711,8 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17665,8 +17738,8 @@ "cog", "stac" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17691,10 +17764,10 @@ "environmental", "oceans" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://sofar-spotter-archive.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17716,11 +17789,11 @@ "weather", "GPS" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket by serial number](http://sondehub-history.s3-website-us-east-1.amazonaws.com/#serial/)", "[Browse Bucket by date/time](http://sondehub-history.s3-website-us-east-1.amazonaws.com/#date/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17746,8 +17819,8 @@ "survey", "geospatial" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17768,8 +17841,8 @@ "earthquakes", "seismology" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17793,8 +17866,8 @@ "disaster response", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17828,8 +17901,8 @@ "telecommunications", "tiles" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17851,8 +17924,8 @@ "satellite imagery", "survey" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": false }, @@ -17873,8 +17946,8 @@ "meteorological", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17901,8 +17974,8 @@ "conservation", "geospatial" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17926,10 +17999,10 @@ "traffic", "transportation" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://data.geo.admin.ch/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17949,8 +18022,8 @@ "geospatial", "satellite imagery" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17973,10 +18046,10 @@ "geospatial", "disaster response" ], - "RequesterPays": null, "Explore": [ "[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -17999,8 +18072,8 @@ "geospatial", "disaster response" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18025,8 +18098,8 @@ "epigenomics", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18056,10 +18129,10 @@ "satellite imagery", "weather" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://noaa-nesdis-tcprimed-pds.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18083,8 +18156,8 @@ "lidar", "stac" ], - "RequesterPays": true, "Explore": null, + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -18108,10 +18181,10 @@ "lidar", "stac" ], - "RequesterPays": null, "Explore": [ "[STAC Catalog](https://usgs-lidar-stac.s3-us-west-2.amazonaws.com/ept/catalog.json)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18137,8 +18210,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18164,8 +18237,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18191,8 +18264,8 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18218,10 +18291,10 @@ "stac", "cog" ], - "RequesterPays": true, "Explore": [ "[STAC Catalog](https://landsatlook.usgs.gov/stac-server/collections)" ], + "RequesterPays": true, "Host": null, "AccountRequired": null }, @@ -18245,11 +18318,11 @@ "image processing", "geospatial" ], - "RequesterPays": false, "Explore": [ "[Browse Bucket](http://umbra-open-data-catalog.s3-website.us-west-2.amazonaws.com/)", "[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/s3.us-west-2.amazonaws.com/umbra-open-data-catalog/stac/catalog.json)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -18279,8 +18352,8 @@ "environmental", "land cover" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18310,10 +18383,10 @@ "environmental", "land cover" ], - "RequesterPays": false, "Explore": [ "[STAC Browser Venus L2A (COG) Catalog](https://radiantearth.github.io/stac-browser/#/external/venus-l2a-cogs.s3.us-east-1.amazonaws.com/catalog.json)" ], + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -18336,8 +18409,8 @@ "elevation", "land cover" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -18360,8 +18433,8 @@ "elevation", "land cover" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -18384,8 +18457,8 @@ "elevation", "land cover" ], - "RequesterPays": false, "Explore": null, + "RequesterPays": false, "Host": null, "AccountRequired": null }, @@ -18411,8 +18484,8 @@ "lidar", "mapping" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18440,10 +18513,10 @@ "oceans", "weather" ], - "RequesterPays": null, "Explore": [ "[S3 Bucket](https://wis2-global-cache.s3.amazonaws.com/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18469,10 +18542,10 @@ "zarr", "turbulence" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://whiffle-wins50-data.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18495,10 +18568,10 @@ "stac", "cog" ], - "RequesterPays": null, "Explore": [ "[STAC 1.0.0-beta.2 endpoint](https://stacindex.org/catalogs/world-bank-light-every-night#/)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18522,8 +18595,8 @@ "CMIP6", "netcdf" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18546,8 +18619,8 @@ "conservation", "life sciences" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18574,10 +18647,10 @@ "machine learning", "satellite imagery" ], - "RequesterPays": null, "Explore": [ "[Browse data using the STAC viewer](https://isdasoil.s3.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18600,8 +18673,8 @@ "transportation", "urban" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": "https://d1qinkmu0ju04f.cloudfront.net", "AccountRequired": null }, @@ -18624,10 +18697,10 @@ "transportation", "urban" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://motional-nuplan.s3.ap-northeast-1.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18651,8 +18724,8 @@ "transportation", "urban" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": "https://d36yt3mvayqw5m.cloudfront.net", "AccountRequired": null }, @@ -18676,10 +18749,10 @@ "transportation", "urban" ], - "RequesterPays": null, "Explore": [ "[Browse Bucket](https://motional-nuscenes.s3.ap-northeast-1.amazonaws.com/index.html)" ], + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18701,8 +18774,8 @@ "disaster response", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null }, @@ -18724,8 +18797,8 @@ "disaster response", "aws-pds" ], - "RequesterPays": null, "Explore": null, + "RequesterPays": null, "Host": null, "AccountRequired": null } diff --git a/aws_geo_datasets.tsv b/aws_geo_datasets.tsv index 711b3c7..9b4e2cf 100644 --- a/aws_geo_datasets.tsv +++ b/aws_geo_datasets.tsv @@ -1,89 +1,90 @@ -Name Description ARN Region Type Documentation Contact ManagedBy UpdateFrequency License Tags RequesterPays Explore Host AccountRequired -(EXPERIMENTAL) NOAA GraphCast Global Forecast System (GFS) (EXPERIMENTAL) - GraphCast GFS data GraphCast GFS data arn:aws:s3:::noaa-nws-graphcastgfs-pds us-east-1 S3 Bucket For the NOAA Product, https://graphcastgfs.readthedocs.io/en/latest/index.html a For questions regarding data content or quality, visit [the NOAA EMC Github site [NOAA](http://www.noaa.gov/) 4 times a day at 00Z, 06Z, 12Z and 18Z NOAA's GraphCast GFS products are released under CC0 license. The products in th aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather ['[Browse Bucket](https://noaa-nws-graphcastgfs-pds.s3.amazonaws.com/index.html)'] +Name Description ARN Region Type Documentation Contact ManagedBy UpdateFrequency License Tags Explore RequesterPays Host AccountRequired +(EXPERIMENTAL) NOAA GraphCast Global Forecast System (GFS) (EXPERIMENTAL) - GraphCast GFS data GraphCast GFS data arn:aws:s3:::noaa-nws-graphcastgfs-pds us-east-1 S3 Bucket For the NOAA Product, https://graphcastgfs.readthedocs.io/en/latest/index.html a For questions regarding data content or quality, visit [the NOAA EMC Github site [NOAA](http://www.noaa.gov/) 4 times a day at 00Z, 06Z, 12Z and 18Z NOAA's GraphCast GFS products are released under CC0 license. The products in th aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather ['[Browse Bucket](https://noaa-nws-graphcastgfs-pds.s3.amazonaws.com/index.html)'] (EXPERIMENTAL) NOAA GraphCast Global Forecast System (GFS) (EXPERIMENTAL) - New data notifications for GraphCast GFS, only Lambda and SQS protocols allowed New data notifications for GraphCast GFS, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:709902155096:NewNWSGRAPHCASTGFSObject us-east-1 SNS Topic For the NOAA Product, https://graphcastgfs.readthedocs.io/en/latest/index.html a For questions regarding data content or quality, visit [the NOAA EMC Github site [NOAA](http://www.noaa.gov/) 4 times a day at 00Z, 06Z, 12Z and 18Z NOAA's GraphCast GFS products are released under CC0 license. The products in th aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather -10m Annual Land Use Land Cover (9-class) 10m Annual Land Use Land Cover (9-class) arn:aws:s3:::io-10m-annual-lulc us-west-2 S3 Bucket https://www.impactobservatory.com/global_maps hello@impactobservatory.com [Impact Observatory](https://www.impactobservatory.com/) A new year is made available annually, each January. A new time series was provi [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, earth observation, environmental, geospatial, satellite imagery, sustainability, stac, cog, land cover, land use, machine learning, mapping, planetary ['[STAC 1.0.0 endpoint](https://api.impactobservatory.com/stac-aws/collections/io-10m-annual-lulc/items)'] +10m Annual Land Use Land Cover (9-class) 10m Annual Land Use Land Cover (9-class) arn:aws:s3:::io-10m-annual-lulc us-west-2 S3 Bucket https://www.impactobservatory.com/global_maps hello@impactobservatory.com [Impact Observatory](https://www.impactobservatory.com/) A new year is made available annually, each January. A new time series was provi [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, earth observation, environmental, geospatial, satellite imagery, sustainability, stac, cog, land cover, land use, machine learning, mapping, planetary ['[STAC 1.0.0 endpoint](https://api.impactobservatory.com/stac-aws/collections/io-10m-annual-lulc/items)'] 2021 Amazon Last Mile Routing Research Challenge Dataset Dataset including training and testing data Folder almrrc2021_data_training inc arn:aws:s3:::amazon-last-mile-challenges us-west-2 S3 Bucket https://github.com/MIT-CAVE/rc-cli/blob/main/templates/data_structures.md lastmile-research-challenge@amazon.com [Amazon](https://www.amazon.com/) None Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. The material transportation, machine learning, deep learning, amazon.science, urban, analytics, geospatial, logistics, last mile, optimization, routing -3-Band Cryo Data | Wide-field Infrared Survey Explorer (WISE) "3-Band Cryo Single-exposure Image Sets: 392,879 calibrated 1024x1024 pix @275""/" arn:aws:s3:::nasa-irsa-wise/wise/cryo-3band us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The WISE 3-Band Cryo Data Release has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, satellite imagery, survey False False +3-Band Cryo Data | Wide-field Infrared Survey Explorer (WISE) "3-Band Cryo Single-exposure Image Sets: 392,879 calibrated 1024x1024 pix @275""/" arn:aws:s3:::nasa-irsa-wise/wise/cryo-3band us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The WISE 3-Band Cryo Data Release has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, satellite imagery, survey False False A region-wide, multi-year set of crop field boundary labels for Africa Field boundary labels and corresponding Planet images arn:aws:s3:::africa-field-boundary-labels us-west-2 S3 Bucket https://github.com/agroimpacts/lacunalabels/ airg@clarku.edu [The Agricultural Impacts Research Group](https://agroimpacts.info/) Updated versions of the dataset are added as they are developed [Planet NICFI participant license agreement](https://assets.planet.com/docs/Plan agriculture, machine learning, land cover, satellite imagery, cog, labeled A2D2: Audi Autonomous Driving Dataset http://a2d2audi arn:aws:s3:::aev-autonomous-driving-dataset eu-central-1 S3 Bucket http://a2d2.audi aevdrivingdataset@audi.de [Audi AG](http://a2d2.audi/) The dataset may be updated with additional or corrected data on a need-to-update https://creativecommons.org/licenses/by-nd/4.0/ autonomous vehicles, deep learning, computer vision, lidar, mapping, machine learning, robotics, aws-pds +AI Weather Prediction (AIWP) Model Reforecasts AIWP data arn:aws:s3:::noaa-oar-mlwp-data us-east-1 S3 Bucket https://noaa-oar-mlwp-data.s3.amazonaws.com/README.txt For questions regarding data availability, content, or quality, contact Dr. Jaco Dr. Jacob Radford (jacob.radford@noaa.gov) 2 times a day, every 12 hours starting at midnight UTC Open Data. There are no restrictions on the use of this data. environmental, meteorological, weather ['[Browse Bucket](https://noaa-oar-mlwp-data.s3.amazonaws.com/index.html)'] ARPA-E PERFORM Forecast data - ARPA-E PERFORM Forecast data ARPA-E PERFORM Forecast data arn:aws:s3:::arpa-e-perform/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar ARPA-E PERFORM Forecast data - Forecasts and Actuals for The Electric Reliability Council of Texas (ERCOT) Forecasts and Actuals for The Electric Reliability Council of Texas (ERCOT) arn:aws:s3:::arpa-e-perform/ERCOT/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar ARPA-E PERFORM Forecast data - Forecasts and Actuals for The Midcontinent Independent System Operator (MISO) Forecasts and Actuals for The Midcontinent Independent System Operator (MISO) arn:aws:s3:::arpa-e-perform/MISO/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar ARPA-E PERFORM Forecast data - Forecasts and Actuals for The New York Independent System Operator (NYISO) Forecasts and Actuals for The New York Independent System Operator (NYISO) arn:aws:s3:::arpa-e-perform/NYISO/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar ARPA-E PERFORM Forecast data - Forecasts and Actuals for The Southwest Power Pool (SPP) Forecasts and Actuals for The Southwest Power Pool (SPP) arn:aws:s3:::arpa-e-perform/SPP/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar ASF SAR Data Products for Disaster Events - ASF Event data S3 bucket ASF Event data S3 bucket arn:aws:s3:::asf-event-data us-west-2 S3 Bucket https://asf-event-data.s3.us-west-2.amazonaws.com/README.md https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) "Irregular, in response to disaster events -" This data falls under the terms and conditions of the [Creative Commons Zero (CC aws-pds, disaster response, satellite imagery, geospatial, cog, stac ['[Browse Bucket](https://asf-event-data.s3.amazonaws.com/index.html)'] +" This data falls under the terms and conditions of the [Creative Commons Zero (CC aws-pds, disaster response, satellite imagery, geospatial, cog, stac ['[Browse Bucket](https://asf-event-data.s3.amazonaws.com/index.html)'] ASF SAR Data Products for Disaster Events - Notifications for new event data Notifications for new event data arn:aws:sns:us-west-2:654654592981:asf-event-data-object_created us-west-2 SNS Topic https://asf-event-data.s3.us-west-2.amazonaws.com/README.md https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) "Irregular, in response to disaster events " This data falls under the terms and conditions of the [Creative Commons Zero (CC aws-pds, disaster response, satellite imagery, geospatial, cog, stac -ASTER L1T Cloud-Optimized GeoTIFFs - Imagery and metadata Imagery and metadata arn:aws:s3:::aster-l1t us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t opendata@descarteslabs.com [Descartes Labs](https://descarteslabs.com/) Daily There are no restrictions on the use of data, unless expressly identified prior aws-pds, earth observation, satellite imagery, geospatial, natural resource, sustainability, mining, cog False +ASTER L1T Cloud-Optimized GeoTIFFs - Imagery and metadata Imagery and metadata arn:aws:s3:::aster-l1t us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t opendata@descarteslabs.com [Descartes Labs](https://descarteslabs.com/) Daily There are no restrictions on the use of data, unless expressly identified prior aws-pds, earth observation, satellite imagery, geospatial, natural resource, sustainability, mining, cog False ASTER L1T Cloud-Optimized GeoTIFFs - New image notifications New image notifications arn:aws:sns:us-west-2:526859492376:aster-l1t-object_created us-west-2 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t opendata@descarteslabs.com [Descartes Labs](https://descarteslabs.com/) Daily There are no restrictions on the use of data, unless expressly identified prior aws-pds, earth observation, satellite imagery, geospatial, natural resource, sustainability, mining, cog Africa Soil Information Service (AfSIS) Soil Chemistry Paired wet and dry chemistry measurements for georeferenced soilscollected by t arn:aws:s3:::afsis us-east-1 S3 Bucket https://github.com/qedsoftware/afsis-soil-chem-tutorial afsis@qed.ai [QED](https://qed.ai/) As required "ODC Open Database License (""[ODbL](https://opendatacommons.org/licenses/odbl/sum" agriculture, aws-pds, environmental, food security, machine learning, life sciences -All-Sky Data | Wide-field Infrared Survey Explorer (WISE) "All-Sky Single-exposure Image Sets: 1,491,686 calibrated 1024x1024 pix @275""/pi" arn:aws:s3:::nasa-irsa-wise/wise/allsky us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The All-Sky Data Release has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, satellite imagery, survey False False -AllWISE Data | Wide-field Infrared Survey Explorer (WISE) The AllWISE Images Atlas includes 18,240 4-band (34, 46, 12, 22 microns) calib arn:aws:s3:::nasa-irsa-wise/wise/allwise us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The AllWISE Data Release has been finalized and will not be updated. However, th https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, survey False False -Amazonia EO satellite on AWS - Amazonia 1 imagery (COG files, quicklooks, metadata) Amazonia 1 imagery (COG files, quicklooks, metadata) arn:aws:s3:::brazil-eosats us-west-2 S3 Bucket http://www.inpe.br/amazonia1 https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, sustainability, disaster response, stac, cog False ['[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)', '[stacindex](https://stacindex.org/catalogs/cbers)'] +All-Sky Data | Wide-field Infrared Survey Explorer (WISE) "All-Sky Single-exposure Image Sets: 1,491,686 calibrated 1024x1024 pix @275""/pi" arn:aws:s3:::nasa-irsa-wise/wise/allsky us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The All-Sky Data Release has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, satellite imagery, survey False False +AllWISE Data | Wide-field Infrared Survey Explorer (WISE) The AllWISE Images Atlas includes 18,240 4-band (34, 46, 12, 22 microns) calib arn:aws:s3:::nasa-irsa-wise/wise/allwise us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The AllWISE Data Release has been finalized and will not be updated. However, th https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, survey False False +Amazonia EO satellite on AWS - Amazonia 1 imagery (COG files, quicklooks, metadata) Amazonia 1 imagery (COG files, quicklooks, metadata) arn:aws:s3:::brazil-eosats us-west-2 S3 Bucket http://www.inpe.br/amazonia1 https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, sustainability, disaster response, stac, cog ['[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)', '[stacindex](https://stacindex.org/catalogs/cbers)'] False Amazonia EO satellite on AWS - Notifications for new quicklooks Notifications for new quicklooks arn:aws:sns:us-west-2:599544552497:NewAM1Quicklook us-west-2 SNS Topic http://www.inpe.br/amazonia1 https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, sustainability, disaster response, stac, cog -Amazonia EO satellite on AWS - STAC static catalog STAC static catalog arn:aws:s3:::br-eo-stac-1-0-0 us-west-2 S3 Bucket http://www.inpe.br/amazonia1 https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, sustainability, disaster response, stac, cog False +Amazonia EO satellite on AWS - STAC static catalog STAC static catalog arn:aws:s3:::br-eo-stac-1-0-0 us-west-2 S3 Bucket http://www.inpe.br/amazonia1 https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, sustainability, disaster response, stac, cog False Amazonia EO satellite on AWS - Topic that receives STAC V100 items as new scenes are ingested Topic that receives STAC V100 items as new scenes are ingested arn:aws:sns:us-west-2:769537946825:br-eo-stac-prod-stacitemtopic4BCE3141-Z8he7LYjqXFe us-west-2 SNS Topic http://www.inpe.br/amazonia1 https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, sustainability, disaster response, stac, cog -Analysis Ready Sentinel-1 Backscatter Imagery - Sentinel-1 RTC tiled data and metadata in a S3 bucket Sentinel-1 RTC tiled data and metadata in a S3 bucket arn:aws:s3:::sentinel-s1-rtc-indigo us-west-2 S3 Bucket https://sentinel-s1-rtc-indigo-docs.s3-us-west-2.amazonaws.com/index.html For questions regarding data methodology or delivery, contact sentinel1@indigoag [Indigo Ag, Inc.](https://www.indigoag.com/) Data updates are paused while we repair the processing pipeline, but the target The use of these data fall under the terms and conditions of the [Indigo Atlas S agriculture, aws-pds, disaster response, earth observation, environmental, geospatial, satellite imagery, cog, stac, synthetic aperture radar ['[STAC V1.0.0 endpoint](https://scottyhq.github.io/sentinel1-rtc-stac/#/)'] +Analysis Ready Sentinel-1 Backscatter Imagery - Sentinel-1 RTC tiled data and metadata in a S3 bucket Sentinel-1 RTC tiled data and metadata in a S3 bucket arn:aws:s3:::sentinel-s1-rtc-indigo us-west-2 S3 Bucket https://sentinel-s1-rtc-indigo-docs.s3-us-west-2.amazonaws.com/index.html For questions regarding data methodology or delivery, contact sentinel1@indigoag [Indigo Ag, Inc.](https://www.indigoag.com/) Data updates are paused while we repair the processing pipeline, but the target The use of these data fall under the terms and conditions of the [Indigo Atlas S agriculture, aws-pds, disaster response, earth observation, environmental, geospatial, satellite imagery, cog, stac, synthetic aperture radar ['[STAC V1.0.0 endpoint](https://scottyhq.github.io/sentinel1-rtc-stac/#/)'] Analysis Ready Sentinel-1 Backscatter Imagery - Simple Notification Service (SNS) topic for notification of new tile uploads Simple Notification Service (SNS) topic for notification of new tile uploads arn:aws:sns:us-west-2:410373799403:sentinel-s1-rtc-indigo-object_created us-west-2 SNS Topic https://sentinel-s1-rtc-indigo-docs.s3-us-west-2.amazonaws.com/index.html For questions regarding data methodology or delivery, contact sentinel1@indigoag [Indigo Ag, Inc.](https://www.indigoag.com/) Data updates are paused while we repair the processing pipeline, but the target The use of these data fall under the terms and conditions of the [Indigo Atlas S agriculture, aws-pds, disaster response, earth observation, environmental, geospatial, satellite imagery, cog, stac, synthetic aperture radar -ArcticDEM - ArcticDEM DEM Mosaics ArcticDEM DEM Mosaics arn:aws:s3:::pgc-opendata-dems/arcticdem/mosaics/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/arcticdem/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics.json)'] -ArcticDEM - ArcticDEM DEM Strips ArcticDEM DEM Strips arn:aws:s3:::pgc-opendata-dems/arcticdem/strips/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/arcticdem/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/strips.json)'] +ArcticDEM - ArcticDEM DEM Mosaics ArcticDEM DEM Mosaics arn:aws:s3:::pgc-opendata-dems/arcticdem/mosaics/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/arcticdem/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics.json)'] +ArcticDEM - ArcticDEM DEM Strips ArcticDEM DEM Strips arn:aws:s3:::pgc-opendata-dems/arcticdem/strips/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/arcticdem/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/strips.json)'] Argoverse Argoverse arn:aws:s3:::argoverse us-east-1 S3 Bucket https://argoverse.github.io/user-guide/ https://github.com/argoverse/av2-api/issues [Argoverse](https://argoverse.org) Infrequently [CC-BY-NC-SA-4.0](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html) aws-pds, autonomous vehicles, computer vision, lidar, robotics, geospatial Astrophysics Division Galaxy Morphology Benchmark Dataset NASA APD Galaxy Morphology dataset arn:aws:s3:::nasa-apd-galaxymorph us-west-2 S3 Bucket https://github.com/erinleeryan/nasa_astro_aiml/tree/main/galaxymorphology Roopesh.Ojha@nasa.gov [NASA](https://osdr.nasa.gov/) No updates There are no restrictions on the use of this data. aws-pds, astronomy, machine learning, satellite imagery, NASA SMD AI -Atmospheric Models from Météo-France Atmospheric Models from Météo-France arn:aws:s3:::mf-nwp-models eu-west-1 S3 Bucket https://mf-models-on-aws.org contact@openmeteodata.com [OpenMeteoData](https://openmeteodata.com) Every 6 hours https://mf-models-on-aws.org/en/doc/license aws-pds, agriculture, climate, disaster response, earth observation, environmental, meteorological, model, weather ['[Browse Bucket](https://mf-nwp-models.s3.amazonaws.com/index.html)'] +Atmospheric Models from Météo-France Atmospheric Models from Météo-France arn:aws:s3:::mf-nwp-models eu-west-1 S3 Bucket https://mf-models-on-aws.org contact@openmeteodata.com [OpenMeteoData](https://openmeteodata.com) Every 6 hours https://mf-models-on-aws.org/en/doc/license aws-pds, agriculture, climate, disaster response, earth observation, environmental, meteorological, model, weather ['[Browse Bucket](https://mf-nwp-models.s3.amazonaws.com/index.html)'] Aurora Multi-Sensor Dataset Aurora Multi-Sensor Dataset arn:aws:s3:::pit30m us-east-1 S3 Bucket A third-party development kit authored by Andrei Bârsan of the University of Tor ams-dataset@aurora.tech Aurora Operations, Inc. This dataset is complete. This data is intended for non-commercial academic use only. It is licensed under aws-pds, autonomous vehicles, computer vision, lidar, mapping, robotics, transportation, urban, weather, traffic, image processing, machine learning, deep learning -Blended TROPOMI+GOSAT Satellite Data Product for Atmospheric Methane Blended TROPOMI+GOSAT netCDF files arn:aws:s3:::blended-tropomi-gosat-methane us-west-2 S3 Bucket https://github.com/nicholasbalasus/write_blended_files/blob/main/PUM.md nicholasbalasus@g.harvard.edu Nicholas Balasus Monthly There are no restrictions on the use of this data, but please contact nicholasba aws-pds, climate, environmental, satellite imagery ['[Browse Bucket](https://s3-us-west-2.amazonaws.com/blended-tropomi-gosat-methane/index.html)'] +Blended TROPOMI+GOSAT Satellite Data Product for Atmospheric Methane Blended TROPOMI+GOSAT netCDF files arn:aws:s3:::blended-tropomi-gosat-methane us-west-2 S3 Bucket https://github.com/nicholasbalasus/write_blended_files/blob/main/PUM.md nicholasbalasus@g.harvard.edu Nicholas Balasus Monthly There are no restrictions on the use of this data, but please contact nicholasba aws-pds, climate, environmental, satellite imagery ['[Browse Bucket](https://s3-us-west-2.amazonaws.com/blended-tropomi-gosat-methane/index.html)'] CAM6 Data Assimilation Research Testbed (DART) Reanalysis: Cloud-Optimized Dataset Project data files arn:aws:s3:::ncar-dart-cam6 us-west-2 S3 Bucket https://doi.org/10.26024/sprq-2d04 rdahelp@ucar.edu [National Center for Atmospheric Research](https://ncar.ucar.edu/) Rare. Additional variables or years outside of 2011-2019 may be added in the fu https://www.ucar.edu/terms-of-use/data atmosphere, land, climate, climate model, data assimilation, forecast, meteorological, weather, geoscience, geospatial, aws-pds, zarr -CBERS on AWS - CBERS imagery (COG files, quicklooks, metadata) CBERS imagery (COG files, quicklooks, metadata) arn:aws:s3:::brazil-eosats us-west-2 S3 Bucket https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog False ['[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)', '[stacindex](https://stacindex.org/catalogs/cbers)'] +CBERS on AWS - CBERS imagery (COG files, quicklooks, metadata) CBERS imagery (COG files, quicklooks, metadata) arn:aws:s3:::brazil-eosats us-west-2 S3 Bucket https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog ['[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)', '[stacindex](https://stacindex.org/catalogs/cbers)'] False CBERS on AWS - Notifications for new CBERS 4 quicklooks, all sensors Notifications for new CBERS 4 quicklooks, all sensors arn:aws:sns:us-west-2:599544552497:NewCB4Quicklook us-west-2 SNS Topic https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog CBERS on AWS - Notifications for new CBERS 4A quicklooks, all sensors Notifications for new CBERS 4A quicklooks, all sensors arn:aws:sns:us-west-2:599544552497:NewCB4AQuicklook us-west-2 SNS Topic https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog -CBERS on AWS - STAC static catalog STAC static catalog arn:aws:s3:::br-eo-stac-1-0-0 us-west-2 S3 Bucket https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog False +CBERS on AWS - STAC static catalog STAC static catalog arn:aws:s3:::br-eo-stac-1-0-0 us-west-2 S3 Bucket https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog False CBERS on AWS - Topic that receives STAC V100 items as new scenes are ingested Topic that receives STAC V100 items as new scenes are ingested arn:aws:sns:us-west-2:769537946825:br-eo-stac-prod-stacitemtopic4BCE3141-Z8he7LYjqXFe us-west-2 SNS Topic https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog -CMAS Data Warehouse - 2016v3 Modeling Platform 2016v3 Modeling Platform arn:aws:s3:::2016v3platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2016v3platform.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - 2018v2 Modeling Platform 2018v2 Modeling Platform arn:aws:s3:::2018v2platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2018v2platform.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - 2019 Modeling Platform 2019 Modeling Platform arn:aws:s3:::2019platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2019platform.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - 2020 Modeling Platform 2020 Modeling Platform arn:aws:s3:::2020platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2020platform.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - AMET Data AMET Data arn:aws:s3:::cmas-amet us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-amet.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - CMAQ 2018 Modeling Platform CMAQ 2018 Modeling Platform arn:aws:s3:::cmas-cmaq-modeling-platform-2018 us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq-modeling-platform-2018.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - CMAQ Benchmark Data CMAQ Benchmark Data arn:aws:s3:::cmas-cmaq us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - CMAQ CONUS-2 Benchmark Data CMAQ CONUS-2 Benchmark Data arn:aws:s3:::cmas-cmaq-conus2-benchmark us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq-conus2-benchmark.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - CMAQ Release Benchmark Data for Easy Download CMAQ Release Benchmark Data for Easy Download arn:aws:s3:::cmaq-release-benchmark-data-for-easy-download us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmaq-release-benchmark-data-for-easy-download.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - CMAS WWLLN Lightning Data CMAS WWLLN Lightning Data arn:aws:s3:::cmas-wwlln-lightning us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-wwlln-lightning.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - EQUATES EPA’s Air QUAlity TimE Series Project Data EQUATES EPA’s Air QUAlity TimE Series Project Data arn:aws:s3:::cmas-equates us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-equates.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - MPAS-CMAQ Input Data MPAS-CMAQ Input Data arn:aws:s3:::mpas-cmaq us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://mpas-cmaq.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - SMOKE 2016 Modeling Platform SMOKE 2016 Modeling Platform arn:aws:s3:::cmas-smoke-modeling-platform-2016 us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-smoke-modeling-platform-2016.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse - SMOKE Test Case SMOKE Test Case arn:aws:s3:::cmas-smoke-testcase us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-smoke-testcase.s3.amazonaws.com/index.html)'] -CMIP6 GCMs downscaled using WRF WRF output files arn:aws:s3:::wrf-cmip6-noversioning us-west-2 S3 Bucket https://dept.atmos.ucla.edu/alexhall/downscaling-cmip6 srahimi@uwyo.edu, leih@ucla.edu [UCLA Center for Climate Science](https://dept.atmos.ucla.edu/) New downscaled results are uploaded as soon as they become available Creative Commons Attribution 4.0 International License aws-pds, agriculture, atmosphere, climate, earth observation, environmental, model, oceans, simulations, weather ['[Browse Bucket](https://wrf-cmip6-noversioning.s3.amazonaws.com/index.html)'] -CRC-SAS/SISSA historical seasonal and subseasonal forecast database CRC-SAS/SISSA Retrospective Daily forecast database arn:aws:s3:::sissa-forecast-database us-west-2 S3 Bucket General information, tutorials and examples, contact us atl sissa-aws@smn.gob.ar For any questions regarding the data set or any general questions, you can conta [SISSA](https://sissa.crc-sas.org/) Static database from 2000-2019 without correction and 2010-2019 with correction. [Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org aws-pds, earth observation, natural resource, weather, forecast, meteorological, agriculture, hydrology ['[Browse Bucket](https://s3-us-west-2.amazonaws.com/sissa-forecast-database/index.html)'] -Capella Space Synthetic Aperture Radar (SAR) Open Dataset - Capella Space Open Data in COG format Capella Space Open Data in COG format arn:aws:s3:::capella-open-data/data/ us-west-2 S3 Bucket Documentation is available under [support.capellaspace.com](https://support.cape opendata@capellaspace.com [Capella Space](https://www.capellaspace.com/) New data is added quarterly. [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, cog, stac, earth observation, satellite imagery, geospatial, image processing, computer vision, synthetic aperture radar False ['[STAC Catalog](https://capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)', '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)'] -Capella Space Synthetic Aperture Radar (SAR) Open Dataset - Capella Space Open Data in TileDB format Capella Space Open Data in TileDB format arn:aws:s3:::capella-open-data/data/tiledb/ us-west-2 S3 Bucket Documentation is available under [support.capellaspace.com](https://support.cape opendata@capellaspace.com [Capella Space](https://www.capellaspace.com/) New data is added quarterly. [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, cog, stac, earth observation, satellite imagery, geospatial, image processing, computer vision, synthetic aperture radar False +CMAS Data Warehouse - 2016v3 Modeling Platform 2016v3 Modeling Platform arn:aws:s3:::2016v3platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2016v3platform.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - 2018v2 Modeling Platform 2018v2 Modeling Platform arn:aws:s3:::2018v2platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2018v2platform.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - 2019 Modeling Platform 2019 Modeling Platform arn:aws:s3:::2019platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2019platform.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - 2020 Modeling Platform 2020 Modeling Platform arn:aws:s3:::2020platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2020platform.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - AMET Data AMET Data arn:aws:s3:::cmas-amet us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-amet.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - CMAQ 2018 Modeling Platform CMAQ 2018 Modeling Platform arn:aws:s3:::cmas-cmaq-modeling-platform-2018 us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq-modeling-platform-2018.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - CMAQ Benchmark Data CMAQ Benchmark Data arn:aws:s3:::cmas-cmaq us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - CMAQ CONUS-2 Benchmark Data CMAQ CONUS-2 Benchmark Data arn:aws:s3:::cmas-cmaq-conus2-benchmark us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq-conus2-benchmark.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - CMAQ Release Benchmark Data for Easy Download CMAQ Release Benchmark Data for Easy Download arn:aws:s3:::cmaq-release-benchmark-data-for-easy-download us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmaq-release-benchmark-data-for-easy-download.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - CMAS WWLLN Lightning Data CMAS WWLLN Lightning Data arn:aws:s3:::cmas-wwlln-lightning us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-wwlln-lightning.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - EQUATES EPA’s Air QUAlity TimE Series Project Data EQUATES EPA’s Air QUAlity TimE Series Project Data arn:aws:s3:::cmas-equates us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-equates.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - MPAS-CMAQ Input Data MPAS-CMAQ Input Data arn:aws:s3:::mpas-cmaq us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://mpas-cmaq.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - SMOKE 2016 Modeling Platform SMOKE 2016 Modeling Platform arn:aws:s3:::cmas-smoke-modeling-platform-2016 us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-smoke-modeling-platform-2016.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse - SMOKE Test Case SMOKE Test Case arn:aws:s3:::cmas-smoke-testcase us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-smoke-testcase.s3.amazonaws.com/index.html)'] +CMIP6 GCMs downscaled using WRF WRF output files arn:aws:s3:::wrf-cmip6-noversioning us-west-2 S3 Bucket https://dept.atmos.ucla.edu/alexhall/downscaling-cmip6 srahimi@uwyo.edu, leih@ucla.edu [UCLA Center for Climate Science](https://dept.atmos.ucla.edu/) New downscaled results are uploaded as soon as they become available Creative Commons Attribution 4.0 International License aws-pds, agriculture, atmosphere, climate, earth observation, environmental, model, oceans, simulations, weather ['[Browse Bucket](https://wrf-cmip6-noversioning.s3.amazonaws.com/index.html)'] +CRC-SAS/SISSA historical seasonal and subseasonal forecast database CRC-SAS/SISSA Retrospective Daily forecast database arn:aws:s3:::sissa-forecast-database us-west-2 S3 Bucket General information, tutorials and examples, contact us atl sissa-aws@smn.gob.ar For any questions regarding the data set or any general questions, you can conta [SISSA](https://sissa.crc-sas.org/) Static database from 2000-2019 without correction and 2010-2019 with correction. [Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org aws-pds, earth observation, natural resource, weather, forecast, meteorological, agriculture, hydrology ['[Browse Bucket](https://s3-us-west-2.amazonaws.com/sissa-forecast-database/index.html)'] +Capella Space Synthetic Aperture Radar (SAR) Open Dataset - Capella Space Open Data in COG format Capella Space Open Data in COG format arn:aws:s3:::capella-open-data/data/ us-west-2 S3 Bucket Documentation is available under [support.capellaspace.com](https://support.cape opendata@capellaspace.com [Capella Space](https://www.capellaspace.com/) New data is added quarterly. [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, cog, stac, earth observation, satellite imagery, geospatial, image processing, computer vision, synthetic aperture radar ['[STAC Catalog](https://capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)', '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)'] False +Capella Space Synthetic Aperture Radar (SAR) Open Dataset - Capella Space Open Data in TileDB format Capella Space Open Data in TileDB format arn:aws:s3:::capella-open-data/data/tiledb/ us-west-2 S3 Bucket Documentation is available under [support.capellaspace.com](https://support.cape opendata@capellaspace.com [Capella Space](https://www.capellaspace.com/) New data is added quarterly. [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, cog, stac, earth observation, satellite imagery, geospatial, image processing, computer vision, synthetic aperture radar False Central Weather Administration OpenData CWA data lake arn:aws:s3:::cwaopendata ap-northeast-1 S3 Bucket https://opendata.cwa.gov.tw/devManual/insrtuction od@cwa.gov.tw [Central Weather Administration](https://www.cwa.gov.tw/) Data is updated as soon as newer one is available. http://data.gov.tw/license aws-pds, climate, earth observation, earthquakes, satellite imagery, weather Central Weather Bureau OpenData CWB data lake arn:aws:s3:::cwbopendata ap-northeast-1 S3 Bucket https://opendata.cwb.gov.tw/devManual/insrtuction od@cwb.gov.tw [Central Weather Bureau](https://www.cwb.gov.tw/) Data is updated as soon as newer one is available. http://data.gov.tw/license aws-pds, climate, earth observation, earthquakes, satellite imagery, weather -Chalmers Cloud Ice Climatology CCIC total ice water path and 2D cloud probability in Zarr format arn:aws:s3:::chalmerscloudiceclimatology us-west-2 S3 Bucket https://ccic.readthedocs.io https://github.com/see-geo/ccic [Geoscience and Remote Sensing at Chalmers University of Technology](https://www Quarterly [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) atmosphere, aws-pds, climate, deep learning, environmental, exploration, geophysics, geoscience, geospatial, global, ice, planetary, satellite imagery, zarr ['[Browse Bucket](https://chalmerscloudiceclimatology.s3.amazonaws.com/index.html)'] +Chalmers Cloud Ice Climatology CCIC total ice water path and 2D cloud probability in Zarr format arn:aws:s3:::chalmerscloudiceclimatology us-west-2 S3 Bucket https://ccic.readthedocs.io https://github.com/see-geo/ccic [Geoscience and Remote Sensing at Chalmers University of Technology](https://www Quarterly [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) atmosphere, aws-pds, climate, deep learning, environmental, exploration, geophysics, geoscience, geospatial, global, ice, planetary, satellite imagery, zarr ['[Browse Bucket](https://chalmerscloudiceclimatology.s3.amazonaws.com/index.html)'] CitrusFarm Dataset CitrusFarm Dataset sequences arn:aws:s3:::ucr-robotics/citrus-farm-dataset us-west-2 S3 Bucket https://ucr-robotics.github.io/Citrus-Farm-Dataset/ Hanzhe Teng (hteng007@ucr.edu), Konstantinos Karydis (kkarydis@ece.ucr.edu) [Autonomous Robots and Control Systems Lab](https://sites.google.com/view/arcs-l NA Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0). aws-pds, robotics, computer vision, agriculture, localization, mapping, lidar, IMU Cloud Indexes for Bowtie, Kraken, HISAT, and Centrifuge This bucket contains genomic indexes for Bowtie, Kraken, HISAT, and Centrifuge arn:aws:s3:::genome-idx us-east-1 S3 Bucket https://benlangmead.github.io/aws-indexes/ https://github.com/BenLangmead/aws-indexes/issues Langmead Lab at Johns Hopkins University & Kim Lab at University of Texas Southw As new data becomes available; roughly quarterly Public Domain aws-pds, genomic, bioinformatics, biology, whole genome sequencing, medicine, reference index, mapping, life sciences Cloud to Street - Microsoft Flood and Clouds Dataset Flood and Cloud Training Dataset arn:aws:s3:::radiant-mlhub/c2smsfloods us-west-2 S3 Bucket https://www.drivendata.org/competitions/81/detect-flood-water/ support@cloudtostreet.info [Radiant Earth Foundation](https://radiant.earth/) Not updated CC-BY-4.0 https://creativecommons.org/licenses/by/4.0/ aws-pds, computer vision, deep learning, machine learning, floods, geospatial, earth observation, satellite imagery, cog, synthetic aperture radar -Co-Produced Climate Data to Support California's Resilience Investments Data catalog arn:aws:s3:::cadcat us-west-2 S3 Bucket https://analytics.cal-adapt.org/data/ analytics@cal-adapt.org Cal-Adapt Analytics Engine https://analytics.cal-adapt.org/ Infrequent, Irregular Varies, see dataset specific metadata atmosphere, aws-pds, climate, climate model, earth observation, geoscience, geospatial, meteorological, simulations, weather, zarr ['[Browse Bucket](https://cadcat.s3.amazonaws.com/index.html)', '[Data Catalog](https://cadcat.s3.amazonaws.com/cae.yaml)'] +Co-Produced Climate Data to Support California's Resilience Investments Data catalog arn:aws:s3:::cadcat us-west-2 S3 Bucket https://analytics.cal-adapt.org/data/ analytics@cal-adapt.org Cal-Adapt Analytics Engine https://analytics.cal-adapt.org/ Infrequent, Irregular Varies, see dataset specific metadata atmosphere, aws-pds, climate, climate model, earth observation, geoscience, geospatial, meteorological, simulations, weather, zarr ['[Browse Bucket](https://cadcat.s3.amazonaws.com/index.html)', '[Data Catalog](https://cadcat.s3.amazonaws.com/cae.yaml)'] Collection of open nation-scale LiDAR datasets Open LiDAR datasets arn:aws:s3:::open-lidar-data eu-central-1 S3 Bucket https://github.com/flai-ai/open-lidar-data info@flai.ai [Flai](https://flai.ai/) When new open dataset is published. The exact version of the licence depends on LiDAR dataset and is not the same fo aws-pds, lidar, earth observation, geoscience, geospatial, land cover, mapping, survey Community Earth System Model Large Ensemble (CESM LENS) Project data files arn:aws:s3:::ncar-cesm-lens us-west-2 S3 Bucket https://doi.org/10.26024/wt24-5j82 rdahelp@ucar.edu [National Center for Atmospheric Research](https://ncar.ucar.edu/) Rare. The LENS experiment is complete, but we may occasionally copy additional f https://www.ucar.edu/terms-of-use/data climate, model, climate model, atmosphere, oceans, land, ice, geospatial, aws-pds, sustainability, zarr -Community Earth System Model v2 ARISE (CESM2 ARISE) Project data files arn:aws:s3:::ncar-cesm2-arise us-east-2 S3 Bucket (https://github.com/NCAR/CESM2-ARISE) opendata-aws-arise@ucar.edu [National Center for Atmospheric Research](https://ncar.ucar.edu/) Rare once complete (August 2022) https://www.ucar.edu/terms-of-use/data climate, model, climate model, atmosphere, oceans, land, ice, geospatial, aws-pds, sustainability ['[Browse Bucket](https://ncar-cesm2-arise.s3.amazonaws.com/index.html)'] +Community Earth System Model v2 ARISE (CESM2 ARISE) Project data files arn:aws:s3:::ncar-cesm2-arise us-east-2 S3 Bucket (https://github.com/NCAR/CESM2-ARISE) opendata-aws-arise@ucar.edu [National Center for Atmospheric Research](https://ncar.ucar.edu/) Rare once complete (August 2022) https://www.ucar.edu/terms-of-use/data climate, model, climate model, atmosphere, oceans, land, ice, geospatial, aws-pds, sustainability ['[Browse Bucket](https://ncar-cesm2-arise.s3.amazonaws.com/index.html)'] Community Earth System Model v2 Large Ensemble (CESM2 LENS) Project data files arn:aws:s3:::ncar-cesm2-lens us-west-2 S3 Bucket https://doi.org/10.26024/y48t-q717 rdahelp@ucar.edu [National Center for Atmospheric Research](https://ncar.ucar.edu/) Rare. The LENS experiment is complete, but we may occasionally copy additional f https://www.ucar.edu/terms-of-use/data climate, model, climate model, atmosphere, oceans, land, ice, geospatial, aws-pds, sustainability, zarr -Copernicus Digital Elevation Model (DEM) - GLO-30 Public in S3 bucket The list of tiles covering specific countries that a GLO-30 Public in S3 bucket The list of tiles covering specific countries that a arn:aws:s3:::copernicus-dem-30m eu-central-1 S3 Bucket https://copernicus-dem-30m.s3.amazonaws.com/readme.html https://forum.sentinel-hub.com/c/aws-copdem/28 [Sinergise](https://www.sinergise.com/) None, except GLO-30 Public can be updated if the public tile list changes. GLO-30 Public and GLO-90 are available on a free basis for the general public un aws-pds, agriculture, elevation, earth observation, satellite imagery, geospatial, disaster response, cog ['[STAC V1.0.0 endpoint](https://copernicus-dem-30m-stac.s3.amazonaws.com/)'] -Copernicus Digital Elevation Model (DEM) - GLO-90 in S3 bucket GLO-90 in S3 bucket arn:aws:s3:::copernicus-dem-90m eu-central-1 S3 Bucket https://copernicus-dem-30m.s3.amazonaws.com/readme.html https://forum.sentinel-hub.com/c/aws-copdem/28 [Sinergise](https://www.sinergise.com/) None, except GLO-30 Public can be updated if the public tile list changes. GLO-30 Public and GLO-90 are available on a free basis for the general public un aws-pds, agriculture, elevation, earth observation, satellite imagery, geospatial, disaster response, cog ['[STAC V1.0.0 endpoint](https://copernicus-dem-90m-stac.s3.amazonaws.com/)'] -Coupled Model Intercomparison Project 6 - Netcdf formatted data managed by the Earth System Grid Federation Netcdf formatted data managed by the Earth System Grid Federation arn:aws:s3:::esgf-world us-east-2 S3 Bucket https://pangeo-data.github.io/pangeo-cmip6-cloud/, https://www.wcrp-climate.org/ If you have any feedback on the CMIP6 data available on AWS please email sustain ESGF and Pangeo Core CMIP6 datasets are added as soon as they are available. See [docs] (https://pangeo-data.github.io/pangeo-cmip6-cloud/licensing_citation. aws-pds, agriculture, atmosphere, climate, earth observation, environmental, model, oceans, simulations, weather ['[Browse Bucket](https://esgf-world.s3.amazonaws.com/index.html)', '[Data Catalog](https://cmip6-nc.s3.amazonaws.com/esgf-world.csv.gz)'] -Coupled Model Intercomparison Project 6 - Zarr formatted data Zarr formatted data arn:aws:s3:::cmip6-pds us-west-2 S3 Bucket https://pangeo-data.github.io/pangeo-cmip6-cloud/, https://www.wcrp-climate.org/ If you have any feedback on the CMIP6 data available on AWS please email sustain ESGF and Pangeo Core CMIP6 datasets are added as soon as they are available. See [docs] (https://pangeo-data.github.io/pangeo-cmip6-cloud/licensing_citation. aws-pds, agriculture, atmosphere, climate, earth observation, environmental, model, oceans, simulations, weather ['[Browse Bucket](https://cmip6-pds.s3.amazonaws.com/index.html#CMIP6/)', '[Data Catalog](https://cmip6-pds.s3.amazonaws.com/pangeo-cmip6.csv)'] -Coupled Model Intercomparison Project Phase 5 (CMIP5) University of Wisconsin-Madison Probabilistic Downscaling Dataset Data files arn:aws:s3:::noaa-nws-uwpd-cmip5-pds us-east-1 S3 Bucket http://djlorenz.github.io/downscaling2/main.html For questions about data development, quality and content, please contact Dr. Da [NOAA](http://www.noaa.gov/) Periodically, as new data becomes available or when corrections are implemented. Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, sustainability, oceans, water, weather ['[Browse Bucket](https://noaa-nws-uwpd-cmip5-pds.s3.amazonaws.com/index.html)'] -Crowdsourced Bathymetry - Crowdsourced bathymetry data Crowdsourced bathymetry data arn:aws:s3:::noaa-dcdb-bathymetry-pds us-east-1 S3 Bucket https://noaa-dcdb-bathymetry-pds.s3.amazonaws.com/docs/readme.html mb.info@noaa.gov [NOAA](http://www.noaa.gov/) New data is added once a week. There are no restrictions on the use of this data. aws-pds, earth observation, oceans ['[Browse Bucket](https://noaa-dcdb-bathymetry-pds.s3.amazonaws.com/index.html)'] +Copernicus Digital Elevation Model (DEM) - GLO-30 Public in S3 bucket The list of tiles covering specific countries that a GLO-30 Public in S3 bucket The list of tiles covering specific countries that a arn:aws:s3:::copernicus-dem-30m eu-central-1 S3 Bucket https://copernicus-dem-30m.s3.amazonaws.com/readme.html https://forum.sentinel-hub.com/c/aws-copdem/28 [Sinergise](https://www.sinergise.com/) None, except GLO-30 Public can be updated if the public tile list changes. GLO-30 Public and GLO-90 are available on a free basis for the general public un aws-pds, agriculture, elevation, earth observation, satellite imagery, geospatial, disaster response, cog ['[STAC V1.0.0 endpoint](https://copernicus-dem-30m-stac.s3.amazonaws.com/)'] +Copernicus Digital Elevation Model (DEM) - GLO-90 in S3 bucket GLO-90 in S3 bucket arn:aws:s3:::copernicus-dem-90m eu-central-1 S3 Bucket https://copernicus-dem-30m.s3.amazonaws.com/readme.html https://forum.sentinel-hub.com/c/aws-copdem/28 [Sinergise](https://www.sinergise.com/) None, except GLO-30 Public can be updated if the public tile list changes. GLO-30 Public and GLO-90 are available on a free basis for the general public un aws-pds, agriculture, elevation, earth observation, satellite imagery, geospatial, disaster response, cog ['[STAC V1.0.0 endpoint](https://copernicus-dem-90m-stac.s3.amazonaws.com/)'] +Coupled Model Intercomparison Project 6 - Netcdf formatted data managed by the Earth System Grid Federation Netcdf formatted data managed by the Earth System Grid Federation arn:aws:s3:::esgf-world us-east-2 S3 Bucket https://pangeo-data.github.io/pangeo-cmip6-cloud/, https://www.wcrp-climate.org/ If you have any feedback on the CMIP6 data available on AWS please email sustain ESGF and Pangeo Core CMIP6 datasets are added as soon as they are available. See [docs] (https://pangeo-data.github.io/pangeo-cmip6-cloud/licensing_citation. aws-pds, agriculture, atmosphere, climate, earth observation, environmental, model, oceans, simulations, weather ['[Browse Bucket](https://esgf-world.s3.amazonaws.com/index.html)', '[Data Catalog](https://cmip6-nc.s3.amazonaws.com/esgf-world.csv.gz)'] +Coupled Model Intercomparison Project 6 - Zarr formatted data Zarr formatted data arn:aws:s3:::cmip6-pds us-west-2 S3 Bucket https://pangeo-data.github.io/pangeo-cmip6-cloud/, https://www.wcrp-climate.org/ If you have any feedback on the CMIP6 data available on AWS please email sustain ESGF and Pangeo Core CMIP6 datasets are added as soon as they are available. See [docs] (https://pangeo-data.github.io/pangeo-cmip6-cloud/licensing_citation. aws-pds, agriculture, atmosphere, climate, earth observation, environmental, model, oceans, simulations, weather ['[Browse Bucket](https://cmip6-pds.s3.amazonaws.com/index.html#CMIP6/)', '[Data Catalog](https://cmip6-pds.s3.amazonaws.com/pangeo-cmip6.csv)'] +Coupled Model Intercomparison Project Phase 5 (CMIP5) University of Wisconsin-Madison Probabilistic Downscaling Dataset Data files arn:aws:s3:::noaa-nws-uwpd-cmip5-pds us-east-1 S3 Bucket http://djlorenz.github.io/downscaling2/main.html For questions about data development, quality and content, please contact Dr. Da [NOAA](http://www.noaa.gov/) Periodically, as new data becomes available or when corrections are implemented. Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, sustainability, oceans, water, weather ['[Browse Bucket](https://noaa-nws-uwpd-cmip5-pds.s3.amazonaws.com/index.html)'] +Crowdsourced Bathymetry - Crowdsourced bathymetry data Crowdsourced bathymetry data arn:aws:s3:::noaa-dcdb-bathymetry-pds us-east-1 S3 Bucket https://noaa-dcdb-bathymetry-pds.s3.amazonaws.com/docs/readme.html mb.info@noaa.gov [NOAA](http://www.noaa.gov/) New data is added once a week. There are no restrictions on the use of this data. aws-pds, earth observation, oceans ['[Browse Bucket](https://noaa-dcdb-bathymetry-pds.s3.amazonaws.com/index.html)'] Crowdsourced Bathymetry - Notifications for CSB data Notifications for CSB data arn:aws:sns:us-east-1:709902155096:NewDCDBBathymetryObject us-east-1 SNS Topic https://noaa-dcdb-bathymetry-pds.s3.amazonaws.com/docs/readme.html mb.info@noaa.gov [NOAA](http://www.noaa.gov/) New data is added once a week. There are no restrictions on the use of this data. aws-pds, earth observation, oceans -DOE's Water Power Technology Office's (WPTO) US Wave dataset - 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Atlantic/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FAtlantic%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset - 32 Year Wave Hindcast (1979-2010) for the Pacific Ocean around US state of Hawai 32 Year Wave Hindcast (1979-2010) for the Pacific Ocean around US state of Hawai arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Hawaii/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FHawaii%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset - 32 Year Wave Hindcast (1979-2010) for the West Coast of the United States at 3-h 32 Year Wave Hindcast (1979-2010) for the West Coast of the United States at 3-h arn:aws:s3:::wpto-pds-us-wave/v1.0.0/West_Coast/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FWest_Coast%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset - 32 Year Wave Hindcast (1979-2010) from select virtual buoys along the Atlantic C 32 Year Wave Hindcast (1979-2010) from select virtual buoys along the Atlantic C arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/Atlantic/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FAtlantic%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset - 32 Year Wave Hindcast (1979-2010) from select virtual buoys along the West Coast 32 Year Wave Hindcast (1979-2010) from select virtual buoys along the West Coast arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/West_Coast/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FWest_Coast%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset - DOE's Water Power Technology Office's Wave Hindcast datasets DOE's Water Power Technology Office's Wave Hindcast datasets arn:aws:s3:::wpto-pds-us-wave/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset - HSDS US Virtual Buoy domains HSDS US Virtual Buoy domains arn:aws:s3:::nrel-pds-hsds/nrel/US_wave/virtual_buoy/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2Fvirtual_buoy%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset - HSDS US Wave domains HSDS US Wave domains arn:aws:s3:::nrel-pds-hsds/nrel/US_wave/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset - Updated version of 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of t Updated version of 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of t arn:aws:s3:::wpto-pds-us-wave/v1.0.1/Atlantic/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.1%2FAtlantic%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset - 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Atlantic/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FAtlantic%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset - 32 Year Wave Hindcast (1979-2010) for the Pacific Ocean around US state of Hawai 32 Year Wave Hindcast (1979-2010) for the Pacific Ocean around US state of Hawai arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Hawaii/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FHawaii%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset - 32 Year Wave Hindcast (1979-2010) for the West Coast of the United States at 3-h 32 Year Wave Hindcast (1979-2010) for the West Coast of the United States at 3-h arn:aws:s3:::wpto-pds-us-wave/v1.0.0/West_Coast/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FWest_Coast%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset - 32 Year Wave Hindcast (1979-2010) from select virtual buoys along the Atlantic C 32 Year Wave Hindcast (1979-2010) from select virtual buoys along the Atlantic C arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/Atlantic/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FAtlantic%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset - 32 Year Wave Hindcast (1979-2010) from select virtual buoys along the West Coast 32 Year Wave Hindcast (1979-2010) from select virtual buoys along the West Coast arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/West_Coast/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FWest_Coast%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset - DOE's Water Power Technology Office's Wave Hindcast datasets DOE's Water Power Technology Office's Wave Hindcast datasets arn:aws:s3:::wpto-pds-us-wave/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset - HSDS US Virtual Buoy domains HSDS US Virtual Buoy domains arn:aws:s3:::nrel-pds-hsds/nrel/US_wave/virtual_buoy/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2Fvirtual_buoy%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset - HSDS US Wave domains HSDS US Wave domains arn:aws:s3:::nrel-pds-hsds/nrel/US_wave/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset - Updated version of 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of t Updated version of 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of t arn:aws:s3:::wpto-pds-us-wave/v1.0.1/Atlantic/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.1%2FAtlantic%2F)'] Daylight Map Distribution of OpenStreetMap - Daylight Earth Table (Parquet) Daylight Earth Table (Parquet) arn:aws:s3:::daylight-openstreetmap/earth us-west-2 S3 Bucket [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds Daylight Map Distribution of OpenStreetMap - Daylight OSM Elements (Parquet) Daylight OSM Elements (Parquet) arn:aws:s3:::daylight-openstreetmap/parquet/osm_elements us-west-2 S3 Bucket [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds Daylight Map Distribution of OpenStreetMap - Daylight OSM Features (Parquet) Daylight OSM Features (Parquet) arn:aws:s3:::daylight-openstreetmap/parquet/osm_features us-west-2 S3 Bucket [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds @@ -91,202 +92,202 @@ Daylight Map Distribution of OpenStreetMap - Daylight OSM PBF Files Daylight OSM Daylight Map Distribution of OpenStreetMap - New OSM PBF Notification New OSM PBF Notification arn:aws:sns:us-west-1:632571768781:Daylight us-west-1 SNS Topic [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds Daylight Map Distribution of OpenStreetMap - New Parquet File Notification New Parquet File Notification arn:aws:sns:us-west-2:632571768781:Analysis_Ready_Daylight us-west-2 SNS Topic [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds Defense Meteorology Satellite Program (DMSP) Auroral Particle Flux DMSP Auroral Particle Flux arn:aws:s3:::dmspssj us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/dmspssj delores.knipp@colorado.edu Space Weather Technology, Research and Education Center (TREC) at University of Infrequent This data is in the '[public domain](https://creativecommons.org/publicdomain/ze aws-pds, solar, space weather, geospatial, earth observation -Demand-Side Grid (dsgrid) Toolkit - Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (E Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (E arn:aws:s3:::oedi-data-lake/dsgrid-2018-efs/ us-west-2 S3 Bucket https://www.nrel.gov/analysis/dsgrid.html elaine.hale@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License data assimilation, electricity, energy, energy modeling, meteorological, transportation, industrial, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dsgrid-2018-efs%2F)'] -Demand-Side Grid (dsgrid) Toolkit - Demand-side grid (dsgrid) Toolkit Datasets Demand-side grid (dsgrid) Toolkit Datasets arn:aws:s3:::nrel-pds-dsgrid/ us-west-2 S3 Bucket https://www.nrel.gov/analysis/dsgrid.html elaine.hale@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License data assimilation, electricity, energy, energy modeling, meteorological, transportation, industrial, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-dsgrid%2F)'] -Demand-Side Grid (dsgrid) Toolkit - Transportation Energy & Mobility Pathway Options (TEMPO) Model Datasets Transportation Energy & Mobility Pathway Options (TEMPO) Model Datasets arn:aws:s3:::nrel-pds-dsgrid/tempo/ us-west-2 S3 Bucket https://www.nrel.gov/analysis/dsgrid.html elaine.hale@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License data assimilation, electricity, energy, energy modeling, meteorological, transportation, industrial, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-dsgrid&prefix=tempo%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - AlphaBuilding - Synthetic Buildings Operation Dataset AlphaBuilding - Synthetic Buildings Operation Dataset arn:aws:s3:::oedi-data-lake/building_synthetic_dataset/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=building_synthetic_dataset%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - BUTTER - Empirical Deep Learning Dataset BUTTER - Empirical Deep Learning Dataset arn:aws:s3:::oedi-data-lake/butter/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=butter%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short- BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short- arn:aws:s3:::oedi-data-lake/buildings-bench/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=buildings-bench)'] -Department of Energy's Open Energy Data Initiative (OEDI) - Data Catalog of the Open Energy Data Initiative (OEDI) Data Catalog of the Open Energy Data Initiative (OEDI) arn:aws:s3:::oedi-data-lake/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake)'] -Department of Energy's Open Energy Data Initiative (OEDI) - Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (E Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (E arn:aws:s3:::oedi-data-lake/dsgrid-2018-efs/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dsgrid-2018-efs%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - Distributed Generation Market Demand (dGen) model Distributed Generation Market Demand (dGen) model arn:aws:s3:::oedi-data-lake/dgen/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dgen%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - End-Use Load Profiles for the US Building Stock End-Use Load Profiles for the US Building Stock arn:aws:s3:::oedi-data-lake/nrel-pds-building-stock/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=nrel-pds-building-stock%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - Lawrence Berkeley National Laboratory (LBNL) Tracking the Sun Lawrence Berkeley National Laboratory (LBNL) Tracking the Sun arn:aws:s3:::oedi-data-lake/tracking-the-sun/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=tracking-the-sun%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - NREL's Annual Technology Baseline (ATB) NREL's Annual Technology Baseline (ATB) arn:aws:s3:::oedi-data-lake/ATB/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=ATB%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - National Renewable Energy Laboratory (NREL) PV Rooftop Database - (PVRDB) National Renewable Energy Laboratory (NREL) PV Rooftop Database - (PVRDB) arn:aws:s3:::oedi-data-lake/pv-rooftop/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pv-rooftop%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - National Renewable Energy Laboratory (NREL) PV Rooftop Database - Puerto Rico (P National Renewable Energy Laboratory (NREL) PV Rooftop Database - Puerto Rico (P arn:aws:s3:::oedi-data-lake/pv-rooftop-pr/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pv-rooftop-pr%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - PR100: Puerto Rico Grid Resilience and Transition to 100% Renewable Energy PR100: Puerto Rico Grid Resilience and Transition to 100% Renewable Energy arn:aws:s3:::oedi-data-lake/PR100/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=PR100%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - Photovoltaic Data Acquisition (PVDAQ) Public Datasets Photovoltaic Data Acquisition (PVDAQ) Public Datasets arn:aws:s3:::oedi-data-lake/pvdaq/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pvdaq%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scena Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scena arn:aws:s3:::oedi-data-lake/SMART-DS/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=SMART-DS%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - University of Miami Coupled Model (UMCM) for Hurricanes Ike and Sandy University of Miami Coupled Model (UMCM) for Hurricanes Ike and Sandy arn:aws:s3:::oedi-data-lake/umcm/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=umcm%2F)'] -Department of Energy's Open Energy Data Initiative (OEDI) - Wind and Structural Loads on Parabolic Trough Solar Collectors at Nevada Solar O Wind and Structural Loads on Parabolic Trough Solar Collectors at Nevada Solar O arn:aws:s3:::oedi-data-lake/NSO/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=NSO%2F)'] -Digital Earth Africa ALOS PALSAR, ALOS-2 PALSAR-2 and JERS-1 - ALOS PALSAR ALOS-2 PALSAR-2 data ALOS PALSAR ALOS-2 PALSAR-2 data arn:aws:s3:::deafrica-input-datasets/alos_palsar_mosaic af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/ALOS_PALSAR_annual_mosa helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) As available, generally annually. Data is available for free under the [terms of use](https://earth.jaxa.jp/policy aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, synthetic aperture radar, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/alos_palsar_mosaic)'] +Demand-Side Grid (dsgrid) Toolkit - Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (E Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (E arn:aws:s3:::oedi-data-lake/dsgrid-2018-efs/ us-west-2 S3 Bucket https://www.nrel.gov/analysis/dsgrid.html elaine.hale@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License data assimilation, electricity, energy, energy modeling, meteorological, transportation, industrial, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dsgrid-2018-efs%2F)'] +Demand-Side Grid (dsgrid) Toolkit - Demand-side grid (dsgrid) Toolkit Datasets Demand-side grid (dsgrid) Toolkit Datasets arn:aws:s3:::nrel-pds-dsgrid/ us-west-2 S3 Bucket https://www.nrel.gov/analysis/dsgrid.html elaine.hale@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License data assimilation, electricity, energy, energy modeling, meteorological, transportation, industrial, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-dsgrid%2F)'] +Demand-Side Grid (dsgrid) Toolkit - Transportation Energy & Mobility Pathway Options (TEMPO) Model Datasets Transportation Energy & Mobility Pathway Options (TEMPO) Model Datasets arn:aws:s3:::nrel-pds-dsgrid/tempo/ us-west-2 S3 Bucket https://www.nrel.gov/analysis/dsgrid.html elaine.hale@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License data assimilation, electricity, energy, energy modeling, meteorological, transportation, industrial, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-dsgrid&prefix=tempo%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - AlphaBuilding - Synthetic Buildings Operation Dataset AlphaBuilding - Synthetic Buildings Operation Dataset arn:aws:s3:::oedi-data-lake/building_synthetic_dataset/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=building_synthetic_dataset%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - BUTTER - Empirical Deep Learning Dataset BUTTER - Empirical Deep Learning Dataset arn:aws:s3:::oedi-data-lake/butter/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=butter%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short- BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short- arn:aws:s3:::oedi-data-lake/buildings-bench/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=buildings-bench)'] +Department of Energy's Open Energy Data Initiative (OEDI) - Data Catalog of the Open Energy Data Initiative (OEDI) Data Catalog of the Open Energy Data Initiative (OEDI) arn:aws:s3:::oedi-data-lake/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake)'] +Department of Energy's Open Energy Data Initiative (OEDI) - Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (E Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (E arn:aws:s3:::oedi-data-lake/dsgrid-2018-efs/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dsgrid-2018-efs%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - Distributed Generation Market Demand (dGen) model Distributed Generation Market Demand (dGen) model arn:aws:s3:::oedi-data-lake/dgen/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dgen%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - End-Use Load Profiles for the US Building Stock End-Use Load Profiles for the US Building Stock arn:aws:s3:::oedi-data-lake/nrel-pds-building-stock/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=nrel-pds-building-stock%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - Lawrence Berkeley National Laboratory (LBNL) Tracking the Sun Lawrence Berkeley National Laboratory (LBNL) Tracking the Sun arn:aws:s3:::oedi-data-lake/tracking-the-sun/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=tracking-the-sun%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - NREL's Annual Technology Baseline (ATB) NREL's Annual Technology Baseline (ATB) arn:aws:s3:::oedi-data-lake/ATB/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=ATB%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - National Renewable Energy Laboratory (NREL) PV Rooftop Database - (PVRDB) National Renewable Energy Laboratory (NREL) PV Rooftop Database - (PVRDB) arn:aws:s3:::oedi-data-lake/pv-rooftop/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pv-rooftop%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - National Renewable Energy Laboratory (NREL) PV Rooftop Database - Puerto Rico (P National Renewable Energy Laboratory (NREL) PV Rooftop Database - Puerto Rico (P arn:aws:s3:::oedi-data-lake/pv-rooftop-pr/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pv-rooftop-pr%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - PR100: Puerto Rico Grid Resilience and Transition to 100% Renewable Energy PR100: Puerto Rico Grid Resilience and Transition to 100% Renewable Energy arn:aws:s3:::oedi-data-lake/PR100/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=PR100%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - Photovoltaic Data Acquisition (PVDAQ) Public Datasets Photovoltaic Data Acquisition (PVDAQ) Public Datasets arn:aws:s3:::oedi-data-lake/pvdaq/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=pvdaq%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scena Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scena arn:aws:s3:::oedi-data-lake/SMART-DS/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=SMART-DS%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - University of Miami Coupled Model (UMCM) for Hurricanes Ike and Sandy University of Miami Coupled Model (UMCM) for Hurricanes Ike and Sandy arn:aws:s3:::oedi-data-lake/umcm/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=umcm%2F)'] +Department of Energy's Open Energy Data Initiative (OEDI) - Wind and Structural Loads on Parabolic Trough Solar Collectors at Nevada Solar O Wind and Structural Loads on Parabolic Trough Solar Collectors at Nevada Solar O arn:aws:s3:::oedi-data-lake/NSO/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/ https://github.com/openEDI/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar, lidar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=NSO%2F)'] +Digital Earth Africa ALOS PALSAR, ALOS-2 PALSAR-2 and JERS-1 - ALOS PALSAR ALOS-2 PALSAR-2 data ALOS PALSAR ALOS-2 PALSAR-2 data arn:aws:s3:::deafrica-input-datasets/alos_palsar_mosaic af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/ALOS_PALSAR_annual_mosa helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) As available, generally annually. Data is available for free under the [terms of use](https://earth.jaxa.jp/policy aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, synthetic aperture radar, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/alos_palsar_mosaic)'] False Digital Earth Africa ALOS PALSAR, ALOS-2 PALSAR-2 and JERS-1 - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-input-datasets-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/ALOS_PALSAR_annual_mosa helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) As available, generally annually. Data is available for free under the [terms of use](https://earth.jaxa.jp/policy aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, synthetic aperture radar, deafrica, stac, cog Digital Earth Africa ALOS PALSAR, ALOS-2 PALSAR-2 and JERS-1 - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-input-datasets-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/ALOS_PALSAR_annual_mosa helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) As available, generally annually. Data is available for free under the [terms of use](https://earth.jaxa.jp/policy aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, synthetic aperture radar, deafrica, stac, cog Digital Earth Africa CHIRPS Rainfall - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-input-datasets-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/CHIRPS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) Monthly. To the extent possible under the law, Pete Peterson has waived all copyright and aws-pds, agriculture, climate, earth observation, food security, geospatial, meteorological, satellite imagery, sustainability, deafrica, stac, cog -Digital Earth Africa CHIRPS Rainfall - CHIRPS daily rainfall CHIRPS daily rainfall arn:aws:s3:::deafrica-input-datasets/rainfall_chirps_daily af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/CHIRPS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) Monthly. To the extent possible under the law, Pete Peterson has waived all copyright and aws-pds, agriculture, climate, earth observation, food security, geospatial, meteorological, satellite imagery, sustainability, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/rainfall_chirps_daily)'] -Digital Earth Africa CHIRPS Rainfall - CHIRPS monthly rainfall CHIRPS monthly rainfall arn:aws:s3:::deafrica-input-datasets/rainfall_chirps_monthly af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/CHIRPS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) Monthly. To the extent possible under the law, Pete Peterson has waived all copyright and aws-pds, agriculture, climate, earth observation, food security, geospatial, meteorological, satellite imagery, sustainability, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/rainfall_chirps_monthly)'] +Digital Earth Africa CHIRPS Rainfall - CHIRPS daily rainfall CHIRPS daily rainfall arn:aws:s3:::deafrica-input-datasets/rainfall_chirps_daily af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/CHIRPS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) Monthly. To the extent possible under the law, Pete Peterson has waived all copyright and aws-pds, agriculture, climate, earth observation, food security, geospatial, meteorological, satellite imagery, sustainability, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/rainfall_chirps_daily)'] False +Digital Earth Africa CHIRPS Rainfall - CHIRPS monthly rainfall CHIRPS monthly rainfall arn:aws:s3:::deafrica-input-datasets/rainfall_chirps_monthly af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/CHIRPS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) Monthly. To the extent possible under the law, Pete Peterson has waived all copyright and aws-pds, agriculture, climate, earth observation, food security, geospatial, meteorological, satellite imagery, sustainability, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/rainfall_chirps_monthly)'] False Digital Earth Africa CHIRPS Rainfall - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-input-datasets-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/CHIRPS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) Monthly. To the extent possible under the law, Pete Peterson has waived all copyright and aws-pds, agriculture, climate, earth observation, food security, geospatial, meteorological, satellite imagery, sustainability, deafrica, stac, cog Digital Earth Africa Coastlines - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-services-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Coastlines_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, climate, coastal, earth observation, geospatial, satellite imagery, sustainability, deafrica -Digital Earth Africa Coastlines - DE Africa Coastlines DE Africa Coastlines arn:aws:s3:::deafrica-services/coastlines af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Coastlines_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, climate, coastal, earth observation, geospatial, satellite imagery, sustainability, deafrica False +Digital Earth Africa Coastlines - DE Africa Coastlines DE Africa Coastlines arn:aws:s3:::deafrica-services/coastlines af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Coastlines_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, climate, coastal, earth observation, geospatial, satellite imagery, sustainability, deafrica False Digital Earth Africa Coastlines - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-services-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Coastlines_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, climate, coastal, earth observation, geospatial, satellite imagery, sustainability, deafrica Digital Earth Africa Cropland Extent Map (2019) - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-services-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.h helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, food security, geospatial, satellite imagery, sustainability, deafrica, stac, cog -Digital Earth Africa Cropland Extent Map (2019) - Cropland extent map 2019 Cropland extent map 2019 arn:aws:s3:::deafrica-services/crop_mask af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.h helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, food security, geospatial, satellite imagery, sustainability, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/crop_mask)'] +Digital Earth Africa Cropland Extent Map (2019) - Cropland extent map 2019 Cropland extent map 2019 arn:aws:s3:::deafrica-services/crop_mask af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.h helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, food security, geospatial, satellite imagery, sustainability, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/crop_mask)'] False Digital Earth Africa Cropland Extent Map (2019) - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-services-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.h helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, food security, geospatial, satellite imagery, sustainability, deafrica, stac, cog Digital Earth Africa Fractional Cover - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-services-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Fractional_Cover_specs. helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, sustainability, deafrica, stac, cog -Digital Earth Africa Fractional Cover - Fractional Cover Annual Summary data Fractional Cover Annual Summary data arn:aws:s3:::deafrica-services/fc_ls_summary_annual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Fractional_Cover_specs. helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, sustainability, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/fc_ls_summary_annual)'] -Digital Earth Africa Fractional Cover - Fractional Cover data Fractional Cover data arn:aws:s3:::deafrica-services/fc_ls af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Fractional_Cover_specs. helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, sustainability, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/fc_ls)'] +Digital Earth Africa Fractional Cover - Fractional Cover Annual Summary data Fractional Cover Annual Summary data arn:aws:s3:::deafrica-services/fc_ls_summary_annual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Fractional_Cover_specs. helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, sustainability, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/fc_ls_summary_annual)'] False +Digital Earth Africa Fractional Cover - Fractional Cover data Fractional Cover data arn:aws:s3:::deafrica-services/fc_ls af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Fractional_Cover_specs. helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, sustainability, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/fc_ls)'] False Digital Earth Africa Fractional Cover - New scene notifications, can subscribe with Lambda New scene notifications, can subscribe with Lambda arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Fractional_Cover_specs. helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, sustainability, deafrica, stac, cog Digital Earth Africa Fractional Cover - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-services-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Fractional_Cover_specs. helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, sustainability, deafrica, stac, cog Digital Earth Africa GeoMAD - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-services-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog -Digital Earth Africa GeoMAD - Landsat 5 and 7 GeoMAD (Annual) data Landsat 5 and 7 GeoMAD (Annual) data arn:aws:s3:::deafrica-services/gm_ls5_ls7_annual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_ls5_ls7_annual)'] -Digital Earth Africa GeoMAD - Landsat 8 GeoMAD (Annual) data Landsat 8 GeoMAD (Annual) data arn:aws:s3:::deafrica-services/gm_ls8_annual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_ls8_annual)'] +Digital Earth Africa GeoMAD - Landsat 5 and 7 GeoMAD (Annual) data Landsat 5 and 7 GeoMAD (Annual) data arn:aws:s3:::deafrica-services/gm_ls5_ls7_annual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_ls5_ls7_annual)'] False +Digital Earth Africa GeoMAD - Landsat 8 GeoMAD (Annual) data Landsat 8 GeoMAD (Annual) data arn:aws:s3:::deafrica-services/gm_ls8_annual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_ls8_annual)'] False Digital Earth Africa GeoMAD - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-services-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog -Digital Earth Africa GeoMAD - Sentinel-2 GeoMAD (Annual) data Sentinel-2 GeoMAD (Annual) data arn:aws:s3:::deafrica-services/gm_s2_annual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_s2_annual)'] -Digital Earth Africa GeoMAD - Sentinel-2 GeoMAD (Semi-Annual) data Sentinel-2 GeoMAD (Semi-Annual) data arn:aws:s3:::deafrica-services/gm_s2_semiannual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_s2_semiannual)'] +Digital Earth Africa GeoMAD - Sentinel-2 GeoMAD (Annual) data Sentinel-2 GeoMAD (Annual) data arn:aws:s3:::deafrica-services/gm_s2_annual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_s2_annual)'] False +Digital Earth Africa GeoMAD - Sentinel-2 GeoMAD (Semi-Annual) data Sentinel-2 GeoMAD (Semi-Annual) data arn:aws:s3:::deafrica-services/gm_s2_semiannual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/GeoMAD_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) GeoMADs for Sentinel-2 and Landsat are updated as their respective time periods DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gm_s2_semiannual)'] False Digital Earth Africa Global Mangrove Watch - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-input-datasets-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Global_Mangrove_Watch_s helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, natural resource, earth observation, coastal, geospatial, satellite imagery, sustainability, deafrica, stac, cog, land cover -Digital Earth Africa Global Mangrove Watch - Global Mangrove Watch Global Mangrove Watch arn:aws:s3:::deafrica-input-datasets/gmw af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Global_Mangrove_Watch_s helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, natural resource, earth observation, coastal, geospatial, satellite imagery, sustainability, deafrica, stac, cog, land cover False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gmw)'] +Digital Earth Africa Global Mangrove Watch - Global Mangrove Watch Global Mangrove Watch arn:aws:s3:::deafrica-input-datasets/gmw af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Global_Mangrove_Watch_s helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, natural resource, earth observation, coastal, geospatial, satellite imagery, sustainability, deafrica, stac, cog, land cover ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gmw)'] False Digital Earth Africa Global Mangrove Watch - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-services-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Global_Mangrove_Watch_s helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) To be defined. DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, natural resource, earth observation, coastal, geospatial, satellite imagery, sustainability, deafrica, stac, cog, land cover Digital Earth Africa Landsat Collection 2 Level 2 - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-landsat-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.htm helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Landsat data are added regularly, usually within a few hours of them being a There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog -Digital Earth Africa Landsat Collection 2 Level 2 - Landsat scenes and metadata Landsat scenes and metadata arn:aws:s3:::deafrica-landsat af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.htm helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Landsat data are added regularly, usually within a few hours of them being a There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/)'] +Digital Earth Africa Landsat Collection 2 Level 2 - Landsat scenes and metadata Landsat scenes and metadata arn:aws:s3:::deafrica-landsat af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.htm helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Landsat data are added regularly, usually within a few hours of them being a There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/)'] False Digital Earth Africa Landsat Collection 2 Level 2 - New scene notifications, can subscribe with Lambda New scene notifications, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-landsat-scene-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.htm helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Landsat data are added regularly, usually within a few hours of them being a There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog Digital Earth Africa Landsat Collection 2 Level 2 - S3 Inventory files S3 Inventory files arn:aws:s3:::deafrica-landsat-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.htm helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Landsat data are added regularly, usually within a few hours of them being a There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog Digital Earth Africa Monthly Normalised Difference Vegetation Index (NDVI) Anomaly - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-services-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/NDVI_Anomaly_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) From September 2022, the Monthly NDVI Anomaly is generated as a low latency prod DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, deafrica, stac, cog -Digital Earth Africa Monthly Normalised Difference Vegetation Index (NDVI) Anomaly - Monthly Normalised Difference Vegetation Index (NDVI) Anomaly Monthly Normalised Difference Vegetation Index (NDVI) Anomaly arn:aws:s3:::deafrica-services/ndvi_anomaly af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/NDVI_Anomaly_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) From September 2022, the Monthly NDVI Anomaly is generated as a low latency prod DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/ndvi_anomaly)'] +Digital Earth Africa Monthly Normalised Difference Vegetation Index (NDVI) Anomaly - Monthly Normalised Difference Vegetation Index (NDVI) Anomaly Monthly Normalised Difference Vegetation Index (NDVI) Anomaly arn:aws:s3:::deafrica-services/ndvi_anomaly af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/NDVI_Anomaly_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) From September 2022, the Monthly NDVI Anomaly is generated as a low latency prod DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/ndvi_anomaly)'] False Digital Earth Africa Monthly Normalised Difference Vegetation Index (NDVI) Anomaly - New scene notifications, can subscribe with Lambda New scene notifications, can subscribe with Lambda arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/NDVI_Anomaly_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) From September 2022, the Monthly NDVI Anomaly is generated as a low latency prod DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, deafrica, stac, cog Digital Earth Africa Monthly Normalised Difference Vegetation Index (NDVI) Anomaly - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-services-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/NDVI_Anomaly_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) From September 2022, the Monthly NDVI Anomaly is generated as a low latency prod DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, deafrica, stac, cog Digital Earth Africa Normalised Difference Vegetation Index (NDVI) Climatology - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-services-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/ndvi_climatology_ls.htm helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) N/A DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, deafrica, stac, cog Digital Earth Africa Normalised Difference Vegetation Index (NDVI) Climatology - New scene notifications, can subscribe with Lambda New scene notifications, can subscribe with Lambda arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/ndvi_climatology_ls.htm helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) N/A DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, deafrica, stac, cog -Digital Earth Africa Normalised Difference Vegetation Index (NDVI) Climatology - Normalised Difference Vegetation Index (NDVI) Climatology Normalised Difference Vegetation Index (NDVI) Climatology arn:aws:s3:::deafrica-services/ndvi_climatology_ls af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/ndvi_climatology_ls.htm helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) N/A DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/ndvi_climatology_ls)'] +Digital Earth Africa Normalised Difference Vegetation Index (NDVI) Climatology - Normalised Difference Vegetation Index (NDVI) Climatology Normalised Difference Vegetation Index (NDVI) Climatology arn:aws:s3:::deafrica-services/ndvi_climatology_ls af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/ndvi_climatology_ls.htm helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) N/A DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/ndvi_climatology_ls)'] False Digital Earth Africa Normalised Difference Vegetation Index (NDVI) Climatology - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-services-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/ndvi_climatology_ls.htm helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) N/A DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, deafrica, stac, cog Digital Earth Africa Sentinel-1 Radiometrically Terrain Corrected - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-1-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-1_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Sentinel-1 data are added regularly. Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog, synthetic aperture radar Digital Earth Africa Sentinel-1 Radiometrically Terrain Corrected - New scene notifications, can subscribe with Lambda New scene notifications, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-1-scene-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-1_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Sentinel-1 data are added regularly. Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog, synthetic aperture radar Digital Earth Africa Sentinel-1 Radiometrically Terrain Corrected - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-sentinel-1-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-1_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Sentinel-1 data are added regularly. Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog, synthetic aperture radar -Digital Earth Africa Sentinel-1 Radiometrically Terrain Corrected - Sentinel-1 tiles and metadata Sentinel-1 tiles and metadata arn:aws:s3:::deafrica-sentinel-1 af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-1_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Sentinel-1 data are added regularly. Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog, synthetic aperture radar False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s1_rtc)'] +Digital Earth Africa Sentinel-1 Radiometrically Terrain Corrected - Sentinel-1 tiles and metadata Sentinel-1 tiles and metadata arn:aws:s3:::deafrica-sentinel-1 af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-1_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Sentinel-1 data are added regularly. Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog, synthetic aperture radar ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s1_rtc)'] False Digital Earth Africa Sentinel-2 Level-2A - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-2-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_spe helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Sentinel-2 scenes are added regularly, usually within few hours after they a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog Digital Earth Africa Sentinel-2 Level-2A - New scene notifications, can subscribe with Lambda New scene notifications, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-2-scene-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_spe helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Sentinel-2 scenes are added regularly, usually within few hours after they a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog Digital Earth Africa Sentinel-2 Level-2A - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-sentinel-2-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_spe helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Sentinel-2 scenes are added regularly, usually within few hours after they a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog -Digital Earth Africa Sentinel-2 Level-2A - Sentinel-2 scenes and metadata Sentinel-2 scenes and metadata arn:aws:s3:::deafrica-sentinel-2 af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_spe helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Sentinel-2 scenes are added regularly, usually within few hours after they a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s2_l2a)'] +Digital Earth Africa Sentinel-2 Level-2A - Sentinel-2 scenes and metadata Sentinel-2 scenes and metadata arn:aws:s3:::deafrica-sentinel-2 af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_spe helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New Sentinel-2 scenes are added regularly, usually within few hours after they a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s2_l2a)'] False Digital Earth Africa Water Observations from Space - Bucket creation event notification, can subscribe with Lambda Bucket creation event notification, can subscribe with Lambda arn:aws:sns:af-south-1:543785577597:deafrica-services-topic af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, water, deafrica, stac, cog Digital Earth Africa Water Observations from Space - New scene notifications, can subscribe with Lambda New scene notifications, can subscribe with Lambda arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs af-south-1 SNS Topic https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, water, deafrica, stac, cog Digital Earth Africa Water Observations from Space - S3 Inventory S3 Inventory arn:aws:s3:::deafrica-services-inventory af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, water, deafrica, stac, cog -Digital Earth Africa Water Observations from Space - Water Observations from Space All-Time Summary data Water Observations from Space All-Time Summary data arn:aws:s3:::deafrica-services/wofs_ls_summary_alltime af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, water, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls_summary_alltime)'] -Digital Earth Africa Water Observations from Space - Water Observations from Space Annual Summary data Water Observations from Space Annual Summary data arn:aws:s3:::deafrica-services/wofs_ls_summary_annual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, water, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls_summary_annual)'] -Digital Earth Africa Water Observations from Space - Water Observations from Space data Water Observations from Space data arn:aws:s3:::deafrica-services/wofs_ls af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, water, deafrica, stac, cog False ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls)'] -District of Columbia - Classified Point Cloud LiDAR - LAS, XML LAS, XML arn:aws:s3:::dc-lidar-2018 us-east-1 S3 Bucket [2015 data](https://github.com/awslabs/open-data-docs/tree/main/docs/dc-lidar-20 dcgis@dc.gov [Washington DC government](https://dc.gov/) The most recent data is from 2018 and 2015 data is available as well. A new data See Washington, DC [Terms of Use](https://dc.gov/page/terms-and-conditions-use) aws-pds, geospatial, cities, us-dc, disaster response ['[Browse Bucket](https://dc-lidar-2018.s3.amazonaws.com/index.html)'] -District of Columbia - Classified Point Cloud LiDAR - LAS, XML, SHP LAS, XML, SHP arn:aws:s3:::dc-lidar-2015 us-east-1 S3 Bucket [2015 data](https://github.com/awslabs/open-data-docs/tree/main/docs/dc-lidar-20 dcgis@dc.gov [Washington DC government](https://dc.gov/) The most recent data is from 2018 and 2015 data is available as well. A new data See Washington, DC [Terms of Use](https://dc.gov/page/terms-and-conditions-use) aws-pds, geospatial, cities, us-dc, disaster response ['[Browse Bucket](https://dc-lidar-2015.s3.amazonaws.com/index.html)'] -Downscaled Climate Data for Alaska (v1.1, August 2023) Dynamically downscaled climate data for Alaska and surrounding regions arn:aws:s3:::wrf-ak-ar5 us-east-1 S3 Bucket https://catalog.snap.uaf.edu/geonetwork/srv/eng/catalog.search#/metadata/7825535 http://directory.iarc.uaf.edu/peter-bieniek Scenarios Network for Alaska + Arctic Planning at the International Arctic Resea as needed https://creativecommons.org/licenses/by/4.0/ aws-pds, agriculture, climate, coastal, earth observation, environmental, weather, aws-pds, sustainability ['[Browse Bucket](http://wrf-ak-ar5.s3-website-us-east-1.amazonaws.com/)'] +Digital Earth Africa Water Observations from Space - Water Observations from Space All-Time Summary data Water Observations from Space All-Time Summary data arn:aws:s3:::deafrica-services/wofs_ls_summary_alltime af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, water, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls_summary_alltime)'] False +Digital Earth Africa Water Observations from Space - Water Observations from Space Annual Summary data Water Observations from Space Annual Summary data arn:aws:s3:::deafrica-services/wofs_ls_summary_annual af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, water, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls_summary_annual)'] False +Digital Earth Africa Water Observations from Space - Water Observations from Space data Water Observations from Space data arn:aws:s3:::deafrica-services/wofs_ls af-south-1 S3 Bucket https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html helpdesk@digitalearthafrica.org [Digital Earth Africa](https://www.digitalearthafrica.org/) New scene-level data is added as new Landsat data is available. New summaries ar DE Africa makes this data available under the Creative Commons Attribute 4.0 lic aws-pds, agriculture, disaster response, earth observation, geospatial, natural resource, satellite imagery, water, deafrica, stac, cog ['[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls)'] False +District of Columbia - Classified Point Cloud LiDAR - LAS, XML LAS, XML arn:aws:s3:::dc-lidar-2018 us-east-1 S3 Bucket [2015 data](https://github.com/awslabs/open-data-docs/tree/main/docs/dc-lidar-20 dcgis@dc.gov [Washington DC government](https://dc.gov/) The most recent data is from 2018 and 2015 data is available as well. A new data See Washington, DC [Terms of Use](https://dc.gov/page/terms-and-conditions-use) aws-pds, geospatial, cities, us-dc, disaster response ['[Browse Bucket](https://dc-lidar-2018.s3.amazonaws.com/index.html)'] +District of Columbia - Classified Point Cloud LiDAR - LAS, XML, SHP LAS, XML, SHP arn:aws:s3:::dc-lidar-2015 us-east-1 S3 Bucket [2015 data](https://github.com/awslabs/open-data-docs/tree/main/docs/dc-lidar-20 dcgis@dc.gov [Washington DC government](https://dc.gov/) The most recent data is from 2018 and 2015 data is available as well. A new data See Washington, DC [Terms of Use](https://dc.gov/page/terms-and-conditions-use) aws-pds, geospatial, cities, us-dc, disaster response ['[Browse Bucket](https://dc-lidar-2015.s3.amazonaws.com/index.html)'] +Downscaled Climate Data for Alaska (v1.1, August 2023) Dynamically downscaled climate data for Alaska and surrounding regions arn:aws:s3:::wrf-ak-ar5 us-east-1 S3 Bucket https://catalog.snap.uaf.edu/geonetwork/srv/eng/catalog.search#/metadata/7825535 http://directory.iarc.uaf.edu/peter-bieniek Scenarios Network for Alaska + Arctic Planning at the International Arctic Resea as needed https://creativecommons.org/licenses/by/4.0/ aws-pds, agriculture, climate, coastal, earth observation, environmental, weather, aws-pds, sustainability ['[Browse Bucket](http://wrf-ak-ar5.s3-website-us-east-1.amazonaws.com/)'] ECMWF real-time forecasts Access ECMWF's open-data for real-time forecasts arn:aws:s3:::ecmwf-forecasts eu-central-1 S3 Bucket [User Documentation](https://confluence.ecmwf.int/display/DAC/ECMWF+open+data%3A https://confluence.ecmwf.int/site/support [European Centre for Medium-Range Weather Forecasts](https://www.ecmwf.int/) The data are released 1 hour after the [real-time dissemination schedule](https: This ECMWF data is published under a Creative Commons Attribution 4.0 Internatio aws-pds, air temperature, atmosphere, meteorological, near-surface air temperature, near-surface relative humidity, near-surface specific humidity, precipitation, weather -EPA Dynamically Downscaled Ensemble (EDDE) - EDDE data are compressed netCDF (https://unidataucaredu/software/netcdf/) file EDDE data are compressed netCDF (https://unidataucaredu/software/netcdf/) file arn:aws:s3:::epa-edde us-east-1 S3 Bucket EDDE Version 1 can be referenced using https://doi.org/10.23719/1530964 Please s spero.tanya@epa.gov; mallard.megan@epa.gov U.S. Environmental Protection Agency, Office of Research and Development, Center Quarterly These datasets are products of the U.S. Government and are intended for public a aws-pds, weather, climate, climate model, climate projections, CMIP5, CMIP6, us, atmosphere, environmental, meteorological, Eulerian, model, simulations, HPC, events, fluid dynamics, geospatial, netcdf, hdf5, physics, geoscience, open source software, post-processing, air temperature, near-surface air temperature, near-surface relative humidity, near-surface specific humidity, precipitation, radiation, elevation, land use, land cover, agriculture, air quality, infrastructure, ecosystems, floods, health, hydrology, water ['[Browse Bucket](https://epa-edde.s3.amazonaws.com/index.html)'] +EPA Dynamically Downscaled Ensemble (EDDE) - EDDE data are compressed netCDF (https://unidataucaredu/software/netcdf/) file EDDE data are compressed netCDF (https://unidataucaredu/software/netcdf/) file arn:aws:s3:::epa-edde us-east-1 S3 Bucket EDDE Version 1 can be referenced using https://doi.org/10.23719/1530964 Please s spero.tanya@epa.gov; mallard.megan@epa.gov U.S. Environmental Protection Agency, Office of Research and Development, Center Quarterly These datasets are products of the U.S. Government and are intended for public a aws-pds, weather, climate, climate model, climate projections, CMIP5, CMIP6, us, atmosphere, environmental, meteorological, Eulerian, model, simulations, HPC, events, fluid dynamics, geospatial, netcdf, hdf5, physics, geoscience, open source software, post-processing, air temperature, near-surface air temperature, near-surface relative humidity, near-surface specific humidity, precipitation, radiation, elevation, land use, land cover, agriculture, air quality, infrastructure, ecosystems, floods, health, hydrology, water ['[Browse Bucket](https://epa-edde.s3.amazonaws.com/index.html)'] EPA Dynamically Downscaled Ensemble (EDDE) - Notifications for EDDE bucket Notifications for EDDE bucket arn:aws:sns:us-east-1:127085394039:epa-edde-object_created us-east-1 SNS Topic EDDE Version 1 can be referenced using https://doi.org/10.23719/1530964 Please s spero.tanya@epa.gov; mallard.megan@epa.gov U.S. Environmental Protection Agency, Office of Research and Development, Center Quarterly These datasets are products of the U.S. Government and are intended for public a aws-pds, weather, climate, climate model, climate projections, CMIP5, CMIP6, us, atmosphere, environmental, meteorological, Eulerian, model, simulations, HPC, events, fluid dynamics, geospatial, netcdf, hdf5, physics, geoscience, open source software, post-processing, air temperature, near-surface air temperature, near-surface relative humidity, near-surface specific humidity, precipitation, radiation, elevation, land use, land cover, agriculture, air quality, infrastructure, ecosystems, floods, health, hydrology, water -EPA Risk-Screening Environmental Indicators RSEI Microdata arn:aws:s3:::epa-rsei-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/epa-rsei-pds https://www.epa.gov/rsei/forms/contact-us-about-rsei-model [Environmental Protection Agency](https://www.epa.gov/rsei/) Updated infrequently US Government work aws-pds, environmental ['[Browse Bucket](https://epa-rsei-pds.s3.amazonaws.com/index.html)'] +EPA Risk-Screening Environmental Indicators RSEI Microdata arn:aws:s3:::epa-rsei-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/epa-rsei-pds https://www.epa.gov/rsei/forms/contact-us-about-rsei-model [Environmental Protection Agency](https://www.epa.gov/rsei/) Updated infrequently US Government work aws-pds, environmental ['[Browse Bucket](https://epa-rsei-pds.s3.amazonaws.com/index.html)'] ERA5-for-WRF Open Data on AWS ERA5-for-WRF Data arn:aws:s3:::era5-for-wrf us-east-1 S3 Bucket https://github.com/moptis/era5-for-wrf/ info@veer.eco [Veer Renewables](http://www.veer.eco/) Monthly. CC BY-SA 4.0 aws-pds, weather, sustainability, atmosphere, electricity, meteorological, model -ESA WorldCover ESA WorldCover in a S3 bucket arn:aws:s3:::esa-worldcover eu-central-1 S3 Bucket Documentation is available [here](https://esa-worldcover.org/en/data-access). https://esa-worldcover.org/en/contact [VITO](https://vito.be) Yearly. CC-BY 4.0 aws-pds, earth observation, agriculture, satellite imagery, geospatial, natural resource, sustainability, cog, disaster response, mapping, synthetic aperture radar, land cover, land use, machine learning, stac False ['[STAC endpoint](https://services.terrascope.be/stac/)'] -ESA WorldCover Sentinel-1 and Sentinel-2 10m Annual Composites - ESA WorldCover S1 composites The bucket contains 1 Sentinel-1 annual composite ESA WorldCover S1 composites The bucket contains 1 Sentinel-1 annual composite arn:aws:s3:::esa-worldcover-s1 eu-central-1 S3 Bucket More information is available on the products' [GitHub](https://github.com/ESA-W https://esa-worldcover.org/en/contact [VITO](https://vito.be) Not updated. CC-BY 4.0 aws-pds, earth observation, agriculture, satellite imagery, geospatial, natural resource, sustainability, cog, disaster response, mapping, synthetic aperture radar, land cover, land use, machine learning, stac False ['[Products Grid](https://esa-worldcover.s3.eu-central-1.amazonaws.com/esa_worldcover_grid_composites.fgb)', '[WorldCover Viewer](https://viewer.esa-worldcover.org/worldcover/?layer=WORLDCOVER_2021_S1_VVVHratio)'] -ESA WorldCover Sentinel-1 and Sentinel-2 10m Annual Composites - ESA WorldCover S2 composites The bucket contains the 3 Sentinel-2 L2A annual co ESA WorldCover S2 composites The bucket contains the 3 Sentinel-2 L2A annual co arn:aws:s3:::esa-worldcover-s2 eu-central-1 S3 Bucket More information is available on the products' [GitHub](https://github.com/ESA-W https://esa-worldcover.org/en/contact [VITO](https://vito.be) Not updated. CC-BY 4.0 aws-pds, earth observation, agriculture, satellite imagery, geospatial, natural resource, sustainability, cog, disaster response, mapping, synthetic aperture radar, land cover, land use, machine learning, stac False ['[Products Grid](https://esa-worldcover.s3.eu-central-1.amazonaws.com/esa_worldcover_grid_composites.fgb)', '[WorldCover Viewer](https://viewer.esa-worldcover.org/worldcover/?layer=WORLDCOVER_2021_S2_NDVI)'] +ESA WorldCover ESA WorldCover in a S3 bucket arn:aws:s3:::esa-worldcover eu-central-1 S3 Bucket Documentation is available [here](https://esa-worldcover.org/en/data-access). https://esa-worldcover.org/en/contact [VITO](https://vito.be) Yearly. CC-BY 4.0 aws-pds, earth observation, agriculture, satellite imagery, geospatial, natural resource, sustainability, cog, disaster response, mapping, synthetic aperture radar, land cover, land use, machine learning, stac ['[STAC endpoint](https://services.terrascope.be/stac/)'] False +ESA WorldCover Sentinel-1 and Sentinel-2 10m Annual Composites - ESA WorldCover S1 composites The bucket contains 1 Sentinel-1 annual composite ESA WorldCover S1 composites The bucket contains 1 Sentinel-1 annual composite arn:aws:s3:::esa-worldcover-s1 eu-central-1 S3 Bucket More information is available on the products' [GitHub](https://github.com/ESA-W https://esa-worldcover.org/en/contact [VITO](https://vito.be) Not updated. CC-BY 4.0 aws-pds, earth observation, agriculture, satellite imagery, geospatial, natural resource, sustainability, cog, disaster response, mapping, synthetic aperture radar, land cover, land use, machine learning, stac ['[Products Grid](https://esa-worldcover.s3.eu-central-1.amazonaws.com/esa_worldcover_grid_composites.fgb)', '[WorldCover Viewer](https://viewer.esa-worldcover.org/worldcover/?layer=WORLDCOVER_2021_S1_VVVHratio)'] False +ESA WorldCover Sentinel-1 and Sentinel-2 10m Annual Composites - ESA WorldCover S2 composites The bucket contains the 3 Sentinel-2 L2A annual co ESA WorldCover S2 composites The bucket contains the 3 Sentinel-2 L2A annual co arn:aws:s3:::esa-worldcover-s2 eu-central-1 S3 Bucket More information is available on the products' [GitHub](https://github.com/ESA-W https://esa-worldcover.org/en/contact [VITO](https://vito.be) Not updated. CC-BY 4.0 aws-pds, earth observation, agriculture, satellite imagery, geospatial, natural resource, sustainability, cog, disaster response, mapping, synthetic aperture radar, land cover, land use, machine learning, stac ['[Products Grid](https://esa-worldcover.s3.eu-central-1.amazonaws.com/esa_worldcover_grid_composites.fgb)', '[WorldCover Viewer](https://viewer.esa-worldcover.org/worldcover/?layer=WORLDCOVER_2021_S2_NDVI)'] False EURO-CORDEX - European component of the Coordinated Regional Downscaling Experiment Project data files arn:aws:s3:::euro-cordex eu-central-1 S3 Bucket https://www.euro-cordex.net/060378/index.php.en cordex-aws@hereon.de [Helmholtz Centre Hereon / GERICS](https://www.climate-service-center.de) We will add more datasets on demand. https://is-enes-data.github.io/cordex_terms_of_use.pdf aws-pds, climate, model, climate model, atmosphere, geospatial, zarr -Earth Observation Data Cubes for Brazil - Earth Observation Data Cubes for Brazil - CBERS 4 Earth Observation Data Cubes for Brazil - CBERS 4 arn:aws:s3:::bdc-cbers us-west-2 S3 Bucket http://brazildatacube.org/en/home-page-2/ brazildatacube@inpe.br [INPE - Brazil Data Cube](http://brazildatacube.org/) New EO data cubes are added as soon as there are produced by the Brazil Data Cub The EO data cubes are produced from free and open images of CBERS-4, Landsat-8 a earth observation, satellite imagery, geoscience, geospatial, image processing, open source software, cog, stac, aws-pds False ['[BDC STAC V0.9.0 endpoint](https://bdc-cbers.s3.us-west-2.amazonaws.com/catalog.json)'] -Earth Observation Data Cubes for Brazil - Earth Observation Data Cubes for Brazil - Sentinel 2A/2B Earth Observation Data Cubes for Brazil - Sentinel 2A/2B arn:aws:s3:::bdc-sentinel-2 us-west-2 S3 Bucket http://brazildatacube.org/en/home-page-2/ brazildatacube@inpe.br [INPE - Brazil Data Cube](http://brazildatacube.org/) New EO data cubes are added as soon as there are produced by the Brazil Data Cub The EO data cubes are produced from free and open images of CBERS-4, Landsat-8 a earth observation, satellite imagery, geoscience, geospatial, image processing, open source software, cog, stac, aws-pds False ['[BDC STAC V0.9.0 endpoint](https://bdc-sentinel-2.s3.us-west-2.amazonaws.com/catalog.json)'] +Earth Observation Data Cubes for Brazil - Earth Observation Data Cubes for Brazil - CBERS 4 Earth Observation Data Cubes for Brazil - CBERS 4 arn:aws:s3:::bdc-cbers us-west-2 S3 Bucket http://brazildatacube.org/en/home-page-2/ brazildatacube@inpe.br [INPE - Brazil Data Cube](http://brazildatacube.org/) New EO data cubes are added as soon as there are produced by the Brazil Data Cub The EO data cubes are produced from free and open images of CBERS-4, Landsat-8 a earth observation, satellite imagery, geoscience, geospatial, image processing, open source software, cog, stac, aws-pds ['[BDC STAC V0.9.0 endpoint](https://bdc-cbers.s3.us-west-2.amazonaws.com/catalog.json)'] False +Earth Observation Data Cubes for Brazil - Earth Observation Data Cubes for Brazil - Sentinel 2A/2B Earth Observation Data Cubes for Brazil - Sentinel 2A/2B arn:aws:s3:::bdc-sentinel-2 us-west-2 S3 Bucket http://brazildatacube.org/en/home-page-2/ brazildatacube@inpe.br [INPE - Brazil Data Cube](http://brazildatacube.org/) New EO data cubes are added as soon as there are produced by the Brazil Data Cub The EO data cubes are produced from free and open images of CBERS-4, Landsat-8 a earth observation, satellite imagery, geoscience, geospatial, image processing, open source software, cog, stac, aws-pds ['[BDC STAC V0.9.0 endpoint](https://bdc-sentinel-2.s3.us-west-2.amazonaws.com/catalog.json)'] False Earth Observation Data Cubes for Brazil - Notifications for new EO Data Cubes CBERS scenes Notifications for new EO Data Cubes CBERS scenes arn:aws:sns:us-west-2:896627514407:bdc-cbers-object_created us-west-2 SNS Topic http://brazildatacube.org/en/home-page-2/ brazildatacube@inpe.br [INPE - Brazil Data Cube](http://brazildatacube.org/) New EO data cubes are added as soon as there are produced by the Brazil Data Cub The EO data cubes are produced from free and open images of CBERS-4, Landsat-8 a earth observation, satellite imagery, geoscience, geospatial, image processing, open source software, cog, stac, aws-pds Earth Observation Data Cubes for Brazil - Notifications for new EO Data Cubes Sentinel-2 scenes Notifications for new EO Data Cubes Sentinel-2 scenes arn:aws:sns:us-west-2:896627514407:bdc-sentinel-2-object_created us-west-2 SNS Topic http://brazildatacube.org/en/home-page-2/ brazildatacube@inpe.br [INPE - Brazil Data Cube](http://brazildatacube.org/) New EO data cubes are added as soon as there are produced by the Brazil Data Cub The EO data cubes are produced from free and open images of CBERS-4, Landsat-8 a earth observation, satellite imagery, geoscience, geospatial, image processing, open source software, cog, stac, aws-pds -Earth Radio Occultation Three types of data exist in a directory hierarchy that includes the contributin arn:aws:s3:::gnss-ro-data us-east-1 S3 Bucket http://github.com/gnss-ro/aws-opendata Stephen Leroy (sleroy@aer.com) Verisk Atmospheric and Environmental Research, Inc. The dataset is updated monthly for UCAR and ROM SAF contributions only. The upda A Creative Commons open-use licence (https://www.ucar.edu/terms-of-use/data) app aws-pds, atmosphere, climate, earth observation, global, signal processing, weather ['[Browse Bucket](https://gnss-ro-data.s3.amazonaws.com/index.html)'] -EarthDEM EarthDEM DEM Strips arn:aws:s3:::pgc-opendata-dems/earthdem/strips/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/earthdem/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added when allowed by licensing restrictions. Mosaic product [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/earthdem/strips.json)'] -End-Use Load Profiles for the U.S. Building Stock End-Use Load Profiles for the US Building Stock arn:aws:s3:::oedi-data-lake/nrel-pds-building-stock/ us-west-2 S3 Bucket https://www.nrel.gov/buildings/end-use-load-profiles.html ComStock@nrel.gov and ResStock@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Twice per year [ComStock License](https://github.com/NREL/ComStock/blob/main/LICENSE.txt) and [ aws-pds, climate, cities, energy, energy modeling, geospatial, metadata, model, open source software, sustainability, utilities ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=nrel-pds-building-stock%2Fend-use-load-profiles-for-us-building-stock%2F)'] -Ensemble Meteorological Dataset for Planet Earth, EM-Earth Ensemble Meteorological Dataset for Planet Earth, EM-Earth arn:aws:s3:::emearth us-west-2 S3 Bucket https://doi.org/10.20383/102.0547 shervan.gharari@usask.ca [Computational Hydrology at the University of Saskatchewan](https://uofs-comphyd N/A Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommo aws-pds, atmosphere, netcdf, near-surface air temperature, precipitation, meteorological ['[Browse Bucket](https://emearth.s3.amazonaws.com/index.html)'] +Earth Radio Occultation Three types of data exist in a directory hierarchy that includes the contributin arn:aws:s3:::gnss-ro-data us-east-1 S3 Bucket http://github.com/gnss-ro/aws-opendata Stephen Leroy (sleroy@aer.com) Verisk Atmospheric and Environmental Research, Inc. The dataset is updated monthly for UCAR and ROM SAF contributions only. The upda A Creative Commons open-use licence (https://www.ucar.edu/terms-of-use/data) app aws-pds, atmosphere, climate, earth observation, global, signal processing, weather ['[Browse Bucket](https://gnss-ro-data.s3.amazonaws.com/index.html)'] +EarthDEM EarthDEM DEM Strips arn:aws:s3:::pgc-opendata-dems/earthdem/strips/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/earthdem/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added when allowed by licensing restrictions. Mosaic product [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/earthdem/strips.json)'] +End-Use Load Profiles for the U.S. Building Stock End-Use Load Profiles for the US Building Stock arn:aws:s3:::oedi-data-lake/nrel-pds-building-stock/ us-west-2 S3 Bucket https://www.nrel.gov/buildings/end-use-load-profiles.html ComStock@nrel.gov and ResStock@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Twice per year [ComStock License](https://github.com/NREL/ComStock/blob/main/LICENSE.txt) and [ aws-pds, climate, cities, energy, energy modeling, geospatial, metadata, model, open source software, sustainability, utilities ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=nrel-pds-building-stock%2Fend-use-load-profiles-for-us-building-stock%2F)'] +Ensemble Meteorological Dataset for Planet Earth, EM-Earth Ensemble Meteorological Dataset for Planet Earth, EM-Earth arn:aws:s3:::emearth us-west-2 S3 Bucket https://doi.org/10.20383/102.0547 shervan.gharari@usask.ca [Computational Hydrology at the University of Saskatchewan](https://uofs-comphyd N/A Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommo aws-pds, atmosphere, netcdf, near-surface air temperature, precipitation, meteorological ['[Browse Bucket](https://emearth.s3.amazonaws.com/index.html)'] Finnish Meteorological Institute Weather Radar Data - Notifications for new GeoTIFF data Notifications for new GeoTIFF data arn:aws:sns:eu-west-1:916174725480:fmi-opendata-radar-geotiff-object_created eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/radar-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 5 minutes Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, weather, meteorological Finnish Meteorological Institute Weather Radar Data - Notifications for new volume data Notifications for new volume data arn:aws:sns:eu-west-1:916174725480:fmi-opendata-radar-volume-hdf5-object_created eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/radar-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 5 minutes Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, weather, meteorological -Finnish Meteorological Institute Weather Radar Data - Radar data as GeoTIFF Radar data as GeoTIFF arn:aws:s3:::fmi-opendata-radar-geotiff eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/radar-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 5 minutes Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, weather, meteorological ['[Browse Bucket](https://fmi-opendata-radar-geotiff.s3.amazonaws.com/index.html)'] -Finnish Meteorological Institute Weather Radar Data - Volume data as HDF5 Volume data as HDF5 arn:aws:s3:::fmi-opendata-radar-volume-hdf5 eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/radar-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 5 minutes Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, weather, meteorological ['[Browse Bucket](https://fmi-opendata-radar-volume-hdf5.s3.amazonaws.com/index.html)'] +Finnish Meteorological Institute Weather Radar Data - Radar data as GeoTIFF Radar data as GeoTIFF arn:aws:s3:::fmi-opendata-radar-geotiff eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/radar-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 5 minutes Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, weather, meteorological ['[Browse Bucket](https://fmi-opendata-radar-geotiff.s3.amazonaws.com/index.html)'] +Finnish Meteorological Institute Weather Radar Data - Volume data as HDF5 Volume data as HDF5 arn:aws:s3:::fmi-opendata-radar-volume-hdf5 eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/radar-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 5 minutes Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, weather, meteorological ['[Browse Bucket](https://fmi-opendata-radar-volume-hdf5.s3.amazonaws.com/index.html)'] Ford Multi-AV Seasonal Dataset All data arn:aws:s3:::ford-multi-av-seasonal us-west-2 S3 Bucket avdata.ford.com avdata.ford.com [Ford Motor Company](https://avdata.ford.com) New data will be added until the entire dataset is released online. This data is intended for non-commercial academic use only. It is licensed under autonomous vehicles, computer vision, lidar, mapping, robotics, transportation, urban, weather, aws-pds -GEOS-Chem Input Data Top-level directory for all GEOS-Chem data arn:aws:s3:::geos-chem us-east-1 S3 Bucket https://geos-chem.readthedocs.io/en/latest/geos-chem-shared-docs/supplemental-gu https://geoschem.github.io/support-team.html [GEOS-Chem Support Team](https://geoschem.github.io/support-team.html) New meteorological and emission data will be added when available. https://geoschem.github.io/license.html aws-pds, climate, weather, meteorological, environmental, air quality, chemistry, atmosphere, model ['[Browse Bucket](https://geos-chem.s3.amazonaws.com/index.html)'] -GEOS-Chem Nested Input Data Top-level directory for all GEOS-Chem nested-grid data arn:aws:s3:::gcgrid us-east-1 S3 Bucket https://geos-chem.readthedocs.io http://geos-chem.org/support-team [GEOS-Chem Support Team](https://geoschem.github.io/support-team.html) New meteorological and emission data will be added when available. http://geos-chem.org/license aws-pds, climate, weather, meteorological, environmental, air quality, chemistry, atmosphere, model ['[Browse Bucket](https://s3.amazonaws.com/gcgrid/index.html)'] +GEOS-Chem Input Data Top-level directory for all GEOS-Chem data arn:aws:s3:::geos-chem us-east-1 S3 Bucket https://geos-chem.readthedocs.io/en/latest/geos-chem-shared-docs/supplemental-gu https://geoschem.github.io/support-team.html [GEOS-Chem Support Team](https://geoschem.github.io/support-team.html) New meteorological and emission data will be added when available. https://geoschem.github.io/license.html aws-pds, climate, weather, meteorological, environmental, air quality, chemistry, atmosphere, model ['[Browse Bucket](https://geos-chem.s3.amazonaws.com/index.html)'] +GEOS-Chem Nested Input Data Top-level directory for all GEOS-Chem nested-grid data arn:aws:s3:::gcgrid us-east-1 S3 Bucket https://geos-chem.readthedocs.io http://geos-chem.org/support-team [GEOS-Chem Support Team](https://geoschem.github.io/support-team.html) New meteorological and emission data will be added when available. http://geos-chem.org/license aws-pds, climate, weather, meteorological, environmental, air quality, chemistry, atmosphere, model ['[Browse Bucket](https://s3.amazonaws.com/gcgrid/index.html)'] Geosnap Data, Center for Geospatial Sciences Data files stored as Apache parquet and GeoTiff in a public bucket arn:aws:s3:::spatial-ucr us-east-1 S3 Bucket https://spatialucr.github.io/geosnap-guide/content/home Eli Knaap [UCR Center for Geospatial Sciences](https://spatial.ucr.edu) Annually BSD aws-pds, urban, geospatial, demographics -Global 30m Height Above Nearest Drainage (HAND) - GLO-30 HAND S3 bucket GLO-30 HAND S3 bucket arn:aws:s3:::glo-30-hand us-west-2 S3 Bucket https://glo-30-hand.s3.us-west-2.amazonaws.com/readme.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) None, except HAND may be updated if the[ Copernicus GLO-30 Public](https://regis Copyright 2022 Alaska Satellite Facility (ASF). Produced using the Copernicus Wo aws-pds, elevation, hydrology, agriculture, disaster response, satellite imagery, geospatial, cog, stac ['[STAC V1.0.0 endpoint](https://stac.asf.alaska.edu/collections/glo-30-hand)', '[Via STAC Browser](https://radiantearth.github.io/stac-browser/#/external/stac.asf.alaska.edu/collections/glo-30-hand)'] +Global 30m Height Above Nearest Drainage (HAND) - GLO-30 HAND S3 bucket GLO-30 HAND S3 bucket arn:aws:s3:::glo-30-hand us-west-2 S3 Bucket https://glo-30-hand.s3.us-west-2.amazonaws.com/readme.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) None, except HAND may be updated if the[ Copernicus GLO-30 Public](https://regis Copyright 2022 Alaska Satellite Facility (ASF). Produced using the Copernicus Wo aws-pds, elevation, hydrology, agriculture, disaster response, satellite imagery, geospatial, cog, stac ['[STAC V1.0.0 endpoint](https://stac.asf.alaska.edu/collections/glo-30-hand)', '[Via STAC Browser](https://radiantearth.github.io/stac-browser/#/external/stac.asf.alaska.edu/collections/glo-30-hand)'] Global 30m Height Above Nearest Drainage (HAND) - Notifications for new data Notifications for new data arn:aws:sns:us-west-2:879002409890:glo-30-hand-object_created us-west-2 SNS Topic https://glo-30-hand.s3.us-west-2.amazonaws.com/readme.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) None, except HAND may be updated if the[ Copernicus GLO-30 Public](https://regis Copyright 2022 Alaska Satellite Facility (ASF). Produced using the Copernicus Wo aws-pds, elevation, hydrology, agriculture, disaster response, satellite imagery, geospatial, cog, stac Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (af-south-1 region) GBIF species occurrence data in Parquet format (af-south-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-af-south-1-object_created af-south-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences -Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (af-south-1 region) GBIF species occurrence data in Parquet format (af-south-1 region) arn:aws:s3:::gbif-open-data-af-south-1 af-south-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-af-south-1.s3.af-south-1.amazonaws.com/index.html)'] +Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (af-south-1 region) GBIF species occurrence data in Parquet format (af-south-1 region) arn:aws:s3:::gbif-open-data-af-south-1 af-south-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-af-south-1.s3.af-south-1.amazonaws.com/index.html)'] Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (ap-southeast-2 region) GBIF species occurrence data in Parquet format (ap-southeast-2 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-ap-southeast-2-object_created ap-southeast-2 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences -Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (ap-southeast-2 region) GBIF species occurrence data in Parquet format (ap-southeast-2 region) arn:aws:s3:::gbif-open-data-ap-southeast-2 ap-southeast-2 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-ap-southeast-2.s3.ap-southeast-2.amazonaws.com/index.html)'] +Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (ap-southeast-2 region) GBIF species occurrence data in Parquet format (ap-southeast-2 region) arn:aws:s3:::gbif-open-data-ap-southeast-2 ap-southeast-2 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-ap-southeast-2.s3.ap-southeast-2.amazonaws.com/index.html)'] Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (eu-central-1 region) GBIF species occurrence data in Parquet format (eu-central-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-eu-central-1-object_created eu-central-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences -Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (eu-central-1 region) GBIF species occurrence data in Parquet format (eu-central-1 region) arn:aws:s3:::gbif-open-data-eu-central-1 eu-central-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-eu-central-1.s3.eu-central-1.amazonaws.com/index.html)'] -Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (sa-east-1 region) GBIF species occurrence data in Parquet format (sa-east-1 region) arn:aws:s3:::gbif-open-data-sa-east-1 sa-east-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-sa-east-1.s3.sa-east-1.amazonaws.com/index.html)'] +Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (eu-central-1 region) GBIF species occurrence data in Parquet format (eu-central-1 region) arn:aws:s3:::gbif-open-data-eu-central-1 eu-central-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-eu-central-1.s3.eu-central-1.amazonaws.com/index.html)'] +Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (sa-east-1 region) GBIF species occurrence data in Parquet format (sa-east-1 region) arn:aws:s3:::gbif-open-data-sa-east-1 sa-east-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-sa-east-1.s3.sa-east-1.amazonaws.com/index.html)'] Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (sa-east-1 region) GBIF species occurrence data in Parquet format (sa-east-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-sa-east-1-object_created sa-east-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (us-east-1 region) GBIF species occurrence data in Parquet format (us-east-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-us-east-1-object_created us-east-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences -Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (us-east-1 region) GBIF species occurrence data in Parquet format (us-east-1 region) arn:aws:s3:::gbif-open-data-us-east-1 us-east-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-us-east-1.s3.us-east-1.amazonaws.com/index.html)'] +Global Biodiversity Information Facility (GBIF) Species Occurrences - GBIF species occurrence data in Parquet format (us-east-1 region) GBIF species occurrence data in Parquet format (us-east-1 region) arn:aws:s3:::gbif-open-data-us-east-1 us-east-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-us-east-1.s3.us-east-1.amazonaws.com/index.html)'] Global Database of Events, Language and Tone (GDELT) - Notifications for new data Notifications for new data arn:aws:sns:us-east-1:928094251383:gdelt-csv us-east-1 SNS Topic http://www.gdeltproject.org/ http://www.gdeltproject.org/about.html#contact Unmanaged Not currently being updated http://www.gdeltproject.org/about.html#termsofuse aws-pds, events, disaster response Global Database of Events, Language and Tone (GDELT) - Project data files Project data files arn:aws:s3:::gdelt-open-data us-east-1 S3 Bucket http://www.gdeltproject.org/ http://www.gdeltproject.org/about.html#contact Unmanaged Not currently being updated http://www.gdeltproject.org/about.html#termsofuse aws-pds, events, disaster response Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter Data Set - 1x1 degree tiled data and metadata in a S3 bucket, Global VRT mosaics (vrt 1x1 degree tiled data and metadata in a S3 bucket, Global VRT mosaics (vrt arn:aws:s3:::sentinel-1-global-coherence-earthbigdata/data/tiles us-west-2 S3 Bucket http://sentinel-1-global-coherence-earthbigdata.s3-website-us-west-2.amazonaws.c For questions regarding data methodology or delivery, contact info@earthbigdata. [Earth Big Data LLC](https://earthbigdata.com/) The data set covers the time period from 1-Dec-2019 to 30-Nov-2020. No updates a The use of these data fall under the terms and conditions of the [Creative Commo global, satellite imagery, ecosystems, agriculture, urban, infrastructure, earth observation, earthquakes, environmental, geology, geophysics, geospatial, mapping, natural resource, cog, synthetic aperture radar, aws-pds Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter Data Set - Global mosaics at 001 degree pixel spacing as cloud optimized GeoTIFFs in a S3 Global mosaics at 001 degree pixel spacing as cloud optimized GeoTIFFs in a S3 arn:aws:s3:::sentinel-1-global-coherence-earthbigdata/data/mosaics us-west-2 S3 Bucket http://sentinel-1-global-coherence-earthbigdata.s3-website-us-west-2.amazonaws.c For questions regarding data methodology or delivery, contact info@earthbigdata. [Earth Big Data LLC](https://earthbigdata.com/) The data set covers the time period from 1-Dec-2019 to 30-Nov-2020. No updates a The use of these data fall under the terms and conditions of the [Creative Commo global, satellite imagery, ecosystems, agriculture, urban, infrastructure, earth observation, earthquakes, environmental, geology, geophysics, geospatial, mapping, natural resource, cog, synthetic aperture radar, aws-pds -Grid Algorithms and Data Analytics Library (GADAL) Sample IEEE123 Bus system for OEDI SI arn:aws:s3:::gadal/gadal_ieee123/ us-west-2 S3 Bucket https://github.com/openEDI/GADAL https://github.com/openEDI/GADAL/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, sustainability, model ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gadal&prefix=gadal_ieee123%2F)'] -Gulfwide Avian Colony Monitoring Survey Photos High resolution(5184 x 3456) images are provided in jpg format (compression qual arn:aws:s3:::twi-aviandata us-east-2 S3 Bucket https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647 avaiandataaws@thewaterinstitute.org [CPRA](https://coastal.la.gov/) and [The Water Institute](https://thewaterinstit ~2 years Creative Commons BY-SA biology, conservation, ecosystems, object detection, labeled, environmental, aws-pds ['[Explore dataset](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647/page/Data-Explorer/)', '[README](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647/page/Project-Information/)', '[Data processing notebook](https://github.com/waterinstitute/avian_data_ingestor/blob/master/doc/Metadata%20for%20DottedImages.ipynb)'] +Grid Algorithms and Data Analytics Library (GADAL) Sample IEEE123 Bus system for OEDI SI arn:aws:s3:::gadal/gadal_ieee123/ us-west-2 S3 Bucket https://github.com/openEDI/GADAL https://github.com/openEDI/GADAL/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, sustainability, model ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=gadal&prefix=gadal_ieee123%2F)'] +Gulfwide Avian Colony Monitoring Survey Photos High resolution(5184 x 3456) images are provided in jpg format (compression qual arn:aws:s3:::twi-aviandata us-east-2 S3 Bucket https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647 avaiandataaws@thewaterinstitute.org [CPRA](https://coastal.la.gov/) and [The Water Institute](https://thewaterinstit ~2 years Creative Commons BY-SA biology, conservation, ecosystems, object detection, labeled, environmental, aws-pds ['[Explore dataset](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647/page/Data-Explorer/)', '[README](https://experience.arcgis.com/experience/010503b4c64b4ff6a7f3570220a53647/page/Project-Information/)', '[Data processing notebook](https://github.com/waterinstitute/avian_data_ingestor/blob/master/doc/Metadata%20for%20DottedImages.ipynb)'] HIRLAM Weather Model - Notifications for new pressure data Notifications for new pressure data arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-rcrhirlam-pressure-grib eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) The data is updated four times a day with analysis hours 00, 06, 12 and 18. Corr Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, climate, weather, meteorological HIRLAM Weather Model - Notifications for new surface data Notifications for new surface data arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-rcrhirlam-surface-grib eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) The data is updated four times a day with analysis hours 00, 06, 12 and 18. Corr Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, climate, weather, meteorological -HIRLAM Weather Model - Pressure GRIB files Pressure GRIB files arn:aws:s3:::fmi-opendata-rcrhirlam-pressure-grib eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) The data is updated four times a day with analysis hours 00, 06, 12 and 18. Corr Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, climate, weather, meteorological ['[Browse Bucket](https://fmi-opendata-rcrhirlam-pressure-grib.s3.amazonaws.com/index.html)'] -HIRLAM Weather Model - Surface GRIB files Surface GRIB files arn:aws:s3:::fmi-opendata-rcrhirlam-surface-grib eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) The data is updated four times a day with analysis hours 00, 06, 12 and 18. Corr Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, climate, weather, meteorological ['[Browse Bucket](https://fmi-opendata-rcrhirlam-surface-grib.s3.amazonaws.com/index.html)'] +HIRLAM Weather Model - Pressure GRIB files Pressure GRIB files arn:aws:s3:::fmi-opendata-rcrhirlam-pressure-grib eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) The data is updated four times a day with analysis hours 00, 06, 12 and 18. Corr Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, climate, weather, meteorological ['[Browse Bucket](https://fmi-opendata-rcrhirlam-pressure-grib.s3.amazonaws.com/index.html)'] +HIRLAM Weather Model - Surface GRIB files Surface GRIB files arn:aws:s3:::fmi-opendata-rcrhirlam-surface-grib eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) The data is updated four times a day with analysis hours 00, 06, 12 and 18. Corr Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, climate, weather, meteorological ['[Browse Bucket](https://fmi-opendata-rcrhirlam-surface-grib.s3.amazonaws.com/index.html)'] High Resolution Canopy Height Maps by WRI and Meta - California Canopy Height maps GeoTIFF files and Geojson files with observation California Canopy Height maps GeoTIFF files and Geojson files with observation arn:aws:s3:::dataforgood-fb-data/forests/v1/California/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial High Resolution Canopy Height Maps by WRI and Meta - Global Canopy Height maps GeoTIFF files and Geojson files with observation date Global Canopy Height maps GeoTIFF files and Geojson files with observation date arn:aws:s3:::dataforgood-fb-data/forests/v1/alsgedi_global_v6_float/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial High Resolution Canopy Height Maps by WRI and Meta - Model weights Model weights arn:aws:s3:::dataforgood-fb-data/forests/v1/models/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial High Resolution Canopy Height Maps by WRI and Meta - Sao Paulo Canopy Height maps GeoTIFF files and Geojson files with observation d Sao Paulo Canopy Height maps GeoTIFF files and Geojson files with observation d arn:aws:s3:::dataforgood-fb-data/forests/v1/sao_paulo/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial High Resolution Canopy Height Maps by WRI and Meta - Sub Saharan Africa Height maps GeoTIFF files and Geojson files with observation Sub Saharan Africa Height maps GeoTIFF files and Geojson files with observation arn:aws:s3:::dataforgood-fb-data/forests/v1/subsaharan_africa/ us-east-1 S3 Bucket https://github.com/facebookresearch/HighResCanopyHeight dataforgood@meta.com [Meta](https://dataforgood.fb.com/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, climate, land cover, agriculture, machine learning, aerial imagery, satellite imagery, image processing, geospatial -High Resolution Downscaled Climate Data for Southeast Alaska High-res dynamically downscaled climate data for Southeast Alaska arn:aws:s3:::wrf-se-ak-ar5 us-west-2 S3 Bucket https://www.sciencebase.gov/catalog/item/5f93658882ce720ee2d598a7 http://directory.iarc.uaf.edu/richard-lader Scenarios Network for Alaska + Arctic Planning at the International Arctic Resea as needed https://creativecommons.org/licenses/by/4.0/ aws-pds, agriculture, climate, coastal, earth observation, environmental, weather, aws-pds, sustainability ['[Browse Bucket](http://wrf-se-ak-ar5.s3-website-us-west-2.amazonaws.com/)'] +High Resolution Downscaled Climate Data for Southeast Alaska High-res dynamically downscaled climate data for Southeast Alaska arn:aws:s3:::wrf-se-ak-ar5 us-west-2 S3 Bucket https://www.sciencebase.gov/catalog/item/5f93658882ce720ee2d598a7 http://directory.iarc.uaf.edu/richard-lader Scenarios Network for Alaska + Arctic Planning at the International Arctic Resea as needed https://creativecommons.org/licenses/by/4.0/ aws-pds, agriculture, climate, coastal, earth observation, environmental, weather, aws-pds, sustainability ['[Browse Bucket](http://wrf-se-ak-ar5.s3-website-us-west-2.amazonaws.com/)'] High Resolution Population Density Maps + Demographic Estimates by CIESIN and Meta - CSV files CSV files arn:aws:s3:::dataforgood-fb-data/csv/ us-east-1 S3 Bucket [Project overview](https://dataforgood.facebook.com/dfg/docs/methodology-high-re disastermaps@fb.com [Meta](https://dataforgood.fb.com/) Quarterly https://creativecommons.org/licenses/by/4.0/ population, demographics, machine learning, aerial imagery, satellite imagery, image processing, geospatial, disaster response, aws-pds High Resolution Population Density Maps + Demographic Estimates by CIESIN and Meta - Cloud-optimized GeoTIFF files Cloud-optimized GeoTIFF files arn:aws:s3:::dataforgood-fb-data/hrsl-cogs/ us-east-1 S3 Bucket [Project overview](https://dataforgood.facebook.com/dfg/docs/methodology-high-re disastermaps@fb.com [Meta](https://dataforgood.fb.com/) Quarterly https://creativecommons.org/licenses/by/4.0/ population, demographics, machine learning, aerial imagery, satellite imagery, image processing, geospatial, disaster response, aws-pds High resolution, annual cropland and landcover maps for selected African countries Field boundary and land cover maps arn:aws:s3:::mappingafrica us-west-2 S3 Bucket https://github.com/agroimpacts/mapping-africa mappingafrica@clarku.edu [The Agricultural Impacts Research Group](https://agroimpacts.info/) New maps are added as developed [Planet NICFI participant license agreement](https://assets.planet.com/docs/Plan aws-pds, agriculture, land cover, satellite imagery, machine learning, deep learning, cog, labeled -Homeland Security and Infrastructure US Cities A Requester Pays Bucket of HSIP data including building footprints, LiDAR, ortho arn:aws:s3:::usgs-lidar-uscities us-west-2 S3 Bucket https://github.com/hobuinc/hsip-lidar https://github.com/hobuinc/hsip-lidar [Hobu, Inc.](https://hobu.co) Periodically US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensin aws-pds, elevation, disaster response, geospatial, lidar True +Homeland Security and Infrastructure US Cities A Requester Pays Bucket of HSIP data including building footprints, LiDAR, ortho arn:aws:s3:::usgs-lidar-uscities us-west-2 S3 Bucket https://github.com/hobuinc/hsip-lidar https://github.com/hobuinc/hsip-lidar [Hobu, Inc.](https://hobu.co) Periodically US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensin aws-pds, elevation, disaster response, geospatial, lidar True IDEAM - Colombian Radar Network Level II data arn:aws:s3:::s3-radaresideam us-east-1 S3 Bucket http://www.pronosticosyalertas.gov.co/archivos-radar atencionalciudadano@ideam.gov.co, radares_ideam@ideam.gov.co [IDEAM](http://www.ideam.gov.co/) Updated level II data is added as soon as it is available. Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, agriculture, earth observation, natural resource, weather, meteorological IGP Coal Plant Asset level data - Coal_Plants_IGP arn:aws:s3:::assetdata-igp/Coal Plants/ ap-south-1 S3 Bucket https://github.com/APAD2024/APAD-Asset-Data/blob/main/Asset%20Data/Coal%20Plants https://github.com/APAD2024/APAD-Asset-Data/issues APAD as needed https://creativecommons.org/licenses/by/4.0/ air quality, energy, meteorological, environmental ISERV ISERV Optical Imagery arn:aws:s3:::nasa-iserv us-west-2 S3 Bucket https://stacindex.org/collections/nasa-iserv support@radiant.earth [Radiant Earth Foundation](https://www.radiant.earth/) Not updated The data is released under a ODC Public Domain Dedication & License 1.0 ([PDDL-1 aws-pds, geospatial, earth observation, satellite imagery, environmental Indiana Statewide Digital Aerial Imagery Catalog State of Indiana digital orthophotography archive arn:aws:s3:::gisimageryingov us-east-2 S3 Bucket https://imagery.gio.in.gov/ sscholer@iot.in.gov Indiana Geographic Information Office The State of Indiana has had a 4-year cycle collecting imagery. The collections Access to Indiana Geographic Information Office Orthoimagery is governed by Crea aerial imagery, aws-pds, earth observation, geospatial, imaging, mapping, cog, natural resource, sustainability, agriculture Indiana Statewide Elevation Catalog State of Indiana Elevation archive arn:aws:s3:::giselevationingov us-east-2 S3 Bucket https://elevation.gio.in.gov/ sscholer@iot.in.gov Indiana Geographic Information Office The State of Indiana has another four-year cycle of collecting orthoimagery and Access to Indiana Geographic Information Office Lidar is governed by Creative Co lidar, aws-pds, earth observation, geospatial, imaging, mapping, natural resource, sustainability, agriculture -Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) - ITS_LIVE Data S3 Bucket ITS_LIVE Data S3 Bucket arn:aws:s3:::its-live-data us-west-2 S3 Bucket https://its-live-data.s3.us-west-2.amazonaws.com/README.html If you have questions about the data itself or the processing methods used, plea [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Up to daily, as new satellite imagery is made available. [Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/ aws-pds, ice, earth observation, satellite imagery, geophysics, geospatial, global, cog, netcdf, zarr, stac ['[Browse Bucket](https://its-live-data.s3.amazonaws.com/index.html)'] +Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) - ITS_LIVE Data S3 Bucket ITS_LIVE Data S3 Bucket arn:aws:s3:::its-live-data us-west-2 S3 Bucket https://its-live-data.s3.us-west-2.amazonaws.com/README.html If you have questions about the data itself or the processing methods used, plea [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Up to daily, as new satellite imagery is made available. [Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/ aws-pds, ice, earth observation, satellite imagery, geophysics, geospatial, global, cog, netcdf, zarr, stac ['[Browse Bucket](https://its-live-data.s3.amazonaws.com/index.html)'] Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) - Notifications for new data Notifications for new data arn:aws:sns:us-west-2:367587189974:its-live-data-object_created us-west-2 SNS Topic https://its-live-data.s3.us-west-2.amazonaws.com/README.html If you have questions about the data itself or the processing methods used, plea [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Up to daily, as new satellite imagery is made available. [Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/ aws-pds, ice, earth observation, satellite imagery, geophysics, geospatial, global, cog, netcdf, zarr, stac -JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations - Scenes and metadata for monoscopic observing mode Scenes and metadata for monoscopic observing mode arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/monoscopic/uncontrolled/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) The Kaguya/SELENE mission has completed. No updates to this dataset are planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_monoscopic_uncontrolled_observations)'] -JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations - Scenes and metadata for spectral profiler (spsupport) observing mode Scenes and metadata for spectral profiler (spsupport) observing mode arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/spsupport/uncontrolled/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) The Kaguya/SELENE mission has completed. No updates to this dataset are planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_spsupport_uncontrolled_observations)'] -JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations - Scenes and metadata for stereoscopic observing mode Scenes and metadata for stereoscopic observing mode arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/stereoscopic/uncontrolled/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) The Kaguya/SELENE mission has completed. No updates to this dataset are planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_stereoscopic_uncontrolled_observations)'] -JMA Himawari-8/9 - Himawari-8 Imagery Himawari-8 Imagery arn:aws:s3:::noaa-himawari8 us-east-1 S3 Bucket https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-himawari8.s3.amazonaws.com/index.html)'] -JMA Himawari-8/9 - Himawari-9 Imagery Himawari-9 Imagery arn:aws:s3:::noaa-himawari9 us-east-1 S3 Bucket https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-himawari9.s3.amazonaws.com/index.html)'] +JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations - Scenes and metadata for monoscopic observing mode Scenes and metadata for monoscopic observing mode arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/monoscopic/uncontrolled/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) The Kaguya/SELENE mission has completed. No updates to this dataset are planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_monoscopic_uncontrolled_observations)'] +JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations - Scenes and metadata for spectral profiler (spsupport) observing mode Scenes and metadata for spectral profiler (spsupport) observing mode arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/spsupport/uncontrolled/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) The Kaguya/SELENE mission has completed. No updates to this dataset are planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_spsupport_uncontrolled_observations)'] +JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations - Scenes and metadata for stereoscopic observing mode Scenes and metadata for stereoscopic observing mode arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/stereoscopic/uncontrolled/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) The Kaguya/SELENE mission has completed. No updates to this dataset are planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_stereoscopic_uncontrolled_observations)'] +JMA Himawari-8/9 - Himawari-8 Imagery Himawari-8 Imagery arn:aws:s3:::noaa-himawari8 us-east-1 S3 Bucket https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-himawari8.s3.amazonaws.com/index.html)'] +JMA Himawari-8/9 - Himawari-9 Imagery Himawari-9 Imagery arn:aws:s3:::noaa-himawari9 us-east-1 S3 Bucket https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-himawari9.s3.amazonaws.com/index.html)'] JMA Himawari-8/9 - New data notifications for Himawari-8, only Lambda and SQS protocols allowed New data notifications for Himawari-8, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewHimawari8Object us-east-1 SNS Topic https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery JMA Himawari-8/9 - New data notifications for Himawari-9, only Lambda and SQS protocols allowed New data notifications for Himawari-9, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewHimawariNineObject us-east-1 SNS Topic https://www.data.jma.go.jp/mscweb/en/himawari89/cloud_service/cloud_service.html For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2.5 minutes for Regions 1, 2, and 3, and .5 minutes fo Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery -Korea Meteorological Administration (KMA) GK-2A Satellite Data - GK2A Imagery GK2A Imagery arn:aws:s3:::noaa-gk2a-pds us-east-1 S3 Bucket https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.fa For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2 minutes for 4 visible channels and 12 infrared chan Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-gk2a-pds.s3.amazonaws.com/index.html)'] +Korea Meteorological Administration (KMA) GK-2A Satellite Data - GK2A Imagery GK2A Imagery arn:aws:s3:::noaa-gk2a-pds us-east-1 S3 Bucket https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.fa For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2 minutes for 4 visible channels and 12 infrared chan Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-gk2a-pds.s3.amazonaws.com/index.html)'] Korea Meteorological Administration (KMA) GK-2A Satellite Data - New data notifications for GK2A, only Lambda and SQS protocols allowed New data notifications for GK2A, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:709902155096:NewGK2AObject us-east-1 SNS Topic https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.fa For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) 10 minutes for Full Disk, 2 minutes for 4 visible channels and 12 infrared chan Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery -KyFromAbove on AWS - Elevation and imagery data resources for the Commonwealth of Kentucky are organi Elevation and imagery data resources for the Commonwealth of Kentucky are organi arn:aws:s3:::kyfromabove us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation False ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html)'] -KyFromAbove on AWS - KyFromAbove Topographic Contours, digital elevation models, point cloud, spot el KyFromAbove Topographic Contours, digital elevation models, point cloud, spot el arn:aws:s3:::kyfromabove/elevation/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation False ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/)'] -KyFromAbove on AWS - KyFromAbove aerial imagery, both nadir and oblique views, can be found in this b KyFromAbove aerial imagery, both nadir and oblique views, can be found in this b arn:aws:s3:::kyfromabove/imagery/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation False ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/)'] -KyFromAbove on AWS - KyFromAbove oblique imagery can be found in this folder The four oblique views KyFromAbove oblique imagery can be found in this folder The four oblique views arn:aws:s3:::kyfromabove/imagery/obliques/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation False ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/obliques/)'] -KyFromAbove on AWS - KyFromAbove ortho imagery for the Commonwealth of Kentucky organized in a 5000x5 KyFromAbove ortho imagery for the Commonwealth of Kentucky organized in a 5000x5 arn:aws:s3:::kyfromabove/imagery/orthos/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation False ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/orthos/)'] -KyFromAbove on AWS - LiDAR-derived Point Cloud tiles for the Commonwealth of Kentucky organized in a LiDAR-derived Point Cloud tiles for the Commonwealth of Kentucky organized in a arn:aws:s3:::kyfromabove/elevation/PointCloud/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation False ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/PointCloud/)'] -KyFromAbove on AWS - LiDAR-derived digital elevation models (DEM) for the Commonwealth of Kentucky or LiDAR-derived digital elevation models (DEM) for the Commonwealth of Kentucky or arn:aws:s3:::kyfromabove/elevation/DEM/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation False ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/DEM/)'] -KyFromAbove on AWS - The data in this bucket includes spot elevations for the entire Commonwealth of The data in this bucket includes spot elevations for the entire Commonwealth of arn:aws:s3:::kyfromabove/elevation/SpotElevations/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation False ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/SpotElevations/)'] -KyFromAbove on AWS - There are three data resources in this folder - 1) KyTopo Map Series quadrangles There are three data resources in this folder - 1) KyTopo Map Series quadrangles arn:aws:s3:::kyfromabove/elevation/KyTopoMapSeries/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation False ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/KyTopoMapSeries/)'] -KyFromAbove on AWS - Topographic contours created from the KyFromAbove Phase 1 LiDAR-derived digital Topographic contours created from the KyFromAbove Phase 1 LiDAR-derived digital arn:aws:s3:::kyfromabove/elevation/Contours/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation False ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/Contours/)'] +KyFromAbove on AWS - Elevation and imagery data resources for the Commonwealth of Kentucky are organi Elevation and imagery data resources for the Commonwealth of Kentucky are organi arn:aws:s3:::kyfromabove us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html)'] False +KyFromAbove on AWS - KyFromAbove Topographic Contours, digital elevation models, point cloud, spot el KyFromAbove Topographic Contours, digital elevation models, point cloud, spot el arn:aws:s3:::kyfromabove/elevation/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/)'] False +KyFromAbove on AWS - KyFromAbove aerial imagery, both nadir and oblique views, can be found in this b KyFromAbove aerial imagery, both nadir and oblique views, can be found in this b arn:aws:s3:::kyfromabove/imagery/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/)'] False +KyFromAbove on AWS - KyFromAbove oblique imagery can be found in this folder The four oblique views KyFromAbove oblique imagery can be found in this folder The four oblique views arn:aws:s3:::kyfromabove/imagery/obliques/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/obliques/)'] False +KyFromAbove on AWS - KyFromAbove ortho imagery for the Commonwealth of Kentucky organized in a 5000x5 KyFromAbove ortho imagery for the Commonwealth of Kentucky organized in a 5000x5 arn:aws:s3:::kyfromabove/imagery/orthos/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#imagery/orthos/)'] False +KyFromAbove on AWS - LiDAR-derived Point Cloud tiles for the Commonwealth of Kentucky organized in a LiDAR-derived Point Cloud tiles for the Commonwealth of Kentucky organized in a arn:aws:s3:::kyfromabove/elevation/PointCloud/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/PointCloud/)'] False +KyFromAbove on AWS - LiDAR-derived digital elevation models (DEM) for the Commonwealth of Kentucky or LiDAR-derived digital elevation models (DEM) for the Commonwealth of Kentucky or arn:aws:s3:::kyfromabove/elevation/DEM/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/DEM/)'] False +KyFromAbove on AWS - The data in this bucket includes spot elevations for the entire Commonwealth of The data in this bucket includes spot elevations for the entire Commonwealth of arn:aws:s3:::kyfromabove/elevation/SpotElevations/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/SpotElevations/)'] False +KyFromAbove on AWS - There are three data resources in this folder - 1) KyTopo Map Series quadrangles There are three data resources in this folder - 1) KyTopo Map Series quadrangles arn:aws:s3:::kyfromabove/elevation/KyTopoMapSeries/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/KyTopoMapSeries/)'] False +KyFromAbove on AWS - Topographic contours created from the KyFromAbove Phase 1 LiDAR-derived digital Topographic contours created from the KyFromAbove Phase 1 LiDAR-derived digital arn:aws:s3:::kyfromabove/elevation/Contours/ us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/kyfromabove More information regarding the KyFromAbove program can be found at https://kyfro [Kentucky Division of Geographic Information](https://kygeonet.ky.gov) KyFromAbove data is typically updated on an annual basis. Each year, a portion o Public Domain with Attribution aws-pds, earth observation, aerial imagery, geospatial, lidar, elevation ['[Browse Bucket](https://kyfromabove.s3.us-west-2.amazonaws.com/index.html#elevation/Contours/)'] False Low Altitude Disaster Imagery (LADI) Dataset LADI dataset: images and labels arn:aws:s3:::ladi us-west-2 S3 Bucket https://github.com/LADI-Dataset/ladi-overview ladi-dataset-admin@mit.edu [MIT Lincoln Laboratory Humanitarian Assistance and Disaster Relief group](https Periodically Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, aerial imagery, coastal, computer vision, disaster response, earth observation, earthquakes, geospatial, imaging, image processing, infrastructure, land, machine learning, mapping, natural resource, seismology, transportation, urban, water MAN TruckScenes - MAN TruckScenes mini MAN TruckScenes mini arn:aws:s3:::man-truckscenes/release/mini/ eu-central-1 S3 Bucket https://www.man.eu/truckscenes truckscenes@man.eu [MAN Truck and Bus SE](https://www.man.eu) The dataset may be updated with additional or corrected data on a need-to-update [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) autonomous vehicles, radar, lidar, IMU, GPS, computer vision, machine learning, deep learning, perception, object detection, object tracking, transportation, logistics, robotics MAN TruckScenes - MAN TruckScenes test MAN TruckScenes test arn:aws:s3:::man-truckscenes/release/test/ eu-central-1 S3 Bucket https://www.man.eu/truckscenes truckscenes@man.eu [MAN Truck and Bus SE](https://www.man.eu) The dataset may be updated with additional or corrected data on a need-to-update [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) autonomous vehicles, radar, lidar, IMU, GPS, computer vision, machine learning, deep learning, perception, object detection, object tracking, transportation, logistics, robotics MAN TruckScenes - MAN TruckScenes trainval MAN TruckScenes trainval arn:aws:s3:::man-truckscenes/release/trainval/ eu-central-1 S3 Bucket https://www.man.eu/truckscenes truckscenes@man.eu [MAN Truck and Bus SE](https://www.man.eu) The dataset may be updated with additional or corrected data on a need-to-update [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) autonomous vehicles, radar, lidar, IMU, GPS, computer vision, machine learning, deep learning, perception, object detection, object tracking, transportation, logistics, robotics -MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - Imagery and metadata Imagery and metadata arn:aws:s3:::astraea-opendata us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True +MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - Imagery and metadata Imagery and metadata arn:aws:s3:::astraea-opendata us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - New data notifications New data notifications arn:aws:sns:us-west-2:791757209086:astraea-opendata-events us-west-2 SNS Topic Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response -MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - S3 Inventory files for MCD43A4 S3 Inventory files for MCD43A4 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MCD43A4 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True -MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - S3 Inventory files for MOD11A1 S3 Inventory files for MOD11A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MOD11A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True -MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - S3 Inventory files for MOD13A1 S3 Inventory files for MOD13A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MOD13A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True -MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - S3 Inventory files for MYD11A1 S3 Inventory files for MYD11A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MYD11A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True -MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - S3 Inventory files for MYD13A1 S3 Inventory files for MYD13A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MYD13A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True +MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - S3 Inventory files for MCD43A4 S3 Inventory files for MCD43A4 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MCD43A4 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True +MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - S3 Inventory files for MOD11A1 S3 Inventory files for MOD11A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MOD11A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True +MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - S3 Inventory files for MOD13A1 S3 Inventory files for MOD13A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MOD13A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True +MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - S3 Inventory files for MYD11A1 S3 Inventory files for MYD11A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MYD11A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True +MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4 - S3 Inventory files for MYD13A1 S3 Inventory files for MYD13A1 arn:aws:s3:::astraea-opendata-inventory/astraea-opendata/modis-assets-MYD13A1 us-west-2 S3 Bucket Documentation is available for this data at the [s22s/astraea-opendata GitHub re https://astraea.earth/ [Astraea](https://astraea.earth/) New scenes are added daily. There are no restrictions on the use of data, unless expressly identified prior aws-pds, agriculture, geospatial, satellite imagery, natural resource, disaster response True MWIS VR Instances Conflict graphs for maximum weight independent set arn:aws:s3:::mwis-vr-instances us-east-1 S3 Bucket https://mwis-vr-instances.s3.amazonaws.com/vrInstances.pdf resendem@amazon.com [Amazon](https://www.amazon.com/) Infrequent MIT-0 amazon.science, traffic, transportation, graph -Materials Project Data - Build Data Build Data arn:aws:s3:::materialsproject-build us-east-1 S3 Bucket https://docs.materialsproject.org materialsproject@lbl.gov [Materials Project](https://materialsproject.org) New versions and objects added as we continuously calculate, parse and build new [Materials Project Terms of Use](https://materialsproject.org/about/terms) aws-pds, chemistry, cloud computing, data assimilation, digital assets, digital preservation, energy, environmental, free software, genome, HPC, information retrieval, infrastructure, json, machine learning, materials science, molecular dynamics, molecule, open source software, physics, post-processing, x-ray crystallography ['[Browse Bucket](https://materialsproject-build.s3.amazonaws.com/index.html)'] -Materials Project Data - Parsed Data Parsed Data arn:aws:s3:::materialsproject-parsed us-east-1 S3 Bucket https://docs.materialsproject.org materialsproject@lbl.gov [Materials Project](https://materialsproject.org) New versions and objects added as we continuously calculate, parse and build new [Materials Project Terms of Use](https://materialsproject.org/about/terms) aws-pds, chemistry, cloud computing, data assimilation, digital assets, digital preservation, energy, environmental, free software, genome, HPC, information retrieval, infrastructure, json, machine learning, materials science, molecular dynamics, molecule, open source software, physics, post-processing, x-ray crystallography ['[Browse Bucket](https://materialsproject-parsed.s3.amazonaws.com/index.html)'] +Materials Project Data - Build Data Build Data arn:aws:s3:::materialsproject-build us-east-1 S3 Bucket https://docs.materialsproject.org materialsproject@lbl.gov [Materials Project](https://materialsproject.org) New versions and objects added as we continuously calculate, parse and build new [Materials Project Terms of Use](https://materialsproject.org/about/terms) aws-pds, chemistry, cloud computing, data assimilation, digital assets, digital preservation, energy, environmental, free software, genome, HPC, information retrieval, infrastructure, json, machine learning, materials science, molecular dynamics, molecule, open source software, physics, post-processing, x-ray crystallography ['[Browse Bucket](https://materialsproject-build.s3.amazonaws.com/index.html)'] +Materials Project Data - Parsed Data Parsed Data arn:aws:s3:::materialsproject-parsed us-east-1 S3 Bucket https://docs.materialsproject.org materialsproject@lbl.gov [Materials Project](https://materialsproject.org) New versions and objects added as we continuously calculate, parse and build new [Materials Project Terms of Use](https://materialsproject.org/about/terms) aws-pds, chemistry, cloud computing, data assimilation, digital assets, digital preservation, energy, environmental, free software, genome, HPC, information retrieval, infrastructure, json, machine learning, materials science, molecular dynamics, molecule, open source software, physics, post-processing, x-ray crystallography ['[Browse Bucket](https://materialsproject-parsed.s3.amazonaws.com/index.html)'] Materials Project Data - Raw Data Raw Data arn:aws:s3:::materialsproject-raw us-east-1 S3 Bucket https://docs.materialsproject.org materialsproject@lbl.gov [Materials Project](https://materialsproject.org) New versions and objects added as we continuously calculate, parse and build new [Materials Project Terms of Use](https://materialsproject.org/about/terms) aws-pds, chemistry, cloud computing, data assimilation, digital assets, digital preservation, energy, environmental, free software, genome, HPC, information retrieval, infrastructure, json, machine learning, materials science, molecular dynamics, molecule, open source software, physics, post-processing, x-ray crystallography -Maxar Open Data Program Imagery and metadata arn:aws:s3:::maxar-opendata us-west-2 S3 Bucket https://www.maxar.com/open-data https://www.maxar.com/open-data [Maxar](https://www.maxar.com/) New data is released in response to activations. Older data may be migrated to t Creative Commons Attribution Non Commercial 4.0 aws-pds, earth observation, disaster response, geospatial, satellite imagery, cog, stac ['[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/maxar-opendata.s3.dualstack.us-west-2.amazonaws.com/events/catalog.json)', '[STAC Catalog](https://stacindex.org/catalogs/maxar-open-data-catalog-ard-format#/)'] -Met Office Global Deterministic 10km on a 2-year rolling archive - Met Office Global Deterministic 10km on a 2-year rolling archive Met Office Global Deterministic 10km on a 2-year rolling archive arn:aws:s3:::met-office-atmospheric-model-data eu-west-2 S3 Bucket https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office](https://www.metoffice.gov.uk/) The Global Deterministic available time steps are provided every hour from 0 to British Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA]( aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, model, near-surface air temperature, near-surface relative humidity, netcdf, weather ['[Browse bucket](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html)'] +Maxar Open Data Program Imagery and metadata arn:aws:s3:::maxar-opendata us-west-2 S3 Bucket https://www.maxar.com/open-data https://www.maxar.com/open-data [Maxar](https://www.maxar.com/) New data is released in response to activations. Older data may be migrated to t Creative Commons Attribution Non Commercial 4.0 aws-pds, earth observation, disaster response, geospatial, satellite imagery, cog, stac ['[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/maxar-opendata.s3.dualstack.us-west-2.amazonaws.com/events/catalog.json)', '[STAC Catalog](https://stacindex.org/catalogs/maxar-open-data-catalog-ard-format#/)'] +Met Office Global Deterministic 10km on a 2-year rolling archive - Met Office Global Deterministic 10km on a 2-year rolling archive Met Office Global Deterministic 10km on a 2-year rolling archive arn:aws:s3:::met-office-atmospheric-model-data eu-west-2 S3 Bucket https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office](https://www.metoffice.gov.uk/) The Global Deterministic available time steps are provided every hour from 0 to British Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA]( aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, model, near-surface air temperature, near-surface relative humidity, netcdf, weather ['[Browse bucket](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html)'] Met Office Global Deterministic 10km on a 2-year rolling archive - Notifications for new atmospheric model data Notifications for new atmospheric model data arn:aws:sns:eu-west-2:633885181284:met-office-atmospheric-model-data-object_created eu-west-2 SNS Topic https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office](https://www.metoffice.gov.uk/) The Global Deterministic available time steps are provided every hour from 0 to British Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA]( aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, model, near-surface air temperature, near-surface relative humidity, netcdf, weather -Met Office Global Ensemble Prediction System (MOGREPS-G) on a 30-day rolling archive - Met Office Global Ensemble Prediction System (MOGREPS-G) on a 30-day rolling arc Met Office Global Ensemble Prediction System (MOGREPS-G) on a 30-day rolling arc arn:aws:s3:::met-office-global-ensemble-model-data eu-west-2 S3 Bucket https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office](https://www.metoffice.gov.uk/) The MOGREPS-G model runs four times per day at 00, 06, 12, 18 UTC. The available British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA]( aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, global, meteorological, model, near-surface air temperature, near-surface relative humidity, netcdf, weather ['[Browse bucket](https://met-office-global-ensemble-model-data.s3.eu-west-2.amazonaws.com/index.html)'] +Met Office Global Ensemble Prediction System (MOGREPS-G) on a 30-day rolling archive - Met Office Global Ensemble Prediction System (MOGREPS-G) on a 30-day rolling arc Met Office Global Ensemble Prediction System (MOGREPS-G) on a 30-day rolling arc arn:aws:s3:::met-office-global-ensemble-model-data eu-west-2 S3 Bucket https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office](https://www.metoffice.gov.uk/) The MOGREPS-G model runs four times per day at 00, 06, 12, 18 UTC. The available British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA]( aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, global, meteorological, model, near-surface air temperature, near-surface relative humidity, netcdf, weather ['[Browse bucket](https://met-office-global-ensemble-model-data.s3.eu-west-2.amazonaws.com/index.html)'] Met Office Global Ensemble Prediction System (MOGREPS-G) on a 30-day rolling archive - Notifications for new global ensemble model data Notifications for new global ensemble model data arn:aws:sns:eu-west-2:633885181284:met-office-global-ensemble-model-data-object_created eu-west-2 SNS Topic https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office](https://www.metoffice.gov.uk/) The MOGREPS-G model runs four times per day at 00, 06, 12, 18 UTC. The available British Crown copyright 2024-2025, the Met Office, is licensed under [CC BY-SA]( aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, global, meteorological, model, near-surface air temperature, near-surface relative humidity, netcdf, weather -Met Office UK Deterministic (UKV)2km on a 2-year rolling archive - Met Office UK Deterministic (UKV)2km on a 2-year rolling archive Met Office UK Deterministic (UKV)2km on a 2-year rolling archive arn:aws:s3:::met-office-atmospheric-model-data eu-west-2 S3 Bucket https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office](https://www.metoffice.gov.uk/) The UKV provides hourly forecasts covering the period T+0 to T+48 hours, produce British Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA]( aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, model, near-surface air temperature, near-surface relative humidity, netcdf, weather ['[Browse bucket](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html)'] +Met Office UK Deterministic (UKV)2km on a 2-year rolling archive - Met Office UK Deterministic (UKV)2km on a 2-year rolling archive Met Office UK Deterministic (UKV)2km on a 2-year rolling archive arn:aws:s3:::met-office-atmospheric-model-data eu-west-2 S3 Bucket https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office](https://www.metoffice.gov.uk/) The UKV provides hourly forecasts covering the period T+0 to T+48 hours, produce British Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA]( aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, model, near-surface air temperature, near-surface relative humidity, netcdf, weather ['[Browse bucket](https://met-office-atmospheric-model-data.s3.eu-west-2.amazonaws.com/index.html)'] Met Office UK Deterministic (UKV)2km on a 2-year rolling archive - Notifications for new atmospheric model data Notifications for new atmospheric model data arn:aws:sns:eu-west-2:633885181284:met-office-atmospheric-model-data-object_created eu-west-2 SNS Topic https://www.metoffice.gov.uk/services/data/external-data-channels servicedesk@metoffice.gov.uk. Service desk is only available Mon – Fri, 09:00 un [Met Office](https://www.metoffice.gov.uk/) The UKV provides hourly forecasts covering the period T+0 to T+48 hours, produce British Crown copyright 2023-2025, the Met Office, is licensed under [CC BY-SA]( aws-pds, air temperature, atmosphere, forecast, geoscience, geospatial, model, near-surface air temperature, near-surface relative humidity, netcdf, weather -Met Office UK Earth System Model (UKESM1) ARISE-SAI geoengineering experiment data CMIP6 standards-compliant netCDF data arn:aws:s3:::met-office-ukesm1-arise eu-west-2 S3 Bucket (https://github.com/MetOffice/arise-cmor-tables) https://github.com/MetOffice/arise-cmor-tables/issues [Met Office](https://www.metoffice.gov.uk) Rare once complete CMIP6 data included is licensed under CC-BY 4.0 (see [here](https://wcrp-cmip.gi climate, model, climate model, atmosphere, oceans, land, ice, geospatial, aws-pds, sustainability, CMIP6 ['[Browse Bucket](https://met-office-ukesm1-arise.s3.amazonaws.com/index.html)'] +Met Office UK Earth System Model (UKESM1) ARISE-SAI geoengineering experiment data CMIP6 standards-compliant netCDF data arn:aws:s3:::met-office-ukesm1-arise eu-west-2 S3 Bucket (https://github.com/MetOffice/arise-cmor-tables) https://github.com/MetOffice/arise-cmor-tables/issues [Met Office](https://www.metoffice.gov.uk) Rare once complete CMIP6 data included is licensed under CC-BY 4.0 (see [here](https://wcrp-cmip.gi climate, model, climate model, atmosphere, oceans, land, ice, geospatial, aws-pds, sustainability, CMIP6 ['[Browse Bucket](https://met-office-ukesm1-arise.s3.amazonaws.com/index.html)'] Multi-Scale Ultra High Resolution (MUR) Sea Surface Temperature (SST) MUR Level 4 SST dataset in Zarr format The zarr-v1/ directory contains a zarr s arn:aws:s3:::mur-sst/zarr-v1 us-west-2 S3 Bucket https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1 podaac@podaac.jpl.nasa.gov [Farallon Institute](https://faralloninstitute.org) The temporal extent of the Zarr store is 2002-06-01 to 2020-01-20. There are no restrictions on the use of these data. aws-pds, earth observation, environmental, natural resource, oceans, satellite imagery, climate, water, weather My School Today Using open-source georeferenced data and satellite data products, we construct t arn:aws:s3:::my-school-today us-west-2 S3 Bucket https://sdgstoday.org/dataset/my-school-today sdgstoday@unsdsn.org SDSN SDGs Today Monthly (weekly updates in progress) This work is licensed under a Creative Commons by Attribution (CC BY 4.0) licens aws-pds, education, geospatial, infrastructure, schools NA-CORDEX - North American component of the Coordinated Regional Downscaling Experiment Project data files arn:aws:s3:::ncar-na-cordex us-west-2 S3 Bucket https://doi.org/10.26024/9xkm-fp81 rdahelp@ucar.edu [National Center for Atmospheric Research](https://ncar.ucar.edu/) Rare. NA-CORDEX is complete, but we may occasionally copy additional fields from https://na-cordex.org/terms-use.html climate, model, climate model, atmosphere, land, geospatial, aws-pds, sustainability, zarr -NAIP on AWS - The data in this bucket is 3 band RGB in Geotiff format It is converted and man The data in this bucket is 3 band RGB in Geotiff format It is converted and man arn:aws:s3:::naip-visualization us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/naip imagerycontent@esri.com [Esri](https://www.esri.com/en-us/home) NAIP data is provided state by state at varying time intervals. Each year, a var Public Domain with Attribution aws-pds, agriculture, earth observation, aerial imagery, geospatial, natural resource, regulatory, cog True -NAIP on AWS - The data in this bucket is 4-band (RGB + NIR) in MRF format and Cloud Optimized The data in this bucket is 4-band (RGB + NIR) in MRF format and Cloud Optimized arn:aws:s3:::naip-analytic us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/naip imagerycontent@esri.com [Esri](https://www.esri.com/en-us/home) NAIP data is provided state by state at varying time intervals. Each year, a var Public Domain with Attribution aws-pds, agriculture, earth observation, aerial imagery, geospatial, natural resource, regulatory, cog True -NAIP on AWS - The data in this bucket is Original Imagery in Geotiff format 4-band (RGB + NIR) The data in this bucket is Original Imagery in Geotiff format 4-band (RGB + NIR) arn:aws:s3:::naip-source us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/naip imagerycontent@esri.com [Esri](https://www.esri.com/en-us/home) NAIP data is provided state by state at varying time intervals. Each year, a var Public Domain with Attribution aws-pds, agriculture, earth observation, aerial imagery, geospatial, natural resource, regulatory, cog True -NASA / USGS Controlled Europa DTMs Scenes and metadata arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_dtms/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/europa_controlled_us https://answers.usgs.gov/ [NASA](https://www.nasa.gov) HiRISE data will be updated as new releases are made to the Planetary Data Syste [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_dtms?.language=en)'] -NASA / USGS Controlled THEMIS Mosaics Scenes and metadata arn:aws:s3:::astrogeo-ard/mars/mo/themis/controlled_mosaics/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/mars/themis_controlled_mosaics/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) None planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/mo_themis_controlled_mosaics)'] -NASA / USGS Europa Controlled Observation Mosaics Scenes and metadata arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_mosaics/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/galileo_sequence_mos https://answers.usgs.gov/ [NASA](https://www.nasa.gov) No future updates planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_mosaics)'] -NASA / USGS Europa Controlled Observations Scenes and metadata arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_observations/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/galileo_individual_i https://answers.usgs.gov/ [NASA](https://www.nasa.gov) No future updates planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_observations)'] -NASA / USGS Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) Targeted DTMs DTMs, orthoimages, error images, and quality assurance metrics arn:aws:s3:::astrogeo-ard/mars/mro/ctx/controlled/usgs/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/mars/ctxdtms/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) Updated as new stereoapirs are processed [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, elevation, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/collections/mro_ctx_controlled_usgs_dtms)'] -NASA / USGS Released HiRISE Digital Terrain Models Scenes and metadata arn:aws:s3:::astrogeo-ard/mars/mro/hirise/controlled/dtm us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/mars/hirise_dtms/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) HiRISE DTMs will be updated as new releases are made by the University of Arizon [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/mro_hirise_socet_dtms)'] -NASA / USGS Uncontrolled HiRISE RDRs Scenes and metadata arn:aws:s3:::astrogeo-ard/mars/mro/hirise/uncontrolled_rdr_observations/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/mars/uncontrolled_hirise/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) HiRISE data will be updated as new releases are made to the Planetary Data Syste [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/collections/mro_hirise_uncontrolled_observations)'] -NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) - The NEX-GDDP-CMIP6 archive Files are in NetCDF format with the CF-17 metadata The NEX-GDDP-CMIP6 archive Files are in NetCDF format with the CF-17 metadata arn:aws:s3:::nex-gddp-cmip6 us-west-2 S3 Bucket https://doi.org/10.7917/OFSG3345 support@nccs.nasa.gov [NASA](https://www.nasa.gov) No future updates planned. As noted in the metadata of each file, the NEX-GDDP-CMIP6 archive wasinitially m aws-pds, CMIP6, climate, climate model, model, global, environmental, earth observation, climate projections, netcdf, near-surface relative humidity, near-surface specific humidity, precipitation, air temperature, NASA Center for Climate Simulation (NCCS), cog False ['[Browse Bucket](https://nex-gddp-cmip6.s3.us-west-2.amazonaws.com/index.html)'] -NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) - The NEX-GDDP-CMIP6-COG archive Files are in Cloud-Optimized GeoTiff (COG The NEX-GDDP-CMIP6-COG archive Files are in Cloud-Optimized GeoTiff (COG arn:aws:s3:::nex-gddp-cmip6-cog us-west-2 S3 Bucket https://doi.org/10.7917/OFSG3345 support@nccs.nasa.gov [NASA](https://www.nasa.gov) No future updates planned. As noted in the metadata of each file, the NEX-GDDP-CMIP6 archive wasinitially m aws-pds, CMIP6, climate, climate model, model, global, environmental, earth observation, climate projections, netcdf, near-surface relative humidity, near-surface specific humidity, precipitation, air temperature, NASA Center for Climate Simulation (NCCS), cog False ['[Browse Bucket](https://nex-gddp-cmip6-cog.s3.us-west-2.amazonaws.com/index.html)'] +NAIP on AWS - The data in this bucket is 3 band RGB in Geotiff format It is converted and man The data in this bucket is 3 band RGB in Geotiff format It is converted and man arn:aws:s3:::naip-visualization us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/naip imagerycontent@esri.com [Esri](https://www.esri.com/en-us/home) NAIP data is provided state by state at varying time intervals. Each year, a var Public Domain with Attribution aws-pds, agriculture, earth observation, aerial imagery, geospatial, natural resource, regulatory, cog True +NAIP on AWS - The data in this bucket is 4-band (RGB + NIR) in MRF format and Cloud Optimized The data in this bucket is 4-band (RGB + NIR) in MRF format and Cloud Optimized arn:aws:s3:::naip-analytic us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/naip imagerycontent@esri.com [Esri](https://www.esri.com/en-us/home) NAIP data is provided state by state at varying time intervals. Each year, a var Public Domain with Attribution aws-pds, agriculture, earth observation, aerial imagery, geospatial, natural resource, regulatory, cog True +NAIP on AWS - The data in this bucket is Original Imagery in Geotiff format 4-band (RGB + NIR) The data in this bucket is Original Imagery in Geotiff format 4-band (RGB + NIR) arn:aws:s3:::naip-source us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/naip imagerycontent@esri.com [Esri](https://www.esri.com/en-us/home) NAIP data is provided state by state at varying time intervals. Each year, a var Public Domain with Attribution aws-pds, agriculture, earth observation, aerial imagery, geospatial, natural resource, regulatory, cog True +NASA / USGS Controlled Europa DTMs Scenes and metadata arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_dtms/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/europa_controlled_us https://answers.usgs.gov/ [NASA](https://www.nasa.gov) HiRISE data will be updated as new releases are made to the Planetary Data Syste [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_dtms?.language=en)'] +NASA / USGS Controlled THEMIS Mosaics Scenes and metadata arn:aws:s3:::astrogeo-ard/mars/mo/themis/controlled_mosaics/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/mars/themis_controlled_mosaics/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) None planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/mo_themis_controlled_mosaics)'] +NASA / USGS Europa Controlled Observation Mosaics Scenes and metadata arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_mosaics/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/galileo_sequence_mos https://answers.usgs.gov/ [NASA](https://www.nasa.gov) No future updates planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_mosaics)'] +NASA / USGS Europa Controlled Observations Scenes and metadata arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_observations/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/galileo_individual_i https://answers.usgs.gov/ [NASA](https://www.nasa.gov) No future updates planned. [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_observations)'] +NASA / USGS Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) Targeted DTMs DTMs, orthoimages, error images, and quality assurance metrics arn:aws:s3:::astrogeo-ard/mars/mro/ctx/controlled/usgs/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/mars/ctxdtms/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) Updated as new stereoapirs are processed [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, elevation, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/collections/mro_ctx_controlled_usgs_dtms)'] +NASA / USGS Released HiRISE Digital Terrain Models Scenes and metadata arn:aws:s3:::astrogeo-ard/mars/mro/hirise/controlled/dtm us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/mars/hirise_dtms/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) HiRISE DTMs will be updated as new releases are made by the University of Arizon [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/mro_hirise_socet_dtms)'] +NASA / USGS Uncontrolled HiRISE RDRs Scenes and metadata arn:aws:s3:::astrogeo-ard/mars/mro/hirise/uncontrolled_rdr_observations/ us-west-2 S3 Bucket https://stac.astrogeology.usgs.gov/docs/data/mars/uncontrolled_hirise/ https://answers.usgs.gov/ [NASA](https://www.nasa.gov) HiRISE data will be updated as new releases are made to the Planetary Data Syste [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, planetary, satellite imagery, stac, cog ['[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/collections/mro_hirise_uncontrolled_observations)'] +NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) - The NEX-GDDP-CMIP6 archive Files are in NetCDF format with the CF-17 metadata The NEX-GDDP-CMIP6 archive Files are in NetCDF format with the CF-17 metadata arn:aws:s3:::nex-gddp-cmip6 us-west-2 S3 Bucket https://doi.org/10.7917/OFSG3345 support@nccs.nasa.gov [NASA](https://www.nasa.gov) No future updates planned. As noted in the metadata of each file, the NEX-GDDP-CMIP6 archive wasinitially m aws-pds, CMIP6, climate, climate model, model, global, environmental, earth observation, climate projections, netcdf, near-surface relative humidity, near-surface specific humidity, precipitation, air temperature, NASA Center for Climate Simulation (NCCS), cog ['[Browse Bucket](https://nex-gddp-cmip6.s3.us-west-2.amazonaws.com/index.html)'] False +NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) - The NEX-GDDP-CMIP6-COG archive Files are in Cloud-Optimized GeoTiff (COG The NEX-GDDP-CMIP6-COG archive Files are in Cloud-Optimized GeoTiff (COG arn:aws:s3:::nex-gddp-cmip6-cog us-west-2 S3 Bucket https://doi.org/10.7917/OFSG3345 support@nccs.nasa.gov [NASA](https://www.nasa.gov) No future updates planned. As noted in the metadata of each file, the NEX-GDDP-CMIP6 archive wasinitially m aws-pds, CMIP6, climate, climate model, model, global, environmental, earth observation, climate projections, netcdf, near-surface relative humidity, near-surface specific humidity, precipitation, air temperature, NASA Center for Climate Simulation (NCCS), cog ['[Browse Bucket](https://nex-gddp-cmip6-cog.s3.us-west-2.amazonaws.com/index.html)'] False NASA High Energy Astrophysics Mission Data - Chandra Mission Data Archive supported by the Chandra X-ray Observatory Chandra Mission Data Archive supported by the Chandra X-ray Observatory arn:aws:s3:::nasa-heasarc/chandra/data/byobsid us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray NASA High Energy Astrophysics Mission Data - Fermi LAT weekly Data Products as well as GBM bursts and triggers (The full mi Fermi LAT weekly Data Products as well as GBM bursts and triggers (The full mi arn:aws:s3:::heasarc-public/fermi/data/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray NASA High Energy Astrophysics Mission Data - Swift Mission Data Archive For more information, see the website of the Swift Swift Mission Data Archive For more information, see the website of the Swift arn:aws:s3:::nasa-heasarc/swift/data/obs/ us-east-1 S3 Bucket The [HEASARC Website](https://heasarc.gsfc.nasa.gov/) The [HEASARC Feedback](https://heasarc.gsfc.nasa.gov/cgi-bin/Feedback) The [HEASARC](https://heasarc.gsfc.nasa.gov/) Various. See [the HEASARC data policy web site](https://heasarc.gsfc.nasa.gov/docs/heasar aws-pds, astronomy, archives, datacenter, imaging, satellite imagery, x-ray @@ -320,238 +321,238 @@ NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA) - The COBE/D NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA) - The COBE/Diffuse Infrared Background Experiment (DIRBE) Total size of 95 GB The COBE/Diffuse Infrared Background Experiment (DIRBE) Total size of 95 GB arn:aws:s3:::nasa-lambda/cobe/dirbe us-west-2 S3 Bucket [The LAMBDA Website](https://lambda.gsfc.nasa.gov/) [LAMBDA Feedback](https://lambda.gsfc.nasa.gov/contact/contact.html) [LAMBDA](https://lambda.gsfc.nasa.gov/) Various. There are no restrictions on the use of this data. aws-pds, astronomy, archives, datacenter, imaging, satellite imagery NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA) - The COBE/Far-InfraRed Absolute Spectrophotometer (FIRAS) Total size of 94 GB The COBE/Far-InfraRed Absolute Spectrophotometer (FIRAS) Total size of 94 GB arn:aws:s3:::nasa-lambda/cobe/firas us-west-2 S3 Bucket [The LAMBDA Website](https://lambda.gsfc.nasa.gov/) [LAMBDA Feedback](https://lambda.gsfc.nasa.gov/contact/contact.html) [LAMBDA](https://lambda.gsfc.nasa.gov/) Various. There are no restrictions on the use of this data. aws-pds, astronomy, archives, datacenter, imaging, satellite imagery NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA) - the Wilkinson Microwave Anisotropy Probe (WMAP)otal size of 2 TB the Wilkinson Microwave Anisotropy Probe (WMAP)otal size of 2 TB arn:aws:s3:::nasa-lambda/cobe/map/dr5 us-west-2 S3 Bucket [The LAMBDA Website](https://lambda.gsfc.nasa.gov/) [LAMBDA Feedback](https://lambda.gsfc.nasa.gov/contact/contact.html) [LAMBDA](https://lambda.gsfc.nasa.gov/) Various. There are no restrictions on the use of this data. aws-pds, astronomy, archives, datacenter, imaging, satellite imagery -NASA Prediction of Worldwide Energy Resources (POWER) - POWER's NetCDF Datastore POWER's NetCDF Datastore arn:aws:s3:::power-datastore us-west-2 S3 Bucket https://power.larc.nasa.gov/docs/ larc-power-project@mail.nasa.gov NASA Near Real Time (NRT); as soon as source data becomes available from our source d There are no restrictions on the use, access, and/or download of data from the N agriculture, air quality, analytics, archives, atmosphere, climate, climate model, data assimilation, deep learning, earth observation, energy, environmental, forecast, geoscience, geospatial, global, netcdf, history, imaging, industry, machine learning, machine translation, metadata, meteorological, model, opendap, radiation, satellite imagery, solar, statistics, sustainability, time series forecasting, water, weather, zarr, aws-pds False ['[Browse Bucket](https://power-datastore.s3.us-west-2.amazonaws.com/index.html)'] -NASA Prediction of Worldwide Energy Resources (POWER) - POWER's Zarr Analysis Ready Data (ARD) Datasets POWER's Zarr Analysis Ready Data (ARD) Datasets arn:aws:s3:::nasa-power us-west-2 S3 Bucket https://power.larc.nasa.gov/docs/ larc-power-project@mail.nasa.gov NASA Near Real Time (NRT); as soon as source data becomes available from our source d There are no restrictions on the use, access, and/or download of data from the N agriculture, air quality, analytics, archives, atmosphere, climate, climate model, data assimilation, deep learning, earth observation, energy, environmental, forecast, geoscience, geospatial, global, netcdf, history, imaging, industry, machine learning, machine translation, metadata, meteorological, model, opendap, radiation, satellite imagery, solar, statistics, sustainability, time series forecasting, water, weather, zarr, aws-pds False ['[Browse Bucket](https://nasa-power.s3.us-west-2.amazonaws.com/index.html)'] +NASA Prediction of Worldwide Energy Resources (POWER) - POWER's NetCDF Datastore POWER's NetCDF Datastore arn:aws:s3:::power-datastore us-west-2 S3 Bucket https://power.larc.nasa.gov/docs/ larc-power-project@mail.nasa.gov NASA Near Real Time (NRT); as soon as source data becomes available from our source d There are no restrictions on the use, access, and/or download of data from the N agriculture, air quality, analytics, archives, atmosphere, climate, climate model, data assimilation, deep learning, earth observation, energy, environmental, forecast, geoscience, geospatial, global, netcdf, history, imaging, industry, machine learning, machine translation, metadata, meteorological, model, opendap, radiation, satellite imagery, solar, statistics, sustainability, time series forecasting, water, weather, zarr, aws-pds ['[Browse Bucket](https://power-datastore.s3.us-west-2.amazonaws.com/index.html)'] False +NASA Prediction of Worldwide Energy Resources (POWER) - POWER's Zarr Analysis Ready Data (ARD) Datasets POWER's Zarr Analysis Ready Data (ARD) Datasets arn:aws:s3:::nasa-power us-west-2 S3 Bucket https://power.larc.nasa.gov/docs/ larc-power-project@mail.nasa.gov NASA Near Real Time (NRT); as soon as source data becomes available from our source d There are no restrictions on the use, access, and/or download of data from the N agriculture, air quality, analytics, archives, atmosphere, climate, climate model, data assimilation, deep learning, earth observation, energy, environmental, forecast, geoscience, geospatial, global, netcdf, history, imaging, industry, machine learning, machine translation, metadata, meteorological, model, opendap, radiation, satellite imagery, solar, statistics, sustainability, time series forecasting, water, weather, zarr, aws-pds ['[Browse Bucket](https://nasa-power.s3.us-west-2.amazonaws.com/index.html)'] False NASA SOTERIA Simulation Testbed Data SOTERIA Testbed Data arn:aws:s3:::nasa-soteria-data us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/ chad.l.stephens@nasa.gov; tyler.fettrow@nasa.gov [NASA](http://www.nasa.gov/) As required [Creative Commons Attribution 4.0 International](https://creativecommons.org/lic workload analysis, neuroimaging, transportation, life sciences -NEOWISE Post-Cryo Data | Wide-field Infrared Survey Explorer (WISE) NEOWISE Post-Cryo Single-exposure Image Sets: 901,271 calibrated 1016x1016 pix @ arn:aws:s3:::nasa-irsa-wise/wise/postcryo us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The NEOWISE Post-Cryo Data Release has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, satellite imagery, survey False False -NEOWISE Reactivation Data | Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) The Single-exposure Image Sets consist of more than 20 million calibrated 1016x1 arn:aws:s3:::nasa-irsa-wise/wise/neowiser us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The NEOWISE-R dataset is updated annually. The data may also be presented in new https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, survey False False -NEXRAD on AWS - NEXRAD Level II archive data NEXRAD Level II archive data arn:aws:s3:::noaa-nexrad-level2 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological ['[Browse Bucket](https://noaa-nexrad-level2.s3.amazonaws.com/index.html)'] +NEOWISE Post-Cryo Data | Wide-field Infrared Survey Explorer (WISE) NEOWISE Post-Cryo Single-exposure Image Sets: 901,271 calibrated 1016x1016 pix @ arn:aws:s3:::nasa-irsa-wise/wise/postcryo us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The NEOWISE Post-Cryo Data Release has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, satellite imagery, survey False False +NEOWISE Reactivation Data | Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) The Single-exposure Image Sets consist of more than 20 million calibrated 1016x1 arn:aws:s3:::nasa-irsa-wise/wise/neowiser us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The NEOWISE-R dataset is updated annually. The data may also be presented in new https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, survey False False +NEXRAD on AWS - NEXRAD Level II archive data NEXRAD Level II archive data arn:aws:s3:::noaa-nexrad-level2 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological ['[Browse Bucket](https://noaa-nexrad-level2.s3.amazonaws.com/index.html)'] NEXRAD on AWS - NEXRAD Level II real-time data NEXRAD Level II real-time data arn:aws:s3:::unidata-nexrad-level2-chunks us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological -NEXRAD on AWS - NEXRAD Level III real-time select data NEXRAD Level III real-time select data arn:aws:s3:::unidata-nexrad-level3 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological ['[Browse Bucket](https://unidata-nexrad-level3.s3.amazonaws.com/index.html)'] +NEXRAD on AWS - NEXRAD Level III real-time select data NEXRAD Level III real-time select data arn:aws:s3:::unidata-nexrad-level3 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological ['[Browse Bucket](https://unidata-nexrad-level3.s3.amazonaws.com/index.html)'] NEXRAD on AWS - Notifications for the Level II archival bucket Notifications for the Level II archival bucket arn:aws:sns:us-east-1:811054952067:NewNEXRADLevel2Archive us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological NEXRAD on AWS - Notifications for the Level III bucket Notifications for the Level III bucket arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel3Object us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological NEXRAD on AWS - Rich notifications for real-time data with filterable fields Rich notifications for real-time data with filterable fields arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2ObjectFilterable us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nexrad support-level2@unidata.ucar.edu [Unidata](https://www.unidata.ucar.edu/) New Level II data is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, natural resource, weather, meteorological -NOAA - hourly position, current, and sea surface temperature from drifters - Hourly position, current, and sea surface temperature data from drifters Hourly position, current, and sea surface temperature data from drifters arn:aws:s3:::noaa-oar-hourly-gdp-pds us-east-1 S3 Bucket https://www.aoml.noaa.gov/phod/gdp/hourly_data.php Please direct scientific inquiries to Dr. Rick Lumpkin (Rick.Lumpkin@noaa.gov) a [NOAA](http://www.noaa.gov/) New data is added as soon as it's available. Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, sustainability, weather, oceans, environmental ['[Browse Bucket](https://noaa-oar-hourly-gdp-pds.s3.amazonaws.com/index.html)'] +NOAA - hourly position, current, and sea surface temperature from drifters - Hourly position, current, and sea surface temperature data from drifters Hourly position, current, and sea surface temperature data from drifters arn:aws:s3:::noaa-oar-hourly-gdp-pds us-east-1 S3 Bucket https://www.aoml.noaa.gov/phod/gdp/hourly_data.php Please direct scientific inquiries to Dr. Rick Lumpkin (Rick.Lumpkin@noaa.gov) a [NOAA](http://www.noaa.gov/) New data is added as soon as it's available. Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, sustainability, weather, oceans, environmental ['[Browse Bucket](https://noaa-oar-hourly-gdp-pds.s3.amazonaws.com/index.html)'] NOAA - hourly position, current, and sea surface temperature from drifters - New data notifications for Hourly position, current, and sea surface temperature New data notifications for Hourly position, current, and sea surface temperature arn:aws:sns:us-east-1:709902155096:NewHourlyGDPObject us-east-1 SNS Topic https://www.aoml.noaa.gov/phod/gdp/hourly_data.php Please direct scientific inquiries to Dr. Rick Lumpkin (Rick.Lumpkin@noaa.gov) a [NOAA](http://www.noaa.gov/) New data is added as soon as it's available. Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, sustainability, weather, oceans, environmental -NOAA / NGA Satellite Computed Bathymetry Assessment-SCuBA - NOAA / NGA Satellite Computed Bathymetry Assessment (SCuBA) data NOAA / NGA Satellite Computed Bathymetry Assessment (SCuBA) data arn:aws:s3:::noaa-nos-scuba-icesat2-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/SCuBA/NOAA_webs For questions regarding data content or quality, email gretchen.imahori@noaa.gov [NOAA’s National Geodetic Survey](https://geodesy.noaa.gov/) Monthly, quarterly, and annually, depending on the dataset. Open Data. There are no restrictions on the use of this data. aws-pds, bathymetry, agriculture, weather, climate, environmental, disaster response, agriculture, transportation, oceans ['[Browse Bucket](https://noaa-nos-scuba-icesat2-pds.s3.amazonaws.com/index.html)'] +NOAA / NGA Satellite Computed Bathymetry Assessment-SCuBA - NOAA / NGA Satellite Computed Bathymetry Assessment (SCuBA) data NOAA / NGA Satellite Computed Bathymetry Assessment (SCuBA) data arn:aws:s3:::noaa-nos-scuba-icesat2-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/SCuBA/NOAA_webs For questions regarding data content or quality, email gretchen.imahori@noaa.gov [NOAA’s National Geodetic Survey](https://geodesy.noaa.gov/) Monthly, quarterly, and annually, depending on the dataset. Open Data. There are no restrictions on the use of this data. aws-pds, bathymetry, agriculture, weather, climate, environmental, disaster response, agriculture, transportation, oceans ['[Browse Bucket](https://noaa-nos-scuba-icesat2-pds.s3.amazonaws.com/index.html)'] NOAA / NGA Satellite Computed Bathymetry Assessment-SCuBA - NOAA / NGA Satellite Computed Bathymetry Assessment (SCuBA) data notifications NOAA / NGA Satellite Computed Bathymetry Assessment (SCuBA) data notifications arn:aws:sns:us-east-1:709902155096:NewICESATObject us-east-1 SNS Topic https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/SCuBA/NOAA_webs For questions regarding data content or quality, email gretchen.imahori@noaa.gov [NOAA’s National Geodetic Survey](https://geodesy.noaa.gov/) Monthly, quarterly, and annually, depending on the dataset. Open Data. There are no restrictions on the use of this data. aws-pds, bathymetry, agriculture, weather, climate, environmental, disaster response, agriculture, transportation, oceans -NOAA 3-D Surge and Tide Operational Forecast System for the Atlantic Basin (STOFS-3D-Atlantic) - NOAA STOFS-3D-Atlantic Forecast Guidance NOAA STOFS-3D-Atlantic Forecast Guidance arn:aws:s3:::noaa-nos-stofs3d-pds us-east-1 S3 Bucket https://noaa-nos-stofs3d-pds.s3.amazonaws.com/README.html For questions regarding data content or quality, visit the STOFS site (https://p [NOAA](http://www.noaa.gov/) One time per day at 12 UTC Open Data. There are no restrictions on the use of this data. aws-pds, global, coastal, marine navigation, disaster response, weather, water, environmental, meteorological, oceans, sustainability, climate ['[Browse Bucket](https://noaa-nos-stofs3d-pds.s3.amazonaws.com/index.html)'] +NOAA 3-D Surge and Tide Operational Forecast System for the Atlantic Basin (STOFS-3D-Atlantic) - NOAA STOFS-3D-Atlantic Forecast Guidance NOAA STOFS-3D-Atlantic Forecast Guidance arn:aws:s3:::noaa-nos-stofs3d-pds us-east-1 S3 Bucket https://noaa-nos-stofs3d-pds.s3.amazonaws.com/README.html For questions regarding data content or quality, visit the STOFS site (https://p [NOAA](http://www.noaa.gov/) One time per day at 12 UTC Open Data. There are no restrictions on the use of this data. aws-pds, global, coastal, marine navigation, disaster response, weather, water, environmental, meteorological, oceans, sustainability, climate ['[Browse Bucket](https://noaa-nos-stofs3d-pds.s3.amazonaws.com/index.html)'] NOAA 3-D Surge and Tide Operational Forecast System for the Atlantic Basin (STOFS-3D-Atlantic) - NOAA STOFS-3D-Atlantic Forecast Guidance New Dataset Notification NOAA STOFS-3D-Atlantic Forecast Guidance New Dataset Notification arn:aws:sns:us-east-1:709902155096:NewICOGS3DObject us-east-1 SNS Topic https://noaa-nos-stofs3d-pds.s3.amazonaws.com/README.html For questions regarding data content or quality, visit the STOFS site (https://p [NOAA](http://www.noaa.gov/) One time per day at 12 UTC Open Data. There are no restrictions on the use of this data. aws-pds, global, coastal, marine navigation, disaster response, weather, water, environmental, meteorological, oceans, sustainability, climate -NOAA Analysis of Record for Calibration (AORC) Dataset 1-km resolution AORC version 11 arn:aws:s3:::noaa-nws-aorc-v1-1-1km us-east-1 S3 Bucket [Analysis of Record for Calibration: Version 1.1 Sources, Methods, and Verificat For questions regarding data content or quality, email the AORC team at aorc.inf [NOAA](http://www.noaa.gov/) To be determined CC-0 - Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nws-aorc-v1-1-1km.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Aerosol Optical Thickness Aerosol Optical Thickness arn:aws:s3:::noaa-cdr-aerosol-optical-thickness-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-aerosol-optical-thickness-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - CMORPH Precip CMORPH Precip arn:aws:s3:::noaa-cdr-precip-cmorph-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-precip-cmorph-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Cloud Properties ISCCP Cloud Properties ISCCP arn:aws:s3:::noaa-cdr-cloud-properties-isccp-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-cloud-properties-isccp-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Cloud Properties Polar Orbiter Cloud Properties Polar Orbiter arn:aws:s3:::noaa-cdr-cloud-properties-polar-orbiter-nasa-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-cloud-properties-polar-orbiter-nasa-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - GPCP Precip Daily GPCP Precip Daily arn:aws:s3:::noaa-cdr-precip-gpcp-daily-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-precip-gpcp-daily-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - GPCP Precip Monthly GPCP Precip Monthly arn:aws:s3:::noaa-cdr-precip-gpcp-monthly-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-precip-gpcp-monthly-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Hydrological Properties Hydrological Properties arn:aws:s3:::noaa-cdr-hydrological-properties-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-hydrological-properties-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - NEXRAD Precip NEXRAD Precip arn:aws:s3:::noaa-cdr-precip-nexrad-qpe-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-precip-nexrad-qpe-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Ocean Heat Content Ocean Heat Content arn:aws:s3:::noaa-cdr-ocean-heat-content-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ocean-heat-content-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Ocean Heatflux Ocean Heatflux arn:aws:s3:::noaa-cdr-ocean-heatflux-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ocean-heatflux-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Ocean Nearsurface Atmos Profiles Ocean Nearsurface Atmos Profiles arn:aws:s3:::noaa-cdr-ocean-nearsurface-atmos-profiles-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ocean-nearsurface-atmos-profiles-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Outgoing Longwave Radiation - Daily Outgoing Longwave Radiation - Daily arn:aws:s3:::noaa-cdr-outgoing-longwave-radiation-daily-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-outgoing-longwave-radiation-daily-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Outgoing Longwave Radiation - Monthly Outgoing Longwave Radiation - Monthly arn:aws:s3:::noaa-cdr-outgoing-longwave-radiation-monthly-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-outgoing-longwave-radiation-monthly-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Ozone - ESRL Ozone - ESRL arn:aws:s3:::noaa-cdr-ozone-esrl-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ozone-esrl-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - PERSIANN Precip PERSIANN Precip arn:aws:s3:::noaa-cdr-precip-persiann-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-precip-persiann-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Solar Spectral Irradiance Solar Spectral Irradiance arn:aws:s3:::noaa-cdr-solar-spectral-irradiance-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-solar-spectral-irradiance-pds.s3.amazonaws.com/index.html)'] -NOAA Atmospheric Climate Data Records - Total Solar Irradiance Total Solar Irradiance arn:aws:s3:::noaa-cdr-total-solar-irradiance-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-total-solar-irradiance-pds.s3.amazonaws.com/index.html)'] -NOAA Climate Forecast System (CFS) - Climate Forecast System (CFS) Model Data Climate Forecast System (CFS) Model Data arn:aws:s3:::noaa-cfs-pds us-east-1 S3 Bucket https://cfs.ncep.noaa.gov/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly, 6-Hourly, and Monthly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-cfs-pds.s3.amazonaws.com/index.html)'] +NOAA Analysis of Record for Calibration (AORC) Dataset 1-km resolution AORC version 11 arn:aws:s3:::noaa-nws-aorc-v1-1-1km us-east-1 S3 Bucket [Analysis of Record for Calibration: Version 1.1 Sources, Methods, and Verificat For questions regarding data content or quality, email the AORC team at aorc.inf [NOAA](http://www.noaa.gov/) To be determined CC-0 - Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nws-aorc-v1-1-1km.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Aerosol Optical Thickness Aerosol Optical Thickness arn:aws:s3:::noaa-cdr-aerosol-optical-thickness-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-aerosol-optical-thickness-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - CMORPH Precip CMORPH Precip arn:aws:s3:::noaa-cdr-precip-cmorph-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-precip-cmorph-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Cloud Properties ISCCP Cloud Properties ISCCP arn:aws:s3:::noaa-cdr-cloud-properties-isccp-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-cloud-properties-isccp-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Cloud Properties Polar Orbiter Cloud Properties Polar Orbiter arn:aws:s3:::noaa-cdr-cloud-properties-polar-orbiter-nasa-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-cloud-properties-polar-orbiter-nasa-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - GPCP Precip Daily GPCP Precip Daily arn:aws:s3:::noaa-cdr-precip-gpcp-daily-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-precip-gpcp-daily-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - GPCP Precip Monthly GPCP Precip Monthly arn:aws:s3:::noaa-cdr-precip-gpcp-monthly-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-precip-gpcp-monthly-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Hydrological Properties Hydrological Properties arn:aws:s3:::noaa-cdr-hydrological-properties-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-hydrological-properties-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - NEXRAD Precip NEXRAD Precip arn:aws:s3:::noaa-cdr-precip-nexrad-qpe-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-precip-nexrad-qpe-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Ocean Heat Content Ocean Heat Content arn:aws:s3:::noaa-cdr-ocean-heat-content-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ocean-heat-content-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Ocean Heatflux Ocean Heatflux arn:aws:s3:::noaa-cdr-ocean-heatflux-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ocean-heatflux-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Ocean Nearsurface Atmos Profiles Ocean Nearsurface Atmos Profiles arn:aws:s3:::noaa-cdr-ocean-nearsurface-atmos-profiles-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ocean-nearsurface-atmos-profiles-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Outgoing Longwave Radiation - Daily Outgoing Longwave Radiation - Daily arn:aws:s3:::noaa-cdr-outgoing-longwave-radiation-daily-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-outgoing-longwave-radiation-daily-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Outgoing Longwave Radiation - Monthly Outgoing Longwave Radiation - Monthly arn:aws:s3:::noaa-cdr-outgoing-longwave-radiation-monthly-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-outgoing-longwave-radiation-monthly-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Ozone - ESRL Ozone - ESRL arn:aws:s3:::noaa-cdr-ozone-esrl-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ozone-esrl-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - PERSIANN Precip PERSIANN Precip arn:aws:s3:::noaa-cdr-precip-persiann-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-precip-persiann-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Solar Spectral Irradiance Solar Spectral Irradiance arn:aws:s3:::noaa-cdr-solar-spectral-irradiance-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-solar-spectral-irradiance-pds.s3.amazonaws.com/index.html)'] +NOAA Atmospheric Climate Data Records - Total Solar Irradiance Total Solar Irradiance arn:aws:s3:::noaa-cdr-total-solar-irradiance-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/products/climate-data-records/atmospheric For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-total-solar-irradiance-pds.s3.amazonaws.com/index.html)'] +NOAA Climate Forecast System (CFS) - Climate Forecast System (CFS) Model Data Climate Forecast System (CFS) Model Data arn:aws:s3:::noaa-cfs-pds us-east-1 S3 Bucket https://cfs.ncep.noaa.gov/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly, 6-Hourly, and Monthly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-cfs-pds.s3.amazonaws.com/index.html)'] NOAA Climate Forecast System (CFS) - New data notifications for CFS, only Lambda and SQS protocols allowed New data notifications for CFS, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewCFSObject us-east-1 SNS Topic https://cfs.ncep.noaa.gov/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly, 6-Hourly, and Monthly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Cloud Optimized Zarr Reference Files (Kerchunk) - Cloud-optimized Zarr Reference Files Cloud-optimized Zarr Reference Files arn:aws:s3:::noaa-nodd-kerchunk-pds us-east-1 S3 Bucket Refer to source datasets documentation For questions regarding data content or quality, visit [Email The Tetra Tech Tea [NOAA's National Ocean Service, the Integrated Ocean Observing System (IOOS)](ht Optimizations run every time new data is uploaded to the source buckets and are Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, oceans, water, weather ['[Browse Bucket](https://noaa-nodd-kerchunk-pds.s3.amazonaws.com/index.html)'] +NOAA Cloud Optimized Zarr Reference Files (Kerchunk) - Cloud-optimized Zarr Reference Files Cloud-optimized Zarr Reference Files arn:aws:s3:::noaa-nodd-kerchunk-pds us-east-1 S3 Bucket Refer to source datasets documentation For questions regarding data content or quality, visit [Email The Tetra Tech Tea [NOAA's National Ocean Service, the Integrated Ocean Observing System (IOOS)](ht Optimizations run every time new data is uploaded to the source buckets and are Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, oceans, water, weather ['[Browse Bucket](https://noaa-nodd-kerchunk-pds.s3.amazonaws.com/index.html)'] NOAA Cloud Optimized Zarr Reference Files (Kerchunk) - New data notifications for Cloud-optimized Zarr Reference Files New data notifications for Cloud-optimized Zarr Reference Files arn:aws:sns:us-east-1:123901341784:NewNODDKerchunkObject us-east-1 SNS Topic Refer to source datasets documentation For questions regarding data content or quality, visit [Email The Tetra Tech Tea [NOAA's National Ocean Service, the Integrated Ocean Observing System (IOOS)](ht Optimizations run every time new data is uploaded to the source buckets and are Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, oceans, water, weather -NOAA Coastal Lidar Data - NOAA Coastal Lidar Dataset NOAA Coastal Lidar Dataset arn:aws:s3:::noaa-nos-coastal-lidar-pds us-east-1 S3 Bucket https://coast.noaa.gov/digitalcoast/data/coastallidar.html and https://coast.noa For any questions regarding data delivery or any general questions regarding the [NOAA](https://www.noaa.gov/) Periodically, as new data becomes available Open Data. There are no restrictions on the use of this data. aws-pds, climate, elevation, disaster response, geospatial, lidar, stac ['[STAC V1.0.0 endpoint](https://noaa-nos-coastal-lidar-pds.s3.us-east-1.amazonaws.com/entwine/stac/catalog.json)'] +NOAA Coastal Lidar Data - NOAA Coastal Lidar Dataset NOAA Coastal Lidar Dataset arn:aws:s3:::noaa-nos-coastal-lidar-pds us-east-1 S3 Bucket https://coast.noaa.gov/digitalcoast/data/coastallidar.html and https://coast.noa For any questions regarding data delivery or any general questions regarding the [NOAA](https://www.noaa.gov/) Periodically, as new data becomes available Open Data. There are no restrictions on the use of this data. aws-pds, climate, elevation, disaster response, geospatial, lidar, stac ['[STAC V1.0.0 endpoint](https://noaa-nos-coastal-lidar-pds.s3.us-east-1.amazonaws.com/entwine/stac/catalog.json)'] NOAA Coastal Lidar Data - NOAA Coastal Lidar Dataset New Dataset Notification NOAA Coastal Lidar Dataset New Dataset Notification arn:aws:sns:us-east-1:709902155096:NewCoastalLidarObject us-east-1 SNS Topic https://coast.noaa.gov/digitalcoast/data/coastallidar.html and https://coast.noa For any questions regarding data delivery or any general questions regarding the [NOAA](https://www.noaa.gov/) Periodically, as new data becomes available Open Data. There are no restrictions on the use of this data. aws-pds, climate, elevation, disaster response, geospatial, lidar, stac -NOAA Continuously Operating Reference Stations (CORS) Network (NCN) NCN Data and Products arn:aws:s3:::noaa-cors-pds us-east-1 S3 Bucket For more information, visit [NCN Data and Products](https://geodesy.noaa.gov/COR - For general inquiries about NCN data and products, email ✉ ngs.cors at noaa.go [NOAA](http://www.noaa.gov/) Most data are available within 1 hour from when they were recorded at the remote There are no restrictions on the use of this data. aws-pds, broadcast ephemeris, Continuously Operating Reference Station (CORS), earth observation, geospatial, GPS, GNSS, mapping, NOAA CORS Network (NCN), post-processing, RINEX, survey ['[Browse NOAA-NCN Bucket](https://noaa-cors-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Gridsat B1 Gridsat B1 arn:aws:s3:::noaa-cdr-gridsat-b1-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-gridsat-b1-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - MSU Brightness Temperature MSU Brightness Temperature arn:aws:s3:::noaa-cdr-msu-brit-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-msu-brit-temp-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Mean Layer Temperature Mean Layer Temperature arn:aws:s3:::noaa-cdr-mean-layer-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-mean-layer-temp-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Mean Layer Temperature - RSS Mean Layer Temperature - RSS arn:aws:s3:::noaa-cdr-mean-layer-temp-rss-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-mean-layer-temp-rss-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Mean Layer Temperature - UAH Mean Layer Temperature - UAH arn:aws:s3:::noaa-cdr-mean-layer-temp-uah-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-mean-layer-temp-uah-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Mean Layer Temperature Lower Stratosphere Mean Layer Temperature Lower Stratosphere arn:aws:s3:::noaa-cdr-mean-layer-temp-lower-strat-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-mean-layer-temp-lower-strat-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Mean Layer Temperature Upper Troposphere Mean Layer Temperature Upper Troposphere arn:aws:s3:::noaa-cdr-mean-layer-temp-upper-trop-lower-strat-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-mean-layer-temp-upper-trop-lower-strat-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Microwave Brightness Temperature Microwave Brightness Temperature arn:aws:s3:::noaa-cdr-microwave-brit-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-microwave-brit-temp-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Microwave Humidity Sounder Brightness Temperature Microwave Humidity Sounder Brightness Temperature arn:aws:s3:::noaa-cdr-microwave-humidity-sounder-brit-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-microwave-humidity-sounder-brit-temp-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Microwave Imager Brightness Temperature - CSU Microwave Imager Brightness Temperature - CSU arn:aws:s3:::noaa-cdr-microwave-imager-brit-temp-csu-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-microwave-imager-brit-temp-csu-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Microwave Imager Brightness Temperature - RSS Microwave Imager Brightness Temperature - RSS arn:aws:s3:::noaa-cdr-microwave-imager-brit-temp-rss-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-microwave-imager-brit-temp-rss-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Microwave Temperature Sounder Brightness Temperature Microwave Temperature Sounder Brightness Temperature arn:aws:s3:::noaa-cdr-microwave-temp-sounder-brit-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-microwave-temp-sounder-brit-temp-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Polar Orbiter Radiances and Cloud Properties - NASA Polar Orbiter Radiances and Cloud Properties - NASA arn:aws:s3:::noaa-cdr-radiances-and-cloud-properties-polar-orbiter-nasa-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-radiances-and-cloud-properties-polar-orbiter-nasa-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Polar Pathfinder Polar Pathfinder arn:aws:s3:::noaa-cdr-polar-pathfinder-fcdr-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-polar-pathfinder-fcdr-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Polar Pathfinder Extended Polar Pathfinder Extended arn:aws:s3:::noaa-cdr-polar-pathfinder-extended-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-polar-pathfinder-extended-pds.s3.amazonaws.com/index.html)'] -NOAA Fundamental Climate Data Records (FCDR) - Water Vapor Brightness Temperature Water Vapor Brightness Temperature arn:aws:s3:::noaa-cdr-ir-water-vapor-brit-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ir-water-vapor-brit-temp-pds.s3.amazonaws.com/index.html)'] -NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17 & 18 - GOES-16 imagery and metadata GOES-16 imagery and metadata arn:aws:s3:::noaa-goes16 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-goes16 "For questions related to specific GOES Products, please visit the ""[GOES-R websi" [NOAA](http://www.noaa.gov/) New data is added as soon as it's available There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-goes16.s3.amazonaws.com/index.html)'] -NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17 & 18 - GOES-17 imagery and metadata GOES-17 imagery and metadata arn:aws:s3:::noaa-goes17 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-goes16 "For questions related to specific GOES Products, please visit the ""[GOES-R websi" [NOAA](http://www.noaa.gov/) New data is added as soon as it's available There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-goes17.s3.amazonaws.com/index.html)'] -NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17 & 18 - GOES-18 imagery and metadata GOES-18 imagery and metadata arn:aws:s3:::noaa-goes18 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-goes16 "For questions related to specific GOES Products, please visit the ""[GOES-R websi" [NOAA](http://www.noaa.gov/) New data is added as soon as it's available There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-goes18.s3.amazonaws.com/index.html)'] +NOAA Continuously Operating Reference Stations (CORS) Network (NCN) NCN Data and Products arn:aws:s3:::noaa-cors-pds us-east-1 S3 Bucket For more information, visit [NCN Data and Products](https://geodesy.noaa.gov/COR - For general inquiries about NCN data and products, email ✉ ngs.cors at noaa.go [NOAA](http://www.noaa.gov/) Most data are available within 1 hour from when they were recorded at the remote There are no restrictions on the use of this data. aws-pds, broadcast ephemeris, Continuously Operating Reference Station (CORS), earth observation, geospatial, GPS, GNSS, mapping, NOAA CORS Network (NCN), post-processing, RINEX, survey ['[Browse NOAA-NCN Bucket](https://noaa-cors-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Gridsat B1 Gridsat B1 arn:aws:s3:::noaa-cdr-gridsat-b1-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-gridsat-b1-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - MSU Brightness Temperature MSU Brightness Temperature arn:aws:s3:::noaa-cdr-msu-brit-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-msu-brit-temp-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Mean Layer Temperature Mean Layer Temperature arn:aws:s3:::noaa-cdr-mean-layer-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-mean-layer-temp-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Mean Layer Temperature - RSS Mean Layer Temperature - RSS arn:aws:s3:::noaa-cdr-mean-layer-temp-rss-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-mean-layer-temp-rss-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Mean Layer Temperature - UAH Mean Layer Temperature - UAH arn:aws:s3:::noaa-cdr-mean-layer-temp-uah-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-mean-layer-temp-uah-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Mean Layer Temperature Lower Stratosphere Mean Layer Temperature Lower Stratosphere arn:aws:s3:::noaa-cdr-mean-layer-temp-lower-strat-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-mean-layer-temp-lower-strat-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Mean Layer Temperature Upper Troposphere Mean Layer Temperature Upper Troposphere arn:aws:s3:::noaa-cdr-mean-layer-temp-upper-trop-lower-strat-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-mean-layer-temp-upper-trop-lower-strat-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Microwave Brightness Temperature Microwave Brightness Temperature arn:aws:s3:::noaa-cdr-microwave-brit-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-microwave-brit-temp-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Microwave Humidity Sounder Brightness Temperature Microwave Humidity Sounder Brightness Temperature arn:aws:s3:::noaa-cdr-microwave-humidity-sounder-brit-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-microwave-humidity-sounder-brit-temp-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Microwave Imager Brightness Temperature - CSU Microwave Imager Brightness Temperature - CSU arn:aws:s3:::noaa-cdr-microwave-imager-brit-temp-csu-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-microwave-imager-brit-temp-csu-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Microwave Imager Brightness Temperature - RSS Microwave Imager Brightness Temperature - RSS arn:aws:s3:::noaa-cdr-microwave-imager-brit-temp-rss-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-microwave-imager-brit-temp-rss-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Microwave Temperature Sounder Brightness Temperature Microwave Temperature Sounder Brightness Temperature arn:aws:s3:::noaa-cdr-microwave-temp-sounder-brit-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-microwave-temp-sounder-brit-temp-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Polar Orbiter Radiances and Cloud Properties - NASA Polar Orbiter Radiances and Cloud Properties - NASA arn:aws:s3:::noaa-cdr-radiances-and-cloud-properties-polar-orbiter-nasa-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-radiances-and-cloud-properties-polar-orbiter-nasa-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Polar Pathfinder Polar Pathfinder arn:aws:s3:::noaa-cdr-polar-pathfinder-fcdr-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-polar-pathfinder-fcdr-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Polar Pathfinder Extended Polar Pathfinder Extended arn:aws:s3:::noaa-cdr-polar-pathfinder-extended-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-polar-pathfinder-extended-pds.s3.amazonaws.com/index.html)'] +NOAA Fundamental Climate Data Records (FCDR) - Water Vapor Brightness Temperature Water Vapor Brightness Temperature arn:aws:s3:::noaa-cdr-ir-water-vapor-brit-temp-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ir-water-vapor-brit-temp-pds.s3.amazonaws.com/index.html)'] +NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17 & 18 - GOES-16 imagery and metadata GOES-16 imagery and metadata arn:aws:s3:::noaa-goes16 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-goes16 "For questions related to specific GOES Products, please visit the ""[GOES-R websi" [NOAA](http://www.noaa.gov/) New data is added as soon as it's available There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-goes16.s3.amazonaws.com/index.html)'] +NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17 & 18 - GOES-17 imagery and metadata GOES-17 imagery and metadata arn:aws:s3:::noaa-goes17 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-goes16 "For questions related to specific GOES Products, please visit the ""[GOES-R websi" [NOAA](http://www.noaa.gov/) New data is added as soon as it's available There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-goes17.s3.amazonaws.com/index.html)'] +NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17 & 18 - GOES-18 imagery and metadata GOES-18 imagery and metadata arn:aws:s3:::noaa-goes18 us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-goes16 "For questions related to specific GOES Products, please visit the ""[GOES-R websi" [NOAA](http://www.noaa.gov/) New data is added as soon as it's available There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery ['[Browse Bucket](https://noaa-goes18.s3.amazonaws.com/index.html)'] NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17 & 18 - New data notifications for GOES-16, only Lambda and SQS protocols allowed New data notifications for GOES-16, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewGOES16Object us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-goes16 "For questions related to specific GOES Products, please visit the ""[GOES-R websi" [NOAA](http://www.noaa.gov/) New data is added as soon as it's available There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17 & 18 - New data notifications for GOES-17, only Lambda and SQS protocols allowed New data notifications for GOES-17, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewGOES17Object us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-goes16 "For questions related to specific GOES Products, please visit the ""[GOES-R websi" [NOAA](http://www.noaa.gov/) New data is added as soon as it's available There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17 & 18 - New data notifications for GOES-18, only Lambda and SQS protocols allowed New data notifications for GOES-18, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewGOES18Object us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-goes16 "For questions related to specific GOES Products, please visit the ""[GOES-R websi" [NOAA](http://www.noaa.gov/) New data is added as soon as it's available There are no restrictions on the use of this data. aws-pds, agriculture, geospatial, weather, earth observation, meteorological, disaster response, satellite imagery -NOAA Global Data Assimilation (DA) Test Data - Global Data Assimilation (DA) System Test Data Global Data Assimilation (DA) System Test Data arn:aws:s3:::noaa-ufs-gdas-pds us-east-1 S3 Bucket https://github.com/NOAA-EMC/GDASApp/wiki For questions regarding data content or quality, post on the ufs-community forum [NOAA](http://www.noaa.gov/) These are stable datasets for use with global DA projects. They will be updated GNU Lesser Public License v2.1: https://www.gnu.org/licenses/old-licenses/lgpl-2 aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather ['[Browse Bucket](https://noaa-ufs-gdas-pds.s3.amazonaws.com/index.html)'] +NOAA Global Data Assimilation (DA) Test Data - Global Data Assimilation (DA) System Test Data Global Data Assimilation (DA) System Test Data arn:aws:s3:::noaa-ufs-gdas-pds us-east-1 S3 Bucket https://github.com/NOAA-EMC/GDASApp/wiki For questions regarding data content or quality, post on the ufs-community forum [NOAA](http://www.noaa.gov/) These are stable datasets for use with global DA projects. They will be updated GNU Lesser Public License v2.1: https://www.gnu.org/licenses/old-licenses/lgpl-2 aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather ['[Browse Bucket](https://noaa-ufs-gdas-pds.s3.amazonaws.com/index.html)'] NOAA Global Data Assimilation (DA) Test Data - New data notifications for Global Data Assimilation (DA) System Test Data, only New data notifications for Global Data Assimilation (DA) System Test Data, only arn:aws:sns:us-east-1:709902155096:NewNWSUFSGDASObject us-east-1 SNS Topic https://github.com/NOAA-EMC/GDASApp/wiki For questions regarding data content or quality, post on the ufs-community forum [NOAA](http://www.noaa.gov/) These are stable datasets for use with global DA projects. They will be updated GNU Lesser Public License v2.1: https://www.gnu.org/licenses/old-licenses/lgpl-2 aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather NOAA Global Ensemble Forecast System (GEFS) - New data notifications for GFS, only Lambda and SQS protocols allowed New data notifications for GFS, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewGEFSObject us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-gefs-pds For questions regarding data content or quality, visit [the NOAA GEFS site](http [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours starting at midnight. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Global Ensemble Forecast System (GEFS) - Project data files Project data files arn:aws:s3:::noaa-gefs-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-gefs-pds For questions regarding data content or quality, visit [the NOAA GEFS site](http [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours starting at midnight. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-gefs-pds.s3.amazonaws.com/index.html)'] -NOAA Global Ensemble Forecast System (GEFS) Re-forecast GEFS Re-forecast in Grib2 Format arn:aws:s3:::noaa-gefs-retrospective us-east-1 S3 Bucket https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data. For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Re-forecasts do not adhere to an update frequency. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-gefs-retrospective.s3.amazonaws.com/index.html)'] -NOAA Global Forecast System (GFS) - GFS Warm Start Initial Conditions GFS Warm Start Initial Conditions arn:aws:s3:::noaa-gfs-warmstart-pds us-east-1 S3 Bucket https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php For questions regarding data content or quality, visit [the NOAA GFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours starting at midnight UTC Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather ['[Browse Bucket](https://noaa-gfs-warmstart-pds.s3.amazonaws.com/index.html)'] -NOAA Global Forecast System (GFS) - GFS data GFS data arn:aws:s3:::noaa-gfs-bdp-pds us-east-1 S3 Bucket https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php For questions regarding data content or quality, visit [the NOAA GFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours starting at midnight UTC Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather ['[Browse Bucket](https://noaa-gfs-bdp-pds.s3.amazonaws.com/index.html)'] +NOAA Global Ensemble Forecast System (GEFS) - Project data files Project data files arn:aws:s3:::noaa-gefs-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-gefs-pds For questions regarding data content or quality, visit [the NOAA GEFS site](http [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours starting at midnight. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-gefs-pds.s3.amazonaws.com/index.html)'] +NOAA Global Ensemble Forecast System (GEFS) Re-forecast GEFS Re-forecast in Grib2 Format arn:aws:s3:::noaa-gefs-retrospective us-east-1 S3 Bucket https://noaa-gefs-retrospective.s3.amazonaws.com/Description_of_reforecast_data. For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Re-forecasts do not adhere to an update frequency. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-gefs-retrospective.s3.amazonaws.com/index.html)'] +NOAA Global Forecast System (GFS) - GFS Warm Start Initial Conditions GFS Warm Start Initial Conditions arn:aws:s3:::noaa-gfs-warmstart-pds us-east-1 S3 Bucket https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php For questions regarding data content or quality, visit [the NOAA GFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours starting at midnight UTC Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather ['[Browse Bucket](https://noaa-gfs-warmstart-pds.s3.amazonaws.com/index.html)'] +NOAA Global Forecast System (GFS) - GFS data GFS data arn:aws:s3:::noaa-gfs-bdp-pds us-east-1 S3 Bucket https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php For questions regarding data content or quality, visit [the NOAA GFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours starting at midnight UTC Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather ['[Browse Bucket](https://noaa-gfs-bdp-pds.s3.amazonaws.com/index.html)'] NOAA Global Forecast System (GFS) - New data notifications for GFS Warm Start IC, only Lambda and SQS protocols allo New data notifications for GFS Warm Start IC, only Lambda and SQS protocols allo arn:aws:sns:us-east-1:123901341784:NewGfsWarmStartObject us-east-1 SNS Topic https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php For questions regarding data content or quality, visit [the NOAA GFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours starting at midnight UTC Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather NOAA Global Forecast System (GFS) - New data notifications for GFS, only Lambda and SQS protocols allowed New data notifications for GFS, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewGFSObject us-east-1 SNS Topic https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/gfs.php For questions regarding data content or quality, visit [the NOAA GFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours starting at midnight UTC Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather -NOAA Global Historical Climatology Network Daily (GHCN-D) Project data files arn:aws:s3:::noaa-ghcn-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-ghcn For questions regarding data content or quality, visit [the NOAA GHCN site](http [NOAA](http://www.noaa.gov/) Daily https://www.ncdc.noaa.gov/ghcnd-data-access aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ghcn-pds.s3.amazonaws.com/index.html)'] +NOAA Global Historical Climatology Network Daily (GHCN-D) Project data files arn:aws:s3:::noaa-ghcn-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-ghcn For questions regarding data content or quality, visit [the NOAA GHCN site](http [NOAA](http://www.noaa.gov/) Daily https://www.ncdc.noaa.gov/ghcnd-data-access aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ghcn-pds.s3.amazonaws.com/index.html)'] NOAA Global Hydro Estimator (GHE) - New data notifications for GHE, only Lambda and SQS protocols allowed New data notifications for GHE, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewGHEObject us-east-1 SNS Topic https://www.ospo.noaa.gov/Products/atmosphere/ghe/index.html For questions regarding product content orquality, visit https://www.ospo.noaa.g [NOAA](http://www.noaa.gov/) 15 minute-instantaneous There are no restrictions on the use of this data. aws-pds, agriculture, meteorological, water, weather -NOAA Global Hydro Estimator (GHE) - Project data files Project data files arn:aws:s3:::noaa-ghe-pds us-east-1 S3 Bucket https://www.ospo.noaa.gov/Products/atmosphere/ghe/index.html For questions regarding product content orquality, visit https://www.ospo.noaa.g [NOAA](http://www.noaa.gov/) 15 minute-instantaneous There are no restrictions on the use of this data. aws-pds, agriculture, meteorological, water, weather ['[Browse Bucket](https://noaa-ghe-pds.s3.amazonaws.com/index.html)'] -NOAA Global Mosaic of Geostationary Satellite Imagery (GMGSI) - NOAA Global Mosaic of Geostationary Satellite Imagery (GMGSI) NOAA Global Mosaic of Geostationary Satellite Imagery (GMGSI) arn:aws:s3:::noaa-gmgsi-pds us-east-1 S3 Bucket [https://www.ospo.noaa.gov/Operations/GOES/index.html](https://www.ospo.noaa.gov For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-gmgsi-pds.s3.amazonaws.com/index.html)'] +NOAA Global Hydro Estimator (GHE) - Project data files Project data files arn:aws:s3:::noaa-ghe-pds us-east-1 S3 Bucket https://www.ospo.noaa.gov/Products/atmosphere/ghe/index.html For questions regarding product content orquality, visit https://www.ospo.noaa.g [NOAA](http://www.noaa.gov/) 15 minute-instantaneous There are no restrictions on the use of this data. aws-pds, agriculture, meteorological, water, weather ['[Browse Bucket](https://noaa-ghe-pds.s3.amazonaws.com/index.html)'] +NOAA Global Mosaic of Geostationary Satellite Imagery (GMGSI) - NOAA Global Mosaic of Geostationary Satellite Imagery (GMGSI) NOAA Global Mosaic of Geostationary Satellite Imagery (GMGSI) arn:aws:s3:::noaa-gmgsi-pds us-east-1 S3 Bucket [https://www.ospo.noaa.gov/Operations/GOES/index.html](https://www.ospo.noaa.gov For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-gmgsi-pds.s3.amazonaws.com/index.html)'] NOAA Global Mosaic of Geostationary Satellite Imagery (GMGSI) - New data notifications for GMGSI, only Lambda and SQS protocols allowed New data notifications for GMGSI, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewGMGSIObject us-east-1 SNS Topic [https://www.ospo.noaa.gov/Operations/GOES/index.html](https://www.ospo.noaa.gov For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Global Real-Time Ocean Forecast System (Global RTOFS) - NOAA Global Real Time Ocean Forecasting System Data NOAA Global Real Time Ocean Forecasting System Data arn:aws:s3:::noaa-nws-rtofs-pds us-east-1 S3 Bucket https://polar.ncep.noaa.gov/global/ For questions regarding data content or quality, visit the RTOFS site (https://p [NOAA](http://www.noaa.gov/) Once a day. The products are available at the following times each day
00 Open Data. There are no restrictions on the use of this data. aws-pds, global, coastal, disaster response, weather, water, environmental, meteorological, oceans, climate ['[Browse Bucket](https://noaa-nws-rtofs-pds.s3.amazonaws.com/index.html)'] +NOAA Global Real-Time Ocean Forecast System (Global RTOFS) - NOAA Global Real Time Ocean Forecasting System Data NOAA Global Real Time Ocean Forecasting System Data arn:aws:s3:::noaa-nws-rtofs-pds us-east-1 S3 Bucket https://polar.ncep.noaa.gov/global/ For questions regarding data content or quality, visit the RTOFS site (https://p [NOAA](http://www.noaa.gov/) Once a day. The products are available at the following times each day
00 Open Data. There are no restrictions on the use of this data. aws-pds, global, coastal, disaster response, weather, water, environmental, meteorological, oceans, climate ['[Browse Bucket](https://noaa-nws-rtofs-pds.s3.amazonaws.com/index.html)'] NOAA Global Real-Time Ocean Forecast System (Global RTOFS) - NOAA Global Real Time Ocean Forecasting System New Dataset Notification NOAA Global Real Time Ocean Forecasting System New Dataset Notification arn:aws:sns:us-east-1:709902155096:NewRTOFSObject us-east-1 SNS Topic https://polar.ncep.noaa.gov/global/ For questions regarding data content or quality, visit the RTOFS site (https://p [NOAA](http://www.noaa.gov/) Once a day. The products are available at the following times each day
00 Open Data. There are no restrictions on the use of this data. aws-pds, global, coastal, disaster response, weather, water, environmental, meteorological, oceans, climate NOAA Global Surface Summary of Day Measurements and metadata arn:aws:s3:::noaa-gsod-pds us-east-1 S3 Bucket http://www.ncdc.noaa.gov/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Data is updated as new products are available. Open Data. There are no restrictions on the use of this data aws-pds, agriculture, environmental, climate, weather, natural resource, regulatory -NOAA Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global) - NOAA STOFS-2D-Global Water Level Forecast Guidance NOAA STOFS-2D-Global Water Level Forecast Guidance arn:aws:s3:::noaa-gestofs-pds us-east-1 S3 Bucket https://noaa-gestofs-pds.s3.amazonaws.com/README.html For questions regarding data content or quality, visit the ESTOFS site (https:// [NOAA](http://www.noaa.gov/) Four times per day, every 6 hours starting at midnight UTC Open Data. There are no restrictions on the use of this data. aws-pds, global, coastal, disaster response, weather, water, environmental, meteorological, oceans, climate ['[Browse Bucket](https://noaa-gestofs-pds.s3.amazonaws.com/index.html)'] +NOAA Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global) - NOAA STOFS-2D-Global Water Level Forecast Guidance NOAA STOFS-2D-Global Water Level Forecast Guidance arn:aws:s3:::noaa-gestofs-pds us-east-1 S3 Bucket https://noaa-gestofs-pds.s3.amazonaws.com/README.html For questions regarding data content or quality, visit the ESTOFS site (https:// [NOAA](http://www.noaa.gov/) Four times per day, every 6 hours starting at midnight UTC Open Data. There are no restrictions on the use of this data. aws-pds, global, coastal, disaster response, weather, water, environmental, meteorological, oceans, climate ['[Browse Bucket](https://noaa-gestofs-pds.s3.amazonaws.com/index.html)'] NOAA Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global) - NOAA STOFS-2D-Global Water Level Forecast Guidance New Dataset Notification NOAA STOFS-2D-Global Water Level Forecast Guidance New Dataset Notification arn:aws:sns:us-east-1:123901341784:NewGESTOFSObject us-east-1 SNS Topic https://noaa-gestofs-pds.s3.amazonaws.com/README.html For questions regarding data content or quality, visit the ESTOFS site (https:// [NOAA](http://www.noaa.gov/) Four times per day, every 6 hours starting at midnight UTC Open Data. There are no restrictions on the use of this data. aws-pds, global, coastal, disaster response, weather, water, environmental, meteorological, oceans, climate -NOAA High-Resolution Rapid Refresh (HRRR) Model - Archive of HRRR data since 2014 Archive of HRRR data since 2014 arn:aws:s3:::noaa-hrrr-bdp-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-hrrr For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly U.S. Government Work aws-pds, agriculture, climate, weather, environmental, disaster response ['[Browse Bucket](https://noaa-hrrr-bdp-pds.s3.amazonaws.com/index.html)'] -NOAA High-Resolution Rapid Refresh (HRRR) Model - HRRR Zarr format near-real time data archive managed by the University of Utah HRRR Zarr format near-real time data archive managed by the University of Utah arn:aws:s3:::hrrrzarr us-west-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-hrrr For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly U.S. Government Work aws-pds, agriculture, climate, weather, environmental, disaster response ['[Browse Bucket](https://hrrrzarr.s3.amazonaws.com/index.html)'] +NOAA High-Resolution Rapid Refresh (HRRR) Model - Archive of HRRR data since 2014 Archive of HRRR data since 2014 arn:aws:s3:::noaa-hrrr-bdp-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-hrrr For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly U.S. Government Work aws-pds, agriculture, climate, weather, environmental, disaster response ['[Browse Bucket](https://noaa-hrrr-bdp-pds.s3.amazonaws.com/index.html)'] +NOAA High-Resolution Rapid Refresh (HRRR) Model - HRRR Zarr format near-real time data archive managed by the University of Utah HRRR Zarr format near-real time data archive managed by the University of Utah arn:aws:s3:::hrrrzarr us-west-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-hrrr For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly U.S. Government Work aws-pds, agriculture, climate, weather, environmental, disaster response ['[Browse Bucket](https://hrrrzarr.s3.amazonaws.com/index.html)'] NOAA High-Resolution Rapid Refresh (HRRR) Model - New data notifications New data notifications arn:aws:sns:us-east-1:123901341784:NewHRRRObject us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-hrrr For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly U.S. Government Work aws-pds, agriculture, climate, weather, environmental, disaster response -NOAA Historical Maps and Charts NOAA Historic Chart Datasets arn:aws:s3:::noaa-nos-historicalcharts-pds us-east-1 S3 Bucket https://historicalcharts.noaa.gov/about.php For any questions regarding data delivery not associated with this platform or a [NOAA](http://www.noaa.gov/) Periodic manual updates when historic charts are added to the collection. Open Data. There are no restrictions on the use of this data. aws-pds, history, mapping, coastal, geospatial, survey ['[Browse Bucket](https://noaa-nos-historicalcharts-pds.s3.amazonaws.com/index.html)'] -NOAA Hurricane Analysis and Forecast System (HAFS) - Hurricane Analysis Forecast System (HAFS) Data Hurricane Analysis Forecast System (HAFS) Data arn:aws:s3:::noaa-nws-hafs-pds us-east-1 S3 Bucket https://wpo.noaa.gov/the-hurricane-analysis-and-forecast-system-hafs/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Event Driven Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nws-hafs-pds.s3.amazonaws.com/index.html)'] +NOAA Historical Maps and Charts NOAA Historic Chart Datasets arn:aws:s3:::noaa-nos-historicalcharts-pds us-east-1 S3 Bucket https://historicalcharts.noaa.gov/about.php For any questions regarding data delivery not associated with this platform or a [NOAA](http://www.noaa.gov/) Periodic manual updates when historic charts are added to the collection. Open Data. There are no restrictions on the use of this data. aws-pds, history, mapping, coastal, geospatial, survey ['[Browse Bucket](https://noaa-nos-historicalcharts-pds.s3.amazonaws.com/index.html)'] +NOAA Hurricane Analysis and Forecast System (HAFS) - Hurricane Analysis Forecast System (HAFS) Data Hurricane Analysis Forecast System (HAFS) Data arn:aws:s3:::noaa-nws-hafs-pds us-east-1 S3 Bucket https://wpo.noaa.gov/the-hurricane-analysis-and-forecast-system-hafs/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Event Driven Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nws-hafs-pds.s3.amazonaws.com/index.html)'] NOAA Hurricane Analysis and Forecast System (HAFS) - New data notifications for HAFS, only Lambda and SQS protocols allowed New data notifications for HAFS, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:709902155096:NewHAFSObject us-east-1 SNS Topic https://wpo.noaa.gov/the-hurricane-analysis-and-forecast-system-hafs/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Event Driven Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Integrated Surface Database (ISD) - ISD in CSV format ISD in CSV format arn:aws:s3:::noaa-global-hourly-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/data/global-hourly/doc/isd-format-document.pdf For questions regarding data content or quality, visit [the NOAA ISD site](https [NOAA](http://www.noaa.gov/) Daily https://www.ncdc.noaa.gov/isd/data-access aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-global-hourly-pds.s3.amazonaws.com/index.html)'] -NOAA Integrated Surface Database (ISD) - ISD in original format ISD in original format arn:aws:s3:::noaa-isd-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/data/global-hourly/doc/isd-format-document.pdf For questions regarding data content or quality, visit [the NOAA ISD site](https [NOAA](http://www.noaa.gov/) Daily https://www.ncdc.noaa.gov/isd/data-access aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-isd-pds.s3.amazonaws.com/index.html)'] -NOAA Joint Polar Satellite System (JPSS) - NOAA JPSS Development Data NOAA JPSS Development Data arn:aws:s3:::noaa-jpss us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-jpss.s3.amazonaws.com/index.html)'] -NOAA Joint Polar Satellite System (JPSS) - NOAA JPSS NOAA-20 Data NOAA JPSS NOAA-20 Data arn:aws:s3:::noaa-nesdis-n20-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nesdis-n20-pds.s3.amazonaws.com/index.html)'] -NOAA Joint Polar Satellite System (JPSS) - NOAA JPSS NOAA-21 Data NOAA JPSS NOAA-21 Data arn:aws:s3:::noaa-nesdis-n21-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nesdis-n21-pds.s3.amazonaws.com/index.html)'] -NOAA Joint Polar Satellite System (JPSS) - NOAA JPSS SNPP (Suomi NPP) Data NOAA JPSS SNPP (Suomi NPP) Data arn:aws:s3:::noaa-nesdis-snpp-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nesdis-snpp-pds.s3.amazonaws.com/index.html)'] +NOAA Integrated Surface Database (ISD) - ISD in CSV format ISD in CSV format arn:aws:s3:::noaa-global-hourly-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/data/global-hourly/doc/isd-format-document.pdf For questions regarding data content or quality, visit [the NOAA ISD site](https [NOAA](http://www.noaa.gov/) Daily https://www.ncdc.noaa.gov/isd/data-access aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-global-hourly-pds.s3.amazonaws.com/index.html)'] +NOAA Integrated Surface Database (ISD) - ISD in original format ISD in original format arn:aws:s3:::noaa-isd-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/data/global-hourly/doc/isd-format-document.pdf For questions regarding data content or quality, visit [the NOAA ISD site](https [NOAA](http://www.noaa.gov/) Daily https://www.ncdc.noaa.gov/isd/data-access aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-isd-pds.s3.amazonaws.com/index.html)'] +NOAA Joint Polar Satellite System (JPSS) - NOAA JPSS Development Data NOAA JPSS Development Data arn:aws:s3:::noaa-jpss us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-jpss.s3.amazonaws.com/index.html)'] +NOAA Joint Polar Satellite System (JPSS) - NOAA JPSS NOAA-20 Data NOAA JPSS NOAA-20 Data arn:aws:s3:::noaa-nesdis-n20-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nesdis-n20-pds.s3.amazonaws.com/index.html)'] +NOAA Joint Polar Satellite System (JPSS) - NOAA JPSS NOAA-21 Data NOAA JPSS NOAA-21 Data arn:aws:s3:::noaa-nesdis-n21-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nesdis-n21-pds.s3.amazonaws.com/index.html)'] +NOAA Joint Polar Satellite System (JPSS) - NOAA JPSS SNPP (Suomi NPP) Data NOAA JPSS SNPP (Suomi NPP) Data arn:aws:s3:::noaa-nesdis-snpp-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nesdis-snpp-pds.s3.amazonaws.com/index.html)'] NOAA Joint Polar Satellite System (JPSS) - New data notifications for JPSS data, only Lambda and SQS protocols allowed New data notifications for JPSS data, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:709902155096:NewJPSSObject us-east-1 SNS Topic https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather +NOAA Joint Polar Satellite System (JPSS) - New data notifications for JPSS data, only Lambda and SQS protocols allowed New data notifications for JPSS data, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:709902155096:NewNOAA21Object us-east-1 SNS Topic https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather NOAA Joint Polar Satellite System (JPSS) - New data notifications for JPSS data, only Lambda and SQS protocols allowed New data notifications for JPSS data, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:709902155096:NewSNPPObject us-east-1 SNS Topic https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather NOAA Joint Polar Satellite System (JPSS) - New data notifications for JPSS data, only Lambda and SQS protocols allowed New data notifications for JPSS data, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:709902155096:NewNOAA20Object us-east-1 SNS Topic https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Joint Polar Satellite System (JPSS) - New data notifications for JPSS data, only Lambda and SQS protocols allowed New data notifications for JPSS data, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:709902155096:NewNOAA21Object us-east-1 SNS Topic https://github.com/NOAA-Big-Data-Program/bdp-data-docs/tree/main/JPSS For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) New data is added as soon as it's available Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Multi-Radar/Multi-Sensor System (MRMS) - NOAA Multi-Radar/Multi-Sensor System (MRMS) NOAA Multi-Radar/Multi-Sensor System (MRMS) arn:aws:s3:::noaa-mrms-pds us-east-1 S3 Bucket https://www.nssl.noaa.gov/projects/mrms/ For specific MRMS data questions, please reach out to the MRMS Team at mrms@noaa [NOAA](http://www.noaa.gov/) Data is delivered in real-time with a 2-minute update cycle. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-mrms-pds.s3.amazonaws.com/index.html)'] +NOAA Multi-Radar/Multi-Sensor System (MRMS) - NOAA Multi-Radar/Multi-Sensor System (MRMS) NOAA Multi-Radar/Multi-Sensor System (MRMS) arn:aws:s3:::noaa-mrms-pds us-east-1 S3 Bucket https://www.nssl.noaa.gov/projects/mrms/ For specific MRMS data questions, please reach out to the MRMS Team at mrms@noaa [NOAA](http://www.noaa.gov/) Data is delivered in real-time with a 2-minute update cycle. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-mrms-pds.s3.amazonaws.com/index.html)'] NOAA Multi-Radar/Multi-Sensor System (MRMS) - New data notifications for MRMS data, only Lambda and SQS protocols allowed New data notifications for MRMS data, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewMRMSObject us-east-1 SNS Topic https://www.nssl.noaa.gov/projects/mrms/ For specific MRMS data questions, please reach out to the MRMS Team at mrms@noaa [NOAA](http://www.noaa.gov/) Data is delivered in real-time with a 2-minute update cycle. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) Multi-Year Reanalysis of Remotely Sensed Storms arn:aws:s3:::noaa-oar-myrorss-pds us-east-1 S3 Bucket https://osf.io/9gzp2/ For any data delivery issues or any questions in general, please contact the NOA [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, meteorological, natural resource, sustainability, weather ['[Browse Bucket](https://noaa-oar-myrorss-pds.s3.amazonaws.com/index.html)'] -NOAA NASA Joint Archive (NNJA) of Observations for Earth System Reanalysis - NNJA Observations for Earth System Reanalysis Data NNJA Observations for Earth System Reanalysis Data arn:aws:s3:::noaa-reanalyses-pds us-east-1 S3 Bucket https://psl.noaa.gov/data/nnja_obs/ For questions regarding data content or quality, visit [the NOAA PSL NNJA](https [NOAA](http://www.noaa.gov/) 1 time a day Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-reanalyses-pds.s3.amazonaws.com/index.html)'] +NOAA Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) Multi-Year Reanalysis of Remotely Sensed Storms arn:aws:s3:::noaa-oar-myrorss-pds us-east-1 S3 Bucket https://osf.io/9gzp2/ For any data delivery issues or any questions in general, please contact the NOA [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, earth observation, meteorological, natural resource, sustainability, weather ['[Browse Bucket](https://noaa-oar-myrorss-pds.s3.amazonaws.com/index.html)'] +NOAA NASA Joint Archive (NNJA) of Observations for Earth System Reanalysis - NNJA Observations for Earth System Reanalysis Data NNJA Observations for Earth System Reanalysis Data arn:aws:s3:::noaa-reanalyses-pds us-east-1 S3 Bucket https://psl.noaa.gov/data/nnja_obs/ For questions regarding data content or quality, visit [the NOAA PSL NNJA](https [NOAA](http://www.noaa.gov/) 1 time a day Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-reanalyses-pds.s3.amazonaws.com/index.html)'] NOAA NASA Joint Archive (NNJA) of Observations for Earth System Reanalysis - New data notifications for NNJA Observations, only Lambda and SQS protocols allo New data notifications for NNJA Observations, only Lambda and SQS protocols allo arn:aws:sns:us-east-1:123901341784:NewNOAAReanalysesObject us-east-1 SNS Topic https://psl.noaa.gov/data/nnja_obs/ For questions regarding data content or quality, visit [the NOAA PSL NNJA](https [NOAA](http://www.noaa.gov/) 1 time a day Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA National Air Quality Forecast Capability (NAQFC) Regional Model Guidance NOAA National Air Quality Forecast Capability (NAQFC) Regional Model Guidance arn:aws:s3:::noaa-nws-naqfc-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/osti-modeling/air-quality For questions regarding data content or quality, visit the NCEP AQM Products web [NOAA](http://www.noaa.gov/) 2 times per day, 0600 and 1200 UTC for O3, PM2.5, and dust; once per day, 0300 U Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather ['[Browse Bucket](https://noaa-nws-naqfc-pds.s3.amazonaws.com/index.html)'] -NOAA National Bathymetric Source Data - NOAA National Bathymetric Source Data NOAA National Bathymetric Source Data arn:aws:s3:::noaa-ocs-nationalbathymetry-pds us-east-1 S3 Bucket https://nauticalcharts.noaa.gov/data/bluetopo.html For general questions or feedback about the data, please submit inquiriesthrough [NOAA](http://www.noaa.gov/) Monthly where new data is available. Creative Commons licenses are attached to each file and, where available, are at aws-pds, earth observation, model, oceans, bathymetry, marine navigation, oceans ['[Browse Bucket](https://noaa-ocs-nationalbathymetry-pds.s3.amazonaws.com/index.html)'] +NOAA National Air Quality Forecast Capability (NAQFC) Regional Model Guidance NOAA National Air Quality Forecast Capability (NAQFC) Regional Model Guidance arn:aws:s3:::noaa-nws-naqfc-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/osti-modeling/air-quality For questions regarding data content or quality, visit the NCEP AQM Products web [NOAA](http://www.noaa.gov/) 2 times per day, 0600 and 1200 UTC for O3, PM2.5, and dust; once per day, 0300 U Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather ['[Browse Bucket](https://noaa-nws-naqfc-pds.s3.amazonaws.com/index.html)'] +NOAA National Bathymetric Source Data - NOAA National Bathymetric Source Data NOAA National Bathymetric Source Data arn:aws:s3:::noaa-ocs-nationalbathymetry-pds us-east-1 S3 Bucket https://nauticalcharts.noaa.gov/data/bluetopo.html For general questions or feedback about the data, please submit inquiriesthrough [NOAA](http://www.noaa.gov/) Monthly where new data is available. Creative Commons licenses are attached to each file and, where available, are at aws-pds, earth observation, model, oceans, bathymetry, marine navigation, oceans ['[Browse Bucket](https://noaa-ocs-nationalbathymetry-pds.s3.amazonaws.com/index.html)'] NOAA National Bathymetric Source Data - NOAA National Bathymetry New Object Notification NOAA National Bathymetry New Object Notification arn:aws:sns:us-east-1:709902155096:NewNationalBathymetryObject us-east-1 SNS Topic https://nauticalcharts.noaa.gov/data/bluetopo.html For general questions or feedback about the data, please submit inquiriesthrough [NOAA](http://www.noaa.gov/) Monthly where new data is available. Creative Commons licenses are attached to each file and, where available, are at aws-pds, earth observation, model, oceans, bathymetry, marine navigation, oceans -NOAA National Blend of Models (NBM) - National Blend of Models (NBM) COG Format National Blend of Models (NBM) COG Format arn:aws:s3:::noaa-nbm-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/mdl/nbm For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Once per hour Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, cog, meteorological, weather ['[Browse Bucket](https://noaa-nbm-pds.s3.amazonaws.com/index.html)'] -NOAA National Blend of Models (NBM) - National Blend of Models (NBM) Grib2 Format National Blend of Models (NBM) Grib2 Format arn:aws:s3:::noaa-nbm-grib2-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/mdl/nbm For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Once per hour Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, cog, meteorological, weather ['[Browse Bucket](https://noaa-nbm-grib2-pds.s3.amazonaws.com/index.html)'] +NOAA National Blend of Models (NBM) - National Blend of Models (NBM) COG Format National Blend of Models (NBM) COG Format arn:aws:s3:::noaa-nbm-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/mdl/nbm For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Once per hour Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, cog, meteorological, weather ['[Browse Bucket](https://noaa-nbm-pds.s3.amazonaws.com/index.html)'] +NOAA National Blend of Models (NBM) - National Blend of Models (NBM) Grib2 Format National Blend of Models (NBM) Grib2 Format arn:aws:s3:::noaa-nbm-grib2-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/mdl/nbm For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Once per hour Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, cog, meteorological, weather ['[Browse Bucket](https://noaa-nbm-grib2-pds.s3.amazonaws.com/index.html)'] NOAA National Blend of Models (NBM) - New data notifications for NBM-COG Format, only Lambda and SQS protocols allowed New data notifications for NBM-COG Format, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewNBMCOGObject us-east-1 SNS Topic https://vlab.noaa.gov/web/mdl/nbm For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Once per hour Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, cog, meteorological, weather NOAA National Blend of Models (NBM) - New data notifications for NBM-Grib2 Format, only Lambda and SQS protocols allow New data notifications for NBM-Grib2 Format, only Lambda and SQS protocols allow arn:aws:sns:us-east-1:123901341784:NewNBMGRIBObject us-east-1 SNS Topic https://vlab.noaa.gov/web/mdl/nbm For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Once per hour Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, cog, meteorological, weather -NOAA National Digital Forecast Database (NDFD) - National Digital Forecast Database (NDFD) Grib2 Format National Digital Forecast Database (NDFD) Grib2 Format arn:aws:s3:::noaa-ndfd-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/mdl/ndfd (For NDFD Product information, instructions, For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) As often as once every half hour (varies by forecast element, forecast projectio Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ndfd-pds.s3.amazonaws.com/index.html)'] +NOAA National Digital Forecast Database (NDFD) - National Digital Forecast Database (NDFD) Grib2 Format National Digital Forecast Database (NDFD) Grib2 Format arn:aws:s3:::noaa-ndfd-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/mdl/ndfd (For NDFD Product information, instructions, For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) As often as once every half hour (varies by forecast element, forecast projectio Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ndfd-pds.s3.amazonaws.com/index.html)'] NOAA National Digital Forecast Database (NDFD) - New data notifications for NDFD, only Lambda and SQS protocols allowed New data notifications for NDFD, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewNDFDObject us-east-1 SNS Topic https://vlab.noaa.gov/web/mdl/ndfd (For NDFD Product information, instructions, For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) As often as once every half hour (varies by forecast element, forecast projectio Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA National Water Model CONUS Retrospective Dataset - The complete archive of NWM data version 12 The complete archive of NWM data version 12 arn:aws:s3:::nwm-archive us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://nwm-archive.s3.amazonaws.com/index.html)'] -NOAA National Water Model CONUS Retrospective Dataset - The complete archive of NWM data version 20 The complete archive of NWM data version 20 arn:aws:s3:::noaa-nwm-retro-v2-0-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-retro-v2-0-pds.s3.amazonaws.com/index.html)'] -NOAA National Water Model CONUS Retrospective Dataset - The complete archive of NWM input forcing and model output data version 21 in N The complete archive of NWM input forcing and model output data version 21 in N arn:aws:s3:::noaa-nwm-retrospective-2-1-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-retrospective-2-1-pds.s3.amazonaws.com/index.html)'] -NOAA National Water Model CONUS Retrospective Dataset - The complete archive of NWM input forcing and model output data version 30 in N The complete archive of NWM input forcing and model output data version 30 in N arn:aws:s3:::noaa-nwm-retrospective-3-0-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-retrospective-3-0-pds.s3.amazonaws.com/index.html)'] -NOAA National Water Model CONUS Retrospective Dataset - The complete archive of NWM model output data version 21 in Zarr format The NW The complete archive of NWM model output data version 21 in Zarr format The NW arn:aws:s3:::noaa-nwm-retrospective-2-1-zarr-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-retrospective-2-1-zarr-pds.s3.amazonaws.com/index.html)'] -NOAA National Water Model CONUS Retrospective Dataset - The streamflow from the NWM version 20 in Zarr format The streamflow from the NWM version 20 in Zarr format arn:aws:s3:::noaa-nwm-retro-v2-zarr-pds us-west-2 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-retro-v2-zarr-pds.s3.amazonaws.com/index.html)'] -NOAA National Water Model Short-Range Forecast - A rolling four week archive of NWM data A rolling four week archive of NWM data arn:aws:s3:::noaa-nwm-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nwm-pds For questions regarding data content or quality, go [here](http://water.noaa.gov [NOAA](http://www.noaa.gov/) Daily Open Data. There are no restrictions on the use of this data aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-pds.s3.amazonaws.com/index.html)'] -NOAA National Water Model Short-Range Forecast - Cloud-optimized zarr reference files managed by RPS Tetra Tech Cloud-optimized zarr reference files managed by RPS Tetra Tech arn:aws:s3:::noaa-nodd-kerchunk-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nwm-pds For questions regarding data content or quality, go [here](http://water.noaa.gov [NOAA](http://www.noaa.gov/) Daily Open Data. There are no restrictions on the use of this data aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nodd-kerchunk-pds.s3.amazonaws.com/index.html#nwm/)'] +NOAA National Water Model CONUS Retrospective Dataset - The complete archive of NWM data version 12 The complete archive of NWM data version 12 arn:aws:s3:::nwm-archive us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://nwm-archive.s3.amazonaws.com/index.html)'] +NOAA National Water Model CONUS Retrospective Dataset - The complete archive of NWM data version 20 The complete archive of NWM data version 20 arn:aws:s3:::noaa-nwm-retro-v2-0-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-retro-v2-0-pds.s3.amazonaws.com/index.html)'] +NOAA National Water Model CONUS Retrospective Dataset - The complete archive of NWM input forcing and model output data version 21 in N The complete archive of NWM input forcing and model output data version 21 in N arn:aws:s3:::noaa-nwm-retrospective-2-1-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-retrospective-2-1-pds.s3.amazonaws.com/index.html)'] +NOAA National Water Model CONUS Retrospective Dataset - The complete archive of NWM input forcing and model output data version 30 in N The complete archive of NWM input forcing and model output data version 30 in N arn:aws:s3:::noaa-nwm-retrospective-3-0-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-retrospective-3-0-pds.s3.amazonaws.com/index.html)'] +NOAA National Water Model CONUS Retrospective Dataset - The complete archive of NWM model output data version 21 in Zarr format The NW The complete archive of NWM model output data version 21 in Zarr format The NW arn:aws:s3:::noaa-nwm-retrospective-2-1-zarr-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-retrospective-2-1-zarr-pds.s3.amazonaws.com/index.html)'] +NOAA National Water Model CONUS Retrospective Dataset - The streamflow from the NWM version 20 in Zarr format The streamflow from the NWM version 20 in Zarr format arn:aws:s3:::noaa-nwm-retro-v2-zarr-pds us-west-2 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md For questions regarding data content or quality, email nws.nwc.ops@noaa.gov.For [NOAA](http://www.noaa.gov/) No updates Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-retro-v2-zarr-pds.s3.amazonaws.com/index.html)'] +NOAA National Water Model Short-Range Forecast - A rolling four week archive of NWM data A rolling four week archive of NWM data arn:aws:s3:::noaa-nwm-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nwm-pds For questions regarding data content or quality, go [here](http://water.noaa.gov [NOAA](http://www.noaa.gov/) Daily Open Data. There are no restrictions on the use of this data aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nwm-pds.s3.amazonaws.com/index.html)'] +NOAA National Water Model Short-Range Forecast - Cloud-optimized zarr reference files managed by RPS Tetra Tech Cloud-optimized zarr reference files managed by RPS Tetra Tech arn:aws:s3:::noaa-nodd-kerchunk-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nwm-pds For questions regarding data content or quality, go [here](http://water.noaa.gov [NOAA](http://www.noaa.gov/) Daily Open Data. There are no restrictions on the use of this data aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation ['[Browse Bucket](https://noaa-nodd-kerchunk-pds.s3.amazonaws.com/index.html#nwm/)'] NOAA National Water Model Short-Range Forecast - New data notifications for NWM, only Lambda and SQS protocols allowed New data notifications for NWM, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewNWMObject us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-nwm-pds For questions regarding data content or quality, go [here](http://water.noaa.gov [NOAA](http://www.noaa.gov/) Daily Open Data. There are no restrictions on the use of this data aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation NOAA North American Mesoscale Forecast System (NAM) - New data notifications for NAM data, only Lambda and SQS protocols allowed New data notifications for NAM data, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewNCEPNAMObject us-east-1 SNS Topic https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/nam.php, http For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Four times daily (0000, 0600, 1200, and 1800 UTC) Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA North American Mesoscale Forecast System (NAM) - North American Mesoscale Forecast System (NAM) Data North American Mesoscale Forecast System (NAM) Data arn:aws:s3:::noaa-nam-pds us-east-1 S3 Bucket https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/nam.php, http For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Four times daily (0000, 0600, 1200, and 1800 UTC) Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nam-pds.s3.amazonaws.com/index.html)'] -NOAA Oceanic Climate Data Records - Sea Ice Concentration Sea Ice Concentration arn:aws:s3:::noaa-cdr-sea-ice-concentration-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, oceans, sustainability, weather ['[Browse Bucket](https://noaa-cdr-sea-ice-concentration-pds.s3.amazonaws.com/index.html)'] -NOAA Oceanic Climate Data Records - Sea Surface Temperature - Optimum Interpolation Sea Surface Temperature - Optimum Interpolation arn:aws:s3:::noaa-cdr-sea-surface-temp-optimum-interpolation-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, oceans, sustainability, weather ['[Browse Bucket](https://noaa-cdr-sea-surface-temp-optimum-interpolation-pds.s3.amazonaws.com/index.html)'] -NOAA Oceanic Climate Data Records - Sea Surface Temperature - WHOI Sea Surface Temperature - WHOI arn:aws:s3:::noaa-cdr-sea-surface-temp-whoi-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, oceans, sustainability, weather ['[Browse Bucket](https://noaa-cdr-sea-surface-temp-whoi-pds.s3.amazonaws.com/index.html)'] -NOAA Office of Coast Survey - Hydrographic Survey Data - NOAA Office of Coast Survey Hydrographic Survey Data NOAA Office of Coast Survey Hydrographic Survey Data arn:aws:s3:::noaa-ocs-hydrodata-pds us-east-1 S3 Bucket https://nauticalcharts.noaa.gov/ For general questions or feedback about the data, please submit inquiriesthrough [NOAA](http://www.noaa.gov/) This bucket is updated as new data source become available Creative Commons licenses are attached to each file and, where available, are at aws-pds, earth observation, model, oceans, bathymetry, marine navigation, oceans ['[Browse Bucket](https://noaa-ocs-hydrodata-pds.s3.amazonaws.com/index.html)'] +NOAA North American Mesoscale Forecast System (NAM) - North American Mesoscale Forecast System (NAM) Data North American Mesoscale Forecast System (NAM) Data arn:aws:s3:::noaa-nam-pds us-east-1 S3 Bucket https://www.emc.ncep.noaa.gov/emc/pages/numerical_forecast_systems/nam.php, http For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Four times daily (0000, 0600, 1200, and 1800 UTC) Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nam-pds.s3.amazonaws.com/index.html)'] +NOAA Oceanic Climate Data Records - Sea Ice Concentration Sea Ice Concentration arn:aws:s3:::noaa-cdr-sea-ice-concentration-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, oceans, sustainability, weather ['[Browse Bucket](https://noaa-cdr-sea-ice-concentration-pds.s3.amazonaws.com/index.html)'] +NOAA Oceanic Climate Data Records - Sea Surface Temperature - Optimum Interpolation Sea Surface Temperature - Optimum Interpolation arn:aws:s3:::noaa-cdr-sea-surface-temp-optimum-interpolation-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, oceans, sustainability, weather ['[Browse Bucket](https://noaa-cdr-sea-surface-temp-optimum-interpolation-pds.s3.amazonaws.com/index.html)'] +NOAA Oceanic Climate Data Records - Sea Surface Temperature - WHOI Sea Surface Temperature - WHOI arn:aws:s3:::noaa-cdr-sea-surface-temp-whoi-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, oceans, sustainability, weather ['[Browse Bucket](https://noaa-cdr-sea-surface-temp-whoi-pds.s3.amazonaws.com/index.html)'] +NOAA Office of Coast Survey - Hydrographic Survey Data - NOAA Office of Coast Survey Hydrographic Survey Data NOAA Office of Coast Survey Hydrographic Survey Data arn:aws:s3:::noaa-ocs-hydrodata-pds us-east-1 S3 Bucket https://nauticalcharts.noaa.gov/ For general questions or feedback about the data, please submit inquiriesthrough [NOAA](http://www.noaa.gov/) This bucket is updated as new data source become available Creative Commons licenses are attached to each file and, where available, are at aws-pds, earth observation, model, oceans, bathymetry, marine navigation, oceans ['[Browse Bucket](https://noaa-ocs-hydrodata-pds.s3.amazonaws.com/index.html)'] NOAA Office of Coast Survey - Hydrographic Survey Data - NOAA Office of Coast Survey Hydrographic Survey Data New Object Notification NOAA Office of Coast Survey Hydrographic Survey Data New Object Notification arn:aws:sns:us-east-1:709902155096:NewOCSHYDROObject us-east-1 SNS Topic https://nauticalcharts.noaa.gov/ For general questions or feedback about the data, please submit inquiriesthrough [NOAA](http://www.noaa.gov/) This bucket is updated as new data source become available Creative Commons licenses are attached to each file and, where available, are at aws-pds, earth observation, model, oceans, bathymetry, marine navigation, oceans -NOAA Operational Forecast System (OFS) - CO-OPS Operational OFS Data (Historical Retention) CO-OPS Operational OFS Data (Historical Retention) arn:aws:s3:::noaa-nos-ofs-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/OFS/README.md For questions regarding data content or quality, visit [the NOAA OFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours with 6-hour nowcasts (WCOFS is updated once a day w Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, oceans, water, weather ['[Browse Bucket](https://noaa-nos-ofs-pds.s3.amazonaws.com/index.html)'] -NOAA Operational Forecast System (OFS) - NOMADS Production OFS Data (30 day rolling retention) NOMADS Production OFS Data (30 day rolling retention) arn:aws:s3:::noaa-ofs-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/OFS/README.md For questions regarding data content or quality, visit [the NOAA OFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours with 6-hour nowcasts (WCOFS is updated once a day w Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, oceans, water, weather ['[Browse Bucket](https://noaa-ofs-pds.s3.amazonaws.com/index.html)'] +NOAA Operational Forecast System (OFS) - CO-OPS Operational OFS Data (Historical Retention) CO-OPS Operational OFS Data (Historical Retention) arn:aws:s3:::noaa-nos-ofs-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/OFS/README.md For questions regarding data content or quality, visit [the NOAA OFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours with 6-hour nowcasts (WCOFS is updated once a day w Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, oceans, water, weather ['[Browse Bucket](https://noaa-nos-ofs-pds.s3.amazonaws.com/index.html)'] +NOAA Operational Forecast System (OFS) - NOMADS Production OFS Data (30 day rolling retention) NOMADS Production OFS Data (30 day rolling retention) arn:aws:s3:::noaa-ofs-pds us-east-1 S3 Bucket https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/OFS/README.md For questions regarding data content or quality, visit [the NOAA OFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours with 6-hour nowcasts (WCOFS is updated once a day w Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, oceans, water, weather ['[Browse Bucket](https://noaa-ofs-pds.s3.amazonaws.com/index.html)'] NOAA Operational Forecast System (OFS) - New data notifications for NOS OFS Historical Retention, only Lambda and SQS pro New data notifications for NOS OFS Historical Retention, only Lambda and SQS pro arn:aws:sns:us-east-1:123901341784:NewNOSOFSObject us-east-1 SNS Topic https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/OFS/README.md For questions regarding data content or quality, visit [the NOAA OFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours with 6-hour nowcasts (WCOFS is updated once a day w Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, oceans, water, weather NOAA Operational Forecast System (OFS) - New data notifications for OFS, only Lambda and SQS protocols allowed New data notifications for OFS, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewOFSObject us-east-1 SNS Topic https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/OFS/README.md For questions regarding data content or quality, visit [the NOAA OFS site](https [NOAA](http://www.noaa.gov/) 4 times a day, every 6 hours with 6-hour nowcasts (WCOFS is updated once a day w Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, oceans, water, weather NOAA Rapid Refresh (RAP) - New data notifications for RAP, only Lambda and SQS protocols allowed New data notifications for RAP, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewRAPObject us-east-1 SNS Topic https://www.nco.ncep.noaa.gov/pmb/products/rap/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Rapid Refresh (RAP) - Rapid Refresh (RAP) Data Rapid Refresh (RAP) Data arn:aws:s3:::noaa-rap-pds us-east-1 S3 Bucket https://www.nco.ncep.noaa.gov/pmb/products/rap/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-rap-pds.s3.amazonaws.com/index.html)'] +NOAA Rapid Refresh (RAP) - Rapid Refresh (RAP) Data Rapid Refresh (RAP) Data arn:aws:s3:::noaa-rap-pds us-east-1 S3 Bucket https://www.nco.ncep.noaa.gov/pmb/products/rap/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-rap-pds.s3.amazonaws.com/index.html)'] NOAA Rapid Refresh Forecast System (RRFS) [Prototype] - New data notifications for RRFS, only Lambda and SQS protocols allowed New data notifications for RRFS, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:709902155096:NewRRFSObject us-east-1 SNS Topic https://vlab.noaa.gov/web/ufs-r2o For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Daily Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Rapid Refresh Forecast System (RRFS) [Prototype] - Rapid Refresh Forecast System (RRFS) Data Rapid Refresh Forecast System (RRFS) Data arn:aws:s3:::noaa-rrfs-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/ufs-r2o For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Daily Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-rrfs-pds.s3.amazonaws.com/index.html)'] +NOAA Rapid Refresh Forecast System (RRFS) [Prototype] - Rapid Refresh Forecast System (RRFS) Data Rapid Refresh Forecast System (RRFS) Data arn:aws:s3:::noaa-rrfs-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/ufs-r2o For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Daily Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-rrfs-pds.s3.amazonaws.com/index.html)'] NOAA Real-Time Mesoscale Analysis (RTMA) / Unrestricted Mesoscale Analysis (URMA) - New data notifications for RTMA data, only Lambda and SQS protocols allowed New data notifications for RTMA data, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewNCEPRTMAObject us-east-1 SNS Topic https://www.nco.ncep.noaa.gov/pmb/products/rtma/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather NOAA Real-Time Mesoscale Analysis (RTMA) / Unrestricted Mesoscale Analysis (URMA) - New data notifications for URMA data, only Lambda and SQS protocols allowed New data notifications for URMA data, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewNCEPURMAObject us-east-1 SNS Topic https://www.nco.ncep.noaa.gov/pmb/products/rtma/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Real-Time Mesoscale Analysis (RTMA) / Unrestricted Mesoscale Analysis (URMA) - Real-Time Mesoscale Analysis (RTMA) Data Real-Time Mesoscale Analysis (RTMA) Data arn:aws:s3:::noaa-rtma-pds us-east-1 S3 Bucket https://www.nco.ncep.noaa.gov/pmb/products/rtma/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-rtma-pds.s3.amazonaws.com/index.html)'] -NOAA Real-Time Mesoscale Analysis (RTMA) / Unrestricted Mesoscale Analysis (URMA) - Unrestricted Mesoscale Analysis (URMA) Data Unrestricted Mesoscale Analysis (URMA) Data arn:aws:s3:::noaa-urma-pds us-east-1 S3 Bucket https://www.nco.ncep.noaa.gov/pmb/products/rtma/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-urma-pds.s3.amazonaws.com/index.html)'] -NOAA Severe Weather Data Inventory (SWDI) - NOAA Severe Weather Data Inventory Dataset (SWDI) NOAA Severe Weather Data Inventory Dataset (SWDI) arn:aws:s3:::noaa-swdi-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc: For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Monthly Open Data. There are no restrictions on the use of this data. Use of the data sh aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-swdi-pds.s3.amazonaws.com/index.html)'] +NOAA Real-Time Mesoscale Analysis (RTMA) / Unrestricted Mesoscale Analysis (URMA) - Real-Time Mesoscale Analysis (RTMA) Data Real-Time Mesoscale Analysis (RTMA) Data arn:aws:s3:::noaa-rtma-pds us-east-1 S3 Bucket https://www.nco.ncep.noaa.gov/pmb/products/rtma/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-rtma-pds.s3.amazonaws.com/index.html)'] +NOAA Real-Time Mesoscale Analysis (RTMA) / Unrestricted Mesoscale Analysis (URMA) - Unrestricted Mesoscale Analysis (URMA) Data Unrestricted Mesoscale Analysis (URMA) Data arn:aws:s3:::noaa-urma-pds us-east-1 S3 Bucket https://www.nco.ncep.noaa.gov/pmb/products/rtma/ For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Hourly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-urma-pds.s3.amazonaws.com/index.html)'] +NOAA Severe Weather Data Inventory (SWDI) - NOAA Severe Weather Data Inventory Dataset (SWDI) NOAA Severe Weather Data Inventory Dataset (SWDI) arn:aws:s3:::noaa-swdi-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc: For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Monthly Open Data. There are no restrictions on the use of this data. Use of the data sh aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-swdi-pds.s3.amazonaws.com/index.html)'] NOAA Severe Weather Data Inventory (SWDI) - NOAA Severe Weather Data Inventory Dataset Notification NOAA Severe Weather Data Inventory Dataset Notification arn:aws:sns:us-east-1:123901341784:NewSWDIObject us-east-1 SNS Topic https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc: For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Monthly Open Data. There are no restrictions on the use of this data. Use of the data sh aws-pds, agriculture, climate, meteorological, weather -NOAA Space Weather Forecast and Observation Data - NOAA Space Weather Prediction Center Forecasts NOAA Space Weather Prediction Center Forecasts arn:aws:s3:::noaa-swpc-pds us-east-1 S3 Bucket https://www.swpc.noaa.gov/products-and-data For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) The update frequencies of the space weather dataset range from one minute observ Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, solar, weather ['[Browse Bucket](https://noaa-swpc-pds.s3.amazonaws.com/index.html)'] +NOAA Space Weather Forecast and Observation Data - NOAA Space Weather Prediction Center Forecasts NOAA Space Weather Prediction Center Forecasts arn:aws:s3:::noaa-swpc-pds us-east-1 S3 Bucket https://www.swpc.noaa.gov/products-and-data For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) The update frequencies of the space weather dataset range from one minute observ Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, solar, weather ['[Browse Bucket](https://noaa-swpc-pds.s3.amazonaws.com/index.html)'] NOAA Space Weather Forecast and Observation Data - New data notifications for NOAA Space Weather Prediction Center Forecasts, only New data notifications for NOAA Space Weather Prediction Center Forecasts, only arn:aws:sns:us-east-1:123901341784:NewSWPCObject us-east-1 SNS Topic https://www.swpc.noaa.gov/products-and-data For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) The update frequencies of the space weather dataset range from one minute observ Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, solar, weather -NOAA Terrestrial Climate Data Records - Leaf Area Index Leaf Area Index arn:aws:s3:::noaa-cdr-leaf-area-index-fapar-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-leaf-area-index-fapar-pds.s3.amazonaws.com/index.html)'] -NOAA Terrestrial Climate Data Records - NDVI NDVI arn:aws:s3:::noaa-cdr-ndvi-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ndvi-pds.s3.amazonaws.com/index.html)'] -NOAA Terrestrial Climate Data Records - Snow Cover Extent Snow Cover Extent arn:aws:s3:::noaa-cdr-snow-cover-ext-north-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-snow-cover-ext-north-pds.s3.amazonaws.com/index.html)'] -NOAA U.S. Climate Gridded Dataset (NClimGrid) - Daily NClimGrid Data Daily NClimGrid Data arn:aws:s3:::noaa-nclimgrid-daily-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc: For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Monthly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nclimgrid-daily-pds.s3.amazonaws.com/index.html)'] -NOAA U.S. Climate Gridded Dataset (NClimGrid) - Monthly NClimGrid Data Monthly NClimGrid Data arn:aws:s3:::noaa-nclimgrid-monthly-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc: For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Monthly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nclimgrid-monthly-pds.s3.amazonaws.com/index.html)'] -NOAA U.S. Climate Gridded Dataset (NClimGrid) - New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowe New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowe arn:aws:sns:us-east-1:123901341784:NewNClimGridMonthlyObject us-east-1 SNS Topic https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc: For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Monthly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather +NOAA Terrestrial Climate Data Records - Leaf Area Index Leaf Area Index arn:aws:s3:::noaa-cdr-leaf-area-index-fapar-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-leaf-area-index-fapar-pds.s3.amazonaws.com/index.html)'] +NOAA Terrestrial Climate Data Records - NDVI NDVI arn:aws:s3:::noaa-cdr-ndvi-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-ndvi-pds.s3.amazonaws.com/index.html)'] +NOAA Terrestrial Climate Data Records - Snow Cover Extent Snow Cover Extent arn:aws:s3:::noaa-cdr-snow-cover-ext-north-pds us-east-1 S3 Bucket https://www.ncdc.noaa.gov/cdr For questions regarding the specific CDR data holdings, please contact NCEI.SAT. [NOAA](http://www.noaa.gov/) Climate Data Records are updated independently. For update frequency for a speci Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-cdr-snow-cover-ext-north-pds.s3.amazonaws.com/index.html)'] +NOAA U.S. Climate Gridded Dataset (NClimGrid) - Daily NClimGrid Data Daily NClimGrid Data arn:aws:s3:::noaa-nclimgrid-daily-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc: For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Monthly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nclimgrid-daily-pds.s3.amazonaws.com/index.html)'] +NOAA U.S. Climate Gridded Dataset (NClimGrid) - Monthly NClimGrid Data Monthly NClimGrid Data arn:aws:s3:::noaa-nclimgrid-monthly-pds us-east-1 S3 Bucket https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc: For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Monthly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nclimgrid-monthly-pds.s3.amazonaws.com/index.html)'] NOAA U.S. Climate Gridded Dataset (NClimGrid) - New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowe New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowe arn:aws:sns:us-east-1:123901341784:NewNClimGridDailyObject us-east-1 SNS Topic https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc: For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Monthly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA U.S. Climate Normals US Climate Normals Data arn:aws:s3:::noaa-normals-pds us-east-1 S3 Bucket [https://www.ncei.noaa.gov/products/us-climate-normals](https://www.ncei.noaa.go For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Data is updated on 10 year cycles or when corrections are implemented. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-normals-pds.s3.amazonaws.com/index.html)'] +NOAA U.S. Climate Gridded Dataset (NClimGrid) - New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowe New data notifications for Daily NClimGrid, only Lambda and SQS protocols allowe arn:aws:sns:us-east-1:123901341784:NewNClimGridMonthlyObject us-east-1 SNS Topic https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc: For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Monthly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather +NOAA U.S. Climate Normals US Climate Normals Data arn:aws:s3:::noaa-normals-pds us-east-1 S3 Bucket [https://www.ncei.noaa.gov/products/us-climate-normals](https://www.ncei.noaa.go For any questions regarding data delivery or any general questions regarding the [NOAA](http://www.noaa.gov/) Data is updated on 10 year cycles or when corrections are implemented. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, sustainability, weather ['[Browse Bucket](https://noaa-normals-pds.s3.amazonaws.com/index.html)'] NOAA Unified Forecast System (UFS) Global Ensemble Forecast System (GEFS) Version 13 Replay - New data notifications for UFS / GEFS Replay Data, only Lambda and SQS protocols New data notifications for UFS / GEFS Replay Data, only Lambda and SQS protocols arn:aws:sns:us-east-1:123901341784:NewUFS-GEFSObject us-east-1 SNS Topic https://psl.noaa.gov/data/ufs_replay/ For questions regarding data content or quality, visit [the NOAA GEFS Replay sit [NOAA](http://www.noaa.gov/) Static Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Unified Forecast System (UFS) Global Ensemble Forecast System (GEFS) Version 13 Replay - UFS / GEFS Replay Data UFS / GEFS Replay Data arn:aws:s3:::-noaa-ufs-gefsv13replay-pds us-east-1 S3 Bucket https://psl.noaa.gov/data/ufs_replay/ For questions regarding data content or quality, visit [the NOAA GEFS Replay sit [NOAA](http://www.noaa.gov/) Static Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ufs-gefsv13replay-pds.s3.amazonaws.com/index.html)'] -NOAA Unified Forecast System (UFS) Hierarchical Testing Framework (HTF) - NOAA Unified Forecast System (UFS) Hierarchical Testing Framework (HTF) Data NOAA Unified Forecast System (UFS) Hierarchical Testing Framework (HTF) Data arn:aws:s3:::noaa-ufs-htf-pds us-east-1 S3 Bucket https://epic-ufs-htf.readthedocs.io/en/develop/ For questions regarding data content or quality, visit https://github.com/NOAA-E [NOAA](http://www.noaa.gov/) The UFS HTF is in its prototype phase and will be updated as new case studies ar Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather, oceans ['[Browse Bucket](https://noaa-ufs-htf-pds.s3.amazonaws.com/index.html)'] +NOAA Unified Forecast System (UFS) Global Ensemble Forecast System (GEFS) Version 13 Replay - UFS / GEFS Replay Data UFS / GEFS Replay Data arn:aws:s3:::-noaa-ufs-gefsv13replay-pds us-east-1 S3 Bucket https://psl.noaa.gov/data/ufs_replay/ For questions regarding data content or quality, visit [the NOAA GEFS Replay sit [NOAA](http://www.noaa.gov/) Static Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ufs-gefsv13replay-pds.s3.amazonaws.com/index.html)'] +NOAA Unified Forecast System (UFS) Hierarchical Testing Framework (HTF) - NOAA Unified Forecast System (UFS) Hierarchical Testing Framework (HTF) Data NOAA Unified Forecast System (UFS) Hierarchical Testing Framework (HTF) Data arn:aws:s3:::noaa-ufs-htf-pds us-east-1 S3 Bucket https://epic-ufs-htf.readthedocs.io/en/develop/ For questions regarding data content or quality, visit https://github.com/NOAA-E [NOAA](http://www.noaa.gov/) The UFS HTF is in its prototype phase and will be updated as new case studies ar Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather, oceans ['[Browse Bucket](https://noaa-ufs-htf-pds.s3.amazonaws.com/index.html)'] NOAA Unified Forecast System (UFS) Hierarchical Testing Framework (HTF) - New data notifications for UFS HTF, only Lambda and SQS protocols allowed New data notifications for UFS HTF, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:709902155096:NewNWSUFSHTFObject us-east-1 SNS Topic https://epic-ufs-htf.readthedocs.io/en/develop/ For questions regarding data content or quality, visit https://github.com/NOAA-E [NOAA](http://www.noaa.gov/) The UFS HTF is in its prototype phase and will be updated as new case studies ar Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather, oceans NOAA Unified Forecast System (UFS) Land Data Assimilation (DA) System - New data notifications for UFS Land Data Assimilation (DA) System, only Lambda a New data notifications for UFS Land Data Assimilation (DA) System, only Lambda a arn:aws:sns:us-east-1:709902155096:NewUFSLANDDAObject us-east-1 SNS Topic https://land-da.readthedocs.io/en/latest/ For questions regarding data content or quality, visit https://github.com/NOAA-E [NOAA](http://www.noaa.gov/) These are stable datasets for use with the Land DA System. They will not be upda The Land DA license page can be found at: https://github.com/NOAA-EPIC/land-offl aws-pds, agriculture, climate, meteorological, weather -NOAA Unified Forecast System (UFS) Land Data Assimilation (DA) System - Unified Forecast System (UFS) Land Data Assimilation (DA) System Unified Forecast System (UFS) Land Data Assimilation (DA) System arn:aws:s3:::noaa-ufs-land-da-pds us-east-1 S3 Bucket https://land-da.readthedocs.io/en/latest/ For questions regarding data content or quality, visit https://github.com/NOAA-E [NOAA](http://www.noaa.gov/) These are stable datasets for use with the Land DA System. They will not be upda The Land DA license page can be found at: https://github.com/NOAA-EPIC/land-offl aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ufs-land-da-pds.s3.amazonaws.com/index.html)'] -NOAA Unified Forecast System (UFS) Marine Reanalysis: 1979-2019 NOAA UFS Marine Reanalysis 1979-2019 data arn:aws:s3:::noaa-ufs-rnrmarine-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/ufs-r2o/ NWS Disclaimer https://www.weather.gov/discla For questions regarding data content or quality, visit https://vlab.noaa.gov/web [NOAA](http://www.noaa.gov/) The UFS-DATM-MOM6-CICE6 model free run output contains continuous files from 197 Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ufs-rnrmarine-pds.s3.amazonaws.com/index.html)'] +NOAA Unified Forecast System (UFS) Land Data Assimilation (DA) System - Unified Forecast System (UFS) Land Data Assimilation (DA) System Unified Forecast System (UFS) Land Data Assimilation (DA) System arn:aws:s3:::noaa-ufs-land-da-pds us-east-1 S3 Bucket https://land-da.readthedocs.io/en/latest/ For questions regarding data content or quality, visit https://github.com/NOAA-E [NOAA](http://www.noaa.gov/) These are stable datasets for use with the Land DA System. They will not be upda The Land DA license page can be found at: https://github.com/NOAA-EPIC/land-offl aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ufs-land-da-pds.s3.amazonaws.com/index.html)'] +NOAA Unified Forecast System (UFS) Marine Reanalysis: 1979-2019 NOAA UFS Marine Reanalysis 1979-2019 data arn:aws:s3:::noaa-ufs-rnrmarine-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/ufs-r2o/ NWS Disclaimer https://www.weather.gov/discla For questions regarding data content or quality, visit https://vlab.noaa.gov/web [NOAA](http://www.noaa.gov/) The UFS-DATM-MOM6-CICE6 model free run output contains continuous files from 197 Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ufs-rnrmarine-pds.s3.amazonaws.com/index.html)'] NOAA Unified Forecast System Short-Range Weather (UFS SRW) Application - New data notifications for UFS Short-Range Weather data, only Lambda and SQS pro New data notifications for UFS Short-Range Weather data, only Lambda and SQS pro arn:aws:sns:us-east-1:709902155096:NewUFSSRWObject us-east-1 SNS Topic https://ufs-srweather-app.readthedocs.io/en/develop/ For questions regarding data content or quality, visit https://github.com/ufs-c [NOAA](http://www.noaa.gov/) These are stable datasets for use with the SRW Application. They will not be upd The UFS SRW Application license page can be found at: https://github.com/ufs-com aws-pds, agriculture, climate, meteorological, weather -NOAA Unified Forecast System Short-Range Weather (UFS SRW) Application - Unified Forecast System Short-Range Weather (UFS SRW) Application data Unified Forecast System Short-Range Weather (UFS SRW) Application data arn:aws:s3:::noaa-ufs-srw-pds us-east-1 S3 Bucket https://ufs-srweather-app.readthedocs.io/en/develop/ For questions regarding data content or quality, visit https://github.com/ufs-c [NOAA](http://www.noaa.gov/) These are stable datasets for use with the SRW Application. They will not be upd The UFS SRW Application license page can be found at: https://github.com/ufs-com aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ufs-srw-pds.s3.amazonaws.com/index.html)'] +NOAA Unified Forecast System Short-Range Weather (UFS SRW) Application - Unified Forecast System Short-Range Weather (UFS SRW) Application data Unified Forecast System Short-Range Weather (UFS SRW) Application data arn:aws:s3:::noaa-ufs-srw-pds us-east-1 S3 Bucket https://ufs-srweather-app.readthedocs.io/en/develop/ For questions regarding data content or quality, visit https://github.com/ufs-c [NOAA](http://www.noaa.gov/) These are stable datasets for use with the SRW Application. They will not be upd The UFS SRW Application license page can be found at: https://github.com/ufs-com aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ufs-srw-pds.s3.amazonaws.com/index.html)'] NOAA Unified Forecast System Subseasonal to Seasonal Prototypes - New data notifications for UFS Prototype, only Lambda and SQS protocols allowed New data notifications for UFS Prototype, only Lambda and SQS protocols allowed arn:aws:sns:us-east-1:123901341784:NewUfsPrototypesObject us-east-1 SNS Topic https://vlab.noaa.gov/web/ufs-r2o/dataproducts NWS Disclaimer https://www.weathe For questions regarding data content or quality, visit https://vlab.noaa.gov/web [NOAA](http://www.noaa.gov/) A Prototype is a retrospective run for the period from 2011 to 2018. The runs ar Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather, oceans -NOAA Unified Forecast System Subseasonal to Seasonal Prototypes - UFS prototype files (5, 6, 7, & 8) UFS prototype files (5, 6, 7, & 8) arn:aws:s3:::noaa-ufs-prototypes-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/ufs-r2o/dataproducts NWS Disclaimer https://www.weathe For questions regarding data content or quality, visit https://vlab.noaa.gov/web [NOAA](http://www.noaa.gov/) A Prototype is a retrospective run for the period from 2011 to 2018. The runs ar Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather, oceans ['[Browse Bucket](https://noaa-ufs-prototypes-pds.s3.amazonaws.com/index.html)'] +NOAA Unified Forecast System Subseasonal to Seasonal Prototypes - UFS prototype files (5, 6, 7, & 8) UFS prototype files (5, 6, 7, & 8) arn:aws:s3:::noaa-ufs-prototypes-pds us-east-1 S3 Bucket https://vlab.noaa.gov/web/ufs-r2o/dataproducts NWS Disclaimer https://www.weathe For questions regarding data content or quality, visit https://vlab.noaa.gov/web [NOAA](http://www.noaa.gov/) A Prototype is a retrospective run for the period from 2011 to 2018. The runs ar Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, disaster response, environmental, meteorological, weather, oceans ['[Browse Bucket](https://noaa-ufs-prototypes-pds.s3.amazonaws.com/index.html)'] NOAA Unified Forecast System Weather Model (UFS-WM) Regression Tests - New data notifications for UFS Weather Model Regression data, only Lambda and SQ New data notifications for UFS Weather Model Regression data, only Lambda and SQ arn:aws:sns:us-east-1:709902155096:NewUFSObject us-east-1 SNS Topic https://ufs-weather-model.readthedocs.io/en/ufs-v1.0.0/index.html For questions regarding data content or quality, visit https://github.com/ufs-c [NOAA](http://www.noaa.gov/) The input and baseline datasets are updated for the latest two-months of develop The UFS-WM has license page at: https://github.com/ufs-community/ufs-weather-mod aws-pds, agriculture, climate, meteorological, weather -NOAA Unified Forecast System Weather Model (UFS-WM) Regression Tests - Unified Forecast System Weather Model (UFS-WM) Regression Tests data Unified Forecast System Weather Model (UFS-WM) Regression Tests data arn:aws:s3:::noaa-ufs-regtests-pds us-east-1 S3 Bucket https://ufs-weather-model.readthedocs.io/en/ufs-v1.0.0/index.html For questions regarding data content or quality, visit https://github.com/ufs-c [NOAA](http://www.noaa.gov/) The input and baseline datasets are updated for the latest two-months of develop The UFS-WM has license page at: https://github.com/ufs-community/ufs-weather-mod aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ufs-regtests-pds.s3.amazonaws.com/index.html)'] -NOAA Wang Sheeley Arge (WSA) Enlil - NOAA WSA-Enlil Products NOAA WSA-Enlil Products arn:aws:s3:::noaa-wsa-enlil-pds us-east-1 S3 Bucket https://www.swpc.noaa.gov/products/wsa-enlil-solar-wind-prediction For any questions regarding WSA-Ensil data, please contact Eric Adamson (eric.ad [NOAA](http://www.noaa.gov/) Only model output from model runs containing CMEs are provided here. As these ru Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, solar, weather ['[Browse Bucket](https://noaa-wsa-enlil-pds.s3.amazonaws.com/index.html)'] +NOAA Unified Forecast System Weather Model (UFS-WM) Regression Tests - Unified Forecast System Weather Model (UFS-WM) Regression Tests data Unified Forecast System Weather Model (UFS-WM) Regression Tests data arn:aws:s3:::noaa-ufs-regtests-pds us-east-1 S3 Bucket https://ufs-weather-model.readthedocs.io/en/ufs-v1.0.0/index.html For questions regarding data content or quality, visit https://github.com/ufs-c [NOAA](http://www.noaa.gov/) The input and baseline datasets are updated for the latest two-months of develop The UFS-WM has license page at: https://github.com/ufs-community/ufs-weather-mod aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-ufs-regtests-pds.s3.amazonaws.com/index.html)'] +NOAA Wang Sheeley Arge (WSA) Enlil - NOAA WSA-Enlil Products NOAA WSA-Enlil Products arn:aws:s3:::noaa-wsa-enlil-pds us-east-1 S3 Bucket https://www.swpc.noaa.gov/products/wsa-enlil-solar-wind-prediction For any questions regarding WSA-Ensil data, please contact Eric Adamson (eric.ad [NOAA](http://www.noaa.gov/) Only model output from model runs containing CMEs are provided here. As these ru Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, solar, weather ['[Browse Bucket](https://noaa-wsa-enlil-pds.s3.amazonaws.com/index.html)'] NOAA Wang Sheeley Arge (WSA) Enlil - New data notifications for NOAA WSA-Enlil Products, only Lambda and SQS protocol New data notifications for NOAA WSA-Enlil Products, only Lambda and SQS protocol arn:aws:sns:us-east-1:709902155096:NewSWPCWSAEnlilObject us-east-1 SNS Topic https://www.swpc.noaa.gov/products/wsa-enlil-solar-wind-prediction For any questions regarding WSA-Ensil data, please contact Eric Adamson (eric.ad [NOAA](http://www.noaa.gov/) Only model output from model runs containing CMEs are provided here. As these ru Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, solar, weather -NOAA Water-Column Sonar Data Archive NCEI Water-Column Sonar Data Archive arn:aws:s3:::noaa-wcsd-pds us-east-1 S3 Bucket https://cires.gitbook.io/ncei-wcsd-archive/ wcd.info@noaa.gov [NOAA](https://www.ngdc.noaa.gov/mgg/wcd/) New water-column sonar data are added regularly as they are provided to the arch The data may be used and redistributed for free but is not intended for legal us aws-pds, earth observation, biodiversity, ecosystems, environmental, geospatial, mapping, oceans ['[Browse Bucket](https://noaa-wcsd-pds.s3.amazonaws.com/index.html)'] -NOAA Wave Ensemble Reforecast - NOAA Wave Ensemble Reforecast Data NOAA Wave Ensemble Reforecast Data arn:aws:s3:::noaa-nws-gefswaves-reforecast-pds us-east-1 S3 Bucket https://github.com/NOAA-EMC/gefswaves_reforecast/wiki https://github.com/NOAA-E For questions related to wave modeling and the ensemble reforecast available, pl [NOAA](http://www.noaa.gov/) Quarterly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nws-gefswaves-reforecast-pds.s3.amazonaws.com/index.html)'] +NOAA Water-Column Sonar Data Archive NCEI Water-Column Sonar Data Archive arn:aws:s3:::noaa-wcsd-pds us-east-1 S3 Bucket https://cires.gitbook.io/ncei-wcsd-archive/ wcd.info@noaa.gov [NOAA](https://www.ngdc.noaa.gov/mgg/wcd/) New water-column sonar data are added regularly as they are provided to the arch The data may be used and redistributed for free but is not intended for legal us aws-pds, earth observation, biodiversity, ecosystems, environmental, geospatial, mapping, oceans ['[Browse Bucket](https://noaa-wcsd-pds.s3.amazonaws.com/index.html)'] +NOAA Wave Ensemble Reforecast - NOAA Wave Ensemble Reforecast Data NOAA Wave Ensemble Reforecast Data arn:aws:s3:::noaa-nws-gefswaves-reforecast-pds us-east-1 S3 Bucket https://github.com/NOAA-EMC/gefswaves_reforecast/wiki https://github.com/NOAA-E For questions related to wave modeling and the ensemble reforecast available, pl [NOAA](http://www.noaa.gov/) Quarterly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather ['[Browse Bucket](https://noaa-nws-gefswaves-reforecast-pds.s3.amazonaws.com/index.html)'] NOAA Wave Ensemble Reforecast - New data notifications for NOAA Wave Ensemble Reforecast, only Lambda and SQS pr New data notifications for NOAA Wave Ensemble Reforecast, only Lambda and SQS pr arn:aws:sns:us-east-1:709902155096:NewWERObject us-east-1 SNS Topic https://github.com/NOAA-EMC/gefswaves_reforecast/wiki https://github.com/NOAA-E For questions related to wave modeling and the ensemble reforecast available, pl [NOAA](http://www.noaa.gov/) Quarterly Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, climate, meteorological, weather -NOAA Whole Atmosphere Model-Ionosphere Plasmasphere Electrodynamics (WAM-IPE) Forecast System (WFS) - NOAA WAM-IPE Products NOAA WAM-IPE Products arn:aws:s3:::noaa-nws-wam-ipe-pds us-east-1 S3 Bucket https://www.swpc.noaa.gov/products/wam-ipe For any questions regarding WAM-IPE data, please contact Adam Kubaryk (adam.kuba [NOAA](http://www.noaa.gov/) The update frequencies of the WAM-IPE dataset range from 10 minutes to 6 hours d CC-0, Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, solar, weather ['[Browse Bucket](https://noaa-nws-wam-ipe-pds.s3.amazonaws.com/index.html)'] +NOAA Whole Atmosphere Model-Ionosphere Plasmasphere Electrodynamics (WAM-IPE) Forecast System (WFS) - NOAA WAM-IPE Products NOAA WAM-IPE Products arn:aws:s3:::noaa-nws-wam-ipe-pds us-east-1 S3 Bucket https://www.swpc.noaa.gov/products/wam-ipe For any questions regarding WAM-IPE data, please contact Adam Kubaryk (adam.kuba [NOAA](http://www.noaa.gov/) The update frequencies of the WAM-IPE dataset range from 10 minutes to 6 hours d CC-0, Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, solar, weather ['[Browse Bucket](https://noaa-nws-wam-ipe-pds.s3.amazonaws.com/index.html)'] NOAA Whole Atmosphere Model-Ionosphere Plasmasphere Electrodynamics (WAM-IPE) Forecast System (WFS) - New data notifications for NOAA WAM-IPE Products, only Lambda and SQS protocols New data notifications for NOAA WAM-IPE Products, only Lambda and SQS protocols arn:aws:sns:us-east-1:709902155096:NewWIFSObject us-east-1 SNS Topic https://www.swpc.noaa.gov/products/wam-ipe For any questions regarding WAM-IPE data, please contact Adam Kubaryk (adam.kuba [NOAA](http://www.noaa.gov/) The update frequencies of the WAM-IPE dataset range from 10 minutes to 6 hours d CC-0, Open Data. There are no restrictions on the use of this data. aws-pds, climate, meteorological, solar, weather -NOAA's Coastal Ocean Reanalysis (CORA) Dataset - NOAA’s Coastal Ocean Reanalysis (CORA) Dataset NetCDF NOAA’s Coastal Ocean Reanalysis (CORA) Dataset NetCDF arn:aws:s3:::noaa-nos-cora-pds us-east-1 S3 Bucket https://tidesandcurrents.noaa.gov/ For questions regarding data content or quality, email CO-OPS.UserServices@noaa. [NOAA’s National Ocean Service, The Center for Operational Oceanographic Product Monthly, quarterly, and annually, depending on the dataset. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation, oceans ['[Browse Bucket](https://noaa-nos-cora-pds.s3.amazonaws.com/index.html)'] +NOAA's Coastal Ocean Reanalysis (CORA) Dataset - NOAA’s Coastal Ocean Reanalysis (CORA) Dataset NetCDF NOAA’s Coastal Ocean Reanalysis (CORA) Dataset NetCDF arn:aws:s3:::noaa-nos-cora-pds us-east-1 S3 Bucket https://tidesandcurrents.noaa.gov/ For questions regarding data content or quality, email CO-OPS.UserServices@noaa. [NOAA’s National Ocean Service, The Center for Operational Oceanographic Product Monthly, quarterly, and annually, depending on the dataset. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation, oceans ['[Browse Bucket](https://noaa-nos-cora-pds.s3.amazonaws.com/index.html)'] NOAA's Coastal Ocean Reanalysis (CORA) Dataset - NOAA’s Coastal Ocean Reanalysis (CORA) Dataset Notifications NOAA’s Coastal Ocean Reanalysis (CORA) Dataset Notifications arn:aws:sns:us-east-1:709902155096:NewNOSCORAObject us-east-1 SNS Topic https://tidesandcurrents.noaa.gov/ For questions regarding data content or quality, email CO-OPS.UserServices@noaa. [NOAA’s National Ocean Service, The Center for Operational Oceanographic Product Monthly, quarterly, and annually, depending on the dataset. Open Data. There are no restrictions on the use of this data. aws-pds, agriculture, weather, climate, environmental, disaster response, agriculture, transportation, oceans NOAA/PMEL Ocean Climate Stations Moorings - New data notifications for OCS moored buoy data, only Lambda and SQS protocols a New data notifications for OCS moored buoy data, only Lambda and SQS protocols a arn:aws:sns:us-east-1:709902155096:NewKeoPapaObject us-east-1 SNS Topic https://www.pmel.noaa.gov/ocs/ http://www.oceansites.org https://dods.ndbc.noaa. For questions regarding data content or quality, users are directed to the OCS w [NOAA](http://www.noaa.gov/) KEO and Papa data on BDP are synchronized with the OceanSITES Global Data Assemb Open Data. There are no restrictions on the use of this data. aws-pds, climate, environmental, oceans, weather -NOAA/PMEL Ocean Climate Stations Moorings - OCS moored buoy data OCS moored buoy data arn:aws:s3:::noaa-oar-keo-papa-pds us-east-1 S3 Bucket https://www.pmel.noaa.gov/ocs/ http://www.oceansites.org https://dods.ndbc.noaa. For questions regarding data content or quality, users are directed to the OCS w [NOAA](http://www.noaa.gov/) KEO and Papa data on BDP are synchronized with the OceanSITES Global Data Assemb Open Data. There are no restrictions on the use of this data. aws-pds, climate, environmental, oceans, weather ['[Browse Bucket](https://noaa-oar-keo-papa-pds.s3.amazonaws.com/index.html)'] -NREL National Solar Radiation Database - HSDS NSRDB domains HSDS NSRDB domains arn:aws:s3:::nrel-pds-hsds/nrel/nsrdb/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fnsrdb%2F)'] -NREL National Solar Radiation Database - Meteorological Statistical Model 1 (MTS1) data (1961-1990) in HDF5 format Meteorological Statistical Model 1 (MTS1) data (1961-1990) in HDF5 format arn:aws:s3:::nrel-pds-nsrdb/mts1 us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=mts1%2F)'] -NREL National Solar Radiation Database - Meteorological Statistical Model 2 (MTS2) data (1991-2005) in HDF5 format Meteorological Statistical Model 2 (MTS2) data (1991-2005) in HDF5 format arn:aws:s3:::nrel-pds-nsrdb/mts2 us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=mts2%2F)'] -NREL National Solar Radiation Database - NREL Solar Radiation Datasets NREL Solar Radiation Datasets arn:aws:s3:::nrel-pds-nsrdb/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb)'] -NREL National Solar Radiation Database - NSRDB 2km-10min data for the all of the Western Hemisphere (GOES full-disc NSRDB 2km-10min data for the all of the Western Hemisphere (GOES full-disc arn:aws:s3:::nrel-pds-nsrdb/full_disc/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=full_disc%2F)'] -NREL National Solar Radiation Database - NSRDB 2km-5min data for the Contiguous United States (CONUS NSRDB 2km-5min data for the Contiguous United States (CONUS arn:aws:s3:::nrel-pds-nsrdb/conus/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=conus%2F)'] -NREL National Solar Radiation Database - NSRDB 4km-10min data from the Himawari satellites starting in 2015 in HDF5 NSRDB 4km-10min data from the Himawari satellites starting in 2015 in HDF5 arn:aws:s3:::nrel-pds-nsrdb/himawari/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=himawari%2F)'] -NREL National Solar Radiation Database - NSRDB 4km-15min data from the Meteosat satellites starting in 2017 in HDF5 NSRDB 4km-15min data from the Meteosat satellites starting in 2017 in HDF5 arn:aws:s3:::nrel-pds-nsrdb/meteosat/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=meteosat%2F)'] -NREL National Solar Radiation Database - NSRDB Suny-India data (2000-2014 NSRDB Suny-India data (2000-2014 arn:aws:s3:::nrel-pds-nsrdb/india/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=india%2F)'] -NREL National Solar Radiation Database - NSRDB synthetically downscaled 2km x 5min for Puerto Rico (1998-2017 NSRDB synthetically downscaled 2km x 5min for Puerto Rico (1998-2017 arn:aws:s3:::nrel-pds-nsrdb/v3/puerto_rico/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Fpuerto_rico%2F)'] -NREL National Solar Radiation Database - NSRDB v3 4km x 30min data (1998-2018 NSRDB v3 4km x 30min data (1998-2018 arn:aws:s3:::nrel-pds-nsrdb/v3/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2F)'] -NREL National Solar Radiation Database - NSRDB v3 typical direct years (TDY NSRDB v3 typical direct years (TDY arn:aws:s3:::nrel-pds-nsrdb/v3/tdy/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftdy%2F)'] -NREL National Solar Radiation Database - NSRDB v3 typical global years (TGY NSRDB v3 typical global years (TGY arn:aws:s3:::nrel-pds-nsrdb/v3/tgy/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftgy%2F)'] -NREL National Solar Radiation Database - NSRDB v3 typical meteorological years (TMY NSRDB v3 typical meteorological years (TMY arn:aws:s3:::nrel-pds-nsrdb/v3/tmy/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftmy%2F)'] -NREL Wind Integration National Dataset - Bangladesh wind resource data (2014-2017) in HDF5 format Bangladesh wind resource data (2014-2017) in HDF5 format arn:aws:s3:::nrel-pds-wtk/bangladesh/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=bangladesh%2F)'] -NREL Wind Integration National Dataset - Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications arn:aws:s3:::nrel-pds-wtk/bchrrr/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=bchrrr%2F)'] -NREL Wind Integration National Dataset - Bias corrected Indonesia wind resource Bias corrected Indonesia wind resource arn:aws:s3:::nrel-pds-wtk/indonesia/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=indonesia%2F)'] -NREL Wind Integration National Dataset - California offshore wind resource data (2000-2022) in HDF5 format California offshore wind resource data (2000-2022) in HDF5 format arn:aws:s3:::nrel-pds-wtk/now23_california/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=now23_california%2F)'] -NREL Wind Integration National Dataset - Central Asia wind resource data (2015) in HDF5 format Central Asia wind resource data (2015) in HDF5 format arn:aws:s3:::nrel-pds-wtk/central_asia/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=central_asia%2F)'] -NREL Wind Integration National Dataset - Data for the Eastern Wind Integration Study (2004-2006 Data for the Eastern Wind Integration Study (2004-2006 arn:aws:s3:::nrel-pds-wtk/eastern_wind/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=eastern_wind%2F)'] -NREL Wind Integration National Dataset - Data for the Western Wind Integration Study (2004-2006 Data for the Western Wind Integration Study (2004-2006 arn:aws:s3:::nrel-pds-wtk/western_wind/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=western_wind%2F)'] -NREL Wind Integration National Dataset - Great Lakes wind resource data (2000-2020) in HDF5 format Great Lakes wind resource data (2000-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/Great_Lakes/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Great_Lakes%2F)'] -NREL Wind Integration National Dataset - Gulf of Mexico wind resource data (2000-2020) in HDF5 format Gulf of Mexico wind resource data (2000-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/gulf_of_mexico/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=gulf_of_mexico%2F)'] -NREL Wind Integration National Dataset - HSDS WIND domains HSDS WIND domains arn:aws:s3:::nrel-pds-hsds/nrel/wtk/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fwtk%2F)'] -NREL Wind Integration National Dataset - HSDS gridded WIND Toolkit domain HSDS gridded WIND Toolkit domain arn:aws:s3:::nrel-pds-hsds/nrel/wtk-us.h5 us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fwtk-us.h5%2F)'] -NREL Wind Integration National Dataset - Hawaii Wind Resource data for (2000-2019) in HDF5 format Hawaii Wind Resource data for (2000-2019) in HDF5 format arn:aws:s3:::nrel-pds-wtk/Hawaii/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Hawaii%2F)'] -NREL Wind Integration National Dataset - India wind resource data (2014) in HDF5 format India wind resource data (2014) in HDF5 format arn:aws:s3:::nrel-pds-wtk/india/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=india%2F)'] -NREL Wind Integration National Dataset - Kazakhstan wind resource data (2015) in HDF5 format Kazakhstan wind resource data (2015) in HDF5 format arn:aws:s3:::nrel-pds-wtk/kazakhstan/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=kazakhstan%2F)'] -NREL Wind Integration National Dataset - Maine wind resource data (2000-2020) in HDF5 format Maine wind resource data (2000-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/maine/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=maine%2F)'] -NREL Wind Integration National Dataset - Mid Atlantic Wind Resource data for (2000-2020) in HDF5 format Mid Atlantic Wind Resource data for (2000-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/Mid_Atlantic/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Mid_Atlantic%2F)'] -NREL Wind Integration National Dataset - Mid Atlantic three-dimensional planetary boundary layer (3D PBL) scheme wind res Mid Atlantic three-dimensional planetary boundary layer (3D PBL) scheme wind res arn:aws:s3:::nrel-pds-wtk/mid_atlantic_3d_pbl/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=mid_atlantic_3d_pbl%2F)'] -NREL Wind Integration National Dataset - Mid Atlantic wind resource data with modeled wakes in HDF5 format Mid Atlantic wind resource data with modeled wakes in HDF5 format arn:aws:s3:::nrel-pds-wtk/NOW-WAKES_Mid_Atlantic/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=NOW-WAKES_Mid_Atlantic%2F)'] -NREL Wind Integration National Dataset - NREL Wind Resource Datasets NREL Wind Resource Datasets arn:aws:s3:::nrel-pds-wtk/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk)'] -NREL Wind Integration National Dataset - NW Pacific Wind Resource data for (2000-2019) in HDF5 format NW Pacific Wind Resource data for (2000-2019) in HDF5 format arn:aws:s3:::nrel-pds-wtk/NW_Pacific/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=NW_Pacific%2F)'] -NREL Wind Integration National Dataset - Offshore California Wind Resource data for (2000-2019) in HDF5 format Offshore California Wind Resource data for (2000-2019) in HDF5 format arn:aws:s3:::nrel-pds-wtk/Offshore_CA/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Offshore_CA%2F)'] -NREL Wind Integration National Dataset - Philippines typical meteorological year data in HDF5 format Philippines typical meteorological year data in HDF5 format arn:aws:s3:::nrel-pds-wtk/philippines_tmy/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=philippines_tmy%2F)'] -NREL Wind Integration National Dataset - Philippines wind resource data (2017) in HDF5 format Philippines wind resource data (2017) in HDF5 format arn:aws:s3:::nrel-pds-wtk/philippines/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=philippines%2F)'] -NREL Wind Integration National Dataset - Puerto Rico wind resource data (2001-2020) in HDF5 format Puerto Rico wind resource data (2001-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/pr100/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=pr100%2F)'] -NREL Wind Integration National Dataset - Source files for WIND Toolkit CONUS (2007-2014) Source files for WIND Toolkit CONUS (2007-2014) arn:aws:s3:::nrel-pds-wtk/conus/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=conus%2F)'] -NREL Wind Integration National Dataset - Source files for WIND Toolkit Canada (2007-2014) Source files for WIND Toolkit Canada (2007-2014) arn:aws:s3:::nrel-pds-wtk/canada/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=canada%2F)'] -NREL Wind Integration National Dataset - Source files for WIND Toolkit Mexico (2007-2014) Source files for WIND Toolkit Mexico (2007-2014) arn:aws:s3:::nrel-pds-wtk/mexico/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=mexico%2F)'] -NREL Wind Integration National Dataset - Source files for wtk-us gridded (2007-2013) Source files for wtk-us gridded (2007-2013) arn:aws:s3:::nrel-pds-wtk/wtk-us/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=wtk-us%2F)'] -NREL Wind Integration National Dataset - South Atlantic offshore wind resource data (2000-2020) in HDF5 format South Atlantic offshore wind resource data (2000-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/south_atlantic/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=south_atlantic)'] -NREL Wind Integration National Dataset - Southeast Asia wind resource data (2017-2021) in HDF5 format Southeast Asia wind resource data (2017-2021) in HDF5 format arn:aws:s3:::nrel-pds-wtk/seasiawind/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=seasiawind%2F)'] -NREL Wind Integration National Dataset - Southeast Asia wind resource data v2 (2007-2021) in HDF5 format Southeast Asia wind resource data v2 (2007-2021) in HDF5 format arn:aws:s3:::nrel-pds-wtk/seasiawind_v2/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=seasiawind_v2%2F)'] -NREL Wind Integration National Dataset - Southeast Asia wind resource data v3 (2007-2021) in HDF5 format Southeast Asia wind resource data v3 (2007-2021) in HDF5 format arn:aws:s3:::nrel-pds-wtk/seasiawind_v3/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=seasiawind_v3%2F)'] -NREL Wind Integration National Dataset - Techno-economic subset of the WIND Toolkit by location in netCDF Techno-economic subset of the WIND Toolkit by location in netCDF arn:aws:s3:::nrel-pds-wtk/wtk-techno-economic/pywtk-data/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=wtk-techno-economic%2Fpywtk-data%2F)'] -NREL Wind Integration National Dataset - Vietnam wind resource data (2016-2018) in HDF5 format Vietnam wind resource data (2016-2018) in HDF5 format arn:aws:s3:::nrel-pds-wtk/vietnam/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=vietnam%2F)'] -NSF NCAR Curated ECMWF Reanalysis 5 (ERA5) - ERA5 NetCDF4 Data Files ERA5 NetCDF4 Data Files arn:aws:s3:::nsf-ncar-era5 us-west-2 S3 Bucket https://doi.org/10.5065/BH6N-5N20 rdahelp@ucar.edu [NSF National Center for Atmospheric Research](https://ncar.ucar.edu/) Monthly, with a 3-4 month lag from realtime https://www.ucar.edu/terms-of-use/data climate, model, atmosphere, land, data assimilation, forecast, meteorological, weather, geoscience, geospatial, aws-pds, netcdf ['[Browse Bucket](https://nsf-ncar-era5.s3.amazonaws.com/index.html)'] +NOAA/PMEL Ocean Climate Stations Moorings - OCS moored buoy data OCS moored buoy data arn:aws:s3:::noaa-oar-keo-papa-pds us-east-1 S3 Bucket https://www.pmel.noaa.gov/ocs/ http://www.oceansites.org https://dods.ndbc.noaa. For questions regarding data content or quality, users are directed to the OCS w [NOAA](http://www.noaa.gov/) KEO and Papa data on BDP are synchronized with the OceanSITES Global Data Assemb Open Data. There are no restrictions on the use of this data. aws-pds, climate, environmental, oceans, weather ['[Browse Bucket](https://noaa-oar-keo-papa-pds.s3.amazonaws.com/index.html)'] +NREL National Solar Radiation Database - HSDS NSRDB domains HSDS NSRDB domains arn:aws:s3:::nrel-pds-hsds/nrel/nsrdb/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fnsrdb%2F)'] +NREL National Solar Radiation Database - Meteorological Statistical Model 1 (MTS1) data (1961-1990) in HDF5 format Meteorological Statistical Model 1 (MTS1) data (1961-1990) in HDF5 format arn:aws:s3:::nrel-pds-nsrdb/mts1 us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=mts1%2F)'] +NREL National Solar Radiation Database - Meteorological Statistical Model 2 (MTS2) data (1991-2005) in HDF5 format Meteorological Statistical Model 2 (MTS2) data (1991-2005) in HDF5 format arn:aws:s3:::nrel-pds-nsrdb/mts2 us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=mts2%2F)'] +NREL National Solar Radiation Database - NREL Solar Radiation Datasets NREL Solar Radiation Datasets arn:aws:s3:::nrel-pds-nsrdb/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb)'] +NREL National Solar Radiation Database - NSRDB 2km-10min data for the all of the Western Hemisphere (GOES full-disc NSRDB 2km-10min data for the all of the Western Hemisphere (GOES full-disc arn:aws:s3:::nrel-pds-nsrdb/full_disc/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=full_disc%2F)'] +NREL National Solar Radiation Database - NSRDB 2km-5min data for the Contiguous United States (CONUS NSRDB 2km-5min data for the Contiguous United States (CONUS arn:aws:s3:::nrel-pds-nsrdb/conus/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=conus%2F)'] +NREL National Solar Radiation Database - NSRDB 4km-10min data from the Himawari satellites starting in 2015 in HDF5 NSRDB 4km-10min data from the Himawari satellites starting in 2015 in HDF5 arn:aws:s3:::nrel-pds-nsrdb/himawari/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=himawari%2F)'] +NREL National Solar Radiation Database - NSRDB 4km-15min data from the Meteosat satellites starting in 2017 in HDF5 NSRDB 4km-15min data from the Meteosat satellites starting in 2017 in HDF5 arn:aws:s3:::nrel-pds-nsrdb/meteosat/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=meteosat%2F)'] +NREL National Solar Radiation Database - NSRDB Suny-India data (2000-2014 NSRDB Suny-India data (2000-2014 arn:aws:s3:::nrel-pds-nsrdb/india/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=india%2F)'] +NREL National Solar Radiation Database - NSRDB synthetically downscaled 2km x 5min for Puerto Rico (1998-2017 NSRDB synthetically downscaled 2km x 5min for Puerto Rico (1998-2017 arn:aws:s3:::nrel-pds-nsrdb/v3/puerto_rico/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Fpuerto_rico%2F)'] +NREL National Solar Radiation Database - NSRDB v3 4km x 30min data (1998-2018 NSRDB v3 4km x 30min data (1998-2018 arn:aws:s3:::nrel-pds-nsrdb/v3/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2F)'] +NREL National Solar Radiation Database - NSRDB v3 typical direct years (TDY NSRDB v3 typical direct years (TDY arn:aws:s3:::nrel-pds-nsrdb/v3/tdy/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftdy%2F)'] +NREL National Solar Radiation Database - NSRDB v3 typical global years (TGY NSRDB v3 typical global years (TGY arn:aws:s3:::nrel-pds-nsrdb/v3/tgy/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftgy%2F)'] +NREL National Solar Radiation Database - NSRDB v3 typical meteorological years (TMY NSRDB v3 typical meteorological years (TMY arn:aws:s3:::nrel-pds-nsrdb/v3/tmy/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ nsrdb@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-nsrdb&prefix=v3%2Ftmy%2F)'] +NREL Wind Integration National Dataset - Bangladesh wind resource data (2014-2017) in HDF5 format Bangladesh wind resource data (2014-2017) in HDF5 format arn:aws:s3:::nrel-pds-wtk/bangladesh/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=bangladesh%2F)'] +NREL Wind Integration National Dataset - Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications arn:aws:s3:::nrel-pds-wtk/bchrrr/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=bchrrr%2F)'] +NREL Wind Integration National Dataset - Bias corrected Indonesia wind resource Bias corrected Indonesia wind resource arn:aws:s3:::nrel-pds-wtk/indonesia/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=indonesia%2F)'] +NREL Wind Integration National Dataset - California offshore wind resource data (2000-2022) in HDF5 format California offshore wind resource data (2000-2022) in HDF5 format arn:aws:s3:::nrel-pds-wtk/now23_california/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=now23_california%2F)'] +NREL Wind Integration National Dataset - Central Asia wind resource data (2015) in HDF5 format Central Asia wind resource data (2015) in HDF5 format arn:aws:s3:::nrel-pds-wtk/central_asia/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=central_asia%2F)'] +NREL Wind Integration National Dataset - Data for the Eastern Wind Integration Study (2004-2006 Data for the Eastern Wind Integration Study (2004-2006 arn:aws:s3:::nrel-pds-wtk/eastern_wind/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=eastern_wind%2F)'] +NREL Wind Integration National Dataset - Data for the Western Wind Integration Study (2004-2006 Data for the Western Wind Integration Study (2004-2006 arn:aws:s3:::nrel-pds-wtk/western_wind/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=western_wind%2F)'] +NREL Wind Integration National Dataset - Great Lakes wind resource data (2000-2020) in HDF5 format Great Lakes wind resource data (2000-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/Great_Lakes/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Great_Lakes%2F)'] +NREL Wind Integration National Dataset - Gulf of Mexico wind resource data (2000-2020) in HDF5 format Gulf of Mexico wind resource data (2000-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/gulf_of_mexico/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=gulf_of_mexico%2F)'] +NREL Wind Integration National Dataset - HSDS WIND domains HSDS WIND domains arn:aws:s3:::nrel-pds-hsds/nrel/wtk/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fwtk%2F)'] +NREL Wind Integration National Dataset - HSDS gridded WIND Toolkit domain HSDS gridded WIND Toolkit domain arn:aws:s3:::nrel-pds-hsds/nrel/wtk-us.h5 us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fwtk-us.h5%2F)'] +NREL Wind Integration National Dataset - Hawaii Wind Resource data for (2000-2019) in HDF5 format Hawaii Wind Resource data for (2000-2019) in HDF5 format arn:aws:s3:::nrel-pds-wtk/Hawaii/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Hawaii%2F)'] +NREL Wind Integration National Dataset - India wind resource data (2014) in HDF5 format India wind resource data (2014) in HDF5 format arn:aws:s3:::nrel-pds-wtk/india/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=india%2F)'] +NREL Wind Integration National Dataset - Kazakhstan wind resource data (2015) in HDF5 format Kazakhstan wind resource data (2015) in HDF5 format arn:aws:s3:::nrel-pds-wtk/kazakhstan/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=kazakhstan%2F)'] +NREL Wind Integration National Dataset - Maine wind resource data (2000-2020) in HDF5 format Maine wind resource data (2000-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/maine/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=maine%2F)'] +NREL Wind Integration National Dataset - Mid Atlantic Wind Resource data for (2000-2020) in HDF5 format Mid Atlantic Wind Resource data for (2000-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/Mid_Atlantic/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Mid_Atlantic%2F)'] +NREL Wind Integration National Dataset - Mid Atlantic three-dimensional planetary boundary layer (3D PBL) scheme wind res Mid Atlantic three-dimensional planetary boundary layer (3D PBL) scheme wind res arn:aws:s3:::nrel-pds-wtk/mid_atlantic_3d_pbl/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=mid_atlantic_3d_pbl%2F)'] +NREL Wind Integration National Dataset - Mid Atlantic wind resource data with modeled wakes in HDF5 format Mid Atlantic wind resource data with modeled wakes in HDF5 format arn:aws:s3:::nrel-pds-wtk/NOW-WAKES_Mid_Atlantic/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=NOW-WAKES_Mid_Atlantic%2F)'] +NREL Wind Integration National Dataset - NREL Wind Resource Datasets NREL Wind Resource Datasets arn:aws:s3:::nrel-pds-wtk/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk)'] +NREL Wind Integration National Dataset - NW Pacific Wind Resource data for (2000-2019) in HDF5 format NW Pacific Wind Resource data for (2000-2019) in HDF5 format arn:aws:s3:::nrel-pds-wtk/NW_Pacific/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=NW_Pacific%2F)'] +NREL Wind Integration National Dataset - Offshore California Wind Resource data for (2000-2019) in HDF5 format Offshore California Wind Resource data for (2000-2019) in HDF5 format arn:aws:s3:::nrel-pds-wtk/Offshore_CA/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=Offshore_CA%2F)'] +NREL Wind Integration National Dataset - Philippines typical meteorological year data in HDF5 format Philippines typical meteorological year data in HDF5 format arn:aws:s3:::nrel-pds-wtk/philippines_tmy/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=philippines_tmy%2F)'] +NREL Wind Integration National Dataset - Philippines wind resource data (2017) in HDF5 format Philippines wind resource data (2017) in HDF5 format arn:aws:s3:::nrel-pds-wtk/philippines/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=philippines%2F)'] +NREL Wind Integration National Dataset - Puerto Rico wind resource data (2001-2020) in HDF5 format Puerto Rico wind resource data (2001-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/pr100/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=pr100%2F)'] +NREL Wind Integration National Dataset - Source files for WIND Toolkit CONUS (2007-2014) Source files for WIND Toolkit CONUS (2007-2014) arn:aws:s3:::nrel-pds-wtk/conus/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=conus%2F)'] +NREL Wind Integration National Dataset - Source files for WIND Toolkit Canada (2007-2014) Source files for WIND Toolkit Canada (2007-2014) arn:aws:s3:::nrel-pds-wtk/canada/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=canada%2F)'] +NREL Wind Integration National Dataset - Source files for WIND Toolkit Mexico (2007-2014) Source files for WIND Toolkit Mexico (2007-2014) arn:aws:s3:::nrel-pds-wtk/mexico/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=mexico%2F)'] +NREL Wind Integration National Dataset - Source files for wtk-us gridded (2007-2013) Source files for wtk-us gridded (2007-2013) arn:aws:s3:::nrel-pds-wtk/wtk-us/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=wtk-us%2F)'] +NREL Wind Integration National Dataset - South Atlantic offshore wind resource data (2000-2020) in HDF5 format South Atlantic offshore wind resource data (2000-2020) in HDF5 format arn:aws:s3:::nrel-pds-wtk/south_atlantic/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=south_atlantic)'] +NREL Wind Integration National Dataset - Southeast Asia wind resource data (2017-2021) in HDF5 format Southeast Asia wind resource data (2017-2021) in HDF5 format arn:aws:s3:::nrel-pds-wtk/seasiawind/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=seasiawind%2F)'] +NREL Wind Integration National Dataset - Southeast Asia wind resource data v2 (2007-2021) in HDF5 format Southeast Asia wind resource data v2 (2007-2021) in HDF5 format arn:aws:s3:::nrel-pds-wtk/seasiawind_v2/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=seasiawind_v2%2F)'] +NREL Wind Integration National Dataset - Southeast Asia wind resource data v3 (2007-2021) in HDF5 format Southeast Asia wind resource data v3 (2007-2021) in HDF5 format arn:aws:s3:::nrel-pds-wtk/seasiawind_v3/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=seasiawind_v3%2F)'] +NREL Wind Integration National Dataset - Techno-economic subset of the WIND Toolkit by location in netCDF Techno-economic subset of the WIND Toolkit by location in netCDF arn:aws:s3:::nrel-pds-wtk/wtk-techno-economic/pywtk-data/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=wtk-techno-economic%2Fpywtk-data%2F)'] +NREL Wind Integration National Dataset - Vietnam wind resource data (2016-2018) in HDF5 format Vietnam wind resource data (2016-2018) in HDF5 format arn:aws:s3:::nrel-pds-wtk/vietnam/ us-west-2 S3 Bucket https://www.nrel.gov/grid/wind-toolkit.html wind-toolkit@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As Needed Creative Commons Attribution 3.0 United States License aws-pds, environmental, geospatial, meteorological ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=vietnam%2F)'] +NSF NCAR Curated ECMWF Reanalysis 5 (ERA5) - ERA5 NetCDF4 Data Files ERA5 NetCDF4 Data Files arn:aws:s3:::nsf-ncar-era5 us-west-2 S3 Bucket https://doi.org/10.5065/BH6N-5N20 rdahelp@ucar.edu [NSF National Center for Atmospheric Research](https://ncar.ucar.edu/) Monthly, with a 3-4 month lag from realtime https://www.ucar.edu/terms-of-use/data climate, model, atmosphere, land, data assimilation, forecast, meteorological, weather, geoscience, geospatial, aws-pds, netcdf ['[Browse Bucket](https://nsf-ncar-era5.s3.amazonaws.com/index.html)'] NSF NCAR Curated ECMWF Reanalysis 5 (ERA5) - Notifications for the NSF NCAR ERA5 bucket Notifications for the NSF NCAR ERA5 bucket arn:aws:sns:us-west-2:891377163634:nsf-ncar-era5-object_created us-west-2 SNS Topic https://doi.org/10.5065/BH6N-5N20 rdahelp@ucar.edu [NSF National Center for Atmospheric Research](https://ncar.ucar.edu/) Monthly, with a 3-4 month lag from realtime https://www.ucar.edu/terms-of-use/data climate, model, atmosphere, land, data assimilation, forecast, meteorological, weather, geoscience, geospatial, aws-pds, netcdf -National Climate Database (NCDB) - HSDS NCDB Domains HSDS NCDB Domains arn:aws:s3:::nrel-pds-hsds/nrel/ncdb/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ Manajit.Sengupta@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar, climate projections, CMIP5, CMIP6 ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fncdb%2F)'] -National Climate Database (NCDB) - NCDB CONUS 4km Hourly CONUS (2006-2100) in HDF5 format NCDB CONUS 4km Hourly CONUS (2006-2100) in HDF5 format arn:aws:s3:::nrel-pds-ncdb/4km-Hourly-CONUS/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ Manajit.Sengupta@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar, climate projections, CMIP5, CMIP6 ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-ncdb&prefix=v3%2F4km-Hourly-CONUS%2F)'] -National Climate Database (NCDB) - National Climate Database (NCDB) National Climate Database (NCDB) arn:aws:s3:::nrel-pds-ncdb/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ Manajit.Sengupta@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar, climate projections, CMIP5, CMIP6 ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-ncdb)'] +National Climate Database (NCDB) - HSDS NCDB Domains HSDS NCDB Domains arn:aws:s3:::nrel-pds-hsds/nrel/ncdb/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ Manajit.Sengupta@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar, climate projections, CMIP5, CMIP6 ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fncdb%2F)'] +National Climate Database (NCDB) - NCDB CONUS 4km Hourly CONUS (2006-2100) in HDF5 format NCDB CONUS 4km Hourly CONUS (2006-2100) in HDF5 format arn:aws:s3:::nrel-pds-ncdb/4km-Hourly-CONUS/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ Manajit.Sengupta@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar, climate projections, CMIP5, CMIP6 ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-ncdb&prefix=v3%2F4km-Hourly-CONUS%2F)'] +National Climate Database (NCDB) - National Climate Database (NCDB) National Climate Database (NCDB) arn:aws:s3:::nrel-pds-ncdb/ us-west-2 S3 Bucket https://nsrdb.nrel.gov/ Manajit.Sengupta@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, earth observation, energy, geospatial, meteorological, solar, climate projections, CMIP5, CMIP6 ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-ncdb)'] National Herbarium of NSW Herbarium Collection Image files arn:aws:s3:::herbariumnsw-pds ap-southeast-2 S3 Bucket https://www.rbgsyd.nsw.gov.au/science/national-herbarium-of-new-south-wales Hannah.McPherson@rbgsyd.nsw.gov.au Royal Botanic Gardens and Domain Trust Quarterly https://creativecommons.org/licenses/by/4.0/legalcode aws-pds, agriculture, biodiversity, biology, climate, digital preservation, ecosystems, environmental -Natural Earth Shapefile downloads in ZIP archives arn:aws:s3:::naturalearth us-west-2 S3 Bucket https://www.naturalearthdata.com/downloads/ https://github.com/nvkelso/natural-earth-vector/issues North American Cartographic Information Society (nacis.org) As needed Public Domain. https://www.naturalearthdata.com/about/terms-of-use/ aws-pds, mapping, geospatial, global, tiles, earth observation, population False +Natural Earth Shapefile downloads in ZIP archives arn:aws:s3:::naturalearth us-west-2 S3 Bucket https://www.naturalearthdata.com/downloads/ https://github.com/nvkelso/natural-earth-vector/issues North American Cartographic Information Society (nacis.org) As needed Public Domain. https://www.naturalearthdata.com/about/terms-of-use/ aws-pds, mapping, geospatial, global, tiles, earth observation, population False New Jersey Statewide Digital Aerial Imagery Catalog New Jersey digital orthophotography archive arn:aws:s3:::njogis-imagery us-west-2 S3 Bucket https://njgin.nj.gov/njgin/edata/imagery/index.html njgin@oit.nj.gov The New Jersey Office of GIS, NJ Office of Information Technology None "The State of New Jersey provides the service ""as is"". The State makes no guarant" aerial imagery, aws-pds, earth observation, geospatial, imaging, mapping, cog New Jersey Statewide LiDAR New Jersey digital elevation data archive, LiDAR derived products arn:aws:s3:::njogis-elevation us-west-2 S3 Bucket https://njgin.nj.gov/njgin/edata/elevation/index.html njgin@oit.nj.gov The New Jersey Office of GIS, NJ Office of Information Technology None "The State of New Jersey provides the service ""as is"". The State makes no guarant" aws-pds, lidar, elevation, geospatial, mapping New York City Taxi and Limousine Commission (TLC) Trip Record Data PARQUET files containing NYC TLC trip data arn:aws:s3:::nyc-tlc us-east-1 S3 Bucket https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page research@tlc.nyc.gov City of New York Taxi and Limousine Commission As soon as new data is available to be shared publicly. http://www1.nyc.gov/home/terms-of-use.page aws-pds, cities, urban, transportation True @@ -561,28 +562,28 @@ Nighttime-Fire-Flare NASA Black Marble Combustion Detections, Earth Science Trai Normalized Difference Urban Index (NDUI) NDUI-2000 on a global land scale arn:aws:s3:::qinglinglab-ndui-2000 us-west-2 S3 Bucket https://github.com/yifwahaha/NDUI_work_flow/blob/master/User's%20Guide%20-%20202 zhangqling@mail.sysu.edu.cn Remote Sensing Big Data Intelligent Application Laboratory, School of Aeronautic Next year's NDUI is added as soon as it is available. There are no restrictions on the use of this data. aws-pds, earth observation, geospatial, urban, satellite imagery Northern California Earthquake Data Seismic waveform data (miniSEED format), metadata (FDSNStationXML format) and ea arn:aws:s3:::ncedc-pds us-west-2 S3 Bucket https://ncedc.org/db/cloud.html stephane@berkeley.edu [Northern California Earthquake Data Center](https://ncedc.org) Daily NCEDC hereby grants the non-exclusive, royalty free, non-transferable, worldwide aws-pds, earth observation, earthquakes, seismology OAQPS 2022 Modeling Platform - Notification for the 2022 Modeling Platform bucket Notification for the 2022 Modeling Platform bucket arn:aws:sns:us-east-1:127085394039:epa-2022-modeling-platform-object_created us-east-1 SNS Topic 2022 WRF Modeling TSD: https://www.epa.gov/system/files/documents/2024-03/wrf_2 Misenis.Chris@epa.gov U.S. Environmental Protection Agency, Office of Air Quality Planning and Standar As needed These datasets are products of the U.S. Government and are intended for public a aws-pds, air quality, regulatory, weather, meteorological -OAQPS 2022 Modeling Platform - The 2022 WRF output are stored as uncompressed netcdf/hdf5 formatted files in t The 2022 WRF output are stored as uncompressed netcdf/hdf5 formatted files in t arn:aws:s3:::epa-2022-modeling-platform us-east-1 S3 Bucket 2022 WRF Modeling TSD: https://www.epa.gov/system/files/documents/2024-03/wrf_2 Misenis.Chris@epa.gov U.S. Environmental Protection Agency, Office of Air Quality Planning and Standar As needed These datasets are products of the U.S. Government and are intended for public a aws-pds, air quality, regulatory, weather, meteorological ['[Browse Bucket](https://epa-2022-modeling-platform.s3.amazonaws.com/index.html)'] +OAQPS 2022 Modeling Platform - The 2022 WRF output are stored as uncompressed netcdf/hdf5 formatted files in t The 2022 WRF output are stored as uncompressed netcdf/hdf5 formatted files in t arn:aws:s3:::epa-2022-modeling-platform us-east-1 S3 Bucket 2022 WRF Modeling TSD: https://www.epa.gov/system/files/documents/2024-03/wrf_2 Misenis.Chris@epa.gov U.S. Environmental Protection Agency, Office of Air Quality Planning and Standar As needed These datasets are products of the U.S. Government and are intended for public a aws-pds, air quality, regulatory, weather, meteorological ['[Browse Bucket](https://epa-2022-modeling-platform.s3.amazonaws.com/index.html)'] Open City Model (OCM) Project data files arn:aws:s3:::opencitymodel us-east-1 S3 Bucket https://github.com/opencitymodel/opencitymodel https://github.com/opencitymodel/opencitymodel#contact BuildZero Quarterly https://github.com/opencitymodel/opencitymodel#license aws-pds, events, cities, geospatial -Open-Meteo Weather API Database Open-Meteo Weather API Database arn:aws:s3:::openmeteo us-west-2 S3 Bucket https://github.com/open-meteo/open-data info@open-meteo.com [Open-Meteo](https://www.open-meteo.com/) Hourly CC-BY 4.0 aws-pds, agriculture, climate, earth observation, meteorological, weather ['[Browse Bucket](https://openmeteo.s3.amazonaws.com/index.html#data/)'] +Open-Meteo Weather API Database Open-Meteo Weather API Database arn:aws:s3:::openmeteo us-west-2 S3 Bucket https://github.com/open-meteo/open-data info@open-meteo.com [Open-Meteo](https://www.open-meteo.com/) Hourly CC-BY 4.0 aws-pds, agriculture, climate, earth observation, meteorological, weather ['[Browse Bucket](https://openmeteo.s3.amazonaws.com/index.html#data/)'] OpenAQ - Daily gzipped CSVs of global air quality measurements fetched from sources all o Daily gzipped CSVs of global air quality measurements fetched from sources all o arn:aws:s3:::openaq-data-archive us-east-1 S3 Bucket https://openaq.org info@openaq.org [OpenAQ](https://openaq.org) Hourly Varies, depends on data provider aws-pds, air quality, cities, environmental, geospatial OpenAQ - OpenAQ API OpenAQ API us-east-1 CloudFront Distribution https://openaq.org info@openaq.org [OpenAQ](https://openaq.org) Hourly Varies, depends on data provider aws-pds, air quality, cities, environmental, geospatial api.openaq.org OpenAQ - SNS topic for new objects in the openaq-data-archive bucket SNS topic for new objects in the openaq-data-archive bucket arn:aws:sns:us-east-1:817926761842:openaq-data-archive-object_created us-east-1 SNS Topic https://openaq.org info@openaq.org [OpenAQ](https://openaq.org) Hourly Varies, depends on data provider aws-pds, air quality, cities, environmental, geospatial -OpenAerialMap on AWS OpenAerialMap files and metadata arn:aws:s3:::oin-hotosm us-east-1 S3 Bucket https://docs.openaerialmap.org/ info@openaerialmap.org [Humanitarian OpenStreetMap Team](https://www.hotosm.org/) New imagery is added as soon as it is uploaded by community contributors. All imagery is publicly licensed CC-BY 4.0, with attribution as contributors of satellite imagery, aerial imagery, earth observation, disaster response, cog ['[Browse Bucket](https://oin-hotosm.s3.amazonaws.com/)'] -OpenEEW OpenEEW arn:aws:s3:::grillo-openeew us-east-1 S3 Bucket https://github.com/openeew/openeew hello@openeew.com [Grillo](https://grillo.io/) Approximately every 5 minutes https://github.com/openeew/openeew#license disaster response, earth observation, earthquakes, aws-pds ['[Browse Bucket](https://grillo-openeew.s3.amazonaws.com/index.html)'] +OpenAerialMap on AWS OpenAerialMap files and metadata arn:aws:s3:::oin-hotosm us-east-1 S3 Bucket https://docs.openaerialmap.org/ info@openaerialmap.org [Humanitarian OpenStreetMap Team](https://www.hotosm.org/) New imagery is added as soon as it is uploaded by community contributors. All imagery is publicly licensed CC-BY 4.0, with attribution as contributors of satellite imagery, aerial imagery, earth observation, disaster response, cog ['[Browse Bucket](https://oin-hotosm.s3.amazonaws.com/)'] +OpenEEW OpenEEW arn:aws:s3:::grillo-openeew us-east-1 S3 Bucket https://github.com/openeew/openeew hello@openeew.com [Grillo](https://grillo.io/) Approximately every 5 minutes https://github.com/openeew/openeew#license disaster response, earth observation, earthquakes, aws-pds ['[Browse Bucket](https://grillo-openeew.s3.amazonaws.com/index.html)'] OpenStreetMap on AWS - Imagery and metadata Imagery and metadata arn:aws:s3:::osm-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds https://github.com/mojodna/osm-pds-pipelines/issues Pacific Atlas Data is updated weekly https://www.openstreetmap.org/copyright aws-pds, geospatial, mapping, osm, disaster response OpenStreetMap on AWS - New data notifications New data notifications arn:aws:sns:us-east-1:800218804198:New_File us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds https://github.com/mojodna/osm-pds-pipelines/issues Pacific Atlas Data is updated weekly https://www.openstreetmap.org/copyright aws-pds, geospatial, mapping, osm, disaster response -OpenUniverse 2024 Matched Rubin and Roman Simulations: Preview - The simulated Roman data products include truth files listing the basic physical The simulated Roman data products include truth files listing the basic physical arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/roman/ us-east-1 S3 Bucket https://irsa.ipac.caltech.edu/data/theory/openuniverse2024 https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The OpenUniverse 2024 Data Preview has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, simulations, survey False False -OpenUniverse 2024 Matched Rubin and Roman Simulations: Preview - The simulated Rubin data products include raw pixel data, calibrated exposures, The simulated Rubin data products include raw pixel data, calibrated exposures, arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/rubin/ us-east-1 S3 Bucket https://irsa.ipac.caltech.edu/data/theory/openuniverse2024 https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The OpenUniverse 2024 Data Preview has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, simulations, survey False False +OpenUniverse 2024 Matched Rubin and Roman Simulations: Preview - The simulated Roman data products include truth files listing the basic physical The simulated Roman data products include truth files listing the basic physical arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/roman/ us-east-1 S3 Bucket https://irsa.ipac.caltech.edu/data/theory/openuniverse2024 https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The OpenUniverse 2024 Data Preview has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, simulations, survey False False +OpenUniverse 2024 Matched Rubin and Roman Simulations: Preview - The simulated Rubin data products include raw pixel data, calibrated exposures, The simulated Rubin data products include raw pixel data, calibrated exposures, arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/rubin/ us-east-1 S3 Bucket https://irsa.ipac.caltech.edu/data/theory/openuniverse2024 https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The OpenUniverse 2024 Data Preview has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, simulations, survey False False Orcasound - bioacoustic data for marine conservation - Archived lossless orca audio data (FLAC) Archived lossless orca audio data (FLAC) arn:aws:s3:::archive-orcasound-net us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing Orcasound - bioacoustic data for marine conservation - Labeled audio data for ML model development Labeled audio data for ML model development arn:aws:s3:::acoustic-sandbox us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing Orcasound - bioacoustic data for marine conservation - Live-streamed orca audio data (HLS) Live-streamed orca audio data (HLS) arn:aws:s3:::streaming-orcasound-net us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing Overture Maps Foundation Open Map Data - New File Notification New File Notification arn:aws:sns:us-west-2:913550007193:overturemaps-us-west-2 us-west-2 SNS Topic Documentation is available at [docs.overturemaps.org](https://docs.overturemaps. info@overturemaps.org [Overture Maps Foundation](https://overturemaps.org) Monthly Overture data is licensed under the Community Database License Agreement Permiss aws-pds, geospatial, global, mapping, osm, parquet, transportation Overture Maps Foundation Open Map Data - Overture Maps Foundation Data (GeoParquet) Overture Maps Foundation Data (GeoParquet) arn:aws:s3:::overturemaps-us-west-2/release/ us-west-2 S3 Bucket Documentation is available at [docs.overturemaps.org](https://docs.overturemaps. info@overturemaps.org [Overture Maps Foundation](https://overturemaps.org) Monthly Overture data is licensed under the Community Database License Agreement Permiss aws-pds, geospatial, global, mapping, osm, parquet, transportation Ozone Monitoring Instrument (OMI) / Aura NO2 Tropospheric Column Density S3 Bucket for OMI NO2 in Cloud-Optimized GeoTiff format arn:aws:s3:::omi-no2-nasa us-west-2 S3 Bucket https://disc.gsfc.nasa.gov/datasets/OMNO2d_003/summary binita.kc@nasa.gov NASA None There are no restrictions on the use of these data. aws-pds, earth observation, geospatial, satellite imagery, air quality, atmosphere, environmental -PALSAR-2 ScanSAR CARD4L (L2.2) PALSAR-2 ScanSAR CARD4L arn:aws:s3:::jaxaalos2/palsar2/L2.2/Africa/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) Every month after 42 days observed Data is available for free under the [terms of use](https://earth.jaxa.jp/policy aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, sustainability, disaster response, synthetic aperture radar, deafrica, stac, cog False -PALSAR-2 ScanSAR Flooding in Rwanda (L2.1) PALSAR-2 ScanSAR L11 & L22 arn:aws:s3:::jaxaalos2/palsar2-scansar/Rwanda/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.e aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) As available. Data is available for free under the terms of use. aws-pds, agriculture, cog, deafrica, disaster response, earth observation, geospatial, natural resource, satellite imagery, stac, sustainability, synthetic aperture radar False -PALSAR-2 ScanSAR Tropical Cycolne Mocha (L2.1) PALSAR-2 ScanSAR L22 arn:aws:s3:::jaxaalos2/palsar2-scansar/Bangladesh/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.e aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) As available. Data is available for free under the terms of use. aws-pds, agriculture, cog, disaster response, earth observation, geospatial, natural resource, satellite imagery, stac, sustainability, synthetic aperture radar False -PALSAR-2 ScanSAR Turkey & Syria Earthquake (L2.1 & L1.1) PALSAR-2 ScanSAR L11 & L22 arn:aws:s3:::jaxaalos2/palsar2-scansar/Turkey-Syria-earthquake/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.e aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) As available. Data is available for free under the [terms of use](https://earth.jaxa.jp/policy aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, sustainability, disaster response, synthetic aperture radar, deafrica, stac, cog False +PALSAR-2 ScanSAR CARD4L (L2.2) PALSAR-2 ScanSAR CARD4L arn:aws:s3:::jaxaalos2/palsar2/L2.2/Africa/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) Every month after 42 days observed Data is available for free under the [terms of use](https://earth.jaxa.jp/policy aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, sustainability, disaster response, synthetic aperture radar, deafrica, stac, cog False +PALSAR-2 ScanSAR Flooding in Rwanda (L2.1) PALSAR-2 ScanSAR L11 & L22 arn:aws:s3:::jaxaalos2/palsar2-scansar/Rwanda/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.e aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) As available. Data is available for free under the terms of use. aws-pds, agriculture, cog, deafrica, disaster response, earth observation, geospatial, natural resource, satellite imagery, stac, sustainability, synthetic aperture radar False +PALSAR-2 ScanSAR Tropical Cycolne Mocha (L2.1) PALSAR-2 ScanSAR L22 arn:aws:s3:::jaxaalos2/palsar2-scansar/Bangladesh/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.e aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) As available. Data is available for free under the terms of use. aws-pds, agriculture, cog, disaster response, earth observation, geospatial, natural resource, satellite imagery, stac, sustainability, synthetic aperture radar False +PALSAR-2 ScanSAR Turkey & Syria Earthquake (L2.1 & L1.1) PALSAR-2 ScanSAR L11 & L22 arn:aws:s3:::jaxaalos2/palsar2-scansar/Turkey-Syria-earthquake/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.e aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) As available. Data is available for free under the [terms of use](https://earth.jaxa.jp/policy aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, sustainability, disaster response, synthetic aperture radar, deafrica, stac, cog False PROJ datum grids Horizontal and vertical adjustment datasets us-east-1 CloudFront Distribution https://github.com/OSGeo/proj-datumgrid-geotiff proj@lists.osgeo.org [PROJ](https://proj.org) New grids are added when made available Per file. Under an Open Source Definition compliant license. Consult the READMEs aws-pds, geospatial, mapping cdn.proj.org Pacific Ocean Sound Recordings - decimated 16 kHz audio recordings decimated 16 kHz audio recordings arn:aws:s3:::pacific-sound-16khz us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings - decimated 2 kHz audio recordings decimated 2 kHz audio recordings arn:aws:s3:::pacific-sound-2khz us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software @@ -598,109 +599,111 @@ Pacific Ocean Sound Recordings - original 256 kHz audio recordings year 2022 ori Pacific Ocean Sound Recordings - original 256 kHz audio recordings year 2023 original 256 kHz audio recordings year 2023 arn:aws:s3:::pacific-sound-256khz-2023 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings - original 256 kHz audio recordings year 2024 original 256 kHz audio recordings year 2024 arn:aws:s3:::pacific-sound-256khz-2024 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings - original 256 kHz audio recordings year 2025 original 256 kHz audio recordings year 2025 arn:aws:s3:::pacific-sound-256khz-2025 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -PoroTomo - HSDS PoroTomo domains HSDS PoroTomo domains arn:aws:s3:::nrel-pds-hsds/nrel/porotomo/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fporotomo%2F)'] -PoroTomo - PoroTomo Datasets PoroTomo Datasets arn:aws:s3:::nrel-pds-porotomo/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo)'] -PoroTomo - PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time M PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time M arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/Resampled/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2FResampled%2F)'] -PoroTomo - PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in HDF5 format PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in HDF5 format arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASH/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASH%2F)'] -PoroTomo - PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in SEG-Y format PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in SEG-Y format arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2F)'] -PoroTomo - PoroTomo Nodal Seismometer Continuous Data PoroTomo Nodal Seismometer Continuous Data arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac%2F)'] -PoroTomo - PoroTomo Nodal Seismometer Field Notes and Metadata PoroTomo Nodal Seismometer Field Notes and Metadata arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_metadata/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_metadata%2F)'] -PoroTomo - PoroTomo Nodal Seismometer Sweep Data PoroTomo Nodal Seismometer Sweep Data arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac_sweep/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac_sweep%2F)'] -PoroTomo - PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASV/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASV%2F)'] -PoroTomo - PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in SEG-Y format PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in SEG-Y format arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASV/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASV%2F)'] +PoroTomo - HSDS PoroTomo domains HSDS PoroTomo domains arn:aws:s3:::nrel-pds-hsds/nrel/porotomo/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fporotomo%2F)'] +PoroTomo - PoroTomo Datasets PoroTomo Datasets arn:aws:s3:::nrel-pds-porotomo/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo)'] +PoroTomo - PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time M PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time M arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/Resampled/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2FResampled%2F)'] +PoroTomo - PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in HDF5 format PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in HDF5 format arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASH/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASH%2F)'] +PoroTomo - PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in SEG-Y format PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in SEG-Y format arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2F)'] +PoroTomo - PoroTomo Nodal Seismometer Continuous Data PoroTomo Nodal Seismometer Continuous Data arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac%2F)'] +PoroTomo - PoroTomo Nodal Seismometer Field Notes and Metadata PoroTomo Nodal Seismometer Field Notes and Metadata arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_metadata/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_metadata%2F)'] +PoroTomo - PoroTomo Nodal Seismometer Sweep Data PoroTomo Nodal Seismometer Sweep Data arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac_sweep/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac_sweep%2F)'] +PoroTomo - PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASV/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASV%2F)'] +PoroTomo - PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in SEG-Y format PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in SEG-Y format arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASV/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASV%2F)'] Prefeitura Municipal de São Paulo (PMSP) LiDAR Point Cloud - São Paulo city's 3D LiDAR - Entwine Point Tiles São Paulo city's 3D LiDAR - Entwine Point Tiles arn:aws:s3:::ept-m3dc-pmsp sa-east-1 S3 Bucket https://github.com/geoinfo-smdu/M3DC geosampa@prefeitura.sp.gov.br [GeoSampa - o mapa digital da cidade de São Paulo](http://geosampa.prefeitura.sp Local survey executed by demand generates new data as local point clouds. [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html) cities, land, lidar, urban, geospatial, elevation, mapping, aws-pds Prefeitura Municipal de São Paulo (PMSP) LiDAR Point Cloud - São Paulo city's 3D LiDAR - LAZ Files São Paulo city's 3D LiDAR - LAZ Files arn:aws:s3:::laz-m3dc-pmsp sa-east-1 S3 Bucket https://github.com/geoinfo-smdu/M3DC geosampa@prefeitura.sp.gov.br [GeoSampa - o mapa digital da cidade de São Paulo](http://geosampa.prefeitura.sp Local survey executed by demand generates new data as local point clouds. [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html) cities, land, lidar, urban, geospatial, elevation, mapping, aws-pds Public Utility Data Liberation Project All PUDL data outputs arn:aws:s3:::pudl.catalyst.coop us-west-2 S3 Bucket You can download the [data directly](https://catalystcoop-pudl.readthedocs.io/en For general questions or feedback about the data, create an GitHub issue or disc [Catalyst Cooperative](https://catalyst.coop/) The federal agencies that publish the raw data PUDL processes release new data, The PUDL data and documentation are published under the [Creative Commons Attrib aws-pds, climate, climate model, energy, environmental, government records, infrastructure, open source software, electricity, energy modeling, utilities QIIME 2 Tutorial Data Source for rendered documentation and tutorial datasets for the QIIME 2 project arn:aws:s3:::qiime2-data us-west-2 S3 Bucket https://use.qiime2.org https://forum.qiime2.org Caporaso Lab Twice per year BSD 3-Clause License aws-pds, bioinformatics, biology, ecosystems, environmental, genetic, genomic, health, microbiome, metagenomics, life sciences RADARSAT-1 Cloud Optimized GeoTIFF (COG) images arn:aws:s3:::radarsat-r1-l1-cog ca-central-1 S3 Bucket https://www.asc-csa.gc.ca/eng/satellites/radarsat1/what-is-radarsat1.asp https://www.eodms-sgdot.nrcan-rncan.gc.ca [Natural Resources Canada](https://nrcan.gc.ca/) Products are added on an adhoc basis driven by prioritized foreign repatriation [Open Government License (OGL)](https://open.canada.ca/en/open-government-licenc earth observation, global, aws-pds, ice, agriculture, disaster response, satellite imagery, geospatial, cog, synthetic aperture radar -RAPID NRT Flood Maps RAPID archive flood maps arn:aws:s3:::rapid-nrt-flood-maps us-west-2 S3 Bucket https://github.com/QingYang6/RAPID-NRT-flood-maps-on-AWS/blob/master/README.md xinyi.shen@uconn.edu; qing.yang6@hotmail.com University of Connecticut; Guangxi University NRT data will be update as soon as SAR images available and done processed. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International aws-pds, agriculture, earth observation, water, environmental, disaster response ['[Browse Bucket](https://rapid-nrt-flood-maps.s3.amazonaws.com/index.html)'] -RCM CEOS Analysis Ready Data | Données prêtes à l'analyse du CEOS pour le MCR RCM CEOS Analysis Ready Data Données prêtes à l'analyse (DPA) du CEOS pour le M arn:aws:s3:::rcm-ceos-ard ca-central-1 S3 Bucket https://www.asc-csa.gc.ca/eng/satellites/radarsat/ eodms-sgdot@nrcan-rncan.gc.ca [Natural Resources Canada](https://www.nrcan.gc.ca/) The initial dataset will be Canada-wide, 30M Compact-Polarization standard cover RCM image products are available free of charge, to the broadest extent possible aws-pds, agriculture, earth observation, satellite imagery, geospatial, sustainability, disaster response, synthetic aperture radar, stac ['[EODMS STAC for RCM CEOS ARD](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/collections/rcm-ard/items/)'] +RAPID NRT Flood Maps RAPID archive flood maps arn:aws:s3:::rapid-nrt-flood-maps us-west-2 S3 Bucket https://github.com/QingYang6/RAPID-NRT-flood-maps-on-AWS/blob/master/README.md xinyi.shen@uconn.edu; qing.yang6@hotmail.com University of Connecticut; Guangxi University NRT data will be update as soon as SAR images available and done processed. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International aws-pds, agriculture, earth observation, water, environmental, disaster response ['[Browse Bucket](https://rapid-nrt-flood-maps.s3.amazonaws.com/index.html)'] +RCM CEOS Analysis Ready Data | Données prêtes à l'analyse du CEOS pour le MCR RCM CEOS Analysis Ready Data Données prêtes à l'analyse (DPA) du CEOS pour le M arn:aws:s3:::rcm-ceos-ard ca-central-1 S3 Bucket https://www.asc-csa.gc.ca/eng/satellites/radarsat/ eodms-sgdot@nrcan-rncan.gc.ca [Natural Resources Canada](https://www.nrcan.gc.ca/) The initial dataset will be Canada-wide, 30M Compact-Polarization standard cover RCM image products are available free of charge, to the broadest extent possible aws-pds, agriculture, earth observation, satellite imagery, geospatial, sustainability, disaster response, synthetic aperture radar, stac ['[EODMS STAC for RCM CEOS ARD](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/collections/rcm-ard/items/)'] Radiant MLHub Radiant MLHub Training Data arn:aws:s3:::radiant-mlhub us-west-2 S3 Bucket http://docs.mlhub.earth/ support@radiant.earth [Radiant Earth Foundation](https://www.radiant.earth/) New training data catalogs are added on a rolling basis Access to Radiant MLHub data is free for everyone. Each dataset has its own lice aws-pds, labeled, machine learning, geospatial, earth observation, satellite imagery, environmental, cog, stac RarePlanes Real and synthetic satellite imagery, annotations, and metadata arn:aws:s3:::rareplanes-public us-west-2 S3 Bucket www.cosmiqworks.org/RarePlanes jss5102@gmail.com and avanetten@iqt.org In-Q-Tel - CosmiQ Works None Planned [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) computer vision, deep learning, earth observation, geospatial, machine learning, satellite imagery, aws-pds, labeled -Reference Elevation Model of Antarctica (REMA) - REMA DEM Mosaics REMA DEM Mosaics arn:aws:s3:::pgc-opendata-dems/rema/mosaics/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/rema/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/mosaics.json)'] -Reference Elevation Model of Antarctica (REMA) - REMA DEM Strips REMA DEM Strips arn:aws:s3:::pgc-opendata-dems/rema/strips/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/rema/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/strips.json)'] +Reference Elevation Model of Antarctica (REMA) - REMA DEM Mosaics REMA DEM Mosaics arn:aws:s3:::pgc-opendata-dems/rema/mosaics/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/rema/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/mosaics.json)'] +Reference Elevation Model of Antarctica (REMA) - REMA DEM Strips REMA DEM Strips arn:aws:s3:::pgc-opendata-dems/rema/strips/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/rema/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/strips.json)'] Reference data for HiFi human WGS HiFi Human WGS Reference data arn:aws:s3:::pacbio-hifi-human-wgs-reference us-west-2 S3 Bucket https://zenodo.org/records/8415406 dl_it-awsopendata@pacificbiosciences.com [Pacific Biosciences of California, Inc](https://www.pacb.com/) "Files are updated to reflect the support for the lastest version of [PacBio WGS " [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, health, life sciences, Homo sapiens, long read sequencing, genetic, mapping, whole genome sequencing, vcf, variant annotation SILAM Air Quality - Notifications for new netcdf surface data Notifications for new netcdf surface data arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-netcdf eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological SILAM Air Quality - Notifications for new zarr surface data Notifications for new zarr surface data arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-zarr eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological -SILAM Air Quality - Surface NetCDF files Surface NetCDF files arn:aws:s3:::fmi-opendata-silam-surface-netcdf eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological ['[Browse Bucket](https://fmi-opendata-silam-surface-netcdf.s3.amazonaws.com/index.html)'] -SILAM Air Quality - Surface Zarr files Surface Zarr files arn:aws:s3:::fmi-opendata-silam-surface-zarr eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological ['[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)'] +SILAM Air Quality - Surface NetCDF files Surface NetCDF files arn:aws:s3:::fmi-opendata-silam-surface-netcdf eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological ['[Browse Bucket](https://fmi-opendata-silam-surface-netcdf.s3.amazonaws.com/index.html)'] +SILAM Air Quality - Surface Zarr files Surface Zarr files arn:aws:s3:::fmi-opendata-silam-surface-zarr eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological ['[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)'] SILO climate data on AWS SILO open data arn:aws:s3:::silo-open-data ap-southeast-2 S3 Bucket https://www.longpaddock.qld.gov.au/silo/gridded-data https://www.longpaddock.qld.gov.au/silo/contact-us Queensland Government Daily SILO datasets are constructed by the [Queensland Government](http://www.qld.gov. aws-pds, agriculture, climate, earth observation, environmental, meteorological, model, sustainability, water, weather -SMN Hi-Res Weather Forecast over Argentina WRF SMN data arn:aws:s3:::smn-ar-wrf us-west-2 S3 Bucket General information, tutorials and examples:[https://odp-aws-smn.github.io/docum For any questions regarding the data set or any general questions, you can conta [SMN](http://www.smn.gov.ar/) New data is added as soon as it's available. Two forecast cycles a day initializ [Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org aws-pds, earth observation, natural resource, weather, meteorological ['[Browse Bucket](https://smn-ar-wrf.s3.amazonaws.com/index.html)'] +SMN Hi-Res Weather Forecast over Argentina WRF SMN data arn:aws:s3:::smn-ar-wrf us-west-2 S3 Bucket General information, tutorials and examples:[https://odp-aws-smn.github.io/docum For any questions regarding the data set or any general questions, you can conta [SMN](http://www.smn.gov.ar/) New data is added as soon as it's available. Two forecast cycles a day initializ [Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org aws-pds, earth observation, natural resource, weather, meteorological ['[Browse Bucket](https://smn-ar-wrf.s3.amazonaws.com/index.html)'] SPARTAN Data All data products (PM25, aerosol chemical components, scattering) provided by S arn:aws:s3:::spartan-cloud us-west-2 S3 Bucket https://www.spartan-network.org/data SPARTAN.PM25@gmail.com The [Atmospheric Composition Analysis Group](https://sites.wustl.edu/acag/) New measurement or estimation products will be added when available, usually mul SPARTAN data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/ aws-pds, environmental, air quality SSL4EO S12 Landsat Multi Product Dataset Satellite imagery and context from Sentinel-12 and Landsat 4-5, 7, 8-9 arn:aws:s3:::ssl4eo-s12-landsat-combined us-west-2 S3 Bucket https://github.com/sunny1401/ssl4eo_multi_satellite_products https://github.com/sunny1401/ssl4eo_multi_satellite_products Sankranti Joshi Not updated https://creativecommons.org/licenses/by-nc-sa/4.0/ satellite imagery -Safecast - Bulk exports of air and radiation measurements Bulk exports of air and radiation measurements arn:aws:s3:::safecast-opendata-public-us-east-1 us-east-1 S3 Bucket https://github.com/Safecast/safecastapi/wiki/Data-Sets https://groups.google.com/forum/#!forum/safecast-devices [Safecast](https://safecast.org/) Continuous Safecast data is published under a [CC0 designation](https://creativecommons.org air quality, aws-pds, climate, environmental, geospatial, radiation ['[Browse Bucket](https://safecast-opendata-public-us-east-1.s3.amazonaws.com/index.html)'] +Safecast - Bulk exports of air and radiation measurements Bulk exports of air and radiation measurements arn:aws:s3:::safecast-opendata-public-us-east-1 us-east-1 S3 Bucket https://github.com/Safecast/safecastapi/wiki/Data-Sets https://groups.google.com/forum/#!forum/safecast-devices [Safecast](https://safecast.org/) Continuous Safecast data is published under a [CC0 designation](https://creativecommons.org air quality, aws-pds, climate, environmental, geospatial, radiation ['[Browse Bucket](https://safecast-opendata-public-us-east-1.s3.amazonaws.com/index.html)'] Safecast - New air and radiation measurement payloads New air and radiation measurement payloads arn:aws:sns:us-west-2:985752656544:ingest-measurements-prd us-west-2 SNS Topic https://github.com/Safecast/safecastapi/wiki/Data-Sets https://groups.google.com/forum/#!forum/safecast-devices [Safecast](https://safecast.org/) Continuous Safecast data is published under a [CC0 designation](https://creativecommons.org air quality, aws-pds, climate, environmental, geospatial, radiation -SatPM2.5 Satellite-Derived Fine Particulate Matter (PM25) concentrations from the Atmosp arn:aws:s3:::v6.pm25.global us-west-2 S3 Bucket https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V6.GL.02 randall.martin@wustl.edu https://sites.wustl.edu/acag/ Yearly Creative Commons Attribution 4.0 International (https://creativecommons.org/lice atmosphere, netcdf, environmental, air quality, health ['[Browse Bucket](https://s3.us-west-2.amazonaws.com/v6.pm25.global/index.html)'] +SatPM2.5 Satellite-Derived Fine Particulate Matter (PM25) concentrations from the Atmosp arn:aws:s3:::v6.pm25.global us-west-2 S3 Bucket https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V6.GL.02 randall.martin@wustl.edu https://sites.wustl.edu/acag/ Yearly Creative Commons Attribution 4.0 International (https://creativecommons.org/lice atmosphere, netcdf, environmental, air quality, health ['[Browse Bucket](https://s3.us-west-2.amazonaws.com/v6.pm25.global/index.html)'] Satellite - Sea surface temperature - Level 3 - Single sensor - 1 day - Day and night time Cloud Optimised AODN dataset of IMOS - SRS - SST - L3S - Single Sensor - 1 day - arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.zarr ap-southeast-2 S3 Bucket https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/a info@aodn.org.au AODN As Needed http://creativecommons.org/licenses/by/4.0/ oceans, satellite imagery Scottish Public Sector LiDAR Dataset LiDAR data (DSM, DTM and Laz) arn:aws:s3:::srsp-open-data eu-west-2 S3 Bucket https://remotesensingdata.gov.scot/data#/list https://remotesensingdata.gov.scot/feedback or email Scottish Government on gi-s [Joint Nature Conservation Committee](https://jncc.gov.uk/) New datasets have historically been added every 2-3 years but there is no guaran All data is made available under the [Open Government Licence v3](http://www.nat lidar, cities, coastal, environmental, urban, elevation, cog, aws-pds Sea Surface Temperature Daily Analysis: European Space Agency Climate Change Initiative product version 2.1 Global daily-mean sea surface temperatures from 1981 onwards, in Zarr format Th arn:aws:s3:::surftemp-sst us-west-2 S3 Bucket https://surftemp.github.io/sst-data-tutorials/ https://www.reading.ac.uk/met/ [University of Reading, Department of Meteorology](https://www.reading.ac.uk/met yearly Creative Commons Licence by attribution (https://creativecommons.org/licenses/by aws-pds, earth observation, oceans, climate, environmental, global, geospatial SeeFar V0 Primary SeeFar dataset containing multi-resolution satellite imagery in cloud-op arn:aws:s3:::seefar-dataset us-east-1 S3 Bucket https://coastalcarbon.ai/seefar James Lowman Coastal Carbon Yearly The SeeFar dataset includes multiple licensing terms, specific to each satellite geospatial, earth observation, satellite imagery, climate, biodiversity, coastal, machine learning, environmental, sustainability, natural resource, global, mapping, aws-pds -Sentinel Near Real-time Canada Mirror | Miroir Sentinel temps quasi réel du Canada Sentinel data over Canada | Données sentinelles au Canada arn:aws:s3:::sentinel-products-ca-mirror ca-central-1 S3 Bucket https://sentinel.esa.int/web/sentinel/home eodms-sgdot@nrcan-rncan.gc.ca [Natural Resources Canada](https://www.nrcan.gc.ca/) Sentinel-1 is an NRT dataset retrieved from ESA within 90 minutes of satellite d The access and use of Copernicus Sentinel data is available on a free, full and aws-pds, agriculture, earth observation, satellite imagery, geospatial, sustainability, disaster response, synthetic aperture radar, stac ['[EODMS STAC for Sentinel products](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/)'] -Sentinel-1 - GRD in a Requester Pays S3 bucket GRD in a Requester Pays S3 bucket arn:aws:s3:::sentinel-s1-l1c eu-central-1 S3 Bucket https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar True ['[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)'] +Sentinel Near Real-time Canada Mirror | Miroir Sentinel temps quasi réel du Canada Sentinel data over Canada | Données sentinelles au Canada arn:aws:s3:::sentinel-products-ca-mirror ca-central-1 S3 Bucket https://sentinel.esa.int/web/sentinel/home eodms-sgdot@nrcan-rncan.gc.ca [Natural Resources Canada](https://www.nrcan.gc.ca/) Sentinel-1 is an NRT dataset retrieved from ESA within 90 minutes of satellite d The access and use of Copernicus Sentinel data is available on a free, full and aws-pds, agriculture, earth observation, satellite imagery, geospatial, sustainability, disaster response, synthetic aperture radar, stac ['[EODMS STAC for Sentinel products](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/)'] +Sentinel-1 - GRD in a Requester Pays S3 bucket GRD in a Requester Pays S3 bucket arn:aws:s3:::sentinel-s1-l1c eu-central-1 S3 Bucket https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar ['[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)'] True Sentinel-1 - S3 Inventory files for L1C and CSV S3 Inventory files for L1C and CSV arn:aws:s3:::sentinel-inventory/ eu-central-1 S3 Bucket https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar Sentinel-1 - SNS topic for notification of new scenes, can subscribe with Lambda SNS topic for notification of new scenes, can subscribe with Lambda arn:aws:sns:eu-central-1:214830741341:SentinelS1L1C eu-central-1 SNS Topic https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar Sentinel-1 Precise Orbit Determination (POD) Products - Notifications for new data Notifications for new data arn:aws:sns:us-west-2:211125554030:s1-orbits-object_created us-west-2 SNS Topic https://s1-orbits.s3.us-west-2.amazonaws.com/README.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Updated as new data becomes available on the [Copernicus Data Space Ecosystem](h Access to Sentinel data is free, full and open for the broad Regional, National, auxiliary data, disaster response, earth observation, earthquakes, floods, geophysics, sentinel-1, synthetic aperture radar -Sentinel-1 Precise Orbit Determination (POD) Products - Sentinel-1 Orbits bucket Sentinel-1 Orbits bucket arn:aws:s3:::s1-orbits us-west-2 S3 Bucket https://s1-orbits.s3.us-west-2.amazonaws.com/README.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Updated as new data becomes available on the [Copernicus Data Space Ecosystem](h Access to Sentinel data is free, full and open for the broad Regional, National, auxiliary data, disaster response, earth observation, earthquakes, floods, geophysics, sentinel-1, synthetic aperture radar ['[AWS S3 Explorer](https://s1-orbits.s3.amazonaws.com/index.html)'] +Sentinel-1 Precise Orbit Determination (POD) Products - Sentinel-1 Orbits bucket Sentinel-1 Orbits bucket arn:aws:s3:::s1-orbits us-west-2 S3 Bucket https://s1-orbits.s3.us-west-2.amazonaws.com/README.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Updated as new data becomes available on the [Copernicus Data Space Ecosystem](h Access to Sentinel data is free, full and open for the broad Regional, National, auxiliary data, disaster response, earth observation, earthquakes, floods, geophysics, sentinel-1, synthetic aperture radar ['[AWS S3 Explorer](https://s1-orbits.s3.amazonaws.com/index.html)'] Sentinel-1 SLC dataset for Germany Public access to Sentinel-1 SLC IW scenes over Germany arn:aws:s3:::sentinel1-slc eu-west-1 S3 Bucket https://github.com/live-eo/sentinel1-slc/ For any enquires regarding the dataset, please email OpenData at Live-EO opendat [LiveEO](https://live-eo.com/) New Sentinel1-SLC IW data are updated regularly in an interval of 6 days, after The data usage will inherit and fully comply with the free and open data policy aws-pds, disaster response, satellite imagery, geospatial, sustainability, earth observation, environmental, synthetic aperture radar Sentinel-1 SLC dataset for South and Southeast Asia, Taiwan, Korea and Japan Public access to Sentinel-1 SLC IW scenes over South and Southeast Asia, Taiwan arn:aws:s3:::sentinel1-slc-seasia-pds ap-southeast-1 S3 Bucket https://github.com/earthobservatory/sentinel1-opds/ For any enquires regarding data delivery, please email ehill@ntu.edu.sg and stch [Earth Observatory of Singapore, Nanyang Technological University](https://earth S1 SLC data for the region of interest will be updated regularly, as it becomes The data usage will inherit and fully comply with the free and open data policy aws-pds, disaster response, satellite imagery, geospatial, earth observation, environmental, synthetic aperture radar -Sentinel-2 - Level 1C scenes and metadata, in Requester Pays S3 bucket Level 1C scenes and metadata, in Requester Pays S3 bucket arn:aws:s3:::sentinel-s2-l1c eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True ['[Earth Search STAC L1C Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)', '[Earth Search STAC Browser L1C Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)', '[STAC V1.0.0 endpoint](https://sentinel-s2-l1c-stac.s3.amazonaws.com/)', '[Earth Viewer by Element 84](https://viewer.aws.element84.com/)'] -Sentinel-2 - Level 2A scenes and metadata, in Requester Pays S3 bucket Level 2A scenes and metadata, in Requester Pays S3 bucket arn:aws:s3:::sentinel-s2-l2a eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True ['[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)'] +Sentinel-2 - Level 1C scenes and metadata, in Requester Pays S3 bucket Level 1C scenes and metadata, in Requester Pays S3 bucket arn:aws:s3:::sentinel-s2-l1c eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac ['[Earth Search STAC L1C Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)', '[Earth Search STAC Browser L1C Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)', '[STAC V1.0.0 endpoint](https://sentinel-s2-l1c-stac.s3.amazonaws.com/)', '[Earth Viewer by Element 84](https://viewer.aws.element84.com/)'] True +Sentinel-2 - Level 2A scenes and metadata, in Requester Pays S3 bucket Level 2A scenes and metadata, in Requester Pays S3 bucket arn:aws:s3:::sentinel-s2-l2a eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac ['[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)'] True Sentinel-2 - New scene notifications for L1C, can subscribe with Lambda New scene notifications for L1C, can subscribe with Lambda arn:aws:sns:eu-west-1:214830741341:NewSentinel2Product eu-west-1 SNS Topic Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac Sentinel-2 - New scene notifications for L2A, can subscribe with Lambda New scene notifications for L2A, can subscribe with Lambda arn:aws:sns:eu-central-1:214830741341:SentinelS2L2A eu-central-1 SNS Topic Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac Sentinel-2 - S3 Inventory files for L1C and CSV S3 Inventory files for L1C and CSV arn:aws:s3:::sentinel-inventory/sentinel-s2-l1c eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac Sentinel-2 - S3 Inventory files for L2A and CSV S3 Inventory files for L2A and CSV arn:aws:s3:::sentinel-inventory/sentinel-s2-l2a eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac -Sentinel-2 - Zipped archives for each L1C product with 3 day retention period, in Requester P Zipped archives for each L1C product with 3 day retention period, in Requester P arn:aws:s3:::sentinel-s2-l1c-zips eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True -Sentinel-2 - Zipped archives for each L2A product with 3 day retention period, in Requester P Zipped archives for each L2A product with 3 day retention period, in Requester P arn:aws:s3:::sentinel-s2-l2a-zips eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True -Sentinel-2 Cloud-Optimized GeoTIFFs - Level 2A scenes and metadata Level 2A scenes and metadata arn:aws:s3:::sentinel-cogs us-west-2 S3 Bucket https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac False ['[Earth Search STAC L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)', '[STAC Browser L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)'] +Sentinel-2 - Zipped archives for each L1C product with 3 day retention period, in Requester P Zipped archives for each L1C product with 3 day retention period, in Requester P arn:aws:s3:::sentinel-s2-l1c-zips eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True +Sentinel-2 - Zipped archives for each L2A product with 3 day retention period, in Requester P Zipped archives for each L2A product with 3 day retention period, in Requester P arn:aws:s3:::sentinel-s2-l2a-zips eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True +Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States - New scene notification New scene notification arn:aws:sns:us-west-2:242201296900:usgs-wma-sentinel-2-aqr-acolite-dsf-object_created us-west-2 SNS Topic https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed tvking@usgs.gov [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. Contains modified Copernicus Sentinel data, which is available under the Creativ aws-pds, earth observation, satellite imagery, geospatial, natural resource, cog, water +Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States - Scenes and metadata Scenes and metadata arn:aws:s3:::usgs-wma-sentinel-2-aqr-acolite-dsf/version_01 us-west-2 S3 Bucket https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed tvking@usgs.gov [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. Contains modified Copernicus Sentinel data, which is available under the Creativ aws-pds, earth observation, satellite imagery, geospatial, natural resource, cog, water +Sentinel-2 Cloud-Optimized GeoTIFFs - Level 2A scenes and metadata Level 2A scenes and metadata arn:aws:s3:::sentinel-cogs us-west-2 S3 Bucket https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac ['[Earth Search STAC L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)', '[STAC Browser L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)'] False Sentinel-2 Cloud-Optimized GeoTIFFs - New scene notifications, can subscribe with Lambda New scene notifications, can subscribe with Lambda arn:aws:sns:us-west-2:608149789419:cirrus-v0-publish us-west-2 SNS Topic https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac Sentinel-2 Cloud-Optimized GeoTIFFs - S3 Inventory files for L1C and CSV S3 Inventory files for L1C and CSV arn:aws:s3:::sentinel-cogs-inventory us-west-2 S3 Bucket https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac -Sentinel-2 L2A 120m Mosaic Sentinel-2 L2A 120m mosaics data in a S3 bucket arn:aws:s3:::sentinel-s2-l2a-mosaic-120 eu-central-1 S3 Bucket Documentation is available [here](https://sentinel-s2-l2a-mosaic-120.s3.amazonaw https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New data will be added annually. CC-BY 4.0, Credit: Contains modified Copernicus data [year] processed by Sentine aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, machine learning, cog False -Sentinel-3 - Sentinel-3 Cloud Optimized GeoTIFF (COG) format Sentinel-3 Cloud Optimized GeoTIFF (COG) format arn:aws:s3:::meeo-s3-cog/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac ['[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)'] +Sentinel-2 L2A 120m Mosaic Sentinel-2 L2A 120m mosaics data in a S3 bucket arn:aws:s3:::sentinel-s2-l2a-mosaic-120 eu-central-1 S3 Bucket Documentation is available [here](https://sentinel-s2-l2a-mosaic-120.s3.amazonaw https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New data will be added annually. CC-BY 4.0, Credit: Contains modified Copernicus data [year] processed by Sentine aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, machine learning, cog False +Sentinel-3 - Sentinel-3 Cloud Optimized GeoTIFF (COG) format Sentinel-3 Cloud Optimized GeoTIFF (COG) format arn:aws:s3:::meeo-s3-cog/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac ['[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)'] Sentinel-3 - Sentinel-3 Near Real Time Data (NRT) format Sentinel-3 Near Real Time Data (NRT) format arn:aws:s3:::meeo-s3/NRT/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac Sentinel-3 - Sentinel-3 Not Time Critical (NTC) format Sentinel-3 Not Time Critical (NTC) format arn:aws:s3:::meeo-s3/NTC/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac Sentinel-3 - Sentinel-3 Short Time Critical (STC) format Sentinel-3 Short Time Critical (STC) format arn:aws:s3:::meeo-s3/STC/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac -Sentinel-5P Level 2 - Sentinel-5p Cloud Optimised GeoTIFF (COGT) TIFF format Sentinel-5p Cloud Optimised GeoTIFF (COGT) TIFF format arn:aws:s3:::meeo-s5p/COGT/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac ['[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)'] +Sentinel-5P Level 2 - Sentinel-5p Cloud Optimised GeoTIFF (COGT) TIFF format Sentinel-5p Cloud Optimised GeoTIFF (COGT) TIFF format arn:aws:s3:::meeo-s5p/COGT/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac ['[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)'] Sentinel-5P Level 2 - Sentinel-5p Near Real Time Data (NRTI) NetCDF format Sentinel-5p Near Real Time Data (NRTI) NetCDF format arn:aws:s3:::meeo-s5p/NRTI/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac Sentinel-5P Level 2 - Sentinel-5p Off Line Data (OFFL) NetCDF format Sentinel-5p Off Line Data (OFFL) NetCDF format arn:aws:s3:::meeo-s5p/OFFL/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac Sentinel-5P Level 2 - Sentinel-5p Reprocessed Data (RPRO) NetCDF format Sentinel-5p Reprocessed Data (RPRO) NetCDF format arn:aws:s3:::meeo-s5p/RPRO/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac -Sofar Spotter Archive Hourly position, wave spectra and bulk wave parameters from global free drifting arn:aws:s3:::sofar-spotter-archive us-west-2 S3 Bucket [Spotter Technical Reference Manual](https://content.sofarocean.com/hubfs/Spotte opendata@sofarocean.com [Sofar Ocean](https://www.sofarocean.com/company/contact-us) As available [Sofar Data Access Agreement](https://sofarocean.notion.site/sofarocean/Sofar-Da aws-pds, climate, meteorological, sustainability, weather, oceans, environmental, oceans ['[Browse Bucket](https://sofar-spotter-archive.s3.amazonaws.com/index.html)'] -SondeHub Radiosonde Telemetry Radiosonde Telemetry as JSON blobs of Universal Telemetry format arn:aws:s3:::sondehub-history us-east-1 S3 Bucket https://github.com/projecthorus/sondehub-infra/wiki/Amazon-Open-Data Michaela Wheeler [SondeHub](https://sondehub.org/) Data is updated as we receive it Creative Commons BY-SA 2.0 aws-pds, climate, environmental, weather, GPS ['[Browse Bucket by serial number](http://sondehub-history.s3-website-us-east-1.amazonaws.com/#serial/)', '[Browse Bucket by date/time](http://sondehub-history.s3-website-us-east-1.amazonaws.com/#date/)'] +Sofar Spotter Archive Hourly position, wave spectra and bulk wave parameters from global free drifting arn:aws:s3:::sofar-spotter-archive us-west-2 S3 Bucket [Spotter Technical Reference Manual](https://content.sofarocean.com/hubfs/Spotte opendata@sofarocean.com [Sofar Ocean](https://www.sofarocean.com/company/contact-us) As available [Sofar Data Access Agreement](https://sofarocean.notion.site/sofarocean/Sofar-Da aws-pds, climate, meteorological, sustainability, weather, oceans, environmental, oceans ['[Browse Bucket](https://sofar-spotter-archive.s3.amazonaws.com/index.html)'] +SondeHub Radiosonde Telemetry Radiosonde Telemetry as JSON blobs of Universal Telemetry format arn:aws:s3:::sondehub-history us-east-1 S3 Bucket https://github.com/projecthorus/sondehub-infra/wiki/Amazon-Open-Data Michaela Wheeler [SondeHub](https://sondehub.org/) Data is updated as we receive it Creative Commons BY-SA 2.0 aws-pds, climate, environmental, weather, GPS ['[Browse Bucket by serial number](http://sondehub-history.s3-website-us-east-1.amazonaws.com/#serial/)', '[Browse Bucket by date/time](http://sondehub-history.s3-website-us-east-1.amazonaws.com/#date/)'] Sounds of Central African landscapes Nouabale-Ndoki landscape sound data arn:aws:s3:::congo8khz-pnnn us-west-2 S3 Bucket https://elephantlisteningproject.org/congo-soundscapes-public-database/ elephant-lp@cornell.edu Center for Conservation Bioacoustics, Cornell University (https://elephantlisten New sound data spanning 4-month time periods added as soon as possible These sound files are freely available for scientific study andexploration, incl aws-pds, biodiversity, ecosystems, biology, land, life sciences, natural resource, survey, geospatial Southern California Earthquake Data Seismic waveform data (miniSEED format) and earthquake catalog (ascii) arn:aws:s3:::scedc-pds us-west-2 S3 Bucket https://scedc.caltech.edu/data/cloud.html scedc@gps.caltech.edu [Southern California Earthquake Data Center](https://scedc.caltech.edu) Daily SCEDC herby grants the non-exclusive, royalty free, non-transferable, worldwide aws-pds, earth observation, earthquakes, seismology SpaceNet Imagery and metadata in a S3 bucket arn:aws:s3:::spacenet-dataset us-east-1 S3 Bucket https://spacenet.ai/ https://spacenet.ai/contact-us/ [SpaceNet](https://spacenet.ai/) New imagery and features are added quarterly Various (See [here](https://spacenet.ai/datasets/) for more details) aws-pds, geospatial, computer vision, machine learning, earth observation, disaster response, satellite imagery Speedtest by Ookla Global Fixed and Mobile Network Performance Maps Parquet and Shapefiles arn:aws:s3:::ookla-open-data us-west-2 S3 Bucket [Performance Maps Overview](https://github.com/teamookla/ookla-open-data) opendata@ookla.com [Ookla](https://www.ookla.com/ookla-for-good) Quarterly """[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)""" analytics, aws-pds, broadband, cities, civic, disaster response, geospatial, global, government spending, infrastructure, internet, mapping, parquet, network traffic, regulatory, telecommunications, tiles -Spitzer Enhanced Imaging Products (SEIP) Super Mosaics SEIP Super Mosaics: 36, 45, 58, 8, and 24 micron mean and median mosaics with arn:aws:s3:::nasa-irsa-spitzer/spitzer/seip us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/data/SPITZER/Enhanced/SEIP/overview.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca This data set may be updated once or twice in the future. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, satellite imagery, survey False False +Spitzer Enhanced Imaging Products (SEIP) Super Mosaics SEIP Super Mosaics: 36, 45, 58, 8, and 24 micron mean and median mosaics with arn:aws:s3:::nasa-irsa-spitzer/spitzer/seip us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/data/SPITZER/Enhanced/SEIP/overview.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca This data set may be updated once or twice in the future. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, satellite imagery, survey False False Storm EVent ImageRy (SEVIR) Dataset of storm imagery arn:aws:s3:::sevir us-west-2 S3 Bucket https://nbviewer.jupyter.org/github/MIT-AI-Accelerator/eie-sevir/blob/master/exa mark.veillette@mit.edu Mark S. Veillette New events will be added to SEVIR yearly There are no restrictions on the use of this data. satellite imagery, weather, meteorological, aws-pds Sub-Meter Canopy Tree Height of California in 2020 by CTrees.org Cloud-optimized GeoTIFF files with names corresponding to image of California fo arn:aws:s3:::ctrees-tree-height-ca-2020/ us-west-2 S3 Bucket [Project overview](https://ctrees.org/products/tree-level) info@ctrees.org [CTrees](https://ctrees.org/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, land cover, deep learning, aerial imagery, image processing, environmental, conservation, geospatial -Swiss Public Transport Stops data files ESRI FGDB, CSV , MapInfo, Interlis arn:aws:s3:::data.geo.admin.ch/ch.bav.haltestellen-oev/data.zip eu-west-1 S3 Bucket https://www.bav.admin.ch/bav/de/home/allgemeine-themen/fachthemen/geoinformation fredi.daellenbach@bav.admin.ch Swiss Geoportal annually You may use this dataset for non-commercial purposes. You may use this dataset f aws-pds, cities, geospatial, infrastructure, mapping, traffic, transportation ['[Browse Bucket](https://data.geo.admin.ch/index.html)'] +Swiss Public Transport Stops data files ESRI FGDB, CSV , MapInfo, Interlis arn:aws:s3:::data.geo.admin.ch/ch.bav.haltestellen-oev/data.zip eu-west-1 S3 Bucket https://www.bav.admin.ch/bav/de/home/allgemeine-themen/fachthemen/geoinformation fredi.daellenbach@bav.admin.ch Swiss Geoportal annually You may use this dataset for non-commercial purposes. You may use this dataset f aws-pds, cities, geospatial, infrastructure, mapping, traffic, transportation ['[Browse Bucket](https://data.geo.admin.ch/index.html)'] Terra Fusion Data Sampler AWS S3 Public Bucket Containing Terra Basic Fusion Hierarchical Data Format 5 (H arn:aws:s3:::terrafusiondatasampler us-west-2 S3 Bucket https://go.illinois.edu/terra-fusion-doc gdi@illinois.edu University of Illinois Static, with a planned update for years 2016-2020 in the future. Creative Commons Level 0 aws-pds, geospatial, satellite imagery -Terrain Tiles - Gridded elevation tiles Gridded elevation tiles arn:aws:s3:::elevation-tiles-prod us-east-1 S3 Bucket https://github.com/tilezen/joerd/tree/master/docs https://github.com/tilezen/joerd/issues Mapzen, a Linux Foundation project New data is added based on community feedback https://github.com/tilezen/joerd/blob/master/docs/attribution.md aws-pds, agriculture, elevation, earth observation, geospatial, disaster response ['[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)'] +Terrain Tiles - Gridded elevation tiles Gridded elevation tiles arn:aws:s3:::elevation-tiles-prod us-east-1 S3 Bucket https://github.com/tilezen/joerd/tree/master/docs https://github.com/tilezen/joerd/issues Mapzen, a Linux Foundation project New data is added based on community feedback https://github.com/tilezen/joerd/blob/master/docs/attribution.md aws-pds, agriculture, elevation, earth observation, geospatial, disaster response ['[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)'] Terrain Tiles - Gridded elevation tiles - replication in EU region Gridded elevation tiles - replication in EU region arn:aws:s3:::elevation-tiles-prod-eu eu-central-1 S3 Bucket https://github.com/tilezen/joerd/tree/master/docs https://github.com/tilezen/joerd/issues Mapzen, a Linux Foundation project New data is added based on community feedback https://github.com/tilezen/joerd/blob/master/docs/attribution.md aws-pds, agriculture, elevation, earth observation, geospatial, disaster response Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription (TaRGET) Released and archived TaRGET II data arn:aws:s3:::targetepigenomics us-west-2 S3 Bucket https://data.targetepigenomics.org/ targetdcc16@gmail.com TaRGET II Data Coordination Center (TaRGET-DCC) TaRGET-DCC offers monthly data releases, although this dataset may not be update External data users may freely download, analyze, and publish results based on a biology, bioinformatics, genetic, genomic, life sciences, environmental, epigenomics, aws-pds -Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) The Tropical Cyclone Precipitation, Infrared, Microwave and Environmental Datase arn:aws:s3:::noaa-nesdis-tcprimed-pds us-east-1 S3 Bucket https://rammb-data.cira.colostate.edu/tcprimed/TCPRIMED_v01r00_documentation.pdf CIRA_tcprimed [at] colostate [dot] edu [CIRA](https://www.cira.colostate.edu/) Annually, several months after the conclusion of the Northern Hemisphere tropica No constraints on data access or use atmosphere, aws-pds, earth observation, environmental, geophysics, geoscience, global, meteorological, model, netcdf, precipitation, satellite imagery, weather ['[Browse Bucket](https://noaa-nesdis-tcprimed-pds.s3.amazonaws.com/index.html)'] -USGS 3DEP LiDAR Point Clouds - A Requester Pays Bucket of Raw LAZ 14 3DEP data Data in this bucket is more co A Requester Pays Bucket of Raw LAZ 14 3DEP data Data in this bucket is more co arn:aws:s3:::usgs-lidar us-west-2 S3 Bucket https://github.com/hobu/usgs-lidar/ https://github.com/hobu/usgs-lidar [Hobu, Inc.](https://hobu.co) Periodically US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensin aws-pds, agriculture, elevation, disaster response, geospatial, lidar, stac True -USGS 3DEP LiDAR Point Clouds - Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs arn:aws:s3:::usgs-lidar-public us-west-2 S3 Bucket https://github.com/hobu/usgs-lidar/ https://github.com/hobu/usgs-lidar [Hobu, Inc.](https://hobu.co) Periodically US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensin aws-pds, agriculture, elevation, disaster response, geospatial, lidar, stac ['[STAC Catalog](https://usgs-lidar-stac.s3-us-west-2.amazonaws.com/ept/catalog.json)'] +Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) The Tropical Cyclone Precipitation, Infrared, Microwave and Environmental Datase arn:aws:s3:::noaa-nesdis-tcprimed-pds us-east-1 S3 Bucket https://rammb-data.cira.colostate.edu/tcprimed/TCPRIMED_v01r00_documentation.pdf CIRA_tcprimed [at] colostate [dot] edu [CIRA](https://www.cira.colostate.edu/) Annually, several months after the conclusion of the Northern Hemisphere tropica No constraints on data access or use atmosphere, aws-pds, earth observation, environmental, geophysics, geoscience, global, meteorological, model, netcdf, precipitation, satellite imagery, weather ['[Browse Bucket](https://noaa-nesdis-tcprimed-pds.s3.amazonaws.com/index.html)'] +USGS 3DEP LiDAR Point Clouds - A Requester Pays Bucket of Raw LAZ 14 3DEP data Data in this bucket is more co A Requester Pays Bucket of Raw LAZ 14 3DEP data Data in this bucket is more co arn:aws:s3:::usgs-lidar us-west-2 S3 Bucket https://github.com/hobu/usgs-lidar/ https://github.com/hobu/usgs-lidar [Hobu, Inc.](https://hobu.co) Periodically US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensin aws-pds, agriculture, elevation, disaster response, geospatial, lidar, stac True +USGS 3DEP LiDAR Point Clouds - Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs arn:aws:s3:::usgs-lidar-public us-west-2 S3 Bucket https://github.com/hobu/usgs-lidar/ https://github.com/hobu/usgs-lidar [Hobu, Inc.](https://hobu.co) Periodically US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensin aws-pds, agriculture, elevation, disaster response, geospatial, lidar, stac ['[STAC Catalog](https://usgs-lidar-stac.s3-us-west-2.amazonaws.com/ept/catalog.json)'] USGS Landsat - New scene notifications, Level 3 Science Products New scene notifications, Level 3 Science Products arn:aws:sns:us-west-2:673253540267:public-c2-level-3-tile-notify-v2 us-west-2 SNS Topic https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog USGS Landsat - New scene notifications, Level-1 and Level-2 Scenes New scene notifications, Level-1 and Level-2 Scenes arn:aws:sns:us-west-2:673253540267:public-c2-notify-v2 us-west-2 SNS Topic https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog USGS Landsat - New scene notifications, US ARD Tiles New scene notifications, US ARD Tiles arn:aws:sns:us-west-2:673253540267:public-c2-ard-tile-notify-v2 us-west-2 SNS Topic https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog -USGS Landsat - Scenes and metadata Scenes and metadata arn:aws:s3:::usgs-landsat/collection02/ us-west-2 S3 Bucket https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog True ['[STAC Catalog](https://landsatlook.usgs.gov/stac-server/collections)'] -Umbra Synthetic Aperture Radar (SAR) Open Data Umbra Spotlight collects including GEC, SICD, SIDD, CPHD data and metadata arn:aws:s3:::umbra-open-data-catalog us-west-2 S3 Bucket https://help.umbra.space/product-guide help@umbra.space [Umbra](http://umbra.space/) New data is added frequently. The frequent updates enable users to analyze the t All data is provided with a Creative Commons License ([CC by 4.0](https://umbra. aws-pds, synthetic aperture radar, stac, satellite imagery, earth observation, image processing, geospatial False ['[Browse Bucket](http://umbra-open-data-catalog.s3-website.us-west-2.amazonaws.com/)', '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/s3.us-west-2.amazonaws.com/umbra-open-data-catalog/stac/catalog.json)'] +USGS Landsat - Scenes and metadata Scenes and metadata arn:aws:s3:::usgs-landsat/collection02/ us-west-2 S3 Bucket https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog ['[STAC Catalog](https://landsatlook.usgs.gov/stac-server/collections)'] True +Umbra Synthetic Aperture Radar (SAR) Open Data Umbra Spotlight collects including GEC, SICD, SIDD, CPHD data and metadata arn:aws:s3:::umbra-open-data-catalog us-west-2 S3 Bucket https://help.umbra.space/product-guide help@umbra.space [Umbra](http://umbra.space/) New data is added frequently. The frequent updates enable users to analyze the t All data is provided with a Creative Commons License ([CC by 4.0](https://umbra. aws-pds, synthetic aperture radar, stac, satellite imagery, earth observation, image processing, geospatial ['[Browse Bucket](http://umbra-open-data-catalog.s3-website.us-west-2.amazonaws.com/)', '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/s3.us-west-2.amazonaws.com/umbra-open-data-catalog/stac/catalog.json)'] False VENUS L2A Cloud-Optimized GeoTIFFs - New Venus L2A dataset notifications, can subscribe with Lambda New Venus L2A dataset notifications, can subscribe with Lambda arn:aws:sns:us-east-1:794383284256:venus-l2a-cogs-object_created us-east-1 SNS Topic https://github.com/earthdaily/venus-on-aws/ Klaus Bachhuber - klaus.bachhuber@earthdaily.com [EarthDaily Analytics](https://earthdaily.com/) New Venus data are added regularly https://creativecommons.org/licenses/by-nc/4.0/ aws-pds, agriculture, earth observation, satellite imagery, geospatial, image processing, natural resource, disaster response, cog, stac, activity detection, environmental, land cover -VENUS L2A Cloud-Optimized GeoTIFFs - Venus L2A dataset (COG) and metadata (STAC) Venus L2A dataset (COG) and metadata (STAC) arn:aws:s3:::venus-l2a-cogs us-east-1 S3 Bucket https://github.com/earthdaily/venus-on-aws/ Klaus Bachhuber - klaus.bachhuber@earthdaily.com [EarthDaily Analytics](https://earthdaily.com/) New Venus data are added regularly https://creativecommons.org/licenses/by-nc/4.0/ aws-pds, agriculture, earth observation, satellite imagery, geospatial, image processing, natural resource, disaster response, cog, stac, activity detection, environmental, land cover False ['[STAC Browser Venus L2A (COG) Catalog](https://radiantearth.github.io/stac-browser/#/external/venus-l2a-cogs.s3.us-east-1.amazonaws.com/catalog.json)'] -Vermont Open Geospatial on AWS - Elevation datsets (primarily lidar based) are organized in this bucket as statew Elevation datsets (primarily lidar based) are organized in this bucket as statew arn:aws:s3:::vtopendata-prd/Elevation us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False -Vermont Open Geospatial on AWS - Imagery datsets are organized in this bucket as statewide file mosaics and by ac Imagery datsets are organized in this bucket as statewide file mosaics and by ac arn:aws:s3:::vtopendata-prd/Imagery us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False -Vermont Open Geospatial on AWS - Landcover datsets are organized in this bucket as statewide file mosaics These Landcover datsets are organized in this bucket as statewide file mosaics These arn:aws:s3:::vtopendata-prd/Landcover us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False +VENUS L2A Cloud-Optimized GeoTIFFs - Venus L2A dataset (COG) and metadata (STAC) Venus L2A dataset (COG) and metadata (STAC) arn:aws:s3:::venus-l2a-cogs us-east-1 S3 Bucket https://github.com/earthdaily/venus-on-aws/ Klaus Bachhuber - klaus.bachhuber@earthdaily.com [EarthDaily Analytics](https://earthdaily.com/) New Venus data are added regularly https://creativecommons.org/licenses/by-nc/4.0/ aws-pds, agriculture, earth observation, satellite imagery, geospatial, image processing, natural resource, disaster response, cog, stac, activity detection, environmental, land cover ['[STAC Browser Venus L2A (COG) Catalog](https://radiantearth.github.io/stac-browser/#/external/venus-l2a-cogs.s3.us-east-1.amazonaws.com/catalog.json)'] False +Vermont Open Geospatial on AWS - Elevation datsets (primarily lidar based) are organized in this bucket as statew Elevation datsets (primarily lidar based) are organized in this bucket as statew arn:aws:s3:::vtopendata-prd/Elevation us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False +Vermont Open Geospatial on AWS - Imagery datsets are organized in this bucket as statewide file mosaics and by ac Imagery datsets are organized in this bucket as statewide file mosaics and by ac arn:aws:s3:::vtopendata-prd/Imagery us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False +Vermont Open Geospatial on AWS - Landcover datsets are organized in this bucket as statewide file mosaics These Landcover datsets are organized in this bucket as statewide file mosaics These arn:aws:s3:::vtopendata-prd/Landcover us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False Virtual Shizuoka, 3D Point Cloud Data Point Cloud Data of Shizuoka Prefecture, Japan arn:aws:s3:::virtual-shizuoka ap-northeast-1 S3 Bucket https://github.com/aigidjp/opendata_virtualshizuoka/README.md virtualshizuoka@aigid.jp [AIGID](https://aigid.jp/) Currently not scheduled Creative Commons Attribution 4.0 International (CC-BY 4.0) and Open Data Commons aws-pds, bathymetry, disaster response, elevation, geospatial, japanese, land, lidar, mapping -WIS2 Global Cache on AWS Core data as defined in the WMO Unified Data Policy (Resolution 1 (Cg-19)) and t arn:aws:s3:::wis2-global-cache eu-west-2 S3 Bucket https://www.metoffice.gov.uk/services/data/external-data-channels gisc-exeter@metoffice.gov.uk [Met Office](https://www.metoffice.gov.uk/) New data added as soon as available from origin WIS2 Nodes. There are no restrictions on the use of this data. Attribution of original sourc aws-pds, atmosphere, forecast, geoscience, climate, earth observation, hydrology, meteorological, model, oceans, weather ['[S3 Bucket](https://wis2-global-cache.s3.amazonaws.com/)'] -Whiffle WINS50 Open Data on AWS Whiffle WINS50 LES Data arn:aws:s3:::whiffle-wins50-data eu-central-1 S3 Bucket https://gitlab.com/whiffle-public/whiffle-open-data support@whiffle.nl [Whiffle](http://www.whiffle.nl/) No updates planned. CC BY-SA 4.0 aws-pds, weather, sustainability, atmosphere, electricity, meteorological, model, zarr, turbulence ['[Browse Bucket](https://whiffle-wins50-data.s3.amazonaws.com/index.html)'] -World Bank - Light Every Night Light Every Night dataset of all VIIRS DNB and DMSP-OLS nighttime satellite data arn:aws:s3:::globalnightlight us-east-1 S3 Bucket https://worldbank.github.io/OpenNightLights/wb-light-every-night-readme.html Trevor Monroe tmonroe@worldbank.org; Benjamin P. Stewart bstewart@worldbankgroup [World Bank Group](https://www.worldbank.org/en/home) Quarterly [World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/b disaster response, earth observation, satellite imagery, aws-pds, stac, cog ['[STAC 1.0.0-beta.2 endpoint](https://stacindex.org/catalogs/world-bank-light-every-night#/)'] +WIS2 Global Cache on AWS Core data as defined in the WMO Unified Data Policy (Resolution 1 (Cg-19)) and t arn:aws:s3:::wis2-global-cache eu-west-2 S3 Bucket https://www.metoffice.gov.uk/services/data/external-data-channels gisc-exeter@metoffice.gov.uk [Met Office](https://www.metoffice.gov.uk/) New data added as soon as available from origin WIS2 Nodes. There are no restrictions on the use of this data. Attribution of original sourc aws-pds, atmosphere, forecast, geoscience, climate, earth observation, hydrology, meteorological, model, oceans, weather ['[S3 Bucket](https://wis2-global-cache.s3.amazonaws.com/)'] +Whiffle WINS50 Open Data on AWS Whiffle WINS50 LES Data arn:aws:s3:::whiffle-wins50-data eu-central-1 S3 Bucket https://gitlab.com/whiffle-public/whiffle-open-data support@whiffle.nl [Whiffle](http://www.whiffle.nl/) No updates planned. CC BY-SA 4.0 aws-pds, weather, sustainability, atmosphere, electricity, meteorological, model, zarr, turbulence ['[Browse Bucket](https://whiffle-wins50-data.s3.amazonaws.com/index.html)'] +World Bank - Light Every Night Light Every Night dataset of all VIIRS DNB and DMSP-OLS nighttime satellite data arn:aws:s3:::globalnightlight us-east-1 S3 Bucket https://worldbank.github.io/OpenNightLights/wb-light-every-night-readme.html Trevor Monroe tmonroe@worldbank.org; Benjamin P. Stewart bstewart@worldbankgroup [World Bank Group](https://www.worldbank.org/en/home) Quarterly [World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/b disaster response, earth observation, satellite imagery, aws-pds, stac, cog ['[STAC 1.0.0-beta.2 endpoint](https://stacindex.org/catalogs/world-bank-light-every-night#/)'] World Bank Climate Change Knowledge Portal (CCKP) World Bank Climate Change Knowledge Portal observed and projected climate datase arn:aws:s3:::wbg-cckp us-west-2 S3 Bucket https://worldbank.github.io/climateknowledgeportal C. MacKenzie Dove cdove@worldbank.org; askclimate@worldbank.org [World Bank Group](https://www.worldbank.org/en/home) Semi-annually [World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/b aws-pds, climate, climate model, earth observation, climate projections, CMIP6, netcdf iNaturalist Licensed Observation Images Image files (eg JPEG) associated with metadata describing the observation asso arn:aws:s3:::inaturalist-open-data us-east-1 S3 Bucket "Documentation can be found We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov", + "ManagedBy": "Dr. Jacob Radford (jacob.radford@noaa.gov)", + "UpdateFrequency": "2 times a day, every 12 hours starting at midnight UTC", + "License": "Open Data. There are no restrictions on the use of this data.", + "Tags": [ + "environmental", + "meteorological", + "weather" + ], + "Explore": [ + "[Browse Bucket](https://noaa-oar-mlwp-data.s3.amazonaws.com/index.html)" + ], + "RequesterPays": null, + "ControlledAccess": null, + "AccountRequired": null, + "Host": null + }, { "Name": "AI2 Diagram Dataset (AI2D)", "Description": "Project data files in a public bucket", @@ -970,8 +994,8 @@ }, { "Name": "ARPA-E PERFORM Forecast data", - "Description": "ARPA-E PERFORM Forecast data", - "ARN": "arn:aws:s3:::arpa-e-perform/", + "Description": "Forecasts and Actuals for The Southwest Power Pool (SPP)", + "ARN": "arn:aws:s3:::arpa-e-perform/SPP/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/PERFORM-Forecasts/documentation", @@ -995,8 +1019,8 @@ }, { "Name": "ARPA-E PERFORM Forecast data", - "Description": "Forecasts and Actuals for The Electric Reliability Council of Texas (ERCOT)", - "ARN": "arn:aws:s3:::arpa-e-perform/ERCOT/", + "Description": "Forecasts and Actuals for The Midcontinent Independent System Operator (MISO)", + "ARN": "arn:aws:s3:::arpa-e-perform/MISO/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/PERFORM-Forecasts/documentation", @@ -1045,8 +1069,8 @@ }, { "Name": "ARPA-E PERFORM Forecast data", - "Description": "Forecasts and Actuals for The Midcontinent Independent System Operator (MISO)", - "ARN": "arn:aws:s3:::arpa-e-perform/MISO/", + "Description": "Forecasts and Actuals for The Electric Reliability Council of Texas (ERCOT)", + "ARN": "arn:aws:s3:::arpa-e-perform/ERCOT/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/PERFORM-Forecasts/documentation", @@ -1070,8 +1094,8 @@ }, { "Name": "ARPA-E PERFORM Forecast data", - "Description": "Forecasts and Actuals for The Southwest Power Pool (SPP)", - "ARN": "arn:aws:s3:::arpa-e-perform/SPP/", + "Description": "ARPA-E PERFORM Forecast data", + "ARN": "arn:aws:s3:::arpa-e-perform/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/PERFORM-Forecasts/documentation", @@ -1095,10 +1119,10 @@ }, { "Name": "ASF SAR Data Products for Disaster Events", - "Description": "ASF Event data S3 bucket", - "ARN": "arn:aws:s3:::asf-event-data", + "Description": "Notifications for new event data", + "ARN": "arn:aws:sns:us-west-2:654654592981:asf-event-data-object_created", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://asf-event-data.s3.us-west-2.amazonaws.com/README.md", "Contact": "https://asf.alaska.edu/asf/contact-us/", "ManagedBy": "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)", @@ -1112,9 +1136,7 @@ "cog", "stac" ], - "Explore": [ - "[Browse Bucket](https://asf-event-data.s3.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -1122,10 +1144,10 @@ }, { "Name": "ASF SAR Data Products for Disaster Events", - "Description": "Notifications for new event data", - "ARN": "arn:aws:sns:us-west-2:654654592981:asf-event-data-object_created", + "Description": "ASF Event data S3 bucket", + "ARN": "arn:aws:s3:::asf-event-data", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://asf-event-data.s3.us-west-2.amazonaws.com/README.md", "Contact": "https://asf.alaska.edu/asf/contact-us/", "ManagedBy": "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)", @@ -1139,7 +1161,9 @@ "cog", "stac" ], - "Explore": null, + "Explore": [ + "[Browse Bucket](https://asf-event-data.s3.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -1147,10 +1171,10 @@ }, { "Name": "ASTER L1T Cloud-Optimized GeoTIFFs", - "Description": "Imagery and metadata", - "ARN": "arn:aws:s3:::aster-l1t", + "Description": "New image notifications", + "ARN": "arn:aws:sns:us-west-2:526859492376:aster-l1t-object_created", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t", "Contact": "opendata@descarteslabs.com", "ManagedBy": "[Descartes Labs](https://descarteslabs.com/)", @@ -1167,17 +1191,17 @@ "cog" ], "Explore": null, - "RequesterPays": false, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "ASTER L1T Cloud-Optimized GeoTIFFs", - "Description": "New image notifications", - "ARN": "arn:aws:sns:us-west-2:526859492376:aster-l1t-object_created", + "Description": "Imagery and metadata", + "ARN": "arn:aws:s3:::aster-l1t", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t", "Contact": "opendata@descarteslabs.com", "ManagedBy": "[Descartes Labs](https://descarteslabs.com/)", @@ -1194,7 +1218,7 @@ "cog" ], "Explore": null, - "RequesterPays": null, + "RequesterPays": false, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -1278,8 +1302,8 @@ }, { "Name": "AgricultureVision", - "Description": "Dataset affiliated with the 2021 CVPR Agricutlure Vision Workshop This includes both the supervised and additional raw imagery, both high-resolution (10cm/pixel) and low-resolution (sentinel-1 10m/pixel) imagery The supervised portion is split train-val-test The full-field imagery is given as a series of folders where each folder corresponds to a field, and the images contained are named according to the date of collection High-resolution images are named _tif Low-resolution images are named _gamma0__lowtif", - "ARN": "arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_challenge_2021_full", + "Description": "Dataset affiliated with the 2021 CVPR Agricutlure Vision Workshop This includes both the supervised and additional raw imagery The supervised portion is split train-val-test The full-field imagery is given as a series of folders where each folder corresponds to a field, and the images contained are named according to the date of collection This is the high-resolution-only subset of cvpr_challenge_2021_full", + "ARN": "arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_challenge_2021", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://arxiv.org/abs/2001.01306", @@ -1303,8 +1327,8 @@ }, { "Name": "AgricultureVision", - "Description": "Dataset affiliated with the 2021 CVPR Agricutlure Vision Workshop This includes both the supervised and additional raw imagery The supervised portion is split train-val-test The full-field imagery is given as a series of folders where each folder corresponds to a field, and the images contained are named according to the date of collection This is the high-resolution-only subset of cvpr_challenge_2021_full", - "ARN": "arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_challenge_2021", + "Description": "Original dataset affiliated with the 2020 CVPR paper Dataset provided as a series of targz files with data for each year and an associated json file dscribing the train/validation/test split", + "ARN": "arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_paper_2020", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://arxiv.org/abs/2001.01306", @@ -1328,8 +1352,8 @@ }, { "Name": "AgricultureVision", - "Description": "Original dataset affiliated with the 2020 CVPR paper Dataset provided as a series of targz files with data for each year and an associated json file dscribing the train/validation/test split", - "ARN": "arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_paper_2020", + "Description": "Dataset affiliated with the 2021 CVPR Agricutlure Vision Workshop This includes both the supervised and additional raw imagery, both high-resolution (10cm/pixel) and low-resolution (sentinel-1 10m/pixel) imagery The supervised portion is split train-val-test The full-field imagery is given as a series of folders where each folder corresponds to a field, and the images contained are named according to the date of collection High-resolution images are named _tif Low-resolution images are named _gamma0__lowtif", + "ARN": "arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_challenge_2021_full", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://arxiv.org/abs/2001.01306", @@ -1750,8 +1774,8 @@ }, { "Name": "Amazonia EO satellite on AWS", - "Description": "Amazonia 1 imagery (COG files, quicklooks, metadata)", - "ARN": "arn:aws:s3:::brazil-eosats", + "Description": "STAC static catalog", + "ARN": "arn:aws:s3:::br-eo-stac-1-0-0", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "http://www.inpe.br/amazonia1", @@ -1771,10 +1795,7 @@ "stac", "cog" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)", - "[stacindex](https://stacindex.org/catalogs/cbers)" - ], + "Explore": null, "RequesterPays": false, "ControlledAccess": null, "AccountRequired": null, @@ -1782,10 +1803,10 @@ }, { "Name": "Amazonia EO satellite on AWS", - "Description": "STAC static catalog", - "ARN": "arn:aws:s3:::br-eo-stac-1-0-0", + "Description": "Notifications for new quicklooks", + "ARN": "arn:aws:sns:us-west-2:599544552497:NewAM1Quicklook", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "http://www.inpe.br/amazonia1", "Contact": "https://lists.osgeo.org/mailman/listinfo/cbers-pds", "ManagedBy": "[AMS Kepler](https://amskepler.com/)", @@ -1804,17 +1825,17 @@ "cog" ], "Explore": null, - "RequesterPays": false, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Amazonia EO satellite on AWS", - "Description": "Notifications for new quicklooks", - "ARN": "arn:aws:sns:us-west-2:599544552497:NewAM1Quicklook", + "Description": "Amazonia 1 imagery (COG files, quicklooks, metadata)", + "ARN": "arn:aws:s3:::brazil-eosats", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "http://www.inpe.br/amazonia1", "Contact": "https://lists.osgeo.org/mailman/listinfo/cbers-pds", "ManagedBy": "[AMS Kepler](https://amskepler.com/)", @@ -1832,8 +1853,11 @@ "stac", "cog" ], - "Explore": null, - "RequesterPays": null, + "Explore": [ + "[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)", + "[stacindex](https://stacindex.org/catalogs/cbers)" + ], + "RequesterPays": false, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -1869,10 +1893,10 @@ }, { "Name": "Analysis Ready Sentinel-1 Backscatter Imagery", - "Description": "Sentinel-1 RTC tiled data and metadata in a S3 bucket", - "ARN": "arn:aws:s3:::sentinel-s1-rtc-indigo", + "Description": "Simple Notification Service (SNS) topic for notification of new tile uploads", + "ARN": "arn:aws:sns:us-west-2:410373799403:sentinel-s1-rtc-indigo-object_created", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://sentinel-s1-rtc-indigo-docs.s3-us-west-2.amazonaws.com/index.html", "Contact": "For questions regarding data methodology or delivery, contact sentinel1@indigoag.com.", "ManagedBy": "[Indigo Ag, Inc.](https://www.indigoag.com/)", @@ -1890,9 +1914,7 @@ "stac", "synthetic aperture radar" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://scottyhq.github.io/sentinel1-rtc-stac/#/)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -1900,10 +1922,10 @@ }, { "Name": "Analysis Ready Sentinel-1 Backscatter Imagery", - "Description": "Simple Notification Service (SNS) topic for notification of new tile uploads", - "ARN": "arn:aws:sns:us-west-2:410373799403:sentinel-s1-rtc-indigo-object_created", + "Description": "Sentinel-1 RTC tiled data and metadata in a S3 bucket", + "ARN": "arn:aws:s3:::sentinel-s1-rtc-indigo", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://sentinel-s1-rtc-indigo-docs.s3-us-west-2.amazonaws.com/index.html", "Contact": "For questions regarding data methodology or delivery, contact sentinel1@indigoag.com.", "ManagedBy": "[Indigo Ag, Inc.](https://www.indigoag.com/)", @@ -1921,7 +1943,9 @@ "stac", "synthetic aperture radar" ], - "Explore": null, + "Explore": [ + "[STAC V1.0.0 endpoint](https://scottyhq.github.io/sentinel1-rtc-stac/#/)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -1973,8 +1997,8 @@ }, { "Name": "ArcticDEM", - "Description": "ArcticDEM DEM Mosaics", - "ARN": "arn:aws:s3:::pgc-opendata-dems/arcticdem/mosaics/", + "Description": "ArcticDEM DEM Strips", + "ARN": "arn:aws:s3:::pgc-opendata-dems/arcticdem/strips/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://www.pgc.umn.edu/data/arcticdem/", @@ -1994,7 +2018,7 @@ "stac" ], "Explore": [ - "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics.json)" + "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/strips.json)" ], "RequesterPays": null, "ControlledAccess": null, @@ -2003,8 +2027,8 @@ }, { "Name": "ArcticDEM", - "Description": "ArcticDEM DEM Strips", - "ARN": "arn:aws:s3:::pgc-opendata-dems/arcticdem/strips/", + "Description": "ArcticDEM DEM Mosaics", + "ARN": "arn:aws:s3:::pgc-opendata-dems/arcticdem/mosaics/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://www.pgc.umn.edu/data/arcticdem/", @@ -2024,7 +2048,7 @@ "stac" ], "Explore": [ - "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/strips.json)" + "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics.json)" ], "RequesterPays": null, "ControlledAccess": null, @@ -2443,8 +2467,8 @@ }, { "Name": "Beat Acute Myeloid Leukemia (AML) 1.0", - "Description": "WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, RNA-SeqSplice Junction Quantification", - "ARN": "arn:aws:s3:::gdc-beataml1-cohort-phs001657-2-controlled", + "Description": "BEATAML10-COHORT RNA-Seq Gene Expression Quantification", + "ARN": "arn:aws:s3:::gdc-beataml1-cohort-phs001657-2-open", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.cancer.gov/about-nci/organization/ccg/blog/2019/beataml", @@ -2463,7 +2487,7 @@ ], "Explore": null, "RequesterPays": null, - "ControlledAccess": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001657.v1.p1&phv=417530&phd=&pha=&pht=9391&phvf=&phdf=&phaf=&phtf=&dssp=1&consent=&temp=1", + "ControlledAccess": null, "AccountRequired": null, "Host": null }, @@ -2495,8 +2519,8 @@ }, { "Name": "Beat Acute Myeloid Leukemia (AML) 1.0", - "Description": "BEATAML10-COHORT RNA-Seq Gene Expression Quantification", - "ARN": "arn:aws:s3:::gdc-beataml1-cohort-phs001657-2-open", + "Description": "WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, RNA-SeqSplice Junction Quantification", + "ARN": "arn:aws:s3:::gdc-beataml1-cohort-phs001657-2-controlled", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.cancer.gov/about-nci/organization/ccg/blog/2019/beataml", @@ -2515,7 +2539,7 @@ ], "Explore": null, "RequesterPays": null, - "ControlledAccess": null, + "ControlledAccess": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001657.v1.p1&phv=417530&phd=&pha=&pht=9391&phvf=&phdf=&phaf=&phtf=&dssp=1&consent=&temp=1", "AccountRequired": null, "Host": null }, @@ -2620,6 +2644,36 @@ "AccountRequired": null, "Host": null }, + { + "Name": "Blue Brain Open Data", + "Description": "Data files representing neurological tissue structures", + "ARN": "arn:aws:s3:::openbluebrain", + "Region": "us-west-2", + "Type": "S3 Bucket", + "Documentation": "https://github.com/BlueBrain/OpenData", + "Contact": "jamesgonzalo.king@epfl.ch", + "ManagedBy": "BBP/EPFL", + "UpdateFrequency": "No updates", + "License": "CC-BY-4.0", + "Tags": [ + "neuroscience", + "simulation neuroscience", + "brain models", + "morphological reconstructions", + "electrophysiology", + "life sciences", + "single neuron models", + "ion channels", + "brain images", + "microcircuit modeling and simulation", + "Mus musculus" + ], + "Explore": null, + "RequesterPays": null, + "ControlledAccess": null, + "AccountRequired": null, + "Host": null + }, { "Name": "BodyM Dataset", "Description": "This S3 bucket has height, weight, gender, measurements and two silhouette images for each type of data", @@ -2752,10 +2806,10 @@ }, { "Name": "CAFE60 reanalysis", - "Description": "CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1 (CAFE60v1) data", - "ARN": "arn:aws:s3:::cafe60-reanalysis-dataset-aws-open-data", + "Description": "Notifications for updates to data", + "ARN": "arn:aws:sns:ap-southeast-2:970429975021:Cafe60-Data-Changes", "Region": "ap-southeast-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://data.csiro.au/dap/ws/v2/collections/49803/support/4029", "Contact": "Terence.O'Kane@csiro.au", "ManagedBy": "[CSIRO](http://csiro.au/)", @@ -2766,9 +2820,7 @@ "climate", "sustainability" ], - "Explore": [ - "[Browse Bucket](https://cafe60-reanalysis-dataset-aws-open-data.s3.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -2776,10 +2828,10 @@ }, { "Name": "CAFE60 reanalysis", - "Description": "Notifications for updates to data", - "ARN": "arn:aws:sns:ap-southeast-2:970429975021:Cafe60-Data-Changes", + "Description": "CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1 (CAFE60v1) data", + "ARN": "arn:aws:s3:::cafe60-reanalysis-dataset-aws-open-data", "Region": "ap-southeast-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://data.csiro.au/dap/ws/v2/collections/49803/support/4029", "Contact": "Terence.O'Kane@csiro.au", "ManagedBy": "[CSIRO](http://csiro.au/)", @@ -2790,7 +2842,9 @@ "climate", "sustainability" ], - "Explore": null, + "Explore": [ + "[Browse Bucket](https://cafe60-reanalysis-dataset-aws-open-data.s3.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -2856,10 +2910,10 @@ }, { "Name": "CBERS on AWS", - "Description": "CBERS imagery (COG files, quicklooks, metadata)", - "ARN": "arn:aws:s3:::brazil-eosats", + "Description": "Topic that receives STAC V100 items as new scenes are ingested", + "ARN": "arn:aws:sns:us-west-2:769537946825:br-eo-stac-prod-stacitemtopic4BCE3141-Z8he7LYjqXFe", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://github.com/fredliporace/cbers-on-aws", "Contact": "https://lists.osgeo.org/mailman/listinfo/cbers-pds", "ManagedBy": "[AMS Kepler](https://amskepler.com/)", @@ -2876,21 +2930,18 @@ "stac", "cog" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)", - "[stacindex](https://stacindex.org/catalogs/cbers)" - ], - "RequesterPays": false, + "Explore": null, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "CBERS on AWS", - "Description": "STAC static catalog", - "ARN": "arn:aws:s3:::br-eo-stac-1-0-0", + "Description": "Notifications for new CBERS 4 quicklooks, all sensors", + "ARN": "arn:aws:sns:us-west-2:599544552497:NewCB4Quicklook", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://github.com/fredliporace/cbers-on-aws", "Contact": "https://lists.osgeo.org/mailman/listinfo/cbers-pds", "ManagedBy": "[AMS Kepler](https://amskepler.com/)", @@ -2908,7 +2959,7 @@ "cog" ], "Explore": null, - "RequesterPays": false, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -2943,10 +2994,10 @@ }, { "Name": "CBERS on AWS", - "Description": "Notifications for new CBERS 4 quicklooks, all sensors", - "ARN": "arn:aws:sns:us-west-2:599544552497:NewCB4Quicklook", + "Description": "CBERS imagery (COG files, quicklooks, metadata)", + "ARN": "arn:aws:s3:::brazil-eosats", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://github.com/fredliporace/cbers-on-aws", "Contact": "https://lists.osgeo.org/mailman/listinfo/cbers-pds", "ManagedBy": "[AMS Kepler](https://amskepler.com/)", @@ -2963,18 +3014,21 @@ "stac", "cog" ], - "Explore": null, - "RequesterPays": null, + "Explore": [ + "[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)", + "[stacindex](https://stacindex.org/catalogs/cbers)" + ], + "RequesterPays": false, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "CBERS on AWS", - "Description": "Topic that receives STAC V100 items as new scenes are ingested", - "ARN": "arn:aws:sns:us-west-2:769537946825:br-eo-stac-prod-stacitemtopic4BCE3141-Z8he7LYjqXFe", + "Description": "STAC static catalog", + "ARN": "arn:aws:s3:::br-eo-stac-1-0-0", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://github.com/fredliporace/cbers-on-aws", "Contact": "https://lists.osgeo.org/mailman/listinfo/cbers-pds", "ManagedBy": "[AMS Kepler](https://amskepler.com/)", @@ -2992,7 +3046,7 @@ "cog" ], "Explore": null, - "RequesterPays": null, + "RequesterPays": false, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -3078,8 +3132,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "MPAS-CMAQ Input Data", - "ARN": "arn:aws:s3:::mpas-cmaq", + "Description": "SMOKE Test Case", + "ARN": "arn:aws:s3:::cmas-smoke-testcase", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3096,7 +3150,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://mpas-cmaq.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://cmas-smoke-testcase.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3105,8 +3159,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "CMAQ CONUS-2 Benchmark Data", - "ARN": "arn:aws:s3:::cmas-cmaq-conus2-benchmark", + "Description": "SMOKE 2016 Modeling Platform", + "ARN": "arn:aws:s3:::cmas-smoke-modeling-platform-2016", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3123,7 +3177,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://cmas-cmaq-conus2-benchmark.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://cmas-smoke-modeling-platform-2016.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3132,8 +3186,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "CMAQ Benchmark Data", - "ARN": "arn:aws:s3:::cmas-cmaq", + "Description": "2019 Modeling Platform", + "ARN": "arn:aws:s3:::2019platform", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3150,7 +3204,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://cmas-cmaq.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://2019platform.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3159,8 +3213,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "CMAQ 2018 Modeling Platform", - "ARN": "arn:aws:s3:::cmas-cmaq-modeling-platform-2018", + "Description": "2020 Modeling Platform", + "ARN": "arn:aws:s3:::2020platform", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3177,7 +3231,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://cmas-cmaq-modeling-platform-2018.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://2020platform.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3186,8 +3240,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "CMAS WWLLN Lightning Data", - "ARN": "arn:aws:s3:::cmas-wwlln-lightning", + "Description": "CMAQ 2018 Modeling Platform", + "ARN": "arn:aws:s3:::cmas-cmaq-modeling-platform-2018", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3204,7 +3258,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://cmas-wwlln-lightning.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://cmas-cmaq-modeling-platform-2018.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3240,8 +3294,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "2020 Modeling Platform", - "ARN": "arn:aws:s3:::2020platform", + "Description": "CMAS WWLLN Lightning Data", + "ARN": "arn:aws:s3:::cmas-wwlln-lightning", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3258,7 +3312,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://2020platform.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://cmas-wwlln-lightning.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3267,8 +3321,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "2019 Modeling Platform", - "ARN": "arn:aws:s3:::2019platform", + "Description": "AMET Data", + "ARN": "arn:aws:s3:::cmas-amet", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3285,7 +3339,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://2019platform.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://cmas-amet.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3294,8 +3348,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "2016v3 Modeling Platform", - "ARN": "arn:aws:s3:::2016v3platform", + "Description": "CMAQ Benchmark Data", + "ARN": "arn:aws:s3:::cmas-cmaq", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3312,7 +3366,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://2016v3platform.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://cmas-cmaq.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3321,8 +3375,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "SMOKE Test Case", - "ARN": "arn:aws:s3:::cmas-smoke-testcase", + "Description": "MPAS-CMAQ Input Data", + "ARN": "arn:aws:s3:::mpas-cmaq", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3339,7 +3393,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://cmas-smoke-testcase.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://mpas-cmaq.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3348,8 +3402,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "SMOKE 2016 Modeling Platform", - "ARN": "arn:aws:s3:::cmas-smoke-modeling-platform-2016", + "Description": "CMAQ CONUS-2 Benchmark Data", + "ARN": "arn:aws:s3:::cmas-cmaq-conus2-benchmark", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3366,7 +3420,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://cmas-smoke-modeling-platform-2016.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://cmas-cmaq-conus2-benchmark.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3375,8 +3429,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "AMET Data", - "ARN": "arn:aws:s3:::cmas-amet", + "Description": "CMAQ Release Benchmark Data for Easy Download", + "ARN": "arn:aws:s3:::cmaq-release-benchmark-data-for-easy-download", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3393,7 +3447,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://cmas-amet.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://cmaq-release-benchmark-data-for-easy-download.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3402,8 +3456,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "2018v2 Modeling Platform", - "ARN": "arn:aws:s3:::2018v2platform", + "Description": "2016v3 Modeling Platform", + "ARN": "arn:aws:s3:::2016v3platform", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3420,7 +3474,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://2018v2platform.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://2016v3platform.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3429,8 +3483,8 @@ }, { "Name": "CMAS Data Warehouse", - "Description": "CMAQ Release Benchmark Data for Easy Download", - "ARN": "arn:aws:s3:::cmaq-release-benchmark-data-for-easy-download", + "Description": "2018v2 Modeling Platform", + "ARN": "arn:aws:s3:::2018v2platform", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://dataverse.unc.edu/dataverse/cmascenter", @@ -3447,7 +3501,7 @@ "climate" ], "Explore": [ - "[Browse Bucket](https://cmaq-release-benchmark-data-for-easy-download.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://2018v2platform.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -3592,8 +3646,8 @@ }, { "Name": "COVID-19 Genome Sequence Dataset", - "Description": "Metadata for sra-pub-sars-cov2 in an Athena-queryable format", - "ARN": "arn:aws:s3:::sra-pub-sars-cov2-metadata-us-east-1", + "Description": "Genomic sequence reads of SARS-CoV-2 and related coronaviridae, organized by NCBI accession Files in the `sra-src` folder are in FASTQ, BAM, or CRAM format (original submission); files in the `run` folder are in sra format and require the SRA Toolkit", + "ARN": "arn:aws:s3:::sra-pub-sars-cov2", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.ncbi.nlm.nih.gov/sra/docs/sra-aws-download/", @@ -3629,8 +3683,8 @@ }, { "Name": "COVID-19 Genome Sequence Dataset", - "Description": "Genomic sequence reads of SARS-CoV-2 and related coronaviridae, organized by NCBI accession Files in the `sra-src` folder are in FASTQ, BAM, or CRAM format (original submission); files in the `run` folder are in sra format and require the SRA Toolkit", - "ARN": "arn:aws:s3:::sra-pub-sars-cov2", + "Description": "Metadata for sra-pub-sars-cov2 in an Athena-queryable format", + "ARN": "arn:aws:s3:::sra-pub-sars-cov2-metadata-us-east-1", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.ncbi.nlm.nih.gov/sra/docs/sra-aws-download/", @@ -3826,7 +3880,7 @@ { "Name": "Cancer Genome Characterization Initiatives - Burkitt Lymphoma, HIV+ Cervical Cancer", "Description": "Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-SeqIsoform Expression Quantification, miRNA-Seq miRNA Expression Quantification", - "ARN": "arn:aws:s3:::gdc-cgci-phs000235-2-open", + "ARN": "arn:aws:s3:::gdc-cgci-blgsp-phs000235-2-open", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://ocg.cancer.gov/programs/cgci", @@ -3851,7 +3905,7 @@ { "Name": "Cancer Genome Characterization Initiatives - Burkitt Lymphoma, HIV+ Cervical Cancer", "Description": "Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-SeqIsoform Expression Quantification, miRNA-Seq miRNA Expression Quantification", - "ARN": "arn:aws:s3:::gdc-cgci-blgsp-phs000235-2-open", + "ARN": "arn:aws:s3:::gdc-cgci-phs000235-2-open", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://ocg.cancer.gov/programs/cgci", @@ -3985,8 +4039,8 @@ }, { "Name": "Cell Organelle Segmentation in Electron Microscopy (COSEM) on AWS", - "Description": "Machine learning models for organelle prediction", - "ARN": "arn:aws:s3:::janelia-cosem-networks", + "Description": "Raw FIB-SEM datasets and derived data", + "ARN": "arn:aws:s3:::janelia-cosem-datasets", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://github.com/janelia-cosem/aws-opendata/blob/master/cosem-introduction.md", @@ -4011,8 +4065,8 @@ }, { "Name": "Cell Organelle Segmentation in Electron Microscopy (COSEM) on AWS", - "Description": "Raw FIB-SEM datasets and derived data", - "ARN": "arn:aws:s3:::janelia-cosem-datasets", + "Description": "Machine learning models for organelle prediction", + "ARN": "arn:aws:s3:::janelia-cosem-networks", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://github.com/janelia-cosem/aws-opendata/blob/master/cosem-introduction.md", @@ -4811,9 +4865,9 @@ }, { "Name": "Coupled Model Intercomparison Project 6", - "Description": "Netcdf formatted data managed by the Earth System Grid Federation", - "ARN": "arn:aws:s3:::esgf-world", - "Region": "us-east-2", + "Description": "Zarr formatted data", + "ARN": "arn:aws:s3:::cmip6-pds", + "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://pangeo-data.github.io/pangeo-cmip6-cloud/, https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6", "Contact": "If you have any feedback on the CMIP6 data available on AWS please email sustainability-data-initiative@amazon.com. We are acepting requests for additional CMIP6 variables and/or models to be made available to AWS but cannot guarantee that your request will be fulfilled. We will prioritize requests that bring value to the largest number of users. Note that we are not providing technical support through this email account.We also seek to identify case studies on how CMIP6 data is being used and will be featuring those stories in future publications and events. If you are interested in seeing your story highlighted, please share it with the ASDI team here: sustainability-data-initiative@amazon.com.", @@ -4833,8 +4887,8 @@ "weather" ], "Explore": [ - "[Browse Bucket](https://esgf-world.s3.amazonaws.com/index.html)", - "[Data Catalog](https://cmip6-nc.s3.amazonaws.com/esgf-world.csv.gz)" + "[Browse Bucket](https://cmip6-pds.s3.amazonaws.com/index.html#CMIP6/)", + "[Data Catalog](https://cmip6-pds.s3.amazonaws.com/pangeo-cmip6.csv)" ], "RequesterPays": null, "ControlledAccess": null, @@ -4843,9 +4897,9 @@ }, { "Name": "Coupled Model Intercomparison Project 6", - "Description": "Zarr formatted data", - "ARN": "arn:aws:s3:::cmip6-pds", - "Region": "us-west-2", + "Description": "Netcdf formatted data managed by the Earth System Grid Federation", + "ARN": "arn:aws:s3:::esgf-world", + "Region": "us-east-2", "Type": "S3 Bucket", "Documentation": "https://pangeo-data.github.io/pangeo-cmip6-cloud/, https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6", "Contact": "If you have any feedback on the CMIP6 data available on AWS please email sustainability-data-initiative@amazon.com. We are acepting requests for additional CMIP6 variables and/or models to be made available to AWS but cannot guarantee that your request will be fulfilled. We will prioritize requests that bring value to the largest number of users. Note that we are not providing technical support through this email account.We also seek to identify case studies on how CMIP6 data is being used and will be featuring those stories in future publications and events. If you are interested in seeing your story highlighted, please share it with the ASDI team here: sustainability-data-initiative@amazon.com.", @@ -4865,8 +4919,8 @@ "weather" ], "Explore": [ - "[Browse Bucket](https://cmip6-pds.s3.amazonaws.com/index.html#CMIP6/)", - "[Data Catalog](https://cmip6-pds.s3.amazonaws.com/pangeo-cmip6.csv)" + "[Browse Bucket](https://esgf-world.s3.amazonaws.com/index.html)", + "[Data Catalog](https://cmip6-nc.s3.amazonaws.com/esgf-world.csv.gz)" ], "RequesterPays": null, "ControlledAccess": null, @@ -5231,8 +5285,8 @@ }, { "Name": "DOE's Water Power Technology Office's (WPTO) US Wave dataset", - "Description": "32 Year Wave Hindcast (1979-2010) for the Pacific Ocean around US state of Hawaii at 3-hour temporal resolution and down to 200m spatial resolution in HDF5 format", - "ARN": "arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Hawaii/", + "Description": "DOE's Water Power Technology Office's Wave Hindcast datasets", + "ARN": "arn:aws:s3:::wpto-pds-us-wave/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/US_Wave.md", @@ -5249,7 +5303,7 @@ "water" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FHawaii%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave)" ], "RequesterPays": null, "ControlledAccess": null, @@ -5258,8 +5312,8 @@ }, { "Name": "DOE's Water Power Technology Office's (WPTO) US Wave dataset", - "Description": "HSDS US Virtual Buoy domains", - "ARN": "arn:aws:s3:::nrel-pds-hsds/nrel/US_wave/virtual_buoy/", + "Description": "32 Year Wave Hindcast (1979-2010) from select virtual buoys along the Atlantic Coast of the United States with 1-hour temporal resolution and direction wave spectrum data in HDF5 format", + "ARN": "arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/Atlantic/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/US_Wave.md", @@ -5276,7 +5330,7 @@ "water" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2Fvirtual_buoy%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FAtlantic%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -5285,8 +5339,8 @@ }, { "Name": "DOE's Water Power Technology Office's (WPTO) US Wave dataset", - "Description": "32 Year Wave Hindcast (1979-2010) from select virtual buoys along the West Coast of the United States with 1-hour temporal resolution and direction wave spectrum data in HDF5 format", - "ARN": "arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/West_Coast/", + "Description": "Updated version of 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at 3-hour temporal resolution and down to 200m spatial resolution in HDF5 format Updates resolve issues with NaNs", + "ARN": "arn:aws:s3:::wpto-pds-us-wave/v1.0.1/Atlantic/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/US_Wave.md", @@ -5303,7 +5357,7 @@ "water" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FWest_Coast%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.1%2FAtlantic%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -5312,8 +5366,8 @@ }, { "Name": "DOE's Water Power Technology Office's (WPTO) US Wave dataset", - "Description": "32 Year Wave Hindcast (1979-2010) from select virtual buoys along the Atlantic Coast of the United States with 1-hour temporal resolution and direction wave spectrum data in HDF5 format", - "ARN": "arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/Atlantic/", + "Description": "32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at 3-hour temporal resolution and down to 200m spatial resolution in HDF5 format", + "ARN": "arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Atlantic/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/US_Wave.md", @@ -5330,7 +5384,7 @@ "water" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FAtlantic%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FAtlantic%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -5339,8 +5393,8 @@ }, { "Name": "DOE's Water Power Technology Office's (WPTO) US Wave dataset", - "Description": "32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at 3-hour temporal resolution and down to 200m spatial resolution in HDF5 format", - "ARN": "arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Atlantic/", + "Description": "32 Year Wave Hindcast (1979-2010) from select virtual buoys along the West Coast of the United States with 1-hour temporal resolution and direction wave spectrum data in HDF5 format", + "ARN": "arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/West_Coast/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/US_Wave.md", @@ -5357,7 +5411,7 @@ "water" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FAtlantic%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FWest_Coast%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -5366,8 +5420,8 @@ }, { "Name": "DOE's Water Power Technology Office's (WPTO) US Wave dataset", - "Description": "Updated version of 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at 3-hour temporal resolution and down to 200m spatial resolution in HDF5 format Updates resolve issues with NaNs", - "ARN": "arn:aws:s3:::wpto-pds-us-wave/v1.0.1/Atlantic/", + "Description": "32 Year Wave Hindcast (1979-2010) for the Pacific Ocean around US state of Hawaii at 3-hour temporal resolution and down to 200m spatial resolution in HDF5 format", + "ARN": "arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Hawaii/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/US_Wave.md", @@ -5384,7 +5438,7 @@ "water" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.1%2FAtlantic%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FHawaii%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -5393,8 +5447,8 @@ }, { "Name": "DOE's Water Power Technology Office's (WPTO) US Wave dataset", - "Description": "DOE's Water Power Technology Office's Wave Hindcast datasets", - "ARN": "arn:aws:s3:::wpto-pds-us-wave/", + "Description": "HSDS US Virtual Buoy domains", + "ARN": "arn:aws:s3:::nrel-pds-hsds/nrel/US_wave/virtual_buoy/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/US_Wave.md", @@ -5411,7 +5465,7 @@ "water" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2Fvirtual_buoy%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -5420,10 +5474,10 @@ }, { "Name": "Daylight Map Distribution of OpenStreetMap", - "Description": "Daylight Earth Table (Parquet)", - "ARN": "arn:aws:s3:::daylight-openstreetmap/earth", + "Description": "New Parquet File Notification", + "ARN": "arn:aws:sns:us-west-2:632571768781:Analysis_Ready_Daylight", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "[Project Website](https://daylightmap.org)", "Contact": "osm@fb.com", "ManagedBy": "[Meta](https://dataforgood.fb.com/)", @@ -5444,10 +5498,10 @@ }, { "Name": "Daylight Map Distribution of OpenStreetMap", - "Description": "New OSM PBF Notification", - "ARN": "arn:aws:sns:us-west-1:632571768781:Daylight", + "Description": "Daylight OSM PBF Files", + "ARN": "arn:aws:s3:::daylight-map-distribution/release/", "Region": "us-west-1", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "[Project Website](https://daylightmap.org)", "Contact": "osm@fb.com", "ManagedBy": "[Meta](https://dataforgood.fb.com/)", @@ -5468,10 +5522,10 @@ }, { "Name": "Daylight Map Distribution of OpenStreetMap", - "Description": "Daylight OSM PBF Files", - "ARN": "arn:aws:s3:::daylight-map-distribution/release/", + "Description": "New OSM PBF Notification", + "ARN": "arn:aws:sns:us-west-1:632571768781:Daylight", "Region": "us-west-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "[Project Website](https://daylightmap.org)", "Contact": "osm@fb.com", "ManagedBy": "[Meta](https://dataforgood.fb.com/)", @@ -5540,10 +5594,10 @@ }, { "Name": "Daylight Map Distribution of OpenStreetMap", - "Description": "New Parquet File Notification", - "ARN": "arn:aws:sns:us-west-2:632571768781:Analysis_Ready_Daylight", + "Description": "Daylight Earth Table (Parquet)", + "ARN": "arn:aws:s3:::daylight-openstreetmap/earth", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "[Project Website](https://daylightmap.org)", "Contact": "osm@fb.com", "ManagedBy": "[Meta](https://dataforgood.fb.com/)", @@ -10963,9 +11017,9 @@ }, { "Name": "Global Biodiversity Information Facility (GBIF) Species Occurrences", - "Description": "GBIF species occurrence data in Parquet format (ap-southeast-2 region)", - "ARN": "arn:aws:sns:af-south-1:288719126026:gbif-open-data-ap-southeast-2-object_created", - "Region": "ap-southeast-2", + "Description": "GBIF species occurrence data in Parquet format (af-south-1 region)", + "ARN": "arn:aws:sns:af-south-1:288719126026:gbif-open-data-af-south-1-object_created", + "Region": "af-south-1", "Type": "SNS Topic", "Documentation": "Documentation can be found [here](https://github.com/gbif/occurrence/blob/master/aws-public-data.md). You can learn more about GBIF [here](https://www.gbif.org).", "Contact": "helpdesk@gbif.org", @@ -11144,9 +11198,9 @@ }, { "Name": "Global Biodiversity Information Facility (GBIF) Species Occurrences", - "Description": "GBIF species occurrence data in Parquet format (af-south-1 region)", - "ARN": "arn:aws:sns:af-south-1:288719126026:gbif-open-data-af-south-1-object_created", - "Region": "af-south-1", + "Description": "GBIF species occurrence data in Parquet format (ap-southeast-2 region)", + "ARN": "arn:aws:sns:af-south-1:288719126026:gbif-open-data-ap-southeast-2-object_created", + "Region": "ap-southeast-2", "Type": "SNS Topic", "Documentation": "Documentation can be found [here](https://github.com/gbif/occurrence/blob/master/aws-public-data.md). You can learn more about GBIF [here](https://www.gbif.org).", "Contact": "helpdesk@gbif.org", @@ -23544,7 +23598,7 @@ }, { "Name": "ONS Open Data Portal", - "Description": "Daily affluent natural energy per equivalent energy reservoir (PT-BR Energia Natural Afluente (ENA) di\u00e1rio por REE - reservatorio equivalente de energia)", + "Description": "Daily affluent natural energy per basin (PT-BR Energia Natural Afluente (ENA) di\u00e1rio por bacia)", "ARN": "arn:aws:s3:::ons-aws-prod-opendata", "Region": "sa-east-1", "Type": "S3 Bucket", @@ -23560,7 +23614,7 @@ "energy" ], "Explore": [ - "[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-ree-reservatorio-equivalente-de-energia)" + "[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-bacia)" ], "RequesterPays": null, "ControlledAccess": null, @@ -24569,7 +24623,7 @@ }, { "Name": "ONS Open Data Portal", - "Description": "Daily affluent natural energy per basin (PT-BR Energia Natural Afluente (ENA) di\u00e1rio por bacia)", + "Description": "Daily affluent natural energy per equivalent energy reservoir (PT-BR Energia Natural Afluente (ENA) di\u00e1rio por REE - reservatorio equivalente de energia)", "ARN": "arn:aws:s3:::ons-aws-prod-opendata", "Region": "sa-east-1", "Type": "S3 Bucket", @@ -24585,7 +24639,7 @@ "energy" ], "Explore": [ - "[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-bacia)" + "[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-ree-reservatorio-equivalente-de-energia)" ], "RequesterPays": null, "ControlledAccess": null, @@ -24987,9 +25041,9 @@ }, { "Name": "Open Observatory of Network Interference (OONI)", - "Description": "Old S3 bucket with cans for older measurements", - "ARN": "arn:aws:s3:::ooni-data", - "Region": "us-east-1", + "Description": "New S3 bucket with JSONL files", + "ARN": "arn:aws:s3:::ooni-data-eu-fra", + "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://ooni.org/data/", "Contact": "https://ooni.org/get-involved/", @@ -25008,9 +25062,9 @@ }, { "Name": "Open Observatory of Network Interference (OONI)", - "Description": "New S3 bucket with JSONL files", - "ARN": "arn:aws:s3:::ooni-data-eu-fra", - "Region": "eu-central-1", + "Description": "Old S3 bucket with cans for older measurements", + "ARN": "arn:aws:s3:::ooni-data", + "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://ooni.org/data/", "Contact": "https://ooni.org/get-involved/", @@ -25083,10 +25137,10 @@ }, { "Name": "OpenAQ", - "Description": "Daily gzipped CSVs of global air quality measurements fetched from sources all over the world", - "ARN": "arn:aws:s3:::openaq-data-archive", + "Description": "OpenAQ API", + "ARN": null, "Region": "us-east-1", - "Type": "S3 Bucket", + "Type": "CloudFront Distribution", "Documentation": "https://openaq.org", "Contact": "info@openaq.org", "ManagedBy": "[OpenAQ](https://openaq.org)", @@ -25103,7 +25157,7 @@ "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, - "Host": null + "Host": "api.openaq.org" }, { "Name": "OpenAQ", @@ -25131,10 +25185,10 @@ }, { "Name": "OpenAQ", - "Description": "OpenAQ API", - "ARN": null, + "Description": "Daily gzipped CSVs of global air quality measurements fetched from sources all over the world", + "ARN": "arn:aws:s3:::openaq-data-archive", "Region": "us-east-1", - "Type": "CloudFront Distribution", + "Type": "S3 Bucket", "Documentation": "https://openaq.org", "Contact": "info@openaq.org", "ManagedBy": "[OpenAQ](https://openaq.org)", @@ -25151,7 +25205,7 @@ "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, - "Host": "api.openaq.org" + "Host": null }, { "Name": "OpenAerialMap on AWS", @@ -25181,8 +25235,8 @@ }, { "Name": "OpenAlex dataset", - "Description": "OpenAlex Entities in JSON Lines format", - "ARN": "arn:aws:s3:::openalex", + "Description": "Openalex Entities decomposed to tab-separated columnar files for backward compatibility with Microsoft Academic Graph", + "ARN": "arn:aws:s3:::openalex-mag-format", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://docs.openalex.org", @@ -25198,7 +25252,7 @@ "aws-pds" ], "Explore": [ - "[Browse Bucket](https://openalex.s3.amazonaws.com/browse.html)" + "[Browse Bucket](https://openalex-mag-format.s3.amazonaws.com/browse.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -25207,8 +25261,8 @@ }, { "Name": "OpenAlex dataset", - "Description": "Openalex Entities decomposed to tab-separated columnar files for backward compatibility with Microsoft Academic Graph", - "ARN": "arn:aws:s3:::openalex-mag-format", + "Description": "OpenAlex Entities in JSON Lines format", + "ARN": "arn:aws:s3:::openalex", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://docs.openalex.org", @@ -25224,7 +25278,7 @@ "aws-pds" ], "Explore": [ - "[Browse Bucket](https://openalex-mag-format.s3.amazonaws.com/browse.html)" + "[Browse Bucket](https://openalex.s3.amazonaws.com/browse.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -25392,10 +25446,10 @@ }, { "Name": "OpenStreetMap on AWS", - "Description": "Imagery and metadata", - "ARN": "arn:aws:s3:::osm-pds", + "Description": "New data notifications", + "ARN": "arn:aws:sns:us-east-1:800218804198:New_File", "Region": "us-east-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds", "Contact": "https://github.com/mojodna/osm-pds-pipelines/issues", "ManagedBy": "Pacific Atlas", @@ -25416,10 +25470,10 @@ }, { "Name": "OpenStreetMap on AWS", - "Description": "New data notifications", - "ARN": "arn:aws:sns:us-east-1:800218804198:New_File", + "Description": "Imagery and metadata", + "ARN": "arn:aws:s3:::osm-pds", "Region": "us-east-1", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds", "Contact": "https://github.com/mojodna/osm-pds-pipelines/issues", "ManagedBy": "Pacific Atlas", @@ -25463,8 +25517,8 @@ }, { "Name": "OpenUniverse 2024 Matched Rubin and Roman Simulations: Preview", - "Description": "The simulated Roman data products include truth files listing the basic physical properties of the simulated astronomical objects; truth images that include the appropriate bandpass and PSF but limited sources of noise; calibrated images that include relevant backgrounds and major sources of noise; and coadded images created using the IMCOM software", - "ARN": "arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/roman/", + "Description": "The simulated Rubin data products include raw pixel data, calibrated exposures, coadds of the calibrated exposures, and catalogs of photometry measured from the simulated images", + "ARN": "arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/rubin/", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://irsa.ipac.caltech.edu/data/theory/openuniverse2024", @@ -25490,9 +25544,9 @@ }, { "Name": "OpenUniverse 2024 Matched Rubin and Roman Simulations: Preview", - "Description": "The simulated Rubin data products include raw pixel data, calibrated exposures, coadds of the calibrated exposures, and catalogs of photometry measured from the simulated images", - "ARN": "arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/rubin/", - "Region": "us-east-1", + "Description": "The simulated Roman data products include truth files listing the basic physical properties of the simulated astronomical objects; truth images that include the appropriate bandpass and PSF but limited sources of noise; calibrated images that include relevant backgrounds and major sources of noise; and coadded images created using the IMCOM software", + "ARN": "arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/roman/", + "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://irsa.ipac.caltech.edu/data/theory/openuniverse2024", "Contact": "https://irsa.ipac.caltech.edu/docs/help_desk.html", @@ -25542,8 +25596,8 @@ }, { "Name": "Orcasound - bioacoustic data for marine conservation", - "Description": "Archived lossless orca audio data (FLAC)", - "ARN": "arn:aws:s3:::archive-orcasound-net", + "Description": "Live-streamed orca audio data (HLS)", + "ARN": "arn:aws:s3:::streaming-orcasound-net", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/orcasound/orcadata/wiki", @@ -25576,8 +25630,8 @@ }, { "Name": "Orcasound - bioacoustic data for marine conservation", - "Description": "Labeled audio data for ML model development", - "ARN": "arn:aws:s3:::acoustic-sandbox", + "Description": "Archived lossless orca audio data (FLAC)", + "ARN": "arn:aws:s3:::archive-orcasound-net", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/orcasound/orcadata/wiki", @@ -25610,8 +25664,8 @@ }, { "Name": "Orcasound - bioacoustic data for marine conservation", - "Description": "Live-streamed orca audio data (HLS)", - "ARN": "arn:aws:s3:::streaming-orcasound-net", + "Description": "Labeled audio data for ML model development", + "ARN": "arn:aws:s3:::acoustic-sandbox", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/orcasound/orcadata/wiki", @@ -25667,10 +25721,10 @@ }, { "Name": "Overture Maps Foundation Open Map Data", - "Description": "Overture Maps Foundation Data (GeoParquet)", - "ARN": "arn:aws:s3:::overturemaps-us-west-2/release/", + "Description": "New File Notification", + "ARN": "arn:aws:sns:us-west-2:913550007193:overturemaps-us-west-2", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "Documentation is available at [docs.overturemaps.org](https://docs.overturemaps.org/)", "Contact": "info@overturemaps.org", "ManagedBy": "[Overture Maps Foundation](https://overturemaps.org)", @@ -25693,10 +25747,10 @@ }, { "Name": "Overture Maps Foundation Open Map Data", - "Description": "New File Notification", - "ARN": "arn:aws:sns:us-west-2:913550007193:overturemaps-us-west-2", + "Description": "Overture Maps Foundation Data (GeoParquet)", + "ARN": "arn:aws:s3:::overturemaps-us-west-2/release/", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "Documentation is available at [docs.overturemaps.org](https://docs.overturemaps.org/)", "Contact": "info@overturemaps.org", "ManagedBy": "[Overture Maps Foundation](https://overturemaps.org)", @@ -25719,8 +25773,8 @@ }, { "Name": "Oxford Nanopore Technologies Benchmark Datasets", - "Description": "Oxford Nanopore Open Datasets", - "ARN": "arn:aws:s3:::ont-open-data", + "Description": "Using nanopore sequencing, researchers have directly identified DNA and RNA base modifications at nucleotide resolution, including 5-methylycytosine, 5-hydroxymethylcytosine, N6-methyladenosine, 5-bromodeoxyuridine in DAN; and N6-methyladenosine in RNA, with detection of other natural or synthetic epigenetic modifications possible through training basecalling algorithms One of the most widespread genomic modifications is 5-methylcytosine (5mC), which most frequently occurs at dinucleotides Compared to whole-genome bisulfite sequencing, the traditional method of 5mC detection, nanopore technology can offer many advantagesThe following cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: GM24385", + "ARN": "arn:aws:s3:::ont-open-data/gm24385_mod_2021.09/extra_analysis/bonito_remora", "Region": "eu-west-1", "Type": "S3 Bucket", "Documentation": "https://labs.epi2me.io/dataindex/", @@ -25747,8 +25801,8 @@ }, { "Name": "Oxford Nanopore Technologies Benchmark Datasets", - "Description": "Nanopore sequencing data of the Genome in a Bottle samples NA24385, NA24149, and NA24143 (HG002-HG004) using the LSK114 sequencing chemistry The direct sequencer output is included, raw signal data stored in fast5 files and basecalled data in fastq file Additional secondary analyses are included, notably alignments of sequence data to the reference genome and variant calls are provided along with statistics derived from theseThe following cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: NA24385, NA24149, and NA24143", - "ARN": "arn:aws:s3:::ont-open-data/giab_lsk114_2022.12", + "Description": "CpG dinucleotides frequently occur in high-density clusters called CpG islands (CGI) and >60% of human genes have their promoters embedded within CGIs Determining the methylation status of cytosines within CpGs is of substantial biological interest: alterations in methylation patterns within promoters is associated with changes in gene expression and disease states such as cancer Exploring methylation differences between tumour samples and normal samples can help to elucidate mechanisms associated with tumour formation and development Nanopore sequencing enables direct detection of methylated cytosines (eg at CpG sites), without the need for bisulfite conversionOxford Nanopore\u2019s Adaptive Sampling offers a flexible method to enrich regions of interest (eg CGIs) by depleting off-target regions during the sequencing run itself with no upfront sample manipulation Here we introduce Reduced Representation Methylation Sequencing (RRMS) to target 310 Mb of the human genome including regions which are highly enriched for CpGs including ~28,000 CpG islands, ~50,600 shores and ~42,700 shelves as well as ~21,600 promoter regions", + "ARN": "arn:aws:s3:::ont-open-data/rrms_2022.07", "Region": "eu-west-1", "Type": "S3 Bucket", "Documentation": "https://labs.epi2me.io/dataindex/", @@ -25775,8 +25829,8 @@ }, { "Name": "Oxford Nanopore Technologies Benchmark Datasets", - "Description": "Using nanopore sequencing, researchers have directly identified DNA and RNA base modifications at nucleotide resolution, including 5-methylycytosine, 5-hydroxymethylcytosine, N6-methyladenosine, 5-bromodeoxyuridine in DAN; and N6-methyladenosine in RNA, with detection of other natural or synthetic epigenetic modifications possible through training basecalling algorithms One of the most widespread genomic modifications is 5-methylcytosine (5mC), which most frequently occurs at dinucleotides Compared to whole-genome bisulfite sequencing, the traditional method of 5mC detection, nanopore technology can offer many advantagesThe following cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: GM24385", - "ARN": "arn:aws:s3:::ont-open-data/gm24385_mod_2021.09/extra_analysis/bonito_remora", + "Description": "Oxford Nanopore Open Datasets", + "ARN": "arn:aws:s3:::ont-open-data", "Region": "eu-west-1", "Type": "S3 Bucket", "Documentation": "https://labs.epi2me.io/dataindex/", @@ -25803,8 +25857,8 @@ }, { "Name": "Oxford Nanopore Technologies Benchmark Datasets", - "Description": "CpG dinucleotides frequently occur in high-density clusters called CpG islands (CGI) and >60% of human genes have their promoters embedded within CGIs Determining the methylation status of cytosines within CpGs is of substantial biological interest: alterations in methylation patterns within promoters is associated with changes in gene expression and disease states such as cancer Exploring methylation differences between tumour samples and normal samples can help to elucidate mechanisms associated with tumour formation and development Nanopore sequencing enables direct detection of methylated cytosines (eg at CpG sites), without the need for bisulfite conversionOxford Nanopore\u2019s Adaptive Sampling offers a flexible method to enrich regions of interest (eg CGIs) by depleting off-target regions during the sequencing run itself with no upfront sample manipulation Here we introduce Reduced Representation Methylation Sequencing (RRMS) to target 310 Mb of the human genome including regions which are highly enriched for CpGs including ~28,000 CpG islands, ~50,600 shores and ~42,700 shelves as well as ~21,600 promoter regions", - "ARN": "arn:aws:s3:::ont-open-data/rrms_2022.07", + "Description": "Nanopore sequencing data of the Genome in a Bottle samples NA24385, NA24149, and NA24143 (HG002-HG004) using the LSK114 sequencing chemistry The direct sequencer output is included, raw signal data stored in fast5 files and basecalled data in fastq file Additional secondary analyses are included, notably alignments of sequence data to the reference genome and variant calls are provided along with statistics derived from theseThe following cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: NA24385, NA24149, and NA24143", + "ARN": "arn:aws:s3:::ont-open-data/giab_lsk114_2022.12", "Region": "eu-west-1", "Type": "S3 Bucket", "Documentation": "https://labs.epi2me.io/dataindex/", @@ -26051,8 +26105,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2015", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2015", + "Description": "original 256 kHz audio recordings year 2018", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2018", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26083,8 +26137,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2016", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2016", + "Description": "decimated 16 kHz audio recordings", + "ARN": "arn:aws:s3:::pacific-sound-16khz", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26115,8 +26169,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2023", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2023", + "Description": "original 256 kHz audio recordings year 2017", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2017", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26147,8 +26201,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2024", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2024", + "Description": "original 256 kHz audio recordings year 2016", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2016", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26179,8 +26233,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2025", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2025", + "Description": "original 256 kHz audio recordings year 2019", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2019", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26211,8 +26265,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "decimated 2 kHz audio recordings", - "ARN": "arn:aws:s3:::pacific-sound-2khz", + "Description": "original 256 kHz audio recordings year 2020", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2020", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26243,8 +26297,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "decimated 16 kHz audio recordings", - "ARN": "arn:aws:s3:::pacific-sound-16khz", + "Description": "decimated 2 kHz audio recordings", + "ARN": "arn:aws:s3:::pacific-sound-2khz", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26275,8 +26329,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "machine learning models", - "ARN": "arn:aws:s3:::pacific-sound-models", + "Description": "original 256 kHz audio recordings year 2022", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2022", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26307,8 +26361,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2020", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2020", + "Description": "original 256 kHz audio recordings year 2023", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2023", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26339,8 +26393,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2019", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2019", + "Description": "original 256 kHz audio recordings year 2024", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2024", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26371,8 +26425,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2018", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2018", + "Description": "original 256 kHz audio recordings year 2025", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2025", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26403,8 +26457,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2017", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2017", + "Description": "machine learning models", + "ARN": "arn:aws:s3:::pacific-sound-models", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26467,8 +26521,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2022", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2022", + "Description": "original 256 kHz audio recordings year 2015", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2015", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -26718,8 +26772,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Nodal Seismometer Continuous Data", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac/", + "Description": "PoroTomo Nodal Seismometer Field Notes and Metadata", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_metadata/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -26735,7 +26789,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_metadata%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26744,8 +26798,8 @@ }, { "Name": "PoroTomo", - "Description": "HSDS PoroTomo domains", - "ARN": "arn:aws:s3:::nrel-pds-hsds/nrel/porotomo/", + "Description": "PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in SEG-Y format", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASV/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -26761,7 +26815,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fporotomo%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASV%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26770,8 +26824,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Nodal Seismometer Field Notes and Metadata", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_metadata/", + "Description": "PoroTomo Datasets", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -26787,7 +26841,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_metadata%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26796,8 +26850,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in SEG-Y format", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASV/", + "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in HDF5 format", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASH/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -26813,7 +26867,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASV%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASH%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26848,8 +26902,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in SEG-Y format", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/", + "Description": "PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASV/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -26865,7 +26919,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASV%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26874,8 +26928,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Datasets", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/", + "Description": "HSDS PoroTomo domains", + "ARN": "arn:aws:s3:::nrel-pds-hsds/nrel/porotomo/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -26891,7 +26945,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fporotomo%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26900,8 +26954,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASV/", + "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in SEG-Y format", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -26917,7 +26971,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASV%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26926,8 +26980,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in HDF5 format", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASH/", + "Description": "PoroTomo Nodal Seismometer Continuous Data", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -26943,7 +26997,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASH%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -27080,10 +27134,10 @@ }, { "Name": "Protein Data Bank 3D Structural Biology Data", - "Description": "Globally cached distribution of the dataset Web frontend also available to browse the dataset and file directory", - "ARN": null, + "Description": "Historical snapshots of archival datasets from 2005 onwards Snapshots are generated annually and at major milestone", + "ARN": "arn:aws:s3:::pdbsnapshots", "Region": "us-west-2", - "Type": "CloudFront Distribution", + "Type": "S3 Bucket", "Documentation": "https://www.wwpdb.org/documentation/file-format", "Contact": "https://www.wwpdb.org/about/contact", "ManagedBy": "[Worldwide Protein Data Bank Partnership](wwpdb.org)", @@ -27112,7 +27166,7 @@ "x-ray crystallography" ], "Explore": [ - "[Browse Dataset](https://s3.rcsb.org)" + "[Browse Bucket](https://pdbsnapshots.s3.us-west-2.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -27121,10 +27175,10 @@ }, { "Name": "Protein Data Bank 3D Structural Biology Data", - "Description": "Historical snapshots of archival datasets from 2005 onwards Snapshots are generated annually and at major milestone", - "ARN": "arn:aws:s3:::pdbsnapshots", + "Description": "Globally cached distribution of the dataset Web frontend also available to browse the dataset and file directory", + "ARN": null, "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "CloudFront Distribution", "Documentation": "https://www.wwpdb.org/documentation/file-format", "Contact": "https://www.wwpdb.org/about/contact", "ManagedBy": "[Worldwide Protein Data Bank Partnership](wwpdb.org)", @@ -27153,7 +27207,7 @@ "x-ray crystallography" ], "Explore": [ - "[Browse Bucket](https://pdbsnapshots.s3.us-west-2.amazonaws.com/index.html)" + "[Browse Dataset](https://s3.rcsb.org)" ], "RequesterPays": null, "ControlledAccess": null, @@ -27184,8 +27238,8 @@ }, { "Name": "PubSeq - Public Sequence Resource", - "Description": "PubSeq submitted datasets (FASTA and JSON metadata)", - "ARN": "arn:aws:s3:::pubseq-datasets", + "Description": "Pubseq output data (Arvados Keep)", + "ARN": "arn:aws:s3:::pubseq-output-data", "Region": "us-east-2", "Type": "S3 Bucket", "Documentation": "https://covid19.genenetwork.org/about", @@ -27219,7 +27273,7 @@ "SPARQL" ], "Explore": [ - "[Browse Bucket](https://pubseq-datasets.s3.amazonaws.com/)" + "[Arvados download](https://covid19.genenetwork.org/download)" ], "RequesterPays": null, "ControlledAccess": null, @@ -27228,8 +27282,8 @@ }, { "Name": "PubSeq - Public Sequence Resource", - "Description": "Pubseq output data (Arvados Keep)", - "ARN": "arn:aws:s3:::pubseq-output-data", + "Description": "PubSeq submitted datasets (FASTA and JSON metadata)", + "ARN": "arn:aws:s3:::pubseq-datasets", "Region": "us-east-2", "Type": "S3 Bucket", "Documentation": "https://covid19.genenetwork.org/about", @@ -27263,7 +27317,7 @@ "SPARQL" ], "Explore": [ - "[Arvados download](https://covid19.genenetwork.org/download)" + "[Browse Bucket](https://pubseq-datasets.s3.amazonaws.com/)" ], "RequesterPays": null, "ControlledAccess": null, @@ -27302,8 +27356,8 @@ }, { "Name": "PyEnvs and CallArgs", - "Description": "PyEnvs", - "ARN": "arn:aws:s3:::pyenvs-and-callargs/pyenvs/", + "Description": "CallArgs", + "ARN": "arn:aws:s3:::pyenvs-and-callargs/callargs/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/amazon-research/function-call-argument-completion", @@ -27323,8 +27377,8 @@ }, { "Name": "PyEnvs and CallArgs", - "Description": "CallArgs", - "ARN": "arn:aws:s3:::pyenvs-and-callargs/callargs/", + "Description": "PyEnvs", + "ARN": "arn:aws:s3:::pyenvs-and-callargs/pyenvs/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/amazon-research/function-call-argument-completion", @@ -27821,8 +27875,8 @@ }, { "Name": "Reference Elevation Model of Antarctica (REMA)", - "Description": "REMA DEM Strips", - "ARN": "arn:aws:s3:::pgc-opendata-dems/rema/strips/", + "Description": "REMA DEM Mosaics", + "ARN": "arn:aws:s3:::pgc-opendata-dems/rema/mosaics/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://www.pgc.umn.edu/data/rema/", @@ -27842,7 +27896,7 @@ "stac" ], "Explore": [ - "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/strips.json)" + "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/mosaics.json)" ], "RequesterPays": null, "ControlledAccess": null, @@ -27851,8 +27905,8 @@ }, { "Name": "Reference Elevation Model of Antarctica (REMA)", - "Description": "REMA DEM Mosaics", - "ARN": "arn:aws:s3:::pgc-opendata-dems/rema/mosaics/", + "Description": "REMA DEM Strips", + "ARN": "arn:aws:s3:::pgc-opendata-dems/rema/strips/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://www.pgc.umn.edu/data/rema/", @@ -27872,7 +27926,7 @@ "stac" ], "Explore": [ - "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/mosaics.json)" + "[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/strips.json)" ], "RequesterPays": null, "ControlledAccess": null, @@ -27939,10 +27993,10 @@ }, { "Name": "Registry of Open Data on AWS", - "Description": "Registry of Open Data on AWS", - "ARN": "arn:aws:s3:::registry.opendata.aws/roda/ndjson/", + "Description": "SNS topic for object create events", + "ARN": "arn:aws:sns:us-east-1:652627389412:roda-object_created", "Region": "us-east-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://github.com/awslabs/open-data-registry#how-are-datasets-added-to-the-registry", "Contact": "opendata@amazon.com", "ManagedBy": "[Amazon Web Services](https://aws.amazon.com/)", @@ -27961,10 +28015,10 @@ }, { "Name": "Registry of Open Data on AWS", - "Description": "SNS topic for object create events", - "ARN": "arn:aws:sns:us-east-1:652627389412:roda-object_created", + "Description": "Registry of Open Data on AWS", + "ARN": "arn:aws:s3:::registry.opendata.aws/roda/ndjson/", "Region": "us-east-1", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://github.com/awslabs/open-data-registry#how-are-datasets-added-to-the-registry", "Contact": "opendata@amazon.com", "ManagedBy": "[Amazon Web Services](https://aws.amazon.com/)", @@ -27983,10 +28037,10 @@ }, { "Name": "SILAM Air Quality", - "Description": "Notifications for new netcdf surface data", - "ARN": "arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-netcdf", + "Description": "Surface Zarr files", + "ARN": "arn:aws:s3:::fmi-opendata-silam-surface-zarr", "Region": "eu-west-1", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3", "Contact": "avoin-data@fmi.fi", "ManagedBy": "[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)", @@ -28000,7 +28054,9 @@ "air quality", "meteorological" ], - "Explore": null, + "Explore": [ + "[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -28008,10 +28064,10 @@ }, { "Name": "SILAM Air Quality", - "Description": "Surface Zarr files", - "ARN": "arn:aws:s3:::fmi-opendata-silam-surface-zarr", + "Description": "Notifications for new zarr surface data", + "ARN": "arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-zarr", "Region": "eu-west-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3", "Contact": "avoin-data@fmi.fi", "ManagedBy": "[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)", @@ -28025,9 +28081,7 @@ "air quality", "meteorological" ], - "Explore": [ - "[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -28035,10 +28089,10 @@ }, { "Name": "SILAM Air Quality", - "Description": "Surface NetCDF files", - "ARN": "arn:aws:s3:::fmi-opendata-silam-surface-netcdf", + "Description": "Notifications for new netcdf surface data", + "ARN": "arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-netcdf", "Region": "eu-west-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3", "Contact": "avoin-data@fmi.fi", "ManagedBy": "[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)", @@ -28052,9 +28106,7 @@ "air quality", "meteorological" ], - "Explore": [ - "[Browse Bucket](https://fmi-opendata-silam-surface-netcdf.s3.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -28062,10 +28114,10 @@ }, { "Name": "SILAM Air Quality", - "Description": "Notifications for new zarr surface data", - "ARN": "arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-zarr", + "Description": "Surface NetCDF files", + "ARN": "arn:aws:s3:::fmi-opendata-silam-surface-netcdf", "Region": "eu-west-1", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3", "Contact": "avoin-data@fmi.fi", "ManagedBy": "[Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/)", @@ -28079,7 +28131,9 @@ "air quality", "meteorological" ], - "Explore": null, + "Explore": [ + "[Browse Bucket](https://fmi-opendata-silam-surface-netcdf.s3.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -28262,10 +28316,10 @@ }, { "Name": "Safecast", - "Description": "Bulk exports of air and radiation measurements", - "ARN": "arn:aws:s3:::safecast-opendata-public-us-east-1", - "Region": "us-east-1", - "Type": "S3 Bucket", + "Description": "New air and radiation measurement payloads", + "ARN": "arn:aws:sns:us-west-2:985752656544:ingest-measurements-prd", + "Region": "us-west-2", + "Type": "SNS Topic", "Documentation": "https://github.com/Safecast/safecastapi/wiki/Data-Sets", "Contact": "https://groups.google.com/forum/#!forum/safecast-devices", "ManagedBy": "[Safecast](https://safecast.org/)", @@ -28279,9 +28333,7 @@ "geospatial", "radiation" ], - "Explore": [ - "[Browse Bucket](https://safecast-opendata-public-us-east-1.s3.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -28289,10 +28341,10 @@ }, { "Name": "Safecast", - "Description": "New air and radiation measurement payloads", - "ARN": "arn:aws:sns:us-west-2:985752656544:ingest-measurements-prd", - "Region": "us-west-2", - "Type": "SNS Topic", + "Description": "Bulk exports of air and radiation measurements", + "ARN": "arn:aws:s3:::safecast-opendata-public-us-east-1", + "Region": "us-east-1", + "Type": "S3 Bucket", "Documentation": "https://github.com/Safecast/safecastapi/wiki/Data-Sets", "Contact": "https://groups.google.com/forum/#!forum/safecast-devices", "ManagedBy": "[Safecast](https://safecast.org/)", @@ -28306,7 +28358,9 @@ "geospatial", "radiation" ], - "Explore": null, + "Explore": [ + "[Browse Bucket](https://safecast-opendata-public-us-east-1.s3.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -28438,8 +28492,8 @@ }, { "Name": "Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD)", - "Description": "Single cell profiling (transcriptomics and epigenomics) data files in a public bucket", - "ARN": "arn:aws:s3:::sea-ad-single-cell-profiling", + "Description": "Spatial transcriptomics data files in a public bucket", + "ARN": "arn:aws:s3:::sea-ad-spatial-transcriptomics", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheimers-disease-brain-cell-atlas-download?edit&language=en", @@ -28466,7 +28520,7 @@ "life sciences" ], "Explore": [ - "[Browse Bucket](https://sea-ad-single-cell-profiling.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://sea-ad-spatial-transcriptomics.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -28475,8 +28529,8 @@ }, { "Name": "Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD)", - "Description": "Spatial transcriptomics data files in a public bucket", - "ARN": "arn:aws:s3:::sea-ad-spatial-transcriptomics", + "Description": "Quantitative neuropathology (full resolution images, processed images, and quantifications) in a public bucket", + "ARN": "arn:aws:s3:::sea-ad-quantitative-neuropathology", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheimers-disease-brain-cell-atlas-download?edit&language=en", @@ -28503,7 +28557,7 @@ "life sciences" ], "Explore": [ - "[Browse Bucket](https://sea-ad-spatial-transcriptomics.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://sea-ad-quantitative-neuropathology.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -28512,8 +28566,8 @@ }, { "Name": "Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD)", - "Description": "Quantitative neuropathology (full resolution images, processed images, and quantifications) in a public bucket", - "ARN": "arn:aws:s3:::sea-ad-quantitative-neuropathology", + "Description": "Single cell profiling (transcriptomics and epigenomics) data files in a public bucket", + "ARN": "arn:aws:s3:::sea-ad-single-cell-profiling", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheimers-disease-brain-cell-atlas-download?edit&language=en", @@ -28540,7 +28594,7 @@ "life sciences" ], "Explore": [ - "[Browse Bucket](https://sea-ad-quantitative-neuropathology.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://sea-ad-single-cell-profiling.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -28611,8 +28665,8 @@ }, { "Name": "Sentinel-1", - "Description": "GRD in a Requester Pays S3 bucket", - "ARN": "arn:aws:s3:::sentinel-s1-l1c", + "Description": "S3 Inventory files for L1C and CSV", + "ARN": "arn:aws:s3:::sentinel-inventory/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html", @@ -28630,20 +28684,18 @@ "cog", "synthetic aperture radar" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)" - ], - "RequesterPays": true, + "Explore": null, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Sentinel-1", - "Description": "S3 Inventory files for L1C and CSV", - "ARN": "arn:aws:s3:::sentinel-inventory/", + "Description": "SNS topic for notification of new scenes, can subscribe with Lambda", + "ARN": "arn:aws:sns:eu-central-1:214830741341:SentinelS1L1C", "Region": "eu-central-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html", "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", "ManagedBy": "[Sinergise](https://www.sinergise.com/)", @@ -28667,10 +28719,10 @@ }, { "Name": "Sentinel-1", - "Description": "SNS topic for notification of new scenes, can subscribe with Lambda", - "ARN": "arn:aws:sns:eu-central-1:214830741341:SentinelS1L1C", + "Description": "GRD in a Requester Pays S3 bucket", + "ARN": "arn:aws:s3:::sentinel-s1-l1c", "Region": "eu-central-1", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html", "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", "ManagedBy": "[Sinergise](https://www.sinergise.com/)", @@ -28686,8 +28738,10 @@ "cog", "synthetic aperture radar" ], - "Explore": null, - "RequesterPays": null, + "Explore": [ + "[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)" + ], + "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -28803,10 +28857,10 @@ }, { "Name": "Sentinel-2", - "Description": "Level 2A scenes and metadata, in Requester Pays S3 bucket", - "ARN": "arn:aws:s3:::sentinel-s2-l2a", + "Description": "New scene notifications for L2A, can subscribe with Lambda", + "ARN": "arn:aws:sns:eu-central-1:214830741341:SentinelS2L2A", "Region": "eu-central-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", "ManagedBy": "[Sinergise](https://www.sinergise.com/)", @@ -28822,19 +28876,17 @@ "disaster response", "stac" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)" - ], - "RequesterPays": true, + "Explore": null, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Sentinel-2", - "Description": "New scene notifications for L2A, can subscribe with Lambda", - "ARN": "arn:aws:sns:eu-central-1:214830741341:SentinelS2L2A", - "Region": "eu-central-1", + "Description": "New scene notifications for L1C, can subscribe with Lambda", + "ARN": "arn:aws:sns:eu-west-1:214830741341:NewSentinel2Product", + "Region": "eu-west-1", "Type": "SNS Topic", "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", @@ -28859,10 +28911,10 @@ }, { "Name": "Sentinel-2", - "Description": "New scene notifications for L1C, can subscribe with Lambda", - "ARN": "arn:aws:sns:eu-west-1:214830741341:NewSentinel2Product", - "Region": "eu-west-1", - "Type": "SNS Topic", + "Description": "Zipped archives for each L2A product with 3 day retention period, in Requester Pays bucket", + "ARN": "arn:aws:s3:::sentinel-s2-l2a-zips", + "Region": "eu-central-1", + "Type": "S3 Bucket", "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", "ManagedBy": "[Sinergise](https://www.sinergise.com/)", @@ -28879,7 +28931,7 @@ "stac" ], "Explore": null, - "RequesterPays": null, + "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -28938,6 +28990,35 @@ "AccountRequired": null, "Host": null }, + { + "Name": "Sentinel-2", + "Description": "Level 2A scenes and metadata, in Requester Pays S3 bucket", + "ARN": "arn:aws:s3:::sentinel-s2-l2a", + "Region": "eu-central-1", + "Type": "S3 Bucket", + "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", + "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", + "ManagedBy": "[Sinergise](https://www.sinergise.com/)", + "UpdateFrequency": "New Sentinel data are added regularly, usually within few hours after they are available on Copernicus OpenHub.", + "License": "Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions).", + "Tags": [ + "aws-pds", + "agriculture", + "earth observation", + "satellite imagery", + "geospatial", + "natural resource", + "disaster response", + "stac" + ], + "Explore": [ + "[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)" + ], + "RequesterPays": true, + "ControlledAccess": null, + "AccountRequired": null, + "Host": null + }, { "Name": "Sentinel-2", "Description": "S3 Inventory files for L1C and CSV", @@ -28998,28 +29079,53 @@ "Host": null }, { - "Name": "Sentinel-2", - "Description": "Zipped archives for each L2A product with 3 day retention period, in Requester Pays bucket", - "ARN": "arn:aws:s3:::sentinel-s2-l2a-zips", - "Region": "eu-central-1", + "Name": "Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States", + "Description": "New scene notification", + "ARN": "arn:aws:sns:us-west-2:242201296900:usgs-wma-sentinel-2-aqr-acolite-dsf-object_created", + "Region": "us-west-2", + "Type": "SNS Topic", + "Documentation": "https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed", + "Contact": "tvking@usgs.gov", + "ManagedBy": "[United States Geological Survey](https://www.usgs.gov)", + "UpdateFrequency": "New scenes are added daily.", + "License": "Contains modified Copernicus Sentinel data, which is available under the Creative Commons CC BY-SA 3.0 IGO license. Please reference King et al., 2024 (doi 10.5066/P904243C) when referring to the aquatic reflectance, and include the statement 'Contains modified Copernicus Sentinel data [Year]' to acknowledge the data originator.", + "Tags": [ + "aws-pds", + "earth observation", + "satellite imagery", + "geospatial", + "natural resource", + "cog", + "water" + ], + "Explore": null, + "RequesterPays": null, + "ControlledAccess": null, + "AccountRequired": null, + "Host": null + }, + { + "Name": "Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States", + "Description": "Scenes and metadata", + "ARN": "arn:aws:s3:::usgs-wma-sentinel-2-aqr-acolite-dsf/version_01", + "Region": "us-west-2", "Type": "S3 Bucket", - "Documentation": "Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/sentinel-s2-l1c/readme.html) and [Sentinel-2 L2A](https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html).", - "Contact": "https://forum.sentinel-hub.com/c/aws-sentinel", - "ManagedBy": "[Sinergise](https://www.sinergise.com/)", - "UpdateFrequency": "New Sentinel data are added regularly, usually within few hours after they are available on Copernicus OpenHub.", - "License": "Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions).", + "Documentation": "https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed", + "Contact": "tvking@usgs.gov", + "ManagedBy": "[United States Geological Survey](https://www.usgs.gov)", + "UpdateFrequency": "New scenes are added daily.", + "License": "Contains modified Copernicus Sentinel data, which is available under the Creative Commons CC BY-SA 3.0 IGO license. Please reference King et al., 2024 (doi 10.5066/P904243C) when referring to the aquatic reflectance, and include the statement 'Contains modified Copernicus Sentinel data [Year]' to acknowledge the data originator.", "Tags": [ "aws-pds", - "agriculture", "earth observation", "satellite imagery", "geospatial", "natural resource", - "disaster response", - "stac" + "cog", + "water" ], "Explore": null, - "RequesterPays": true, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -29054,8 +29160,8 @@ }, { "Name": "Sentinel-2 Cloud-Optimized GeoTIFFs", - "Description": "Level 2A scenes and metadata", - "ARN": "arn:aws:s3:::sentinel-cogs", + "Description": "S3 Inventory files for L1C and CSV", + "ARN": "arn:aws:s3:::sentinel-cogs-inventory", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/cirrus-geo/cirrus-earth-search", @@ -29074,19 +29180,16 @@ "cog", "stac" ], - "Explore": [ - "[Earth Search STAC L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)", - "[STAC Browser L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)" - ], - "RequesterPays": false, + "Explore": null, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Sentinel-2 Cloud-Optimized GeoTIFFs", - "Description": "S3 Inventory files for L1C and CSV", - "ARN": "arn:aws:s3:::sentinel-cogs-inventory", + "Description": "Level 2A scenes and metadata", + "ARN": "arn:aws:s3:::sentinel-cogs", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/cirrus-geo/cirrus-earth-search", @@ -29105,8 +29208,11 @@ "cog", "stac" ], - "Explore": null, - "RequesterPays": null, + "Explore": [ + "[Earth Search STAC L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)", + "[STAC Browser L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)" + ], + "RequesterPays": false, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -29140,8 +29246,8 @@ }, { "Name": "Sentinel-3", - "Description": "Sentinel-3 Cloud Optimized GeoTIFF (COG) format", - "ARN": "arn:aws:s3:::meeo-s3-cog/", + "Description": "Sentinel-3 Near Real Time Data (NRT) format", + "ARN": "arn:aws:s3:::meeo-s3/NRT/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Description.md", @@ -29160,9 +29266,7 @@ "cog", "stac" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -29170,8 +29274,8 @@ }, { "Name": "Sentinel-3", - "Description": "Sentinel-3 Short Time Critical (STC) format", - "ARN": "arn:aws:s3:::meeo-s3/STC/", + "Description": "Sentinel-3 Not Time Critical (NTC) format", + "ARN": "arn:aws:s3:::meeo-s3/NTC/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Description.md", @@ -29198,8 +29302,8 @@ }, { "Name": "Sentinel-3", - "Description": "Sentinel-3 Not Time Critical (NTC) format", - "ARN": "arn:aws:s3:::meeo-s3/NTC/", + "Description": "Sentinel-3 Short Time Critical (STC) format", + "ARN": "arn:aws:s3:::meeo-s3/STC/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Description.md", @@ -29226,8 +29330,8 @@ }, { "Name": "Sentinel-3", - "Description": "Sentinel-3 Near Real Time Data (NRT) format", - "ARN": "arn:aws:s3:::meeo-s3/NRT/", + "Description": "Sentinel-3 Cloud Optimized GeoTIFF (COG) format", + "ARN": "arn:aws:s3:::meeo-s3-cog/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Description.md", @@ -29246,7 +29350,9 @@ "cog", "stac" ], - "Explore": null, + "Explore": [ + "[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -29254,8 +29360,8 @@ }, { "Name": "Sentinel-5P Level 2", - "Description": "Sentinel-5p Reprocessed Data (RPRO) NetCDF format", - "ARN": "arn:aws:s3:::meeo-s5p/RPRO/", + "Description": "Sentinel-5p Near Real Time Data (NRTI) NetCDF format", + "ARN": "arn:aws:s3:::meeo-s5p/NRTI/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Description.md", @@ -29282,8 +29388,8 @@ }, { "Name": "Sentinel-5P Level 2", - "Description": "Sentinel-5p Near Real Time Data (NRTI) NetCDF format", - "ARN": "arn:aws:s3:::meeo-s5p/NRTI/", + "Description": "Sentinel-5p Off Line Data (OFFL) NetCDF format", + "ARN": "arn:aws:s3:::meeo-s5p/OFFL/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Description.md", @@ -29310,8 +29416,8 @@ }, { "Name": "Sentinel-5P Level 2", - "Description": "Sentinel-5p Cloud Optimised GeoTIFF (COGT) TIFF format", - "ARN": "arn:aws:s3:::meeo-s5p/COGT/", + "Description": "Sentinel-5p Reprocessed Data (RPRO) NetCDF format", + "ARN": "arn:aws:s3:::meeo-s5p/RPRO/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Description.md", @@ -29330,9 +29436,7 @@ "cog", "stac" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -29340,8 +29444,8 @@ }, { "Name": "Sentinel-5P Level 2", - "Description": "Sentinel-5p Off Line Data (OFFL) NetCDF format", - "ARN": "arn:aws:s3:::meeo-s5p/OFFL/", + "Description": "Sentinel-5p Cloud Optimised GeoTIFF (COGT) TIFF format", + "ARN": "arn:aws:s3:::meeo-s5p/COGT/", "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Description.md", @@ -29360,7 +29464,9 @@ "cog", "stac" ], - "Explore": null, + "Explore": [ + "[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -29990,10 +30096,10 @@ }, { "Name": "Sudachi Language Resources", - "Description": "Cloudfront CDN mirror", - "ARN": null, + "Description": "SudachiDict: Binary format of the mophological analysis dictionarieschiVe: Pretrained word embedding in various formats", + "ARN": "arn:aws:s3:::sudachi", "Region": "ap-northeast-1", - "Type": "CloudFront Distribution", + "Type": "S3 Bucket", "Documentation": "https://worksapplications.github.io/Sudachi/", "Contact": "sudachi@worksap.co.jp", "ManagedBy": "[Works Applications](https://www.worksap.co.jp/about/csr/nlp/)", @@ -30007,14 +30113,14 @@ "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, - "Host": "d2ej7fkh96fzlu.cloudfront.net" + "Host": null }, { "Name": "Sudachi Language Resources", - "Description": "SudachiDict: Binary format of the mophological analysis dictionarieschiVe: Pretrained word embedding in various formats", - "ARN": "arn:aws:s3:::sudachi", + "Description": "Cloudfront CDN mirror", + "ARN": null, "Region": "ap-northeast-1", - "Type": "S3 Bucket", + "Type": "CloudFront Distribution", "Documentation": "https://worksapplications.github.io/Sudachi/", "Contact": "sudachi@worksap.co.jp", "ManagedBy": "[Works Applications](https://www.worksap.co.jp/about/csr/nlp/)", @@ -30028,12 +30134,12 @@ "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, - "Host": null + "Host": "d2ej7fkh96fzlu.cloudfront.net" }, { "Name": "Sup3rCC", - "Description": "Sup3rCC", - "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/", + "Description": "Sup3rCC Generative Models", + "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/models/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/sup3r", @@ -30049,7 +30155,7 @@ "climate model" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=models%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -30058,8 +30164,8 @@ }, { "Name": "Sup3rCC", - "Description": "Sup3rCC Generative Models", - "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/models/", + "Description": "Sup3rCC - CONUS - MRI ESM 20 - SSP585 - r1i1p1f1", + "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/conus_mriesm20_ssp585_r1i1p1f1/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/sup3r", @@ -30075,7 +30181,7 @@ "climate model" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=models%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_mriesm20_ssp585_r1i1p1f1%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -30084,8 +30190,8 @@ }, { "Name": "Sup3rCC", - "Description": "Sup3rCC - CONUS - MRI ESM 20 - SSP585 - r1i1p1f1", - "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/conus_mriesm20_ssp585_r1i1p1f1/", + "Description": "Sup3rCC", + "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/sup3r", @@ -30101,7 +30207,7 @@ "climate model" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_mriesm20_ssp585_r1i1p1f1%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc)" ], "RequesterPays": null, "ControlledAccess": null, @@ -30345,9 +30451,9 @@ }, { "Name": "Terrain Tiles", - "Description": "Gridded elevation tiles", - "ARN": "arn:aws:s3:::elevation-tiles-prod", - "Region": "us-east-1", + "Description": "Gridded elevation tiles - replication in EU region", + "ARN": "arn:aws:s3:::elevation-tiles-prod-eu", + "Region": "eu-central-1", "Type": "S3 Bucket", "Documentation": "https://github.com/tilezen/joerd/tree/master/docs", "Contact": "https://github.com/tilezen/joerd/issues", @@ -30362,9 +30468,7 @@ "geospatial", "disaster response" ], - "Explore": [ - "[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -30372,9 +30476,9 @@ }, { "Name": "Terrain Tiles", - "Description": "Gridded elevation tiles - replication in EU region", - "ARN": "arn:aws:s3:::elevation-tiles-prod-eu", - "Region": "eu-central-1", + "Description": "Gridded elevation tiles", + "ARN": "arn:aws:s3:::elevation-tiles-prod", + "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://github.com/tilezen/joerd/tree/master/docs", "Contact": "https://github.com/tilezen/joerd/issues", @@ -30389,7 +30493,9 @@ "geospatial", "disaster response" ], - "Explore": null, + "Explore": [ + "[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -30418,8 +30524,8 @@ }, { "Name": "The Cancer Genome Atlas", - "Description": "Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-SeqIsoform Expression Quantification, miRNA Expression Quantification, Genotyping Array CopyNumber Segment, Genotyping Array Masked Copy Number Segment, Genotyping Array Gene Level CopyNumber Scores, WXS Masked Somatic Mutation", - "ARN": "arn:aws:s3:::tcga-2-open", + "Description": "WXS/RNA-Seq/miRNA-Seq/ATAC-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw SomaticMutation, WXS Aggregated Somatic Mutation", + "ARN": "arn:aws:s3:::tcga-2-controlled", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga", @@ -30437,14 +30543,14 @@ ], "Explore": null, "RequesterPays": null, - "ControlledAccess": null, + "ControlledAccess": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178.v1.p1", "AccountRequired": null, "Host": null }, { "Name": "The Cancer Genome Atlas", - "Description": "WXS/RNA-Seq/miRNA-Seq/ATAC-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw SomaticMutation, WXS Aggregated Somatic Mutation", - "ARN": "arn:aws:s3:::tcga-2-controlled", + "Description": "Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-SeqIsoform Expression Quantification, miRNA Expression Quantification, Genotyping Array CopyNumber Segment, Genotyping Array Masked Copy Number Segment, Genotyping Array Gene Level CopyNumber Scores, WXS Masked Somatic Mutation", + "ARN": "arn:aws:s3:::tcga-2-open", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga", @@ -30462,7 +30568,7 @@ ], "Explore": null, "RequesterPays": null, - "ControlledAccess": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178.v1.p1", + "ControlledAccess": null, "AccountRequired": null, "Host": null }, @@ -31065,8 +31171,8 @@ }, { "Name": "USGS Landsat", - "Description": "New scene notifications, Level-1 and Level-2 Scenes", - "ARN": "arn:aws:sns:us-west-2:673253540267:public-c2-notify-v2", + "Description": "New scene notifications, US ARD Tiles", + "ARN": "arn:aws:sns:us-west-2:673253540267:public-c2-ard-tile-notify-v2", "Region": "us-west-2", "Type": "SNS Topic", "Documentation": "https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-data-access", @@ -31093,8 +31199,8 @@ }, { "Name": "USGS Landsat", - "Description": "New scene notifications, US ARD Tiles", - "ARN": "arn:aws:sns:us-west-2:673253540267:public-c2-ard-tile-notify-v2", + "Description": "New scene notifications, Level 3 Science Products", + "ARN": "arn:aws:sns:us-west-2:673253540267:public-c2-level-3-tile-notify-v2", "Region": "us-west-2", "Type": "SNS Topic", "Documentation": "https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-data-access", @@ -31121,8 +31227,8 @@ }, { "Name": "USGS Landsat", - "Description": "New scene notifications, Level 3 Science Products", - "ARN": "arn:aws:sns:us-west-2:673253540267:public-c2-level-3-tile-notify-v2", + "Description": "New scene notifications, Level-1 and Level-2 Scenes", + "ARN": "arn:aws:sns:us-west-2:673253540267:public-c2-notify-v2", "Region": "us-west-2", "Type": "SNS Topic", "Documentation": "https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-data-access", @@ -31436,8 +31542,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2022_01", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-01/", + "Description": "UniProt 2021_04", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-04/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31466,8 +31572,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2022_02", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-02/", + "Description": "UniProt 2022_01", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-01/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31526,8 +31632,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2021_04", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-04/", + "Description": "UniProt 2023_05", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-05/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31556,8 +31662,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2022_05", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-05/", + "Description": "UniProt 2022_04", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-04/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31586,8 +31692,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2023_01", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-01/", + "Description": "UniProt 2022_05", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-05/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31616,8 +31722,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2023_02", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-02/", + "Description": "UniProt 2023_01", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-01/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31646,8 +31752,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2023_03", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-03/", + "Description": "UniProt 2023_02", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-02/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31676,8 +31782,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2023_04", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-04/", + "Description": "UniProt 2023_03", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-03/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31706,8 +31812,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2023_05", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-05/", + "Description": "UniProt 2023_04", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-04/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31736,8 +31842,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2024_01", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-01/", + "Description": "UniProt 2022_02", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-02/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31766,8 +31872,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2022_04", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-04/", + "Description": "UniProt 2024_01", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-01/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31916,8 +32022,8 @@ }, { "Name": "Vermont Open Geospatial on AWS", - "Description": "Landcover datsets are organized in this bucket as statewide file mosaics These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention STATEWIDE__cm_LANDCOVER_", - "ARN": "arn:aws:s3:::vtopendata-prd/Landcover", + "Description": "Imagery datsets are organized in this bucket as statewide file mosaics and by acquisition year (often a portion of the state in any given year) These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention; 1) Statewide - STATEWIDE__cm__<#BANDS>Band, 2) By Acquisition Year - _cm__<#BANDS>Band Individual tiles are also available as lossless COGs under the /_Tiles subfolder", + "ARN": "arn:aws:s3:::vtopendata-prd/Imagery", "Region": "us-east-2", "Type": "S3 Bucket", "Documentation": "https://vcgi.vermont.gov/data-and-programs/", @@ -31941,8 +32047,8 @@ }, { "Name": "Vermont Open Geospatial on AWS", - "Description": "Imagery datsets are organized in this bucket as statewide file mosaics and by acquisition year (often a portion of the state in any given year) These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention; 1) Statewide - STATEWIDE__cm__<#BANDS>Band, 2) By Acquisition Year - _cm__<#BANDS>Band Individual tiles are also available as lossless COGs under the /_Tiles subfolder", - "ARN": "arn:aws:s3:::vtopendata-prd/Imagery", + "Description": "Landcover datsets are organized in this bucket as statewide file mosaics These data are available in Cloud Optimized GeoTIFF (COG) format and use the following naming convention STATEWIDE__cm_LANDCOVER_", + "ARN": "arn:aws:s3:::vtopendata-prd/Landcover", "Region": "us-east-2", "Type": "S3 Bucket", "Documentation": "https://vcgi.vermont.gov/data-and-programs/", @@ -32279,8 +32385,8 @@ }, { "Name": "Wind AI Bench", - "Description": "Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 9k Shapes Data Sets", - "ARN": "arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/9K_airfoils/", + "Description": "Wind AI Bench Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets] (https://dataopeneiorg/submissions/5884)", + "ARN": "arn:aws:s3:::nrel-pds-windai/wind_plant_power/floris/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/windAI_bench", @@ -32295,7 +32401,7 @@ "machine learning" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F9k_airfoils%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=wind_plant_power%2Ffloris%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -32304,8 +32410,8 @@ }, { "Name": "Wind AI Bench", - "Description": "Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 2k Shapes Data Sets", - "ARN": "arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/2K_airfoils/", + "Description": "Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 9k Shapes Data Sets", + "ARN": "arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/9K_airfoils/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/windAI_bench", @@ -32320,7 +32426,7 @@ "machine learning" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F2k_airfoils%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F9k_airfoils%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -32329,8 +32435,8 @@ }, { "Name": "Wind AI Bench", - "Description": "Wind AI Bench Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets] (https://dataopeneiorg/submissions/5884)", - "ARN": "arn:aws:s3:::nrel-pds-windai/wind_plant_power/floris/", + "Description": "Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 2k Shapes Data Sets", + "ARN": "arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/2K_airfoils/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/windAI_bench", @@ -32345,7 +32451,7 @@ "machine learning" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=wind_plant_power%2Ffloris%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F2k_airfoils%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -32607,8 +32713,8 @@ }, { "Name": "iHART Whole Genome Sequencing Data Set", - "Description": "BAM, gVCF, and VCF files from The iHART whole genome sequencing study, Phase I", - "ARN": "arn:aws:s3:::ihart-release", + "Description": "gVCF and VCF files from The iHART whole genome sequencing study, control data sets", + "ARN": "arn:aws:s3:::ihart-brain", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "http://www.ihart.org/data", @@ -32634,8 +32740,8 @@ }, { "Name": "iHART Whole Genome Sequencing Data Set", - "Description": "BAM, gVCF, and VCF files from The iHART whole genome sequencing study, Phase II", - "ARN": "arn:aws:s3:::ihart-main", + "Description": "Cram, gVCF, and VCF files from The iHART whole genome sequencing study, Phase I+II, GRCh38", + "ARN": "arn:aws:s3:::ihart-hg38", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "http://www.ihart.org/data", @@ -32661,8 +32767,8 @@ }, { "Name": "iHART Whole Genome Sequencing Data Set", - "Description": "gVCF and VCF files from The iHART whole genome sequencing study, control data sets", - "ARN": "arn:aws:s3:::ihart-psp", + "Description": "BAM, gVCF, and VCF files from The iHART whole genome sequencing study, Phase II", + "ARN": "arn:aws:s3:::ihart-main", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "http://www.ihart.org/data", @@ -32688,8 +32794,8 @@ }, { "Name": "iHART Whole Genome Sequencing Data Set", - "Description": "gVCF and VCF files from The iHART whole genome sequencing study, control data sets", - "ARN": "arn:aws:s3:::ihart-brain", + "Description": "BAM, gVCF, and VCF files from The iHART whole genome sequencing study, Phase I", + "ARN": "arn:aws:s3:::ihart-release", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "http://www.ihart.org/data", @@ -32715,8 +32821,8 @@ }, { "Name": "iHART Whole Genome Sequencing Data Set", - "Description": "Cram, gVCF, and VCF files from The iHART whole genome sequencing study, Phase I+II, GRCh38", - "ARN": "arn:aws:s3:::ihart-hg38", + "Description": "gVCF and VCF files from The iHART whole genome sequencing study, control data sets", + "ARN": "arn:aws:s3:::ihart-psp", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "http://www.ihart.org/data", @@ -32818,10 +32924,10 @@ }, { "Name": "nuPlan", - "Description": "Globally cached distribution of the nuPlan Dataset Web frontend is available to browse the dataset", - "ARN": null, + "Description": "nuPlan Dataset", + "ARN": "arn:aws:s3:::motional-nuplan", "Region": "ap-northeast-1", - "Type": "CloudFront Distribution", + "Type": "S3 Bucket", "Documentation": "https://nuplan.org", "Contact": "https://nuplan.org", "ManagedBy": "[Motional, Inc.](https://motional.com)", @@ -32835,18 +32941,20 @@ "transportation", "urban" ], - "Explore": null, + "Explore": [ + "[Browse Bucket](https://motional-nuplan.s3.ap-northeast-1.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, - "Host": "https://d1qinkmu0ju04f.cloudfront.net" + "Host": null }, { "Name": "nuPlan", - "Description": "nuPlan Dataset", - "ARN": "arn:aws:s3:::motional-nuplan", + "Description": "Globally cached distribution of the nuPlan Dataset Web frontend is available to browse the dataset", + "ARN": null, "Region": "ap-northeast-1", - "Type": "S3 Bucket", + "Type": "CloudFront Distribution", "Documentation": "https://nuplan.org", "Contact": "https://nuplan.org", "ManagedBy": "[Motional, Inc.](https://motional.com)", @@ -32860,13 +32968,11 @@ "transportation", "urban" ], - "Explore": [ - "[Browse Bucket](https://motional-nuplan.s3.ap-northeast-1.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, - "Host": null + "Host": "https://d1qinkmu0ju04f.cloudfront.net" }, { "Name": "nuScenes", @@ -32924,10 +33030,10 @@ }, { "Name": "real-changesets", - "Description": "real-changesets", - "ARN": "arn:aws:s3:::real-changesets", + "Description": "New File Notification", + "ARN": "arn:aws:sns:us-west-2:877446169145:real-changesets-object_created", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://github.com/osmus/osmcha-charter-project/blob/main/real-changesets-docs.md", "Contact": "team@openstreetmap.us", "ManagedBy": "OpenStreetMap US", @@ -32948,10 +33054,10 @@ }, { "Name": "real-changesets", - "Description": "New File Notification", - "ARN": "arn:aws:sns:us-west-2:877446169145:real-changesets-object_created", + "Description": "real-changesets", + "ARN": "arn:aws:s3:::real-changesets", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://github.com/osmus/osmcha-charter-project/blob/main/real-changesets-docs.md", "Contact": "team@openstreetmap.us", "ManagedBy": "OpenStreetMap US", diff --git a/aws_open_datasets.tsv b/aws_open_datasets.tsv index f6bbd0d..834d5cf 100644 --- a/aws_open_datasets.tsv +++ b/aws_open_datasets.tsv @@ -10,10 +10,10 @@ Name Description ARN Region Type Documentation Contact ManagedBy UpdateFrequency 10m Annual Land Use Land Cover (9-class) 10m Annual Land Use Land Cover (9-class) arn:aws:s3:::io-10m-annual-lulc us-west-2 S3 Bucket https://www.impactobservatory.com/global_maps hello@impactobservatory.com [Impact Observatory](https://www.impactobservatory.com/) A new year is made available annually, each January. A new time series was provi [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, earth observation, environmental, geospatial, satellite imagery, sustainability, stac, cog, land cover, land use, machine learning, mapping, planetary ['[STAC 1.0.0 endpoint](https://api.impactobservatory.com/stac-aws/collections/io-10m-annual-lulc/items)'] 1940 Census Population Schedules, Enumeration District Maps, and Enumeration District Descriptions 1940 Census arn:aws:s3:::nara-1940-census us-east-2 S3 Bucket https://www.archives.gov/developer/1940-census public.dataset.program@nara.gov National Archives and Records Administration (NARA) Not updated US Government work nara, census, archives, 1940 census, demography, aws-pds 1950 Census Population Schedules, Enumeration District Maps, and Enumeration District Descriptions 1950 Census arn:aws:s3:::nara-1950-census us-east-2 S3 Bucket https://www.archives.gov/developer/1950-census public.dataset.program@nara.gov National Archives and Records Administration (NARA) Not updated US Government work nara, census, archives, 1950 census, demography, aws-pds -2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File Census Open Data S3 Inventory arn:aws:s3:::uscb-opendata-inventory us-west-2 S3 Bucket [2010 Census Production Settings Demographic and Housing Characteristics (DHC) D 2020DAS@census.gov [United States Census Bureau](http://www.census.gov/) Not Updated CC0 1.0 Universal aws-pds, census, differential privacy, disclosure avoidance, ethnicity, group quarters, hispanic, latino, housing, housing units, noisy measurements, population, race, redistricting, voting age 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File 2010 Census Demographic and Housing Characteristics Noisy Measurement File arn:aws:s3:::uscb-2020-product-releases/decennial/dhc/2010/nmf/2010-dhc-nmf-county-partitioned us-west-2 S3 Bucket [2010 Census Production Settings Demographic and Housing Characteristics (DHC) D 2020DAS@census.gov [United States Census Bureau](http://www.census.gov/) Not Updated CC0 1.0 Universal aws-pds, census, differential privacy, disclosure avoidance, ethnicity, group quarters, hispanic, latino, housing, housing units, noisy measurements, population, race, redistricting, voting age -2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File The 2010 Census Redistricting Data (PL 94-171) Noisy Measurement File arn:aws:s3:::uscb-2020-product-releases/decennial/redistricting/2010/nmf/2010-pl94-nmf-state-partitioned us-west-2 S3 Bucket [2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration 2020DAS@census.gov [United States Census Bureau](http://www.census.gov/) Last updated November 10, 2023: Modifications to identifiers within the parquet CC0 1.0 Universal aws-pds, census, differential privacy, disclosure avoidance, ethnicity, group quarters, hispanic, latino, housing, housing units, noisy measurements, population, race, redistricting, voting age +2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File Census Open Data S3 Inventory arn:aws:s3:::uscb-opendata-inventory us-west-2 S3 Bucket [2010 Census Production Settings Demographic and Housing Characteristics (DHC) D 2020DAS@census.gov [United States Census Bureau](http://www.census.gov/) Not Updated CC0 1.0 Universal aws-pds, census, differential privacy, disclosure avoidance, ethnicity, group quarters, hispanic, latino, housing, housing units, noisy measurements, population, race, redistricting, voting age 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File Census Open Data S3 Inventory arn:aws:s3:::uscb-opendata-inventory us-west-2 S3 Bucket [2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration 2020DAS@census.gov [United States Census Bureau](http://www.census.gov/) Last updated November 10, 2023: Modifications to identifiers within the parquet CC0 1.0 Universal aws-pds, census, differential privacy, disclosure avoidance, ethnicity, group quarters, hispanic, latino, housing, housing units, noisy measurements, population, race, redistricting, voting age +2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File The 2010 Census Redistricting Data (PL 94-171) Noisy Measurement File arn:aws:s3:::uscb-2020-product-releases/decennial/redistricting/2010/nmf/2010-pl94-nmf-state-partitioned us-west-2 S3 Bucket [2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration 2020DAS@census.gov [United States Census Bureau](http://www.census.gov/) Last updated November 10, 2023: Modifications to identifiers within the parquet CC0 1.0 Universal aws-pds, census, differential privacy, disclosure avoidance, ethnicity, group quarters, hispanic, latino, housing, housing units, noisy measurements, population, race, redistricting, voting age 2020 Census Demographic and Housing Characteristics (DHC) Noisy Measurement File Census Open Data S3 Inventory arn:aws:s3:::uscb-opendata-inventory us-west-2 S3 Bucket [Demographic and Housing Characteristics (DHC) Noisy Measurement File (2023-10-2 2020DAS@census.gov [United States Census Bureau](http://www.census.gov/) Not Updated CC0 1.0 Universal aws-pds, census, differential privacy, disclosure avoidance, ethnicity, group quarters, housing, housing units, noisy measurements, population, race, redistricting, voting age 2020 Census Demographic and Housing Characteristics (DHC) Noisy Measurement File The 2020 Census Demographic and Housing Characteristics Noisy Measurement File arn:aws:s3:::uscb-2020-product-releases/decennial/dhc/2020/nmf/2020-dhc-nmf us-west-2 S3 Bucket [Demographic and Housing Characteristics (DHC) Noisy Measurement File (2023-10-2 2020DAS@census.gov [United States Census Bureau](http://www.census.gov/) Not Updated CC0 1.0 Universal aws-pds, census, differential privacy, disclosure avoidance, ethnicity, group quarters, housing, housing units, noisy measurements, population, race, redistricting, voting age 2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File Census Open Data S3 Inventory arn:aws:s3:::uscb-opendata-inventory us-west-2 S3 Bucket [2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File README File 2020DAS@census.gov [United States Census Bureau](http://www.census.gov/) Not Updated CC0 1.0 Universal aws-pds, census, differential privacy, disclosure avoidance, ethnicity, group quarters, housing, housing units, noisy measurements, population, race, redistricting, voting age @@ -28,26 +28,27 @@ A Realistic Cyber Defense Dataset (CSE-CIC-IDS2018) Network traffic and log file A region-wide, multi-year set of crop field boundary labels for Africa Field boundary labels and corresponding Planet images arn:aws:s3:::africa-field-boundary-labels us-west-2 S3 Bucket https://github.com/agroimpacts/lacunalabels/ airg@clarku.edu [The Agricultural Impacts Research Group](https://agroimpacts.info/) Updated versions of the dataset are added as they are developed [Planet NICFI participant license agreement](https://assets.planet.com/docs/Plan agriculture, machine learning, land cover, satellite imagery, cog, labeled A2D2: Audi Autonomous Driving Dataset http://a2d2audi arn:aws:s3:::aev-autonomous-driving-dataset eu-central-1 S3 Bucket http://a2d2.audi aevdrivingdataset@audi.de [Audi AG](http://a2d2.audi/) The dataset may be updated with additional or corrected data on a need-to-update https://creativecommons.org/licenses/by-nd/4.0/ autonomous vehicles, deep learning, computer vision, lidar, mapping, machine learning, robotics, aws-pds ABEJA CC JA Text corpus arn:aws:s3:::abeja-cc-ja ap-northeast-1 S3 Bucket https://github.com/abeja-inc/Megatron-LM/blob/main/docs/dataset/about_data.md abeja-datascience@abejainc.com [ABEJA inc.](https://www.abejainc.com/) None This data is available for anyone to use under the [Common Crawl Terms of Use](h natural language processing, web archive, internet, japanese +AI Weather Prediction (AIWP) Model Reforecasts AIWP data arn:aws:s3:::noaa-oar-mlwp-data us-east-1 S3 Bucket https://noaa-oar-mlwp-data.s3.amazonaws.com/README.txt For questions regarding data availability, content, or quality, contact Dr. Jaco Dr. Jacob Radford (jacob.radford@noaa.gov) 2 times a day, every 12 hours starting at midnight UTC Open Data. There are no restrictions on the use of this data. environmental, meteorological, weather ['[Browse Bucket](https://noaa-oar-mlwp-data.s3.amazonaws.com/index.html)'] AI2 Diagram Dataset (AI2D) Project data files in a public bucket arn:aws:s3:::ai2-public-datasets us-west-2 S3 Bucket https://allenai.org/data/diagrams info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/) aws-pds, machine learning AI2 Meaningful Citations Data Set Project data files in a public bucket arn:aws:s3:::ai2-public-datasets us-west-2 S3 Bucket https://allenai.org/data/meaningful-citations info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY](https://creativecommons.org/licenses/by/4.0) aws-pds, machine learning, csv AI2 Reasoning Challenge (ARC) 2018 Project data files in a public bucket arn:aws:s3:::ai2-public-datasets us-west-2 S3 Bucket https://allenai.org/data/arc info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/) aws-pds, machine learning, json, csv AI2 TabMCQ: Multiple Choice Questions aligned with the Aristo Tablestore Project data files in a public bucket arn:aws:s3:::ai2-public-datasets/tablestore-questions/ us-west-2 S3 Bucket https://allenai.org/data/tablestore-questions info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/) aws-pds, machine learning, natural language processing AI2 Tablestore (November 2015 Snapshot) Project data files in a public bucket arn:aws:s3:::ai2-public-datasets/tablestore/ us-west-2 S3 Bucket https://allenai.org/data/tablestore info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/) aws-pds, machine learning, natural language processing -ARPA-E PERFORM Forecast data ARPA-E PERFORM Forecast data arn:aws:s3:::arpa-e-perform/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar -ARPA-E PERFORM Forecast data Forecasts and Actuals for The Electric Reliability Council of Texas (ERCOT) arn:aws:s3:::arpa-e-perform/ERCOT/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar -ARPA-E PERFORM Forecast data Forecasts and Actuals for The New York Independent System Operator (NYISO) arn:aws:s3:::arpa-e-perform/NYISO/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar -ARPA-E PERFORM Forecast data Forecasts and Actuals for The Midcontinent Independent System Operator (MISO) arn:aws:s3:::arpa-e-perform/MISO/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar ARPA-E PERFORM Forecast data Forecasts and Actuals for The Southwest Power Pool (SPP) arn:aws:s3:::arpa-e-perform/SPP/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar -ASF SAR Data Products for Disaster Events ASF Event data S3 bucket arn:aws:s3:::asf-event-data us-west-2 S3 Bucket https://asf-event-data.s3.us-west-2.amazonaws.com/README.md https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Irregular, in response to disaster events This data falls under the terms and conditions of the [Creative Commons Zero (CC aws-pds, disaster response, satellite imagery, geospatial, cog, stac ['[Browse Bucket](https://asf-event-data.s3.amazonaws.com/index.html)'] +ARPA-E PERFORM Forecast data Forecasts and Actuals for The Midcontinent Independent System Operator (MISO) arn:aws:s3:::arpa-e-perform/MISO/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar +ARPA-E PERFORM Forecast data Forecasts and Actuals for The New York Independent System Operator (NYISO) arn:aws:s3:::arpa-e-perform/NYISO/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar +ARPA-E PERFORM Forecast data Forecasts and Actuals for The Electric Reliability Council of Texas (ERCOT) arn:aws:s3:::arpa-e-perform/ERCOT/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar +ARPA-E PERFORM Forecast data ARPA-E PERFORM Forecast data arn:aws:s3:::arpa-e-perform/ us-west-2 S3 Bucket https://github.com/PERFORM-Forecasts/documentation https://github.com/openEDI/PERFORM-Forecasts/documentation/issues [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, energy, environmental, geospatial, model, solar ASF SAR Data Products for Disaster Events Notifications for new event data arn:aws:sns:us-west-2:654654592981:asf-event-data-object_created us-west-2 SNS Topic https://asf-event-data.s3.us-west-2.amazonaws.com/README.md https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Irregular, in response to disaster events This data falls under the terms and conditions of the [Creative Commons Zero (CC aws-pds, disaster response, satellite imagery, geospatial, cog, stac -ASTER L1T Cloud-Optimized GeoTIFFs Imagery and metadata arn:aws:s3:::aster-l1t us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t opendata@descarteslabs.com [Descartes Labs](https://descarteslabs.com/) Daily There are no restrictions on the use of data, unless expressly identified prior aws-pds, earth observation, satellite imagery, geospatial, natural resource, sustainability, mining, cog False +ASF SAR Data Products for Disaster Events ASF Event data S3 bucket arn:aws:s3:::asf-event-data us-west-2 S3 Bucket https://asf-event-data.s3.us-west-2.amazonaws.com/README.md https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Irregular, in response to disaster events This data falls under the terms and conditions of the [Creative Commons Zero (CC aws-pds, disaster response, satellite imagery, geospatial, cog, stac ['[Browse Bucket](https://asf-event-data.s3.amazonaws.com/index.html)'] ASTER L1T Cloud-Optimized GeoTIFFs New image notifications arn:aws:sns:us-west-2:526859492376:aster-l1t-object_created us-west-2 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t opendata@descarteslabs.com [Descartes Labs](https://descarteslabs.com/) Daily There are no restrictions on the use of data, unless expressly identified prior aws-pds, earth observation, satellite imagery, geospatial, natural resource, sustainability, mining, cog +ASTER L1T Cloud-Optimized GeoTIFFs Imagery and metadata arn:aws:s3:::aster-l1t us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/aster-l1t opendata@descarteslabs.com [Descartes Labs](https://descarteslabs.com/) Daily There are no restrictions on the use of data, unless expressly identified prior aws-pds, earth observation, satellite imagery, geospatial, natural resource, sustainability, mining, cog False AWS Public Blockchain Data AWS Public Blockchain Data arn:aws:s3:::aws-public-blockchain us-east-2 S3 Bucket https://github.com/aws-samples/digital-assets-examples/blob/main/analytics/ aws-blockchain-data@amazon.com [Amazon Web Services](https://aws.amazon.com/) New data is delivered daily to the current date folders Parquet files. https://github.com/aws-samples/digital-assets-examples/blob/main/LICENSE blockchain, web3 AWS iGenomes AWS-iGenomes S3 Bucket arn:aws:s3:::ngi-igenomes eu-west-1 S3 Bucket https://ewels.github.io/AWS-iGenomes/ https://github.com/ewels/AWS-iGenomes/issues [SciLifeLab](https://opensource.scilifelab.se/) New data are added when available. Multiple - please see [data origins](https://github.com/ewels/AWS-iGenomes#data- amazon.science, agriculture, biology, genetic, genomic, life sciences, reference index, Caenorhabditis elegans, Danio rerio, Homo sapiens, Mus musculus, Rattus norvegicus Africa Soil Information Service (AfSIS) Soil Chemistry Paired wet and dry chemistry measurements for georeferenced soilscollected by t arn:aws:s3:::afsis us-east-1 S3 Bucket https://github.com/qedsoftware/afsis-soil-chem-tutorial afsis@qed.ai [QED](https://qed.ai/) As required "ODC Open Database License (""[ODbL](https://opendatacommons.org/licenses/odbl/sum" agriculture, aws-pds, environmental, food security, machine learning, life sciences -AgricultureVision Dataset affiliated with the 2021 CVPR Agricutlure Vision Workshop This includes arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_challenge_2021_full us-east-1 S3 Bucket https://arxiv.org/abs/2001.01306 support@intelinair.com Intelinair, Inc. Periodically Provided in the bucket. aws-pds, aerial imagery, agriculture, computer vision, deep learning, machine learning False AgricultureVision Dataset affiliated with the 2021 CVPR Agricutlure Vision Workshop This includ arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_challenge_2021 us-east-1 S3 Bucket https://arxiv.org/abs/2001.01306 support@intelinair.com Intelinair, Inc. Periodically Provided in the bucket. aws-pds, aerial imagery, agriculture, computer vision, deep learning, machine learning False AgricultureVision Original dataset affiliated with the 2020 CVPR paper Dataset provided as a ser arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_paper_2020 us-east-1 S3 Bucket https://arxiv.org/abs/2001.01306 support@intelinair.com Intelinair, Inc. Periodically Provided in the bucket. aws-pds, aerial imagery, agriculture, computer vision, deep learning, machine learning False +AgricultureVision Dataset affiliated with the 2021 CVPR Agricutlure Vision Workshop This includes arn:aws:s3:::intelinair-data-releases/agriculture-vision/cvpr_challenge_2021_full us-east-1 S3 Bucket https://arxiv.org/abs/2001.01306 support@intelinair.com Intelinair, Inc. Periodically Provided in the bucket. aws-pds, aerial imagery, agriculture, computer vision, deep learning, machine learning False Airborne Object Tracking Dataset The training dataset is further split into smaller directories Each subdirector arn:aws:s3:::airborne-obj-detection-challenge-training us-east-1 S3 Bucket https://www.aicrowd.com/challenges/airborne-object-tracking-challenge airborne-object-tracking-challenge@amazon.com [Amazon](https://www.amazon.com/) Not updated Community Data License Agreement – Permissive, Version 1.0 https://cdla.dev/perm amazon.science, computer vision, deep learning, machine learning ['[Explore dataset](https://github.com/amazon-research/siam-mot/blob/main/readme/model_zoo.md)', '[README](https://gitlab.aicrowd.com/amazon-prime-air/airborne-detection-starter-kit/-/blob/master/docs/DATASET.md)'] All-Sky Data | Wide-field Infrared Survey Explorer (WISE) "All-Sky Single-exposure Image Sets: 1,491,686 calibrated 1024x1024 pix @275""/pi" arn:aws:s3:::nasa-irsa-wise/wise/allsky us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The All-Sky Data Release has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, satellite imagery, survey False False AllWISE Data | Wide-field Infrared Survey Explorer (WISE) The AllWISE Images Atlas includes 18,240 4-band (34, 46, 12, 22 microns) calib arn:aws:s3:::nasa-irsa-wise/wise/allwise us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/Missions/wise.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The AllWISE Data Release has been finalized and will not be updated. However, th https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, survey False False @@ -63,16 +64,16 @@ Amazon Berkeley Objects Dataset Product listings and metadata in gzip-compressed Amazon Bin Image Dataset Over 500,000 bin JPEG images and corresponding JSON metadata files describing it arn:aws:s3:::aft-vbi-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/aft-vbi-pds amazon-bin-images@amazon.com [Amazon](https://www.amazon.com/) Not updated Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States (CC BY-N amazon.science, computer vision, machine learning ['[Browse Bucket](https://aft-vbi-pds.s3.amazonaws.com/index.html)'] Amazon Seller Contact Intent Sequence Amazon customer support contact intent sequence from sellers in an S3 bucket arn:aws:s3:::ascis us-west-2 S3 Bucket https://ascis.s3.us-west-2.amazonaws.com/README.md Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 [Amazon](https://www.amazon.com/) None https://cdla.dev/permissive-1-0/ amazon.science, machine learning, temporal point process, Hawkes Process Amazon-PQA Amazon product questions and their answers, along with the public product inform arn:aws:s3:::amazon-pqa us-east-1 S3 Bucket https://amazon-pqa.s3.amazonaws.com/readme.txt vitamin@amazon.com Amazon None https://cdla.dev/permissive-1-0/ amazon.science, natural language processing, machine learning -Amazonia EO satellite on AWS Amazonia 1 imagery (COG files, quicklooks, metadata) arn:aws:s3:::brazil-eosats us-west-2 S3 Bucket http://www.inpe.br/amazonia1 https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, sustainability, disaster response, stac, cog ['[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)', '[stacindex](https://stacindex.org/catalogs/cbers)'] False Amazonia EO satellite on AWS STAC static catalog arn:aws:s3:::br-eo-stac-1-0-0 us-west-2 S3 Bucket http://www.inpe.br/amazonia1 https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, sustainability, disaster response, stac, cog False Amazonia EO satellite on AWS Notifications for new quicklooks arn:aws:sns:us-west-2:599544552497:NewAM1Quicklook us-west-2 SNS Topic http://www.inpe.br/amazonia1 https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, sustainability, disaster response, stac, cog +Amazonia EO satellite on AWS Amazonia 1 imagery (COG files, quicklooks, metadata) arn:aws:s3:::brazil-eosats us-west-2 S3 Bucket http://www.inpe.br/amazonia1 https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, sustainability, disaster response, stac, cog ['[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)', '[stacindex](https://stacindex.org/catalogs/cbers)'] False Amazonia EO satellite on AWS Topic that receives STAC V100 items as new scenes are ingested arn:aws:sns:us-west-2:769537946825:br-eo-stac-prod-stacitemtopic4BCE3141-Z8he7LYjqXFe us-west-2 SNS Topic http://www.inpe.br/amazonia1 https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, sustainability, disaster response, stac, cog -Analysis Ready Sentinel-1 Backscatter Imagery Sentinel-1 RTC tiled data and metadata in a S3 bucket arn:aws:s3:::sentinel-s1-rtc-indigo us-west-2 S3 Bucket https://sentinel-s1-rtc-indigo-docs.s3-us-west-2.amazonaws.com/index.html For questions regarding data methodology or delivery, contact sentinel1@indigoag [Indigo Ag, Inc.](https://www.indigoag.com/) Data updates are paused while we repair the processing pipeline, but the target The use of these data fall under the terms and conditions of the [Indigo Atlas S agriculture, aws-pds, disaster response, earth observation, environmental, geospatial, satellite imagery, cog, stac, synthetic aperture radar ['[STAC V1.0.0 endpoint](https://scottyhq.github.io/sentinel1-rtc-stac/#/)'] Analysis Ready Sentinel-1 Backscatter Imagery Simple Notification Service (SNS) topic for notification of new tile uploads arn:aws:sns:us-west-2:410373799403:sentinel-s1-rtc-indigo-object_created us-west-2 SNS Topic https://sentinel-s1-rtc-indigo-docs.s3-us-west-2.amazonaws.com/index.html For questions regarding data methodology or delivery, contact sentinel1@indigoag [Indigo Ag, Inc.](https://www.indigoag.com/) Data updates are paused while we repair the processing pipeline, but the target The use of these data fall under the terms and conditions of the [Indigo Atlas S agriculture, aws-pds, disaster response, earth observation, environmental, geospatial, satellite imagery, cog, stac, synthetic aperture radar +Analysis Ready Sentinel-1 Backscatter Imagery Sentinel-1 RTC tiled data and metadata in a S3 bucket arn:aws:s3:::sentinel-s1-rtc-indigo us-west-2 S3 Bucket https://sentinel-s1-rtc-indigo-docs.s3-us-west-2.amazonaws.com/index.html For questions regarding data methodology or delivery, contact sentinel1@indigoag [Indigo Ag, Inc.](https://www.indigoag.com/) Data updates are paused while we repair the processing pipeline, but the target The use of these data fall under the terms and conditions of the [Indigo Atlas S agriculture, aws-pds, disaster response, earth observation, environmental, geospatial, satellite imagery, cog, stac, synthetic aperture radar ['[STAC V1.0.0 endpoint](https://scottyhq.github.io/sentinel1-rtc-stac/#/)'] Animal Tracking - Acoustic Telemetry - Quality controlled detections Cloud Optimised AODN dataset of IMOS - Animal Tracking Facility - Acoustic Track arn:aws:s3:::aodn-cloud-optimised/receiver_animal_acoustic_tagging_delayed_qc.parquet ap-southeast-2 S3 Bucket https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/5 info@aodn.org.au AODN As Needed http://creativecommons.org/licenses/by/4.0/ oceans, marine mammals, life sciences Answer Reformulation Answer Reformulation Dataset arn:aws:s3:::answer-reformulation-pds us-west-2 S3 Bucket https://answer-reformulation-pds.s3-us-west-2.amazonaws.com/README.txt filicesf@amazon.com [Amazon](https://www.amazon.com/) Not currently being updated [cc-by-sa 4.0](https://creativecommons.org/licenses/by-sa/4.0/) amazon.science, natural language processing, machine learning -ArcticDEM ArcticDEM DEM Mosaics arn:aws:s3:::pgc-opendata-dems/arcticdem/mosaics/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/arcticdem/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics.json)'] ArcticDEM ArcticDEM DEM Strips arn:aws:s3:::pgc-opendata-dems/arcticdem/strips/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/arcticdem/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/strips.json)'] +ArcticDEM ArcticDEM DEM Mosaics arn:aws:s3:::pgc-opendata-dems/arcticdem/mosaics/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/arcticdem/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics.json)'] Argo marine floats data and metadata from Global Data Assembly Centre (Argo GDAC) Argo GDAC data and metadata arn:aws:s3:::argo-gdac-sandbox eu-west-3 S3 Bucket http://www.argodatamgt.org/Documentation codac@ifremer.fr [Euro-Argo](https://www.euro-argo.eu/) Data is updated daily. Open data, there are no restrictions on the use of this data. https://creativeco aws-pds, climate, oceans, chemical biology, chemistry, datacenter, digital assets, geochemistry, geophysics, geoscience, marine, netcdf ['[Browse Bucket](https://argo-gdac-sandbox.s3.eu-west-3.amazonaws.com/pub/index.html#pub/)'] Argoverse Argoverse arn:aws:s3:::argoverse us-east-1 S3 Bucket https://argoverse.github.io/user-guide/ https://github.com/argoverse/av2-api/issues [Argoverse](https://argoverse.org) Infrequently [CC-BY-NC-SA-4.0](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html) aws-pds, autonomous vehicles, computer vision, lidar, robotics, geospatial Aristo Mini Corpus Project data files in a public bucket arn:aws:s3:::ai2-public-datasets us-west-2 S3 Bucket https://allenai.org/data/aristo-mini info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/) aws-pds, machine learning, json, csv @@ -88,65 +89,66 @@ Automated Segmentation of Intracellular Substructures in Electron Microscopy (AS Automatic Speech Recognition (ASR) Error Robustness Datatasets with ASR Errors arn:aws:s3:::asr-error-robustness us-east-1 S3 Bucket https://github.com/anjiefang/asr-error-robustness njfn@amazon.com [Amazon](https://www.amazon.com/) N/A See https://github.com/anjiefang/asr-error-robustness amazon.science, natural language processing, deep learning, machine learning, speech recognition Baby Open Brains (BOBs) Repository on AWS BOBs Repository data arn:aws:s3:::bobsrepository us-east-2 S3 Bucket https://bobsrepository.readthedocs.io/en/latest/ Eric Feczko (feczk001@umn.edu) & Sally M. Stoyell (stoye003@umn.edu) Masonic Institute for the Developing Brain (MIDB) Open Data Initiative The repository is updated when: (1) all brain segmentations have undergone furth CC-By Attribution 4.0 International neuroimaging, magnetic resonance imaging, neuroscience, pediatric, nifti, segmentation, life sciences ['[Browse Bucket](https://bobsrepository.s3.amazonaws.com/index.html)'] Basic Local Alignment Sequences Tool (BLAST) Databases BLAST databases with associated files in a public S3 bucket arn:aws:s3:::ncbi-blast-databases us-east-1 S3 Bucket https://github.com/ncbi/blast_plus_docs https://support.nlm.nih.gov/support/create-case/ [National Library of Medicine (NLM)](http://nlm.nih.gov/) Periodically """[NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/gen" aws-pds, bioinformatics, biology, health, life sciences, genetic, genomic, transcriptomics, protein, reference index -Beat Acute Myeloid Leukemia (AML) 1.0 WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutat arn:aws:s3:::gdc-beataml1-cohort-phs001657-2-controlled us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/blog/2019/beataml dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genetic, genomic, Homo sapiens, STRIDES https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001657.v1.p1&phv=417530&phd=&pha=&pht=9391&phvf=&phdf=&phaf=&phtf=&dssp=1&consent=&temp=1 -Beat Acute Myeloid Leukemia (AML) 1.0 BEATAML10-CRENOLANIB Clinical Supplement arn:aws:s3:::gdc-beataml1.0-crenolanib-phs001628-2-open us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/blog/2019/beataml dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genetic, genomic, Homo sapiens, STRIDES Beat Acute Myeloid Leukemia (AML) 1.0 BEATAML10-COHORT RNA-Seq Gene Expression Quantification arn:aws:s3:::gdc-beataml1-cohort-phs001657-2-open us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/blog/2019/beataml dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genetic, genomic, Homo sapiens, STRIDES +Beat Acute Myeloid Leukemia (AML) 1.0 BEATAML10-CRENOLANIB Clinical Supplement arn:aws:s3:::gdc-beataml1.0-crenolanib-phs001628-2-open us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/blog/2019/beataml dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genetic, genomic, Homo sapiens, STRIDES +Beat Acute Myeloid Leukemia (AML) 1.0 WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutat arn:aws:s3:::gdc-beataml1-cohort-phs001657-2-controlled us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/blog/2019/beataml dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genetic, genomic, Homo sapiens, STRIDES https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001657.v1.p1&phv=417530&phd=&pha=&pht=9391&phvf=&phdf=&phaf=&phtf=&dssp=1&consent=&temp=1 Biodiversity Heritage Library Metadata and Page Images Image files (JPEG-2000) and associated metadata describing the image and the boo arn:aws:s3:::bhl-open-data us-east-2 S3 Bucket "Documentation can be found at ou" feedback@biodiversitylibrary.org [The Biodiversity Heritage Library](https://biodiversitylibrary.org/) Metadata is updated monthly. Images are updated weekly. Public Domain, CC0, or Creative Commons. Exact licenses are found in the related biodiversity, bioinformatics, life sciences Biological and Physical Sciences (BPS) Microscopy Benchmark Training Dataset NASA BPS Microscopy Benchmark Training Data arn:aws:s3:::nasa-bps-training-data/Microscopy/ us-west-2 S3 Bucket https://docs.google.com/document/d/e/2PACX-1vTIjUPenLxVX0stErsBbK884QMJW_Ur1mqHJ lauren.m.sanders@nasa.gov [NASA](https://osdr.nasa.gov/) New fluorescence microscopy mouse fibroblast nuclei data is added whenever it is There are no restrictions on the use of this data. aws-pds, fluorescence imaging, genetic, genetic maps, microscopy, GeneLab, NASA SMD AI, life sciences Biological and Physical Sciences (BPS) RNA Sequencing Benchmark Training Dataset NASA BPS RNA Sequencing Benchmark Training Data arn:aws:s3:::nasa-bps-training-data/RNAsequencing/ us-west-2 S3 Bucket https://docs.google.com/document/d/e/2PACX-1vSvHfRYav25TVSJfQE2MvEKJIM7LUnpmAyRQ lauren.m.sanders@nasa.gov [NASA](https://osdr.nasa.gov/) New spaceflight liver RNA sequencing data is added whenever it is available. There are no restrictions on the use of this data. aws-pds, space biology, gene expression, genetic, genetic maps, GeneLab, NASA SMD AI, life sciences Blended TROPOMI+GOSAT Satellite Data Product for Atmospheric Methane Blended TROPOMI+GOSAT netCDF files arn:aws:s3:::blended-tropomi-gosat-methane us-west-2 S3 Bucket https://github.com/nicholasbalasus/write_blended_files/blob/main/PUM.md nicholasbalasus@g.harvard.edu Nicholas Balasus Monthly There are no restrictions on the use of this data, but please contact nicholasba aws-pds, climate, environmental, satellite imagery ['[Browse Bucket](https://s3-us-west-2.amazonaws.com/blended-tropomi-gosat-methane/index.html)'] +Blue Brain Open Data Data files representing neurological tissue structures arn:aws:s3:::openbluebrain us-west-2 S3 Bucket https://github.com/BlueBrain/OpenData jamesgonzalo.king@epfl.ch BBP/EPFL No updates CC-BY-4.0 neuroscience, simulation neuroscience, brain models, morphological reconstructions, electrophysiology, life sciences, single neuron models, ion channels, brain images, microcircuit modeling and simulation, Mus musculus BodyM Dataset This S3 bucket has height, weight, gender, measurements and two silhouette image arn:aws:s3:::amazon-bodym us-west-2 S3 Bucket https://adversarialbodysim.github.io/ Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 Amazon None Creative Commons Attribution-Non Commercial 4.0 International Public License - h amazon.science, computer vision, deep learning Boltz-1 Training Data S3 Bucket containing the Boltz-1 data arn:aws:s3:::boltz1 us-east-2 S3 Bucket https://github.com/jwohlwend/boltz/blob/main/docs/training.md jwohlwend@csail.mit.edu MIT CSAIL - Regina Barzilay Group None MIT License deep learning, protein folding, molecular docking, open source software, life sciences Boreas Autonomous Driving Dataset Boreas dataset arn:aws:s3:::boreas us-west-2 S3 Bucket https://github.com/utiasASRL/pyboreas/blob/master/DATA_REFERENCE.md boreas@robotics.utias.utoronto.ca [ASRL](http://asrl.utias.utoronto.ca) New driving sequences will be added as they are collected. [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) autonomous vehicles, robotics, computer vision, lidar, aws-pds BossDB Open Neuroimagery Datasets Large 3D volumes of neuroimaging data and image processing products such as segm arn:aws:s3:::bossdb-open-data us-east-1 S3 Bucket https://bossdb.org/ brock.wester@jhuapl.edu [Johns Hopkins University Applied Physics Laboratory](https://https://jhuapl.edu New datasets are added as soon as it is available. Minor updates on existing dat Creative Commons 4.0 International (CC BY 4.0) aws-pds, life sciences, imaging, neuroscience, neuroimaging, electron microscopy, x-ray tomography, x-ray microtomography, x-ray, magnetic resonance imaging, light-sheet microscopy, calcium imaging, volumetric imaging Broad Genome References This dataset includes two human genome references assembled by the Genome Refere arn:aws:s3:::broad-references us-east-1 S3 Bucket https://s3.amazonaws.com/broad-references/broad-references-readme.html hensonc@broadinstitute.org Broad Institute Monthly CC0 1.0 Universal (CC0 1.0) Public Domain Dedication aws-pds, biology, bioinformatics, cancer, genetic, genomic, life sciences, reference index, Homo sapiens -CAFE60 reanalysis CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1 (CA arn:aws:s3:::cafe60-reanalysis-dataset-aws-open-data ap-southeast-2 S3 Bucket https://data.csiro.au/dap/ws/v2/collections/49803/support/4029 Terence.O'Kane@csiro.au [CSIRO](http://csiro.au/) 6 Monthly (Approx) Creative Commons Attribution-ShareAlike 4.0 International Licence aws-pds, climate, sustainability ['[Browse Bucket](https://cafe60-reanalysis-dataset-aws-open-data.s3.amazonaws.com/index.html)'] CAFE60 reanalysis Notifications for updates to data arn:aws:sns:ap-southeast-2:970429975021:Cafe60-Data-Changes ap-southeast-2 SNS Topic https://data.csiro.au/dap/ws/v2/collections/49803/support/4029 Terence.O'Kane@csiro.au [CSIRO](http://csiro.au/) 6 Monthly (Approx) Creative Commons Attribution-ShareAlike 4.0 International Licence aws-pds, climate, sustainability +CAFE60 reanalysis CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1 (CA arn:aws:s3:::cafe60-reanalysis-dataset-aws-open-data ap-southeast-2 S3 Bucket https://data.csiro.au/dap/ws/v2/collections/49803/support/4029 Terence.O'Kane@csiro.au [CSIRO](http://csiro.au/) 6 Monthly (Approx) Creative Commons Attribution-ShareAlike 4.0 International Licence aws-pds, climate, sustainability ['[Browse Bucket](https://cafe60-reanalysis-dataset-aws-open-data.s3.amazonaws.com/index.html)'] CAM6 Data Assimilation Research Testbed (DART) Reanalysis: Cloud-Optimized Dataset Project data files arn:aws:s3:::ncar-dart-cam6 us-west-2 S3 Bucket https://doi.org/10.26024/sprq-2d04 rdahelp@ucar.edu [National Center for Atmospheric Research](https://ncar.ucar.edu/) Rare. Additional variables or years outside of 2011-2019 may be added in the fu https://www.ucar.edu/terms-of-use/data atmosphere, land, climate, climate model, data assimilation, forecast, meteorological, weather, geoscience, geospatial, aws-pds, zarr CAncer MEtastases in LYmph nOdes challeNge (CAMELYON) Dataset Whole slide images with corresponding annotations including tumor, stroma and tu arn:aws:s3:::camelyon-dataset us-west-2 S3 Bucket https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6007545/ https://camelyon17.grand-challenge.org/ Radboud University Medical Center As required CC0 aws-pds, life sciences, cancer, computational pathology, grand-challenge.org, histopathology, deep learning, computer vision +CBERS on AWS Topic that receives STAC V100 items as new scenes are ingested arn:aws:sns:us-west-2:769537946825:br-eo-stac-prod-stacitemtopic4BCE3141-Z8he7LYjqXFe us-west-2 SNS Topic https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog +CBERS on AWS Notifications for new CBERS 4 quicklooks, all sensors arn:aws:sns:us-west-2:599544552497:NewCB4Quicklook us-west-2 SNS Topic https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog +CBERS on AWS Notifications for new CBERS 4A quicklooks, all sensors arn:aws:sns:us-west-2:599544552497:NewCB4AQuicklook us-west-2 SNS Topic https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog CBERS on AWS CBERS imagery (COG files, quicklooks, metadata) arn:aws:s3:::brazil-eosats us-west-2 S3 Bucket https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog ['[STAC V1.0.0 endpoint](https://stac.amskepler.com/v100)', '[stacindex](https://stacindex.org/catalogs/cbers)'] False CBERS on AWS STAC static catalog arn:aws:s3:::br-eo-stac-1-0-0 us-west-2 S3 Bucket https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog False -CBERS on AWS Notifications for new CBERS 4A quicklooks, all sensors arn:aws:sns:us-west-2:599544552497:NewCB4AQuicklook us-west-2 SNS Topic https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog -CBERS on AWS Notifications for new CBERS 4 quicklooks, all sensors arn:aws:sns:us-west-2:599544552497:NewCB4Quicklook us-west-2 SNS Topic https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog -CBERS on AWS Topic that receives STAC V100 items as new scenes are ingested arn:aws:sns:us-west-2:769537946825:br-eo-stac-prod-stacitemtopic4BCE3141-Z8he7LYjqXFe us-west-2 SNS Topic https://github.com/fredliporace/cbers-on-aws https://lists.osgeo.org/mailman/listinfo/cbers-pds [AMS Kepler](https://amskepler.com/) Daily https://creativecommons.org/licenses/by-sa/3.0/ aws-pds, agriculture, earth observation, geospatial, imaging, satellite imagery, disaster response, stac, cog CCAFS-Climate Data ARC GRID, and ARC ASCII format compressed arn:aws:s3:::cgiardata us-west-2 S3 Bucket http://www.ccafs-climate.org http://www.ccafs-climate.org/contact/ [International Center for Tropical Agriculture](https://ciat.cgiar.org/) Every three months Creative Commons Attribution-NonCommercial 4.0 International License http://crea aws-pds, agriculture, food security, climate, sustainability CESM-HR CESM-HR PI-CTRL data arn:aws:s3:::cesm-hr-pi-ctrl us-east-1 S3 Bucket https://github.com/MESACLIP/cesm-hr-pi-ctrl-aws ping@tamu.edu [TAMU](https://www.tamu.com/) Rare. The CESM-HR PI-CTRL experiment is complete. Updates are expected only if a This dataset is created in collaboration with NCAR and the NCAR’s “Creative Comm aws-pds, climate, climate model, climate projections, CMIP6, ocean circulation, ocean currents, ocean velocity, ocean sea surface height, ocean simulation ['[Browse Bucket](http://pi-ctrl-bucket.s3-website.us-east-2.amazonaws.com)'] CIViC (Clinical Interpretation of Variants in Cancer) Monthly CIViC data dumps including gene, variant, assertion, and evidence tables arn:aws:s3:::civic-aws-opendata us-west-2 S3 Bucket https://docs.civicdb.org/ help@civicdb.org The McDonnell Genome Institute at Washington University School of Medicine First of each month [CC0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, genetic, genomic, life sciences, vcf -CMAS Data Warehouse MPAS-CMAQ Input Data arn:aws:s3:::mpas-cmaq us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://mpas-cmaq.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse CMAQ CONUS-2 Benchmark Data arn:aws:s3:::cmas-cmaq-conus2-benchmark us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq-conus2-benchmark.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse CMAQ Benchmark Data arn:aws:s3:::cmas-cmaq us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse CMAQ 2018 Modeling Platform arn:aws:s3:::cmas-cmaq-modeling-platform-2018 us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq-modeling-platform-2018.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse CMAS WWLLN Lightning Data arn:aws:s3:::cmas-wwlln-lightning us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-wwlln-lightning.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse EQUATES EPA’s Air QUAlity TimE Series Project Data arn:aws:s3:::cmas-equates us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-equates.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse 2020 Modeling Platform arn:aws:s3:::2020platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2020platform.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse 2019 Modeling Platform arn:aws:s3:::2019platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2019platform.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse 2016v3 Modeling Platform arn:aws:s3:::2016v3platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2016v3platform.s3.amazonaws.com/index.html)'] CMAS Data Warehouse SMOKE Test Case arn:aws:s3:::cmas-smoke-testcase us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-smoke-testcase.s3.amazonaws.com/index.html)'] CMAS Data Warehouse SMOKE 2016 Modeling Platform arn:aws:s3:::cmas-smoke-modeling-platform-2016 us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-smoke-modeling-platform-2016.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse 2019 Modeling Platform arn:aws:s3:::2019platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2019platform.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse 2020 Modeling Platform arn:aws:s3:::2020platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2020platform.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse CMAQ 2018 Modeling Platform arn:aws:s3:::cmas-cmaq-modeling-platform-2018 us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq-modeling-platform-2018.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse EQUATES EPA’s Air QUAlity TimE Series Project Data arn:aws:s3:::cmas-equates us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-equates.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse CMAS WWLLN Lightning Data arn:aws:s3:::cmas-wwlln-lightning us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-wwlln-lightning.s3.amazonaws.com/index.html)'] CMAS Data Warehouse AMET Data arn:aws:s3:::cmas-amet us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-amet.s3.amazonaws.com/index.html)'] -CMAS Data Warehouse 2018v2 Modeling Platform arn:aws:s3:::2018v2platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2018v2platform.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse CMAQ Benchmark Data arn:aws:s3:::cmas-cmaq us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse MPAS-CMAQ Input Data arn:aws:s3:::mpas-cmaq us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://mpas-cmaq.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse CMAQ CONUS-2 Benchmark Data arn:aws:s3:::cmas-cmaq-conus2-benchmark us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmas-cmaq-conus2-benchmark.s3.amazonaws.com/index.html)'] CMAS Data Warehouse CMAQ Release Benchmark Data for Easy Download arn:aws:s3:::cmaq-release-benchmark-data-for-easy-download us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://cmaq-release-benchmark-data-for-easy-download.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse 2016v3 Modeling Platform arn:aws:s3:::2016v3platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2016v3platform.s3.amazonaws.com/index.html)'] +CMAS Data Warehouse 2018v2 Modeling Platform arn:aws:s3:::2018v2platform us-east-1 S3 Bucket https://dataverse.unc.edu/dataverse/cmascenter cmas@unc.edu [CMAS CENTER](https://cmascenter.org/) New data is added as soon as it is available. There are no restrictions on the use of this data. US EPA License (https://paste aws-pds, air quality, meteorological, geospatial, environmental, climate ['[Browse Bucket](https://2018v2platform.s3.amazonaws.com/index.html)'] CMIP6 GCMs downscaled using WRF WRF output files arn:aws:s3:::wrf-cmip6-noversioning us-west-2 S3 Bucket https://dept.atmos.ucla.edu/alexhall/downscaling-cmip6 srahimi@uwyo.edu, leih@ucla.edu [UCLA Center for Climate Science](https://dept.atmos.ucla.edu/) New downscaled results are uploaded as soon as they become available Creative Commons Attribution 4.0 International License aws-pds, agriculture, atmosphere, climate, earth observation, environmental, model, oceans, simulations, weather ['[Browse Bucket](https://wrf-cmip6-noversioning.s3.amazonaws.com/index.html)'] CMS 2008-2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF) in OMOP Common Data Model Project data files arn:aws:s3:::synpuf-omop us-east-1 S3 Bucket https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 [Amazon Web Sevices](https://aws.amazon.com/) Not updated https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use amazon.science, bioinformatics, health, life sciences, natural language processing, us COBRA Whole slide images with corresponding labels including skin cancer and basal cel arn:aws:s3:::cobra-pathology us-west-2 S3 Bucket https://daangeijs.github.io/cobra/ https://www.computationalpathologygroup.eu/members/daan-geijs/ Radboud University Medical Center As required CC BY-SA-NC 4.0 aws-pds, life sciences, cancer, computational pathology, deep learning, histopathology, computer vision COCO - Common Objects in Context - fast.ai datasets Datasets arn:aws:s3:::fast-ai-coco us-east-1 S3 Bucket http://course.fast.ai/datasets info@fast.ai [fast.ai](http://www.fast.ai/) As required Creative Commons http://cocodataset.org/#termsofuse aws-pds, deep learning, computer vision, machine learning COVID-19 Data Lake Collected COVID-19 related datasets arn:aws:s3:::covid19-lake us-east-2 S3 Bucket https://aws.amazon.com/blogs/big-data/a-public-data-lake-for-analysis-of-covid-1 aws-covid-19-data-lake@amazon.com [Amazon Web Services](https://aws.amazon.com/) Periodically Varies by dataset amazon.science, bioinformatics, biology, coronavirus, COVID-19, health, life sciences, MERS, medicine, SARS ['[Browse Bucket](https://covid19-lake.s3.amazonaws.com/index.html)'] -COVID-19 Genome Sequence Dataset Metadata for sra-pub-sars-cov2 in an Athena-queryable format arn:aws:s3:::sra-pub-sars-cov2-metadata-us-east-1 us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/sra/docs/sra-aws-download/ https://support.nlm.nih.gov/support/create-case/ [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/geno aws-pds, bioinformatics, biology, coronavirus, COVID-19, fastq, bam, cram, genomic, genetic, health, life sciences, MERS, SARS, virus, STRIDES, whole genome sequencing, transcriptomics COVID-19 Genome Sequence Dataset Genomic sequence reads of SARS-CoV-2 and related coronaviridae, organized by NCB arn:aws:s3:::sra-pub-sars-cov2 us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/sra/docs/sra-aws-download/ https://support.nlm.nih.gov/support/create-case/ [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/geno aws-pds, bioinformatics, biology, coronavirus, COVID-19, fastq, bam, cram, genomic, genetic, health, life sciences, MERS, SARS, virus, STRIDES, whole genome sequencing, transcriptomics +COVID-19 Genome Sequence Dataset Metadata for sra-pub-sars-cov2 in an Athena-queryable format arn:aws:s3:::sra-pub-sars-cov2-metadata-us-east-1 us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/sra/docs/sra-aws-download/ https://support.nlm.nih.gov/support/create-case/ [National Library of Medicine (NLM)](http://nlm.nih.gov/) Daily [NIH Genomic Data Sharing Policy](https://osp.od.nih.gov/scientific-sharing/geno aws-pds, bioinformatics, biology, coronavirus, COVID-19, fastq, bam, cram, genomic, genetic, health, life sciences, MERS, SARS, virus, STRIDES, whole genome sequencing, transcriptomics COVID-19 Harmonized Data COVID-19 Harmonized Dataset arn:aws:s3:::covid19-harmonized-dataset us-east-2 S3 Bucket https://www.stitchdata.com/docs/integrations/saas/covid-19 covid19.dataset@talend.com [Talend / Stitch](http://www.stitchdata.com/) New COVID-19 data is added twice daily There are no restrictions on the use of this data. aws-pds, COVID-19, coronavirus, life sciences ['[Browse Bucket](https://covid19-harmonized-dataset.s3.amazonaws.com/index.html)'] COVID-19 Molecular Structure and Therapeutics Hub Data storage of for the MolSSI and BioExcel COVID-19 Hub Includes atomistic str arn:aws:s3:::molssi-bioexcel-covid-19-structure-therapeutics-hub us-east-1 S3 Bucket https://covid.molssi.org/ info@molssi.org [Molecular Sciences Software Institute (MolSSI)](https://molssi.org/) and [BioEx Data contributions come from external researchers and groups at a roughly weekly Most data will be in an open license provided by the contributing individual(s). aws-pds, biology, bioinformatics, coronavirus, COVID-19, life sciences, molecular docking, pharmaceutical COVID-19 Open Research Dataset (CORD-19) S3 bucket with CORD-19 dataset files arn:aws:s3:::ai2-semanticscholar-cord-19 us-west-2 S3 Bucket https://pages.semanticscholar.org/coronavirus-research partnerships@allenai.org Allen Institute for AI Weekly Open (see license file for details) aws-pds, COVID-19, coronavirus, life sciences, SARS, MERS CRC-SAS/SISSA historical seasonal and subseasonal forecast database CRC-SAS/SISSA Retrospective Daily forecast database arn:aws:s3:::sissa-forecast-database us-west-2 S3 Bucket General information, tutorials and examples, contact us atl sissa-aws@smn.gob.ar For any questions regarding the data set or any general questions, you can conta [SISSA](https://sissa.crc-sas.org/) Static database from 2000-2019 without correction and 2010-2019 with correction. [Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org aws-pds, earth observation, natural resource, weather, forecast, meteorological, agriculture, hydrology ['[Browse Bucket](https://s3-us-west-2.amazonaws.com/sissa-forecast-database/index.html)'] CZ CELLxGENE Discover Census CZ CELLxGENE Discover Census Data arn:aws:s3:::cellxgene-census-public-us-west-2/cell-census us-west-2 S3 Bucket https://chanzuckerberg.github.io/cellxgene-census/ cellxgene@chanzuckerberg.com [Chan Zuckerberg Initiative Foundation](http://www.chanzuckerberg.com/) New releases are published weekly. Long-term supported (LTS) releases are publis CC BY license aws-pds, single-cell transcriptomics, transcriptomics, cell biology, bioinformatics, life sciences Cancer Cell Line Encyclopedia (CCLE) RNA-Seq Aligned Reads, WXS Aligned Reads, WGS Aligned Reads arn:aws:s3:::gdc-ccle-2-open us-east-1 S3 Bucket https://portals.broadinstitute.org/ccle/about dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genetic, genomic, life sciences, transcriptomics, whole genome sequencing, Homo sapiens, STRIDES -Cancer Genome Characterization Initiatives - Burkitt Lymphoma, HIV+ Cervical Cancer Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantificat arn:aws:s3:::gdc-cgci-phs000235-2-open us-east-1 S3 Bucket https://ocg.cancer.gov/programs/cgci dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genomic, life sciences, transcriptomics, STRIDES Cancer Genome Characterization Initiatives - Burkitt Lymphoma, HIV+ Cervical Cancer Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantificat arn:aws:s3:::gdc-cgci-blgsp-phs000235-2-open us-east-1 S3 Bucket https://ocg.cancer.gov/programs/cgci dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genomic, life sciences, transcriptomics, STRIDES +Cancer Genome Characterization Initiatives - Burkitt Lymphoma, HIV+ Cervical Cancer Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantificat arn:aws:s3:::gdc-cgci-phs000235-2-open us-east-1 S3 Bucket https://ocg.cancer.gov/programs/cgci dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genomic, life sciences, transcriptomics, STRIDES Cancer Genome Characterization Initiatives - Burkitt Lymphoma, HIV+ Cervical Cancer WGS/Targeted Sequencing/RNA-Seq/miRNA-Seq Aligned Reads, RNA-Seq Splice Junction arn:aws:s3:::gdc-cgci-phs000235-2-controlled us-east-1 S3 Bucket https://ocg.cancer.gov/programs/cgci dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genomic, life sciences, transcriptomics, STRIDES https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000235.v14.p2 Capella Space Synthetic Aperture Radar (SAR) Open Dataset Capella Space Open Data in COG format arn:aws:s3:::capella-open-data/data/ us-west-2 S3 Bucket Documentation is available under [support.capellaspace.com](https://support.cape opendata@capellaspace.com [Capella Space](https://www.capellaspace.com/) New data is added quarterly. [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, cog, stac, earth observation, satellite imagery, geospatial, image processing, computer vision, synthetic aperture radar ['[STAC Catalog](https://capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)', '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)'] False Capella Space Synthetic Aperture Radar (SAR) Open Dataset Capella Space Open Data in TileDB format arn:aws:s3:::capella-open-data/data/tiledb/ us-west-2 S3 Bucket Documentation is available under [support.capellaspace.com](https://support.cape opendata@capellaspace.com [Capella Space](https://www.capellaspace.com/) New data is added quarterly. [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, cog, stac, earth observation, satellite imagery, geospatial, image processing, computer vision, synthetic aperture radar False Catalina Sky Survey (CSS) subset data on AWS Catalina Sky Survey Raw Data arn:aws:s3:::pds-css-archive us-west-2 S3 Bucket https://sbn.psi.edu/pds/resource/css.html pds-operator@jpl.nasa.gov [Planetary Data Systems Small Bodies Node (SBN)](https://sbn.psi.edu/pds/support CSS data are delivered nightly into the PDS archive starting with the night of J There are no restrictions on the use of this data. aws-pds, planetary, astronomy, object detection, survey ['[Browse archive](https://sbnarchive.psi.edu/pds4/surveys/gbo.ast.catalina.survey/data_raw/V06/2023/23Jul15/)'] -Cell Organelle Segmentation in Electron Microscopy (COSEM) on AWS Machine learning models for organelle prediction arn:aws:s3:::janelia-cosem-networks us-east-1 S3 Bucket https://github.com/janelia-cosem/aws-opendata/blob/master/cosem-introduction.md COSEMData@janelia.hhmi.org [Janelia Research Campus](https://www.janelia.org/) New datasets and derived data are added as soon as they are available. CC-BY-4.0 aws-pds, cell biology, computer vision, electron microscopy, life sciences, imaging, organelle Cell Organelle Segmentation in Electron Microscopy (COSEM) on AWS Raw FIB-SEM datasets and derived data arn:aws:s3:::janelia-cosem-datasets us-east-1 S3 Bucket https://github.com/janelia-cosem/aws-opendata/blob/master/cosem-introduction.md COSEMData@janelia.hhmi.org [Janelia Research Campus](https://www.janelia.org/) New datasets and derived data are added as soon as they are available. CC-BY-4.0 aws-pds, cell biology, computer vision, electron microscopy, life sciences, imaging, organelle +Cell Organelle Segmentation in Electron Microscopy (COSEM) on AWS Machine learning models for organelle prediction arn:aws:s3:::janelia-cosem-networks us-east-1 S3 Bucket https://github.com/janelia-cosem/aws-opendata/blob/master/cosem-introduction.md COSEMData@janelia.hhmi.org [Janelia Research Campus](https://www.janelia.org/) New datasets and derived data are added as soon as they are available. CC-BY-4.0 aws-pds, cell biology, computer vision, electron microscopy, life sciences, imaging, organelle Cell Painting Gallery Cell Painting data, comprising fluorescence microscopy cell images (TIFF), extra arn:aws:s3:::cellpainting-gallery us-east-1 S3 Bucket https://github.com/broadinstitute/cellpainting-gallery cellpainting@broadinstitute.org Carpenter-Singh and Cimini Labs at the Broad Institute Typically when an associated publication is posted on biorxiv CC0 1.0 Universal (CC0 1.0) Public Domain Dedication, but please do cite the cor aws-pds, bioinformatics, biology, cancer, cell biology, cell imaging, cell painting, chemical biology, computer vision, csv, deep learning, fluorescence imaging, genetic, high-throughput imaging, image processing, image-based profiling, imaging, machine learning, medicine, microscopy, organelle, life sciences ['[Documentation](https://github.com/broadinstitute/cellpainting-gallery)', '[Browse Bucket](https://cellpainting-gallery.s3.amazonaws.com/index.html)'] Cell Painting Image Collection Images, extracted features and aggregated profiles are available as a S3 bucket arn:aws:s3:::cytodata us-east-1 S3 Bucket https://github.com/cytodata/cytodata-hackathon-2018 "Post on https://forum.image.sc/ and tag with ""cellpainting""" The Broad Institute irregularly CC0 1.0 Universal (CC0 1.0) Public Domain Dedication aws-pds, microscopy, biology, life sciences, imaging, high-throughput imaging, cell imaging, cell painting, fluorescence imaging Central Weather Administration OpenData CWA data lake arn:aws:s3:::cwaopendata ap-northeast-1 S3 Bucket https://opendata.cwa.gov.tw/devManual/insrtuction od@cwa.gov.tw [Central Weather Administration](https://www.cwa.gov.tw/) Data is updated as soon as newer one is available. http://data.gov.tw/license aws-pds, climate, earth observation, earthquakes, satellite imagery, weather @@ -175,8 +177,8 @@ Consented Activities of People The Consented Activities of People (CAP) dataset Copernicus Digital Elevation Model (DEM) GLO-30 Public in S3 bucket The list of tiles covering specific countries that a arn:aws:s3:::copernicus-dem-30m eu-central-1 S3 Bucket https://copernicus-dem-30m.s3.amazonaws.com/readme.html https://forum.sentinel-hub.com/c/aws-copdem/28 [Sinergise](https://www.sinergise.com/) None, except GLO-30 Public can be updated if the public tile list changes. GLO-30 Public and GLO-90 are available on a free basis for the general public un aws-pds, agriculture, elevation, earth observation, satellite imagery, geospatial, disaster response, cog ['[STAC V1.0.0 endpoint](https://copernicus-dem-30m-stac.s3.amazonaws.com/)'] Copernicus Digital Elevation Model (DEM) GLO-90 in S3 bucket arn:aws:s3:::copernicus-dem-90m eu-central-1 S3 Bucket https://copernicus-dem-30m.s3.amazonaws.com/readme.html https://forum.sentinel-hub.com/c/aws-copdem/28 [Sinergise](https://www.sinergise.com/) None, except GLO-30 Public can be updated if the public tile list changes. GLO-30 Public and GLO-90 are available on a free basis for the general public un aws-pds, agriculture, elevation, earth observation, satellite imagery, geospatial, disaster response, cog ['[STAC V1.0.0 endpoint](https://copernicus-dem-90m-stac.s3.amazonaws.com/)'] Corn Kernel Counting Dataset Folder contains the terms of use, paper, and zip file of data in COOC format Ea arn:aws:s3:::intelinair-data-releases/corn-kernel-counting us-east-1 S3 Bucket https://www.frontiersin.org/articles/10.3389/frobt.2021.627009/abstract support@intelinair.com Intelinair, Inc. Periodically Provided in the bucket. aws-pds, agriculture, computer vision, machine learning False -Coupled Model Intercomparison Project 6 Netcdf formatted data managed by the Earth System Grid Federation arn:aws:s3:::esgf-world us-east-2 S3 Bucket https://pangeo-data.github.io/pangeo-cmip6-cloud/, https://www.wcrp-climate.org/ If you have any feedback on the CMIP6 data available on AWS please email sustain ESGF and Pangeo Core CMIP6 datasets are added as soon as they are available. See [docs] (https://pangeo-data.github.io/pangeo-cmip6-cloud/licensing_citation. aws-pds, agriculture, atmosphere, climate, earth observation, environmental, model, oceans, simulations, weather ['[Browse Bucket](https://esgf-world.s3.amazonaws.com/index.html)', '[Data Catalog](https://cmip6-nc.s3.amazonaws.com/esgf-world.csv.gz)'] Coupled Model Intercomparison Project 6 Zarr formatted data arn:aws:s3:::cmip6-pds us-west-2 S3 Bucket https://pangeo-data.github.io/pangeo-cmip6-cloud/, https://www.wcrp-climate.org/ If you have any feedback on the CMIP6 data available on AWS please email sustain ESGF and Pangeo Core CMIP6 datasets are added as soon as they are available. See [docs] (https://pangeo-data.github.io/pangeo-cmip6-cloud/licensing_citation. aws-pds, agriculture, atmosphere, climate, earth observation, environmental, model, oceans, simulations, weather ['[Browse Bucket](https://cmip6-pds.s3.amazonaws.com/index.html#CMIP6/)', '[Data Catalog](https://cmip6-pds.s3.amazonaws.com/pangeo-cmip6.csv)'] +Coupled Model Intercomparison Project 6 Netcdf formatted data managed by the Earth System Grid Federation arn:aws:s3:::esgf-world us-east-2 S3 Bucket https://pangeo-data.github.io/pangeo-cmip6-cloud/, https://www.wcrp-climate.org/ If you have any feedback on the CMIP6 data available on AWS please email sustain ESGF and Pangeo Core CMIP6 datasets are added as soon as they are available. See [docs] (https://pangeo-data.github.io/pangeo-cmip6-cloud/licensing_citation. aws-pds, agriculture, atmosphere, climate, earth observation, environmental, model, oceans, simulations, weather ['[Browse Bucket](https://esgf-world.s3.amazonaws.com/index.html)', '[Data Catalog](https://cmip6-nc.s3.amazonaws.com/esgf-world.csv.gz)'] Coupled Model Intercomparison Project Phase 5 (CMIP5) University of Wisconsin-Madison Probabilistic Downscaling Dataset Data files arn:aws:s3:::noaa-nws-uwpd-cmip5-pds us-east-1 S3 Bucket http://djlorenz.github.io/downscaling2/main.html For questions about data development, quality and content, please contact Dr. Da [NOAA](http://www.noaa.gov/) Periodically, as new data becomes available or when corrections are implemented. Open Data. There are no restrictions on the use of this data. aws-pds, climate, coastal, disaster response, environmental, meteorological, sustainability, oceans, water, weather ['[Browse Bucket](https://noaa-nws-uwpd-cmip5-pds.s3.amazonaws.com/index.html)'] CoversBR metadata of songs, features files and audio streaming arn:aws:s3:::covers-song-br us-west-2 S3 Bucket https://github.com/SPLab-IT/CoversBR https://github.com/SPLab-IT/CoversBR/issues Dirceu G Silva New metadata, songs features files and audio streamings for live song identifica The code in this repository is licensed under Apache 2.0The metadata and the pre aws-pds, copyright monitoring, cover song identification, live song identification, music, music features dataset, music information retrieval, music recognition Covid Job Impacts - US Hiring Data Since March 1 2020 GreenwichHR daily US hiring data (normalized to March 1 2020) arn:aws:s3:::greenwichhr-covidjobimpacts us-east-2 S3 Bucket https://greenwichhr-covidjobimpacts.s3.us-east-2.amazonaws.com/ghr_data_specs_co info@Greenwich.HR [Greenwich.HR](http://www.greenwich.hr/) Daily This data is made available under a [Creative Commons license](https://creativec aws-pds, COVID-19, hiring, economics, financial markets, market data @@ -190,19 +192,19 @@ DIWASA Rainfed and Irrigated Cropland Map for Africa high-confidence cropland ma DNAStack COVID19 SRA Data SARS-CoV-2 raw sequencing and output data (FASTQ, BAM, FASTA, VCF) arn:aws:s3:::dnastack-covid-19-sra-data us-west-2 S3 Bucket https://github.com/DNAstack/dnastack-open-data [DNAstack](bioinformatics@dnastack.com) [DNAstack](https://dnastack.com/) Rolling [DNAstack terms of use](https://dnastack.com/terms-of-use/) aws-pds, bam, bioinformatics, coronavirus, COVID-19, fasta, fastq, global, genetic, genomic, health, life sciences, long read sequencing, SARS-CoV-2, vcf, virus, whole genome sequencing ['[Browse bucket](https://dnastack-covid-19-sra-data.s3.amazonaws.com/)'] False DOE's Water Power Technology Office's (WPTO) US Wave dataset 32 Year Wave Hindcast (1979-2010) for the West Coast of the United States at 3-h arn:aws:s3:::wpto-pds-us-wave/v1.0.0/West_Coast/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FWest_Coast%2F)'] DOE's Water Power Technology Office's (WPTO) US Wave dataset HSDS US Wave domains arn:aws:s3:::nrel-pds-hsds/nrel/US_wave/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset 32 Year Wave Hindcast (1979-2010) for the Pacific Ocean around US state of Hawai arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Hawaii/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FHawaii%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset HSDS US Virtual Buoy domains arn:aws:s3:::nrel-pds-hsds/nrel/US_wave/virtual_buoy/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2Fvirtual_buoy%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset 32 Year Wave Hindcast (1979-2010) from select virtual buoys along the West Coast arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/West_Coast/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FWest_Coast%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset DOE's Water Power Technology Office's Wave Hindcast datasets arn:aws:s3:::wpto-pds-us-wave/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave)'] DOE's Water Power Technology Office's (WPTO) US Wave dataset 32 Year Wave Hindcast (1979-2010) from select virtual buoys along the Atlantic C arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/Atlantic/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FAtlantic%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Atlantic/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FAtlantic%2F)'] DOE's Water Power Technology Office's (WPTO) US Wave dataset Updated version of 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of t arn:aws:s3:::wpto-pds-us-wave/v1.0.1/Atlantic/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.1%2FAtlantic%2F)'] -DOE's Water Power Technology Office's (WPTO) US Wave dataset DOE's Water Power Technology Office's Wave Hindcast datasets arn:aws:s3:::wpto-pds-us-wave/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave)'] -Daylight Map Distribution of OpenStreetMap Daylight Earth Table (Parquet) arn:aws:s3:::daylight-openstreetmap/earth us-west-2 S3 Bucket [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds -Daylight Map Distribution of OpenStreetMap New OSM PBF Notification arn:aws:sns:us-west-1:632571768781:Daylight us-west-1 SNS Topic [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds +DOE's Water Power Technology Office's (WPTO) US Wave dataset 32 Year Wave Hindcast (1979-2010) for the Atlantic Coast of the United States at arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Atlantic/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FAtlantic%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset 32 Year Wave Hindcast (1979-2010) from select virtual buoys along the West Coast arn:aws:s3:::wpto-pds-us-wave/v1.0.0/virtual_buoy/West_Coast/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2Fvirtual_buoy%2FWest_Coast%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset 32 Year Wave Hindcast (1979-2010) for the Pacific Ocean around US state of Hawai arn:aws:s3:::wpto-pds-us-wave/v1.0.0/Hawaii/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=wpto-pds-us-wave&prefix=v1.0.0%2FHawaii%2F)'] +DOE's Water Power Technology Office's (WPTO) US Wave dataset HSDS US Virtual Buoy domains arn:aws:s3:::nrel-pds-hsds/nrel/US_wave/virtual_buoy/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/US_Wave.md Levi.Kilcher@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed https://github.com/openEDI/documentation/blob/master/US_Wave.md aws-pds, earth observation, energy, geospatial, meteorological, water ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2FUS_wave%2Fvirtual_buoy%2F)'] +Daylight Map Distribution of OpenStreetMap New Parquet File Notification arn:aws:sns:us-west-2:632571768781:Analysis_Ready_Daylight us-west-2 SNS Topic [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds Daylight Map Distribution of OpenStreetMap Daylight OSM PBF Files arn:aws:s3:::daylight-map-distribution/release/ us-west-1 S3 Bucket [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds +Daylight Map Distribution of OpenStreetMap New OSM PBF Notification arn:aws:sns:us-west-1:632571768781:Daylight us-west-1 SNS Topic [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds Daylight Map Distribution of OpenStreetMap Daylight OSM Features (Parquet) arn:aws:s3:::daylight-openstreetmap/parquet/osm_features us-west-2 S3 Bucket [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds Daylight Map Distribution of OpenStreetMap Daylight OSM Elements (Parquet) arn:aws:s3:::daylight-openstreetmap/parquet/osm_elements us-west-2 S3 Bucket [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds -Daylight Map Distribution of OpenStreetMap New Parquet File Notification arn:aws:sns:us-west-2:632571768781:Analysis_Ready_Daylight us-west-2 SNS Topic [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds +Daylight Map Distribution of OpenStreetMap Daylight Earth Table (Parquet) arn:aws:s3:::daylight-openstreetmap/earth us-west-2 S3 Bucket [Project Website](https://daylightmap.org) osm@fb.com [Meta](https://dataforgood.fb.com/) Quarterly [Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/1-0/) geospatial, osm, mapping, disaster response, aws-pds Defense Meteorology Satellite Program (DMSP) Auroral Particle Flux DMSP Auroral Particle Flux arn:aws:s3:::dmspssj us-west-2 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/dmspssj delores.knipp@colorado.edu Space Weather Technology, Research and Education Center (TREC) at University of Infrequent This data is in the '[public domain](https://creativecommons.org/publicdomain/ze aws-pds, solar, space weather, geospatial, earth observation Demand-Side Grid (dsgrid) Toolkit Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (E arn:aws:s3:::oedi-data-lake/dsgrid-2018-efs/ us-west-2 S3 Bucket https://www.nrel.gov/analysis/dsgrid.html elaine.hale@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License data assimilation, electricity, energy, energy modeling, meteorological, transportation, industrial, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=oedi-data-lake&prefix=dsgrid-2018-efs%2F)'] Demand-Side Grid (dsgrid) Toolkit Demand-side grid (dsgrid) Toolkit Datasets arn:aws:s3:::nrel-pds-dsgrid/ us-west-2 S3 Bucket https://www.nrel.gov/analysis/dsgrid.html elaine.hale@nrel.gov [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License data assimilation, electricity, energy, energy modeling, meteorological, transportation, industrial, solar ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-dsgrid%2F)'] @@ -388,14 +390,14 @@ GeoNet Aotearoa New Zealand Data Data files arn:aws:s3:::geonet-open-data ap-sou Geosnap Data, Center for Geospatial Sciences Data files stored as Apache parquet and GeoTiff in a public bucket arn:aws:s3:::spatial-ucr us-east-1 S3 Bucket https://spatialucr.github.io/geosnap-guide/content/home Eli Knaap [UCR Center for Geospatial Sciences](https://spatial.ucr.edu) Annually BSD aws-pds, urban, geospatial, demographics Global 30m Height Above Nearest Drainage (HAND) Notifications for new data arn:aws:sns:us-west-2:879002409890:glo-30-hand-object_created us-west-2 SNS Topic https://glo-30-hand.s3.us-west-2.amazonaws.com/readme.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) None, except HAND may be updated if the[ Copernicus GLO-30 Public](https://regis Copyright 2022 Alaska Satellite Facility (ASF). Produced using the Copernicus Wo aws-pds, elevation, hydrology, agriculture, disaster response, satellite imagery, geospatial, cog, stac Global 30m Height Above Nearest Drainage (HAND) GLO-30 HAND S3 bucket arn:aws:s3:::glo-30-hand us-west-2 S3 Bucket https://glo-30-hand.s3.us-west-2.amazonaws.com/readme.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) None, except HAND may be updated if the[ Copernicus GLO-30 Public](https://regis Copyright 2022 Alaska Satellite Facility (ASF). Produced using the Copernicus Wo aws-pds, elevation, hydrology, agriculture, disaster response, satellite imagery, geospatial, cog, stac ['[STAC V1.0.0 endpoint](https://stac.asf.alaska.edu/collections/glo-30-hand)', '[Via STAC Browser](https://radiantearth.github.io/stac-browser/#/external/stac.asf.alaska.edu/collections/glo-30-hand)'] -Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (ap-southeast-2 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-ap-southeast-2-object_created ap-southeast-2 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences +Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (af-south-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-af-south-1-object_created af-south-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (eu-central-1 region) arn:aws:s3:::gbif-open-data-eu-central-1 eu-central-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-eu-central-1.s3.eu-central-1.amazonaws.com/index.html)'] Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (us-east-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-us-east-1-object_created us-east-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (sa-east-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-sa-east-1-object_created sa-east-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (eu-central-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-eu-central-1-object_created eu-central-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (us-east-1 region) arn:aws:s3:::gbif-open-data-us-east-1 us-east-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-us-east-1.s3.us-east-1.amazonaws.com/index.html)'] Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (sa-east-1 region) arn:aws:s3:::gbif-open-data-sa-east-1 sa-east-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-sa-east-1.s3.sa-east-1.amazonaws.com/index.html)'] -Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (af-south-1 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-af-south-1-object_created af-south-1 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences +Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (ap-southeast-2 region) arn:aws:sns:af-south-1:288719126026:gbif-open-data-ap-southeast-2-object_created ap-southeast-2 SNS Topic Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (af-south-1 region) arn:aws:s3:::gbif-open-data-af-south-1 af-south-1 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-af-south-1.s3.af-south-1.amazonaws.com/index.html)'] Global Biodiversity Information Facility (GBIF) Species Occurrences GBIF species occurrence data in Parquet format (ap-southeast-2 region) arn:aws:s3:::gbif-open-data-ap-southeast-2 ap-southeast-2 S3 Bucket Documentation can be found [here](https://github.com/gbif/occurrence/blob/master helpdesk@gbif.org The Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) Snapshots of GBIF are taken on a monthly basis [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/) under the GBIF [term aws-pds, earth observation, biodiversity, bioinformatics, conservation, life sciences ['[Browse bucket](https://gbif-open-data-ap-southeast-2.s3.ap-southeast-2.amazonaws.com/index.html)'] Global Carbon Budget Data Global Carbon Budget Open Data - Trendy arn:aws:s3:::gcbo-opendata eu-west-2 S3 Bucket https://globalcarbonbudgetdata.org/ For any data queries please contact gcbo-data@exeter.ac.uk. Global Carbon Budget Office at the University of Exeter, UK Annual Open Data. There are no restrictions on the use of this data. climate, land, oceans @@ -858,7 +860,7 @@ OAQPS 2022 Modeling Platform Notification for the 2022 Modeling Platform bucket ONS Open Data Portal Turbinable spilled energy (PT-BR Energia vertida turbinável) arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/energia-vertida-turbinavel)'] ONS Open Data Portal Daily affluent natural energy per subsystem (PT-BR Energia Natural Afluente (ENA arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-subsistema)'] ONS Open Data Portal Power plant availability data (PT-BR Disponibilidade de Usinas) arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/disponibilidade_usina)'] -ONS Open Data Portal Daily affluent natural energy per equivalent energy reservoir (PT-BR Energia Nat arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-ree-reservatorio-equivalente-de-energia)'] +ONS Open Data Portal Daily affluent natural energy per basin (PT-BR Energia Natural Afluente (ENA) di arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-bacia)'] ONS Open Data Portal Hourly charge curve (PT-BR Curva de carga horária) arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/curva-carga)'] ONS Open Data Portal Unit Variable Cost of thermal power plants (PT-BR Custo Variável Unitário (CVU) arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/cvu-usitermica)'] ONS Open Data Portal Reservoir hydrological data - Daily Basis (PT-BR Dados hidrológicos de reservató arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/dados-hidrologicos-res)'] @@ -899,7 +901,7 @@ ONS Open Data Portal Fluviometric data (PT-BR Grandezas fluviométricas) arn:aws ONS Open Data Portal Generation per power plant on an hourly basis (PT-BR Geração por usina em base h arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/geracao-usina-2)'] ONS Open Data Portal Performance indicator of generation functions per generating unit on an yearly b arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/ind_disponibilidade_fgeracao_uge_anual)'] ONS Open Data Portal Semi-hourly marginal cost of operation (PT-BR Custo Marginal de Operação (CMO) S arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/cmo-semi-horario)'] -ONS Open Data Portal Daily affluent natural energy per basin (PT-BR Energia Natural Afluente (ENA) di arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-bacia)'] +ONS Open Data Portal Daily affluent natural energy per equivalent energy reservoir (PT-BR Energia Nat arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/ena-diario-por-ree-reservatorio-equivalente-de-energia)'] ONS Open Data Portal Verfied energy charge (PT-BR Carga de energia verificada) arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/carga-energia-verificada)'] ONS Open Data Portal Scheduled energy charge (PT-BR Carga de energia programda) arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/carga-energia-programada)'] ONS Open Data Portal Monthly energy charge (PT-BR Carga de energia mensal) arn:aws:s3:::ons-aws-prod-opendata sa-east-1 S3 Bucket https://dados.ons.org.br/dataset/ dadosabertos@ons.org.br [ONS - National Electric System Operator](https://www.ons.org.br/) diary CC-BY 4.0 aws-pds, electricity, hydrography, energy ['[Browse Dataset](https://dados.ons.org.br/dataset/carga-mensal)'] @@ -916,38 +918,38 @@ Open Bioinformatics Reference Data for Galaxy The data is organized as versioned Open City Model (OCM) Project data files arn:aws:s3:::opencitymodel us-east-1 S3 Bucket https://github.com/opencitymodel/opencitymodel https://github.com/opencitymodel/opencitymodel#contact BuildZero Quarterly https://github.com/opencitymodel/opencitymodel#license aws-pds, events, cities, geospatial Open Food Facts Images Open Food Facts image dataset arn:aws:s3:::openfoodfacts-images eu-west-3 S3 Bucket https://openfoodfacts.github.io/openfoodfacts-server/api/aws-images-dataset contact@openfoodfacts.org [Open Food Facts](https://world.openfoodfacts.org) Monthly All data contained in this dataset is licenced under the [Creative Commons Attri aws-pds, machine learning, image processing Open NeuroData Neuroglancer precomputed volumes in a public bucket arn:aws:s3:::open-neurodata us-east-1 S3 Bucket https://neurodata.io/help/download/ support@neurodata.io [NeuroData](https://neurodata.io/ocp) The dataset may be updated with additional or corrected data on a need-to-update ODC-By v1.0 unless otherwise specified aws-pds, biology, image processing, neuroimaging, neuroscience, electron microscopy, life sciences, light-sheet microscopy, magnetic resonance imaging, array tomography -Open Observatory of Network Interference (OONI) Old S3 bucket with cans for older measurements arn:aws:s3:::ooni-data us-east-1 S3 Bucket https://ooni.org/data/ https://ooni.org/get-involved/ Open Observatory of Network Interference Hourly Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International https:// aws-pds, internet Open Observatory of Network Interference (OONI) New S3 bucket with JSONL files arn:aws:s3:::ooni-data-eu-fra eu-central-1 S3 Bucket https://ooni.org/data/ https://ooni.org/get-involved/ Open Observatory of Network Interference Hourly Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International https:// aws-pds, internet +Open Observatory of Network Interference (OONI) Old S3 bucket with cans for older measurements arn:aws:s3:::ooni-data us-east-1 S3 Bucket https://ooni.org/data/ https://ooni.org/get-involved/ Open Observatory of Network Interference Hourly Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International https:// aws-pds, internet Open VLF: Scientific Open Data Initiative for CRAAM's SAVNET and AWESOME VLF Data. The Open VLF Files Total size of 736 GB arn:aws:s3:::craam-files-bucket sa-east-1 S3 Bucket [Open VLF](https://open-vlf.web.app) [Open VLF Feedback](https://open-vlf.web.app/markdown/the-project) [CRAAM Mackenzie](https://www.mackenzie.br/centro-de-radio-astronomia-e-astrofis Various. Data since 2006, and still updated. Follow the announcements and what i There are no restrictions on the use of this data. archives, astronomy, atmosphere, aws-pds, global, open source software, signal processing, life sciences Open-Meteo Weather API Database Open-Meteo Weather API Database arn:aws:s3:::openmeteo us-west-2 S3 Bucket https://github.com/open-meteo/open-data info@open-meteo.com [Open-Meteo](https://www.open-meteo.com/) Hourly CC-BY 4.0 aws-pds, agriculture, climate, earth observation, meteorological, weather ['[Browse Bucket](https://openmeteo.s3.amazonaws.com/index.html#data/)'] -OpenAQ Daily gzipped CSVs of global air quality measurements fetched from sources all o arn:aws:s3:::openaq-data-archive us-east-1 S3 Bucket https://openaq.org info@openaq.org [OpenAQ](https://openaq.org) Hourly Varies, depends on data provider aws-pds, air quality, cities, environmental, geospatial -OpenAQ SNS topic for new objects in the openaq-data-archive bucket arn:aws:sns:us-east-1:817926761842:openaq-data-archive-object_created us-east-1 SNS Topic https://openaq.org info@openaq.org [OpenAQ](https://openaq.org) Hourly Varies, depends on data provider aws-pds, air quality, cities, environmental, geospatial OpenAQ OpenAQ API us-east-1 CloudFront Distribution https://openaq.org info@openaq.org [OpenAQ](https://openaq.org) Hourly Varies, depends on data provider aws-pds, air quality, cities, environmental, geospatial api.openaq.org +OpenAQ SNS topic for new objects in the openaq-data-archive bucket arn:aws:sns:us-east-1:817926761842:openaq-data-archive-object_created us-east-1 SNS Topic https://openaq.org info@openaq.org [OpenAQ](https://openaq.org) Hourly Varies, depends on data provider aws-pds, air quality, cities, environmental, geospatial +OpenAQ Daily gzipped CSVs of global air quality measurements fetched from sources all o arn:aws:s3:::openaq-data-archive us-east-1 S3 Bucket https://openaq.org info@openaq.org [OpenAQ](https://openaq.org) Hourly Varies, depends on data provider aws-pds, air quality, cities, environmental, geospatial OpenAerialMap on AWS OpenAerialMap files and metadata arn:aws:s3:::oin-hotosm us-east-1 S3 Bucket https://docs.openaerialmap.org/ info@openaerialmap.org [Humanitarian OpenStreetMap Team](https://www.hotosm.org/) New imagery is added as soon as it is uploaded by community contributors. All imagery is publicly licensed CC-BY 4.0, with attribution as contributors of satellite imagery, aerial imagery, earth observation, disaster response, cog ['[Browse Bucket](https://oin-hotosm.s3.amazonaws.com/)'] -OpenAlex dataset OpenAlex Entities in JSON Lines format arn:aws:s3:::openalex us-east-1 S3 Bucket https://docs.openalex.org team@ourresearch.org [OurResearch](https://ourresearch.org/) Approximately monthly [CC0](https://creativecommons.org/publicdomain/zero/1.0/) graph, json, metadata, scholarly communication, aws-pds ['[Browse Bucket](https://openalex.s3.amazonaws.com/browse.html)'] OpenAlex dataset Openalex Entities decomposed to tab-separated columnar files for backward compat arn:aws:s3:::openalex-mag-format us-east-1 S3 Bucket https://docs.openalex.org team@ourresearch.org [OurResearch](https://ourresearch.org/) Approximately monthly [CC0](https://creativecommons.org/publicdomain/zero/1.0/) graph, json, metadata, scholarly communication, aws-pds ['[Browse Bucket](https://openalex-mag-format.s3.amazonaws.com/browse.html)'] +OpenAlex dataset OpenAlex Entities in JSON Lines format arn:aws:s3:::openalex us-east-1 S3 Bucket https://docs.openalex.org team@ourresearch.org [OurResearch](https://ourresearch.org/) Approximately monthly [CC0](https://creativecommons.org/publicdomain/zero/1.0/) graph, json, metadata, scholarly communication, aws-pds ['[Browse Bucket](https://openalex.s3.amazonaws.com/browse.html)'] OpenCRAVAT OpenCRAVAT Store EU arn:aws:s3:::opencravat-store-eu-west-2 eu-west-2 S3 Bucket https://open-cravat.readthedocs.io support@opencravat.org KarchinLab, Potomac IT Group Data is mirrored daily. Update frequencies of individual annotators depend on th "License varies per-annotator. Commercial users must check the ""commercial_warnin" aws-pds, genetic, genomic, life sciences, variant annotation, sqlite, tertiary analysis OpenCRAVAT OpenCRAVAT Store US arn:aws:s3:::opencravat-store-aws us-east-1 S3 Bucket https://open-cravat.readthedocs.io support@opencravat.org KarchinLab, Potomac IT Group Data is mirrored daily. Update frequencies of individual annotators depend on th "License varies per-annotator. Commercial users must check the ""commercial_warnin" aws-pds, genetic, genomic, life sciences, variant annotation, sqlite, tertiary analysis OpenCell on AWS Live-cell confocal fluorescence microscopy images of the OpenCell library of flu arn:aws:s3:::czb-opencell us-west-2 S3 Bucket https://opencell.czbiohub.org/download opencell@czbiohub.org [Chan Zuckerberg Biohub](https://www.czbiohub.org/) This is the final version of the dataset. https://github.com/czbiohub/opencell/blob/master/LICENSE aws-pds, biology, cell biology, life sciences, imaging, cell imaging, fluorescence imaging, microscopy, computer vision, machine learning OpenEEW OpenEEW arn:aws:s3:::grillo-openeew us-east-1 S3 Bucket https://github.com/openeew/openeew hello@openeew.com [Grillo](https://grillo.io/) Approximately every 5 minutes https://github.com/openeew/openeew#license disaster response, earth observation, earthquakes, aws-pds ['[Browse Bucket](https://grillo-openeew.s3.amazonaws.com/index.html)'] OpenNeuro MRI, MEG, EEG, iEEG, and ECoG datasets from OpenNeuro arn:aws:s3:::openneuro.org us-east-1 S3 Bucket http://openneuro.org Support form at https://openneuro.org [Stanford University Center for Reproducible Neuroscience](https://reproducibili New datasets deposited every 4-6 days CC0 aws-pds, biology, imaging, life sciences, neurobiology, neuroimaging OpenProteinSet A repository of MSAs and template hits arn:aws:s3:::openfold us-east-1 S3 Bucket https://docs.google.com/document/d/1R90-VJSLQEbot7tgXF3zb068Y1ZJAmsckQ_t2sJTv2c/ https://github.com/aqlaboratory/openfold/issues OpenFold Never [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) openfold, msa, protein, protein template, protein folding, alphafold, open source software, life sciences, aws-pds -OpenStreetMap on AWS Imagery and metadata arn:aws:s3:::osm-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds https://github.com/mojodna/osm-pds-pipelines/issues Pacific Atlas Data is updated weekly https://www.openstreetmap.org/copyright aws-pds, geospatial, mapping, osm, disaster response OpenStreetMap on AWS New data notifications arn:aws:sns:us-east-1:800218804198:New_File us-east-1 SNS Topic https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds https://github.com/mojodna/osm-pds-pipelines/issues Pacific Atlas Data is updated weekly https://www.openstreetmap.org/copyright aws-pds, geospatial, mapping, osm, disaster response +OpenStreetMap on AWS Imagery and metadata arn:aws:s3:::osm-pds us-east-1 S3 Bucket https://github.com/awslabs/open-data-docs/tree/main/docs/osm-pds https://github.com/mojodna/osm-pds-pipelines/issues Pacific Atlas Data is updated weekly https://www.openstreetmap.org/copyright aws-pds, geospatial, mapping, osm, disaster response OpenSurfaces OpenSurfaces data arn:aws:s3:::labelmaterial us-east-1 S3 Bucket http://opensurfaces.cs.cornell.edu/publications/opensurfaces/ snavely@cs.cornell.edu Cornell University Static dataset (not updated) The annotations are licensed under a Creative Commons Attribution 4.0 Internatio computer vision, aws-pds ['[Browse data on project webpage](http://opensurfaces.cs.cornell.edu/)'] -OpenUniverse 2024 Matched Rubin and Roman Simulations: Preview The simulated Roman data products include truth files listing the basic physical arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/roman/ us-east-1 S3 Bucket https://irsa.ipac.caltech.edu/data/theory/openuniverse2024 https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The OpenUniverse 2024 Data Preview has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, simulations, survey False False OpenUniverse 2024 Matched Rubin and Roman Simulations: Preview The simulated Rubin data products include raw pixel data, calibrated exposures, arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/rubin/ us-east-1 S3 Bucket https://irsa.ipac.caltech.edu/data/theory/openuniverse2024 https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The OpenUniverse 2024 Data Preview has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, simulations, survey False False +OpenUniverse 2024 Matched Rubin and Roman Simulations: Preview The simulated Roman data products include truth files listing the basic physical arn:aws:s3:::nasa-irsa-simulations/openuniverse2024/roman/ us-east-1 S3 Bucket https://irsa.ipac.caltech.edu/data/theory/openuniverse2024 https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The OpenUniverse 2024 Data Preview has been finalized and will not be updated. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, object detection, parquet, satellite imagery, simulations, survey False False Opioid Industry Documents Archive (OIDA) Data on AWS Raw data from the Opioid Industry Documents Archive (OIDA), including documents arn:aws:s3:::opioid-industry-documents-archive-dataset-bucket us-east-1 S3 Bucket https://opioid-industry-documents-archive-dataset-bucket.s3.amazonaws.com/index. opioidarchive@jh.edu Johns Hopkins University monthly https://www.industrydocuments.ucsf.edu/opioids/help/copyright/ aws-pds, archives, text analysis, txt, pharmaceutical, life sciences +Orcasound - bioacoustic data for marine conservation Live-streamed orca audio data (HLS) arn:aws:s3:::streaming-orcasound-net us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing Orcasound - bioacoustic data for marine conservation Archived lossless orca audio data (FLAC) arn:aws:s3:::archive-orcasound-net us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing Orcasound - bioacoustic data for marine conservation Labeled audio data for ML model development arn:aws:s3:::acoustic-sandbox us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing -Orcasound - bioacoustic data for marine conservation Live-streamed orca audio data (HLS) arn:aws:s3:::streaming-orcasound-net us-west-2 S3 Bucket https://github.com/orcasound/orcadata/wiki info@orcasound.net Orcasound Typical latency is 10-120 seconds https://creativecommons.org/licenses/by-nc-sa/4.0/ aws-pds, biodiversity, biology, coastal, conservation, deep learning, ecosystems, environmental, geospatial, labeled, machine learning, mapping, oceans, open source software, signal processing Oregon Health & Science University Chronic Neutrophilic Leukemia Dataset RNA-Seq Gene Expression Quantification arn:aws:s3:::gdc-ohsu-cnl-phs001799-2-open us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001799.v dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genomic, life sciences -Overture Maps Foundation Open Map Data Overture Maps Foundation Data (GeoParquet) arn:aws:s3:::overturemaps-us-west-2/release/ us-west-2 S3 Bucket Documentation is available at [docs.overturemaps.org](https://docs.overturemaps. info@overturemaps.org [Overture Maps Foundation](https://overturemaps.org) Monthly Overture data is licensed under the Community Database License Agreement Permiss aws-pds, geospatial, global, mapping, osm, parquet, transportation Overture Maps Foundation Open Map Data New File Notification arn:aws:sns:us-west-2:913550007193:overturemaps-us-west-2 us-west-2 SNS Topic Documentation is available at [docs.overturemaps.org](https://docs.overturemaps. info@overturemaps.org [Overture Maps Foundation](https://overturemaps.org) Monthly Overture data is licensed under the Community Database License Agreement Permiss aws-pds, geospatial, global, mapping, osm, parquet, transportation -Oxford Nanopore Technologies Benchmark Datasets Oxford Nanopore Open Datasets arn:aws:s3:::ont-open-data eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False -Oxford Nanopore Technologies Benchmark Datasets Nanopore sequencing data of the Genome in a Bottle samples NA24385, NA24149, and arn:aws:s3:::ont-open-data/giab_lsk114_2022.12 eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False +Overture Maps Foundation Open Map Data Overture Maps Foundation Data (GeoParquet) arn:aws:s3:::overturemaps-us-west-2/release/ us-west-2 S3 Bucket Documentation is available at [docs.overturemaps.org](https://docs.overturemaps. info@overturemaps.org [Overture Maps Foundation](https://overturemaps.org) Monthly Overture data is licensed under the Community Database License Agreement Permiss aws-pds, geospatial, global, mapping, osm, parquet, transportation Oxford Nanopore Technologies Benchmark Datasets Using nanopore sequencing, researchers have directly identified DNA and RNA base arn:aws:s3:::ont-open-data/gm24385_mod_2021.09/extra_analysis/bonito_remora eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False Oxford Nanopore Technologies Benchmark Datasets CpG dinucleotides frequently occur in high-density clusters called CpG islands ( arn:aws:s3:::ont-open-data/rrms_2022.07 eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False +Oxford Nanopore Technologies Benchmark Datasets Oxford Nanopore Open Datasets arn:aws:s3:::ont-open-data eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False +Oxford Nanopore Technologies Benchmark Datasets Nanopore sequencing data of the Genome in a Bottle samples NA24385, NA24149, and arn:aws:s3:::ont-open-data/giab_lsk114_2022.12 eu-west-1 S3 Bucket https://labs.epi2me.io/dataindex/ support@nanoporetech.com Oxford Nanopore Technologies Additional datasets will be added periodically. Updates and amendents will be ma Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommo aws-pds, bioinformatics, biology, fastq, fast5, genomic, life sciences, Homo sapiens, whole genome sequencing False Ozone Monitoring Instrument (OMI) / Aura NO2 Tropospheric Column Density S3 Bucket for OMI NO2 in Cloud-Optimized GeoTiff format arn:aws:s3:::omi-no2-nasa us-west-2 S3 Bucket https://disc.gsfc.nasa.gov/datasets/OMNO2d_003/summary binita.kc@nasa.gov NASA None There are no restrictions on the use of these data. aws-pds, earth observation, geospatial, satellite imagery, air quality, atmosphere, environmental PALSAR-2 ScanSAR CARD4L (L2.2) PALSAR-2 ScanSAR CARD4L arn:aws:s3:::jaxaalos2/palsar2/L2.2/Africa/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) Every month after 42 days observed Data is available for free under the [terms of use](https://earth.jaxa.jp/policy aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, sustainability, disaster response, synthetic aperture radar, deafrica, stac, cog False PALSAR-2 ScanSAR Flooding in Rwanda (L2.1) PALSAR-2 ScanSAR L11 & L22 arn:aws:s3:::jaxaalos2/palsar2-scansar/Rwanda/ us-west-2 S3 Bucket https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.e aproject@jaxa.jp [JAXA](https://www.jaxa.jp/) As available. Data is available for free under the terms of use. aws-pds, agriculture, cog, deafrica, disaster response, earth observation, geospatial, natural resource, satellite imagery, stac, sustainability, synthetic aperture radar False @@ -956,20 +958,20 @@ PALSAR-2 ScanSAR Turkey & Syria Earthquake (L2.1 & L1.1) PALSAR-2 ScanSAR L11 & PASS: Perturb-and-Select Summarizer for Product Reviews A collection of summaries generated by PASS for the FewSum Product Reviews datas arn:aws:s3:::pass-summary-fewsum us-east-1 S3 Bucket https://pass-summary-fewsum.s3.amazonaws.com/README.md noved@amazon.com [Amazon](https://www.amazon.com/) Not updated This data is available for anyone to use under the terms of the CDLA-Sharing lic amazon.science, natural language processing, text analysis ['[pass_generated_summaries.jsonl](https://pass-summary-fewsum.s3.amazonaws.com/pass_gen_summaries_fewsum_amazon_val_test.jsonl)'] PD12M Image files arn:aws:s3:::pd12m us-west-2 S3 Bucket https://huggingface.co/datasets/Spawning/PD12M info@spawning.ai Spawning Data will be adjusted as infringing works are discovered, improved provenance is https://cdla.dev/permissive-2-0/ image processing, machine learning, media, art, deep learning, labeled PROJ datum grids Horizontal and vertical adjustment datasets us-east-1 CloudFront Distribution https://github.com/OSGeo/proj-datumgrid-geotiff proj@lists.osgeo.org [PROJ](https://proj.org) New grids are added when made available Per file. Under an Open Source Definition compliant license. Consult the READMEs aws-pds, geospatial, mapping cdn.proj.org -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2015 arn:aws:s3:::pacific-sound-256khz-2015 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2018 arn:aws:s3:::pacific-sound-256khz-2018 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings decimated 16 kHz audio recordings arn:aws:s3:::pacific-sound-16khz us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2017 arn:aws:s3:::pacific-sound-256khz-2017 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2016 arn:aws:s3:::pacific-sound-256khz-2016 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2019 arn:aws:s3:::pacific-sound-256khz-2019 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2020 arn:aws:s3:::pacific-sound-256khz-2020 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings decimated 2 kHz audio recordings arn:aws:s3:::pacific-sound-2khz us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2022 arn:aws:s3:::pacific-sound-256khz-2022 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2023 arn:aws:s3:::pacific-sound-256khz-2023 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2024 arn:aws:s3:::pacific-sound-256khz-2024 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2025 arn:aws:s3:::pacific-sound-256khz-2025 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings decimated 2 kHz audio recordings arn:aws:s3:::pacific-sound-2khz us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings decimated 16 kHz audio recordings arn:aws:s3:::pacific-sound-16khz us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings machine learning models arn:aws:s3:::pacific-sound-models us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2020 arn:aws:s3:::pacific-sound-256khz-2020 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2019 arn:aws:s3:::pacific-sound-256khz-2019 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2018 arn:aws:s3:::pacific-sound-256khz-2018 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2017 arn:aws:s3:::pacific-sound-256khz-2017 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2021 arn:aws:s3:::pacific-sound-256khz-2021 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software -Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2022 arn:aws:s3:::pacific-sound-256khz-2022 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software +Pacific Ocean Sound Recordings original 256 kHz audio recordings year 2015 arn:aws:s3:::pacific-sound-256khz-2015 us-west-2 S3 Bucket https://docs.mbari.org/pacific-sound/ dcline@mbari.org [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) daily CC-BY 4.0 aws-pds, acoustics, biodiversity, ecosystems, biology, marine mammals, oceans, climate, coastal, deep learning, machine learning, environmental, open source software Pan-STARRS PS1 Survey PS1 DR1 and DR2 image files arn:aws:s3:::stpubdata/ps1 us-east-1 S3 Bucket https://outerspace.stsci.edu/display/PANSTARRS/ archive@stsci.edu [Space Telescope Science Institute](http://www.stsci.edu/) Never STScI hereby grants the non-exclusive, royalty-free, non-transferable, worldwide aws-pds, astronomy False Pancreatic Cancer Organoid Profiling RNA-Seq Gene Expression Quantification arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-open us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genetic, genomic, transcriptomics, whole genome sequencing, STRIDES, life sciences Pancreatic Cancer Organoid Profiling WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic M arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-controlled us-east-1 S3 Bucket https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, cancer, genetic, genomic, transcriptomics, whole genome sequencing, STRIDES, life sciences https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1 @@ -979,28 +981,28 @@ Physionet https://s3amazonawscom/physionet-pds/indexhtml arn:aws:s3:::physionet- Platinum Pedigree https://githubcom/Platinum-Pedigree-Consortium/Platinum-Pedigree-Datasets arn:aws:s3:::platinum-pedigree-data us-west-1 S3 Bucket https://github.com/Platinum-Pedigree-Consortium https://github.com/Platinum-Pedigree-Consortium/Platinum-Pedigree-Datasets/issue Platinum Pedigree Consortium As needed [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) genomic, genotyping, long read sequencing, bioinformatics, Homo sapiens, life sciences, whole genome sequencing Pohang Canal Dataset: A Multimodal Maritime Dataset for Autonomous Navigation in Restricted Waters Pohang Canal dataset arn:aws:s3:::pohang-canal-dataset us-west-2 S3 Bucket https://sites.google.com/view/pohang-canal-dataset/home morin-lab@kaist.ac.kr [MORIN](http://morin.kaist.ac.kr) Not updated [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) aws-pds, autonomous vehicles, marine navigation, robotics, computer vision, lidar PoroTomo PoroTomo Nodal Seismometer Sweep Data arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac_sweep/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac_sweep%2F)'] -PoroTomo PoroTomo Nodal Seismometer Continuous Data arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac%2F)'] -PoroTomo HSDS PoroTomo domains arn:aws:s3:::nrel-pds-hsds/nrel/porotomo/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fporotomo%2F)'] PoroTomo PoroTomo Nodal Seismometer Field Notes and Metadata arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_metadata/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_metadata%2F)'] PoroTomo PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in SEG-Y format arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASV/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASV%2F)'] -PoroTomo PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time M arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/Resampled/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2FResampled%2F)'] -PoroTomo PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in SEG-Y format arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2F)'] PoroTomo PoroTomo Datasets arn:aws:s3:::nrel-pds-porotomo/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo)'] -PoroTomo PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASV/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASV%2F)'] PoroTomo PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in HDF5 format arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASH/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASH%2F)'] +PoroTomo PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time M arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/Resampled/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2FResampled%2F)'] +PoroTomo PoroTomo Vertical Distributed Acoustic Sensing (DASV) Data in HDF5 format arn:aws:s3:::nrel-pds-porotomo/DAS/H5/DASV/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FH5%2FDASV%2F)'] +PoroTomo HSDS PoroTomo domains arn:aws:s3:::nrel-pds-hsds/nrel/porotomo/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-hsds&prefix=nrel%2Fporotomo%2F)'] +PoroTomo PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data in SEG-Y format arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2F)'] +PoroTomo PoroTomo Nodal Seismometer Continuous Data arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac/ us-west-2 S3 Bucket https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md Thomas Coleman (thomas.coleman@silixa.com) [National Renewable Energy Laboratory](https://www.nrel.gov/) As needed Creative Commons Attribution 3.0 United States License aws-pds, geothermal, seismology, image processing, geospatial ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac%2F)'] Poseidon 3D Seismic, Australia Poseidon 3D Seismic MDIO volumes and Reports arn:aws:s3:::tgs-opendata-poseidon us-west-2 S3 Bucket TBD For any questions regarding the datasets and MDIO, email the TGS Open Data Team [TGS](https://www.tgs.com) Dataset is static. CC BY 4.0 seismology, geophysics, exploration ['[Browse Bucket](https://tgs-opendata-poseidon.s3.amazonaws.com/index.html)'] Pre- and post-purchase product questions S3 bucket with dataset arn:aws:s3:::pre-post-purchase-questions us-east-1 S3 Bucket https://pre-post-purchase-questions.s3.amazonaws.com/README.txt litalku@amazon.com [Amazon](https://www.amazon.com/) Not currently being updated [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) amazon.science, natural language processing, machine learning ['[PrePostQuestions.csv](https://pre-post-purchase-questions.s3.amazonaws.com/PrePostQuestions.csv)'] Prefeitura Municipal de São Paulo (PMSP) LiDAR Point Cloud São Paulo city's 3D LiDAR - Entwine Point Tiles arn:aws:s3:::ept-m3dc-pmsp sa-east-1 S3 Bucket https://github.com/geoinfo-smdu/M3DC geosampa@prefeitura.sp.gov.br [GeoSampa - o mapa digital da cidade de São Paulo](http://geosampa.prefeitura.sp Local survey executed by demand generates new data as local point clouds. [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html) cities, land, lidar, urban, geospatial, elevation, mapping, aws-pds Prefeitura Municipal de São Paulo (PMSP) LiDAR Point Cloud São Paulo city's 3D LiDAR - LAZ Files arn:aws:s3:::laz-m3dc-pmsp sa-east-1 S3 Bucket https://github.com/geoinfo-smdu/M3DC geosampa@prefeitura.sp.gov.br [GeoSampa - o mapa digital da cidade de São Paulo](http://geosampa.prefeitura.sp Local survey executed by demand generates new data as local point clouds. [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html) cities, land, lidar, urban, geospatial, elevation, mapping, aws-pds Product Comparison Dataset for Online Shopping Product Comparison Dataset for Online Shopping arn:aws:s3:::prod-comp-shopping-dataset us-west-2 S3 Bucket https://prod-comp-shopping-dataset.s3.us-west-2.amazonaws.com/README.md Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 [Amazon](https://www.amazon.com/) None [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) product comparison, online shopping, amazon.science, natural language processing, machine learning ['[final_prodcomp_dataset_cleaned.tsv](https://prod-comp-shopping-dataset.s3.us-west-2.amazonaws.com/final_prodcomp_dataset_cleaned.tsv)'] -Protein Data Bank 3D Structural Biology Data Globally cached distribution of the dataset Web frontend also available to brow us-west-2 CloudFront Distribution https://www.wwpdb.org/documentation/file-format https://www.wwpdb.org/about/contact [Worldwide Protein Data Bank Partnership](wwpdb.org) New and updated data files are published weekly and released on Wednesdays 0:00 https://creativecommons.org/publicdomain/zero/1.0/ aws-pds, amino acid, archives, bioinformatics, biomolecular modeling, cell biology, chemical biology, COVID-19, electron microscopy, electron tomography, enzyme, life sciences, molecule, nuclear magnetic resonance, pharmaceutical, protein, protein template, SARS-CoV-2, structural biology, x-ray crystallography ['[Browse Dataset](https://s3.rcsb.org)'] Protein Data Bank 3D Structural Biology Data Historical snapshots of archival datasets from 2005 onwards Snapshots are gener arn:aws:s3:::pdbsnapshots us-west-2 S3 Bucket https://www.wwpdb.org/documentation/file-format https://www.wwpdb.org/about/contact [Worldwide Protein Data Bank Partnership](wwpdb.org) New and updated data files are published weekly and released on Wednesdays 0:00 https://creativecommons.org/publicdomain/zero/1.0/ aws-pds, amino acid, archives, bioinformatics, biomolecular modeling, cell biology, chemical biology, COVID-19, electron microscopy, electron tomography, enzyme, life sciences, molecule, nuclear magnetic resonance, pharmaceutical, protein, protein template, SARS-CoV-2, structural biology, x-ray crystallography ['[Browse Bucket](https://pdbsnapshots.s3.us-west-2.amazonaws.com/index.html)'] +Protein Data Bank 3D Structural Biology Data Globally cached distribution of the dataset Web frontend also available to brow us-west-2 CloudFront Distribution https://www.wwpdb.org/documentation/file-format https://www.wwpdb.org/about/contact [Worldwide Protein Data Bank Partnership](wwpdb.org) New and updated data files are published weekly and released on Wednesdays 0:00 https://creativecommons.org/publicdomain/zero/1.0/ aws-pds, amino acid, archives, bioinformatics, biomolecular modeling, cell biology, chemical biology, COVID-19, electron microscopy, electron tomography, enzyme, life sciences, molecule, nuclear magnetic resonance, pharmaceutical, protein, protein template, SARS-CoV-2, structural biology, x-ray crystallography ['[Browse Dataset](https://s3.rcsb.org)'] Provision of Web-Scale Parallel Corpora for Official European Languages (ParaCrawl) Parallel Corpora to/from English for all official EU languages arn:aws:s3:::web-language-models us-east-1 S3 Bucket https://paracrawl.eu/releases.html For questions regarding the datasets contact Kenneth Heafield, email kheafiel@in [ParaCrawl](https://paracrawl.eu) New data is added according to ParaCrawl release schedule. "Creative Commons CC0 license (""no rights reserved"")." aws-pds, machine translation, natural language processing -PubSeq - Public Sequence Resource PubSeq submitted datasets (FASTA and JSON metadata) arn:aws:s3:::pubseq-datasets us-east-2 S3 Bucket https://covid19.genenetwork.org/about https://covid19.genenetwork.org/contact [UTHSC GeneNetwork](https://covid19.genenetwork.org/) Rolling dataset. Creative Commons Attribution 4.0 International (CC BY 4.0) unless otherwise spec aws-pds, bam, bioinformatics, biology, coronavirus, COVID-19, fasta, fastq, fast5, genetic, genomic, health, json, life sciences, long read sequencing, open source software, MERS, metadata, medicine, RDF, SARS, SARS-CoV-2, SPARQL ['[Browse Bucket](https://pubseq-datasets.s3.amazonaws.com/)'] PubSeq - Public Sequence Resource Pubseq output data (Arvados Keep) arn:aws:s3:::pubseq-output-data us-east-2 S3 Bucket https://covid19.genenetwork.org/about https://covid19.genenetwork.org/contact [UTHSC GeneNetwork](https://covid19.genenetwork.org/) Rolling dataset. Creative Commons Attribution 4.0 International (CC BY 4.0) unless otherwise spec aws-pds, bam, bioinformatics, biology, coronavirus, COVID-19, fasta, fastq, fast5, genetic, genomic, health, json, life sciences, long read sequencing, open source software, MERS, metadata, medicine, RDF, SARS, SARS-CoV-2, SPARQL ['[Arvados download](https://covid19.genenetwork.org/download)'] +PubSeq - Public Sequence Resource PubSeq submitted datasets (FASTA and JSON metadata) arn:aws:s3:::pubseq-datasets us-east-2 S3 Bucket https://covid19.genenetwork.org/about https://covid19.genenetwork.org/contact [UTHSC GeneNetwork](https://covid19.genenetwork.org/) Rolling dataset. Creative Commons Attribution 4.0 International (CC BY 4.0) unless otherwise spec aws-pds, bam, bioinformatics, biology, coronavirus, COVID-19, fasta, fastq, fast5, genetic, genomic, health, json, life sciences, long read sequencing, open source software, MERS, metadata, medicine, RDF, SARS, SARS-CoV-2, SPARQL ['[Browse Bucket](https://pubseq-datasets.s3.amazonaws.com/)'] Public Utility Data Liberation Project All PUDL data outputs arn:aws:s3:::pudl.catalyst.coop us-west-2 S3 Bucket You can download the [data directly](https://catalystcoop-pudl.readthedocs.io/en For general questions or feedback about the data, create an GitHub issue or disc [Catalyst Cooperative](https://catalyst.coop/) The federal agencies that publish the raw data PUDL processes release new data, The PUDL data and documentation are published under the [Creative Commons Attrib aws-pds, climate, climate model, energy, environmental, government records, infrastructure, open source software, electricity, energy modeling, utilities -PyEnvs and CallArgs PyEnvs arn:aws:s3:::pyenvs-and-callargs/pyenvs/ us-west-2 S3 Bucket https://github.com/amazon-research/function-call-argument-completion Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 Amazon None Planned Please note that while we are providing this aggregation of code snippets unlice machine learning, code completion PyEnvs and CallArgs CallArgs arn:aws:s3:::pyenvs-and-callargs/callargs/ us-west-2 S3 Bucket https://github.com/amazon-research/function-call-argument-completion Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 Amazon None Planned Please note that while we are providing this aggregation of code snippets unlice machine learning, code completion +PyEnvs and CallArgs PyEnvs arn:aws:s3:::pyenvs-and-callargs/pyenvs/ us-west-2 S3 Bucket https://github.com/amazon-research/function-call-argument-completion Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 Amazon None Planned Please note that while we are providing this aggregation of code snippets unlice machine learning, code completion QIIME 2 Tutorial Data Source for rendered documentation and tutorial datasets for the QIIME 2 project arn:aws:s3:::qiime2-data us-west-2 S3 Bucket https://use.qiime2.org https://forum.qiime2.org Caporaso Lab Twice per year BSD 3-Clause License aws-pds, bioinformatics, biology, ecosystems, environmental, genetic, genomic, health, microbiome, metagenomics, life sciences Quoref Project data files in a public bucket arn:aws:s3:::ai2-public-datasets us-west-2 S3 Bucket https://allenai.org/data/quoref info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY](https://creativecommons.org/licenses/by/4.0) aws-pds, machine learning, natural language processing RACECAR Dataset The RACECAR dataset is the first open dataset for full-scale and high-speed auto arn:aws:s3:::racecar-dataset us-west-2 S3 Bucket https://github.com/linklab-uva/RACECAR_DATA Prof. Madhur Behl (madhur.behl@viginia.edu) Amar Kulkarni (ark8su@virginia.edu) This dataset was constructed during a single racing season (2021-22). Future sea Creative Commons Attribution-NonCommercial 4.0 International Public License [(CC aws-pds, autonomous vehicles, autonomous racing, robotics, computer vision, perception, lidar, radar, GNSS, image processing, localization, object detection, object tracking @@ -1018,16 +1020,16 @@ RSNA Screening Mammography Breast Cancer Detection (RSNA-SMBC) Dataset Zip archi Radiant MLHub Radiant MLHub Training Data arn:aws:s3:::radiant-mlhub us-west-2 S3 Bucket http://docs.mlhub.earth/ support@radiant.earth [Radiant Earth Foundation](https://www.radiant.earth/) New training data catalogs are added on a rolling basis Access to Radiant MLHub data is free for everyone. Each dataset has its own lice aws-pds, labeled, machine learning, geospatial, earth observation, satellite imagery, environmental, cog, stac RarePlanes Real and synthetic satellite imagery, annotations, and metadata arn:aws:s3:::rareplanes-public us-west-2 S3 Bucket www.cosmiqworks.org/RarePlanes jss5102@gmail.com and avanetten@iqt.org In-Q-Tel - CosmiQ Works None Planned [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) computer vision, deep learning, earth observation, geospatial, machine learning, satellite imagery, aws-pds, labeled Reasoning Over Paragraph Effects in Situations (ROPES) Project data files in a public bucket arn:aws:s3:::ai2-public-datasets us-west-2 S3 Bucket https://allenai.org/data/ropes info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY](https://creativecommons.org/licenses/by/4.0) aws-pds, machine learning, natural language processing, json -Reference Elevation Model of Antarctica (REMA) REMA DEM Strips arn:aws:s3:::pgc-opendata-dems/rema/strips/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/rema/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/strips.json)'] Reference Elevation Model of Antarctica (REMA) REMA DEM Mosaics arn:aws:s3:::pgc-opendata-dems/rema/mosaics/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/rema/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/mosaics.json)'] +Reference Elevation Model of Antarctica (REMA) REMA DEM Strips arn:aws:s3:::pgc-opendata-dems/rema/strips/ us-west-2 S3 Bucket https://www.pgc.umn.edu/data/rema/ pgc-support@umn.edu [Polar Geospatial Center](https://www.pgc.umn.edu/) New DEM strips are added twice yearly. Mosaic products are added as soon as the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecom aws-pds, elevation, earth observation, geospatial, mapping, open source software, satellite imagery, cog, stac ['[Browse STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/strips.json)'] Reference data for HiFi human WGS HiFi Human WGS Reference data arn:aws:s3:::pacbio-hifi-human-wgs-reference us-west-2 S3 Bucket https://zenodo.org/records/8415406 dl_it-awsopendata@pacificbiosciences.com [Pacific Biosciences of California, Inc](https://www.pacb.com/) Files are updated to reflect the support for the lastest version of[PacBio WGS V [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) aws-pds, health, life sciences, Homo sapiens, long read sequencing, genetic, mapping, whole genome sequencing, vcf, variant annotation Refgenie reference genome assets Refgenie S3 Bucket arn:aws:s3:::awspds.refgenie.databio.org us-east-1 S3 Bucket http://refgenie.databio.org https://github.com/databio/refgenie/issues Sheffield lab at the University of Virginia As new data becomes available (roughly quarterly) Public domain aws-pds, biology, bioinformatics, genetic, genomic, infrastructure, life sciences, single-cell transcriptomics, transcriptomics, whole genome sequencing -Registry of Open Data on AWS Registry of Open Data on AWS arn:aws:s3:::registry.opendata.aws/roda/ndjson/ us-east-1 S3 Bucket https://github.com/awslabs/open-data-registry#how-are-datasets-added-to-the-regi opendata@amazon.com [Amazon Web Services](https://aws.amazon.com/) Automatically when new datasets are added [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) amazon.science, json, metadata Registry of Open Data on AWS SNS topic for object create events arn:aws:sns:us-east-1:652627389412:roda-object_created us-east-1 SNS Topic https://github.com/awslabs/open-data-registry#how-are-datasets-added-to-the-regi opendata@amazon.com [Amazon Web Services](https://aws.amazon.com/) Automatically when new datasets are added [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) amazon.science, json, metadata -SILAM Air Quality Notifications for new netcdf surface data arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-netcdf eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological +Registry of Open Data on AWS Registry of Open Data on AWS arn:aws:s3:::registry.opendata.aws/roda/ndjson/ us-east-1 S3 Bucket https://github.com/awslabs/open-data-registry#how-are-datasets-added-to-the-regi opendata@amazon.com [Amazon Web Services](https://aws.amazon.com/) Automatically when new datasets are added [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) amazon.science, json, metadata SILAM Air Quality Surface Zarr files arn:aws:s3:::fmi-opendata-silam-surface-zarr eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological ['[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)'] -SILAM Air Quality Surface NetCDF files arn:aws:s3:::fmi-opendata-silam-surface-netcdf eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological ['[Browse Bucket](https://fmi-opendata-silam-surface-netcdf.s3.amazonaws.com/index.html)'] SILAM Air Quality Notifications for new zarr surface data arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-zarr eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological +SILAM Air Quality Notifications for new netcdf surface data arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-silam-surface-netcdf eu-west-1 SNS Topic http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological +SILAM Air Quality Surface NetCDF files arn:aws:s3:::fmi-opendata-silam-surface-netcdf eu-west-1 S3 Bucket http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3 avoin-data@fmi.fi [Finnish Meteorological Institute](https://www.ilmatieteenlaitos.fi/) 1 time a day Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, earth observation, climate, weather, air quality, meteorological ['[Browse Bucket](https://fmi-opendata-silam-surface-netcdf.s3.amazonaws.com/index.html)'] SILO climate data on AWS SILO open data arn:aws:s3:::silo-open-data ap-southeast-2 S3 Bucket https://www.longpaddock.qld.gov.au/silo/gridded-data https://www.longpaddock.qld.gov.au/silo/contact-us Queensland Government Daily SILO datasets are constructed by the [Queensland Government](http://www.qld.gov. aws-pds, agriculture, climate, earth observation, environmental, meteorological, model, sustainability, water, weather SMN Hi-Res Weather Forecast over Argentina WRF SMN data arn:aws:s3:::smn-ar-wrf us-west-2 S3 Bucket General information, tutorials and examples:[https://odp-aws-smn.github.io/docum For any questions regarding the data set or any general questions, you can conta [SMN](http://www.smn.gov.ar/) New data is added as soon as it's available. Two forecast cycles a day initializ [Creative Commons Attribution 2.5 Argentina License](https://creativecommons.org aws-pds, earth observation, natural resource, weather, meteorological ['[Browse Bucket](https://smn-ar-wrf.s3.amazonaws.com/index.html)'] SPARTAN Data All data products (PM25, aerosol chemical components, scattering) provided by S arn:aws:s3:::spartan-cloud us-west-2 S3 Bucket https://www.spartan-network.org/data SPARTAN.PM25@gmail.com The [Atmospheric Composition Analysis Group](https://sites.wustl.edu/acag/) New measurement or estimation products will be added when available, usually mul SPARTAN data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/ aws-pds, environmental, air quality @@ -1035,45 +1037,47 @@ SPaRCNet data:Seizures, Rhythmic and Periodic Patterns in ICU Electroencephalogr SSL4EO S12 Landsat Multi Product Dataset Satellite imagery and context from Sentinel-12 and Landsat 4-5, 7, 8-9 arn:aws:s3:::ssl4eo-s12-landsat-combined us-west-2 S3 Bucket https://github.com/sunny1401/ssl4eo_multi_satellite_products https://github.com/sunny1401/ssl4eo_multi_satellite_products Sankranti Joshi Not updated https://creativecommons.org/licenses/by-nc-sa/4.0/ satellite imagery STOIC2021 Training The data set contains 2000 CT scans stored as compressed mha files Each file c arn:aws:s3:::stoic2021-training us-west-2 S3 Bucket https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384 support@grand-challenge.org Radboud University Medical Center The full training set was published at the release. CC-BY-NC 4.0 aws-pds, life sciences, computed tomography, computer vision, coronavirus, COVID-19, grand-challenge.org, imaging, SARS-CoV-2 SUCHO Ukrainian Cultural Heritage Web Archives WACZ archives arn:aws:s3:::sucho-opendata eu-central-1 S3 Bucket https://www.sucho.org/tutorials info@sucho.org Saving Ukrainian Cultural Heritage Online (SUCHO) Periodically Public Domain (CC0) ukraine, internet, cultural preservation, aws-pds -Safecast Bulk exports of air and radiation measurements arn:aws:s3:::safecast-opendata-public-us-east-1 us-east-1 S3 Bucket https://github.com/Safecast/safecastapi/wiki/Data-Sets https://groups.google.com/forum/#!forum/safecast-devices [Safecast](https://safecast.org/) Continuous Safecast data is published under a [CC0 designation](https://creativecommons.org air quality, aws-pds, climate, environmental, geospatial, radiation ['[Browse Bucket](https://safecast-opendata-public-us-east-1.s3.amazonaws.com/index.html)'] Safecast New air and radiation measurement payloads arn:aws:sns:us-west-2:985752656544:ingest-measurements-prd us-west-2 SNS Topic https://github.com/Safecast/safecastapi/wiki/Data-Sets https://groups.google.com/forum/#!forum/safecast-devices [Safecast](https://safecast.org/) Continuous Safecast data is published under a [CC0 designation](https://creativecommons.org air quality, aws-pds, climate, environmental, geospatial, radiation +Safecast Bulk exports of air and radiation measurements arn:aws:s3:::safecast-opendata-public-us-east-1 us-east-1 S3 Bucket https://github.com/Safecast/safecastapi/wiki/Data-Sets https://groups.google.com/forum/#!forum/safecast-devices [Safecast](https://safecast.org/) Continuous Safecast data is published under a [CC0 designation](https://creativecommons.org air quality, aws-pds, climate, environmental, geospatial, radiation ['[Browse Bucket](https://safecast-opendata-public-us-east-1.s3.amazonaws.com/index.html)'] SatPM2.5 Satellite-Derived Fine Particulate Matter (PM25) concentrations from the Atmosp arn:aws:s3:::v6.pm25.global us-west-2 S3 Bucket https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V6.GL.02 randall.martin@wustl.edu https://sites.wustl.edu/acag/ Yearly Creative Commons Attribution 4.0 International (https://creativecommons.org/lice atmosphere, netcdf, environmental, air quality, health ['[Browse Bucket](https://s3.us-west-2.amazonaws.com/v6.pm25.global/index.html)'] Satellite - Sea surface temperature - Level 3 - Single sensor - 1 day - Day and night time Cloud Optimised AODN dataset of IMOS - SRS - SST - L3S - Single Sensor - 1 day - arn:aws:s3:::aodn-cloud-optimised/satellite_ghrsst_l3s_1day_daynighttime_single_sensor_australia.zarr ap-southeast-2 S3 Bucket https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/a info@aodn.org.au AODN As Needed http://creativecommons.org/licenses/by/4.0/ oceans, satellite imagery Scottish Public Sector LiDAR Dataset LiDAR data (DSM, DTM and Laz) arn:aws:s3:::srsp-open-data eu-west-2 S3 Bucket https://remotesensingdata.gov.scot/data#/list https://remotesensingdata.gov.scot/feedback or email Scottish Government on gi-s [Joint Nature Conservation Committee](https://jncc.gov.uk/) New datasets have historically been added every 2-3 years but there is no guaran All data is made available under the [Open Government Licence v3](http://www.nat lidar, cities, coastal, environmental, urban, elevation, cog, aws-pds Sea Around Us Global Fisheries Catch Data Global Fisheries Catch Data arn:aws:s3:::fisheries-catch-data us-west-2 S3 Bucket https://www.seaaroundus.org/ubc-cic-sea-around-us-project-collaboration/ https://www.seaaroundus.org/feedback/ [Sea Around Us](https://www.seaaroundus.org/) The full dataset is computed only once or twice a year or when there is a signif This data is available for anyone to use under the [Sea Around Us Terms of Use]( aws-pds, fisheries, ecosystems, biodiversity, marine Sea Surface Temperature Daily Analysis: European Space Agency Climate Change Initiative product version 2.1 Global daily-mean sea surface temperatures from 1981 onwards, in Zarr format Th arn:aws:s3:::surftemp-sst us-west-2 S3 Bucket https://surftemp.github.io/sst-data-tutorials/ https://www.reading.ac.uk/met/ [University of Reading, Department of Meteorology](https://www.reading.ac.uk/met yearly Creative Commons Licence by attribution (https://creativecommons.org/licenses/by aws-pds, earth observation, oceans, climate, environmental, global, geospatial -Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) Single cell profiling (transcriptomics and epigenomics) data files in a public b arn:aws:s3:::sea-ad-single-cell-profiling us-west-2 S3 Bucket https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheime awspublicdataset@alleninstitute.org [Allen Institute](http://www.alleninstitute.org/) Annually https://alleninstitute.org/legal/terms-use/ aws-pds, biology, cell biology, cell imaging, epigenomics, gene expression, histopathology, Homo sapiens, imaging, medicine, microscopy, neurobiology, neuroscience, single-cell transcriptomics, transcriptomics, life sciences ['[Browse Bucket](https://sea-ad-single-cell-profiling.s3.amazonaws.com/index.html)'] Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) Spatial transcriptomics data files in a public bucket arn:aws:s3:::sea-ad-spatial-transcriptomics us-west-2 S3 Bucket https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheime awspublicdataset@alleninstitute.org [Allen Institute](http://www.alleninstitute.org/) Annually https://alleninstitute.org/legal/terms-use/ aws-pds, biology, cell biology, cell imaging, epigenomics, gene expression, histopathology, Homo sapiens, imaging, medicine, microscopy, neurobiology, neuroscience, single-cell transcriptomics, transcriptomics, life sciences ['[Browse Bucket](https://sea-ad-spatial-transcriptomics.s3.amazonaws.com/index.html)'] Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) Quantitative neuropathology (full resolution images, processed images, and quant arn:aws:s3:::sea-ad-quantitative-neuropathology us-west-2 S3 Bucket https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheime awspublicdataset@alleninstitute.org [Allen Institute](http://www.alleninstitute.org/) Annually https://alleninstitute.org/legal/terms-use/ aws-pds, biology, cell biology, cell imaging, epigenomics, gene expression, histopathology, Homo sapiens, imaging, medicine, microscopy, neurobiology, neuroscience, single-cell transcriptomics, transcriptomics, life sciences ['[Browse Bucket](https://sea-ad-quantitative-neuropathology.s3.amazonaws.com/index.html)'] +Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) Single cell profiling (transcriptomics and epigenomics) data files in a public b arn:aws:s3:::sea-ad-single-cell-profiling us-west-2 S3 Bucket https://portal.brain-map.org/explore/seattle-alzheimers-disease/seattle-alzheime awspublicdataset@alleninstitute.org [Allen Institute](http://www.alleninstitute.org/) Annually https://alleninstitute.org/legal/terms-use/ aws-pds, biology, cell biology, cell imaging, epigenomics, gene expression, histopathology, Homo sapiens, imaging, medicine, microscopy, neurobiology, neuroscience, single-cell transcriptomics, transcriptomics, life sciences ['[Browse Bucket](https://sea-ad-single-cell-profiling.s3.amazonaws.com/index.html)'] SeeFar V0 Primary SeeFar dataset containing multi-resolution satellite imagery in cloud-op arn:aws:s3:::seefar-dataset us-east-1 S3 Bucket https://coastalcarbon.ai/seefar James Lowman Coastal Carbon Yearly The SeeFar dataset includes multiple licensing terms, specific to each satellite geospatial, earth observation, satellite imagery, climate, biodiversity, coastal, machine learning, environmental, sustainability, natural resource, global, mapping, aws-pds Sentinel Near Real-time Canada Mirror | Miroir Sentinel temps quasi réel du Canada Sentinel data over Canada | Données sentinelles au Canada arn:aws:s3:::sentinel-products-ca-mirror ca-central-1 S3 Bucket https://sentinel.esa.int/web/sentinel/home eodms-sgdot@nrcan-rncan.gc.ca [Natural Resources Canada](https://www.nrcan.gc.ca/) Sentinel-1 is an NRT dataset retrieved from ESA within 90 minutes of satellite d The access and use of Copernicus Sentinel data is available on a free, full and aws-pds, agriculture, earth observation, satellite imagery, geospatial, sustainability, disaster response, synthetic aperture radar, stac ['[EODMS STAC for Sentinel products](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/)'] -Sentinel-1 GRD in a Requester Pays S3 bucket arn:aws:s3:::sentinel-s1-l1c eu-central-1 S3 Bucket https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar ['[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)'] True Sentinel-1 S3 Inventory files for L1C and CSV arn:aws:s3:::sentinel-inventory/ eu-central-1 S3 Bucket https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar Sentinel-1 SNS topic for notification of new scenes, can subscribe with Lambda arn:aws:sns:eu-central-1:214830741341:SentinelS1L1C eu-central-1 SNS Topic https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar +Sentinel-1 GRD in a Requester Pays S3 bucket arn:aws:s3:::sentinel-s1-l1c eu-central-1 S3 Bucket https://roda.sentinel-hub.com/sentinel-s1-l1c/GRD/readme.html https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, disaster response, cog, synthetic aperture radar ['[STAC V1.0.0 endpoint](https://sentinel-s1-l1c-stac.s3.amazonaws.com/)'] True Sentinel-1 Precise Orbit Determination (POD) Products Sentinel-1 Orbits bucket arn:aws:s3:::s1-orbits us-west-2 S3 Bucket https://s1-orbits.s3.us-west-2.amazonaws.com/README.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Updated as new data becomes available on the [Copernicus Data Space Ecosystem](h Access to Sentinel data is free, full and open for the broad Regional, National, auxiliary data, disaster response, earth observation, earthquakes, floods, geophysics, sentinel-1, synthetic aperture radar ['[AWS S3 Explorer](https://s1-orbits.s3.amazonaws.com/index.html)'] Sentinel-1 Precise Orbit Determination (POD) Products Notifications for new data arn:aws:sns:us-west-2:211125554030:s1-orbits-object_created us-west-2 SNS Topic https://s1-orbits.s3.us-west-2.amazonaws.com/README.html https://asf.alaska.edu/asf/contact-us/ [The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/) Updated as new data becomes available on the [Copernicus Data Space Ecosystem](h Access to Sentinel data is free, full and open for the broad Regional, National, auxiliary data, disaster response, earth observation, earthquakes, floods, geophysics, sentinel-1, synthetic aperture radar Sentinel-1 SLC dataset for Germany Public access to Sentinel-1 SLC IW scenes over Germany arn:aws:s3:::sentinel1-slc eu-west-1 S3 Bucket https://github.com/live-eo/sentinel1-slc/ For any enquires regarding the dataset, please email OpenData at Live-EO opendat [LiveEO](https://live-eo.com/) New Sentinel1-SLC IW data are updated regularly in an interval of 6 days, after The data usage will inherit and fully comply with the free and open data policy aws-pds, disaster response, satellite imagery, geospatial, sustainability, earth observation, environmental, synthetic aperture radar Sentinel-1 SLC dataset for South and Southeast Asia, Taiwan, Korea and Japan Public access to Sentinel-1 SLC IW scenes over South and Southeast Asia, Taiwan arn:aws:s3:::sentinel1-slc-seasia-pds ap-southeast-1 S3 Bucket https://github.com/earthobservatory/sentinel1-opds/ For any enquires regarding data delivery, please email ehill@ntu.edu.sg and stch [Earth Observatory of Singapore, Nanyang Technological University](https://earth S1 SLC data for the region of interest will be updated regularly, as it becomes The data usage will inherit and fully comply with the free and open data policy aws-pds, disaster response, satellite imagery, geospatial, earth observation, environmental, synthetic aperture radar -Sentinel-2 Level 2A scenes and metadata, in Requester Pays S3 bucket arn:aws:s3:::sentinel-s2-l2a eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac ['[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)'] True Sentinel-2 New scene notifications for L2A, can subscribe with Lambda arn:aws:sns:eu-central-1:214830741341:SentinelS2L2A eu-central-1 SNS Topic Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac Sentinel-2 New scene notifications for L1C, can subscribe with Lambda arn:aws:sns:eu-west-1:214830741341:NewSentinel2Product eu-west-1 SNS Topic Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac +Sentinel-2 Zipped archives for each L2A product with 3 day retention period, in Requester P arn:aws:s3:::sentinel-s2-l2a-zips eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True Sentinel-2 Zipped archives for each L1C product with 3 day retention period, in Requester P arn:aws:s3:::sentinel-s2-l1c-zips eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True Sentinel-2 S3 Inventory files for L2A and CSV arn:aws:s3:::sentinel-inventory/sentinel-s2-l2a eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac +Sentinel-2 Level 2A scenes and metadata, in Requester Pays S3 bucket arn:aws:s3:::sentinel-s2-l2a eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac ['[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)'] True Sentinel-2 S3 Inventory files for L1C and CSV arn:aws:s3:::sentinel-inventory/sentinel-s2-l1c eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac Sentinel-2 Level 1C scenes and metadata, in Requester Pays S3 bucket arn:aws:s3:::sentinel-s2-l1c eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac ['[Earth Search STAC L1C Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)', '[Earth Search STAC Browser L1C Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l1c)', '[STAC V1.0.0 endpoint](https://sentinel-s2-l1c-stac.s3.amazonaws.com/)', '[Earth Viewer by Element 84](https://viewer.aws.element84.com/)'] True -Sentinel-2 Zipped archives for each L2A product with 3 day retention period, in Requester P arn:aws:s3:::sentinel-s2-l2a-zips eu-central-1 S3 Bucket Documentation is available for [Sentinel-2 L1C](https://roda.sentinel-hub.com/se https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac True +Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States New scene notification arn:aws:sns:us-west-2:242201296900:usgs-wma-sentinel-2-aqr-acolite-dsf-object_created us-west-2 SNS Topic https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed tvking@usgs.gov [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. Contains modified Copernicus Sentinel data, which is available under the Creativ aws-pds, earth observation, satellite imagery, geospatial, natural resource, cog, water +Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States Scenes and metadata arn:aws:s3:::usgs-wma-sentinel-2-aqr-acolite-dsf/version_01 us-west-2 S3 Bucket https://www.sciencebase.gov/catalog/item/640f612dd34e254fd352e1ed tvking@usgs.gov [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. Contains modified Copernicus Sentinel data, which is available under the Creativ aws-pds, earth observation, satellite imagery, geospatial, natural resource, cog, water Sentinel-2 Cloud-Optimized GeoTIFFs New scene notifications, can subscribe with Lambda arn:aws:sns:us-west-2:608149789419:cirrus-v0-publish us-west-2 SNS Topic https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac -Sentinel-2 Cloud-Optimized GeoTIFFs Level 2A scenes and metadata arn:aws:s3:::sentinel-cogs us-west-2 S3 Bucket https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac ['[Earth Search STAC L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)', '[STAC Browser L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)'] False Sentinel-2 Cloud-Optimized GeoTIFFs S3 Inventory files for L1C and CSV arn:aws:s3:::sentinel-cogs-inventory us-west-2 S3 Bucket https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac +Sentinel-2 Cloud-Optimized GeoTIFFs Level 2A scenes and metadata arn:aws:s3:::sentinel-cogs us-west-2 S3 Bucket https://github.com/cirrus-geo/cirrus-earth-search opendata@element84.com [Element 84](https://www.element84.com/) New Sentinel data are added regularly, usually within few hours after they are a Access to Sentinel data is free, full and open for the broad Regional, National, aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, cog, stac ['[Earth Search STAC L2A Collection](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)', '[STAC Browser L2A Collection](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)'] False Sentinel-2 L2A 120m Mosaic Sentinel-2 L2A 120m mosaics data in a S3 bucket arn:aws:s3:::sentinel-s2-l2a-mosaic-120 eu-central-1 S3 Bucket Documentation is available [here](https://sentinel-s2-l2a-mosaic-120.s3.amazonaw https://forum.sentinel-hub.com/c/aws-sentinel [Sinergise](https://www.sinergise.com/) New data will be added annually. CC-BY 4.0, Credit: Contains modified Copernicus data [year] processed by Sentine aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, machine learning, cog False -Sentinel-3 Sentinel-3 Cloud Optimized GeoTIFF (COG) format arn:aws:s3:::meeo-s3-cog/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac ['[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)'] -Sentinel-3 Sentinel-3 Short Time Critical (STC) format arn:aws:s3:::meeo-s3/STC/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac -Sentinel-3 Sentinel-3 Not Time Critical (NTC) format arn:aws:s3:::meeo-s3/NTC/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac Sentinel-3 Sentinel-3 Near Real Time Data (NRT) format arn:aws:s3:::meeo-s3/NRT/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac -Sentinel-5P Level 2 Sentinel-5p Reprocessed Data (RPRO) NetCDF format arn:aws:s3:::meeo-s5p/RPRO/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac +Sentinel-3 Sentinel-3 Not Time Critical (NTC) format arn:aws:s3:::meeo-s3/NTC/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac +Sentinel-3 Sentinel-3 Short Time Critical (STC) format arn:aws:s3:::meeo-s3/STC/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac +Sentinel-3 Sentinel-3 Cloud Optimized GeoTIFF (COG) format arn:aws:s3:::meeo-s3-cog/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Descri sentinel3@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, oceans, earth observation, environmental, geospatial, land, satellite imagery, cog, stac ['[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)'] Sentinel-5P Level 2 Sentinel-5p Near Real Time Data (NRTI) NetCDF format arn:aws:s3:::meeo-s5p/NRTI/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac -Sentinel-5P Level 2 Sentinel-5p Cloud Optimised GeoTIFF (COGT) TIFF format arn:aws:s3:::meeo-s5p/COGT/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac ['[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)'] Sentinel-5P Level 2 Sentinel-5p Off Line Data (OFFL) NetCDF format arn:aws:s3:::meeo-s5p/OFFL/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac +Sentinel-5P Level 2 Sentinel-5p Reprocessed Data (RPRO) NetCDF format arn:aws:s3:::meeo-s5p/RPRO/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac +Sentinel-5P Level 2 Sentinel-5p Cloud Optimised GeoTIFF (COGT) TIFF format arn:aws:s3:::meeo-s5p/COGT/ eu-central-1 S3 Bucket https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Desc sentinel5p@meeo.it [Meteorological Environmental Earth Observation](http://www.meeo.it/) Daily https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice aws-pds, air quality, atmosphere, earth observation, environmental, geospatial, satellite imagery, cog, stac ['[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)'] Serratus: Ultra-deep Search for Novel Viruses - Versioned Data Release Versioned and structured data releases from the Serratus project Current versio arn:aws:s3:::lovelywater2 us-east-1 S3 Bucket https://github.com/ababaian/serratus/wiki/Access-Data-Release https://github.com/ababaian/serratus/issues Serratus / UBC Cloud Innovation Centre Quarterly [CC-0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aws-pds, life sciences, genetic, genomic, bam, virus, COVID-19, SARS, SARS-CoV-2, MERS Ships of Opportunity - Biogeochemical sensors - Delayed mode Cloud Optimised AODN dataset of IMOS - SOOP Underway CO2 Measurements Research G arn:aws:s3:::aodn-cloud-optimised/vessel_co2_delayed_qc.parquet ap-southeast-2 S3 Bucket https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/6 info@aodn.org.au AODN As Needed http://creativecommons.org/licenses/by/4.0/ oceans, chemistry Ships of Opportunity - Expendable bathythermographs - Real time Cloud Optimised AODN dataset of IMOS - SOOP Expendable Bathythermographs (XBT) R arn:aws:s3:::aodn-cloud-optimised/vessel_xbt_realtime_nonqc.parquet ap-southeast-2 S3 Bucket https://catalogue-imos.aodn.org.au/geonetwork/srv/eng/catalog.search#/metadata/3 info@aodn.org.au AODN As Needed http://creativecommons.org/licenses/by/4.0/ oceans @@ -1099,11 +1103,11 @@ Speedtest by Ookla Global Fixed and Mobile Network Performance Maps Parquet and Spitzer Enhanced Imaging Products (SEIP) Super Mosaics SEIP Super Mosaics: 36, 45, 58, 8, and 24 micron mean and median mosaics with arn:aws:s3:::nasa-irsa-spitzer/spitzer/seip us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/data/SPITZER/Enhanced/SEIP/overview.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca This data set may be updated once or twice in the future. https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, imaging, satellite imagery, survey False False Storm EVent ImageRy (SEVIR) Dataset of storm imagery arn:aws:s3:::sevir us-west-2 S3 Bucket https://nbviewer.jupyter.org/github/MIT-AI-Accelerator/eie-sevir/blob/master/exa mark.veillette@mit.edu Mark S. Veillette New events will be added to SEVIR yearly There are no restrictions on the use of this data. satellite imagery, weather, meteorological, aws-pds Sub-Meter Canopy Tree Height of California in 2020 by CTrees.org Cloud-optimized GeoTIFF files with names corresponding to image of California fo arn:aws:s3:::ctrees-tree-height-ca-2020/ us-west-2 S3 Bucket [Project overview](https://ctrees.org/products/tree-level) info@ctrees.org [CTrees](https://ctrees.org/) TBD https://creativecommons.org/licenses/by/4.0/ aws-pds, cog, earth observation, land cover, deep learning, aerial imagery, image processing, environmental, conservation, geospatial -Sudachi Language Resources Cloudfront CDN mirror ap-northeast-1 CloudFront Distribution https://worksapplications.github.io/Sudachi/ sudachi@worksap.co.jp [Works Applications](https://www.worksap.co.jp/about/csr/nlp/) The dictionaries are updated every few months to include neologism and fixes for Apache-2.0 aws-pds, natural language processing d2ej7fkh96fzlu.cloudfront.net Sudachi Language Resources SudachiDict: Binary format of the mophological analysis dictionarieschiVe: Pret arn:aws:s3:::sudachi ap-northeast-1 S3 Bucket https://worksapplications.github.io/Sudachi/ sudachi@worksap.co.jp [Works Applications](https://www.worksap.co.jp/about/csr/nlp/) The dictionaries are updated every few months to include neologism and fixes for Apache-2.0 aws-pds, natural language processing -Sup3rCC Sup3rCC arn:aws:s3:::nrel-pds-sup3rcc/ us-west-2 S3 Bucket https://github.com/NREL/sup3r Grant Buster (grant.buster@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annual Creative Commons Attribution 4.0 United States License aws-pds, energy, solar, air temperature, climate model ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc)'] +Sudachi Language Resources Cloudfront CDN mirror ap-northeast-1 CloudFront Distribution https://worksapplications.github.io/Sudachi/ sudachi@worksap.co.jp [Works Applications](https://www.worksap.co.jp/about/csr/nlp/) The dictionaries are updated every few months to include neologism and fixes for Apache-2.0 aws-pds, natural language processing d2ej7fkh96fzlu.cloudfront.net Sup3rCC Sup3rCC Generative Models arn:aws:s3:::nrel-pds-sup3rcc/models/ us-west-2 S3 Bucket https://github.com/NREL/sup3r Grant Buster (grant.buster@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annual Creative Commons Attribution 4.0 United States License aws-pds, energy, solar, air temperature, climate model ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=models%2F)'] Sup3rCC Sup3rCC - CONUS - MRI ESM 20 - SSP585 - r1i1p1f1 arn:aws:s3:::nrel-pds-sup3rcc/conus_mriesm20_ssp585_r1i1p1f1/ us-west-2 S3 Bucket https://github.com/NREL/sup3r Grant Buster (grant.buster@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annual Creative Commons Attribution 4.0 United States License aws-pds, energy, solar, air temperature, climate model ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc&prefix=conus_mriesm20_ssp585_r1i1p1f1%2F)'] +Sup3rCC Sup3rCC arn:aws:s3:::nrel-pds-sup3rcc/ us-west-2 S3 Bucket https://github.com/NREL/sup3r Grant Buster (grant.buster@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annual Creative Commons Attribution 4.0 United States License aws-pds, energy, solar, air temperature, climate model ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc)'] Swiss Public Transport Stops data files ESRI FGDB, CSV , MapInfo, Interlis arn:aws:s3:::data.geo.admin.ch/ch.bav.haltestellen-oev/data.zip eu-west-1 S3 Bucket https://www.bav.admin.ch/bav/de/home/allgemeine-themen/fachthemen/geoinformation fredi.daellenbach@bav.admin.ch Swiss Geoportal annually You may use this dataset for non-commercial purposes. You may use this dataset f aws-pds, cities, geospatial, infrastructure, mapping, traffic, transportation ['[Browse Bucket](https://data.geo.admin.ch/index.html)'] Synthea Coherent Data Set Synthetic data set that includes FHIR resources, DICOM images, genomic data, phy arn:aws:s3:::synthea-open-data/coherent/ us-east-1 S3 Bucket https://doi.org/10.3390/electronics11081199 synthea-list@groups.mitre.org [The MITRE Corporation](https://www.mitre.org) Rarely [Creative Commons Attribution 4.0 International License](https://creativecommons aws-pds, health, bioinformatics, life sciences, medicine, csv, dicom, genomic, imaging Synthea synthetic patient generator data in OMOP Common Data Model Project data files arn:aws:s3:::synthea-omop us-east-1 S3 Bucket https://github.com/synthetichealth/synthea/wiki Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 [Amazon Web Sevices](https://aws.amazon.com/) Not updated https://github.com/synthetichealth/synthea/blob/master/LICENSE aws-pds, bioinformatics, health, life sciences, natural language processing, us @@ -1113,11 +1117,11 @@ Tabula Muris https://githubcom/czbiohub/tabula-muris arn:aws:s3:::czb-tabula-mur Tabula Muris Senis https://githubcom/czbiohub/tabula-muris-senis arn:aws:s3:::czb-tabula-muris-senis us-west-2 S3 Bucket https://github.com/czbiohub/tabula-muris-senis/blob/master/tabula-muris-senis-on If you have questions about the data, you can create an Issue at https://github. [Chan Zuckerberg Biohub](https://www.czbiohub.org/) This is the first version of the dataset and it will be updated after the manusc https://github.com/czbiohub/tabula-muris-senis/blob/master/LICENSE aws-pds, biology, encyclopedic, genomic, health, life sciences, medicine, single-cell transcriptomics Tabula Sapiens http://tabula-sapiens-portaldsczbiohuborg arn:aws:s3:::czb-tabula-sapiens us-west-2 S3 Bucket http://tabula-sapiens-portal.ds.czbiohub.org/ https://github.com/czbiohub/tabula-muris-senis/issues [Chan Zuckerberg Biohub](https://www.czbiohub.org/) This is the first version of the dataset and it will be updated once per month u http://tabula-sapiens-portal.ds.czbiohub.org/whereisthedata aws-pds, biology, encyclopedic, genetic, genomic, health, life sciences, medicine, single-cell transcriptomics https://docs.google.com/forms/d/e/1FAIpQLSeeB0N7TrklXbCbpc6nDi5e77uad3uZDZ4WCMV77jwhVzxUtQ/viewform Terra Fusion Data Sampler AWS S3 Public Bucket Containing Terra Basic Fusion Hierarchical Data Format 5 (H arn:aws:s3:::terrafusiondatasampler us-west-2 S3 Bucket https://go.illinois.edu/terra-fusion-doc gdi@illinois.edu University of Illinois Static, with a planned update for years 2016-2020 in the future. Creative Commons Level 0 aws-pds, geospatial, satellite imagery -Terrain Tiles Gridded elevation tiles arn:aws:s3:::elevation-tiles-prod us-east-1 S3 Bucket https://github.com/tilezen/joerd/tree/master/docs https://github.com/tilezen/joerd/issues Mapzen, a Linux Foundation project New data is added based on community feedback https://github.com/tilezen/joerd/blob/master/docs/attribution.md aws-pds, agriculture, elevation, earth observation, geospatial, disaster response ['[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)'] Terrain Tiles Gridded elevation tiles - replication in EU region arn:aws:s3:::elevation-tiles-prod-eu eu-central-1 S3 Bucket https://github.com/tilezen/joerd/tree/master/docs https://github.com/tilezen/joerd/issues Mapzen, a Linux Foundation project New data is added based on community feedback https://github.com/tilezen/joerd/blob/master/docs/attribution.md aws-pds, agriculture, elevation, earth observation, geospatial, disaster response +Terrain Tiles Gridded elevation tiles arn:aws:s3:::elevation-tiles-prod us-east-1 S3 Bucket https://github.com/tilezen/joerd/tree/master/docs https://github.com/tilezen/joerd/issues Mapzen, a Linux Foundation project New data is added based on community feedback https://github.com/tilezen/joerd/blob/master/docs/attribution.md aws-pds, agriculture, elevation, earth observation, geospatial, disaster response ['[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)'] Textbook Question Answering (TQA) Project data files in a public bucket arn:aws:s3:::ai2-public-datasets us-west-2 S3 Bucket https://allenai.org/data/tqa info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/) aws-pds, machine learning -The Cancer Genome Atlas Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantificat arn:aws:s3:::tcga-2-open us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/t dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genomic, whole genome sequencing, STRIDES The Cancer Genome Atlas WXS/RNA-Seq/miRNA-Seq/ATAC-Seq Aligned Reads, WXS Annotated Somatic Mutation, WX arn:aws:s3:::tcga-2-controlled us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/t dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genomic, whole genome sequencing, STRIDES https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178.v1.p1 +The Cancer Genome Atlas Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantificat arn:aws:s3:::tcga-2-open us-east-1 S3 Bucket https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/t dcf-support@datacommons.io [Center for Translational Data Science at The University of Chicago](https://ctd Genomic Data Commons (GDC) is source of truth for this dataset; GDC offers month NIH Genomic Data Sharing Policy: https://gdc.cancer.gov/access-data/data-access- aws-pds, life sciences, cancer, genomic, whole genome sequencing, STRIDES The Genome Modeling System https://gmsdatas3amazonawscom/indexhtml arn:aws:s3:::gmsdata us-west-2 S3 Bucket https://github.com/genome/gms/wiki https://github.com/genome/gms/issues Genome Institute at the Washington University School of Medicine in St. Louis Not updated [GNU Lesser General Public License v3.0](https://github.com/genome/gms/blob/ubun aws-pds, genetic, genomic, life sciences ['[Browse Bucket](https://gmsdata.s3.amazonaws.com/index.html)'] The Human Connectome Project https://wwwhumanconnectomeorg/study/hcp-young-adult/overview arn:aws:s3:::hcp-openaccess us-east-1 S3 Bucket http://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data hcp-users@humanconnectome.org [Connectome Coordination Facility](https://www.humanconnectome.org/ccf-staff) Uncertain [HCP Data Use Agreement](https://www.humanconnectome.org/storage/app/media/data_ aws-pds, biology, imaging, neurobiology, neuroimaging, neuroscience, life sciences https://wiki.humanconnectome.org/docs/How%20To%20Connect%20to%20Connectome%20Data%20via%20AWS.html The Human Microbiome Project https://awsamazoncom/datasets/human-microbiome-project/ arn:aws:s3:::human-microbiome-project us-west-2 S3 Bucket https://commonfund.nih.gov/hmp https://commonfund.nih.gov/hmp/related_activities [The National Institutes of Health Office of Strategic Coordination - The Common Uncertain The data is publicly available to the community free of charge. aws-pds, life sciences, genetic, genomic, metagenomics, microbiome, fasta, amino acid, fastq @@ -1141,9 +1145,9 @@ UK Biobank Pharma Proteomics Project (UKB-PPP) Population-specific GWAS summary USGS 3DEP LiDAR Point Clouds Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs arn:aws:s3:::usgs-lidar-public us-west-2 S3 Bucket https://github.com/hobu/usgs-lidar/ https://github.com/hobu/usgs-lidar [Hobu, Inc.](https://hobu.co) Periodically US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensin aws-pds, agriculture, elevation, disaster response, geospatial, lidar, stac ['[STAC Catalog](https://usgs-lidar-stac.s3-us-west-2.amazonaws.com/ept/catalog.json)'] USGS 3DEP LiDAR Point Clouds A Requester Pays Bucket of Raw LAZ 14 3DEP data Data in this bucket is more co arn:aws:s3:::usgs-lidar us-west-2 S3 Bucket https://github.com/hobu/usgs-lidar/ https://github.com/hobu/usgs-lidar [Hobu, Inc.](https://hobu.co) Periodically US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensin aws-pds, agriculture, elevation, disaster response, geospatial, lidar, stac True USGS COAWST (Coupled Ocean Atmosphere Wave and Sediment Transport) Forecast Model Archive, US East and Gulf Coasts A collection of NetCDF4 files, kerchunk generated JSON files, and an Intake cata arn:aws:s3:::usgs-coawst us-west-2 S3 Bucket https://www.sciencebase.gov/catalog/item/610acd4fd34ef8d7056893da jbzambon@fathomscience.com Fathom Science None CC0 aws-pds, oceans -USGS Landsat New scene notifications, Level-1 and Level-2 Scenes arn:aws:sns:us-west-2:673253540267:public-c2-notify-v2 us-west-2 SNS Topic https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog USGS Landsat New scene notifications, US ARD Tiles arn:aws:sns:us-west-2:673253540267:public-c2-ard-tile-notify-v2 us-west-2 SNS Topic https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog USGS Landsat New scene notifications, Level 3 Science Products arn:aws:sns:us-west-2:673253540267:public-c2-level-3-tile-notify-v2 us-west-2 SNS Topic https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog +USGS Landsat New scene notifications, Level-1 and Level-2 Scenes arn:aws:sns:us-west-2:673253540267:public-c2-notify-v2 us-west-2 SNS Topic https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog USGS Landsat Scenes and metadata arn:aws:s3:::usgs-landsat/collection02/ us-west-2 S3 Bucket https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-d https://answers.usgs.gov/ [United States Geological Survey](https://www.usgs.gov) New scenes are added daily. There are no restrictions on Landsat data downloaded from the USGS; it can be us aws-pds, agriculture, earth observation, satellite imagery, geospatial, natural resource, disaster response, stac, cog ['[STAC Catalog](https://landsatlook.usgs.gov/stac-server/collections)'] True USearch Molecules Project data files in a public bucket arn:aws:s3:::usearch-molecules us-west-2 S3 Bucket https://github.com/ashvardanian/usearch-molecules ash.vardanian@unum.cloud [Ash Vardanian](https://ashvardanian.com) Not updated [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) aws-pds, life sciences, biology, chemical biology, pharmaceutical Umbra Synthetic Aperture Radar (SAR) Open Data Umbra Spotlight collects including GEC, SICD, SIDD, CPHD data and metadata arn:aws:s3:::umbra-open-data-catalog us-west-2 S3 Bucket https://help.umbra.space/product-guide help@umbra.space [Umbra](http://umbra.space/) New data is added frequently. The frequent updates enable users to analyze the t All data is provided with a Creative Commons License ([CC by 4.0](https://umbra. aws-pds, synthetic aperture radar, stac, satellite imagery, earth observation, image processing, geospatial ['[Browse Bucket](http://umbra-open-data-catalog.s3-website.us-west-2.amazonaws.com/)', '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/s3.us-west-2.amazonaws.com/umbra-open-data-catalog/stac/catalog.json)'] False @@ -1154,24 +1158,24 @@ UniProt UniProt 2024_05 arn:aws:s3:::aws-open-data-uniprot-rdf/2024-05/ eu-west- UniProt UniProt 2021_01 arn:aws:s3:::aws-open-data-uniprot-rdf/2021-01/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2021_02 arn:aws:s3:::aws-open-data-uniprot-rdf/2021-02/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2021_03 arn:aws:s3:::aws-open-data-uniprot-rdf/2021-03/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL +UniProt UniProt 2021_04 arn:aws:s3:::aws-open-data-uniprot-rdf/2021-04/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2022_01 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-01/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL -UniProt UniProt 2022_02 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-02/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2022_03 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-03/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL -UniProt UniProt 2021_04 arn:aws:s3:::aws-open-data-uniprot-rdf/2021-04/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL +UniProt UniProt 2023_05 arn:aws:s3:::aws-open-data-uniprot-rdf/2023-05/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL +UniProt UniProt 2022_04 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-04/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2022_05 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-05/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2023_01 arn:aws:s3:::aws-open-data-uniprot-rdf/2023-01/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2023_02 arn:aws:s3:::aws-open-data-uniprot-rdf/2023-02/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2023_03 arn:aws:s3:::aws-open-data-uniprot-rdf/2023-03/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2023_04 arn:aws:s3:::aws-open-data-uniprot-rdf/2023-04/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL -UniProt UniProt 2023_05 arn:aws:s3:::aws-open-data-uniprot-rdf/2023-05/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL +UniProt UniProt 2022_02 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-02/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL UniProt UniProt 2024_01 arn:aws:s3:::aws-open-data-uniprot-rdf/2024-01/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL -UniProt UniProt 2022_04 arn:aws:s3:::aws-open-data-uniprot-rdf/2022-04/ eu-west-3 S3 Bucket https://www.uniprot.org/help/about https://www.uniprot.org/contact [SIB Swiss Institute of Bioinformatics](https://sp.sib.swiss/) on behalf of the Under 1 months after a new UniProt release. http://creativecommons.org/licenses/by/4.0/ aws-pds, chemistry, protein, enzyme, molecule, life sciences, bioinformatics, biology, RDF, graph, SPARQL University of British Columbia Sunflower Genome Dataset UBC Sunflower Genome Data 1 arn:aws:s3:::ubc-sunflower-genome us-west-2 S3 Bucket https://rieseberglab.github.io/ubc-sunflower-genome/ UBC Botany Sunflower The Rieseberg Lab at the University of British Columbia Twice per year. Public Domain aws-pds, agriculture, biodiversity, bioinformatics, biology, food security, genetic, genomic, life sciences, whole genome sequencing VENUS L2A Cloud-Optimized GeoTIFFs New Venus L2A dataset notifications, can subscribe with Lambda arn:aws:sns:us-east-1:794383284256:venus-l2a-cogs-object_created us-east-1 SNS Topic https://github.com/earthdaily/venus-on-aws/ Klaus Bachhuber - klaus.bachhuber@earthdaily.com [EarthDaily Analytics](https://earthdaily.com/) New Venus data are added regularly https://creativecommons.org/licenses/by-nc/4.0/ aws-pds, agriculture, earth observation, satellite imagery, geospatial, image processing, natural resource, disaster response, cog, stac, activity detection, environmental, land cover VENUS L2A Cloud-Optimized GeoTIFFs Venus L2A dataset (COG) and metadata (STAC) arn:aws:s3:::venus-l2a-cogs us-east-1 S3 Bucket https://github.com/earthdaily/venus-on-aws/ Klaus Bachhuber - klaus.bachhuber@earthdaily.com [EarthDaily Analytics](https://earthdaily.com/) New Venus data are added regularly https://creativecommons.org/licenses/by-nc/4.0/ aws-pds, agriculture, earth observation, satellite imagery, geospatial, image processing, natural resource, disaster response, cog, stac, activity detection, environmental, land cover ['[STAC Browser Venus L2A (COG) Catalog](https://radiantearth.github.io/stac-browser/#/external/venus-l2a-cogs.s3.us-east-1.amazonaws.com/catalog.json)'] False Variant Effect Predictor (VEP) and the Loss-Of-Function Transcript Effect Estimator (LOFTEE) Plugin VEP and LOFTEE data arn:aws:s3:::hail-vep-pipeline us-east-1 S3 Bucket https://hail-vep-pipeline.public.tennex.io/ https://www.tennex.io/contact [Tennex](https://www.tennex.io/) New packages are added as soon as they are available and confirmed to work with [VEP](https://uswest.ensembl.org/info/about/publications.html) use is governed b aws-pds, genome wide association study, genomic, life sciences, vep, loftee -Vermont Open Geospatial on AWS Landcover datsets are organized in this bucket as statewide file mosaics These arn:aws:s3:::vtopendata-prd/Landcover us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False Vermont Open Geospatial on AWS Imagery datsets are organized in this bucket as statewide file mosaics and by ac arn:aws:s3:::vtopendata-prd/Imagery us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False +Vermont Open Geospatial on AWS Landcover datsets are organized in this bucket as statewide file mosaics These arn:aws:s3:::vtopendata-prd/Landcover us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False Vermont Open Geospatial on AWS Elevation datsets (primarily lidar based) are organized in this bucket as statew arn:aws:s3:::vtopendata-prd/Elevation us-east-2 S3 Bucket https://vcgi.vermont.gov/data-and-programs/ If you have specific questions please contact - vcgi@vermont.gov [Vermont Center for Geographic Information](https://vcgi.vermont.gov) Vermont acquires statewide imagery approximately once every other year. Lidar is Public Domain with Attribution earth observation, aerial imagery, geospatial, lidar, elevation, land cover False Virginia Coastal Resilience Master Plan, Phase 1 - December 2021 Data Product List See readmetxt file for more information on the folder struc arn:aws:s3:::vadcr-frp us-east-1 S3 Bucket https://www.dcr.virginia.gov/crmp/ flood.resilience@dcr.virginia.gov [Virginia Department of Conservation and Recreation](https://www.dcr.virginia.go Every 5 years or as data becomes available Conditions of Release - Data is available by permission of the Virginia Departme aws-pds, coastal, floods ['[Browse Data](https://vadcr-frp.s3.us-east-1.amazonaws.com/index.html)'] Virtual Shizuoka, 3D Point Cloud Data Point Cloud Data of Shizuoka Prefecture, Japan arn:aws:s3:::virtual-shizuoka ap-northeast-1 S3 Bucket https://github.com/aigidjp/opendata_virtualshizuoka/README.md virtualshizuoka@aigid.jp [AIGID](https://aigid.jp/) Currently not scheduled Creative Commons Attribution 4.0 International (CC-BY 4.0) and Open Data Commons aws-pds, bathymetry, disaster response, elevation, geospatial, japanese, land, lidar, mapping @@ -1184,9 +1188,9 @@ WIS2 Global Cache on AWS Core data as defined in the WMO Unified Data Policy (Re Whiffle WINS50 Open Data on AWS Whiffle WINS50 LES Data arn:aws:s3:::whiffle-wins50-data eu-central-1 S3 Bucket https://gitlab.com/whiffle-public/whiffle-open-data support@whiffle.nl [Whiffle](http://www.whiffle.nl/) No updates planned. CC BY-SA 4.0 aws-pds, weather, sustainability, atmosphere, electricity, meteorological, model, zarr, turbulence ['[Browse Bucket](https://whiffle-wins50-data.s3.amazonaws.com/index.html)'] WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation WikiSum Dataset arn:aws:s3:::wikisum us-east-1 S3 Bucket https://wikisum.s3.amazonaws.com/README.txt nachshon@amazon.com, orenk@amazon.com [Amazon](https://www.amazon.com/) Not currently being updated Dataset is published under [CC-NC-SA-3.0](https://creativecommons.org/licenses/b amazon.science, natural language processing, machine learning ['[wikisum.zip](https://wikisum.s3.amazonaws.com/WikiSumDataset.zip)', '[wikisum-human-eval.zip](https://wikisum.s3.amazonaws.com/HumanEvaluation.zip)'] Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021 https://doiorg/1013026/34va-7q14 arn:aws:s3:::physionet-open/challenge-2021/ us-east-1 S3 Bucket https://doi.org/10.13026/34va-7q14 https://physionet.org/about/#contact_us [PhysioNet](https://physionet.org/) Not updated Creative Commons Attribution 4.0 International Public License aws-pds +Wind AI Bench Wind AI Bench Flow Redirection and Induction in Steady State (FLORIS) Wind Plant arn:aws:s3:::nrel-pds-windai/wind_plant_power/floris/ us-west-2 S3 Bucket https://github.com/NREL/windAI_bench Ryan King (ryan.king@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License aws-pds, energy, benchmark, machine learning ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=wind_plant_power%2Ffloris%2F)'] Wind AI Bench Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 9k Shapes Data Sets arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/9K_airfoils/ us-west-2 S3 Bucket https://github.com/NREL/windAI_bench Ryan King (ryan.king@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License aws-pds, energy, benchmark, machine learning ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F9k_airfoils%2F)'] Wind AI Bench Wind AI Bench Airfoil Computational Fluid Dynamics (CFD) - 2k Shapes Data Sets arn:aws:s3:::nrel-pds-windai/aerodynamic_shapes/2D/2K_airfoils/ us-west-2 S3 Bucket https://github.com/NREL/windAI_bench Ryan King (ryan.king@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License aws-pds, energy, benchmark, machine learning ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=aerodynamic_shapes%2F2D%2F2k_airfoils%2F)'] -Wind AI Bench Wind AI Bench Flow Redirection and Induction in Steady State (FLORIS) Wind Plant arn:aws:s3:::nrel-pds-windai/wind_plant_power/floris/ us-west-2 S3 Bucket https://github.com/NREL/windAI_bench Ryan King (ryan.king@nrel.gov) [National Renewable Energy Laboratory](https://www.nrel.gov/) Annually Creative Commons Attribution 4.0 United States License aws-pds, energy, benchmark, machine learning ['[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=wind_plant_power%2Ffloris%2F)'] Wizard of Tasks Wizard of Tasks Dataset arn:aws:s3:::wizard-of-tasks us-west-2 S3 Bucket https://wizard-of-tasks.s3.us-west-2.amazonaws.com/README.md giusecas@amazon.com [Amazon](https://www.amazon.com/) Not currently being updated [cc-by-sa 4.0](https://creativecommons.org/licenses/by-sa/4.0/) conversation data, dialog, amazon.science, natural language processing, machine learning ['[wizard_of_tasks_cooking_v1.0.json](https://wizard-of-tasks.s3.us-west-2.amazonaws.com/wizard_of_tasks_cooking_v1.0.json)', '[wizard_of_tasks_diy_v1.0.json](https://wizard-of-tasks.s3.us-west-2.amazonaws.com/wizard_of_tasks_diy_v1.0.json)'] World Bank - Light Every Night Light Every Night dataset of all VIIRS DNB and DMSP-OLS nighttime satellite data arn:aws:s3:::globalnightlight us-east-1 S3 Bucket https://worldbank.github.io/OpenNightLights/wb-light-every-night-readme.html Trevor Monroe tmonroe@worldbank.org; Benjamin P. Stewart bstewart@worldbankgroup [World Bank Group](https://www.worldbank.org/en/home) Quarterly [World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/b disaster response, earth observation, satellite imagery, aws-pds, stac, cog ['[STAC 1.0.0-beta.2 endpoint](https://stacindex.org/catalogs/world-bank-light-every-night#/)'] World Bank Climate Change Knowledge Portal (CCKP) World Bank Climate Change Knowledge Portal observed and projected climate datase arn:aws:s3:::wbg-cckp us-west-2 S3 Bucket https://worldbank.github.io/climateknowledgeportal C. MacKenzie Dove cdove@worldbank.org; askclimate@worldbank.org [World Bank Group](https://www.worldbank.org/en/home) Semi-annually [World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/b aws-pds, climate, climate model, earth observation, climate projections, CMIP6, netcdf @@ -1197,19 +1201,19 @@ YouTube 8 Million - Data Lakehouse Ready Lakehouse ready YT8M as Glue Parquet fi YouTube 8 Million - Data Lakehouse Ready Replica of the two locations above in us-east-1 arn:aws:s3:::aws-roda-ml-datalake-us-east-1/ us-east-1 S3 Bucket https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install. https://github.com/aws-samples/data-lake-as-code/issues [Amazon Web Services](https://aws.amazon.com/) Google Research has not updated the dataset since 2019. https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_attribut amazon.science, computer vision, machine learning, labeled, parquet, video ZEST: ZEroShot learning from Task descriptions Project data files in a public bucket arn:aws:s3:::ai2-public-datasets/zest/ us-west-2 S3 Bucket https://allenai.org/data/zest info@allenai.org [Allen Institute for AI](https://allenai.org) Not updated [CC BY](https://creativecommons.org/licenses/by/4.0) aws-pds, machine learning, natural language processing ZINC Database 3D molecular docking structure files in db2gz, sdf and mol2 formats arn:aws:s3:::zinc3d us-east-1 S3 Bucket http://wiki.docking.org/index.php/ZINC15:Resources [John Irwin](chemistry4biology@gmail.com) [John Irwin](chemistry4biology@gmail.com) Monthly ZINC is free as in beer. You may not redistribute without the written permission aws-pds, life sciences, biology, chemical biology, pharmaceutical, molecular docking, protein -iHART Whole Genome Sequencing Data Set BAM, gVCF, and VCF files from The iHART whole genome sequencing study, Phase I arn:aws:s3:::ihart-release us-east-1 S3 Bucket http://www.ihart.org/data ihart2-org@stanford.edu [Stanford University](https://wall-lab.stanford.edu/projects/ihart/) The dataset may be updated with additional or corrected data on a need-to-update Data use is subject to the access and publication polices of the iHART. More inf aws-pds, autism spectrum disorder, genetic, genomic, life sciences, whole genome sequencing, bam, vcf http://www.ihart.org/access -iHART Whole Genome Sequencing Data Set BAM, gVCF, and VCF files from The iHART whole genome sequencing study, Phase II arn:aws:s3:::ihart-main us-east-1 S3 Bucket http://www.ihart.org/data ihart2-org@stanford.edu [Stanford University](https://wall-lab.stanford.edu/projects/ihart/) The dataset may be updated with additional or corrected data on a need-to-update Data use is subject to the access and publication polices of the iHART. More inf aws-pds, autism spectrum disorder, genetic, genomic, life sciences, whole genome sequencing, bam, vcf http://www.ihart.org/access -iHART Whole Genome Sequencing Data Set gVCF and VCF files from The iHART whole genome sequencing study, control data se arn:aws:s3:::ihart-psp us-east-1 S3 Bucket http://www.ihart.org/data ihart2-org@stanford.edu [Stanford University](https://wall-lab.stanford.edu/projects/ihart/) The dataset may be updated with additional or corrected data on a need-to-update Data use is subject to the access and publication polices of the iHART. More inf aws-pds, autism spectrum disorder, genetic, genomic, life sciences, whole genome sequencing, bam, vcf http://www.ihart.org/access iHART Whole Genome Sequencing Data Set gVCF and VCF files from The iHART whole genome sequencing study, control data se arn:aws:s3:::ihart-brain us-east-1 S3 Bucket http://www.ihart.org/data ihart2-org@stanford.edu [Stanford University](https://wall-lab.stanford.edu/projects/ihart/) The dataset may be updated with additional or corrected data on a need-to-update Data use is subject to the access and publication polices of the iHART. More inf aws-pds, autism spectrum disorder, genetic, genomic, life sciences, whole genome sequencing, bam, vcf http://www.ihart.org/access iHART Whole Genome Sequencing Data Set Cram, gVCF, and VCF files from The iHART whole genome sequencing study, Phase I+ arn:aws:s3:::ihart-hg38 us-east-1 S3 Bucket http://www.ihart.org/data ihart2-org@stanford.edu [Stanford University](https://wall-lab.stanford.edu/projects/ihart/) The dataset may be updated with additional or corrected data on a need-to-update Data use is subject to the access and publication polices of the iHART. More inf aws-pds, autism spectrum disorder, genetic, genomic, life sciences, whole genome sequencing, bam, vcf http://www.ihart.org/access +iHART Whole Genome Sequencing Data Set BAM, gVCF, and VCF files from The iHART whole genome sequencing study, Phase II arn:aws:s3:::ihart-main us-east-1 S3 Bucket http://www.ihart.org/data ihart2-org@stanford.edu [Stanford University](https://wall-lab.stanford.edu/projects/ihart/) The dataset may be updated with additional or corrected data on a need-to-update Data use is subject to the access and publication polices of the iHART. More inf aws-pds, autism spectrum disorder, genetic, genomic, life sciences, whole genome sequencing, bam, vcf http://www.ihart.org/access +iHART Whole Genome Sequencing Data Set BAM, gVCF, and VCF files from The iHART whole genome sequencing study, Phase I arn:aws:s3:::ihart-release us-east-1 S3 Bucket http://www.ihart.org/data ihart2-org@stanford.edu [Stanford University](https://wall-lab.stanford.edu/projects/ihart/) The dataset may be updated with additional or corrected data on a need-to-update Data use is subject to the access and publication polices of the iHART. More inf aws-pds, autism spectrum disorder, genetic, genomic, life sciences, whole genome sequencing, bam, vcf http://www.ihart.org/access +iHART Whole Genome Sequencing Data Set gVCF and VCF files from The iHART whole genome sequencing study, control data se arn:aws:s3:::ihart-psp us-east-1 S3 Bucket http://www.ihart.org/data ihart2-org@stanford.edu [Stanford University](https://wall-lab.stanford.edu/projects/ihart/) The dataset may be updated with additional or corrected data on a need-to-update Data use is subject to the access and publication polices of the iHART. More inf aws-pds, autism spectrum disorder, genetic, genomic, life sciences, whole genome sequencing, bam, vcf http://www.ihart.org/access iNaturalist Licensed Observation Images Image files (eg JPEG) associated with metadata describing the observation asso arn:aws:s3:::inaturalist-open-data us-east-1 S3 Bucket "Documentation can be found 2.5 and 10nm) from King Sejong Station collected in 2019", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-01-01", "end_date": "2019-08-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -124074,7 +124074,7 @@ { "id": "KOPRI-KPDC-00001214_4", "title": "Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2019", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2019-01-01", "end_date": "2019-08-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -124126,7 +124126,7 @@ { "id": "KOPRI-KPDC-00001218_3", "title": "Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2018", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-01-01", "end_date": "2018-12-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -124139,7 +124139,7 @@ { "id": "KOPRI-KPDC-00001218_3", "title": "Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2018", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2018-01-01", "end_date": "2018-12-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -124152,7 +124152,7 @@ { "id": "KOPRI-KPDC-00001219_3", "title": "Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2017", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-01-01", "end_date": "2017-12-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -124165,7 +124165,7 @@ { "id": "KOPRI-KPDC-00001219_3", "title": "Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2017", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2017-01-01", "end_date": "2017-12-31", "bbox": "-58.78, -62.22, -58.78, -62.22", @@ -124711,7 +124711,7 @@ { "id": "KOPRI-KPDC-00001265_3", "title": "All-sky aurora (proton) image, KHO Longyearbyen, 2019", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-01-01", "end_date": "2019-04-15", "bbox": "16.03412, 78.15174, 16.03412, 78.15174", @@ -124724,7 +124724,7 @@ { "id": "KOPRI-KPDC-00001265_3", "title": "All-sky aurora (proton) image, KHO Longyearbyen, 2019", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2019-01-01", "end_date": "2019-04-15", "bbox": "16.03412, 78.15174, 16.03412, 78.15174", @@ -124854,7 +124854,7 @@ { "id": "KOPRI-KPDC-00001275_3", "title": "All-sky airglow image, King Sejong Station, 2019", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-03-11", "end_date": "2019-09-30", "bbox": "-58.78804, -62.22268, -58.78804, -62.22268", @@ -124867,7 +124867,7 @@ { "id": "KOPRI-KPDC-00001275_3", "title": "All-sky airglow image, King Sejong Station, 2019", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2019-03-11", "end_date": "2019-09-30", "bbox": "-58.78804, -62.22268, -58.78804, -62.22268", @@ -127844,7 +127844,7 @@ { "id": "KOPRI-KPDC-00001505_5", "title": "All-sky airglow image, King Sejong Station, 2020", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2020-02-18", "end_date": "2020-09-23", "bbox": "-58.47, -62.13, -58.47, -62.13", @@ -127857,7 +127857,7 @@ { "id": "KOPRI-KPDC-00001505_5", "title": "All-sky airglow image, King Sejong Station, 2020", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-02-18", "end_date": "2020-09-23", "bbox": "-58.47, -62.13, -58.47, -62.13", @@ -127896,7 +127896,7 @@ { "id": "KOPRI-KPDC-00001508_4", "title": "All-sky aurora (proton) image, KHO Longyearbyen, 2020", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-10-19", "bbox": "16.12, 78.48, 16.12, 78.48", @@ -127909,7 +127909,7 @@ { "id": "KOPRI-KPDC-00001508_4", "title": "All-sky aurora (proton) image, KHO Longyearbyen, 2020", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-10-19", "bbox": "16.12, 78.48, 16.12, 78.48", @@ -127922,7 +127922,7 @@ { "id": "KOPRI-KPDC-00001509_1", "title": "2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2019-01-19", "end_date": "2020-01-26", "bbox": "-58.788436, -62.240056, -58.719694, -62.218583", @@ -127935,7 +127935,7 @@ { "id": "KOPRI-KPDC-00001509_1", "title": "2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-01-19", "end_date": "2020-01-26", "bbox": "-58.788436, -62.240056, -58.719694, -62.218583", @@ -129885,7 +129885,7 @@ { "id": "KOPRI-KPDC-00001671_3", "title": "2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station)", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-02-14", "end_date": "2019-02-15", "bbox": "163.984928, -74.73604, 164.57053, -74.610485", @@ -129898,7 +129898,7 @@ { "id": "KOPRI-KPDC-00001671_3", "title": "2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station)", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2019-02-14", "end_date": "2019-02-15", "bbox": "163.984928, -74.73604, 164.57053, -74.610485", @@ -129911,7 +129911,7 @@ { "id": "KOPRI-KPDC-00001672_3", "title": "2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station)", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-01-29", "end_date": "2017-02-06", "bbox": "163.984928, -74.73604, 164.57053, -74.610485", @@ -129924,7 +129924,7 @@ { "id": "KOPRI-KPDC-00001672_3", "title": "2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station)", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2017-01-29", "end_date": "2017-02-06", "bbox": "163.984928, -74.73604, 164.57053, -74.610485", @@ -131497,7 +131497,7 @@ { "id": "KOPRI-KPDC-00001797_2", "title": "Age characteristics of Antarctic scallops (Adamussium colbecki)", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2019-02-21", "end_date": "2019-03-01", "bbox": "164.243867, -74.627661, 164.243867, -74.627661", @@ -131510,7 +131510,7 @@ { "id": "KOPRI-KPDC-00001797_2", "title": "Age characteristics of Antarctic scallops (Adamussium colbecki)", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-02-21", "end_date": "2019-03-01", "bbox": "164.243867, -74.627661, 164.243867, -74.627661", @@ -131562,7 +131562,7 @@ { "id": "KOPRI-KPDC-00001804_2", "title": "Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2020", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2020-01-10", "end_date": "2021-03-11", "bbox": "-58.789338, -62.240538, -58.721474, -62.220364", @@ -131575,7 +131575,7 @@ { "id": "KOPRI-KPDC-00001804_2", "title": "Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2020", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-01-10", "end_date": "2021-03-11", "bbox": "-58.789338, -62.240538, -58.721474, -62.220364", @@ -132121,7 +132121,7 @@ { "id": "KOPRI-KPDC-00001851_2", "title": "All-sky aurora (electron) image, Jang Bogo Station, 2021", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_KOPRI STAC Catalog", "state_date": "2021-03-01", "end_date": "2021-09-30", "bbox": "164.2, -74.623333, 164.2, -74.623333", @@ -132134,7 +132134,7 @@ { "id": "KOPRI-KPDC-00001851_2", "title": "All-sky aurora (electron) image, Jang Bogo Station, 2021", - "catalog": "AMD_KOPRI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2021-03-01", "end_date": "2021-09-30", "bbox": "164.2, -74.623333, 164.2, -74.623333", @@ -133447,7 +133447,7 @@ { "id": "L1B_Wind_Products_3.0", "title": "Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers", - "catalog": "ALL STAC Catalog", + "catalog": "ESA STAC Catalog", "state_date": "2020-04-20", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -133460,7 +133460,7 @@ { "id": "L1B_Wind_Products_3.0", "title": "Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers", - "catalog": "ESA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-04-20", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -133473,7 +133473,7 @@ { "id": "L2B_Wind_Products_3.0", "title": "Aeolus Scientific L2B Rayleigh/Mie wind product", - "catalog": "ALL STAC Catalog", + "catalog": "ESA STAC Catalog", "state_date": "2020-04-20", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -133486,7 +133486,7 @@ { "id": "L2B_Wind_Products_3.0", "title": "Aeolus Scientific L2B Rayleigh/Mie wind product", - "catalog": "ESA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-04-20", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -133499,7 +133499,7 @@ { "id": "L2C_Wind_products_5.0", "title": "Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing", - "catalog": "ALL STAC Catalog", + "catalog": "ESA STAC Catalog", "state_date": "2020-07-09", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -133512,7 +133512,7 @@ { "id": "L2C_Wind_products_5.0", "title": "Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing", - "catalog": "ESA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-07-09", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -135371,7 +135371,7 @@ { "id": "LGB_10m_traverse_1", "title": "10 m firn temperature data: LGB traverses 1990-95", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1989-11-01", "end_date": "1995-02-28", "bbox": "54, -77, 78, -69", @@ -135384,7 +135384,7 @@ { "id": "LGB_10m_traverse_1", "title": "10 m firn temperature data: LGB traverses 1990-95", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1989-11-01", "end_date": "1995-02-28", "bbox": "54, -77, 78, -69", @@ -136567,7 +136567,7 @@ { "id": "LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001", "title": "Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-01-12", "end_date": "2019-03-01", "bbox": "123, -75, 123, -75", @@ -136580,7 +136580,7 @@ { "id": "LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001", "title": "Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2018-01-12", "end_date": "2019-03-01", "bbox": "123, -75, 123, -75", @@ -137087,7 +137087,7 @@ { "id": "Last_Day_Spring_Snow_1528_1", "title": "ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2000-04-01", "end_date": "2016-07-02", "bbox": "-175.76, 52.17, -97.95, 68.97", @@ -137100,7 +137100,7 @@ { "id": "Last_Day_Spring_Snow_1528_1", "title": "ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2000-04-01", "end_date": "2016-07-02", "bbox": "-175.76, 52.17, -97.95, 68.97", @@ -137204,7 +137204,7 @@ { "id": "LiDAR_Tundra_Forest_AK_1782_1", "title": "ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-06-14", "end_date": "2016-06-25", "bbox": "-149.76, 67.97, -149.71, 68.02", @@ -137217,7 +137217,7 @@ { "id": "LiDAR_Tundra_Forest_AK_1782_1", "title": "ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-06-14", "end_date": "2016-06-25", "bbox": "-149.76, 67.97, -149.71, 68.02", @@ -142349,19 +142349,6 @@ "description": "MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. 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Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. 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Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. 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The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. 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", - "license": "proprietary" - }, { "id": "MERRA2_CNN_HAQAST_PM25_1", "title": "MERRA2_CNN_HAQAST bias corrected global hourly surface total PM2.5 mass concentration, V1 (MERRA2_CNN_HAQAST_PM25) at GES DISC", @@ -143015,7 +142755,7 @@ { "id": "MFLL_CO2_Weighting_Functions_1891_1", "title": "ACT-America: L2 Weighting Functions for Airborne Lidar Column-avg CO2, Eastern USA", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-05-27", "end_date": "2018-05-20", "bbox": "-106.05, 27.23, -71.91, 49.11", @@ -143028,7 +142768,7 @@ { "id": "MFLL_CO2_Weighting_Functions_1891_1", "title": "ACT-America: L2 Weighting Functions for Airborne Lidar Column-avg CO2, Eastern USA", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-05-27", "end_date": "2018-05-20", "bbox": "-106.05, 27.23, -71.91, 49.11", @@ -143041,7 +142781,7 @@ { "id": "MFLL_XCO2_Range_10Hz_1892_1", "title": "ACT-America: L2 Remotely Sensed Column-avg CO2 by Airborne Lidar, Lite, Eastern USA", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-05-27", "end_date": "2018-05-20", "bbox": "-106.05, 27.23, -71.91, 49.11", @@ -143054,7 +142794,7 @@ { "id": "MFLL_XCO2_Range_10Hz_1892_1", "title": "ACT-America: L2 Remotely Sensed Column-avg CO2 by Airborne Lidar, Lite, Eastern USA", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-05-27", "end_date": "2018-05-20", "bbox": "-106.05, 27.23, -71.91, 49.11", @@ -150464,7 +150204,7 @@ { "id": "MODIS_MAIAC_Reflectance_1700_1", "title": "ABoVE: Corrected MODIS MAIAC Reflectance at Tower Sites, Alaska and Canada, 2000-2016", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2000-02-24", "end_date": "2016-07-31", "bbox": "-157.41, 42.64, -74.04, 71.32", @@ -150477,7 +150217,7 @@ { "id": "MODIS_MAIAC_Reflectance_1700_1", "title": "ABoVE: Corrected MODIS MAIAC Reflectance at Tower Sites, Alaska and Canada, 2000-2016", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2000-02-24", "end_date": "2016-07-31", "bbox": "-157.41, 42.64, -74.04, 71.32", @@ -151673,7 +151413,7 @@ { "id": "MURI_Camouflage_0", "title": "A Multi University Research Initiative (MURI) Camouflage Project", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2010-06-14", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -151686,7 +151426,7 @@ { "id": "MURI_Camouflage_0", "title": "A Multi University Research Initiative (MURI) Camouflage Project", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "2010-06-14", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -153363,7 +153103,7 @@ { "id": "Main_Melt_Onset_Dates_1841_1.1", "title": "ABoVE: Passive Microwave-derived Annual Snowpack Main Melt Onset Date Maps, 1988-2018", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1988-02-09", "end_date": "2018-02-10", "bbox": "-180, 51.61, -107.83, 72.41", @@ -153376,7 +153116,7 @@ { "id": "Main_Melt_Onset_Dates_1841_1.1", "title": "ABoVE: Passive Microwave-derived Annual Snowpack Main Melt Onset Date Maps, 1988-2018", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1988-02-09", "end_date": "2018-02-10", "bbox": "-180, 51.61, -107.83, 72.41", @@ -153389,7 +153129,7 @@ { "id": "MaineInvasives", "title": "A Historical Record of Sponges, Bryozoa and Ascidians on the Coast of Maine: 1843-1980 (Bigelow Laboratory for Ocean Sciences)", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1843-01-01", "end_date": "1980-12-31", "bbox": "-70.7, 42.6, -66.9, 45.2", @@ -153402,7 +153142,7 @@ { "id": "MaineInvasives", "title": "A Historical Record of Sponges, Bryozoa and Ascidians on the Coast of Maine: 1843-1980 (Bigelow Laboratory for Ocean Sciences)", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1843-01-01", "end_date": "1980-12-31", "bbox": "-70.7, 42.6, -66.9, 45.2", @@ -153571,7 +153311,7 @@ { "id": "MassGIS_GISDATA.COQHMOSAICSCDS_POLY", "title": "2001 MrSID Mosaics CD-ROM Index", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2006-08-03", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -153584,7 +153324,7 @@ { "id": "MassGIS_GISDATA.COQHMOSAICSCDS_POLY", "title": "2001 MrSID Mosaics CD-ROM Index", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2006-08-03", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -153675,7 +153415,7 @@ { "id": "MassGIS_GISDATA.COQMOSAICSCDS2005_POLY.", "title": "2005 MrSID Mosaics CD-ROM Index", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2006-08-03", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -153688,7 +153428,7 @@ { "id": "MassGIS_GISDATA.COQMOSAICSCDS2005_POLY.", "title": "2005 MrSID Mosaics CD-ROM Index", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2006-08-03", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -153727,7 +153467,7 @@ { "id": "MassGIS_GISDATA.IMG_BWORTHOS", "title": "1:5,000 Black and White Digital Orthophoto Images", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1992-01-01", "end_date": "1999-12-31", "bbox": "-73.54455, 41.198524, -69.87159, 42.908627", @@ -153740,7 +153480,7 @@ { "id": "MassGIS_GISDATA.IMG_BWORTHOS", "title": "1:5,000 Black and White Digital Orthophoto Images", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1992-01-01", "end_date": "1999-12-31", "bbox": "-73.54455, 41.198524, -69.87159, 42.908627", @@ -153779,7 +153519,7 @@ { "id": "MassGIS_GISDATA.IMG_COQ2005", "title": "1:5,000 Color Ortho Imagery (2005)", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2005-04-01", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -153792,7 +153532,7 @@ { "id": "MassGIS_GISDATA.IMG_COQ2005", "title": "1:5,000 Color Ortho Imagery (2005)", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2005-04-01", "end_date": "", "bbox": "-73.54455, 41.19853, -69.8716, 42.908627", @@ -153909,7 +153649,7 @@ { "id": "Maxwell_Bay_Beaches_data", "title": "Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "0500-01-01", "end_date": "2007-04-30", "bbox": "-59, -62.3, -58.833, -62.1", @@ -153922,7 +153662,7 @@ { "id": "Maxwell_Bay_Beaches_data", "title": "Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "0500-01-01", "end_date": "2007-04-30", "bbox": "-59, -62.3, -58.833, -62.1", @@ -154195,7 +153935,7 @@ { "id": "Monthly_Hydrological_Fluxes_1647_1", "title": "ABoVE: Monthly Hydrological Fluxes for Canada and Alaska, 1979-2018", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1979-01-01", "end_date": "2018-04-01", "bbox": "-172.25, 41.75, -53.43, 83.12", @@ -154208,7 +153948,7 @@ { "id": "Monthly_Hydrological_Fluxes_1647_1", "title": "ABoVE: Monthly Hydrological Fluxes for Canada and Alaska, 1979-2018", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1979-01-01", "end_date": "2018-04-01", "bbox": "-172.25, 41.75, -53.43, 83.12", @@ -156470,7 +156210,7 @@ { "id": "NBId0023_101", "title": "Africa Holdridge Life Zone Classification (Vegetation and Climate)", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "16, -35, 55, 40", @@ -156483,7 +156223,7 @@ { "id": "NBId0023_101", "title": "Africa Holdridge Life Zone Classification (Vegetation and Climate)", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "16, -35, 55, 40", @@ -156496,7 +156236,7 @@ { "id": "NBId0024_101", "title": "Africa Soil Classification by Wilson and Henderson-Sellers", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "12.88, 6.67, 24.97, 24.19", @@ -156509,7 +156249,7 @@ { "id": "NBId0024_101", "title": "Africa Soil Classification by Wilson and Henderson-Sellers", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "12.88, 6.67, 24.97, 24.19", @@ -156522,7 +156262,7 @@ { "id": "NBId0025_101", "title": "Africa Soil Classification by Zobler", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -156535,7 +156275,7 @@ { "id": "NBId0025_101", "title": "Africa Soil Classification by Zobler", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -156548,7 +156288,7 @@ { "id": "NBId0036_101", "title": "Africa Lakes and Rivers (World Data Bank II)", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -156561,7 +156301,7 @@ { "id": "NBId0036_101", "title": "Africa Lakes and Rivers (World Data Bank II)", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -156652,7 +156392,7 @@ { "id": "NBId0053_101", "title": "Africa Revised FNOC Percent Water Cover", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -156665,7 +156405,7 @@ { "id": "NBId0053_101", "title": "Africa Revised FNOC Percent Water Cover", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-30, -45, 60, 40", @@ -156899,7 +156639,7 @@ { "id": "NBId0203_101", "title": "Africa Water Balance high/lowland crops, 1987", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -156912,7 +156652,7 @@ { "id": "NBId0203_101", "title": "Africa Water Balance high/lowland crops, 1987", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -156964,7 +156704,7 @@ { "id": "NBId0211_101", "title": "Africa Irrigation Potential, Best soils, 1987", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -156977,7 +156717,7 @@ { "id": "NBId0211_101", "title": "Africa Irrigation Potential, Best soils, 1987", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -156990,7 +156730,7 @@ { "id": "NBId0216_101", "title": "Africa Number of Wet Days per Year and Wind Velocity, 1984", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -157003,7 +156743,7 @@ { "id": "NBId0216_101", "title": "Africa Number of Wet Days per Year and Wind Velocity, 1984", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-20, -35, 55, 40", @@ -157198,7 +156938,7 @@ { "id": "NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1", "title": "2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2004-12-17", "end_date": "2005-11-30", "bbox": "-179.488, -77.642, -166.989, -49.014", @@ -157211,7 +156951,7 @@ { "id": "NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1", "title": "2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-12-17", "end_date": "2005-11-30", "bbox": "-179.488, -77.642, -166.989, -49.014", @@ -157250,7 +156990,7 @@ { "id": "NCAR_DS474.0", "title": "AARI Russian North Polar Drifting Station Data, from NSIDC", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1937-05-01", "end_date": "1991-03-31", "bbox": "-180, -90, 180, 90", @@ -157263,7 +157003,7 @@ { "id": "NCAR_DS474.0", "title": "AARI Russian North Polar Drifting Station Data, from NSIDC", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1937-05-01", "end_date": "1991-03-31", "bbox": "-180, -90, 180, 90", @@ -157302,7 +157042,7 @@ { "id": "NCAR_DS744.7", "title": "ADEOS Scatterometer Winds, Level 2B", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-06-04", "end_date": "2002-06-27", "bbox": "-180, -90, 180, 90", @@ -157315,7 +157055,7 @@ { "id": "NCAR_DS744.7", "title": "ADEOS Scatterometer Winds, Level 2B", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2002-06-04", "end_date": "2002-06-27", "bbox": "-180, -90, 180, 90", @@ -157354,7 +157094,7 @@ { "id": "NCEI DSI 1167_01_Not Applicable", "title": "Active Marine Station Metadata", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2012-05-18", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -157367,7 +157107,7 @@ { "id": "NCEI DSI 1167_01_Not Applicable", "title": "Active Marine Station Metadata", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-05-18", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -157692,7 +157432,7 @@ { "id": "NCEI DSI 9799_Not Applicable", "title": "African Historical Precipitation Data", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1850-01-01", "end_date": "1984-12-31", "bbox": "-25, -31, 52, 28", @@ -157705,7 +157445,7 @@ { "id": "NCEI DSI 9799_Not Applicable", "title": "African Historical Precipitation Data", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1850-01-01", "end_date": "1984-12-31", "bbox": "-25, -31, 52, 28", @@ -158537,7 +158277,7 @@ { "id": "NESP_2015_SRW_3", "title": "2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2015-02-09", "end_date": "2015-07-09", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -158550,7 +158290,7 @@ { "id": "NESP_2015_SRW_3", "title": "2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2015-02-09", "end_date": "2015-07-09", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -158563,7 +158303,7 @@ { "id": "NESP_2016_SRW_3", "title": "2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2016-08-24", "end_date": "2016-08-29", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -158576,7 +158316,7 @@ { "id": "NESP_2016_SRW_3", "title": "2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-08-24", "end_date": "2016-08-29", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -158589,7 +158329,7 @@ { "id": "NESP_2017_SRW_1", "title": "2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2017-08-23", "end_date": "2017-08-27", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -158602,7 +158342,7 @@ { "id": "NESP_2017_SRW_1", "title": "2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-08-23", "end_date": "2017-08-27", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -158615,7 +158355,7 @@ { "id": "NESP_2018_SRW_1", "title": "2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-08-18", "end_date": "2018-08-23", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -158628,7 +158368,7 @@ { "id": "NESP_2018_SRW_1", "title": "2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2018-08-18", "end_date": "2018-08-23", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -158641,7 +158381,7 @@ { "id": "NESP_2019_SRW_1", "title": "2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2019-08-18", "end_date": "2019-08-24", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -158654,7 +158394,7 @@ { "id": "NESP_2019_SRW_1", "title": "2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-08-18", "end_date": "2019-08-24", "bbox": "113.02734, -36.59789, 138.69141, -29.993", @@ -158771,7 +158511,7 @@ { "id": "NGA183\n _1.0", "title": "Active Layer Hydrology in an Arctic Tundra Ecosystem: Quantifying Water Sources and Cycling Using Water Stable Isotopes: Supporting Data", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -158784,7 +158524,7 @@ { "id": "NGA183\n _1.0", "title": "Active Layer Hydrology in an Arctic Tundra Ecosystem: Quantifying Water Sources and Cycling Using Water Stable Isotopes: Supporting Data", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -158992,7 +158732,7 @@ { "id": "NIPR_PMG_AIR_ARCHIVE_ANT", "title": "Air samples for archive", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-02-01", "end_date": "2009-01-31", "bbox": "39.5, -69, 39.5, -69", @@ -159005,7 +158745,7 @@ { "id": "NIPR_PMG_AIR_ARCHIVE_ANT", "title": "Air samples for archive", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1995-02-01", "end_date": "2009-01-31", "bbox": "39.5, -69, 39.5, -69", @@ -161267,7 +161007,7 @@ { "id": "NSF-ANT-1142074-penguins_1.0", "title": "Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-12-15", "end_date": "2013-01-31", "bbox": "165.9, -77.6, 169.4, -76.9", @@ -161280,7 +161020,7 @@ { "id": "NSF-ANT-1142074-penguins_1.0", "title": "Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2012-12-15", "end_date": "2013-01-31", "bbox": "165.9, -77.6, 169.4, -76.9", @@ -161579,7 +161319,7 @@ { "id": "NSF-ANT09-44042", "title": "Acoustic Assessment of Southern Ocean Salps and Their Ecosystem Impact", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2010-09-01", "end_date": "2013-08-31", "bbox": "-70, -66, -50, -59", @@ -161592,7 +161332,7 @@ { "id": "NSF-ANT09-44042", "title": "Acoustic Assessment of Southern Ocean Salps and Their Ecosystem Impact", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2010-09-01", "end_date": "2013-08-31", "bbox": "-70, -66, -50, -59", @@ -161631,7 +161371,7 @@ { "id": "NSF-ANT09-44411", "title": "Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2010-09-15", "end_date": "2015-08-31", "bbox": "-180, -90, 180, -60", @@ -161644,7 +161384,7 @@ { "id": "NSF-ANT09-44411", "title": "Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2010-09-15", "end_date": "2015-08-31", "bbox": "-180, -90, 180, -60", @@ -161735,7 +161475,7 @@ { "id": "NSF-ANT10-43517", "title": "A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2011-07-01", "end_date": "2015-06-30", "bbox": "163.5, -78.32, 165.35, -77.57", @@ -161748,7 +161488,7 @@ { "id": "NSF-ANT10-43517", "title": "A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-07-01", "end_date": "2015-06-30", "bbox": "163.5, -78.32, 165.35, -77.57", @@ -161761,7 +161501,7 @@ { "id": "NSF-ANT10-43554_1", "title": "Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-07-01", "end_date": "2015-06-30", "bbox": "161.5, -77.5, 161.5, -77.5", @@ -161774,7 +161514,7 @@ { "id": "NSF-ANT10-43554_1", "title": "Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2011-07-01", "end_date": "2015-06-30", "bbox": "161.5, -77.5, 161.5, -77.5", @@ -161787,7 +161527,7 @@ { "id": "NSF-ANT10-43621", "title": "A Comparison of Conjugate Auroral Electojet Indices", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2011-06-01", "end_date": "2013-05-31", "bbox": "-180, -79.5, 180, -54.5", @@ -161800,7 +161540,7 @@ { "id": "NSF-ANT10-43621", "title": "A Comparison of Conjugate Auroral Electojet Indices", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-06-01", "end_date": "2013-05-31", "bbox": "-180, -79.5, 180, -54.5", @@ -161891,7 +161631,7 @@ { "id": "NSF-ANT12-41487", "title": "A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2012-06-01", "end_date": "2013-05-31", "bbox": "-180, -90, 180, 90", @@ -161904,7 +161644,7 @@ { "id": "NSF-ANT12-41487", "title": "A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-06-01", "end_date": "2013-05-31", "bbox": "-180, -90, 180, 90", @@ -161917,7 +161657,7 @@ { "id": "NSF-ANT13-55533_1", "title": "A Multi-decadal Record of Antarctic Benthos: Image Analysis to Maximize Data Utilization", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2013-10-01", "end_date": "2015-09-30", "bbox": "163, -78.5, 167, -78", @@ -161930,7 +161670,7 @@ { "id": "NSF-ANT13-55533_1", "title": "A Multi-decadal Record of Antarctic Benthos: Image Analysis to Maximize Data Utilization", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2013-10-01", "end_date": "2015-09-30", "bbox": "163, -78.5, 167, -78", @@ -162749,7 +162489,7 @@ { "id": "NSIDC-0212_1", "title": "Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1", - "catalog": "ALL STAC Catalog", + "catalog": "NSIDCV0 STAC Catalog", "state_date": "2003-01-14", "end_date": "2003-02-03", "bbox": "130, 30, 150, 40", @@ -162762,7 +162502,7 @@ { "id": "NSIDC-0212_1", "title": "Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1", - "catalog": "NSIDCV0 STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-01-14", "end_date": "2003-02-03", "bbox": "130, 30, 150, 40", @@ -163061,7 +162801,7 @@ { "id": "NSIDC-0326_1", "title": "Ablation Rates of Taylor Glacier, Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2002-11-19", "end_date": "2011-01-12", "bbox": "160.1, -77.9, 162.2, -77.6", @@ -163074,7 +162814,7 @@ { "id": "NSIDC-0326_1", "title": "Ablation Rates of Taylor Glacier, Antarctica", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-11-19", "end_date": "2011-01-12", "bbox": "160.1, -77.9, 162.2, -77.6", @@ -163451,7 +163191,7 @@ { "id": "NSIDC-0504_1", "title": "Alkanes in Firn Air Samples, Antarctica and Greenland", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2005-12-01", "end_date": "2009-01-31", "bbox": "-38.3833, -79.47, 112.09, 72.5833", @@ -163464,7 +163204,7 @@ { "id": "NSIDC-0504_1", "title": "Alkanes in Firn Air Samples, Antarctica and Greenland", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2005-12-01", "end_date": "2009-01-31", "bbox": "-38.3833, -79.47, 112.09, 72.5833", @@ -163646,7 +163386,7 @@ { "id": "NSIDC-0539_1", "title": "Abrupt Change in Atmospheric CO2 During the Last Ice Age", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2009-01-01", "end_date": "2012-12-31", "bbox": "-148.82, -81.66, -119.83, -80.01", @@ -163659,7 +163399,7 @@ { "id": "NSIDC-0539_1", "title": "Abrupt Change in Atmospheric CO2 During the Last Ice Age", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2009-01-01", "end_date": "2012-12-31", "bbox": "-148.82, -81.66, -119.83, -80.01", @@ -163867,7 +163607,7 @@ { "id": "NSIDC-0634_1", "title": "Alaska Tidewater Glacier Terminus Positions, Version 1", - "catalog": "ALL STAC Catalog", + "catalog": "NSIDCV0 STAC Catalog", "state_date": "1948-01-01", "end_date": "2012-12-31", "bbox": "-151, 56.5, -132, 61.5", @@ -163880,7 +163620,7 @@ { "id": "NSIDC-0634_1", "title": "Alaska Tidewater Glacier Terminus Positions, Version 1", - "catalog": "NSIDCV0 STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1948-01-01", "end_date": "2012-12-31", "bbox": "-151, 56.5, -132, 61.5", @@ -165141,7 +164881,7 @@ { "id": "NorthSlope_NEE_TVPRM_1920_1", "title": "ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2008-01-01", "end_date": "2017-12-31", "bbox": "-177.47, 56.09, -128.59, 77.26", @@ -165154,7 +164894,7 @@ { "id": "NorthSlope_NEE_TVPRM_1920_1", "title": "ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2008-01-01", "end_date": "2017-12-31", "bbox": "-177.47, 56.09, -128.59, 77.26", @@ -166740,7 +166480,7 @@ { "id": "OCTS_L1_2", "title": "ADEOS-I OCTS Level-1A Data, version 2", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -166753,7 +166493,7 @@ { "id": "OCTS_L1_2", "title": "ADEOS-I OCTS Level-1A Data, version 2", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -166766,7 +166506,7 @@ { "id": "OCTS_L2_IOP_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Regional Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -166779,7 +166519,7 @@ { "id": "OCTS_L2_IOP_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Regional Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -166818,7 +166558,7 @@ { "id": "OCTS_L2_OC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Ocean Color (OC) Regional Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -166831,7 +166571,7 @@ { "id": "OCTS_L2_OC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Ocean Color (OC) Regional Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -166896,7 +166636,7 @@ { "id": "OCTS_L3b_CHL_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -166909,7 +166649,7 @@ { "id": "OCTS_L3b_CHL_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -166922,7 +166662,7 @@ { "id": "OCTS_L3b_IOP_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -166935,7 +166675,7 @@ { "id": "OCTS_L3b_IOP_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -166974,7 +166714,7 @@ { "id": "OCTS_L3b_KD_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -166987,7 +166727,7 @@ { "id": "OCTS_L3b_KD_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167000,7 +166740,7 @@ { "id": "OCTS_L3b_KD_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167013,7 +166753,7 @@ { "id": "OCTS_L3b_KD_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167026,7 +166766,7 @@ { "id": "OCTS_L3b_PAR_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Binned Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167039,7 +166779,7 @@ { "id": "OCTS_L3b_PAR_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Binned Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167052,7 +166792,7 @@ { "id": "OCTS_L3b_PAR_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167065,7 +166805,7 @@ { "id": "OCTS_L3b_PAR_2022.0", "title": "ADEOS-I OCTS Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167130,7 +166870,7 @@ { "id": "OCTS_L3b_POC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Binned Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167143,7 +166883,7 @@ { "id": "OCTS_L3b_POC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Binned Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167286,7 +167026,7 @@ { "id": "OCTS_L3m_IOP_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167299,7 +167039,7 @@ { "id": "OCTS_L3m_IOP_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167338,7 +167078,7 @@ { "id": "OCTS_L3m_KD_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167351,7 +167091,7 @@ { "id": "OCTS_L3m_KD_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167364,7 +167104,7 @@ { "id": "OCTS_L3m_KD_2022.0", "title": "ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167377,7 +167117,7 @@ { "id": "OCTS_L3m_KD_2022.0", "title": "ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167416,7 +167156,7 @@ { "id": "OCTS_L3m_PAR_2022.0", "title": "ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167429,7 +167169,7 @@ { "id": "OCTS_L3m_PAR_2022.0", "title": "ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167442,7 +167182,7 @@ { "id": "OCTS_L3m_PIC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167455,7 +167195,7 @@ { "id": "OCTS_L3m_PIC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167494,7 +167234,7 @@ { "id": "OCTS_L3m_POC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167507,7 +167247,7 @@ { "id": "OCTS_L3m_POC_2014", "title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "1996-11-01", "end_date": "1997-06-30", "bbox": "-180, -90, 180, 90", @@ -167520,7 +167260,7 @@ { "id": "OCTS_L3m_POC_2022.0", "title": "ADEOS-I OCTS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167533,7 +167273,7 @@ { "id": "OCTS_L3m_POC_2022.0", "title": "ADEOS-I OCTS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167572,7 +167312,7 @@ { "id": "OCTS_L3m_RRS_2022.0", "title": "ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0", - "catalog": "ALL STAC Catalog", + "catalog": "OB_CLOUD STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167585,7 +167325,7 @@ { "id": "OCTS_L3m_RRS_2022.0", "title": "ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0", - "catalog": "OB_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-10-31", "end_date": "1997-06-29", "bbox": "-180, -90, 180, 90", @@ -167624,7 +167364,7 @@ { "id": "OFR_94-212", "title": "A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1980-05-01", "end_date": "1988-09-06", "bbox": "-122, 46, -122, 46", @@ -167637,7 +167377,7 @@ { "id": "OFR_94-212", "title": "A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1980-05-01", "end_date": "1988-09-06", "bbox": "-122, 46, -122, 46", @@ -167650,7 +167390,7 @@ { "id": "OFR_95-55", "title": "A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-03-20", "end_date": "1994-07-07", "bbox": "-154, 56, -152, 62", @@ -167663,7 +167403,7 @@ { "id": "OFR_95-55", "title": "A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-03-20", "end_date": "1994-07-07", "bbox": "-154, 56, -152, 62", @@ -168986,19 +168726,6 @@ "description": "This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes.", "license": "proprietary" }, - { - "id": "OMCLDRR_003", - "title": "OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 NRT", - "catalog": "OMINRT STAC Catalog", - "state_date": "2004-07-15", - "end_date": "", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000100-OMINRT.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000100-OMINRT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMCLDRR_003", - "description": "The reprocessed Aura OMI Version 003 Level 2 Cloud Data Product OMCLDRR is made available (in April 2012) to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). http://disc.gsfc.nasa.gov/Aura/OMI/omcldrr_v003.shtml ) Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product (OMCLDRR) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. OMCLDRR files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml . A short OMCLDRR Readme Document that includes brief algorithm description and data quality is also provided by the OMCLDRR Algorithm lead. The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products are archived at the NASA GES DISC. For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/instruments/omi/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMCLDRR data file contains one swath which consists of two groups: Data fields: Two Effective Cloud Fraction and two Cloud Top Pressures that are based on two different clear and cloudy scene reflectivity criteria, Chlorophyll Amount, Effective Reflectivity (394.1 micron), UV Aerosol Index (based on 360 and 388 nm), and many Auxiliary Algorithm Parameter and Quality Flags. Geolocation Fields: Latitude, Longitude, Time, Solar Zenith Angle, Viewing Zenith Angle, Relative Azimuth Angle, Terrain Height, and Ground Pixel Quality Flags. OMI Atmospheric data and documents are available from the following sites: http://disc.gsfc.nasa.gov/Aura/OMI/ http://mirador.gsfc.nasa.gov/", - "license": "proprietary" - }, { "id": "OMCLDRR_003", "title": "OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC", @@ -169012,6 +168739,19 @@ "description": "The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.", "license": "proprietary" }, + { + "id": "OMCLDRR_003", + "title": "OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 NRT", + "catalog": "OMINRT STAC Catalog", + "state_date": "2004-07-15", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000100-OMINRT.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000100-OMINRT.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMCLDRR_003", + "description": "The reprocessed Aura OMI Version 003 Level 2 Cloud Data Product OMCLDRR is made available (in April 2012) to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). http://disc.gsfc.nasa.gov/Aura/OMI/omcldrr_v003.shtml ) Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product (OMCLDRR) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. OMCLDRR files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml . A short OMCLDRR Readme Document that includes brief algorithm description and data quality is also provided by the OMCLDRR Algorithm lead. The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products are archived at the NASA GES DISC. For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/instruments/omi/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMCLDRR data file contains one swath which consists of two groups: Data fields: Two Effective Cloud Fraction and two Cloud Top Pressures that are based on two different clear and cloudy scene reflectivity criteria, Chlorophyll Amount, Effective Reflectivity (394.1 micron), UV Aerosol Index (based on 360 and 388 nm), and many Auxiliary Algorithm Parameter and Quality Flags. Geolocation Fields: Latitude, Longitude, Time, Solar Zenith Angle, Viewing Zenith Angle, Relative Azimuth Angle, Terrain Height, and Ground Pixel Quality Flags. OMI Atmospheric data and documents are available from the following sites: http://disc.gsfc.nasa.gov/Aura/OMI/ http://mirador.gsfc.nasa.gov/", + "license": "proprietary" + }, { "id": "OMCLDRR_004", "title": "OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V004 (OMCLDRR) at GES DISC", @@ -169555,7 +169295,7 @@ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3333494968-GES_DISC.umm_json", "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3333494968-GES_DISC.html", "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections/OMNO2_004", - "description": "The Version 4.0 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V4.0 updates include: (1) use of a new daily and OMI \ufb01eld of view speci\ufb01c geometry dependent surface Lambertian Equivalent Re\ufb02ectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (e\ufb00ective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2 slant column data and the GLER product for terrain re\ufb02ectivity; (3) use of a more accurate terrain pressure calculated using OMI ground pixel-averaged terrain height and monthly mean GMI terrain pressure; and (4) improved treatment over snow/ice surfaces by using the concept of scene LER and scene pressure. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data. The short name for the Level-2 swath type column NO2 products is OMNO2. Other OMNO2-associated NO2 products include the Level-2 gridded column product, OMNO2G, and the Level-3 gridded column product, OMNO2d. The OMNO2 files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each Level-2 file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMNO2 is ~24 MB.", + "description": "The Collection 4, Version 5 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major updates include: (1) use of a new daily and OMI \ufb01eld of view speci\ufb01c geometry dependent surface Lambertian Equivalent Re\ufb02ectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (e\ufb00ective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2 slant column data and the GLER product for terrain re\ufb02ectivity; (3) use of a more accurate terrain pressure calculated using OMI ground pixel-averaged terrain height and monthly mean GMI terrain pressure; and (4) improved treatment over snow/ice surfaces by using the concept of scene LER and scene pressure. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data. The short name for the Level-2 swath type column NO2 products is OMNO2. Other OMNO2-associated NO2 products include the Level-2 gridded column product, OMNO2G, and the Level-3 gridded column product, OMNO2d. The OMNO2 files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each Level-2 file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMNO2 is ~24 MB.", "license": "proprietary" }, { @@ -170078,19 +169818,6 @@ "description": "This Level-2G daily global gridded product OMSO2G is based on the pixel level OMI Level-2 SO2 product OMSO2. OMSO2G data product is a special Level-2 gridded product where pixel level products are binned into 0.125x0.125 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999 . All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMSO2G data product contains almost all parameters that are contained in OMSO2 files. For example, in addition to three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm, and ancillary parameters, e.g., UV aerosol index, cloud fraction, cloud pressure, geolocation, solar and satellite viewing angles, and quality flags. The OMSO2G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 146 Mbytes.", "license": "proprietary" }, - { - "id": "OMSO2_003", - "title": "OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 NRT", - "catalog": "OMINRT STAC Catalog", - "state_date": "2004-07-15", - "end_date": "", - "bbox": "-180, -90, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.html", - "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMSO2_003", - "description": "The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004 (1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Space Office (NSO) in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO,NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. The Sulfer Dioxide Product 'OMSO2' from the Aura-OMI is now publicly available from NASA GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMSO2 product contains three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm. It also contains quality flags, geolocation and other ancillary information. The shortname for this Level-2 OMI total column SO2 product is OMSO2 and the algorithm leads for this product are NASA/UMBC OMI scientists Drs. Nikolay Krotkov (nickolay.a.krotkov@nasa.gov),Kai Yang(kai.yang@nasa.gov) and Arlin J. Krueger(krueger@umbc.edu). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 21 Mbytes. On-line spatial and parameter subset options are available during data download A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMSO2 Readme Document that includes brief algorithm description and documents that provides known data quality related issues are available from the UMBC OMI site ( http://so2.gsfc.nasa.gov/docs.php ) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://so2.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/. For the full set of Aura products and other atmospheric composition data available from the GES DISC, please see the links below. http://disc.sci.gsfc.nasa.gov/Aura/ http://disc.gsfc.nasa.gov/acdisc/", - "license": "proprietary" - }, { "id": "OMSO2_003", "title": "OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 (OMSO2) at GES DISC", @@ -170104,6 +169831,19 @@ "description": "The Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources. Files also contain quality flags, geolocation and other ancillary information. The lead scientist for the OMSO2 product is Can Li. The OMSO2 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the daylit half of an orbit (~53 minutes). There are approximately 14 orbits per day. The resolution of the data is 13x24 km2 at nadir, with a swath width of 2600 km and 60 pixels per scan line every 2 seconds.", "license": "proprietary" }, + { + "id": "OMSO2_003", + "title": "OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 NRT", + "catalog": "OMINRT STAC Catalog", + "state_date": "2004-07-15", + "end_date": "", + "bbox": "-180, -90, 180, 90", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.html", + "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMSO2_003", + "description": "The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004 (1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Space Office (NSO) in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO,NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. The Sulfer Dioxide Product 'OMSO2' from the Aura-OMI is now publicly available from NASA GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMSO2 product contains three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm. It also contains quality flags, geolocation and other ancillary information. The shortname for this Level-2 OMI total column SO2 product is OMSO2 and the algorithm leads for this product are NASA/UMBC OMI scientists Drs. Nikolay Krotkov (nickolay.a.krotkov@nasa.gov),Kai Yang(kai.yang@nasa.gov) and Arlin J. Krueger(krueger@umbc.edu). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 21 Mbytes. On-line spatial and parameter subset options are available during data download A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMSO2 Readme Document that includes brief algorithm description and documents that provides known data quality related issues are available from the UMBC OMI site ( http://so2.gsfc.nasa.gov/docs.php ) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://so2.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/. For the full set of Aura products and other atmospheric composition data available from the GES DISC, please see the links below. http://disc.sci.gsfc.nasa.gov/Aura/ http://disc.gsfc.nasa.gov/acdisc/", + "license": "proprietary" + }, { "id": "OMSO2_CPR_003", "title": "OMI/Aura Level 2 Sulphur Dioxide (SO2) Trace Gas Column Data 1-Orbit Subset and Collocated Swath along CloudSat V003 (OMSO2_CPR) at GES DISC", @@ -171771,7 +171511,7 @@ { "id": "PASSCAL_ALAR", "title": "Aleutian Arc Seismic Experiment", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -171784,7 +171524,7 @@ { "id": "PASSCAL_ALAR", "title": "Aleutian Arc Seismic Experiment", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -172356,7 +172096,7 @@ { "id": "POSTER-2005 Sig Hurricanes_Not Applicable", "title": "2005 Significant U.S. Hurricane Strikes Poster", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2005-07-10", "end_date": "2005-10-24", "bbox": "-102, 12, -69, 40.5", @@ -172369,7 +172109,7 @@ { "id": "POSTER-2005 Sig Hurricanes_Not Applicable", "title": "2005 Significant U.S. Hurricane Strikes Poster", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2005-07-10", "end_date": "2005-10-24", "bbox": "-102, 12, -69, 40.5", @@ -172980,7 +172720,7 @@ { "id": "Passive_Microwave_Snowoff_Data_1711_1.1", "title": "ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1988-01-01", "end_date": "2018-12-31", "bbox": "-180, 37.98, 180, 90", @@ -172993,7 +172733,7 @@ { "id": "Passive_Microwave_Snowoff_Data_1711_1.1", "title": "ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1988-01-01", "end_date": "2018-12-31", "bbox": "-180, 37.98, 180, 90", @@ -173071,7 +172811,7 @@ { "id": "Permafrost_ActiveLayer_NSlope_1759_1", "title": "ABoVE: Active Layer Soil Characterization of Permafrost Sites, Northern Alaska, 2018", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-08-22", "end_date": "2018-08-26", "bbox": "-149.31, 68.61, -148.56, 69.81", @@ -173084,7 +172824,7 @@ { "id": "Permafrost_ActiveLayer_NSlope_1759_1", "title": "ABoVE: Active Layer Soil Characterization of Permafrost Sites, Northern Alaska, 2018", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2018-08-22", "end_date": "2018-08-26", "bbox": "-149.31, 68.61, -148.56, 69.81", @@ -173175,7 +172915,7 @@ { "id": "Photos_ThermokarstLakes_AK_1845_1", "title": "ABoVE: Aerial Photographs of Frozen Lakes near Fairbanks, Alaska, October 2014", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2014-10-08", "end_date": "2014-10-08", "bbox": "-147.95, 64.86, -147.76, 64.94", @@ -173188,7 +172928,7 @@ { "id": "Photos_ThermokarstLakes_AK_1845_1", "title": "ABoVE: Aerial Photographs of Frozen Lakes near Fairbanks, Alaska, October 2014", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2014-10-08", "end_date": "2014-10-08", "bbox": "-147.95, 64.86, -147.76, 64.94", @@ -173318,7 +173058,7 @@ { "id": "PolInSAR_Canopy_Height_1589_1", "title": "AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-02-27", "end_date": "2016-03-08", "bbox": "9.29, -0.35, 11.83, 0.24", @@ -173331,7 +173071,7 @@ { "id": "PolInSAR_Canopy_Height_1589_1", "title": "AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-02-27", "end_date": "2016-03-08", "bbox": "9.29, -0.35, 11.83, 0.24", @@ -173747,7 +173487,7 @@ { "id": "Profile_based_PBL_heights_1706_1.1", "title": "ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-07-18", "end_date": "2019-07-26", "bbox": "-106.36, 28.65, -73.13, 49.49", @@ -173760,7 +173500,7 @@ { "id": "Profile_based_PBL_heights_1706_1.1", "title": "ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-07-18", "end_date": "2019-07-26", "bbox": "-106.36, 28.65, -73.13, 49.49", @@ -174696,7 +174436,7 @@ { "id": "RSFDCE_KLIM4", "title": "Absolute Minimum of Air Temperature. Year By Year Data", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1881-01-01", "end_date": "1965-12-31", "bbox": "25, 23.21, -175, 71", @@ -174709,7 +174449,7 @@ { "id": "RSFDCE_KLIM4", "title": "Absolute Minimum of Air Temperature. Year By Year Data", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1881-01-01", "end_date": "1965-12-31", "bbox": "25, 23.21, -175, 71", @@ -174722,7 +174462,7 @@ { "id": "RSFDCE_KLIM5", "title": "Air Temperature 01.00 P.M. Year By Year Date", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1881-01-01", "end_date": "1965-12-31", "bbox": "25, 23.21, -175, 71", @@ -174735,7 +174475,7 @@ { "id": "RSFDCE_KLIM5", "title": "Air Temperature 01.00 P.M. Year By Year Date", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1881-01-01", "end_date": "1965-12-31", "bbox": "25, 23.21, -175, 71", @@ -174813,7 +174553,7 @@ { "id": "Rain-on-Snow_Data_1611_1", "title": "ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2002-11-01", "end_date": "2016-12-31", "bbox": "-175.4, 48.62, -111.54, 73.85", @@ -174826,7 +174566,7 @@ { "id": "Rain-on-Snow_Data_1611_1", "title": "ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-11-01", "end_date": "2016-12-31", "bbox": "-175.4, 48.62, -111.54, 73.85", @@ -175021,7 +174761,7 @@ { "id": "RiSCC_Outcomes_Bibliography_1", "title": "A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1994-01-01", "end_date": "2006-12-31", "bbox": "-180, -70, 180, -50", @@ -175034,7 +174774,7 @@ { "id": "RiSCC_Outcomes_Bibliography_1", "title": "A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1994-01-01", "end_date": "2006-12-31", "bbox": "-180, -70, 180, -50", @@ -175047,7 +174787,7 @@ { "id": "RiSCC_Research_Support_Bibliography_1", "title": "A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1875-01-01", "end_date": "2004-12-31", "bbox": "-180, -70, 180, -50", @@ -175060,7 +174800,7 @@ { "id": "RiSCC_Research_Support_Bibliography_1", "title": "A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1875-01-01", "end_date": "2004-12-31", "bbox": "-180, -70, 180, -50", @@ -178752,7 +178492,7 @@ { "id": "SIPEX_II_AUV_1", "title": "3-D mapping of sea ice draft with an autonomous underwater vehicle", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2012-09-28", "end_date": "2012-10-13", "bbox": "115, -65, 125, -60", @@ -178765,7 +178505,7 @@ { "id": "SIPEX_II_AUV_1", "title": "3-D mapping of sea ice draft with an autonomous underwater vehicle", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-09-28", "end_date": "2012-10-13", "bbox": "115, -65, 125, -60", @@ -179922,7 +179662,7 @@ { "id": "SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0", "title": "ACEX 2004 ODEN TRACK", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2004-08-08", "end_date": "2004-09-13", "bbox": "19.045, 69.727, 175.94, 89.999", @@ -179935,7 +179675,7 @@ { "id": "SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0", "title": "ACEX 2004 ODEN TRACK", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-08-08", "end_date": "2004-09-13", "bbox": "19.045, 69.727, 175.94, 89.999", @@ -179948,7 +179688,7 @@ { "id": "SMHI_IPY_ACEX-2004-Seismic", "title": "ACEX 2004 Seismic", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2004-08-08", "end_date": "2004-09-13", "bbox": "139.0632, 87.917, 140.31, 87.977", @@ -179961,7 +179701,7 @@ { "id": "SMHI_IPY_ACEX-2004-Seismic", "title": "ACEX 2004 Seismic", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-08-08", "end_date": "2004-09-13", "bbox": "139.0632, 87.917, 140.31, 87.977", @@ -179974,7 +179714,7 @@ { "id": "SMHI_IPY_ACEX-2004-Sites_1.0", "title": "ACEX 2004 Sites", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2004-08-08", "end_date": "2004-09-13", "bbox": "-4.05029, 69.727, 19.045, 89.999", @@ -179987,7 +179727,7 @@ { "id": "SMHI_IPY_ACEX-2004-Sites_1.0", "title": "ACEX 2004 Sites", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-08-08", "end_date": "2004-09-13", "bbox": "-4.05029, 69.727, 19.045, 89.999", @@ -180000,7 +179740,7 @@ { "id": "SMHI_IPY_AGAVE2007-track_1.0", "title": "AGAVE2007 track", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2007-07-01", "end_date": "2007-08-09", "bbox": "-180, -90, 180, 90", @@ -180013,7 +179753,7 @@ { "id": "SMHI_IPY_AGAVE2007-track_1.0", "title": "AGAVE2007 track", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2007-07-01", "end_date": "2007-08-09", "bbox": "-180, -90, 180, 90", @@ -180026,7 +179766,7 @@ { "id": "SMHI_IPY_ALIS", "title": "ALIS, Auroral Large Imaging System", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1993-12-23", "end_date": "2009-02-18", "bbox": "18.8, 67.3, 21.7, 69.3", @@ -180039,7 +179779,7 @@ { "id": "SMHI_IPY_ALIS", "title": "ALIS, Auroral Large Imaging System", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1993-12-23", "end_date": "2009-02-18", "bbox": "18.8, 67.3, 21.7, 69.3", @@ -184810,26 +184550,26 @@ { "id": "SPL1AP_002", "title": "SMAP L1A Radiometer Time-Ordered Parsed Telemetry V002", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -86.4, 180, 86.4", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661641-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661641-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1AP_002", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000001801-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000001801-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1AP_002", "description": "

Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts of the following:

  • The first four raw moments of the fullband channel for both vertical and horizontal polarizations
  • The complex cross-correlations of the fullband channel
  • The 16 subband channels for both vertical and horizontal polarizations
", "license": "proprietary" }, { "id": "SPL1AP_002", "title": "SMAP L1A Radiometer Time-Ordered Parsed Telemetry V002", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -86.4, 180, 86.4", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000001801-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000001801-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1AP_002", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661641-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661641-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1AP_002", "description": "

Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts of the following:

  • The first four raw moments of the fullband channel for both vertical and horizontal polarizations
  • The complex cross-correlations of the fullband channel
  • The 16 subband channels for both vertical and horizontal polarizations
", "license": "proprietary" }, @@ -185187,52 +184927,52 @@ { "id": "SPL1CTB_006", "title": "SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1CTB_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1CTB_006", "description": "This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product.", "license": "proprietary" }, { "id": "SPL1CTB_006", "title": "SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1CTB_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1CTB_006", "description": "This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product.", "license": "proprietary" }, { "id": "SPL1CTB_E_004", "title": "SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1CTB_E_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1CTB_E_004", "description": "This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal.", "license": "proprietary" }, { "id": "SPL1CTB_E_004", "title": "SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1CTB_E_004", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1CTB_E_004", "description": "This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal.", "license": "proprietary" }, @@ -185382,104 +185122,104 @@ { "id": "SPL2SMAP_S_003", "title": "SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -60, 180, 60", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMAP_S_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMAP_S_003", "description": "This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution.", "license": "proprietary" }, { "id": "SPL2SMAP_S_003", "title": "SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -60, 180, 60", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMAP_S_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMAP_S_003", "description": "This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution.", "license": "proprietary" }, { "id": "SPL2SMA_003", "title": "SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMA_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMA_003", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, { "id": "SPL2SMA_003", "title": "SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMA_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMA_003", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, { "id": "SPL2SMP_009", "title": "SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_009", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_009", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data.", "license": "proprietary" }, { "id": "SPL2SMP_009", "title": "SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_009", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_009", "description": "This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data.", "license": "proprietary" }, { "id": "SPL2SMP_E_006", "title": "SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_E_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_E_006", "description": "This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product].", "license": "proprietary" }, { "id": "SPL2SMP_E_006", "title": "SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 90", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_E_006", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_E_006", "description": "This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product].", "license": "proprietary" }, @@ -185499,26 +185239,26 @@ { "id": "SPL3FTA_003", "title": "SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, 45, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766057-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766057-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3FTA_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303849-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303849-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3FTA_003", "description": "This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, { "id": "SPL3FTA_003", "title": "SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, 45, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303849-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303849-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3FTA_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766057-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766057-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3FTA_003", "description": "This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, @@ -185577,26 +185317,26 @@ { "id": "SPL3SMAP_003", "title": "SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303847-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303847-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMAP_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2872767144-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2872767144-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMAP_003", "description": "This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, { "id": "SPL3SMAP_003", "title": "SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture V003", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-04-13", "end_date": "2015-07-07", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2872767144-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2872767144-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMAP_003", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303847-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303847-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMAP_003", "description": "This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, @@ -185629,26 +185369,26 @@ { "id": "SPL3SMP_009", "title": "SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMP_009", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463935-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463935-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMP_009", "description": "This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, { "id": "SPL3SMP_009", "title": "SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463935-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463935-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMP_009", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMP_009", "description": "This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).", "license": "proprietary" }, @@ -185681,78 +185421,78 @@ { "id": "SPL4CMDL_007", "title": "SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange V007", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2534576405-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2534576405-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4CMDL_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665243-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665243-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4CMDL_007", "description": "The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, { "id": "SPL4CMDL_007", "title": "SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange V007", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665243-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665243-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4CMDL_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2534576405-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2534576405-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4CMDL_007", "description": "The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, { "id": "SPL4SMAU_007", "title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMAU_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMAU_007", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products:
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)
  • SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).
For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, { "id": "SPL4SMAU_007", "title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMAU_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMAU_007", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products:
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)
  • SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).
For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, { "id": "SPL4SMGP_007", "title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007", - "catalog": "NSIDC_CPRD STAC Catalog", + "catalog": "NSIDC_ECS STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMGP_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMGP_007", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, { "id": "SPL4SMGP_007", "title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007", - "catalog": "NSIDC_ECS STAC Catalog", + "catalog": "NSIDC_CPRD STAC Catalog", "state_date": "2015-03-31", "end_date": "", "bbox": "-180, -85.044, 180, 85.044", - "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json", - "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.html", - "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMGP_007", + "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json", + "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.html", + "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMGP_007", "description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.", "license": "proprietary" }, @@ -186383,7 +186123,7 @@ { "id": "SRDB_V5_1827_5", "title": "A Global Database of Soil Respiration Data, Version 5.0", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1961-01-01", "end_date": "2017-12-31", "bbox": "-163.71, -78.02, 175.9, 81.8", @@ -186396,7 +186136,7 @@ { "id": "SRDB_V5_1827_5", "title": "A Global Database of Soil Respiration Data, Version 5.0", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1961-01-01", "end_date": "2017-12-31", "bbox": "-163.71, -78.02, 175.9, 81.8", @@ -189113,7 +188853,7 @@ { "id": "Sat_ActiveLayer_Thickness_Maps_1760_1", "title": "ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2001-01-01", "end_date": "2015-12-31", "bbox": "-179.18, 55.57, -132.58, 70.21", @@ -189126,7 +188866,7 @@ { "id": "Sat_ActiveLayer_Thickness_Maps_1760_1", "title": "ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2001-01-01", "end_date": "2015-12-31", "bbox": "-179.18, 55.57, -132.58, 70.21", @@ -189945,7 +189685,7 @@ { "id": "Seasonality_Tundra_Vegetation_1606_1", "title": "ABoVE: Climate Drivers of Pan-Arctic Tundra Vegetation Productivity, 1982-2015", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1982-01-01", "end_date": "2015-12-31", "bbox": "-180, 70, 180, 90", @@ -189958,7 +189698,7 @@ { "id": "Seasonality_Tundra_Vegetation_1606_1", "title": "ABoVE: Climate Drivers of Pan-Arctic Tundra Vegetation Productivity, 1982-2015", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1982-01-01", "end_date": "2015-12-31", "bbox": "-180, 70, 180, 90", @@ -190153,7 +189893,7 @@ { "id": "Skelton_Aeromag_Data", "title": "Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition)", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1997-01-01", "end_date": "1998-12-31", "bbox": "153.5, -79.7, 166.7, -77.5", @@ -190166,7 +189906,7 @@ { "id": "Skelton_Aeromag_Data", "title": "Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition)", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1997-01-01", "end_date": "1998-12-31", "bbox": "153.5, -79.7, 166.7, -77.5", @@ -190309,7 +190049,7 @@ { "id": "Snowpack_Dall_Sheep_Track_1583_1", "title": "ABoVE: Dall Sheep Track Sinking Depths, Snow Depth, Hardness, and Density, 2017", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2017-03-19", "end_date": "2017-03-22", "bbox": "-143.06, 62.26, -143.01, 62.28", @@ -190322,7 +190062,7 @@ { "id": "Snowpack_Dall_Sheep_Track_1583_1", "title": "ABoVE: Dall Sheep Track Sinking Depths, Snow Depth, Hardness, and Density, 2017", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-03-19", "end_date": "2017-03-22", "bbox": "-143.06, 62.26, -143.01, 62.28", @@ -190465,7 +190205,7 @@ { "id": "Soil_Temperature_Profiles_AK_1767_1", "title": "ABoVE: Soil Temperature Profiles, USArray Seismic Stations, AK and Canada, 2016-2019", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-06-25", "end_date": "2019-08-22", "bbox": "-163.18, 63.89, -134.34, 69.92", @@ -190478,7 +190218,7 @@ { "id": "Soil_Temperature_Profiles_AK_1767_1", "title": "ABoVE: Soil Temperature Profiles, USArray Seismic Stations, AK and Canada, 2016-2019", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-06-25", "end_date": "2019-08-22", "bbox": "-163.18, 63.89, -134.34, 69.92", @@ -190517,7 +190257,7 @@ { "id": "Southern_Boreal_Plot_Attribute_1740_1", "title": "ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-05-30", "end_date": "2016-06-16", "bbox": "-109.17, 54.09, -104.69, 57.36", @@ -190530,7 +190270,7 @@ { "id": "Southern_Boreal_Plot_Attribute_1740_1", "title": "ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-05-30", "end_date": "2016-06-16", "bbox": "-109.17, 54.09, -104.69, 57.36", @@ -191544,7 +191284,7 @@ { "id": "TEMR_RSFCE", "title": "Air Temperature Time Series", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1883-01-01", "end_date": "1987-12-31", "bbox": "25, 23.21, -175, 71", @@ -191557,7 +191297,7 @@ { "id": "TEMR_RSFCE", "title": "Air Temperature Time Series", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1883-01-01", "end_date": "1987-12-31", "bbox": "25, 23.21, -175, 71", @@ -199396,7 +199136,7 @@ { "id": "Tundra_Greeness_Temp_Trends_1893_1", "title": "ABoVE: Landsat Tundra Greenness and Summer Air Temperatures, Arctic Tundra, 1985-2016", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1985-07-01", "end_date": "2016-08-31", "bbox": "-180, 31.49, 180, 90", @@ -199409,7 +199149,7 @@ { "id": "Tundra_Greeness_Temp_Trends_1893_1", "title": "ABoVE: Landsat Tundra Greenness and Summer Air Temperatures, Arctic Tundra, 1985-2016", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1985-07-01", "end_date": "2016-08-31", "bbox": "-180, 31.49, 180, 90", @@ -199435,7 +199175,7 @@ { "id": "Turbid9_0", "title": "2004 Measurements made in the Chesapeake Bay", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-10-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -199448,7 +199188,7 @@ { "id": "Turbid9_0", "title": "2004 Measurements made in the Chesapeake Bay", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "2004-10-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -200306,7 +200046,7 @@ { "id": "UM0405_26_aerosol_optical", "title": "Aerosol optical thickness - UM0405_26_aerosol_optical", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2004-12-31", "end_date": "2005-01-25", "bbox": "18, -68, 115, -32", @@ -200319,7 +200059,7 @@ { "id": "UM0405_26_aerosol_optical", "title": "Aerosol optical thickness - UM0405_26_aerosol_optical", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-12-31", "end_date": "2005-01-25", "bbox": "18, -68, 115, -32", @@ -200332,7 +200072,7 @@ { "id": "UM0506_26_aerosol_optical", "title": "Aerosol optical thickness", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2006-01-03", "end_date": "2006-01-30", "bbox": "18, -68, 115, -32", @@ -200345,7 +200085,7 @@ { "id": "UM0506_26_aerosol_optical", "title": "Aerosol optical thickness", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2006-01-03", "end_date": "2006-01-30", "bbox": "18, -68, 115, -32", @@ -200358,7 +200098,7 @@ { "id": "UM0708_25_multi-frequency_acoustic", "title": "Acoustic data of multi-frequency acoustic system", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2007-12-24", "end_date": "2008-02-14", "bbox": "-180, -90, 180, 90", @@ -200371,7 +200111,7 @@ { "id": "UM0708_25_multi-frequency_acoustic", "title": "Acoustic data of multi-frequency acoustic system", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2007-12-24", "end_date": "2008-02-14", "bbox": "-180, -90, 180, 90", @@ -200384,7 +200124,7 @@ { "id": "UM0809_33_nano", "title": "Abundance and composition of nano, picoplankton, microzooplankton", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2009-01-12", "end_date": "2009-01-25", "bbox": "38, -70, 75, -60", @@ -200397,7 +200137,7 @@ { "id": "UM0809_33_nano", "title": "Abundance and composition of nano, picoplankton, microzooplankton", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2009-01-12", "end_date": "2009-01-25", "bbox": "38, -70, 75, -60", @@ -200423,7 +200163,7 @@ { "id": "UNEP_GRID_SF_AFRICA_third version", "title": "Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1960-01-01", "end_date": "1990-12-31", "bbox": "-18, -35, 52, 35", @@ -200436,7 +200176,7 @@ { "id": "UNEP_GRID_SF_AFRICA_third version", "title": "Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1960-01-01", "end_date": "1990-12-31", "bbox": "-18, -35, 52, 35", @@ -200891,7 +200631,7 @@ { "id": "USAP-1544526_1", "title": "Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2016-09-01", "end_date": "2017-08-31", "bbox": "160, -77.8, 163.7, -76.5", @@ -200904,7 +200644,7 @@ { "id": "USAP-1544526_1", "title": "Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-09-01", "end_date": "2017-08-31", "bbox": "160, -77.8, 163.7, -76.5", @@ -200930,7 +200670,7 @@ { "id": "USAP-1643722_1", "title": "A High Resolution Atmospheric Methane Record from the South Pole Ice Core", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-02-01", "end_date": "2019-01-31", "bbox": "180, -90, 180, -90", @@ -200943,7 +200683,7 @@ { "id": "USAP-1643722_1", "title": "A High Resolution Atmospheric Methane Record from the South Pole Ice Core", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2017-02-01", "end_date": "2019-01-31", "bbox": "180, -90, 180, -90", @@ -201008,7 +200748,7 @@ { "id": "USAP-1644234_1", "title": "A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2017-07-15", "end_date": "2022-06-30", "bbox": "166.17, -77.7, 167.75, -77.3", @@ -201021,7 +200761,7 @@ { "id": "USAP-1644234_1", "title": "A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-07-15", "end_date": "2022-06-30", "bbox": "166.17, -77.7, 167.75, -77.3", @@ -201034,7 +200774,7 @@ { "id": "USAP-1656344_1", "title": "A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2016-08-01", "end_date": "2018-07-31", "bbox": "-64.1, -65, -63.9, -64.75", @@ -201047,7 +200787,7 @@ { "id": "USAP-1656344_1", "title": "A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-08-01", "end_date": "2018-07-31", "bbox": "-64.1, -65, -63.9, -64.75", @@ -201060,7 +200800,7 @@ { "id": "USAP-1744755_1", "title": "A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2018-05-01", "end_date": "2022-04-30", "bbox": "-80, -70, -30, -45", @@ -201073,7 +200813,7 @@ { "id": "USAP-1744755_1", "title": "A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "2018-05-01", "end_date": "2022-04-30", "bbox": "-80, -70, -30, -45", @@ -201580,7 +201320,7 @@ { "id": "USAP-9615281_1", "title": "Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure", - "catalog": "ALL STAC Catalog", + "catalog": "AMD_USAPDC STAC Catalog", "state_date": "1997-08-15", "end_date": "2002-07-31", "bbox": "-170, -84, -135, -76", @@ -201593,7 +201333,7 @@ { "id": "USAP-9615281_1", "title": "Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure", - "catalog": "AMD_USAPDC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1997-08-15", "end_date": "2002-07-31", "bbox": "-170, -84, -135, -76", @@ -201619,7 +201359,7 @@ { "id": "USARC_AERIAL_PHOTOS", "title": "Aerial Photography of Antarctica", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, -62.83", @@ -201632,7 +201372,7 @@ { "id": "USARC_AERIAL_PHOTOS", "title": "Aerial Photography of Antarctica", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, -62.83", @@ -201645,7 +201385,7 @@ { "id": "USArray_Ground_Temperature_1680_1.1", "title": "ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2016-05-13", "end_date": "2021-07-08", "bbox": "-165.35, 59.25, -141.59, 71", @@ -201658,7 +201398,7 @@ { "id": "USArray_Ground_Temperature_1680_1.1", "title": "ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2016-05-13", "end_date": "2021-07-08", "bbox": "-165.35, 59.25, -141.59, 71", @@ -201879,7 +201619,7 @@ { "id": "USGS-DDS_30_P-10_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-121.388916, 34.890034, -118.58517, 37.83907", @@ -201892,7 +201632,7 @@ { "id": "USGS-DDS_30_P-10_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-121.388916, 34.890034, -118.58517, 37.83907", @@ -202334,7 +202074,7 @@ { "id": "USGS_DDS_P13_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Ventura Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-120.58227, 33.84158, -117.37425, 34.824276", @@ -202347,7 +202087,7 @@ { "id": "USGS_DDS_P13_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Ventura Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-120.58227, 33.84158, -117.37425, 34.824276", @@ -202490,7 +202230,7 @@ { "id": "USGS_DDS_P17_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Idaho - Snake River Downwarp Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-117.24303, 41.99332, -111.04548, 49.00115", @@ -202503,7 +202243,7 @@ { "id": "USGS_DDS_P17_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Idaho - Snake River Downwarp Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-117.24303, 41.99332, -111.04548, 49.00115", @@ -202516,7 +202256,7 @@ { "id": "USGS_DDS_P17_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Idaho - Snake River Downwarp Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-117.24303, 41.99332, -111.04548, 49.00115", @@ -202529,7 +202269,7 @@ { "id": "USGS_DDS_P17_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Idaho - Snake River Downwarp Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-117.24303, 41.99332, -111.04548, 49.00115", @@ -202568,7 +202308,7 @@ { "id": "USGS_DDS_P18_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Western Great Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-122.29004, 32.717037, -114.13121, 44.563953", @@ -202581,7 +202321,7 @@ { "id": "USGS_DDS_P18_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Western Great Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-122.29004, 32.717037, -114.13121, 44.563953", @@ -202594,7 +202334,7 @@ { "id": "USGS_DDS_P19_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-117.02622, 35.002083, -111.170425, 43.022377", @@ -202607,7 +202347,7 @@ { "id": "USGS_DDS_P19_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-117.02622, 35.002083, -111.170425, 43.022377", @@ -202646,7 +202386,7 @@ { "id": "USGS_DDS_P20_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Uinta - Piceance Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-111.486916, 38.14689, -105.87804, 40.85869", @@ -202659,7 +202399,7 @@ { "id": "USGS_DDS_P20_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Uinta - Piceance Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-111.486916, 38.14689, -105.87804, 40.85869", @@ -202672,7 +202412,7 @@ { "id": "USGS_DDS_P20_continuous", "title": "1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-111.486916, 38.14689, -105.87804, 40.85869", @@ -202685,7 +202425,7 @@ { "id": "USGS_DDS_P20_continuous", "title": "1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-111.486916, 38.14689, -105.87804, 40.85869", @@ -202724,7 +202464,7 @@ { "id": "USGS_DDS_P2_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-173.22636, 58.49761, -140.99017, 68.01999", @@ -202737,7 +202477,7 @@ { "id": "USGS_DDS_P2_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-173.22636, 58.49761, -140.99017, 68.01999", @@ -202789,7 +202529,7 @@ { "id": "USGS_DS-845_PierScoutDatabase_1.0", "title": "A pier-scour database: 2,427 field and laboratory measurements of pier scour", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "19.6, 16.916668, -52.62, 83.1", @@ -202802,7 +202542,7 @@ { "id": "USGS_DS-845_PierScoutDatabase_1.0", "title": "A pier-scour database: 2,427 field and laboratory measurements of pier scour", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "19.6, 16.916668, -52.62, 83.1", @@ -202945,7 +202685,7 @@ { "id": "USGS_DS_2006_224", "title": "Aeromagnetic Survey of Taylor Mountains Area in Southwest Alaska, A Website for the Distribution of Data", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2004-04-17", "end_date": "2004-05-31", "bbox": "-160, 60, -156, 61", @@ -202958,7 +202698,7 @@ { "id": "USGS_DS_2006_224", "title": "Aeromagnetic Survey of Taylor Mountains Area in Southwest Alaska, A Website for the Distribution of Data", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-04-17", "end_date": "2004-05-31", "bbox": "-160, 60, -156, 61", @@ -204297,7 +204037,7 @@ { "id": "USGS_NPS_AcadiaAccuracy_Final", "title": "Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2003-10-01", "end_date": "2003-10-01", "bbox": "-75.262726, 43.99941, -68.044304, 44.48051", @@ -204310,7 +204050,7 @@ { "id": "USGS_NPS_AcadiaAccuracy_Final", "title": "Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-10-01", "end_date": "2003-10-01", "bbox": "-75.262726, 43.99941, -68.044304, 44.48051", @@ -204349,7 +204089,7 @@ { "id": "USGS_NPS_AcadiaParkBoundary_Final", "title": "Acadia National Park Vegetation Mapping Project - Park Boundary", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2003-10-01", "end_date": "2003-10-01", "bbox": "-68.944374, 43.99941, -68.02303, 44.48051", @@ -204362,7 +204102,7 @@ { "id": "USGS_NPS_AcadiaParkBoundary_Final", "title": "Acadia National Park Vegetation Mapping Project - Park Boundary", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-10-01", "end_date": "2003-10-01", "bbox": "-68.944374, 43.99941, -68.02303, 44.48051", @@ -204453,7 +204193,7 @@ { "id": "USGS_OFR-97-792", "title": "500,000 Year-old Stable Isotopic Record from Devils Hole, USGS OFR-97-792", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-116.3, 36.42, -116.3, 36.42", @@ -204466,7 +204206,7 @@ { "id": "USGS_OFR-97-792", "title": "500,000 Year-old Stable Isotopic Record from Devils Hole, USGS OFR-97-792", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-116.3, 36.42, -116.3, 36.42", @@ -206130,7 +205870,7 @@ { "id": "USGS_OFR_2004_1058", "title": "2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2002-01-01", "end_date": "", "bbox": "-168, 46, -126, 76", @@ -206143,7 +205883,7 @@ { "id": "USGS_OFR_2004_1058", "title": "2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-01-01", "end_date": "", "bbox": "-168, 46, -126, 76", @@ -206702,7 +206442,7 @@ { "id": "USGS_OFR_2005_1148_1.0", "title": "Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-80.82, 39.43, -74.41, 42.56", @@ -206715,7 +206455,7 @@ { "id": "USGS_OFR_2005_1148_1.0", "title": "Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-80.82, 39.43, -74.41, 42.56", @@ -208145,7 +207885,7 @@ { "id": "USGS_P-11_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-123.80987, 34.66294, -118.997696, 39.082233", @@ -208158,7 +207898,7 @@ { "id": "USGS_P-11_cells", "title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-12-01", "end_date": "1990-12-01", "bbox": "-123.80987, 34.66294, -118.997696, 39.082233", @@ -208171,7 +207911,7 @@ { "id": "USGS_P-11_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-123.80987, 34.66294, -118.997696, 39.082233", @@ -208184,7 +207924,7 @@ { "id": "USGS_P-11_conventional", "title": "1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "1996-12-31", "bbox": "-123.80987, 34.66294, -118.997696, 39.082233", @@ -208353,7 +208093,7 @@ { "id": "USGS_SESC_SturgeonBiblio_3", "title": "A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi.", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -208366,7 +208106,7 @@ { "id": "USGS_SESC_SturgeonBiblio_3", "title": "A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi.", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -208561,7 +208301,7 @@ { "id": "USGS_SOFIA_Eco_hist_db_2008_present_2", "title": "2008 - Present Ecosystem History of South Florida's Estuaries Database version 2", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2008-03-16", "end_date": "2012-09-30", "bbox": "-81.83, 24.75, -80, 26.5", @@ -208574,7 +208314,7 @@ { "id": "USGS_SOFIA_Eco_hist_db_2008_present_2", "title": "2008 - Present Ecosystem History of South Florida's Estuaries Database version 2", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2008-03-16", "end_date": "2012-09-30", "bbox": "-81.83, 24.75, -80, 26.5", @@ -208886,7 +208626,7 @@ { "id": "USGS_SOFIA_aerial-photos", "title": "Aerial Photos of the 1940s", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1940-02-14", "end_date": "1940-08-21", "bbox": "-81.9, 24.41, -79.98, 26.22", @@ -208899,7 +208639,7 @@ { "id": "USGS_SOFIA_aerial-photos", "title": "Aerial Photos of the 1940s", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1940-02-14", "end_date": "1940-08-21", "bbox": "-81.9, 24.41, -79.98, 26.22", @@ -208938,7 +208678,7 @@ { "id": "USGS_SOFIA_atlss_prog", "title": "Across Trophic Level System Simulation (ATLSS) Program", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1996-01-01", "end_date": "", "bbox": "-81.30333, 24.696152, -80.26212, 25.847113", @@ -208951,7 +208691,7 @@ { "id": "USGS_SOFIA_atlss_prog", "title": "Across Trophic Level System Simulation (ATLSS) Program", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1996-01-01", "end_date": "", "bbox": "-81.30333, 24.696152, -80.26212, 25.847113", @@ -209172,7 +208912,7 @@ { "id": "USGS_SOFIA_coupled_sw-gw_model", "title": "A Coupled Surface Water and Ground-Water Model to Simulate Past, Present, and Future Hydrologic Conditions in DOI Managed Lands", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1995-01-01", "end_date": "2009-09-30", "bbox": "-81.56, 25.02, -80, 25.75", @@ -209185,7 +208925,7 @@ { "id": "USGS_SOFIA_coupled_sw-gw_model", "title": "A Coupled Surface Water and Ground-Water Model to Simulate Past, Present, and Future Hydrologic Conditions in DOI Managed Lands", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-01-01", "end_date": "2009-09-30", "bbox": "-81.56, 25.02, -80, 25.75", @@ -209315,7 +209055,7 @@ { "id": "USGS_SOFIA_eco_hist_db1995-2007_version 7", "title": "1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1994-09-27", "end_date": "2007-04-03", "bbox": "-81.83, 24.75, -80, 26.5", @@ -209328,7 +209068,7 @@ { "id": "USGS_SOFIA_eco_hist_db1995-2007_version 7", "title": "1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1994-09-27", "end_date": "2007-04-03", "bbox": "-81.83, 24.75, -80, 26.5", @@ -211122,7 +210862,7 @@ { "id": "USGS_cont1994", "title": "1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-117.07194, 34.095333, -115.98976, 34.64026", @@ -211135,7 +210875,7 @@ { "id": "USGS_cont1994", "title": "1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-117.07194, 34.095333, -115.98976, 34.64026", @@ -212006,7 +211746,7 @@ { "id": "UTC_1990countyboundaries", "title": "1990 County Boundaries of the United States", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1972-01-01", "end_date": "1990-12-31", "bbox": "-177.1, 13.71, -61.48, 76.63", @@ -212019,7 +211759,7 @@ { "id": "UTC_1990countyboundaries", "title": "1990 County Boundaries of the United States", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1972-01-01", "end_date": "1990-12-31", "bbox": "-177.1, 13.71, -61.48, 76.63", @@ -216036,7 +215776,7 @@ { "id": "VMS_Genomics_1", "title": "2010/11 VMS Geonomics sampling - data collected from the VMS (Voyage Marine Science) voyage of the Aurora Australis", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-01-04", "end_date": "2011-02-06", "bbox": "140, -67, 150, -42", @@ -216049,7 +215789,7 @@ { "id": "VMS_Genomics_1", "title": "2010/11 VMS Geonomics sampling - data collected from the VMS (Voyage Marine Science) voyage of the Aurora Australis", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2011-01-04", "end_date": "2011-02-06", "bbox": "140, -67, 150, -42", @@ -219078,7 +218818,7 @@ { "id": "WARd0002_108", "title": "Administration Division Maps Of Poland", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "24, 14, 49, 54", @@ -219091,7 +218831,7 @@ { "id": "WARd0002_108", "title": "Administration Division Maps Of Poland", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "24, 14, 49, 54", @@ -219442,7 +219182,7 @@ { "id": "WIND_3DP", "title": "3-D Plasma and Energetic Particle Investigation on WIND", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1994-11-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -219455,7 +219195,7 @@ { "id": "WIND_3DP", "title": "3-D Plasma and Energetic Particle Investigation on WIND", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1994-11-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -219780,7 +219520,7 @@ { "id": "WYGISC_HYDRO100K", "title": "1:100,000-scale Hydrography for Wyoming (enhanced DLGs)", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-111.36555, 40.944794, -103.783806, 44.99391", @@ -219793,7 +219533,7 @@ { "id": "WYGISC_HYDRO100K", "title": "1:100,000-scale Hydrography for Wyoming (enhanced DLGs)", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-111.36555, 40.944794, -103.783806, 44.99391", @@ -219806,7 +219546,7 @@ { "id": "WYGISC_HYDRO24K", "title": "1:24,000-scale Hydrography for ortions Wyoming, various sources", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1967-01-01", "end_date": "1971-12-31", "bbox": "-111, 41, -104, 45", @@ -219819,7 +219559,7 @@ { "id": "WYGISC_HYDRO24K", "title": "1:24,000-scale Hydrography for ortions Wyoming, various sources", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1967-01-01", "end_date": "1971-12-31", "bbox": "-111, 41, -104, 45", @@ -219832,7 +219572,7 @@ { "id": "WYGISC_LANDUSE", "title": "Agricultural Land Use of Wyoming", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1980-01-01", "end_date": "1982-12-31", "bbox": "-111.09, 40.95, -103.88, 45.107", @@ -219845,7 +219585,7 @@ { "id": "WYGISC_LANDUSE", "title": "Agricultural Land Use of Wyoming", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1980-01-01", "end_date": "1982-12-31", "bbox": "-111.09, 40.95, -103.88, 45.107", @@ -219975,7 +219715,7 @@ { "id": "Wetland_VegClassification_PAD_2069_1", "title": "ABoVE: Wetland Vegetation Classification for Peace-Athabasca Delta, Canada, 2019", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2019-07-15", "end_date": "2019-09-15", "bbox": "-112.11, 58.21, -110.83, 59.14", @@ -219988,7 +219728,7 @@ { "id": "Wetland_VegClassification_PAD_2069_1", "title": "ABoVE: Wetland Vegetation Classification for Peace-Athabasca Delta, Canada, 2019", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-07-15", "end_date": "2019-09-15", "bbox": "-112.11, 58.21, -110.83, 59.14", @@ -220079,7 +219819,7 @@ { "id": "Wildfires_2014_NWT_Canada_1307_1", "title": "ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1997-07-07", "end_date": "2015-07-15", "bbox": "-121.6, 60.33, -110.68, 64.25", @@ -220092,7 +219832,7 @@ { "id": "Wildfires_2014_NWT_Canada_1307_1", "title": "ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1997-07-07", "end_date": "2015-07-15", "bbox": "-121.6, 60.33, -110.68, 64.25", @@ -220105,7 +219845,7 @@ { "id": "Wildfires_Date_of_Burning_1559_1.1", "title": "ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2001-01-01", "end_date": "2019-12-31", "bbox": "-178.84, 41.75, -53.83, 70.16", @@ -220118,7 +219858,7 @@ { "id": "Wildfires_Date_of_Burning_1559_1.1", "title": "ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2001-01-01", "end_date": "2019-12-31", "bbox": "-178.84, 41.75, -53.83, 70.16", @@ -220131,7 +219871,7 @@ { "id": "Wildfires_NWT_Canada_1548_1", "title": "ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "2015-05-20", "end_date": "2016-08-08", "bbox": "-135.54, 59.93, -106.76, 68.33", @@ -220144,7 +219884,7 @@ { "id": "Wildfires_NWT_Canada_1548_1", "title": "ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2015-05-20", "end_date": "2016-08-08", "bbox": "-135.54, 59.93, -106.76, 68.33", @@ -220391,7 +220131,7 @@ { "id": "XAERDT_L2_AHI_H08_1", "title": "AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km", - "catalog": "LAADS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-01-01", "end_date": "2022-12-31", "bbox": "-180, -90, 180, 90", @@ -220404,7 +220144,7 @@ { "id": "XAERDT_L2_AHI_H08_1", "title": "AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km", - "catalog": "ALL STAC Catalog", + "catalog": "LAADS STAC Catalog", "state_date": "2019-01-01", "end_date": "2022-12-31", "bbox": "-180, -90, 180, 90", @@ -220417,7 +220157,7 @@ { "id": "XAERDT_L2_AHI_H09_1", "title": "AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km", - "catalog": "LAADS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2022-12-13", "end_date": "2022-12-31", "bbox": "-180, -90, 180, 90", @@ -220430,7 +220170,7 @@ { "id": "XAERDT_L2_AHI_H09_1", "title": "AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km", - "catalog": "ALL STAC Catalog", + "catalog": "LAADS STAC Catalog", "state_date": "2022-12-13", "end_date": "2022-12-31", "bbox": "-180, -90, 180, 90", @@ -221054,7 +220794,7 @@ { "id": "aces1am_1", "title": "ACES Aircraft and Mechanical Data", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -221067,7 +220807,7 @@ { "id": "aces1am_1", "title": "ACES Aircraft and Mechanical Data", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -221080,7 +220820,7 @@ { "id": "aces1cont_1", "title": "ACES CONTINUOUS DATA V1", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -221093,7 +220833,7 @@ { "id": "aces1cont_1", "title": "ACES CONTINUOUS DATA V1", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -221106,7 +220846,7 @@ { "id": "aces1efm_1", "title": "ACES ELECTRIC FIELD MILL V1", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -221119,7 +220859,7 @@ { "id": "aces1efm_1", "title": "ACES ELECTRIC FIELD MILL V1", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -221132,7 +220872,7 @@ { "id": "aces1log_1", "title": "ACES LOG DATA", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -221145,7 +220885,7 @@ { "id": "aces1log_1", "title": "ACES LOG DATA", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -221184,7 +220924,7 @@ { "id": "aces1trig_1", "title": "ACES TRIGGERED DATA", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -221197,7 +220937,7 @@ { "id": "aces1trig_1", "title": "ACES TRIGGERED DATA", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-07-10", "end_date": "2002-08-30", "bbox": "-85, 23, -81, 26", @@ -221288,7 +221028,7 @@ { "id": "active_layer_arcss_grid_atqasuk_alaska_2010", "title": "Active Layer ARCSS grid Atqasuk, Alaska 2010", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2010-07-10", "end_date": "2010-08-16", "bbox": "-156, 70, -158, 71", @@ -221301,7 +221041,7 @@ { "id": "active_layer_arcss_grid_atqasuk_alaska_2010", "title": "Active Layer ARCSS grid Atqasuk, Alaska 2010", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2010-07-10", "end_date": "2010-08-16", "bbox": "-156, 70, -158, 71", @@ -221340,7 +221080,7 @@ { "id": "active_layer_arcss_grid_atqasuk_alaska_2012", "title": "Active Layer ARCSS grid Atqasuk, Alaska 2012", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2012-06-09", "end_date": "2012-08-18", "bbox": "-156, 70, -157, 71", @@ -221353,7 +221093,7 @@ { "id": "active_layer_arcss_grid_atqasuk_alaska_2012", "title": "Active Layer ARCSS grid Atqasuk, Alaska 2012", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-06-09", "end_date": "2012-08-18", "bbox": "-156, 70, -157, 71", @@ -221496,7 +221236,7 @@ { "id": "active_layer_nims_grid_barrow_alaska_2011", "title": "Active Layer NIMS grid Barrow, Alaska 2011", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2011-06-14", "end_date": "2011-08-09", "bbox": "-156.6, 71, -156.5, 71.5", @@ -221509,7 +221249,7 @@ { "id": "active_layer_nims_grid_barrow_alaska_2011", "title": "Active Layer NIMS grid Barrow, Alaska 2011", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-06-14", "end_date": "2011-08-09", "bbox": "-156.6, 71, -156.5, 71.5", @@ -221522,7 +221262,7 @@ { "id": "active_layer_nims_grid_barrow_alaska_2012", "title": "Active Layer NIMS grid Barrow, Alaska 2012", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-06-09", "end_date": "2012-08-18", "bbox": "-156.6, 71, -156.5, 71.5", @@ -221535,7 +221275,7 @@ { "id": "active_layer_nims_grid_barrow_alaska_2012", "title": "Active Layer NIMS grid Barrow, Alaska 2012", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2012-06-09", "end_date": "2012-08-18", "bbox": "-156.6, 71, -156.5, 71.5", @@ -221678,7 +221418,7 @@ { "id": "aerial_casa_2010_11_1", "title": "Aerial photography flown for the Australian Antarctic Division from CASA 212-400 aircraft, 2010-11", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2011-01-02", "end_date": "2011-02-06", "bbox": "89.17, -72.37, 112.42, -65.69", @@ -221691,7 +221431,7 @@ { "id": "aerial_casa_2010_11_1", "title": "Aerial photography flown for the Australian Antarctic Division from CASA 212-400 aircraft, 2010-11", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2011-01-02", "end_date": "2011-02-06", "bbox": "89.17, -72.37, 112.42, -65.69", @@ -221704,7 +221444,7 @@ { "id": "aerial_mosaics_macquarie_2017_2", "title": "Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-01-15", "end_date": "2017-02-15", "bbox": "158.874, -54.506, 158.954, -54.483", @@ -221717,7 +221457,7 @@ { "id": "aerial_mosaics_macquarie_2017_2", "title": "Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2017-01-15", "end_date": "2017-02-15", "bbox": "158.874, -54.506, 158.954, -54.483", @@ -221730,7 +221470,7 @@ { "id": "aerial_photo_sea_ice_1", "title": "Aerial photographs of sea ice flown by the Australian Antarctic Division", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2003-09-10", "end_date": "", "bbox": "-58.2, -69.67, 118.85, -64.03", @@ -221743,7 +221483,7 @@ { "id": "aerial_photo_sea_ice_1", "title": "Aerial photographs of sea ice flown by the Australian Antarctic Division", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-09-10", "end_date": "", "bbox": "-58.2, -69.67, 118.85, -64.03", @@ -221808,7 +221548,7 @@ { "id": "aerial_photo_sea_ice_SIPEX_1", "title": "Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2007-08-29", "end_date": "2007-10-16", "bbox": "109.1, -66.7, 118.85, -64.03", @@ -221821,7 +221561,7 @@ { "id": "aerial_photo_sea_ice_SIPEX_1", "title": "Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2007-08-29", "end_date": "2007-10-16", "bbox": "109.1, -66.7, 118.85, -64.03", @@ -221847,7 +221587,7 @@ { "id": "aerial_photographs_from_columbia_glacier_1976-2010", "title": "Aerial Photographs from Columbia Glacier, 1976-2010", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1976-07-24", "end_date": "2011-06-15", "bbox": "-146.895, 61.22, -146.895, 61.22", @@ -221860,7 +221600,7 @@ { "id": "aerial_photographs_from_columbia_glacier_1976-2010", "title": "Aerial Photographs from Columbia Glacier, 1976-2010", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1976-07-24", "end_date": "2011-06-15", "bbox": "-146.895, 61.22, -146.895, 61.22", @@ -221925,7 +221665,7 @@ { "id": "aerosol-data-davos-wolfgang_1.0", "title": "Aerosol Data Davos Wolfgang", - "catalog": "ALL STAC Catalog", + "catalog": "ENVIDAT STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-01-01", "bbox": "9.853594, 46.835577, 9.853594, 46.835577", @@ -221938,7 +221678,7 @@ { "id": "aerosol-data-davos-wolfgang_1.0", "title": "Aerosol Data Davos Wolfgang", - "catalog": "ENVIDAT STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-01-01", "bbox": "9.853594, 46.835577, 9.853594, 46.835577", @@ -221951,7 +221691,7 @@ { "id": "aerosol-data-weissfluhjoch_1.0", "title": "Aerosol Data Weissfluhjoch", - "catalog": "ALL STAC Catalog", + "catalog": "ENVIDAT STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-01-01", "bbox": "9.806475, 46.832964, 9.806475, 46.832964", @@ -221964,7 +221704,7 @@ { "id": "aerosol-data-weissfluhjoch_1.0", "title": "Aerosol Data Weissfluhjoch", - "catalog": "ENVIDAT STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-01-01", "end_date": "2020-01-01", "bbox": "9.806475, 46.832964, 9.806475, 46.832964", @@ -222224,7 +221964,7 @@ { "id": "agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0", "title": "Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis", - "catalog": "ALL STAC Catalog", + "catalog": "ENVIDAT STAC Catalog", "state_date": "2022-01-01", "end_date": "2022-01-01", "bbox": "5.95587, 45.81802, 10.49203, 47.80838", @@ -222237,7 +221977,7 @@ { "id": "agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0", "title": "Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis", - "catalog": "ENVIDAT STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2022-01-01", "end_date": "2022-01-01", "bbox": "5.95587, 45.81802, 10.49203, 47.80838", @@ -222471,7 +222211,7 @@ { "id": "allADCP_GB", "title": "Acoustic Doppler Current Profiler (ADCP) observations, Georges Bank area, April-June 1995, GLOBEC.", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-04-25", "end_date": "1995-06-16", "bbox": "-68, 40.5, -67, 41.5", @@ -222484,7 +222224,7 @@ { "id": "allADCP_GB", "title": "Acoustic Doppler Current Profiler (ADCP) observations, Georges Bank area, April-June 1995, GLOBEC.", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1995-04-25", "end_date": "1995-06-16", "bbox": "-68, 40.5, -67, 41.5", @@ -222640,7 +222380,7 @@ { "id": "amprimpacts_1", "title": "Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2020-01-18", "end_date": "2023-03-02", "bbox": "-124.153, 26.507, -64.366, 49.31", @@ -222653,7 +222393,7 @@ { "id": "amprimpacts_1", "title": "Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2020-01-18", "end_date": "2023-03-02", "bbox": "-124.153, 26.507, -64.366, 49.31", @@ -222848,7 +222588,7 @@ { "id": "amsua15sp_1", "title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "1998-08-03", "end_date": "", "bbox": "-180, -90, 180, 89.756", @@ -222861,7 +222601,7 @@ { "id": "amsua15sp_1", "title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1998-08-03", "end_date": "", "bbox": "-180, -90, 180, 89.756", @@ -222874,7 +222614,7 @@ { "id": "amsua16sp_1", "title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2001-05-27", "end_date": "2009-07-30", "bbox": "-180, -89.91, 180, 89.73", @@ -222887,7 +222627,7 @@ { "id": "amsua16sp_1", "title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2001-05-27", "end_date": "2009-07-30", "bbox": "-180, -89.91, 180, 89.73", @@ -223043,7 +222783,7 @@ { "id": "apr3cpex_1", "title": "Airborne Precipitation Radar 3rd Generation (APR-3) CPEX", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2017-05-27", "end_date": "2017-06-24", "bbox": "-96.0262, 16.8091, -69.2994, 28.9042", @@ -223056,7 +222796,7 @@ { "id": "apr3cpex_1", "title": "Airborne Precipitation Radar 3rd Generation (APR-3) CPEX", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2017-05-27", "end_date": "2017-06-24", "bbox": "-96.0262, 16.8091, -69.2994, 28.9042", @@ -223303,7 +223043,7 @@ { "id": "aster_1", "title": "Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2000-10-08", "end_date": "", "bbox": "-180, -90, 180, -53", @@ -223316,7 +223056,7 @@ { "id": "aster_1", "title": "Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2000-10-08", "end_date": "", "bbox": "-180, -90, 180, -53", @@ -224993,7 +224733,7 @@ { "id": "breeding_success_BI_1", "title": "Adelie penguin breeding success for Bechervaise Island, Mawson", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1990-10-01", "end_date": "2005-02-01", "bbox": "62.8055, -67.5916, 62.825, -67.5861", @@ -225006,7 +224746,7 @@ { "id": "breeding_success_BI_1", "title": "Adelie penguin breeding success for Bechervaise Island, Mawson", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1990-10-01", "end_date": "2005-02-01", "bbox": "62.8055, -67.5916, 62.825, -67.5861", @@ -225045,7 +224785,7 @@ { "id": "bromwich_0337948_1", "title": "A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1979-01-01", "end_date": "2002-08-31", "bbox": "-180, -90, 180, -60", @@ -225058,7 +224798,7 @@ { "id": "bromwich_0337948_1", "title": "A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1979-01-01", "end_date": "2002-08-31", "bbox": "-180, -90, 180, -60", @@ -225071,7 +224811,7 @@ { "id": "brownbay_bathy_dem_1", "title": "A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1997-02-01", "end_date": "2000-02-05", "bbox": "110.54, -66.281, 110.548, -66.279", @@ -225084,7 +224824,7 @@ { "id": "brownbay_bathy_dem_1", "title": "A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1997-02-01", "end_date": "2000-02-05", "bbox": "110.54, -66.281, 110.548, -66.279", @@ -225201,7 +224941,7 @@ { "id": "c05fa2267e6e03d0e5b9bb6429fdbb974a8194a1", "title": "3 year daily average solar exposure map Mali 3Km GRAS August 2008-2011", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2008-03-01", "end_date": "2011-03-31", "bbox": "-15, 8, 5, 28", @@ -225214,7 +224954,7 @@ { "id": "c05fa2267e6e03d0e5b9bb6429fdbb974a8194a1", "title": "3 year daily average solar exposure map Mali 3Km GRAS August 2008-2011", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2008-03-01", "end_date": "2011-03-31", "bbox": "-15, 8, 5, 28", @@ -225279,7 +225019,7 @@ { "id": "c2d1361c3fcbc6c7b60b35791f7bbc45bf8079dc", "title": "3 year daily average solar exposure map Mali 3Km GRAS January 2008-2011", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-15, 8, 5, 28", @@ -225292,7 +225032,7 @@ { "id": "c2d1361c3fcbc6c7b60b35791f7bbc45bf8079dc", "title": "3 year daily average solar exposure map Mali 3Km GRAS January 2008-2011", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-15, 8, 5, 28", @@ -225968,7 +225708,7 @@ { "id": "capeden_sat_ikonos_1", "title": "A georeferenced high resolution satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast acquired on 26, 31 January 2001", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2001-01-26", "end_date": "2001-01-31", "bbox": "142.5153, -67.0697, 143.03, -66.9478", @@ -225981,7 +225721,7 @@ { "id": "capeden_sat_ikonos_1", "title": "A georeferenced high resolution satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast acquired on 26, 31 January 2001", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2001-01-26", "end_date": "2001-01-31", "bbox": "142.5153, -67.0697, 143.03, -66.9478", @@ -226579,7 +226319,7 @@ { "id": "climate_pressure_1", "title": "ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air pressure", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1901-01-01", "end_date": "1998-12-31", "bbox": "-180, -80, 180, -17", @@ -226592,7 +226332,7 @@ { "id": "climate_pressure_1", "title": "ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air pressure", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1901-01-01", "end_date": "1998-12-31", "bbox": "-180, -80, 180, -17", @@ -226618,7 +226358,7 @@ { "id": "climate_temps_1", "title": "ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1901-01-01", "end_date": "2002-12-31", "bbox": "-180, -80, 180, -17", @@ -226631,7 +226371,7 @@ { "id": "climate_temps_1", "title": "ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1901-01-01", "end_date": "2002-12-31", "bbox": "-180, -80, 180, -17", @@ -230752,7 +230492,7 @@ { "id": "fife_AF_filtr_wyo_7_1", "title": "Aircraft Flux-Filtered: U of Wy. (FIFE)", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1987-08-11", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -230765,7 +230505,7 @@ { "id": "fife_AF_filtr_wyo_7_1", "title": "Aircraft Flux-Filtered: U of Wy. (FIFE)", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1987-08-11", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -230778,7 +230518,7 @@ { "id": "fife_AF_raw_nae_9_1", "title": "Aircraft Flux-Raw: NRCC (FIFE)", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1987-06-26", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -230791,7 +230531,7 @@ { "id": "fife_AF_raw_nae_9_1", "title": "Aircraft Flux-Raw: NRCC (FIFE)", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1987-06-26", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -230804,7 +230544,7 @@ { "id": "fife_AF_raw_ncar_11_1", "title": "Aircraft Flux-Raw: Univ. Col. (FIFE)", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1987-05-26", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -230817,7 +230557,7 @@ { "id": "fife_AF_raw_ncar_11_1", "title": "Aircraft Flux-Raw: Univ. Col. (FIFE)", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1987-05-26", "end_date": "1989-10-31", "bbox": "-102, 37, -95, 40", @@ -231753,7 +231493,7 @@ { "id": "fife_sur_met_rain_30m_2_1", "title": "30 Minute Rainfall Data (FIFE)", - "catalog": "ORNL_CLOUD STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1987-05-29", "end_date": "1987-10-26", "bbox": "-96.6, 39.08, -96.55, 39.11", @@ -231766,7 +231506,7 @@ { "id": "fife_sur_met_rain_30m_2_1", "title": "30 Minute Rainfall Data (FIFE)", - "catalog": "ALL STAC Catalog", + "catalog": "ORNL_CLOUD STAC Catalog", "state_date": "1987-05-29", "end_date": "1987-10-26", "bbox": "-96.6, 39.08, -96.55, 39.11", @@ -233469,7 +233209,7 @@ { "id": "geodata_0331", "title": "Agriculture Value Added - Percent of GDP", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1960-01-01", "end_date": "2009-12-31", "bbox": "-180, -90, 180, -60.5033", @@ -233482,7 +233222,7 @@ { "id": "geodata_0331", "title": "Agriculture Value Added - Percent of GDP", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1960-01-01", "end_date": "2009-12-31", "bbox": "-180, -90, 180, -60.5033", @@ -235211,7 +234951,7 @@ { "id": "geodata_1672", "title": "Agricultural Area", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1961-01-01", "end_date": "2008-12-31", "bbox": "-180, -90, 180, -60.5033", @@ -235224,7 +234964,7 @@ { "id": "geodata_1672", "title": "Agricultural Area", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1961-01-01", "end_date": "2008-12-31", "bbox": "-180, -90, 180, -60.5033", @@ -236329,7 +236069,7 @@ { "id": "geodata_2134", "title": "Agricultural Area Irrigated", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2001-01-01", "end_date": "2008-12-31", "bbox": "-180, -90, 180, -60.5033", @@ -236342,7 +236082,7 @@ { "id": "geodata_2134", "title": "Agricultural Area Irrigated", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2001-01-01", "end_date": "2008-12-31", "bbox": "-180, -90, 180, -60.5033", @@ -236602,7 +236342,7 @@ { "id": "geodata_2217", "title": "Agricultural Area Certified Organic", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-01-01", "end_date": "2008-12-31", "bbox": "-180, -90, 180, -60.5033", @@ -236615,7 +236355,7 @@ { "id": "geodata_2217", "title": "Agricultural Area Certified Organic", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "2003-01-01", "end_date": "2008-12-31", "bbox": "-180, -90, 180, -60.5033", @@ -236628,7 +236368,7 @@ { "id": "geodata_2222", "title": "Adjusted Human Water Security Threat", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, -60.5033", @@ -236641,7 +236381,7 @@ { "id": "geodata_2222", "title": "Adjusted Human Water Security Threat", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, -60.5033", @@ -238188,7 +237928,7 @@ { "id": "gomc_219", "title": "2001 Long Island Sound Study Ambient Water Quality and Monitoring Program", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-74.3, 40.5, -71.75, 41.5", @@ -238201,7 +237941,7 @@ { "id": "gomc_219", "title": "2001 Long Island Sound Study Ambient Water Quality and Monitoring Program", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-74.3, 40.5, -71.75, 41.5", @@ -238240,7 +237980,7 @@ { "id": "gomc_40", "title": "Air Quality Monitoring In New Brunswick", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-145.27, 37.3, -48.11, 87.61", @@ -238253,7 +237993,7 @@ { "id": "gomc_40", "title": "Air Quality Monitoring In New Brunswick", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-145.27, 37.3, -48.11, 87.61", @@ -238318,7 +238058,7 @@ { "id": "gov.noaa.ncdc:C01598_Beta4", "title": "Adaptive Ecosystem Climatology Beta 4 Model and Satellite Climatology", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1980-01-01", "end_date": "2012-12-31", "bbox": "-98, 18.091, -77.36, 30.73", @@ -238331,7 +238071,7 @@ { "id": "gov.noaa.ncdc:C01598_Beta4", "title": "Adaptive Ecosystem Climatology Beta 4 Model and Satellite Climatology", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1980-01-01", "end_date": "2012-12-31", "bbox": "-98, 18.091, -77.36, 30.73", @@ -238552,7 +238292,7 @@ { "id": "gov.noaa.nodc:0000015_Not Applicable", "title": "Alkalinity, dissolved oxygen, nutrients, pH, phosphate, salinity, silicate, and temperature collected by bottle from multiple cruises in the Southern Oceans from 1/15/1958 - 3/2/1990 (NCEI Accession 0000015)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1958-01-15", "end_date": "1990-03-02", "bbox": "6.05, -70.233333, -47.033333, -26.05", @@ -238565,7 +238305,7 @@ { "id": "gov.noaa.nodc:0000015_Not Applicable", "title": "Alkalinity, dissolved oxygen, nutrients, pH, phosphate, salinity, silicate, and temperature collected by bottle from multiple cruises in the Southern Oceans from 1/15/1958 - 3/2/1990 (NCEI Accession 0000015)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1958-01-15", "end_date": "1990-03-02", "bbox": "6.05, -70.233333, -47.033333, -26.05", @@ -238643,7 +238383,7 @@ { "id": "gov.noaa.nodc:0000052_Not Applicable", "title": "1988 Resurrection Bay Zooplankton Data Set from 01 March 1988 to 28 June 1988 (NCEI Accession 0000052)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1988-03-01", "end_date": "1988-06-28", "bbox": "-149.4083, 59.9117, -149.3583, 60.02", @@ -238656,7 +238396,7 @@ { "id": "gov.noaa.nodc:0000052_Not Applicable", "title": "1988 Resurrection Bay Zooplankton Data Set from 01 March 1988 to 28 June 1988 (NCEI Accession 0000052)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1988-03-01", "end_date": "1988-06-28", "bbox": "-149.4083, 59.9117, -149.3583, 60.02", @@ -238981,7 +238721,7 @@ { "id": "gov.noaa.nodc:0000501_Not Applicable", "title": "A unified, long-term, Caribbean-wide initiative to identity the factors responsible for sustaining mangrove wetland, seagrass meadow, and coral reef productivity, February 1993 - October 1998 (NCEI Accession 0000501)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1993-02-12", "end_date": "1998-10-15", "bbox": "-90.583333, 9.583333, -59.633333, 24.05", @@ -238994,7 +238734,7 @@ { "id": "gov.noaa.nodc:0000501_Not Applicable", "title": "A unified, long-term, Caribbean-wide initiative to identity the factors responsible for sustaining mangrove wetland, seagrass meadow, and coral reef productivity, February 1993 - October 1998 (NCEI Accession 0000501)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1993-02-12", "end_date": "1998-10-15", "bbox": "-90.583333, 9.583333, -59.633333, 24.05", @@ -239488,7 +239228,7 @@ { "id": "gov.noaa.nodc:0001746_Not Applicable", "title": "ALINE time series (NCEI Accession 0001746)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1989-01-01", "end_date": "2001-01-01", "bbox": "141, 37, 150, 44", @@ -239501,7 +239241,7 @@ { "id": "gov.noaa.nodc:0001746_Not Applicable", "title": "ALINE time series (NCEI Accession 0001746)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1989-01-01", "end_date": "2001-01-01", "bbox": "141, 37, 150, 44", @@ -239527,7 +239267,7 @@ { "id": "gov.noaa.nodc:0001941_Not Applicable", "title": "Aerial surveys of bowhead and beluga whales along with incidental sighting of other marine mammals in the Bering, Beaufort and Chukchi Seas for the Bowhead Whale Aerial Survey Project (BWASP), 1979 - 2004 (NCEI Accession 0001941)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1979-04-01", "end_date": "2004-10-18", "bbox": "-174.01, 57.72, -125.25, 76.14", @@ -239540,7 +239280,7 @@ { "id": "gov.noaa.nodc:0001941_Not Applicable", "title": "Aerial surveys of bowhead and beluga whales along with incidental sighting of other marine mammals in the Bering, Beaufort and Chukchi Seas for the Bowhead Whale Aerial Survey Project (BWASP), 1979 - 2004 (NCEI Accession 0001941)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1979-04-01", "end_date": "2004-10-18", "bbox": "-174.01, 57.72, -125.25, 76.14", @@ -239579,7 +239319,7 @@ { "id": "gov.noaa.nodc:0002170_Not Applicable", "title": "22 Real-time XBT replacements assembled by Canada Department of Fisheries and Oceans (DFO) for the Global Temperature-Salinity Profile Program (GTSPP), dates ranging from 05/26/2004 to 05/27/2004 (NCEI Accession 0002170)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2004-05-27", "end_date": "2004-05-27", "bbox": "9.106, 31.684, 33.058, 44.043", @@ -239592,7 +239332,7 @@ { "id": "gov.noaa.nodc:0002170_Not Applicable", "title": "22 Real-time XBT replacements assembled by Canada Department of Fisheries and Oceans (DFO) for the Global Temperature-Salinity Profile Program (GTSPP), dates ranging from 05/26/2004 to 05/27/2004 (NCEI Accession 0002170)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2004-05-27", "end_date": "2004-05-27", "bbox": "9.106, 31.684, 33.058, 44.043", @@ -239631,7 +239371,7 @@ { "id": "gov.noaa.nodc:0002193_Not Applicable", "title": "A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002193)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1999-09-01", "end_date": "2002-08-01", "bbox": "-96, 23.47, -85.47, 29.33", @@ -239644,7 +239384,7 @@ { "id": "gov.noaa.nodc:0002193_Not Applicable", "title": "A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002193)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1999-09-01", "end_date": "2002-08-01", "bbox": "-96, 23.47, -85.47, 29.33", @@ -239657,7 +239397,7 @@ { "id": "gov.noaa.nodc:0002196_Not Applicable", "title": "Acoustic doppler current meter data collected in support of the Minerals Management Service-supported Deep Water Program in the the Gulf of Mexico, 1999 - 2003 (NCEI Accession 0002196)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1999-09-01", "end_date": "2003-08-01", "bbox": "-96, 23.47, -85.47, 29.33", @@ -239670,7 +239410,7 @@ { "id": "gov.noaa.nodc:0002196_Not Applicable", "title": "Acoustic doppler current meter data collected in support of the Minerals Management Service-supported Deep Water Program in the the Gulf of Mexico, 1999 - 2003 (NCEI Accession 0002196)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1999-09-01", "end_date": "2003-08-01", "bbox": "-96, 23.47, -85.47, 29.33", @@ -239878,7 +239618,7 @@ { "id": "gov.noaa.nodc:0014906_Not Applicable", "title": "Aerial sightings of bowhead whales and other marine mammals by the US Department of the Interior's Minerals Management Service, 1979 - 2006, in the Bering, Chukchi and Beaufort Seas (NCEI Accession 0014906)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1979-04-01", "end_date": "2006-10-31", "bbox": "-174.01, 57.72, -125.25, 76.14", @@ -239891,7 +239631,7 @@ { "id": "gov.noaa.nodc:0014906_Not Applicable", "title": "Aerial sightings of bowhead whales and other marine mammals by the US Department of the Interior's Minerals Management Service, 1979 - 2006, in the Bering, Chukchi and Beaufort Seas (NCEI Accession 0014906)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1979-04-01", "end_date": "2006-10-31", "bbox": "-174.01, 57.72, -125.25, 76.14", @@ -239982,7 +239722,7 @@ { "id": "gov.noaa.nodc:0046934_Not Applicable", "title": "Acropora Spatial Survey Data of the Upper Florida Keys National Marine Sanctuary, 2005 - 2007 (NCEI Accession 0046934)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2005-01-01", "end_date": "2007-12-31", "bbox": "-81.41079, 24.54466, -80.19632, 25.29129", @@ -239995,7 +239735,7 @@ { "id": "gov.noaa.nodc:0046934_Not Applicable", "title": "Acropora Spatial Survey Data of the Upper Florida Keys National Marine Sanctuary, 2005 - 2007 (NCEI Accession 0046934)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2005-01-01", "end_date": "2007-12-31", "bbox": "-81.41079, 24.54466, -80.19632, 25.29129", @@ -240099,7 +239839,7 @@ { "id": "gov.noaa.nodc:0061208_Not Applicable", "title": "Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2005-11-13", "end_date": "2007-05-23", "bbox": "-93.58, 27.85, -92.45, 28.3", @@ -240112,7 +239852,7 @@ { "id": "gov.noaa.nodc:0061208_Not Applicable", "title": "Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2005-11-13", "end_date": "2007-05-23", "bbox": "-93.58, 27.85, -92.45, 28.3", @@ -242036,7 +241776,7 @@ { "id": "gov.noaa.nodc:0125596_Not Applicable", "title": "Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2009-03-18", "end_date": "2012-12-10", "bbox": "-51.493, -34.504, -44.498, -34.499", @@ -242049,7 +241789,7 @@ { "id": "gov.noaa.nodc:0125596_Not Applicable", "title": "Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2009-03-18", "end_date": "2012-12-10", "bbox": "-51.493, -34.504, -44.498, -34.499", @@ -242088,7 +241828,7 @@ { "id": "gov.noaa.nodc:0127525_Not Applicable", "title": "Abundance and behavior of parrotfishes (Labridae, Scarinae) in the upper Florida Keys from 2013-06-19 to 2013-07-30 (NCEI Accession 0127525)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2013-06-19", "end_date": "2013-07-30", "bbox": "-80.38, 25, -80.21, 25.22", @@ -242101,7 +241841,7 @@ { "id": "gov.noaa.nodc:0127525_Not Applicable", "title": "Abundance and behavior of parrotfishes (Labridae, Scarinae) in the upper Florida Keys from 2013-06-19 to 2013-07-30 (NCEI Accession 0127525)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2013-06-19", "end_date": "2013-07-30", "bbox": "-80.38, 25, -80.21, 25.22", @@ -242257,7 +241997,7 @@ { "id": "gov.noaa.nodc:0138863_Not Applicable", "title": "Acoustics short-term passive monitoring using sonobuoys in the Bering, Chukchi, and Western Beaufort Seas conducted by Alaska Fisheries Scientific Center, National Marine Mammal Laboratory from 2007-08-01 to 2015-09-28 (NCEI Accession 0138863)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2007-08-01", "end_date": "2015-09-28", "bbox": "-177.5925, 53.52167, -141.62497, 72.86938", @@ -242270,7 +242010,7 @@ { "id": "gov.noaa.nodc:0138863_Not Applicable", "title": "Acoustics short-term passive monitoring using sonobuoys in the Bering, Chukchi, and Western Beaufort Seas conducted by Alaska Fisheries Scientific Center, National Marine Mammal Laboratory from 2007-08-01 to 2015-09-28 (NCEI Accession 0138863)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2007-08-01", "end_date": "2015-09-28", "bbox": "-177.5925, 53.52167, -141.62497, 72.86938", @@ -242309,7 +242049,7 @@ { "id": "gov.noaa.nodc:0143303_Not Applicable", "title": "Acoustics long-term passive monitoring using moored autonomous recorders in the Bering, Chukchi, and Western Beaufort Seas conducted by Alaska Fisheries Scientific Center, National Marine Mammal Laboratory from 2007-08-15 to 2015-04-30 (NCEI Accession 0143303)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2007-08-15", "end_date": "2015-04-30", "bbox": "171.7, 53.63, -0.78, 78.837", @@ -242322,7 +242062,7 @@ { "id": "gov.noaa.nodc:0143303_Not Applicable", "title": "Acoustics long-term passive monitoring using moored autonomous recorders in the Bering, Chukchi, and Western Beaufort Seas conducted by Alaska Fisheries Scientific Center, National Marine Mammal Laboratory from 2007-08-15 to 2015-04-30 (NCEI Accession 0143303)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2007-08-15", "end_date": "2015-04-30", "bbox": "171.7, 53.63, -0.78, 78.837", @@ -242413,7 +242153,7 @@ { "id": "gov.noaa.nodc:0148759_Not Applicable", "title": "AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2009-08-11", "end_date": "2016-02-20", "bbox": "-38.146, 66.329, -38.146, 66.329", @@ -242426,7 +242166,7 @@ { "id": "gov.noaa.nodc:0148759_Not Applicable", "title": "AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2009-08-11", "end_date": "2016-02-20", "bbox": "-38.146, 66.329, -38.146, 66.329", @@ -242517,7 +242257,7 @@ { "id": "gov.noaa.nodc:0156424_Not Applicable", "title": "Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1950-01-01", "end_date": "1996-12-31", "bbox": "-180, 58, 180, 90", @@ -242530,7 +242270,7 @@ { "id": "gov.noaa.nodc:0156424_Not Applicable", "title": "Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1950-01-01", "end_date": "1996-12-31", "bbox": "-180, 58, 180, 90", @@ -242660,7 +242400,7 @@ { "id": "gov.noaa.nodc:0157074_Not Applicable", "title": "ACOUSTIC TRAVEL TIME collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) from March 1995 to March 1997 (NCEI Accession 0157074)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-03-20", "end_date": "1997-03-28", "bbox": "143.63333, -52.08133, 143.805, -47.99867", @@ -242673,7 +242413,7 @@ { "id": "gov.noaa.nodc:0157074_Not Applicable", "title": "ACOUSTIC TRAVEL TIME collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) from March 1995 to March 1997 (NCEI Accession 0157074)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1995-03-20", "end_date": "1997-03-28", "bbox": "143.63333, -52.08133, 143.805, -47.99867", @@ -242712,7 +242452,7 @@ { "id": "gov.noaa.nodc:0159386_Not Applicable", "title": "Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2002-10-02", "end_date": "2002-10-04", "bbox": "-88.672, 22.203, -84.062, 26.433", @@ -242725,7 +242465,7 @@ { "id": "gov.noaa.nodc:0159386_Not Applicable", "title": "Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2002-10-02", "end_date": "2002-10-04", "bbox": "-88.672, 22.203, -84.062, 26.433", @@ -242738,7 +242478,7 @@ { "id": "gov.noaa.nodc:0159419_Not Applicable", "title": "ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2013-10-17", "end_date": "2013-10-20", "bbox": "-94.9828, 26.16133, -88, 29.69641", @@ -242751,7 +242491,7 @@ { "id": "gov.noaa.nodc:0159419_Not Applicable", "title": "ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2013-10-17", "end_date": "2013-10-20", "bbox": "-94.9828, 26.16133, -88, 29.69641", @@ -242777,7 +242517,7 @@ { "id": "gov.noaa.nodc:0161311_Not Applicable", "title": "A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1979-01-01", "end_date": "1982-12-31", "bbox": "-88.431, 30.2129, -87.328, 31.0701", @@ -242790,7 +242530,7 @@ { "id": "gov.noaa.nodc:0161311_Not Applicable", "title": "A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1979-01-01", "end_date": "1982-12-31", "bbox": "-88.431, 30.2129, -87.328, 31.0701", @@ -242894,7 +242634,7 @@ { "id": "gov.noaa.nodc:0163192_Not Applicable", "title": "A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1998-07-12", "end_date": "2005-07-27", "bbox": "-86.2279, 27.4432, -80.1996, 30.7692", @@ -242907,7 +242647,7 @@ { "id": "gov.noaa.nodc:0163192_Not Applicable", "title": "A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1998-07-12", "end_date": "2005-07-27", "bbox": "-86.2279, 27.4432, -80.1996, 30.7692", @@ -243453,7 +243193,7 @@ { "id": "gov.noaa.nodc:0172043_Not Applicable", "title": "ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2012-11-28", "end_date": "2012-12-19", "bbox": "-94.0863, 25.7961, -87.2228, 28.9733", @@ -243466,7 +243206,7 @@ { "id": "gov.noaa.nodc:0172043_Not Applicable", "title": "ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2012-11-28", "end_date": "2012-12-19", "bbox": "-94.0863, 25.7961, -87.2228, 28.9733", @@ -243479,7 +243219,7 @@ { "id": "gov.noaa.nodc:0172377_Not Applicable", "title": "Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2015-07-21", "end_date": "2016-08-05", "bbox": "-64.9199, 17.63764, -64.47889, 17.82709", @@ -243492,7 +243232,7 @@ { "id": "gov.noaa.nodc:0172377_Not Applicable", "title": "Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2015-07-21", "end_date": "2016-08-05", "bbox": "-64.9199, 17.63764, -64.47889, 17.82709", @@ -243622,7 +243362,7 @@ { "id": "gov.noaa.nodc:0175786_Not Applicable", "title": "Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1986-04-01", "end_date": "2017-06-27", "bbox": "-89.85889, 29.8917, -87.6955, 30.68067", @@ -243635,7 +243375,7 @@ { "id": "gov.noaa.nodc:0175786_Not Applicable", "title": "Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1986-04-01", "end_date": "2017-06-27", "bbox": "-89.85889, 29.8917, -87.6955, 30.68067", @@ -243687,7 +243427,7 @@ { "id": "gov.noaa.nodc:0185753_Not Applicable", "title": "Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2006-09-01", "end_date": "2012-12-31", "bbox": "-84.5, 43.2, -79.8, 46.3", @@ -243700,7 +243440,7 @@ { "id": "gov.noaa.nodc:0185753_Not Applicable", "title": "Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2006-09-01", "end_date": "2012-12-31", "bbox": "-84.5, 43.2, -79.8, 46.3", @@ -243817,7 +243557,7 @@ { "id": "gov.noaa.nodc:0206155_Not Applicable", "title": "2019 Summer Hypoxia Survey of Alabama Shelf CTD Data (2019-06-04 to 2019-08-02) (NCEI Accession 0206155)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-06-04", "end_date": "2019-08-02", "bbox": "-88.418, 29.4782, -88.004, 30.2166", @@ -243830,7 +243570,7 @@ { "id": "gov.noaa.nodc:0206155_Not Applicable", "title": "2019 Summer Hypoxia Survey of Alabama Shelf CTD Data (2019-06-04 to 2019-08-02) (NCEI Accession 0206155)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2019-06-04", "end_date": "2019-08-02", "bbox": "-88.418, 29.4782, -88.004, 30.2166", @@ -243895,7 +243635,7 @@ { "id": "gov.noaa.nodc:0209071_Not Applicable", "title": "ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean from 2009-12-01 to 2010-03-23 (NCEI Accession 0209071)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2009-12-01", "end_date": "2010-03-23", "bbox": "11.2067, -5.8778, 11.2067, -5.8778", @@ -243908,7 +243648,7 @@ { "id": "gov.noaa.nodc:0209071_Not Applicable", "title": "ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean from 2009-12-01 to 2010-03-23 (NCEI Accession 0209071)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2009-12-01", "end_date": "2010-03-23", "bbox": "11.2067, -5.8778, 11.2067, -5.8778", @@ -243947,7 +243687,7 @@ { "id": "gov.noaa.nodc:0209222_Not Applicable", "title": "Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Lake Guardian in Lake Michigan, Great Lakes from 2015-07-20 to 2015-07-29 (NCEI Accession 0209222)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2015-07-20", "end_date": "2015-07-29", "bbox": "-88.1, 41.6, -84.75, 46.2", @@ -243960,7 +243700,7 @@ { "id": "gov.noaa.nodc:0209222_Not Applicable", "title": "Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Lake Guardian in Lake Michigan, Great Lakes from 2015-07-20 to 2015-07-29 (NCEI Accession 0209222)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2015-07-20", "end_date": "2015-07-29", "bbox": "-88.1, 41.6, -84.75, 46.2", @@ -243973,7 +243713,7 @@ { "id": "gov.noaa.nodc:0209226_Not Applicable", "title": "Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2006-01-01", "end_date": "2099-12-31", "bbox": "-82.9771, 24.4437, -80.0646, 26.3438", @@ -243986,7 +243726,7 @@ { "id": "gov.noaa.nodc:0209226_Not Applicable", "title": "Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2006-01-01", "end_date": "2099-12-31", "bbox": "-82.9771, 24.4437, -80.0646, 26.3438", @@ -244012,7 +243752,7 @@ { "id": "gov.noaa.nodc:0209357_Not Applicable", "title": "A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2000-01-01", "end_date": "2020-03-01", "bbox": "-180, -90, 180, 90", @@ -244025,7 +243765,7 @@ { "id": "gov.noaa.nodc:0209357_Not Applicable", "title": "A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2000-01-01", "end_date": "2020-03-01", "bbox": "-180, -90, 180, 90", @@ -244038,7 +243778,7 @@ { "id": "gov.noaa.nodc:0210577_Not Applicable", "title": "Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from 2014-07-15 to 2018-11-11 (NCEI Accession 0210577)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2014-07-15", "end_date": "2018-11-11", "bbox": "-162, 11, -50, 43", @@ -244051,7 +243791,7 @@ { "id": "gov.noaa.nodc:0210577_Not Applicable", "title": "Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from 2014-07-15 to 2018-11-11 (NCEI Accession 0210577)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2014-07-15", "end_date": "2018-11-11", "bbox": "-162, 11, -50, 43", @@ -244220,7 +243960,7 @@ { "id": "gov.noaa.nodc:0231662_Not Applicable", "title": "ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "2019-07-15", "end_date": "2019-07-15", "bbox": "-124.355093, 44.282964, -124.054485, 44.625023", @@ -244233,7 +243973,7 @@ { "id": "gov.noaa.nodc:0231662_Not Applicable", "title": "ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2019-07-15", "end_date": "2019-07-15", "bbox": "-124.355093, 44.282964, -124.054485, 44.625023", @@ -244389,7 +244129,7 @@ { "id": "gov.noaa.nodc:7000981_Not Applicable", "title": "A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-06-01", "end_date": "1970-07-01", "bbox": "-29.33, 50.01, -14.2, 55.56", @@ -244402,7 +244142,7 @@ { "id": "gov.noaa.nodc:7000981_Not Applicable", "title": "A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1970-06-01", "end_date": "1970-07-01", "bbox": "-29.33, 50.01, -14.2, 55.56", @@ -244441,7 +244181,7 @@ { "id": "gov.noaa.nodc:7100048_Not Applicable", "title": "AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms From NE Pacific (limit-180) from 1969-08-01 to 1969-08-31 (NCEI Accession 7100048)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1969-08-01", "end_date": "1969-08-31", "bbox": "-85, 7, -75, 12", @@ -244454,7 +244194,7 @@ { "id": "gov.noaa.nodc:7100048_Not Applicable", "title": "AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms From NE Pacific (limit-180) from 1969-08-01 to 1969-08-31 (NCEI Accession 7100048)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1969-08-01", "end_date": "1969-08-31", "bbox": "-85, 7, -75, 12", @@ -244610,7 +244350,7 @@ { "id": "gov.noaa.nodc:7300282_Not Applicable", "title": "AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms from 1968-07-01 to 1970-12-31 (NCEI Accession 7300282)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1968-07-01", "end_date": "1970-12-31", "bbox": "113.9, -46.6, 179.8, -0.2", @@ -244623,7 +244363,7 @@ { "id": "gov.noaa.nodc:7300282_Not Applicable", "title": "AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms from 1968-07-01 to 1970-12-31 (NCEI Accession 7300282)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1968-07-01", "end_date": "1970-12-31", "bbox": "113.9, -46.6, 179.8, -0.2", @@ -244857,7 +244597,7 @@ { "id": "gov.noaa.nodc:7601613_Not Applicable", "title": "AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1972-01-01", "end_date": "1974-06-30", "bbox": "-77, 37, -76, 39", @@ -244870,7 +244610,7 @@ { "id": "gov.noaa.nodc:7601613_Not Applicable", "title": "AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1972-01-01", "end_date": "1974-06-30", "bbox": "-77, 37, -76, 39", @@ -244948,7 +244688,7 @@ { "id": "gov.noaa.nodc:7700058_Not Applicable", "title": "AIR PRESSURE and Other Data from YELCHO From Drake Passage from 1976-02-27 to 1976-04-08 (NCEI Accession 7700058)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1976-02-27", "end_date": "1976-04-08", "bbox": "-70, -90, -50, -70", @@ -244961,7 +244701,7 @@ { "id": "gov.noaa.nodc:7700058_Not Applicable", "title": "AIR PRESSURE and Other Data from YELCHO From Drake Passage from 1976-02-27 to 1976-04-08 (NCEI Accession 7700058)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1976-02-27", "end_date": "1976-04-08", "bbox": "-70, -90, -50, -70", @@ -244974,7 +244714,7 @@ { "id": "gov.noaa.nodc:7700179_Not Applicable", "title": "AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1919-09-29", "end_date": "1976-04-26", "bbox": "-60, 44, 48, 80.5", @@ -244987,7 +244727,7 @@ { "id": "gov.noaa.nodc:7700179_Not Applicable", "title": "AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1919-09-29", "end_date": "1976-04-26", "bbox": "-60, 44, 48, 80.5", @@ -247977,7 +247717,7 @@ { "id": "gov.noaa.nodc:9300196_Not Applicable", "title": "Algal Species and other data collected from photographs in Southeast Atlantic Ocean from 1991-06-11 to 1993-03-22 (NCEI Accession 9300196)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1991-06-11", "end_date": "1993-03-22", "bbox": "-88, 17, -85, 22", @@ -247990,7 +247730,7 @@ { "id": "gov.noaa.nodc:9300196_Not Applicable", "title": "Algal Species and other data collected from photographs in Southeast Atlantic Ocean from 1991-06-11 to 1993-03-22 (NCEI Accession 9300196)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1991-06-11", "end_date": "1993-03-22", "bbox": "-88, 17, -85, 22", @@ -248692,7 +248432,7 @@ { "id": "gov.noaa.nodc:9700063_Not Applicable", "title": "AIR PRESSURE and Other Data from NOODIN From Great Lakes from 1995-06-20 to 1996-11-14 (NCEI Accession 9700063)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1995-06-20", "end_date": "1996-11-14", "bbox": "-91.7, 47, -91.7, 47", @@ -248705,7 +248445,7 @@ { "id": "gov.noaa.nodc:9700063_Not Applicable", "title": "AIR PRESSURE and Other Data from NOODIN From Great Lakes from 1995-06-20 to 1996-11-14 (NCEI Accession 9700063)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-06-20", "end_date": "1996-11-14", "bbox": "-91.7, 47, -91.7, 47", @@ -248861,7 +248601,7 @@ { "id": "gov.noaa.nodc:9800085_Not Applicable", "title": "AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1995-01-09 to 1995-12-28 (NCEI Accession 9800085)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1995-01-09", "end_date": "1995-12-28", "bbox": "56.5, 9.9, 68.8, 24.1", @@ -248874,7 +248614,7 @@ { "id": "gov.noaa.nodc:9800085_Not Applicable", "title": "AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1995-01-09 to 1995-12-28 (NCEI Accession 9800085)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-01-09", "end_date": "1995-12-28", "bbox": "56.5, 9.9, 68.8, 24.1", @@ -249082,7 +248822,7 @@ { "id": "gov.noaa.nodc:9900022_Not Applicable", "title": "AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms from 1998-08-01 to 1998-12-31 (NCEI Accession 9900022)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1998-08-01", "end_date": "1998-12-31", "bbox": "-124.1, 44.6, -124, 44.8", @@ -249095,7 +248835,7 @@ { "id": "gov.noaa.nodc:9900022_Not Applicable", "title": "AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms from 1998-08-01 to 1998-12-31 (NCEI Accession 9900022)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1998-08-01", "end_date": "1998-12-31", "bbox": "-124.1, 44.6, -124, 44.8", @@ -249108,7 +248848,7 @@ { "id": "gov.noaa.nodc:9900054_Not Applicable", "title": "Algal species and other data collected using photographs in the South Pacific Ocean from 1992-01-02 to 1992-12-31 (NCEI Accession 9900054)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1992-01-02", "end_date": "1992-12-31", "bbox": "-170.8, -14.4, -170.6, -14.3", @@ -249121,7 +248861,7 @@ { "id": "gov.noaa.nodc:9900054_Not Applicable", "title": "Algal species and other data collected using photographs in the South Pacific Ocean from 1992-01-02 to 1992-12-31 (NCEI Accession 9900054)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1992-01-02", "end_date": "1992-12-31", "bbox": "-170.8, -14.4, -170.6, -14.3", @@ -249134,7 +248874,7 @@ { "id": "gov.noaa.nodc:9900094_Not Applicable", "title": "AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-01-01 to 1999-04-29 (NCEI Accession 9900094)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1999-01-01", "end_date": "1999-04-29", "bbox": "-124, 44.6, -124, 44.6", @@ -249147,7 +248887,7 @@ { "id": "gov.noaa.nodc:9900094_Not Applicable", "title": "AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-01-01 to 1999-04-29 (NCEI Accession 9900094)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1999-01-01", "end_date": "1999-04-29", "bbox": "-124, 44.6, -124, 44.6", @@ -249199,7 +248939,7 @@ { "id": "gov.noaa.nodc:9900159_Not Applicable", "title": "1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159)", - "catalog": "NOAA_NCEI STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1999-06-16", "end_date": "1999-07-18", "bbox": "-124, 45, -122, 49.5", @@ -249212,7 +248952,7 @@ { "id": "gov.noaa.nodc:9900159_Not Applicable", "title": "1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159)", - "catalog": "ALL STAC Catalog", + "catalog": "NOAA_NCEI STAC Catalog", "state_date": "1999-06-16", "end_date": "1999-07-18", "bbox": "-124, 45, -122, 49.5", @@ -253853,7 +253593,7 @@ { "id": "grinstedSBB-ECM-VIDEO", "title": "2km long Surface Conductivity Profile and video recording, Scharffenbergbotnen", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-11.042684, -74.57969, 11.11278, -74.566", @@ -253866,7 +253606,7 @@ { "id": "grinstedSBB-ECM-VIDEO", "title": "2km long Surface Conductivity Profile and video recording, Scharffenbergbotnen", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-11.042684, -74.57969, 11.11278, -74.566", @@ -254854,7 +254594,7 @@ { "id": "heard_dem_terrasar_1", "title": "A Digital Elevation Model of Heard Island derived from TerraSAR satellite imagery", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2009-10-31", "end_date": "2009-11-14", "bbox": "73.185, -53.266, 74.02, -52.931", @@ -254867,7 +254607,7 @@ { "id": "heard_dem_terrasar_1", "title": "A Digital Elevation Model of Heard Island derived from TerraSAR satellite imagery", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2009-10-31", "end_date": "2009-11-14", "bbox": "73.185, -53.266, 74.02, -52.931", @@ -255985,7 +255725,7 @@ { "id": "insects_subsaharanAfrica", "title": "A Checklist of the Insects of Subsaharan Africa", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2000-01-01", "end_date": "", "bbox": "13.68, -35.9, 33.98, -21.27", @@ -255998,7 +255738,7 @@ { "id": "insects_subsaharanAfrica", "title": "A Checklist of the Insects of Subsaharan Africa", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2000-01-01", "end_date": "", "bbox": "13.68, -35.9, 33.98, -21.27", @@ -256245,7 +255985,7 @@ { "id": "joughin_0631973", "title": "Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1980-01-01", "end_date": "2009-12-31", "bbox": "-124.8, -80.8, -86.7, -73.9", @@ -256258,7 +255998,7 @@ { "id": "joughin_0631973", "title": "Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1980-01-01", "end_date": "2009-12-31", "bbox": "-124.8, -80.8, -86.7, -73.9", @@ -257025,7 +256765,7 @@ { "id": "lake_erie_aug_2014_0", "title": "2014 Lake Erie measurements", - "catalog": "ALL STAC Catalog", + "catalog": "OB_DAAC STAC Catalog", "state_date": "2014-08-18", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -257038,7 +256778,7 @@ { "id": "lake_erie_aug_2014_0", "title": "2014 Lake Erie measurements", - "catalog": "OB_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2014-08-18", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -257467,7 +257207,7 @@ { "id": "law_dome_700yr_na_1", "title": "700 Year Record of Winter Sodium Concentrations (May June July averages) from Law Dome", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1301-01-01", "end_date": "1995-12-31", "bbox": "112.806946, -66.76972, 112.806946, -66.76972", @@ -257480,7 +257220,7 @@ { "id": "law_dome_700yr_na_1", "title": "700 Year Record of Winter Sodium Concentrations (May June July averages) from Law Dome", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1301-01-01", "end_date": "1995-12-31", "bbox": "112.806946, -66.76972, 112.806946, -66.76972", @@ -257597,7 +257337,7 @@ { "id": "lawdome_1979_field_data_1", "title": "Accumulation, Air Temperature, Barometric Pressure and Magnetic Readings from Law Dome and Wilkes Land, 1979", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1979-01-01", "end_date": "1979-12-31", "bbox": "110, -68, 115, -65", @@ -257610,7 +257350,7 @@ { "id": "lawdome_1979_field_data_1", "title": "Accumulation, Air Temperature, Barometric Pressure and Magnetic Readings from Law Dome and Wilkes Land, 1979", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1979-01-01", "end_date": "1979-12-31", "bbox": "110, -68, 115, -65", @@ -259742,7 +259482,7 @@ { "id": "mendocino_mathison_peak_nff_sr", "title": "Airborne laser swath mapping (ALSM) data of the San Andreas fault", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2003-02-05", "end_date": "2003-02-11", "bbox": "-123.81387, 39.31092, -123.720085, 39.333496", @@ -259755,7 +259495,7 @@ { "id": "mendocino_mathison_peak_nff_sr", "title": "Airborne laser swath mapping (ALSM) data of the San Andreas fault", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "2003-02-05", "end_date": "2003-02-11", "bbox": "-123.81387, 39.31092, -123.720085, 39.333496", @@ -261237,7 +260977,7 @@ { "id": "newcomb_bay_bathy_dem_1", "title": "A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "1997-02-01", "end_date": "2000-02-05", "bbox": "110.512, -66.282, 110.566, -66.256", @@ -261250,7 +260990,7 @@ { "id": "newcomb_bay_bathy_dem_1", "title": "A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1997-02-01", "end_date": "2000-02-05", "bbox": "110.512, -66.282, 110.566, -66.256", @@ -261588,7 +261328,7 @@ { "id": "nwrc_amphibianslowermiss", "title": "A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1999-09-05", "end_date": "1999-12-05", "bbox": "-91.95, 31.15, -91.25, 32.4333", @@ -261601,7 +261341,7 @@ { "id": "nwrc_amphibianslowermiss", "title": "A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1999-09-05", "end_date": "1999-12-05", "bbox": "-91.95, 31.15, -91.25, 32.4333", @@ -261978,7 +261718,7 @@ { "id": "pfynwaldgasexchange_1.0", "title": "2013-2020 gas exchange at Pfynwald", - "catalog": "ENVIDAT STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2021-01-01", "end_date": "2021-01-01", "bbox": "7.6105556, 46.3001905, 7.6163921, 46.3047564", @@ -261991,7 +261731,7 @@ { "id": "pfynwaldgasexchange_1.0", "title": "2013-2020 gas exchange at Pfynwald", - "catalog": "ALL STAC Catalog", + "catalog": "ENVIDAT STAC Catalog", "state_date": "2021-01-01", "end_date": "2021-01-01", "bbox": "7.6105556, 46.3001905, 7.6163921, 46.3047564", @@ -263434,7 +263174,7 @@ { "id": "robinson_adelie_colonies_1", "title": "Adelie penguin colonies in the Robinson Group, Antarctica: surveys conducted during 2005/06 and 2006/07", - "catalog": "ALL STAC Catalog", + "catalog": "AU_AADC STAC Catalog", "state_date": "2005-09-30", "end_date": "2007-03-31", "bbox": "63.233334, -67.51667, 63.85, -67.36667", @@ -263447,7 +263187,7 @@ { "id": "robinson_adelie_colonies_1", "title": "Adelie penguin colonies in the Robinson Group, Antarctica: surveys conducted during 2005/06 and 2006/07", - "catalog": "AU_AADC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2005-09-30", "end_date": "2007-03-31", "bbox": "63.233334, -67.51667, 63.85, -67.36667", @@ -264838,7 +264578,7 @@ { "id": "scarmarbin_1647", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea.", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -264851,7 +264591,7 @@ { "id": "scarmarbin_1647", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea.", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -264890,7 +264630,7 @@ { "id": "scarmarbin_1649", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida.", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -264903,7 +264643,7 @@ { "id": "scarmarbin_1649", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida.", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -265046,7 +264786,7 @@ { "id": "scarmarbin_1808", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997).", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -265059,7 +264799,7 @@ { "id": "scarmarbin_1808", "title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997).", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -265098,7 +264838,7 @@ { "id": "scarmarbin_ABBED", "title": "Admiralty Bay Benthos Biodiversity Database [SCAR-MarBIN]", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1906-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -265111,7 +264851,7 @@ { "id": "scarmarbin_ABBED", "title": "Admiralty Bay Benthos Biodiversity Database [SCAR-MarBIN]", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1906-01-01", "end_date": "", "bbox": "-180, -90, 180, 90", @@ -270987,7 +270727,7 @@ { "id": "urn:ogc:def:EOP:VITO:VGT_S10_1", "title": "10 Days Synthesis of SPOT VEGETATION Images (VGT-S10)", - "catalog": "FEDEO STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1998-04-01", "end_date": "2014-05-31", "bbox": "-180, -56, 180, 75", @@ -271000,7 +270740,7 @@ { "id": "urn:ogc:def:EOP:VITO:VGT_S10_1", "title": "10 Days Synthesis of SPOT VEGETATION Images (VGT-S10)", - "catalog": "ALL STAC Catalog", + "catalog": "FEDEO STAC Catalog", "state_date": "1998-04-01", "end_date": "2014-05-31", "bbox": "-180, -56, 180, 75", @@ -271117,7 +270857,7 @@ { "id": "usgs_nps_agatefossilbeds", "title": "Agate Fossil Beds National Monument, Field Plots Data Base for Vegetation Mapping", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-07-10", "end_date": "1995-08-15", "bbox": "-103.8, 42.40833, -103.7, 42.44167", @@ -271130,7 +270870,7 @@ { "id": "usgs_nps_agatefossilbeds", "title": "Agate Fossil Beds National Monument, Field Plots Data Base for Vegetation Mapping", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1995-07-10", "end_date": "1995-08-15", "bbox": "-103.8, 42.40833, -103.7, 42.44167", @@ -271143,7 +270883,7 @@ { "id": "usgs_nps_agatefossilbedsspatial", "title": "Agate Fossil Beds National Monument Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1995-07-29", "end_date": "1995-07-29", "bbox": "-103.8, 42.40833, -103.7, 42.44167", @@ -271156,7 +270896,7 @@ { "id": "usgs_nps_agatefossilbedsspatial", "title": "Agate Fossil Beds National Monument Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1995-07-29", "end_date": "1995-07-29", "bbox": "-103.8, 42.40833, -103.7, 42.44167", @@ -271390,7 +271130,7 @@ { "id": "usgs_npwrc_graywolves_Version 30APR2001", "title": "Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-168, 43.5, -75, 55", @@ -271403,7 +271143,7 @@ { "id": "usgs_npwrc_graywolves_Version 30APR2001", "title": "Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-168, 43.5, -75, 55", @@ -271429,7 +271169,7 @@ { "id": "usgs_npwrc_manitobaspiders_Version 16JUL97", "title": "A Checklist of Manitoba Spiders (Araneae) with Notes on Geographic Relationships", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-145.27, 37.3, -48.11, 87.61", @@ -271442,7 +271182,7 @@ { "id": "usgs_npwrc_manitobaspiders_Version 16JUL97", "title": "A Checklist of Manitoba Spiders (Araneae) with Notes on Geographic Relationships", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-145.27, 37.3, -48.11, 87.61", @@ -271507,7 +271247,7 @@ { "id": "usgsbrdasc00000004", "title": "Air quality monitoring protocol - Denali National Park and Preserve", - "catalog": "ALL STAC Catalog", + "catalog": "SCIOPS STAC Catalog", "state_date": "1992-01-01", "end_date": "1998-01-01", "bbox": "-149, 63, -148, 64", @@ -271520,7 +271260,7 @@ { "id": "usgsbrdasc00000004", "title": "Air quality monitoring protocol - Denali National Park and Preserve", - "catalog": "SCIOPS STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1992-01-01", "end_date": "1998-01-01", "bbox": "-149, 63, -148, 64", @@ -271585,7 +271325,7 @@ { "id": "usgsbrdnpwrcb00000013_Version 30SEP2002", "title": "A Bibliography of Fisheries Biology in North and South Dakota", - "catalog": "ALL STAC Catalog", + "catalog": "CEOS_EXTRA STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-104, 43, -96, 49", @@ -271598,7 +271338,7 @@ { "id": "usgsbrdnpwrcb00000013_Version 30SEP2002", "title": "A Bibliography of Fisheries Biology in North and South Dakota", - "catalog": "CEOS_EXTRA STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "1970-01-01", "end_date": "", "bbox": "-104, 43, -96, 49", @@ -272222,7 +271962,7 @@ { "id": "wbandimpacts_1", "title": "ACHIEVE W-Band Cloud Radar IMPACTS", - "catalog": "GHRC_DAAC STAC Catalog", + "catalog": "ALL STAC Catalog", "state_date": "2023-01-23", "end_date": "2023-03-01", "bbox": "-72.861, 41.368, -71.655, 42.268", @@ -272235,7 +271975,7 @@ { "id": "wbandimpacts_1", "title": "ACHIEVE W-Band Cloud Radar IMPACTS", - "catalog": "ALL STAC Catalog", + "catalog": "GHRC_DAAC STAC Catalog", "state_date": "2023-01-23", "end_date": "2023-03-01", "bbox": "-72.861, 41.368, -71.655, 42.268", diff --git a/nasa_cmr_catalog.tsv b/nasa_cmr_catalog.tsv index 7212b21..57f47e2 100644 --- a/nasa_cmr_catalog.tsv +++ b/nasa_cmr_catalog.tsv @@ -8936,12 +8936,12 @@ KOPRI-KPDC-00000616_1 Benthos image data from transect line, coastal of Jang Bog KOPRI-KPDC-00000617_1 Black Carbon data at Jang Bogo station, 2015 AMD_KOPRI STAC Catalog 2015-02-14 164.228333, -74.623333, 164.228333, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244298913-AMD_KOPRI.umm_json The aethalometer is used to measure atmospheric black carbon concentration every 5 minute over Jang Bogo station. Monitoring of Black Carbon concentration over Jang Bogo station proprietary KOPRI-KPDC-00000618_1 Soil and Fresh/Sea water samples from Barton Peninsular collected in 2015-2016 AMD_KOPRI STAC Catalog 2016-01-18 2016-02-21 -58.80624, -62.24449, -58.69884, -62.20679 https://cmr.earthdata.nasa.gov/search/concepts/C2244299282-AMD_KOPRI.umm_json Analysis of microbial community structure and diversity in soil and fresh/sea water samples from Barton Peninsular in Antarctica Investigation to the terrestrial biodiversity in Barton Peninsular for the monitoring by environment change proprietary KOPRI-KPDC-00000619_1 Environmental data about King George Islands collected in 2016 AMD_KOPRI STAC Catalog 2015-01-31 2015-02-21 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244299653-AMD_KOPRI.umm_json Microclimate data from King George Islands collected in 2016. Investigate relationship between biota proprietary -KOPRI-KPDC-00000620_1 2015-2016 JBS_micro-climate data_HOBO_soil temp.,PAR,air temp.,relative humidity ALL STAC Catalog 2015-02-09 2015-02-13 164.191389, -74.632806, 164.229972, -74.613 https://cmr.earthdata.nasa.gov/search/concepts/C2244300021-AMD_KOPRI.umm_json Micro-climate data set from The Jang Bogo Station in Terra Nova Bay collected during 1 year, 2015 proprietary KOPRI-KPDC-00000620_1 2015-2016 JBS_micro-climate data_HOBO_soil temp.,PAR,air temp.,relative humidity AMD_KOPRI STAC Catalog 2015-02-09 2015-02-13 164.191389, -74.632806, 164.229972, -74.613 https://cmr.earthdata.nasa.gov/search/concepts/C2244300021-AMD_KOPRI.umm_json Micro-climate data set from The Jang Bogo Station in Terra Nova Bay collected during 1 year, 2015 proprietary +KOPRI-KPDC-00000620_1 2015-2016 JBS_micro-climate data_HOBO_soil temp.,PAR,air temp.,relative humidity ALL STAC Catalog 2015-02-09 2015-02-13 164.191389, -74.632806, 164.229972, -74.613 https://cmr.earthdata.nasa.gov/search/concepts/C2244300021-AMD_KOPRI.umm_json Micro-climate data set from The Jang Bogo Station in Terra Nova Bay collected during 1 year, 2015 proprietary KOPRI-KPDC-00000621_1 Soil and Fresh water samples of the Antarctic Jang Bogo Station from Terra Nova Bay collected in 2016 AMD_KOPRI STAC Catalog 2016-01-07 2016-02-21 164.192056, -74.633361, 164.23725, -74.612056 https://cmr.earthdata.nasa.gov/search/concepts/C2244300323-AMD_KOPRI.umm_json Analysis of microbial community structure and diversity in soil and water samples of the Antarctic Jang Bogo Station from Terra Nova Bay in Antarctica Investigation to the terrestrial biodiversity in Terra Nova Bay for the monitoring by environment change proprietary KOPRI-KPDC-00000622_1 Sampling activity for identification between biotic (ciliate) and abiotic data from Barton Peninsular in Antarctica during the summer season in 2015/2016. AMD_KOPRI STAC Catalog 2015-12-04 2015-12-18 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300508-AMD_KOPRI.umm_json Identification of ciliate biota and environmental data of habitats from Antarctica (Barton Peninsular) Identification of the relationship between biotic sample and abiotic data proprietary -KOPRI-KPDC-00000623_1 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2015 AMD_KOPRI STAC Catalog 2015-03-01 2016-02-01 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244300569-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2015 Long term monitoring proprietary KOPRI-KPDC-00000623_1 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2015 ALL STAC Catalog 2015-03-01 2016-02-01 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244300569-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2015 Long term monitoring proprietary +KOPRI-KPDC-00000623_1 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2015 AMD_KOPRI STAC Catalog 2015-03-01 2016-02-01 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244300569-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2015 Long term monitoring proprietary KOPRI-KPDC-00000624_1 Lichen samples from South Shetland Islands collected in 2016 AMD_KOPRI STAC Catalog 2016-02-01 2016-02-21 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300624-AMD_KOPRI.umm_json Lichen samples from Barton Peninsular collected in 2016 Ecophysiological study of lichen proprietary KOPRI-KPDC-00000625_2 Climate Measurement Around the King Sejong Station, Antarctica in 2016 AMD_KOPRI STAC Catalog 2016-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244305947-AMD_KOPRI.umm_json Meteorological observation was carried out at the King Sejong Station in 2016. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, horizontal global solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report. To understand weather phenomema and to monitor at Antarctic Peninsula proprietary KOPRI-KPDC-00000626_1 Soil samples of the Antarctic King Sejong Station from Barton Peninsular collected in 2016 AMD_KOPRI STAC Catalog 2016-02-19 2016-02-19 -58.788436, -62.224964, -58.786192, -62.22415 https://cmr.earthdata.nasa.gov/search/concepts/C2244300652-AMD_KOPRI.umm_json Analysis of microbial community structure and diversity in soil samples of the Antarctic King Sejong Station from Barton Peninsular in Antarctica Investigation to the terrestrial biodiversity in Barton peninsular for the monitoring by environment change proprietary @@ -8992,8 +8992,8 @@ KOPRI-KPDC-00000670_1 Nano-SMPS data AMD_KOPRI STAC Catalog 2016-11-15 2016-11-1 KOPRI-KPDC-00000671_1 Meteorological data over the Northern Hemisphere using WRF model AMD_KOPRI STAC Catalog 2008-05-01 2008-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244299016-AMD_KOPRI.umm_json Meteorological data are obtained from the Weather Research and Forecasting (WRF) v3.4.1 model in conjunction with NCEP reanalyzed data during the 4 months from May to August 2008. The data are provides on an hourly basis. The WRF domain covers the areas of Northern Hemisphere with 54x54 km^2 horizontal resolution. Meteorological input data for 3D- chemistry and transport model modeling proprietary KOPRI-KPDC-00000672_1 Soil enzyme activities before and 1, 3 years after climate manipulation AMD_KOPRI STAC Catalog 2016-11-17 2016-11-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244299362-AMD_KOPRI.umm_json Activities of Hydrolases (beta-glucosidase, cellobiase, N-acetyl-glucosaminidase, and aminopeptidase) and phenol oxidase in soil under warming and precipitation increase Ancillary data including dissolved organic carbon (DOC) content, specific UV absorbance (SUVA), and carbon stable isotope ratio of plant leaves To determine the effects of climate change on soil enzyme activities that is related to decomposition proprietary KOPRI-KPDC-00000673_1 CO2 and CH4 fluxes before and 1, 3 years after climate manipulation AMD_KOPRI STAC Catalog 2016-11-17 2016-11-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244299684-AMD_KOPRI.umm_json Snapshot of CO2 and CH4 fluxes between soil and atmosphere under warming and precipitation increase Abundance of methanogen and methanotroph in soil under warming and precipitation increase To determine the effects of climate change on GHGs flux proprietary -KOPRI-KPDC-00000674_1 Air temperature and air humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2016 AMD_KOPRI STAC Catalog 2016-06-17 2016-06-27 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300011-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2015.06~2016.06) were collected. To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000674_1 Air temperature and air humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2016 ALL STAC Catalog 2016-06-17 2016-06-27 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300011-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2015.06~2016.06) were collected. To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary +KOPRI-KPDC-00000674_1 Air temperature and air humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2016 AMD_KOPRI STAC Catalog 2016-06-17 2016-06-27 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300011-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2015.06~2016.06) were collected. To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000675_1 Soil moisture and soil temperature data collected from climate manipulation plots in Cambridge Bay, Canada in 2016 AMD_KOPRI STAC Catalog 2016-06-17 2016-06-27 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300267-AMD_KOPRI.umm_json Micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 1 year (2015.06~2016.06) were collected. To monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000676_1 Permafrost core samples in Council, Alaska, USA in 2016 AMD_KOPRI STAC Catalog 2016-08-24 2016-08-31 -165, 64, -165, 64 https://cmr.earthdata.nasa.gov/search/concepts/C2244300480-AMD_KOPRI.umm_json Bulk and core samples from four sites of tussock and inter-tussock areas were collected in August. 2016. In the active layer, soil pits were made and bulk samples were collected from the face of opened pits. After describing soil profiles in the active layer, soil cores were acquired by SIPRI corer. In most sampling points, about 2-m deep soil samples were collected. To conduct laboratory soil incubation study proprietary KOPRI-KPDC-00000677_1 Eddy covariance data of Alaska permafrost site in 2016 AMD_KOPRI STAC Catalog 2016-04-21 2016-09-22 -163.705333, 64.843333, -163.705333, 64.843333 https://cmr.earthdata.nasa.gov/search/concepts/C2244300555-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from late April to September 2016 at Council, Alaska. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux over permafrost region proprietary @@ -9024,8 +9024,8 @@ KOPRI-KPDC-00000703_1 Soil physical and chemical properties in Council, Alaska i KOPRI-KPDC-00000704_1 Soil physical and chemical properties in Cambridge Bay, Canada in 2012 AMD_KOPRI STAC Catalog 2012-06-28 2012-07-14 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244296757-AMD_KOPRI.umm_json Physical and chemical properties of soil which were collected in 2012 (before climate manipulation) were analyzed. Soils from 0-5 and 5-10 cm depths were sampled. To monitor the changes in soil physical and chemical properties by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000705_1 Soil physical and chemical properties in Cambridge Bay, Canada in 2013 AMD_KOPRI STAC Catalog 2013-07-31 2013-08-09 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244296821-AMD_KOPRI.umm_json Physical and chemical properties of soil which were collected in 2013 after one year of climate manipulation were analyzed. Soils from 0-5 and 5-10 cm depths were sampled. To monitor the changes in soil physical and chemical properties by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000706_1 Soil physical and chemical properties in Cambridge Bay, Canada in 2015 AMD_KOPRI STAC Catalog 2015-07-30 2015-08-07 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244296843-AMD_KOPRI.umm_json Physical and chemical properties of soil which were collected in 2015 after three years of climate manipulation were analyzed. Soils from organic and mineral layers were sampled. To monitor the changes in soil physical and chemical properties by increasing temperature by open top chambers and increasing precipitation proprietary -KOPRI-KPDC-00000707_3 3D floorplan for CAD of Jang Bogo Station ALL STAC Catalog 2011-01-01 2011-01-31 164.228817, -74.624017, 164.228817, -74.624017 https://cmr.earthdata.nasa.gov/search/concepts/C2244296823-AMD_KOPRI.umm_json 3D floorplan for CAD of Jang Bogo Station To use for Numerical Weather Prediction Model proprietary KOPRI-KPDC-00000707_3 3D floorplan for CAD of Jang Bogo Station AMD_KOPRI STAC Catalog 2011-01-01 2011-01-31 164.228817, -74.624017, 164.228817, -74.624017 https://cmr.earthdata.nasa.gov/search/concepts/C2244296823-AMD_KOPRI.umm_json 3D floorplan for CAD of Jang Bogo Station To use for Numerical Weather Prediction Model proprietary +KOPRI-KPDC-00000707_3 3D floorplan for CAD of Jang Bogo Station ALL STAC Catalog 2011-01-01 2011-01-31 164.228817, -74.624017, 164.228817, -74.624017 https://cmr.earthdata.nasa.gov/search/concepts/C2244296823-AMD_KOPRI.umm_json 3D floorplan for CAD of Jang Bogo Station To use for Numerical Weather Prediction Model proprietary KOPRI-KPDC-00000708_1 Multiprotein-bridging factor 1c-like gene sequence from an Antarctic moss Polytrichastrum alpinum AMD_KOPRI STAC Catalog 2017-03-03 2017-03-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244297364-AMD_KOPRI.umm_json PaMBF1c (Multiprotein-bridging factor 1c-like) gene considered as an abiotic stimulus related genes from an Antarctic moss Polytrichastrum alpinum Investigation of molecular adaptation mechanism of the Antarcic moss to Antarctic environment proprietary KOPRI-KPDC-00000709_1 AMIGOS data in the Drygalski Ice Tongue, 2012 AMD_KOPRI STAC Catalog 2012-01-31 2012-12-31 164.294346, -75.412399, 165.17164, -75.348164 https://cmr.earthdata.nasa.gov/search/concepts/C2244295101-AMD_KOPRI.umm_json GPS, camera, and weather (air temperature, humidity, pressure, wind speed, wind direction) measurements from the AMIGOS systems in the Drygalski Ice Tongue Monitoring the movement and environmental change of Drygalski Ice Tongue proprietary KOPRI-KPDC-00000710_1 Hydro-Carbon Hydrate Accumulations in the Okhotsk Sea III AMD_KOPRI STAC Catalog 2006-05-24 2006-06-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295224-AMD_KOPRI.umm_json We had very remarkable results from the CHAOS-1 (2003) and CHAOS-2 (2005) project; lots of gas flares in the water column, many gas venting structures on the seafloor, gas hydrate samples including massive gas hydrate chunk (about 45 cm thick) near the seafloor, and gas hydrate-related structures in deep sub-bottom depth. These results encourage us to continue and expend the CHAOS project. Since the previous expedition focused on the relatively small area where gas hydrate-related phenomena has been known to be active, the basic aim of the CHAOS-III expedition is to improve understanding on gas hydrate-related phenomena in the Sea of Okhotsk in terms of multidisciplinary areas including geology, chemistry, oceanology and biology. 1. Detection of new gas hydrate-related structures including gas flares and gas venting structures. 2. Definition of the boundaries of the gas hydrate province 3. Mapping of the seafloor expressions related with gas hydrates and gas seepages using side-scan sonar. 4. Recognition of size, shape, and morphology of gas seepages on the seafloor. 5. High-resolution seismic investigation to examine inner structures and the gas hydrate stability condition in gas hydrate-baring sediments in detail. 6. Detection of gas flares in the water column emitted from gas seepages. 7. Study on hydrated water and dissociated gas sample 8. Chemistry of gas, gas hydrate, hydrate-forming fluids and carbonates including isotopic analysis. 9. Determination of methane concentration in the water column. 10. Underway survey to understand distribution of methane and dioxide in surface water and its controlling factor. 11. Detailed investigation of marine sedimentological environment in the gas hydrate area 12. Mechanism of formation-dissociation for gas-hydrates. 13. Interrelation of methane fluxes and mercury 14. Organic geochemical information related to the origin and composition of sedimentary organic matter. 15. Identification of biomarkers of microorganisms associated methane cycle. 16. Understanding of the composition of microbial community in gas hydrate environment proprietary @@ -9043,8 +9043,8 @@ KOPRI-KPDC-00000721_1 Lichen samples from South Shetland Islands collected in 20 KOPRI-KPDC-00000722_1 Lichen samples from Punta Arenas in Chile collected in 2014 AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -71.416667, -53.6, -71.416667, -53.6 https://cmr.earthdata.nasa.gov/search/concepts/C2244295591-AMD_KOPRI.umm_json Lichen samples from Chile collected in 2014. Locality, habitat information for 165 lichen samples Investigation to diversity, morphology and phylogeography in lichen proprietary KOPRI-KPDC-00000723_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2012 ALL STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295614-AMD_KOPRI.umm_json Yearly air temperature data from Barton Peninsular collected in 2012 Long term monitoring proprietary KOPRI-KPDC-00000723_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2012 AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295614-AMD_KOPRI.umm_json Yearly air temperature data from Barton Peninsular collected in 2012 Long term monitoring proprietary -KOPRI-KPDC-00000724_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013 AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295626-AMD_KOPRI.umm_json Yearly air temperauter and relative humidity data from Barton Peninsular collected in 2013 Long term monitoring proprietary KOPRI-KPDC-00000724_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013 ALL STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295626-AMD_KOPRI.umm_json Yearly air temperauter and relative humidity data from Barton Peninsular collected in 2013 Long term monitoring proprietary +KOPRI-KPDC-00000724_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013 AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295626-AMD_KOPRI.umm_json Yearly air temperauter and relative humidity data from Barton Peninsular collected in 2013 Long term monitoring proprietary KOPRI-KPDC-00000725_1 Water isotope composition in a GV7 3-m snow pit (2013-2014) AMD_KOPRI STAC Catalog 2014-10-10 2014-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295745-AMD_KOPRI.umm_json A 3 m snow pit was collected at GV7 (Antarctica) in the 2013-2014 summer season. Its water isotope composition (dD, d18O) was determined using cavity ringdown spectroscopy (PICARRO). To detect annual (seasonal) layering of snowpack. proprietary KOPRI-KPDC-00000726_1 NEEM project_ice core AMD_KOPRI STAC Catalog 2014-10-10 2014-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295831-AMD_KOPRI.umm_json We obtained ice cores after participating the North Greenland Eemian Ice Drilling program. We reconstruct the high-resolution ice record of a shift of mineral dust sources in response to climate transition between the Last Glacial Maximum(~25,000 yr BP) and Holocene(8,000 yr BP) by analyzing trace elements including rare earth elements from a Greenland NEEM ice core. proprietary KOPRI-KPDC-00000727_1 ARA05C BC AMD_KOPRI STAC Catalog 2014-10-10 2014-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244296153-AMD_KOPRI.umm_json ARA05C BC proprietary @@ -9081,8 +9081,8 @@ KOPRI-KPDC-00000756_1 Gravity cores from Antarctic Weddell Sea(JV10-GC01) AMD_KO KOPRI-KPDC-00000757_1 Physical and chemical properties of soil cores from Council, Alaska in 2016 AMD_KOPRI STAC Catalog 2017-06-01 2017-09-20 -163.7, 64.85, -163.7, 64.85 https://cmr.earthdata.nasa.gov/search/concepts/C2244299727-AMD_KOPRI.umm_json Several soil physical and chemical properties (moisture content, bulk density, C and N content, etc.) were analyzed from soil samples acquired in tussock and inter-tussock areas in August. 2016. To use for the basic information in the laboratory incubation study and to understand the site characteristics proprietary KOPRI-KPDC-00000758_1 Crystal structure and functional characterization of an isoaspartyl dipeptidase (CpsIadA) from Colwellia psychrerythraea strain 34H AMD_KOPRI STAC Catalog 2017-06-21 2017-06-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244300669-AMD_KOPRI.umm_json Isoaspartyl dipeptidase (IadA) is an enzyme that catalyzes the hydrolysis of an isoaspartyl dipeptide-like moiety, which can be inappropriately formed in proteins, between the β-carboxyl group side chain of Asp and the amino group of the following amino acid. Here, we have determined the structures of an isoaspartyl dipeptidase (CpsIadA) from Colwellia psychrerythraea, both ligand-free and that complexed with β-isoaspartyl lysine, at 1.85-Å and 2.33-Å resolution, respectively. In both structures, CpsIadA formed an octamer with two Zn ions in the active site. A structural comparison with Escherichia coli isoaspartyl dipeptidase (EcoIadA) revealed a major difference in the structure of the active site. For metal ion coordination, CpsIadA has a Glu166 residue in the active site, whereas EcoIadA has a post-translationally carbamylated-lysine 162 residue. Site-directed mutagenesis studies confirmed that the Glu166 residue is critical for CpsIadA enzymatic activity. This residue substitution from lysine to glutamate induces the protrusion of the β12-α8 loop into the active site to compensate for the loss of length of the side chain. In addition, the α3-β9 loop of CpsIadA adopts a different conformation compared to EcoIadA, which induces a change in the structure of the substrate-binding pocket. Despite CpsIadA having a different active-site residue composition and substrate-binding pocket, there is only a slight difference in CpsIadA substrate specificity compared with EcoIadA. Comparative sequence analysis classified IadA-containing bacteria and archaea into two groups based on the active-site residue composition, with Type I IadAs having a glutamate residue and Type II IadAs having a carbamylated-lysine residue. CpsIadA has maximal activity at pH 8±8.5 and 45ÊC, and was completely inactivated at 60ÊC. Despite being isolated from a psychrophilic bacteria, CpsIadA is thermostable probably owing to its octameric structure. This is the first conclusive description of the structure and properties of a Type I IadA. To determine the structures of an isoaspartyl dipeptidase IadA from a psychrophilic bacterium Colwellia psychrerythraea strain 34H (CpsIadA) in both the ligand-free form and that complexed with β-isoaspartyl lysine proprietary KOPRI-KPDC-00000759_1 X-ray diffraction data of EaEST AMD_KOPRI STAC Catalog 2016-04-03 2016-04-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244300649-AMD_KOPRI.umm_json A novel microbial esterase, EaEST, from a psychrophilic bacterium Exiguobacterium antarcticum B7, was identified and characterized. To our knowledge, this is the first report describing structural analysis and biochemical characterization of an esterase isolated from the genus Exiguobacterium. Crystal structure of EaEST, determined at a resolution of 1.9 Å, showed that the enzyme has a canonical α/β hydrolase fold with an α-helical cap domain and a catalytic triad consisting of Ser96, Asp220, and His248. Interestingly, the active site of the structure of EaEST is occupied by a peracetate molecule, which is the product of perhydrolysis of acetate. This result suggests that EaEST may have perhydrolase activity. The activity assay showed that EaEST has significant perhydrolase and esterase activity with respect to short-chain p-nitrophenyl esters (≤C8), naphthyl derivatives, phenyl acetate, and glyceryl tributyrate. However, the S96A single mutant had low esterase and perhydrolase activity. Moreover, the L30A mutant showed low levels of protein expression and solubility as well as preference for different substrates. On conducting an enantioselectivity analysis using R- and S-methyl-3-hydroxy-2-methylpropionate, a preference for R-enantiomers was observed. Surprisingly, immobilized EaEST was found to not only retain 200% of its initial activity after incubation for 1 h at 80°C, but also retained more than 60% of its initial activity after 20 cycles of reutilization. This research will serve as basis for future engineering of this esterase for biotechnological and industrial applications. Our goal was to identify a novel cold-active esterase from a polar microorganism. We identified and characterized a novel esterase, EaEST, from a psychrophilic bacterium Exiguobacterium antarcticum B7. Further structural and functional analysis indicated that EaEST had dual activity of a perhydrolase and an esterase. It is known that perhydrolysis is a side activity of esterases and it may be useful in industrial and organic synthesis. Moreover, the peracetate-bound EaEST structure reported in our study provides the first snapshot of the peracetate binding mode, and a comparison of the structure of EaEST with that of PfEST (PDB code 3HI4) reveals a comprehensive structural basis for the conformational changes of this enzyme induced by binding of different substrates. proprietary -KOPRI-KPDC-00000760_1 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017 AMD_KOPRI STAC Catalog 2016-12-28 2017-02-15 153.936483, -75.389942, 159.216086, -75.059956 https://cmr.earthdata.nasa.gov/search/concepts/C2244300682-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation ice surface / bed elevation proprietary KOPRI-KPDC-00000760_1 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017 ALL STAC Catalog 2016-12-28 2017-02-15 153.936483, -75.389942, 159.216086, -75.059956 https://cmr.earthdata.nasa.gov/search/concepts/C2244300682-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation ice surface / bed elevation proprietary +KOPRI-KPDC-00000760_1 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017 AMD_KOPRI STAC Catalog 2016-12-28 2017-02-15 153.936483, -75.389942, 159.216086, -75.059956 https://cmr.earthdata.nasa.gov/search/concepts/C2244300682-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation ice surface / bed elevation proprietary KOPRI-KPDC-00000761_1 Comparison of diversity of ciliate between Barton peninsula in Antarctica and Korea using NGS technique. AMD_KOPRI STAC Catalog 2017-05-04 2017-06-18 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300615-AMD_KOPRI.umm_json Identification of ciliate diversity from Korea and Antarctica (Barton Peninsular) Comparison of both data to know the specific ciliate in Antarctica proprietary KOPRI-KPDC-00000762_1 Greenland NEEM 2009S1 shallow ice core trace elements concentrations AMD_KOPRI STAC Catalog 2017-09-27 2017-09-27 -51.06, 77.45, -51.06, 77.45 https://cmr.earthdata.nasa.gov/search/concepts/C2244300703-AMD_KOPRI.umm_json The first high resolution records of atmospherc trace metals for 1711~1969 were recovered from Greenland NEEM shallow ice core together with ions records. These records reveal increases in various atmospheric metals since the Industrial Revolution. Also, the comparion between these records and those from other Greenland ice cores represents regional differences in anthropogenic contributions. Researches for changes in atmospheric trace element over Greenland after the Industrial Revolution and contributions from natural/anthropogenic sources proprietary KOPRI-KPDC-00000763_1 CPS2 AMD_KOPRI STAC Catalog 2013-02-20 2013-02-27 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244300790-AMD_KOPRI.umm_json CPS2 is termed as cell-protection substances 2 capable of protection of the cells and lowering freezing points below melting points. Antarctic freshwater green microalga, Chloromonas sp. was reported to produce and secrete CPS2. CPS2 genes will be utilized to protect the skin and tissue cells by applying any valuable products. proprietary @@ -9099,8 +9099,8 @@ KOPRI-KPDC-00000771_1 Italian Seismic Line 2017 AMD_KOPRI STAC Catalog 2017-02-0 KOPRI-KPDC-00000772_1 List of marine benthic invertebrate animal species around King Sejong Station (2017) AMD_KOPRI STAC Catalog 2017-09-29 2017-09-29 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295664-AMD_KOPRI.umm_json Survey of marine benthic invertebrate biota by diving around King Sejong Station Diversity of marine benthic invertebrates proprietary KOPRI-KPDC-00000773_2 Comparison of diversity of ciliate between Jang Bogo Station in Antarctica and Korea using NGS technique (Site261_2014) AMD_KOPRI STAC Catalog 2021-08-02 2021-08-02 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244305169-AMD_KOPRI.umm_json Identification of ciliate diversity from Korea and Antarctica (Jang Bogo Station) Comparison of both data to know the specific ciliate in Antarctica proprietary KOPRI-KPDC-00000774_1 ANA07C Multi-Channel Seismic Survey Lines AMD_KOPRI STAC Catalog 2017-02-04 2017-02-05 166, -75, 170, -74.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244295683-AMD_KOPRI.umm_json Multi-Channel seismic data were collected during the 2016-2017 ANA07C cruise in the Ross Sea, Antarctic Ocean The major purpose of this survey is to investigate stratography and the structure of sediments across the Terror Rift, Antarctica. proprietary -KOPRI-KPDC-00000775_1 Aerosol Size Distribution from King Sejong Station collected in 2010-2016. ALL STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298745-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary KOPRI-KPDC-00000775_1 Aerosol Size Distribution from King Sejong Station collected in 2010-2016. AMD_KOPRI STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298745-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary +KOPRI-KPDC-00000775_1 Aerosol Size Distribution from King Sejong Station collected in 2010-2016. ALL STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298745-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary KOPRI-KPDC-00000776_1 Meterological data at BearPeninsula in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-04-11 -115.56512, -74.1877, -115.56512, -74.1877 https://cmr.earthdata.nasa.gov/search/concepts/C2244300521-AMD_KOPRI.umm_json Meterological observation at BearPeninsula DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change in Antarctic region. Primary climate factors including solar radiation wind speed and direction, air temperature, pressure and relative humidity has been monitored using automatic weather monitoring system at Bear Peninsula. One hourly averaged data are stored at a data logger and an Argos Satellite transmitter is used to transmit daily data. The objectives of this monitoring are to record the past and current climate change through continuous operation of AWS, and to understand characteristics of meteorological phenomena at Bear Peninsula. Monitoring on meteorology at Bear Peninsula. proprietary KOPRI-KPDC-00000777_2 Fossils from North Greenland (2016) AMD_KOPRI STAC Catalog 2016-07-25 2016-08-12 -42.228333, 82.793333, -42.228333, 82.793333 https://cmr.earthdata.nasa.gov/search/concepts/C2244305474-AMD_KOPRI.umm_json This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 600 kg of fossils were collected during 2016 expedition. The Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion. proprietary KOPRI-KPDC-00000778_1 GV7_S2_dust data AMD_KOPRI STAC Catalog 2017-10-10 2017-10-10 158.85, -70.683333, 158.85, -70.683333 https://cmr.earthdata.nasa.gov/search/concepts/C2244300372-AMD_KOPRI.umm_json GV7_S2_dust data MS4_GV7 S22 dust data proprietary @@ -9206,8 +9206,8 @@ KOPRI-KPDC-00000875_1 Eddy covariance data at DASAN Station in 2016 AMD_KOPRI ST KOPRI-KPDC-00000876_1 Eddy covariance data at DASAN Station in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-09-19 11.865833, 78.921944, 11.865833, 78.921944 https://cmr.earthdata.nasa.gov/search/concepts/C2244295705-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2017 at Ny-Alesund where Arctic DASAN station is located. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux at DASAN Station proprietary KOPRI-KPDC-00000877_1 CCN(Cloud Condensation Nuclei) data at Zeppelin station in January-November, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-11-30 11.888889, 78.906667, 11.888889, 78.906667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295663-AMD_KOPRI.umm_json The CCNC(Cloud Condensation Nuclei Counter) is used to measure atmospheric CCN(Cloud Condensation Nuclei) concentration over Zeppelin station. Monitoring of CCN(Cloud Condensation Nuclei) concentration over Zeppelin station proprietary KOPRI-KPDC-00000878_1 LED NDVI measured at Council site of Alaska in 2016 AMD_KOPRI STAC Catalog 2017-12-05 2017-12-05 -163.711, 64.844, -163.711, 64.844 https://cmr.earthdata.nasa.gov/search/concepts/C2244301187-AMD_KOPRI.umm_json A vegetation index NDVI was measured during growing season at the Council site, 70-miles northeast from the Nome, Alaska. The sensor was developed by Seoul National University (Prof. Young-Ryul Ryu) and provided for in-situ installation. The sensor is composed of one pair of upward/downward looking LEDs to obtain reflectivity in each bandwidth. We can calculate NDVI (normalized difference vegetation index) using this sensor to monitor vegetation activity. To monitor high-temporal variation of vegetaion activity at permafrost region, west Alaska. proprietary -KOPRI-KPDC-00000879_1 Air temperature and air humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2017 ALL STAC Catalog 2016-06-19 2017-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300975-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2016.06~2017.06) were collected. To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000879_1 Air temperature and air humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2017 AMD_KOPRI STAC Catalog 2016-06-19 2017-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300975-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2016.06~2017.06) were collected. To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary +KOPRI-KPDC-00000879_1 Air temperature and air humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2017 ALL STAC Catalog 2016-06-19 2017-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300975-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2016.06~2017.06) were collected. To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000880_1 Soil moisture and soil temperature data collected from climate manipulation plots in Cambridge Bay, Canada in 2017 AMD_KOPRI STAC Catalog 2016-06-19 2017-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300926-AMD_KOPRI.umm_json Micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 1 year (2016.06~2017.06) were collected. To monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00000881_1 CO2 auto-chamber data of Council site in 2017 AMD_KOPRI STAC Catalog 2016-09-22 2017-09-13 -163.705333, 64.843333, -163.705333, 64.843333 https://cmr.earthdata.nasa.gov/search/concepts/C2244301146-AMD_KOPRI.umm_json CO2 fluxes at dominant vegetation types were measured using custom-made auto-chamber system during summertime in 2017 at Council site, Alaska. Auto-chamber system was consisted of a gas-analyzer (LI-840) connected with 15-chambers, which are controlled by electronic board with 225-second opening for each chamber. As a result, whole-chamber cycle is completed in a hour. CO2 data is recorded every 10-second by CR1000 logger. Also, soil temperature and moisture at 5-cm depth at each chamber were recorded at 10-min interval. To monitor and understand CO2 flux of dominant vegetation types of Alaska permafrost site. proprietary KOPRI-KPDC-00000882_1 Upper air observation data at Jang Bogo Station in 2016 AMD_KOPRI STAC Catalog 2016-02-01 2016-12-21 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244300540-AMD_KOPRI.umm_json Regular upper air observation is made once a day at 00 UTC from February to November by using auto and manual lauch of radio sondes. Data of pressure, temperature, relative humidity, wind speed and wind direction are sampled and recorded every two-second. The minimum observation height is over 20 km. Monitoring of changes in meteorological variables with altitude over Jang Bogo station proprietary @@ -9274,8 +9274,8 @@ KOPRI-KPDC-00000942_1 Moderate Resolution Imaging Spectroradiometer in Antarctic KOPRI-KPDC-00000943_1 Moderate Resolution Imaging Spectroradiometer in Arctic (MODIS) / Aqua, 2014 AMD_KOPRI STAC Catalog 2014-01-01 2014-12-31 180, 60, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244297144-AMD_KOPRI.umm_json MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002. Derive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans. proprietary KOPRI-KPDC-00000944_1 Moderate Resolution Imaging Spectroradiometer in Arctic (MODIS) / Aqua, 2015 AMD_KOPRI STAC Catalog 2015-01-01 2015-12-31 180, 60, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244297192-AMD_KOPRI.umm_json MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002. Derive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans. proprietary KOPRI-KPDC-00000945_1 Moderate Resolution Imaging Spectroradiometer in Antarctic (MODIS) / Aqua, 2015 AMD_KOPRI STAC Catalog 2015-01-01 2015-12-31 180, -90, -180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2244297229-AMD_KOPRI.umm_json MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002. Derive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans. proprietary -KOPRI-KPDC-00000946_1 Advanced TIROS Operational Vertical Sounder (ATOVS) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-03-01 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297301-AMD_KOPRI.umm_json The Advanced TIROS Operational Vertical Sounder (ATOVS) consists of High Resolution Infrared Radiation Sounder (HIRS), the Advanced Microwave Sounding Unit-A (AMSU-A) and AMSU-B for retrieving temperature, humidity and ozone sounding in all weather conditions. The data were obtained around the Jang Bogo Station in Antarctic. To derive products including cloud, ozone, surface elevation, surface pressure, temperature around the Jang Bogo Station. proprietary KOPRI-KPDC-00000946_1 Advanced TIROS Operational Vertical Sounder (ATOVS) around the Jang Bogo Station, 2015-2016 ALL STAC Catalog 2015-03-01 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297301-AMD_KOPRI.umm_json The Advanced TIROS Operational Vertical Sounder (ATOVS) consists of High Resolution Infrared Radiation Sounder (HIRS), the Advanced Microwave Sounding Unit-A (AMSU-A) and AMSU-B for retrieving temperature, humidity and ozone sounding in all weather conditions. The data were obtained around the Jang Bogo Station in Antarctic. To derive products including cloud, ozone, surface elevation, surface pressure, temperature around the Jang Bogo Station. proprietary +KOPRI-KPDC-00000946_1 Advanced TIROS Operational Vertical Sounder (ATOVS) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-03-01 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297301-AMD_KOPRI.umm_json The Advanced TIROS Operational Vertical Sounder (ATOVS) consists of High Resolution Infrared Radiation Sounder (HIRS), the Advanced Microwave Sounding Unit-A (AMSU-A) and AMSU-B for retrieving temperature, humidity and ozone sounding in all weather conditions. The data were obtained around the Jang Bogo Station in Antarctic. To derive products including cloud, ozone, surface elevation, surface pressure, temperature around the Jang Bogo Station. proprietary KOPRI-KPDC-00000947_1 Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-03-03 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297622-AMD_KOPRI.umm_json The AVHRR is a six channel scanning radiometer providing three solar channels in the visible-near infrared region and three thermal infrared channels and obtained data around the Jang Bogo Station in Antarctic. To derive products including cloud cover, surface temperature, land-water boundaries, snow and ice detection around the Jang Bogo Station. proprietary KOPRI-KPDC-00000947_1 Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016 ALL STAC Catalog 2015-03-03 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297622-AMD_KOPRI.umm_json The AVHRR is a six channel scanning radiometer providing three solar channels in the visible-near infrared region and three thermal infrared channels and obtained data around the Jang Bogo Station in Antarctic. To derive products including cloud cover, surface temperature, land-water boundaries, snow and ice detection around the Jang Bogo Station. proprietary KOPRI-KPDC-00000948_1 Moderate Resolution Imaging Spectroradiometer (MODIS) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-03-30 2016-02-03 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297939-AMD_KOPRI.umm_json MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data around the Jang Bogo Station in Antarctic. To derive products including vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans around the Jang Bogo Station. proprietary @@ -9327,8 +9327,8 @@ KOPRI-KPDC-00000995_1 Sea Ice from SW of James Ross Island AMD_KOPRI STAC Catalo KOPRI-KPDC-00000996_1 Sea Ice from W of James Ross Island AMD_KOPRI STAC Catalog 2018-04-20 -58.543322, -64.147707, -58.543322, -64.147707 https://cmr.earthdata.nasa.gov/search/concepts/C2244299635-AMD_KOPRI.umm_json 2018 W of James Ross Island Sea Ice, Antarctic Climate change observation proprietary KOPRI-KPDC-00000997_1 Identification of growth rate of Antarctic terrestrial ciliates based on temperature around King Sejong Station (2017/18) AMD_KOPRI STAC Catalog 2017-12-06 2018-01-24 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300273-AMD_KOPRI.umm_json Identification of growth rate of ciliates from Barton Peninsular, South Shetland Islands in Antarctica To show the growth rate of ciliates based on temperature in Antarctica proprietary KOPRI-KPDC-00000998_2 ANA08C Marine Magnetic Data AMD_KOPRI STAC Catalog 2018-03-13 164.4, -75.5, 165.9, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244301255-AMD_KOPRI.umm_json Marine magnetic data were collected during the ANA08C Expedition in the 2017-2018 austral summer in the Ross Sea, Antarctica proprietary -KOPRI-KPDC-00000999_2 2018 Multibeam bathymetry data in the Ross Sea, Antarctica ALL STAC Catalog 2018-03-13 164.4, -75.5, 165.9, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244301265-AMD_KOPRI.umm_json Multibeam bathymetry data were collected during the ANA08C Expedition in the Ross Sea, Antarctica proprietary KOPRI-KPDC-00000999_2 2018 Multibeam bathymetry data in the Ross Sea, Antarctica AMD_KOPRI STAC Catalog 2018-03-13 164.4, -75.5, 165.9, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244301265-AMD_KOPRI.umm_json Multibeam bathymetry data were collected during the ANA08C Expedition in the Ross Sea, Antarctica proprietary +KOPRI-KPDC-00000999_2 2018 Multibeam bathymetry data in the Ross Sea, Antarctica ALL STAC Catalog 2018-03-13 164.4, -75.5, 165.9, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244301265-AMD_KOPRI.umm_json Multibeam bathymetry data were collected during the ANA08C Expedition in the Ross Sea, Antarctica proprietary KOPRI-KPDC-00001000_2 Sub-bottom profile data in the Ross Sea, Antarctica AMD_KOPRI STAC Catalog 2018-03-13 164.4, -75.5, 165.9, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244301275-AMD_KOPRI.umm_json Sub-bottom profile (SBP) data were collected during the ANA08C Expedition in the Ross Sea, Antarctica proprietary KOPRI-KPDC-00001001_1 De novo transcriptome assembly of the moss Sanionia uncinata in response to relative water content reduction in the Antarctic natural habitat AMD_KOPRI STAC Catalog 2016-03-21 -58.771667, -62.220278, -58.771667, -62.220278 https://cmr.earthdata.nasa.gov/search/concepts/C2244300607-AMD_KOPRI.umm_json Despite the importance, the molecular responses of S. uncinata related to the decrease in water availability in the long-term future have not yet been identified. To explain physiological and molecular change induced by dehydration, we performed de novo transcriptome assembly. Using the short-read assembly program, 32,100 unigenes were assembled with an N50 of 1,296 bp. proprietary KOPRI-KPDC-00001002_1 EGRIP SP TE AMD_KOPRI STAC Catalog 2018-06-01 2018-06-30 -35.9915, 75.6268, -35.9915, 75.6268 https://cmr.earthdata.nasa.gov/search/concepts/C2244300642-AMD_KOPRI.umm_json Greenland EastGRIP 2017 snow pit trace metals Investigation of seasonal changes in atmospheric trace metals over northeastern Greenland proprietary @@ -9337,8 +9337,8 @@ KOPRI-KPDC-00001004_1 Stable water isotope composition of the Styx ice core (v3) KOPRI-KPDC-00001005_1 GV7_S2_Pu AMD_KOPRI STAC Catalog 2018-10-04 2018-10-04 158.85, -70.683333, 158.85, -70.683333 https://cmr.earthdata.nasa.gov/search/concepts/C2244300677-AMD_KOPRI.umm_json "Atmospheric nuclear explosions during the period from the 1940s to the 1980s are the major anthropogenic source of plutonium (Pu) in the environment. In this work, we analyzed fg g-1 levels of artificial Pu, released predominantly by atmospheric nuclear weapons tests. We measured 351 samples which collected a 78 m-depth fire core at the site of GV7 (S 70°41 ´17.1"", E 158°51´48.9"", 1950 m a.s.l.), Northern Victoria Land, East Antarctica. To determine the Pu concentration in the samples, we used an inductively coupled plasma sector field mass spectrometry coupled with an Apex high-efficiency sample introduction system, which has the advantages of small sample consumption and simple sample preparation." proprietary KOPRI-KPDC-00001006_1 Styx_Pu AMD_KOPRI STAC Catalog 2018-10-04 2018-10-04 163.683333, -73.85, 163.683333, -73.85 https://cmr.earthdata.nasa.gov/search/concepts/C2244300691-AMD_KOPRI.umm_json Atmospheric nuclear explosions during the period from the 1940s to the 1980s are the major anthropogenic source of plutonium (Pu) in the environment. In this work, we analyzed fg g-1 levels of artificial Pu, released predominantly by atmospheric nuclear weapons tests. proprietary KOPRI-KPDC-00001007_1 Ubi:DaGolS2, rice transgenic line overexpressing DaGolS2 from Deschampsia antarctica AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 -58.791222, -62.2365, -58.719472, -62.224972 https://cmr.earthdata.nasa.gov/search/concepts/C2244300474-AMD_KOPRI.umm_json Deschampsia antarctica is an Antarctic hairgrass that grows on the west coast of the Antarctic peninsula. In this report, we have identified and characterized DaGolS2, that is a member of the galactinol synthase group 2. To investigate its possible cellular role in cold tolerance, a transgenic rice system was employed. DaGolS2-overexpressing transgenic rice plants (Ubi:DaGolS2) exhibited markedly increased tolerance to cold and drought stress compared to wild-type plants without growth defects; however, overexpression of DaGolS2 exerted little effect on tolerance to salt stress. These results suggest that overexpression of DaGolS2 directly and indirectly confers enhanced tolerance to cold and drought stresses. proprietary -KOPRI-KPDC-00001008_2 2018 KOPRI North Greenland Sirius Passet collection 1 AMD_KOPRI STAC Catalog 2021-08-02 2021-08-02 -42.228333, 82.793333, -42.228333, 82.793333 https://cmr.earthdata.nasa.gov/search/concepts/C2244301304-AMD_KOPRI.umm_json This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016-2018 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 2000 kg of fossils were collected during 2016-2018 expedition. The Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion. proprietary KOPRI-KPDC-00001008_2 2018 KOPRI North Greenland Sirius Passet collection 1 ALL STAC Catalog 2021-08-02 2021-08-02 -42.228333, 82.793333, -42.228333, 82.793333 https://cmr.earthdata.nasa.gov/search/concepts/C2244301304-AMD_KOPRI.umm_json This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016-2018 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 2000 kg of fossils were collected during 2016-2018 expedition. The Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion. proprietary +KOPRI-KPDC-00001008_2 2018 KOPRI North Greenland Sirius Passet collection 1 AMD_KOPRI STAC Catalog 2021-08-02 2021-08-02 -42.228333, 82.793333, -42.228333, 82.793333 https://cmr.earthdata.nasa.gov/search/concepts/C2244301304-AMD_KOPRI.umm_json This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016-2018 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 2000 kg of fossils were collected during 2016-2018 expedition. The Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion. proprietary KOPRI-KPDC-00001009_2 Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations, Arctic, 2016 AMD_KOPRI STAC Catalog 2016-01-01 2016-12-31 180, 30, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244306767-AMD_KOPRI.umm_json This Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations (NRTSI) data set provides sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. Starting with 1 April 2016, data from DMSP-F18 are used. The SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument. SSMIS data are received daily from the Comprehensive Large Array-data Stewardship System (CLASS) at the National Oceanic and Atmospheric Administration (NOAA) and are gridded onto a polar stereographic grid. Investigators generate sea ice concentrations from these data using the NASA Team algorithm. proprietary KOPRI-KPDC-00001010_2 Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations, Arctic, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 180, 30, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244306790-AMD_KOPRI.umm_json This Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations (NRTSI) data set provides sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. Starting with 1 April 2016, data from DMSP-F18 are used. The SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument. SSMIS data are received daily from the Comprehensive Large Array-data Stewardship System (CLASS) at the National Oceanic and Atmospheric Administration (NOAA) and are gridded onto a polar stereographic grid. Investigators generate sea ice concentrations from these data using the NASA Team algorithm. proprietary KOPRI-KPDC-00001011_2 Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1, Arctic, 2006 AMD_KOPRI STAC Catalog 2006-01-01 2006-12-31 180, 30.98, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244306537-AMD_KOPRI.umm_json This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km. This product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors. proprietary @@ -9464,8 +9464,8 @@ KOPRI-KPDC-00001125_4 NanoSMPS particle number concentration in 2017 AMD_KOPRI S KOPRI-KPDC-00001126_5 NanoSMPS particle number concentration in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 11.894, 78.908, 11.894, 78.908 https://cmr.earthdata.nasa.gov/search/concepts/C2244301557-AMD_KOPRI.umm_json The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region. proprietary KOPRI-KPDC-00001127_3 NanoSMPS particle number concentration in 2016 AMD_KOPRI STAC Catalog 2016-10-01 2016-12-31 11.894, 78.908, 11.894, 78.908 https://cmr.earthdata.nasa.gov/search/concepts/C2244301534-AMD_KOPRI.umm_json The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region. proprietary KOPRI-KPDC-00001128_1 Soil moisture and soil temperature data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 AMD_KOPRI STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300912-AMD_KOPRI.umm_json Micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06 ~ 2018. 06) were collected. To monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation proprietary -KOPRI-KPDC-00001129_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 ALL STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300871-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06~2018.06) were collected To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00001129_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 AMD_KOPRI STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300871-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06~2018.06) were collected To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary +KOPRI-KPDC-00001129_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 ALL STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300871-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06~2018.06) were collected To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary KOPRI-KPDC-00001130_1 Atmospheric DMS mixing ratio measured from Storhofdi, Iceland in 2017-2018. AMD_KOPRI STAC Catalog 2017-04-04 2018-08-18 -20.29, 63.4, -20.29, 63.4 https://cmr.earthdata.nasa.gov/search/concepts/C2244300807-AMD_KOPRI.umm_json Custum-made DMS analyzer was installed at the Storhofdi observatory, Iceland, and monitored the atmospheric DMS mixing ratio in 2017-208. Analyzing in-situ DMs mixing ratio Storhofdi, Iceland. proprietary KOPRI-KPDC-00001131_1 NDVI data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 AMD_KOPRI STAC Catalog 2018-07-04 2018-09-05 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300832-AMD_KOPRI.umm_json NDVI(Normalized Difference Vegetation Index) from climate manipulation (increasing snow cover) plot for 2 months (2018.7.4 ~ 9.5) were collected proprietary KOPRI-KPDC-00001132_1 Eddy covariance data of Canada permafrost site in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 -105.058917, 69.13025, -105.058917, 69.13025 https://cmr.earthdata.nasa.gov/search/concepts/C2244301100-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2017 at Cambridge bay, Canada. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer and open-path CH4 gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux over permafrost region proprietary @@ -9493,8 +9493,8 @@ KOPRI-KPDC-00001153_2 Profile of Meteorological data at the Jang Bogo Station, A KOPRI-KPDC-00001154_2 Data for observation and prediction to model responses of Antarctic hairgrass AMD_KOPRI STAC Catalog 2017-01-01 2018-12-31 -58.771667, -62.220278, -58.771667, -62.220278 https://cmr.earthdata.nasa.gov/search/concepts/C2244303875-AMD_KOPRI.umm_json In order to model the distribution and physiological response of Antarctic hairgrass, we obtained 2,127 data points (Po, average 118.7) of distributions and physiological response observations in the vicinity of Sejong Station, King George Island, Antarctica in 2017. In addition, we obtained 2,127 data points for this species. With these data, the prediction accuracy of the model acquired in 2018 was 83.3%. proprietary KOPRI-KPDC-00001155_2 Data for observation and prediction to model responses of Antarctic pearlwort AMD_KOPRI STAC Catalog 2016-01-01 2017-12-31 -58.771667, -62.220278, -58.771667, -62.220278 https://cmr.earthdata.nasa.gov/search/concepts/C2244303548-AMD_KOPRI.umm_json In order to model the distribution and physiological response of Antarctic pearlwort, we obtained 1,150 data points (Po, average 96.7) of distributions and physiological response observations in the vicinity of Sejong Station, King George Island, Antarctica in 2016. In addition, we obtained 1,150 data points for this species. With these data, the prediction accuracy of the model acquired in 2017 was 78.84%. proprietary KOPRI-KPDC-00001156_4 Neutral wind and temperature from FPI, King Sejong Station, Antarctica, 2018 AMD_KOPRI STAC Catalog 2018-03-01 2018-10-31 -58.7766, -62.2206, -58.7766, -62.2206 https://cmr.earthdata.nasa.gov/search/concepts/C2244306106-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at KSS station, Antarctica Study of the atmosphere wave activities in the upper atmosphere in the southern high-latitude proprietary -KOPRI-KPDC-00001157_3 All-Sky airglow image, Jang Bogo Station, Antarctica, 2017 ALL STAC Catalog 2017-01-01 2017-10-01 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306682-AMD_KOPRI.umm_json Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at Jang Bogo Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001157_3 All-Sky airglow image, Jang Bogo Station, Antarctica, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-10-01 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306682-AMD_KOPRI.umm_json Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at Jang Bogo Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary +KOPRI-KPDC-00001157_3 All-Sky airglow image, Jang Bogo Station, Antarctica, 2017 ALL STAC Catalog 2017-01-01 2017-10-01 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306682-AMD_KOPRI.umm_json Airglow (OI 557.7nm, OI 630.0nm, Na 589.7nm, OH Meinel band) image measured from all-sky camera at Jang Bogo Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001158_1 Upper O3 observation data at Jang Bogo Station in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-09-30 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244297194-AMD_KOPRI.umm_json Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second. Monitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station. proprietary KOPRI-KPDC-00001159_1 O3 observation data using BREWER at Jang Bogo Station in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-09-30 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244299602-AMD_KOPRI.umm_json The Brewer Ozone spectroscopy (BREWER) accurately measures the amount of light from a certain wavelength (286.5 nm to 363 nm) that absorbs ozone and is a total of ozone. Monitoring of changes in meteorological variables (O3) at Jang Bogo station. proprietary KOPRI-KPDC-00001160_2 Upper air observation data at Jang Bogo Station during YOPP-SH(Year of Polar Prediction-Southern Hemisphere) in 2018/19 AMD_KOPRI STAC Catalog 2018-11-16 2019-02-11 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244296754-AMD_KOPRI.umm_json Upper air observation is made once a day at 1800UTC during YOPP-SH (from 16 NOV. 2018 and 11 FEB 2019) by using auto and manual lauch of radio sondes. Data of pressure, temperature, relative humidity, wind speed and wind direction are sampled and recorded every a second. The minimum observation height is over 20 km. Monitoring of changes in meteorological variables with altitude over Jang Bogo station proprietary @@ -9545,15 +9545,15 @@ KOPRI-KPDC-00001210_1 X-ray diffraction data of CpsORN AMD_KOPRI STAC Catalog 20 KOPRI-KPDC-00001211_1 X-ray diffraction data of SfSFGH AMD_KOPRI STAC Catalog 2017-07-28 2017-07-28 129.316333, 36.0235, 129.316333, 36.0235 https://cmr.earthdata.nasa.gov/search/concepts/C2244301218-AMD_KOPRI.umm_json A novel cold-active S-formylglutathione hydrolase (SfSFGH) from Shewanella frigidimarina, composed of 279 amino acids with a molecular mass of ~31.0 kDa was identified, expressed, and characterized. Sequence analysis of SfSFGH revealed a conserved pentapeptide of G-X-S-X-G that is found in various lipolytic enzymes along with a putative catalytic triad of Ser148-Asp224-His257. Activity analysis showed that SfSFGH was active towards short-chain esters, such as p-nitrophenyl acetate, butyrate, hexanoate, and octanoate. The optimum pH for enzyme activity was slightly alkaline (pH 8.0). To investigate the active site configuration of SfSFGH, we determined the crystal structure of SfSFGH at 2.32 ? resolution. Structural analysis showed that a Trp182 residue is located in the active site entrance, allowing it to act as a gatekeeper residue to control substrate binding in SfSFGH. Mutation of Trp182 to Ala allowed SfSFGH to accommodate a longer chain of substrates. It is thought that the W182A mutation may increase the substrate-binding pocket and decrease the steric effect for larger substrates in SfSFGH. Consequently, the W182A mutant has broader substrate specificity compared to wild-type SfSFGH. Moreover, SfSFGH displayed more than 50% of its initial activity in the presence of various chemicals, including 30% EtOH, 1% Triton X-100, 1% SDS, and 5 M urea. Taken together, this study provides useful structure-function data of a SFGH family member and may inform protein engineering strategies for industrial applications of SfSFGH. proprietary KOPRI-KPDC-00001212_1 X-ray diffraction data of GerE AMD_KOPRI STAC Catalog 2015-10-15 2015-10-15 129.316333, 36.0235, 129.316333, 36.0235 https://cmr.earthdata.nasa.gov/search/concepts/C2244301233-AMD_KOPRI.umm_json In cold and harsh environments such as glaciers and sediments in ice cores, microbes can survive by forming spores. Spores are composed of a thick coat protein, which protects against external factors such as heat-shock, high salinity, and nutrient deficiency. GerE is a key transcription factor involved in spore coat protein expression in the mother cell during sporulation. GerE regulates transcription during the late sporulation stage by directly binding to the promoter of cotB gene. Here, we report the crystal structure of PaGerE at 2.09 ? resolution from Paenisporosarcina sp. TG-14, which was isolated from the Taylor glacier. The PaGerE structure is composed of four α-helices and adopts a helix-turn-helix architecture with 68 amino acid residues. Based on our DNA binding analysis, the PaGerE binds to the promoter region of CotB to affect protein expression. Additionally, our structural comparison studies suggest that DNA binding by PaGerE causes a conformational change in the α4-helix region, which may strongly induce dimerization of PaGerE. proprietary KOPRI-KPDC-00001213_4 Atmospheric dimethyl sulfide (DMS) mixing ratio observed at King Sejong Station in 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-04-03 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301273-AMD_KOPRI.umm_json 1) Abstract (English) Atmospheric DMS mixing ratio measured at King Sejong Station in 2019 (from 1 Jan to 4 April) by using custom-made trapping and desorption system equipped with pulsed flame photometric detector. 2) Purpose (English) Monitoring of atmospheric DMS mixing ration at King Sejong Station. proprietary -KOPRI-KPDC-00001214_4 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-08-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301205-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary KOPRI-KPDC-00001214_4 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2019 ALL STAC Catalog 2019-01-01 2019-08-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301205-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary +KOPRI-KPDC-00001214_4 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-08-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301205-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary KOPRI-KPDC-00001215_3 Cloud Condensation Nuclei concentration at King Sejong Station collected in 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-08-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244302431-AMD_KOPRI.umm_json Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN Monitoring of Aerosol CCN from King Sejong Station. proprietary KOPRI-KPDC-00001216_3 Cloud Condensation Nuclei concentration at King Sejong Station collected in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301742-AMD_KOPRI.umm_json Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN Monitoring of Aerosol CCN from King Sejong Station. proprietary KOPRI-KPDC-00001217_3 Cloud Condensation Nuclei concentration at King Sejong Station collected in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244302084-AMD_KOPRI.umm_json Cloud Condensation Nuclei Counter(CCNC) measures the number of aerosol CCN Monitoring of Aerosol CCN from King Sejong Station. proprietary -KOPRI-KPDC-00001218_3 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301229-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary KOPRI-KPDC-00001218_3 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2018 ALL STAC Catalog 2018-01-01 2018-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301229-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary -KOPRI-KPDC-00001219_3 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301244-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary +KOPRI-KPDC-00001218_3 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301229-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary KOPRI-KPDC-00001219_3 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2017 ALL STAC Catalog 2017-01-01 2017-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301244-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary +KOPRI-KPDC-00001219_3 Aerosol Number Concentration (> 2.5 and 10nm) from King Sejong Station collected in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244301244-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 2.5 and 10nm in diameter Monitoring of Aerosol Number Concentration (>2.5 and 10nm) at King Sejong Station. proprietary KOPRI-KPDC-00001220_2 Aerosol Size Distribution from King Sejong Station collected in 2019. AMD_KOPRI STAC Catalog 2019-01-01 2019-06-30 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244305477-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary KOPRI-KPDC-00001220_2 Aerosol Size Distribution from King Sejong Station collected in 2019. ALL STAC Catalog 2019-01-01 2019-06-30 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244305477-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary KOPRI-KPDC-00001221_3 KPDC MAXDOAS For Halogen gases at KSJ 2018-2019 AMD_KOPRI STAC Catalog 2018-12-09 2019-06-12 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244306023-AMD_KOPRI.umm_json Spectrum intensity for gaseous halogen compounds measured at King Sejong Station in 2018-2019 (from 9 Dec 2018 to 12 June 2019) by using Multi-Axis Differential Optic Absorption Spectroscopy (Max-DOAS) Monitoring of atmospheric halogen compounds at King Sejong Station. proprietary @@ -9595,8 +9595,8 @@ KOPRI-KPDC-00001261_1 Phytoplankton abundance in the Sea water of the Kongsfjord KOPRI-KPDC-00001262_4 Ionospheric scintillation, Kiruna Sweden, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 21.06242, 67.87654, 21.06242, 67.87654 https://cmr.earthdata.nasa.gov/search/concepts/C2244306224-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Kiruna, Sweden Study of the ionospheric irregularity in the northern high latitude proprietary KOPRI-KPDC-00001263_3 Neutral wind and temperature, Kiruna Sweden, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-04-15 21.06242, 67.87654, 21.06242, 67.87654 https://cmr.earthdata.nasa.gov/search/concepts/C2244306088-AMD_KOPRI.umm_json Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) at Esrange Space Center, Kiruna, Sweden Study of the atmosphere wave activities in the upper atmosphere in the northern high-latitude proprietary KOPRI-KPDC-00001264_4 Mesospheric temperature, Kiruna Sweden, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-04-15 21.06242, 67.87654, 21.06242, 67.87654 https://cmr.earthdata.nasa.gov/search/concepts/C2244306165-AMD_KOPRI.umm_json Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Kiruna, Study of the long-term trend of mesospheric temperature in the northern high latitude proprietary -KOPRI-KPDC-00001265_3 All-sky aurora (proton) image, KHO Longyearbyen, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-04-15 16.03412, 78.15174, 16.03412, 78.15174 https://cmr.earthdata.nasa.gov/search/concepts/C2244305996-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary KOPRI-KPDC-00001265_3 All-sky aurora (proton) image, KHO Longyearbyen, 2019 ALL STAC Catalog 2019-01-01 2019-04-15 16.03412, 78.15174, 16.03412, 78.15174 https://cmr.earthdata.nasa.gov/search/concepts/C2244305996-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary +KOPRI-KPDC-00001265_3 All-sky aurora (proton) image, KHO Longyearbyen, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-04-15 16.03412, 78.15174, 16.03412, 78.15174 https://cmr.earthdata.nasa.gov/search/concepts/C2244305996-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary KOPRI-KPDC-00001266_4 Ionospheric scintillation, Dasan Station, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-12-31 11.9342, 78.9272, 11.9342, 78.9272 https://cmr.earthdata.nasa.gov/search/concepts/C2244306538-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan station, Arctic Study of the ionospheric irregularity in the northern high latitude proprietary KOPRI-KPDC-00001267_3 Neutral wind and temperature, Dasan Station, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-04-15 11.9333, 78.9167, 11.9333, 78.9167 https://cmr.earthdata.nasa.gov/search/concepts/C2244306103-AMD_KOPRI.umm_json Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) at Dasan station, Arctic region Study of the atmosphere wave activities in the upper atmosphere in the northern high-latitude proprietary KOPRI-KPDC-00001268_2 The measurement of geomagnetic field at Jang Bogo Station, Antarctica at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244301263-AMD_KOPRI.umm_json The value of geomagnetic field intensity observed at Jang Bogo Station, Antarctica To investigate the interaction between ionosphere and geomagnetic disturbances proprietary @@ -9606,8 +9606,8 @@ KOPRI-KPDC-00001271_2 Ionospheric total electron content monitoring system over KOPRI-KPDC-00001272_2 Neutron Monitor installed at Jang Bogo Station, Antarctica at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244301215-AMD_KOPRI.umm_json The Neutron Monitor observes the neutron flux incoming from space to earth's atmosphere over JBS, Antarctica. To study the variation of neutron flux with the strength of solar activity and the relationship between neutron flux and atmospheric constituents. proprietary KOPRI-KPDC-00001273_2 Neutral wind data from FPI installed at Jang Bogo Station, Antarctica at 2019 AMD_KOPRI STAC Catalog 2019-03-11 2019-09-30 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244301235-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from FPI instrument at JBS station, Antarctica Study of the atmospheric wave activities in MLT and thermosphere regions over the southern high-latitude proprietary KOPRI-KPDC-00001274_2 Plasma density and drift velocity in ionoephre over Jang Bogo Station, Antarctica at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244305912-AMD_KOPRI.umm_json Ionospheric plasma density and drift velocity measured from VIPIR at JBS station, Antarctica Comprehensive study of ionosphere on plasma-neutral interaction over the southern high-latitude proprietary -KOPRI-KPDC-00001275_3 All-sky airglow image, King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306051-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001275_3 All-sky airglow image, King Sejong Station, 2019 ALL STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306051-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary +KOPRI-KPDC-00001275_3 All-sky airglow image, King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306051-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001276_3 Neutral wind and temperature, King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306024-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, and 250km measured from Fabry-Perot Interferometer (FPI) at King Sejong Station Study of the atmosphere wave activities in the upper atmosphere in the southern high-latitude proprietary KOPRI-KPDC-00001277_3 Ionospheric scintillation, King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306035-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at King Sejong Station Study of the ionospheric irregularity in the southern high latitude proprietary KOPRI-KPDC-00001278_4 Neutral wind and temperature (MR), King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 -58.78462, -62.2238, -58.78462, -62.2238 https://cmr.earthdata.nasa.gov/search/concepts/C2244306123-AMD_KOPRI.umm_json Neutral wind (80 – 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica Study of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude proprietary @@ -9836,14 +9836,14 @@ KOPRI-KPDC-00001501_2 Temporal variation of marine phytoplankton in the surface KOPRI-KPDC-00001502_4 Soil physicochemical data from Barton and Weaver peninsula in King George Island at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-01-31 -58.8, -62.233333, -58.766664, -62.2 https://cmr.earthdata.nasa.gov/search/concepts/C2244301429-AMD_KOPRI.umm_json Physicochemical data (pH, EC, TC, TIC, TN and soil texture) of glacier foreland soil samples obtained from Barton and Weaver Peninsula in King George Island at 2019 proprietary KOPRI-KPDC-00001503_4 Fungal NGS data from Barton and Weaver peninsula in King George Island at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-01-31 -58.8, -62.233333, -58.766664, -62.2 https://cmr.earthdata.nasa.gov/search/concepts/C2244301480-AMD_KOPRI.umm_json These data were obtained to examine fungal community structure and reveal the correlation between soil physicochemical factors and soil fungal composition in glacial foreland of the Antarctic. proprietary KOPRI-KPDC-00001504_1 Soil and freshwater samples of the Antarctic King Sejong Station from Barton Peninsular collected in 2019 AMD_KOPRI STAC Catalog 2020-01-10 2020-01-21 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244301324-AMD_KOPRI.umm_json Analysis of microbial community structure and diversity in soil and freshwater samples of the Antarctic King Sejong Station from Barton Peninsular in Antarctica Investigation to the terrestrial biodiversity in Barton peninsular for the monitoring by environment change proprietary -KOPRI-KPDC-00001505_5 All-sky airglow image, King Sejong Station, 2020 ALL STAC Catalog 2020-02-18 2020-09-23 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244307204-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001505_5 All-sky airglow image, King Sejong Station, 2020 AMD_KOPRI STAC Catalog 2020-02-18 2020-09-23 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244307204-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary +KOPRI-KPDC-00001505_5 All-sky airglow image, King Sejong Station, 2020 ALL STAC Catalog 2020-02-18 2020-09-23 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244307204-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary KOPRI-KPDC-00001506_6 Ionospheric scintillation, Kiruna Sweden, 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-10-20 21.03, 67.53, 21.03, 67.53 https://cmr.earthdata.nasa.gov/search/concepts/C2244307220-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Kiruna, Sweden Study of the ionospheric irregularity in the northern high latitude proprietary KOPRI-KPDC-00001507_6 Ionospheric scintillation, Dasan Station, 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-12-31 11.9342, 78.9272, 11.9342, 78.9272 https://cmr.earthdata.nasa.gov/search/concepts/C2244306380-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan station, Arctic Study of the ionospheric irregularity in the northern high latitude proprietary -KOPRI-KPDC-00001508_4 All-sky aurora (proton) image, KHO Longyearbyen, 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-10-19 16.12, 78.48, 16.12, 78.48 https://cmr.earthdata.nasa.gov/search/concepts/C2244307127-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary KOPRI-KPDC-00001508_4 All-sky aurora (proton) image, KHO Longyearbyen, 2020 ALL STAC Catalog 2020-01-01 2020-10-19 16.12, 78.48, 16.12, 78.48 https://cmr.earthdata.nasa.gov/search/concepts/C2244307127-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary -KOPRI-KPDC-00001509_1 2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity ALL STAC Catalog 2019-01-19 2020-01-26 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244301374-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2019 proprietary +KOPRI-KPDC-00001508_4 All-sky aurora (proton) image, KHO Longyearbyen, 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-10-19 16.12, 78.48, 16.12, 78.48 https://cmr.earthdata.nasa.gov/search/concepts/C2244307127-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary KOPRI-KPDC-00001509_1 2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity AMD_KOPRI STAC Catalog 2019-01-19 2020-01-26 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244301374-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2019 proprietary +KOPRI-KPDC-00001509_1 2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity ALL STAC Catalog 2019-01-19 2020-01-26 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244301374-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2019 proprietary KOPRI-KPDC-00001510_2 Snow cover map of the Barton Peninsula, King George Island, Antarctica AMD_KOPRI STAC Catalog 1986-01-28 2020-01-19 -58.747839, -62.229025, -58.747839, -62.229025 https://cmr.earthdata.nasa.gov/search/concepts/C2244306359-AMD_KOPRI.umm_json Snow cover on the Barton Peninsula, Antarctica extracted from time-series Landsat satellite data proprietary KOPRI-KPDC-00001511_3 Bacterial NGS data from Barton and Weaver peninsula in King George Island at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-01-31 -58.8, -62.233333, -58.766664, -62.2 https://cmr.earthdata.nasa.gov/search/concepts/C2244306368-AMD_KOPRI.umm_json These data were obtained to examine bacterial community structure and reveal the correlation between soil physicochemical factors and soil bacterial composition in glacial foreland of the Antarctic. proprietary KOPRI-KPDC-00001512_2 2019/20 season Korean Route Traverse based GPS GIS data ALL STAC Catalog 2019-11-07 2020-01-18 149.040453, -77.04815, 164.228789, -74.62405 https://cmr.earthdata.nasa.gov/search/concepts/C2244306379-AMD_KOPRI.umm_json GOAL ○ Development of Korean route and infrastructure such as research camp to approach the Antarctic inland ○ Establishment of support system for the Antarctic inland researches RESEARCH CONTENTS ○ A safe and reliable route expedition to the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches ○ Construction of logistic camps at the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches proprietary @@ -9993,10 +9993,10 @@ KOPRI-KPDC-00001666_2 Wind data on ARAON DaDis for Antarctic cruise, 2020/2021 A KOPRI-KPDC-00001667_2 Upper O3 observation data at Jang Bogo Station in 2019 AMD_KOPRI STAC Catalog 2019-01-17 2019-11-28 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244306388-AMD_KOPRI.umm_json Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second. Monitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station. proprietary KOPRI-KPDC-00001668_2 Upper O3 observation data at Jang Bogo Station in 2020 AMD_KOPRI STAC Catalog 2020-01-16 2020-12-17 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244306563-AMD_KOPRI.umm_json Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second. Monitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station. proprietary KOPRI-KPDC-00001669_2 Upper O3 observation data at Jang Bogo Station in 2021 AMD_KOPRI STAC Catalog 2021-01-02 2021-06-10 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244306666-AMD_KOPRI.umm_json Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second. Monitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station. proprietary -KOPRI-KPDC-00001671_3 2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station) AMD_KOPRI STAC Catalog 2019-02-14 2019-02-15 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306725-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2018&19 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary KOPRI-KPDC-00001671_3 2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station) ALL STAC Catalog 2019-02-14 2019-02-15 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306725-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2018&19 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary -KOPRI-KPDC-00001672_3 2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station) AMD_KOPRI STAC Catalog 2017-01-29 2017-02-06 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306756-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2016&17 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary +KOPRI-KPDC-00001671_3 2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station) AMD_KOPRI STAC Catalog 2019-02-14 2019-02-15 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306725-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2018&19 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary KOPRI-KPDC-00001672_3 2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station) ALL STAC Catalog 2017-01-29 2017-02-06 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306756-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2016&17 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary +KOPRI-KPDC-00001672_3 2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station) AMD_KOPRI STAC Catalog 2017-01-29 2017-02-06 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306756-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2016&17 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary KOPRI-KPDC-00001673_2 Multibeam data, Australian-Antarctic Ridge (AAR) and the Pacific-Antarctic Ridge (PAR), 2020/21 season AMD_KOPRI STAC Catalog 2020-11-28 2020-11-29 -179.79775, -66.58295, -176.64499, -64.11792 https://cmr.earthdata.nasa.gov/search/concepts/C2244306908-AMD_KOPRI.umm_json During 2020/2021 summer season, due to sea ice, we obtained high resolution bathymetric data and marine magnetic data for only one short spreading-segment in “large-scaled spreading and fracture zones (or leaky transform faults)” located between the Australian-Antarctic Ridge (AAR) and the Pacific-Antarctic Ridge (PAR). It is expected that it will be able to contribute to the investigations for the tectonic evolution of the Antarctica related to the Australian-Pacific-Antarctic plates and the evolution of the Zealandia-Antarctic mantle, through the bathymetric and magnetic data that will be accumulated in the future. proprietary KOPRI-KPDC-00001674_4 WRF model namelist input for Arctic winter climate change studies AMD_KOPRI STAC Catalog 2021-01-01 2021-01-01 180, 56, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244307132-AMD_KOPRI.umm_json "Attached is a namelist for polar region optimized version of WRF model. It was used for the study ""Short-term Atmospheric Response to Recent Arctic Sea Ice Loss"" (submitted to Geophysical Research Letters)." proprietary KOPRI-KPDC-00001675_2 Flux of individual lipid biomarkers in the KAMS1 and KAMS2 which collected from August 2017 to August 2018. AMD_KOPRI STAC Catalog 2017-08-18 2018-08-13 177.056033, 75.244383, -171.981267, 75.79935 https://cmr.earthdata.nasa.gov/search/concepts/C2244306291-AMD_KOPRI.umm_json Flux of individual lipid biomarkers in the KAMS1 and KAMS2 which collected from August 2017 to August 2018. Table 1 contained TMF, POC, SIC and Chla data set. Table 2 and 3 contained individual lipid biomarkers data in the KAMS1 and KAMS2, respectively. proprietary @@ -10117,13 +10117,13 @@ KOPRI-KPDC-00001793_2 SHRIMP zircon U-Pb age data for the Abbott alkali feldspar KOPRI-KPDC-00001794_2 Ship-borne radiosonde observation data over the Arctic Ocean in the 2021 Araon summer expedition(ARA12B,ARA12C) AMD_KOPRI STAC Catalog 2021-07-19 2021-09-12 179.974635, 58.66413, 179.741158, 80.002337 https://cmr.earthdata.nasa.gov/search/concepts/C2244304238-AMD_KOPRI.umm_json The radiosonde balloon sounding observations were performed from 18 July 2021 to 12 September 2021 to obtain the Arctic Ocean high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at two times daily intervals(00 and 12UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system. proprietary KOPRI-KPDC-00001795_2 Ship-borne radiosonde observation data over the Arctic Ocean in the 2021 Araon summer expedition(ARA12A) AMD_KOPRI STAC Catalog 2021-07-08 2021-07-14 158.04125, 45.86753, -173.457097, 56.675897 https://cmr.earthdata.nasa.gov/search/concepts/C2244304627-AMD_KOPRI.umm_json The radiosonde balloon sounding observations were performed from 8 July 2021 to 14 July 2021 to obtain the Bering Sea high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at four times daily intervals(00,06,12, and 18UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system. proprietary KOPRI-KPDC-00001796_2 miRNA sequencing data of Field and lab culture Sanionia uncinata AMD_KOPRI STAC Catalog 2015-02-01 2021-08-30 126.646833, -62.219722, -58.767778, 37.368722 https://cmr.earthdata.nasa.gov/search/concepts/C2244306533-AMD_KOPRI.umm_json To investigate miRNA profiling of antartic moss Sanionia uncinata during seasonal changes Using field samples and lab cultre samples proprietary -KOPRI-KPDC-00001797_2 Age characteristics of Antarctic scallops (Adamussium colbecki) ALL STAC Catalog 2019-02-21 2019-03-01 164.243867, -74.627661, 164.243867, -74.627661 https://cmr.earthdata.nasa.gov/search/concepts/C2244306570-AMD_KOPRI.umm_json Age measurement of Antarctic scallops by shell height proprietary KOPRI-KPDC-00001797_2 Age characteristics of Antarctic scallops (Adamussium colbecki) AMD_KOPRI STAC Catalog 2019-02-21 2019-03-01 164.243867, -74.627661, 164.243867, -74.627661 https://cmr.earthdata.nasa.gov/search/concepts/C2244306570-AMD_KOPRI.umm_json Age measurement of Antarctic scallops by shell height proprietary +KOPRI-KPDC-00001797_2 Age characteristics of Antarctic scallops (Adamussium colbecki) ALL STAC Catalog 2019-02-21 2019-03-01 164.243867, -74.627661, 164.243867, -74.627661 https://cmr.earthdata.nasa.gov/search/concepts/C2244306570-AMD_KOPRI.umm_json Age measurement of Antarctic scallops by shell height proprietary KOPRI-KPDC-00001798_2 Fast Ice Map in the Terra Nova Bay AMD_KOPRI STAC Catalog 2017-05-14 2018-01-09 164.259926, -74.65589, 164.259926, -74.65589 https://cmr.earthdata.nasa.gov/search/concepts/C2244306604-AMD_KOPRI.umm_json Extraction of fast ice area using satellite data proprietary KOPRI-KPDC-00001800_2 Species list and coverage of benthic animals in Ross Sea, Antarctica AMD_KOPRI STAC Catalog 2017-11-01 2019-11-30 168.024447, -77.84013, 168.024447, -77.84013 https://cmr.earthdata.nasa.gov/search/concepts/C2244306640-AMD_KOPRI.umm_json Species list and coverage of benthic animals in Ross Sea, Antarctica proprietary KOPRI-KPDC-00001801_2 Ecological index of benthic animals in Ross Sea, Antarctica AMD_KOPRI STAC Catalog 2017-11-01 2019-11-30 168.024447, -77.84013, 168.024447, -77.84013 https://cmr.earthdata.nasa.gov/search/concepts/C2244306667-AMD_KOPRI.umm_json Biodiversity analysis of benthic animals in Ross Sea, Antarctica proprietary -KOPRI-KPDC-00001804_2 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2020 ALL STAC Catalog 2020-01-10 2021-03-11 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244306700-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2020 Long term monitoring proprietary KOPRI-KPDC-00001804_2 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2020 AMD_KOPRI STAC Catalog 2020-01-10 2021-03-11 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244306700-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2020 Long term monitoring proprietary +KOPRI-KPDC-00001804_2 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2020 ALL STAC Catalog 2020-01-10 2021-03-11 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244306700-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2020 Long term monitoring proprietary KOPRI-KPDC-00001809_2 mRNA sequencing data of lab culture Sanionia uncinata AMD_KOPRI STAC Catalog 2021-02-01 2021-03-31 126.646833, 37.368742, 126.646833, 37.368742 https://cmr.earthdata.nasa.gov/search/concepts/C2244306716-AMD_KOPRI.umm_json To investigate climate factors which regulate life cycle of antartic moss Sanionia uncinata Lab culter Sanionia uncinata were treated with the condition that mimic the climate condition of King George Isaland proprietary KOPRI-KPDC-00001810_2 Global surface air temperature for the 2000 - 2019 winter from the CAM6 hindcast simulation AMD_KOPRI STAC Catalog 2000-10-01 2020-02-28 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244306727-AMD_KOPRI.umm_json Developing a seasonal prediction system with atmosphere global climate model CAM6, monthly surface air temperature data was generated from the 2000-2019 wintertime hindcast simulation. proprietary KOPRI-KPDC-00001811_3 Neutral wind and temperature (MR), King Sejong Station, 2021 AMD_KOPRI STAC Catalog 2021-01-01 2021-09-28 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244307232-AMD_KOPRI.umm_json Neutral wind (80 – 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica Study of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude proprietary @@ -10165,8 +10165,8 @@ KOPRI-KPDC-00001846_2 Major ionic species measured at ice core from Tourmaline P KOPRI-KPDC-00001847_2 Trace elements in GV7 snow pit AMD_KOPRI STAC Catalog 2013-12-22 2013-12-24 158.863583, -70.688083, 158.863583, -70.688083 https://cmr.earthdata.nasa.gov/search/concepts/C2244305965-AMD_KOPRI.umm_json Trace elements in GV7 snow pit investigation of climate change mechanism by observation and simulation of polar climate for the past and present proprietary KOPRI-KPDC-00001848_2 Trace elements in Hercules Neve snow pit AMD_KOPRI STAC Catalog 2015-12-16 2015-12-16 165.410756, -73.052936, 165.410756, -73.052936 https://cmr.earthdata.nasa.gov/search/concepts/C2244305998-AMD_KOPRI.umm_json Trace elements in Hercules Neve snow pit investigation of climate change mechanism by observation and simulation of polar climate for the past and present proprietary KOPRI-KPDC-00001850_3 Continuous monitoring of pCO2 and its relevant parameters in the coast of the Jang Bogo Station, Antarctica, in 2021 AMD_KOPRI STAC Catalog 2021-01-01 2021-06-30 164.2, -74.616667, 164.2, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244307445-AMD_KOPRI.umm_json In order to conduct long-term monitoring of the acidification of the coastal waters around Antarctica, ocean pCO2 and its relevant physical, chemical, biological parameters start monitoring in 2020. These include atmospheric CO2 concentration, ocean pCO2, seawater temperature, salinity, dissolved oxygen, pH, chlorophyll-a, CDOM, and, turbidity. proprietary -KOPRI-KPDC-00001851_2 All-sky aurora (electron) image, Jang Bogo Station, 2021 ALL STAC Catalog 2021-03-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306019-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station. Study of the aurora characterisitcs in the southern high latitude. proprietary KOPRI-KPDC-00001851_2 All-sky aurora (electron) image, Jang Bogo Station, 2021 AMD_KOPRI STAC Catalog 2021-03-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306019-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station. Study of the aurora characterisitcs in the southern high latitude. proprietary +KOPRI-KPDC-00001851_2 All-sky aurora (electron) image, Jang Bogo Station, 2021 ALL STAC Catalog 2021-03-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306019-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station. Study of the aurora characterisitcs in the southern high latitude. proprietary KOPRI-KPDC-00001852_2 Neutral wind and temperature, Jang Bogo Station, 2021 AMD_KOPRI STAC Catalog 2021-03-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306033-AMD_KOPRI.umm_json Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) Jang Bogo Station, Antarctica Study of the atmospheric wave activities in the upper atmosphere in the southern high-latitude. proprietary KOPRI-KPDC-00001853_2 Electron density, plasma drift, Jang Bogo Station, 2021 AMD_KOPRI STAC Catalog 2020-11-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306043-AMD_KOPRI.umm_json Electron density profile, plasma drift velocity, and ionospheric tile information measured from VIPIR (ionosonde) at Jang Bogo Station. Study of the ionospheric characteristics in the southern high latitude. proprietary KOPRI-KPDC-00001854_2 Neutron count, Jang Bogo Station, 2021 AMD_KOPRI STAC Catalog 2020-11-01 2021-09-30 164.2, -74.623333, 164.2, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306067-AMD_KOPRI.umm_json Cosmic ray origin neutron count from space measured from neutron monitor at Jang Bogo Station, Antarctica. Study of the variation of neutron count in the southern high latitude. proprietary @@ -10267,12 +10267,12 @@ Kuparuk_Veg_Maps_1378_1 Maps of Vegetation Types and Physiographic Features, Kup Kuroshio_Area_0 Measurements in the Kuroshio current OB_DAAC STAC Catalog 1997-11-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360413-OB_DAAC.umm_json Measurements in the Kuroshio, western boundary current in the North Pacific Ocean, from 1997. proprietary Kyle-Ferrar_Igneous_Province 40Ar/39Ar dates of Jurassic igneous rocks from Antarctica SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C1214612994-SCIOPS.umm_json Plagioclase mineral separates from basaltic extrusive (lavas) and instrusive (dolerite and gabbro) samples from the Dronning Maud Land area of Antarctica were dated by the incremental heating 40Ar/39Ar method. 32 individual samples were dated with 11 samples having duplicate analyses. proprietary Kyle-Ferrar_Igneous_Province 40Ar/39Ar dates of Jurassic igneous rocks from Antarctica ALL STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C1214612994-SCIOPS.umm_json Plagioclase mineral separates from basaltic extrusive (lavas) and instrusive (dolerite and gabbro) samples from the Dronning Maud Land area of Antarctica were dated by the incremental heating 40Ar/39Ar method. 32 individual samples were dated with 11 samples having duplicate analyses. proprietary -L1B_Wind_Products_3.0 Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers ALL STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689596-ESA.umm_json The Level 1B wind product of the Aeolus mission contains the preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers, which are generated in Near Real Time. Standard atmospheric correction (Rayleigh channel), receiver response and bias correction is applied. The product is generated within 3 hours after data acquisition. proprietary L1B_Wind_Products_3.0 Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers ESA STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689596-ESA.umm_json The Level 1B wind product of the Aeolus mission contains the preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers, which are generated in Near Real Time. Standard atmospheric correction (Rayleigh channel), receiver response and bias correction is applied. The product is generated within 3 hours after data acquisition. proprietary -L2B_Wind_Products_3.0 Aeolus Scientific L2B Rayleigh/Mie wind product ALL STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689544-ESA.umm_json The Level 2B wind product of the Aeolus mission is a geo-located consolidated HLOS (horizontal line-of-sight) wind observation with actual atmospheric correction applied to Rayleigh channel. The product is generated by within 3 hours after data acquisition. proprietary +L1B_Wind_Products_3.0 Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers ALL STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689596-ESA.umm_json The Level 1B wind product of the Aeolus mission contains the preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers, which are generated in Near Real Time. Standard atmospheric correction (Rayleigh channel), receiver response and bias correction is applied. The product is generated within 3 hours after data acquisition. proprietary L2B_Wind_Products_3.0 Aeolus Scientific L2B Rayleigh/Mie wind product ESA STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689544-ESA.umm_json The Level 2B wind product of the Aeolus mission is a geo-located consolidated HLOS (horizontal line-of-sight) wind observation with actual atmospheric correction applied to Rayleigh channel. The product is generated by within 3 hours after data acquisition. proprietary -L2C_Wind_products_5.0 Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing ALL STAC Catalog 2020-07-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2619280864-ESA.umm_json The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds. proprietary +L2B_Wind_Products_3.0 Aeolus Scientific L2B Rayleigh/Mie wind product ALL STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689544-ESA.umm_json The Level 2B wind product of the Aeolus mission is a geo-located consolidated HLOS (horizontal line-of-sight) wind observation with actual atmospheric correction applied to Rayleigh channel. The product is generated by within 3 hours after data acquisition. proprietary L2C_Wind_products_5.0 Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing ESA STAC Catalog 2020-07-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2619280864-ESA.umm_json The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds. proprietary +L2C_Wind_products_5.0 Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing ALL STAC Catalog 2020-07-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2619280864-ESA.umm_json The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds. proprietary L2SW_Open_3.0 SMOS NRT L2 Swath Wind Speed ESA STAC Catalog 2018-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689620-ESA.umm_json SMOS retrieved surface wind speed gridded maps (with a spatial sampling of 1/4 x 1/4 degrees) are available in NetCDF format. Each product contains parts of ascending and descending orbits and it is generated by Ifremer, starting from the SMOS L1B data products, in Near Real Time i.e. within 4 to 6 hours from sensing time. Before using this dataset, please check the read-me-first note available in the Resources section below. proprietary L3SW_Open_4.0 SMOS L3 Daily Wind Speed ESA STAC Catalog 2018-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689536-ESA.umm_json SMOS L3WS products are daily composite maps of the collected SMOS L2 swath wind products for a specific day, provided with the same grid than the Level 2 wind data (SMOS L2WS NRT) but separated into ascending and descending passes. This product is available the day after sensing from Ifremer, in NetCDF format. Before using this dataset, please check the read-me-first note available in the Resources section below. proprietary L3S_LEO_AM-STAR-v2.80_2.80 GHRSST NOAA/STAR ACSPO v2.80 0.02 degree L3S Dataset from mid-Morning LEO Satellites (GDS v2) POCLOUD STAC Catalog 2006-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2050135480-POCLOUD.umm_json NOAA STAR produces two lines of gridded 0.02 degree super-collated L3S LEO sub-skin Sea Surface Temperature (SST) datasets, one from the NOAA afternoon JPSS (L3S_LEO_PM) satellites and the other from the EUMETSAT mid-morning Metop (L3S_LEO_AM) satellites. The L3S_LEO_AM is derived from three Low Earth Orbiting (LEO) Metop-FG satellites: Metop-A, -B and -C . The Metop-FG satellite program was jointly established by the European Space Agency (ESA) and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The US National Oceanic and Atmospheric Administration (NOAA) under the joint NOAA/EUMETSAT Initial Joint Polar System Agreement, has contributed three Advanced Very High Resolution Radiometer (AVHRR) sensors capable of collecting and transmitting data in the Full Resolution Area Coverage (FRAC; 1km/nadir) format. The L3S_LEO_AM dataset is produced by aggregating three L3U datasets from MetOp-FG satellites (MetOp-A, -B and -C; all hosted in PO.DAAC) and covers from Dec 2006-present. The L3S_LEO_AM SST dataset is reported in two files per 24-hour interval, daytime and nighttime (nominal Metop local equator crossing times around 09:30/21:30, respectively), in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The Near Real Time (NRT) L3S-LEO data are archived at PO.DAAC with approximately 6 hours latency, and then replaced by the Re-ANalysis (RAN) files about 2 months later, with identical file names. The dataset is validated against quality controlled in situ data, provided by the NOAA in situ SST Quality Monitor system (iQuam; Xu and Ignatov, 2014), and monitored in another NOAA system, SST Quality Monitor (SQUAM; Dash et al, 2010). The L3S SST imagery and local coverage are continuously evaluated, and checked for consistency with other Level 2, 3 and 4 datasets in the ACSPO Regional Monitor for SST (ARMS) system. NOAA plans to include data from other mid-morning platforms and sensors, such as MetOp-SG METImage and Terra MODIS, into L3S_LEO_AM. More information about the dataset can be found under the Documentation and Citation tabs. proprietary @@ -10415,8 +10415,8 @@ LEOLSTCMG30_002 Low Earth Orbit Land Surface Temperature Monthly Global Gridded LEO_0 Long-term Ecosystem Observatory (LEO) oceanographic and meteorological data collection system OB_DAAC STAC Catalog 2001-07-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360429-OB_DAAC.umm_json Measurements from the LEO station off the Atlantic Coast of New Jersey in 2001. proprietary LEVEL_1C__3_5.0 Proba-V 1Km, 333m, and 100m products ESA STAC Catalog 2013-11-28 -180, -56, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C1965336924-ESA.umm_json The Proba-V VEGETATION Raw products (Level 1C/P) and synthesis products (Level 3, S1 = daily, S5 = 5 days, S10 = decade) ensure coverage of all significant landmasses worldwide with, in the case of a 10-day synthesis product, a minimum effect of cloud cover, resulting from selection of cloud-free acquisitions during the 10-day period. It ensures a daily coverage between Lat. 35°N and 75°N, and between 35°S and 56°S, and a full coverage every two days at equator. The VEGETATION instrument is pre-programmed with an indefinite repeated sequence of acquisitions. This nominal acquisition scenario allows a continuous series of identical products to be generated, aiming to map land cover and vegetation growth across the entire planet every two days.Products overview • Projection: Plate carrée projection • Spectral bands: All 4 + NDVI • Format: HDF5 & GeoTiFF The Proba-V VEGETATION Level 3 synthesis products are divided into so called granules, each measuring 10 degrees x 10 degrees, each granule being delivered as a single file. Level 3 products are: - Syntesys S1, with resolution 100m (TOA, TOC and TOC NDVI reflectance), 333m (TOA and TOC reflectance) and 1km (TOA and TOC reflectance) - Syntesys S5, with resolution 100m (TOA, TOC and TOC NDVI reflectance) - Syntesys S10, with resolution 333m (TOC and TOC NDVI reflectance) and 1km (TOC and TOC NDVI reflectance) proprietary LF_Bibliography_1 Bibliography of papers relevant to longline fishing. AU_AADC STAC Catalog 1972-01-01 -180, -80, 180, 85 https://cmr.earthdata.nasa.gov/search/concepts/C1214313596-AU_AADC.umm_json The bibliography covers a wealth of published, 'grey', and unpublished literature addressing the effects of longline fishing on seabird mortality. The scope is global, but with a special emphasis on the Southern Ocean. Information on longline methodology is included and attention is given to materials that cover the various mitigation methods in use, tested or proposed. Further, information on the relevant aspects of the ecology of affected seabird species is covered, especially that dealing with mortality levels, at-sea distributions and population and conservation biology. Data sources covered include the scientific literature, popular publications, newspaper articles, videos, brochures, maps and posters, as well as government, NGO and IGO reports. proprietary -LGB_10m_traverse_1 10 m firn temperature data: LGB traverses 1990-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313574-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Ten metre depth (10 m) firn temperatures, as a proxy indicator of annual mean surface temperature at a site, were recorded approximately every 30 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. 10 m depth firn temperatures were recorded manually in field notebooks and the data transferred to spreadsheet files (MS Excel). Summary data (30 km spatial resolution) can be obtained from CRC Research Note No.09 'Surface Mass Balance and Snow Surface Properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary LGB_10m_traverse_1 10 m firn temperature data: LGB traverses 1990-95 ALL STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313574-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Ten metre depth (10 m) firn temperatures, as a proxy indicator of annual mean surface temperature at a site, were recorded approximately every 30 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. 10 m depth firn temperatures were recorded manually in field notebooks and the data transferred to spreadsheet files (MS Excel). Summary data (30 km spatial resolution) can be obtained from CRC Research Note No.09 'Surface Mass Balance and Snow Surface Properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary +LGB_10m_traverse_1 10 m firn temperature data: LGB traverses 1990-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313574-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Ten metre depth (10 m) firn temperatures, as a proxy indicator of annual mean surface temperature at a site, were recorded approximately every 30 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. 10 m depth firn temperatures were recorded manually in field notebooks and the data transferred to spreadsheet files (MS Excel). Summary data (30 km spatial resolution) can be obtained from CRC Research Note No.09 'Surface Mass Balance and Snow Surface Properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary LGB_Del_traverse_1 Delta Oxygen-18 isotope data: LGB traverses 1989-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313576-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Several shallow depth ice cores (15-60 m) were drilled at selected sites along 2014 km of the main traverse track from LGB00 (68.6543 S, 61.1201 E) near Mawson Station to LGB72 (69.9209 S,76.4933 E) near Davis Station, and at selected sites along a western traverse line from LGB00 toward Enderby Land. Surface cores (2 m) were collected at 30 km intervals along the entire route from LGB00-LGB72. Ice cores have been kept in cool storage at a local cold room storage facility. Isotope data from the cores have been saved in various spreadsheet files (mainly MS Excel). Initial summary data can be obtained from CRC Research Note No.09 'Surface mass balance and snow surface properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary LGB_Gra_traverse_1 Earth gravity field for ice thickness data: LGB traverses 1989-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313598-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. LaCoste and Romberg gravity meters were used to record measurements of the Earth's gravity field approximately every 2 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. Gravity readings were also obtained at 5 km intervals along a 516 km upper western offset track (50 km parallel upslope from main route) from LGBUW485 (68.6458 S, 60.0272 E) to LGBUW000 (72.6508 S, 55.9275 E). Raw data were stored as meter readings in field notebooks, transferred manually to spreadsheet files (MS Excel). Processed data were stored in spreadsheet files (MS Excel). The data available at the url below are stored in various formats. Summary data (2 km spatial resolution) can be obtained from CRC Research Note No.27 'Ice Thicknesses and Surface and Bedrock Elevations from the Lambert Glacier Basin Traverses 1990-95'. Documents providing archive details of the logbooks are available for download from the provided URL. This work was completed as part of ASAC projects 3 and 2216. Logbook(s): - Gravity Meter Log 89/90 - LGBT Gravity #2 1992-93 - Glaciology Gravity Readings LGBT 1990-91 proprietary LGB_Ht_traverse_1 Ice sheet surface elevation data: LGB traverses 1989-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313577-AU_AADC.umm_json The ANARE Lambert Glacier Basin (LGB) series of oversnow traverses were conducted during the period 1989-95. Field operations were carried out along the proximity of the 2500 m elevation contour around the interior basin between Mawson and Davis stations. The main traverse route covered some 2014 km of track from LGB00 at 68.6543 S, 61.1201 E, and LGB72 at 69.9209 S, 76.4933 E. An offset route (50 km upslope) parallels the main traverse track around the western half of the basin. Raw data were stored in binary files containing pressure, temperature, navigational position and a variety of other parameters at an approximately 10 m spacing associated with each 2 km long section of track. Processed data were stored as 2 km averaged ice sheet surface elevation spreadsheet files (MS Excel). The data available at the url below are stored in various formats. Summary data (2 km spatial average) can be obtained from CRC Research Note No. 27 'Ice Thicknesses and Surface and Bedrock Elevations from the Lambert Glacier Basin Traverses 1989-95'. This work was completed as part of ASAC projects 3 and 2216. proprietary @@ -10507,8 +10507,8 @@ LSC_biomarkers Evaluation of Selected Histologic and Immunologic Biomarkers in F LSC_immunereprohistologic Immune, Reproductive and Histologic Biomarker Evaluation in Fish Collected for the Columbia and Rio Grande River Basin BEST Program, 1997 CEOS_EXTRA STAC Catalog 1997-08-01 2001-03-01 -115, 30, -105, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231553382-CEOS_EXTRA.umm_json "This study is part of a larger project entitled ""Contaminants and Biomarkers in Fish in the Columbia River and Rio Grande Basins, 1997"" ( Mid-Continent Ecological Science Center) This project is part of the Biomonitoring of Environmental Status and Trends (BEST) program. The BEST program incorporates both analytical chemistry arid a suite of biological responses to describe and track contaminant exposure and effects. Our part of this program is to measure and evaluate selected histologic, immunological and reproductive biomarkers. Our objectives are: to document the presence of selected histologic lesions which have been validated or widely accepted as indicators of contaminant exposure; to determine if there is evidence of immunosuppression using immune system biomarkers; evaluate gonad histology utilizing new potential biomarkers; determine if changes in gonad histology correlate with circulating vitellogenin levels; determine if these findings correlate with contaminant presence or concentration. Information was obtained from http://www.lsc.usgs.gov" proprietary LSM_807_1 Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies ORNL_CLOUD STAC Catalog 1996-01-15 1996-01-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2956539244-ORNL_CLOUD.umm_json The NCAR LSM 1.0 is a land surface model developed to examine biogeophysical and biogeochemical land-atmosphere interactions, especially the effects of land surfaces on climate and atmospheric chemistry. It can be run coupled to an atmospheric model or uncoupled, in a stand-alone mode, if an atmospheric forcing is provided. The model runs on a spatial grid that can range from one point to global. The model was designed for coupling to atmospheric numerical models. Consequently, there is a compromise between computational efficiency and the complexity with which the necessary atmospheric, ecological, and hydrologic processes are parameterized. The model is not meant to be a detailed micrometeorological model, but rather a simplified treatment of surface fluxes that reproduces at minimal computational cost the essential characteristics of land-atmosphere interactions important for climate simulations. The model is a complete executable code with its own time-stepping driver, initialization (subroutine lsmini), and main calling routine (subroutine lsmdrv). When coupled to an atmospheric model, the atmospheric model is the time-stepping driver. There is one call to subroutine lsmini during initialization to initialize all land points in the domain; there is one call per time step to subroutine lsmdrv to calculate surface fluxes and update the ecological, hydrological, and thermal state for all land points in the domain. The model writes its own restart and history files. These can be turned off if appropriate. Available for downloading from the ORNL DAAC are the LMS Model Documentation and User's Guide, the model source code, input data set, and scripts for running the model. Applications of the model are described in two additional companion files. proprietary LS_TM_ARC Landsat TM Image Data Archived in China Remote Sensing Satellite Ground Station CEOS_EXTRA STAC Catalog 1986-06-01 90, 20, 140, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2226631645-CEOS_EXTRA.umm_json Landsat 5 was launched on March 1, 1984, carrying a seven-band TM sensor, and still operates properly at present. The satellite takes a sun-synchronized orbit with 705km altitude and 98.22 deg. inclination. A TM scene covers 185km by 170km earth surface approximately, with 30m ground resolution for band 1,2,3,4,5,7 and 120m for band6. For a particular place, the revisit cycle of the satellite is 16 days. Chaina Remote Sensing Satellite Ground Station(CRSGS) was inaugurated and become operational in Dec. 1986. Up to now it is the most important source of remote sensing satellite data in China for earth resouce exploration and environment monitoring. CRSGS has provided a large amount of satellite remote sensing products to more than 400 users, domestic and abroad. Applications of TM images have resulted in great economic and social benefits in a wide range of areas of national economy: resource survey and utilization, environment monitoring, geographic cartography, minerarl exploration, disaster detecting and assessing, etc. TM data received by CRSGS since 1986 have been archived. Through a Catalogue Archive and Browse System(CABS), users can retrieve useful information about data of interests. A image(or a group of images) could be searched according to date, location(latitude-longitude or path-row), and quality, etc. Text catalogue is available for all TM data in the archival. In addition to text contents, sub-sampled browse images are available for data acquired after Apr.,1994. The major products of CRSGS are TM data on CCTs, floppy disks and imagery on films or papaer prints. Products fall into two categories with respect to processing methods. 1. Standard processing includes systematic correction, precision correction, and geocoding, etc. 2.Special product(user dependent) includes multi-scene mosaicking, image classification, user defined annotation or administrative boundary adding, special juts enhancement, etc. proprietary -LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001 Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019 SCIOPS STAC Catalog 2018-01-12 2019-03-01 123, -75, 123, -75 https://cmr.earthdata.nasa.gov/search/concepts/C1605658799-SCIOPS.umm_json The data set comprise data measured with a Single Particle Soot Photometer (SP2) at the Italian/French Dome Concordia station in Antarctica. The station is located at 75°05′59″S 123°19′56″E at an elevation of 3233 m. The data was collected at the ATMOS clean air facility at the station between 1.12.2018 - 3.1.2019. The SP2 is a single particle instrument which recorded every particle detected for the duration of the measurements. Physical parameters derived from the recorded data include optical size of the particles and soot-core size of the soot containing particles. proprietary LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001 Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019 ALL STAC Catalog 2018-01-12 2019-03-01 123, -75, 123, -75 https://cmr.earthdata.nasa.gov/search/concepts/C1605658799-SCIOPS.umm_json The data set comprise data measured with a Single Particle Soot Photometer (SP2) at the Italian/French Dome Concordia station in Antarctica. The station is located at 75°05′59″S 123°19′56″E at an elevation of 3233 m. The data was collected at the ATMOS clean air facility at the station between 1.12.2018 - 3.1.2019. The SP2 is a single particle instrument which recorded every particle detected for the duration of the measurements. Physical parameters derived from the recorded data include optical size of the particles and soot-core size of the soot containing particles. proprietary +LTCPAA_DOMECONCORDIA_2018_2019_SP2_AEROSOL_SOOT_SIZEDISTRIBUTIONS_001 Aerosol optical size distribution and soot core size distribution measured by a Single Particle Soot Photometer (SP2) for 30 days in summer 2018-2019 SCIOPS STAC Catalog 2018-01-12 2019-03-01 123, -75, 123, -75 https://cmr.earthdata.nasa.gov/search/concepts/C1605658799-SCIOPS.umm_json The data set comprise data measured with a Single Particle Soot Photometer (SP2) at the Italian/French Dome Concordia station in Antarctica. The station is located at 75°05′59″S 123°19′56″E at an elevation of 3233 m. The data was collected at the ATMOS clean air facility at the station between 1.12.2018 - 3.1.2019. The SP2 is a single particle instrument which recorded every particle detected for the duration of the measurements. Physical parameters derived from the recorded data include optical size of the particles and soot-core size of the soot containing particles. proprietary LTER_0 Long Term Ecological Research Network (LTER) OB_DAAC STAC Catalog 1981-09-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360464-OB_DAAC.umm_json Measurements from the Long Term Ecological Research Network (LTER) between 1981 and 1999. proprietary LUH2_GCB2019_1851_1 LUH2-GCB2019: Land-Use Harmonization 2 Update for the Global Carbon Budget, 850-2019 ORNL_CLOUD STAC Catalog 0850-01-01 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2756847743-ORNL_CLOUD.umm_json This dataset, referred to as LUH2-GCB2019, includes 0.25-degree gridded, global maps of fractional land-use states, transitions, and management practices for the period 0850-2019. The LUH2-GCB2019 dataset is an update to the previous Land-Use Harmonization Version 2 (LUH2-GCB) datasets prepared as required input to land models in the annual Global Carbon Budget (GCB) assessments, including land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, afforestation, and crop rotations. Compared with previous LUH2-GCB datasets, the LUH2-GCB2019 takes advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil, as far back as 1950. LUH2-GCB datasets are used by bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB. proprietary LULC_Nigeria_Ethiopia_SAfrica_2367_1 Annual Land Use and Urban Land Cover: Ethiopia, Nigeria, and South Africa, 2016-2020 ORNL_CLOUD STAC Catalog 2016-01-01 2020-12-31 2.57, -35.34, 49.69, 16.21 https://cmr.earthdata.nasa.gov/search/concepts/C3235688636-ORNL_CLOUD.umm_json This dataset provides a two-tier annual Land Use (LU) and Urban Land Cover (LC) product suite over three African countries, Ethiopia, Nigeria, and South Africa, across a 5-year period of 2016-2020. Remote sensing data sources were used to create 30-m resolution LU maps (Tier-1), which were then utilized to delineate urban boundaries for 10-m resolution LC classes (Tier-2). Random Forest machine learning classifier models were trained on reference data for each tier and country (but one model was trained across all years); models were validated using a separate reference data set for each tier and country. Tier-1 LU maps were based on the 30-m Landsat time series, and Tier-2 urban LC maps were based on the 10-m Sentinel-2 time series. Additional data sources included climate, topography, night-time light, and soils. The overall map accuracy was 65-80% for Tier-1 maps and 60-80% for Tier-2 maps, depending on the year and country. The data are provided in cloud optimized GeoTIFF (COG) format. proprietary @@ -10547,8 +10547,8 @@ Landsat_8 Landsat 8 USGS_LTA STAC Catalog 2013-02-11 -180, -82.71, 180, 82.74 h Landsat_MSS_ESA_Archive_9.0 Landsat MSS ESA Archive ESA STAC Catalog 1975-04-21 1993-12-31 -22, -24, 44, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1965336926-ESA.umm_json This dataset contains all the Landsat 1 to Landsat 5 Multi Spectral Scanner (MSS) high-quality ortho-rectified L1T dataset acquired by ESA over the Fucino, Kiruna (active from April to September only) and Maspalomas (on campaign basis) visibility masks. The acquired Landsat MSS scene covers approximately 183 x 172.8 km. A standard full scene is nominally centred on the intersection between a path and row (the actual image centre can vary by up to 200m). The altitude changed from 917 Km to 705 km and therefore two World Reference Systems (WRS) were. A full image is composed of 3460 pixels x 2880 lines with a pixel size of 60m. Level 1 Geometrically and terrain corrected GTC products (L1T) are available: it is the most accurate level of processing as it incorporates Ground Control Points (GCPs) and a Digital Elevation Model (DEM) to provide systematic geometric and topographic accuracy, with geodetic accuracy dependent on the number, spatial distribution and accuracy of the GCPs over the scene extent, and the resolution of the DEM used. proprietary Landsat_RBV_8.0 Landsat RBV ESA STAC Catalog 1978-11-01 2018-08-01 20, -90, 50, 75 https://cmr.earthdata.nasa.gov/search/concepts/C3325393983-ESA.umm_json This dataset contains Landsat 3 Return Beam Vidicon (RBV) products, acquired by ESA by the Fucino ground station over its visibility mask. The data (673 scenes) are the result of the digitalization of the original 70 millimetre (mm) black and white film rolls. The RBV instrument was mounted on board the Landsat 1 to 3 satellites between 1972 and 1983, with 80 meter resolution. Three independent co-aligned television cameras, one for each spectral band (band 1: blue-green, band 2: yellow-red, band 3: NIR), constituted this instrument. The RBV system was redesigned for Landsat 3 to use two cameras operating in one broad spectral band (green to near-infrared; 0.505–0.750 µm), mounted side-by-side, with panchromatic spectral response and higher spatial resolution than on Landsat-1 and Landsat-2. Each of the cameras produced a swath of about 90 km (for a total swath of 180 km), with a spatial resolution of 40 m. proprietary Large_River_DOC_Export_0 Export of dissolved organic carbon (DOC) by large rivers OB_DAAC STAC Catalog 2015-05-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360426-OB_DAAC.umm_json Measurements taken as a part of a project to quanitfy and assess the export of dissolved organic carbon by large rivers. proprietary -Last_Day_Spring_Snow_1528_1 ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016 ALL STAC Catalog 2000-04-01 2016-07-02 -175.76, 52.17, -97.95, 68.97 https://cmr.earthdata.nasa.gov/search/concepts/C2162119017-ORNL_CLOUD.umm_json "This dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution. Pixels in the daily snow cover fraction grids from April 1 through July 31 were flagged as ""Snow"" if the snow fraction exceeded 0.15, resulting in a time series of binary daily snow cover grids for each year. The annual last day of spring snow for each pixel was identified by day of the year ranging from 91 (April 1) to 183 (July 2)." proprietary Last_Day_Spring_Snow_1528_1 ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016 ORNL_CLOUD STAC Catalog 2000-04-01 2016-07-02 -175.76, 52.17, -97.95, 68.97 https://cmr.earthdata.nasa.gov/search/concepts/C2162119017-ORNL_CLOUD.umm_json "This dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution. Pixels in the daily snow cover fraction grids from April 1 through July 31 were flagged as ""Snow"" if the snow fraction exceeded 0.15, resulting in a time series of binary daily snow cover grids for each year. The annual last day of spring snow for each pixel was identified by day of the year ranging from 91 (April 1) to 183 (July 2)." proprietary +Last_Day_Spring_Snow_1528_1 ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016 ALL STAC Catalog 2000-04-01 2016-07-02 -175.76, 52.17, -97.95, 68.97 https://cmr.earthdata.nasa.gov/search/concepts/C2162119017-ORNL_CLOUD.umm_json "This dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution. Pixels in the daily snow cover fraction grids from April 1 through July 31 were flagged as ""Snow"" if the snow fraction exceeded 0.15, resulting in a time series of binary daily snow cover grids for each year. The annual last day of spring snow for each pixel was identified by day of the year ranging from 91 (April 1) to 183 (July 2)." proprietary Leaf_Carbon_Nutrients_1106_1 A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves ALL STAC Catalog 1970-01-01 2009-12-31 -159.7, -50, 176.9, 68.5 https://cmr.earthdata.nasa.gov/search/concepts/C2784383820-ORNL_CLOUD.umm_json This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set. proprietary Leaf_Carbon_Nutrients_1106_1 A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves ORNL_CLOUD STAC Catalog 1970-01-01 2009-12-31 -159.7, -50, 176.9, 68.5 https://cmr.earthdata.nasa.gov/search/concepts/C2784383820-ORNL_CLOUD.umm_json This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set. proprietary Leaf_Photosynthesis_Traits_1224_1 A Global Data Set of Leaf Photosynthetic Rates, Leaf N and P, and Specific Leaf Area ALL STAC Catalog 1993-01-01 2010-12-31 -122.4, -43.2, 176.13, 58.42 https://cmr.earthdata.nasa.gov/search/concepts/C2784384781-ORNL_CLOUD.umm_json This global data set of photosynthetic rates and leaf nutrient traits was compiled from a comprehensive literature review. It includes estimates of Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), leaf nitrogen content (N), leaf phosphorus content (P), and specific leaf area (SLA) data from both experimental and ambient field conditions, for a total of 325 species and treatment combinations. Both the original published Vcmax and Jmax values as well as estimates at standard temperature are reported. The maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) are primary determinants of photosynthetic rates in plants, and modeled carbon fluxes are highly sensitive to these parameters. Previous studies have shown that Vcmax and Jmax correlate with leaf nitrogen across species and regions, and locally across species with leaf phosphorus and specific leaf area, yet no universal relationship suitable for global-scale models is currently available. These data are suitable for exploring the general relationships of Vcmax and Jmax with each other and with leaf N, P and SLA. This data set contains one *.csv file. proprietary @@ -10556,8 +10556,8 @@ Leaf_Photosynthesis_Traits_1224_1 A Global Data Set of Leaf Photosynthetic Rates Level_2A_aerosol_cloud_optical_products_3.0 Aeolus L2A Aerosol/Cloud optical product ESA STAC Catalog 2021-05-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207498185-ESA.umm_json The Level 2A aerosol/cloud optical products of the Aeolus mission include geo-located consolidated backscatter and extinction profiles, backscatter-to-extinction coefficient, LIDAR ratio, scene classification, heterogeneity index and attenuated backscatter signals. Resolution - Horizontal resolution of L2A optical properties at observation scale (~87 km); Exceptions are group properties (horizontal accumulation of measurements from ~3 km to ~87 km) and attenuated backscatters (~3 km); Note: the resolution of "groups" in the L2A can only go down to 5 measurements at the moment, i.e. ~15 km horizontal resolution. This could be configured to go to 1 measurement - Vertical resolution 250-2000 m (Defined by Range Bin Settings https://earth.esa.int/eogateway/instruments/aladin/overview-of-the-main-wind-rbs-changes). proprietary Level_2A_aerosol_cloud_optical_products_3.0 Aeolus L2A Aerosol/Cloud optical product ALL STAC Catalog 2021-05-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207498185-ESA.umm_json The Level 2A aerosol/cloud optical products of the Aeolus mission include geo-located consolidated backscatter and extinction profiles, backscatter-to-extinction coefficient, LIDAR ratio, scene classification, heterogeneity index and attenuated backscatter signals. Resolution - Horizontal resolution of L2A optical properties at observation scale (~87 km); Exceptions are group properties (horizontal accumulation of measurements from ~3 km to ~87 km) and attenuated backscatters (~3 km); Note: the resolution of "groups" in the L2A can only go down to 5 measurements at the moment, i.e. ~15 km horizontal resolution. This could be configured to go to 1 measurement - Vertical resolution 250-2000 m (Defined by Range Bin Settings https://earth.esa.int/eogateway/instruments/aladin/overview-of-the-main-wind-rbs-changes). proprietary LiDAR_Forest_Inventory_Brazil_1644_1 LiDAR Surveys over Selected Forest Research Sites, Brazilian Amazon, 2008-2018 ORNL_CLOUD STAC Catalog 2008-01-01 2018-12-31 -68.3, -26.7, -39.06, -1.58 https://cmr.earthdata.nasa.gov/search/concepts/C2398128915-ORNL_CLOUD.umm_json This dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo. The point clouds have been georeferenced, noise-filtered, and corrected for misalignment of overlapping flight lines. They are provided in 1 km2 tiles. The data were collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time. proprietary -LiDAR_Tundra_Forest_AK_1782_1 ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016 ALL STAC Catalog 2016-06-14 2016-06-25 -149.76, 67.97, -149.71, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401877-ORNL_CLOUD.umm_json This dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016. Data were acquired for each plot from multiple scan positions with a Leica ScanStation C10 green wavelength laser instrument. After processing the point spacing is < 1 cm. TLS enables resolution of 3-dimensional landscape features that can be used to derive ecologically important metrics of canopy structure and surface topography at high spatial resolution. proprietary LiDAR_Tundra_Forest_AK_1782_1 ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016 ORNL_CLOUD STAC Catalog 2016-06-14 2016-06-25 -149.76, 67.97, -149.71, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401877-ORNL_CLOUD.umm_json This dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016. Data were acquired for each plot from multiple scan positions with a Leica ScanStation C10 green wavelength laser instrument. After processing the point spacing is < 1 cm. TLS enables resolution of 3-dimensional landscape features that can be used to derive ecologically important metrics of canopy structure and surface topography at high spatial resolution. proprietary +LiDAR_Tundra_Forest_AK_1782_1 ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016 ALL STAC Catalog 2016-06-14 2016-06-25 -149.76, 67.97, -149.71, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401877-ORNL_CLOUD.umm_json This dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016. Data were acquired for each plot from multiple scan positions with a Leica ScanStation C10 green wavelength laser instrument. After processing the point spacing is < 1 cm. TLS enables resolution of 3-dimensional landscape features that can be used to derive ecologically important metrics of canopy structure and surface topography at high spatial resolution. proprietary LiDAR_Veg_Ht_Idaho_1532_1 LiDAR Data, DEM, and Maximum Vegetation Height Product from Southern Idaho, 2014 ORNL_CLOUD STAC Catalog 2014-08-23 2014-08-31 -116.89, 42.28, -114.68, 43.33 https://cmr.earthdata.nasa.gov/search/concepts/C2767326506-ORNL_CLOUD.umm_json This dataset provides the point cloud data derived from small footprint waveform LiDAR data collected in August 2014 over Reynolds Creek Experimental Watershed and Hollister in southern Idaho. The LiDAR data have been georeferenced, noise-filtered, and corrected for misalignment for overlapping flight lines and are provided in 1 km tiles. High resolution digital elevation models and maps of maximum vegetation height derived from the LiDAR data are provided for each site. proprietary Lidar_Bibliography_1 A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments AU_AADC STAC Catalog 1961-01-01 62, -68, 159, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313620-AU_AADC.umm_json A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments - the bibliography has been compiled by Andrew Klekociuk of the Australian Antarctic Division (Space and Atmospheric Sciences section of the Ice, Oceans Atmosphere and Climate Program). At the 4th of June, 2007, the bibliography contained 996 references. The bibliography can also be searched via the scientific bibliographies database available at the URL given below. The fields in this dataset are: year author title journal proprietary Lidar_Bibliography_1 A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments ALL STAC Catalog 1961-01-01 62, -68, 159, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313620-AU_AADC.umm_json A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments - the bibliography has been compiled by Andrew Klekociuk of the Australian Antarctic Division (Space and Atmospheric Sciences section of the Ice, Oceans Atmosphere and Climate Program). At the 4th of June, 2007, the bibliography contained 996 references. The bibliography can also be searched via the scientific bibliographies database available at the URL given below. The fields in this dataset are: year author title journal proprietary @@ -10952,61 +10952,41 @@ MERGED_TP_J1_OSTM_OST_GMSL_ASCII_V52_5.2 Global Mean Sea Level Trend from Integr MERIS_L1_FRS_4 ENVISAT MERIS Full Resolution, Full Swath (FRS) Data, version 4 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1569867387-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L1_RR_4 ENVISAT MERIS Reduced Resolution (RR) Data, version 4 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1569867388-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L2_FRS_IOP_2022.0 ENVISAT MERIS Level-2 Regional Full Resolution, Full Swath (FRS) Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281901057-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L2_FRS_IOP_R2022.0 ENVISAT MERIS Regional Full Resolution, Full Swath (FRS) Inherent Optical Properties (IOP) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672029-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L2_FRS_OC_2022.0 ENVISAT MERIS Level-2 Regional Full Resolution, Full Swath (FRS) Ocean Color (OC) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778845-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L2_FRS_OC_R2022.0 ENVISAT MERIS Regional Full Resolution, Full Swath (FRS) Ocean Color (OC) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672030-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L2_ILW_4 ENVISAT MERIS Regional Inland Waters (ILW) Data, version 4 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2954423359-OB_DAAC.umm_json The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site. proprietary MERIS_L2_RR_IOP_2022.0 ENVISAT MERIS Level-2 Regional Reduced Resolution (RR) Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281901072-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L2_RR_IOP_R2022.0 ENVISAT MERIS Regional Reduced Resolution (RR) Inherent Optical Properties (IOP) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672032-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L2_RR_OC_2022.0 ENVISAT MERIS Level-2 Regional Reduced Resolution (RR) Ocean Color (OC) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778850-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L2_RR_OC_R2022.0 ENVISAT MERIS Regional Reduced Resolution (RR) Ocean Color (OC) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672033-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3b_CHL_2022.0 ENVISAT MERIS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778854-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3b_CHL_R2022.0 ENVISAT MERIS Global Binned Chlorophyll (CHL) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672034-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3b_CYANTC_5.0 ENVISAT MERIS Global Binned CyAN Project, True Color (TC) Data, version 5.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561580570-OB_DAAC.umm_json Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. proprietary MERIS_L3b_CYAN_5.0 ENVISAT MERIS Global Binned Cyanobacteria Index (CI) Data, version 5.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561580568-OB_DAAC.umm_json Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. proprietary MERIS_L3b_ILW_4 ENVISAT MERIS Regional Binned Inland Waters (ILW) Data, version 4 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2954423607-OB_DAAC.umm_json The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site. proprietary MERIS_L3b_IOP_2022.0 ENVISAT MERIS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778868-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3b_IOP_R2022.0 ENVISAT MERIS Global Binned Inherent Optical Properties (IOP) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672035-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3b_KD_2022.0 ENVISAT MERIS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778872-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3b_KD_R2022.0 ENVISAT MERIS Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672036-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3b_PAR_2022.0 ENVISAT MERIS Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778878-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3b_PAR_R2022.0 ENVISAT MERIS Global Binned Photosynthetically Available Radiation (PAR) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672040-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3b_PIC_2022.0 ENVISAT MERIS Level-3 Global Binned Particulate Inorganic Carbon (PIC) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778885-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3b_PIC_R2022.0 ENVISAT MERIS Global Binned Particulate Inorganic Carbon (PIC) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672041-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3b_POC_2022.0 ENVISAT MERIS Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778891-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3b_POC_R2022.0 ENVISAT MERIS Global Binned Particulate Organic Carbon (POC) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672042-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3b_RRS_2022.0 ENVISAT MERIS Level-3 Global Binned Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778899-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3b_RRS_R2022.0 ENVISAT MERIS Global Binned Remote-Sensing Reflectance (RRS) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672043-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3m_CHL_2022.0 ENVISAT MERIS Level-3 Global Mapped Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778904-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3m_CHL_R2022.0 ENVISAT MERIS Global Mapped Chlorophyll (CHL) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672044-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3m_CYANTC_5.0 ENVISAT MERIS Global Mapped CyAN Project, True Color (TC) Data, version 5.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561580577-OB_DAAC.umm_json Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. proprietary MERIS_L3m_CYAN_5.0 ENVISAT MERIS Global Mapped Cyanobacteria Index (CI) Data, version 5.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561580575-OB_DAAC.umm_json Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data. proprietary MERIS_L3m_ILW_4 ENVISAT MERIS Regional Mapped Inland Waters (ILW) Data, version 4 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2954423744-OB_DAAC.umm_json The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site. proprietary MERIS_L3m_IOP_2022.0 ENVISAT MERIS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778909-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3m_IOP_R2022.0 ENVISAT MERIS Global Mapped Inherent Optical Properties (IOP) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672045-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3m_KD_2022.0 ENVISAT MERIS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778916-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3m_KD_R2022.0 ENVISAT MERIS Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672046-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3m_PAR_2022.0 ENVISAT MERIS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778919-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3m_PAR_R2022.0 ENVISAT MERIS Global Mapped Photosynthetically Available Radiation (PAR) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672047-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3m_PIC_2022.0 ENVISAT MERIS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778924-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3m_PIC_R2022.0 ENVISAT MERIS Global Mapped Particulate Inorganic Carbon (PIC) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672049-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3m_POC_2022.0 ENVISAT MERIS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778927-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3m_POC_R2022.0 ENVISAT MERIS Global Mapped Particulate Organic Carbon (POC) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672050-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L3m_RRS_2022.0 ENVISAT MERIS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3281778928-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L3m_RRS_R2022.0 ENVISAT MERIS Global Mapped Remote-Sensing Reflectance (RRS) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2210672051-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L4b_GSM_2022.0 ENVISAT MERIS Level-4 Global Binned Garver-Siegel-Maritorena Model (GSM) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3288082129-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L4b_GSM_R2022.0 ENVISAT MERIS 4B Global Binned Garver-Siegel-Maritorena Model (GSM) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2802700386-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERIS_L4m_GSM_2022.0 ENVISAT MERIS Level-4 Global Mapped Garver-Siegel-Maritorena Model (GSM) Data, version 2022.0 OB_CLOUD STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3288082302-OB_CLOUD.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary -MERIS_L4m_GSM_R2022.0 ENVISAT MERIS 4M Global Mapped Garver-Siegel-Maritorena Model (GSM) Data, version R2022.0 OB_DAAC STAC Catalog 2002-03-21 2012-05-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2802700390-OB_DAAC.umm_json MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration. proprietary MERRA2_CNN_HAQAST_PM25_1 MERRA2_CNN_HAQAST bias corrected global hourly surface total PM2.5 mass concentration, V1 (MERRA2_CNN_HAQAST_PM25) at GES DISC GES_DISC STAC Catalog 2000-01-01 2024-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3094710982-GES_DISC.umm_json This product provides MERRA-2 bias-corrected global hourly surface total PM2.5 mass concentration with the same horizontal spatial resolution as MERRA-2, covering a temporal range from 2000 to 2024. It is derived using a machine learning (ML) approach with a convolutional neural network (CNN) method and is specifically developed for the NASA Health and Air Quality Applied Sciences Team (HAQAST). The dataset consists of two parameters: MERRA2_CNN_Surface_PM25 and QFLAG. MERRA2_CNN_Surface_PM25, a 3-dimensional variable (time, latitude, longitude), represents the surface PM2.5 concentrations in µg/m³. QFLAG denotes the quality of data at each grid point, where 4 indicates the highest quality and 1 indicates the lowest quality. It is recommended to use QFLAG values of 3 and 4 for quantitative analysis. proprietary MER_FRS_1P_8.0 Envisat MERIS Full Resolution - Level 1 [MER_FRS_1P/ME_1_FRG] ESA STAC Catalog 2002-05-17 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207506362-ESA.umm_json The MERIS Level 1 Full Resolution (FR) product contains the Top of Atmosphere (TOA) upwelling spectral radiance measures. The in-band reference irradiances for the 15 MERIS bands are computed by averaging the in-band solar irradiance of each pixel. The in-band solar irradiance of each pixel is computed by integrating the reference solar spectrum with the band-pass of each pixel. The MERIS FR Level 1 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. Each measurement and annotation data file is in NetCDF 4. The Level 1 product is composed of 22 data files: 15 files containing radiances at each band (one band per file), accompanied by the associated error estimates, and 7 annotation data files. The 15 sun spectral flux values provided in the instrument data file of the Level 1 products are the in-band reference irradiances adjusted for the Earth-sun distance at the time of measurement. The band-pass of each pixel is derived from on-ground and in-flight characterisation via an instrument model. The values "Band wavelength" and "Bandwidth" provided in the Manifest file of the Level 1b products are the averaged band-pass of each pixel over the instrument field of view. Auxiliary data are also listed in the Manifest file associated to each product. The Level 1 FR product covers the complete instrument swath. The product duration is not fixed and it can span up to the time interval of the input Level 0 (for a maximum of 20 minutes). Thus the estimated size of the Level 1 FR is dependent on the start/stop time of the acquired segment. During the Envisat mission, acquisition of MERIS Full Resolution data was subject to dedicated planning based on on-demand ordering and coverage of specific areas according to operational recommendations and considerations. See yearly and global density maps to get a better overview of the MERIS FR coverage. proprietary MER_FRS_2P_8.0 Envisat MERIS Full Resolution - Level 2 [MER_FRS_2P/ME_2_FRG] ESA STAC Catalog 2002-05-17 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207506787-ESA.umm_json MERIS FR Level 2 is a Full-Resolution Geophysical product for Ocean, Land and Atmosphere. Each MERIS Level 2 geophysical product is derived from a MERIS Level 1 product and auxiliary parameter files specific to the MERIS Level 2 processing. The MERIS FR Level 2 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. The data package is composed of NetCDF 4 files containing instrumental and scientific measurements, and a Manifest file which contains metadata information related to the description of the product. A Level 2 product is composed of 64 measurement files containing: 13 files containing Water-leaving reflectance, 13 files containing Land surface reflectance and 13 files containing the TOA reflectance (for all bands except those dedicated to measurement of atmospheric gas - M11 and M15), and several files containing additional measurement on Ocean, Land and Atmospheric parameters and annotation. The Auxiliary data used are listed in the Manifest file associated to each product. The Level 2 FR product covers the complete instrument swath. The product duration is not fixed and it can span up to the time interval of the input Level 0/Level 1. Thus the estimated size of the Level 2 FR is dependent on the start/stop time of the acquired segment. During the Envisat mission, acquisition of MERIS Full Resolution data was subject to dedicated planning based on on-demand ordering and coverage of specific areas according to operational recommendations and considerations. See yearly and global density maps to get a better overview of the MERIS FR coverage. proprietary MESSR_MOS-1_L2_Data_NA MESSR/MOS-1 L2 Data JAXA STAC Catalog 1987-02-24 1995-11-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130302-JAXA.umm_json MESSR/MOS-1 L2 Data is obtained from the MESSR sensor onboard MOS-1, Japan's first marine observation satellite, and produced by the National Space Development Agency of Japan:NASDA. MOS-1, Japan's first marine observation satellite, is Sun-synchronous sub-recurrent Orbit satellite launched on February 19, 1987 as a link in a global satellite observation system for more effective natural resource utilization and for environmental protection. The MESSR is multi-spectral radiometers and has swath of 100 km. This dataset includes radiometric and geometric corrected applied raw data.Map projection is UTM, SOM, PS. The provided format is CEOS. The spatial resolution is 50 m. proprietary MESSR_MOS-1b_L2_Data_NA MESSR/MOS-1b L2 Data JAXA STAC Catalog 1990-03-09 1996-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133853-JAXA.umm_json MESSR/MOS-1b L2 Data is obtained from the MESSR sensor onboard MOS-1b, Japan's first marine observation satellite, and produced by the National Space Development Agency of Japan:NASDA. MOS-1b which has the same functions as MOS-1 is Sun-synchronous sub-recurrent Orbit satellite launched on February 7, 1990 as a link in a global satellite observation system for more effective natural resource utilization and for environmental protection. The MESSR is multi-spectral radiometers and has swath of 100 km. This dataset includes radiometric and geometric corrected applied raw data.Map projction is UTM, SOM, PS. The provided format is CEOS. The spatial resolution is 50 m. proprietary -MFLL_CO2_Weighting_Functions_1891_1 ACT-America: L2 Weighting Functions for Airborne Lidar Column-avg CO2, Eastern USA ALL STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2704977536-ORNL_CLOUD.umm_json This dataset provides vertical weighting function coefficients of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. The MFLL-measured column-averaged CO2 values have certain distinct vertical weights on CO2 profiles depending on the meteorological conditions and the wavelengths used at the measurement time and location. This product includes the instrument location at the time of measurement in geographic coordinates and altitude, along with a vector of weighting function values representing conditions along the nadir direction. proprietary MFLL_CO2_Weighting_Functions_1891_1 ACT-America: L2 Weighting Functions for Airborne Lidar Column-avg CO2, Eastern USA ORNL_CLOUD STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2704977536-ORNL_CLOUD.umm_json This dataset provides vertical weighting function coefficients of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. The MFLL-measured column-averaged CO2 values have certain distinct vertical weights on CO2 profiles depending on the meteorological conditions and the wavelengths used at the measurement time and location. This product includes the instrument location at the time of measurement in geographic coordinates and altitude, along with a vector of weighting function values representing conditions along the nadir direction. proprietary -MFLL_XCO2_Range_10Hz_1892_1 ACT-America: L2 Remotely Sensed Column-avg CO2 by Airborne Lidar, Lite, Eastern USA ORNL_CLOUD STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2704971204-ORNL_CLOUD.umm_json This dataset provides a direct subset (i.e., the Lite version) of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1-second column CO2 reporting frequency, is included, but not limited to, latitude, longitude, altitude, and attitude. proprietary +MFLL_CO2_Weighting_Functions_1891_1 ACT-America: L2 Weighting Functions for Airborne Lidar Column-avg CO2, Eastern USA ALL STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2704977536-ORNL_CLOUD.umm_json This dataset provides vertical weighting function coefficients of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. The MFLL-measured column-averaged CO2 values have certain distinct vertical weights on CO2 profiles depending on the meteorological conditions and the wavelengths used at the measurement time and location. This product includes the instrument location at the time of measurement in geographic coordinates and altitude, along with a vector of weighting function values representing conditions along the nadir direction. proprietary MFLL_XCO2_Range_10Hz_1892_1 ACT-America: L2 Remotely Sensed Column-avg CO2 by Airborne Lidar, Lite, Eastern USA ALL STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2704971204-ORNL_CLOUD.umm_json This dataset provides a direct subset (i.e., the Lite version) of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1-second column CO2 reporting frequency, is included, but not limited to, latitude, longitude, altitude, and attitude. proprietary +MFLL_XCO2_Range_10Hz_1892_1 ACT-America: L2 Remotely Sensed Column-avg CO2 by Airborne Lidar, Lite, Eastern USA ORNL_CLOUD STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2704971204-ORNL_CLOUD.umm_json This dataset provides a direct subset (i.e., the Lite version) of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1-second column CO2 reporting frequency, is included, but not limited to, latitude, longitude, altitude, and attitude. proprietary MI03_resp_nutrients_GC1_1 GC-FID analysis of soil respirometery experiment. Soil from Macquarie Island, sampled in 2003. AU_AADC STAC Catalog 2003-01-01 2003-12-31 158.76892, -54.78406, 158.96667, -54.47802 https://cmr.earthdata.nasa.gov/search/concepts/C1214313661-AU_AADC.umm_json Field samples were collected from the Main Power House at Macquarie Island - coordinates.... The soil sample used for the respirometer trial was made up as a composite of 8 cores, namely: MPH1, MPH3, MPH4, MPH5, MPH7, MPH8 and MPH9. Each core was analysed for petroleum hydrocarbons (PHCs) at 0.05 m intervals. Intervals containing between 2500 and 5000 mg/kg PHC were then combined into a bulked sample used in the respirometer test. The sample was homogenised by placing all the soil (4.5 kg) into a large mixing bowl and stirring with a flat stirrer. The respirometer experiment was conducted by Jim Walworth and Andrew Pond at the University of Arizona. The objective was to optimise the nutrient status for microbial degradation of PHC's. The respirometer used was an N-Con closed system, with 24 flasks. There were 5 treatments and a control, each run in quadriplate. The control was unammended while treatments were 125, 250, 375, 500, and 625 mg nitrogen/kg of soil (on a dry soil weight basis). See: Sheet 'Sample details' for sample barcode, user ID and sample mass summary. Sheet 'GC-FID Data', cells A1-A18 = sample ID, GC injection file and processing notes Sheet 'GC-FID Data', Rows 10 and 11 contain TPH estimates and estimated standard uncertainty for the TPH value Sheet 'GC-FID Data', cells A21-A125 = compounds or GC elution windows measured Sheet 'GC-FID Data', cells B21-B56 = compound [CAS numbers] Sheet 'GC-FID Data', cells C21-AL125 = GC-FID area responses Sheet 'GC-FID Data', cells C128-AL232 = Estimated standard uncertainties for all GC-FID area responses (from blank drifts,local signal/noise etc) Chemical analysis details........Sample Extraction A 0.5mL volume of internal standard solution containing a mixture of compounds (cyclo-octane at c.1000mg/L, d8-naphthalene at 100mg/L, p-terphenyl at 100 mg/L and 1-bromoeicosane at 1000mg/L) dissolved in hexane, was pipetted onto the soil with a calibrated positive displacement pipette. This was followed by the addition of 10mL of hexane and 10mL of water. The vials were then tumbled end over end (50rpm) overnight and centrifuged at 1500 rpm. 1.8mL of the clear hexane layer was transferred by Pasteur pipette into a 2mL vial for Gas Chromatography Flame Ionisation Detector (GC-FID) analysis Chemical analysis details........GC-FID parameters The download file also includes a paper produced from this data. This work was completed as part of ASAC project 1163 (ASAC_1163). proprietary MI08_soil_properties_1 Characteristics of soil collected on Macquarie Island in 2008. AU_AADC STAC Catalog 2008-01-01 2008-01-31 158.93, -54.51, 158.94, -54.49 https://cmr.earthdata.nasa.gov/search/concepts/C1214313645-AU_AADC.umm_json Samples were collected on Macquarie Island from three sites: the main powerhouse, the fuel farm and a reference site on the isthmus by the Bioremediation Project team in January 2008. Soil characteristics including conductivity, pH, total petroleum hydrocarbons, total carbon, nitrate, nitrite, ammonium, fluoride, bromide, chloride, sulphate and phosphate were measured. The data consists of two files, the rtf file contains the methods used and the csv file contains the soil characteristics. Samples are identified by a barcode which is the barcode number assigned by the Bioremediation Project Sample Tracking Database. This work was carried out as part of AAS project 1163. proprietary MI1AC_2 MISR Level 1A Calibration Data V002 LARC STAC Catalog 1999-12-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179031451-LARC.umm_json MI1AC_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1A Calibration data in DN. The data numbers have been commuted from 12-bit to 16-bit, byte-aligned half-word version 2. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. proprietary @@ -11576,8 +11556,8 @@ MODIS_CCaN_NDVI_Trends_Alaska_1666_1 ABoVE: MODIS- and CCAN-Derived NDVI and Tre MODIS_CR_Equal_Angle_3h_1.0 MODIS_CR_Equal_Angle_3h GES_DISC STAC Catalog 2002-12-31 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089272156-GES_DISC.umm_json The MODIS Collection 6.1 Equal-Angle Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures. proprietary MODIS_CR_Equal_Angle_Daily_1.0 MODIS_CR_Equal_Angle_Daily GES_DISC STAC Catalog 2002-12-31 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089272480-GES_DISC.umm_json The MODIS Collection 6.1 Equal-Angle Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures. proprietary MODIS_CR_Equal_Area_3h_1.0 MODIS_CR_Equal_Area_3h GES_DISC STAC Catalog 2002-12-31 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2084194432-GES_DISC.umm_json The MODIS Collection 6.1 Equal-Area Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures. proprietary -MODIS_MAIAC_Reflectance_1700_1 ABoVE: Corrected MODIS MAIAC Reflectance at Tower Sites, Alaska and Canada, 2000-2016 ORNL_CLOUD STAC Catalog 2000-02-24 2016-07-31 -157.41, 42.64, -74.04, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2143403511-ORNL_CLOUD.umm_json This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances by two methods at each of 62 flux tower sites (1 km x 1 km area) across the ABoVE domain in Alaska and western Canada from 2000 to 2015/2016. The original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) using two independent algorithms: the first based on the original BRDF (Bidirectional Reflectance Distribution Function) parameters provided by the MAIAC team, and the second based on a machine learning approach (random forests). The corrected data preserve the original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12). The resulting tower site sub-daily timeseries of angular corrected surface reflectances are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena. proprietary MODIS_MAIAC_Reflectance_1700_1 ABoVE: Corrected MODIS MAIAC Reflectance at Tower Sites, Alaska and Canada, 2000-2016 ALL STAC Catalog 2000-02-24 2016-07-31 -157.41, 42.64, -74.04, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2143403511-ORNL_CLOUD.umm_json This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances by two methods at each of 62 flux tower sites (1 km x 1 km area) across the ABoVE domain in Alaska and western Canada from 2000 to 2015/2016. The original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) using two independent algorithms: the first based on the original BRDF (Bidirectional Reflectance Distribution Function) parameters provided by the MAIAC team, and the second based on a machine learning approach (random forests). The corrected data preserve the original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12). The resulting tower site sub-daily timeseries of angular corrected surface reflectances are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena. proprietary +MODIS_MAIAC_Reflectance_1700_1 ABoVE: Corrected MODIS MAIAC Reflectance at Tower Sites, Alaska and Canada, 2000-2016 ORNL_CLOUD STAC Catalog 2000-02-24 2016-07-31 -157.41, 42.64, -74.04, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2143403511-ORNL_CLOUD.umm_json This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances by two methods at each of 62 flux tower sites (1 km x 1 km area) across the ABoVE domain in Alaska and western Canada from 2000 to 2015/2016. The original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) using two independent algorithms: the first based on the original BRDF (Bidirectional Reflectance Distribution Function) parameters provided by the MAIAC team, and the second based on a machine learning approach (random forests). The corrected data preserve the original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12). The resulting tower site sub-daily timeseries of angular corrected surface reflectances are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena. proprietary MODIS_PAR_1140_1 NACP: MODIS Daily Land Incident 4-km PAR Images For North America, 2003-2005 ORNL_CLOUD STAC Catalog 2003-01-01 2005-12-31 -180, 0, 0, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2631225371-ORNL_CLOUD.umm_json This data set contains daily Moderate Resolution Imaging Spectroradiometer (MODIS) land incident photosynthetically active radiation (PAR) images over North America for the years 2003 - 2005 and was created to fill the need for daily PAR estimates. Incident PAR is the solar radiation in the range of 400 to 700 nm reaching the earth's surface and plays an important role in modeling terrestrial ecosystem productivity. The daily images were derived by integrating MODIS/Terra and MODIS/Aqua instantaneous PAR data where the instantaneous PAR data is estimated directly from Terra or Aqua MODIS 5-min L1b swath data (Liang et al., 2006 and Wang et al., 2010). The spatial distribution of this data set includes the MODIS tile subsets covering North America, Central America, portions of South America, and Greenland, available for the years 2003 - 2005. There are 45,376 *.hdf files with a spatial resolution of 4 km x 4 km in sinusoidal projection distributed by year in three compressed data files: 2003.zip, 2004.zip, and 2005.zip. Contained within each daily file are 4 separate image files: DirectPar, DiffusePAR, TotalPAR, and Observation Count. There are 46 MODIS tiles that cover the study area extent. proprietary MODIS_T-JPL-L2P-v2019.0_2019.0 GHRSST Level 2P Global Sea Surface Skin Temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Terra satellite (GDS2) POCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1940475563-POCLOUD.umm_json NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project, and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous R2014.0 datasets which can be found at https://doi.org/10.5067/GHMDT-2PJ02 proprietary MODIS_TERRA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0 MODIS Terra Level 3 SST MID-IR 8 day 4km Nighttime V2019.0 POCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2036882246-POCLOUD.umm_json Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODTM-8D4N4 proprietary @@ -11669,8 +11649,8 @@ MS_Sound_0 Mississippi (MS) Sound optical measurements OB_DAAC STAC Catalog 2005 MTSAT2-OSPO-L2P-v1.0_1.0 GHRSST Level 2P Western Pacific Regional Skin Sea Surface Temperature from the Multifunctional Transport Satellite 2 (MTSAT-2) (GDS version 2) POCLOUD STAC Catalog 2013-08-01 2015-12-04 64, -80, -134, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2499940520-POCLOUD.umm_json Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and its radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis by using Bayesian Cloud Mask algorithm at the Office of Satellite and Product Operations (OSPO). L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. proprietary MUR-JPL-L4-GLOB-v4.1_4.1 GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1) POCLOUD STAC Catalog 2002-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1996881146-POCLOUD.umm_json "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains additional variables for some granules including a SST anomaly derived from a MUR climatology and the temporal distance to the nearest IR measurement for each pixel.This dataset is funded by the NASA MEaSUREs program ( http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata ""history:"" attribute to determine if a granule is near-realtime or retrospective." proprietary MUR25-JPL-L4-GLOB-v04.2_4.2 GHRSST Level 4 MUR 0.25deg Global Foundation Sea Surface Temperature Analysis (v4.2) POCLOUD STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2036880657-POCLOUD.umm_json A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.25 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains an additional SST anomaly variable derived from a MUR climatology (average between 2003 and 2014). This dataset was originally funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ) and the NASA CEOS COVERAGE project and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. proprietary -MURI_Camouflage_0 A Multi University Research Initiative (MURI) Camouflage Project OB_DAAC STAC Catalog 2010-06-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360494-OB_DAAC.umm_json A Multi University Research Initiative was funded to study the biological response to the dynamic, polarized light field in distinct water types. During June 2010, a campaign was undertaken in the coastal waters off Port Aransas, Texas to study the angular/temporal distribution of polarization in multiple environment types (eutrophic sediment laden coastal waters, oligotrophic off-shore), as well as the polarization-reflectance responses of several organisms. In addition to radiometric polarization measurements, water column IOPs, Rrs, benthic reflectance, and pigment concentration measurements were collected. Later campaigns expanded this research in the coastal waters off the Florida Keys. proprietary MURI_Camouflage_0 A Multi University Research Initiative (MURI) Camouflage Project ALL STAC Catalog 2010-06-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360494-OB_DAAC.umm_json A Multi University Research Initiative was funded to study the biological response to the dynamic, polarized light field in distinct water types. During June 2010, a campaign was undertaken in the coastal waters off Port Aransas, Texas to study the angular/temporal distribution of polarization in multiple environment types (eutrophic sediment laden coastal waters, oligotrophic off-shore), as well as the polarization-reflectance responses of several organisms. In addition to radiometric polarization measurements, water column IOPs, Rrs, benthic reflectance, and pigment concentration measurements were collected. Later campaigns expanded this research in the coastal waters off the Florida Keys. proprietary +MURI_Camouflage_0 A Multi University Research Initiative (MURI) Camouflage Project OB_DAAC STAC Catalog 2010-06-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360494-OB_DAAC.umm_json A Multi University Research Initiative was funded to study the biological response to the dynamic, polarized light field in distinct water types. During June 2010, a campaign was undertaken in the coastal waters off Port Aransas, Texas to study the angular/temporal distribution of polarization in multiple environment types (eutrophic sediment laden coastal waters, oligotrophic off-shore), as well as the polarization-reflectance responses of several organisms. In addition to radiometric polarization measurements, water column IOPs, Rrs, benthic reflectance, and pigment concentration measurements were collected. Later campaigns expanded this research in the coastal waters off the Florida Keys. proprietary MURI_HI_0 A Multi University Research Initiative (MURI) near the Hawaiian Islands OB_DAAC STAC Catalog 2012-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360508-OB_DAAC.umm_json Measurements taken by the RV Kilo Moana in 2012 near the Hawaiian Islands. proprietary MURI_HI_0 A Multi University Research Initiative (MURI) near the Hawaiian Islands ALL STAC Catalog 2012-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360508-OB_DAAC.umm_json Measurements taken by the RV Kilo Moana in 2012 near the Hawaiian Islands. proprietary MUSE_0 Monterey Ocean Observing System (MOOS) Upper-water-column Science Experiment (MUSE) OB_DAAC STAC Catalog 2002-07-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360509-OB_DAAC.umm_json Measurements made near Monterey Bay under the MOOS Upper-water-column Science Experiment (MUSE). proprietary @@ -11799,10 +11779,10 @@ Macquarie_Quickbird_2Nov2010_1 Macquarie Island Quickbird Image (2 November 2010 Macquarie_Royals_1962-1968_1 Macquarie Island Royal Penguin studies. Also includes Skua predation study and band resights. 1962 - 1968 AU_AADC STAC Catalog 1962-01-01 1968-12-31 158.76892, -54.78247, 158.95706, -54.48041 https://cmr.earthdata.nasa.gov/search/concepts/C1214311177-AU_AADC.umm_json Scans from one or more field books from observations made at Macquarie Island between 1962 and 1968. The observations were of Royal Penguins, and also of Skua predation and band resights. The following names have been mentioned in the scans: Susan Ingham John Warham John Ling David Nicolls I.T. Simpson Duncan Mackenzie Peter Shaughnessy D. Edwards R.Carrick Merilees Kerry Peter Ormay Schmidt Major S. Harris proprietary Macquarie_Tide_Gauges_2 Macquarie Island Tide Gauge Data 1993-2007 AU_AADC STAC Catalog 1993-11-01 2007-04-30 158.76068, -54.78802, 158.95844, -54.47323 https://cmr.earthdata.nasa.gov/search/concepts/C1667370487-AU_AADC.umm_json Over time there have been a number of tide gauges deployed at Macquarie Island Station. The data download files contain further information about the gauges, but some of the information has been summarised here. Note that this metadata record only describes tide gauge data from 1993 to 2007. More recent data are described elsewhere. Macquarie Island used Aquatrak and Druck tide gauges during this period. Documentation from the older metadata record: Documentation dated 2001-06-12 The Macquarie Island Tide Gauge System The Macquarie Island Tide Gauge was first commissioned in November 1993. Since then every year attempts have been made to improve the performance of the system. The next improvement involves the installation of radio modems to effect a network link to the tide gauge dataloggers. Other improvements planned are include using the wave guide temperatures to correct the water heights for variations in the velocity of sound in air due to temperature gradients in the waveguide. The system consists of two separate sensors contained in separate housings on a rock shelf on the northern side of Garden Cove. One of the sensors is an Aquatrack acoustic type and the other is a Druck pressure transducer. Both housings contain a Platypus Engineering data logger and a battery. The housings consist each of an Admiralty Bronze ring bolted down to a concrete plinth and a glass fibre reinforced cover held down by a single central bolt and nut. Primary power for both installations comes from a solar panel array mounted on the northern side of the rock ridge behind the rock shelf. The solar panels are attached to an aluminium frame which is bolted to a galvanized steel frame cemented into holes in the rock face. The bolts are made of nylon with nylon washers so that the aluminium frame is not in contact with the galvanized frame. Mounted below the panels is a sealed plastic box with a hinged door. A multicore data cable runs from this box to the tide gauge housings. This cable is run inside a length of plastic conduit along with the power cable. The conduit is concealed in the vegetation and at the lower level is cemented into slots cut into the rock The batteries in the housing are kept charged by the solar panels but are isolated via power diodes, one in each housing. Either or both of the housing batteries or only the solar panel battery may be removed without interruption to data logging. The voltage of either housing battery may be found by interrogation of the appropriate data logger. Tide Gauge Bore Holes. Both gauges obtain access to the ocean via an inclined hole about 12 metres long inclined at approximately 34 and 39 degrees to the horizontal. Both holes are lined with a plastic pipe which is normally not removable. In the Aquatrack sensor hole a 50mm ABS pressure pipe runs down inside the liner and is fitted with a brass strainer and orifice at the lower end. This strainer protrudes into the ocean somewhat clear of the sea floor (see figure). Inside the 50mm pipe runs a 15mm diameter plastic pipe. The bottom end of this is fitted with a 600mm length of red brass tubing and stops about 100mm from the orifice at the bottom of the pipe. The 15mm pipe is held central in the 50mm pipe by three armed spiders placed about every metre down the pipe. The top end of both pipes is secured by a flange with two O rings and stainless steel screws. On top of the 15mm pipe is mounted the Aquatrack acoustic sensor the 15mm pipe acting as a waveguide for sound pulses from the sensor (see figure ). The Aquatrack sensor measures the distance of the water surface from a reference point on the sensor. About one metre down the wave guide is a small hole. This has two functions. One is to act as vent to allow water to rise and fall in the wave guide and the other is to provide an acoustic reflection at a known distance down the wave guide. This allows compensation for velocity of sound changes due to temperature changes. The Aquatrak wave guide has a series of thermistors placed along its length. The bottom one is always submerged and is used to measure the seawater temperature..The top one is placed just below the Sensor and the others evenly spaced along the length of the waveguide. The temperature readings from these can be used to compensate for the change in the velocity of sound due to density changes. This feature has not yet been used. The Druck Sensor has a single thermistor placed beside it which measures seawater temperature. System Components. The Aquatrak Installation houses four main components. 1. The Aquatrack Sensor and Waveguide Assembly. The sensor itself is in a waterproof plastic tube with a cable with a waterproof connector which plugs into the Bartek controller. 2. The Bartek Controller, housed in a waterproof diecast box with waterproof connectors. This lies in the centre of the installation housing. 3. The Platypus Engineering Datalogger 4. The Battery, a 15 Ah, 12 volt sealed gel cell lead acid battery. It is charged from the solar a diode. The battery lies in the main housing opposite the Datalogger . The Druck Installation houses four main components 1. The Druck Pressure Sensor, fitted to the end of a 13 metre cable, submerged in seawater about 10 metres down the borehole. The cable has five conductors and an air vent enclosed within it. 2. The Pressure Sensor Amplifier housed in a waterproof diecast box. This box has a vent leading to a vented bottle filled with silica gel to keep the transducer air vent dry. 3. A Datalogger As above. 4. A battery as above The Solar Panel Installation has three main parts. 1. Three Photo Voltaic Solar Panels, two 60 Watt and one 30 Watt. These are mounted on an aluminium frame attached to a hotdip galvanised steel frame with insulating bolts. 2. A sealed plastic box mounted below the panels containing a12V 24 Ah Battery and a regulator and the radio modem equipment. (The modems are not currently fitted.) 3. Antennae and cables protected with flexible conduit. Data Retrieval Data have been retrieved at approximately 30 day intervals from the Garden Cove gauges by using a portable computer to download the data loggers. The connector for this is in the enclosure by the solar panels allowing the loggers to be accessed during bad weather. Documentation dated 2008-10-17 1. In April 2007, the dataloggers and radio modems at Macquarie Island Tide Gauge site were replaced with Campbell Scientific CR1000 dataloggers. 2. This change enabled data to be streamed from the pressure sensor datalogger every 30 seconds. 3. There has been no change to scaling of records from the Aquatrak sensor as generation of ranges is done by the Aquatrak controller, the datalogger only saving and transmitting the records. Records from the pressure sensor however are now not converted to heights but saved and streamed as raw A/D conversion values. It is intended that appropriate scales and offsets for this sensor be derived after a Floating GPS Buoy exercise. 4. Data is streamed from the pressure sensor logger as this is the only sensor that can be supply 30 seconds average values. This logger also streams 3 minute average values. 5. The aquatrak sensor logger streams 3 minute average value ranges. 6. Data is streamed in NVP (name/Value Pair) format as defined by BoM. 7. Embedded in the streams are battery voltage and aquatrak waveguide temperature values. proprietary MagMix_0 MagMix project OB_DAAC STAC Catalog 2008-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360470-OB_DAAC.umm_json Estuarine and coastal systems play important roles in society, serving as port facilities, productive fisheries and rookeries, and scenic recreational areas. However, these same values to society mean that these areas can be significantly affected by human activities. Inputs of nutrients, organic matter, and trace metals are among these impacts. The MagMix project seeks to understand the transport and cycling of nutrients and trace elements and relate that to biogeochemical and optical properties in river-dominated coastal systems. The area of study is the outflow region of the Mississippi and Atchafalaya rivers in the northern Gulf of Mexico. The Mississippi River carries high concentrations of plant nutrients derived from fertilizer use on farms in the heartland of the US. These excess nutrients stimulate plant growth in the surface waters of the Louisiana Shelf. These plants, in turn, sink to the bottom waters of the shelf where they serve as food for respiring organisms. The input of this excess food then stimulates an excess of respiration thereby depleting the shelf bottom waters of oxygen during the summer. These oxygen-depleted (or hypoxic) waters then become a dead zone avoided by animals. The overall goal of this research project is to better understand the mixing processes and their relationship to optical and biogeochemical properties as the waters of the Mississippi River and the Atchafalaya River enter the Gulf of Mexico. proprietary -Main_Melt_Onset_Dates_1841_1.1 ABoVE: Passive Microwave-derived Annual Snowpack Main Melt Onset Date Maps, 1988-2018 ORNL_CLOUD STAC Catalog 1988-02-09 2018-02-10 -180, 51.61, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2143401742-ORNL_CLOUD.umm_json This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) observations, and compared to the established Freeze-Thaw ESDR (FT-ESDR) spring onset date. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes across the ABoVE domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary Main_Melt_Onset_Dates_1841_1.1 ABoVE: Passive Microwave-derived Annual Snowpack Main Melt Onset Date Maps, 1988-2018 ALL STAC Catalog 1988-02-09 2018-02-10 -180, 51.61, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2143401742-ORNL_CLOUD.umm_json This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) observations, and compared to the established Freeze-Thaw ESDR (FT-ESDR) spring onset date. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes across the ABoVE domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary -MaineInvasives A Historical Record of Sponges, Bryozoa and Ascidians on the Coast of Maine: 1843-1980 (Bigelow Laboratory for Ocean Sciences) SCIOPS STAC Catalog 1843-01-01 1980-12-31 -70.7, 42.6, -66.9, 45.2 https://cmr.earthdata.nasa.gov/search/concepts/C1214593917-SCIOPS.umm_json Records of the occurrences of marine and estuarine sponges, bryozoans and ascideans on the coast of Maine have been compiled from the historic literature spanning the time frame of 1843 to 1980. These records variously include information on location, abundance, depth and habitat notes. Also available in many cases are common synonymies and scientific author. Sources include the primary literature, scientific and technical reports and unpublished records and field notes of marine researchers. The taxonomy of the species has been verified on the website WoRMS and by taxonomic experts. A few records need further investigation. These data have been georeferenced and entered into the OBIS database providing world-wide access and various search capabilities. proprietary +Main_Melt_Onset_Dates_1841_1.1 ABoVE: Passive Microwave-derived Annual Snowpack Main Melt Onset Date Maps, 1988-2018 ORNL_CLOUD STAC Catalog 1988-02-09 2018-02-10 -180, 51.61, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2143401742-ORNL_CLOUD.umm_json This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) observations, and compared to the established Freeze-Thaw ESDR (FT-ESDR) spring onset date. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes across the ABoVE domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary MaineInvasives A Historical Record of Sponges, Bryozoa and Ascidians on the Coast of Maine: 1843-1980 (Bigelow Laboratory for Ocean Sciences) ALL STAC Catalog 1843-01-01 1980-12-31 -70.7, 42.6, -66.9, 45.2 https://cmr.earthdata.nasa.gov/search/concepts/C1214593917-SCIOPS.umm_json Records of the occurrences of marine and estuarine sponges, bryozoans and ascideans on the coast of Maine have been compiled from the historic literature spanning the time frame of 1843 to 1980. These records variously include information on location, abundance, depth and habitat notes. Also available in many cases are common synonymies and scientific author. Sources include the primary literature, scientific and technical reports and unpublished records and field notes of marine researchers. The taxonomy of the species has been verified on the website WoRMS and by taxonomic experts. A few records need further investigation. These data have been georeferenced and entered into the OBIS database providing world-wide access and various search capabilities. proprietary +MaineInvasives A Historical Record of Sponges, Bryozoa and Ascidians on the Coast of Maine: 1843-1980 (Bigelow Laboratory for Ocean Sciences) SCIOPS STAC Catalog 1843-01-01 1980-12-31 -70.7, 42.6, -66.9, 45.2 https://cmr.earthdata.nasa.gov/search/concepts/C1214593917-SCIOPS.umm_json Records of the occurrences of marine and estuarine sponges, bryozoans and ascideans on the coast of Maine have been compiled from the historic literature spanning the time frame of 1843 to 1980. These records variously include information on location, abundance, depth and habitat notes. Also available in many cases are common synonymies and scientific author. Sources include the primary literature, scientific and technical reports and unpublished records and field notes of marine researchers. The taxonomy of the species has been verified on the website WoRMS and by taxonomic experts. A few records need further investigation. These data have been georeferenced and entered into the OBIS database providing world-wide access and various search capabilities. proprietary Maps_AGB_North_Slope_AK_1565_1 ABoVE: Gridded 30-m Aboveground Biomass, Shrub Dominance, North Slope, AK, 2007-2016 ORNL_CLOUD STAC Catalog 2007-06-01 2016-08-31 -168.58, 64.73, -111.55, 76.23 https://cmr.earthdata.nasa.gov/search/concepts/C2170971358-ORNL_CLOUD.umm_json This dataset includes 30-m gridded estimates of total plant aboveground biomass (AGB), the shrub AGB, and the shrub dominance (shrub/plant AGB) for non-water portions of the Beaufort Coastal Plain and Brooks Foothills ecoregions of the North Slope of Alaska. The estimates were derived by linking biomass harvests from 28 published field site datasets with NDVI from a regional Landsat mosaic derived from Landsat 5 and 7 satellite imagery. The data cover the period 2007-06-01 to 2016-08-31. proprietary Maps_AGB_North_Slope_AK_1565_1 ABoVE: Gridded 30-m Aboveground Biomass, Shrub Dominance, North Slope, AK, 2007-2016 ALL STAC Catalog 2007-06-01 2016-08-31 -168.58, 64.73, -111.55, 76.23 https://cmr.earthdata.nasa.gov/search/concepts/C2170971358-ORNL_CLOUD.umm_json This dataset includes 30-m gridded estimates of total plant aboveground biomass (AGB), the shrub AGB, and the shrub dominance (shrub/plant AGB) for non-water portions of the Beaufort Coastal Plain and Brooks Foothills ecoregions of the North Slope of Alaska. The estimates were derived by linking biomass harvests from 28 published field site datasets with NDVI from a regional Landsat mosaic derived from Landsat 5 and 7 satellite imagery. The data cover the period 2007-06-01 to 2016-08-31. proprietary Marine Debris Archive (MARIDA)_1 Marine Debris Archive (MARIDA) MLHUB STAC Catalog 2020-01-01 2023-01-01 -88.8557904, -29.8973351, 129.0745722, 56.4061985 https://cmr.earthdata.nasa.gov/search/concepts/C2781412537-MLHUB.umm_json Marine Debris Archive (MARIDA) is a marine debris-oriented dataset on Sentinel-2 satellite images. It also includes various sea features (clear & turbid water, waves, etc.) and floating materials (Sargassum macroalgae, ships, natural organic material, etc) that co-exist. MARIDA is primarily focused on the weakly supervised pixel-level semantic segmentation task. proprietary @@ -11815,24 +11795,24 @@ Marlon_Lewis_92_0 Marlon Lewis drifting buoys 1992 OB_DAAC STAC Catalog 1992-08- Marn10k_1 Marine Plain 1:10000 Topographic GIS Dataset AU_AADC STAC Catalog 1958-01-06 1979-01-26 78.0007, -68.666, 78.216, -68.597 https://cmr.earthdata.nasa.gov/search/concepts/C1214313613-AU_AADC.umm_json This dataset details features of Marine Plain in the Vestfold Hills, Antarctica. The dataset includes coastline, 5 metre interval contours and lake shores. These data were captured from aerial photography and are the basis of the Marine Plain Orthophoto Map published for the Australian Antarctic Division in 1993. This map is available from a URL provided in this metadata record. proprietary Maryland_Temperature_Humidity_1319_1 In-situ Air Temperature and Relative Humidity in Greenbelt, MD, 2013-2015 ORNL_CLOUD STAC Catalog 2013-09-05 2015-12-28 -76.86, 38.99, -76.84, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2736724792-ORNL_CLOUD.umm_json This data set describes the temperature and relative humidity at 12 locations around Goddard Space Flight Center in Greenbelt MD at 15 minute intervals between November 2013 and November 2015. These data were collected to study the impact of surface type on heating in a campus setting and to improve the understanding of urban heating and potential mitigation strategies on the campus scale. Sensors were mounted on posts at 2 m above surface and placed on 7 different surface types around the centre: asphalt parking lot, bright surface roof, grass field, forest, and stormwater mitigation features (bio-retention pond and rain garden). Data were also recorded in an office setting and a garage, both pre- and post-deployment, for calibration purposes. This dataset could be used to validate satellite-based study or could be used as a stand-alone study of the impact of surface type on heating in a campus setting. proprietary MassBay_LongTerm Long-Term Oceanographic Observations in Massachusetts Bay, 1989-2006 CEOS_EXTRA STAC Catalog 1989-01-01 2006-12-31 -71, 42, -70.5, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552981-CEOS_EXTRA.umm_json This data report presents long-term oceanographic observations made in western Massachusetts Bay at long-term site LT-A (42° 22.6' N., 70° 47.0' W.; nominal water depth 32 meters) from December 1989 through February 2006 and long-term site B LT-B (42° 9.8' N., 70° 38.4' W.; nominal water depth 22 meters) from October 1997 through February 2004. The observations were collected as part of a U.S. Geological Survey (USGS) study designed to understand the transport and long-term fate of sediments and associated contaminants in Massachusetts Bay. The observations include time-series measurements of current, temperature, salinity, light transmission, pressure, oxygen, fluorescence, and sediment-trapping rate. About 160 separate mooring or tripod deployments were made on about 90 research cruises to collect these long-term observations. This report presents a description of the 16-year field program and the instrumentation used to make the measurements, an overview of the data set, more than 2,500 pages of statistics and plots that summarize the data, and the digital data in Network Common Data Form (NetCDF) format. This research was conducted by the USGS in cooperation with the Massachusetts Water Resources Authority and the U.S. Coast Guard. proprietary -MassGIS_GISDATA.COQHMOSAICSCDS_POLY 2001 MrSID Mosaics CD-ROM Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592880-SCIOPS.umm_json CD-ROM index scheme for the 2001 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQHMOSAICSCDS_POLY 2001 MrSID Mosaics CD-ROM Index ALL STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592880-SCIOPS.umm_json CD-ROM index scheme for the 2001 color ortho image MrSID mosaics. proprietary +MassGIS_GISDATA.COQHMOSAICSCDS_POLY 2001 MrSID Mosaics CD-ROM Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592880-SCIOPS.umm_json CD-ROM index scheme for the 2001 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm 2001 MrSID Mosaics DVD Index SCIOPS STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592858-SCIOPS.umm_json DVD index scheme for the 2001 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm 2001 MrSID Mosaics DVD Index ALL STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592858-SCIOPS.umm_json DVD index scheme for the 2001 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQHMOSAICS_POLY 2001 MrSID Mosaics Index ALL STAC Catalog 2002-08-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592815-SCIOPS.umm_json This data layer is used to index the half-meter MrSID mosaics for the 2001/03 1:5,000 Color Ortho Imagery. proprietary MassGIS_GISDATA.COQHMOSAICS_POLY 2001 MrSID Mosaics Index SCIOPS STAC Catalog 2002-08-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592815-SCIOPS.umm_json This data layer is used to index the half-meter MrSID mosaics for the 2001/03 1:5,000 Color Ortho Imagery. proprietary MassGIS_GISDATA.COQMOSAICS2005_POLY 2005 MrSID Mosaics Index ALL STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592900-SCIOPS.umm_json Index scheme for the 2005 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQMOSAICS2005_POLY 2005 MrSID Mosaics Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592900-SCIOPS.umm_json Index scheme for the 2005 color ortho image MrSID mosaics. proprietary -MassGIS_GISDATA.COQMOSAICSCDS2005_POLY. 2005 MrSID Mosaics CD-ROM Index ALL STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592882-SCIOPS.umm_json CD-ROM index scheme for the 2005 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQMOSAICSCDS2005_POLY. 2005 MrSID Mosaics CD-ROM Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592882-SCIOPS.umm_json CD-ROM index scheme for the 2005 color ortho image MrSID mosaics. proprietary +MassGIS_GISDATA.COQMOSAICSCDS2005_POLY. 2005 MrSID Mosaics CD-ROM Index ALL STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592882-SCIOPS.umm_json CD-ROM index scheme for the 2005 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY 2005 MrSID Mosaics DVD Index SCIOPS STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592901-SCIOPS.umm_json DVD index scheme for the 2005 color ortho image MrSID mosaics. proprietary MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY 2005 MrSID Mosaics DVD Index ALL STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592901-SCIOPS.umm_json DVD index scheme for the 2005 color ortho image MrSID mosaics. proprietary -MassGIS_GISDATA.IMG_BWORTHOS 1:5,000 Black and White Digital Orthophoto Images ALL STAC Catalog 1992-01-01 1999-12-31 -73.54455, 41.198524, -69.87159, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592889-SCIOPS.umm_json "These medium resolution images provide a high-quality ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). As of March 31, 2000, the entire state is available. The imagery was captured during the spring from 1992 through 1999. Pixel resolution is 0.5 meters. In ArcSDE the layer is named IMG_BWORTHOS." proprietary MassGIS_GISDATA.IMG_BWORTHOS 1:5,000 Black and White Digital Orthophoto Images SCIOPS STAC Catalog 1992-01-01 1999-12-31 -73.54455, 41.198524, -69.87159, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592889-SCIOPS.umm_json "These medium resolution images provide a high-quality ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). As of March 31, 2000, the entire state is available. The imagery was captured during the spring from 1992 through 1999. Pixel resolution is 0.5 meters. In ArcSDE the layer is named IMG_BWORTHOS." proprietary +MassGIS_GISDATA.IMG_BWORTHOS 1:5,000 Black and White Digital Orthophoto Images ALL STAC Catalog 1992-01-01 1999-12-31 -73.54455, 41.198524, -69.87159, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592889-SCIOPS.umm_json "These medium resolution images provide a high-quality ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). As of March 31, 2000, the entire state is available. The imagery was captured during the spring from 1992 through 1999. Pixel resolution is 0.5 meters. In ArcSDE the layer is named IMG_BWORTHOS." proprietary MassGIS_GISDATA.IMG_COQ2001 1:5,000 Color Ortho Imagery ALL STAC Catalog 2001-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592921-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). MassGIS/EOEA and the Massachusetts Highway Department jointly funded the project. The photography for the mainland was captured in April 2001 when deciduous trees were mostly bare and the ground was generally free of snow." proprietary MassGIS_GISDATA.IMG_COQ2001 1:5,000 Color Ortho Imagery SCIOPS STAC Catalog 2001-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592921-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). MassGIS/EOEA and the Massachusetts Highway Department jointly funded the project. The photography for the mainland was captured in April 2001 when deciduous trees were mostly bare and the ground was generally free of snow." proprietary -MassGIS_GISDATA.IMG_COQ2005 1:5,000 Color Ortho Imagery (2005) SCIOPS STAC Catalog 2005-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592911-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). The photography for the entire commonwealth was captured in April 2005 when deciduous trees were mostly bare and the ground was generally free of snow. Image type is 4-band (RGBN) natural color (Red, Green, Blue) and Near infrared in 8 bits (values ranging 0-255) per band format. Image horizontal accuracy is +/-3 meters at the 95% confidence level at the nominal scale of 1:5,000. This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software. The project was funded by the Executive Office of Environmental Affairs, the Department of Environmental Protection, the Massachusetts Highway Department, and the Department of Public Health." proprietary MassGIS_GISDATA.IMG_COQ2005 1:5,000 Color Ortho Imagery (2005) ALL STAC Catalog 2005-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592911-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). The photography for the entire commonwealth was captured in April 2005 when deciduous trees were mostly bare and the ground was generally free of snow. Image type is 4-band (RGBN) natural color (Red, Green, Blue) and Near infrared in 8 bits (values ranging 0-255) per band format. Image horizontal accuracy is +/-3 meters at the 95% confidence level at the nominal scale of 1:5,000. This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software. The project was funded by the Executive Office of Environmental Affairs, the Department of Environmental Protection, the Massachusetts Highway Department, and the Department of Public Health." proprietary +MassGIS_GISDATA.IMG_COQ2005 1:5,000 Color Ortho Imagery (2005) SCIOPS STAC Catalog 2005-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592911-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). The photography for the entire commonwealth was captured in April 2005 when deciduous trees were mostly bare and the ground was generally free of snow. Image type is 4-band (RGBN) natural color (Red, Green, Blue) and Near infrared in 8 bits (values ranging 0-255) per band format. Image horizontal accuracy is +/-3 meters at the 95% confidence level at the nominal scale of 1:5,000. This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software. The project was funded by the Executive Office of Environmental Affairs, the Department of Environmental Protection, the Massachusetts Highway Department, and the Department of Public Health." proprietary MassGIS_GISDATA.VCPEATLAND_POLY Acidic Peatland Community Systems SCIOPS STAC Catalog 2003-04-01 -71.36416, 41.53563, -70.51623, 42.859413 https://cmr.earthdata.nasa.gov/search/concepts/C1214592150-SCIOPS.umm_json Acidic Peatland Community Systems include evergreen forest and shrub bogs, Atlantic White Cedar (AWC) swamps and bogs, and shrub and graminoid fens. This data was created by starting with the DEP Wetlands, creating a new set of just the bog, coniferous and mixed forested wetland types, and then adding, deleting and changing polygon shapes and labels based on aerial photo interpretation of the 1999/2000 photos and field information. In some areas where this wetland layer did not exist, the wetlands were interpreted and digitized from the aerial photos. The Acidic Peatland datalayer is named VCPEATLAND_POLY in ArcSDE. This layer is part of the MassGIS Priority Natural Vegetation Communities dataset, which depicts the distribution of the eight natural community systems identified by the Massachusetts Natural Heritage and Endangered Species Program (NHESP) as most critical to the conservation of the Commonwealth‚Äôs biological diversity (Barbour et al., 1998). proprietary MassGIS_GISDATA.VCPEATLAND_POLY Acidic Peatland Community Systems ALL STAC Catalog 2003-04-01 -71.36416, 41.53563, -70.51623, 42.859413 https://cmr.earthdata.nasa.gov/search/concepts/C1214592150-SCIOPS.umm_json Acidic Peatland Community Systems include evergreen forest and shrub bogs, Atlantic White Cedar (AWC) swamps and bogs, and shrub and graminoid fens. This data was created by starting with the DEP Wetlands, creating a new set of just the bog, coniferous and mixed forested wetland types, and then adding, deleting and changing polygon shapes and labels based on aerial photo interpretation of the 1999/2000 photos and field information. In some areas where this wetland layer did not exist, the wetlands were interpreted and digitized from the aerial photos. The Acidic Peatland datalayer is named VCPEATLAND_POLY in ArcSDE. This layer is part of the MassGIS Priority Natural Vegetation Communities dataset, which depicts the distribution of the eight natural community systems identified by the Massachusetts Natural Heritage and Endangered Species Program (NHESP) as most critical to the conservation of the Commonwealth‚Äôs biological diversity (Barbour et al., 1998). proprietary MatthewsVegetation_419_1 Global Vegetation Types, 1971-1982 (Matthews) ORNL_CLOUD STAC Catalog 1971-01-01 1982-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2808090466-ORNL_CLOUD.umm_json A global digital data base of vegetation was compiled at 1 degree latitude by 1 degree longitude resolution, drawing on approximately 100 published sources. Vegetation data from varied sources were consistently recorded using the hierarchical UNESCO classification system. The raw data base distinguishes about 180 vegetation types that have been collapsed to 32. proprietary @@ -11841,8 +11821,8 @@ Mawson_SAM_1 Mawson Station GIS Dataset AU_AADC STAC Catalog 1996-03-18 1996-03- Mawson_Tide_Gauges_2 Mawson Tide Gauge Data 1992-2016 AU_AADC STAC Catalog 1992-03-05 2016-11-04 62.83356, -67.61863, 62.90771, -67.58619 https://cmr.earthdata.nasa.gov/search/concepts/C1667370710-AU_AADC.umm_json "Over time there have been a number of tide gauges deployed at Mawson Station, Antarctica. The data download files contain further information about the gauges, but some of the information has been summarised here. Note that this metadata record only describes tide gauge data from 1992 to 2016. More recent data are described elsewhere. Tide Gauge 1 (TG001) 1992-03-05 - 1992-05-13 This folder contains monthly download files from the first deployment of a submerged tide gauge at Mawson in March 1992. These files are ASCII hexadecimal files. They need to be converted to decimal. The resultant values are absolute seawater pressures in mbar. Tide Gauge 4 (TG004) 1993-03-22 - 1999-12-29 This folder contains the following folders:- old_tidedata monthly download files from the second deployment of a submerged tide gauge at Mawson in March 1993. These files are ASCII hexadecimal files. They need to be converted to decimal. The resultant values are absolute seawater pressures in mbar. raw memory images from submerged tide gauge. file extension is memory bank number. These files are processed by a utility called tgxtract.exe which creates files in same format as those in old_tidedata folder. These file have extension .srt. They are then converted to decimal pressure values. interim files produced during processing of .raw files. output output file (.srt) which have been sent to BoM. Tide Gauge 13 (TG013) 2014-06-04 - 2016-11-04 Tide Gauge 20 (TG020) 1999-11-05 - 2009-12-21 This folder contains the following folders:- raw memory images from submerged tide gauge. file extension is memory bank number. These files are processed by a utility called tgxtract.exe which creates files in same format as original download format. These file have extension .srt. These files are ASCII hexadecimal files. They need to be converted to decimal. The resultant values are absolute seawater pressures in mbar. interim files produced during processing of .raw files. output output file (.srt) which have been sent to BoM. Tide Gauge 41 (TG041) 2008-03-02 - 2010-11-16 This folder contains the following folders:- raw memory images from submerged tide gauge. file extension is memory bank number. These files are processed by a utility called tgxtract.exe which creates files in same format as original download format. These file have extension .srt. These files are ASCII hexadecimal files. They need to be converted to decimal. The resultant values are absolute seawater pressures in mbar. interim files produced during processing of .raw files. output output file (.srt) which have been sent to BoM. Documentation from older metadata record: Documentation dated 2001-03-26 Mawson Submerged Tide Gauge The gauge used at Mawson was designed in 1991/2 by Platypus Engineering, Hobart, Tasmania. It was intended to be submerged in about 7 metres of water in a purpose made concrete mooring in the shape of a truncated pyramid. The gauge measures pressure using a Paroscientific Digiquartz Pressure Transducer with a full scale pressure of 30 psi absolute. The accuracy of the transducer is 1 in 10,000 of full scale over the calibrated temperature range. The overall accuracy of the system is better than +/- 3 mm for a known water density. Data is retrieved from the gauges by lowering a coil assembly on the end of a cable over a projecting knob on the top of the gauge and by use of an interface unit ,a serial connection can be established to the gauge. Time setting and data retrieval can be then achieved. The first of these gauges were first deployed Mawson in early 1992 in a a mooring in Horseshoe Harbour. The gauge was found to have some communications problems and was removed in May 1992. Tidal records from 6/3/92 to present have been retrieved from it. A new gauge was deployed at Mawson in March 1993. Data has been retrieved from these gauges irregularly since then. The records are complete since deployment except for a few days in late 1995. The loss was caused by a fault in the software which allows directory entries to overwrites data when the directory memory has been filled. The first gauge used at Mawson in 1992 was refitted with a higher pressure transducer and was later deployed at Heard Island in Atlas Cove. Conversion of raw data to tidal records is done as detailed in document DATAFORMAT1.DOC . As the current gauge is expected to require a new battery soon, a new mooring has been placed close to the original and a new gauge has been deployed. Levelling Several attempts have been made at precise levelling of the gauge. The first was in the Summer of 1995/6. Roger Handsworth, Tom Gordon and Natasha Adams physically measured the level of the top of the gauge in its mooring and derived a reading when a known column of water was over the gauge. The next attempt was in the Summer of 1996/7 when Roger Handsworth and Paul Delaney made timed water level measurements close to the gauge and the tide gauge benchmark. From this work, and from tidal records, a value for MSL for Mawson was derived. Permanent Gauge In the summer of 1995/6 two possible sites for a permanent Aquatrak type tide gauge were identified. As neither of these sites were approved, a survey in the Summer of 1996/7 identified two more suitable sites. One of these, the site at the base of East arm, near the Variometer Building, was approved and a bore hole was drilled to exit about 6 metres below MSL. A power cable was run from the variometer building to provide two phase 240V power to the site. A heated borehole liner containing an Aquatrak wave guide and a Druck pressure transducer was inserted into the bore hole. Two datalogger will be added to the installation in 2001 to complete the installation. A radio modem will be used to link the dataloggers to the AAD network. Documentation dated 2008-10-17 Mawson A new submerged gauge ,TG41, was deployed at Mawson on 2008-03-03. Submerged Tide gauge TG20 was removed on 2008-08-26. There is a useful overlap of data between the gauges of about 104 days. The dataloggers used in the shored based tide gauge installation have been replaced with Campbell Scientific CR1000 dataloggers. The aquatrak shore based gauge at Mawson has not been operating since march 2008. The shore base pressure gauge is still operating." proprietary MawsonsHuts2008_2009_1 Mawson's Huts Preservation Program 2007/2008, 2008/2009 and 2009/2010 Data Entry AU_AADC STAC Catalog 2008-10-01 2010-03-31 142.65, -67.1, 142.67, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214313539-AU_AADC.umm_json "723 images where loaded into the AAD image library, ""Image Antarctica"" and attached to records in the Antarctic Heritage Register database. The images documented the condition of the interior and exterior of Mawsons Huts located at Cape Denison including the main hut, the absolute hut, the magnetograph hut and the transit hut during the 2007/2008 season and the 2008/2009 season. The images were taken in both high resolution jpgs as well as raw files. The camera used was a Nikon D80. Also included were images of conserved artefacts as well as details of the conservation treatments uploaded to the Antarctic Heritage Register Database and linked to specific catalogue records. 2011-04-21 - the record was updated to include a file of data from the 2009/2010 season. Raw data from 2008/2009 and 2009/2010 have also been archived in the AADC servers, and are available to AAD personnel upon request." proprietary Mawsons_Huts_Dataloggers_2 Dataloggers at Mawson's Hut, Cape Denison - microclimate measurements AU_AADC STAC Catalog 1998-01-26 2008-01-30 142.66, -67.009, 142.662, -67.007 https://cmr.earthdata.nasa.gov/search/concepts/C1214313538-AU_AADC.umm_json Dataloggers were installed in a number of locations inside and outside Mawson's Huts at Cape Denison. The dataloggers measure temperature and relative humidity for the purpose of helping gauge corrosivity in the huts. The data are used to assess whether the removal of ice and snow from inside the Hut is affecting the internal microclimate and, therefore, the condition of the building fabric and other artefacts. Currently the data are downloaded by the Research Centre for Materials Conservation and the Built Environment at the Australian Museum, Sydney. Copies of the data are stored in the Australian Antarctic Data Centre. The fields in this dataset are: Date Time Temperature Relative Humidity Thermocouple Site proprietary -Maxwell_Bay_Beaches_data Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands SCIOPS STAC Catalog 0500-01-01 2007-04-30 -59, -62.3, -58.833, -62.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214590771-SCIOPS.umm_json This data set includes elevations, OSL ages, and one suspect radiocarbon date from several raised beaches around Maxwell Bay in the South Shetland Islands. It also includes some basic textural parameters (grain size, sorting, and roundness) from modern beaches, talus slopes, and moraines in the area. We also compiled a map of recent moraines in the Gerlache Straight. proprietary Maxwell_Bay_Beaches_data Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands ALL STAC Catalog 0500-01-01 2007-04-30 -59, -62.3, -58.833, -62.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214590771-SCIOPS.umm_json This data set includes elevations, OSL ages, and one suspect radiocarbon date from several raised beaches around Maxwell Bay in the South Shetland Islands. It also includes some basic textural parameters (grain size, sorting, and roundness) from modern beaches, talus slopes, and moraines in the area. We also compiled a map of recent moraines in the Gerlache Straight. proprietary +Maxwell_Bay_Beaches_data Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands SCIOPS STAC Catalog 0500-01-01 2007-04-30 -59, -62.3, -58.833, -62.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214590771-SCIOPS.umm_json This data set includes elevations, OSL ages, and one suspect radiocarbon date from several raised beaches around Maxwell Bay in the South Shetland Islands. It also includes some basic textural parameters (grain size, sorting, and roundness) from modern beaches, talus slopes, and moraines in the area. We also compiled a map of recent moraines in the Gerlache Straight. proprietary McMurdo_Predator_Prey_Acoustics Acoustic records near McMurdo Station, Antarctica, 2012 - 2015. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106925-SCIOPS.umm_json Sonar data were collected to determine prey fields (krill, fishes) in McMurdo Sound, Antarctica proprietary McMurdo_Predator_Prey_Acoustics Acoustic records near McMurdo Station, Antarctica, 2012 - 2015. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106925-SCIOPS.umm_json Sonar data were collected to determine prey fields (krill, fishes) in McMurdo Sound, Antarctica proprietary McMurdo_Predator_Prey_Adelie_Penguins Adelie Penguins at Cape Royds, Antarctica, 2012 - 2015. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106459-SCIOPS.umm_json Adelie penguin data will be deposited in the California Avian Data Center (CADC) hosted by Point Blue Conservation Science (http://data.prbo.org/apps/penguinscience/). proprietary @@ -11863,8 +11843,8 @@ Microbiome_0 Tara microbiome OB_DAAC STAC Catalog 2020-12-26 2022-12-31 -180, -9 Mid-latitude_soils_705_2 Northern and Mid-Latitude Soil Database, Version 1, R1 ORNL_CLOUD STAC Catalog 2001-01-01 2001-12-31 -180, 50.9, -129.3, 71.4 https://cmr.earthdata.nasa.gov/search/concepts/C2216863233-ORNL_CLOUD.umm_json The U.S. Department of Agriculture, Agriculture and Agri-Food Canada, the Russian Academy of Agricultural Sciences, the University of Copenhagen Institute of Geography, the European Soil Bureau, the University of Manchester Institute of Landscape Ecology, MTT Agrifood Research Finland, and the Agricultural Research Institute Iceland have shared data and expertise in order to develop the Northern and Mid Latitude Soil Database (Cryosol Working Group, 2001). This database was the source of data for the current product. The spatial coverage of the Northern and Mid Latitude Soil Database is the polar and mid-latitude regions of the northern hemisphere: Alaska, Canada, Conterminous United States, Eurasia (except Italy), Greenland, Iceland, Kazakstan, Mexico, Mongolia, Italy, and Svalbard. The Northern and Mid-Latitude Soil Database represents the proportion (percentage) of polygon encompassed by the dominant soil or nonsoil. Soils include turbels, orthels, histels, histosols, mollisols, vertisols, aridisols, andisols, entisols, spodosols, inceptisols (and hapludolls), alfisols (cryalf and udalf), natric great groups, aqu-suborders, glaciers, and rocklands. Also included are data on the circumpolar distribution of gelisols (turbels, orthels, and histels), and the ice content (low, medium, or high) of circumpolar soil materials (from the International Permafrost Association, 1997). The resulting maps show the dominant soil of the spatial polygon unless the polygon is over 90 percent rock or ice. Data are in the U.S. soil classification system and includes the distribution of soil types (%) within a map unit (polygon). Data are available in ESRI shapefile format and include the same attribute values with the exception of Italy, which does not contain distribution values. proprietary Missouri_Reservoirs_RSWQ_0 Retrospective analysis of anthropogenic change in Midwest reservoirs: Integrating earth observing data with statewide reservoir monitoring programs OB_DAAC STAC Catalog 2023-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2785397264-OB_DAAC.umm_json The dataset comprises in-situ hyperspectral data acquired using the on-water approach (aka skylight-blocked approach), using a combination of a downwelling irradiance sensor and an upwelling radiance sensor. These sensors are specifically TriOS RAMSES hyperspectral radiometers, each associated with two calibration files. The data collection was conducted across different reservoirs in the state of Missouri USA. This NASA-funded project directly addresses how Earth-observing satellite data can better inform critical links between the biogeochemical and optical properties of inland waters. It achieves this by using satellite imagery and in-situ measurements from two long-running water quality monitoring programs in the state of Missouri that annually record more than one thousand measurements of nitrogen, phosphorus, chlorophyll-a, Secchi depth, particulate organic and inorganic matter, and cyanotoxins across 100 reservoirs. proprietary MonthlyWetland_CH4_WetCHARTsV2_2346_1.3.3 CMS: Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.3.3) ORNL_CLOUD STAC Catalog 2001-01-01 2022-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3236621594-ORNL_CLOUD.umm_json This dataset provides global monthly wetland methane (CH4) emissions estimates at 0.5 by 0.5-degree resolution for the period 2001-01-01 to 2022-08-31 that were derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies that encompass the main sources of uncertainty in wetland CH4 emissions. There are 18 model configurations. WetCHARTs v1.3.3 is an updated product of WetCHARTs v1.3.1 dataset. The intended use of this product is as a process-informed wetland CH4 emission data set for atmospheric chemistry and transport modeling. Users can compare estimates by model configuration to explore variability and sensitivity with respect to ensemble members. The data are provided in netCDF format. proprietary -Monthly_Hydrological_Fluxes_1647_1 ABoVE: Monthly Hydrological Fluxes for Canada and Alaska, 1979-2018 ALL STAC Catalog 1979-01-01 2018-04-01 -172.25, 41.75, -53.43, 83.12 https://cmr.earthdata.nasa.gov/search/concepts/C2170971533-ORNL_CLOUD.umm_json This dataset provides modeled estimates of monthly hydrological fluxes at 0.25-degree resolution over Alaska and Canada for the years 1979-2018. The estimates were derived from the Variable Infiltration Capacity (VIC) macroscale hydrological model version 4.1.2 with water and energy balance schemes at 0.25-degree spatial and daily temporal resolution for this 38-year period. The gridded output data products are monthly average water balance variables including precipitation (P), evapotranspiration (E), 'P minus E', evaporation, soil moisture in three soil layers, base flow and runoff, snow depth, snow water equivalent (SWE), and snow sublimation, and energy balance variables including surface temperature, albedo, latent and sensible heat flux, ground heat flux, short- and long-wave and other radiative fluxes. The daily modeled values for precipitation and evapotranspiration were also aggregated to water years and precipitation was also aggregated to a 30-year climate normal average. proprietary Monthly_Hydrological_Fluxes_1647_1 ABoVE: Monthly Hydrological Fluxes for Canada and Alaska, 1979-2018 ORNL_CLOUD STAC Catalog 1979-01-01 2018-04-01 -172.25, 41.75, -53.43, 83.12 https://cmr.earthdata.nasa.gov/search/concepts/C2170971533-ORNL_CLOUD.umm_json This dataset provides modeled estimates of monthly hydrological fluxes at 0.25-degree resolution over Alaska and Canada for the years 1979-2018. The estimates were derived from the Variable Infiltration Capacity (VIC) macroscale hydrological model version 4.1.2 with water and energy balance schemes at 0.25-degree spatial and daily temporal resolution for this 38-year period. The gridded output data products are monthly average water balance variables including precipitation (P), evapotranspiration (E), 'P minus E', evaporation, soil moisture in three soil layers, base flow and runoff, snow depth, snow water equivalent (SWE), and snow sublimation, and energy balance variables including surface temperature, albedo, latent and sensible heat flux, ground heat flux, short- and long-wave and other radiative fluxes. The daily modeled values for precipitation and evapotranspiration were also aggregated to water years and precipitation was also aggregated to a 30-year climate normal average. proprietary +Monthly_Hydrological_Fluxes_1647_1 ABoVE: Monthly Hydrological Fluxes for Canada and Alaska, 1979-2018 ALL STAC Catalog 1979-01-01 2018-04-01 -172.25, 41.75, -53.43, 83.12 https://cmr.earthdata.nasa.gov/search/concepts/C2170971533-ORNL_CLOUD.umm_json This dataset provides modeled estimates of monthly hydrological fluxes at 0.25-degree resolution over Alaska and Canada for the years 1979-2018. The estimates were derived from the Variable Infiltration Capacity (VIC) macroscale hydrological model version 4.1.2 with water and energy balance schemes at 0.25-degree spatial and daily temporal resolution for this 38-year period. The gridded output data products are monthly average water balance variables including precipitation (P), evapotranspiration (E), 'P minus E', evaporation, soil moisture in three soil layers, base flow and runoff, snow depth, snow water equivalent (SWE), and snow sublimation, and energy balance variables including surface temperature, albedo, latent and sensible heat flux, ground heat flux, short- and long-wave and other radiative fluxes. The daily modeled values for precipitation and evapotranspiration were also aggregated to water years and precipitation was also aggregated to a 30-year climate normal average. proprietary MultiInstrumentFusedXCO2_3 Multi-Instrument Fused bias-corrected XCO2 and other select fields aggregated as Level 4 daily files V3 (MultiInstrumentFusedXCO2) GES_DISC STAC Catalog 2014-09-06 2020-07-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2219373930-GES_DISC.umm_json Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT) bias corrected data. This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page. proprietary MultiInstrumentFusedXCO2_4 Multi-Instrument Fused bias-corrected XCO2 and other select fields aggregated as Level 3 daily files V4 (MultiInstrumentFusedXCO2) GES_DISC STAC Catalog 2014-09-06 2021-05-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3278456754-GES_DISC.umm_json Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT) bias corrected data. This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page. proprietary MumfordCove_0 Measurements from Mumford Cove, Connecticut OB_DAAC STAC Catalog 2015-10-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360493-OB_DAAC.umm_json Measurements made in and around Mumford Cove, Connecticut since 2015. proprietary @@ -12038,22 +12018,22 @@ NBId0019_101 FAO Major Elevation Zones of Africa (GIS Coverage) CEOS_EXTRA STAC NBId0020_101 Countries, Coasts and Islands of Africa (Global Change Data Base - Digital Boundaries and Coastlines) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848088-CEOS_EXTRA.umm_json New-ID: NBI20 Countries, Coasts and Islands Dataset documentation (Micro World Data Bank II) Files: COASTS.E00 Code: 100051-001 COUNTRY.E00 100052-001 ISLANDS.E00 100054-001 Vector Members Original files were in IDRISI VEC format coverted to Arc/Info. The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. Micro World Data Bank II (MWDB-II) comprising Coastlines, Country boundries and Islands data sets is part of NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II and is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact: NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The COASTS file shows African Coastlines The COUNTRY file shows African Country Boundaries without coast, no names - only lines The ISLANDS file shows African Islands References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, Vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map: digitized from available sources Publication Date: Jun 1992 Projection: Lat/Lon Type: Polygon and line Format: Arc/Info Export non-compressed proprietary NBId0022_101 Africa Olson World Ecosystems CEOS_EXTRA STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232846860-CEOS_EXTRA.umm_json "New-ID: NBI22 OLSON WORLD ECOSYSTEMS DATASET DOCUMENTATION File: AFWE20.IMG Code: 100032-001 Raster Member This IMG file is in IDRISI format Olson World Ecosystems data base is part of Global Change Data Base produced by The World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC) and for cooperative project called Global Ecosystems Database Project between NDAA(National Oceanic & Atmospheric Administration, USA)/NGDC and the U.S. Environmental Protection Agency. The software (known as IDRISI) was developed and adopted for this project at Clark University. The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California, has joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/Latitude) projection. Each data set is accompanied by an ASCII documentation file. Which contains information necessary for use of the data set in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWE20 file shows Olson ecosystem classes version 1.4 References: Olson, J.S. Earth""'""s Vegetation and Atmospheric Carbon Dioxide, in Carbon Dioxide Review: 1982. Ed. by W.C. Clark (1983), Exford Univ. Press, New York, pp.388-398. Olson, J.S., J.A. Watts, and L.J. Allison. Carbon in Live Vegetation of Major World Ecosystems (1983). Report ORNL-5862, Oark Ridge Laboratory, Oak Ridge, Tennessee. Olson, J.S. and J.A. Watts. Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation (1982). Oak Ridge National Laboratory, Oak Ridge, Tennesse (map). Source map : from available maps and observations. Publication Date : 1989 Projection : lat/lon. Type : Raster Format : IDRISI" proprietary NBId0022_101 Africa Olson World Ecosystems ALL STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232846860-CEOS_EXTRA.umm_json "New-ID: NBI22 OLSON WORLD ECOSYSTEMS DATASET DOCUMENTATION File: AFWE20.IMG Code: 100032-001 Raster Member This IMG file is in IDRISI format Olson World Ecosystems data base is part of Global Change Data Base produced by The World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC) and for cooperative project called Global Ecosystems Database Project between NDAA(National Oceanic & Atmospheric Administration, USA)/NGDC and the U.S. Environmental Protection Agency. The software (known as IDRISI) was developed and adopted for this project at Clark University. The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California, has joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/Latitude) projection. Each data set is accompanied by an ASCII documentation file. Which contains information necessary for use of the data set in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWE20 file shows Olson ecosystem classes version 1.4 References: Olson, J.S. Earth""'""s Vegetation and Atmospheric Carbon Dioxide, in Carbon Dioxide Review: 1982. Ed. by W.C. Clark (1983), Exford Univ. Press, New York, pp.388-398. Olson, J.S., J.A. Watts, and L.J. Allison. Carbon in Live Vegetation of Major World Ecosystems (1983). Report ORNL-5862, Oark Ridge Laboratory, Oak Ridge, Tennessee. Olson, J.S. and J.A. Watts. Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation (1982). Oak Ridge National Laboratory, Oak Ridge, Tennesse (map). Source map : from available maps and observations. Publication Date : 1989 Projection : lat/lon. Type : Raster Format : IDRISI" proprietary -NBId0023_101 Africa Holdridge Life Zone Classification (Vegetation and Climate) CEOS_EXTRA STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847334-CEOS_EXTRA.umm_json New-ID: NBI23 Holdridge Life Zone is a coverage showing zone classification, vegetation relation to climate and vice versa. proprietary NBId0023_101 Africa Holdridge Life Zone Classification (Vegetation and Climate) ALL STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847334-CEOS_EXTRA.umm_json New-ID: NBI23 Holdridge Life Zone is a coverage showing zone classification, vegetation relation to climate and vice versa. proprietary -NBId0024_101 Africa Soil Classification by Wilson and Henderson-Sellers CEOS_EXTRA STAC Catalog 1970-01-01 12.88, 6.67, 24.97, 24.19 https://cmr.earthdata.nasa.gov/search/concepts/C2232848824-CEOS_EXTRA.umm_json New-ID: NBI24 Wilson and Henderson-Sellers soil classes and soil class reliability. The Wilson and Henderson-Sellers Soil Classes Dataset Files: AFWSOILS.IMG Code: 100043-001 AFWSOILR.IMG 100043-002 Raster Members The IMG files are in IDRISI format. The Wilson and Henderson-Sellers soils data set is part of Wilson Henderson-Sellers land cover and soils for global circulation modeling project was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II. This data Bank is provided on a Database on diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : Roy Jenne, NCAR, P.O. Box 3000, Boulder, CO 80307-3000 The AFWSOILS file shows Wilson/Henderson-Sellers Soil Classes The ASWSOILR file shows Wilson/Henderson-Sellers Soil Class Reliability References: Wilson, M.F/ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general ciruclation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World. Oxford Regional Economic Atlas of USSR and Eastern Europe Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary +NBId0023_101 Africa Holdridge Life Zone Classification (Vegetation and Climate) CEOS_EXTRA STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847334-CEOS_EXTRA.umm_json New-ID: NBI23 Holdridge Life Zone is a coverage showing zone classification, vegetation relation to climate and vice versa. proprietary NBId0024_101 Africa Soil Classification by Wilson and Henderson-Sellers ALL STAC Catalog 1970-01-01 12.88, 6.67, 24.97, 24.19 https://cmr.earthdata.nasa.gov/search/concepts/C2232848824-CEOS_EXTRA.umm_json New-ID: NBI24 Wilson and Henderson-Sellers soil classes and soil class reliability. The Wilson and Henderson-Sellers Soil Classes Dataset Files: AFWSOILS.IMG Code: 100043-001 AFWSOILR.IMG 100043-002 Raster Members The IMG files are in IDRISI format. The Wilson and Henderson-Sellers soils data set is part of Wilson Henderson-Sellers land cover and soils for global circulation modeling project was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II. This data Bank is provided on a Database on diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : Roy Jenne, NCAR, P.O. Box 3000, Boulder, CO 80307-3000 The AFWSOILS file shows Wilson/Henderson-Sellers Soil Classes The ASWSOILR file shows Wilson/Henderson-Sellers Soil Class Reliability References: Wilson, M.F/ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general ciruclation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World. Oxford Regional Economic Atlas of USSR and Eastern Europe Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary -NBId0025_101 Africa Soil Classification by Zobler ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848306-CEOS_EXTRA.umm_json New-ID: NBI25 Africa ZOBLER Soil Type, Soil Texture, Surface Slope Classes Dataset Documentation Files: AFZSOILS.IMG Code: 100090-001 AFZTEX.IMG 100090-002 AFZSUBSD.IMG 100090-003 AFZSP3.IMG 100090-004 AFZPHS.IMG 100090-005 AFZSLOPE.IMG 100092-001 Raster Members The IMG files are in IDRISI format The Zobler soil type, soil texture and surface slope dataset was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of a larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFZSOILS file shows Zobler soil types The AFZTEX file shows Zobler soil texture The AFZSUBSD file shows subsidiary soil units The AFZSP3 file shows Zobler special codes The AFZPHS file shows Zobler phase codes The AFZSLOPE file shows Zobler surface slope References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. -----. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map : various Publication Date : 1987 Projection : Lat/lon Type : Raster Format : IDRISI proprietary +NBId0024_101 Africa Soil Classification by Wilson and Henderson-Sellers CEOS_EXTRA STAC Catalog 1970-01-01 12.88, 6.67, 24.97, 24.19 https://cmr.earthdata.nasa.gov/search/concepts/C2232848824-CEOS_EXTRA.umm_json New-ID: NBI24 Wilson and Henderson-Sellers soil classes and soil class reliability. The Wilson and Henderson-Sellers Soil Classes Dataset Files: AFWSOILS.IMG Code: 100043-001 AFWSOILR.IMG 100043-002 Raster Members The IMG files are in IDRISI format. The Wilson and Henderson-Sellers soils data set is part of Wilson Henderson-Sellers land cover and soils for global circulation modeling project was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II. This data Bank is provided on a Database on diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : Roy Jenne, NCAR, P.O. Box 3000, Boulder, CO 80307-3000 The AFWSOILS file shows Wilson/Henderson-Sellers Soil Classes The ASWSOILR file shows Wilson/Henderson-Sellers Soil Class Reliability References: Wilson, M.F/ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general ciruclation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World. Oxford Regional Economic Atlas of USSR and Eastern Europe Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary NBId0025_101 Africa Soil Classification by Zobler CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848306-CEOS_EXTRA.umm_json New-ID: NBI25 Africa ZOBLER Soil Type, Soil Texture, Surface Slope Classes Dataset Documentation Files: AFZSOILS.IMG Code: 100090-001 AFZTEX.IMG 100090-002 AFZSUBSD.IMG 100090-003 AFZSP3.IMG 100090-004 AFZPHS.IMG 100090-005 AFZSLOPE.IMG 100092-001 Raster Members The IMG files are in IDRISI format The Zobler soil type, soil texture and surface slope dataset was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of a larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFZSOILS file shows Zobler soil types The AFZTEX file shows Zobler soil texture The AFZSUBSD file shows subsidiary soil units The AFZSP3 file shows Zobler special codes The AFZPHS file shows Zobler phase codes The AFZSLOPE file shows Zobler surface slope References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. -----. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map : various Publication Date : 1987 Projection : Lat/lon Type : Raster Format : IDRISI proprietary -NBId0036_101 Africa Lakes and Rivers (World Data Bank II) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849206-CEOS_EXTRA.umm_json New-ID: NBI36 Africa Lakes and Rivers. Lakes and Rivers Dataset documentation (Micro World Data Bank II) Files: LAKES.VEC Code: 100055-001 RIVERS.VEC 100061-001 AFRIVER.IMG 100002-001 Raster Members The VEC and IMG files are in IDRISI format Africa lakes and rivers datasets are part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The LAKES file shows African lakes The RIVERS file shows African rivers The AFRIVER file shows African rivers References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : digitized from available sources Publication Date : 1988 Projection : Lat/lon Type : Raster Format : IDRISI proprietary +NBId0025_101 Africa Soil Classification by Zobler ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848306-CEOS_EXTRA.umm_json New-ID: NBI25 Africa ZOBLER Soil Type, Soil Texture, Surface Slope Classes Dataset Documentation Files: AFZSOILS.IMG Code: 100090-001 AFZTEX.IMG 100090-002 AFZSUBSD.IMG 100090-003 AFZSP3.IMG 100090-004 AFZPHS.IMG 100090-005 AFZSLOPE.IMG 100092-001 Raster Members The IMG files are in IDRISI format The Zobler soil type, soil texture and surface slope dataset was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of a larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFZSOILS file shows Zobler soil types The AFZTEX file shows Zobler soil texture The AFZSUBSD file shows subsidiary soil units The AFZSP3 file shows Zobler special codes The AFZPHS file shows Zobler phase codes The AFZSLOPE file shows Zobler surface slope References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. -----. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map : various Publication Date : 1987 Projection : Lat/lon Type : Raster Format : IDRISI proprietary NBId0036_101 Africa Lakes and Rivers (World Data Bank II) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849206-CEOS_EXTRA.umm_json New-ID: NBI36 Africa Lakes and Rivers. Lakes and Rivers Dataset documentation (Micro World Data Bank II) Files: LAKES.VEC Code: 100055-001 RIVERS.VEC 100061-001 AFRIVER.IMG 100002-001 Raster Members The VEC and IMG files are in IDRISI format Africa lakes and rivers datasets are part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The LAKES file shows African lakes The RIVERS file shows African rivers The AFRIVER file shows African rivers References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : digitized from available sources Publication Date : 1988 Projection : Lat/lon Type : Raster Format : IDRISI proprietary +NBId0036_101 Africa Lakes and Rivers (World Data Bank II) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849206-CEOS_EXTRA.umm_json New-ID: NBI36 Africa Lakes and Rivers. Lakes and Rivers Dataset documentation (Micro World Data Bank II) Files: LAKES.VEC Code: 100055-001 RIVERS.VEC 100061-001 AFRIVER.IMG 100002-001 Raster Members The VEC and IMG files are in IDRISI format Africa lakes and rivers datasets are part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The LAKES file shows African lakes The RIVERS file shows African rivers The AFRIVER file shows African rivers References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : digitized from available sources Publication Date : 1988 Projection : Lat/lon Type : Raster Format : IDRISI proprietary NBId0041_101 FNOC Elevation (meters), Terrain and Surface Characteristics for Africa CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847281-CEOS_EXTRA.umm_json New-ID: NBI41 Africa FNOC Elevation (meters), Terrain and Surface characteristics. Africa Elevation (meters), Terrain, and Surface Characteristics Dataset Documentation Files: AFMAX.IMG Code: 100082-001 AFMIN.IMG 100082-002 AFMOD.IMG 100082-003 Raster Members The IMG files are in IDRISI format Africa elevation dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFMAX file shows maximum elevation (meters) The AFMIN file shows minimum elevation (meters) The AFMOD shows modal elevation (meters) Reference: Cuming, Michael J. and Barbara A. Hawkins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary NBId0042_101 NOAA Monthly 10-Minute Normalized Vegetation Index (April 1985-December 1988) for Africa CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848152-CEOS_EXTRA.umm_json "New-ID: NBI42 NOAA monthly Normalized Vegetation Index (NDVI) for Africa. NOAA Monthly 10-Min Normalized Vegetation Index Dataset (APRIL 1985 - DECEMBER 1988) Files: AFAPR85.IMG-AFDEC85.IMG Code: 100041-001 AFJAN86.IMG-AFDEC86.IMG 100041-001 AFJAN87.IMG-AFDEC87.IMG 100041-001 AFJAN88.IMG-AFDEC88.IMG 100041-001 Raster Members The IMG files are in IDRISI format Africa monthly 10-min normalized difference vegetation index dataset is part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA AFAPR85-AFDEC88 (45 months) show monthly Normalized Vegetation Index (NDVI) References: Kidwell, Katherin B. (ed.). Global Vegetion Index User""'""s Guide (1990). NOAA/NHESDIS/SDSD. for additional references see Appendix A-26-A32 of the Global Change Data Base documentation Source map : digitized from available maps and reprocessed Publication Date : Jun 1992 Projection : Lat/lon Type : Raster Format : IDRISI" proprietary NBId0043_101 Africa Integrated Elevation and Bathymetry CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847599-CEOS_EXTRA.umm_json "New-ID: NBI43 Africa Integrated Elevation and Bathymetry (feet). Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1992 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary NBId0043_101 Africa Integrated Elevation and Bathymetry ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847599-CEOS_EXTRA.umm_json "New-ID: NBI43 Africa Integrated Elevation and Bathymetry (feet). Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1992 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary NBId0044_101 Africa Ocean Mask CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849137-CEOS_EXTRA.umm_json "New-ID: NBI44 Ocean mask for Africa. Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1985 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary NBId0044_101 Africa Ocean Mask ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849137-CEOS_EXTRA.umm_json "New-ID: NBI44 Ocean mask for Africa. Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1985 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary -NBId0053_101 Africa Revised FNOC Percent Water Cover CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847596-CEOS_EXTRA.umm_json New-ID: NBI53 Africa Revised FNOC Percent Water Cover Dataset Documentation File: AFWATER.IMG Code: 100082-005 Raster Member The IMG file is in IDRISI format The percent water cover dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWATER file shows the revised FNOC percent water cover for Africa. Reference: Cuming, Michael J. and Barbara A. Hwakins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lon/lat Type : Raster Format : IDRISI proprietary NBId0053_101 Africa Revised FNOC Percent Water Cover ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847596-CEOS_EXTRA.umm_json New-ID: NBI53 Africa Revised FNOC Percent Water Cover Dataset Documentation File: AFWATER.IMG Code: 100082-005 Raster Member The IMG file is in IDRISI format The percent water cover dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWATER file shows the revised FNOC percent water cover for Africa. Reference: Cuming, Michael J. and Barbara A. Hwakins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lon/lat Type : Raster Format : IDRISI proprietary +NBId0053_101 Africa Revised FNOC Percent Water Cover CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847596-CEOS_EXTRA.umm_json New-ID: NBI53 Africa Revised FNOC Percent Water Cover Dataset Documentation File: AFWATER.IMG Code: 100082-005 Raster Member The IMG file is in IDRISI format The percent water cover dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWATER file shows the revised FNOC percent water cover for Africa. Reference: Cuming, Michael J. and Barbara A. Hwakins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lon/lat Type : Raster Format : IDRISI proprietary NBId0079_101 Lake Chad Datasets, Africa CEOS_EXTRA STAC Catalog 1970-01-01 13, 7, 24, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2232848788-CEOS_EXTRA.umm_json The Lake Chad Dataset which is a detailed case study of the UNEP/FAO/ESRI Family was developed by UNEP/GRID, on behalf of the UNEP/Fresh Water Unit for the Lake Chad Commission on Sustainable Development. Lake Chad Dataset covers parts of 7 countries: Cameroon, Chad, Nigeria and Niger, Sudan, Central African Republic and Libya and is a clip (regional version) of Africa Outline Dataset (NBI01). The base maps used for the continental version were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit and the Rand-McNally New International Atlas (1982) to clarify unit boundaries. Files: ADMIN.E00 Code: 115001-001 BASE.E00 115002-001 COUNTRIES.E00 115003-001 Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The ADMIN polygon dataset showing administrative areas for 7 countries around Lake Chad. The BASE is a polygon Dataset showing the countries with inland water bodies. The COUNTRIES is a polygon Dataset showing only the country boundaries. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. FAO/UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris FAO. Maps and Statistical Data by Administrative Unit (unpublished) Rand-McNally. New International Atlas (1982). Rand-McNally & Company. Chicago Source: FAO/UNESCO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1988 Projection: Miller Type: Polygon and line Format: Arc/Info Export, non-compressed Related Datasets: All the Lake Chad Datasets of the UNEP/FAO/ESRI family. proprietary NBId0083_101 Kenya Administrative Units, Drainage, Agro-Climatic Zones, Rainfall, Infrastructure CEOS_EXTRA STAC Catalog 1970-01-01 33, -5, 43, 6 https://cmr.earthdata.nasa.gov/search/concepts/C2232847488-CEOS_EXTRA.umm_json Description: These datasets (Administrative Units, Drainage, Agro-Climatic Zones, Rainfall, Infrastructure) were scanned by the Canadian Land data Systems Division, Land Directorate, Dept of Environment, Ottawa, Canada. This was in response to the request to GRID by the Kenya Ministry of Agriculture to assist in creating the datasets. The source information and scales are varied; Rivers, Agroecological Zones, Soils, Boundaries, Towns, Lakes, Transport, and the Districts, Provinces (administrative boundary), Elevation were based on the scale of 1: 1 000 000 and of which the source information was derived from Ministry of Agriculture and Survey of Kenya maps. The Landuse dataset was based on the Kenya Rangeland Ecological Monitoring Unit (KREMU now DRSRS) map at the scale of 1: 1 000 000.The Mean Annual Rainfall dataset was based on an East Africa map(1966) at the scale of 1: 2 000 000 Rainfall data was originally provided by Kenya Meteorological Department. These were collected from a total of 79 Stations for the period between 1982-1988. More records were added by GRID which extended the period to 1991 The data consists of the rainfall,Potential Evapotranspiration (PET) and Temperature information. Sample Files: RAINFALL.E00 FILL8291.PLU, PETALL.DBF/.NDX, ADD82,83,84,85,86.DAT (Others available on request) Vector Members: - Files are in an ArcInfo Export format proprietary NBId0089_101 Kenya Soils (GIS Coverage from UNEP/GRID Nairobi) CEOS_EXTRA STAC Catalog 1979-12-30 1982-12-30 33, -5, 43, 6 https://cmr.earthdata.nasa.gov/search/concepts/C2232848109-CEOS_EXTRA.umm_json "New-ID: NBI89 SOIL MAP OF KENYA. Produced by the Republic of Kenya, Kenya Soil Survey in the Ministry of Agriculture Nairobi. Agro-climatic classification and map preparation was done by H. M. H. Braun and other staff of the Kenya soil survey. Cartography and lithography was done by the Soil Survey Insitute Wageningen, The Netherlands. There are three items in the info table which are of importance namely TYPE1, TYPE2 and SOIL. TYPE1 and TYPE2 are an alpha-numeric code which represent the soil type in the item SOIL. This code was given in order to facilitate manipulation and calculations of the info tables, which is more easily done using integers rather than using character strings. TYPE1 is the first part of the character string in the item SOIL and TYPE2 is the second part of the character string in the item SOIL, as seen in the info table below in SOIL# 19. For details on the actual soil types and associated information see the documentation ""Exploratory Soil Map and Agro-climatic Zone Map of Kenya, 1980. MAP TITLE Exploratory Soil Map and Agro-climatic Zone Map of Kenya, 1980. Arc/info table AREA PERIMETER SOIL# SOIL-ID TYPE1 TYPE2 SOIL -47.552 39.567 1 0 0 0 '' 0.068 3.258 2 9009 479 0 ' H9' 0.013 0.634 3 9010 645 0 ' Y5' 0.000 0.053 4 9011 60937 0 ' Ux7' 0.001 0.132 5 9012 403 0 ' A3' 0.002 0.284 6 9013 645 0 ' Y5' 0.009 0.524 7 9014 60937 0 ' Ux7' 0.001 0.150 8 9015 479 0 ' H9' 0.009 0.602 9 9016 516 0 ' L6' 0.052 1.562 10 9017 645 0 ' Y5' 0.022 0.975 11 9018 558821 0 ' Ps21' 0.127 2.573 12 9019 558821 0 ' Ps21' 0.000 0.085 13 9020 479 0 ' H9' 0.073 4.595 14 9021 403 0 ' A3' 0.238 5.943 15 9022 60937 0 ' Ux7' 0.002 0.231 16 9023 458 0 ' F8' 0.142 3.913 17 9024 408 0 ' A8' 0.004 0.263 18 9025 479 0 ' H9' 0.004 0.249 19 9026 431 55813 ' D1 + Pl3' 0.018 0.855 20 9027 408 0 ' A8' 0.044 1.360 21 9028 479 0 ' H9'" proprietary @@ -12071,15 +12051,15 @@ NBId0153_101 Benito River dataset of Equatorial Guinea CEOS_EXTRA STAC Catalog 1 NBId0161_101 Climate Dataset of Senegal CEOS_EXTRA STAC Catalog 1970-01-01 -17.53, 12.02, -10.89, 17.14 https://cmr.earthdata.nasa.gov/search/concepts/C2232849116-CEOS_EXTRA.umm_json New-ID: NBI161 The Climate Dataset of Senegal documentation Files: SENEGAL4.IMG Code: 146005-001 SENEGAL5.IMG 146006-001 SENEGAL6.IMG 146007-001 Raster Members IMG files are in IDRISI format The Senegal Climate Dataset was originally digitized for the UNEP/FAO/ESRI Database for Africa from hand-drawn maps provided by FAO for the Desertification Hazard Mapping project. GRID-Geneva rasterized the coverages for UNEP/GRID/WHO/CISFAM Senegal Database with a cell size of 30 seconds and two minutes lat/lon (approximately one- and four kilometeter-square pixels, respectively). Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy The SENEGAL4 file shows mean annual wind velocity meters per second (8 classes). The SENEGAL5 file shows number of wet days per year (6 classes). The SENEGAL6 file shows mean annual rainfall in millimeters (10 classes). REMARK: file may have limited applicability at national scale as was extracted from continental. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP. CISFAM. Consolidated Information System for Famine Management in Africa, Phase I Report (Apr. 1987), Univ. of Louvain, Brussels, Belgium. Source and scale : unknown Report Publication Date : Dec 1988 Projection : lat/lon Type : Raster Format : IDRISI Related Datasets : All UNEP/FAO/ESRI climate Datasets proprietary NBId0169_101 Baringo (Kenya) Pilot Study for Desertification Assessment and Mapping CEOS_EXTRA STAC Catalog 1984-01-01 1992-12-30 35, -1, 36, 0 https://cmr.earthdata.nasa.gov/search/concepts/C2232849286-CEOS_EXTRA.umm_json The purpose of the Kenya Pilot Study was to evaluate the FAO/UNEP Provisional Methodology for Assessment and Mapping of Desertification, and to recommend an effective, simple methodology for desertification assessment within Kenya. The FAO/UNEP Provisional Methodology (1984) proposes seven processes for consideration in desertification assessment: degradation of vegetation, water erosion, wind erosion, salinization, reduction of organic content, soil crusting and compaction. In late 1985, a pilot project for the assessment of the FAO/UNEP Methodology within Kenya was proposed, and in 1987 a memorandum of understanding between the Government of Kenya and UNEP for the implementation of that study was signed. The study areas were: 1) Models can be useful to assist in desertification assessment. Models can be developed from FAO/UNEP Methodology. 2) Any modeling output requires verification. 3) Ground survey and remote sensing can be important sources of data. 4) An evaluation of data and methodologies necessary to allow verification of desertification assessment modeling is required. 5) A human use component should be incorporated into desertification assessment that considers management implications and social, as well as, economic context. 6) Computer implementation of desertificaiton assessment can be effective, however, procedures should be well defined. This study within the Baringo Study Area was designed to address these concerns. The Baringo Study Area identified in this study would be typical of such a training area. The models developed during this study could be applied to the general region. The study area lies between 0 15'-1 N and 35 30' -36 30' E. It is located between the Laikipia escarpment to the East and the Tugen Hills to the West. Topographic elevations vary from 900m on the Njemps flats to 2000m in the Puka, Tangulbei and Pokot highlands. The size of the study area is approximately 15ookm2. 4.0 DATA COLLECTION A wide variety of data was collected. Detailed data was required to provide a basis for evaluating more general cost effective data gathering techniques and to provide a basis for model verification, particularly the socio/economic data. Physical Environment Topographic Data Topographic contours were digitized directly from 1:250,000 Survey of Kenya topographic maps. The contour interval was 200 feet. A digital elevation model was constructed using triangular irregular networks (TIN). Soil Data Soil types were mapped at 1:100,000 scale using existing soil maps, manual interpretation of SPOT imagery, and field investigations (Figure 3). During field trips, soil samples were taken from each soil unit and analyzed by the Kenya National Agricultural Center. 4.2 Climate Data 4.2.1 Rainfall Data Rainfall data from the Kenya Meteorological Department was analyzed for 33 stations within and surrounding the study area. A rainfall erosivity index was calculated based on the Fourier Index (R). 12 RE (p /P) 12 where P = annual rainfall p = monthly rainfall A relationship between this erosivity index and the annual rainfall for each station was calculated using linear regression (Bake, 1988). A map of rainfall erosivity was generated for the study area by relating annual rainfall isoheyts to the following: y = 0.108x - 0.68 This data was coded and digitized. Wind Erosion Potential The following required conditions were determined to create high wind erosion potential (Kinuthia, 1989): 1) Annual rainfall less than 300mm. 2) P/E greater than zero and less than 1, where: P=mean monthly rainfall (cm). E=mean monthly PET (cm). 3) Wind velocity greater than 4 m/s at 10m height. Vegetation Data A vegetation map for the study area was produced at a scale of 1:100,000 through manual interpretation of a SPOT image and field investigations (Figure 6). A structural classification system as adopted by DRSRS was used for naming vegetation types (Grunb). Systematic Reconnaissance Flight Data Since 1977, DRSRS has been conducting aerial surveys of Kenyan rangelands. In addition to data on the number of wildlife and livestock, observations of land use and environmental condition are also made. Socio/economic Data Social Factors A wide variety of data was collected through literature review and a field administered questionnaire. Nutritional status was estimated by measurement of childrens' mid upper arm. Such data is useful for a Level 1 type assessment. Permanent Structures Data For the Level 2 assessment, data on permanent structures was extracted from DRSRS SRF data. This data was used to indicate presence and concentration of sedentary populations. Example Files: VDS.E00 (Vegetation degradation) DES.E00 (Plant Species) Others available on request. proprietary NBId0177_101 Laikipia (Kenya) Research Programme GIS Datasets CEOS_EXTRA STAC Catalog 1990-01-01 1994-12-30 36, 0, 37, 1 https://cmr.earthdata.nasa.gov/search/concepts/C2232848187-CEOS_EXTRA.umm_json Laikipia Research Programme GIS Datasets are divided into two main different study area scales: the Regional level [Laikipia district, the Ewaso Ng'iro Basin] and the Local level [Land parcels-farm(s), catchments of a few kilometer square]. Coordinate Reference System Coverage data is organized thematically as a series of layers. The coordinate reference systems used in LRP dataset are:- (a) global coordinate system - Universal Transverse Mercator (UTM), (b) Local coordinate system. Digitizing Scale and Fuzzy Tolerance The initial digitizing scale for the LRP GIS Dataset is dependent on the scale of the study areas. There are two major research levels carried by LRP namely Regional and Local. The scales used for regional level are 1:250,000 and 1:50,000. FUZZY TOLERANCE is the minimum distance between coordinates in a coverage. The resolution of a coverage is defined by the minimum distance separating the coordinates used to store coverage features. Resolution is limited by the map scale in initial digitizing. The fuzzy tolerance can be calculated as follows for digitizing table: Initial Scale for Coverage of Fuzzy Tolerance Digitizing Units Value 1;250,000 Meters 6.35 1:50,000 Meters 1.25 1:10,000 Meters 0.25 1:5,000 Meters 0.125 1:2,500 Meters 0.0625 Files: Roads.E00 (Roads) Settle.E00 (Settlement Pattern) Centres.E00 (Urban Centres) (other files exist also) proprietary -NBId0203_101 Africa Water Balance high/lowland crops, 1987 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847252-CEOS_EXTRA.umm_json "The Africa Water Balance data set which is prepared by watershed and by country, belongs to the group of ""Irrigation and Water Resources Potential"" study. It covers 55 countries and 25 major basins which contain 335 watersheds. The digitized data base for Africa and the World was originally prepared for an FAO/UNEP project on Desertification in 1982-1984. UNEP financed preparation and analysis of the digitized map data and FAO prepared the data and methodology. The main input maps (all in Miller Oblated Stereographic projection) are the 1975 UNESCO Geological Map of Africa (originally at a scale of 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and Administrative Units map - all at a scale 1:5 m. The methodology was based on water balance approach. This determines the suitability of the soil for irrigation and estimates the amount of water the soil requires. Estimates of the surface and groundwater are then compared to the potential irrigation use. If use exceeds available water resources, the irrigable area is correspondingly reduced; in the event of water surplus, some of the water is routed to the downstream basin. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except lowland crops). For further details refer to FAO contact for the 1987 FAO Irrigation and Water Resources Potential for Africa AGL/MISC/11/87. FAO, Land and Water Development Division via Delle Terme di Caracalla, 00100, Rome, Italy Vector Member The file is in Arc/Info Export format. Reference: FAO. Irrigation and Water Resources Potential for Africa. (1987) FAO. Final Report UNEP/FAO world and Africa GIS data base (1984), unpublished publication of ESRI, FAO and UNEP. UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000." proprietary NBId0203_101 Africa Water Balance high/lowland crops, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847252-CEOS_EXTRA.umm_json "The Africa Water Balance data set which is prepared by watershed and by country, belongs to the group of ""Irrigation and Water Resources Potential"" study. It covers 55 countries and 25 major basins which contain 335 watersheds. The digitized data base for Africa and the World was originally prepared for an FAO/UNEP project on Desertification in 1982-1984. UNEP financed preparation and analysis of the digitized map data and FAO prepared the data and methodology. The main input maps (all in Miller Oblated Stereographic projection) are the 1975 UNESCO Geological Map of Africa (originally at a scale of 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and Administrative Units map - all at a scale 1:5 m. The methodology was based on water balance approach. This determines the suitability of the soil for irrigation and estimates the amount of water the soil requires. Estimates of the surface and groundwater are then compared to the potential irrigation use. If use exceeds available water resources, the irrigable area is correspondingly reduced; in the event of water surplus, some of the water is routed to the downstream basin. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except lowland crops). For further details refer to FAO contact for the 1987 FAO Irrigation and Water Resources Potential for Africa AGL/MISC/11/87. FAO, Land and Water Development Division via Delle Terme di Caracalla, 00100, Rome, Italy Vector Member The file is in Arc/Info Export format. Reference: FAO. Irrigation and Water Resources Potential for Africa. (1987) FAO. Final Report UNEP/FAO world and Africa GIS data base (1984), unpublished publication of ESRI, FAO and UNEP. UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000." proprietary +NBId0203_101 Africa Water Balance high/lowland crops, 1987 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847252-CEOS_EXTRA.umm_json "The Africa Water Balance data set which is prepared by watershed and by country, belongs to the group of ""Irrigation and Water Resources Potential"" study. It covers 55 countries and 25 major basins which contain 335 watersheds. The digitized data base for Africa and the World was originally prepared for an FAO/UNEP project on Desertification in 1982-1984. UNEP financed preparation and analysis of the digitized map data and FAO prepared the data and methodology. The main input maps (all in Miller Oblated Stereographic projection) are the 1975 UNESCO Geological Map of Africa (originally at a scale of 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and Administrative Units map - all at a scale 1:5 m. The methodology was based on water balance approach. This determines the suitability of the soil for irrigation and estimates the amount of water the soil requires. Estimates of the surface and groundwater are then compared to the potential irrigation use. If use exceeds available water resources, the irrigable area is correspondingly reduced; in the event of water surplus, some of the water is routed to the downstream basin. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except lowland crops). For further details refer to FAO contact for the 1987 FAO Irrigation and Water Resources Potential for Africa AGL/MISC/11/87. FAO, Land and Water Development Division via Delle Terme di Caracalla, 00100, Rome, Italy Vector Member The file is in Arc/Info Export format. Reference: FAO. Irrigation and Water Resources Potential for Africa. (1987) FAO. Final Report UNEP/FAO world and Africa GIS data base (1984), unpublished publication of ESRI, FAO and UNEP. UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000." proprietary NBId0207_101 IGADD Member Countries Crop types and distribution by administrative units, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 22, -12, 51, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2232849119-CEOS_EXTRA.umm_json "The IGADD (Inter-Governmental Authority on Drought and Development) crop zones dataset is part of the Africa UNEP/FAO/ESRI Crops Data. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. The data was provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service, Land and Water Development Division, Italy. The datasets were then developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Administrative Units map and the World Atlas of Agriculture (1969). All sources were re-registered to the base map by comparing known features on the base map and the source maps. In the original Database (Africa), a considerable study was made of crop water requirements for a range of crops in the various African climates during the time of the year when irrigation would be required. It was found that a relatively simple relationship exists between annual rainfall and the crop irrigation water requirements for the African food grain crops. It was also observed that water requirements for food grains vary between fruit and vegetable crops on the one side and fiber crops and fodder on the other. No attempt was made to produce complex crop patterns. There is a maximum of 13 crop types in one country. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO/UNESCO Soil Map of the Africa (1977). Scale 1:5000000. UNESCO, Paris. FAO. Administration units map. Scale 1:5 000 000. Rome. FAO. Irrigation and Water Resources Potential for Africa. (1987) Source :UNESCO/FAO Soil Map of the World. Scale 1:5000000 Publication Date :Nov 1987 Projection :Miller Type :Polygon Format :Arc/Info Export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets FAO Irrigable Data sets 100050: "" IRRIGLB lowland crops, best soils "" IRRIGLT lowland crops, best plus suitable soils "" IRRIGUB upland crops, best soils "" IRRIGUT upland crops, best plus suitable soils FAO Soil water balance 100053: "" WATBALLB lowland crops, best soils "" WATBALLT lowland crops, best plus suitable soils "" WATBALUB upland crops, best soils "" WATBALUT upland crops, best plus suitable soils FAO Agro-ecological zones AEZBLL08 North-west of continent AEZBLL09 North-east of continent AEZBLL10 South of continent" proprietary NBId0208_101 Africa Major Human Settlements and Landuse, 1984 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848068-CEOS_EXTRA.umm_json The Africa Human Settlements and Landuse data sets form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the base map those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Miller Type :Points Format :Arc/Info export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments : no outline of Africa proprietary NBId0208_101 Africa Major Human Settlements and Landuse, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848068-CEOS_EXTRA.umm_json The Africa Human Settlements and Landuse data sets form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the base map those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Miller Type :Points Format :Arc/Info export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments : no outline of Africa proprietary -NBId0211_101 Africa Irrigation Potential, Best soils, 1987 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848204-CEOS_EXTRA.umm_json The Africa Irrigation Potential data set, which represents the best soils suitable for upland, is part of the FAO Irrigation and Water Resources Potential Database. The main input maps were the 1977 FAO/UNESCO Soil Map of the Africa, UNESCO Geological World Atlas (scale 1:10 m), Mean Annual Rainfall map from hand drawn FAO/AGS climate maps, Template with water related features, Administrative Units map, and Watersheds map. All maps, apart from where specified were at a scale of 1:5 million, and all in Miller Oblated Stereographic projection. The soil suitability for irrigation was determined by evaluating the properties of all soil components: dominant soil, associations and inclusions, phases, slope, drainage, and texture. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops). The soils source includes a list of attributes for each soil unit including: slope, drainage, texture and phase (re: UNEP/FAO/ESRI ITU 100004). Then for both cases (lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops)), two maps were generated. One with all soils which are suitable, and one where slope, texture, drainage and phase were considered. Each different soil type is classed according to suitability, S1 irrigation with no constraints, S2 irrigation with some constraints, N1 not suitable without major improvements, N2 permanently not suitable. Because one soil unit can consist of more soil components (unit Af26-a can mean 30%Bf and 70% Af) the suitability is expressed in percentage of the unit that is suitable (1 >50% suitable, 2 = 25-50% etc.). Then the soil characteristics are used to refine the ranking. This refining is done were the original soil rank is increased decreased or changed from their original suitability to a new suitability (so or soil gets new class S1, N1 etc. or ranking changes like, -1 lower soil rank by one, +1 raise soil rank with one). The Ranking of Soils is as follows The soils considered not suitable are: Lithosols, Arenosols, Rendzinas, Yermosols, Podzols, Thionic Fluvisols, Miscellaneous land units such as rock debris, desert debris, Gypsum units, Soils with stonic, lythic or petrogypsic phase. proprietary NBId0211_101 Africa Irrigation Potential, Best soils, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848204-CEOS_EXTRA.umm_json The Africa Irrigation Potential data set, which represents the best soils suitable for upland, is part of the FAO Irrigation and Water Resources Potential Database. The main input maps were the 1977 FAO/UNESCO Soil Map of the Africa, UNESCO Geological World Atlas (scale 1:10 m), Mean Annual Rainfall map from hand drawn FAO/AGS climate maps, Template with water related features, Administrative Units map, and Watersheds map. All maps, apart from where specified were at a scale of 1:5 million, and all in Miller Oblated Stereographic projection. The soil suitability for irrigation was determined by evaluating the properties of all soil components: dominant soil, associations and inclusions, phases, slope, drainage, and texture. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops). The soils source includes a list of attributes for each soil unit including: slope, drainage, texture and phase (re: UNEP/FAO/ESRI ITU 100004). Then for both cases (lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops)), two maps were generated. One with all soils which are suitable, and one where slope, texture, drainage and phase were considered. Each different soil type is classed according to suitability, S1 irrigation with no constraints, S2 irrigation with some constraints, N1 not suitable without major improvements, N2 permanently not suitable. Because one soil unit can consist of more soil components (unit Af26-a can mean 30%Bf and 70% Af) the suitability is expressed in percentage of the unit that is suitable (1 >50% suitable, 2 = 25-50% etc.). Then the soil characteristics are used to refine the ranking. This refining is done were the original soil rank is increased decreased or changed from their original suitability to a new suitability (so or soil gets new class S1, N1 etc. or ranking changes like, -1 lower soil rank by one, +1 raise soil rank with one). The Ranking of Soils is as follows The soils considered not suitable are: Lithosols, Arenosols, Rendzinas, Yermosols, Podzols, Thionic Fluvisols, Miscellaneous land units such as rock debris, desert debris, Gypsum units, Soils with stonic, lythic or petrogypsic phase. proprietary -NBId0216_101 Africa Number of Wet Days per Year and Wind Velocity, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849224-CEOS_EXTRA.umm_json "The Africa Number of Wet Days per year and Wind Velocity data sets are part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa in this case. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). ""Internal Publication from ESRI, FAO and UNEP ""FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris ""FAO. Map of Mean Annual Rainfall and general Climate zones for P/Pet for Africa. (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resources, Management and Conservation Service. FAO, Rome ""FAO. Maps of Mean annual Wind Velocity for Africa (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome"" ""FAO. Maps of Number of Wet Days per Year (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Type :Polygon and line Format :Arc/Info Export non-compressed Related Datasets :All UNEP/FAO/ESRI Data sets" proprietary +NBId0211_101 Africa Irrigation Potential, Best soils, 1987 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848204-CEOS_EXTRA.umm_json The Africa Irrigation Potential data set, which represents the best soils suitable for upland, is part of the FAO Irrigation and Water Resources Potential Database. The main input maps were the 1977 FAO/UNESCO Soil Map of the Africa, UNESCO Geological World Atlas (scale 1:10 m), Mean Annual Rainfall map from hand drawn FAO/AGS climate maps, Template with water related features, Administrative Units map, and Watersheds map. All maps, apart from where specified were at a scale of 1:5 million, and all in Miller Oblated Stereographic projection. The soil suitability for irrigation was determined by evaluating the properties of all soil components: dominant soil, associations and inclusions, phases, slope, drainage, and texture. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops). The soils source includes a list of attributes for each soil unit including: slope, drainage, texture and phase (re: UNEP/FAO/ESRI ITU 100004). Then for both cases (lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops)), two maps were generated. One with all soils which are suitable, and one where slope, texture, drainage and phase were considered. Each different soil type is classed according to suitability, S1 irrigation with no constraints, S2 irrigation with some constraints, N1 not suitable without major improvements, N2 permanently not suitable. Because one soil unit can consist of more soil components (unit Af26-a can mean 30%Bf and 70% Af) the suitability is expressed in percentage of the unit that is suitable (1 >50% suitable, 2 = 25-50% etc.). Then the soil characteristics are used to refine the ranking. This refining is done were the original soil rank is increased decreased or changed from their original suitability to a new suitability (so or soil gets new class S1, N1 etc. or ranking changes like, -1 lower soil rank by one, +1 raise soil rank with one). The Ranking of Soils is as follows The soils considered not suitable are: Lithosols, Arenosols, Rendzinas, Yermosols, Podzols, Thionic Fluvisols, Miscellaneous land units such as rock debris, desert debris, Gypsum units, Soils with stonic, lythic or petrogypsic phase. proprietary NBId0216_101 Africa Number of Wet Days per Year and Wind Velocity, 1984 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849224-CEOS_EXTRA.umm_json "The Africa Number of Wet Days per year and Wind Velocity data sets are part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa in this case. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). ""Internal Publication from ESRI, FAO and UNEP ""FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris ""FAO. Map of Mean Annual Rainfall and general Climate zones for P/Pet for Africa. (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resources, Management and Conservation Service. FAO, Rome ""FAO. Maps of Mean annual Wind Velocity for Africa (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome"" ""FAO. Maps of Number of Wet Days per Year (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Type :Polygon and line Format :Arc/Info Export non-compressed Related Datasets :All UNEP/FAO/ESRI Data sets" proprietary +NBId0216_101 Africa Number of Wet Days per Year and Wind Velocity, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849224-CEOS_EXTRA.umm_json "The Africa Number of Wet Days per year and Wind Velocity data sets are part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa in this case. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). ""Internal Publication from ESRI, FAO and UNEP ""FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris ""FAO. Map of Mean Annual Rainfall and general Climate zones for P/Pet for Africa. (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resources, Management and Conservation Service. FAO, Rome ""FAO. Maps of Mean annual Wind Velocity for Africa (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome"" ""FAO. Maps of Number of Wet Days per Year (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Type :Polygon and line Format :Arc/Info Export non-compressed Related Datasets :All UNEP/FAO/ESRI Data sets" proprietary NBId0218_101 Africa Surface Hydrography, 1984 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848062-CEOS_EXTRA.umm_json The First-Third Order Stream Network member of the African Surface Hydrography data set is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP), as part of a project initiated by the same. The base map used was the FAO/UNESCO Soil Map of the World, scale 1:5000000 (1977) in Miller Oblated Stereographic projection. All sources were re-registered to the base map by comparing known features on the base map and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm by US Geological Survey and ESRI) to create coverage for one-degree graticules. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1977). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World.(1977). Scale 1:5000000. UNESCO, Paris Source :FAO/UNESCO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Feature type :line Related Data sets :All UNEP/FAO/ESRI Data sets, Outline of Africa OUTLINE3.E00, HYDRMAJLL, HYDRMINLL (Surface Hydrography), Hydrologic Basins Comment : No boundary (outline) for Africa. proprietary NBId0218_101 Africa Surface Hydrography, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848062-CEOS_EXTRA.umm_json The First-Third Order Stream Network member of the African Surface Hydrography data set is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP), as part of a project initiated by the same. The base map used was the FAO/UNESCO Soil Map of the World, scale 1:5000000 (1977) in Miller Oblated Stereographic projection. All sources were re-registered to the base map by comparing known features on the base map and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm by US Geological Survey and ESRI) to create coverage for one-degree graticules. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1977). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World.(1977). Scale 1:5000000. UNESCO, Paris Source :FAO/UNESCO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Feature type :line Related Data sets :All UNEP/FAO/ESRI Data sets, Outline of Africa OUTLINE3.E00, HYDRMAJLL, HYDRMINLL (Surface Hydrography), Hydrologic Basins Comment : No boundary (outline) for Africa. proprietary NBId0220_101 Africa Rainfall and Maximum Temperature Measuring Stations (12 average monthly), 1989 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849335-CEOS_EXTRA.umm_json "The Africa Rain Measuring Stations data set, for monthly rainfall is part of the UNEP/ILRAD, now ILRI East Coast Fever (ECF) Database project. The point data was reformatted (Miller, scale 1:5 000 000) from CIAT tabular data based on 12 average monthly rainfall, evaporation, and minimum/maximum temperature. The data was used in the calculation of interpolated surfaces for rainfall and temperature distribution as the basis for modeling of climatic stress factors that constrain the distribution of ticks that transfer ECF. Vector Member The file is in Arc/Info Export format. The RAINSTNS point data represents rainfall measuring stations (12 average monthly) should go with file DATREAD.ME References: P. Lessard, R. L'Eppattenier, R.A. Norval, B.D. Perry, T.T. Dolan, K. Kundert, H. Croze, J.B. Walker, A.D. Irvin. Geographic Information System for studying the Epidemiology of East Coast Fever (Theileria parva) (1989). K. Kundert. Isolating East Coast Fever High risk Areas (1989). Arc/Info European User Conference, Rome, October 1989. CSIRO. Users guide to CLIMEX, A computer program for comparing climates in ecology. CSIRO Aust. Div Rep No.35, pp.-29 Source : CIAT tabular data Publication Date :Jan 1989 Projection :Miller Type :Point Format :Arc/Info Export non-compressed ""Related Data sets :East Coast Fever (100057-002-/66-002): ECFMAP, TICKSUIT, BUFFALO2, CATTLE, CATTYP, BUFCAT2, RAPOLY, RAPNTS, RDPNTS, RNPNTS and RZPNTS. Comment : No boundary (outline) for Africa" proprietary @@ -12094,20 +12074,20 @@ NBId0248_101 Africa Wilson & Henderson-Sellers Secondary Vegetation Classes and NBId0248_101 Africa Wilson & Henderson-Sellers Secondary Vegetation Classes and Class Reliability, 1985 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848868-CEOS_EXTRA.umm_json "The Wilson and Henderson-Sellers Secondary Vegetation Classes and Class Reliability data sets are part of the ""Wilson Henderson-Sellers land cover and soils for global circulation modeling project "" and were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the US National Geophysical Data Center (NGDC). The data sets are part of the World Data Bank II. This data Bank is provided in a Database on diskette called """"The Global Change Data Base"""". The Data Bank II is part of larger project called ""Global Ecosystems Database Project"". This is a cooperative effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the US Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The data sets are accompanied by an ASCII documentation file which contains information necessary for the use of the dataset in GIS or other software. References: Wilson, M.F./ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World Publication Date : 1985 Projection : lat/lon Type : Raster Format : IDRISI" proprietary NBId0270_101 Desertification Atlas (Africa) Maps 1-17 CEOS_EXTRA STAC Catalog 1990-01-01 1992-12-30 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847403-CEOS_EXTRA.umm_json INTRODUCTION Desertification/Land Degradation - The Background More than 6.1 billion hectares, over one third of the Earth's land area, is dryland. Nearly one billion hectares of this area are naturally hvperarid deserts, with very low biological productivity. The remaining 5.1 billion hectares are made up of arid, semiarid and dry subhumid areas, part of which have become desert since the dawn of civilization while other parts of these areas are still being degraded by human action today. These lands are the habitat and the source of livelihood for one quarter of the world's population. They are areas characterized by the persistent natural menace of recurrent drought, a natural hazard accentuated by imbalanced management of natural resources. Particularly acute drought years in the Sahelian region of Africa from 1968 to 1973, and their tragic effects on the peoples of the region, drew worldwide attention to the problems of human survival and development in drylands, particularly on desert margins. These problems have been addressed by the United Nations (UN) General Assembly, in conformity with the Charter of the United Nations. The UN General Assembly's Resolution 3202 (vi) of 1 May 1974 recommended that the international community undertake concrete and speedy measures to arrest desertification and assist the economic development of affected areas. The Economic and Social Council's Resolution 1878 (LVII) of 16 July 1974 requested all the concerned organizations of the UN system to pursue a broad attack on the drought problem. Decisions of the Governing Councils of the UN Development Programme (UNDP) and the UN Environment Programme (UNEP) emphasized the need for undertaking measures to check the spread of desert conditions. The General Assembly then decided, by Resolution 3337 (xxix) of 17 December 1974, to initiate concerted international action to combat desertification and, in order to provide an impetus to this action, to convene a UN Conference on Desertification (UNCOD), between 29 August and 9 September 1977 in Nairobi, Kenya, which would produce an effective, comprehensive and coordinated programme for solving the problem. For the purposes of this atlas, desertification/land degradation is defined as: Land degradation in arid, semiarid and dry subhumid areas resulting mainly from adverse human impact. proprietary NBId0288_101 Desertification Atlas (Global) Maps 1-20 CEOS_EXTRA STAC Catalog 1990-01-01 1992-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848998-CEOS_EXTRA.umm_json INTRODUCTION Desertification/Land Degradation - The Background More than 6.1 billion hectares, over one third of the Earth's land area, is dryland. Nearly one billion hectares of this area are naturally hvperarid deserts, with very low biological productivity. The remaining 5.1 billion hectares are made up of arid, semiarid and dry subhumid areas, part of which have become desert since the dawn of civilization while other parts of these areas are still being degraded by human action today. These lands are the habitat and the source of livelihood for one quarter of the world's population. They are areas characterized by the persistent natural menace of recurrent drought, a natural hazard accentuated by imbalanced management of natural resources. Particularly acute drought years in the Sahelian region of Africa from 1968 to 1973, and their tragic effects on the peoples of the region, drew worldwide attention to the problems of human survival and development in drylands, particularly on desert margins. These problems have been addressed by the United Nations (UN) General Assembly, in conformity with the Charter of the United Nations. The UN General Assembly's Resolution 3202 (vi) of 1 May 1974 recommended that the international community undertake concrete and speedy measures to arrest desertification and assist the economic development of affected areas. The Economic and Social Council's Resolution 1878 (LVII) of 16 July 1974 requested all the concerned organizations of the UN system to pursue a broad attack on the drought problem. Decisions of the Governing Councils of the UN Development Programme (UNDP) and the UN Environment Programme (UNEP) emphasized the need for undertaking measures to check the spread of desert conditions. The General Assembly then decided, by Resolution 3337 (xxix) of 17 December 1974, to initiate concerted international action to combat desertification and, in order to provide an impetus to this action, to convene a UN Conference on Desertification (UNCOD), between 29 August and 9 September 1977 in Nairobi, Kenya, which would produce an effective, comprehensive and coordinated programme for solving the problem. For the purposes of this atlas, desertification/land degradation is defined as: Land degradation in arid, semiarid and dry subhumid areas resulting mainly from adverse human impact. proprietary -NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1 2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates ALL STAC Catalog 2004-12-17 2005-11-30 -179.488, -77.642, -166.989, -49.014 https://cmr.earthdata.nasa.gov/search/concepts/C1214590838-SCIOPS.umm_json This data set contains concentration and rate data for the following sulfur compounds: dimethylsulfide (DMS), dimethylsulfoxide (DMSO) and dimethylsulfoniopropionate (DMSP). Data were obtained in a transect from New Zealand to the Ross Sea, Antarctica, and in the Ross Sea Polynya. Data were obtained during two research cruises to the Ross Sea aboard the RIV Nathaniel B. Palmer in December 2004 to January 2005 (NBP04-09) and in October to November 2005 (NBP05-08). A data set is also provide for biological data (bacterial biomass, bacterial productivity), CTD data and GUV irradiance data obtained during our Nathanial B. Palmer (NBP) cruises to the Ross Sea in 2004 and 2005 (NBP04-09 and NBP05-08). proprietary NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1 2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates SCIOPS STAC Catalog 2004-12-17 2005-11-30 -179.488, -77.642, -166.989, -49.014 https://cmr.earthdata.nasa.gov/search/concepts/C1214590838-SCIOPS.umm_json This data set contains concentration and rate data for the following sulfur compounds: dimethylsulfide (DMS), dimethylsulfoxide (DMSO) and dimethylsulfoniopropionate (DMSP). Data were obtained in a transect from New Zealand to the Ross Sea, Antarctica, and in the Ross Sea Polynya. Data were obtained during two research cruises to the Ross Sea aboard the RIV Nathaniel B. Palmer in December 2004 to January 2005 (NBP04-09) and in October to November 2005 (NBP05-08). A data set is also provide for biological data (bacterial biomass, bacterial productivity), CTD data and GUV irradiance data obtained during our Nathanial B. Palmer (NBP) cruises to the Ross Sea in 2004 and 2005 (NBP04-09 and NBP05-08). proprietary +NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1 2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates ALL STAC Catalog 2004-12-17 2005-11-30 -179.488, -77.642, -166.989, -49.014 https://cmr.earthdata.nasa.gov/search/concepts/C1214590838-SCIOPS.umm_json This data set contains concentration and rate data for the following sulfur compounds: dimethylsulfide (DMS), dimethylsulfoxide (DMSO) and dimethylsulfoniopropionate (DMSP). Data were obtained in a transect from New Zealand to the Ross Sea, Antarctica, and in the Ross Sea Polynya. Data were obtained during two research cruises to the Ross Sea aboard the RIV Nathaniel B. Palmer in December 2004 to January 2005 (NBP04-09) and in October to November 2005 (NBP05-08). A data set is also provide for biological data (bacterial biomass, bacterial productivity), CTD data and GUV irradiance data obtained during our Nathanial B. Palmer (NBP) cruises to the Ross Sea in 2004 and 2005 (NBP04-09 and NBP05-08). proprietary NCALDAS_NOAH0125_D_2.0 NCA-LDAS Noah-3.3 Land Surface Model L4 Daily 0.125 x 0.125 degree V2.0 (NCALDAS_NOAH0125_D) at GES DISC GES_DISC STAC Catalog 1979-01-02 2016-12-31 -125, 25, -67, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1454297282-GES_DISC.umm_json The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs). The overall goal is to provide the highest quality terrestrial hydrology products that enable improved scientific understanding, adaptation, and management of water and related energy resources during a changing climate. An overview of NCA-LDAS and its capability for developing climate change indicators are provided in Jasinski et al. (2019). Details on the data assimilation used in NCA-LDAS are described in Kumar et al. (2019). Sample mean annual trends are provided in the NCA-LDAS V2.0 README document. This NCA-LDAS version 2.0 data product was simulated for the continental United States for the satellite era from January 1979 to December 2016. The core of NCA-LDAS is the multivariate assimilation of past and current satellite based data records within the Noah Version 3.3 land-surface model (LSM) at 1/8th degree resolution using NASA's Land Information System (LIS; Kumar et al. 2006) software framework during the Earth observing satellite era. The temporal resolution is daily. NCA-LDAS V001 data will no longer be available and have been superseded by V2.0. NCA-LDAS includes 42 variables including land-surface fluxes (e.g. precipitation, radiation and latent and sensible heat, etc.), stores (e.g. soil moisture and snow), states (e.g., surface temperature), and routing variables (e.g., runoff, streamflow, flooded area, etc.), driven by the atmospheric forcing data from North American Land Data Assimilation System Phase 2 (NLDAS-2; Xia et al., 2012). NCA-LDAS builds upon NLDAS through the addition of multivariate assimilation of earth observations such as soil moisture (Kumar et al, 2014), snow (Liu et al, 2015; Kumar et al, 2015a) and irrigation (Ozdagon et al, 2010; Kumar et al, 2015b). The EDRs that have been assimilated into the NCA-LDAS include soil moisture and snow depth from principally microwave sensors including SMMR, SSM/I, AMSR-E, ASCAT, AMSR-2, SMOS, and SMAP, irrigation intensity estimates from MODIS, and snow covered area from MODIS and from the multisensor IMS snow product. proprietary NCALDAS_NOAH0125_Trends_2.0 NCA-LDAS Noah-3.3 Land Surface Model L4 Trends 0.125 x 0.125 degree V2.0 (NCALDAS_NOAH0125_Trends) at GES DISC GES_DISC STAC Catalog 1979-10-01 2015-09-30 -125, 25, -67, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1646132439-GES_DISC.umm_json The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs). The overall goal is to provide the highest quality terrestrial hydrology products that enable improved scientific understanding, adaptation, and management of water and related energy resources during a changing climate. This dataset consists of a suite of historical trends in terrestrial hydrology over the conterminous United States estimated for the water years of 1980-2015 using the NCA-LDAS daily reanalysis. NCA-LDAS provides gridded daily outputs from the uncoupled Noah version 3.3 land surface model (LSM) at 1/8th degree resolution forced with NLDAS-2 meteorology (Xia et al., 2012), rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products (Jasinski et al., 2019; Kumar et al., 2019). Trends in annual hydrologic indicators are reported using the nonparametric Mann-Kendall test at p < 0.1 significance. An additional precipitation trend field (annual total), with no significance test applied, is included for comparison purposes. Collectively, these fields represent the bulk of the results presented in Jasinski et al. (2019). proprietary -NCAR_DS474.0 AARI Russian North Polar Drifting Station Data, from NSIDC ALL STAC Catalog 1937-05-01 1991-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214056415-SCIOPS.umm_json This dataset consists of 31 Russian north polar drifting stations which took observations of surface variables for the periods 1937-1938 and 1950-1991. We received the latest version of this data from the Arctic and Antarctic Research Institute (AARI) via the National Snow and Ice Data Center (NSIDC). proprietary NCAR_DS474.0 AARI Russian North Polar Drifting Station Data, from NSIDC SCIOPS STAC Catalog 1937-05-01 1991-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214056415-SCIOPS.umm_json This dataset consists of 31 Russian north polar drifting stations which took observations of surface variables for the periods 1937-1938 and 1950-1991. We received the latest version of this data from the Arctic and Antarctic Research Institute (AARI) via the National Snow and Ice Data Center (NSIDC). proprietary +NCAR_DS474.0 AARI Russian North Polar Drifting Station Data, from NSIDC ALL STAC Catalog 1937-05-01 1991-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214056415-SCIOPS.umm_json This dataset consists of 31 Russian north polar drifting stations which took observations of surface variables for the periods 1937-1938 and 1950-1991. We received the latest version of this data from the Arctic and Antarctic Research Institute (AARI) via the National Snow and Ice Data Center (NSIDC). proprietary NCAR_DS510.5 A Quality-Controlled Dataset for Long-Term U.S. Snowfall Trends ALL STAC Catalog 1890-01-01 2007-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110939-SCIOPS.umm_json NCDC's U.S. Cooperative Summary of Data (DSI3200) dataset was screened for stations with long continuous observations for use in assessing 20th-century U.S. snowfall trends. The result is a subset of 424 stations with quality-controlled snowfall, precipitation, and temperature data for snow-season months (October through May). Most of the stations have observations that begin prior to the winter of 1930-31, making for station periods of longer than 77 winters. Several stations have data as far back as the 1890s. proprietary NCAR_DS510.5 A Quality-Controlled Dataset for Long-Term U.S. Snowfall Trends SCIOPS STAC Catalog 1890-01-01 2007-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110939-SCIOPS.umm_json NCDC's U.S. Cooperative Summary of Data (DSI3200) dataset was screened for stations with long continuous observations for use in assessing 20th-century U.S. snowfall trends. The result is a subset of 424 stations with quality-controlled snowfall, precipitation, and temperature data for snow-season months (October through May). Most of the stations have observations that begin prior to the winter of 1930-31, making for station periods of longer than 77 winters. Several stations have data as far back as the 1890s. proprietary -NCAR_DS744.7 ADEOS Scatterometer Winds, Level 2B SCIOPS STAC Catalog 2002-06-04 2002-06-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214055329-SCIOPS.umm_json Sea surface wind estimated by scatterometer instruments on the ADEOS satellite. JPL PO.DAAC [http://podaac.jpl.nasa.gov/] has initiated reprocessing of all ADEOS and QuikSCAT data with superior algorithms for retrievals in high wind speed and light rain areas. This reprocessing could affect this dataset. proprietary NCAR_DS744.7 ADEOS Scatterometer Winds, Level 2B ALL STAC Catalog 2002-06-04 2002-06-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214055329-SCIOPS.umm_json Sea surface wind estimated by scatterometer instruments on the ADEOS satellite. JPL PO.DAAC [http://podaac.jpl.nasa.gov/] has initiated reprocessing of all ADEOS and QuikSCAT data with superior algorithms for retrievals in high wind speed and light rain areas. This reprocessing could affect this dataset. proprietary +NCAR_DS744.7 ADEOS Scatterometer Winds, Level 2B SCIOPS STAC Catalog 2002-06-04 2002-06-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214055329-SCIOPS.umm_json Sea surface wind estimated by scatterometer instruments on the ADEOS satellite. JPL PO.DAAC [http://podaac.jpl.nasa.gov/] has initiated reprocessing of all ADEOS and QuikSCAT data with superior algorithms for retrievals in high wind speed and light rain areas. This reprocessing could affect this dataset. proprietary NCAR_DS871.0 ADAPTE: Minimum and Maximum Temperature and Relative Humidity for Latin American Cities Data ALL STAC Catalog 2000-01-01 2006-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110979-SCIOPS.umm_json Temperature data classified as maximum, mean, and minimum temperature and relative humidity measures from the meteorological station located at the regional airport in Bogota and Buenos Aries, called the National Service of Hydrology and Meteorology. Mexico data was collected from the National Polytechnic Institute of Mexico and National Meteorological System. In Santiago, Chile weather data was provided by the air pollution monitoring network with stations across the city, the REDCAM2 (Red de Monitoreo Automatica de la Calidad del Aire Metropolitana) Automatic Monitoring Network of Metropolitan Air Quality. The data from these stations were averaged to obtain temperature values for the Gran Santiago region. Daily temperature and relative humidity readings were made by automatic-recording instruments. proprietary NCAR_DS871.0 ADAPTE: Minimum and Maximum Temperature and Relative Humidity for Latin American Cities Data SCIOPS STAC Catalog 2000-01-01 2006-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110979-SCIOPS.umm_json Temperature data classified as maximum, mean, and minimum temperature and relative humidity measures from the meteorological station located at the regional airport in Bogota and Buenos Aries, called the National Service of Hydrology and Meteorology. Mexico data was collected from the National Polytechnic Institute of Mexico and National Meteorological System. In Santiago, Chile weather data was provided by the air pollution monitoring network with stations across the city, the REDCAM2 (Red de Monitoreo Automatica de la Calidad del Aire Metropolitana) Automatic Monitoring Network of Metropolitan Air Quality. The data from these stations were averaged to obtain temperature values for the Gran Santiago region. Daily temperature and relative humidity readings were made by automatic-recording instruments. proprietary -NCEI DSI 1167_01_Not Applicable Active Marine Station Metadata ALL STAC Catalog 2012-05-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093639-NOAA_NCEI.umm_json The Active Marine Station Metadata is a daily metadata report for active marine bouy and C-MAN (Coastal Marine Automated Network) platforms from the National Data Buoy Center (NDBC). Metadata includes the station id, latitude/longitude (resolution to thousandths of a degree), the station name, the station owner, the program the station is associated with (e.g., TAO, NDBC, tsunami, NOS, etc.), station type (e.g., buoy, fixed, oil rig, etc.), notification if the station observes meteorology, currents, and water quality (signified by 'y' for yes and 'n' for no). If there is a 'y' associated with one of these tags, then the station has reported data in that category within the last 8 hours (or 24 hours for DART stations--Deep-Ocean Assessment Reporting of Tsunamis). If there is an 'n', data has not been received within those times. Stations are removed from the list when they are dismantled. The metadata information is written to a daily XML-formatted file. proprietary NCEI DSI 1167_01_Not Applicable Active Marine Station Metadata NOAA_NCEI STAC Catalog 2012-05-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093639-NOAA_NCEI.umm_json The Active Marine Station Metadata is a daily metadata report for active marine bouy and C-MAN (Coastal Marine Automated Network) platforms from the National Data Buoy Center (NDBC). Metadata includes the station id, latitude/longitude (resolution to thousandths of a degree), the station name, the station owner, the program the station is associated with (e.g., TAO, NDBC, tsunami, NOS, etc.), station type (e.g., buoy, fixed, oil rig, etc.), notification if the station observes meteorology, currents, and water quality (signified by 'y' for yes and 'n' for no). If there is a 'y' associated with one of these tags, then the station has reported data in that category within the last 8 hours (or 24 hours for DART stations--Deep-Ocean Assessment Reporting of Tsunamis). If there is an 'n', data has not been received within those times. Stations are removed from the list when they are dismantled. The metadata information is written to a daily XML-formatted file. proprietary +NCEI DSI 1167_01_Not Applicable Active Marine Station Metadata ALL STAC Catalog 2012-05-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093639-NOAA_NCEI.umm_json The Active Marine Station Metadata is a daily metadata report for active marine bouy and C-MAN (Coastal Marine Automated Network) platforms from the National Data Buoy Center (NDBC). Metadata includes the station id, latitude/longitude (resolution to thousandths of a degree), the station name, the station owner, the program the station is associated with (e.g., TAO, NDBC, tsunami, NOS, etc.), station type (e.g., buoy, fixed, oil rig, etc.), notification if the station observes meteorology, currents, and water quality (signified by 'y' for yes and 'n' for no). If there is a 'y' associated with one of these tags, then the station has reported data in that category within the last 8 hours (or 24 hours for DART stations--Deep-Ocean Assessment Reporting of Tsunamis). If there is an 'n', data has not been received within those times. Stations are removed from the list when they are dismantled. The metadata information is written to a daily XML-formatted file. proprietary NCEI DSI 2001_01_Not Applicable Climate Forecast System Version 2 (CFSv2) Operational Forecasts NOAA_NCEI STAC Catalog 2011-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093673-NOAA_NCEI.umm_json The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the interaction between the Earth's oceans, land and atmosphere. The four-times-daily, 9-month control runs, consist of all 6-hourly forecasts, and the monthly means and variable time-series (all variables). The CFSv2 outputs include: 2-D Energetics (EGY); 2-D Surface and Radiative Fluxes (FLX); 3-D Pressure Level Data (PGB); 3-D Isentropic Level Data (IPV); 3-D Ocean Data (OCN); Low-resolution output (GRBLOW); Dumps (DMP); and High- and Low-resolution Initial Conditions (HIC and LIC). The monthly CDAS variable timeseries includes all variables. The CFSv2 period of record begins on April 1, 2011 and continues onward. CFS output is in GRIB-2 file format. proprietary NCEI DSI 2002_01_Not Applicable Climate Forecast System Version 2 (CFSv2) Operational Analysis NOAA_NCEI STAC Catalog 2011-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093682-NOAA_NCEI.umm_json The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the interaction between the Earth's oceans, land and atmosphere. The CFSv2 Operational Analysis or Climate Data Assimilation System (CDAS), consist of all 6-Hourly CDAS, and the monthly CDAS monthly means and variable time-series (all variables). The CFSv2 outputs include: 2-D Energetics (EGY); 2-D Surface and Radiative Fluxes (FLX); 3-D Pressure Level Data (PGB); 3-D Isentropic Level Data (IPV); 3-D Ocean Data (OCN); Low-resolution output (GRBLOW); Dumps (DMP); and High- and Low-resolution Initial Conditions (HIC and LIC). The monthly CDAS variable timeseries includes all variables. The CFSv2 period of record begins on April 1, 2011 and continues onward. CFS output is in GRIB-2 file format. proprietary NCEI DSI 3298_01 (original)_Not Applicable Climate Record Books Keyed Data NOAA_NCEI STAC Catalog 1850-01-01 1990-12-31 134, -15, -64, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2102893128-NOAA_NCEI.umm_json Climate Record Books (CRB) Data were keyed as part of the Climate Database Modernization Program (CDMP). These original keyed files as well as documentation relating to the format and keying process is available within the 3298_01 archive. The Northeast Regional Climate Center (NRCC) reformatted and performed quality control checks on the data, ensuring that the data could be used in high quality datasets and applications. Data and documentation for this data is available within the 3298_02 archive. The dataset consists of 171 stations that are located throughout the US. Variables include: maximum temperature, minimum temperature, average temperature, precipitation, and snowfall. Temporal resolution is daily, but observation times are not available for this dataset. However, data coverage varies by station. The records for individual stations range in length from 9 months to 121 years. Parts of the records may be duplicated in other, higher-priority ACIS data sources. proprietary @@ -12132,8 +12112,8 @@ NCEI DSI 9694_01_Not Applicable Cedar Hill Tower Data NOAA_NCEI STAC Catalog 196 NCEI DSI 9715_01_Not Applicable Climatological Data National Summary (CDNS) Monthly Surface NOAA_NCEI STAC Catalog 1961-01-01 1964-12-31 134, -15, -64, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2102893102-NOAA_NCEI.umm_json These data are keyed (digitized) data from the images of the Climatological Data National Summary containing monthly summaries for cities in the United States (and territories). Variables include temperature, precipitation, station and sea level pressure, average dew point, average relative humidity, weather occurrence, wind, cloudiness/sunshine and degree days. Period of record is 1961-1964. proprietary NCEI DSI 9795_01_Not Applicable Climate Diagnostics Data Base NOAA_NCEI STAC Catalog 1978-10-01 1983-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102892556-NOAA_NCEI.umm_json The Climatic Diagnostics Database, DSI-9795, is a historical data set created by the Climate Analysis Center using global climatic data from the period October 1, 1978 through September 30, 1983. The Climate Diagnostics Database contains monthly averages of selected fields from the National Meteorological Center's (NMC; now National Centers for Environmental Prediction, NCEP) Global Data Assimilation System (GDAS). The major parameters are monthly averages of the following elements for constant pressure levels of 1000-, 850-, 700-, 500-, 300-, 250-, 200-, 100-, and 50-millibars: 1. U (West/East) component of wind (meters/second), 2. V (South/North) component of wind (meters/second), 3. Temperature (Deg. K), 4. Geopotential height (geopotential meters), 5. Vertical velocity (millibars/second), 6. Specific humidity (grams/kilogram) 7. Vorticity (seconds-1), 8. Pressure (millibars), 9. Sums squared of U (West/East) component of wind (meters/second), 10. Sums squared of V (South/North) component of wind (meters/second), 11. Sums squared of temperature (K), 12. Sums squared of geopotential height (geopotential meters). 13. Sums squared of vertical velocity (millibars/second), 14. Sums squared of specific humidity (grams/kilogram), 15. Sums squared of vertical velocity (seconds-1), 16. Sum of cross product UV wind components (m2s-2), East-West transport of poleward momentum, 17. Sum of cross product U and temperature (ms-1K), East-West transport of heat, 18. Sum of cross product U and geopotential height (ms-1gpm), East-West transport of mass, 19. Sum of cross product U and vertical velocity (mmbs-2), East-West transport of vertical momentum, 20. Sum of cross product U and specific humidity (mgs-1Kg-1), East-West transport of moisture, 21. Sum of cross product U and vorticity (ms-2), East-West transport of relative vorticity, 22. Sum of cross product V and temperature, North-South transport of heat, 23. Sum of cross product V and geopotential height (ms-1gpm), North-South transport of mass, 24. Sum of cross product V and vertical velocity (mmbs-2), North-South transport of vertical momentum, 25. Sum of cross products V and specific humidity (mgs-1Kg-1), North-South transport of moisture, 26. Sum of cross products V and vorticity (ms-2), North-South transport of relative vorticity, 27. Stretching of vortex tubes (s-2). proprietary NCEI DSI 9796_01_Not Applicable Atmospheric Handbook Data Tables NOAA_NCEI STAC Catalog 1896-01-01 1982-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102892524-NOAA_NCEI.umm_json Atmospheric Handbook Data Tables consists of one combined file containing 226 data files. The files contains information, programs, and data largely taken from results published in scientific journals. In general, sections of files are grouped according to the atmospheric area. Atmospheric data tables in this data set are described in World Data Center A for Meteorology and World Data Center A for Solar Terrestrial Physics Report UAG-89. Data areas cover attenuation coefficients for the atmosphere and H2O; 1962 standard atmospheres; cloud drop size distributions for water and ice spheres; solar spectral irradiance (NIMBUS and SMM satellite solar irradiance data); sky spectral radiance; Rayleigh coefficients for air; refractive indices for air, ice, liquid H2O, and various atmospheric aerosols; and relative reflectance for ice and H2O. proprietary -NCEI DSI 9799_Not Applicable African Historical Precipitation Data NOAA_NCEI STAC Catalog 1850-01-01 1984-12-31 -25, -31, 52, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2102892476-NOAA_NCEI.umm_json African Historical Precipitation Data is digital data set DSI-9799, archived at the National Climatic Data Center (NCDC). This data is a collection from various sources of data from Africa, including publications, hand-written data secured from visiting scientists, and visits to African nations. The activity was supported by funds provided by the Agency for International Development (AID). The geographic coverage is selected stations from Africa in the following regions: Subequatorial, Tropical West, Sahel, Horn. Not included are most of northern and southern Africa. The time period covered is variable; earliest is 1850 and latest is 1984. The major parameter is sequential monthly total precipitation (mm). proprietary NCEI DSI 9799_Not Applicable African Historical Precipitation Data ALL STAC Catalog 1850-01-01 1984-12-31 -25, -31, 52, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2102892476-NOAA_NCEI.umm_json African Historical Precipitation Data is digital data set DSI-9799, archived at the National Climatic Data Center (NCDC). This data is a collection from various sources of data from Africa, including publications, hand-written data secured from visiting scientists, and visits to African nations. The activity was supported by funds provided by the Agency for International Development (AID). The geographic coverage is selected stations from Africa in the following regions: Subequatorial, Tropical West, Sahel, Horn. Not included are most of northern and southern Africa. The time period covered is variable; earliest is 1850 and latest is 1984. The major parameter is sequential monthly total precipitation (mm). proprietary +NCEI DSI 9799_Not Applicable African Historical Precipitation Data NOAA_NCEI STAC Catalog 1850-01-01 1984-12-31 -25, -31, 52, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2102892476-NOAA_NCEI.umm_json African Historical Precipitation Data is digital data set DSI-9799, archived at the National Climatic Data Center (NCDC). This data is a collection from various sources of data from Africa, including publications, hand-written data secured from visiting scientists, and visits to African nations. The activity was supported by funds provided by the Agency for International Development (AID). The geographic coverage is selected stations from Africa in the following regions: Subequatorial, Tropical West, Sahel, Horn. Not included are most of northern and southern Africa. The time period covered is variable; earliest is 1850 and latest is 1984. The major parameter is sequential monthly total precipitation (mm). proprietary NCEI DSI 9873_01_Not Applicable Baseline Surface Radiation Network (BSRN) Solar Radiation Data (Disposition Review) NOAA_NCEI STAC Catalog 1993-01-01 2008-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102893059-NOAA_NCEI.umm_json "The dataset DSI 9873 is a subset of the Baseline Surface Radiation Network data monitored by NOAA ESRL Global Radiation (G-Rad) group in Boulder, Colorado. The ""STAR"" network is a name that Ells (Ellsworth Dutton, deceased) came up with for the NOAA Global Monitoring Division (formerly CMDL) radiation measurements at GMD's baseline sites at Barrow, Mauna Loa, American Samoa, Boulder Atmospheric Observatory (BAO tower), South Pole, and other sites at Kwajalein, Bermuda, and Trinidad Head (CA). Before STAR, they were just referred to as ""Baseline sites"". As the NCEI archive only contains a subset (The ""STAR"" stations continue to operate, so their data set does extend beyond 2008), users are encouraged to contact the ESRL Global Monitoring Division for the most up-to-date information. Per MACI team: The dataset DSI 9873 is a subset of the Baseline Surface Radiation Network data monitored by NOAA ESRL Global Radiation (G-Rad) group in Boulder, Colorado. Dave Longenecker is the data manager in Boulder and he provides the data to the global network (see online resource URL). In a phone conversation with Mara Sprain, 22 Aug 2016, Dave related that he didn't know we had this small subset. He had no direction to provide us with additional data. This dataset needs a submission agreement (if it's to be maintained) or it should be a candidate for removal. It's duplicated both in Boulder (FTP) and Germany (FTP and PANGAEA). From John Augustine email, 19 Aug 2016: The ""STAR"" network is a name that Ells (Ellsworth Dutton, deceased) came up with for the NOAA Global Monitoring Division (formerly CMDL) radiation measurements at GMD's baseline sites at Barrow, Mauna Loa, American Samoa, Boulder Atmospheric Observatory (BAO tower), South Pole, and other sites at Kwajalein, Bermuda, and Trinidad Head (CA). Before STAR, they were just referred to as ""Baseline sites"". When NCDC found out about these measurements (circa 2008), they requested that their data be submitted there. I wrote a program for Ells to do that and several years of data were submitted. I am not sure how up-to-date those submissions are because I don't do them. If you want metadata on the Baseline sites, you will have to contact Dave Longenecker (david.u.longenecker@noaa.gov). He has been the data manager for them for many years. Bermuda and Kwajalein have been supported by NASA, but they cut those funds this year. I am not sure whether they will continue. Bermuda has not operated for about three years because of communication problems and other issues. It will be brought back up soon. The ""STAR"" stations continue to operate, so their data set does extend beyond 2008. Data are also (?) held in Colorado archive." proprietary NCEI DSI 9926_01_Not Applicable Bulletin W Monthly Summary Data NOAA_NCEI STAC Catalog 1891-01-01 1960-01-01 134, -15, -64, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2102893120-NOAA_NCEI.umm_json Monthly station summaries of precipitation (including snowfall), maximum temperature and minimum temperature are provided. Also included are number of days with temperature and precipitation meeting defined threshold values. Also included are extreme highest and lowest temperature, and years of record. Period of record is generally 1891-1960, with coverage in the United States, Puerto Rico, the U.S. Virgin Islands and the Pacific islands. proprietary NCEI DSI 9949_01_Not Applicable Automation of Field Operations and Services (AFOS) National Weather Service (NWS) Service Records and Retention System (SRRS) Data NOAA_NCEI STAC Catalog 1983-05-31 2001-08-05 134, -15, -64, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2107093299-NOAA_NCEI.umm_json Service Records and Retention System (SRRS) is historical digital data set DSI-9949, a collection of products created by the U.S. National Weather Service (NWS) and archived at the National Centers for Environmental Information (NCEI) [formerly National Climatic Data Center (NCDC)]. SRRS was a network of computers and associated hardware whose purpose was to transmit and store a large number of NWS products and make them available as needed. Basic meteorological and hydrological data, analyses, forecasts, and warnings are distributed among NWS offices over the AFOS (Automation of Field Operations and Services) communications system since 1978. These include PIREP (aircraft reports from pilots), AIRMET (aeronautical meteorological bulletins), SIGMET (significant meteorological information), surface and upper air plotted unanalyzed maps, air stagnation, precipitable water, Forecasts such as wind and temperature aloft, thickness and analysis, fire weather, area, local, zone, state, agricultural advisory, and terminal; and Warnings such as marine, severe weather, hurricane and tornado. The AFOS system was developed to increase the productivity and effectiveness of NWS personnel and to increase the timeliness and quality of their warning and forecasting services. This format version of the SRRS data was archived at NCEI from 1983 to 2001 (when a new format was created). The NCEI can service requests for products from the SRRS; two types of products are available to the user: 1) graphic displays of meteorological analyses and forecast charts (limited), and 2) alphanumeric displays of narrative summaries and meteorological/hydrological data. The following is a partial list of historical SRRS products available through the NCDC: rawinsonde data above 100 MB; AIREPS buoy reports; coastal flood warning; Coast Guard surface report; climatological report (daily and misc, incl monthly reports); weather advisory Coastal Waters Forecast Center (CWSU); weather statement; 3- to 5-day extended forecast; average 6- to 10-day weather outlook (local and national); aviation area forecast winds aloft forecast; flash flood statements, watches and warnings; flood statement; flood warning forecast; medium range guidance; FOUS relative humidity/temperature guidance; FOUS prog max/min temp/POP guidance; FOUS wind/cloud guidance; Great Lakes forecast; hurricane local statement; high seas forecast; international aviation observations; local forecast; local storm report; rawinsonde observation - mandatory levels;, METAR formatted surface weather observation; marine weather statement; short term rorecast; non-precipitation warnings/watches/advisories; nearshore marine forecast (Great Lakes only), offshore aviation area forecast; offshore forecast; other marine products, other surface weather observations, pilot report plain language, ship report, state pilot report, collective recreational report; narrative radar summary radar observation; hydrology-meteorology data report; river summary; river forecast; miscellaneous river product; river recreation statement; ; regional weather summary; surface aviation observation; preliminary notice of watch and canc msg SVR; local storm watch and warning; cancelation msg SELS watch; point information message; state forecast discussion ; state forecast rawinsonde observation - significant levels; surface ship report at intermediate synoptic time; surface ship report at non-synoptic time; surface ship report at synoptic time; special weather statement international; SIGMET severe local storm watch and area outline; special marine warning; intermediate surface synoptic observation; main surface synoptic observation; severe thunderstorm warning; severe weather statement; severe storm outlook; narrative state weather summary; terminal forecast; tropical cyclone discussion; marine/aviation tropical cyclone advisory; public tropical cyclone advisory; tornado warning; transcribed weather broadcast; tropical weather discussion; tropical weather outlook and summary; AIRMET SIGMET zone forecast; terminal forecast (prior to 7/1/96); winter weather warnings, watches, advisories; marine advisory/warning; special marine warning; miscellaneous product convective SIGMET ; local ice forecast; area forecast discussion; public information statement. SRRS (DSI-9949) by the Gateway SRRS (DSI-9957; C00583). NWS products after 2001 can be obtained from those systems, from NCEI. proprietary @@ -12197,16 +12177,16 @@ NEMSN5L2_001 NEMS/Nimbus-5 Level 2 Output Data V001 (NEMSN5L2) at GES DISC GES_D NES-LTER_0 Northeast U.S. Shelf (NES), Long-Term Ecological Research (LTER) OB_DAAC STAC Catalog 2018-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208430341-OB_DAAC.umm_json The Northeast U.S. Shelf (NES) Long-Term Ecological Research (LTER) project integrates observations, experiments, and models to understand and predict how planktonic food webs are changing, and how those changes impact the productivity of higher trophic levels. The NES-LTER is co-located with the Northeast U.S. Continental Shelf Large Marine Ecosystem, spanning the Middle Atlantic Bight and Gulf of Maine. Our focal cross-shelf transect extends about 150 km southward from Martha's Vineyard, MA, to just beyond the shelf break. proprietary NESP_2015_SRW 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1381760732-SCIOPS.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the ?western? Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the ?eastern? subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected ?western? count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary NESP_2015_SRW 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia SCIOPS STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1381760732-SCIOPS.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the ?western? Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the ?eastern? subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected ?western? count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary -NESP_2015_SRW_3 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1333031622-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary NESP_2015_SRW_3 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1333031622-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary -NESP_2016_SRW_3 2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2016-08-24 2016-08-29 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1412710076-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2016. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2016. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary +NESP_2015_SRW_3 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1333031622-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary NESP_2016_SRW_3 2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2016-08-24 2016-08-29 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1412710076-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2016. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2016. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary -NESP_2017_SRW_1 2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2017-08-23 2017-08-27 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847804-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2017. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 25-year period 1993-2017. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future proprietary +NESP_2016_SRW_3 2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2016-08-24 2016-08-29 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1412710076-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2016. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2016. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary NESP_2017_SRW_1 2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2017-08-23 2017-08-27 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847804-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2017. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 25-year period 1993-2017. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future proprietary -NESP_2018_SRW_1 2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2018-08-18 2018-08-23 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847807-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary +NESP_2017_SRW_1 2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2017-08-23 2017-08-27 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847804-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2017. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 25-year period 1993-2017. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future proprietary NESP_2018_SRW_1 2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2018-08-18 2018-08-23 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847807-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary -NESP_2019_SRW_1 2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2019-08-18 2019-08-24 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847810-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2019. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 27-year period 1993-2019. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary +NESP_2018_SRW_1 2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2018-08-18 2018-08-23 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847807-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary NESP_2019_SRW_1 2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2019-08-18 2019-08-24 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847810-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2019. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 27-year period 1993-2019. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary +NESP_2019_SRW_1 2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2019-08-18 2019-08-24 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847810-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2019. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 27-year period 1993-2019. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary NEUROST_SSH-SST_L4_V2024.0_2024.0 Daily NeurOST L4 Sea Surface Height and Surface Geostrophic Currents POCLOUD STAC Catalog 2010-01-01 2024-06-15 -180, -70, 180, 79.9 https://cmr.earthdata.nasa.gov/search/concepts/C3085229833-POCLOUD.umm_json This Daily NeurOST Level 4 Sea Surface Height and Surface Geostrophic Currents analysis product from the University of Washington and JPL was mapped by a neural network trained with sparse Level 3 nadir altimetry observations (CMEMS, E.U. Copernicus Marine Service Information) and the MUR Level 4 gridded sea surface temperature product (PO.DAAC). proprietary NEWS_WEB_ACLIM_1.0 NASA Energy and Water cycle Study (NEWS) Annual Climatology of the 1st decade of the 21st Century V1.0 (NEWS_WEB_ACLIM) at GES DISC GES_DISC STAC Catalog 1998-01-01 2010-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233781718-GES_DISC.umm_json NASA Energy and Water cycle Study (NEWS) Climatology of the 1st decade of the 21st Century Dataset summarizes the original observationally-based mean fluxes of water and energy budget components during the first decade of the 21st Century, for each continent and ocean basin on monthly and annual scales as well as means over all oceans, all continents, and the globe. A careful accounting of uncertainty in the estimates is included. Also, it includes optimized versions of all component fluxes that simultaneously satisfy energy and water cycle balance constraints. The NEWS Climatology contains two data products: an annual climatology data product and a monthly climatology data product. This data product is the annual climatology product. The climatology base period is roughly 1998-2010, where individual datasets cover various periods starting as early as 1998 and as late as 2002, not all extending to 2010. The continents and ocean basins boundaries map is used in this study to compute regional means. The ocean basin data was provided by Kyle Hilburn and Chelle Gentemann at Remote Sensing Systems. The land portion and some inland water bodies of the data are delineated into continents according to general definitions found in Wikipedia and relevant past studies. The data are distributed with four different units (1000 km^3/year, W/m^2, cm/year, and mm/day), in three formats (NetCDF, xlsx, and csv). proprietary NEWS_WEB_MCLIM_1.0 NASA Energy and Water cycle Study (NEWS) Monthly Climatology of the 1st decade of the 21st Century V1.0 (NEWS_WEB_MCLIM) at GES DISC GES_DISC STAC Catalog 1998-01-01 2010-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233781717-GES_DISC.umm_json NASA Energy and Water cycle Study (NEWS) Climatology of the 1st decade of the 21st Century Dataset summarizes the original observationally-based mean fluxes of water and energy budget components during the first decade of the 21st Century, for each continent and ocean basin on monthly and annual scales as well as means over all oceans, all continents, and the globe. A careful accounting of uncertainty in the estimates is included. Also, it includes optimized versions of all component fluxes that simultaneously satisfy energy and water cycle balance constraints. The NEWS Climatology contains two data products: an annual climatology data product and a monthly climatology data product. This data product is the monthly climatology product. The climatology base period is roughly 1998-2010, where individual datasets cover various periods starting as early as 1998 and as late as 2002, not all extending to 2010. The continents and ocean basins boundaries map is used in this study to compute regional means. The ocean basin data was provided by Kyle Hilburn and Chelle Gentemann at Remote Sensing Systems. The land portion and some inland water bodies of the data are delineated into continents according to general definitions found in Wikipedia and relevant past studies. The data are distributed with four different units (1000 km^3/month, W/m^2, cm/month, and mm/day), in three formats (NetCDF, xlsx, and csv). proprietary @@ -12217,10 +12197,10 @@ NFRDI_0 National Fisheries Research and Development Institute (NFRDI) OB_DAAC ST _1.0" Advanced Terrestrial Simulator ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388528-SCIOPS.umm_json The Advanced Terrestrial Simulator (formerly sometimes known as the Arctic Terrestrial Simulator) is a code for solving ecosystem-based, integrated, distributed hydrology. Capabilities are largely based on solving various forms of Richards equation coupled to a surface flow equation, along with the needed sources and sinks for ecosystem and climate models. This can (but need not) include thermal processes (especially ice for frozen soils), evapo-transpiration, albedo-driven surface energy balances, snow, biogeochemistry, plant dynamics, deformation, transport, and much more. proprietary "NGA178 _1.0" Advanced Terrestrial Simulator SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388528-SCIOPS.umm_json The Advanced Terrestrial Simulator (formerly sometimes known as the Arctic Terrestrial Simulator) is a code for solving ecosystem-based, integrated, distributed hydrology. Capabilities are largely based on solving various forms of Richards equation coupled to a surface flow equation, along with the needed sources and sinks for ecosystem and climate models. This can (but need not) include thermal processes (especially ice for frozen soils), evapo-transpiration, albedo-driven surface energy balances, snow, biogeochemistry, plant dynamics, deformation, transport, and much more. proprietary -"NGA183 - _1.0" Active Layer Hydrology in an Arctic Tundra Ecosystem: Quantifying Water Sources and Cycling Using Water Stable Isotopes: Supporting Data ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388529-SCIOPS.umm_json Data include results from water isotope analyses (one *.csv file) for samples collected in Utqiagvik (Barrow), Alaska during August and September 2012. Samples were from surface and soil pore waters from 17 drainages that could be interlake (basins with polygonal terrain), different-aged drain thaw lake basins (young, medium, old, or ancient), or a combination of different aged basins. Samples taken in different drainage flow types at three different depths at each location in and around the Barrow Environmental Observatory. Precipitation stable isotope data are also included (added in October 2019 with no changes to previously released data). This dataset used in Throckmorton, et.al. 2016.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM). proprietary "NGA183 _1.0" Active Layer Hydrology in an Arctic Tundra Ecosystem: Quantifying Water Sources and Cycling Using Water Stable Isotopes: Supporting Data SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388529-SCIOPS.umm_json Data include results from water isotope analyses (one *.csv file) for samples collected in Utqiagvik (Barrow), Alaska during August and September 2012. Samples were from surface and soil pore waters from 17 drainages that could be interlake (basins with polygonal terrain), different-aged drain thaw lake basins (young, medium, old, or ancient), or a combination of different aged basins. Samples taken in different drainage flow types at three different depths at each location in and around the Barrow Environmental Observatory. Precipitation stable isotope data are also included (added in October 2019 with no changes to previously released data). This dataset used in Throckmorton, et.al. 2016.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM). proprietary +"NGA183 + _1.0" Active Layer Hydrology in an Arctic Tundra Ecosystem: Quantifying Water Sources and Cycling Using Water Stable Isotopes: Supporting Data ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388529-SCIOPS.umm_json Data include results from water isotope analyses (one *.csv file) for samples collected in Utqiagvik (Barrow), Alaska during August and September 2012. Samples were from surface and soil pore waters from 17 drainages that could be interlake (basins with polygonal terrain), different-aged drain thaw lake basins (young, medium, old, or ancient), or a combination of different aged basins. Samples taken in different drainage flow types at three different depths at each location in and around the Barrow Environmental Observatory. Precipitation stable isotope data are also included (added in October 2019 with no changes to previously released data). This dataset used in Throckmorton, et.al. 2016.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM). proprietary "NGA232 _1.0" A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra: Supporting Data SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388919-SCIOPS.umm_json Remote sensing data collected from Brookhaven National Laboratory’s (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform – the Osprey – operated by the Terrestrial Ecosystem Science and Technology (TEST) group. Data was collected from a single flight over the Kougarok hillslope site on 26 July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface “skin” temperature imagery, as well as surface reflectance at 1nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model. Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM). proprietary "NGA232 @@ -12238,8 +12218,8 @@ NIPR-GEO-1 Airborne Magnetic Survey Data in Antarctica by JARE SCIOPS STAC Catal NIPR-GEO-1 Airborne Magnetic Survey Data in Antarctica by JARE ALL STAC Catalog 1980-01-01 20, -72, 60, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214584952-SCIOPS.umm_json The digital data which can be supplied are total intensity raw data, and not reduced to magnetic anomaly data. However, the user can analyze the data by him/herself with the Data Reports. The data processing is still being made at NIPR. proprietary NIPR_GEO_SEIS_SEAL_MIZUHO Acitve source digital seismic waveforms by SEAL exploration ALL STAC Catalog 2000-01-01 38, -70, 45, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214590137-SCIOPS.umm_json "Deep Seismic Surveys (DSS) were carried out in 2000 and 2002 austral summers on the continental ice-sheet of the Lutzow-Holm Complex (LHC), Eastern Dronning Maud Land, East Antarctica . The surveys were carried out as a program of the ""Structure and Evolution of the East Antarctic Lithosphere (SEAL)"" by JARE. Detailed crustal velocity models and reflection sections were obtained in the LHC. In both surveys, more than 170 plant-type 2 Hz geophones were installed on the continental ice-sheet totally 190 km in length. A total of 8,300kg dynamite charge at the fourteen sites on the Mizuho Plateau gave information concerning the deep structure of a continental margin of the LHC. Archived digital waveforms are available from Library Server of Polar Data Center of NIPR." proprietary NIPR_GEO_SEIS_SEAL_MIZUHO Acitve source digital seismic waveforms by SEAL exploration SCIOPS STAC Catalog 2000-01-01 38, -70, 45, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214590137-SCIOPS.umm_json "Deep Seismic Surveys (DSS) were carried out in 2000 and 2002 austral summers on the continental ice-sheet of the Lutzow-Holm Complex (LHC), Eastern Dronning Maud Land, East Antarctica . The surveys were carried out as a program of the ""Structure and Evolution of the East Antarctic Lithosphere (SEAL)"" by JARE. Detailed crustal velocity models and reflection sections were obtained in the LHC. In both surveys, more than 170 plant-type 2 Hz geophones were installed on the continental ice-sheet totally 190 km in length. A total of 8,300kg dynamite charge at the fourteen sites on the Mizuho Plateau gave information concerning the deep structure of a continental margin of the LHC. Archived digital waveforms are available from Library Server of Polar Data Center of NIPR." proprietary -NIPR_PMG_AIR_ARCHIVE_ANT Air samples for archive SCIOPS STAC Catalog 1995-02-01 2009-01-31 39.5, -69, 39.5, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214590122-SCIOPS.umm_json Air samples for archive proprietary NIPR_PMG_AIR_ARCHIVE_ANT Air samples for archive ALL STAC Catalog 1995-02-01 2009-01-31 39.5, -69, 39.5, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214590122-SCIOPS.umm_json Air samples for archive proprietary +NIPR_PMG_AIR_ARCHIVE_ANT Air samples for archive SCIOPS STAC Catalog 1995-02-01 2009-01-31 39.5, -69, 39.5, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214590122-SCIOPS.umm_json Air samples for archive proprietary NISE_2 Near-Real-Time SSM/I EASE-Grid Daily Global Ice Concentration and Snow Extent V002 NSIDC_ECS STAC Catalog 1995-05-04 2009-09-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1647528934-NSIDC_ECS.umm_json "The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation. This NISE Version 2 product contains SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) F13 satellite. For DMSP-F16, SSMIS-derived data, see NISE Version 3. For DMSP-F17, SSMIS-derived data, see NISE Version 4. For DMSP-F18, SSMIS-derived data, see NISE Version 5." proprietary NISE_3 Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent V003 NSIDC_ECS STAC Catalog 2012-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1997866870-NSIDC_ECS.umm_json "The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation. This NISE Version 3 product contains DMSP-F16, SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F16 satellite. For DMSP-F18, SSMIS-derived data, see NISE Version 5. For DMSP-F17, SSMIS-derived data, see NISE Version 4. For the older, DMSP-F13, Special Sensor Microwave Imager (SSMI) derived data, see NISE Version 2." proprietary NISE_4 Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent V004 NSIDC_ECS STAC Catalog 2009-08-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1450086509-NSIDC_ECS.umm_json "The Near-real-time Ice and Snow Extent (NISE) data set provides daily, global maps of sea ice concentrations and snow extent. These data are not suitable for time series, anomalies, or trends analyses. They are meant to provide a best estimate of current ice and snow conditions based on information and algorithms available at the time the data are acquired. Near-real-time products are not intended for operational use in assessing sea ice conditions for navigation. This NISE Version 4 product contains DMSP-F17, SSMIS-derived sea ice concentrations and snow extents derived from the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F17 satellite. For DMSP-F16, SSMIS-derived data, see NISE Version 3. For DMSP-F18, SSMIS-derived data, see NISE Version 5. For the older, DMSP-F13, Special Sensor Microwave Imager (SSMI) derived data, see NISE Version 2." proprietary @@ -12413,8 +12393,8 @@ NSCAT_LEVEL_2_V2_2 NSCAT Level 2 Ocean Wind Vector Geophysical Data Record POCLO NSCAT_LEVEL_3_BROWSE_IMAGES_2 NSCAT Level 3 Daily Gridded Ocean Surface Wind Vector Browse Images (JPL) POCLOUD STAC Catalog 1996-09-15 1997-06-29 -180, -75, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2617226745-POCLOUD.umm_json This dataset provides browse images of the NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors, which are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function. Information and access to the Level 3 source data used to generate these browse images may be accessed at: http://podaac.jpl.nasa.gov/dataset/NSCAT%20LEVEL%203. proprietary NSCAT_LEVEL_3_V2_2 NSCAT Level 3 Daily Gridded Ocean Surface Wind Vectors (JPL) POCLOUD STAC Catalog 1996-09-15 1997-06-30 -180, -75, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2617226815-POCLOUD.umm_json The NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea-ice. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function. proprietary NSCAT_W25_RMGDR_V2_2 NSCAT High Resolution R-MGDR, Selected Ocean Wind Vectors (JPL) POCLOUD STAC Catalog 1996-09-15 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617226887-POCLOUD.umm_json The NASA Scatterometer (NSCAT) Level 2.5 high-resolution reduced MGDR contains only wind vector data (sigma-0 is excluded) in 25 km wind vector cell (WVC) swaths which contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing or ambiguous. In the presence of land or sea ice winds values are set to 0. Wind vectors are processed using the NSCAT-2 geophysical model function. proprietary -NSF-ANT-1142074-penguins_1.0 Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 2012-12-15 2013-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1219899602-SCIOPS.umm_json Satellite positions and dive data collected on Adelie penguins in the 2012-13 season for purposes of evaluating food-web dynamics.. proprietary NSF-ANT-1142074-penguins_1.0 Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science ALL STAC Catalog 2012-12-15 2013-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1219899602-SCIOPS.umm_json Satellite positions and dive data collected on Adelie penguins in the 2012-13 season for purposes of evaluating food-web dynamics.. proprietary +NSF-ANT-1142074-penguins_1.0 Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 2012-12-15 2013-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1219899602-SCIOPS.umm_json Satellite positions and dive data collected on Adelie penguins in the 2012-13 season for purposes of evaluating food-web dynamics.. proprietary NSF-ANT02-28842 Boron in Antarctic granulite-facies rocks: under what conditions is boron retained in the middle crust? AMD_USAPDC STAC Catalog 2003-06-01 2009-11-30 76, -69.5, 76.5, -69.3 https://cmr.earthdata.nasa.gov/search/concepts/C2534797156-AMD_USAPDC.umm_json This award, provided by the Antarctic Geology and Geophysics Program of the Office of Polar Programs, supports a project to investigate the role and fate of Boron in high-grade metamorphic rocks of the Larsemann Hills region of Antarctica. Trace elements provide valuable information on the changes sedimentary rocks undergo as temperature and pressure increase during burial. One such element, boron, is particularly sensitive to increasing temperature because of its affinity for aqueous fluids, which are lost as rocks are buried. Boron contents of unmetamorphosed pelitic sediments range from 20 to over 200 parts per million, but rarely exceed 5 parts per million in rocks subjected to conditions of the middle and lower crust, that is, temperatures of 700 degrees C or more in the granulite-facies, which is characterized by very low water activities at pressures of 5 to 10 kbar (18-35 km burial). Devolatization reactions with loss of aqueous fluid and partial melting with removal of melt have been cited as primary causes for boron depletion under granulite-facies conditions. Despite the pervasiveness of both these processes, rocks rich in boron are locally found in the granulite-facies, that is, there are mechanisms for retaining boron during the metamorphic process. The Larsemann Hills, Prydz Bay, Antarctica, are a prime example. More than 20 lenses and layered bodies containing four borosilicate mineral species crop out over a 50 square kilometer area, which thus would be well suited for research on boron-rich granulite-facies metamorphic rocks. While most investigators have focused on the causes for loss of boron, this work will investigate how boron is retained during high-grade metamorphism. Field observations and mapping in the Larsemann Hills, chemical analyses of minerals and their host rocks, and microprobe age dating will be used to identify possible precursors and deduce how the precursor materials recrystallized into borosilicate rocks under granulite-facies conditions. The working hypothesis is that high initial boron content facilitates retention of boron during metamorphism because above a certain threshold boron content, a mechanism 'kicks in' that facilitates retention of boron in metamorphosed rocks. For example, in a rock with large amounts of the borosilicate tourmaline, such as stratabound tourmalinite, the breakdown of tourmaline to melt could result in the formation of prismatine and grandidierite, two borosilicates found in the Larsemann Hills. This situation is rarely observed in rocks with modest boron content, in which breakdown of tourmaline releases boron into partial melts, which in turn remove boron when they leave the system. Stratabound tourmalinite is associated with manganese-rich quartzite, phosphorus-rich rocks and sulfide concentrations that could be diagnostic for recognizing a tourmalinite protolith in a highly metamorphosed complex where sedimentary features have been destroyed by deformation. Because partial melting plays an important role in the fate of boron during metamorphism, our field and laboratory research will focus on the relationship between the borosilicate units, granite pegmatites and other granitic intrusives. The results of our study will provide information on cycling of boron at deeper levels in the Earth's crust and on possible sources of boron for granites originating from deep-seated rocks. An undergraduate student will participate in the electron microprobe age-dating of monazite and xenotime as part of a senior project, thereby integrating the proposed research into the educational mission of the University of Maine. In response to a proposal for fieldwork, the Australian Antarctic Division, which maintains Davis station near the Larsemann Hills, has indicated that they will support the Antarctic fieldwork. proprietary NSF-ANT04-36190_1 Biodiversity, Buoyancy and Morphological Studies of Non-Antarctic Notothenioid Fishes AMD_USAPDC STAC Catalog 2005-04-01 2009-03-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532069293-AMD_USAPDC.umm_json Patterns of biodiversity, as revealed by basic research in organismal biology, may be derived from ecological and evolutionary processes expressed in unique settings, such as Antarctica. The polar regions and their faunas are commanding increased attention as declining species diversity, environmental change, commercial fisheries, and resource management are now being viewed in a global context. Commercial fishing is known to have a direct and pervasive effect on marine biodiversity, and occurs in the Southern Ocean as far south as the Ross Sea. The nature of fish biodiversity in the Antarctic is different than in all other ocean shelf areas. Waters of the Antarctic continental shelf are ice covered for most of the year and water temperatures are nearly constant at -1.5 C. In these waters components of the phyletically derived Antarctic clade of Notothenioids dominate fish diversity. In some regions, including the southwestern Ross Sea, Notothenioids are overwhelmingly dominant in terms of number of species, abundance, and biomass. Such dominance by a single taxonomic group is unique among shelf faunas of the world. In the absence of competition from a taxonomically diverse fauna, Notothenioids underwent a habitat or depth related diversification keyed to the utilization of unfilled niches in the water column, especially pelagic or partially pelagic zooplanktivory and piscivory. This has been accomplished in the absence of a swim bladder for buoyancy control. They also may form a special type of adaptive radiation known as a species flock, which is an assemblage of a disproportionately high number of related species that have evolved rapidly within a defined area where most species are endemic. Diversification in buoyancy is the hallmark of the notothenioid radiation. Buoyancy is the feature of notothenioid biology that determines whether a species lives on the substrate, in the water column or both. Buoyancy also influences other key aspects of life history including swimming, feeding and reproduction and thus has implications for the role of the species in the ecosystem. With similarities to classic evolutionary hot spots, the Antarctic shelf and its Notothenioid radiation merit further exploration. The 2004 'International Collaborative Expedition to collect and study Fish Indigenous to Sub-Antarctic Habitats,' or, 'ICEFISH,' provided a platform for collection of notothenioid fishes from sub-Antarctic waters between South America and Africa, which will be examined in this project. This study will determine buoyancy for samples of all notothenioid species captured during the ICEFISH cruise. This essential aspect of the biology is known for only 19% of the notothenioid fauna. Also, the gross and microscopic anatomy of brains and sense organs of the phyletically basal families Bovichtidae, Eleginopidae, and of the non-Antarctic species of the primarily Antarctic family Nototheniidae will be examined. The fish biodiversity and endemicity in poorly known localities along the ICEFISH cruise track, seamounts and deep trenches will be quantified. Broader impacts include improved information for comprehending and conserving biodiversity, a scientific and societal priority. proprietary NSF-ANT04-39906_1 Abandoned Elephant Seal Colonies in Antarctica: Integration of Genetic, Isotopic, and Geologic Approaches toward Understanding Holocene Environmental Change ALL STAC Catalog 2005-09-15 2009-08-31 162, -78, 168, -72 https://cmr.earthdata.nasa.gov/search/concepts/C2532069615-AMD_USAPDC.umm_json During previous NSF-sponsored research, the PI's discovered that southern elephant seal colonies once existed along the Victoria Land coast (VLC) of Antarctica, a region where they are no longer observed. Molted seal skin and hair occur along 300 km of coastline, more than 1000 km from any extant colony. The last record of a seal at a former colony site is at ~A.D. 1600. Because abandonment occurred prior to subantarctic sealing, disappearance of the VLC colony probably was due to environmental factors, possibly cooling and encroachment of land-fast, perennial sea ice that made access to haul-out sites difficult. The record of seal inhabitation along the VLC, therefore, has potential as a proxy for climate change. Elephant seals are a predominantly subantarctic species with circumpolar distribution. Genetic studies have revealed significant differentiation among populations, particularly with regard to that at Macquarie I., which is the extant population nearest to the abandoned VLC colony. Not only is the Macquarie population unique genetically, but it is has undergone unexplained decline of 2%/yr over the last 50 years3. In a pilot study, genetic analyses showed a close relationship between the VLC seals and those at Macquarie I. An understanding of the relationship between the two populations, as well as of the environmental pressures that led to the demise of the VLC colonies, will provide a better understanding of present-day population genetic structure, the effect of environmental change on seal populations, and possibly the reasons underlying the modern decline at Macquarie Island. This project addresses several key research problems: (1) Why did elephant seals colonize and then abandon the VLC? (2) What does the elephant seal record reveal about Holocene climate change and sea-ice conditions? (3) What were the foraging strategies of the seals and did these strategies change over time as climate varied? (4) How does the genetic structure of the VLC seals relate to extant populations? (5) How did genetic diversity change over time and with colony decline? (6) Using ancient samples to estimate mtDNA mutation rates, what can be learned about VLC population dynamics over time? (7) What was the ecological relationship between elephant seals and Adelie penguins that occupied the same sites, but apparently at different times? The proposed work includes the professional training of young researchers and incorporation of data into graduate and undergraduate courses. Because of extreme isolation of the Antarctic continent since the Early Oligocene, one expects a unique invertebrate benthic fauna with a high degree of endemism. Yet some invertebrate taxa that constitute important ecological components of sedimentary benthic communities include more than 40 percent non-endemic species (e.g., benthic polychaetes). To account for non-endemic species, intermittent genetic exchange must occur between Antarctic and other (e.g. South American) populations. The most likely mechanism for such gene flow, at least for in-faunal and mobile macrobenthos, is dispersal of planktonic larvae across the sub- Antarctic and Antarctic polar fronts. To test for larval dispersal as a mechanism of maintaining genetic continuity across polar fronts, the scientists propose to (1) take plankton samples along transects across Drake passage during both the austral summer and winter seasons while concurrently collecting the appropriate hydrographic data. Such data will help elucidate the hydrographic mechanisms that allow dispersal across Drake Passage. Using a molecular phylogenetic approach, they will (2) compare seemingly identical adult forms from Antarctic and South America continents to identify genetic breaks, historical gene flow, and control for the presence of cryptic species. (3) Similar molecular tools will be used to relate planktonic larvae to their adult forms. Through this procedure, they propose to link the larval forms respectively to their Antarctic or South America origins. The proposed work builds on previous research that provides the basis for this effort to develop a synthetic understanding of historical gene flow and present day dispersal mechanism in South American/Drake Passage/ Antarctic Peninsular region. Furthermore, this work represents one of the first attempts to examine recent gene flow in Antarctic benthic invertebrates. Graduate students and a postdoctoral fellow will be trained during this research proprietary @@ -12437,34 +12417,34 @@ NSF-ANT07-39464_1 Atmosphere-Ocean-Ice Interaction in a Coastal Polynya AMD_USAP NSF-ANT08-37988 Antarctic Climate Reconstruction Utilizing the US ITASE Ice Core Array (2009-2012) AMD_USAPDC STAC Catalog 2009-06-01 2013-05-31 -180, -90, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C2532069463-AMD_USAPDC.umm_json This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This award supports a project to reconstruct the past physical and chemical climate of Antarctica, with an emphasis on the region surrounding the Ross Sea Embayment, using >60 ice cores collected in this region by US ITASE and by Australian, Brazilian, Chilean, and New Zealand ITASE teams. The ice core records are annually resolved and exceptionally well dated, and will provide, through the analyses of stable isotopes, major soluble ions and for some trace elements, instrumentally calibrated proxies for past temperature, precipitation, atmospheric circulation, chemistry of the atmosphere, sea ice extent, and volcanic activity. These records will be used to understand the role of solar, volcanic, and human forcing on Antarctic climate and to investigate the character of recent abrupt climate change over Antarctica in the context of broader Southern Hemisphere and global climate variability. The intellectual merit of the project is that ITASE has resulted in an array of ice core records, increasing the spatial resolution of observations of recent Antarctic climate variability by more than an order of magnitude and provides the basis for assessment of past and current change and establishes a framework for monitoring of future climate change in the Southern Hemisphere. This comes at a critical time as global record warming and other impacts are noted in the Southern Ocean, the Antarctic Peninsula, and on the Antarctic ice sheet. The broader impacts of the project are that Post-doctoral and graduate students involved in the project will benefit from exposure to observational and modeling approaches to climate change research and working meetings to be held at the two collaborating institutions plus other prominent climate change institutions. The results are of prime interest to the public and the media Websites hosted by the two collaborating institutions contain climate change position papers, scientific exchanges concerning current climate change issues, and scientific contribution series. proprietary NSF-ANT08-38955_1 Alternative Nutritional Strategies in Antarctic Protists AMD_USAPDC STAC Catalog 2009-08-01 2013-07-31 71.504166, -76.585556, 71.60472, -76.159164 https://cmr.earthdata.nasa.gov/search/concepts/C2532069762-AMD_USAPDC.umm_json This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Most organisms meet their carbon and energy needs using photosynthesis (phototrophy) or ingestion/assimilation of organic substances (heterotrophy). However, a nutritional strategy that combines phototrophy and heterotrophy - mixotrophy - is geographically and taxonomically widespread in aquatic systems. While the presence of mixotrophs in the Southern Ocean is known only recently, preliminary evidence indicates a significant role in Southern Ocean food webs. Recent work on Southern Ocean dinoflagellate, Kleptodinium, suggests that it sequesters functional chloroplasts of the bloom-forming haptophyte, Phaeocystis antarctica. This dinoflagellate is abundant in the Ross Sea, has been reported elsewhere in the Southern Ocean, and may have a circumpolar distribution. By combining nutritional modes. mixotrophy may offer competitive advantages over pure autotrophs and heterotrophs. The goals of this project are to understand the importance of alternative nutritional strategies for Antarctic species that combine phototrophic and phagotrophic processes in the same organism. The research will combine field investigations of plankton and ice communities in the Southern Ocean with laboratory experiments on Kleptodinium and recently identified mixotrophs from our Antarctic culture collections. The research will address: 1) the relative contributions of phototrophy and phagotrophy in Antarctic mixotrophs; 2) the nature of the relationship between Kleptodinium and its kleptoplastids; 3) the distributions and abundances of mixotrophs and Kleptodinium in the Southern Ocean during austral spring/summer; and 4) the impacts of mixotrophs and Kleptodinium on prey populations, the factors influencing these behaviors and the physiological conditions of these groups in their natural environment. The project will contribute to the maintenance of a culture collection of heterotrophic, phototrophic and mixotrophic Antarctic protists that are available to the scientific community, and it will train graduate and undergraduate students at Temple University. Research findings and activities will be summarized for non-scientific audiences through the PIs' websites and through other public forums, and will involve middle school teachers via collaboration with COSEE-New England. proprietary NSF-ANT08-38996_1 Ammonia Oxidation Versus Heterotrophy in Crenarchaeota Populations from Marine Environments West of the Antarctic Peninsula AMD_USAPDC STAC Catalog 2009-08-15 2013-12-31 -79, -71, -64, -63 https://cmr.earthdata.nasa.gov/search/concepts/C2532069861-AMD_USAPDC.umm_json Ammonia oxidation is the first step in the conversion of regenerated nitrogen to dinitrogen gas, a 3-step pathway mediated by 3 distinct guilds of bacteria and archaea. Ammonia oxidation and the overall process of nitrification-denitrification have received relatively little attention in polar oceans where the effects of climate change on biogeochemical rates are likely to be pronounced. Previous work on Ammonia Oxidizing Archaea (AOA) in the Palmer LTER study area West of the Antarctic Peninsula (WAP), has suggested strong vertical segregation of crenarchaeote metabolism, with the 'winter water' (WW, ~50-100 m depth range) dominated by non-AOA crenarchaeotes, while Crenarchaeota populations in the 'circumpolar deep water' (CDW), which lies immediately below the winter water (150-3500 m), are dominated by AOA. Analysis of a limited number of samples from the Arctic Ocean did not reveal a comparable vertical segregation of AOA, and suggested that AOA and Crenarchaeota abundance is much lower there than in the Antarctic. These findings led to 3 hypotheses that will be tested in this project: 1) the apparent low abundance of Crenarchaeota and AOA in Arctic Ocean samples may be due to spatial or temporal variability in populations; 2) the WW population of Crenarchaeota in the WAP is dominated by a heterotroph; 3) the WW population of Crenarchaeota in the WAP 'grows in' during spring and summer after this water mass forms. The study will contribute substantially to understanding an important aspect of the nitrogen cycle in the Palmer LTER (Long Term Ecological Research) study area by providing insights into the ecology and physiology of AOA. The natural segregation of crenarchaeote phenotypes in waters of the WAP, coupled with metagenomic studies in progress in the same area by others (A. Murray, H. Ducklow), offers the possibility of major breakthroughs in understanding of the metabolic capabilities of these organisms. This knowledge is needed to model how water column nitrification will respond to changes in polar ecosystems accompanying global climate change. The Principal Investigator will participate fully in the education and outreach efforts of the Palmer LTER, including making highlights of our findings available for posting to their project web site and participating in outreach (for example, Schoolyard LTER). The research also will involve undergraduates (including the field work if possible) and will support high school interns in the P.I.'s laboratory over the summer. proprietary -NSF-ANT09-44042 Acoustic Assessment of Southern Ocean Salps and Their Ecosystem Impact AMD_USAPDC STAC Catalog 2010-09-01 2013-08-31 -70, -66, -50, -59 https://cmr.earthdata.nasa.gov/search/concepts/C2532069797-AMD_USAPDC.umm_json The importance of gelatinous zooplankton in marine systems worldwide is increasing. In Southern Ocean, increasing salp densities could have a detrimental effect on higher predators, including penguins, fur seals, and baleen whales. The proposed research is a methods-develoment project that will improve the capability to indirectly assess abundances and distributions of salps in the Southern Ocean through acoustic surveys. Hydrographic, net tow, and acoustic backscatter data will be collected in the waters surrounding the South Shetland Islands and the Antarctic peninsula, where both krill and salps are found and compete for food. Shipboard experimental manipulations and measurements will lead to improved techniques for assessment of salp biomass acoustically. Experiments will focus on material properties (density and sound speed), size and shape of salps, as well as how these physical properties will vary with the salp\'s environment, feeding rate, and reproductive status. In the field, volume backscattering data from an acoustic echosounder will be collected at the same locations as the net tows to enable comparison of net and acoustic estimates of salp abundance. A physics-based scattering model for salps will be developed and validated, to determine if multiple acoustic frequencies can be used to discriminate between scattering associated with krill swarms and that from salp blooms. During the same period as the Antarctic field work, a parallel outreach and education study will be undertaken in Long Island, New York examining local gelatinous zooplankton. This study will enable project participants to learn and practice research procedures and methods before traveling to Antarctica; provide a comparison time-series that will be used for educational purposes; and include many more students and teachers in the research project than would be able to participate in the Antarctic field component. proprietary NSF-ANT09-44042 Acoustic Assessment of Southern Ocean Salps and Their Ecosystem Impact ALL STAC Catalog 2010-09-01 2013-08-31 -70, -66, -50, -59 https://cmr.earthdata.nasa.gov/search/concepts/C2532069797-AMD_USAPDC.umm_json The importance of gelatinous zooplankton in marine systems worldwide is increasing. In Southern Ocean, increasing salp densities could have a detrimental effect on higher predators, including penguins, fur seals, and baleen whales. The proposed research is a methods-develoment project that will improve the capability to indirectly assess abundances and distributions of salps in the Southern Ocean through acoustic surveys. Hydrographic, net tow, and acoustic backscatter data will be collected in the waters surrounding the South Shetland Islands and the Antarctic peninsula, where both krill and salps are found and compete for food. Shipboard experimental manipulations and measurements will lead to improved techniques for assessment of salp biomass acoustically. Experiments will focus on material properties (density and sound speed), size and shape of salps, as well as how these physical properties will vary with the salp\'s environment, feeding rate, and reproductive status. In the field, volume backscattering data from an acoustic echosounder will be collected at the same locations as the net tows to enable comparison of net and acoustic estimates of salp abundance. A physics-based scattering model for salps will be developed and validated, to determine if multiple acoustic frequencies can be used to discriminate between scattering associated with krill swarms and that from salp blooms. During the same period as the Antarctic field work, a parallel outreach and education study will be undertaken in Long Island, New York examining local gelatinous zooplankton. This study will enable project participants to learn and practice research procedures and methods before traveling to Antarctica; provide a comparison time-series that will be used for educational purposes; and include many more students and teachers in the research project than would be able to participate in the Antarctic field component. proprietary +NSF-ANT09-44042 Acoustic Assessment of Southern Ocean Salps and Their Ecosystem Impact AMD_USAPDC STAC Catalog 2010-09-01 2013-08-31 -70, -66, -50, -59 https://cmr.earthdata.nasa.gov/search/concepts/C2532069797-AMD_USAPDC.umm_json The importance of gelatinous zooplankton in marine systems worldwide is increasing. In Southern Ocean, increasing salp densities could have a detrimental effect on higher predators, including penguins, fur seals, and baleen whales. The proposed research is a methods-develoment project that will improve the capability to indirectly assess abundances and distributions of salps in the Southern Ocean through acoustic surveys. Hydrographic, net tow, and acoustic backscatter data will be collected in the waters surrounding the South Shetland Islands and the Antarctic peninsula, where both krill and salps are found and compete for food. Shipboard experimental manipulations and measurements will lead to improved techniques for assessment of salp biomass acoustically. Experiments will focus on material properties (density and sound speed), size and shape of salps, as well as how these physical properties will vary with the salp\'s environment, feeding rate, and reproductive status. In the field, volume backscattering data from an acoustic echosounder will be collected at the same locations as the net tows to enable comparison of net and acoustic estimates of salp abundance. A physics-based scattering model for salps will be developed and validated, to determine if multiple acoustic frequencies can be used to discriminate between scattering associated with krill swarms and that from salp blooms. During the same period as the Antarctic field work, a parallel outreach and education study will be undertaken in Long Island, New York examining local gelatinous zooplankton. This study will enable project participants to learn and practice research procedures and methods before traveling to Antarctica; provide a comparison time-series that will be used for educational purposes; and include many more students and teachers in the research project than would be able to participate in the Antarctic field component. proprietary NSF-ANT09-44358 Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels - NSF-ANT09-44358 AMD_USAPDC STAC Catalog 2010-09-15 2015-08-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C2532070119-AMD_USAPDC.umm_json While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Ad?lie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources. proprietary NSF-ANT09-44358 Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels - NSF-ANT09-44358 ALL STAC Catalog 2010-09-15 2015-08-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C2532070119-AMD_USAPDC.umm_json While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Ad?lie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources. proprietary -NSF-ANT09-44411 Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels ALL STAC Catalog 2010-09-15 2015-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532069734-AMD_USAPDC.umm_json While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Adélie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources. proprietary NSF-ANT09-44411 Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels AMD_USAPDC STAC Catalog 2010-09-15 2015-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532069734-AMD_USAPDC.umm_json While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Adélie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources. proprietary +NSF-ANT09-44411 Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels ALL STAC Catalog 2010-09-15 2015-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532069734-AMD_USAPDC.umm_json While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Adélie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources. proprietary NSF-ANT09-44532 Application of Detrital Zircon Isotope Characteristics and Sandstone Analysis of Beacon Strata to the Tectonic Evolution of the Antarctic Sector of Gondwana AMD_USAPDC STAC Catalog 2010-07-01 2013-06-30 158.9, -85.1, 165.73, -83 https://cmr.earthdata.nasa.gov/search/concepts/C2532069801-AMD_USAPDC.umm_json Intellectual Merit: The goal of this project is to address relationships between foreland basins and their tectonic settings by combining detrital zircon isotope characteristics and sedimentological data. To accomplish this goal the PIs will develop a detailed geochronology and analyze Hf- and O-isotopes of detrital zircons in sandstones of the Devonian Taylor Group and the Permian-Triassic Victoria Group. These data will allow them to better determine provenance and basin fill, and to understand the nature of the now ice covered source regions in East and West Antarctica. The PIs will document possible unexposed/unknown crustal terrains in West Antarctica, investigate sub-glacial terrains of East Antarctica that were exposed to erosion during Devonian to Triassic time, and determine the evolving provenance and tectonic history of the Devonian to Triassic Gondwana basins in the central Transantarctic Mountains. Detrital zircon data will be interpreted in the context of fluvial dispersal/drainage patterns, sandstone petrology, and sequence stratigraphy. This interpretation will identify source terrains and evolving sediment provenances. Paleocurrent analysis and sequence stratigraphy will determine the timing and nature of changing tectonic conditions associated with development of the depositional basins and document the tectonic history of the Antarctic sector of Gondwana. Results from this study will answer questions about the Panthalassan margin of Gondwana, the Antarctic craton, and the Beacon depositional basin and their respective roles in global tectonics and the geologic and biotic history of Antarctica. The Beacon basin and adjacent uplands played an important role in the development and demise of Gondwanan glaciation through modification of polar climates, development of peat-forming mires, colonization of the landscape by plants, and were a migration route for Mesozoic vertebrates into Antarctica. Broader impacts: This proposal includes support for two graduate students who will participate in the fieldwork, and also support for other students to participate in laboratory studies. Results of the research will be incorporated in classroom teaching at the undergraduate and graduate levels and will help train the next generation of field geologists. Interactions with K-12 science classes will be achieved by video/computer conferencing and satellite phone connections from Antarctica. Another outreach effort is the developing cooperation between the Byrd Polar Research Center and the Center of Science and Industry in Columbus. proprietary NSF-ANT09-44653_1 Annual Satellite Era Accumulation Patterns Over WAIS Divide: A Study Using Shallow Ice Cores, Near-Surface Radars and Satellites AMD_USAPDC STAC Catalog 2010-08-01 2015-07-31 -110, -80, -119.4, -78.1 https://cmr.earthdata.nasa.gov/search/concepts/C2532069942-AMD_USAPDC.umm_json This award supports a project to broaden the knowledge of annual accumulation patterns over the West Antarctic Ice Sheet by processing existing near-surface radar data taken on the US ITASE traverse in 2000 and by gathering and validating new ultra/super-high-frequency (UHF) radar images of near surface layers (to depths of ~15 m), expanding abilities to monitor recent annual accumulation patterns from point source ice cores to radar lines. Shallow (15 m) ice cores will be collected in conjunction with UHF radar images to confirm that radar echoed returns correspond with annual layers, and/or sub-annual density changes in the near-surface snow, as determined from ice core stable isotopes. This project will additionally improve accumulation monitoring from space-borne instruments by comparing the spatial-radar-derived-annual accumulation time series to the passive microwave time series dating back over 3 decades and covering most of Antarctica. The intellectual merit of this project is that mapping the spatial and temporal variations in accumulation rates over the Antarctic ice sheet is essential for understanding ice sheet responses to climate forcing. Antarctic precipitation rate is projected to increase up to 20% in the coming century from the predicted warming. Accumulation is a key component for determining ice sheet mass balance and, hence, sea level rise, yet our ability to measure annual accumulation variability over the past 5 decades (satellite era) is mostly limited to point-source ice cores. Developing a radar and ice core derived annual accumulation dataset will provide validation data for space-born remote sensing algorithms, climate models and, additionally, establish accumulation trends. The broader impacts of the project are that it will advance discovery and understanding within the climatology, glaciology and remote sensing communities by verifying the use of UHF radars to monitor annual layers as determined by visual, chemical and isotopic analysis from corresponding shallow ice cores and will provide a dataset of annual to near-annual accumulation measurements over the past ~5 decades across WAIS divide from existing radar data and proposed radar data. By determining if temporal changes in the passive microwave signal are correlated with temporal changes in accumulation will help assess the utility of passive microwave remote sensing to monitor accumulation rates over ice sheets for future decades. The project will promote teaching, training and learning, and increase representation of underrepresented groups by becoming involved in the NASA History of Winter project and Thermochron Mission and by providing K-12 teachers with training to monitor snow accumulation and temperature here in the US, linking polar research to the student's backyard. The project will train both undergraduate and graduate students in polar research and will encouraging young investigators to become involved in careers in science. In particular, two REU students will participate in original research projects as part of this larger project, from development of a hypothesis to presentation and publication of the results. The support of a new, young woman scientist will help to increase gender diversity in polar research. proprietary NSF-ANT09-44727 ASPIRE: Amundsen Sea Polynya International Research Expedition AMD_USAPDC STAC Catalog 2010-10-01 2014-09-30 -118.3, -74.2, -111, -71.6 https://cmr.earthdata.nasa.gov/search/concepts/C2532069918-AMD_USAPDC.umm_json ASPIRE is an NSF-funded project that will examine the ecology of the Amundsen Sea during the Austral summer of 2010. ASPIRE includes an international team of trace metal and carbon chemists, phytoplankton physiologists, microbial and zooplankton ecologists, and physical oceanographers, that will investigate why and how the Amundsen Sea Polynya is so much more productive than other polynyas and whether interannual variability can provide insight to climate-sensitive mechanisms driving carbon fluxes. This project will compliment the existing ASPIRE effort by using 1) experimental manipulations to understand photoacclimation of the dominant phytoplankton taxa under conditions of varying light and trace metal abundance, 2) nutrient addition bioassays to determine the importance of trace metal versus nitrogen limitation of phytoplankton growth, and 3) a numerical ecosystem model to understand the importance of differences in mixing regime, flow field, and Fe sources in controlling phytoplankton bloom dynamics and community composition in this unusually productive polynya system. The research strategy will integrate satellite remote sensing, field-based experimental manipulations, and numerical modeling. Outreach and education include participation in Stanford's Summer Program for Professional Development for Science Teachers, Stanford's School of Earth Sciences high school internship program, and development of curriculum for local science training centers, including the Chabot Space and Science Center. Undergraduate participation and training will include support for both graduate students and undergraduate assistants. proprietary NSF-ANT10-43145_1 Bromide in Snow in the Sea Ice Zone AMD_USAPDC STAC Catalog 2011-08-15 2015-07-31 164.1005, -77.8645, 166.7398, -77.1188 https://cmr.earthdata.nasa.gov/search/concepts/C2532070132-AMD_USAPDC.umm_json A range of chemical and microphysical pathways in polar latitudes, including spring time (tropospheric) ozone depletion, oxidative pathways for mercury, and cloud condensation nuclei (CCN) production leading to changes in the cloud cover and attendant surface energy budgets, have been invoked as being dependent upon the emission of halogen gases formed in sea-ice. The prospects for climate warming induced reductions in sea ice extent causing alteration of these incompletely known surface-atmospheric feedbacks and interactions requires confirmation of mechanistic details in both laboratory studies and field campaigns. One such mechanistic question is how bromine (BrO and Br) enriched snow migrates or is formed through processes in sea-ice, prior to its subsequent mobilization as an aerosol fraction into the atmosphere by strong winds. Once aloft, it may react with ozone and other atmospheric species. Dartmouth researchers will collect snow from the surface of sea ice, from freely blowing snow and in sea-ice cores from Cape Byrd, Ross Sea. A range of spectroscopic, microanalytic and and microstructural approaches will be subsequently used to determine the Br distribution gradients through sea-ice, in order to shed light on how sea-ice first forms and then releases bromine species into the polar atmospheric boundary layer. proprietary NSF-ANT10-43485_1 A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea ALL STAC Catalog 2011-07-01 2015-06-30 -160, -78, -150, -68 https://cmr.earthdata.nasa.gov/search/concepts/C2532069944-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary NSF-ANT10-43485_1 A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 -160, -78, -150, -68 https://cmr.earthdata.nasa.gov/search/concepts/C2532069944-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary -NSF-ANT10-43517 A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea ALL STAC Catalog 2011-07-01 2015-06-30 163.5, -78.32, 165.35, -77.57 https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary NSF-ANT10-43517 A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 163.5, -78.32, 165.35, -77.57 https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary -NSF-ANT10-43554_1 Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 161.5, -77.5, 161.5, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532070458-AMD_USAPDC.umm_json The PIs propose to address the question of whether ice surface melting zones developed at high elevations during warm climatic phases in the Transantarctic Mountains. Evidence from sediment cores drilled by the ANDRILL program indicates that open water in the Ross Sea could have been a source of warmth during Pliocene and Pleistocene. The question is whether marine warmth penetrated inland to the ice sheet margins. The glacial record may be ill suited to answer this question, as cold-based glaciers may respond too slowly to register brief warmth. Questions also surround possible orbital controls on regional climate and ice sheet margins. Northern Hemisphere insolation at obliquity and precession timescales is thought to control Antarctic climate through oceanic or atmospheric connections, but new thinking suggests that the duration of Southern Hemisphere summer may be more important. The PIs propose to use high elevation alluvial deposits in the Transantarctic Mountains as a proxy for inland warmth. These relatively young fans, channels, and debris flow levees stand out as visible evidence for the presence of melt water in an otherwise ancient, frozen landscape. Based on initial analyses of an alluvial fan in the Olympus Range, these deposits are sensitive recorders of rare melt events that occur at orbital timescales. For their study they will 1) map alluvial deposits using aerial photography, satellite imagery and GPS assisted field surveys to establish water sources and to quantify parameters effecting melt water production, 2) date stratigraphic sequences within these deposits using OSL, cosmogenic nuclide, and interbedded volcanic ash chronologies, 3) use paired nuclide analyses to estimate exposure and burial times, and rates of deposition and erosion, and 4) use micro and regional scale climate modeling to estimate paleoenvironmental conditions associated with melt events. This study will produce a record of inland melting from sites adjacent to ice sheet margins to help determine controls on regional climate along margins of the East Antarctic Ice Sheet to aid ice sheet and sea level modeling studies. The proposal will support several graduate and undergraduates. A PhD student will be supported on existing funding. The PIs will work with multiple K-12 schools to conduct interviews and webcasts from Antarctica and they will make follow up visits to classrooms after the field season is complete. proprietary +NSF-ANT10-43517 A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea ALL STAC Catalog 2011-07-01 2015-06-30 163.5, -78.32, 165.35, -77.57 https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary NSF-ANT10-43554_1 Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins ALL STAC Catalog 2011-07-01 2015-06-30 161.5, -77.5, 161.5, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532070458-AMD_USAPDC.umm_json The PIs propose to address the question of whether ice surface melting zones developed at high elevations during warm climatic phases in the Transantarctic Mountains. Evidence from sediment cores drilled by the ANDRILL program indicates that open water in the Ross Sea could have been a source of warmth during Pliocene and Pleistocene. The question is whether marine warmth penetrated inland to the ice sheet margins. The glacial record may be ill suited to answer this question, as cold-based glaciers may respond too slowly to register brief warmth. Questions also surround possible orbital controls on regional climate and ice sheet margins. Northern Hemisphere insolation at obliquity and precession timescales is thought to control Antarctic climate through oceanic or atmospheric connections, but new thinking suggests that the duration of Southern Hemisphere summer may be more important. The PIs propose to use high elevation alluvial deposits in the Transantarctic Mountains as a proxy for inland warmth. These relatively young fans, channels, and debris flow levees stand out as visible evidence for the presence of melt water in an otherwise ancient, frozen landscape. Based on initial analyses of an alluvial fan in the Olympus Range, these deposits are sensitive recorders of rare melt events that occur at orbital timescales. For their study they will 1) map alluvial deposits using aerial photography, satellite imagery and GPS assisted field surveys to establish water sources and to quantify parameters effecting melt water production, 2) date stratigraphic sequences within these deposits using OSL, cosmogenic nuclide, and interbedded volcanic ash chronologies, 3) use paired nuclide analyses to estimate exposure and burial times, and rates of deposition and erosion, and 4) use micro and regional scale climate modeling to estimate paleoenvironmental conditions associated with melt events. This study will produce a record of inland melting from sites adjacent to ice sheet margins to help determine controls on regional climate along margins of the East Antarctic Ice Sheet to aid ice sheet and sea level modeling studies. The proposal will support several graduate and undergraduates. A PhD student will be supported on existing funding. The PIs will work with multiple K-12 schools to conduct interviews and webcasts from Antarctica and they will make follow up visits to classrooms after the field season is complete. proprietary -NSF-ANT10-43621 A Comparison of Conjugate Auroral Electojet Indices ALL STAC Catalog 2011-06-01 2013-05-31 -180, -79.5, 180, -54.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069751-AMD_USAPDC.umm_json The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input. proprietary +NSF-ANT10-43554_1 Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 161.5, -77.5, 161.5, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532070458-AMD_USAPDC.umm_json The PIs propose to address the question of whether ice surface melting zones developed at high elevations during warm climatic phases in the Transantarctic Mountains. Evidence from sediment cores drilled by the ANDRILL program indicates that open water in the Ross Sea could have been a source of warmth during Pliocene and Pleistocene. The question is whether marine warmth penetrated inland to the ice sheet margins. The glacial record may be ill suited to answer this question, as cold-based glaciers may respond too slowly to register brief warmth. Questions also surround possible orbital controls on regional climate and ice sheet margins. Northern Hemisphere insolation at obliquity and precession timescales is thought to control Antarctic climate through oceanic or atmospheric connections, but new thinking suggests that the duration of Southern Hemisphere summer may be more important. The PIs propose to use high elevation alluvial deposits in the Transantarctic Mountains as a proxy for inland warmth. These relatively young fans, channels, and debris flow levees stand out as visible evidence for the presence of melt water in an otherwise ancient, frozen landscape. Based on initial analyses of an alluvial fan in the Olympus Range, these deposits are sensitive recorders of rare melt events that occur at orbital timescales. For their study they will 1) map alluvial deposits using aerial photography, satellite imagery and GPS assisted field surveys to establish water sources and to quantify parameters effecting melt water production, 2) date stratigraphic sequences within these deposits using OSL, cosmogenic nuclide, and interbedded volcanic ash chronologies, 3) use paired nuclide analyses to estimate exposure and burial times, and rates of deposition and erosion, and 4) use micro and regional scale climate modeling to estimate paleoenvironmental conditions associated with melt events. This study will produce a record of inland melting from sites adjacent to ice sheet margins to help determine controls on regional climate along margins of the East Antarctic Ice Sheet to aid ice sheet and sea level modeling studies. The proposal will support several graduate and undergraduates. A PhD student will be supported on existing funding. The PIs will work with multiple K-12 schools to conduct interviews and webcasts from Antarctica and they will make follow up visits to classrooms after the field season is complete. proprietary NSF-ANT10-43621 A Comparison of Conjugate Auroral Electojet Indices AMD_USAPDC STAC Catalog 2011-06-01 2013-05-31 -180, -79.5, 180, -54.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069751-AMD_USAPDC.umm_json The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input. proprietary +NSF-ANT10-43621 A Comparison of Conjugate Auroral Electojet Indices ALL STAC Catalog 2011-06-01 2013-05-31 -180, -79.5, 180, -54.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069751-AMD_USAPDC.umm_json The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input. proprietary NSF-ANT10-44978 BICEP2 and SPUD - A Search for Inflation with Degree-Scale Polarimetry from the South Pole AMD_USAPDC STAC Catalog 2008-05-15 2017-09-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532070162-AMD_USAPDC.umm_json BICEP2 and SPUD - A Search for Inflation with Degree-Scale Polarimetry from the South Pole. The proposed work is a four-year program of research activities directed toward upgrading the BICEP (Background Imaging of Cosmic Extragalactic Polarization) telescope operating at South Pole since early 2006 to reach far =stretching goals of detection of the Cosmic Gravitational-wave Background (CGB). This telescope is a first Cosmic Microwave Background (CMB) B-mode polarimeter, specifically designed to search for CGB signatures while mapping ~2% of the southern sky that is free of the Milky Way foreground galactic radiation at 100 GH and 150 GHz. The BICEP1 telescope will reach its designed sensitivity by the end of 2008. A coordinated series of upgrades to BICEP1 will provide the increased sensitivity and more exacting control of instrumental effects and potential confusion from galactic foregrounds necessary to search for the B-mode signal more deeply through space. A powerful new 150 GHz receiver, BICEP2, will replace the current detector at the beginning of 2009, increasing the mapping speed almost ten-fold. In 2010, the first of a series of compact, mechanically-cooled receivers (called SPUD - Small Polarimeter Upgrade for DASI) will be deployed on the existing DASI mount and tower, providing similar mapping speed at 100 GHz in parallel with BICEP2. The latter instrument will reach (and exceed with the addition of a SPUD polarimeter) the target sensitivity r = 0.15 set forth by the Interagency (NSF/NASA/DoE) Task Force on CMB Research for a future space mission dedicated to the detection and characterization of primordial gravitational waves. This Task Force has identified detection of the Inflation's gravitational waves as the number one priority for the modern cosmology. More broadly, as the cosmology captures a lot of the public imagination, it is a remarkably effective vehicle for stimulating interest in basic science. The CGB detection would be to Inflation what the discovery of the CMB radiation was to the Big Bang. The project will contribute to the training of the next generation of cosmologists by integrating graduate and undergraduate education with the technology and instrumentation development, astronomical observations and scientific analysis. Sharing of the forefront research results with public extends the new knowledge beyond the universities. This project will be undertaken in collaboration between the California Institute of Technology and the University of Chicago. proprietary NSF-ANT10-48343_1 CAREER: Deciphering Antarctic Climate Variability during the Temperate/Polar Transition and Improving Climate Change Literacy in Louisiana through a Companion Outreach Program AMD_USAPDC STAC Catalog 2011-03-01 2016-02-29 57.217, -70.373, 153.359, -63.664 https://cmr.earthdata.nasa.gov/search/concepts/C2532069731-AMD_USAPDC.umm_json Intellectual Merit: The PI proposes a high-resolution paleoenvironmental study of pollen, spore, fresh-water algae, and dinoflagellate cyst assemblages to investigate the palynological record of sudden warming events in the Antarctic as recorded by the ANDRILL SMS drill core and terrestrial sections. These data will be used to derive causal mechanisms for these rapid climate events. Terrestrial samples will be obtained at various altitudes in the Dry Valleys region. The pollen and spores will provide data on atmospheric conditions, while the algae will provide data on sea-surface conditions. These data will help identify the triggers for sudden climatic shifts. If they are caused by changes in oceanic currents, a signal will be visible in the dinocyst assemblages first as currents influence their distribution. Conversely, if these shifts are triggered by atmospheric factors, then the shifts will first affect plants and be visible in the pollen record. Broader impacts: The PI proposes a suite of activities to bring field-based climate change research to a broader audience. The PI will advise a diverse group of students and educators. The palynological data collected as part of this research will be utilized, in part, to develop new lectures on Antarctic palynology and these new lectures will be made available via a collaboration with the LSU HHMI program. In addition, the PI will direct three Louisiana middle-school teachers as they pursue a Masters of Natural Science for science educators. These teachers will help the PI develop a professional development program for science teachers. Community-based activities will be organized to raise science awareness and alert students and the public of opportunities in science. proprietary NSF-ANT10-63592_1 Application for an Early-concept Grant for Exploratory Reasearch (EAGER) to develop a Pathway/Genome Database (PGDB) for the Southern Ocean Haptophyte Phaeocystis Antarctica. AMD_USAPDC STAC Catalog 2011-05-15 2015-04-30 -75.8, -67.12, -62.37, -61.08 https://cmr.earthdata.nasa.gov/search/concepts/C2532069964-AMD_USAPDC.umm_json Phaeocystis antarctica is capable of forming blooms that are denser and more extensive than any other member of the Southern Ocean phytoplankton community. The factors that enable P Antarctica to dominate its competitors are not clear but are likely related to its colonial lifestyle. The goal of the project is to map all the reactions in metabolic pathways that are key to defining the ecological niche of Phaeocystis antarctica by developing a Pathway/Genome Database (PGDB) using Pathway Tools software. The investigators will assign proteins and enzymes to key pathways in P. Antarctica, continually improve and edit the database as the full Phaeocystis genome comes online, and host the database on the BioCyc webpage. The end product will be the first database for a eukaryotic phytoplankton genome where researchers can query extant metabolic pathways and place new proteins and enzymes of interest within metabolic networks. The risk is that a substantial percentage of catalytic enzymes may belong to pathways that are poorly characterized. The science impact is to link genomes to metabolic potential in the context of Phaeocystis life history but also in comparison to other organisms across the tree of life. The education and outreach includes work with a high school teacher and intern and curriculum development. proprietary NSF-ANT11-42018_1 Adaptive Responses of Phaeocystis Populations in Antarctic Ecosystems AMD_USAPDC STAC Catalog 2011-05-15 2015-04-30 -75.8, -67.12, -62.37, -61.08 https://cmr.earthdata.nasa.gov/search/concepts/C2532070261-AMD_USAPDC.umm_json Global climate change is having significant effects on areas of the Southern Ocean, and a better understanding of this ecosystem will permit predictions about the large-scale implications of these shifts. The haptophyte Phaeocystis antarctica is an important component of the phytoplankton communities in this region, but little is known about the factors controlling its distribution. Preliminary data suggest that P. antarctica posses unique adaptations that allow it to thrive in regions with dynamic light regimes. This research will extend these results to identify the physiological and genetic mechanisms that affect the growth and distribution of P. antarctica. This work will use field and laboratory-based studies and a suite of modern molecular techniques to better understand the biogeography and physiology of this key organism. Results will be widely disseminated through publications as well as through presentations at national and international meetings. In addition, raw data will be made available through open-access databases. This project will support the research and training of two graduate students and will foster an established international collaboration with Dutch scientists. Researchers on this project will participate in outreach programs targeting K12 teachers as well as high school students. proprietary NSF-ANT11-42018_1 Adaptive Responses of Phaeocystis Populations in Antarctic Ecosystems ALL STAC Catalog 2011-05-15 2015-04-30 -75.8, -67.12, -62.37, -61.08 https://cmr.earthdata.nasa.gov/search/concepts/C2532070261-AMD_USAPDC.umm_json Global climate change is having significant effects on areas of the Southern Ocean, and a better understanding of this ecosystem will permit predictions about the large-scale implications of these shifts. The haptophyte Phaeocystis antarctica is an important component of the phytoplankton communities in this region, but little is known about the factors controlling its distribution. Preliminary data suggest that P. antarctica posses unique adaptations that allow it to thrive in regions with dynamic light regimes. This research will extend these results to identify the physiological and genetic mechanisms that affect the growth and distribution of P. antarctica. This work will use field and laboratory-based studies and a suite of modern molecular techniques to better understand the biogeography and physiology of this key organism. Results will be widely disseminated through publications as well as through presentations at national and international meetings. In addition, raw data will be made available through open-access databases. This project will support the research and training of two graduate students and will foster an established international collaboration with Dutch scientists. Researchers on this project will participate in outreach programs targeting K12 teachers as well as high school students. proprietary NSF-ANT11-42102 An Integrated Ecological Investigation of McMurdo Dry Valley's Active Soil Microbial Communities AMD_USAPDC STAC Catalog 2012-07-01 2015-06-30 161, -77.5, 164, -77 https://cmr.earthdata.nasa.gov/search/concepts/C2532070421-AMD_USAPDC.umm_json The McMurdo Dry Valleys in Antarctica are among the coldest, driest habitats on the planet. Previous research has documented the presence of surprisingly diverse microbial communities in the soils of the Dry Valleys despite these extreme conditions. However, the degree to which these organisms are active is unknown; it is possible that much of this diversity reflects microbes that have blown into this environment that are subsequently preserved in these cold, dry conditions. This research will use modern molecular techniques to answer a fundamental question regarding these communities: which organisms are active and how do they live in such extreme conditions? The research will include manipulations to explore how changes in water, salt and carbon affect the microbial community, to address the role that these organisms play in nutrient cycling in this environment. The results of this work will provide a broader understanding of how life adapts to such extreme conditions as well as the role of dormancy in the life history of microorganisms. Results will be widely disseminated through publications as well as through presentations at national and international meetings; raw data will be made available through a high-profile web-based portal. The research will support two graduate students, two undergraduate research assistants and a postdoctoral fellow. The results will be incorporated into a webinar targeted to secondary and post-secondary educators and a complimentary hands-on class activity kit will be developed and made available to various teacher and outreach organizations. proprietary -NSF-ANT12-41487 A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network ALL STAC Catalog 2012-06-01 2013-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069735-AMD_USAPDC.umm_json This award will support the participation of US scientists in an international planning workshop devoted to discussions of how to best facilitate and coordinate international efforts for terrestrial system studies at the McMurdo Dry Valleys of Antarctica. To date, various aspects of the different Dry Valley landscape features (lakes, soils, glaciers, streams) and their biota have been studied most intensively by US and New Zealand scientists, but these efforts could significantly improve their explanatory power if they were coordinated so as to reduce redundancy, decrease environmental degradation and, most importantly, produce comparable datasets. Additionally, many of the present environmental management programs are based on the past baseline composition and location of biotic communities. As these communities become rearranged across the valleys in the future there is interest in assessing whether today's management plans are adequate. To efficiently move these research programs forward for the McMurdo Dry Valleys requires a coordinated, interdisciplinary, long-term data monitoring and observation network. The ultimate objectives of the workshop are to: i) identify the optimal, complementary suites of measurements required to assess and address key processes associated with environmental change in Dry Valley ecosystems; ii) develop standards and protocols for gathering the most critical biotic and abiotic measurements associated with the key processes driving environmental change; iii) generate a draft data coordination and development plan that will maximize the utility of these data; iv) assess the effectiveness of current McMurdo Dry Valley ASMA (Antarctic Special Management Area) environmental protection guidelines. proprietary NSF-ANT12-41487 A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network AMD_USAPDC STAC Catalog 2012-06-01 2013-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069735-AMD_USAPDC.umm_json This award will support the participation of US scientists in an international planning workshop devoted to discussions of how to best facilitate and coordinate international efforts for terrestrial system studies at the McMurdo Dry Valleys of Antarctica. To date, various aspects of the different Dry Valley landscape features (lakes, soils, glaciers, streams) and their biota have been studied most intensively by US and New Zealand scientists, but these efforts could significantly improve their explanatory power if they were coordinated so as to reduce redundancy, decrease environmental degradation and, most importantly, produce comparable datasets. Additionally, many of the present environmental management programs are based on the past baseline composition and location of biotic communities. As these communities become rearranged across the valleys in the future there is interest in assessing whether today's management plans are adequate. To efficiently move these research programs forward for the McMurdo Dry Valleys requires a coordinated, interdisciplinary, long-term data monitoring and observation network. The ultimate objectives of the workshop are to: i) identify the optimal, complementary suites of measurements required to assess and address key processes associated with environmental change in Dry Valley ecosystems; ii) develop standards and protocols for gathering the most critical biotic and abiotic measurements associated with the key processes driving environmental change; iii) generate a draft data coordination and development plan that will maximize the utility of these data; iv) assess the effectiveness of current McMurdo Dry Valley ASMA (Antarctic Special Management Area) environmental protection guidelines. proprietary -NSF-ANT13-55533_1 A Multi-decadal Record of Antarctic Benthos: Image Analysis to Maximize Data Utilization ALL STAC Catalog 2013-10-01 2015-09-30 163, -78.5, 167, -78 https://cmr.earthdata.nasa.gov/search/concepts/C2532070231-AMD_USAPDC.umm_json Antarctic benthic communities are characterized by many species of sponges (Phylum Porifera), long thought to exhibit extremely slow demographic patterns of settlement, growth and reproduction. This project will analyze many hundreds of diver and remotely operated underwater vehicle photographs documenting a unique, episodic settlement event that occurred between 2000 and 2010 in McMurdo Sound that challenges this paradigm of slow growth. Artificial structures were placed on the seafloor between 1967 and 1974 at several sites, but no sponges were observed to settle on these structures until 2004. By 2010 some 40 species of sponges had settled and grown to be surprisingly large. Given the paradigm of slow settlement and growth supported by the long observation period (37 years, 1967-2004), this extraordinary large-scale settlement and rapid growth over just a 6-year time span is astonishing. This project utilizes image processing software (ImageJ) to obtain metrics (linear dimensions to estimate size, frequency, percent cover) for sponges and other fauna visible in the photographs. It uses R to conduct multidimensional scaling to ordinate community data and ANOSIM to test for differences of community data among sites and times and structures. It will also use SIMPER and ranked species abundances to discriminate species responsible for any differences. This work focuses on Antarctic sponges, but the observations of massive episodic recruitment and growth are important to understanding seafloor communities worldwide. Ecosystems are composed of populations, and populations are ecologically described by their distribution and abundance. A little appreciated fact is that sponges often dominate marine communities, but because sponges are so hard to study, most workers focus on other groups such as corals, kelps, or bivalves. Because most sponges settle and grow slowly their life history is virtually unstudied. The assumption of relative stasis of the Antarctic seafloor community is common, and this project will shatter this paradigm by documenting a dramatic episodic event. Finally, the project takes advantage of old transects from the 1960s and 1970s and compares them with extensive 2010 surveys of the same habitats and sometimes the same intact transect lines, offering a long-term perspective of community change. The investigators will publish these results in peer-reviewed journals, give presentations to the general public and will involve students from local outreach programs, high schools, and undergraduates at UCSD to help with the analysis. proprietary +NSF-ANT12-41487 A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network ALL STAC Catalog 2012-06-01 2013-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069735-AMD_USAPDC.umm_json This award will support the participation of US scientists in an international planning workshop devoted to discussions of how to best facilitate and coordinate international efforts for terrestrial system studies at the McMurdo Dry Valleys of Antarctica. To date, various aspects of the different Dry Valley landscape features (lakes, soils, glaciers, streams) and their biota have been studied most intensively by US and New Zealand scientists, but these efforts could significantly improve their explanatory power if they were coordinated so as to reduce redundancy, decrease environmental degradation and, most importantly, produce comparable datasets. Additionally, many of the present environmental management programs are based on the past baseline composition and location of biotic communities. As these communities become rearranged across the valleys in the future there is interest in assessing whether today's management plans are adequate. To efficiently move these research programs forward for the McMurdo Dry Valleys requires a coordinated, interdisciplinary, long-term data monitoring and observation network. The ultimate objectives of the workshop are to: i) identify the optimal, complementary suites of measurements required to assess and address key processes associated with environmental change in Dry Valley ecosystems; ii) develop standards and protocols for gathering the most critical biotic and abiotic measurements associated with the key processes driving environmental change; iii) generate a draft data coordination and development plan that will maximize the utility of these data; iv) assess the effectiveness of current McMurdo Dry Valley ASMA (Antarctic Special Management Area) environmental protection guidelines. proprietary NSF-ANT13-55533_1 A Multi-decadal Record of Antarctic Benthos: Image Analysis to Maximize Data Utilization AMD_USAPDC STAC Catalog 2013-10-01 2015-09-30 163, -78.5, 167, -78 https://cmr.earthdata.nasa.gov/search/concepts/C2532070231-AMD_USAPDC.umm_json Antarctic benthic communities are characterized by many species of sponges (Phylum Porifera), long thought to exhibit extremely slow demographic patterns of settlement, growth and reproduction. This project will analyze many hundreds of diver and remotely operated underwater vehicle photographs documenting a unique, episodic settlement event that occurred between 2000 and 2010 in McMurdo Sound that challenges this paradigm of slow growth. Artificial structures were placed on the seafloor between 1967 and 1974 at several sites, but no sponges were observed to settle on these structures until 2004. By 2010 some 40 species of sponges had settled and grown to be surprisingly large. Given the paradigm of slow settlement and growth supported by the long observation period (37 years, 1967-2004), this extraordinary large-scale settlement and rapid growth over just a 6-year time span is astonishing. This project utilizes image processing software (ImageJ) to obtain metrics (linear dimensions to estimate size, frequency, percent cover) for sponges and other fauna visible in the photographs. It uses R to conduct multidimensional scaling to ordinate community data and ANOSIM to test for differences of community data among sites and times and structures. It will also use SIMPER and ranked species abundances to discriminate species responsible for any differences. This work focuses on Antarctic sponges, but the observations of massive episodic recruitment and growth are important to understanding seafloor communities worldwide. Ecosystems are composed of populations, and populations are ecologically described by their distribution and abundance. A little appreciated fact is that sponges often dominate marine communities, but because sponges are so hard to study, most workers focus on other groups such as corals, kelps, or bivalves. Because most sponges settle and grow slowly their life history is virtually unstudied. The assumption of relative stasis of the Antarctic seafloor community is common, and this project will shatter this paradigm by documenting a dramatic episodic event. Finally, the project takes advantage of old transects from the 1960s and 1970s and compares them with extensive 2010 surveys of the same habitats and sometimes the same intact transect lines, offering a long-term perspective of community change. The investigators will publish these results in peer-reviewed journals, give presentations to the general public and will involve students from local outreach programs, high schools, and undergraduates at UCSD to help with the analysis. proprietary +NSF-ANT13-55533_1 A Multi-decadal Record of Antarctic Benthos: Image Analysis to Maximize Data Utilization ALL STAC Catalog 2013-10-01 2015-09-30 163, -78.5, 167, -78 https://cmr.earthdata.nasa.gov/search/concepts/C2532070231-AMD_USAPDC.umm_json Antarctic benthic communities are characterized by many species of sponges (Phylum Porifera), long thought to exhibit extremely slow demographic patterns of settlement, growth and reproduction. This project will analyze many hundreds of diver and remotely operated underwater vehicle photographs documenting a unique, episodic settlement event that occurred between 2000 and 2010 in McMurdo Sound that challenges this paradigm of slow growth. Artificial structures were placed on the seafloor between 1967 and 1974 at several sites, but no sponges were observed to settle on these structures until 2004. By 2010 some 40 species of sponges had settled and grown to be surprisingly large. Given the paradigm of slow settlement and growth supported by the long observation period (37 years, 1967-2004), this extraordinary large-scale settlement and rapid growth over just a 6-year time span is astonishing. This project utilizes image processing software (ImageJ) to obtain metrics (linear dimensions to estimate size, frequency, percent cover) for sponges and other fauna visible in the photographs. It uses R to conduct multidimensional scaling to ordinate community data and ANOSIM to test for differences of community data among sites and times and structures. It will also use SIMPER and ranked species abundances to discriminate species responsible for any differences. This work focuses on Antarctic sponges, but the observations of massive episodic recruitment and growth are important to understanding seafloor communities worldwide. Ecosystems are composed of populations, and populations are ecologically described by their distribution and abundance. A little appreciated fact is that sponges often dominate marine communities, but because sponges are so hard to study, most workers focus on other groups such as corals, kelps, or bivalves. Because most sponges settle and grow slowly their life history is virtually unstudied. The assumption of relative stasis of the Antarctic seafloor community is common, and this project will shatter this paradigm by documenting a dramatic episodic event. Finally, the project takes advantage of old transects from the 1960s and 1970s and compares them with extensive 2010 surveys of the same habitats and sometimes the same intact transect lines, offering a long-term perspective of community change. The investigators will publish these results in peer-reviewed journals, give presentations to the general public and will involve students from local outreach programs, high schools, and undergraduates at UCSD to help with the analysis. proprietary NSF-ANT90-24544 Atmospheric Boundary Layer Measurements on the Weddell Sea Drifting Station AMD_USAPDC STAC Catalog 1992-02-21 1992-06-05 -53.8, -71.4, -43.2, -61.2 https://cmr.earthdata.nasa.gov/search/concepts/C2534797194-AMD_USAPDC.umm_json Location: Ice camp on perennial sea ice in the southwestern corner of the Weddell Sea, Antarctic The first direct radiative and turbulent surface flux measurements ever made over floating Antarctic sea ice. The data are from Ice Station Weddell as it drifted in the western Weddell Sea from February to late May 1992. Data Types: Hourly measurements of the turbulent surface fluxes of momentum and sensible and latent heat by eddy covariance at a height of 4.65 m above snow-covered sea ice. Instruments were a 3-axis sonic anemometer/thermometer and a Lyman-alpha hygrometer. Hourly, surface-level measurements of the four radiation components: in-coming and out-going longwave and shortwave radiation. Instruments were hemispherical pyranometers and pyrgeometers. Hourly mean values of standard meteorological variables: air temperature, dew point temperature, wind speed and direction, barometric pressure, surface temperature. Instruments were a propeller-vane for wind speed and direction and cooled-mirror dew-point hygrometers and platinum resistance thermometers for dew-points and temperatures. Surface temperature came from a Barnes PRT-5 infrared thermometer. Flux Data The entire data kit is bundled as a zip file named ISW_Flux_Data.zip The main data file is comma delimited. The README file is ASCII. The associated reprints of publications are in pdf. Radiosounding data: On Ice Station Weddell, typically twice a day from 21 February through 4 June 1992 made with both tethered (i.e., only boundary-layer profiles) and (more rarely) free-flying sondes that did not measure wind speed. (168 soundings). ISW Radiosoundings The entire data kit is bundled as a zip file named ISW_Radiosounding.zip. The README file is in ASCII. Two summary files that include the list of sounding and the declinations are in ASCII. The 168 individual sounding files are in ASCII. Two supporting publications that describe the data and some analyses are in pdf. Radiosounding data collected from the Russian ship Akademic Fedorov from 26 May through 5 June 1992 at 6-hourly intervals as it approached Ice Station Weddell from the north. These soundings include wind vector, temperature, humidity, and pressure. (40 soundings) Akademic Federov Radiosoundings The entire data kit is bundled as a zip file named Akad_Federov_Radiosounding.zip. The README file is in ASCII. A summary file that lists the soundings is in ASCII. The 40 individual sounding files are in ASCII. Two supporting publications that describe the data and some analyses are in pdf. Documentation: Andreas, E. L, and K. J. Claffey, 1995: Air-ice drag coefficients in the western Weddell Sea: 1. Values deduced from profile measurements. Journal of Geophysical Research, 100, 4821–4831. Andreas, E. L, K. J. Claffey, and A. P. Makshtas, 2000: Low-level atmospheric jets and inversions over the western Weddell Sea. Boundary-Layer Meteorology, 97, 459–486. Andreas, E. L, R. E. Jordan, and A. P. Makshtas, 2004: Simulations of snow, ice, and near-surface atmospheric processes on Ice Station Weddell. Journal of Hydrometeorology, 5, 611–624. Andreas, E. L, R. E. Jordan, and A. P. Makshtas, 2005: Parameterizing turbulent exchange over sea ice: The Ice Station Weddell results. Boundary-Layer Meteorology, 114, 439–460. Andreas, E. L, P. O. G. Persson, R. E. Jordan, T. W. Horst, P. S. Guest, A. A. Grachev, and C. W. Fairall, 2010: Parameterizing turbulent exchange over sea ice in winter. Journal of Hydrometeorology, 11, 87–104. Claffey, K. J., E. L Andreas, and A. P. Makshtas, 1994: Upper-air data collected on Ice Station Weddell. Special Report 94-25, U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, NH, 62 pp. ISW Group, 1993: Weddell Sea exploration from ice station. Eos, Transactions, American Geophysical Union, 74, 121–126. Makshtas, A. P., E. L Andreas, P. N. Svyaschennikov, and V. F. Timachev, 1999: Accounting for clouds in sea ice models. Atmospheric Research, 52, 77–113. proprietary NSF-BWZ_0 National Science Foundation (NSF)-Blue Water Zone (BWZ) measurements OB_DAAC STAC Catalog 2004-02-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360531-OB_DAAC.umm_json Measurements taken in the Blue Water Zone (BWZ) under NSF funding near Antarctica and Drakes Passage in 2004 to 2006. proprietary NSF_Gulf_Rapid_0 NSF Collaborative Research: A RAPID response to Hurricane Harvey impacts on coastal carbon cycle, metabolic balance and ocean acidification OB_DAAC STAC Catalog 2017-09-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1719969318-OB_DAAC.umm_json Collaborative Research: A RAPID response to Hurricane Harvey's impacts on coastal carbon cycle, metabolic balance and ocean acidification. proprietary @@ -12527,8 +12507,8 @@ NSIDC-0202_1 Atmospheric CO2 Trapped in the Ice Core from Siple Dome, Antarctica NSIDC-0209_1 Baltic Sea Experiment (BALTEX) Ground-Based Radar Polar Volume Data, Version 1 NSIDCV0 STAC Catalog 2002-09-01 2003-05-31 18.39, 57.24, 18.39, 57.24 https://cmr.earthdata.nasa.gov/search/concepts/C1386204148-NSIDCV0.umm_json This data set includes non-Doppler polar volume reflectivity data from the Baltic Sea Experiment (BALTEX). Data were collected on Sweden's Gotland Island, using an Ericsson radar mounted at 56 m above sea level. proprietary NSIDC-0210_1 Double Rain Gauge Network, Iowa, Version 1 NSIDCV0 STAC Catalog 2002-06-18 2003-11-13 -91.75, 41.5, -91.5, 41.75 https://cmr.earthdata.nasa.gov/search/concepts/C1386204149-NSIDCV0.umm_json This data set includes rainfall data from 25 sites in Iowa, centered on the Iowa City Municipal Airport. proprietary NSIDC-0211_1 CLPX-Model: Rapid Update Cycle 40km (RUC-40) Model Output Reduced Data, Version 1 NSIDCV0 STAC Catalog 2002-10-01 2003-06-30 -108.615, 38.394, -103.971, 42.568 https://cmr.earthdata.nasa.gov/search/concepts/C1386250242-NSIDCV0.umm_json The Rapid Update Cycle, version 2 at 40km (RUC-2, known to the Cold Land Processes community as RUC40) model is a Mesoscale Analysis and Prediction System (MAPS) data set that uses the Model Output Reduced Data Set (MORDS) version. This data set has been subsetted for use in the Cold Land Processes Field Experiment (CLPX). proprietary -NSIDC-0212_1 Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1 ALL STAC Catalog 2003-01-14 2003-02-03 130, 30, 150, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1386204153-NSIDCV0.umm_json This data set includes 94 GHz co- and cross-polarized radar reflectivity. The Airborne Cloud Radar (ACR) sensor was mounted to a NASA P-3 aircraft flown over the Sea of Japan, the Western Pacific Ocean, and the Japanese Islands. proprietary NSIDC-0212_1 Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1 NSIDCV0 STAC Catalog 2003-01-14 2003-02-03 130, 30, 150, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1386204153-NSIDCV0.umm_json This data set includes 94 GHz co- and cross-polarized radar reflectivity. The Airborne Cloud Radar (ACR) sensor was mounted to a NASA P-3 aircraft flown over the Sea of Japan, the Western Pacific Ocean, and the Japanese Islands. proprietary +NSIDC-0212_1 Airborne Cloud Radar (ACR) Reflectivity, Wakasa Bay, Japan, Version 1 ALL STAC Catalog 2003-01-14 2003-02-03 130, 30, 150, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1386204153-NSIDCV0.umm_json This data set includes 94 GHz co- and cross-polarized radar reflectivity. The Airborne Cloud Radar (ACR) sensor was mounted to a NASA P-3 aircraft flown over the Sea of Japan, the Western Pacific Ocean, and the Japanese Islands. proprietary NSIDC-0218_1 Greenland Ice Sheet Melt Characteristics Derived from Passive Microwave Data, Version 1 NSIDCV0 STAC Catalog 1979-04-02 2007-12-31 -73, 60, -10, 84 https://cmr.earthdata.nasa.gov/search/concepts/C1386250243-NSIDCV0.umm_json The Greenland ice sheet melt extent data, acquired as part of the NASA Program for Arctic Regional Climate Assessment (PARCA), is a daily (or every other day, prior to August 1987) estimate of the spatial extent of wet snow on the Greenland ice sheet since 1979. It is derived from passive microwave satellite brightness temperature characteristics using the Cross-Polarized Gradient Ratio (XPGR) of Abdalati and Steffen (1997). It is physically based on the changes in microwave emission characteristics observable in data from the Scanning Multi-channel Microwave Radiometer (SMMR) and the Special Sensor Microwave/Imager (SSM/I) instruments when surface snow melts. It is not a direct measure of the snow wetness but rather is a binary indicator of the state of melt of each SMMR and SSM/I pixel on the ice sheet for each day of observation. It is, however, a useful proxy for the amount of melt that occurs on the Greenland ice sheet. The data are provided in a variety of formats including raw data in ASCII format, gridded daily data in binary format, and annual and complete time series climatologies in gridded binary and GeoTIFF format. All data are in a 60 x 109 pixel subset of the standard Northern Hemisphere polar stereographic grid with a 25 km resolution and are available via FTP. proprietary NSIDC-0223_1 Elevation Change of the Southern Greenland Ice Sheet from 1978-88, Version 1 NSIDCV0 STAC Catalog 1978-01-01 1988-12-31 -52, 61, -30, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1386204165-NSIDCV0.umm_json Southern Greenland ice sheet elevation change estimates are derived from SEASAT and GEOSAT radar altimetry data from 1978 to 1988. Data are confined to 61-72 deg N, 30-50 deg W, above 1700 m elevation. The addition of GEOSAT Geodetic Mission (GM) data results in twice as many crossover points and 50% greater coverage than previous studies. Coverage above 2000 m elevation is improved to 90%, and about 75% of the area between 1700 m and 2000 m is now covered. Data are in ASCII text format, available via FTP, and consist of elevation change rate (dH/dt, cm/year) and corresponding error estimates in 50 km grid cells. proprietary NSIDC-0240_1 Antarctic Aerogeophysics Data AMD_USAPDC STAC Catalog 1994-01-01 2004-12-31 -90, -75, 90, -68.73 https://cmr.earthdata.nasa.gov/search/concepts/C2532073961-AMD_USAPDC.umm_json The data that the Support Office for Aerogeophysical Research (SOAR) provides include various aerogeophysical measurements taken in the West Antarctic Ice Shelf (WAIS) from 1994 to 2001. The instruments used in experiments include ice-penetrating radar, laser altimetry and magnetics, and an integrated aerogeophysical platform that includes airborne gravity with carrier-phase GPS to support kinematic differential positioning. SOAR is a part of the University of Texas Institute for Geophysics (UTIG) and provides several types of data associated with various campaigns over the years. This material is based on work supported by the National Science Foundation under Grants: OPP-9120464, 9319369, 9319379, and 9911617. proprietary @@ -12551,8 +12531,8 @@ NSIDC-0314_1 Atmospheric CO2 and Climate: Byrd Ice Core, Antarctica AMD_USAPDC S NSIDC-0315_1 Atmospheric CO2 and Climate: Taylor Dome Ice Core, Antarctica AMD_USAPDC STAC Catalog 1970-01-01 158, -77.666667, 158, -77.666667 https://cmr.earthdata.nasa.gov/search/concepts/C2532070838-AMD_USAPDC.umm_json Using new and existing ice core CO2 data from 65 - 30 ka BP a new chronology for Taylor Dome ice core CO2 is established and synchronized with Greenland ice core records to study how high latitude climate change and the carbon cycle were linked during the last glacial period. The new data and chronology should provide a better target for models attempting to explain CO2 variability and abrupt climate change. proprietary NSIDC-0318_1 Antarctic Mean Annual Temperature Map AMD_USAPDC STAC Catalog 1957-01-01 2003-12-31 -180, -90, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C2532070844-AMD_USAPDC.umm_json The Mean Annual Temperature map was calculated by creating a contour map using compiled 10 meter firn temperature data from NSIDC and other mean annual temperature data from both cores and stations. The 10 meter data contains temperature measurements dating back to 1957 and the International Geophysical Year, including measurements from several major recent surveys. Data cover the entire continental ice sheet and several ice shelves, but coverage density is generally low. Data are stored in Microsoft Excel and Tagged Image File Format (TIFF), and are available sporadically from 1957 to 2003 via FTP. proprietary NSIDC-0321_1 Global EASE-Grid 8-day Blended SSM/I and MODIS Snow Cover, Version 1 NSIDCV0 STAC Catalog 2000-03-05 2008-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1386250333-NSIDCV0.umm_json This data set comprises global, 8-day Snow-Covered Area (SCA) and Snow Water Equivalent (SWE) data from 2000 through 2008. Global SWE data are derived from the Special Sensor Microwave Imager (SSM/I) and are enhanced with MODIS/Terra Snow Cover 8-Day Level 3 Global 0.05 degree Climate Modeling Grid (CMG) data. Global data are gridded to the Northern and Southern 25 km Equal-Area Scalable Earth Grids (EASE-Grids). These data are suitable for continental-scale time-series studies of snow cover and snow water equivalent. proprietary -NSIDC-0326_1 Ablation Rates of Taylor Glacier, Antarctica ALL STAC Catalog 2002-11-19 2011-01-12 160.1, -77.9, 162.2, -77.6 https://cmr.earthdata.nasa.gov/search/concepts/C2532070867-AMD_USAPDC.umm_json This data set provides glacier surface ablation rates for a network of approximately 250 sites on Taylor Glacier, spanning a period from 2003 to 2011. Here sublimation is the dominant ablation mechanism, though a few sites have accumulation. Ablation data are provided in meters water equivalent per year. Data are available via FTP in space-delimited ASCII format. proprietary NSIDC-0326_1 Ablation Rates of Taylor Glacier, Antarctica AMD_USAPDC STAC Catalog 2002-11-19 2011-01-12 160.1, -77.9, 162.2, -77.6 https://cmr.earthdata.nasa.gov/search/concepts/C2532070867-AMD_USAPDC.umm_json This data set provides glacier surface ablation rates for a network of approximately 250 sites on Taylor Glacier, spanning a period from 2003 to 2011. Here sublimation is the dominant ablation mechanism, though a few sites have accumulation. Ablation data are provided in meters water equivalent per year. Data are available via FTP in space-delimited ASCII format. proprietary +NSIDC-0326_1 Ablation Rates of Taylor Glacier, Antarctica ALL STAC Catalog 2002-11-19 2011-01-12 160.1, -77.9, 162.2, -77.6 https://cmr.earthdata.nasa.gov/search/concepts/C2532070867-AMD_USAPDC.umm_json This data set provides glacier surface ablation rates for a network of approximately 250 sites on Taylor Glacier, spanning a period from 2003 to 2011. Here sublimation is the dominant ablation mechanism, though a few sites have accumulation. Ablation data are provided in meters water equivalent per year. Data are available via FTP in space-delimited ASCII format. proprietary NSIDC-0334_1 Airborne Laser Altimetry of the Thwaites Glacier Catchment, West Antarctica AMD_USAPDC STAC Catalog 2004-12-10 2005-01-29 -130, -80, -95, -75 https://cmr.earthdata.nasa.gov/search/concepts/C2532070878-AMD_USAPDC.umm_json This data set includes airborne altimetry collected over the catchment and main trunk of Thwaites Glacier, one of Antarctica's most active ice streams. The airborne altimetry comprises 35,000 line-kilometers sampled at 20 meters along track. The full dataset has an internal error of �20 cm; a primary subset has an error of �8 cm. We find a +20 cm bias with Geoscience Laser Altimeter System data over a flat interior region. These data will serve as an additional temporal reference for the evolution of Thwaites Glacier surface, as well as aid the construction of future high resolution Digital Elevation Models (DEM). Line data are available in space-delimited ASCII format and are available via FTP. proprietary NSIDC-0334_1 Airborne Laser Altimetry of the Thwaites Glacier Catchment, West Antarctica ALL STAC Catalog 2004-12-10 2005-01-29 -130, -80, -95, -75 https://cmr.earthdata.nasa.gov/search/concepts/C2532070878-AMD_USAPDC.umm_json This data set includes airborne altimetry collected over the catchment and main trunk of Thwaites Glacier, one of Antarctica's most active ice streams. The airborne altimetry comprises 35,000 line-kilometers sampled at 20 meters along track. The full dataset has an internal error of �20 cm; a primary subset has an error of �8 cm. We find a +20 cm bias with Geoscience Laser Altimeter System data over a flat interior region. These data will serve as an additional temporal reference for the evolution of Thwaites Glacier surface, as well as aid the construction of future high resolution Digital Elevation Models (DEM). Line data are available in space-delimited ASCII format and are available via FTP. proprietary NSIDC-0336_1 Antarctic Subglacial Lake Classification Inventory AMD_USAPDC STAC Catalog 1998-12-01 2001-02-28 -160, -90, 15, -70 https://cmr.earthdata.nasa.gov/search/concepts/C2532070882-AMD_USAPDC.umm_json This data set is an Antarctic radar-based subglacial lake classification collection, which focuses on the radar reflection properties of each given lake. The Subglacial lakes are separated into four categories specified by radar reflection properties. Additional information includes: latitude, longitude, length (in kilometers), hydro-potential (in meters), bed elevation (in meters above WGS84), and ice thickness (in meters). Source data used to compile this data set were collected between 1998 and 2001. Data are available via FTP as a Microsoft Excel Spreadsheet (XLS), and Tagged Image File Format (TIF). proprietary @@ -12581,8 +12561,8 @@ NSIDC-0478_2 MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data V002 NSID NSIDC-0481_4 MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from InSAR V004 NSIDC_ECS STAC Catalog 2008-06-12 2023-09-20 -70, 60, -20, 82 https://cmr.earthdata.nasa.gov/search/concepts/C2076118670-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides velocity estimates determined from Interferometric Synthetic Aperture Radar (InSAR) data for major glacier outlet areas in Greenland, some of which have shown profound velocity changes over the MEaSUREs observation period. The InSAR Selected Glacier Site Velocity Maps are produced from image pairs measured by the German Aerospace Center's (DLR) twin satellites TerraSAR-X / TanDEM-X (TSX / TDX). The measurements in this data set are provided in addition to the ice sheet-wide data from the related data set, MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data. See Greenland Ice Mapping Project (GrIMP) for more related data." proprietary NSIDC-0484_2 MEaSUREs InSAR-Based Antarctica Ice Velocity Map V002 NSIDC_ECS STAC Catalog 1996-01-01 2016-12-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1414573008-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, provides the first comprehensive, high-resolution, digital mosaics of ice motion in Antarctica assembled from multiple satellite interferometric, synthetic-aperture radar systems. Data were largely acquired during the International Polar Years 2007 to 2009, as well as between 2013 and 2016. Additional data acquired between 1996 and 2016 were used as needed to maximize coverage. See Antarctic Ice Sheet Velocity and Mapping Data for related data." proprietary NSIDC-0498_2 MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry V002 NSIDC_ECS STAC Catalog 1992-02-07 2014-12-17 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1573480652-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides 22 years of comprehensive high-resolution mapping of grounding lines in Antarctica from 1992 to 2014. The data were derived using differential satellite synthetic aperture radar interferometry (DInSAR) measurements from the following platforms: Earth Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2), RADARSAT-1, RADARSAT-2, the Advanced Land Observing System Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR), Cosmo Skymed, and Copernicus Sentinel-1. See Antarctic Ice Sheet Velocity and Mapping Data for related data." proprietary -NSIDC-0504_1 Alkanes in Firn Air Samples, Antarctica and Greenland ALL STAC Catalog 2005-12-01 2009-01-31 -38.3833, -79.47, 112.09, 72.5833 https://cmr.earthdata.nasa.gov/search/concepts/C2532070980-AMD_USAPDC.umm_json This data set contains ethane, propane, and n-butane measurements in firn air from the South Pole and the West Antarctic Ice Sheet (WAIS) Divide in Antarctica, and from Summit, Greenland. The WAIS Divide and South Pole samples were collected in December to January of of 2005/06 and 2008/09, respectively. The Summit firn was sampled in the summer of 2006. Analyses were conducted on a gas chromatography - mass spectrometry (GC-MS) system at the University of California, Irvine. Measurements and the associated uncertainties are reported as dry air molar mixing ratios in part per trillion (ppt). The reported measurements for each sampling depth represent a mean of multiple measurements on more than one flask in most cases. Data are available via FTP in Microsoft Excel (.xls) format. proprietary NSIDC-0504_1 Alkanes in Firn Air Samples, Antarctica and Greenland AMD_USAPDC STAC Catalog 2005-12-01 2009-01-31 -38.3833, -79.47, 112.09, 72.5833 https://cmr.earthdata.nasa.gov/search/concepts/C2532070980-AMD_USAPDC.umm_json This data set contains ethane, propane, and n-butane measurements in firn air from the South Pole and the West Antarctic Ice Sheet (WAIS) Divide in Antarctica, and from Summit, Greenland. The WAIS Divide and South Pole samples were collected in December to January of of 2005/06 and 2008/09, respectively. The Summit firn was sampled in the summer of 2006. Analyses were conducted on a gas chromatography - mass spectrometry (GC-MS) system at the University of California, Irvine. Measurements and the associated uncertainties are reported as dry air molar mixing ratios in part per trillion (ppt). The reported measurements for each sampling depth represent a mean of multiple measurements on more than one flask in most cases. Data are available via FTP in Microsoft Excel (.xls) format. proprietary +NSIDC-0504_1 Alkanes in Firn Air Samples, Antarctica and Greenland ALL STAC Catalog 2005-12-01 2009-01-31 -38.3833, -79.47, 112.09, 72.5833 https://cmr.earthdata.nasa.gov/search/concepts/C2532070980-AMD_USAPDC.umm_json This data set contains ethane, propane, and n-butane measurements in firn air from the South Pole and the West Antarctic Ice Sheet (WAIS) Divide in Antarctica, and from Summit, Greenland. The WAIS Divide and South Pole samples were collected in December to January of of 2005/06 and 2008/09, respectively. The Summit firn was sampled in the summer of 2006. Analyses were conducted on a gas chromatography - mass spectrometry (GC-MS) system at the University of California, Irvine. Measurements and the associated uncertainties are reported as dry air molar mixing ratios in part per trillion (ppt). The reported measurements for each sampling depth represent a mean of multiple measurements on more than one flask in most cases. Data are available via FTP in Microsoft Excel (.xls) format. proprietary NSIDC-0515_1 Annual Layers at Siple Dome, Antarctica, from Borehole Optical Stratigraphy AMD_USAPDC STAC Catalog 2000-12-15 2001-11-15 -148.82, -81.66, -148.82, -81.66 https://cmr.earthdata.nasa.gov/search/concepts/C2532070824-AMD_USAPDC.umm_json Researchers gathered data on annual snow layers at Siple Dome, Antarctica, using borehole optical stratigraphy. This data set contains annual layer depths and firn optical brightness. The brightness log is a record of reflectivity of the firn, and peaks in brightness are interpreted to be fine-grained high-density winter snow, as part of the wind slab depth-hoar couplet. Data are available via FTP in ASCII text (.txt) format proprietary NSIDC-0516_1 Antarctic Peninsula 100 m Digital Elevation Model Derived from ASTER GDEM AMD_USAPDC STAC Catalog 2000-01-01 2009-12-31 -70, -70, -55, -63 https://cmr.earthdata.nasa.gov/search/concepts/C2532070816-AMD_USAPDC.umm_json This data set provides a 100 meter resolution surface topography Digital Elevation Model (DEM) of the Antarctic Peninsula. The DEM is based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) data. proprietary NSIDC-0517_1 AGASEA Ice Thickness Profile Data from the Amundsen Sea Embayment, Antarctica ALL STAC Catalog 2004-12-10 2005-01-29 -125, -83, -90, -73 https://cmr.earthdata.nasa.gov/search/concepts/C2532070806-AMD_USAPDC.umm_json This data set contains line-based radar-derived ice thickness and bed elevation data, collected as part of the Airborne Geophysical Survey of the Amundsen Embayment (AGASEA) expedition, which took place over Thwaites Glacier in West Antarctica from 2004 to 2005. The data set includes ice thickness, ice sheet bed elevation, and ice sheet surface elevation, derived from ice-penetrating radar and aircraft GPS positions. The data are spaced on a 15 km by 15 km grid over the entire catchment of the glacier, and sampled at approximately 15 meters along track. Most of the radar data used for this dataset has been processed using a 1-D focusing algorithm, to reduce the along track resolution to tens of meters, to improve boundary conditions for ice sheet models. Data are available via FTP in space-delimited ASCII format. proprietary @@ -12596,8 +12576,8 @@ NSIDC-0533_1 MEaSUREs Greenland Surface Melt Daily 25km EASE-Grid 2.0 V001 NSIDC NSIDC-0534_1 MEaSUREs Northern Hemisphere State of Cryosphere Daily 25km EASE-Grid 2.0 V001 NSIDC_ECS STAC Catalog 1999-01-01 2012-12-31 -180, 0, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1402083137-NSIDC_ECS.umm_json This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between three different snow cover data sources. proprietary NSIDC-0535_1 MEaSUREs Northern Hemisphere State of Cryosphere Weekly 100km EASE-Grid 2.0 V001 NSIDC_ECS STAC Catalog 1979-01-02 2012-12-31 -180, 0, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1628163642-NSIDC_ECS.umm_json This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, reports the location of Northern Hemisphere snow cover and sea ice extent, the status of melt onset across Greenland and Arctic sea ice, and the level of agreement between snow cover maps derived from two different sources. proprietary NSIDC-0538_1 Bubble Number-density Data and Modeled Paleoclimates AMD_USAPDC STAC Catalog 2008-01-10 2008-06-18 -112.3, -79.433333, -112.3, -79.433333 https://cmr.earthdata.nasa.gov/search/concepts/C2532070716-AMD_USAPDC.umm_json This data set includes bubble number-density measured at depths from 120 meters to 560 meters at 20-meter intervals in both horizontal and vertical samples. The data set also includes modeled temperature reconstructions based on the model developed by Spencer and others (2006). proprietary -NSIDC-0539_1 Abrupt Change in Atmospheric CO2 During the Last Ice Age AMD_USAPDC STAC Catalog 2009-01-01 2012-12-31 -148.82, -81.66, -119.83, -80.01 https://cmr.earthdata.nasa.gov/search/concepts/C2532070709-AMD_USAPDC.umm_json During the last glacial period atmospheric carbon dioxide and temperature in Antarctica varied in a similar fashion on millennial time scales, but previous work indicates that these changes were gradual. In a detailed analysis of one event, we now find that approximately half of the CO2 increase that occurred during the 1500 year cold period between Dansgaard-Oeschger (DO) Events 8 and 9 happened rapidly, over less than two centuries. This rise in CO2 was synchronous with, or slightly later than, a rapid increase of Antarctic temperature inferred from stable isotopes. proprietary NSIDC-0539_1 Abrupt Change in Atmospheric CO2 During the Last Ice Age ALL STAC Catalog 2009-01-01 2012-12-31 -148.82, -81.66, -119.83, -80.01 https://cmr.earthdata.nasa.gov/search/concepts/C2532070709-AMD_USAPDC.umm_json During the last glacial period atmospheric carbon dioxide and temperature in Antarctica varied in a similar fashion on millennial time scales, but previous work indicates that these changes were gradual. In a detailed analysis of one event, we now find that approximately half of the CO2 increase that occurred during the 1500 year cold period between Dansgaard-Oeschger (DO) Events 8 and 9 happened rapidly, over less than two centuries. This rise in CO2 was synchronous with, or slightly later than, a rapid increase of Antarctic temperature inferred from stable isotopes. proprietary +NSIDC-0539_1 Abrupt Change in Atmospheric CO2 During the Last Ice Age AMD_USAPDC STAC Catalog 2009-01-01 2012-12-31 -148.82, -81.66, -119.83, -80.01 https://cmr.earthdata.nasa.gov/search/concepts/C2532070709-AMD_USAPDC.umm_json During the last glacial period atmospheric carbon dioxide and temperature in Antarctica varied in a similar fashion on millennial time scales, but previous work indicates that these changes were gradual. In a detailed analysis of one event, we now find that approximately half of the CO2 increase that occurred during the 1500 year cold period between Dansgaard-Oeschger (DO) Events 8 and 9 happened rapidly, over less than two centuries. This rise in CO2 was synchronous with, or slightly later than, a rapid increase of Antarctic temperature inferred from stable isotopes. proprietary NSIDC-0541_1 Allan Hills Stable Water Isotopes AMD_USAPDC STAC Catalog 2009-01-01 2011-12-31 159, -76.83, 159.25, -75.67 https://cmr.earthdata.nasa.gov/search/concepts/C2532070698-AMD_USAPDC.umm_json This data set includes stable water isotope values at 10 m resolution along an approximately 5 km transect through the main icefield of the Allan Hills Blue Ice Area, and at 15 cm within a 225 m core drilled at the midpoint of the transect. proprietary NSIDC-0543_1 AMSR-E/Aqua Monthly Global Microwave Land Surface Emissivity, Version 1 NSIDCV0 STAC Catalog 2002-07-01 2008-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1386205524-NSIDCV0.umm_json This data set is a global land emissivity product using passive microwave observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). The data set complements existing land emissivity products from the Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Sounding Unit (AMSU) by adding land emissivity estimates at two lower frequencies, 6.9 and 10.65 GHz (C- and X-band, respectively). Observations at these low frequencies penetrate deeper into the soil layer. Land surface emissivity estimates for this data set were collected at the following vertically and horizontally polarized (V-pol and H-pol) frequencies: 6.9, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz. Ancillary data used in the analysis, such as surface skin temperature and cloud mask, were obtained from International Satellite Cloud Climatology Project (ISCCP). Atmospheric properties were obtained from TIROS Operational Vertical Sounder (TOVS) observations to determine the small upwelling and downwelling atmospheric emissions as well as the atmospheric transmission. The data set is in monthly format that is extracted from instantaneous emissivity estimates. Data are stored in HDF4 files and are available via FTP. proprietary NSIDC-0545_1 MEaSUREs InSAR-Based Ice Velocity of the Amundsen Sea Embayment, Antarctica V001 NSIDC_ECS STAC Catalog 1996-01-01 2012-12-31 -127.3826, -80.4614, 82.8345, -71.9876 https://cmr.earthdata.nasa.gov/search/concepts/C1353062858-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, provides high-resolution, digital mosaics of ice motion in the Amundsen Sea Embayment (ASE) and West Antarctica, including the Pine Island, Thwaites, Haynes, Pope, Smith, and Kohler glaciers. The mosaics were assembled from interferometric synthetic-aperture radar (InSAR) data acquired in 1996, 2000, 2002, and 2006-2012 by various satellites. See Antarctic Ice Sheet Velocity and Mapping Data for related data." proprietary @@ -12613,8 +12593,8 @@ NSIDC-0611_4 EASE-Grid Sea Ice Age, Version 4 NSIDCV0 STAC Catalog 1984-01-01 20 NSIDC-0627_1 Borehole Temperatures at Pine Island Glacier, Antarctica AMD_USAPDC STAC Catalog 2012-12-20 2013-05-10 -100.5, -75.1, -100.5, -75.1 https://cmr.earthdata.nasa.gov/search/concepts/C2532070657-AMD_USAPDC.umm_json This data set is a time series of borehole temperatures at different depths from three thermistor strings deployed in three boreholes drilled through the Pine Island Glacier ice shelf, Antarctica. proprietary NSIDC-0630_1 MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR V001 NSIDC_ECS STAC Catalog 1978-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1371883515-NSIDC_ECS.umm_json This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, is an improved, enhanced-resolution, gridded passive microwave Earth System Data Record (ESDR) for monitoring cryospheric and hydrologic time series from SMMR, SSM/I-SSMIS, and AMSR-E. It is derived from the most mature and available Level-2 satellite passive microwave records from 1978 through the present. proprietary NSIDC-0630_2 Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR V002 NSIDC_ECS STAC Catalog 1978-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464104-NSIDC_ECS.umm_json The Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR, Version 2 data set is a multi-sensor Level 3 Earth Science Data Record (ESDR) with improvements upon Version 1 in cross-sensor calibration and quality checking, modern file formats, better quality control, improved projection grids, and local time-of-day (LTOD) processing. These data are gridded to three EASE-Grid 2.0 projections (North Azimuthal, South Azimuthal, and Cylindrical) and include enhanced-resolution imagery, as well as coarse-resolution, averaged imagery. Inputs include brightness temperature data from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), Special Sensor Microwave Imager/Sounder (SSMIS), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2). proprietary -NSIDC-0634_1 Alaska Tidewater Glacier Terminus Positions, Version 1 ALL STAC Catalog 1948-01-01 2012-12-31 -151, 56.5, -132, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C1386250732-NSIDCV0.umm_json This data set contains Alaska tidewater glacier terminus positions digitized from USGS topographic maps and Landsat images. proprietary NSIDC-0634_1 Alaska Tidewater Glacier Terminus Positions, Version 1 NSIDCV0 STAC Catalog 1948-01-01 2012-12-31 -151, 56.5, -132, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C1386250732-NSIDCV0.umm_json This data set contains Alaska tidewater glacier terminus positions digitized from USGS topographic maps and Landsat images. proprietary +NSIDC-0634_1 Alaska Tidewater Glacier Terminus Positions, Version 1 ALL STAC Catalog 1948-01-01 2012-12-31 -151, 56.5, -132, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C1386250732-NSIDCV0.umm_json This data set contains Alaska tidewater glacier terminus positions digitized from USGS topographic maps and Landsat images. proprietary NSIDC-0637_1 Borehole Temperature Measurement in WDC05A in January 2008 and January 2009 AMD_USAPDC STAC Catalog 2008-01-01 2009-01-01 -112.125, -79.463, -112.125, -79.463 https://cmr.earthdata.nasa.gov/search/concepts/C2532071518-AMD_USAPDC.umm_json This data set includes borehole temperature measurements performed in January 2008 and January 2009 at the West Antarctic Ice sheet divide from the 300 m hole WDC05A. proprietary NSIDC-0642_2 MEaSUREs Annual Greenland Outlet Glacier Terminus Positions from SAR Mosaics V002 NSIDC_ECS STAC Catalog 1972-09-16 2021-03-25 -75, 60, -14, 83 https://cmr.earthdata.nasa.gov/search/concepts/C2139015179-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of annual, digitized (polyline) ice front positions for 239 outlet glaciers in Greenland. Ice front positions are derived from Sentinel-1A, Sentinel-1B, and RADARSAT-1 synthetic aperture radar (SAR) mosaics, plus imagery from Landsat 1 through Landsat 5 and Landsat 7 and Landsat 8. Although temporal coverage varies by glacier, data are available for the winter seasons 1972–1973 through 2020–2021. Data are provided as shapefiles. See Greenland Ice Mapping Project (GrIMP) for related data." proprietary NSIDC-0644_1 Greenland Annual Accumulation along the EGIG Line, 1959–2004, from Airborne Radar and Neutron Probe Densities, Version 1 NSIDCV0 STAC Catalog 1959-10-01 2004-09-30 -42.838297, 70.585609, -36.232431, 71.207715 https://cmr.earthdata.nasa.gov/search/concepts/C1436304012-NSIDCV0.umm_json This data set reports mean annual snow accumulation rates in meters water equivalent (m·w.e.) from 1959 to 2004 along a 250 km segment of the Expéditions Glaciologiques Internationales au Groenland (EGIG) line. Accumulation rates are derived from Airborne SAR/Interferometric Radar Altimeter System (ASIRAS) data and high resolution neutron-probe (NP) density profiles. proprietary @@ -12711,8 +12691,8 @@ NmTHIRmtg-1T_1 Nimbus Temperature-Humidity Infrared Radiometer Global Montage Gr Nome_Veg_Plots_1372_1 Arctic Vegetation Plots at Nome, Alaska, 1951 ORNL_CLOUD STAC Catalog 1951-07-30 1951-08-02 -165.26, 64.63, -165.26, 64.63 https://cmr.earthdata.nasa.gov/search/concepts/C2170969899-ORNL_CLOUD.umm_json This data set provides environmental, soil, and vegetation data collected in July and August 1951 from 80 study plots in the Nome River Valley about 10 miles northeast of Nome, Alaska on the Seward Peninsula. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in plant communities that were found to occur in 5 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species and cover, and soil characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping and analysis of geo-botanical factors in the Nome River Valley and across Alaska. proprietary Non-Forest_Trees_Sahara_Sahel_1832_1 An Unexpectedly Large Count of Trees in the West African Sahara and Sahel ORNL_CLOUD STAC Catalog 2005-11-01 2018-03-31 -18, 11.35, -5.49, 24.03 https://cmr.earthdata.nasa.gov/search/concepts/C2761798565-ORNL_CLOUD.umm_json This dataset provides georeferenced polygon vectors of individual tree canopy geometries for dryland areas in West African Sahara and Sahel that were derived using deep learning applied to 50-cm resolution satellite imagery. More than 1.8 billion non-forest trees (i.e., woody plants with a crown size over 3 m2) over about 1.3 million km2 were identified from panchromatic and pansharpened normalized difference vegetation index (NDVI) images at 0.5-m spatial resolution using an automatic tree detection framework based on supervised deep-learning techniques. Combined with existing and future fieldwork, these data lay the foundation for a comprehensive database that contains information on all individual trees outside of forests and could provide accurate estimates of woody carbon in arid and semi-arid areas throughout the Earth for the first time. proprietary Nongrowing_Season_CO2_Flux_1692_1 Synthesis of Winter In Situ Soil CO2 Flux in pan-Arctic and Boreal Regions, 1989-2017 ORNL_CLOUD STAC Catalog 1989-09-01 2017-04-30 -163.71, 53.88, 161.99, 78.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143403370-ORNL_CLOUD.umm_json This dataset provides a synthesis of winter ( September-April) in situ soil CO2 flux measurement data from locations across pan-Arctic and Boreal permafrost regions. The in situ data were compiled from 66 published and 21 unpublished studies conducted from 1989-2017. The data sources (publication references) are provided. Sampling sites spanned pan-Arctic Boreal and tundra regions (>53 Deg N) in continuous, discontinuous, and isolated/sporadic permafrost zones. The CO2 flux measurements were aggregated at the monthly level, or seasonally when monthly data were not available, and are reported as the daily average (g C m-2 day-1) over the interval. Soil moisture and temperature data plus environmental and ecological model driver data (e.g., vegetation type and productivity, soil substrate availability) are also included based on gridded satellite remote sensing and reanalysis sources. proprietary -NorthSlope_NEE_TVPRM_1920_1 ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017 ORNL_CLOUD STAC Catalog 2008-01-01 2017-12-31 -177.47, 56.09, -128.59, 77.26 https://cmr.earthdata.nasa.gov/search/concepts/C2240727916-ORNL_CLOUD.umm_json This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC). proprietary NorthSlope_NEE_TVPRM_1920_1 ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017 ALL STAC Catalog 2008-01-01 2017-12-31 -177.47, 56.09, -128.59, 77.26 https://cmr.earthdata.nasa.gov/search/concepts/C2240727916-ORNL_CLOUD.umm_json This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC). proprietary +NorthSlope_NEE_TVPRM_1920_1 ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017 ORNL_CLOUD STAC Catalog 2008-01-01 2017-12-31 -177.47, 56.09, -128.59, 77.26 https://cmr.earthdata.nasa.gov/search/concepts/C2240727916-ORNL_CLOUD.umm_json This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC). proprietary North_Carolina_Coast_0 Measurements made off the North Carolina coast OB_DAAC STAC Catalog 2001-04-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360528-OB_DAAC.umm_json Measurements made off the North Carolina coast. proprietary North_Carolina_Sabrina_0 Measurements from the Outer Banks and coastal regions of North Carolina onboard the R/V Sabrina OB_DAAC STAC Catalog 2002-09-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360529-OB_DAAC.umm_json Measurements taken by the research vessel Sabrina in the Outer Banks and coastal regions of North Carolina in 2002 and 2003. proprietary North_Sea_0 Measurements taken in the North Sea in 1994 OB_DAAC STAC Catalog 1994-07-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360530-OB_DAAC.umm_json Measurements taken in the North Sea in 1994. proprietary @@ -12834,38 +12814,38 @@ OCO3_L2_Standard_11 OCO-3 Level 2 geolocated XCO2 retrievals results, physical m OCO3_L2_Standard_11r OCO-3 Level 2 geolocated XCO2 retrievals results, physical model, Retrospective Processing V11r (OCO3_L2_Standard) at GES DISC GES_DISC STAC Catalog 2019-08-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2910086890-GES_DISC.umm_json Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. proprietary OCTS_L1_1 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034340-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L1_1 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034340-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L1_2 ADEOS-I OCTS Level-1A Data, version 2 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834679-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L1_2 ADEOS-I OCTS Level-1A Data, version 2 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834679-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L2_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Regional Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034360-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L1_2 ADEOS-I OCTS Level-1A Data, version 2 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834679-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L2_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Regional Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034360-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L2_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Regional Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034360-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L2_IOP_2022.0 ADEOS-I OCTS Level-2 Regional Inherent Optical Properties (IOP) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834690-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L2_IOP_2022.0 ADEOS-I OCTS Level-2 Regional Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834690-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L2_OC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Ocean Color (OC) Regional Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034380-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L2_OC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Ocean Color (OC) Regional Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034380-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L2_OC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Ocean Color (OC) Regional Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034380-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L2_OC_2022.0 ADEOS-I OCTS Level-2 Regional Ocean Color (OC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834711-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L2_OC_2022.0 ADEOS-I OCTS Level-2 Regional Ocean Color (OC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834711-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034361-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034361-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_CHL_2022.0 ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834719-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_CHL_2022.0 ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834719-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034381-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_CHL_2022.0 ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834719-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034381-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034381-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_IOP_2022.0 ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834731-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_IOP_2022.0 ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834731-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034362-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034362-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_KD_2022.0 ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834737-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034362-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_KD_2022.0 ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834737-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034341-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_KD_2022.0 ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834737-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034341-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_PAR_2022.0 ADEOS-I OCTS Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834749-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034341-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_PAR_2022.0 ADEOS-I OCTS Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834749-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_PAR_2022.0 ADEOS-I OCTS Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834749-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034363-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034363-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_PIC_2022.0 ADEOS-I OCTS Level-3 Global Binned Particulate Inorganic Carbon (PIC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834762-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_PIC_2022.0 ADEOS-I OCTS Level-3 Global Binned Particulate Inorganic Carbon (PIC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834762-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3b_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034382-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034382-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3b_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034382-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_POC_2022.0 ADEOS-I OCTS Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834780-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_POC_2022.0 ADEOS-I OCTS Level-3 Global Binned Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834780-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3b_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034364-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary @@ -12876,36 +12856,36 @@ OCTS_L3m_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll OCTS_L3m_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034342-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_CHL_2022.0 ADEOS-I OCTS Level-3 Global Mapped Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834809-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_CHL_2022.0 ADEOS-I OCTS Level-3 Global Mapped Chlorophyll (CHL) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834809-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034365-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034365-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034365-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_IOP_2022.0 ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834819-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_IOP_2022.0 ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834819-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034383-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034383-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_KD_2022.0 ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834825-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034383-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_KD_2022.0 ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834825-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_KD_2022.0 ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834825-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034366-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034366-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_PAR_2022.0 ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834829-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PAR_2022.0 ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834829-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034384-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_PAR_2022.0 ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834829-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034384-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034384-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PIC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834831-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_PIC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834831-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034367-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034367-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_POC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834842-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034367-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_POC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834842-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_POC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834842-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034385-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034385-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary -OCTS_L3m_RRS_2022.0 ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834849-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary OCTS_L3m_RRS_2022.0 ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834849-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary +OCTS_L3m_RRS_2022.0 ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834849-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary ODIN.SMR_5.0 ODIN SMR data products ESA STAC Catalog 2001-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689700-ESA.umm_json The latest Odin Sub-Millimetre Radiometer (SMR) datasets have been generated by Chalmers University of Technology and Molflow within the Odin-SMR Recalibration and Harmonisation project (http://odin.rss.chalmers.se/), funded by the European Space Agency (ESA) to create a fully consistent and homogeneous dataset from the 20 years of satellite operations. The Odin satellite was launched in February 2001 as a joint undertaking between Sweden, Canada, France and Finland, and is part of the ESA Third Party Missions (TPM) programme since 2007. The complete Odin-SMR data archive was reprocessed applying a revised calibration scheme and upgraded algorithms. The Level 1b dataset is entirely reconsolidated, while Level 2 products are regenerated for the main mesospheric and stratospheric frequency modes (i.e., FM 01, 02, 08, 13, 14, 19, 21, 22, 24). The resulting dataset represents the first full-mission reprocessing campaign of the mission, which is still in operation. proprietary ODU_CBM_0 Old Dominion University (ODU) - Chesapeake Bay Mouth (CBM) measurements OB_DAAC STAC Catalog 2004-05-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360566-OB_DAAC.umm_json Measurements made of the Chesapeake Bay Mouth (CBM) by Old Dominion University (ODU) between 2004 and 2006. proprietary -OFR_94-212 A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212 CEOS_EXTRA STAC Catalog 1980-05-01 1988-09-06 -122, 46, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2232411623-CEOS_EXTRA.umm_json Airborne monitoring of Mount St. Helens by the USGS began in May 1980 for sulfur dioxide emissions and in July 1980 for carbon dioxide emissions. A correlation spectrometer, or COSPEC, was used to measure sulfur dioxide in Mount St. Helens' plume. The upward-looking COSPEC was mounted in a fixed-wing aircraft and flown below and at right angles to the plume. Typically, three to six traverses were made underneath the plume to determine the SO2 burden (concentration x pathlength) within a cross-section of the plume. Knowing the burden along with the plume width and plume velocity (assumed to be the same as ambient wind speed), we could then calculate the emission rate of SO2. The use of correlation spectroscopy for determining the sulfur dioxide output of volcanoes is well established and the technique has been discussed in detail by a number of investigators (Malinconico, 1979; Casadevall and others, 1981; Stoiber and others, 1983). Carbon dioxide in the Mount St. Helens plume was measured by an infrared spectrometer tuned to the 4.26 um CO2 absorption band. An external sample tube was attached to the fuselage of a twin-engine aircraft to deliver outside air to the gas cell of the spectrometer. The aircraft was then flown at several different elevations through the plume at right angles to plume trajectory to define plume area and carbon dioxide concentration in a vertical cross-section of the plume. These two parameters along with the density of CO2 for the altitude of the plume and the plume velocity (assumed as above to be equal to ambient wind speed) were then used to calculate the CO2 emission rate (Harris and others, 1981). proprietary OFR_94-212 A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212 ALL STAC Catalog 1980-05-01 1988-09-06 -122, 46, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2232411623-CEOS_EXTRA.umm_json Airborne monitoring of Mount St. Helens by the USGS began in May 1980 for sulfur dioxide emissions and in July 1980 for carbon dioxide emissions. A correlation spectrometer, or COSPEC, was used to measure sulfur dioxide in Mount St. Helens' plume. The upward-looking COSPEC was mounted in a fixed-wing aircraft and flown below and at right angles to the plume. Typically, three to six traverses were made underneath the plume to determine the SO2 burden (concentration x pathlength) within a cross-section of the plume. Knowing the burden along with the plume width and plume velocity (assumed to be the same as ambient wind speed), we could then calculate the emission rate of SO2. The use of correlation spectroscopy for determining the sulfur dioxide output of volcanoes is well established and the technique has been discussed in detail by a number of investigators (Malinconico, 1979; Casadevall and others, 1981; Stoiber and others, 1983). Carbon dioxide in the Mount St. Helens plume was measured by an infrared spectrometer tuned to the 4.26 um CO2 absorption band. An external sample tube was attached to the fuselage of a twin-engine aircraft to deliver outside air to the gas cell of the spectrometer. The aircraft was then flown at several different elevations through the plume at right angles to plume trajectory to define plume area and carbon dioxide concentration in a vertical cross-section of the plume. These two parameters along with the density of CO2 for the altitude of the plume and the plume velocity (assumed as above to be equal to ambient wind speed) were then used to calculate the CO2 emission rate (Harris and others, 1981). proprietary -OFR_95-55 A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994 CEOS_EXTRA STAC Catalog 1990-03-20 1994-07-07 -154, 56, -152, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2232411611-CEOS_EXTRA.umm_json This report contains all of the available daily sulfur dioxide and carbon dioxide emission rates from Cook Inlet volcanoes as determined by the U.S. Geological Survey (USGS) from March 1990 through July 1994. Airborne sulfur dioxide gas sampling of the Cook Inlet volcanoes (Redoubt, Spurr, Iliamna, and Augustine) began in 1986 when several measurements were carried out at Augustine volcano during the eruption of 1986. Systematic monitoring for sulfur dioxide and carbon dioxide began in March 1990 at Redoubt volcano and continues to the present. Intermittent measurements at Augustine and Iliamna volcanoes began in 1990 and continues to the present. Intermittent measurements began at Spurr volcano in 1991, and were continued at more regular intervals from June, 1992 through the 1992 eruption at the Crater Peak vent to the present. proprietary +OFR_94-212 A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212 CEOS_EXTRA STAC Catalog 1980-05-01 1988-09-06 -122, 46, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2232411623-CEOS_EXTRA.umm_json Airborne monitoring of Mount St. Helens by the USGS began in May 1980 for sulfur dioxide emissions and in July 1980 for carbon dioxide emissions. A correlation spectrometer, or COSPEC, was used to measure sulfur dioxide in Mount St. Helens' plume. The upward-looking COSPEC was mounted in a fixed-wing aircraft and flown below and at right angles to the plume. Typically, three to six traverses were made underneath the plume to determine the SO2 burden (concentration x pathlength) within a cross-section of the plume. Knowing the burden along with the plume width and plume velocity (assumed to be the same as ambient wind speed), we could then calculate the emission rate of SO2. The use of correlation spectroscopy for determining the sulfur dioxide output of volcanoes is well established and the technique has been discussed in detail by a number of investigators (Malinconico, 1979; Casadevall and others, 1981; Stoiber and others, 1983). Carbon dioxide in the Mount St. Helens plume was measured by an infrared spectrometer tuned to the 4.26 um CO2 absorption band. An external sample tube was attached to the fuselage of a twin-engine aircraft to deliver outside air to the gas cell of the spectrometer. The aircraft was then flown at several different elevations through the plume at right angles to plume trajectory to define plume area and carbon dioxide concentration in a vertical cross-section of the plume. These two parameters along with the density of CO2 for the altitude of the plume and the plume velocity (assumed as above to be equal to ambient wind speed) were then used to calculate the CO2 emission rate (Harris and others, 1981). proprietary OFR_95-55 A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994 ALL STAC Catalog 1990-03-20 1994-07-07 -154, 56, -152, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2232411611-CEOS_EXTRA.umm_json This report contains all of the available daily sulfur dioxide and carbon dioxide emission rates from Cook Inlet volcanoes as determined by the U.S. Geological Survey (USGS) from March 1990 through July 1994. Airborne sulfur dioxide gas sampling of the Cook Inlet volcanoes (Redoubt, Spurr, Iliamna, and Augustine) began in 1986 when several measurements were carried out at Augustine volcano during the eruption of 1986. Systematic monitoring for sulfur dioxide and carbon dioxide began in March 1990 at Redoubt volcano and continues to the present. Intermittent measurements at Augustine and Iliamna volcanoes began in 1990 and continues to the present. Intermittent measurements began at Spurr volcano in 1991, and were continued at more regular intervals from June, 1992 through the 1992 eruption at the Crater Peak vent to the present. proprietary +OFR_95-55 A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994 CEOS_EXTRA STAC Catalog 1990-03-20 1994-07-07 -154, 56, -152, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2232411611-CEOS_EXTRA.umm_json This report contains all of the available daily sulfur dioxide and carbon dioxide emission rates from Cook Inlet volcanoes as determined by the U.S. Geological Survey (USGS) from March 1990 through July 1994. Airborne sulfur dioxide gas sampling of the Cook Inlet volcanoes (Redoubt, Spurr, Iliamna, and Augustine) began in 1986 when several measurements were carried out at Augustine volcano during the eruption of 1986. Systematic monitoring for sulfur dioxide and carbon dioxide began in March 1990 at Redoubt volcano and continues to the present. Intermittent measurements at Augustine and Iliamna volcanoes began in 1990 and continues to the present. Intermittent measurements began at Spurr volcano in 1991, and were continued at more regular intervals from June, 1992 through the 1992 eruption at the Crater Peak vent to the present. proprietary OFR_95-78_1 Geometeorological data collected by the USGS Desert Winds Project at Gold Spring, Great Basin Desert, northeastern Arizona, 1979-1992 CEOS_EXTRA STAC Catalog 1979-01-27 1992-12-31 -111, 35, -111, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231550505-CEOS_EXTRA.umm_json This data set contains meteorological data files pertaining to the Gold Spring Geomet research site. Documentation files and data-accessing display software are also included. The meteorological data are wind speed, peak gust, wind direction, precipitation, air temperature, soil temperature, barometric pressure, and humidity. Data from the monitoring station are voluminous; 14 observations from each station are made as often as ten times per hour, totaling more than a million observations per station per year. proprietary OISSS_L4_multimission_7day_v1_1.0 Multi-Mission Optimally Interpolated Sea Surface Salinity Global Dataset V1 POCLOUD STAC Catalog 2011-08-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2095055342-POCLOUD.umm_json This is a level 4 product on a 0.25-degree spatial and 4-day temporal grid. The product is derived from the level 2 swath data of three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. The product offers a continuous record from August 28, 2011 to present by concatenating the measurements from Aquarius (September 2011 - June 2015) and SMAP (April 2015 present). ESAs SMOS data was used to fill the gap in SMAP data between June and July 2019, when the SMAP satellite was in a safe mode. The two-month overlap (April - June 2015) between Aquarius and SMAP was used to ensure consistency and continuity in data record. The product covers the global ocean, including the Arctic and Antarctic in the areas free of sea ice, but does not cover internal seas such as Mediterranean and Baltic Sea. In-situ salinity from Argo floats and moored buoys are used to derive a large-scale bias correction and to ensure consistency and accuracy of the OISSS dataset. This dataset is produced by the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide. proprietary OISSS_L4_multimission_7day_v2_2.0 Multi-Mission Optimally Interpolated Sea Surface Salinity Global Dataset V2 POCLOUD STAC Catalog 2011-08-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2589160971-POCLOUD.umm_json This is a level 4 product on a 0.25-degree spatial and 4-day temporal grid. The product is derived from the level 2 swath data of three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. The product offers a continuous record from August 28, 2011 to present by concatenating the measurements from Aquarius (September 2011 - June 2015) and SMAP (April 2015 present). ESAs SMOS data was used to fill the gap in SMAP data between June and July 2019, when the SMAP satellite was in a safe mode. The two-month overlap (April - June 2015) between Aquarius and SMAP was used to ensure consistency and continuity in data record. The product covers the global ocean, including the Arctic and Antarctic in the areas free of sea ice, but does not cover internal seas such as Mediterranean and Baltic Sea. In-situ salinity from Argo floats and moored buoys are used to derive a large-scale bias correction and to ensure consistency and accuracy of the OISSS dataset. This dataset is produced by the Earth and Space Research (ESR), Seattle, WA and the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide. proprietary @@ -13007,8 +12987,8 @@ OMCLDO2Z_003 OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) Zoomed 1-Or OMCLDO2_003 OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) 1-Orbit L2 Swath 13x24km V003 (OMCLDO2) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966787-GES_DISC.umm_json The reprocessed OMI/Aura Level-2 cloud data product OMCLDO2 is now available from the NASA GoddardEarth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed in late 2011. OMI provides two cloud products based on two different algorithms, the Rotational Raman Scattering method, and O2-O2 absorption method using the DOAS technique. This level-2 global cloud product, with a pixel resolution of 13x24 km2at nadir, is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains cloud pressure, cloud fraction, slant column O2-O2, ozone, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The lead scientist for this product is Dr. Pepijn Veefkind. The OMCLDO2 product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 15.096 MB in size. There are approximately 14 orbits per day thus the total data volume is approximately 200 GB/day. proprietary OMCLDO2_CPR_003 OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) 200-km swath subset along CloudSat track V003 (OMCLDO2_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350939-GES_DISC.umm_json This the OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) subset along CloudSat track, for the purposes of the A-Train mission. The original product uses the DOAS technique method. This level-2 global cloud product at the pixel resolution (13x24 km2 at nadir) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product contains cloud pressure, cloud fraction, slant column O2-O2 and O3, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this Level-2 OMI cloud pressure and fraction (O2-O2 absorption) subset along CloudSat track product is OMCLDO2_CPR) proprietary OMCLDRRG_003 OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMCLDRRG) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136100-GES_DISC.umm_json This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes. proprietary -OMCLDRR_003 OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000100-OMINRT.umm_json The reprocessed Aura OMI Version 003 Level 2 Cloud Data Product OMCLDRR is made available (in April 2012) to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). http://disc.gsfc.nasa.gov/Aura/OMI/omcldrr_v003.shtml ) Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product (OMCLDRR) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. OMCLDRR files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml . A short OMCLDRR Readme Document that includes brief algorithm description and data quality is also provided by the OMCLDRR Algorithm lead. The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products are archived at the NASA GES DISC. For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/instruments/omi/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMCLDRR data file contains one swath which consists of two groups: Data fields: Two Effective Cloud Fraction and two Cloud Top Pressures that are based on two different clear and cloudy scene reflectivity criteria, Chlorophyll Amount, Effective Reflectivity (394.1 micron), UV Aerosol Index (based on 360 and 388 nm), and many Auxiliary Algorithm Parameter and Quality Flags. Geolocation Fields: Latitude, Longitude, Time, Solar Zenith Angle, Viewing Zenith Angle, Relative Azimuth Angle, Terrain Height, and Ground Pixel Quality Flags. OMI Atmospheric data and documents are available from the following sites: http://disc.gsfc.nasa.gov/Aura/OMI/ http://mirador.gsfc.nasa.gov/ proprietary OMCLDRR_003 OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. proprietary +OMCLDRR_003 OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000100-OMINRT.umm_json The reprocessed Aura OMI Version 003 Level 2 Cloud Data Product OMCLDRR is made available (in April 2012) to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). http://disc.gsfc.nasa.gov/Aura/OMI/omcldrr_v003.shtml ) Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product (OMCLDRR) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. OMCLDRR files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml . A short OMCLDRR Readme Document that includes brief algorithm description and data quality is also provided by the OMCLDRR Algorithm lead. The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products are archived at the NASA GES DISC. For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/instruments/omi/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMCLDRR data file contains one swath which consists of two groups: Data fields: Two Effective Cloud Fraction and two Cloud Top Pressures that are based on two different clear and cloudy scene reflectivity criteria, Chlorophyll Amount, Effective Reflectivity (394.1 micron), UV Aerosol Index (based on 360 and 388 nm), and many Auxiliary Algorithm Parameter and Quality Flags. Geolocation Fields: Latitude, Longitude, Time, Solar Zenith Angle, Viewing Zenith Angle, Relative Azimuth Angle, Terrain Height, and Ground Pixel Quality Flags. OMI Atmospheric data and documents are available from the following sites: http://disc.gsfc.nasa.gov/Aura/OMI/ http://mirador.gsfc.nasa.gov/ proprietary OMCLDRR_004 OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V004 (OMCLDRR) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3159637081-GES_DISC.umm_json This is the Aura Ozone Monitoring Instrument (OMI) Version 004 Level 2 Cloud Data Product OMCLDRR. OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. proprietary OMCLDRR_CPR_003 OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 200-km swath subset along CloudSat track V003 (OMCLDRR_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350980-GES_DISC.umm_json This is the OMI/Aura Cloud Pressure and Fraction (Raman Scattering) subset along CloudSat tracks, for the purposes of the A-Train mission. The original data product uses the Rotational Raman Scattering method. This level-2 global cloud product provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). The goal of this subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this Level-2 OMI cloud pressure and fraction subset along CloudSat tracks product is OMCLDRR_CPR) proprietary OMDOAO3G_003 OMI/Aura Ozone (O3) DOAS Total Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMDOAO3G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136103-GES_DISC.umm_json This Level-2G daily global gridded product OMDOAO3G is based on the pixel level OMI Level-2 DOAO3 product OMDOAO3. This Level-2G global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. In addition to the total ozone column this product also contains some auxiliary derived and ancillary input parameters, e.g. ozone slant column density, ozone ghost column density, etc. The short name for this Level-2 OMI ozone product is OMDOAO3G and the lead algorithm scientist for this product and for OMDOAO3 (the data source of OMDOAO3G) is Dr. Pepijn Veefkind from KNMI. The OMDOAO3G product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (approximately 14 orbits) and is roughly 80 MB in size. proprietary @@ -13050,7 +13030,7 @@ OMMYDAGEO_003 OMI/Aura and MODIS/Aqua Aerosol Geo-colocation Product 1-Orbit L2 OMMYDCLD_003 OMI/Aura and MODIS/Aqua Merged Cloud Product 1-Orbit L2 Swath 13x24 km V003 (OMMYDCLD) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1265734652-GES_DISC.umm_json The OMI/Aura and MODIS/Aqua Merged Cloud Product 1-Orbit L2 Swath 13x24 km (OMMYDCLD) is a Level-2 orbital product that combines cloud parameters retrieved by the Ozone Mapping Instrument (OMI) on the Aura satellite with collocated statistical information for cloud parameters retrieved by the Moderate Resolution Imaging Spectrometer (MODIS) on the Aqua spacecraft. This product is designed to take advantage of the synergy between OMI and MODIS, which both fly on satellites in the NASA A-Train constellation of Earth-observing satellites that follow similar orbital tracks and collect near-simultaneous observations. This product can be used for cloud-clearing, detection of multi-layered clouds, and other applications that may exploit these multi-spectral measurements. The algorithm for the OMMYDCLD product co-locates daytime cloud parameters from MODIS onto the OMI visible (VIS) pixel for a given OMI orbit and generates statistical information from the collocated MODIS pixels. For each OMI granule, the orbit start and end times are used to select the corresponding 5-minute MODIS granules for processing. A contiguous list of MODIS granules spanning the full duration of the OMI orbit are selected based on the relative time lag between Aqua and Aura. The algorithm lead for this product is NASA OMI scientist Dr. Joanna Joiner. The OMMYDCLD data files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the swath model, and follows the same conventions used by the other OMI Level-2 data products. Each file contains data from the day lit portion of an orbit (about 53 minutes). There are approximately 14 orbits per day. The file size for the OMMYDCLD data product is about 8 Mbytes. proprietary OMNO2G_003 OMI/Aura NO2 Total and Tropospheric Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMNO2G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136121-GES_DISC.umm_json This Level-2G daily global gridded product OMNO2G is based on the pixel level OMI Level-2 NO2 product OMNO2. OMNO2G data product is a special Level-2 Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a rid box are saved Without Averaging. Nitrogen dioxide is an important chemical species in both the stratosphere, where it plays a key role in ozone chemistry, and in the troposphere, where it is a precursor to ozone production. In the troposphere, it is produced in various combustion processes and in lightning and is an indicator of poor air quality. The OMNO2G data product contains almost all parameters that are contained in OMNO2 product. The OMNO2G data are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of the orbit (~14 orbits). The average file size for the OMNO2G data product is about 115 Mbytes. proprietary OMNO2_003 OMI/Aura Nitrogen Dioxide (NO2) Total and Tropospheric Column 1-orbit L2 Swath 13x24 km V003 (OMNO2) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966842-GES_DISC.umm_json The Version 4.0 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V4.0 updates include: (1) use of a new daily and OMI field of view specific geometry dependent surface Lambertian Equivalent Reflectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (effective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2 slant column data and the GLER product for terrain reflectivity; (3) use of a more accurate terrain pressure calculated using OMI ground pixel-averaged terrain height and monthly mean GMI terrain pressure; and (4) improved treatment over snow/ice surfaces by using the concept of scene LER and scene pressure. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data. The short name for the Level-2 swath type column NO2 products is OMNO2. Other OMNO2-associated NO2 products include the Level-2 gridded column product, OMNO2G, and the Level-3 gridded column product, OMNO2d. The OMNO2 files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each Level-2 file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMNO2 is ~24 MB. proprietary -OMNO2_004 OMI/Aura Nitrogen Dioxide (NO2) Total and Tropospheric Column 1-orbit L2 Swath 13x24 km V004 (OMNO2) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3333494968-GES_DISC.umm_json The Version 4.0 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V4.0 updates include: (1) use of a new daily and OMI field of view specific geometry dependent surface Lambertian Equivalent Reflectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (effective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2 slant column data and the GLER product for terrain reflectivity; (3) use of a more accurate terrain pressure calculated using OMI ground pixel-averaged terrain height and monthly mean GMI terrain pressure; and (4) improved treatment over snow/ice surfaces by using the concept of scene LER and scene pressure. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data. The short name for the Level-2 swath type column NO2 products is OMNO2. Other OMNO2-associated NO2 products include the Level-2 gridded column product, OMNO2G, and the Level-3 gridded column product, OMNO2d. The OMNO2 files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each Level-2 file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMNO2 is ~24 MB. proprietary +OMNO2_004 OMI/Aura Nitrogen Dioxide (NO2) Total and Tropospheric Column 1-orbit L2 Swath 13x24 km V004 (OMNO2) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3333494968-GES_DISC.umm_json The Collection 4, Version 5 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major updates include: (1) use of a new daily and OMI field of view specific geometry dependent surface Lambertian Equivalent Reflectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (effective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2 slant column data and the GLER product for terrain reflectivity; (3) use of a more accurate terrain pressure calculated using OMI ground pixel-averaged terrain height and monthly mean GMI terrain pressure; and (4) improved treatment over snow/ice surfaces by using the concept of scene LER and scene pressure. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data. The short name for the Level-2 swath type column NO2 products is OMNO2. Other OMNO2-associated NO2 products include the Level-2 gridded column product, OMNO2G, and the Level-3 gridded column product, OMNO2d. The OMNO2 files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each Level-2 file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMNO2 is ~24 MB. proprietary OMNO2_CPR_003 OMI/Aura Level 2 Nitrogen Dioxide (NO2) Trace Gas Column Data 1-Orbit subset Swath along CloudSat track 1-Orbit Swath 13x24 km GES_DISC STAC Catalog 2006-06-02 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350981-GES_DISC.umm_json This is a CloudSat-collocated subset of the original product OMNO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 NO2 subset is OMNO2_CPR_V003) proprietary OMNO2d_003 OMI/Aura NO2 Cloud-Screened Total and Tropospheric Column L3 Global Gridded 0.25 degree x 0.25 degree V3 (OMNO2d) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136111-GES_DISC.umm_json "This is Level-3 daily global gridded (0.25x0.25 degree) Nitrogen Dioxide Product (OMNO2d). OMNO2d data product is a Level-3 Gridded Product where pixel level data of good quality are binned and ""averaged"" into 0.25x0.25 degree global grids. This product contains Total column NO2 and Total Tropospheric Column NO2, for all atmospheric conditions, and for sky conditions where cloud fraction is less than 30 percent. Nitrogen dioxide is an important chemical species in both, the stratosphere where it plays a key role in ozone chemistry, and in the troposphere where it is a precursor to ozone production. In the troposphere, it is produced in various combustion processes and in lightning and is an indicator of poor air quality. The OMNO2d data are stored in version 5 EOS Hierarchical Data Format (HDF-EOS). Each file contains data from the day lit portion of the orbit (~14 orbits). The average file size for the OMNO2d data product is about 12 Mbytes." proprietary OMNO2d_004 OMI/Aura NO2 Cloud-Screened Total and Tropospheric Column L3 Global Gridded 0.25 degree x 0.25 degree V004 (OMNO2d) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3333493715-GES_DISC.umm_json "This is Level-3 daily global gridded (0.25x0.25 degree) Nitrogen Dioxide Product (OMNO2d). OMNO2d data product is a Level-3 Gridded Product where pixel level data of good quality are binned and ""averaged"" into 0.25x0.25 degree global grids. This product contains Total column NO2 and Total Tropospheric Column NO2, for all atmospheric conditions, and for sky conditions where cloud fraction is less than 30 percent. Nitrogen dioxide is an important chemical species in both, the stratosphere where it plays a key role in ozone chemistry, and in the troposphere where it is a precursor to ozone production. In the troposphere, it is produced in various combustion processes and in lightning and is an indicator of poor air quality. The OMNO2d data are stored in version 5 EOS Hierarchical Data Format (HDF-EOS). Each file contains data from the day lit portion of the orbit (~14 orbits). The average file size for the OMNO2d data product is about 12 Mbytes." proprietary @@ -13091,8 +13071,8 @@ OMPS_NPP_NPBUVO3_L2_2 OMPS-NPP L2 NP Ozone (O3) Vertical Profile swath orbital G OMPS_NPP_NPBUVO3_L2_2.9 OMPS-NPP L2 NP Ozone (O3) Vertical Profile swath orbital V2.9 (OMPS_NPP_NPBUVO3_L2) at GES DISC GES_DISC STAC Catalog 2011-11-13 -180, -82, 180, 82 https://cmr.earthdata.nasa.gov/search/concepts/C2821060582-GES_DISC.umm_json The OMPS-NPP L2 NP Ozone (O3) Total Column swath orbital product provides ozone profile retrievals from the Ozone Mapping and Profiling Suite (OMPS) Nadir-Profiler (NP) instrument on the Suomi-NPP satellite. The V8 ozone profile algorithm relies on nadir profiler measurements made in the 250 to 310 nm range, as well as from measurements from the nadir mapper in the 300 to 380 nm range. Ozone mixing ratios are reported at 15 pressure levels between 50 and 0.5 hPa. Additionally, this data product contains measurements of total ozone, UV aerosol index and reflectivities at 331 and 380 nm. Each granule contains data from the daylight portion of each orbit measured for a full day. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 80 profiles. The NP footprint size is 250 km x 250 km. The files are written using the Hierarchical Data Format Version 5 or HDF5. proprietary OMPS_NPP_NPEV_L1B_2 OMPS/NPP L1B NP Radiance EV Calibrated Geolocated Swath Orbital V2 (OMPS_NPP_NPEV_L1B) at GES DISC GES_DISC STAC Catalog 2011-11-13 -180, -82, 180, 82 https://cmr.earthdata.nasa.gov/search/concepts/C1279850611-GES_DISC.umm_json The OMPS-NPP L1B NP Radiance EV Calibrated Geolocated Swath Orbital collection contains calibrated and geolocated radiances from 300 to 380 nm measured by the OMPS Nadir-Profiler sensor on the Suomi-NPP satellite. Each granule typically contains data from the daylight portion of a single orbit (about 50 minutes). Spatial coverage is nearly global (-82 to 82 degrees latitude), and there are about 14.5 orbits per day each with a single nadir measurement along the satellite track. proprietary OMSO2G_003 OMI/Aura Sulphur Dioxide (SO2) Total Column Daily L2 Global Gridded 0.125 degree x 0.125 degree V3 (OMSO2G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136113-GES_DISC.umm_json This Level-2G daily global gridded product OMSO2G is based on the pixel level OMI Level-2 SO2 product OMSO2. OMSO2G data product is a special Level-2 gridded product where pixel level products are binned into 0.125x0.125 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999 . All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMSO2G data product contains almost all parameters that are contained in OMSO2 files. For example, in addition to three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm, and ancillary parameters, e.g., UV aerosol index, cloud fraction, cloud pressure, geolocation, solar and satellite viewing angles, and quality flags. The OMSO2G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 146 Mbytes. proprietary -OMSO2_003 OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.umm_json The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004 (1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Space Office (NSO) in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO,NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. The Sulfer Dioxide Product 'OMSO2' from the Aura-OMI is now publicly available from NASA GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMSO2 product contains three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm. It also contains quality flags, geolocation and other ancillary information. The shortname for this Level-2 OMI total column SO2 product is OMSO2 and the algorithm leads for this product are NASA/UMBC OMI scientists Drs. Nikolay Krotkov (nickolay.a.krotkov@nasa.gov),Kai Yang(kai.yang@nasa.gov) and Arlin J. Krueger(krueger@umbc.edu). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 21 Mbytes. On-line spatial and parameter subset options are available during data download A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMSO2 Readme Document that includes brief algorithm description and documents that provides known data quality related issues are available from the UMBC OMI site ( http://so2.gsfc.nasa.gov/docs.php ) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://so2.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/. For the full set of Aura products and other atmospheric composition data available from the GES DISC, please see the links below. http://disc.sci.gsfc.nasa.gov/Aura/ http://disc.gsfc.nasa.gov/acdisc/ proprietary OMSO2_003 OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 (OMSO2) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966837-GES_DISC.umm_json The Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources. Files also contain quality flags, geolocation and other ancillary information. The lead scientist for the OMSO2 product is Can Li. The OMSO2 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the daylit half of an orbit (~53 minutes). There are approximately 14 orbits per day. The resolution of the data is 13x24 km2 at nadir, with a swath width of 2600 km and 60 pixels per scan line every 2 seconds. proprietary +OMSO2_003 OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000121-OMINRT.umm_json The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004 (1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Space Office (NSO) in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO,NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. The Sulfer Dioxide Product 'OMSO2' from the Aura-OMI is now publicly available from NASA GSFC Earth Sciences (GES) Data and Information Services Center (DISC) for public access. OMSO2 product contains three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm. It also contains quality flags, geolocation and other ancillary information. The shortname for this Level-2 OMI total column SO2 product is OMSO2 and the algorithm leads for this product are NASA/UMBC OMI scientists Drs. Nikolay Krotkov (nickolay.a.krotkov@nasa.gov),Kai Yang(kai.yang@nasa.gov) and Arlin J. Krueger(krueger@umbc.edu). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 21 Mbytes. On-line spatial and parameter subset options are available during data download A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml A short OMSO2 Readme Document that includes brief algorithm description and documents that provides known data quality related issues are available from the UMBC OMI site ( http://so2.gsfc.nasa.gov/docs.php ) For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://so2.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/. For the full set of Aura products and other atmospheric composition data available from the GES DISC, please see the links below. http://disc.sci.gsfc.nasa.gov/Aura/ http://disc.gsfc.nasa.gov/acdisc/ proprietary OMSO2_CPR_003 OMI/Aura Level 2 Sulphur Dioxide (SO2) Trace Gas Column Data 1-Orbit Subset and Collocated Swath along CloudSat V003 (OMSO2_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350970-GES_DISC.umm_json "This is a CloudSat-collocated subset of the original product OMSO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated subset of the original product OMSO2 Product is OMSO2_CPR_V003) This document describes the original OMI SO2 product (OMSO2) produced from global mode UV measurements of the Ozone Monitoring Instrument (OMI). OMI was launched on July 15, 2004 on the EOS Aura satellite, which is in a sun-synchronous ascending polar orbit with 1:45pm local equator crossing time. The data collection started on August 17, 2004 (orbit 482) and continues to this day with only minor data gaps. The minimum SO2 mass detectable by OMI is about two orders of magnitude smaller than the detection threshold of the legacy Total Ozone Mapping Spectrometer (TOMS) SO2 data (1978-2005) [Krueger et al 1995]. This is due to smaller OMI footprint and the use of wavelengths better optimized for separating O3 from SO2. The product file, called a data granule, covers the sunlit portion of the orbit with an approximately 2600 km wide swath containing 60 pixels per viewing line. During normal operations, 14 or 15 granules are produced daily, providing fully contiguous coverage of the globe. Currently, OMSO2 products are not produced when OMI goes into the ""zoom mode"" for one day every 452 orbits (~32 days). For each OMI pixel we provide 4 different estimates of the column density of SO2 in Dobson Units (1DU=2.69x10^16 molecules/cm2) obtained by making different assumptions about the vertical distribution of the SO2. However, it is important to note that in most cases the precise vertical distribution of SO2 is unimportant. The users can use either the SO2 plume height, or the center of mass altitude (CMA) derived from SO2 vertical distribution, to interpolate between the 4 values: 1)Planetary Boundary Layer (PBL) SO2 column (ColumnAmountSO2_PBL), corresponding to CMA of 0.9 km. 2)Lower tropospheric SO2 column (ColumnAmountSO2_TRL), corresponding to CMA of 2.5 km. 3)Middle tropospheric SO2 column, (ColumnAmountSO2_TRM), usually produced by volcanic degassing, corresponding to CMA of 7.5 km, 4)Upper tropospheric and Stratospheric SO2 column (ColumnAmountSO2_STL), usually produced by explosive volcanic eruption, corresponding to CMA of 17 km. The accuracy and precision of the derived SO2 columns vary significantly with the SO2 CMA and column amount, observational geometry, and slant column ozone. OMI becomes more sensitive to SO2 above clouds and snow/ice, and less sensitive to SO2 below clouds. Preliminary error estimates are discussed below (see Data Quality Assessment). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 9 Mbytes." proprietary OMSO2e_003 OMI/Aura Sulfur Dioxide (SO2) Total Column Daily L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3 (OMSO2e) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136112-GES_DISC.umm_json "The OMI science team produces this Level-3 Aura/OMI Global OMSO2e Data Products (0.25 degree Latitude/Longitude grids). In this Level-3 daily global SO2 data product, each grid contains only one observation of Total Column Density of SO2 in the Planetary Boundary Layer (PBL), based on an improved Principal Component Analysis (PCA) Algorithm. This single observation is the ""best pixel"", selected from all ""good"" L2 pixels of OMSO2 that overlap this grid and have UTC time between UTC times of 00:00:00 and 23:59:59.999. In addition to the SO2 Vertical column value some ancillary parameters, e.g., cloud fraction, terrain height, scene number, solar and satellite viewing angles, row anomaly flags, and quality flags have been also made available corresponding to the best selected SO2 data pixel in each grid. The OMSO2e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the grid model." proprietary OMTO3G_003 OMI/Aura Ozone (O3) Total Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMTO3G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136114-GES_DISC.umm_json This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved Without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes. proprietary @@ -13221,8 +13201,8 @@ PAL-LTER_0 Palmer Station Antarctica (PAL) Long Term Ecological Research Network PARASOLRB_CPR_001 POLDER/Parasol L2 Radiation Budget subset along CloudSat track V001 (PARASOLRB_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2010-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350976-GES_DISC.umm_json This is the POLDER/Parasol Level-2 Radiation Budget Subset, collocated with the CloudSat track. The subset is processed at the A-Train Data Depot of the GES DISC, NASA. The algorithm first converts the original POLDER binary data, which is Level-2 but nevertheless in a sinusoidal grid, into HDF4 format, and thus stores the full-sized data in HDF4. Then, it calculates the CloudSat ground track coordinates, and proceeds to extract the closest POLDER grid cells. Along with the extraction, the algorithm re-orders the subset grid cells in a line-by-line fashion, so that the output subset is in array format and resembles a swath. This array has a cross-track dimension of 11 columns. That makes about 200-km-wide coverage. All original parameters are preserved in the subset. As it is collocated with CloudSat, the subset is automatically collocated with CALIPSO as well. proprietary PASSCAL_ABBA Adirondack Broad Band Array (ABBA) SCIOPS STAC Catalog 1995-01-01 1996-12-31 -74.5, 43.5, -73.8, 44.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214608962-SCIOPS.umm_json Objective: Determination of anistropy and depth/characteristics of discontinuties in the mantle and the Moho beneath the Adirondacks. Preliminary results: Azimuthal Anisotropy is oriented ENE-WSW with a delay time of about 1 s. Discontinuity studies are still in progress. proprietary PASSCAL_ABBA Adirondack Broad Band Array (ABBA) ALL STAC Catalog 1995-01-01 1996-12-31 -74.5, 43.5, -73.8, 44.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214608962-SCIOPS.umm_json Objective: Determination of anistropy and depth/characteristics of discontinuties in the mantle and the Moho beneath the Adirondacks. Preliminary results: Azimuthal Anisotropy is oriented ENE-WSW with a delay time of about 1 s. Discontinuity studies are still in progress. proprietary -PASSCAL_ALAR Aleutian Arc Seismic Experiment ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214610603-SCIOPS.umm_json "27 instruments were deployed at 18 different locations in the Aleutian Islands to record the airguns from the Ewing as it shot offshore. The full data report is available in PDF at the following URL: ""http://www.iris.edu/data/reports/1996/96-016.pdf""" proprietary PASSCAL_ALAR Aleutian Arc Seismic Experiment SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214610603-SCIOPS.umm_json "27 instruments were deployed at 18 different locations in the Aleutian Islands to record the airguns from the Ewing as it shot offshore. The full data report is available in PDF at the following URL: ""http://www.iris.edu/data/reports/1996/96-016.pdf""" proprietary +PASSCAL_ALAR Aleutian Arc Seismic Experiment ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214610603-SCIOPS.umm_json "27 instruments were deployed at 18 different locations in the Aleutian Islands to record the airguns from the Ewing as it shot offshore. The full data report is available in PDF at the following URL: ""http://www.iris.edu/data/reports/1996/96-016.pdf""" proprietary PASSCAL_KRAFLA 1994 Krafla Undershooting Experiment SCIOPS STAC Catalog 1970-01-01 -24.55, 62.81, -12.79, 67.01 https://cmr.earthdata.nasa.gov/search/concepts/C1214610676-SCIOPS.umm_json Thirty-eight instruments were used to shoot two perpendicular refraction profiles across the Krafla central volcano. The North/South profile is 20 km long while the East/West profile is 55 km long. Average station spacing was 500 m in the caldera and 1-4 km elswhere. A total of three shots were used in the NS profile and 6 shots were used in the EW profile. proprietary PASSCAL_KRAFLA 1994 Krafla Undershooting Experiment ALL STAC Catalog 1970-01-01 -24.55, 62.81, -12.79, 67.01 https://cmr.earthdata.nasa.gov/search/concepts/C1214610676-SCIOPS.umm_json Thirty-eight instruments were used to shoot two perpendicular refraction profiles across the Krafla central volcano. The North/South profile is 20 km long while the East/West profile is 55 km long. Average station spacing was 500 m in the caldera and 1-4 km elswhere. A total of three shots were used in the NS profile and 6 shots were used in the EW profile. proprietary PASSCAL_WABASH A comprehensive geophysical investigation to assess seismic hazards in the coassesment of seismicity in the Wabash Valley ALL STAC Catalog 1995-11-01 1996-06-30 -88.1706, 38.2057, -88.1706, 38.2057 https://cmr.earthdata.nasa.gov/search/concepts/C1214608969-SCIOPS.umm_json Recent paleoseismic evidence had shown there were 5-8 magnitude greater than 6 earthquakes in this region in the past 20,000 years. The study area has always been at the fringe of previously operated seismic networks. A focused, short-term deployment was designed to lower the detection threshold to determine seismicity rates for the region for comparison with estimates derived from paleoseismicity. The researchers hoped to relate observed seismicity to faults mapped in the subsurface through new seismic reflection data made available to the Illinois Basin Consortium. proprietary @@ -13266,8 +13246,8 @@ POSTER-2004 Hurricanes_Not Applicable 2004 Landfalling Hurricanes Poster ALL STA POSTER-2004 Hurricanes_Not Applicable 2004 Landfalling Hurricanes Poster NOAA_NCEI STAC Catalog 2004-08-13 2004-09-25 -91, 8, -33, 46.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093388-NOAA_NCEI.umm_json "The 2004 U.S. Landfalling Hurricanes poster is a special edition poster which contains two sets of images of Hurricanes Charley, Frances, Ivan, and Jeanne, created from NOAA's operational satellites. In addtion to the images, the poster has a map depicting the general track of each storm; information on each storm's landfall location, date of landfall, and category level at time of landfall; as well as, a Saffir-Simpson Hurricane Scale chart. Poster size is 34""x27""." proprietary POSTER-2005 Atl Hurricanes_Not Applicable 2005 Atlantic Hurricanes Poster NOAA_NCEI STAC Catalog 2005-07-03 2005-12-08 -97, 20, -65, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093322-NOAA_NCEI.umm_json "The 2005 Atlantic Hurricanes poster features high quality satellite images of 15 hurricanes which formed in the Atlantic Basin (includes Gulf of Mexico and Caribbean Sea) in the year 2005 which was the busiest season on record. The images show each storm near maximum intensity. Also, under each image there is additional information including, lowest pressure, maximum sustained winds, date range of the storm, highest category level reached on the Saffir-Simpson Hurricane Scale, and approximate position of each storm when the image was taken. Poster size is 35""x30""." proprietary POSTER-2005 Atl Hurricanes_Not Applicable 2005 Atlantic Hurricanes Poster ALL STAC Catalog 2005-07-03 2005-12-08 -97, 20, -65, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093322-NOAA_NCEI.umm_json "The 2005 Atlantic Hurricanes poster features high quality satellite images of 15 hurricanes which formed in the Atlantic Basin (includes Gulf of Mexico and Caribbean Sea) in the year 2005 which was the busiest season on record. The images show each storm near maximum intensity. Also, under each image there is additional information including, lowest pressure, maximum sustained winds, date range of the storm, highest category level reached on the Saffir-Simpson Hurricane Scale, and approximate position of each storm when the image was taken. Poster size is 35""x30""." proprietary -POSTER-2005 Sig Hurricanes_Not Applicable 2005 Significant U.S. Hurricane Strikes Poster ALL STAC Catalog 2005-07-10 2005-10-24 -102, 12, -69, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093260-NOAA_NCEI.umm_json "The 2005 Significant U.S. Hurricane Strikes poster is one of two special edition posters for the Atlantic Hurricanes. This beautiful poster contains two sets of images of five hurricanes that impacted the United States in 2005, namely Katrina, Ophelia, Rita and Wilma. The images were created from NOAA's geostationary and polar-orbiting environmental satellites. In addition to the images, the poster has a map depicting the general track of each storm, a color temperature scale to read the hurricane cloud top temperatures, high level information on each storm, the category at time of landfall; as well as, a Saffir-Simpson Hurricane Scale. Poster size is 36""x32""." proprietary POSTER-2005 Sig Hurricanes_Not Applicable 2005 Significant U.S. Hurricane Strikes Poster NOAA_NCEI STAC Catalog 2005-07-10 2005-10-24 -102, 12, -69, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093260-NOAA_NCEI.umm_json "The 2005 Significant U.S. Hurricane Strikes poster is one of two special edition posters for the Atlantic Hurricanes. This beautiful poster contains two sets of images of five hurricanes that impacted the United States in 2005, namely Katrina, Ophelia, Rita and Wilma. The images were created from NOAA's geostationary and polar-orbiting environmental satellites. In addition to the images, the poster has a map depicting the general track of each storm, a color temperature scale to read the hurricane cloud top temperatures, high level information on each storm, the category at time of landfall; as well as, a Saffir-Simpson Hurricane Scale. Poster size is 36""x32""." proprietary +POSTER-2005 Sig Hurricanes_Not Applicable 2005 Significant U.S. Hurricane Strikes Poster ALL STAC Catalog 2005-07-10 2005-10-24 -102, 12, -69, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093260-NOAA_NCEI.umm_json "The 2005 Significant U.S. Hurricane Strikes poster is one of two special edition posters for the Atlantic Hurricanes. This beautiful poster contains two sets of images of five hurricanes that impacted the United States in 2005, namely Katrina, Ophelia, Rita and Wilma. The images were created from NOAA's geostationary and polar-orbiting environmental satellites. In addition to the images, the poster has a map depicting the general track of each storm, a color temperature scale to read the hurricane cloud top temperatures, high level information on each storm, the category at time of landfall; as well as, a Saffir-Simpson Hurricane Scale. Poster size is 36""x32""." proprietary PRECIP_AMSR2_GCOMW1_1 NASA MEASURES Precipitation Ensemble based on AMSR2 GCOMW1 NASA PPS L1C V05 TBs 1-orbit L2 Swath 10x10km V1 (PRECIP_AMSR2_GCOMW1) at GES DISC GES_DISC STAC Catalog 2012-07-02 2021-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2368305620-GES_DISC.umm_json The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Advanced Microwave Scanning Radiometer-2 (AMSR-2) flown on the Global Climate Observing Mission-Water 1 (GCOM-W1). Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2012 to 2020 with one file per orbit. proprietary PRECIP_AMSRE_AQUA_1 NASA MEASURES Precipitation Ensemble based on AMSRE AQUA NASA PPS L1C V05 Tbs 1-orbit L2 Swath 12x12km V1 (PRECIP_AMSRE_AQUA) at GES DISC GES_DISC STAC Catalog 2002-06-01 2011-10-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2368306433-GES_DISC.umm_json The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Advanced Microwave Scanning Radiometer-E (AMSR-E) flown on the AQUA satellite. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2002 to 2011 with one file per orbit. proprietary PRECIP_GMI_GPM_1 NASA MEASURES Precipitation Ensemble based on GMI GPM NASA PPS L1C V05 Tbs 1-orbit L2 Swath 10x10km V1 (PRECIP_GMI_GPM) at GES DISC GES_DISC STAC Catalog 2014-03-04 2021-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2368306937-GES_DISC.umm_json The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) flown on the GPM satellite. Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2014 to 2020 with one file per orbit. proprietary @@ -13314,23 +13294,23 @@ PVST_SMARTS_0 Validating PACE aerosol columnar properties and OCI water-leaving PVST_VDIUP_0 Validation of Ocean Surface Downwelling Irradiance and Its Underwater Propagation for the PACE Mission OB_DAAC STAC Catalog 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3252791852-OB_DAAC.umm_json This project contributes to the validation of global surface radiation products and diffuse attenuation coefficients (Kd) generated by the PACE mission, essential for quantifying net primary production. The radiation products include instantaneous, daily mean, planar, and scalar fluxes products, in particular daily mean photosynthetically available radiation (PAR). In-situ observations are gathered through a network of automatic stations measuring hyperspectral downward planar irradiance (Ed(0+)) at selected AERONET-OC sites, and BGC-Argo profilers equipped with hyperspectral Ed sensors. BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System https://doi.org/10.17882/42182. Link to BGC-Argo GDAC for raw float data: https://data-argo.ifremer.fr/aux/coriolis/. proprietary PanamaCity_0 Panama City, Florida optical measurements in 1993 OB_DAAC STAC Catalog 1993-10-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360586-OB_DAAC.umm_json Measurements taken in the Gulf of Mexico near Panama City, Florida in 1993. proprietary Panhandle_OWQ_0 Optical Water quality measurements made in the Florida Panhandle estuaries OB_DAAC STAC Catalog 2015-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360587-OB_DAAC.umm_json Measurements made in the Florida Panhandle estuaries in partnership with USF and FWC-FWRI. proprietary -Passive_Microwave_Snowoff_Data_1711_1.1 ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018 ORNL_CLOUD STAC Catalog 1988-01-01 2018-12-31 -180, 37.98, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2221862177-ORNL_CLOUD.umm_json This dataset provides annual maps of the snowoff (SO) date from 1988-2018 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary Passive_Microwave_Snowoff_Data_1711_1.1 ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018 ALL STAC Catalog 1988-01-01 2018-12-31 -180, 37.98, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2221862177-ORNL_CLOUD.umm_json This dataset provides annual maps of the snowoff (SO) date from 1988-2018 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary +Passive_Microwave_Snowoff_Data_1711_1.1 ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018 ORNL_CLOUD STAC Catalog 1988-01-01 2018-12-31 -180, 37.98, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2221862177-ORNL_CLOUD.umm_json This dataset provides annual maps of the snowoff (SO) date from 1988-2018 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary Patagonian_Coastal_0 Measurements off the Argentinian coast near Drakes Passage OB_DAAC STAC Catalog 2008-12-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360588-OB_DAAC.umm_json Measurements made in the South Atlantic Ocean in 2008 and 2009 off the Argentinian coast near Drakes Passage. proprietary Peatland_carbon_balance_1382_1 Global Peatland Carbon Balance and Land Use Change CO2 Emissions Through the Holocene ORNL_CLOUD STAC Catalog 1000-01-01 2001-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216864221-ORNL_CLOUD.umm_json This data set provides a time series of global peatland carbon balance and carbon dioxide emissions from land use change throughout the Holocene (the past 11,000 yrs). Global peatland carbon balance was quantified using a) a continuous net carbon balance history throughout the Holocene derived from a data set of 64 dated peat cores, and b) global model simulations with the LPX-Bern model hindcasting the dynamics of past peatland distribution and carbon balance. CO2 emissions from land-use change are based on published scenarios for anthropogenic land use change (HYDE 3.1, HYDE 3.2, KK10) covering the last 10,000 years. This combination of model estimates with CO2 budget constraints narrows the range of past anthropogenic land use change emissions and their contribution to past carbon cycle changes. proprietary Pelican_PCO2_0 Partial pressure of carbon dioxide (PCO2) onboard the Pelican research vessel OB_DAAC STAC Catalog 2006-04-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360591-OB_DAAC.umm_json Measurements from the Pelican research vessel made off the southern coast of Louisiana in the Gulf of Mexico from 2006. proprietary PenBaySurvey_0 Penobscot Bay Optical Survey OB_DAAC STAC Catalog 2007-11-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360592-OB_DAAC.umm_json Measurements made in the Penobscot Bay between 2007 and 2008. proprietary PermafrostThaw_CarbonEmissions_1872_1 Projections of Permafrost Thaw and Carbon Release for RCP 4.5 and 8.5, 1901-2299 ORNL_CLOUD STAC Catalog 1901-01-01 2300-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2254686682-ORNL_CLOUD.umm_json This dataset consists of an ensemble of model projections from 1901 to 2299 for the northern hemisphere permafrost domain. The model projections include monthly average values for a common set of diagnostic outputs at a spatial resolution of 0.5 x 0.5 degrees latitude and longitude. The model simulations resulted from a synthesis effort organized by the Permafrost Carbon Network to evaluate the impacts of climate change on the carbon cycle in permafrost regions in the high northern latitudes. The model teams used different historical input weather data, but most used driver data developed by the Climate Research Unit - National Centers for Environmental Prediction (CRUNCEP) as modified for the Multiscale Terrestrial Model Intercomparison Project (MsTMIP). The teams scaled the driver data for the projections using output from global climate models from the fifth Coupled Model Intercomparison Project (CMIP5). The synthesis evaluated the terrestrial carbon cycle in the modern era and projected future emissions of carbon under two climate warming scenarios: Representative Concentration Pathways 4.5 and 8.5 (RCP45 and RCP85) from CMIP5. RCP45 represents emissions resulting in a global climate close to the target climate in the Paris Accord. RCP85 represents unconstrained greenhouse gas emissions. proprietary -Permafrost_ActiveLayer_NSlope_1759_1 ABoVE: Active Layer Soil Characterization of Permafrost Sites, Northern Alaska, 2018 ORNL_CLOUD STAC Catalog 2018-08-22 2018-08-26 -149.31, 68.61, -148.56, 69.81 https://cmr.earthdata.nasa.gov/search/concepts/C2143402217-ORNL_CLOUD.umm_json This dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska. From North to South, the study sites include Franklin Bluffs, Sagwon, Happy Valley, Ice Cut, and Imnavait Creek. Measurements were made from August 22 to August 26, 2018. The purpose of the field campaign was to characterize the dielectric properties of permafrost active layer soils in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign. proprietary Permafrost_ActiveLayer_NSlope_1759_1 ABoVE: Active Layer Soil Characterization of Permafrost Sites, Northern Alaska, 2018 ALL STAC Catalog 2018-08-22 2018-08-26 -149.31, 68.61, -148.56, 69.81 https://cmr.earthdata.nasa.gov/search/concepts/C2143402217-ORNL_CLOUD.umm_json This dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska. From North to South, the study sites include Franklin Bluffs, Sagwon, Happy Valley, Ice Cut, and Imnavait Creek. Measurements were made from August 22 to August 26, 2018. The purpose of the field campaign was to characterize the dielectric properties of permafrost active layer soils in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign. proprietary +Permafrost_ActiveLayer_NSlope_1759_1 ABoVE: Active Layer Soil Characterization of Permafrost Sites, Northern Alaska, 2018 ORNL_CLOUD STAC Catalog 2018-08-22 2018-08-26 -149.31, 68.61, -148.56, 69.81 https://cmr.earthdata.nasa.gov/search/concepts/C2143402217-ORNL_CLOUD.umm_json This dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska. From North to South, the study sites include Franklin Bluffs, Sagwon, Happy Valley, Ice Cut, and Imnavait Creek. Measurements were made from August 22 to August 26, 2018. The purpose of the field campaign was to characterize the dielectric properties of permafrost active layer soils in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign. proprietary Permafrost_Thaw_Depth_YK_1598_1 ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016 ALL STAC Catalog 2009-06-27 2016-07-17 -165.69, 61.17, -165.03, 61.29 https://cmr.earthdata.nasa.gov/search/concepts/C2162142273-ORNL_CLOUD.umm_json This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy. proprietary Permafrost_Thaw_Depth_YK_1598_1 ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016 ORNL_CLOUD STAC Catalog 2009-06-27 2016-07-17 -165.69, 61.17, -165.03, 61.29 https://cmr.earthdata.nasa.gov/search/concepts/C2162142273-ORNL_CLOUD.umm_json This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy. proprietary PhenoCam_V2_1674_2 PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018 ORNL_CLOUD STAC Catalog 1999-11-16 2018-12-31 -158.15, -22.97, 119.22, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2764826583-ORNL_CLOUD.umm_json This data set provides a time series of vegetation phenological observations for 393 sites across diverse ecosystems of the world (mostly North America) from 2000-2018. The phenology data were derived from conventional visible-wavelength automated digital camera imagery collected through the PhenoCam Network at each site. From each acquired image, RGB (red, green, blue) color channel information was extracted and means and other statistics calculated for a region-of-interest (ROI) that delineates an area of specific vegetation type. From the high-frequency (typically, 30 minute) imagery collected over several years, time series characterizing vegetation color, including canopy greenness, plus greenness rising and greenness falling transition dates, were summarized over 1- and 3-day intervals. proprietary Phenocam_Images_V2_1689_2 PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018 ORNL_CLOUD STAC Catalog 1999-11-16 2018-12-31 -158.15, -22.97, 119.22, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2764728896-ORNL_CLOUD.umm_json This dataset provides a time series of visible-wavelength digital camera imagery collected through the PhenoCam Network at each of 393 sites predominantly in North America from 2000-2018. The raw imagery was used to derive information on phenology, including time series of vegetation color, canopy greenness, and phenology transition dates for the PhenoCam Dataset v2.0. proprietary Phenology_AmeriFlux_Neon_Sites_2033_1 Land Surface Phenology, Eddy Covariance Tower Sites, North America, 2017-2021 ORNL_CLOUD STAC Catalog 2017-01-01 2021-12-31 -176.13, 14.34, -57.3, 70.98 https://cmr.earthdata.nasa.gov/search/concepts/C2764693210-ORNL_CLOUD.umm_json This land surface phenology (LSP) dataset provides spatially explicit data related to the timing of phenological changes such as the start, peak, and end of vegetation activity, vegetation index metrics and associated quality assurance flags. The data are for the growing seasons of 2017-2021 for 10-km x 10-km windows centered over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. The dataset is derived at 3-m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. These LSP data can be used to assess satellite-based LSP products, to evaluate predictions from land surface models, and to analyze processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes. The data are provided in NetCDF format along with geospatial area-of-interest information and visualizations of the analysis window for each site in GeoJSON and HTML formats. proprietary Phenology_Deciduous_Forest_1570_1 Landsat-derived Spring and Autumn Phenology, Eastern US - Canadian Forests, 1984-2013 ORNL_CLOUD STAC Catalog 1984-01-01 2013-12-31 -124.42, 29.63, -60.4, 62.04 https://cmr.earthdata.nasa.gov/search/concepts/C2764880255-ORNL_CLOUD.umm_json This dataset provides Landsat phenology algorithm (LPA) derived start and end of growing seasons (SOS and EOS) at 500-m resolution for deciduous and mixed forest areas of 75 selected Landsat sidelap regions across the Eastern United States and Canada. The data are a 30-year time series (1984-2013) of derived spring and autumn phenology for forested areas of the Eastern Temperate Forest, Northern Forest, and Taiga ecoregions. proprietary -Photos_ThermokarstLakes_AK_1845_1 ABoVE: Aerial Photographs of Frozen Lakes near Fairbanks, Alaska, October 2014 ALL STAC Catalog 2014-10-08 2014-10-08 -147.95, 64.86, -147.76, 64.94 https://cmr.earthdata.nasa.gov/search/concepts/C2143401765-ORNL_CLOUD.umm_json This dataset includes high resolution orthophotographs of 21 lakes in the region of Fairbanks, Alaska, USA. Aerial photographs were taken on October 8, 2014, three days after lake-ice formation. These photographs were used to identify open holes in lake ice that indicate the location of hotspot seeps associated with the releases of methane from thawing permafrost. Aerial photography can be used to measure changes in lake areas and to observe patterns in the formation of lake ice and other early winter lake conditions. proprietary Photos_ThermokarstLakes_AK_1845_1 ABoVE: Aerial Photographs of Frozen Lakes near Fairbanks, Alaska, October 2014 ORNL_CLOUD STAC Catalog 2014-10-08 2014-10-08 -147.95, 64.86, -147.76, 64.94 https://cmr.earthdata.nasa.gov/search/concepts/C2143401765-ORNL_CLOUD.umm_json This dataset includes high resolution orthophotographs of 21 lakes in the region of Fairbanks, Alaska, USA. Aerial photographs were taken on October 8, 2014, three days after lake-ice formation. These photographs were used to identify open holes in lake ice that indicate the location of hotspot seeps associated with the releases of methane from thawing permafrost. Aerial photography can be used to measure changes in lake areas and to observe patterns in the formation of lake ice and other early winter lake conditions. proprietary +Photos_ThermokarstLakes_AK_1845_1 ABoVE: Aerial Photographs of Frozen Lakes near Fairbanks, Alaska, October 2014 ALL STAC Catalog 2014-10-08 2014-10-08 -147.95, 64.86, -147.76, 64.94 https://cmr.earthdata.nasa.gov/search/concepts/C2143401765-ORNL_CLOUD.umm_json This dataset includes high resolution orthophotographs of 21 lakes in the region of Fairbanks, Alaska, USA. Aerial photographs were taken on October 8, 2014, three days after lake-ice formation. These photographs were used to identify open holes in lake ice that indicate the location of hotspot seeps associated with the releases of methane from thawing permafrost. Aerial photography can be used to measure changes in lake areas and to observe patterns in the formation of lake ice and other early winter lake conditions. proprietary Pingo_Veg_Plots_1507_1 Arctic Vegetation Plots from Pingo Communities, North Slope, Alaska, 1984-1986 ORNL_CLOUD STAC Catalog 1983-01-01 1983-12-31 -149.95, 69.71, -147.66, 70.5 https://cmr.earthdata.nasa.gov/search/concepts/C2170970856-ORNL_CLOUD.umm_json This data set provides vegetation species and vegetation plot data collected between 1983 and 1985 from 293 study plots on 41 pingos on the North Slope of Alaska. The pingos were located within the Arctic Coastal Plain in the Kuparuk, Prudhoe Bay, Kadleroshilik, and Toolik River areas. Specific attributes include dominant vegetation species, cover, soil pH, moisture, and physical characteristics of the plots. proprietary PlanetScope.Full.Archive_7.0 PlanetScope Full Archive ESA STAC Catalog 2016-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336933-ESA.umm_json "The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user. This kind of product is designed for users with advanced image processing and geometric correction capabilities. Basic Scene Product Components and Format Product Components Image File (GeoTIFF format) Metadata File (XML format) Rational Polynomial Coefficients (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Accuracy <10 m RMSE The Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres. Ortho Scene Product Components and Format Product Components Image File (GeoTIFF format) Metadata File (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 3-band natural colour (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Projection UTM WGS84 Accuracy <10 m RMSE PlanetScope Ortho Scene product is available in the following: PlanetScope Visual Ortho Scene product is orthorectified and colour-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite. PlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications. PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary PlanetScopeESAarchive_8.0 PlanetScope ESA archive ESA STAC Catalog 2018-11-15 2018-11-21 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2547572362-ESA.umm_json "The PlanetScope ESA archive collection consists of PlanetScope products requested by ESA supported projects over their areas of interest around the world and that ESA collected over the years. The dataset regularly grows as ESA collects new products. Three product lines for PlanetScope imagery are offered, for all of them the Ground Sampling Distance at nadir is 3.7 m (at reference altitude 475 km). EO-SIP Product Type Product description Processing Level PSC_DEF_S3 3 bands – Analytic and Visual - Basic and Ortho Scene level 1B and 3B PSC_DEF_S4 4 bands – Analytic and Visual - Basic and Ortho Scene level 1B and 3B PSC_DEF_OT 3 bands, 4 bands and 5 bands – Analytic and Visual - Ortho Tile level 3A The Basic Scene product is a single-frame scaled Top of Atmosphere Radiance (at sensor) and sensor-corrected product. The product is not orthorectified or corrected for terrain distortions, radiometric and sensor corrections are applied to the data. The Ortho Scenes product is a single-frame scaled Top of Atmosphere Radiance (at sensor) or Surface Reflectance image product. The product is radiometrically, sensor and geometrically corrected and is projected to a cartographic map (UTM/WGS84). The Ortho Tiles are multiple orthorectified scenes in a single strip that have been merged and then divided according to a defined grid. Radiometric and sensor corrections are applied, the imagery is orthorectified and projected to a UTM projection. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/socat/PlanetScope available on the Third Party Missions Dissemination Service. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary @@ -13340,8 +13320,8 @@ Pleiades.HiRI.archive.and.new_9.0 Pleiades full archive and tasking ESA STAC Cat Pleiades.Neo.full.archive.and.tasking_9.0 Pléiades Neo full archive and tasking ESA STAC Catalog 2021-04-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2547572735-ESA.umm_json "Very High Resolution optical Pléiades Neo data at 30 cm PAN resolution (1.2 m 6-bands Multispectral) are available as part of the Airbus provision with twice daily revisit capability over the entire globe. The swath width is 14 km (footprint at nadir). Band combinations: • Panchromatic one band Black & White image at 0.3 m resolution • Pansharpened colour image at 0.3 m resolution: Natural colour (3 bands RGB), false colour (3 bands NIRRG), 4 bands (RGB+NIR), 6 bands • Multispectral colour image in 4 bands (RGB+NIR) or 6 bands (also Deep blue and Red Edge) at 1.2 m resolution • Bundle 0.3 m panchromatic image and 1.2 m multispectral image (4 or 6 bands) simultaneously acquired Geometric processing levels: • Primary: The Primary product is the processing level closest to the natural image acquired by the sensor. This product restores perfect collection conditions: the sensor is placed in rectilinear geometry, and the image is clear of all radiometric distortion. • Ortho: The Ortho product is a georeferenced image in Earth geometry, corrected from acquisition and terrain off-nadir effects. Acquisition modes: • Mono • Stereo • Tristereo • HD15: 15cm resolution for Panchromatic, 60cm resolution for Multispectral: Mono image resampled by using machine learning model which increase sharpness and fineness of details without introducing any fake data. To complement the traditional and fully customised ordering and download of selected SPOT, Pleiades or Pleiades Neo images in a variety of data formats, you can also subscribe to the OneAtlas Living Library package where the entire OneAtlas optical archive of ortho images is updated on a daily basis and made available for streaming or download. The Living Library consist of: • less-than-18-months-old Pansharpened and Bundle imagery • a curation of SPOT images with no cloud cover and less than 30° incidence angle • Pléiades images acquired worldwide with maximum 15% cloud cover and 30° Incidence Angle • Pléiades Neo premium imagery selection with 2% cloud cover and 30° incidence angle These are the available subscription packages (to be consumed withing one year from the activation) OneAtlas Living Library subscription package 1: up to 230 km2 Pleiades Neo or 430 km2 Pleiades or 1.500 km2 SPOT in download, up to 500 km2 Pleiades Neo or 2.000 km2 Pleiades or 7.500 km2 SPOT in streaming OneAtlas Living Library subscription package 2: up to 654 km2 Pleiades Neo or 1.214 km2 Pleiades or 4.250 km2 SPOT in download, up to 1417 km2 Pleiades Neo or 5.666 km2 Pleiades or 21.250 km2 SPOT in streaming OneAtlas Living Library subscription package 3: up to 1.161 km2 Pleiades Neo or 2.156 km2 Pleiades or 7.545 km2 SPOT in download, up to 2.515 km2 Pleiades Neo or 10.060 km2 Pleiades or 37.723 km2 SPOT in streaming All details about the data provision, data access conditions and quota assignment procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/SPOT-Pleiades-data-terms-of-applicability.pdf available in the Resources section. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary Plot_Data_Noatak_Seward_AK_1919_1 Burned and Unburned Field Site Data, Noatak, Seward, and North Slope, AK, 2016-2018 ORNL_CLOUD STAC Catalog 2016-07-22 2018-08-27 -164.93, 65.02, -148.64, 69.66 https://cmr.earthdata.nasa.gov/search/concepts/C2240727642-ORNL_CLOUD.umm_json This dataset includes field measurements from unburned and burned 10 m x 10 m and 1 m x 1 m plots in the Noatak, Seward, and North Slope regions of the Alaskan tundra during July through August in the years 2016-2018. The data include vegetation coverage, soil moisture, soil temperature, soil thickness, thaw depth, and weather measurements. Measurements were recorded using ocular assessments and standard equipment. Plot photographs are included. proprietary Plumes_and_Blooms_0 Plumes and Blooms OB_DAAC STAC Catalog 1996-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360616-OB_DAAC.umm_json The Plumes and Blooms program is a joint collaboration among UCSB faculty, student and staff researchers at the Institute of Computational Earth System Science (ICESS), NOAA researchers at the Coastal Services Center (Charleston, SC) and the NOAA sanctuary managers of the Channel Islands National Marine Sanctuary (CINMS). Since August, 1996, monthly research cruises have been conducted to collect measurements. These measurements include temperature and salinity, ocean color spectra, and water column profiles of red light transmission and chlorophyll fluorescence (indexes of suspended particulate load and phytoplankton abundance). The transect observations begin at the shelf waters north of Santa Rosa island and end at an area off Goleta Point. These repeat observations are combined with satellite imagery to build a time-series of the changing ocean color conditions in the Santa Barbara Channel. proprietary -PolInSAR_Canopy_Height_1589_1 AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon ALL STAC Catalog 2016-02-27 2016-03-08 9.29, -0.35, 11.83, 0.24 https://cmr.earthdata.nasa.gov/search/concepts/C2734258687-ORNL_CLOUD.umm_json This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency. proprietary PolInSAR_Canopy_Height_1589_1 AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon ORNL_CLOUD STAC Catalog 2016-02-27 2016-03-08 9.29, -0.35, 11.83, 0.24 https://cmr.earthdata.nasa.gov/search/concepts/C2734258687-ORNL_CLOUD.umm_json This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency. proprietary +PolInSAR_Canopy_Height_1589_1 AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon ALL STAC Catalog 2016-02-27 2016-03-08 9.29, -0.35, 11.83, 0.24 https://cmr.earthdata.nasa.gov/search/concepts/C2734258687-ORNL_CLOUD.umm_json This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency. proprietary Polar-VPRM_Alaskan-NEE_1314_1 CARVE Modeled Gross Ecosystem CO2 Exchange and Respiration, Alaska, 2012-2014 ORNL_CLOUD STAC Catalog 2012-01-01 2014-12-31 -179, 55, -134, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2236236883-ORNL_CLOUD.umm_json This data set provides 3-hourly estimates of gross ecosystem CO2 exchange (GEE) and respiration (autotrophic and heterotrophic) for the state of Alaska from 2012 to 2014. The data were generated using the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM) and are provided at ~ 1 km2 [1/4-degree (longitude) by 1/6-degree (latitude)] pixel resolution. The PolarVPRM produces high-frequency estimates of GEE of CO2 for North American biomes from remotely-sensed data sets. For Alaska, the model used meteorological inputs from the North American regional re-analysis (NARR) and inputs of fractional snow cover and land surface water index (LSWI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Land surface greenness was factored into the model from three sources: 1) Enhanced Vegetation Index (EVI) from MODIS; 2) Solar Induced Florescence (SIF) from the Orbiting Carbon Observatory 2 (OCO-2); and 3) SIF from the Global Ozone Monitoring Experiment 2 (GOME-2). Three independent estimates of GEE are included in the data set, one for each source of greenness observations. proprietary PolarWindsII_DAWN_DC8_1 Polar Winds II - Doppler Aerosol WiNd (DAWN) - DC8 LARC_ASDC STAC Catalog 2015-05-11 2015-05-25 -59, 49, 15.5, 70.5 https://cmr.earthdata.nasa.gov/search/concepts/C1440079415-LARC_ASDC.umm_json PolarWindsII_DAWN_DC8_1 is the Polar Winds II - Doppler Aerosol WiNd (DAWN) - DC8 data product. Data collection for this product is complete. Beginning in the fall of 2014, NASA sponsored two airborne field campaigns, collectively called Polar Winds, designed to fly the Doppler Aerosol WiNd (DAWN) lidar and other instruments to take airborne wind measurements of the Arctic atmosphere, specifically over and off the coasts of Greenland during Oct-Nov 2014 and May 2015. In particular, Polar Winds conducted a series of science experiments focusing on the measurement and analyses of lower tropospheric winds and aerosols associated with coastal katabatic flows, barrier winds, the Greenland Tip Jet, boundary layer circulations such as rolls and OLEs (Organized Large Eddies), and near surface winds over open water, transitional ice zones and the Greenland Ice Cap. Polar Winds I was based in Kangerlussuaq, Greenland and flew DAWN on board the NASA King Air UC-12B during Oct-Nov 2014 while Polar Winds II was based in Keflavik, Iceland and utilized the NASA DC-8 aircraft to fly DAWN and Dropsondes over the Arctic in May 2015. In total, twenty-four individual missions with over 80 hours of research flights were flown in the Arctic region near Greenland and Iceland during Polar Winds. The focus instrument for the wind measurements taken over the Arctic during Polar Winds was the DAWN airborne wind lidar. At a wavelength of 2.05 microns and at 250 mj per pulse, DAWN is the most powerful airborne Doppler Wind Lidar available today for airborne missions. DAWN has previously been flown on the NASA DC-8 during the 2010 Genesis and Rapid Intensification Processes (GRIP) campaign and on the NASA C-12 for wind field characterization off the coast of Virginia. In addition to DAWN, Polar Winds utilized the High Definition Sounding System (HDSS) dropsonde delivery system developed by Yankee Environmental Services to drop almost 100 dropsondes during Polar Wind II to obtain additional high-resolution vertical wind profiles during most missions. These dropsondes also provided needed calibration/validation for the much newer DAWN measurements. proprietary PolarWindsI_DAWN_KingAirUC-12B_1 Polar Winds I - Doppler Aerosol WiNd (DAWN) - KingAirUC-12B LARC_ASDC STAC Catalog 2014-10-29 2014-11-13 -58, 59, -42, 69 https://cmr.earthdata.nasa.gov/search/concepts/C1457763994-LARC_ASDC.umm_json PolarWindsI_DAWN_KingAirUC-12B is the Polar Winds I - Doppler Aerosol WiNd (DAWN) - KingAirUC-12B data product. Data for this was collected using the DAWN instrument flown on the NASA Langley Beechcraft UC-12B Huron aircraft. Data collection for this product is complete. Polar Winds I was based in Kangerlussuaq, Greenland and flew DAWN on board the NASA King Air UC-12B during Oct-Nov 2014 while Polar Winds II was based in Keflavik, Iceland and utilized the NASA DC-8 aircraft to fly DAWN and Dropsondes over the Arctic in May 2015. In total, twenty-four individual missions with over 80 hours of research flights were flown in the Arctic region near Greenland and Iceland during Polar Winds. The focus instrument for the wind measurements taken over the Arctic during Polar Winds was the DAWN airborne wind lidar. At a wavelength of 2.05 microns and at 250 mj per pulse, DAWN is the most powerful airborne Doppler Wind Lidar available today for airborne missions. DAWN has previously been flown on the NASA DC-8 during the 2010 Genesis and Rapid Intensification Processes (GRIP) campaign and on the NASA UC-12 for wind field characterization off the coast of Virginia. In addition to DAWN, Polar Winds utilized the High Definition Sounding System (HDSS) dropsonde delivery system developed by Yankee Environmental Services to drop almost 100 dropsondes during Polar Wind II to obtain additional high-resolution vertical wind profiles during most missions. These dropsondes also provided needed calibration/validation for the much newer DAWN measurements. Beginning in the fall of 2014, NASA sponsored two airborne field campaigns, collectively called Polar Winds, designed to fly the Doppler Aerosol WiNd (DAWN) lidar and other instruments to take airborne wind measurements of the Arctic atmosphere, specifically over and off the coasts of Greenland during Oct-Nov 2014 and May 2015. In particular, Polar Winds conducted a series of science experiments focusing on the measurement and analyses of lower tropospheric winds and aerosols associated with coastal katabatic flows, barrier winds, the Greenland Tip Jet, boundary layer circulations such as rolls and OLEs (Organized Large Eddies), and near surface winds over open water, transitional ice zones and the Greenland Ice Cap. proprietary @@ -13373,8 +13353,8 @@ PreDeltaX_Vegetation_Structure_1805_1 Pre-Delta-X: Vegetation Species, Structure PreDeltaX_Water_Level_Data_1801_1 Pre-Delta-X: Water Levels across Wax Lake Outlet, Atchafalaya Basin, LA, USA, 2016 ORNL_CLOUD STAC Catalog 2016-10-13 2016-10-20 -91.45, 29.51, -91.36, 29.74 https://cmr.earthdata.nasa.gov/search/concepts/C2025123345-ORNL_CLOUD.umm_json This dataset provides absolute water level elevations derived for 10 locations across the Wax Lake Delta, Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Field measurements were made during the Pre-Delta-X campaign on October 13-20, 2016. Relative water level measurements were recorded every five minutes during a one-week period using in situ pressure transducers (Solinst) to measure water surface elevation change with millimeter accuracy. The Solinst system combines a total pressure transducer (TPT) and a temperature detector. Once underwater, the TPT measures the sum of the atmosphere and water pressure above the sensor. Atmospheric pressure fluctuations must be accounted for to obtain the height of the water column above the TPT. An absolute elevation correction was applied to the water level data using an iterative approach with the USGS Calumet Station water level height and Airborne Snow Observatory (ASO) lidar water level profiles. These Pre-Delta-X water level measurements served to calibrate and validate the campaign's remote sensing observations and hydrodynamic models. proprietary Pre_LBA_ABRACOS_899_1.1 Pre-LBA Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) Data ORNL_CLOUD STAC Catalog 1991-01-01 1996-12-31 -75, -18, -46, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2762262185-ORNL_CLOUD.umm_json The data set presents the principal data from the Anglo-BRazilian Amazonian Climate Observation Study (ABRACOS) (Gash et al, 1996) and provides quality controlled information from five of the study topics considered by the project in five zipped files containing ASCII text data. The five study topics include Micrometeorology, Climate, Carbon Dioxide and Water Vapor, Plant Physiology, and Soil Moisture. The objectives of the ABRACOS were to monitor Amazonian climate and improve the understanding of the consequences of deforestation and to provide data for the calibration and validation of GCMs and GCM sub-models of Amazonian forest and post-deforestation pasture (Shuttleworth et al, 1991). Three areas were instrumented, each with different soils, dry season intensities and deforestation densities (Gash et al, 1996). In each area, an automatic weather station and soil moisture measurement equipment were installed: in a primary forest site and in nearby cattle pasture, for monitoring climate and soil status throughout the year. Additional intensive periods of study (or Missions), of varying duration, were operated at these sites: for calibration purposes, to understand the physical processes relevant to each site, and for detailed comparisons between sites. These data were collected under the ABRACOS project and made available by the UK Institute of Hydrology and the Instituto Nacional de Pesquisas Espaciais (Brazil). ABRACOS is a collaboration between the Agencia Brasileira de Cooperacao and the UK Overseas Development Administration. The processed, quality controlled and integrated data in the documented Pre-LBA data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually. proprietary Proantar_0 Measurements off James Ross Island, Antarctica OB_DAAC STAC Catalog 2005-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360623-OB_DAAC.umm_json Measurements made off James Ross Island near Antarctica in 2005. proprietary -Profile_based_PBL_heights_1706_1.1 ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA ALL STAC Catalog 2016-07-18 2019-07-26 -106.36, 28.65, -73.13, 49.49 https://cmr.earthdata.nasa.gov/search/concepts/C2677222693-ORNL_CLOUD.umm_json This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. proprietary Profile_based_PBL_heights_1706_1.1 ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA ORNL_CLOUD STAC Catalog 2016-07-18 2019-07-26 -106.36, 28.65, -73.13, 49.49 https://cmr.earthdata.nasa.gov/search/concepts/C2677222693-ORNL_CLOUD.umm_json This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. proprietary +Profile_based_PBL_heights_1706_1.1 ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA ALL STAC Catalog 2016-07-18 2019-07-26 -106.36, 28.65, -73.13, 49.49 https://cmr.earthdata.nasa.gov/search/concepts/C2677222693-ORNL_CLOUD.umm_json This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. proprietary Prudhoe_Bay_ArcSEES_Veg_Plots_1555_1 Arctic Vegetation Plots, Prudhoe Bay ArcSEES Road Study, Lake Colleen, Alaska, 2014 ORNL_CLOUD STAC Catalog 2014-08-06 2014-08-13 -148.47, 70.22, -148.47, 70.22 https://cmr.earthdata.nasa.gov/search/concepts/C2162122325-ORNL_CLOUD.umm_json This dataset provides environmental, soil, and vegetation data collected from study plots in the vicinity of Lake Colleen off the Spine Road at Prudhoe Bay, Alaska, during August of 2014. Data include vegetation species, leaf area index (LAI), percent cover classes, soil moisture and color, and plot characteristics including geology, topographic position, slope, aspect, and plot disturbance. proprietary Prudhoe_Bay_Veg_Maps_1387_1 Geobotanical and Impact Map Collection for Prudhoe Bay Oilfield, Alaska, 1972-2010 ORNL_CLOUD STAC Catalog 1949-01-01 2010-07-31 -150.17, 69.97, -146.97, 71.03 https://cmr.earthdata.nasa.gov/search/concepts/C2162616071-ORNL_CLOUD.umm_json This data set provides a collection of maps of geoecological characteristics of areas within the Beechey Point quadrangle near Prudhoe Bay on the North slope of Alaska: a geobotanical atlas of the Prudhoe Bay region, a land cover map of the Beechey Point quadrangle, and cumulative impact maps in the Prudhoe Bay Oilfield for ten dates from 1968 to 2010. The geobotanical atlas is based on aerial photographs and covers 145 square kilometers of the Prudhoe Bay Oilfield. The land cover map of the Beechey Point quadrangle was derived from the Landsat multispectral scanner, aerial photography, and other field and cartographic methods. The cumulative impact maps of the Prudhoe Bay Oilfield show historical infrastructure and natural changes digitized from aerial photos taken in each successive analysis year (1968, 1970, 1972, 1973, 1977, 1979, 1983, 1990, 2001, and 2010). Nine geoecological attributes are included: dominant vegetation, secondary vegetation, tertiary vegetation, percentage open water, landform, dominant surface form, secondary surface form, dominant soil, and secondary soil. These data document environmental changes in an Arctic region that is affected by both climate change and rapid industrial development. proprietary Prudhoe_Bay_Veg_Plots_1360_1 Arctic Vegetation Plots at Prudhoe Bay, Alaska, 1973-1980 ORNL_CLOUD STAC Catalog 1973-01-01 1980-12-31 -148.95, 70.25, -148.29, 70.38 https://cmr.earthdata.nasa.gov/search/concepts/C2170969598-ORNL_CLOUD.umm_json This data set provides environmental, soil, and vegetation data collected between 1973 and 1980 from 89 study plots in the Prudhoe Bay region of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for study plots subjectively located in 43 plant communities and 4 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation, species, and cover; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for classification, mapping, and analysis of geobotanical factors in the Prudhoe Bay region and across Alaska. proprietary @@ -13446,17 +13426,17 @@ RSCAT_LEVEL_2B_OWV_COMP_12_V1.1_1.1 RapidScat Level 2B Ocean Wind Vectors in 12. RSCAT_LEVEL_2B_OWV_COMP_12_V1.2_1.2 RapidScat Level 2B Ocean Wind Vectors in 12.5km Slice Composites Version 1.2 POCLOUD STAC Catalog 2015-08-19 2016-08-19 -180, -61, 180, 61 https://cmr.earthdata.nasa.gov/search/concepts/C2526576305-POCLOUD.umm_json "This dataset contains the RapidScat Level 2B 12.5km Version 1.2 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 data forward from orbital revolution number 5127, corresponding to 19 August 2015; the overlapping time period starting on 19 August 2015 corresponds to the first time period of the recorded low signal-to-noise ratio (SNR). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the ""Data Access"" tab above. This Version 1.2 dataset differs from the previous Version 1.1 dataset as follows: 1) L1B sigma-0 has been re-calibrated during the periods of low signal-to-noise ratio (SNR) and 2) during low SNR periods the L1B sigma-0 calibration is determined using re-pointed L1B QuikSCAT data. It is advised for users to avoid using the ""wind_obj"" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the ""ambiguity_obj"" variable. The ""wind_obj"" variable contains DIRTH probabilities (which are derived form the ""ambiguity_obj"" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/." proprietary RSCAT_LEVEL_2B_OWV_COMP_12_V1.3_1.3 RapidScat Level 2B Ocean Wind Vectors in 12.5km Slice Composites Version 1.3 POCLOUD STAC Catalog 2016-02-11 2016-08-19 -180, -61, 180, 61 https://cmr.earthdata.nasa.gov/search/concepts/C2526576326-POCLOUD.umm_json "This dataset contains the RapidScat Level 2B 12.5km Version 1.3 science-quality ocean surface wind vectors, which are intended as a replacement and continuation of the Version 1.1 and 1.2 data forward from orbital revolution number 7873, corresponding to 11 February 2016; on 11 Feb 2016, RapidScat entered it's 3rd low signal to noise ratio (SNR) state and the initial calibration of low SNR 3 was preliminary during the Version 1.2 release. The fundamental difference between Version 1.3 and the previous Version 1.2 datasets is that the L1B sigma-0 has been re-calibrated during the periods of low SNR states 3 and 4 using re-pointed QuikSCAT data. The Version 1.1 should still be considered valid up to the first rev of version 1.2 (5127), and similarly version 1.2 shall be considered valid up to the first rev of version 1.3 (7873). The Level 2B wind vectors are binned on a 12.5 km Wind Vector Cell (WVC) grid and processed using the Level 2A Sigma-0 dataset. RapidScat is a Ku-band dual beam circular rotating scatterometer retaining much of the same hardware and functionality of QuikSCAT, with exception of the antenna sub-system and digital interface to the International Space Station (ISS) Columbus module, which is where RapidScat is mounted. The NASA mission is officially referred to as ISS-RapidScat. Unlike QuikSCAT, ISS-RapidScat is not in sun-synchronous orbit, and flies at roughly half the altitude with a low inclination angle that restricts data coverage to the tropics and mid-latitude regions; the extent of latitudinal coverage stretches from approximately 61 degrees North to 61 degrees South. Furthermore, there is no consistent local time of day retrieval. This dataset is provided in a netCDF-3 file format that follows the netCDF-4 classic model (i.e., generated by the netCDF-4 API) and made available via FTP and OPeNDAP. For data access, please click on the ""Data Access"" tab above. It is advised for users to avoid using the ""wind_obj"" variable in this dataset since it is minimally applicable and meant primarily for quality assurance; for users who wish to access the objective function values for each ambiguity, it is suggested to use only the ""ambiguity_obj"" variable. The ""wind_obj"" variable contains DIRTH probabilities (which are derived form the ""ambiguity_obj"" objective function values) in the range of 0 to 1 indicating the conditional probability that the true direction is within + or - 2.5 degrees of the retrieved wind direction given the observed backscatter measurements in the cell. If you have any questions or concerns, please visit our Forum at https://podaac.jpl.nasa.gov/forum/." proprietary RSES_PCM_1 Cosmogenic dating AU_AADC STAC Catalog 2001-12-20 63.6203, -75.2756, 73.7101, -69.7425 https://cmr.earthdata.nasa.gov/search/concepts/C1214313722-AU_AADC.umm_json The data set consists of cosmogenic exposure ages for samples collected by Research School of Earth Sciences in the Prince Charles Mountains and vicinity. Thus far work has been carried out in the 2001/2002, 2002/2003, 2003/2004 and 2004/2005 field seasons. Currently, the only data publicly available is an excel spreadsheet detailing sampling locations. The objectives of this project were: To develop a comprehensive understanding of the Lambert Glacier of East Antarctica, from the time of the last maximum glaciation to the present, through an integrated and interdisciplinary study combining new field evidence - ice retreat history from cosmogenic exposure dating, geodetic measurements of crustal rebound, satellite measurements of present ice heights and changes therein - with other geological and glaciological data and numerical geophysical modelling advances. The project contributes to the quantitative characterisation of the complex interactions between ice-sheets, oceans and solid earth within the climate system. Outcomes have implications for geophysics, glaciology, geomorphology, climate, and past and future sea-level change. This work was completed as part of ASAC projects 2502 and 2516 (ASAC_2502 and ASAC_2516). The fields in this dataset are: Sample Date Collector Type Lithology Location Elevation Latitude Longitude proprietary -RSFDCE_KLIM4 Absolute Minimum of Air Temperature. Year By Year Data SCIOPS STAC Catalog 1881-01-01 1965-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608674-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by West Sybiria Computer Centre in 1977 and containes data from 1078 stations of the USSR. Data is currently stored on magnetic tape (800 bit/inch). proprietary RSFDCE_KLIM4 Absolute Minimum of Air Temperature. Year By Year Data ALL STAC Catalog 1881-01-01 1965-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608674-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by West Sybiria Computer Centre in 1977 and containes data from 1078 stations of the USSR. Data is currently stored on magnetic tape (800 bit/inch). proprietary -RSFDCE_KLIM5 Air Temperature 01.00 P.M. Year By Year Date SCIOPS STAC Catalog 1881-01-01 1965-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608673-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by West Subiria Computer Centre in 1977 and containes data from 1078 stations of the USSR. Data is currently stored on magnetic tape (800 bit/inch). proprietary +RSFDCE_KLIM4 Absolute Minimum of Air Temperature. Year By Year Data SCIOPS STAC Catalog 1881-01-01 1965-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608674-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by West Sybiria Computer Centre in 1977 and containes data from 1078 stations of the USSR. Data is currently stored on magnetic tape (800 bit/inch). proprietary RSFDCE_KLIM5 Air Temperature 01.00 P.M. Year By Year Date ALL STAC Catalog 1881-01-01 1965-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608673-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by West Subiria Computer Centre in 1977 and containes data from 1078 stations of the USSR. Data is currently stored on magnetic tape (800 bit/inch). proprietary +RSFDCE_KLIM5 Air Temperature 01.00 P.M. Year By Year Date SCIOPS STAC Catalog 1881-01-01 1965-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608673-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by West Subiria Computer Centre in 1977 and containes data from 1078 stations of the USSR. Data is currently stored on magnetic tape (800 bit/inch). proprietary RSS18_AVIRIS_L1B_449_1 BOREAS RSS-18 Level 1B AVIRIS At-Sensor Radiance Imagery ORNL_CLOUD STAC Catalog 1996-08-14 1996-08-14 -106.49, 53.45, -105.03, 54.32 https://cmr.earthdata.nasa.gov/search/concepts/C2929128157-ORNL_CLOUD.umm_json This dataset holds Level 1B (L1B) radiance data collected by the AVIRIS-Classic instrument near Prince Albert, Saskatchewan, Canada, on August 14, 1996. This imagery was acquired for the Boreal Ecosystem-Atmosphere Study (BOREAS) project in the boreal forests of central Canada. BOREAS focused on improving the understanding of exchanges of radiative energy, sensible heat, water, CO2 and trace gases between the boreal forest and the lower atmosphere. NASA's AVIRIS-Classic is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-Classic measures reflected radiance in 224 contiguous bands at approximately 10-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 400-2500 nm. The AVIRIS-Classic sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub meter range. For these data, AVIRIS-Classic was deployed on NASA's ER-2 high altitude aircraft. These spectra are acquired as images with 20-meter spatial resolution, 11 km swath width, and flight lines up to 800 km in length. The measurements are spectrally, radiometrically, and geometrically calibrated. There are seven flight lines subdivided into 66 scenes. The dataset includes the radiance imagery cube for each scene along with calibration and navigation information. The radiance data are in instrument coordinates, georeferenced by center of each scan line, and provided in a binary file. Metadata are included in a mixture of binary and text file formats. proprietary RSS_WindSat_L1C_TB_V08.0_8.0 RSS WindSat L1C Calibrated TB Version 8 POCLOUD STAC Catalog 2003-02-01 2020-10-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2559430954-POCLOUD.umm_json The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). The dataset contains the Level 1C WindSat Top of the Atmosphere (TOA) TB processed by RSS. The WindSat radiances are turned into TOA TB after correction for hot and cold calibration anomalies, receiver non-linearities, sensor pointing errors, antenna cross-polarization contamination, spillover, Faraday rotation and polarization alignment. The data are resampled on a fixed regular 0.125 deg Earth grid using Backus-Gilbert Optimum Interpolation. The sampling is done separately for fore and aft looks. The 10.7, 18.7, 23.8, 37.0 GHz channels are resampled to the 10.7 GHz spatial resolution. The 6.8 GHz channels are given at their native spatial resolution. The 10.7, 18.7, 23.8, 37.0 GHz channels are absolutely calibrated using the GMI sensor as calibration reference. The 6.8 GHz channels are calibrated using the open ocean with the RSS ocean emission model and the Amazon rain forest as calibration targets. The Faraday rotation angle (FRA) and geometric polarization basis rotation angle (PRA) were added in the last run. proprietary Radarsat-2_8.0 RADARSAT-2 ESA Archive ESA STAC Catalog 2008-07-27 2021-04-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689631-ESA.umm_json The RADARSAT-2 ESA archive collection consists of RADARSAT-2 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. Following Beam modes are available: Standard, Wide Swath, Fine Resolution, Extended Low Incidence, Extended High Incidence, ScanSAR Narrow and ScanSAR Wide. Standard Beam Mode allows imaging over a wide range of incidence angles with a set of image quality characteristics which provides a balance between fine resolution and wide coverage, and between spatial and radiometric resolutions. Standard Beam Mode operates with any one of eight beams, referred to as S1 to S8, in single and dual polarisation . The nominal incidence angle range covered by the full set of beams is 20 degrees (at the inner edge of S1) to 52 degrees (at the outer edge of S8). Each individual beam covers a nominal ground swath of 100 km within the total standard beam accessibility swath of more than 500 km. BEAM MODE: Standard PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 8.0 or 11.8 x 5.1 (SLC), 8.0 x 8.0 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 9.0 or 13.5 x 7.7 (SLC), 26.8 - 17.3 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 100 x 100 Range of Angle of Incidence (deg): 20 - 52 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: • Single: HH or VV or HV or VH • Dual: HH + HV or VV + VH Wide Swath Beam Mode allows imaging of wider swaths than Standard Beam Mode, but at the expense of slightly coarser spatial resolution. The three Wide Swath beams, W1, W2 and W3, provide coverage of swaths of approximately 170 km, 150 km and 130 km in width respectively, and collectively span a total incidence angle range from 20 degrees to 45 degrees. Polarisation can be single and dual. BEAM MODE: Wide PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 11.8 x 5.1 (SLC), 10 x 10 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 13.5 x 7.7 (SLC), 40.0 - 19.2 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 150 x 150 Range of Angle of Incidence (deg): 20 - 45 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: • Single: HH or VV or HV or VH • Dual: HH + HV or VV + VH Fine Resolution Beam Mode is intended for applications which require finer spatial resolution. Products from this beam mode have a nominal ground swath of 50 km. Nine Fine Resolution physical beams, F23 to F21, and F1 to F6 are available to cover the incidence angle range from 30 to 50 degrees. For each of these beams, the swath can optionally be centred with respect to the physical beam or it can be shifted slightly to the near or far range side. Thanks to these additional swath positioning choices, overlaps of more than 50% are provided between adjacent swaths. RADARSAT-2 can operate in single and dual polarisation for this beam mode. BEAM MODE: Fine PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 4.7 x 5.1 (SLC), 3.13 x 3.13 (SGX), 6.25 x 6.25 (SSG, SPG) Resolution - Range x Azimuth (m): 5.2 x 7.7 (SLC), 10.4 - 6.8 x 7.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 50 x 50 Range of Angle of Incidence (deg): 30 - 50 No. of Looks - Range x Azimuth: 1 x 1 (SLC,SGX, SGF, SSG, SPG) Polarisations - Options: • Single: HH or VV or HV or VH • Dual: HH + HV or VV + VH In the Extended Low Incidence Beam Mode, a single Extended Low Incidence Beam, EL1, is provided for imaging in the incidence angle range from 10 to 23 degrees with a nominal ground swath coverage of 170 km. Some minor degradation of image quality can be expected due to operation of the antenna beyond its optimum scan angle range. Only single polarisation is available. BEAM MODE: Extended Low PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 8.0 x 5.1 (SLC), 10.0 x 10.0 (SGX), 12.5 x 12.5 (SSG, SPG) Nominal Resolution - Range x Azimuth (m): 9.0 x 7.7 (SLC), 52.7 - 23.3 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 170 x 170 Range of Angle of Incidence (deg): 10 - 23 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: Single Pol HH In the Extended High Incidence Beam Mode, six Extended High Incidence Beams, EH1 to EH6, are available for imaging in the 49 to 60 degree incidence angle range. Since these beams operate outside the optimum scan angle range of the SAR antenna, some degradation of image quality, becoming progressively more severe with increasing incidence angle, can be expected when compared with the Standard Beams. Swath widths are restricted to a nominal 80 km for the inner three beams, and 70 km for the outer beams. Only single polarisation available. BEAM MODE: Extended High PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 11.8 x 5.1 (SLC), 8.0 x 8.0 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 13.5 x 7.7 (SLC), 18.2 - 15.9 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 75 x 75 Range of Angle of Incidence (deg): 49 - 60 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: Single Pol HH ScanSAR Narrow Beam Mode provides coverage of a ground swath approximately double the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCNA, which uses physical beams W1 and W2, and SCNB, which uses physical beams W2, S5, and S6. Both options provide coverage of swath widths of about 300 km. The SCNA combination provides coverage over the incidence angle range from 20 to 39 degrees. The SCNB combination provides coverage over the incidence angle range 31 to 47 degrees. RADARSAT-2 can operate in single and dual polarisation for this beam mode. BEAM MODE: ScanSAR Narrow PRODUCT: SCN, SCF, SCS Nominal Pixel Spacing - Range x Azimuth (m) : 25 x 25 Nominal Resolution - Range x Azimuth (m):81-38 x 40-70 Nominal Scene Size - Range x Azimuth (km): 300 x 300 Range of Angle of Incidence (deg): 20 - 46 No. of Looks - Range x Azimuth: 2 x 2 Polarisations - Options: • Single Co or Cross: HH or VV or HV or VH • Dual: HH + HV or VV + VH ScanSAR Wide Beam Mode provides coverage of a ground swath approximately triple the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCWA, which uses physical beams W1, W2, W3, and S7, and SCWB, which uses physical beams W1, W2, S5 and S6. The SCWA combination allows imaging of a swath of more than 500 km covering an incidence angle range of 20 to 49 degrees. The SCWB combination allows imaging of a swath of more than 450 km covering the incidence angle. Polarisation can be single and dual. BEAM MODE: ScanSAR Wide PRODUCT: SCW, SCF, SCS Nominal Pixel Spacing - Range x Azimuth (m) : 50 x 50 Resolution - Range x Azimuth (m): 163.0 - 73 x 78-106 Nominal Scene Size - Range x Azimuth (km): 500 x 500 Range of Angle of Incidence (deg): 20 - 49 No. of Looks - Range x Azimuth: 4 x 2 Polarisations - Options: • Single Co or Cross: HH or VV or HV or VH • Dual: HH + HV or VV + VH These are the different products : SLC (Single Look Complex): Amplitude and phase information is preserved. Data is in slant range. Georeferenced and aligned with the satellite track SGF (Path Image): Data is converted to ground range and may be multi-look processed. Scene is oriented in direction of orbit path. Georeferenced and aligned with the satellite track. SGX (Path Image Plus): Same as SGF except processed with refined pixel spacing as needed to fully encompass the image data bandwidths. Georeferenced and aligned with the satellite track SSG(Map Image): Image is geocorrected to a map projection. SPG (Precision Map Image): Image is geocorrected to a map projection. Ground control points (GCP) are used to improve positional accuracy. SCN(ScanSAR Narrow)/SCF(ScanSAR Wide) : ScanSAR Narrow/Wide beam mode product with original processing options and metadata fields (for backwards compatibility only). Georeferenced and aligned with the satellite track SCF (ScanSAR Fine): ScanSAR product equivalent to SGF with additional processing options and metadata fields. Georeferenced and aligned with the satellite track SCS(ScanSAR Sampled) : Same as SCF except with finer sampling. Georeferenced and aligned with the satellite track proprietary Radial_Growth_PRI_1781_1 ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019 ORNL_CLOUD STAC Catalog 2018-05-01 2019-09-13 -149.76, 67.97, -149.72, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401854-ORNL_CLOUD.umm_json This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics. proprietary Radial_Growth_PRI_1781_1 ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019 ALL STAC Catalog 2018-05-01 2019-09-13 -149.76, 67.97, -149.72, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401854-ORNL_CLOUD.umm_json This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics. proprietary -Rain-on-Snow_Data_1611_1 ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016 ALL STAC Catalog 2002-11-01 2016-12-31 -175.4, 48.62, -111.54, 73.85 https://cmr.earthdata.nasa.gov/search/concepts/C2162145449-ORNL_CLOUD.umm_json This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual months of November to March 2002-2011 and November to March 2012-2016, and annual water year summary maps for 2003-2011 and 2013-2016. ROS events were defined as changes in passive microwave (PM) detection in surface snow wetness and isothermal states induced by atmospheric processes often associated with winter rainfall. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. The daily ROS record encompassed the months when snowmelt from solar irradiance is minimal and snow cover is widespread and relatively consistent throughout the region. Daily ROS geospatial classification across Alaska was derived by combining snow cover and daily microwave brightness temperature retrievals sensitive to landscape freeze-thaw dynamics from overlapping (1) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A2 eight-day maximum snow cover extent (SCE) product and (2) Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (2002-2011) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (2012-to present) Microwave Radiation Imager (MWRI) observations at 19 GHz and 37 GHz. proprietary Rain-on-Snow_Data_1611_1 ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016 ORNL_CLOUD STAC Catalog 2002-11-01 2016-12-31 -175.4, 48.62, -111.54, 73.85 https://cmr.earthdata.nasa.gov/search/concepts/C2162145449-ORNL_CLOUD.umm_json This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual months of November to March 2002-2011 and November to March 2012-2016, and annual water year summary maps for 2003-2011 and 2013-2016. ROS events were defined as changes in passive microwave (PM) detection in surface snow wetness and isothermal states induced by atmospheric processes often associated with winter rainfall. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. The daily ROS record encompassed the months when snowmelt from solar irradiance is minimal and snow cover is widespread and relatively consistent throughout the region. Daily ROS geospatial classification across Alaska was derived by combining snow cover and daily microwave brightness temperature retrievals sensitive to landscape freeze-thaw dynamics from overlapping (1) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A2 eight-day maximum snow cover extent (SCE) product and (2) Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (2002-2011) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (2012-to present) Microwave Radiation Imager (MWRI) observations at 19 GHz and 37 GHz. proprietary +Rain-on-Snow_Data_1611_1 ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016 ALL STAC Catalog 2002-11-01 2016-12-31 -175.4, 48.62, -111.54, 73.85 https://cmr.earthdata.nasa.gov/search/concepts/C2162145449-ORNL_CLOUD.umm_json This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual months of November to March 2002-2011 and November to March 2012-2016, and annual water year summary maps for 2003-2011 and 2013-2016. ROS events were defined as changes in passive microwave (PM) detection in surface snow wetness and isothermal states induced by atmospheric processes often associated with winter rainfall. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. The daily ROS record encompassed the months when snowmelt from solar irradiance is minimal and snow cover is widespread and relatively consistent throughout the region. Daily ROS geospatial classification across Alaska was derived by combining snow cover and daily microwave brightness temperature retrievals sensitive to landscape freeze-thaw dynamics from overlapping (1) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A2 eight-day maximum snow cover extent (SCE) product and (2) Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (2002-2011) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (2012-to present) Microwave Radiation Imager (MWRI) observations at 19 GHz and 37 GHz. proprietary RapidEye.ESA.archive_7.0 RapidEye ESA archive ESA STAC Catalog 2009-02-22 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C1965336937-ESA.umm_json The RapidEye ESA archive is a subset of the RapidEye Full archive that ESA collected over the years. The dataset regularly grows as ESA collects new RapidEye products. proprietary RapidEye.Full.archive_6.0 RapidEye Full Archive ESA STAC Catalog 2009-02-01 2020-03-31 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2547572717-ESA.umm_json The RapidEye Level 3A Ortho Tile, both Visual (in natural colour) and Analytic (multispectral), full archive and new tasking products are available as part of Planet imagery offer. The RapidEye Ortho Tile product (L3A) is radiometric, sensor and geometrically corrected (by using DEMs with a post spacing of between 30 and 90 meters) and aligned to a cartographic map projection. Ground Control Points (GCPs) are used in the creation of every image and the accuracy of the product will vary from region to region based on available GCPs. Product Components and Format: • Image File – GeoTIFF file that contains image data and geolocation information • Metadata File – XML format metadata file • Unusable Data Mask (UDM) file – GeoTIFF format Bands: 3-band natural color (blue, green, red) or 5-band multispectral image (blue, green, red, red edge, near-infrared) Ground Sampling Distance (nadir): 6.5 m at nadir (average at reference altitude 475 km) Projection: UTM WGS84 Accuracy: depends on the quality of the reference data used (GCPs and DEMs) The products are available as part of the Planet provision from RapidEye, Skysat and PlanetScope constellations.RapidEye collection has worldwide coverage: the Planet Explorer Catalogue (https://www.planet.com/explorer/) can be accessed (Planet registration requested) to discover and check the data readiness. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf). proprietary RapidEye.South.America_6.0 RapidEye South America ESA STAC Catalog 2012-07-12 2015-12-13 -81, -41, 54, 1 https://cmr.earthdata.nasa.gov/search/concepts/C1965336940-ESA.umm_json ESA, in collaboration with BlackBridge, has collected this RapidEye dataset of level 3A tiles covering more than 6 million km2 of South American countries: Paraguay, Ecuador, Chile, Bolivia, Peru, Uruguay and Argentina. The area is fully covered with low cloud coverage proprietary @@ -13471,10 +13451,10 @@ RemSensPOC_0 Remote-sensing-derived particulate organic carbon (POC) validation ResourceSat-1-IRS-P6.archive_6.0 ResourceSat-1/IRS-P6 full archive ESA STAC Catalog 2003-11-01 2013-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336942-ESA.umm_json ResourceSat-1 (also known as IRS-P6) archive products are available as below. • LISS-IV MN: Mono-Chromatic, Resolution 5 m, Coverage 70 km x 70 km, Radiometrically and Ortho (DN) corrected, Acquisition in Neustrelitz 2004 - 2010, Global Archive 2003 - 2013 • LISS-III: Multi-spectral, Resolution 20 m, Coverage 140 km x 140 km, Radiometrically and Ortho (DN) corrected (ortho delivered without Band 5), Acquisition in Neustrelitz 2004 - 2013, Global Archive 2003 - 2013 • AWiFS: Multi-spectral, Resolution 60 m, Coverage 370 km x 370 km, Radiometrically and Ortho (DN) corrected, Acquisition in Neustrelitz 2004 - 2013, Global Archive 2003 - 2013 Note: • LISS-IV: Mono-Chromatic, the band is selectable. In practice the red is used. • For LISS-IV MN and LISS-III ortho corrected: If unavailable, user has to supply ground control information and DEM in suitable qualityFor AWiFS ortho corrected: service based on in house available ground control information and DEM The products are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. ‘ResourceSat-1 archive’ collection has worldwide coverage: for data acquired over Neustrelitz footprint, the users can browse the EOWEB GeoPortal catalogue (http://www.euromap.de/products/serv_003.html) to search archived products; worldwide data (out the Neustrelitz footprint) can be requested by contacting GAF user support to check the readiness since no catalogue is not available. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Indian-Data-Terms-Of-Applicability.pdf). proprietary ResourceSat-2.archive.and.tasking_6.0 ResourceSat-2 full archive and tasking ESA STAC Catalog 2011-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336944-ESA.umm_json ResourceSat-2 (also known as IRS-R2) archive and tasking products are available as below: Sensor: LISS-IV Type: Mono-Chromatic Resolution (m): 5 Coverage (km x km): 70 x 70 System or radiometrically corrected and Ortho corrected (DN) Neustralitz archive: 2014 Global archive: 2011 Sensor: LISS-III Type: Multi-spectral Resolution (m): 20 Coverage (km x km): 140 x 140 System or radiometrically corrected, Ortho corrected (DN) and Ortho corrected (TOA reflectance) Neustralitz archive: 2014 Global archive: 2011 Sensor: AWiFS Type: Multi-spectral Resolution (m): 60 Coverage (km x km): 370 x 370 System or radiometrically corrected, Ortho corrected (DN) and Ortho corrected (TOA reflectance) Neustralitz archive: 2014 Global archive: 2011 Note: • LISS-IV: Mono-Chromatic, the band is selectable. In practice the red is used.For LISS-IV MN and LISS-III ortho corrected: If unavailable, user has to supply ground control information and DEM in suitable qualityFor AWiFS ortho corrected: service based on in house available ground control information and DEM The products are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. ‘ResourceSat-2 archive and tasking’ collection has worldwide coverage: for data acquired over Neustrelitz footprint, the users can browse the EOWEB GeoPortal catalogue (http://www.euromap.de/products/serv_003.html) to search archived products; worldwide data (out the Neustrelitz footprint) can be requested by contacting GAF user support to check the readiness since no catalogue is not available. All details about the data provision, data access conditions and quota assignment procedure are described in the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Indian-Data-Terms-Of-Applicability.pdf). proprietary Respiration_622_1 Global Annual Soil Respiration Data (Raich and Schlesinger 1992) ORNL_CLOUD STAC Catalog 1963-01-01 1992-01-01 -156.4, -37.5, 146.5, 71.18 https://cmr.earthdata.nasa.gov/search/concepts/C2216863171-ORNL_CLOUD.umm_json This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates. proprietary -RiSCC_Outcomes_Bibliography_1 A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions AU_AADC STAC Catalog 1994-01-01 2006-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311230-AU_AADC.umm_json A bibliography of references relating to the outcomes of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1994 to 2006. The bibliography was compiled by Dana Bergstrom, and contains 162 references. proprietary RiSCC_Outcomes_Bibliography_1 A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions ALL STAC Catalog 1994-01-01 2006-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311230-AU_AADC.umm_json A bibliography of references relating to the outcomes of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1994 to 2006. The bibliography was compiled by Dana Bergstrom, and contains 162 references. proprietary -RiSCC_Research_Support_Bibliography_1 A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions AU_AADC STAC Catalog 1875-01-01 2004-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311231-AU_AADC.umm_json A bibliography of references relating to the research support of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1875 to 2004. The bibliography was compiled by Dana Bergstrom, and contains 76 references. proprietary +RiSCC_Outcomes_Bibliography_1 A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions AU_AADC STAC Catalog 1994-01-01 2006-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311230-AU_AADC.umm_json A bibliography of references relating to the outcomes of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1994 to 2006. The bibliography was compiled by Dana Bergstrom, and contains 162 references. proprietary RiSCC_Research_Support_Bibliography_1 A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions ALL STAC Catalog 1875-01-01 2004-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311231-AU_AADC.umm_json A bibliography of references relating to the research support of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1875 to 2004. The bibliography was compiled by Dana Bergstrom, and contains 76 references. proprietary +RiSCC_Research_Support_Bibliography_1 A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions AU_AADC STAC Catalog 1875-01-01 2004-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311231-AU_AADC.umm_json A bibliography of references relating to the research support of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1875 to 2004. The bibliography was compiled by Dana Bergstrom, and contains 76 references. proprietary River_Ice_Breakup_Freezeup_1697_1 ABoVE: River Ice Breakup and Freeze-up Stages, Yukon River Basin, Alaska, 1972-2016 ORNL_CLOUD STAC Catalog 1972-11-04 2016-11-30 -160.07, 62.9, -142.99, 66.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143403517-ORNL_CLOUD.umm_json This dataset provides estimates of river ice breakup and freeze-up stages along selected reaches of the Yukon and Tanana Rivers in the Yukon River Basin in interior Alaska from 1972-2016. Time series of Landsat satellite images were visually interpreted to identify the day of year and characteristics of the different stages of river ice seasonality. The stages of breakup or freeze-up were distinguished from one another based on the spatial extent and patterns of open water and ice cover. Images were displayed as false color composites, with the shortwave infrared (SWIR), near infrared (NIR), and green bands represented by red, green, and blue. proprietary River_Ice_Breakup_Freezeup_1697_1 ABoVE: River Ice Breakup and Freeze-up Stages, Yukon River Basin, Alaska, 1972-2016 ALL STAC Catalog 1972-11-04 2016-11-30 -160.07, 62.9, -142.99, 66.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143403517-ORNL_CLOUD.umm_json This dataset provides estimates of river ice breakup and freeze-up stages along selected reaches of the Yukon and Tanana Rivers in the Yukon River Basin in interior Alaska from 1972-2016. Time series of Landsat satellite images were visually interpreted to identify the day of year and characteristics of the different stages of river ice seasonality. The stages of breakup or freeze-up were distinguished from one another based on the spatial extent and patterns of open water and ice cover. Images were displayed as false color composites, with the shortwave infrared (SWIR), near infrared (NIR), and green bands represented by red, green, and blue. proprietary RoyalPenguin1955-1969_1 Breeding biology of the Royal Penguin (Eudypted chrysolophus)at Macquarie Island 1955-1969 AU_AADC STAC Catalog 1955-01-01 1969-12-31 158.76892, -54.78247, 158.95569, -54.48201 https://cmr.earthdata.nasa.gov/search/concepts/C1214313721-AU_AADC.umm_json The data are contained in a number of log books in hand written form (now scanned onto CD ROM. They were gathered according to a protocol updated annually by the Principal Investigator, DR Robert Carrick (now deceased). Details are contained in the paper Carrick R (1972) Population ecology of the Australian black-backed magpie, royal penguin, and silver gull. in: Population ecology of migratory birds - A symposium. US Dept of the Interior, Fish and wildlife service. Wildlife Research Report 2. pp 41-99. The only other information on the Royal penguin population to come from these investigations is the PhD Thesis of G.T. Smith, Studies on the behaviour and reproduction of the Royal penguin Eudyptes chrysolophus schlegeli. Australian National University April 1970. The log books contain a vast array of observations on the Royal penguin. Major observations/studies include banding of chicks and adults, breeding chronology, egg laying, breeding success, arrival weights, movements within and between colonies. The protocols for the collection of the data are missing although some instructions and notes are included in the volumes. Some data have also been entered into an excel spreadsheet. proprietary @@ -13758,8 +13738,8 @@ SIMBAD_DESCHAMPS_LOA_0 Measurements using the SIMBAD radiometer by the Laboratoi SIO-Pier_0 Scripps Ocean Institute (SOI) pier measurements OB_DAAC STAC Catalog 2007-04-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360662-OB_DAAC.umm_json Measurements made from the Scripps Ocean Institute pier in 2007. proprietary SIPEX_ASPECT_1 ASPeCt Sea Ice Data from the SIPEX Voyage of the Aurora Australis in 2007-2008 AU_AADC STAC Catalog 2007-09-09 2007-10-11 116.43, -65.6, 129.133, -61.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214311291-AU_AADC.umm_json ASPeCt is an expert group on multi-disciplinary Antarctic sea ice zone research within the SCAR Physical Sciences program. Established in 1996, ASPeCt has the key objective of improving our understanding of the Antarctic sea ice zone through focussed and ongoing field programs, remote sensing and numerical modelling. The program is designed to complement, and contribute to, other international science programs in Antarctica as well as existing and proposed research programs within national Antarctic programs. ASPeCt also includes a component of data rescue of valuable historical sea ice zone information. The overall aim of ASPeCt is to understand and model the role of Antarctic sea ice in the coupled atmosphere-ice-ocean system. This requires an understanding of key processes, and the determination of physical, chemical, and biological properties of the sea ice zone. These are addressed by objectives which are: 1) To establish the distribution of the basic physical properties of sea ice that are important to air-sea interaction and to biological processes within the Antarctic sea-ice zone (ice and snow cover thickness distributions; structural, chemical and thermal properties of the snow and ice; upper ocean hydrography; floe size and lead distribution). These data are required to derive forcing and validation fields for climate models and to determine factors controlling the biology and ecology of the sea ice-associated biota. 2) To understand the key sea-ice zone processes necessary for improved parameterization of these processes in coupled models. These ASPeCt measurements were taken onboard the Aurora Australis during the SIPEX voyage in the 2007-2008 summer season. proprietary SIPEX_II_ASPECT_1 ASPeCt ship-based observations during the SIPEX II voyage of the Aurora Australis, 2012 AU_AADC STAC Catalog 2012-09-22 2012-11-11 113, -66, 125, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214311294-AU_AADC.umm_json This dataset contains observations of ice conditions taken from the bridge of the RV Aurora Australis during SIPEX 2012, following the Scientific Committee on Antarctic Research/CliC Antarctic Sea Ice Processes and Climate [ASPeCt] protocols. See aspect.antarctica.gov.au Observations include total and partial concentration, ice type, thickness, floe size, topography, and snow cover in each of three primary ice categories; open water characteristics, and weather summary. The dataset is comprised of the scanned pages of a single logbook, which holds hourly observations taken by observers while the ship was moving through sea-ice zone. The following persons assisted in the collection of these data: Dr R. Massom, AAD, Member of observation team Mr A. Steer, AAD, Member of observation team Prof S. Warren, UW(Seattle), USA, Member of observation team Dr J. Hutchings, IARC, UAF, USA, Member of observation team Dr T. Toyota, Inst Low Temp Science, Japan, Member of observation team Dr T. Tamura, NIPR, Japan, Member of EM observation team Dr G. Dieckmann, AWI, Germany, Member of observation team Dr E. Maksym, WHOI, USA, Member of observation team Mr R. Stevens, IMAS, Trainee on observation team Dr J. Melbourne-Thomas, ACE CRC, Trainee on observation team Dr A. Giles, ACE CRC, Trainee on observation team Ms M. Zhia, IMAS, Trainee on observation team Ms J. Jansens, IMAS, Trainee on observation team Mr R. Humphries, Univ Wollengong, Trainee on observation team Mr C. Sampson, Univ Utah, USA, Trainee on observation team Mr Olivier Lecomte, Univ Catholique, Louvain-la-Neuve, Belgium, Trainee on observation team Mr D. Lubbers, Univ Utah, USA, Trainee on observation team Ms M. Zatko, UW(Seattle), USA, Trainee on observation team Ms C. Gionfriddo, Uni Melbourne, Trainee on observation team Mr K. Nakata, EES, Japan, Trainee on observation team proprietary -SIPEX_II_AUV_1 3-D mapping of sea ice draft with an autonomous underwater vehicle ALL STAC Catalog 2012-09-28 2012-10-13 115, -65, 125, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214311295-AU_AADC.umm_json We set out to achieve floe-scale 3-D mapping of sea ice draft and bio-optical parameters using a Multibeam SONAR and Hyperspectral radiometer mounted to an Autonomous Underwater Vehicle (AUV). The AUV utilised was the 'JAGUAR' Seabed-class vehicle from the Deep Submergence Laboratory at the WoodsHole Oceanographic Institution. The AUV comes with a CTD and ADCP. However these are not deployed as scientific sensors and therefore are unsupported in terms of metadata. In particular the CTD was not calibrated before or during the voyage. The AUV used a LongBaseLine system formed by three transponders to navigate to and from the survey grid. Two were located on the ice and the third was deployed from the back of the ship with an acoustic communications modem. Once at the survey grid beneath the sea ice, the AUV used the DVL to navigate using bottom-tracking of the underside of the sea ice. We conducted 4 missions beneath sea-ice during the SIPEX-II voyage. The current status of the data is that is in un-processed and unavailable until final processing is completed in 2013. Persons interested in the data should contact Dr Guy Williams directly for further information and preliminary figures relating to the AUV missions. The files currently in the archive are in raw form. Some preliminary data is provided for stations 2, 3, 4 and 6 as: floe-2-20120926.mat floe-3-20121003.mat floe-4-20121006.mat floe-6-20121013.mat These can be accessed using the Seabed_plot routines (MATLAB) in this folder. There is a readme file provided called what-is-this.txt Also included is the video footage taken from the AUV using a GoPro HD Hero. Video Codec: avc1 Resolution: 1920x1080 pixels Frame Rate: 29.970030 f/s Audio Codec: mp4a Audio Bitrate: 1536 kb/s Finally, plots of the data for ice stations 2,3,4 and 6 are included in the preliminary figures folder. The file names indicate which ice station the plots are from. proprietary SIPEX_II_AUV_1 3-D mapping of sea ice draft with an autonomous underwater vehicle AU_AADC STAC Catalog 2012-09-28 2012-10-13 115, -65, 125, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214311295-AU_AADC.umm_json We set out to achieve floe-scale 3-D mapping of sea ice draft and bio-optical parameters using a Multibeam SONAR and Hyperspectral radiometer mounted to an Autonomous Underwater Vehicle (AUV). The AUV utilised was the 'JAGUAR' Seabed-class vehicle from the Deep Submergence Laboratory at the WoodsHole Oceanographic Institution. The AUV comes with a CTD and ADCP. However these are not deployed as scientific sensors and therefore are unsupported in terms of metadata. In particular the CTD was not calibrated before or during the voyage. The AUV used a LongBaseLine system formed by three transponders to navigate to and from the survey grid. Two were located on the ice and the third was deployed from the back of the ship with an acoustic communications modem. Once at the survey grid beneath the sea ice, the AUV used the DVL to navigate using bottom-tracking of the underside of the sea ice. We conducted 4 missions beneath sea-ice during the SIPEX-II voyage. The current status of the data is that is in un-processed and unavailable until final processing is completed in 2013. Persons interested in the data should contact Dr Guy Williams directly for further information and preliminary figures relating to the AUV missions. The files currently in the archive are in raw form. Some preliminary data is provided for stations 2, 3, 4 and 6 as: floe-2-20120926.mat floe-3-20121003.mat floe-4-20121006.mat floe-6-20121013.mat These can be accessed using the Seabed_plot routines (MATLAB) in this folder. There is a readme file provided called what-is-this.txt Also included is the video footage taken from the AUV using a GoPro HD Hero. Video Codec: avc1 Resolution: 1920x1080 pixels Frame Rate: 29.970030 f/s Audio Codec: mp4a Audio Bitrate: 1536 kb/s Finally, plots of the data for ice stations 2,3,4 and 6 are included in the preliminary figures folder. The file names indicate which ice station the plots are from. proprietary +SIPEX_II_AUV_1 3-D mapping of sea ice draft with an autonomous underwater vehicle ALL STAC Catalog 2012-09-28 2012-10-13 115, -65, 125, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214311295-AU_AADC.umm_json We set out to achieve floe-scale 3-D mapping of sea ice draft and bio-optical parameters using a Multibeam SONAR and Hyperspectral radiometer mounted to an Autonomous Underwater Vehicle (AUV). The AUV utilised was the 'JAGUAR' Seabed-class vehicle from the Deep Submergence Laboratory at the WoodsHole Oceanographic Institution. The AUV comes with a CTD and ADCP. However these are not deployed as scientific sensors and therefore are unsupported in terms of metadata. In particular the CTD was not calibrated before or during the voyage. The AUV used a LongBaseLine system formed by three transponders to navigate to and from the survey grid. Two were located on the ice and the third was deployed from the back of the ship with an acoustic communications modem. Once at the survey grid beneath the sea ice, the AUV used the DVL to navigate using bottom-tracking of the underside of the sea ice. We conducted 4 missions beneath sea-ice during the SIPEX-II voyage. The current status of the data is that is in un-processed and unavailable until final processing is completed in 2013. Persons interested in the data should contact Dr Guy Williams directly for further information and preliminary figures relating to the AUV missions. The files currently in the archive are in raw form. Some preliminary data is provided for stations 2, 3, 4 and 6 as: floe-2-20120926.mat floe-3-20121003.mat floe-4-20121006.mat floe-6-20121013.mat These can be accessed using the Seabed_plot routines (MATLAB) in this folder. There is a readme file provided called what-is-this.txt Also included is the video footage taken from the AUV using a GoPro HD Hero. Video Codec: avc1 Resolution: 1920x1080 pixels Frame Rate: 29.970030 f/s Audio Codec: mp4a Audio Bitrate: 1536 kb/s Finally, plots of the data for ice stations 2,3,4 and 6 are included in the preliminary figures folder. The file names indicate which ice station the plots are from. proprietary SIPEX_II_Aerosols_1 In-situ total aerosol number using condensation particle counters as observed during the SIPEX II voyage of the Aurora Australis, 2012 AU_AADC STAC Catalog 2012-09-23 2012-10-24 119, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311293-AU_AADC.umm_json "The current dataset includes total aerosol count from two different Condensation Particle Counters (CPCs). The two CPCs measure total aerosol number in two different size ranges: - TSI Model 3025A measures particles with diameters larger than 3 nm (files are in the 3025_3nm folder) - TSI Model 3772 measures particles with diameters larger than 10 nm (files are in the 3772_10nm folder) The two CPCs are measuring from the same sample air and as such, the difference between the two measurements gives a measurement of total aerosol concentration in the 3-10 nm size range, known as the nucleation mode. Instrument setup: The instruments are setup inside an insulated shipping container mounted on the hatch covers directly aft of the forecastle. A 100 L pump is used to pull sample air from a 3 m high mast located on the starboard side of the forecastle. The air is pulled through 17 m of 50 mm antistatic (copper coil) polyurethane tubing and 2 m of 50 mm stainless steel pipe for connection and extensions. A 1 m length of one quarter inch stainless steel tubing penetrates into the container and directly through the wall of the polyurethane tubing for sampling off the primary flow to the CPCs. The inserted stainless steel tubing is oriented in such a way that sampled aerosol experience minimal turns to avoid sample loss. Approximately 1.7 m of flexible conductive tubing extends to a Y-piece which directs flow into each CPC. Butanol contaminated exhaust from the CPCs is pushed out of the container by two 10 LPM pumps. Data Processing: Raw data is calibrated for each instrument's recorded flow rate, and an inlet efficiency to correct for losses in the long inlet. Data is then resampled to minute time resolution, and filtered for logged events, wind directions which sampled ship exhaust, and outliers in the dataset. This produced a dataset which represented the sampling of clean Antarctic background atmosphere. The dataset includes both aerosol number concentrations from each instrument giving total number of particles above 3 nm and 10 nm respectively, as well as the different between these values, which gives a measure of newly formed particles in the nucleation mode between 3-10 nm (New Particle Formation, NPF). Associated uncertainties are included in the dataset." proprietary SIPEX_II_Albedo_1 Albedos for 300-2500nm for thin sea ice covered with frost flowers, nilas, snow, and slush collected during SIPEX II ALL STAC Catalog 2012-09-14 2012-11-04 113, -66, 147, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311265-AU_AADC.umm_json This dataset contains albedo data for several varieties of sea ice and snow from 300-2500 nm measured during the SIPEX II voyage (2012). An Analytical Spectral Device (ASD) spectrophotometer records the amount of radiation impingent on a cosine collector, which contains a spectralon diffuser plate. The radiation that hits the diffuser plate is scattered equally in all directions (isotropically). A portion of the radiation incident on the plate is scattered in the direction of a fiber optic cable, which is connected to the ASD. The ASD separates the incoming radiation into 3-10 nm wavelength bins, thus creating a radiation spectrum spanning 300-2500 nm. The cosine collector can be oriented both upwards towards the sky and downward towards the snow and/or sea ice to measure the spectral signature of both the downwelling (from the sky) and upwelling (from the snow/ice) radiation. For each site, we record 5 upwelling and 5 downwelling spectral signatures. MATLAB or a similar analysis package is required to open the spectrum files that are created by the ASD. The ASD files are raw files and named in a sequence, starting with 'spectrum.000'. MATLAB or similar scripts can been written to convert the ASD spectrum data to .mat files. The spectra in the processed files are used to calculate the albedos for various snow and ice types when the ratio of upwelling to downwelling radiation is computed. We use two upwelling scans per one downwelling scan to compute the albedo. Also included is some photography of frost flowers and other examples of ice that was observed. proprietary SIPEX_II_Albedo_1 Albedos for 300-2500nm for thin sea ice covered with frost flowers, nilas, snow, and slush collected during SIPEX II AU_AADC STAC Catalog 2012-09-14 2012-11-04 113, -66, 147, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311265-AU_AADC.umm_json This dataset contains albedo data for several varieties of sea ice and snow from 300-2500 nm measured during the SIPEX II voyage (2012). An Analytical Spectral Device (ASD) spectrophotometer records the amount of radiation impingent on a cosine collector, which contains a spectralon diffuser plate. The radiation that hits the diffuser plate is scattered equally in all directions (isotropically). A portion of the radiation incident on the plate is scattered in the direction of a fiber optic cable, which is connected to the ASD. The ASD separates the incoming radiation into 3-10 nm wavelength bins, thus creating a radiation spectrum spanning 300-2500 nm. The cosine collector can be oriented both upwards towards the sky and downward towards the snow and/or sea ice to measure the spectral signature of both the downwelling (from the sky) and upwelling (from the snow/ice) radiation. For each site, we record 5 upwelling and 5 downwelling spectral signatures. MATLAB or a similar analysis package is required to open the spectrum files that are created by the ASD. The ASD files are raw files and named in a sequence, starting with 'spectrum.000'. MATLAB or similar scripts can been written to convert the ASD spectrum data to .mat files. The spectra in the processed files are used to calculate the albedos for various snow and ice types when the ratio of upwelling to downwelling radiation is computed. We use two upwelling scans per one downwelling scan to compute the albedo. Also included is some photography of frost flowers and other examples of ice that was observed. proprietary @@ -13848,16 +13828,16 @@ SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3_5.3 RSS SMAP Level 3 Sea Surface Salinity Stand SMAP_RSS_L3_SSS_SMI_MONTHLY_V5_5.0 RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V5.0 Validated Dataset POCLOUD STAC Catalog 2015-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208416221-POCLOUD.umm_json The version 5.0 SMAP-SSS level 3, monthly gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS) with a one-month latency. The major changes in Version 5.0 from Version 4 are: (1) the addition of formal uncertainty estimates to all salinity retrieval products. (2) Sea-ice flagging and sea-ice side-lobe correction based on direct ingestion of AMSR-2 brightness temperature (TB) measurements. This is in contrast to Version 4 and earlier versions in which the sea-ice correction was based on an external sea-ice concentration product. The use of AMSR-2 TB measurements in the SMAP Version 5 products allows for salinity retrievals closer to the sea-ice edge and aids in the detection of large icebergs near the Antarctic. Monthly data files for this product are averages over one-month time intervals. SMAP data begins on April 1,2015 and is ongoing, with a one-month latency in processing and availability. L3 products are global in extent with a default spatial resolution of approximately 70KM. The datasets are gridded at 0.25degree x 0.25degree. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval. proprietary SMAP_RSS_L3_SSS_SMI_MONTHLY_V6_6.0 RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V6.0 Validated Dataset POCLOUD STAC Catalog 2015-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2832226365-POCLOUD.umm_json The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V6.0 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.

The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. The RSS SMAP L3 monthly product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, rain filtered SMAP sea surface salinity, collocated wind speed, data and ancillary reference surface salinity data from HYCOM. Each data file is available in netCDF-4 file format and is averaged over one-month time intervals with about 7-day latency (after the end of the averaging period). Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km. proprietary SMERGE_RZSM0_40CM_2.0 Smerge-Noah-CCI root zone soil moisture 0-40 cm L4 daily 0.125 x 0.125 degree V2.0 (SMERGE_RZSM0_40CM) at GES DISC GES_DISC STAC Catalog 1979-01-02 2019-05-10 -125, 25, -67, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1569839798-GES_DISC.umm_json Smerge-Noah-CCI root zone soil moisture 0-40 cm L4 daily 0.125 x 0.125 degree V2.0 is a multi-decadal root-zone soil moisture product. Smerge is developed by merging the North American Land Data Assimilation System (NLDAS) land surface model output with surface satellite retrievals from the European Space Agency Climate Change Initiative. The data have a 0.125 degree resolution at a daily time-step, covering the entire continental United States and spanning nearly four decades (January 1979 to May 2019). This data product contains root-zone soil moisture of 0 - 40 cm layer, Climate Change Initiative (CCI) derived soil moisture anomalies of 0-40 cm layer, and a soil moisture data estimation flag. This data product is the recommended replacement for the AMSR-E/Aqua root zone soil moisture L3 1 day 25 km x 25 km descending and 2-Layer Palmer Water Balance Model V001 product (LPRM_AMSRE_D_RZSM3), which will be removed from archive on June 27, 2022. Smerge provides a better root zone soil moisture estimation because it has higher data quality and longer temporal coverage. proprietary -SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0 ACEX 2004 ODEN TRACK ALL STAC Catalog 2004-08-08 2004-09-13 19.045, 69.727, 175.94, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595274-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0 ACEX 2004 ODEN TRACK SCIOPS STAC Catalog 2004-08-08 2004-09-13 19.045, 69.727, 175.94, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595274-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary -SMHI_IPY_ACEX-2004-Seismic ACEX 2004 Seismic ALL STAC Catalog 2004-08-08 2004-09-13 139.0632, 87.917, 140.31, 87.977 https://cmr.earthdata.nasa.gov/search/concepts/C1214595276-SCIOPS.umm_json Reflection seismic profiles aquired during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary +SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0 ACEX 2004 ODEN TRACK ALL STAC Catalog 2004-08-08 2004-09-13 19.045, 69.727, 175.94, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595274-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary SMHI_IPY_ACEX-2004-Seismic ACEX 2004 Seismic SCIOPS STAC Catalog 2004-08-08 2004-09-13 139.0632, 87.917, 140.31, 87.977 https://cmr.earthdata.nasa.gov/search/concepts/C1214595276-SCIOPS.umm_json Reflection seismic profiles aquired during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary -SMHI_IPY_ACEX-2004-Sites_1.0 ACEX 2004 Sites ALL STAC Catalog 2004-08-08 2004-09-13 -4.05029, 69.727, 19.045, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595252-SCIOPS.umm_json The site location for the cores retrieved during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary +SMHI_IPY_ACEX-2004-Seismic ACEX 2004 Seismic ALL STAC Catalog 2004-08-08 2004-09-13 139.0632, 87.917, 140.31, 87.977 https://cmr.earthdata.nasa.gov/search/concepts/C1214595276-SCIOPS.umm_json Reflection seismic profiles aquired during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary SMHI_IPY_ACEX-2004-Sites_1.0 ACEX 2004 Sites SCIOPS STAC Catalog 2004-08-08 2004-09-13 -4.05029, 69.727, 19.045, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595252-SCIOPS.umm_json The site location for the cores retrieved during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary -SMHI_IPY_AGAVE2007-track_1.0 AGAVE2007 track SCIOPS STAC Catalog 2007-07-01 2007-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595299-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the Arctic Gakkel Vents Expedition (AGAVE) 2007. proprietary +SMHI_IPY_ACEX-2004-Sites_1.0 ACEX 2004 Sites ALL STAC Catalog 2004-08-08 2004-09-13 -4.05029, 69.727, 19.045, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595252-SCIOPS.umm_json The site location for the cores retrieved during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary SMHI_IPY_AGAVE2007-track_1.0 AGAVE2007 track ALL STAC Catalog 2007-07-01 2007-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595299-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the Arctic Gakkel Vents Expedition (AGAVE) 2007. proprietary -SMHI_IPY_ALIS ALIS, Auroral Large Imaging System ALL STAC Catalog 1993-12-23 2009-02-18 18.8, 67.3, 21.7, 69.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214595251-SCIOPS.umm_json ALIS consists of unmanned imaging stations located in Northern Scandinavia in a grid of about 50×50 km. Each station is equipped with an imager having a high-resolution monochrome 1024×1024 pixel CCD detector and a filter wheel with six positions for narrow-band interference filters. The field of view is 70 degrees diagonally for most imagers, but there are also two units with a 90 degrees field of view. The imagers are mounted in a positioning system and can be pointed so that several imagers can view a common volume. ALIS is operated on campaign basis. Filter sequences and pointing directions are freely selectable. proprietary +SMHI_IPY_AGAVE2007-track_1.0 AGAVE2007 track SCIOPS STAC Catalog 2007-07-01 2007-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595299-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the Arctic Gakkel Vents Expedition (AGAVE) 2007. proprietary SMHI_IPY_ALIS ALIS, Auroral Large Imaging System SCIOPS STAC Catalog 1993-12-23 2009-02-18 18.8, 67.3, 21.7, 69.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214595251-SCIOPS.umm_json ALIS consists of unmanned imaging stations located in Northern Scandinavia in a grid of about 50×50 km. Each station is equipped with an imager having a high-resolution monochrome 1024×1024 pixel CCD detector and a filter wheel with six positions for narrow-band interference filters. The field of view is 70 degrees diagonally for most imagers, but there are also two units with a 90 degrees field of view. The imagers are mounted in a positioning system and can be pointed so that several imagers can view a common volume. ALIS is operated on campaign basis. Filter sequences and pointing directions are freely selectable. proprietary +SMHI_IPY_ALIS ALIS, Auroral Large Imaging System ALL STAC Catalog 1993-12-23 2009-02-18 18.8, 67.3, 21.7, 69.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214595251-SCIOPS.umm_json ALIS consists of unmanned imaging stations located in Northern Scandinavia in a grid of about 50×50 km. Each station is equipped with an imager having a high-resolution monochrome 1024×1024 pixel CCD detector and a filter wheel with six positions for narrow-band interference filters. The field of view is 70 degrees diagonally for most imagers, but there are also two units with a 90 degrees field of view. The imagers are mounted in a positioning system and can be pointed so that several imagers can view a common volume. ALIS is operated on campaign basis. Filter sequences and pointing directions are freely selectable. proprietary SMMRN7IM_001 SMMR/Nimbus-7 Color Images V001 (SMMRN7IM) at GES DISC GES_DISC STAC Catalog 1978-10-30 1983-11-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1616514843-GES_DISC.umm_json "SMMRN7IM is the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Color Image data product scanned from 17"" x 15"" color prints and saved as JPEG-2000 files. Sea surface temperature, sea surface winds, total atmospheric water vapor over oceans, total atmospheric liquid water over oceans, including brightness temperature parameters are available as both 6-day composites and 1-month averages between 64 south and north latitudes in Mercator projection. Sea ice fraction, sea ice and ocean surface temperature, sea ice concentration, including brightness temperature parameters are available as both 3-day and 1-month averages in north and south polar stereographic projections. Images may contain between one and three measured parameters. These SMMR images are available from 30 October 1978 through 2 November 1983. The principal investigator for the SMMR experiment was Dr. Per Gloersen from NASA GSFC. These products were previously available from the NSSDC under the ids ESAD-00007, ESAD-00056, ESAD-00123, ESAD-00124, ESAD-00162, ESAD-00172, ESAD-00173, ESAD-00176 ESAD-00177, ESAD-00178, and ESAD-00241 (old ids 78-098A-08I-S)." proprietary SMMR_ALW_PRABHAKARA_1 Scanning Multichannel Microwave Radiometer (SMMR) Monthly Mean Atmospheric Liquid Water (ALW) By Prabhakara LARC_ASDC STAC Catalog 1979-02-01 1984-05-31 180, -48, -180, 48 https://cmr.earthdata.nasa.gov/search/concepts/C1336972900-LARC_ASDC.umm_json SMMR_ALW_PRABHAKARA data are Special Multichannel Microwave Radiometer (SMMR) Monthly Mean Atmospheric Liquid Water (ALW) data by Prabhakara.The Prabhakara Scanning Multichannel Microwave Radiometer (SMMR) Atmospheric Liquid Water (ALW) files were generated by Dr. Prabhakara Cuddapah at the Goddard Space Flight Center (GSFC) using SMMR Antenna Temperatures. A discussion of the SMMR Antenna Temperatures is available from the Langley Distributed Active Archive Center (DAAC). Each ALW file contains one month of 3 degree by 5 degree gridded mean liquid water. Each element of data is in units of mg/cm2. The data spans the period from February 1979 to May 1984. proprietary SMMR_IWV_PRABHAKARA_1 Scanning Multichannel Microwave Radiometer (SMMR) Monthly Mean Integrated Water Vapor (IWV) By Prabhakara LARC_ASDC STAC Catalog 1979-01-01 1983-09-30 -180, -75, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C1336972882-LARC_ASDC.umm_json SMMR_IWV_PRABHAKARA data are Special Multichannel Microwave Radiometer (SMMR) Monthly Mean Integrated Water Vapor (IWV) data by Prabhakara.The Scanning Multichannel Microwave Radiometer (SMMR) Prabhakara integrated atmospheric water vapor (IWV) files were generated by Dr. Prabhakara Cuddapah at the Goddard Space Flight Center (GSFC) using SMMR Antenna Temperatures. A discussion of the SMMR Antenna Temperatures is available from the Langley Research Center Distributed Active Archive Center (DAAC). Each IWV file contains one month of 3 degree by 5 degree gridded mean water vapor. A scale factor of 0.1 must be applied to convert the data into units of g/cm2. The data spans the period from October 1979 to September 1983. proprietary @@ -14224,8 +14204,8 @@ SOR4XPSD_LOW_012 SORCE XPS Level 4 Solar Spectral Irradiance 1.0nm Res 24-Hour M SORTIE_0 Spectral Ocean Radiance Transfer Investigation and Experiment (SORTIE) program OB_DAAC STAC Catalog 2007-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360665-OB_DAAC.umm_json Measurements made under the SORTIE (Spectral Ocean Radiance Transfer Investigation and Experiment) program between 2007 and 2009. proprietary SPACE_PHOTOS Space Acquired Photography USGS_LTA STAC Catalog 1965-03-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566702-USGS_LTA.umm_json Gemini photography was acquired between March 23, 1965 and November 15, 1966. The images were collected as part of the Synoptic Terrain Photography and the Synoptic Weather Photography experiments during Gemini Missions III through XII. Hand-held cameras were used to obtain photographs of geologic, oceanic, and meteorologic targets. The Gemini archive consists primarily of 70-mm black and white (B/W), color, and color-infrared (CIR) film. All Gemini photographs are distributed by the USGS Earth Resources Observation and Science (EROS) Center as digital products only. Skylab photography was acquired between May 22, 1973 and February 8, 1974 during three manned flights. The Skylab Earth Resources Experiment Package used two photographic remote sensing systems. The Multispectral Photographic Camera (S190A), was a six-camera array, in which each camera used 70-mm film and a six-inch focal length lens. The acquired film ranges from narrow-band B/W to broad-band color and CIR. The Earth Terrain Camera (S190B) consisted of a single high-resolution camera which used five-inch film and an 18-inch focal length lens. The acquired film includes B/W, black and white infrared (BIR), color, and CIR. All Skylab photographs are distributed by the USGS EDC as digital products only. Shuttle Large Format Camera (LFC) images were acquired from the Space Shuttle Challenger Mission on October 5-13, 1984. The LFC was mounted in the cargo bay, and was operated via signals from ground controllers. The archived imagery includes 9 x 18 inch B/W, natural color, and CIR film. Shuttle LFC photographs are distributed by the USGS EDC as digital products only. proprietary SPANBR Automatic Atmospheric Sun Photometer Data for Brazil CEOS_EXTRA STAC Catalog 1992-06-01 -65, -28, -45, -2 https://cmr.earthdata.nasa.gov/search/concepts/C2227456147-CEOS_EXTRA.umm_json A network of 9 automatic sunphotometers operates in Brazil. Direct sun and sky radiances are acquired every hour by a weather resistant Cimel spectral radiometer in the wavelengths of 340, 440, 670,870, 940, and 1020 nm and transmitted automatically through the NOAA data collection system geostationary link for near real-time processing into spectral aerosol optical thickness, wavelength exponent and precipitable water. Evaluation of the atmospheric effects of biomass burning emissions from June-November are among the primary targets of the measurements. ftp://ftp.pmel.noaa.gov proprietary -SPL1AP_002 SMAP L1A Radiometer Time-Ordered Parsed Telemetry V002 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2938661641-NSIDC_CPRD.umm_json

Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts of the following:

  • The first four raw moments of the fullband channel for both vertical and horizontal polarizations
  • The complex cross-correlations of the fullband channel
  • The 16 subband channels for both vertical and horizontal polarizations
proprietary SPL1AP_002 SMAP L1A Radiometer Time-Ordered Parsed Telemetry V002 NSIDC_ECS STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C1000001801-NSIDC_ECS.umm_json

Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts of the following:

  • The first four raw moments of the fullband channel for both vertical and horizontal polarizations
  • The complex cross-correlations of the fullband channel
  • The 16 subband channels for both vertical and horizontal polarizations
proprietary +SPL1AP_002 SMAP L1A Radiometer Time-Ordered Parsed Telemetry V002 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2938661641-NSIDC_CPRD.umm_json

Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts of the following:

  • The first four raw moments of the fullband channel for both vertical and horizontal polarizations
  • The complex cross-correlations of the fullband channel
  • The 16 subband channels for both vertical and horizontal polarizations
proprietary SPL1A_001_1 SMAP_L1A_RADAR_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214473171-ASF.umm_json SMAP Level 1A Radar Product proprietary SPL1A_002_2 SMAP_L1A_RADAR_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243149604-ASF.umm_json SMAP Level 1A Radar Product Version 2 proprietary SPL1A_METADATA_001_1 SMAP_L1A_RADAR_METADATA_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214473426-ASF.umm_json SMAP Level 1A Radar Product Metadata proprietary @@ -14253,10 +14233,10 @@ SPL1B_SO_LoRes_METADATA_003_3 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_METADATA_V003 ASF ST SPL1B_SO_LoRes_QA_001_1 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_QA_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214474243-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Data Quality Info proprietary SPL1B_SO_LoRes_QA_002_2 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_QA_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243216659-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Data Quality Info Version 2 proprietary SPL1B_SO_LoRes_QA_003_3 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_QA_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243129847-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Data Quality Info Version 3 proprietary -SPL1CTB_006 SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.umm_json This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product. proprietary SPL1CTB_006 SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.umm_json This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product. proprietary -SPL1CTB_E_004 SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.umm_json This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. proprietary +SPL1CTB_006 SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.umm_json This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product. proprietary SPL1CTB_E_004 SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.umm_json This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. proprietary +SPL1CTB_E_004 SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.umm_json This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. proprietary SPL1C_S0_HiRes_001_1 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214473367-ASF.umm_json SMAP Level 1C Sigma Naught High Res Product proprietary SPL1C_S0_HiRes_002_2 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243268956-ASF.umm_json SMAP Level 1C Sigma Naught High Res Product Version 2 proprietary SPL1C_S0_HiRes_003_3 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243144528-ASF.umm_json SMAP Level 1C Sigma Naught High Res Product Version 3 proprietary @@ -14268,35 +14248,35 @@ SPL1C_S0_HiRes_QA_002_2 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_QA_V002 ASF STAC Catalog SPL1C_S0_HiRes_QA_003_3 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_QA_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243140611-ASF.umm_json SMAP Level 1C Sigma Naught High Res Data Quality Info Version 3 proprietary SPL2SMAP_003 SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303829-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL2SMAP_003 SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2830464428-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary -SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary -SPL2SMA_003 SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary +SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary SPL2SMA_003 SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary -SPL2SMP_009 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary +SPL2SMA_003 SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL2SMP_009 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary -SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary +SPL2SMP_009 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary +SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary SPL2SMP_NRT_107 Near Real-time SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V107 NSIDC_ECS STAC Catalog 2024-12-05 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2312096175-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product SPL2SMP (https://doi.org/10.5067/LPJ8F0TAK6E0)." proprietary -SPL3FTA_003 SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, 45, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872766057-NSIDC_CPRD.umm_json This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3FTA_003 SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, 45, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303849-NSIDC_ECS.umm_json This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary +SPL3FTA_003 SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, 45, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872766057-NSIDC_CPRD.umm_json This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3FTP_004 SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.umm_json This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0. proprietary SPL3FTP_004 SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.umm_json This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0. proprietary SPL3FTP_E_004 SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.umm_json This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal. proprietary SPL3FTP_E_004 SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.umm_json This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal. proprietary -SPL3SMAP_003 SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303847-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMAP_003 SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872767144-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary +SPL3SMAP_003 SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303847-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMA_003 SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMA_003 SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary -SPL3SMP_009 SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMP_009 SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463935-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary +SPL3SMP_009 SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary SPL3SMP_E_006 SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776463943-NSIDC_ECS.umm_json This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. proprietary SPL3SMP_E_006 SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2938664763-NSIDC_CPRD.umm_json This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. proprietary -SPL4CMDL_007 SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2534576405-NSIDC_ECS.umm_json The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4CMDL_007 SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665243-NSIDC_CPRD.umm_json The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary -SPL4SMAU_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products:
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)
  • SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).
For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary +SPL4CMDL_007 SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2534576405-NSIDC_ECS.umm_json The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMAU_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products:
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)
  • SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).
For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary -SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary +SPL4SMAU_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products:
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)
  • SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).
For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary +SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMLM_007 SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938666109-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPL4SMLM_007 SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2537926833-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary SPOT-6.and.7.ESA.archive_9.0 SPOT-6 and 7 ESA archive ESA STAC Catalog 2012-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336951-ESA.umm_json The SPOT 6 and 7 ESA archive is a dataset of SPOT 6 and SPOT 7 products that ESA collected over the years. The dataset regularly grows as ESA collects new SPOT 6 and 7 products. SPOT 6 and 7 Primary and Ortho products can be available in the following modes: Panchromatic image at 1.5m resolution Pansharpened colour image at 1.5m resolution Multispectral image in 4 spectral bands at 6m resolution Bundle (1.5m panchromatic image + 6m multispectral image) Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/socat/SPOT6-7 available on the Third Party Missions Dissemination Service. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided. proprietary @@ -14345,8 +14325,8 @@ SPURS2_WAVEGLIDER_1.0 SPURS-2 Waveglider data for the E. Tropical Pacific field SPURS2_XBAND_1.0 SPURS-2 shipboard X-band radar backscatter data for the E. Tropical Pacific field campaign POCLOUD STAC Catalog 2017-10-21 2017-11-13 -129.131, 8.927, -122.151, 10.355 https://cmr.earthdata.nasa.gov/search/concepts/C2781659132-POCLOUD.umm_json The SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar. Raw data could not be saved. The screenshots show qualitative (uncalibrated) echoes of backscatter from rain. For full details on the screenshots, how they should be used, and what they show about rainfall, please refer to our publication: Thompson, E.J., W.E. Asher, A.T. Jessup, and K. Drushka. 2019. High-Resolution Rain Maps from an X-band Marine Radar and Their Use in Understanding Ocean Freshening. Oceanography 32(2):58–65, https://doi.org/10.5670/oceanog.2019.213 . The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. proprietary SPURS2_XBAND_IMG_1.0 SPURS-2 shipboard X-band radar backscatter images for the 2016 E. Tropical Pacific field campaign POCLOUD STAC Catalog 2016-08-31 2016-09-22 -129.131, 8.927, -122.151, 10.355 https://cmr.earthdata.nasa.gov/search/concepts/C2931233351-POCLOUD.umm_json The SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar. Raw data could not be saved. The screenshots show qualitative (uncalibrated) echoes of backscatter from rain. For full details on the screenshots, how they should be used, and what they show about rainfall, please refer to our publication: Thompson, E.J., W.E. Asher, A.T. Jessup, and K. Drushka. 2019. High-Resolution Rain Maps from an X-band Marine Radar and Their Use in Understanding Ocean Freshening. Oceanography 32(2):58–65, https://doi.org/10.5670/oceanog.2019.213 . The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. proprietary SPURS2_XBT_1.0 SPURS-2 research vessel Expendable Bathythermograph (XBT) profile data for E. Tropical Pacific R/V Revelle cruises POCLOUD STAC Catalog 2016-08-14 2017-11-15 -157.88, 5.06, -118.32, 21.26 https://cmr.earthdata.nasa.gov/search/concepts/C2491772372-POCLOUD.umm_json The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Expendable bathythermograph (XBT) casts were undertaken at stations during both of the SPURS-2 R/V Revelle cruises. Launched off the side of the ship, XBT probes provide vertical profile measurements of the water column at fixed locations. There were a total of 25 and 11 XBT deployments made during the first and second R/V Revelle cruises respectively. There is one XBT data file per cruise, each containing the temperature profile data from all instrument deployments undertaken during that cruise. proprietary -SRDB_V5_1827_5 A Global Database of Soil Respiration Data, Version 5.0 ALL STAC Catalog 1961-01-01 2017-12-31 -163.71, -78.02, 175.9, 81.8 https://cmr.earthdata.nasa.gov/search/concepts/C2216864433-ORNL_CLOUD.umm_json The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science. proprietary SRDB_V5_1827_5 A Global Database of Soil Respiration Data, Version 5.0 ORNL_CLOUD STAC Catalog 1961-01-01 2017-12-31 -163.71, -78.02, 175.9, 81.8 https://cmr.earthdata.nasa.gov/search/concepts/C2216864433-ORNL_CLOUD.umm_json The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science. proprietary +SRDB_V5_1827_5 A Global Database of Soil Respiration Data, Version 5.0 ALL STAC Catalog 1961-01-01 2017-12-31 -163.71, -78.02, 175.9, 81.8 https://cmr.earthdata.nasa.gov/search/concepts/C2216864433-ORNL_CLOUD.umm_json The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science. proprietary SRE4_SAB_gammaclones_1 Clone library using primers for gammaproteobacteria from an SAB treatment in the SRE4 experiment AU_AADC STAC Catalog 2002-12-01 2002-12-31 110, -66, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313841-AU_AADC.umm_json A clone library was created from DNA extracted from an SAB-treated sample from the SRE4 in situ biodegradation experiment. The clone libary was created using one universal primer and one primer designed to be specific for the gammaproteobacteria. Sequences of approximately 600 bp were obtained. The samples used in this experiment were collected from O'Brien Bay, near Casey Station in the Windmill Islands. Gammaproteobacteria clone library Clone library created from SRE4 T2 SAB sample using primers 10F (GAG TTT GAT CCT GGC TCA G ) and GAMR (GGT AAG GTT CTT CGC GTT GCA T). Clones sequenced on a CEQ8000 Genetic Analysis system (Beckman-Coulter) and alignments were done in BioEdit v 5.0.9. Text file SRE4gammaclonesalign is a text version of BioEdit file SRE4gammaclones. This work was completed as part of ASAC project 2672 (ASAC_2672). proprietary SRE4_desulfobaculaDGGE_1 Band pattern data from Desulfobacula-group specific DGGE for the SRE4 experiment AU_AADC STAC Catalog 2001-10-25 2003-03-30 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313816-AU_AADC.umm_json Samples are from the SRE4 experiment - an in situ experiment to determine fate and effects of different types of oils in the Antarctic marine environment. For details see: Powell S.M., Snape I., Bowman J.P., Thompson B.A.W., Stark J.S., McCammon S.A., Riddle M.J. 2005. A comparison of the short term effects of diesel fuel and lubricant oils on Antarctic benthic microbial communities. Journal of Experimental Marine Biology and Ecology 322:53-65. Samples were analysed by denaturing gradient gel electrophoresis (DGGE) with primers specific for the Desulfobacula group. Samples A,B,C,D,E,F,G,H,I are all initial samples collected different days Samples beginning T0 are predeployment samples, the next number refers to the batch. Samples beginning T2 are 1 year samples with: C = control S = SAB L = lubricant U = used lubricant B = biodegradable lubricant PCR conditions were as follows: Primers: 764F: ACAATGGTAAATGAGGGCA 1392RC: CGCCCGCCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCCCACGGGCGG TGTGTAC 50 ul (micro litre) reactions with Advantage II taq (Clontech) following manufacturer's recommendations with 20 pmol (pico mol) each primer and 20 ng (nano gram) template DNA. Cycling: 94C 5 minutes 10 cycles of: 94C 1 minutes 65C 1 minutes (-1C per cycle) 72C 2 minutes 20 cycles of: 94C 1 minutes 55C 1 minutes 72C 2 minutes 72C 30 minutes DGGE carried out using the D-Code system (BioRad). Gel: 8% acrylamide 30 - 65% denaturant with 2 cm stacking gel (15% acrylamide) 1 x TAE, 60 degrees C, 70V 16 hours The gels were pre-run for 20 minutes then half reaction volume was loaded and the lanes flushed out after 15 minutes. Gels were stained with SYBRGold. Images were captured using Storm Phosphorimager and ImageQuant v5.2 software(.gel files). Samples were only compared within a gel. Band pattern results are in the file desulfodgge.xls. For each comparison made there is a separate sheet in this file (see below). The first column in each sheet is the band position (or band name) and the remaining columns are samples with the first row being the sample name. '0' '1' indicate the band was 'absent' or 'present'. Comparison Image files (.gel and .tif) results sheets Background variation 140704f; 140704b 140704f and 140704b predeployment batches 180604f; 180406b 180604f and 180604b effect of setup 150704 150704 immediate effect of oil 250604f; 250604b 250604f and 250604b 1 year samples (T2) 040804f; 040804b 040804f and 040804b This work was completed as part of ASAC projects 1228 and 2201 (ASAC_1228, ASAC_2201). proprietary SRE4_gammaproteobacteriaDGGE_1 Band pattern data from Gammaproteobacteria-group specific DGGE AU_AADC STAC Catalog 2001-10-25 2003-03-30 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313817-AU_AADC.umm_json Samples are from the SRE4 experiment - an in situ experiment to determine fate and effects of different types of oils in the Antarctic marine environment. For details see: Powell S.M., Snape I., Bowman J.P., Thompson B.A.W., Stark J.S., McCammon S.A., Riddle M.J. 2005. A comparison of the short term effects of diesel fuel and lubricant oils on Antarctic benthic microbial communities. Journal of Experimental Marine Biology and Ecology 322:53-65. Samples were analysed by denaturing gradient gel electrophoresis (DGGE) with primers specific for the Gammaproteobacteria. Samples used were from Time2 (1 year) Initial: T-1C; T-1E Control: T2C SAB treatment: T2S PCR conditions: Primers: GAMFC: CGC CCG CCG CGC CCC GCG CCC GGC CCG CCG CCC CCG CCC GGG TTA ATC GGA ATT ACT GG GAMR: GGT AAG GTT CTT CGC GTT GCA T 50 ul (micro litre) reactions with HotStar (qiagen) mix, 5ul Q solution, 10 pmol (pico mol) each primer and 20 ng (nano gram) template DNA cycling: 94C 15 minutes 35 cycles of: 94C 1 minutes 55C 1 minutes 72C 1 minutes 72C 20 minutes DGGE was performed using D-Code system (BioRad). Gel: 8% acryloamide, 30 - 65% denaturant with 2 cm stacking gel 1 x TAE, 60 degrees C, 80V 16 hours Gel was pre-run for 20 minutes and lanes were flushed out after 15 minutes. Gel was stained with Sybrgold. Image captured using Storm Phosphorimager and ImageQuant v5.2 software (.gel files). The image files are called 151105#2.gel and 151105.tif Band pattern results are in gammadgge.xls. The first column is the band position (or band name) and the remaining columns are samples with the first row being the sample name. The numbers indicates how many times the band appeared for that sample out of 2 DGGE runs. This work was completed as part of ASAC projects 1228 and 2201 (ASAC_1228, ASAC_2201). proprietary @@ -14555,8 +14535,8 @@ Salt_Marsh_Biomass_CONUS_2348_1 Aboveground Biomass Estimates for Salt Marsh for San_Diego_Coastal_Project_0 San Diego Coastal Project OB_DAAC STAC Catalog 2004-11-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360636-OB_DAAC.umm_json Measurements near the Southern Californias coast made under the San Diego Coastal Project between 2004 and 2006. proprietary Sargassum_GOM_0 Importance of pelagic Sargassum to fisheries management in the Northern Gulf of Mexico OB_DAAC STAC Catalog 2017-07-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360637-OB_DAAC.umm_json Measurements made under the Linking habitat to recruitment: evaluating the importance of pelagic Sargassum to fisheries management in the Gulf of Mexico, in the Northern Gulf of Mexico. Collaboration with USF and USM. proprietary Saskatchewan_Soils_125m_SSA_1346_2 BOREAS Agriculture Canada Central Saskatchewan Vector Soils Data, R1 ORNL_CLOUD STAC Catalog 1980-01-01 2001-02-06 -110.45, 52.86, -99.87, 55.06 https://cmr.earthdata.nasa.gov/search/concepts/C2773240578-ORNL_CLOUD.umm_json This data set provides soil descriptions for forested areas in the BOREAS southern study area (SSA) in central Saskatchewan, Canada provided by Agriculture Canada. The data contain soil code, modifiers, extent, and soil names for the primary, secondary, and tertiary soil units within each polygon. proprietary -Sat_ActiveLayer_Thickness_Maps_1760_1 ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015 ALL STAC Catalog 2001-01-01 2015-12-31 -179.18, 55.57, -132.58, 70.21 https://cmr.earthdata.nasa.gov/search/concepts/C2143402571-ORNL_CLOUD.umm_json This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided. proprietary Sat_ActiveLayer_Thickness_Maps_1760_1 ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015 ORNL_CLOUD STAC Catalog 2001-01-01 2015-12-31 -179.18, 55.57, -132.58, 70.21 https://cmr.earthdata.nasa.gov/search/concepts/C2143402571-ORNL_CLOUD.umm_json This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided. proprietary +Sat_ActiveLayer_Thickness_Maps_1760_1 ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015 ALL STAC Catalog 2001-01-01 2015-12-31 -179.18, 55.57, -132.58, 70.21 https://cmr.earthdata.nasa.gov/search/concepts/C2143402571-ORNL_CLOUD.umm_json This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided. proprietary SatelliteDerived_Forest_Mexico_2320_1 Satellite-Derived Forest Extent Likelihood Map for Mexico ORNL_CLOUD STAC Catalog 2010-01-01 2020-12-31 -120.31, 12.48, -84.29, 34.51 https://cmr.earthdata.nasa.gov/search/concepts/C2905454214-ORNL_CLOUD.umm_json This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas. proprietary Scambos_PLR1441432 A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions SCIOPS STAC Catalog 2014-06-01 2015-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214604828-SCIOPS.umm_json The investigators propose to build and test a multi-sensor, automated measurement station for monitoring Arctic and Antarctic ice-ocean environments. The system, based on a previously successful design, will incorporate weather and climate sensors, camera, snow and firn sensors, instruments to measure ice motion, ice and ocean thermal profilers, hydrophone, and salinity sensors. This new system will have two-way communications for real-time data delivery and is designed for rapid deployment by a small field group. proprietary Scambos_PLR1441432 A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions ALL STAC Catalog 2014-06-01 2015-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214604828-SCIOPS.umm_json The investigators propose to build and test a multi-sensor, automated measurement station for monitoring Arctic and Antarctic ice-ocean environments. The system, based on a previously successful design, will incorporate weather and climate sensors, camera, snow and firn sensors, instruments to measure ice motion, ice and ocean thermal profilers, hydrophone, and salinity sensors. This new system will have two-way communications for real-time data delivery and is designed for rapid deployment by a small field group. proprietary @@ -14619,8 +14599,8 @@ Seabirds_AAT_1 Distribution and abundance of breeding seabirds in the AAT AU_AAD Seabirds_HIMI_1 Distribution and abundance of breeding seabirds at Heard Island and the McDonald Islands AU_AADC STAC Catalog 1901-01-01 70, -55, 75, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214313740-AU_AADC.umm_json Distribution and abundance of breeding seabirds at Heard I and the McDonald Is. This dataset comprises a broad range of component datasets derived from ground surveys aerial photography and oblique photography. Since the data have also been derived from old station logs for the 1947-54 period, and from published and unpublished records for the 1947-present day period. Aerial and oblique photography has been used to obtain supplementary information on distribution and abundance of seabirds in the region. Recent surveys, 2000/01 onwards, have made use of GPS for more precise geographic information on seabird nests and colonies. At present there are a number of child metadata records attached to this record. See the link above for details. proprietary Seagrass_Mapping_Florida_0 Water quality measurements near the Big Bend Seagrasses Aquatic Preserve, Florida OB_DAAC STAC Catalog 2010-05-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360643-OB_DAAC.umm_json Water quality measurements taken near the Big Bend Seagrasses Aquatic Preserve in Florida. proprietary Searcher_0 Measurements from the Baltic Sea in 1999 OB_DAAC STAC Catalog 1999-07-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360656-OB_DAAC.umm_json Measurements from the Baltic Sea in 1999. proprietary -Seasonality_Tundra_Vegetation_1606_1 ABoVE: Climate Drivers of Pan-Arctic Tundra Vegetation Productivity, 1982-2015 ALL STAC Catalog 1982-01-01 2015-12-31 -180, 70, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2162145436-ORNL_CLOUD.umm_json This dataset provides a summary of potential climate drivers of Arctic tundra vegetation productivity that have been compiled for growing seasons from 1982 to 2015. The scale of interest is the entire pan-arctic non-alpine tundra and the continental subdivisions of the North American and the Eurasian Arctic North of 70 degrees. These climate drivers include (1) maximum normalized difference vegetation index (MaxNDVI) and time-integrated NDVI (TI-NDVI), (2) summer sea ice concentrations, (3) oceanic heat content, (4) land surface temperature, and (5) summer warmth index (SWI). Data are provided variously as timeseries and weekly and bi-weekly averages over selected time ranges and study regions with calculated trends and trend significance. Data collected over 33 years were compiled to observe seasonal trends of vegetation productivity and to detect dynamics between arctic vegetation and climate drivers. proprietary Seasonality_Tundra_Vegetation_1606_1 ABoVE: Climate Drivers of Pan-Arctic Tundra Vegetation Productivity, 1982-2015 ORNL_CLOUD STAC Catalog 1982-01-01 2015-12-31 -180, 70, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2162145436-ORNL_CLOUD.umm_json This dataset provides a summary of potential climate drivers of Arctic tundra vegetation productivity that have been compiled for growing seasons from 1982 to 2015. The scale of interest is the entire pan-arctic non-alpine tundra and the continental subdivisions of the North American and the Eurasian Arctic North of 70 degrees. These climate drivers include (1) maximum normalized difference vegetation index (MaxNDVI) and time-integrated NDVI (TI-NDVI), (2) summer sea ice concentrations, (3) oceanic heat content, (4) land surface temperature, and (5) summer warmth index (SWI). Data are provided variously as timeseries and weekly and bi-weekly averages over selected time ranges and study regions with calculated trends and trend significance. Data collected over 33 years were compiled to observe seasonal trends of vegetation productivity and to detect dynamics between arctic vegetation and climate drivers. proprietary +Seasonality_Tundra_Vegetation_1606_1 ABoVE: Climate Drivers of Pan-Arctic Tundra Vegetation Productivity, 1982-2015 ALL STAC Catalog 1982-01-01 2015-12-31 -180, 70, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2162145436-ORNL_CLOUD.umm_json This dataset provides a summary of potential climate drivers of Arctic tundra vegetation productivity that have been compiled for growing seasons from 1982 to 2015. The scale of interest is the entire pan-arctic non-alpine tundra and the continental subdivisions of the North American and the Eurasian Arctic North of 70 degrees. These climate drivers include (1) maximum normalized difference vegetation index (MaxNDVI) and time-integrated NDVI (TI-NDVI), (2) summer sea ice concentrations, (3) oceanic heat content, (4) land surface temperature, and (5) summer warmth index (SWI). Data are provided variously as timeseries and weekly and bi-weekly averages over selected time ranges and study regions with calculated trends and trend significance. Data collected over 33 years were compiled to observe seasonal trends of vegetation productivity and to detect dynamics between arctic vegetation and climate drivers. proprietary Secret_0 Studies of Ecological and Chemical Responses to Environmental Trends (SECRET) OB_DAAC STAC Catalog 1998-08-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360657-OB_DAAC.umm_json Measurements spanning from the California coast to Hawaii in the mid-Pacific Ocean from 1998 to 2006. proprietary Semantic Segmentation of Crop Type in Ghana_1 Semantic Segmentation of Crop Type in Ghana MLHUB STAC Catalog 2020-01-01 2023-01-01 -2, 8, 1, 11 https://cmr.earthdata.nasa.gov/search/concepts/C2781412078-MLHUB.umm_json Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and a severe lack of training data. To address this gap in the literature, we provide the first crop type semantic segmentation dataset of small holder farms, specifically in Ghana and South Sudan. We are also the first to utilize high resolution, high frequency satellite data in segmenting small holder farms. The dataset includes time series of satellite imagery from Sentinel-1, Sentinel-2, and PlanetScope satellites throughout 2016 and 2017. For each tile/chip in the dataset, there are time series of imagery from each of the satellites, as well as a corresponding label that defines the crop type at each pixel. The label has only one value at each pixel location, and assumes that the crop type remains the same across the full time span of the satellite image time series. In many cases where ground truth was not available, pixels have no label and are set to a value of 0. proprietary Semantic Segmentation of Crop Type in South Sudan_1 Semantic Segmentation of Crop Type in South Sudan MLHUB STAC Catalog 2020-01-01 2023-01-01 24, 1, 36, 13 https://cmr.earthdata.nasa.gov/search/concepts/C2781412590-MLHUB.umm_json Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and a severe lack of training data. To address this gap in the literature, we provide the first crop type semantic segmentation dataset of small holder farms, specifically in Ghana and South Sudan. We are also the first to utilize high resolution, high frequency satellite data in segmenting small holder farms. The dataset includes time series of satellite imagery from Sentinel-1, Sentinel-2, and PlanetScope satellites throughout 2016 and 2017. For each tile/chip in the dataset, there are time series of imagery from each of the satellites, as well as a corresponding label that defines the crop type at each pixel. The label has only one value at each pixel location, and assumes that the crop type remains the same across the full time span of the satellite image time series. In many cases where ground truth was not available, pixels have no label and are set to a value of 0. proprietary @@ -14635,8 +14615,8 @@ SiB4_Global_HalfDegree_Hourly_1847_1 SiB4 Modeled Global 0.5-Degree Hourly Carbo SiB4_Global_HalfDegree_Monthly_1848_1 SiB4 Modeled Global 0.5-Degree Monthly Carbon Fluxes and Pools, 2000-2018 ORNL_CLOUD STAC Catalog 2000-01-01 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2345882961-ORNL_CLOUD.umm_json "This dataset provides global monthly output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Monthly output includes carbon, carbonyl sulfide (COS), and energy fluxes; solar-induced fluorescence (SIF); carbon pools; soil moisture and temperatures in the top three layers; total column soil water and plant available water; and environmental potentials used to scale photosynthesis. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the ""npft"" dimension (01-15) in each data file. The PFT three-character abbreviations (""pft_names"" variable) are listed in the same order as the ""npft"" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the ""pft_area"" variable for each cell." proprietary Siberian_Biomass_Wildfire_1321_1 Siberian Boreal Forest Aboveground Biomass and Fire Scar Maps, Russia, 1969-2007 ORNL_CLOUD STAC Catalog 1969-07-01 2007-07-26 156.61, 64.77, 166.47, 69.9 https://cmr.earthdata.nasa.gov/search/concepts/C2773255198-ORNL_CLOUD.umm_json This data set provides 30-meter resolution mapped estimates of Cajander larch (Larix cajanderi) aboveground biomass (AGB), circa 2007, and a map of burn perimeters for 116 forest fires that occurred from 1966-2007. The data cover ~100,000 km2 of the Kolyma River Basin in northeastern Siberia, Sakha Republic, Russia. proprietary Siberian_Larch_Stand_Age_1364_1 Distribution of Estimated Stand Age Across Siberian Larch Forests, 1989-2012 ORNL_CLOUD STAC Catalog 1989-01-01 2012-12-31 90, 49, 143, 67 https://cmr.earthdata.nasa.gov/search/concepts/C2767498872-ORNL_CLOUD.umm_json This data set provides mapped estimates of the stand age of young (less than 25 years old) larch forests across Siberia from 1989-2012 at 30-m resolution. The age estimates were derived from Landsat-based composites and tree cover for years 2000 and 2012 developed by the Global Forest Change (GFC) project and the stand-replacing fire mapping (SRFM) data set. This approach is based on the assumption that the relationship between the spectral signature of a burned or unburned forest stand acquired by Landsat ETM+ and TM sensors and stand age before and after the year 2000 is similar, thus allowing for training an algorithm on the data from the post-2000 era and applying the algorithm to infer stand age for the pre-2000 era. The output map combines the modeled forest disturbances before 2000 and direct observations of forest loss after 2000 to deliver a 24-year stand age distribution map. proprietary -Skelton_Aeromag_Data Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition) ALL STAC Catalog 1997-01-01 1998-12-31 153.5, -79.7, 166.7, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552348-CEOS_EXTRA.umm_json The Transantarctic Mountains (TAM) rift-flank uplift has developed along the ancestral margin of the East Antarctic craton, and forms the boundary between the craton and the thinned lithosphere of the West Antarctic rift system. Geodynamic processes associated with the exceptionally large-magnitude uplift of the mountain belt remain poorly constrained, but may involve interaction of rift-related mechanical and thermal processes and the inherited mechanical elements of the cratonic lithosphere. The Transantarctic Mountain Aerogeophysical Research Activities (TAMARA) program proposes to document the regional structural architecture of a key segment of the Transantarctic Mountains in the region around the Royal Society Range where the rift flank is offset along a transverse accommodation zone. proprietary Skelton_Aeromag_Data Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition) CEOS_EXTRA STAC Catalog 1997-01-01 1998-12-31 153.5, -79.7, 166.7, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552348-CEOS_EXTRA.umm_json The Transantarctic Mountains (TAM) rift-flank uplift has developed along the ancestral margin of the East Antarctic craton, and forms the boundary between the craton and the thinned lithosphere of the West Antarctic rift system. Geodynamic processes associated with the exceptionally large-magnitude uplift of the mountain belt remain poorly constrained, but may involve interaction of rift-related mechanical and thermal processes and the inherited mechanical elements of the cratonic lithosphere. The Transantarctic Mountain Aerogeophysical Research Activities (TAMARA) program proposes to document the regional structural architecture of a key segment of the Transantarctic Mountains in the region around the Royal Society Range where the rift flank is offset along a transverse accommodation zone. proprietary +Skelton_Aeromag_Data Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition) ALL STAC Catalog 1997-01-01 1998-12-31 153.5, -79.7, 166.7, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552348-CEOS_EXTRA.umm_json The Transantarctic Mountains (TAM) rift-flank uplift has developed along the ancestral margin of the East Antarctic craton, and forms the boundary between the craton and the thinned lithosphere of the West Antarctic rift system. Geodynamic processes associated with the exceptionally large-magnitude uplift of the mountain belt remain poorly constrained, but may involve interaction of rift-related mechanical and thermal processes and the inherited mechanical elements of the cratonic lithosphere. The Transantarctic Mountain Aerogeophysical Research Activities (TAMARA) program proposes to document the regional structural architecture of a key segment of the Transantarctic Mountains in the region around the Royal Society Range where the rift flank is offset along a transverse accommodation zone. proprietary SkySat.Full.Archive.and.New.Tasking_9.0 SkySat Full Archive and New Tasking ESA STAC Catalog 2013-11-13 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C1965336955-ESA.umm_json "The SkySat Level 1 Basic Scene, Level 3B Ortho Scene and Level 3B Consolidated full archive and new tasking products are available as part of the Planet imagery offer. The SkySat Basic Scene product is uncalibrated and in a raw digital number format, not corrected for any geometric distortions inherent to the imaging process. Rational Polynomial Coefficients (RPCs) are provided to enable orthorectification by the user. • Basic Panchromatic Scene product – unorthorectified, radiometrically corrected, panchromatic (PAN) imagery. • Basic Panchromatic DN Scene product – unorthorectified, panchromatic (PAN) imagery. • Basic L1A Panchromatic DN Scene product – unorthorectified, pre-super resolution, panchromatic (PAN) imagery. • Basic Analytic Scene product – unorthorectified, radiometrically corrected, 4-band multispectral (BGR-NIR) imagery. • Basic Analytic DN Scene product – unorthorectified, 4-band multispectral (BGR-NIR) imagery. Basic Scene Product Components and Format Product Components and Format • Image File (GeoTIFF format) • Metadata File (JSON format) • Rational Polynomial Coefficients (Text File) • UDM File (GeoTIFF format) Image Configurations • 1-band Panchromatic/Panchromatic DN Image (PAN) • 4-band Analytic/Analytic DN Image (Blue, Green, Red, NIR) Ground Sampling Distance (nadir) • SkySat-1 & -2: 0.86 m (PAN), 1.0 m (MS) • SkySat-3 to -15: 0.65 m (PAN), 0.8 m (MS). 0.72 m (PAN) and 1.0 m (MS) for data acquired prior to 30/06/2020 • SkySat-16 to -21: 0.57 m (PAN), 0.75 m (MS) Geolocation Accuracy <50 m RMSE The SkySat Ortho Scene product is sensor- and geometrically-corrected (using DEMs with a post spacing of 30 – 90 m) and is projected to a cartographic map projection; the accuracy of the product varies from region-to-region based on available GCPs. • Ortho Panchromatic Scene product – orthorectified, radiometrically corrected, panchromatic (PAN) imagery. • Ortho Panchromatic DN Scene product – orthorectified, panchromatic (PAN), uncalibrated digital number imagery. • Ortho Analytic Scene product – orthorectified, 4-band multispectral (BGR-NIR) imagery. Radiometric corrections are applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance. • Ortho Analytic DN Scene product – orthorectified, 4-band multispectral (BGR-NIR), uncalibrated digital number imagery. Radiometric corrections are applied to correct for any sensor artifacts. • Ortho Pansharpened Multispectral Scene product – orthorectified, pansharpened, 4-band (BGR-NIR) imagery. • Ortho Visual Scene product – orthorectified, pansharpened, colour-corrected (using a colour curve) 3-band (RGB) imagery. Ortho Scene Product Components and Format Product Components and Format • Image File (GeoTIFF format) • Metadata File (JSON format) • Rational Polynomial Coefficients (Text File) • UDM File (GeoTIFF format) Image Configurations • 1-band Panchromatic/Panchromatic DN Image (PAN) • 4-band Analytic/Analytic DN Image (Blue, Green, Red, NIR) • 4-band Pansharpened Multispectral Image (Blue, Green, Red, NIR) • 3-band Pansharpened (Visual) Image (Red, Green, Blue) Orthorectified Pixel Size 50 cm Projection UTM WGS84 Geolocation Accuracy <10 m RMSE The SkySat Ortho Collect product is created by composing SkySat Ortho Scene products along an imaging strip into segments typically unifying ~60 individual SkySat Ortho Scenes, resulting in an image with a footprint of approximately 20 km x 5.9 km. The products may contain artifacts resulting from the composing process, particular offsets in areas of stitched source scenes. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary SkySatESAarchive_8.0 Skysat ESA archive ESA STAC Catalog 2016-02-29 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2547572338-ESA.umm_json "The SkySat ESA archive collection consists of SkySat products requested by ESA supported projects over their areas of interest around the world and that ESA collected over the years. The dataset regularly grows as ESA collects new products. Two different product types are offered, Ground Sampling Distance at nadir up to 65 cm for panchromatic and up to 0.8m for multi-spectral. EO-SIP Product Type Product Description Content SSC_DEF_SC Basic and Ortho scene Level 1B 4-bands Analytic /DN Basic scene Level 1B 4-bands Panchromatic /DN Basic scene Level 1A 1-band Panchromatic DN Pre Sup resolution Basic scene Level 3B 3-bands Visual Ortho Scene Level 3B 4-bands Pansharpened Multispectral Ortho Scene Level 3B 4-bands Analytic/DN/SR Ortho Scene Level 3B 1-band Panchromatic /DN Ortho Scene SSC_DEF_CO Ortho Collect Visual 3-band Pansharpened Image Multispectral 4-band Pansharpened Image Multispectral 4-band Analytic/DN/SR Image (B, G, R, N) 1-band Panchromatic Image The Basic Scene product is uncalibrated, not radiometrically corrected for atmosphere or for any geometric distortions inherent in the imaging process: Analytic - unorthorectified, radiometrically corrected, multispectral BGRN Analytic DN - unorthorectified, multispectral BGRN Panchromatic - unorthorectified, radiometrically corrected, panchromatic (PAN) Panchromatic DN - unorthorectified, panchromatic (PAN) L1A Panchromatic DN - unorthorectified, pre-super resolution, panchromatic (PAN) The Ortho Scene product is sensor and geometrically corrected, and is projected to a cartographic map projection: Visual - orthorectified, pansharpened, and colour-corrected (using a colour curve) 3-band RGB Imagery Pansharpened Multispectral - orthorectified, pansharpened 4-band BGRN Imagery Analytic SR - orthorectified, multispectral BGRN. Atmospherically corrected Surface Reflectance product. Analytic - orthorectified, multispectral BGRN. Radiometric corrections applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance. Analytic DN - orthorectified, multispectral BGRN, uncalibrated digital number imagery product Radiometric corrections applied to correct for any sensor artifacts Panchromatic - orthorectified, radiometrically correct, panchromatic (PAN) Panchromatic DN - orthorectified, panchromatic (PAN), uncalibrated digital number imagery product The Ortho Collect product is created by composing SkySat Ortho Scenes along an imaging strip. The product may contain artifacts resulting from the composing process, particular offsets in areas of stitched source scenes. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/SkySat/ available on the Third Party Missions Dissemination Service. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary Smallholder Cashew Plantations in Benin_1 Smallholder Cashew Plantations in Benin MLHUB STAC Catalog 2020-01-01 2023-01-01 2.4636579, 9.0570625, 2.5618896, 9.1603783 https://cmr.earthdata.nasa.gov/search/concepts/C2781412245-MLHUB.umm_json This dataset contains labels for cashew plantations in a 120 km^2 area in the center of Benin. Each pixel is classified for Well-managed plantation, Poorly-managed plantation, No plantation and other classes. The labels are generated using a combination of ground data collection with a handheld GPS device, and final corrections based on Airbus Pléiades imagery. proprietary @@ -14647,8 +14627,8 @@ Snow_Cover_Extent_and_Depth_1757_1 ABoVE: High Resolution Cloud-Free Snow Cover Snow_Depth_Data_Images_1656_1 Snow Depth, Stratigraphy, and Temperature in Wrangell St Elias NP, Alaska, 2016-2018 ORNL_CLOUD STAC Catalog 2016-09-01 2018-03-20 -143.32, 62.26, -143, 62.39 https://cmr.earthdata.nasa.gov/search/concepts/C2170971586-ORNL_CLOUD.umm_json This dataset includes data from late-March snow surveys and hourly digital camera images from two study areas within the Wrangell St Elias National Park, Alaska. These data comprise snow density, stratigraphy, and temperature profiles obtained by snow pits; and snow depth data obtained from transects between snow pits. Daily snow depths, adjacent to each pit, were derived from hourly camera images of snow stakes placed adjacent to each pit. These data were collected to constrain and validate a physically-based, spatially-distributed snow evolution model used to simulate snow conditions in Dall sheep habitat. The two study areas are both located within the Jacksina Park Unit (JPU). The first study area, surveyed in 2017, included the northern end of Jaeger Mesa and an area near Rambler mine in the North East of the JPU. The second study area, surveyed in 2018, was within the upper watershed of Pass Creek in the North of the JPU. The remote cameras operated from September 2016 to August 2017 on Jaeger Mesa/Rambler Mine and from September 2017 to July 2018 at Pass Creek. proprietary Snow_Wildlife_Tracks_AK_WA_2188_1 Snow Properties and Wildlife Tracks in Washington and Alaska ORNL_CLOUD STAC Catalog 2021-01-09 2023-03-13 -150.01, 48.05, -117.17, 63.97 https://cmr.earthdata.nasa.gov/search/concepts/C2772851281-ORNL_CLOUD.umm_json This dataset contains three field seasons of snow-wildlife observations conducted at 707 sites from January 2021 to March 2023 in Washington and Alaska, spanning a broad range of snow conditions. Relatively fresh tracks (usually <24 h) of common large mammal predators (bobcats, coyotes, cougars, and wolves) and their ungulate prey (caribou, Dall sheep, moose, mule deer, and white-tailed deer) were investigated to determine how snow affects predator-prey interactions. The track sink depth and dimensions (width and length) of three consecutive footprints were measured from one individual. Age class was recorded for moose based either on visual confirmation of an individual creating snow tracks or based on track dimensions. The ability to differentiate age classes for smaller ungulates was more uncertain, so age classes for deer, caribou, or sheep were not specified. Animal gait was identified using a simple classification scheme. Data also include animal species, snow density, hardness, total ice, surface temperature, and vegetation type. To best capture snow hardness, surface penetrability and hand-hardness were measured throughout the snowpack. The data are provided in comma-separated values (CSV) format. proprietary Snowmelt_timing_maps_V2_1712_2 Snowmelt Timing Maps Derived from MODIS for North America, Version 2, 2001-2018 ORNL_CLOUD STAC Catalog 2001-01-01 2018-12-31 -180, 10, 0, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2764725108-ORNL_CLOUD.umm_json This data set provides snowmelt timing maps (STMs), cloud interference maps, and a map with the count of calculated snowmelt timing values for North America. The STMs are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard 8-day composite snow-cover product MOD10A2 collection 6 for the period 2001-01-01 to 2018-12-31. The STMs were created by conducting a time-series analysis of the MOD10A2 snow maps to identify the DOY of snowmelt on a per-pixel basis. Snowmelt timing (no-snow) was defined as a snow-free reading following two consecutive snow-present readings for a given 500-m pixel. The count of STM values is also reported, which represents the number of years on record in the STMs from 2001-2018. proprietary -Snowpack_Dall_Sheep_Track_1583_1 ABoVE: Dall Sheep Track Sinking Depths, Snow Depth, Hardness, and Density, 2017 ALL STAC Catalog 2017-03-19 2017-03-22 -143.06, 62.26, -143.01, 62.28 https://cmr.earthdata.nasa.gov/search/concepts/C2162140002-ORNL_CLOUD.umm_json This dataset contains Dall sheep (Ovis dalli dalli) hoof sinking depths in snow tracks, and snow depth, hardness, and density measurements in snow pits adjacent to the tracks. Snow measurements were collected between March 19-22, 2017 at sites on Jaeger Mesa in the Wrangell Mountains (WRST), Alaska. Estimated sheep age classes and track site coordinates are also provided. proprietary Snowpack_Dall_Sheep_Track_1583_1 ABoVE: Dall Sheep Track Sinking Depths, Snow Depth, Hardness, and Density, 2017 ORNL_CLOUD STAC Catalog 2017-03-19 2017-03-22 -143.06, 62.26, -143.01, 62.28 https://cmr.earthdata.nasa.gov/search/concepts/C2162140002-ORNL_CLOUD.umm_json This dataset contains Dall sheep (Ovis dalli dalli) hoof sinking depths in snow tracks, and snow depth, hardness, and density measurements in snow pits adjacent to the tracks. Snow measurements were collected between March 19-22, 2017 at sites on Jaeger Mesa in the Wrangell Mountains (WRST), Alaska. Estimated sheep age classes and track site coordinates are also provided. proprietary +Snowpack_Dall_Sheep_Track_1583_1 ABoVE: Dall Sheep Track Sinking Depths, Snow Depth, Hardness, and Density, 2017 ALL STAC Catalog 2017-03-19 2017-03-22 -143.06, 62.26, -143.01, 62.28 https://cmr.earthdata.nasa.gov/search/concepts/C2162140002-ORNL_CLOUD.umm_json This dataset contains Dall sheep (Ovis dalli dalli) hoof sinking depths in snow tracks, and snow depth, hardness, and density measurements in snow pits adjacent to the tracks. Snow measurements were collected between March 19-22, 2017 at sites on Jaeger Mesa in the Wrangell Mountains (WRST), Alaska. Estimated sheep age classes and track site coordinates are also provided. proprietary SoilResp_HeterotrophicResp_1928_1 Global Gridded 1-km Soil and Soil Heterotrophic Respiration Derived from SRDB v5 ORNL_CLOUD STAC Catalog 1961-01-01 2016-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2345796019-ORNL_CLOUD.umm_json This dataset provides global gridded estimates of annual soil respiration (Rs) and soil heterotrophic respiration (Rh) and associated uncertainties at 1 km resolution. Mean soil respiration was estimated using a quantile regression forest model utilizing data from the global Soil Respiration Database Version 5 (SRDB-V5) and covariates of mean annual temperature, seasonal precipitation, and vegetative cover. The SRDB holds results of field studies of soil respiration from around the globe. A total of 4,115 records from 1,036 studies were selected from SRDB-V5. SRDB-V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. These soil respiration records were combined with global meteorological, land cover, and topographic data and then evaluated with variable selection using random forests. The standard deviation and coefficient of variation of Rs are included and were also derived from the same model. Global heterotrophic respiration was calculated from Rs estimates. The data are produced in part from SRDB-V5 inputs that cover the period 1961-2016. proprietary SoilSCAPE_1339_1 Soil Moisture Profiles and Temperature Data from SoilSCAPE Sites, USA ORNL_CLOUD STAC Catalog 2011-08-03 2019-12-14 -120.99, 31.74, -83.66, 42.3 https://cmr.earthdata.nasa.gov/search/concepts/C2736724942-ORNL_CLOUD.umm_json This data set contains in-situ soil moisture profile and soil temperature data collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations since August 2011. At its maximum, the network consisted of over 200 wireless sensor installations (nodes), with a range of 6 to 27 nodes per site. The soil moisture sensors (EC-5 and 5-TM from Decagon Devices) were installed at three to four depths, nominally at 5, 20, and 50 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. Temperature sensors were installed at 5 cm depth at six of the sites. Data collection started in August 2011 and continues at eight sites through the present. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional (airborne, e.g. NASA's Airborne Microwave Observation of Subcanopy and Subsurface Mission - AirMOSS) and national (spaceborne, e.g. NASA's Soil Moisture Active Passive - SMAP) scales. proprietary SoilSCAPE_V2_2049_2 Soil Moisture Profiles and Temperature Data from SoilSCAPE Sites, Version 2 ORNL_CLOUD STAC Catalog 2021-12-03 2023-02-03 -110.05, -36.72, 174.62, 37.2 https://cmr.earthdata.nasa.gov/search/concepts/C2736725173-ORNL_CLOUD.umm_json This dataset contains in-situ soil moisture profile and soil temperature data collected at 30-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites since 2021 in the United States and New Zealand. The SoilSCAPE network has used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data over varying durations since 2011. Since 2021, the SoilSCAPE has upgraded the two previously active sites in Arizona and added several new sites in the United States and New Zealand. These new sites typically use the METER Teros-12 soil moisture sensor. At its maximum, the new network consisted of 57 wireless sensor installations (nodes), with a range of 6 to 8 nodes per site. Each SoilSCAPE site contains multiple wireless end-devices (EDs). Each ED supports up to five soil moisture probes typically installed at 5, 10, 20, and 30 cm below the surface. Sites in Arizona have soil moisture probes installed at up to 75 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional and national (e.g. NASA's Cyclone Global Navigation Satellite System - CYGNSS and Soil Moisture Active Passive - SMAP) scales. The data are provided in NetCDF format. proprietary @@ -14659,12 +14639,12 @@ Soil_Moisture_Alaska_Alberta_2123_1 Hourly Soil Moisture Logger Data, Alberta an Soil_Sensors_1 Data collected from in-situ soil sensors placed at Macquarie Island and Casey Station AU_AADC STAC Catalog 2005-01-01 110.52394, -66.28192, 158.9392, -54.498737 https://cmr.earthdata.nasa.gov/search/concepts/C1214313810-AU_AADC.umm_json "Data are collected for the purposes of monitoring on-ground works at Australian Antarctic stations associated with the remediation of petroleum hydrocarbon contaminated soil. Output datasets consist of soil oxygen (%), soil temperature (C), soil moisture content (VWC - Volumetric Water Content %), and aeration manifold pressure as measured by buried sensors (O2, T C, VWC) or manifold instruments (pressure). Sensor types are either: AD590 (temperature C) AD592 (temperature C) Figaro KE25 (% oxygen) Vegetronix VH400 (Volumetric Water Content %) 26PCD (Pressure, kPa) Sensors are attached via instrument cables to Datataker dt80 series loggers, which are housed in waterproof containers mounted on buildings, or inside buildings at Australian Antarctic stations. At the Macquarie Island isthmus, oxygen sensors are attached to buried groundwater monitoring wells (screened PVC tubes, known as mini-piezometers). Pressure sensors are attached to air distribution manifolds (part of an in-situ aeration distribution network), and temperature sensors are buried in the soil profile. Sensor nomenclature is as follows: FF0807/1/O2 (Fuel Farm, 2008 installation, mini-piezometer number 07, Sensor 1, Oxygen sensor) MPH_PS_3 (Main Power House, pressure sensor number 03) Biopiles consist of excavated soil placed in temporary, geo-engineered liner cells. Soil oxygen, soil temperature, and soil moisture content are typically measured at 50 cm height intervals from within the soil piles. Temperature and moisture are also typically measured from within the subgrade and liner materials - common nomenclature for sensor names are as follows: BP1/0.5SS_G11/O2 (Biopile 1, buried 0.5 m in soil profile, location G11, Oxygen sensor) BP1/AGM_G1/T(Biopile 1, Above GeoMembrane, Location G1, Temperature sensor) BP6/AGCL_N1/M (Biopile 6, Above Geosynthetic Clay Liner, Location N1, Moisture sensor) BP6/IGCL_N9/M (Biopile 6, Inside Geosynthetic Clay Liner, Location N9, Moisture sensor) EXT/-30SS_E1/M (External soil location, 30 cm below sediment surface, Sensor 1, Moisture sensor) Permeable Reactive Barrier (PRB's) are permeable gates emplaced within the regolith to treat hydrocarbon contaminated groundwater/meltwater and prevent offsite migration of contaminants (primarily hydrocarbons). The barriers have undergone several design iterations, but have consisted of staged (3 sections) permeable reactive or non-reactive filter media (Granular Activated Carbon, Silica sand, Zeolite, MaxBac (TM), Zeopro (TM), Zero Valent Iron), which are placed in buried galvanised shipping cages. The original PRB (installed 2005/06) is named ""PRB"", the second smaller PRB (named the Upper PRB or ""UPRB"" due to its higher elevation in the ) was installed in 2010/11 to treat contaminated groundwater around the MPH settling tank bund and protected the area cleaned as part of the MPH excavation. From this date, the original PRB has also been referred to as the ""lower PRB"". Sensor nomenclature is as follows: C_MP9/700/T (MiniPiezometer 9, 700 mm below ground surface, Temperature sensor) C_CG3_3/600/02 (Cage 3,Section 3, 600 mm below ground surface, Oxygen sensor) These data are downloaded from the sensors to the Australian Antarctic Division on a daily basis. Data are collected by the sensors every 5-20 minutes. As of 2013-03-04, the following personnel have been involved in the project: Greg Hince (AAD) - Project Manager, Field Remediation (11/12-ongoing). Principle Contact Ian Snape (AAD) - Project Principal (Macquarie Island and Casey Station), Macquarie Island 2008 field team. Geoff Stevens (University of Melbourne) - Project Principal - Casey Lower PRB installation Ben Raymond (AAD) - Calibration and Installation of sensors for Macquarie Island 08/09 field season, maintenance of database and remote troubleshooting of dataloggers. Tim Spedding (ex AAD) - Field Project Manager (08/09-10/11), Macquarie Island 2008 field team Dan Wilkins (AAD) - Datalogger management and system design (2009 onwards), Casey station sensor installation 10/11 and 11/12. John Rayner (ex AAD) - System design - Oxygen sensors. Macquarie Island 2008 field team. Installation of lower PRB (Casey) in 05/06. Lauren Wise (AAD) - Field maintenance and system operation (Macquarie Island, 10/11 and 12/13) Rebecca McWatters (AAD)- Casey Station sensors installation 10/11, 11/12, 12/13 Susan Ferguson (ex AAD) - Macquarie Island 2008 field team, Macquarie Island system maintenance 2009. Brett Quinton (ex AAD) - Macquarie Island system maintenance 2009 Charles Sutherland (AAD contractor/expeditioner) - Macquarie Island system maintenance 12/13 field season Robby Kilpatrick (AAD contractor/expeditioner) - Calibration and Installation of sensors for Macquarie Island 11/12 field season Kathryn Mumford (AAS Project Co-investigator, University of Melbourne) - Installation of lower PRB (Casey) in 05/06. Tom Statham (University of Melbourne, PhD student) - System installation, Casey 10/11 Warren Nichols - Oxygen sensor modifications (resin encasement) Rebecca Miller (AAD contractor/expeditioner) - Calibration and Installation of sensors for Casey EPH biopile - 12/13 Field Season Dan Jones (Queens University, Canada) - Calibration and Installation of sensors for Casey EPH biopile - 12/13 Field Season Various members of AAD Telecommunications Team (on ground troubleshooting and maintenance)" proprietary Soil_Temp_Moisture_Alaska_1869_1 ABoVE: Soil Temperature and VWC at Unburned and Burned Sites Across Alaska, 2016-2023 ORNL_CLOUD STAC Catalog 2016-08-11 2023-09-02 -163.24, 61.27, -146.56, 68.99 https://cmr.earthdata.nasa.gov/search/concepts/C2143401688-ORNL_CLOUD.umm_json This dataset provides soil temperature and volumetric water content (VWC) measurements at 15 cm depth collected at 12 selected boreal and tundra sites located across Alaska. Each site is equipped with a HOBO MicroStation Data Logger that hosts two soil temperature sensors (HOBO S-TMB-M006 Temperature Smart Sensor), and two soil moisture sensors (HOBO S-SMD-M005 10HS Soil Moisture Smart Sensor). Each sensor was installed horizontally at a depth of 15 cm within the soil profile. Samples of soil from seven sites were taken to a laboratory for determination of site-specific soil moisture sensor calibration curves to correct raw measurements. Data were nominally recorded at an hourly frequency and downloaded from the sites at least annually for the period 2016-08-11 to 2023-09-02, but data coverage varies by site. These measurements were collected at the same sites as previously archived CO2 efflux and thaw depth data. The data are provided in comma-separated values (CSV) format. proprietary Soil_Temp_Moisture_Alaska_1869_1 ABoVE: Soil Temperature and VWC at Unburned and Burned Sites Across Alaska, 2016-2023 ALL STAC Catalog 2016-08-11 2023-09-02 -163.24, 61.27, -146.56, 68.99 https://cmr.earthdata.nasa.gov/search/concepts/C2143401688-ORNL_CLOUD.umm_json This dataset provides soil temperature and volumetric water content (VWC) measurements at 15 cm depth collected at 12 selected boreal and tundra sites located across Alaska. Each site is equipped with a HOBO MicroStation Data Logger that hosts two soil temperature sensors (HOBO S-TMB-M006 Temperature Smart Sensor), and two soil moisture sensors (HOBO S-SMD-M005 10HS Soil Moisture Smart Sensor). Each sensor was installed horizontally at a depth of 15 cm within the soil profile. Samples of soil from seven sites were taken to a laboratory for determination of site-specific soil moisture sensor calibration curves to correct raw measurements. Data were nominally recorded at an hourly frequency and downloaded from the sites at least annually for the period 2016-08-11 to 2023-09-02, but data coverage varies by site. These measurements were collected at the same sites as previously archived CO2 efflux and thaw depth data. The data are provided in comma-separated values (CSV) format. proprietary -Soil_Temperature_Profiles_AK_1767_1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, AK and Canada, 2016-2019 ALL STAC Catalog 2016-06-25 2019-08-22 -163.18, 63.89, -134.34, 69.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143402511-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 16 monitoring sites in Alaska, USA, and at one site in Yukon, Canada. The six sites are collocated with seismic stations of the USArray program. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m. Measurements were made from 2016-2018 at two sites, 2017-2019 at four sites, and 2018-2019 at 11 sites using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. proprietary Soil_Temperature_Profiles_AK_1767_1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, AK and Canada, 2016-2019 ORNL_CLOUD STAC Catalog 2016-06-25 2019-08-22 -163.18, 63.89, -134.34, 69.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143402511-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 16 monitoring sites in Alaska, USA, and at one site in Yukon, Canada. The six sites are collocated with seismic stations of the USArray program. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m. Measurements were made from 2016-2018 at two sites, 2017-2019 at four sites, and 2018-2019 at 11 sites using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. proprietary +Soil_Temperature_Profiles_AK_1767_1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, AK and Canada, 2016-2019 ALL STAC Catalog 2016-06-25 2019-08-22 -163.18, 63.89, -134.34, 69.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143402511-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 16 monitoring sites in Alaska, USA, and at one site in Yukon, Canada. The six sites are collocated with seismic stations of the USArray program. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m. Measurements were made from 2016-2018 at two sites, 2017-2019 at four sites, and 2018-2019 at 11 sites using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. proprietary Sonoma_County_Forest_AGB_1764_1 CMS: LiDAR Biomass Improved for High Biomass Forests, Sonoma County, CA, USA, 2013 ORNL_CLOUD STAC Catalog 2013-09-01 2013-09-01 -123.54, 38.11, -122.34, 38.85 https://cmr.earthdata.nasa.gov/search/concepts/C2389021440-ORNL_CLOUD.umm_json This data set provides estimates of above-ground woody biomass and uncertainty at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha), were generated using a combination of airborne LiDAR data and field plot measurements with a parametric modeling approach. The relationship between field estimated and airborne LiDAR estimated aboveground biomass density is represented as a parametric model that predicts biomass as a function of canopy cover and 50th percentile and 90th percentile LiDAR heights at a 30-m resolution. To estimate uncertainty, the biomass model was re-fit 1,000 times through a sampling of the variance-covariance matrix of the fitted parametric model. This produced 1,000 estimates of biomass per pixel. The 5th and 95th percentiles, and the standard deviation of these pixel biomass estimates, were calculated. proprietary South Africa Crop Type Competition_1 South Africa Crop Type Competition MLHUB STAC Catalog 2020-01-01 2023-01-01 17.818514, -34.1538276, 19.7650866, -30.7480751 https://cmr.earthdata.nasa.gov/search/concepts/C2781412651-MLHUB.umm_json This dataset was produced as part of the [Radiant Earth Spot the Crop Challenge](https://zindi.africa/hackathons/radiant-earth-spot-the-crop-hackathon). The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 and Sentinel-1 satellites. proprietary -Southern_Boreal_Plot_Attribute_1740_1 ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016 ORNL_CLOUD STAC Catalog 2016-05-30 2016-06-16 -109.17, 54.09, -104.69, 57.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143402623-ORNL_CLOUD.umm_json This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques. proprietary Southern_Boreal_Plot_Attribute_1740_1 ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016 ALL STAC Catalog 2016-05-30 2016-06-16 -109.17, 54.09, -104.69, 57.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143402623-ORNL_CLOUD.umm_json This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques. proprietary +Southern_Boreal_Plot_Attribute_1740_1 ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016 ORNL_CLOUD STAC Catalog 2016-05-30 2016-06-16 -109.17, 54.09, -104.69, 57.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143402623-ORNL_CLOUD.umm_json This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques. proprietary Southern_Ocean_Drifter_0 Southern Pacific Ocean drifter measurements in 1996 OB_DAAC STAC Catalog 1996-09-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360666-OB_DAAC.umm_json Measurements taken by a drifter in the Southern Pacific Ocean in 1996. proprietary Spire.live.and.historical.data_8.0 Spire live and historical data ESA STAC Catalog 2016-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689697-ESA.umm_json "The data collected by Spire from it's 100 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested: GNSS Polarimetric Radio Occultation (STRATOS) Novel Polarimetric Radio Occultation (PRO) measurements collected by three Spire satellites are available over 15-May-2023 to 30-November-2023. PRO differ from regular RO (described below) in that the H and V polarizations of the signal are available, as opposed to only Right-Handed Circularly Polarized (RHCP) signals in regular RO. The differential phase shift between H and V correlates with the presence of hydrometeors (ice crystals, rain, snow, etc.). When combined, the H and V information provides the same information on atmospheric thermodynamic properties as RO: temperature, humidity, and pressure, based on the signal’s bending angle. Various levels of the products are provided. GNSS Reflectometry (STRATOS) GNSS Reflectometry (GNSS-R) is a technique to measure Earth’s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: Near-Nadir incidence LHCP reflections collected by the Spire GNSS-R satellites, and Grazing-Angle GNSS-R (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. The Near-Nadir GNSS-R collects DDM (Delay Doppler Map) reflectivity measurements. These are used to compute ocean wind / wave conditions and soil moisture over land. The Grazing-Angle GNSS-R collects 50 Hz reflectivity and additionally carrier phase observations. These are used for altimetry and characterization of smooth surfaces (such as ice and inland water). Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while near-nadir GNSS-R data are available from December 2020 to the present. Name Temporal coverage Spatial coverage Description Data format and content Application Polarimetric Radio Occultation (PRO) measurements 15-May-2023 to 30-November-2023 Global PRO measurements observe the properties of GNSS signals as they pass through by Earth's atmosphere, similar to regular RO measurements. The polarization state of the signals is recorded separately for H and V polarizations to provide information on the anisotropy of hydrometeors along the propagation path. leoOrb.sp3. This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file PRO measurements add a sensitivity to ice and precipitation content alongside the traditional RO measurements of the atmospheric temperature, pressure, and water vapor. proObs. Level 0 - Raw open loop carrier phase measurements at 50 Hz sampling for both linear polarization components (horizontal and vertical) of the occulted GNSS signal. h(v)(c)atmPhs. Level 1B - Atmospheric excess phase delay computed for each individual linear polarization component (hatmPhs, vatmPhs) and for the combined (“H” + “V”) signal (catmPhs). Also contains values for signal-to-noise ratio, transmitter and receiver positions and open loop model information. polPhs. Level 1C - Combines the information from the hatmPhs and vatmPhs files while removing phase continuities due to phase wrapping and navigation bit modulation. patmPrf. Level 2 - Bending angle, dry refractivity, and dry temperature as a function of mean sea level altitude and impact parameter derived from the “combined” excess phase delay (catmPhs) Near-Nadir GNSS Reflectometry (NN GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the near-nadir pointing GNSS-R antennas, based on Delay Doppler Maps (DDMs). gbrRCS.nc. Level 1B - Along-track calibrated bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. NN GNSS-R measurements are used to measure ocean surface winds and characterize land surfaces for applications such as soil moisture, freeze/thaw monitoring, flooding detection, inland water body delineation, sea ice classification, etc. gbrNRCS.nc. Level 1B - Along-track calibrated bistatic and normalized radar cross-sections measured by Spire conventional GNSS-R satellites. gbrSSM.nc. Level 2 - Along-track SNR, reflectivity, and retrievals of soil moisture (and associated uncertainties) and probability of frozen ground. gbrOcn.nc. Level 2 - Along-track retrievals of mean square slope (MSS) of the sea surface, wind speed, sigma0, and associated uncertainties. Grazing angle GNSS Reflectometry (GA GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the limb-facing RO antennas, based on open-loop tracking outputs: 50 Hz collections of accumulated I/Q observations. grzRfl.nc. Level 1B - Along-track SNR, reflectivity, phase delay (with respect to an open loop model) and low-level observables and bistatic radar geometries such as receiver, specular reflection, and the transmitter locations. GA GNSS-R measurements are used to 1) characterize land surfaces for applications such as sea ice classification, freeze/thaw monitoring, inland water body detection and delineation, etc., and 2) measure relative altimetry with dm-level precision for inland water bodies, river slopes, sea ice freeboard, etc., but also water vapor characterization from delay based on tropospheric delays. grzIce.nc. Level 2 - Along-track water vs sea ice classification, along with sea ice type classification. grzAlt.nc. Level 2 - Along-track phase-delay, ionosphere-corrected altimetry, tropospheric delay, and ancillary models (mean sea surface, tides). Additionally, the following products (better detailed in the ToA) can be requested but the acceptance is not guaranteed and shall be evaluated on a case-by-case basis: Other STRATOS measurements: profiles of the Earth’s atmosphere and ionosphere, from December 2018 ADS-B Data Stream: monthly subscription to global ADS-B satellite data, available from December 2018 AIS messages: AIS messages observed from Spire satellites (S-AIS) and terrestrial from partner sensor stations (T-AIS), monthly subscription available from June 2016 The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639 ." proprietary Stream_GIS_USGS Digital Line Graphs of U.S. Streams for the EPA Clean Air Mapping and Analysis Program (C-MAP) CEOS_EXTRA STAC Catalog 1970-01-01 -127.77, 23.25, -65.71, 48.15 https://cmr.earthdata.nasa.gov/search/concepts/C2231553171-CEOS_EXTRA.umm_json This is a 1:2,000,000 coverage of streams for the conterminous United States. This coverage was intended for use as a background display for the National Water Summary program. The stream layer was extracted from the 1:2,000,000 Digital Line Graph files. Originally, each state was stored as a separate coverage. In this version, the individual state coverages all have been appended. [Summary provided by EPA] proprietary @@ -14742,8 +14722,8 @@ TEMPO_O3TOT_L3_V03 TEMPO gridded ozone total column V03 (PROVISIONAL) LARC_CLOUD TEMPO_RADT_L1_V03 TEMPO geolocated Earth radiances twilight V03 (PROVISIONAL) LARC_CLOUD STAC Catalog 2023-08-01 -170, 10, -10, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2930766795-LARC_CLOUD.umm_json Level 1 twilight radiance files provide radiance measured during twilight hours to capture city lights at TEMPO’s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on radiometrically calibrated and geolocated radiances for the UV and visible bands, corresponding noise, geolocation, viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes image processing steps to produce radiometrically calibrated radiances with nominal navigation. These data reached provisional validation on December 9, 2024. proprietary TEMPO_RAD_L1_V02 TEMPO geolocated Earth radiances V02 (BETA) LARC_CLOUD STAC Catalog 2023-08-01 -170, 10, -10, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2842845562-LARC_CLOUD.umm_json Level 1 radiance files provide radiance information at TEMPO’s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on radiometrically and wavelength calibrated and geolocated radiances for the UV and visible bands, corresponding noise, parameterized wavelength grid, geolocation, viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes multiple steps: (1) Image processing to produce radiometrically calibrated radiance, (2) Additional wavelength calibration to improve wavelength registration, (3) Image Navigation and Registration (INR) using GOES-R data, and (4) post INR processing geolocation tagging and polarization correction. Please refer to the ATBD for details. These data are beta. Beta maturity is defined as: the product is minimally validated but may still contain significant errors; it is based on product quick looks using the initial calibration parameters. Because the products at this stage have minimal validation, users should refrain from making conclusive public statements regarding science and applications of the data products until a product is designated at the provisional validation status. The TEMPO Level 1 ATBD is still being finalized. For access to Version 1.0 ATBD, please contact the ASDC at larc-dl-asdc-tempo@mail.nasa.gov. proprietary TEMPO_RAD_L1_V03 TEMPO geolocated Earth radiances V03 (PROVISIONAL) LARC_CLOUD STAC Catalog 2023-08-01 -170, 10, -10, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2930759336-LARC_CLOUD.umm_json Level 1 radiance files provide radiance information at TEMPO’s native spatial resolution, ~10 km^2 at the center of the Field of Regard (FOR), for individual granules. Each granule covers the entire North-South TEMPO FOR but only a portion of the East-West FOR. The files are provided in netCDF4 format, and contain information on radiometrically and wavelength calibrated and geolocated radiances for the UV and visible bands, corresponding noise, parameterized wavelength grid, geolocation, viewing geometry, quality flags and other ancillary information. The product is produced using the L0-1b processor which includes multiple steps: (1) Image processing to produce radiometrically calibrated radiance, (2) Additional wavelength calibration to improve wavelength registration, (3) Image Navigation and Registration (INR) using GOES-R data, and (4) post INR processing geolocation tagging. These data reached provisional validation on December 9, 2024. proprietary -TEMR_RSFCE Air Temperature Time Series SCIOPS STAC Catalog 1883-01-01 1987-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608675-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by Computer Centre North Administration for hydrometeorology in 1990 and containes air temperature from 68 stations in Arhangelsk, Vologda regions and Komi ASSR in Russia. Data is currently stored on magnetic tape (800 bit/inch). proprietary TEMR_RSFCE Air Temperature Time Series ALL STAC Catalog 1883-01-01 1987-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608675-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by Computer Centre North Administration for hydrometeorology in 1990 and containes air temperature from 68 stations in Arhangelsk, Vologda regions and Komi ASSR in Russia. Data is currently stored on magnetic tape (800 bit/inch). proprietary +TEMR_RSFCE Air Temperature Time Series SCIOPS STAC Catalog 1883-01-01 1987-12-31 25, 23.21, -175, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214608675-SCIOPS.umm_json Hydrometeorological data on the conditions of the environment are held by the Russian State Fund of data. This dataset was created by Computer Centre North Administration for hydrometeorology in 1990 and containes air temperature from 68 stations in Arhangelsk, Vologda regions and Komi ASSR in Russia. Data is currently stored on magnetic tape (800 bit/inch). proprietary TG02_Balloon_VOC_1110_1 LBA-ECO TG-02 Biogenic VOC Emissions from Brazilian Amazon Forest and Pasture Sites ORNL_CLOUD STAC Catalog 1998-03-22 2000-02-16 -62.2, -10.08, -54.97, -0.86 https://cmr.earthdata.nasa.gov/search/concepts/C2768941787-ORNL_CLOUD.umm_json This data set reports concentrations of biogenic volatile organic compounds (BVOCs) collected from tethered balloon-sampling platforms above selected forest and pasture sites in the Brazilian Amazon in March 1998, February 1999, and February 2000. The air samples were collected from forested sites in Brazil: the Tapajos forest (Para) in the Tapajos/Xingu moist forest; Balbina (Amazonas) in the Uatuma moist forest; and Jaru (Rondonia) in the Purus/Madeira moist forest. Two other sites were also located in Rondonia: at a forest reserve (Rebio Jaru) and a pasture (Fazenda Nossa Senhora Aparecida). The BVOCs measured included isoprene, alpha and beta pinene, camphene, sabinene, myrcene, limonene, and other monoterpenes. Approximately 24 to 40 soundings, including as many as four VOC samples collected simultaneously at various altitudes, were made at each site. There is one comma-delimited data file with this data set. proprietary TG03_AERONET_AOT_1128_1 LBA-ECO TG-03 Aeronet Aerosol Optical Thickness Measurements, Brazil: 1993-2005 ORNL_CLOUD STAC Catalog 1993-01-01 2005-01-01 -70.31, -20.45, -48.28, -1.2 https://cmr.earthdata.nasa.gov/search/concepts/C2768942874-ORNL_CLOUD.umm_json This data set includes aerosol optical thickness measurements from the CIMEL sunphotometer for 22 sites in Brazil during the period from 1993-2005. The AERONET (AErosol RObotic NETwork) program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and the PHOtometrie pour le Traitement Operationnel de Normalisation Satellitaire (PHOTONS) and greatly expanded by AEROCAN (the Canadian sunphotometer network) and other agency, institute and university partners. The goal is to assess aerosol optical properties and validate satellite retrievals of aerosol optical properties. The network imposes standardization of instruments, calibration, and processing. Data from this collaboration provides globally distributed observations of spectral aerosol optical depths, inversion products, and precipitable water in geographically diverse aerosol regimes. Three levels of data are available from the AERONET website: Level 1.0 (unscreened), Level 1.5 (cloud-screened), and Level 2.0 (cloud-screened and quality-assured). Data provided here are Level 2.0. There are 22 comma-delimited data files with this data set and one companion text file which contains the latitude, longitude, and elevation of the 22 sites. proprietary TG03_Aeronet_Solar_Flux_1137_1 LBA-ECO TG-03 Solar Surface Irradiance and PAR, Brazilian Amazon: 1999-2004 ORNL_CLOUD STAC Catalog 1999-01-01 2004-12-31 -67.87, -15.73, -54.95, -1.92 https://cmr.earthdata.nasa.gov/search/concepts/C2781384398-ORNL_CLOUD.umm_json This data set includes solar surface irradiance from Kipp and Zonen CM-21 pyranometers, both total unfiltered and filtered (RG695), and photosynthetically active radiation (PAR) from Skye-Probetech SKE-510 PAR sensors. Measurements were made at six sites acrosss the Brazilian Amazon during the period from 1999 to 2004. These sites were co-located with AERONET (AErosol RObotic NETwork) program sites. There are 17 comma-delimited data files (.csv) with this data set. The AERONET program is an inclusive federation of ground-based remote sensing aerosol networks established by AERONET and the PHOtometrie pour le Traitement Operationnel de Normalisation Satellitaire (PHOTONS) and greatly expanded by AEROCAN (the Canadian sunphotometer network) and other agency, institute and university partners. The goal is to assess aerosol optical properties and validate satellite retrievals of those properties. The network imposes standardization of instruments, calibration, and processing. proprietary @@ -15346,11 +15326,11 @@ Tropical Cyclone Wind Estimation Competition_1 Tropical Cyclone Wind Estimation TundraTransect_VegRefl_Soil_2232_1 Spectral Reflectance and Ancillary Data, Tundra Transect, North Slope, AK, 2000-2022 ORNL_CLOUD STAC Catalog 2000-06-30 2022-08-08 -156.6, 71.32, -156.6, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2840820936-ORNL_CLOUD.umm_json This dataset provides visible-near infrared spectral reflectance, descriptions of vegetation cover, surface temperature, the total fraction of absorbed photosynthetically active radiation (fAPAR, 2001 only), permafrost active layer depth, elevation, and soil temperature at 5 cm depth. Measurements were made at every meter along a 100-m transect aligned mainly in an east-west direction, located approximately 300 m southeast of the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) baseline observatory near Utqiagvik, Alaska. Reflectance measurements were collected at nearly weekly intervals through the growing seasons of 2000 to 2002 to describe characteristics of green-up, peak growth, and senescence. Reflectance measurements were also collected once near peak growth in 2022. Ancillary measurements were collected at intervals through the 2001 and 2002 growing seasons. proprietary TundraVeg_Reflectance_Soil_CO2_1960_1 Tundra Plant Reflectance, CO2 Exchange, PAM Fluorometry, and Pigments, AK, 2001-2002 ORNL_CLOUD STAC Catalog 2001-06-08 2002-08-16 -157.41, 70.45, -156.6, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2262495116-ORNL_CLOUD.umm_json This dataset provides measurements at tundra plots collected near Utqiagvik and Atqasuk, AK, including visible-near infrared spectral reflectance, chamber gas exchange measurements of CO2, pulse amplitude modulated (PAM) fluorometry, chlorophyll pigment contents, along with surface temperature, permafrost active layer depth, and soil temperature at 5 cm, through the growing seasons of 2001 and 2002. At all plots, spectral reflectance was measured using a portable spectrometer configured with a straight fiber optic foreoptic, surface temperatures were measured using a handheld Everest Infrared Thermometer, and thaw depth (or active layer depth) was measured using a metal rod graduated in centimeter intervals. At small plots (~15 cm) at Utqiagvik (referred to as Patch plots) chambers were constructed that enclosed an individual patch to determine photosynthetic rate and estimate respiration rate (made by covering the chamber in a dark cloth). Efficiency using PAM fluorometer, ambient yield estimations, and rapid light curve measurements were taken. Chlorophyll concentration was measured with a portable spectrometer configured as a spectrophotometer. At larger plots (approximately 1 m2) which were part of the International Tundra EXperiment (ITEX plots) at Utqiagvik (referred to as Barrow) and Atqasuk, a sub-sample of five control and five warmed plots at each site were fitted with 0.45 m diameter polyvinyl chloride collars for chamber flux measurements. To determine the total fraction of absorbed photosynthetically active radiation (fAPAR), a series of photosynthetically active radiation (PAR) measurements were made using a custom-made light bar consisting of a linear array of GaAsP sensors mounted within an aluminum U-bar under a white plastic diffuser. In addition, a visual estimate was made of the fraction of standing dead vegetation based on percent cover. The data are provided in comma-separated values (*.csv) format. In addition, photographs of plots and instruments are provided. proprietary Tundra_Fire_Veg_Plots_1547_1 Arctic Vegetation Plots in Burned and Unburned Tundra, Alaska, 2011-2012 ORNL_CLOUD STAC Catalog 2011-07-14 2012-07-30 -164.69, 65.36, -146.65, 70.09 https://cmr.earthdata.nasa.gov/search/concepts/C2162122251-ORNL_CLOUD.umm_json This dataset provides environmental and vegetation data collected in late June and July of 2011 and of 2012 from study plots located in tundra fire scars and adjacent unburned tundra areas on the Seward Peninsula and the northern foothills of the Brooks Range in Arctic Alaska. The surveys focused on upland tundra settings and provide information on vegetative differences between the burned and unburned sites. The sampling design established a chronosequence of sites that varied in time since last fire to better understand post-fire vegetation successional trajectories. Complete species lists and their cover abundance data are provided for both study areas. Environmental data include the baseline plot descriptive information for vegetation, soils, and site factors. No soil samples were collected. proprietary -Tundra_Greeness_Temp_Trends_1893_1 ABoVE: Landsat Tundra Greenness and Summer Air Temperatures, Arctic Tundra, 1985-2016 ORNL_CLOUD STAC Catalog 1985-07-01 2016-08-31 -180, 31.49, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2143401680-ORNL_CLOUD.umm_json This dataset provides annual tundra greenness and summer air temperatures at a resolution of 50 km over the pan-Arctic tundra biome above 31.5 degrees over the time period 1985 to 2016. Annual tundra greenness was assessed using the maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites. Summer air temperatures were quantified using the Summer Warmth Index (SWI) derived from an ensemble of five global temperature datasets. Tabular data include NDVImax, SWI, and estimates of uncertainty using Monte Carlo simulations at 45,334 vegetated sampling sites. Raster data provide (1) annual SWI from 1985 to 2016; (2) temporal trends in annual NDVImax and SWI from 1985 to 2016 and from 2000 to 2016; and (3) temporal correlations between annual NDVImax - SWI during these two periods. Each raster also includes estimates of uncertainty that were generated using Monte Carlo simulations. This dataset provides a new pan-Arctic product for assessing inter-annual variability in tundra using moderate resolution observations from the Landsat satellites. proprietary Tundra_Greeness_Temp_Trends_1893_1 ABoVE: Landsat Tundra Greenness and Summer Air Temperatures, Arctic Tundra, 1985-2016 ALL STAC Catalog 1985-07-01 2016-08-31 -180, 31.49, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2143401680-ORNL_CLOUD.umm_json This dataset provides annual tundra greenness and summer air temperatures at a resolution of 50 km over the pan-Arctic tundra biome above 31.5 degrees over the time period 1985 to 2016. Annual tundra greenness was assessed using the maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites. Summer air temperatures were quantified using the Summer Warmth Index (SWI) derived from an ensemble of five global temperature datasets. Tabular data include NDVImax, SWI, and estimates of uncertainty using Monte Carlo simulations at 45,334 vegetated sampling sites. Raster data provide (1) annual SWI from 1985 to 2016; (2) temporal trends in annual NDVImax and SWI from 1985 to 2016 and from 2000 to 2016; and (3) temporal correlations between annual NDVImax - SWI during these two periods. Each raster also includes estimates of uncertainty that were generated using Monte Carlo simulations. This dataset provides a new pan-Arctic product for assessing inter-annual variability in tundra using moderate resolution observations from the Landsat satellites. proprietary +Tundra_Greeness_Temp_Trends_1893_1 ABoVE: Landsat Tundra Greenness and Summer Air Temperatures, Arctic Tundra, 1985-2016 ORNL_CLOUD STAC Catalog 1985-07-01 2016-08-31 -180, 31.49, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2143401680-ORNL_CLOUD.umm_json This dataset provides annual tundra greenness and summer air temperatures at a resolution of 50 km over the pan-Arctic tundra biome above 31.5 degrees over the time period 1985 to 2016. Annual tundra greenness was assessed using the maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites. Summer air temperatures were quantified using the Summer Warmth Index (SWI) derived from an ensemble of five global temperature datasets. Tabular data include NDVImax, SWI, and estimates of uncertainty using Monte Carlo simulations at 45,334 vegetated sampling sites. Raster data provide (1) annual SWI from 1985 to 2016; (2) temporal trends in annual NDVImax and SWI from 1985 to 2016 and from 2000 to 2016; and (3) temporal correlations between annual NDVImax - SWI during these two periods. Each raster also includes estimates of uncertainty that were generated using Monte Carlo simulations. This dataset provides a new pan-Arctic product for assessing inter-annual variability in tundra using moderate resolution observations from the Landsat satellites. proprietary Tundra_Leaf_Spectra_2005_1 Tundra Plant Leaf-level Spectral Reflectance and Chlorophyll Fluorescence, 2019-2021 ORNL_CLOUD STAC Catalog 2019-07-19 2021-09-30 -156.6, 64.83, -147.81, 71.31 https://cmr.earthdata.nasa.gov/search/concepts/C2262495547-ORNL_CLOUD.umm_json This dataset provides leaf-level visible-near infrared spectral reflectance, chlorophyll fluorescence spectra, species, plant functional type (PFT), and chlorophyll content of common high latitude plant samples collected near Fairbanks, Utqiagvik, and Toolik, Alaska, U.S., during the summers of 2019, 2020, and 2021. A FluoWat leaf clip was used to measure leaf-level visible-near infrared spectral reflectance and chlorophyll fluorescence spectra. Fluorescence yield (Fyield) was calculated as the ratio of the emitted fluorescence divided by the absorbed radiation for the wavelengths from 400 nm up to the wavelength of the cut off for the FluoWat low pass filter (either 650 or 700 nm). Chlorophyll content of samples was measured using a CCM-300 Chlorophyll Content. The data are provided in comma-separated values (.csv) format. proprietary -Turbid9_0 2004 Measurements made in the Chesapeake Bay OB_DAAC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360689-OB_DAAC.umm_json Measurements made in the Chesapeake Bay in 2004. proprietary Turbid9_0 2004 Measurements made in the Chesapeake Bay ALL STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360689-OB_DAAC.umm_json Measurements made in the Chesapeake Bay in 2004. proprietary +Turbid9_0 2004 Measurements made in the Chesapeake Bay OB_DAAC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360689-OB_DAAC.umm_json Measurements made in the Chesapeake Bay in 2004. proprietary Turkish_Seas_0 Turkish Seas pigment measurements OB_DAAC STAC Catalog 1997-09-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360690-OB_DAAC.umm_json Chlorophyll-a and pigment measurements made in the seas surrounding Turkey between 1997 and 1999. proprietary UAEM1LME_002 MISR Level 1B2 Local Mode Ellipsoid Radiance Data subset for the UAE region V002 LARC STAC Catalog 2004-08-02 2004-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1627523796-LARC.umm_json Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Local Mode Ellipsoid Radiance Data subset for the UAE region V002 contains the ellipsoid projected TOA parameters for the single local mode scene, resampled to WGS84 ellipsoid. proprietary UAEM1LMT_002 MISR Level 1B2 Local Mode Terrain Radiance Data subset for the UAE region V002 LARC STAC Catalog 2004-08-02 2004-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1627523809-LARC.umm_json Multi-angle Imaging SpectroRadiometer (MISR) is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the fate of sunlight in Earth environment, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. MISR Level 1B2 Local Mode Terrain Radiance Data subset for the UAE region V002 contains the terrain-projected TOA radiance for the single local mode scene, resampled at the surface and topographically corrected. proprietary @@ -15416,17 +15396,17 @@ UAV_Imagery_BigLakeTrail_1834_1 Multispectral Imagery, NDVI, and Terrain Models, UCLA_DEALIASED_SASS_L3_1 SEASAT SCATTEROMETER DEALIASED OCEAN WIND VECTORS (JPL-UCLA-AES) POCLOUD STAC Catalog 1978-07-07 1978-10-11 -180, -70, 180, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2617197672-POCLOUD.umm_json Contains dealiased ocean wind vector components (zonal and meridional) derived from the Seasat-A Scatterometer (SASS) provided on a global 1x1 degree grid. Dealiasing of the SASS data was achieved manually using ship observations in a joint effort between JPL, UCLA and AES. This data set underwent restoration in 1997. Data are provided in ASCII text files at six hour intervals. proprietary UKASSEL_GLOBAL_IRRIGATED_AREA A Digital Global Map of Irrigated Areas ALL STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214608839-SCIOPS.umm_json "For the purpose of global modeling of water use and crop production, a digital global map of irrigated areas was developed. The map depicts the areal percentage of each 0.5 deg. by 0.5 deg grid cell that was equipped for irrigation in 1995. It was derived by combininginformation from large-scale maps with outlines of irrigated areas (one or more countries per map), FAO data on total irrigated area per country in 1995 and national data on total irrigated area per county, drainage basin or federal state. In the documentation of the map, the data and map sources as well as the map generation process is described, and the data uncertainty is discussed. ""http://www.usf.uni-kassel.de/usf/archiv/dokumente/kwws/kwws.4.pdf"" We plan to improve this map in the future. Therefore, comments, information and data that might contribute to this effort are highly welcome." proprietary UKASSEL_GLOBAL_IRRIGATED_AREA A Digital Global Map of Irrigated Areas SCIOPS STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214608839-SCIOPS.umm_json "For the purpose of global modeling of water use and crop production, a digital global map of irrigated areas was developed. The map depicts the areal percentage of each 0.5 deg. by 0.5 deg grid cell that was equipped for irrigation in 1995. It was derived by combininginformation from large-scale maps with outlines of irrigated areas (one or more countries per map), FAO data on total irrigated area per country in 1995 and national data on total irrigated area per county, drainage basin or federal state. In the documentation of the map, the data and map sources as well as the map generation process is described, and the data uncertainty is discussed. ""http://www.usf.uni-kassel.de/usf/archiv/dokumente/kwws/kwws.4.pdf"" We plan to improve this map in the future. Therefore, comments, information and data that might contribute to this effort are highly welcome." proprietary -UM0405_26_aerosol_optical Aerosol optical thickness - UM0405_26_aerosol_optical ALL STAC Catalog 2004-12-31 2005-01-25 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1221420727-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary UM0405_26_aerosol_optical Aerosol optical thickness - UM0405_26_aerosol_optical SCIOPS STAC Catalog 2004-12-31 2005-01-25 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1221420727-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary -UM0506_26_aerosol_optical Aerosol optical thickness ALL STAC Catalog 2006-01-03 2006-01-30 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1214595208-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary +UM0405_26_aerosol_optical Aerosol optical thickness - UM0405_26_aerosol_optical ALL STAC Catalog 2004-12-31 2005-01-25 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1221420727-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary UM0506_26_aerosol_optical Aerosol optical thickness SCIOPS STAC Catalog 2006-01-03 2006-01-30 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1214595208-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary -UM0708_25_multi-frequency_acoustic Acoustic data of multi-frequency acoustic system ALL STAC Catalog 2007-12-24 2008-02-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595173-SCIOPS.umm_json Vertical profiles of volume backscattering strength recorded by multi-frequency acoustic system for estimate size-abundance spectra of small zooplankton. The system was horizontally mounted on CTD frame and the observation was vertically performed from surface to 200 m at 23 stations. proprietary +UM0506_26_aerosol_optical Aerosol optical thickness ALL STAC Catalog 2006-01-03 2006-01-30 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1214595208-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary UM0708_25_multi-frequency_acoustic Acoustic data of multi-frequency acoustic system SCIOPS STAC Catalog 2007-12-24 2008-02-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595173-SCIOPS.umm_json Vertical profiles of volume backscattering strength recorded by multi-frequency acoustic system for estimate size-abundance spectra of small zooplankton. The system was horizontally mounted on CTD frame and the observation was vertically performed from surface to 200 m at 23 stations. proprietary -UM0809_33_nano Abundance and composition of nano, picoplankton, microzooplankton ALL STAC Catalog 2009-01-12 2009-01-25 38, -70, 75, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214595104-SCIOPS.umm_json Water samples from 5 depths (0-100 m) were collected by Niskin bottles at 9 stations (L1, L3, L5, L9, L12, L37, L33, Ⅰ-10, Ⅱ-7) off Lützow-Holm Bay during Umitaka-maru cruise (Jan-Feb. 2008). The waters were fixed by 0.2% of lugol's acid solution (500 ml), 0.3% of bouin solution (500 ml) and 20 % of glutaraldehyde (100ml).　　　　　　　　　　　　　　　　　　　　　http://biows.ac.jp/~plankton/um0809-1a.png proprietary +UM0708_25_multi-frequency_acoustic Acoustic data of multi-frequency acoustic system ALL STAC Catalog 2007-12-24 2008-02-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595173-SCIOPS.umm_json Vertical profiles of volume backscattering strength recorded by multi-frequency acoustic system for estimate size-abundance spectra of small zooplankton. The system was horizontally mounted on CTD frame and the observation was vertically performed from surface to 200 m at 23 stations. proprietary UM0809_33_nano Abundance and composition of nano, picoplankton, microzooplankton SCIOPS STAC Catalog 2009-01-12 2009-01-25 38, -70, 75, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214595104-SCIOPS.umm_json Water samples from 5 depths (0-100 m) were collected by Niskin bottles at 9 stations (L1, L3, L5, L9, L12, L37, L33, Ⅰ-10, Ⅱ-7) off Lützow-Holm Bay during Umitaka-maru cruise (Jan-Feb. 2008). The waters were fixed by 0.2% of lugol's acid solution (500 ml), 0.3% of bouin solution (500 ml) and 20 % of glutaraldehyde (100ml).　　　　　　　　　　　　　　　　　　　　　http://biows.ac.jp/~plankton/um0809-1a.png proprietary +UM0809_33_nano Abundance and composition of nano, picoplankton, microzooplankton ALL STAC Catalog 2009-01-12 2009-01-25 38, -70, 75, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214595104-SCIOPS.umm_json Water samples from 5 depths (0-100 m) were collected by Niskin bottles at 9 stations (L1, L3, L5, L9, L12, L37, L33, Ⅰ-10, Ⅱ-7) off Lützow-Holm Bay during Umitaka-maru cruise (Jan-Feb. 2008). The waters were fixed by 0.2% of lugol's acid solution (500 ml), 0.3% of bouin solution (500 ml) and 20 % of glutaraldehyde (100ml).　　　　　　　　　　　　　　　　　　　　　http://biows.ac.jp/~plankton/um0809-1a.png proprietary UMD_GEOL388_0 Measurements from the Atlantic Ocean made by the University of Maryland (UMD) OB_DAAC STAC Catalog 2003-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360691-OB_DAAC.umm_json Measurements from the Atlantic Ocean made by the University of Maryland between New England, Bermuda, and Brazil in 2003. proprietary -UNEP_GRID_SF_AFRICA_third version Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1960-01-01 1990-12-31 -18, -35, 52, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2232848311-CEOS_EXTRA.umm_json The African administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change This documentation describes the third version of a database of administrative units with associated population figures for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while the second version represented an update and expansion of this first product (Deichmann 1994, WRI 1995). The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. proprietary UNEP_GRID_SF_AFRICA_third version Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls ALL STAC Catalog 1960-01-01 1990-12-31 -18, -35, 52, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2232848311-CEOS_EXTRA.umm_json The African administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change This documentation describes the third version of a database of administrative units with associated population figures for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while the second version represented an update and expansion of this first product (Deichmann 1994, WRI 1995). The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. proprietary +UNEP_GRID_SF_AFRICA_third version Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1960-01-01 1990-12-31 -18, -35, 52, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2232848311-CEOS_EXTRA.umm_json The African administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change This documentation describes the third version of a database of administrative units with associated population figures for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while the second version represented an update and expansion of this first product (Deichmann 1994, WRI 1995). The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. proprietary UNEP_GRID_SF_ASIA Asia Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 26, -12, 155, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2232847540-CEOS_EXTRA.umm_json The Asian administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. This project (which has been carried out as a cooperative activity between NCGIA, CGIAR and UNEP/GRID between Oct. 1995 and present) has pooled available data sets, many of which had been assembled for the global demography project. All data were checked, international boundaries and coastlines were replaced with a standard template, the attribute database was redesigned, and new, more reliable population estimates for subnational units were produced for all countries. From the resulting data sets, raster surfaces representing population distribution and population density were created in collaboration between NCGIA and GRID-Geneva. proprietary UNEP_GRID_SF_GLOBAL Global Population Distribution Database from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232849256-CEOS_EXTRA.umm_json Population databases are forming the backbone of many important studies modelling the complex interactions between population growth and environmental degradation, predicting the effects of global climate change on humans, and assessing the risks of various hazards such as floods, air pollution and radiation. Detailed information on population size, growth and distribution (along with many other environmental parameters) is of fundamental importance to such efforts. This database includes rural population distributions, population distrbution for cities and gridded global population distributions. This project has provided a population database depicting the worldwide distribution of population in a 1X1 latitude/longitude grid system. The database is unique, firstly, in that it makes use of the most recent data available (1990). Secondly, it offers true apportionment for each grid cell that is, if a cell contains populations from two different countries, each is assigned a percentage of the grid cell area, rather than artificially assigning the whole cell to one or the other country (this is especially important for European countries). Thirdly, the database gives the percentage of a country's total population accounted for in each cell. So if a country's total in a given year around 1990 (1989 or 1991, for example) is known, then population in each cell can be calculated by using the percentage given in the database with the assumption that the growth rate in each cell of the country is the same. And lastly, this dataset is easy to be updated for each country as new national population figures become available. proprietary UNEP_GRID_SF_LATINAMERICA_1.0 Latin America and Caribbean Population Distribution Database from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1960-01-01 1990-12-31 -120, -60, -31, 36 https://cmr.earthdata.nasa.gov/search/concepts/C2232848778-CEOS_EXTRA.umm_json The Latin America population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. This documentation describes the Latin American Population Database, a collaborative effort between the International Center for Tropical Agriculture (CIAT), the United Nations Environment Program (UNEP-GRID, Sioux Falls) and the World Resources Institute (WRI). This work is intended to provide a population database that compliments previous work carried out for Asia and Africa. This data set is more detailed than the Africa and Asia data sets. Population estimates for 1960, 1970, 1980, 1990 and 2000 are also provided. The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). proprietary @@ -15461,21 +15441,21 @@ USAP-1542778 Climate History and Flow Processes from Physical Analyses of the SP USAP-1543383_1 Antarctic Fish and MicroRNA Control of Development and Physiology AMD_USAPDC STAC Catalog 2016-09-01 2019-08-31 -66, -66, -58, -62 https://cmr.earthdata.nasa.gov/search/concepts/C2532072220-AMD_USAPDC.umm_json microRNAs (miRNAs) are key post-transcriptional regulators of gene expression that modulate development and physiology in temperate animals. Although miRNAs act by binding to messenger RNAs (mRNAs), a process that is strongly sensitive to temperature, miRNAs have yet not been studied in Antarctic animals, including Notothenioid fish, which dominate the Southern Ocean. This project will compare miRNA regulation in 1) Antarctic vs. temperate fish to learn the roles of miRNA regulation in adaptation to constant cold; and in 2) bottom-dwelling, dense-boned, red-blooded Nototheniods vs. high buoyancy, osteopenic, white-blooded icefish to understand miRNA regulation in specialized organs after the evolution of the loss of hemoglobin genes and red blood cells, the origin of enlarged heart and vasculature, and the evolution of increased buoyancy, which arose by decreased bone mineralization and increased lipid deposition. Aim 1 is to test the hypothesis that Antarctic fish evolved miRNA-related genome specializations in response to constant cold. The project will compare four Antarctic Notothenioid species to two temperate Notothenioids and two temperate laboratory species to test the hypotheses that (a) Antarctic fish evolved miRNA genome repertoires by loss of ancestral genes and/or gain of new genes, (b) express miRNAs that are involved in cold tolerance, and (c) respond to temperature change by changing miRNA gene expression. Aim 2 is to test the hypothesis that the evolution of icefish from red-blooded bottom-dwelling ancestors was accompanied by an altered miRNA genomic repertoire, sequence, and/or expression. The project will test the hypotheses that (a) miRNAs in icefish evolved in sequence and/or in expression in icefish specializations, including head kidney (origin of red blood cells); heart (changes in vascular system), cranium and pectoral girdle (reduced bone mineral density); and skeletal muscle (lipid deposition), and (b) miRNAs that evolved in icefish specializations had ancestral functions related to their derived roles in icefish, as determined by functional tests of zebrafish orthologs of icefish miRNAs in developing zebrafish. The program will isolate, sequence, and determine the expression of miRNAs and mRNAs using high-throughput transcriptomics and novel software. Results will show how the microRNA system evolves in vertebrate animals pushed to physiological extremes and provide insights into the prospects of key species in the most rapidly warming part of the globe. proprietary USAP-1543498_1 A Full Lifecycle Approach to Understanding Adélie Penguin Response to Changing Pack Ice Conditions in the Ross Sea ALL STAC Catalog 2016-06-01 165, -78, -150, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532074621-AMD_USAPDC.umm_json "The Ross Sea region of the Southern Ocean is experiencing growing sea ice cover in both extent and duration. These trends contrast those of the well-studied, western Antarctic Peninsula area, where sea ice has been disappearing. Unlike the latter, little is known about how expanding sea ice coverage might affect the regional Antarctic marine ecosystem. This project aims to better understand some of the potential effects of the changing ice conditions on the marine ecosystem using the widely-recognized indicator species - the Adélie Penguin. A four-year effort will build on previous results spanning 19 seasons at Ross Island to explore how successes or failures in each part of the penguin's annual cycle are effected by ice conditions and how these carry over to the next annual recruitment cycle, especially with respect to the penguin's condition upon arrival in the spring. Education and public outreach activities will continually be promoted through the PenguinCam and PenguinScience websites (sites with greater than 1 million hits a month) and ""NestCheck"" (a site that is logged-on by >300 classrooms annually that allows students to follow penguin families in their breeding efforts). To encourage students in pursuing educational and career pathways in the Science Technology Engineering and Math fields, the project will also provide stories from the field in a Penguin Journal, develop classroom-ready activities aligned with New Generation Science Standards, increase the availability of instructional presentations as powerpoint files and short webisodes. The project will provide additional outreach activities through local, state and national speaking engagements about penguins, Antarctic science and climate change. The annual outreach efforts are aimed at reaching over 15,000 students through the website, 300 teachers through presentations and workshops, and 500 persons in the general public. The project also will train four interns (undergraduate and graduate level), two post-doctoral researchers, and a science writer/photographer.

The project will accomplish three major goals, all of which relate to how Adélie Penguins adapt to, or cope with environmental change. Specifically the project seeks to determine 1) how changing winter sea ice conditions in the Ross Sea region affect penguin migration, behavior and survival and alter the carry-over effects (COEs) to subsequent reproduction; 2) the interplay between extrinsic and intrinsic factors influencing COEs over multiple years of an individual's lifetime; and 3) how local environmental change may affect population change via impacts to nesting habitat, interacting with individual quality and COEs. Retrospective analyses will be conducted using 19 years of colony based data and collect additional information on individually marked, known-age and known-history penguins, from new recruits to possibly senescent individuals. Four years of new information will be gained from efforts based at two colonies (Cape Royds and Crozier), using radio frequency identification tags to automatically collect data on breeding and foraging effort of marked, known-history birds to explore penguin response to resource availability within the colony as well as between colonies (mates, nesting material, habitat availability). Additional geolocation/time-depth recorders will be used to investigate travels and foraging during winter of these birds. The combined efforts will allow an assessment of the effects of penguin behavior/success in one season on its behavior in the next (e.g. how does winter behavior affect arrival time and body condition on subsequent breeding). It is at the individual level that penguins are responding successfully, or not, to ongoing marine habitat change in the Ross Sea region." proprietary USAP-1543498_1 A Full Lifecycle Approach to Understanding Adélie Penguin Response to Changing Pack Ice Conditions in the Ross Sea AMD_USAPDC STAC Catalog 2016-06-01 165, -78, -150, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532074621-AMD_USAPDC.umm_json "The Ross Sea region of the Southern Ocean is experiencing growing sea ice cover in both extent and duration. These trends contrast those of the well-studied, western Antarctic Peninsula area, where sea ice has been disappearing. Unlike the latter, little is known about how expanding sea ice coverage might affect the regional Antarctic marine ecosystem. This project aims to better understand some of the potential effects of the changing ice conditions on the marine ecosystem using the widely-recognized indicator species - the Adélie Penguin. A four-year effort will build on previous results spanning 19 seasons at Ross Island to explore how successes or failures in each part of the penguin's annual cycle are effected by ice conditions and how these carry over to the next annual recruitment cycle, especially with respect to the penguin's condition upon arrival in the spring. Education and public outreach activities will continually be promoted through the PenguinCam and PenguinScience websites (sites with greater than 1 million hits a month) and ""NestCheck"" (a site that is logged-on by >300 classrooms annually that allows students to follow penguin families in their breeding efforts). To encourage students in pursuing educational and career pathways in the Science Technology Engineering and Math fields, the project will also provide stories from the field in a Penguin Journal, develop classroom-ready activities aligned with New Generation Science Standards, increase the availability of instructional presentations as powerpoint files and short webisodes. The project will provide additional outreach activities through local, state and national speaking engagements about penguins, Antarctic science and climate change. The annual outreach efforts are aimed at reaching over 15,000 students through the website, 300 teachers through presentations and workshops, and 500 persons in the general public. The project also will train four interns (undergraduate and graduate level), two post-doctoral researchers, and a science writer/photographer.

The project will accomplish three major goals, all of which relate to how Adélie Penguins adapt to, or cope with environmental change. Specifically the project seeks to determine 1) how changing winter sea ice conditions in the Ross Sea region affect penguin migration, behavior and survival and alter the carry-over effects (COEs) to subsequent reproduction; 2) the interplay between extrinsic and intrinsic factors influencing COEs over multiple years of an individual's lifetime; and 3) how local environmental change may affect population change via impacts to nesting habitat, interacting with individual quality and COEs. Retrospective analyses will be conducted using 19 years of colony based data and collect additional information on individually marked, known-age and known-history penguins, from new recruits to possibly senescent individuals. Four years of new information will be gained from efforts based at two colonies (Cape Royds and Crozier), using radio frequency identification tags to automatically collect data on breeding and foraging effort of marked, known-history birds to explore penguin response to resource availability within the colony as well as between colonies (mates, nesting material, habitat availability). Additional geolocation/time-depth recorders will be used to investigate travels and foraging during winter of these birds. The combined efforts will allow an assessment of the effects of penguin behavior/success in one season on its behavior in the next (e.g. how does winter behavior affect arrival time and body condition on subsequent breeding). It is at the individual level that penguins are responding successfully, or not, to ongoing marine habitat change in the Ross Sea region." proprietary -USAP-1544526_1 Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica ALL STAC Catalog 2016-09-01 2017-08-31 160, -77.8, 163.7, -76.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069950-AMD_USAPDC.umm_json Cryptoendoliths are organisms that colonize microscopic cavities of rocks, which give them protection and allow them to inhabit extreme environments, such as the cold, arid desert of the Dry Valleys of Antarctica. Fossilized cryptoendoliths preserve the forms and features of organisms from the past and thus provide a unique opportunity to study the organisms' life histories and environments. To study this fossil record, there needs to be a better understanding of what environmental conditions allow these fossils to form. A climate gradient currently exists in the Dry Valleys that allows us to study living, dead, and fossilized cryptoendoliths from mild to increasingly harsh environments; providing insight to the limits of life and how these fossils are formed. This project will develop instruments to detect the biological activity of the live microorganisms and conduct laboratory experiments to determine the environmental limits of their survival. The project also will characterize the chemical and structural features of the living, dead, and fossilized cryptoendoliths to understand how they become fossilized. Knowing how microorganisms are preserved as fossils in cold and dry environments like Antarctica can help to refine methods that can be used to search for and identify evidence for extraterrestrial life in similar habitats on planets such as Mars. This project includes training of graduate and undergraduate students. Little is known about cryptoendolithic microfossils and their formation processes in cold, arid terrestrial habitats of the Dry Valleys of Antarctica, where a legacy of activity is discernible in the form of biosignatures including inorganic materials and microbial fossils that preserve and indicate traces of past biological activity. The overarching goals of the proposed work are: (1) to determine how rates of microbial respiration and biodegradation of organic matter control microbial fossilization; and (2) to characterize microbial fossils and their living counterparts to elucidate mechanisms for fossilization. Using samples collected across an increasingly harsher (more cold and dry) climatic gradient that encompasses living, dead, and fossilized cryptoendolithic microorganisms, the proposed work will: (1) develop an instrument to be used in the field that can measure small concentrations of CO2 in cryptoendolithic habitats in situ; (2) use microscopy techniques to characterize endolithic microorganisms as well as the chemical and morphological characteristics of biosignatures and microbial fossils. A metagenomic survey of microbial communities in these samples will be used to characterize differences in diversity, identify if specific microorganisms (e.g. prokaryotes, eukaryotes) are more capable of surviving under these harsh climatic conditions, and to corroborate microscopic observations of the viability states of these microorganisms. proprietary USAP-1544526_1 Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica AMD_USAPDC STAC Catalog 2016-09-01 2017-08-31 160, -77.8, 163.7, -76.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069950-AMD_USAPDC.umm_json Cryptoendoliths are organisms that colonize microscopic cavities of rocks, which give them protection and allow them to inhabit extreme environments, such as the cold, arid desert of the Dry Valleys of Antarctica. Fossilized cryptoendoliths preserve the forms and features of organisms from the past and thus provide a unique opportunity to study the organisms' life histories and environments. To study this fossil record, there needs to be a better understanding of what environmental conditions allow these fossils to form. A climate gradient currently exists in the Dry Valleys that allows us to study living, dead, and fossilized cryptoendoliths from mild to increasingly harsh environments; providing insight to the limits of life and how these fossils are formed. This project will develop instruments to detect the biological activity of the live microorganisms and conduct laboratory experiments to determine the environmental limits of their survival. The project also will characterize the chemical and structural features of the living, dead, and fossilized cryptoendoliths to understand how they become fossilized. Knowing how microorganisms are preserved as fossils in cold and dry environments like Antarctica can help to refine methods that can be used to search for and identify evidence for extraterrestrial life in similar habitats on planets such as Mars. This project includes training of graduate and undergraduate students. Little is known about cryptoendolithic microfossils and their formation processes in cold, arid terrestrial habitats of the Dry Valleys of Antarctica, where a legacy of activity is discernible in the form of biosignatures including inorganic materials and microbial fossils that preserve and indicate traces of past biological activity. The overarching goals of the proposed work are: (1) to determine how rates of microbial respiration and biodegradation of organic matter control microbial fossilization; and (2) to characterize microbial fossils and their living counterparts to elucidate mechanisms for fossilization. Using samples collected across an increasingly harsher (more cold and dry) climatic gradient that encompasses living, dead, and fossilized cryptoendolithic microorganisms, the proposed work will: (1) develop an instrument to be used in the field that can measure small concentrations of CO2 in cryptoendolithic habitats in situ; (2) use microscopy techniques to characterize endolithic microorganisms as well as the chemical and morphological characteristics of biosignatures and microbial fossils. A metagenomic survey of microbial communities in these samples will be used to characterize differences in diversity, identify if specific microorganisms (e.g. prokaryotes, eukaryotes) are more capable of surviving under these harsh climatic conditions, and to corroborate microscopic observations of the viability states of these microorganisms. proprietary +USAP-1544526_1 Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica ALL STAC Catalog 2016-09-01 2017-08-31 160, -77.8, 163.7, -76.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069950-AMD_USAPDC.umm_json Cryptoendoliths are organisms that colonize microscopic cavities of rocks, which give them protection and allow them to inhabit extreme environments, such as the cold, arid desert of the Dry Valleys of Antarctica. Fossilized cryptoendoliths preserve the forms and features of organisms from the past and thus provide a unique opportunity to study the organisms' life histories and environments. To study this fossil record, there needs to be a better understanding of what environmental conditions allow these fossils to form. A climate gradient currently exists in the Dry Valleys that allows us to study living, dead, and fossilized cryptoendoliths from mild to increasingly harsh environments; providing insight to the limits of life and how these fossils are formed. This project will develop instruments to detect the biological activity of the live microorganisms and conduct laboratory experiments to determine the environmental limits of their survival. The project also will characterize the chemical and structural features of the living, dead, and fossilized cryptoendoliths to understand how they become fossilized. Knowing how microorganisms are preserved as fossils in cold and dry environments like Antarctica can help to refine methods that can be used to search for and identify evidence for extraterrestrial life in similar habitats on planets such as Mars. This project includes training of graduate and undergraduate students. Little is known about cryptoendolithic microfossils and their formation processes in cold, arid terrestrial habitats of the Dry Valleys of Antarctica, where a legacy of activity is discernible in the form of biosignatures including inorganic materials and microbial fossils that preserve and indicate traces of past biological activity. The overarching goals of the proposed work are: (1) to determine how rates of microbial respiration and biodegradation of organic matter control microbial fossilization; and (2) to characterize microbial fossils and their living counterparts to elucidate mechanisms for fossilization. Using samples collected across an increasingly harsher (more cold and dry) climatic gradient that encompasses living, dead, and fossilized cryptoendolithic microorganisms, the proposed work will: (1) develop an instrument to be used in the field that can measure small concentrations of CO2 in cryptoendolithic habitats in situ; (2) use microscopy techniques to characterize endolithic microorganisms as well as the chemical and morphological characteristics of biosignatures and microbial fossils. A metagenomic survey of microbial communities in these samples will be used to characterize differences in diversity, identify if specific microorganisms (e.g. prokaryotes, eukaryotes) are more capable of surviving under these harsh climatic conditions, and to corroborate microscopic observations of the viability states of these microorganisms. proprietary USAP-1643534_1 Biological and Physical Drivers of Oxygen Saturation and Net Community Production Variability along the Western Antarctic Peninsula AMD_USAPDC STAC Catalog 2016-06-15 2023-07-15 -83, -73, -56, -62 https://cmr.earthdata.nasa.gov/search/concepts/C2532075509-AMD_USAPDC.umm_json "This project seeks to make detailed measurements of the oxygen content of the surface ocean along the Western Antarctic Peninsula. Detailed maps of changes in net oxygen content will be combined with measurements of the surface water chemistry and phytoplankton distributions. The project will determine the extent to which on-shore or offshore phytoplankton blooms along the peninsula are likely to lead to different amounts of carbon being exported to the deeper ocean. The project will analyze oxygen in relation to argon that will allow determination of the physical and biological contributions to surface ocean oxygen dynamics. These assessments will be combined with spatial and temporal distributions of nutrients (iron and macronutrients) and irradiances. This will allow the investigators to unravel the complex interplay between ice dynamics, iron and physical mixing dynamics as they relate to Net Community Production (NCP) in the region. NCP measurements will be normalized to Particulate Organic Carbon (POC) and be used to help identify area of ""High Biomass and Low NCP"" and those with ""Low Biomass and High NCP"" as a function of microbial plankton community composition. The team will use machine learning methods- including decision tree assemblages and genetic programming- to identify plankton groups key to facilitating biological carbon fluxes. Decomposing the oxygen signal along the West Antarctic Peninsula will also help elucidate biotic and abiotic drivers of the O2 saturation to further contextualize the growing inventory of oxygen measurements (e.g. by Argo floats) throughout the global oceans." proprietary -USAP-1643722_1 A High Resolution Atmospheric Methane Record from the South Pole Ice Core AMD_USAPDC STAC Catalog 2017-02-01 2019-01-31 180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2534799946-AMD_USAPDC.umm_json This award supports a project to measure the concentration of the gas methane in air trapped in an ice core collected from the South Pole. The data will provide an age scale (age as a function of depth) by matching the South Pole methane changes with similar data from other ice cores for which the age vs. depth relationship is well known. The ages provided will allow all other gas measurements made on the South Pole core (by the PI and other NSF supported investigators) to be interpreted accurately as a function of time. This is critical because a major goal of the South Pole coring project is to understand the history of rare gases in the atmosphere like carbon monoxide, carbon dioxide, ethane, propane, methyl chloride, and methyl bromide. Relatively little is known about what controls these gases in the atmosphere despite their importance to atmospheric chemistry and climate. Undergraduate assistants will work on the project and be introduced to independent research through their work. The PI will continue visits to local middle schools to introduce students to polar science, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) as part of the project. Methane concentrations from a major portion (2 depth intervals, excluding the brittle ice-zone which is being measured at Penn State University) of the new South Pole ice core will be used to create a gas chronology by matching the new South Pole ice core record with that from the well-dated WAIS Divide ice core record. In combination with measurements made at Penn State, this will provide gas dating for the entire 50,000-year record. Correlation will be made using a simple but powerful mid-point method that has been previously demonstrated, and other methods of matching records will be explored. The intellectual merit of this work is that the gas chronology will be a fundamental component of this ice core project, and will be used by the PI and other investigators for dating records of atmospheric composition, and determining the gas age-ice age difference independently of glaciological models, which will constrain processes that affected firn densification in the past. The methane data will also provide direct stratigraphic markers of important perturbations to global biogeochemical cycles (e.g., rapid methane variations synchronous with abrupt warming and cooling in the Northern Hemisphere) that will tie other ice core gas records directly to those perturbations. A record of the total air content will also be produced as a by-product of the methane measurements and will contribute to understanding of this parameter. The broader impacts include that the work will provide a fundamental data set for the South Pole ice core project and the age scale (or variants of it) will be used by all other investigators working on gas records from the core. The project will employ an undergraduate assistant(s) in both years who will conduct an undergraduate research project which will be part of the student's senior thesis or other research paper. The project will also offer at least one research position for the Oregon State University Summer REU site program. Visits to local middle schools, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) will also be part of the project. proprietary USAP-1643722_1 A High Resolution Atmospheric Methane Record from the South Pole Ice Core ALL STAC Catalog 2017-02-01 2019-01-31 180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2534799946-AMD_USAPDC.umm_json This award supports a project to measure the concentration of the gas methane in air trapped in an ice core collected from the South Pole. The data will provide an age scale (age as a function of depth) by matching the South Pole methane changes with similar data from other ice cores for which the age vs. depth relationship is well known. The ages provided will allow all other gas measurements made on the South Pole core (by the PI and other NSF supported investigators) to be interpreted accurately as a function of time. This is critical because a major goal of the South Pole coring project is to understand the history of rare gases in the atmosphere like carbon monoxide, carbon dioxide, ethane, propane, methyl chloride, and methyl bromide. Relatively little is known about what controls these gases in the atmosphere despite their importance to atmospheric chemistry and climate. Undergraduate assistants will work on the project and be introduced to independent research through their work. The PI will continue visits to local middle schools to introduce students to polar science, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) as part of the project. Methane concentrations from a major portion (2 depth intervals, excluding the brittle ice-zone which is being measured at Penn State University) of the new South Pole ice core will be used to create a gas chronology by matching the new South Pole ice core record with that from the well-dated WAIS Divide ice core record. In combination with measurements made at Penn State, this will provide gas dating for the entire 50,000-year record. Correlation will be made using a simple but powerful mid-point method that has been previously demonstrated, and other methods of matching records will be explored. The intellectual merit of this work is that the gas chronology will be a fundamental component of this ice core project, and will be used by the PI and other investigators for dating records of atmospheric composition, and determining the gas age-ice age difference independently of glaciological models, which will constrain processes that affected firn densification in the past. The methane data will also provide direct stratigraphic markers of important perturbations to global biogeochemical cycles (e.g., rapid methane variations synchronous with abrupt warming and cooling in the Northern Hemisphere) that will tie other ice core gas records directly to those perturbations. A record of the total air content will also be produced as a by-product of the methane measurements and will contribute to understanding of this parameter. The broader impacts include that the work will provide a fundamental data set for the South Pole ice core project and the age scale (or variants of it) will be used by all other investigators working on gas records from the core. The project will employ an undergraduate assistant(s) in both years who will conduct an undergraduate research project which will be part of the student's senior thesis or other research paper. The project will also offer at least one research position for the Oregon State University Summer REU site program. Visits to local middle schools, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) will also be part of the project. proprietary +USAP-1643722_1 A High Resolution Atmospheric Methane Record from the South Pole Ice Core AMD_USAPDC STAC Catalog 2017-02-01 2019-01-31 180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2534799946-AMD_USAPDC.umm_json This award supports a project to measure the concentration of the gas methane in air trapped in an ice core collected from the South Pole. The data will provide an age scale (age as a function of depth) by matching the South Pole methane changes with similar data from other ice cores for which the age vs. depth relationship is well known. The ages provided will allow all other gas measurements made on the South Pole core (by the PI and other NSF supported investigators) to be interpreted accurately as a function of time. This is critical because a major goal of the South Pole coring project is to understand the history of rare gases in the atmosphere like carbon monoxide, carbon dioxide, ethane, propane, methyl chloride, and methyl bromide. Relatively little is known about what controls these gases in the atmosphere despite their importance to atmospheric chemistry and climate. Undergraduate assistants will work on the project and be introduced to independent research through their work. The PI will continue visits to local middle schools to introduce students to polar science, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) as part of the project. Methane concentrations from a major portion (2 depth intervals, excluding the brittle ice-zone which is being measured at Penn State University) of the new South Pole ice core will be used to create a gas chronology by matching the new South Pole ice core record with that from the well-dated WAIS Divide ice core record. In combination with measurements made at Penn State, this will provide gas dating for the entire 50,000-year record. Correlation will be made using a simple but powerful mid-point method that has been previously demonstrated, and other methods of matching records will be explored. The intellectual merit of this work is that the gas chronology will be a fundamental component of this ice core project, and will be used by the PI and other investigators for dating records of atmospheric composition, and determining the gas age-ice age difference independently of glaciological models, which will constrain processes that affected firn densification in the past. The methane data will also provide direct stratigraphic markers of important perturbations to global biogeochemical cycles (e.g., rapid methane variations synchronous with abrupt warming and cooling in the Northern Hemisphere) that will tie other ice core gas records directly to those perturbations. A record of the total air content will also be produced as a by-product of the methane measurements and will contribute to understanding of this parameter. The broader impacts include that the work will provide a fundamental data set for the South Pole ice core project and the age scale (or variants of it) will be used by all other investigators working on gas records from the core. The project will employ an undergraduate assistant(s) in both years who will conduct an undergraduate research project which will be part of the student's senior thesis or other research paper. The project will also offer at least one research position for the Oregon State University Summer REU site program. Visits to local middle schools, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) will also be part of the project. proprietary USAP-1643864_1 Collaborative Research: Borehole Logging to Classify Volcanic Signatures in Antarctic Ice AMD_USAPDC STAC Catalog 2017-05-08 -112.085, -79.467, -112.085, -79.467 https://cmr.earthdata.nasa.gov/search/concepts/C2532074603-AMD_USAPDC.umm_json This dataset comprises new photographs and measurements of a WAIS Divide vertical thin section, WDC-06A 420 VTS, previously prepared and measured by J. Fitzpatrick, D. E. Voigt, and R. Alley (dataset DOI: 10.7265/N5W093VM; http://www.usap-dc.org/view/dataset/609605) as part of a larger study of the WAIS Divide ice core (Fitzpatrick, J. et al, 2014, Physical properties of the WAIS Divide ice core, Journal of Glaciology, 60, 224, 1181-1198. (doi:10.3189/2014JoG14J100). These images were taken as a design test of our new automated lightweight c-axis analyzer, dubbed ALPACA, which implements the ice fabric analysis functionality of the Wilen system used by Fitzpatrick et al. in an easily-portable, field-deployable form factor. proprietary USAP-1644004_1 Collaborative Research: Foraging Ecology and Physiology of the Leopard Seal AMD_USAPDC STAC Catalog 2017-10-01 2022-09-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2560369942-AMD_USAPDC.umm_json This research project is a multidisciplinary effort that brings together a diverse team of scientists from multiple institutions together to understand the foraging behavior and physiology of leopard seals and their role in the Southern Ocean food web. The project will examine the physiology and behavior of leopard seals to in an effort to determine their ability to respond to potential changes in their habitat and foraging areas. Using satellite tracking devices the team will examine the movement and diving behavior of leopard seals and couple this information with measurements of their physiological capacity. The project will determine whether leopard seals- who feed on diverse range of prey- are built differently than their deep diving relatives the Weddell and elephant seal who feed on fish and squid. The team will also determine whether leopard seals are operating at or near their physiological capability to determine how much, if any, ?reserve capacity? they might have to forage and live in changing environments. A better understanding of their home ranges, movement patterns, and general behavior will also be informative to help in managing human-leopard seal interactions. The highly visual nature of the data and analysis for this project lends itself to public and educational display and outreach, particularly as they relate to the changing Antarctic habitats. The project will use the research results to educate the public on the unique physiological and ecological adaptations to extreme environments seen in diving marine mammals, including adaptations to exercise under low oxygen conditions and energy utilization, which affect and dictate the lifestyle of these exceptional organisms. The results of the project will also contribute to the broader understanding that may enhance the aims of managing marine living resources. The leopard seal is an apex predator in the Antarctic ecosystem. This project seeks to better understand the ability of the leopard seal to cope with a changing environment. The project will first examine the foraging behavior and habitat utilization of leopard seals using satellite telemetry. Specifically, satellite telemetry tags will be used to obtain dive profiles and movement data for individuals across multiple years. Diet and trophic level positions across multiple temporal scales will then be determined from physiological samples (e.g., blood, vibrissae, blubber fatty acids, stable isotopes, fecal matter). Oceanographic data will be integrated with these measures to develop habitat models that will be used to assess habitat type, habitat utilization, habitat preference, and home range areas for individual animals. Diet composition for individual seals will be evaluated to determine whether specific animals are generalists or specialists. Second, the team will investigate the physiological adaptations that allow leopard seals to be apex predators and determine to what extent leopard seals are working at or near their physiological limit. Diving behavior and physiology of leopard seals will be evaluated (for instance the aerobic dive limit for individual animals and skeletal muscle adaptations will be determined for diving under hypoxic conditions). Data from time-depth recorders will be used to determine foraging strategies for individual seals, and these diving characteristics will be related to physiological variables (e.g., blood volume, muscle oxygen stores) to better understand the link between foraging behavior and physiology. The team will compare myoglobin storage in swimming muscles associated with both forelimb and hind limb propulsion and the use of anaerobic versus aerobic metabolic systems while foraging. proprietary USAP-1644073_1 Collaborative Research: Cobalamin and Iron Co-Limitation Of Phytoplankton Species in Terra Nova Bay AMD_USAPDC STAC Catalog 2017-08-18 2020-08-31 -116, -79, 160, -72 https://cmr.earthdata.nasa.gov/search/concepts/C2532074465-AMD_USAPDC.umm_json Phytoplankton blooms in the coastal waters of the Ross Sea, Antarctica are typically dominated by either diatoms or Phaeocystis Antarctica (a flagellated algae that often can form large colonies in a gelatinous matrix). The project seeks to determine if an association of bacterial populations with Phaeocystis antarctica colonies can directly supply Phaeocystis with Vitamin B12, which can be an important co-limiting micronutrient in the Ross Sea. The supply of an essential vitamin coupled with the ability to grow at lower iron concentrations may put Phaeocystis at a competitive advantage over diatoms. Because Phaeocystis cells can fix more carbon than diatoms and Phaeocystis are not grazed as efficiently as diatoms, the project will help in refining understanding of carbon dynamics in the region as well as the basis of the food web webs. Such understanding also has the potential to help refine predictive ecological models for the region. The project will conduct public outreach activities and will contribute to undergraduate and graduate research. Engagement of underrepresented students will occur during summer student internships. A collaboration with Italian Antarctic researchers, who have been studying the Terra Nova Bay ecosystem since the 1980s, aims to enhance the project and promote international scientific collaborations. The study will test whether a mutualistic symbioses between attached bacteria and Phaeocystis provides colonial cells a mechanism for alleviating chronic Vitamin B12 co-limitation effects thereby conferring them with a competitive advantage over diatom communities. The use of drifters in a time series study will provide the opportunity to track in both space and time a developing algal bloom in Terra Nova Bay and to determine community structure and the physiological nutrient status of microbial populations. A combination of flow cytometry, proteomics, metatranscriptomics, radioisotopic and stable isotopic labeling experiments will determine carbon and nutrient uptake rates and the role of bacteria in mitigating potential vitamin B12 and iron limitation. Membrane inlet and proton transfer reaction mass spectrometry will also be used to estimate net community production and release of volatile organic carbon compounds that are climatically active. Understanding how environmental parameters can influence microbial community dynamics in Antarctic coastal waters will advance an understanding of how changes in ocean stratification and chemistry could impact the biogeochemistry and food web dynamics of Southern Ocean ecosystems. proprietary USAP-1644197_1 Collaborative Research: New Constraints on Post-Glacial Rebound and Holocene Environmental History along the Northern Antarctic Peninsula from Raised Beaches AMD_USAPDC STAC Catalog 2017-08-08 2021-08-31 -65, -65, -55, -61 https://cmr.earthdata.nasa.gov/search/concepts/C2605088269-AMD_USAPDC.umm_json Glacier ice loss from Antarctica has the potential to lead to a significant rise in global sea level. One line of evidence for accelerated glacier ice loss has been an increase in the rate at which the land has been rising across the Antarctic Peninsula as measured by GPS receivers. However, GPS observations of uplift are limited to the last two decades. One goal of this study is to determine how these newly observed rates of uplift compare to average rates of uplift across the Antarctic Peninsula over a longer time interval. Researchers reconstructed past sea levels using the age and elevation of ancient beaches now stranded above sea level on the low-lying coastal hills of the Antarctica Peninsula and determined the rate of uplift over the last 5,000 years. The researchers analyzed the structure of the beaches using ground-penetrating radar and the characteristics of beach sediments to understand how sea-level rise and past climate changes are recorded in beach deposits. We found that unlike most views of how sea level changed across Antarctica over the last 5,000 years, its history is complex with periods of increasing rates of sea-level fall as well as short periods of potential sea-level rise. We attribute these oscillations in the nature of sea-level change across the Antarctic Peninsula to changes in the ice sheet over the last 5,000 years. These changes in sea level also suggest our understanding of the Earth structure beneath the Antarctic Peninsula need to be revised. The beach deposits themselves also record periods of climate change as reflected in the size and shape of their cobbles. This project has lead to the training of five graduate students, three undergraduate students, and outreach talks to k-12 schools in three communities. proprietary -USAP-1644234_1 A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus ALL STAC Catalog 2017-07-15 2022-06-30 166.17, -77.7, 167.75, -77.3 https://cmr.earthdata.nasa.gov/search/concepts/C2586847142-AMD_USAPDC.umm_json Nontechnical Description: The age of rocks and soils at the surface of the Earth can help answer multiple questions that are important for human welfare, including: when did volcanoes erupt and are they likely to erupt again? when did glaciers advance and what do they tell us about climate? what is the frequency of hazards such as landslides, floods, and debris flows? how long does it take soils to form and is erosion of soils going to make farming unsustainable? One method that is used thousands of times every year to address these questions is called 'cosmogenic surface-exposure dating'. This method takes advantage of cosmic rays, which are powerful protons and neutrons produced by supernova that constantly bombard the Earth's atmosphere. Some cosmic rays reach Earth's surface and produce nuclear reactions that result in rare isotopes. Measuring the quantity of the rare isotopes enables the length of time that the rock or soil has been exposed to the atmosphere to be calculated. The distribution of cosmic rays around the globe depends on Earth's magnetic field, and this distribution must be accurately known if useful exposure ages are to be obtained. Currently there are two remaining theories, narrowed down from many, of how to calculate this distribution. Measurements from a site that is at both high altitude and high latitude (close to the poles) are needed to test the two theories. This study involves both field and lab research and includes a Ph.D. student and an undergraduate student. The research team will collect rocks from lava flows on an active volcano in Antarctica named Mount Erebus and measure the amounts of two rare isotopes: 36Cl and 3He. The age of eruption of the samples will be determined using a highly accurate method that does not depend on cosmic rays, called 40Ar/39Ar dating. The two cosmic-ray theories will be used to calculate the ages of the samples using the 36Cl and 3He concentrations and will then be compared to the ages calculated from the 40Ar/39Ar dating. The accurate cosmic-ray theory will be the one that gives the same ages as the 40Ar/39Ar dating. Identification of the accurate theory will enable use of the cosmogenic surface dating methods anywhere on earth. Technical Description: Nuclides produced by cosmic rays in rocks at the surface of the earth are widely used for Quaternary geochronology and geomorphic studies and their use is increasing every year. The recently completed CRONUS-Earth Project (Cosmic-Ray Produced Nuclides on Earth) has systematically evaluated the production rates and theoretical underpinnings of cosmogenic nuclides. However, the CRONUS-Earth Project was not able to discriminate between the two leading theoretical approaches: the original Lal model (St) and the new Lifton-Sato-Dunai model (LSD). Mathematical models used to scale the production of the nuclides as a function of location on the earth, elevation, and magnetic field configuration are an essential component of this dating method. The inability to distinguish between the two models was because the predicted production rates did not differ sufficiently at the location of the calibration sites. The cosmogenic-nuclide production rates that are predicted by the two models differ significantly from each other at Erebus volcano, Antarctica. Mount Erebus is therefore an excellent site for testing which production model best describes actual cosmogenic-nuclide production variations over the globe. The research team recently measured 3He and 36Cl in mineral separates extracted from Erebus lava flows. The exposure ages for each nuclide were reproducible within each flow (~2% standard deviation) and in very good agreement between the 3He and the 36Cl ages. However, the ages calculated by the St and LSD scaling methods differ by ~15-25% due to the sensitivity of the production rate to the scaling at this latitude and elevation. These results lend confidence that Erebus qualifies as a suitable high- latitude/high-elevation calibration site. The remaining component that is still lacking is accurate and reliable independent (i.e., non-cosmogenic) ages, however, published 40Ar/39Ar ages are too imprecise and typically biased to older ages due to excess argon contained in melt inclusions. The research team's new 40Ar/39Ar data show that previous problems with Erebus anorthoclase geochronology are now overcome with modern mass spectrometry and better sample preparation. This indicates a high likelihood of success for this proposal in defining an accurate global scaling model. Although encouraging, much remains to be accomplished. This project will sample lava flows over 3 km in elevation and determine their 40Ar/39Ar and exposure ages. These combined data will discriminate between the two scaling methods, resulting in a preferred scaling model for global cosmogenic geochronology. The LSD method contains two sub-methods, the 'plain' LSD scales all nuclides the same, whereas LSDn scales each nuclide individually. The project can discriminate between these models using 3He and 36Cl data from lava flows at different elevations, because the first model predicts that the production ratio for these two nuclides will be invariant with elevation and the second that there should be ~10% difference over the range of elevations to be sampled. Finally, the project will provide a local, finite-age calibration site for cosmogenic-nuclide investigations in Antarctica. proprietary USAP-1644234_1 A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus AMD_USAPDC STAC Catalog 2017-07-15 2022-06-30 166.17, -77.7, 167.75, -77.3 https://cmr.earthdata.nasa.gov/search/concepts/C2586847142-AMD_USAPDC.umm_json Nontechnical Description: The age of rocks and soils at the surface of the Earth can help answer multiple questions that are important for human welfare, including: when did volcanoes erupt and are they likely to erupt again? when did glaciers advance and what do they tell us about climate? what is the frequency of hazards such as landslides, floods, and debris flows? how long does it take soils to form and is erosion of soils going to make farming unsustainable? One method that is used thousands of times every year to address these questions is called 'cosmogenic surface-exposure dating'. This method takes advantage of cosmic rays, which are powerful protons and neutrons produced by supernova that constantly bombard the Earth's atmosphere. Some cosmic rays reach Earth's surface and produce nuclear reactions that result in rare isotopes. Measuring the quantity of the rare isotopes enables the length of time that the rock or soil has been exposed to the atmosphere to be calculated. The distribution of cosmic rays around the globe depends on Earth's magnetic field, and this distribution must be accurately known if useful exposure ages are to be obtained. Currently there are two remaining theories, narrowed down from many, of how to calculate this distribution. Measurements from a site that is at both high altitude and high latitude (close to the poles) are needed to test the two theories. This study involves both field and lab research and includes a Ph.D. student and an undergraduate student. The research team will collect rocks from lava flows on an active volcano in Antarctica named Mount Erebus and measure the amounts of two rare isotopes: 36Cl and 3He. The age of eruption of the samples will be determined using a highly accurate method that does not depend on cosmic rays, called 40Ar/39Ar dating. The two cosmic-ray theories will be used to calculate the ages of the samples using the 36Cl and 3He concentrations and will then be compared to the ages calculated from the 40Ar/39Ar dating. The accurate cosmic-ray theory will be the one that gives the same ages as the 40Ar/39Ar dating. Identification of the accurate theory will enable use of the cosmogenic surface dating methods anywhere on earth. Technical Description: Nuclides produced by cosmic rays in rocks at the surface of the earth are widely used for Quaternary geochronology and geomorphic studies and their use is increasing every year. The recently completed CRONUS-Earth Project (Cosmic-Ray Produced Nuclides on Earth) has systematically evaluated the production rates and theoretical underpinnings of cosmogenic nuclides. However, the CRONUS-Earth Project was not able to discriminate between the two leading theoretical approaches: the original Lal model (St) and the new Lifton-Sato-Dunai model (LSD). Mathematical models used to scale the production of the nuclides as a function of location on the earth, elevation, and magnetic field configuration are an essential component of this dating method. The inability to distinguish between the two models was because the predicted production rates did not differ sufficiently at the location of the calibration sites. The cosmogenic-nuclide production rates that are predicted by the two models differ significantly from each other at Erebus volcano, Antarctica. Mount Erebus is therefore an excellent site for testing which production model best describes actual cosmogenic-nuclide production variations over the globe. The research team recently measured 3He and 36Cl in mineral separates extracted from Erebus lava flows. The exposure ages for each nuclide were reproducible within each flow (~2% standard deviation) and in very good agreement between the 3He and the 36Cl ages. However, the ages calculated by the St and LSD scaling methods differ by ~15-25% due to the sensitivity of the production rate to the scaling at this latitude and elevation. These results lend confidence that Erebus qualifies as a suitable high- latitude/high-elevation calibration site. The remaining component that is still lacking is accurate and reliable independent (i.e., non-cosmogenic) ages, however, published 40Ar/39Ar ages are too imprecise and typically biased to older ages due to excess argon contained in melt inclusions. The research team's new 40Ar/39Ar data show that previous problems with Erebus anorthoclase geochronology are now overcome with modern mass spectrometry and better sample preparation. This indicates a high likelihood of success for this proposal in defining an accurate global scaling model. Although encouraging, much remains to be accomplished. This project will sample lava flows over 3 km in elevation and determine their 40Ar/39Ar and exposure ages. These combined data will discriminate between the two scaling methods, resulting in a preferred scaling model for global cosmogenic geochronology. The LSD method contains two sub-methods, the 'plain' LSD scales all nuclides the same, whereas LSDn scales each nuclide individually. The project can discriminate between these models using 3He and 36Cl data from lava flows at different elevations, because the first model predicts that the production ratio for these two nuclides will be invariant with elevation and the second that there should be ~10% difference over the range of elevations to be sampled. Finally, the project will provide a local, finite-age calibration site for cosmogenic-nuclide investigations in Antarctica. proprietary -USAP-1656344_1 A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition ALL STAC Catalog 2016-08-01 2018-07-31 -64.1, -65, -63.9, -64.75 https://cmr.earthdata.nasa.gov/search/concepts/C2532071951-AMD_USAPDC.umm_json "This EAGER project will compare gene expression patterns in the planktonic communities under ice covers that form in coastal embayment's in the Antarctic Peninsula. Previous efforts taking advantage of unique ice conditions in November and December of 2015 allowed researchers to conduct an experiment to examine the role of sea ice cover on microbial carbon and energy transfer during the winter-spring transition. The EAGER effort will enable the researchers to conduct the ""omics"" analyses of the phytoplankton to determine predominant means by which energy is acquired and used in these settings. This EAGER effort will apply new expertise to fill an existing gap in ecological observations along the West Antarctic Peninsula. The principle product of the proposed work will be a novel dataset to be analyzed and by an early career researcher from an underserved community (veteran). The critical baseline data contained in this dataset enable a comparison of eukaryotic and prokaryotic gene expression patterns to establish the relative importance of chemoautotrophy, heterotrophy, mixotrophy, and phototrophy during the experiments. this information and data will be made immediately available to the broader scientific community, and will enable the development of further hypotheses on ecosystem change as sea ice cover changes in the region. Very little gene expression data is currently available for the Antarctic marine environment, and no gene expression data is available during the ecologically critical winter to spring transition. Moreover, ice cover in bays is common along the West Antarctic Peninsula yet the opportunity to study cryptophyte phytoplankton physiology beneath such ice conditions in coastal embayments is rare." proprietary +USAP-1644234_1 A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus ALL STAC Catalog 2017-07-15 2022-06-30 166.17, -77.7, 167.75, -77.3 https://cmr.earthdata.nasa.gov/search/concepts/C2586847142-AMD_USAPDC.umm_json Nontechnical Description: The age of rocks and soils at the surface of the Earth can help answer multiple questions that are important for human welfare, including: when did volcanoes erupt and are they likely to erupt again? when did glaciers advance and what do they tell us about climate? what is the frequency of hazards such as landslides, floods, and debris flows? how long does it take soils to form and is erosion of soils going to make farming unsustainable? One method that is used thousands of times every year to address these questions is called 'cosmogenic surface-exposure dating'. This method takes advantage of cosmic rays, which are powerful protons and neutrons produced by supernova that constantly bombard the Earth's atmosphere. Some cosmic rays reach Earth's surface and produce nuclear reactions that result in rare isotopes. Measuring the quantity of the rare isotopes enables the length of time that the rock or soil has been exposed to the atmosphere to be calculated. The distribution of cosmic rays around the globe depends on Earth's magnetic field, and this distribution must be accurately known if useful exposure ages are to be obtained. Currently there are two remaining theories, narrowed down from many, of how to calculate this distribution. Measurements from a site that is at both high altitude and high latitude (close to the poles) are needed to test the two theories. This study involves both field and lab research and includes a Ph.D. student and an undergraduate student. The research team will collect rocks from lava flows on an active volcano in Antarctica named Mount Erebus and measure the amounts of two rare isotopes: 36Cl and 3He. The age of eruption of the samples will be determined using a highly accurate method that does not depend on cosmic rays, called 40Ar/39Ar dating. The two cosmic-ray theories will be used to calculate the ages of the samples using the 36Cl and 3He concentrations and will then be compared to the ages calculated from the 40Ar/39Ar dating. The accurate cosmic-ray theory will be the one that gives the same ages as the 40Ar/39Ar dating. Identification of the accurate theory will enable use of the cosmogenic surface dating methods anywhere on earth. Technical Description: Nuclides produced by cosmic rays in rocks at the surface of the earth are widely used for Quaternary geochronology and geomorphic studies and their use is increasing every year. The recently completed CRONUS-Earth Project (Cosmic-Ray Produced Nuclides on Earth) has systematically evaluated the production rates and theoretical underpinnings of cosmogenic nuclides. However, the CRONUS-Earth Project was not able to discriminate between the two leading theoretical approaches: the original Lal model (St) and the new Lifton-Sato-Dunai model (LSD). Mathematical models used to scale the production of the nuclides as a function of location on the earth, elevation, and magnetic field configuration are an essential component of this dating method. The inability to distinguish between the two models was because the predicted production rates did not differ sufficiently at the location of the calibration sites. The cosmogenic-nuclide production rates that are predicted by the two models differ significantly from each other at Erebus volcano, Antarctica. Mount Erebus is therefore an excellent site for testing which production model best describes actual cosmogenic-nuclide production variations over the globe. The research team recently measured 3He and 36Cl in mineral separates extracted from Erebus lava flows. The exposure ages for each nuclide were reproducible within each flow (~2% standard deviation) and in very good agreement between the 3He and the 36Cl ages. However, the ages calculated by the St and LSD scaling methods differ by ~15-25% due to the sensitivity of the production rate to the scaling at this latitude and elevation. These results lend confidence that Erebus qualifies as a suitable high- latitude/high-elevation calibration site. The remaining component that is still lacking is accurate and reliable independent (i.e., non-cosmogenic) ages, however, published 40Ar/39Ar ages are too imprecise and typically biased to older ages due to excess argon contained in melt inclusions. The research team's new 40Ar/39Ar data show that previous problems with Erebus anorthoclase geochronology are now overcome with modern mass spectrometry and better sample preparation. This indicates a high likelihood of success for this proposal in defining an accurate global scaling model. Although encouraging, much remains to be accomplished. This project will sample lava flows over 3 km in elevation and determine their 40Ar/39Ar and exposure ages. These combined data will discriminate between the two scaling methods, resulting in a preferred scaling model for global cosmogenic geochronology. The LSD method contains two sub-methods, the 'plain' LSD scales all nuclides the same, whereas LSDn scales each nuclide individually. The project can discriminate between these models using 3He and 36Cl data from lava flows at different elevations, because the first model predicts that the production ratio for these two nuclides will be invariant with elevation and the second that there should be ~10% difference over the range of elevations to be sampled. Finally, the project will provide a local, finite-age calibration site for cosmogenic-nuclide investigations in Antarctica. proprietary USAP-1656344_1 A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition AMD_USAPDC STAC Catalog 2016-08-01 2018-07-31 -64.1, -65, -63.9, -64.75 https://cmr.earthdata.nasa.gov/search/concepts/C2532071951-AMD_USAPDC.umm_json "This EAGER project will compare gene expression patterns in the planktonic communities under ice covers that form in coastal embayment's in the Antarctic Peninsula. Previous efforts taking advantage of unique ice conditions in November and December of 2015 allowed researchers to conduct an experiment to examine the role of sea ice cover on microbial carbon and energy transfer during the winter-spring transition. The EAGER effort will enable the researchers to conduct the ""omics"" analyses of the phytoplankton to determine predominant means by which energy is acquired and used in these settings. This EAGER effort will apply new expertise to fill an existing gap in ecological observations along the West Antarctic Peninsula. The principle product of the proposed work will be a novel dataset to be analyzed and by an early career researcher from an underserved community (veteran). The critical baseline data contained in this dataset enable a comparison of eukaryotic and prokaryotic gene expression patterns to establish the relative importance of chemoautotrophy, heterotrophy, mixotrophy, and phototrophy during the experiments. this information and data will be made immediately available to the broader scientific community, and will enable the development of further hypotheses on ecosystem change as sea ice cover changes in the region. Very little gene expression data is currently available for the Antarctic marine environment, and no gene expression data is available during the ecologically critical winter to spring transition. Moreover, ice cover in bays is common along the West Antarctic Peninsula yet the opportunity to study cryptophyte phytoplankton physiology beneath such ice conditions in coastal embayments is rare." proprietary -USAP-1744755_1 A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean AMD_USAPDC STAC Catalog 2018-05-01 2022-04-30 -80, -70, -30, -45 https://cmr.earthdata.nasa.gov/search/concepts/C2545372297-AMD_USAPDC.umm_json Current generation of coupled climate models, that are used to make climate projections, lack the resolution to adequately resolve ocean mesoscale (10 - 100km) processes, exhibiting significant biases in the ocean carbon uptake. Mesoscale processes include many features including jets, fronts and eddies that are crucial for bio-physical interactions, air-sea CO2 exchange and the supply of iron to the surface ocean. This modeling project will support the eddy resolving regional simulations to understand the mechanisms that drives bio-physical interaction and air-sea exchange of carbon dioxide. proprietary +USAP-1656344_1 A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition ALL STAC Catalog 2016-08-01 2018-07-31 -64.1, -65, -63.9, -64.75 https://cmr.earthdata.nasa.gov/search/concepts/C2532071951-AMD_USAPDC.umm_json "This EAGER project will compare gene expression patterns in the planktonic communities under ice covers that form in coastal embayment's in the Antarctic Peninsula. Previous efforts taking advantage of unique ice conditions in November and December of 2015 allowed researchers to conduct an experiment to examine the role of sea ice cover on microbial carbon and energy transfer during the winter-spring transition. The EAGER effort will enable the researchers to conduct the ""omics"" analyses of the phytoplankton to determine predominant means by which energy is acquired and used in these settings. This EAGER effort will apply new expertise to fill an existing gap in ecological observations along the West Antarctic Peninsula. The principle product of the proposed work will be a novel dataset to be analyzed and by an early career researcher from an underserved community (veteran). The critical baseline data contained in this dataset enable a comparison of eukaryotic and prokaryotic gene expression patterns to establish the relative importance of chemoautotrophy, heterotrophy, mixotrophy, and phototrophy during the experiments. this information and data will be made immediately available to the broader scientific community, and will enable the development of further hypotheses on ecosystem change as sea ice cover changes in the region. Very little gene expression data is currently available for the Antarctic marine environment, and no gene expression data is available during the ecologically critical winter to spring transition. Moreover, ice cover in bays is common along the West Antarctic Peninsula yet the opportunity to study cryptophyte phytoplankton physiology beneath such ice conditions in coastal embayments is rare." proprietary USAP-1744755_1 A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean ALL STAC Catalog 2018-05-01 2022-04-30 -80, -70, -30, -45 https://cmr.earthdata.nasa.gov/search/concepts/C2545372297-AMD_USAPDC.umm_json Current generation of coupled climate models, that are used to make climate projections, lack the resolution to adequately resolve ocean mesoscale (10 - 100km) processes, exhibiting significant biases in the ocean carbon uptake. Mesoscale processes include many features including jets, fronts and eddies that are crucial for bio-physical interactions, air-sea CO2 exchange and the supply of iron to the surface ocean. This modeling project will support the eddy resolving regional simulations to understand the mechanisms that drives bio-physical interaction and air-sea exchange of carbon dioxide. proprietary +USAP-1744755_1 A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean AMD_USAPDC STAC Catalog 2018-05-01 2022-04-30 -80, -70, -30, -45 https://cmr.earthdata.nasa.gov/search/concepts/C2545372297-AMD_USAPDC.umm_json Current generation of coupled climate models, that are used to make climate projections, lack the resolution to adequately resolve ocean mesoscale (10 - 100km) processes, exhibiting significant biases in the ocean carbon uptake. Mesoscale processes include many features including jets, fronts and eddies that are crucial for bio-physical interactions, air-sea CO2 exchange and the supply of iron to the surface ocean. This modeling project will support the eddy resolving regional simulations to understand the mechanisms that drives bio-physical interaction and air-sea exchange of carbon dioxide. proprietary USAP-1744828_1 Collaborative Proposal: A High-Latitude Conjugate Area Array Experiment to Investigate Solar Wind - Magnetosphere - Ionosphere Coupling AMD_USAPDC STAC Catalog 2018-08-15 2022-07-31 6, -85, 89, -69 https://cmr.earthdata.nasa.gov/search/concepts/C2532075157-AMD_USAPDC.umm_json This proposal is directed toward an investigation of the coupling phenomena between the solar wind and the Earth's magnetosphere and ionosphere, particularly on the day side of the Earth and observed simultaneously at high latitudes in both northern and southern hemispheres. Through past NSF support, several magnetometers have been deployed in Antarctica, Greenland, and Svalbard, while new collaborations have been developed with the Polar Research Institute of China (PRIC) to further increase coverage through data sharing. This project will expand the existing Virginia Tech-PRIC partnership to include New Jersey Institute of Technology, University of New Hampshire, and the Technical University of Denmark and (1) construct two new stations to be deployed by PRIC along a chain from Zhongshan station to Dome A to complete a conjugate area array, (2) integrate data from all stations into a common format, and (3) address two focused science questions. Both instrument deployment and data processing efforts are motivated by a large number of solar wind-magnetosphere-ionosphere (SWMI) coupling science questions; this project will address two questions pertaining to Ultra Low Frequency (ULF) waves: (1) What is the global ULF response to Hot Flow Anomalies (HFA) and how is it affected by asymmetries in the SWMI system? (2) How do dawn-dusk and north-south asymmetries in the coupled SWMI system affect global ULF wave properties during periods with large, steady east-west Interplanetary Magnetic field (IMF By)? This proposal requires fieldwork in the Antarctic, but all fieldwork will be conducted by PRIC. proprietary USAP-1744989_1 A Multi-scale Approach to Understanding Spatial and Population Variability in Emperor Penguins ALL STAC Catalog 2018-07-15 2022-06-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2705787178-AMD_USAPDC.umm_json This project on emperor penguin populations will quantify penguin presence/absence, and colony size and trajectory, across the entire Antarctic continent using high-resolution satellite imagery. For a subset of the colonies, population estimates derived from high-resolution satellite images will be compared with those determined by aerial surveys - these results have been uploaded to MAPPPD (penguinmap.com) and are freely available for use. This validated information will be used to determine population estimates for all emperor penguin colonies through iterations of supervised classification and maximum likelihood calculations on the high-resolution imagery. The effect of spatial, geophysical, and environmental variables on population size and decadal-scale trends will be assessed using generalized linear models. This research will result in a first ever empirical result for emperor penguin population trends and habitat suitability, and will leverage currently-funded NSF infrastructure and hosting sites to publish results in near-real time to the public. proprietary USAP-1744989_1 A Multi-scale Approach to Understanding Spatial and Population Variability in Emperor Penguins AMD_USAPDC STAC Catalog 2018-07-15 2022-06-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2705787178-AMD_USAPDC.umm_json This project on emperor penguin populations will quantify penguin presence/absence, and colony size and trajectory, across the entire Antarctic continent using high-resolution satellite imagery. For a subset of the colonies, population estimates derived from high-resolution satellite images will be compared with those determined by aerial surveys - these results have been uploaded to MAPPPD (penguinmap.com) and are freely available for use. This validated information will be used to determine population estimates for all emperor penguin colonies through iterations of supervised classification and maximum likelihood calculations on the high-resolution imagery. The effect of spatial, geophysical, and environmental variables on population size and decadal-scale trends will be assessed using generalized linear models. This research will result in a first ever empirical result for emperor penguin population trends and habitat suitability, and will leverage currently-funded NSF infrastructure and hosting sites to publish results in near-real time to the public. proprietary @@ -15514,13 +15494,13 @@ USAP-2149070_1 ANT LIA: Collaborative Research: Adaptations of Southern Ocean Di USAP-2232891_1 ANT LIA: The Role of Sex Determination in the Radiation of Antarctic Notothenioid Fish AMD_USAPDC STAC Catalog 2023-08-15 2027-07-31 -180, -90, 180, -37 https://cmr.earthdata.nasa.gov/search/concepts/C2759058324-AMD_USAPDC.umm_json Antarctic animals face tremendous threats as Antarctic ice sheets melt and temperatures rise. About 34 million years ago, when Antarctica began to cool, most species of fish became locally extinct. A group called the notothenioids, however, survived due to the evolution of antifreeze. The group eventually split into over 120 species. Why did this group of Antarctic fishes evolve into so many species? One possible reason why a single population splits into two species relates to sex genes and sex chromosomes. Diverging species often have either different sex determining genes (genes that specify whether an individual’s gonads become ovaries or testes) or have different sex chromosomes (chromosomes that differ between males and females within a species, like the human X and Y chromosomes). We know the sex chromosomes of only a few notothenioid species and know the genetic basis for sex determination in none of them. The aims of this research are to: 1) identify sex chromosomes in species representing every major group of Antarctic notothenioid fish; 2) discover possible sex determining genes in every major group of Antarctic notothenioid fish; and 3) find sex chromosomes and possible sex determining genes in two groups of temperate, warmer water, notothenioid fish. These warmer water fish include groups that never experienced the frigid Southern Ocean and groups that had ancestors inhabiting Antarctic oceans that later adjusted to warmer waters. This project will help explain the mechanisms that led to the division of a group of species threatened by climate change. This information is critical to conserve declining populations of Antarctic notothenioids, which are major food sources for other Antarctic species such as bird and seals. The project will offer a diverse group of undergraduates the opportunity to develop a permanent exhibit at the Eugene Science Center Museum. The exhibit will describe the Antarctic environment and explain its rapid climate change. It will also introduce the continent’s bizarre fishes that live below the freezing point of water. The project will collaborate with the university’s Science and Comics Initiative and students in the English Department’s Comics Studies Minor to prepare short graphic novels explaining Antarctic biogeography, icefish specialties, and the science of this project as it develops. proprietary USAP-2240780_1 ANT LIA: Collaborative Research: Mixotrophic Grazing as a Strategy to meet Nutritional Requirements in the Iron and Manganese Deficient Southern Ocean AMD_USAPDC STAC Catalog 2023-02-15 2026-01-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2639396983-AMD_USAPDC.umm_json Mixotrophic microorganisms that are capable of both photosynthetic and heterotrophic forms of metabolism are key contributors to primary productivity and organic carbon remineralization in the Southern Ocean. However, uncertainties in their grazing behavior and physiology prevent an accurate understanding of microbial food web dynamics and biogeochemical cycling in the Antarctic ecosystem. Polar mixotrophs have evolved to withstand extreme seasonality, including variable light, sea ice, temperature, and micronutrient concentrations. In particular, the Southern Ocean appears to be the only region of the world’s ocean where the bioessential trace metals iron (Fe) and manganese (Mn) are low enough to inhibit photosynthetic growth. The molecular physiology of mixotrophs experiencing Fe and Mn growth limitation has not yet been examined, and we lack an understanding of how seasonal changes in the availability of these micronutrients influence mixotrophic growth dynamics. We aim to examine whether grazing affords mixotrophs an ecological advantage in the Fe and Mn-deficient Southern Ocean, and to characterize the shifts in metabolic processes that occur during transitions in micronutrient conditions. Culture studies will directly measure growth responses, grazing behavior, and changes in elemental stoichiometry in response to Fe and Mn limitation. Transcriptomic analyses will reveal the metabolic underpinnings of trophic behavior and micronutrient stress responses, with implications for key biogeochemical processes such as carbon fixation, remineralization, and nutrient cycling. proprietary USAP-2324998_1 ANT LIA: Collaborative Research: Evolutionary Patterns and Mechanisms of Trait Diversification in the Antarctic Notothenioid Radiation AMD_USAPDC STAC Catalog 2022-10-01 2025-01-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C3333666817-AMD_USAPDC.umm_json Part I: Nontechnical description The ecologically important notothenioid fish of the Southern Ocean surrounding Antarctica will be studied to address questions central to polar, evolutionary, and adaptational biology. The rapid diversification of the notothenioids into >120 species following a period of Antarctic glaciation and cooling of the Southern Ocean is thought to have been facilitated by key evolutionary innovations, including antifreeze glycoproteins to prevent freezing and bone reduction to increase buoyancy. In this project, a large dataset of genomic sequences will be used to evaluate the genetic mechanisms that underlie the broad pattern of novel trait evolution in these fish, including traits relevant to human diseases (e.g., bone density, renal function, and anemia). The team will develop new STEM-based research and teaching modules for undergraduate education at Northeastern University. The work will provide specific research training to scholars at all levels, including a post-doctoral researcher, a graduate student, undergraduate students, and high school students. The team will also contribute to public outreach, including, in part, the develop of teaching videos in molecular evolutionary biology and accompanying educational supplements. Part II: Technical description The researchers will leverage their comprehensive notothenioid phylogenomic dataset comprising >250,000 protein-coding exons and conserved non-coding elements across 44 ingroup and 2 outgroup species to analyze the genetic origins of three iconic notothenioid traits: (1) loss of erythrocytes by the icefish clade in a cold, stable and highly-oxygenated marine environment. (2) reduction in bone mass and retention of juvenile skeletal characteristics as buoyancy mechanisms to facilitate foraging. And (3) loss of kidney glomeruli to retain energetically expensive antifreeze glycoproteins. The team will first track patterns of change in erythroid-related genes throughout the notothenioid phylogeny. They will then examine whether repetitive evolution of a pedomorphic skeleton in notothenioids is based on parallel or divergent evolution of genetic regulators of heterochrony. Third, they will determine whether there is mutational bias in the mechanisms of loss and re-emergence of kidney glomeruli. Finally, identified genetic mechanisms of evolutionary change will be validated by experimental testing using functional genomic strategies in the zebrafish model system. proprietary -USAP-9615281_1 Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure ALL STAC Catalog 1997-08-15 2002-07-31 -170, -84, -135, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532072225-AMD_USAPDC.umm_json This award supports a collaborative project that combines air and ground geological-geophysical investigations to understand the tectonic and geological development of the boundary between the Ross Sea Rift and the Marie Byrd Land (MBL) volcanic province. The project will determine the Cenozoic tectonic history of the region and whether Neogene structures that localized outlet glacier flow developed within the context of Cenozoic rifting on the eastern Ross Embayment margin, or within the volcanic province in MBL. The geological structure at the boundary between the Ross Embayment and western MBL may be a result of: 1) Cenozoic extension on the eastern shoulder of the Ross Sea rift; 2) uplift and crustal extension related to Neogene mantle plume activity in western MBL; or a combination of the two. Faulting and volcanism, mountain uplift, and glacier downcutting appear to now be active in western MBL, where generally East-to-West-flowing outlet glaciers incise Paleozoic and Mesozoic bedrock, and deglaciated summits indicate a previous North-South glacial flow direction. This study requires data collection using SOAR (Support Office for Aerogeophysical Research, a facility supported by Office of Polar Programs which utilizes high precision differential GPS to support a laser altimeter, ice-penetrating radar, a towed proton magnetometer, and a Bell BGM-3 gravimeter). This survey requires data for 37,000 square kilometers using 5.3 kilometer line spacing with 15.6 kilometer tie lines, and 86,000 square kilometers using a grid of 10.6 by 10.6 kilometer spacing. Data will be acquired over several key features in the region including, among other, the eastern edge of the Ross Sea rift, over ice stream OEO, the transition from the Edward VII Peninsula plateau to the Ford Ranges, the continuation to the east of a gravity high known from previous reconnaissance mapping over the Fosdick Metamorphic Complex, an d the extent of the high-amplitude magnetic anomalies (volcanic centers?) detected southeast of the northern Ford Ranges by other investigators. SOAR products will include glaciology data useful for studying driving stresses, glacial flow and mass balance in the West Antarctic Ice Sheet (WAIS). The ground program is centered on the southern Ford Ranges. Geologic field mapping will focus on small scale brittle structures for regional kinematic interpretation, on glaciated surfaces and deposits, and on datable volcanic rocks for geochronologic control. The relative significance of fault and joint sets, the timing relationships between them, and the probable context of their formation will also be determined. Exposure ages will be determined for erosion surfaces and moraines. Interpretation of potential field data will be aided by on ground sampling for magnetic properties and density as well as ground based gravity measurements. Oriented samples will be taken for paleomagnetic studies. Combined airborne and ground investigations will obtain basic data for describing the geology and structure at the eastern boundary of the Ross Embayment both in outcrop and ice covered areas, and may be used to distinguish between Ross Sea rift- related structural activity from uplift and faulting on the perimeter of the MBL dome and volcanic province. Outcrop geology and structure will be extrapolated with the aerogeophysical data to infer the geology that resides beneath the WAIS. The new knowledge of Neogene tectonics in western MBL will contribute to a comprehensive model for the Cenozoic Ross rift and to understanding of the extent of plume activity in MBL. Both are important for determining the influence of Neogene tectonics on the ice streams and WAIS. proprietary USAP-9615281_1 Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure AMD_USAPDC STAC Catalog 1997-08-15 2002-07-31 -170, -84, -135, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532072225-AMD_USAPDC.umm_json This award supports a collaborative project that combines air and ground geological-geophysical investigations to understand the tectonic and geological development of the boundary between the Ross Sea Rift and the Marie Byrd Land (MBL) volcanic province. The project will determine the Cenozoic tectonic history of the region and whether Neogene structures that localized outlet glacier flow developed within the context of Cenozoic rifting on the eastern Ross Embayment margin, or within the volcanic province in MBL. The geological structure at the boundary between the Ross Embayment and western MBL may be a result of: 1) Cenozoic extension on the eastern shoulder of the Ross Sea rift; 2) uplift and crustal extension related to Neogene mantle plume activity in western MBL; or a combination of the two. Faulting and volcanism, mountain uplift, and glacier downcutting appear to now be active in western MBL, where generally East-to-West-flowing outlet glaciers incise Paleozoic and Mesozoic bedrock, and deglaciated summits indicate a previous North-South glacial flow direction. This study requires data collection using SOAR (Support Office for Aerogeophysical Research, a facility supported by Office of Polar Programs which utilizes high precision differential GPS to support a laser altimeter, ice-penetrating radar, a towed proton magnetometer, and a Bell BGM-3 gravimeter). This survey requires data for 37,000 square kilometers using 5.3 kilometer line spacing with 15.6 kilometer tie lines, and 86,000 square kilometers using a grid of 10.6 by 10.6 kilometer spacing. Data will be acquired over several key features in the region including, among other, the eastern edge of the Ross Sea rift, over ice stream OEO, the transition from the Edward VII Peninsula plateau to the Ford Ranges, the continuation to the east of a gravity high known from previous reconnaissance mapping over the Fosdick Metamorphic Complex, an d the extent of the high-amplitude magnetic anomalies (volcanic centers?) detected southeast of the northern Ford Ranges by other investigators. SOAR products will include glaciology data useful for studying driving stresses, glacial flow and mass balance in the West Antarctic Ice Sheet (WAIS). The ground program is centered on the southern Ford Ranges. Geologic field mapping will focus on small scale brittle structures for regional kinematic interpretation, on glaciated surfaces and deposits, and on datable volcanic rocks for geochronologic control. The relative significance of fault and joint sets, the timing relationships between them, and the probable context of their formation will also be determined. Exposure ages will be determined for erosion surfaces and moraines. Interpretation of potential field data will be aided by on ground sampling for magnetic properties and density as well as ground based gravity measurements. Oriented samples will be taken for paleomagnetic studies. Combined airborne and ground investigations will obtain basic data for describing the geology and structure at the eastern boundary of the Ross Embayment both in outcrop and ice covered areas, and may be used to distinguish between Ross Sea rift- related structural activity from uplift and faulting on the perimeter of the MBL dome and volcanic province. Outcrop geology and structure will be extrapolated with the aerogeophysical data to infer the geology that resides beneath the WAIS. The new knowledge of Neogene tectonics in western MBL will contribute to a comprehensive model for the Cenozoic Ross rift and to understanding of the extent of plume activity in MBL. Both are important for determining the influence of Neogene tectonics on the ice streams and WAIS. proprietary +USAP-9615281_1 Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure ALL STAC Catalog 1997-08-15 2002-07-31 -170, -84, -135, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532072225-AMD_USAPDC.umm_json This award supports a collaborative project that combines air and ground geological-geophysical investigations to understand the tectonic and geological development of the boundary between the Ross Sea Rift and the Marie Byrd Land (MBL) volcanic province. The project will determine the Cenozoic tectonic history of the region and whether Neogene structures that localized outlet glacier flow developed within the context of Cenozoic rifting on the eastern Ross Embayment margin, or within the volcanic province in MBL. The geological structure at the boundary between the Ross Embayment and western MBL may be a result of: 1) Cenozoic extension on the eastern shoulder of the Ross Sea rift; 2) uplift and crustal extension related to Neogene mantle plume activity in western MBL; or a combination of the two. Faulting and volcanism, mountain uplift, and glacier downcutting appear to now be active in western MBL, where generally East-to-West-flowing outlet glaciers incise Paleozoic and Mesozoic bedrock, and deglaciated summits indicate a previous North-South glacial flow direction. This study requires data collection using SOAR (Support Office for Aerogeophysical Research, a facility supported by Office of Polar Programs which utilizes high precision differential GPS to support a laser altimeter, ice-penetrating radar, a towed proton magnetometer, and a Bell BGM-3 gravimeter). This survey requires data for 37,000 square kilometers using 5.3 kilometer line spacing with 15.6 kilometer tie lines, and 86,000 square kilometers using a grid of 10.6 by 10.6 kilometer spacing. Data will be acquired over several key features in the region including, among other, the eastern edge of the Ross Sea rift, over ice stream OEO, the transition from the Edward VII Peninsula plateau to the Ford Ranges, the continuation to the east of a gravity high known from previous reconnaissance mapping over the Fosdick Metamorphic Complex, an d the extent of the high-amplitude magnetic anomalies (volcanic centers?) detected southeast of the northern Ford Ranges by other investigators. SOAR products will include glaciology data useful for studying driving stresses, glacial flow and mass balance in the West Antarctic Ice Sheet (WAIS). The ground program is centered on the southern Ford Ranges. Geologic field mapping will focus on small scale brittle structures for regional kinematic interpretation, on glaciated surfaces and deposits, and on datable volcanic rocks for geochronologic control. The relative significance of fault and joint sets, the timing relationships between them, and the probable context of their formation will also be determined. Exposure ages will be determined for erosion surfaces and moraines. Interpretation of potential field data will be aided by on ground sampling for magnetic properties and density as well as ground based gravity measurements. Oriented samples will be taken for paleomagnetic studies. Combined airborne and ground investigations will obtain basic data for describing the geology and structure at the eastern boundary of the Ross Embayment both in outcrop and ice covered areas, and may be used to distinguish between Ross Sea rift- related structural activity from uplift and faulting on the perimeter of the MBL dome and volcanic province. Outcrop geology and structure will be extrapolated with the aerogeophysical data to infer the geology that resides beneath the WAIS. The new knowledge of Neogene tectonics in western MBL will contribute to a comprehensive model for the Cenozoic Ross rift and to understanding of the extent of plume activity in MBL. Both are important for determining the influence of Neogene tectonics on the ice streams and WAIS. proprietary USAP-9725024_1 Circumpolar Deep Water and the West Antarctic Ice Sheet AMD_USAPDC STAC Catalog 1988-03-01 2002-02-28 140, -68, 150, -65 https://cmr.earthdata.nasa.gov/search/concepts/C2532072042-AMD_USAPDC.umm_json This project will study the dynamics of Circumpolar Deep Water intruding on the continental shelf of the West Antarctic coast, and the effect of this intrusion on the production of cold, dense bottom water, and melting at the base of floating glaciers and ice tongues. It will concentrate on the Amundsen Sea shelf, specifically in the region of the Pine Island Glacier, the Thwaites Glacier, and the Getz Ice Shelf. Circumpolar Deep Water (CDW) is a relatively warm water mass (warmer than +1.0 deg Celsius) which is normally confined to the outer edge of the continental shelf by an oceanic front separating this water mass from colder and saltier shelf waters. In the Amundsen Sea however, the deeper parts of the continental shelf are filled with nearly undiluted CDW, which is mixed upward, delivering significant amounts of heat to the base of the floating glacier tongues and the ice shelf. The melt rate beneath the Pine Island Glacier averages ten meters of ice per year with local annual rates reaching twenty meters. By comparison, melt rates beneath the Ross Ice Shelf are typically twenty to forty centimeters of ice per year. In addition, both the Pine Island and the Thwaites Glacier are extremely fast-moving, and have a significant effect on the regional ice mass balance of West Antarctica. This project therefore has an important connection to antarctic glaciology, particularly in assessing the combined effect of global change on the antarctic environment. The particular objectives of the project are (1) to delineate the frontal structure on the continental shelf sufficiently to define quantitatively the major routes of CDW inflow, meltwater outflow, and the westward evolution of CDW influence; (2) to use the obtained data set to validate a three-dimensional model of sub-ice ocean circulation that is currently under construction, and (3) to refine the estiamtes of in situ melting on the mass balance of the antarctic ice sheet. The observational program will be carried out from the research vessel Nathaniel B. Palmer in February and March, 1999. proprietary -USARC_AERIAL_PHOTOS Aerial Photography of Antarctica ALL STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231551700-CEOS_EXTRA.umm_json "The USARC maintains all US Aerial Antarctic Mapping photography and USGS flight indexes of the Antarctic. There are over 500,000 photographs in the collection. Most photographs are 9"" x 9"" black and white images taken with three Fairchild cameras each with a metrogon lense resulting in trimetrogon photography (left oblique, vertical and right oblique photographs). Special-purpose photographs showing sites of specific scientific interest ""vertical and handheld oblique as well as photographs taken from helicopters"" are also on file. Some color photographs are also available. Line indexes to identify coverage are available for most aerial photographic missions. Contact prints in either matte or glossy finish are available for inspection or stereoscopic viewing. Special feature options, such as ice and rock enhancements, may be special ordered." proprietary USARC_AERIAL_PHOTOS Aerial Photography of Antarctica CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231551700-CEOS_EXTRA.umm_json "The USARC maintains all US Aerial Antarctic Mapping photography and USGS flight indexes of the Antarctic. There are over 500,000 photographs in the collection. Most photographs are 9"" x 9"" black and white images taken with three Fairchild cameras each with a metrogon lense resulting in trimetrogon photography (left oblique, vertical and right oblique photographs). Special-purpose photographs showing sites of specific scientific interest ""vertical and handheld oblique as well as photographs taken from helicopters"" are also on file. Some color photographs are also available. Line indexes to identify coverage are available for most aerial photographic missions. Contact prints in either matte or glossy finish are available for inspection or stereoscopic viewing. Special feature options, such as ice and rock enhancements, may be special ordered." proprietary -USArray_Ground_Temperature_1680_1.1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021 ALL STAC Catalog 2016-05-13 2021-07-08 -165.35, 59.25, -141.59, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2143403529-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location. proprietary +USARC_AERIAL_PHOTOS Aerial Photography of Antarctica ALL STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231551700-CEOS_EXTRA.umm_json "The USARC maintains all US Aerial Antarctic Mapping photography and USGS flight indexes of the Antarctic. There are over 500,000 photographs in the collection. Most photographs are 9"" x 9"" black and white images taken with three Fairchild cameras each with a metrogon lense resulting in trimetrogon photography (left oblique, vertical and right oblique photographs). Special-purpose photographs showing sites of specific scientific interest ""vertical and handheld oblique as well as photographs taken from helicopters"" are also on file. Some color photographs are also available. Line indexes to identify coverage are available for most aerial photographic missions. Contact prints in either matte or glossy finish are available for inspection or stereoscopic viewing. Special feature options, such as ice and rock enhancements, may be special ordered." proprietary USArray_Ground_Temperature_1680_1.1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021 ORNL_CLOUD STAC Catalog 2016-05-13 2021-07-08 -165.35, 59.25, -141.59, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2143403529-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location. proprietary +USArray_Ground_Temperature_1680_1.1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021 ALL STAC Catalog 2016-05-13 2021-07-08 -165.35, 59.25, -141.59, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2143403529-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location. proprietary USDA0113 Groundwater Quality in Beaver Creek Watershed, Tennessee CEOS_EXTRA STAC Catalog 1992-07-01 1992-08-31 -90.74, 34.56, -81.22, 37.12 https://cmr.earthdata.nasa.gov/search/concepts/C2232411621-CEOS_EXTRA.umm_json Analysis for 400 domestic wells for selected constituents. Reconnaissance of Ground Water Quality in Beaver Creek Watershed, Shelby, Tipton, Fayette, and Haywood counties, Tennessee. Collection Organization: USDA-CSREES/USGS - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by UTAES staff, trained volunteers, and USGS Personnel - USGS conducted field and laboratory analysis. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 400 wells; 20 parameters per sample. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Dissemination Media: USGS Data Base Access Instructions: Contact the data center. proprietary USDA0114 Groundwater Quality in Bedford and Coffee Counties, Tennessee CEOS_EXTRA STAC Catalog 1991-06-01 1991-07-31 -90.74, 34.56, -81.22, 37.12 https://cmr.earthdata.nasa.gov/search/concepts/C2232411616-CEOS_EXTRA.umm_json Analysis for 200 domestic wells and springs for selected constituents. Reconnaissance of Ground Water Quality in Bedford and Coffee Counties, TN. Collection Organization: USDA-CSREES/USGS - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by UTAES staff, trained volunteers, and USGS Personnel - USGS conducted field and laboratory analysis. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 200 wells/springs; 7 parameters per sample. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Media: USGS Data Base Access Instructions: Contact the data center. proprietary USDA0115 Groundwater Quality in Tennessee CEOS_EXTRA STAC Catalog 1984-01-01 1990-12-31 -90.74, 34.56, -81.22, 37.12 https://cmr.earthdata.nasa.gov/search/concepts/C2232411608-CEOS_EXTRA.umm_json Analysis of 150 wells for selected constituents, reconnaissance of Ground Water Quality in Tennessee. Collection Organization: USDA-CSREES - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by USGS staff. USGS conducted field and laboratory analysis at their national lab. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 150 wells on farmsteads across Tennessee; 7 parameters per well. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Media: USGS Data Base. Access Instructions: Contact the data center. proprietary @@ -15537,8 +15517,8 @@ USGS-DDS-33_1.0 3-D Reservoir Characterization of the House Creek Oil Field, Pow USGS-DDS-33_1.0 3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00 CEOS_EXTRA STAC Catalog 1970-01-01 -111.4, 40.65, -103.7, 45.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553827-CEOS_EXTRA.umm_json "The Upper Cretaceous Sussex ""B"" sandstone was deposited as a probable transgressive-marine sand-ridge complex in a mid-shelf position. The ""B"" sandstone is bounded by upper and basal disconformities and encased in mudstones and low-porosity and low-permeability sandstones of the Cody Shale. Reservoir characteristics are controlled primarily by depositional and diagenetic heterogeneity at megascopic (field), macroscopic (well), and microscopic (rock sample) levels. To simplify, this means production of oil is controlled by stacking and interbedding of sandstone and mudstone beds and by geochemical changes through time that affect flow of fluids through the rock. More than 24.8 million barrels of oil (MMBO) have been produced from the Sussex ""B"" sandstone in the House Creek field, Powder River Basin, Wyoming. Greatest oil production, porosity, and permeability, the thickest reservoir sandstone intervals, and best lateral continuity of the primary reservoir facies are all located parallel and proximal to field axes. Decrease in reservoir quality west of the axes is due to greater heterogeneity from interbedding of low- and moderate-depositional-energy facies, with associated drop in porosity and permeability. Decrease in production east of the axes results primarily from a combination of seaward thinning of the primary reservoir facies and non-deposition of sand ridges. The House Creek field has two axis orientations; these are related to depositional patterns of the four sand ridges. Deposition of the ""B"" sandstone began in the southeastern corner of the field with sand ridge 1; axis orientation is about north 20 degrees west. Later-deposited sand ridges 2 through 4 are located west and north of sand ridge 1; their axis orientations are approximately north 32 degrees west. Progressive northward deposition of later sand ridges is probably concurrent with uplift of the northeast-trending Belle Fourche arch. Movement along the arch and of lineaments may have caused topographic highs that localized Sussex and Shannon deposition proximal to the arch. [Summary provided by the USGS.]" proprietary USGS-DDS-74_2.0 Long-term Oceanographic Observations in Western Massachusetts Bay Offshore of Boston, Massachusetts: Data Report for 1989-2002 CEOS_EXTRA STAC Catalog 1989-12-01 2002-12-01 -71, 42, -70.5, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231551840-CEOS_EXTRA.umm_json Long-term oceanographic observations have been made at two locations in western Massachusetts Bay: (1) Site A (42ý 22.6' N, 70ý 47.0' W, 33 m water depth) from from 1989 to 2002, and (2) Site B (42ý 9.8' N, 70ý 38.4' W, 21 m deter depth) from 1997 to 2002. Site A is approximately 1 km south of the new ocean outfall that began discharging treated sewage effluent from the Boston metropolitan area into Massachusetts Bay in September 2000. These long-term oceanographic observations have been collected by the U.S. Geological Survey (USGS) in partnership with the Massachusetts Water Resources Authority (MWRA) and with logistical support from the U. S. Coast Guard (USCG). This report presents time series data collected through December 2002, updating a similar report that presented data through December 2000 (Butman and others, 2002). The long-term observations at these two stations are part of a USGS study designed to understand the transport and long-term fate of sediments and associated contaminants in the Massachusetts Bays (see //woodshole.er.usgs.gov/project-pages/bostonharbor / and Butman and Bothner, 1997). The long-term observations document seasonal and inter-annual changes in currents, hydrography, and suspended-matter concentration in western Massachusetts Bay, and the importance of infrequent catastrophic events, such as major storms or hurricanes, in sediment resuspension and transport. They also provide observations for testing numerical models of circulation. This data report presents a description of the field program and instrumentation, an overview of the data through summary plots and statistics, and the data in NetCDF and ASCII format for the period December 1989 through December 2002. The objective of this report is to make the data available in digital form, and to provide summary plots and statistics to facilitate browsing of the long-term data set . [Summary provided by the USGS.] proprietary USGS-DDS-79 Coastal Erosion and Wetland Change in Louisiana: Selected USGS Products CEOS_EXTRA STAC Catalog 1970-01-01 -94.3, 28.67, -88.54, 33.29 https://cmr.earthdata.nasa.gov/search/concepts/C2231552152-CEOS_EXTRA.umm_json Louisiana contains 25 percent of the vegetated wetlands and 40 percent of the tidal wetlands in the 48 conterminous States. These critical natural systems are being lost. Louisiana leads the Nation in coastal erosion and wetland loss as a result of a complex combination of natural processes (e.g. storms, sea-level rise, subsidence) and manmade alterations to the Mississippi River and the wetlands over the past 200 years. Erosion of several of the barrier islands, which lie offshore of the estuaries and wetlands and buffer and protect these important ecosystems from the open marine environment, exceeds 20 meters/year. The average rate of shoreline erosion is over 10 meters/year. Within the past 100 years, Louisiana's barrier islands have decreased in area by more than 40 percent, and some islands have lost more than 75 percent of their land area. If these loss rates continue, several of the barriers are expected to erode completely within the next three decades. Their disappearance will contribute to further loss and deterioration of wetlands and back-barrier estuaries and increase the risk to infrastructure. Coastal wetland environments, which include associated bays and estuaries, support a rich harvest of renewable natural resources with an estimated annual value of over $1 billion. More than 30 percent of the Nation's fisheries come from these wetlands, as well as 25 percent of oil and gas coming through the wetlands. Louisiana also has the highest rate of wetland loss: 80 percent of the Nation's total loss of wetlands has occurred in this State. The rate of wetland loss in the Mississippi River delta plain is estimated to be about 70 square kilometers/year -- the equivalent of a football field every 20 minutes. If these rates continue, an estimated 4,000 square kilometers of wetlands will be lost in the next 50 years. Losses of this magnitude have direct implications on the Nation's energy supplies, economic security, and environmental integrity. Over the past two decades, the USGS, working in partnership with other scientists in universities and State agencies, has led the research effort to document barrier erosion and wetland loss and understand the natural and manmade causes responsible. Some products resulting from this research, included in this DVD, are providing the baseline data and information being used for Federal-State wetlands restoration programs underway and being planned. [Summary provided by the USGS.] proprietary -USGS-DDS_30_P-10_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231552106-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 10 (San Joaquin Basin) are listed here by play number, type, and name: Number Type Name 1001 conventional Pliocene Non-associated Gas 1002 conventional Southeast Stable Shelf 1003 conventional Lower Bakersfield Arch 1004 conventional West Side Fold Belt Sourced by Post-Lower Miocene Rocks. 1005 conventional West Side Fold Belt Sourced by Pre-Middle Miocene Rocks 1006 conventional Northeast Shelf of Neogene Basin 1007 conventional Northern Area Non-associated Gas 1008 conventional Tejon Platform 1009 conventional South End Thrust Salient 1010 conventional East Central Basin and Slope North of Bakersfield Arch 1011 conventional Deep Overpressured Fractured Rocks of West Side Fold and Overthrust Belt proprietary USGS-DDS_30_P-10_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231552106-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 10 (San Joaquin Basin) are listed here by play number, type, and name: Number Type Name 1001 conventional Pliocene Non-associated Gas 1002 conventional Southeast Stable Shelf 1003 conventional Lower Bakersfield Arch 1004 conventional West Side Fold Belt Sourced by Post-Lower Miocene Rocks. 1005 conventional West Side Fold Belt Sourced by Pre-Middle Miocene Rocks 1006 conventional Northeast Shelf of Neogene Basin 1007 conventional Northern Area Non-associated Gas 1008 conventional Tejon Platform 1009 conventional South End Thrust Salient 1010 conventional East Central Basin and Slope North of Bakersfield Arch 1011 conventional Deep Overpressured Fractured Rocks of West Side Fold and Overthrust Belt proprietary +USGS-DDS_30_P-10_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231552106-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 10 (San Joaquin Basin) are listed here by play number, type, and name: Number Type Name 1001 conventional Pliocene Non-associated Gas 1002 conventional Southeast Stable Shelf 1003 conventional Lower Bakersfield Arch 1004 conventional West Side Fold Belt Sourced by Post-Lower Miocene Rocks. 1005 conventional West Side Fold Belt Sourced by Pre-Middle Miocene Rocks 1006 conventional Northeast Shelf of Neogene Basin 1007 conventional Northern Area Non-associated Gas 1008 conventional Tejon Platform 1009 conventional South End Thrust Salient 1010 conventional East Central Basin and Slope North of Bakersfield Arch 1011 conventional Deep Overpressured Fractured Rocks of West Side Fold and Overthrust Belt proprietary USGS-DDS_30_P10_conventional 1995 National Oil and Gas Assessment Conventional Plays within the San Joaquin Basin Province CEOS_EXTRA STAC Catalog 1970-01-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231550316-CEOS_EXTRA.umm_json The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. proprietary USGS-DDS_30_P10_conventional 1995 National Oil and Gas Assessment Conventional Plays within the San Joaquin Basin Province ALL STAC Catalog 1970-01-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231550316-CEOS_EXTRA.umm_json The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. proprietary USGS-DS-91_1.1 Depth to the Juan De Fuca Slab Beneath the Cascadia Subduction Margin: A 3-D Model for Sorting Earthquakes CEOS_EXTRA STAC Catalog 1970-01-01 -130, 40, -120, 51 https://cmr.earthdata.nasa.gov/search/concepts/C2231552778-CEOS_EXTRA.umm_json The USGS presents an updated model of the Juan de Fuca slab beneath southern British Columbia, Washington, Oregon, and northern California, and use this model to separate earthquakes occurring above and below the slab surface. The model is based on depth contours previously published by Flück and others (1997). Our model attempts to rectify a number of shortcomings in the original model and to update it with new work. The most significant improvements include (1) a gridded slab surface in geo-referenced (ArcGIS) format, (2) continuation of the slab surface to its full northern and southern edges, (3) extension of the slab surface from 50-km depth down to 110-km beneath the Cascade arc volcanoes, and (4) revision of the slab shape based on new seismic-reflection and seismic-refraction studies. We have used this surface to sort earthquakes and present some general observations and interpretations of seismicity patterns revealed by our analysis. In addition, we provide files of earthquakes above and below the slab surface and a 3-D animation or fly-through showing a shaded-relief map with plate boundaries, the slab surface, and hypocenters for use as a visualization tool. [Summary provided by the USGS.] proprietary @@ -15572,8 +15552,8 @@ USGS_DDS_P12_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within th USGS_DDS_P12_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Santa Maria Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231553039-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number, type, and name: Number Type Name 1201 conventional Anticlinal Trends - Onshore 1202 conventional Basin Margin 1204 conventional Diagenetic 1211 conventional Anticlinal Trends - Offshore State Waters proprietary USGS_DDS_P12_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Santa Maria Basin Province - USGS_DDS_P12_conventional CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231551861-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number and name: Number Name 1201 Anticlinal Trends - Onshore 1202 Basin Margin 1204 Diagenetic 1211 Anticlinal Trends - Offshore State Waters proprietary USGS_DDS_P12_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Santa Maria Basin Province - USGS_DDS_P12_conventional ALL STAC Catalog 1996-01-01 1996-12-31 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231551861-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number and name: Number Name 1201 Anticlinal Trends - Onshore 1202 Basin Margin 1204 Diagenetic 1211 Anticlinal Trends - Offshore State Waters proprietary -USGS_DDS_P13_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Ventura Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231554781-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 13 (Ventura Basin) are listed here by play number, type, and name: Number Type Name 1301 conventional Paleogene - Onshore 1302 conventional Neogene - Onshore 1304 conventional Cretaceous 1311 conventional Paleogene - Offshore State Waters 1312 conventional Neogene - Offshore State Waters proprietary USGS_DDS_P13_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Ventura Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231554781-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 13 (Ventura Basin) are listed here by play number, type, and name: Number Type Name 1301 conventional Paleogene - Onshore 1302 conventional Neogene - Onshore 1304 conventional Cretaceous 1311 conventional Paleogene - Offshore State Waters 1312 conventional Neogene - Offshore State Waters proprietary +USGS_DDS_P13_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Ventura Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231554781-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 13 (Ventura Basin) are listed here by play number, type, and name: Number Type Name 1301 conventional Paleogene - Onshore 1302 conventional Neogene - Onshore 1304 conventional Cretaceous 1311 conventional Paleogene - Offshore State Waters 1312 conventional Neogene - Offshore State Waters proprietary USGS_DDS_P13_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Ventura Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231550109-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 13 (Ventura Basin) are listed here by play number and name: Number Name 1301 Paleogene - Onshore 1302 Neogene - Onshore 1304 Cretaceous 1311 Paleogene - Offshore State Waters 1312 Neogene - Offshore State Waters proprietary USGS_DDS_P13_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Ventura Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231550109-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 13 (Ventura Basin) are listed here by play number and name: Number Name 1301 Paleogene - Onshore 1302 Neogene - Onshore 1304 Cretaceous 1311 Paleogene - Offshore State Waters 1312 Neogene - Offshore State Waters proprietary USGS_DDS_P14_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Los Angeles Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231552049-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number, type, and name: Number Type Name 1401 conventional Santa Monica Fault System and Las Cienegas Fault and Block 1402 conventional Southwestern Shelf and Adjacent Offshore State Lands 1403 conventional Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 conventional Whittier Fault Zone and Fullerton Embayment 1405 conventional Northern Shelf and Northern Flank of Central Syncline 1406 conventional Anaheim Nose 1407 conventional Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary @@ -15584,31 +15564,31 @@ USGS_DDS_P15_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within th USGS_DDS_P15_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Diego - Oceanside Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -117.75433, 32.527184, -115.904816, 34.236046 https://cmr.earthdata.nasa.gov/search/concepts/C2231553715-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 15 (San Diego - Oceanside) are listed here by play number, type, and name. proprietary USGS_DDS_P16_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Salton Trough Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -116.66911, 32.634293, -114.74501, 34.02059 https://cmr.earthdata.nasa.gov/search/concepts/C2231548651-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 16 (Salton Trough) are listed here by play number, type, and name. proprietary USGS_DDS_P16_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Salton Trough Province ALL STAC Catalog 1990-12-01 1990-12-01 -116.66911, 32.634293, -114.74501, 34.02059 https://cmr.earthdata.nasa.gov/search/concepts/C2231548651-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 16 (Salton Trough) are listed here by play number, type, and name. proprietary -USGS_DDS_P17_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Idaho - Snake River Downwarp Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231550494-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number, type, and name: Number Type Name 1701 conventional Miocene Lacustrine (Lake Bruneau) 1702 conventional Pliocene Lacustrine (Lake Idaho) 1703 conventional Pre-Miocene 1704 conventional Older Tertiary proprietary USGS_DDS_P17_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Idaho - Snake River Downwarp Province ALL STAC Catalog 1990-12-01 1990-12-01 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231550494-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number, type, and name: Number Type Name 1701 conventional Miocene Lacustrine (Lake Bruneau) 1702 conventional Pliocene Lacustrine (Lake Idaho) 1703 conventional Pre-Miocene 1704 conventional Older Tertiary proprietary -USGS_DDS_P17_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Idaho - Snake River Downwarp Province ALL STAC Catalog 1996-01-01 1996-12-31 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231548537-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number and name: Number Name 1701 Miocene Lacustrine (Lake Bruneau) 1702 Pliocene Lacustrine (Lake Idaho) 1703 Pre-Miocene 1704 Older Tertiary proprietary +USGS_DDS_P17_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Idaho - Snake River Downwarp Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231550494-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number, type, and name: Number Type Name 1701 conventional Miocene Lacustrine (Lake Bruneau) 1702 conventional Pliocene Lacustrine (Lake Idaho) 1703 conventional Pre-Miocene 1704 conventional Older Tertiary proprietary USGS_DDS_P17_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Idaho - Snake River Downwarp Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231548537-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number and name: Number Name 1701 Miocene Lacustrine (Lake Bruneau) 1702 Pliocene Lacustrine (Lake Idaho) 1703 Pre-Miocene 1704 Older Tertiary proprietary +USGS_DDS_P17_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Idaho - Snake River Downwarp Province ALL STAC Catalog 1996-01-01 1996-12-31 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231548537-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number and name: Number Name 1701 Miocene Lacustrine (Lake Bruneau) 1702 Pliocene Lacustrine (Lake Idaho) 1703 Pre-Miocene 1704 Older Tertiary proprietary USGS_DDS_P18_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Western Great Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -122.29004, 32.717037, -114.13121, 44.563953 https://cmr.earthdata.nasa.gov/search/concepts/C2231554181-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 18 (Western Great Basin) are listed here by play number, type, and name: Number Type Name 1801 conventional Hornbrook Basin-Modoc Plateau 1802 conventional Eastern Oregon Neogene Basins 1803 conventional Permian-Triassic Source Rocks Northwestern Nevada and East Central and Eastern Oregon 1804 conventional Cretaceous Source Rocks, Northwestern Nevada 1805 conventional Neogene Source Rocks, Northwestern Nevada and Eastern California proprietary USGS_DDS_P18_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Western Great Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -122.29004, 32.717037, -114.13121, 44.563953 https://cmr.earthdata.nasa.gov/search/concepts/C2231554181-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 18 (Western Great Basin) are listed here by play number, type, and name: Number Type Name 1801 conventional Hornbrook Basin-Modoc Plateau 1802 conventional Eastern Oregon Neogene Basins 1803 conventional Permian-Triassic Source Rocks Northwestern Nevada and East Central and Eastern Oregon 1804 conventional Cretaceous Source Rocks, Northwestern Nevada 1805 conventional Neogene Source Rocks, Northwestern Nevada and Eastern California proprietary -USGS_DDS_P18_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Western Great Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -122.29004, 32.717037, -114.13121, 44.563953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549693-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 18 (Western Great Basin) are listed here by play number and name: Number Name 1801 Hornbrook Basin-Modoc Plateau 1802 Eastern Oregon Neogene Basins 1803 Permian-Triassic Source Rocks Northwestern Nevada and East Central and Eastern Oregon 1804 Cretaceous Source Rocks, Northwestern Nevada 1805 Neogene Source Rocks, Northwestern Nevada and Eastern California proprietary USGS_DDS_P18_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Western Great Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -122.29004, 32.717037, -114.13121, 44.563953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549693-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 18 (Western Great Basin) are listed here by play number and name: Number Name 1801 Hornbrook Basin-Modoc Plateau 1802 Eastern Oregon Neogene Basins 1803 Permian-Triassic Source Rocks Northwestern Nevada and East Central and Eastern Oregon 1804 Cretaceous Source Rocks, Northwestern Nevada 1805 Neogene Source Rocks, Northwestern Nevada and Eastern California proprietary -USGS_DDS_P19_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231552402-CEOS_EXTRA.umm_json "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number, type, and name: Number Type Name 1901 conventional Unconformity ""A"" 1902 conventional Late Paleozoic 1903 conventional Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 conventional Younger Tertiary Basins 1906 conventional Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 conventional Sevier Frontal Zone" proprietary +USGS_DDS_P18_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Western Great Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -122.29004, 32.717037, -114.13121, 44.563953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549693-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 18 (Western Great Basin) are listed here by play number and name: Number Name 1801 Hornbrook Basin-Modoc Plateau 1802 Eastern Oregon Neogene Basins 1803 Permian-Triassic Source Rocks Northwestern Nevada and East Central and Eastern Oregon 1804 Cretaceous Source Rocks, Northwestern Nevada 1805 Neogene Source Rocks, Northwestern Nevada and Eastern California proprietary USGS_DDS_P19_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231552402-CEOS_EXTRA.umm_json "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number, type, and name: Number Type Name 1901 conventional Unconformity ""A"" 1902 conventional Late Paleozoic 1903 conventional Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 conventional Younger Tertiary Basins 1906 conventional Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 conventional Sevier Frontal Zone" proprietary +USGS_DDS_P19_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231552402-CEOS_EXTRA.umm_json "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number, type, and name: Number Type Name 1901 conventional Unconformity ""A"" 1902 conventional Late Paleozoic 1903 conventional Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 conventional Younger Tertiary Basins 1906 conventional Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 conventional Sevier Frontal Zone" proprietary USGS_DDS_P19_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231551249-CEOS_EXTRA.umm_json "The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number and name: Number Name 1901 Unconformity ""A"" 1902 Late Paleozoic 1903 Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 Younger Tertiary Basins 1906 Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 Sevier Frontal Zone" proprietary USGS_DDS_P19_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231551249-CEOS_EXTRA.umm_json "The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number and name: Number Name 1901 Unconformity ""A"" 1902 Late Paleozoic 1903 Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 Younger Tertiary Basins 1906 Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 Sevier Frontal Zone" proprietary -USGS_DDS_P20_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231553991-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number, type, and name: Number Type Name 2001 conventional Piceance Tertiary Conventional 2002 conventional Uinta Tertiary Oil and Gas 2003 conventional Upper Cretaceous Conventional 2004 conventional Cretaceous Dakota to Jurassic 2005 conventional Permian-Pennsylvanian Sandstones and Carbonates 2007 continuous Tight Gas Piceance Mesaverde Williams Fork 2009 continuous Cretaceous Self-Sourced Fractured Shales Oil 2010 continuous Tight Gas Piceance Mesaverde Iles 2014 conventional Basin Margin Subthrusts 2015 continuous Tight Gas Uinta Tertiary East 2016 continuous Tight Gas Uinta Tertiary West 2018 continuous Basin Flank Uinta Mesaverde 2020 continuous Deep Synclinal Uinta Mesaverde 2050 coalbed gas Uinta Basin - Book Cliffs 2051 coalbed gas Uinta Basin - Sego 2052 coalbed gas Uinta Basin - Emery 2053 coalbed gas Piceance Basin - White River Dome 2054 coalbed gas Piceance Basin - Western Basin Margin 2055 coalbed gas Piceance Basin - Grand Hogback 2056 coalbed gas Piceance Basin - Divide Creek Anticline 2057 coalbed gas Piceance Basin - Igneous Intrusion proprietary USGS_DDS_P20_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Uinta - Piceance Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231553991-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number, type, and name: Number Type Name 2001 conventional Piceance Tertiary Conventional 2002 conventional Uinta Tertiary Oil and Gas 2003 conventional Upper Cretaceous Conventional 2004 conventional Cretaceous Dakota to Jurassic 2005 conventional Permian-Pennsylvanian Sandstones and Carbonates 2007 continuous Tight Gas Piceance Mesaverde Williams Fork 2009 continuous Cretaceous Self-Sourced Fractured Shales Oil 2010 continuous Tight Gas Piceance Mesaverde Iles 2014 conventional Basin Margin Subthrusts 2015 continuous Tight Gas Uinta Tertiary East 2016 continuous Tight Gas Uinta Tertiary West 2018 continuous Basin Flank Uinta Mesaverde 2020 continuous Deep Synclinal Uinta Mesaverde 2050 coalbed gas Uinta Basin - Book Cliffs 2051 coalbed gas Uinta Basin - Sego 2052 coalbed gas Uinta Basin - Emery 2053 coalbed gas Piceance Basin - White River Dome 2054 coalbed gas Piceance Basin - Western Basin Margin 2055 coalbed gas Piceance Basin - Grand Hogback 2056 coalbed gas Piceance Basin - Divide Creek Anticline 2057 coalbed gas Piceance Basin - Igneous Intrusion proprietary -USGS_DDS_P20_continuous 1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231554716-CEOS_EXTRA.umm_json The purpose of the play map is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Continuous oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2007 Tight Gas Piceance Mesaverde Williams Fork 2009 Cretaceous Self-Sourced Fractured Shales Oil 2010 Tight Gas Piceance Mesaverde Iles 2015 Tight Gas Uinta Tertiary East 2016 Tight Gas Uinta Tertiary West 2018 Basin Flank Uinta Mesaverde 2020 Deep Synclinal Uinta Mesaverde 2050 Uinta Basin - Book Cliffs 2051 Uinta Basin - Sego 2052 Uinta Basin - Emery 2053 Piceance Basin - White River Dome 2054 Piceance Basin - Western Basin Margin 2055 Piceance Basin - Grand Hogback 2056 Piceance Basin - Divide Creek Anticline 2057 Piceance Basin - Igneous Intrusion proprietary +USGS_DDS_P20_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231553991-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number, type, and name: Number Type Name 2001 conventional Piceance Tertiary Conventional 2002 conventional Uinta Tertiary Oil and Gas 2003 conventional Upper Cretaceous Conventional 2004 conventional Cretaceous Dakota to Jurassic 2005 conventional Permian-Pennsylvanian Sandstones and Carbonates 2007 continuous Tight Gas Piceance Mesaverde Williams Fork 2009 continuous Cretaceous Self-Sourced Fractured Shales Oil 2010 continuous Tight Gas Piceance Mesaverde Iles 2014 conventional Basin Margin Subthrusts 2015 continuous Tight Gas Uinta Tertiary East 2016 continuous Tight Gas Uinta Tertiary West 2018 continuous Basin Flank Uinta Mesaverde 2020 continuous Deep Synclinal Uinta Mesaverde 2050 coalbed gas Uinta Basin - Book Cliffs 2051 coalbed gas Uinta Basin - Sego 2052 coalbed gas Uinta Basin - Emery 2053 coalbed gas Piceance Basin - White River Dome 2054 coalbed gas Piceance Basin - Western Basin Margin 2055 coalbed gas Piceance Basin - Grand Hogback 2056 coalbed gas Piceance Basin - Divide Creek Anticline 2057 coalbed gas Piceance Basin - Igneous Intrusion proprietary USGS_DDS_P20_continuous 1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231554716-CEOS_EXTRA.umm_json The purpose of the play map is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Continuous oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2007 Tight Gas Piceance Mesaverde Williams Fork 2009 Cretaceous Self-Sourced Fractured Shales Oil 2010 Tight Gas Piceance Mesaverde Iles 2015 Tight Gas Uinta Tertiary East 2016 Tight Gas Uinta Tertiary West 2018 Basin Flank Uinta Mesaverde 2020 Deep Synclinal Uinta Mesaverde 2050 Uinta Basin - Book Cliffs 2051 Uinta Basin - Sego 2052 Uinta Basin - Emery 2053 Piceance Basin - White River Dome 2054 Piceance Basin - Western Basin Margin 2055 Piceance Basin - Grand Hogback 2056 Piceance Basin - Divide Creek Anticline 2057 Piceance Basin - Igneous Intrusion proprietary +USGS_DDS_P20_continuous 1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231554716-CEOS_EXTRA.umm_json The purpose of the play map is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Continuous oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2007 Tight Gas Piceance Mesaverde Williams Fork 2009 Cretaceous Self-Sourced Fractured Shales Oil 2010 Tight Gas Piceance Mesaverde Iles 2015 Tight Gas Uinta Tertiary East 2016 Tight Gas Uinta Tertiary West 2018 Basin Flank Uinta Mesaverde 2020 Deep Synclinal Uinta Mesaverde 2050 Uinta Basin - Book Cliffs 2051 Uinta Basin - Sego 2052 Uinta Basin - Emery 2053 Piceance Basin - White River Dome 2054 Piceance Basin - Western Basin Margin 2055 Piceance Basin - Grand Hogback 2056 Piceance Basin - Divide Creek Anticline 2057 Piceance Basin - Igneous Intrusion proprietary USGS_DDS_P20_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231552272-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2001 Piceance Tertiary Conventional 2002 Uinta Tertiary Oil and Gas 2003 Upper Cretaceous Conventional 2004 Cretaceous Dakota to Jurassic 2005 Permian-Pennsylvanian Sandstones and Carbonates 2014 Basin Margin Subthrusts proprietary USGS_DDS_P20_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231552272-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2001 Piceance Tertiary Conventional 2002 Uinta Tertiary Oil and Gas 2003 Upper Cretaceous Conventional 2004 Cretaceous Dakota to Jurassic 2005 Permian-Pennsylvanian Sandstones and Carbonates 2014 Basin Margin Subthrusts proprietary -USGS_DDS_P2_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231550471-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 2 (Central Alaska) are listed here by play number, type, and name: Number Type Name 201 conventional Central Alaska Cenozoic Gas 202 conventional Central Alaska Mesozoic Gas 203 conventional Central Alaska Paleozoic Oil 204 conventional Kandik Pre-Mid-Cretaceous Strata 205 conventional Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary USGS_DDS_P2_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province ALL STAC Catalog 1990-12-01 1990-12-01 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231550471-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 2 (Central Alaska) are listed here by play number, type, and name: Number Type Name 201 conventional Central Alaska Cenozoic Gas 202 conventional Central Alaska Mesozoic Gas 203 conventional Central Alaska Paleozoic Oil 204 conventional Kandik Pre-Mid-Cretaceous Strata 205 conventional Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary +USGS_DDS_P2_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231550471-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 2 (Central Alaska) are listed here by play number, type, and name: Number Type Name 201 conventional Central Alaska Cenozoic Gas 202 conventional Central Alaska Mesozoic Gas 203 conventional Central Alaska Paleozoic Oil 204 conventional Kandik Pre-Mid-Cretaceous Strata 205 conventional Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary USGS_DDS_P2_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Alaska Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231551071-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 2 (Central Alaska) are listed here by play number and name: Number Name 201 Central Alaska Cenozoic Gas 202 Central Alaska Mesozoic Gas 203 Central Alaska Paleozoic Oil 204 Kandik Pre-Mid-Cretaceous Strata 205 Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary USGS_DDS_P2_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Alaska Province ALL STAC Catalog 1996-01-01 1996-12-31 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231551071-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 2 (Central Alaska) are listed here by play number and name: Number Name 201 Central Alaska Cenozoic Gas 202 Central Alaska Mesozoic Gas 203 Central Alaska Paleozoic Oil 204 Kandik Pre-Mid-Cretaceous Strata 205 Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary USGS_DOQ USGS Digital Orthophoto Quadrangles USGS_LTA STAC Catalog 1970-01-01 -126, 24, -66, 49 https://cmr.earthdata.nasa.gov/search/concepts/C1220566203-USGS_LTA.umm_json A Digital Orthophoto Quadrangle (DOQ) is a computer-generated image of an aerial photograph in which the image displacement caused by terrain relief and camera tilt has been removed. The DOQ combines the image characteristics of the original photograph with the georeferenced qualities of a map. DOQs are black and white (B/W), natural color, or color-infrared (CIR) images with 1-meter ground resolution. The USGS produces three types of DOQs: 1. 3.75-minute (quarter-quad) DOQs cover an area measuring 3.75-minutes longitude by 3.75-minutes latitude. Most of the U.S. is currently available, and the remaining locations should be complete by 2004. Quarter-quad DOQs are available in both Native and GeoTIFF formats. Native format consists of an ASCII keyword header followed by a series of 8-bit binary image lines for B/W and 24-bit band-interleaved-by-pixel (BIP) for color. DOQs in native format are cast to the Universal Transverse Mercator (UTM) projection and referenced to either the North American Datum (NAD) of 1927 (NAD27) or the NAD of 1983 (NAD83). GeoTIFF format consists of a georeferenced Tagged Image File Format (TIFF), with all geographic referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W quarter quad is 40-45 megabytes, and a color file is generally 140-150 megabytes. Quarter-quad DOQs are distributed via File Transfer Protocol (FTP) as uncompressed files. 2. 7.5-minute (full-quad) DOQs cover an area measuring 7.5-minutes longitude by 7.5-minutes latitude. Full-quad DOQs are mostly available for Oregon, Washington, and Alaska. Limited coverage may also be available for other states. Full-quad DOQs are available in both Native and GeoTIFF formats. Native is formatted with an ASCII keyword header followed by a series of 8-bit binary image lines for B/W. DOQs in native format are cast to the UTM projection and referenced to either NAD27 or NAD83. GeoTIFF is a georeferenced Tagged Image File Format with referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W full quad is 140-150 megabytes. Full-quad DOQs are distributed via FTP as uncompressed files. 3. Seamless DOQs are available for free download from the Seamless site. DOQs on this site are the most current version and are available for the conterminous U.S. [Summary provided by the USGS.] proprietary -USGS_DS-845_PierScoutDatabase_1.0 A pier-scour database: 2,427 field and laboratory measurements of pier scour ALL STAC Catalog 1970-01-01 19.6, 16.916668, -52.62, 83.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231553801-CEOS_EXTRA.umm_json The U.S. Geological Survey conducted a literature review to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet called the 2014 USGS Pier-Scour Database (PSDb-2014) consisting of 569 laboratory and 1,858 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 23 States within the United States and from 6 other countries. The digital spreadsheet is available on the Internet and offers a valuable resource to engineers and researchers seeking to understand pier-scour relations in the laboratory and field. proprietary USGS_DS-845_PierScoutDatabase_1.0 A pier-scour database: 2,427 field and laboratory measurements of pier scour CEOS_EXTRA STAC Catalog 1970-01-01 19.6, 16.916668, -52.62, 83.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231553801-CEOS_EXTRA.umm_json The U.S. Geological Survey conducted a literature review to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet called the 2014 USGS Pier-Scour Database (PSDb-2014) consisting of 569 laboratory and 1,858 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 23 States within the United States and from 6 other countries. The digital spreadsheet is available on the Internet and offers a valuable resource to engineers and researchers seeking to understand pier-scour relations in the laboratory and field. proprietary +USGS_DS-845_PierScoutDatabase_1.0 A pier-scour database: 2,427 field and laboratory measurements of pier scour ALL STAC Catalog 1970-01-01 19.6, 16.916668, -52.62, 83.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231553801-CEOS_EXTRA.umm_json The U.S. Geological Survey conducted a literature review to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet called the 2014 USGS Pier-Scour Database (PSDb-2014) consisting of 569 laboratory and 1,858 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 23 States within the United States and from 6 other countries. The digital spreadsheet is available on the Internet and offers a valuable resource to engineers and researchers seeking to understand pier-scour relations in the laboratory and field. proprietary USGS_DS_2006_171 JAMSTEC multibeam surveys and submersible dives around the Hawaiian Islands: A collaborative Japan-USA exploration of Hawaii's deep seafloor CEOS_EXTRA STAC Catalog 1998-01-01 2002-12-31 -161, 16.75, -152.99988, 25.25005 https://cmr.earthdata.nasa.gov/search/concepts/C2231554487-CEOS_EXTRA.umm_json This database release, USGS Data Series 171, contains data collected during four Japan-USA collaborative cruises that characterize the seafloor around the Hawaiian Islands. The Japan Agency for Marine-Earth Science and Technology (JAMSTEC) sponsored cruises in 1998, 1999, 2001, and 2002, to build a greater understanding of the deep marine geology around the Hawaiian Islands. During these cruises, scientists surveyed over 600,000 square kilometers of the seafloor with a hull-mounted multibeam seafloor-mapping sonar system (SEA BEAM® 2112), observed the seafloor and collected samples using robotic and manned submersible dives, collected dredge and piston-core samples, and performed single-channel seismic surveys. To date, 32 research papers have been published describing results from these cruises. For a list of these articles see the bibliography. This digital database was compiled with ESRI ArcInfo version 7.2.2 and ArcGIS 9.0. The GIS files contain multibeam bathymetry, and acoustic backscatter data in ESRI grid format, and dive, seafloor sampling, and siesmic location data in ESRI shapefile format; ArcInfo-compatible GIS software is therefore required to use the files of this database. Metadata for the GIS files are available as text files. The GIS files were also symbolized and used to create Portable Document Format (PDF) files that are ready to be printed. Adobe Reader or other software that can translate PDFs is necessary to print these files. [Summary provided by the USGS.] proprietary USGS_DS_2006_177 Digital database of recently active traces of the Hayward Fault, California CEOS_EXTRA STAC Catalog 1970-01-01 -128, 35, -120, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231553624-CEOS_EXTRA.umm_json The purpose of this map is to show the location of and evidence for recent movement on active fault traces within the Hayward Fault Zone, California. The mapped traces represent the integration of the following three different types of data: (1) geomorphic expression, (2) creep (aseismic fault slip),and (3) trench exposures. This publication is a major revision of an earlier map (Lienkaemper, 1992), which both brings up to date the evidence for faulting and makes it available formatted both as a digital database for use within a geographic information system (GIS) and for broader public access interactively using widely available viewing software. The pamphlet describes in detail the types of scientific observations used to make the map, gives references pertaining to the fault and the evidence of faulting, and provides guidance for use of and limitations of the map. [Summary provided by the USGS.] proprietary USGS_DS_2006_180_1.0 Capitol Lake, Washington, 2004 Data Summary CEOS_EXTRA STAC Catalog 2004-09-21 2005-02-28 -122.9142, 47.0219, -122.9034, 47.0447 https://cmr.earthdata.nasa.gov/search/concepts/C2231548768-CEOS_EXTRA.umm_json At the request of the Washington Department of Ecology (WDOE), the US Geological Survey (USGS) collected bathymetry data in Capital Lake, Olympia, Wash., on September 21, 2004. The data are to be used to calculate sediment infilling rates within the lake as well as for developing the bottom boundary conditions for numerical models of water quality, sediment transport, and morphological change. In addition, the USGS collected sediment samples in Capitol Lake in February, 2005, to help characterize bottom sediment for numerical model calculations and substrate assessment. [Summary provided by the USGS.] proprietary @@ -15619,8 +15599,8 @@ USGS_DS_2006_203 Archive of Digital Boomer Seismic Reflection Data Collected Dur USGS_DS_2006_216 Base-Flow Yields of Watersheds in the Berkeley County Area, West Virginia CEOS_EXTRA STAC Catalog 2005-07-25 2006-05-04 -78.1, 39.15, -77.5, 39.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231554130-CEOS_EXTRA.umm_json Base-flow yields at approximately 50 percent of the annual mean ground-water recharge rate were estimated for watersheds in the Berkeley County area, W.Va. These base-flow yields were determined from two sets of discharge measurements made July 25-28, 2005, and May 4, 2006. Two sections of channel along Opequon Creek had net flow losses that are expressed as negative base-flow watershed yields; these and other base-flow watershed yields in the eastern half of the study area ranged from -940 to 2,280 gallons per day per acre ((gal/d)/acre) and averaged 395 (gal/d)/acre. The base-flow yields for watersheds in the western half of the study area ranged from 275 to 482 (gal/d)/acre and averaged 376 (gal/d)/acre. [Summary provided by the USGS.] proprietary USGS_DS_2006_220 Hurricane Rita Surge Data, Southwestern Louisiana and Southeastern Texas, September to November 2005 CEOS_EXTRA STAC Catalog 1970-01-01 -98, 29, -90, 33 https://cmr.earthdata.nasa.gov/search/concepts/C2231548576-CEOS_EXTRA.umm_json Pressure transducers and high-water marks were used to document the inland water levels related to storm surge generated by Hurricane Rita in southwestern Louisiana and southeastern Texas. On September 22-23, 2005, an experimental monitoring network consisting of 47 pressure transducers (sensors) was deployed at 33 sites over an area of about 4,000 square miles to record the timing, extent, and magnitude of inland hurricane storm surge and coastal flooding. Sensors were programmed to record date and time, temperature, and barometric or water pressure. Water pressure was corrected for changes in barometric pressure and salinity. Elevation surveys using global-positioning systems and differential levels were used to relate all storm-surge water-level data, reference marks, benchmarks, sensor measuring points, and high-water marks to the North American Vertical Datum of 1988 (NAVD 88). The resulting data indicated that storm-surge water levels over 14 feet above NAVD 88 occurred at three locations and rates of water-level rise greater than 5 feet per hour occurred at three locations near the Louisiana coast. Quality-assurance measures were used to assess the variability and accuracy of the water-level data recorded by the sensors. Water-level data from sensors were similar to data from co-located sensors, permanent U.S. Geological Survey streamgages, and water-surface elevations performed by field staff. Water-level data from sensors at selected locations were compared to corresponding high-water mark elevations. In general, the water-level data from sensors were similar to elevations of high quality high-water marks, while reporting consistently higher than elevations of lesser quality high-water marks. [Summary provided by the USGS.] proprietary USGS_DS_2006_221 Land-Cover and Imperviousness Data for Regional Areas near Denver, Colorado; Dallas-Fort Worth, Texas; and Milwaukee-Green Bay, Wisconsin - 2001 CEOS_EXTRA STAC Catalog 1999-01-01 2002-12-31 -106, 31, -86, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2231548697-CEOS_EXTRA.umm_json This report describes the processing and results of land-cover and impervious surface derivation for parts of three metropolitan areas being studied as part of the U.S. Geological Survey's (USGS) National Water-Quality Assessment (NAWQA) Program Effects of Urbanization on Stream Ecosystems (EUSE). The data were derived primarily from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery from the period 1999-2002, and are provided as 30-meter resolution raster datasets. Data were produced to a standard consistent with data being produced as part of the USGS National Land Cover Database 2001 (NLCD01) Program, and were derived in cooperation with, and assistance from, NLCD01 personnel. The data were intended as surrogates for NLCD01 data because of the EUSE Program's time-critical need for updated land-cover for parts of the United States that would not be available in time from the NLCD01 Program. Six datasets are described in this report: separate land-cover (15-class categorical data) and imperviousness (0-100 percent continuous data) raster datasets for parts of the general Denver, Colorado area (South Platte River Basin), Dallas-Fort Worth, Texas area (Trinity River Basin), and Milwaukee-Green Bay, Wisconsin area (Western Lake Michigan Drainages). [Summary provided by the USGS.] proprietary -USGS_DS_2006_224 Aeromagnetic Survey of Taylor Mountains Area in Southwest Alaska, A Website for the Distribution of Data ALL STAC Catalog 2004-04-17 2004-05-31 -160, 60, -156, 61 https://cmr.earthdata.nasa.gov/search/concepts/C2231550160-CEOS_EXTRA.umm_json An airborne high-resolution magnetic and coincidental horizontal magnetic graviometer survey was completed over the Taylor Mountains area in southwest Alaska. The flying was undertaken by McPhar Geosurveys Ltd. on behalf of the United States Geological Survey (USGS). First tests and calibration flights were completed by April 7, 2004, and data acquisition was initiated on April 17, 2004. The final data acquisition and final test/calibrations flight was completed on May 31, 2004. Data acquired during the survey totaled 8,971.15 line-miles. [Summary provided by the USGS.] proprietary USGS_DS_2006_224 Aeromagnetic Survey of Taylor Mountains Area in Southwest Alaska, A Website for the Distribution of Data CEOS_EXTRA STAC Catalog 2004-04-17 2004-05-31 -160, 60, -156, 61 https://cmr.earthdata.nasa.gov/search/concepts/C2231550160-CEOS_EXTRA.umm_json An airborne high-resolution magnetic and coincidental horizontal magnetic graviometer survey was completed over the Taylor Mountains area in southwest Alaska. The flying was undertaken by McPhar Geosurveys Ltd. on behalf of the United States Geological Survey (USGS). First tests and calibration flights were completed by April 7, 2004, and data acquisition was initiated on April 17, 2004. The final data acquisition and final test/calibrations flight was completed on May 31, 2004. Data acquired during the survey totaled 8,971.15 line-miles. [Summary provided by the USGS.] proprietary +USGS_DS_2006_224 Aeromagnetic Survey of Taylor Mountains Area in Southwest Alaska, A Website for the Distribution of Data ALL STAC Catalog 2004-04-17 2004-05-31 -160, 60, -156, 61 https://cmr.earthdata.nasa.gov/search/concepts/C2231550160-CEOS_EXTRA.umm_json An airborne high-resolution magnetic and coincidental horizontal magnetic graviometer survey was completed over the Taylor Mountains area in southwest Alaska. The flying was undertaken by McPhar Geosurveys Ltd. on behalf of the United States Geological Survey (USGS). First tests and calibration flights were completed by April 7, 2004, and data acquisition was initiated on April 17, 2004. The final data acquisition and final test/calibrations flight was completed on May 31, 2004. Data acquired during the survey totaled 8,971.15 line-miles. [Summary provided by the USGS.] proprietary USGS_DS_2006_234_1.0 Nevada Magnetic and Gravity Maps and Data: A Website for the Distribution of Data CEOS_EXTRA STAC Catalog 1970-01-01 -120, 35, -114, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231548572-CEOS_EXTRA.umm_json Magnetic anomalies are due to variations in the Earth's magnetic field caused by the uneven distribution of magnetic minerals (primarily magnetite) in the rocks that make up the upper part of the Earth's crust. The features and patterns of the magnetic anomalies can be used to delineate details of subsurface geology, including the locations of buried faults and magnetite-bearing rocks and the depth to the base of sedimentary basins. This information is valuable for mineral exploration, geologic mapping, and environmental studies. The Nevada magnetic map is constructed from grids that combine information (see data processing details) collected in 82 separate magnetic surveys conducted between 1947 and 2004. The data from these surveys are of varying quality. The design and specifications (terrain clearance, sampling rates, line spacing, and reduction procedures) varied from survey to survey depending on the purpose of the project and the technology of that time. [Summary provided by the USGS.] proprietary USGS_DS_2007_119 Archive of Digital Boomer Seismic Reflection Data Collected During USGS Field Activity 04SGI01 in the Withlacoochee River of West-Central Florida, March 2004 CEOS_EXTRA STAC Catalog 2004-03-01 2004-03-05 -82.4575, 28.519396, -82.168434, 29.043365 https://cmr.earthdata.nasa.gov/search/concepts/C2231550488-CEOS_EXTRA.umm_json In March of 2004, the U.S. Geological Survey conducted a geophysical survey in the Withlacoochee River of west-central Florida. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. [Summary provided by the USGS.] proprietary USGS_DS_2007_242 Archive of Digital Chirp Seismic Reflection Data Collected During USGS Cruise 05SCC01 Offshore of Port Fourchon and Timbalier Bay, Louisiana, August 2005 CEOS_EXTRA STAC Catalog 2005-08-08 2005-08-11 -90.417816, 29.022211, -89.955574, 29.114426 https://cmr.earthdata.nasa.gov/search/concepts/C2231551890-CEOS_EXTRA.umm_json In August of 2005, the U.S. Geological Survey conducted geophysical surveys offshore of Port Fourchon and Timbalier Bay, Louisiana, and in nearby waterbodies. This report serves as an archive of unprocessed digital chirp seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. [Summary provided by the USGS.] proprietary @@ -15723,20 +15703,20 @@ USGS_Map_MF-2385_1.0 Map and map database of susceptibility to slope failure by USGS_NAWQA_HG_DEP Atmospheric Deposition of Mercury in the Boston Area CEOS_EXTRA STAC Catalog 1970-01-01 -78, 40, -70, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231550487-CEOS_EXTRA.umm_json Atmospheric deposition has been found to be the dominant source of mercury (Hg) in New England's aquatic environment (Krabbenhoft and others, 1999; Northeast States for Coordinated Air Use Management (NESCAUM) and others, 1998). Little is known about atmospheric mercury deposition in urban areas because most atmospheric monitoring to date has been done in rural areas. Preliminary water, sediment, and fish tissue data, collected by U.S. Geological Survey's New England Coastal Basins (NECB) study as part of the National Water Quality Assessment (NAWQA) program, shows elevated concentrations of mercury in the Boston metropolitan area. The NECB Mercury Deposition Network is a four-site, 2-year data collection effort by the USGS to help define the levels of mercury in precipitation and identify how atmospheric mercury may be contributing to mercury in the aquatic ecosystem. [Summary provided by the USGS.] proprietary USGS_NEIC_NEARRT Current and Near Real Time Earthquake Data from the USGS/National Earthquake Information Center (NEIC) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551913-CEOS_EXTRA.umm_json The National Earthquake Information Center (NEIC of the U.S. Geological Survey provides current earthquake information and data including interactive earthquake maps, near real time earthquake data, fast moment and broadband solutions, and lists of earthquakes for the past 3 weeks. Current earthquake information and data are located at: http://earthquake.usgs.gov/ Near real time earthquake data is located at: http://earthquake.usgs.gov/ Archives of past earthquakes can be found at: http://earthquake.usgs.gov/earthquakes/eqinthenews/ proprietary USGS_NHD_CATCH National Hydrography Dataset Catchment Delineations CEOS_EXTRA STAC Catalog 1970-01-01 -170, 17, -46, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2231554271-CEOS_EXTRA.umm_json Topographically-based catchments will be delineated for all stream-reach segments of the National Hydrography Dataset (NHD) within the entire conterminous United States. The NHD is a digital hydrographic dataset produced by the USGS, in cooperation with the U.S. Environmental Protection Agency (USEPA), that shows streams, lakes, ponds, and wetlands for the Nation at an initial scale of 1:100,000. This effort is being supported by the USEPA and USGS and is intended to benefit a wide variety of water-quality and stream-flow studies across the nation. The catchment-delineation technique is the same as that developed for use in the New England SPARROW model. The New England SPARROW model was the first to utilize the detail of the National Hydrography Dataset (NHD) as the underlying stream-reach network. Final products for this project will be the completion of NHD catchment delineations for the conterminous United States, which will be part of the NHDPlus project to be completed and made available in 2006. proprietary -USGS_NPS_AcadiaAccuracy_Final Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points ALL STAC Catalog 2003-10-01 2003-10-01 -75.262726, 43.99941, -68.044304, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231554200-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). Thematic accuracy requirements of the VMP specify 80% accuracy for each map class (theme) that represents National Vegetation Classification System (NVCS) associations (vegetation communities). The UMESC selected 728 field sites, all within Acadia National Park fee and easement lands, for a thematic accuracy assessment (AA) to the vegetation map. The sites were randomly generated, stratified to map class themes that represent NVCS natural/semi-natural vegetation communities using VMP standards. Certain modifications to the process were necessary to accommodate logistical challenges. Local botanists collected field data for 724 of the sites during the 1999 field season. Thematic AA used 688 sites. Sites not used for the analysis were due to the elimination of an entire map class because of irreconcilable classification concepts (19 sites), or to other reasons including unmanageable error with GPS coordinate, duplicate site location, and incomplete field data (17 sites). Regardless of their use in the analysis, all 724 AA sites collected are represented in the Accuracy Assessment Site Spatial Database. proprietary USGS_NPS_AcadiaAccuracy_Final Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -75.262726, 43.99941, -68.044304, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231554200-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). Thematic accuracy requirements of the VMP specify 80% accuracy for each map class (theme) that represents National Vegetation Classification System (NVCS) associations (vegetation communities). The UMESC selected 728 field sites, all within Acadia National Park fee and easement lands, for a thematic accuracy assessment (AA) to the vegetation map. The sites were randomly generated, stratified to map class themes that represent NVCS natural/semi-natural vegetation communities using VMP standards. Certain modifications to the process were necessary to accommodate logistical challenges. Local botanists collected field data for 724 of the sites during the 1999 field season. Thematic AA used 688 sites. Sites not used for the analysis were due to the elimination of an entire map class because of irreconcilable classification concepts (19 sites), or to other reasons including unmanageable error with GPS coordinate, duplicate site location, and incomplete field data (17 sites). Regardless of their use in the analysis, all 724 AA sites collected are represented in the Accuracy Assessment Site Spatial Database. proprietary +USGS_NPS_AcadiaAccuracy_Final Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points ALL STAC Catalog 2003-10-01 2003-10-01 -75.262726, 43.99941, -68.044304, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231554200-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). Thematic accuracy requirements of the VMP specify 80% accuracy for each map class (theme) that represents National Vegetation Classification System (NVCS) associations (vegetation communities). The UMESC selected 728 field sites, all within Acadia National Park fee and easement lands, for a thematic accuracy assessment (AA) to the vegetation map. The sites were randomly generated, stratified to map class themes that represent NVCS natural/semi-natural vegetation communities using VMP standards. Certain modifications to the process were necessary to accommodate logistical challenges. Local botanists collected field data for 724 of the sites during the 1999 field season. Thematic AA used 688 sites. Sites not used for the analysis were due to the elimination of an entire map class because of irreconcilable classification concepts (19 sites), or to other reasons including unmanageable error with GPS coordinate, duplicate site location, and incomplete field data (17 sites). Regardless of their use in the analysis, all 724 AA sites collected are represented in the Accuracy Assessment Site Spatial Database. proprietary USGS_NPS_AcadiaFieldPlots_Final Acadia National Park Vegetation Mapping Project - Field Plot Points CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -68.65603, 44.017136, -68.045715, 44.404953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549568-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of mapping and classifying the vegetation, vegetation sample plots were collected and analyzed, identifying 53 National Vegetation Classification System natural/semi-natural associations (vegetation communities). Local botanists, via contract with The Nature Conservancy, collected 179 vegetation plot samples at Acadia National Park (NP) during the 1997-1999 field seasons. Maine Natural Areas Program performed ordination analysis using the field plot data and other existing vegetation data of the area. Vegetation communities of Acadia NP are defined and described at the local and global scale. All 179 vegetation plot samples are represented in the Vegetation Field Plot Spatial Database with selected data fields from the Project's PLOTS database. proprietary USGS_NPS_AcadiaFieldPlots_Final Acadia National Park Vegetation Mapping Project - Field Plot Points ALL STAC Catalog 2003-10-01 2003-10-01 -68.65603, 44.017136, -68.045715, 44.404953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549568-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of mapping and classifying the vegetation, vegetation sample plots were collected and analyzed, identifying 53 National Vegetation Classification System natural/semi-natural associations (vegetation communities). Local botanists, via contract with The Nature Conservancy, collected 179 vegetation plot samples at Acadia National Park (NP) during the 1997-1999 field seasons. Maine Natural Areas Program performed ordination analysis using the field plot data and other existing vegetation data of the area. Vegetation communities of Acadia NP are defined and described at the local and global scale. All 179 vegetation plot samples are represented in the Vegetation Field Plot Spatial Database with selected data fields from the Project's PLOTS database. proprietary -USGS_NPS_AcadiaParkBoundary_Final Acadia National Park Vegetation Mapping Project - Park Boundary ALL STAC Catalog 2003-10-01 2003-10-01 -68.944374, 43.99941, -68.02303, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231550835-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of the mapping project, various spatial database boundary coverages were either produced or modified from their original source. These boundary coverages are: 1) Project Boundary, 2) Map Data Boundary, 3) Park Boundary, and 4) Quad Boundary. The spatial coverages are projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. proprietary USGS_NPS_AcadiaParkBoundary_Final Acadia National Park Vegetation Mapping Project - Park Boundary CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -68.944374, 43.99941, -68.02303, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231550835-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of the mapping project, various spatial database boundary coverages were either produced or modified from their original source. These boundary coverages are: 1) Project Boundary, 2) Map Data Boundary, 3) Park Boundary, and 4) Quad Boundary. The spatial coverages are projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. proprietary +USGS_NPS_AcadiaParkBoundary_Final Acadia National Park Vegetation Mapping Project - Park Boundary ALL STAC Catalog 2003-10-01 2003-10-01 -68.944374, 43.99941, -68.02303, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231550835-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of the mapping project, various spatial database boundary coverages were either produced or modified from their original source. These boundary coverages are: 1) Project Boundary, 2) Map Data Boundary, 3) Park Boundary, and 4) Quad Boundary. The spatial coverages are projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. proprietary USGS_NPS_AcadiaSpatialVeg_Final Acadia National Park Vegetation Mapping Project - Spatial Vegetation Data ALL STAC Catalog 1997-05-27 1997-05-28 -69, 43.99574, -67.99682, 44.50385 https://cmr.earthdata.nasa.gov/search/concepts/C2231552959-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey (USGS) Upper Midwest Environmental Sciences Center (UMESC) has produced the Vegetation Spatial Database Coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). The vegetation map is of Acadia National Park (NP) and extended environs, providing 99,693 hectares (246,347 acres) of map data. Of this coverage, 52,872 hectares (130,650 acres) is non-vegetated ocean, bay, and estuary (53% of coverage). Acadia NP comprises 19,276 hectares (47,633 acres) of the total data coverage area (19%, 40% not counting ocean and estuary data). Over 7,120 polygons make up the coverage, each with map class description and, for vegetation classes, physiognomic feature information. The spatial database provides crosswalk information to all National Vegetation Classification System (NVCS) floristic and physiognomic levels, and to other established classification systems (NatureServe's U.S. Terrestrial Ecological System Classification, Maine Natural Community Classification, and the USGS Land Use and Land Cover Classification). This mapping project has identified 53 NVCS associations (vegetation communities) at Acadia National Park through analyses of vegetation sample data. These associations are represented in the map coverage with 33 map classes. With all vegetation types, land use classes, and park specific categories combined, 57 map classes define the ground features within the project area (58 classes including the class for no map data). Each polygon within the spatial database map is identified with one of these map classes. In addition, physiognomic modifiers are added to map classes representing vegetation to describe the vegetation structure within a polygon (density, pattern, and height). The spatial database was produced from the interpretation of spring 1997 1:15,840-scale color infrared aerial photographs. The standard minimum mapping unit (MMU) applied is 0.5 hectares (1.25 acres). The interpreted data were transferred and automated using base maps produced from USGS digital orthophoto quadrangles. The finished spatial database is a single seamless coverage, projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. The estimated overall thematic accuracy for vegetation map classes is 80%. proprietary USGS_NPS_AcadiaSpatialVeg_Final Acadia National Park Vegetation Mapping Project - Spatial Vegetation Data CEOS_EXTRA STAC Catalog 1997-05-27 1997-05-28 -69, 43.99574, -67.99682, 44.50385 https://cmr.earthdata.nasa.gov/search/concepts/C2231552959-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey (USGS) Upper Midwest Environmental Sciences Center (UMESC) has produced the Vegetation Spatial Database Coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). The vegetation map is of Acadia National Park (NP) and extended environs, providing 99,693 hectares (246,347 acres) of map data. Of this coverage, 52,872 hectares (130,650 acres) is non-vegetated ocean, bay, and estuary (53% of coverage). Acadia NP comprises 19,276 hectares (47,633 acres) of the total data coverage area (19%, 40% not counting ocean and estuary data). Over 7,120 polygons make up the coverage, each with map class description and, for vegetation classes, physiognomic feature information. The spatial database provides crosswalk information to all National Vegetation Classification System (NVCS) floristic and physiognomic levels, and to other established classification systems (NatureServe's U.S. Terrestrial Ecological System Classification, Maine Natural Community Classification, and the USGS Land Use and Land Cover Classification). This mapping project has identified 53 NVCS associations (vegetation communities) at Acadia National Park through analyses of vegetation sample data. These associations are represented in the map coverage with 33 map classes. With all vegetation types, land use classes, and park specific categories combined, 57 map classes define the ground features within the project area (58 classes including the class for no map data). Each polygon within the spatial database map is identified with one of these map classes. In addition, physiognomic modifiers are added to map classes representing vegetation to describe the vegetation structure within a polygon (density, pattern, and height). The spatial database was produced from the interpretation of spring 1997 1:15,840-scale color infrared aerial photographs. The standard minimum mapping unit (MMU) applied is 0.5 hectares (1.25 acres). The interpreted data were transferred and automated using base maps produced from USGS digital orthophoto quadrangles. The finished spatial database is a single seamless coverage, projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. The estimated overall thematic accuracy for vegetation map classes is 80%. proprietary USGS_NSHMP National Seismic Hazard Maps from the USGS National Seismic Hazard Mapping Project CEOS_EXTRA STAC Catalog 1970-01-01 170, 18, -65, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231550531-CEOS_EXTRA.umm_json The National Seismic Hazard Mapping Project (NSHMP) provides online maps. The hazard maps depict probabilistic ground motions and spectral response with 10%, 5%, and 2% probabilities of exceedance (PE) in 50 years. These maps correspond to return times of approximately 500, 1000, and 2500 years, respectively. The maps are based on the assumption that earthquake occurrence is Poissonian, so that the probability of occurrence is time-independent. The maps cover all of the U.S. including Hawaii and Alaska along with other pertinent information related to earthquake hazards. proprietary USGS_NWRC_LA_LandChange_1932-2010 Land Area Change in Coastal Louisiana from 1932 to 2010 CEOS_EXTRA STAC Catalog 1932-01-01 2010-12-31 -94, 29, -89, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2231549617-CEOS_EXTRA.umm_json The analyses of landscape change presented in this dataset use historical surveys, aerial data, and satellite data to track landscape changes in coastal Louisiana. Persistent loss and gain data are presented for 1932-2010. The U.S. Geological Survey (USGS) analyzed landscape changes in coastal Louisiana by determining land and water classifications for 17 datasets. These datasets include survey data from 1932, aerial data from 1956, and Landsat Multispectral Scanner System (MSS) and Thematic Mapper (TM) data from the 1970s to 2010. proprietary USGS_OF99-535_1.0 Middle Pliocene Paleoenvironmental Reconstruction: PRISM2 CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231553168-CEOS_EXTRA.umm_json As part of the USGS Global Change Research effort, the PRISM (Pliocene Research, Interpretation and Synoptic Mapping) Project has documented the characteristics of middle Pliocene climate on a global scale. The middle Pliocene was selected for detailed study because it spans the transition from relatively warm global climates when glaciers were absent or greatly reduced in the Northern Hemisphere to the generally cooler climates of the Pleistocene with expanded Northern Hemisphere ice sheets and prominent glacial-interglacial cycles. The purpose of this report is to document and explain the PRISM2 mid Pliocene reconstruction. The PRISM2 reconstruction consists of a series of 28 global scale data sets (Table 1) on a 2° latitude by 2° longitude grid. As such, it is the most complete and detailed global reconstruction of climate and environmental conditions older than the last glacial. PRISM2 evolved from a series of studies that summarized conditions at a large number of marine and terrestrial sites and areas (eg. Cronin and Dowsett, 1991; Poore and Sloan, 1996). The first global reconstruction of mid Pliocene climate (PRISM1) was based upon 64 marine sites and 74 terrestrial sites and included data sets representing annual vegetation and land ice, monthly sea surface temperature (SST) and sea-ice, sea level and topography on a 2°x2° grid (Dowsett et al. (1996) and Thompson and Fleming (1996)). The current reconstruction (PRISM2) is a revision of PRISM1 that incorporates several important differences: 1) Additional sites were added to the marine portion of the reconstruction to improve previous coverage. Sites from the Mediterranean Sea and Indian Ocean are incorporated for the first time in PRISM2. 2) All Pliocene sea surface temperature (SST) estimates were recalculated based upon a new core top calibration to the Reynolds and Smith (1995) adjusted optimum interpolation (AOI) SST data set. This reduced some of the problems previously encountered when different fossil groups were calibrated to different modern climatologies (Climate / Long Range Investigation Mapping and Predictions [CLIMAP], Goddard Institute for Space Sciences [GISS], Advanced Very High Resolution Radiometer [AVHRR], etc.). 3) PRISM2 uses a +25m rise in sea level for the Pliocene (PRISM1 used +35m), in keeping with much new data that has become available. 4) Although the change in global ice volume between PRISM1 and PRISM2 is minor, PRISM2 uses model results from Prentice (personal communication) to guide the areal and topographic distribution of Antarctic ice. This results in a more realistic Antarctic ice configuration in tune with the +25m sea level rise. proprietary USGS_OFR-03-13 Cascadia Tsunami Deposit Database CEOS_EXTRA STAC Catalog 1970-01-01 -130, 36, -116, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2231550569-CEOS_EXTRA.umm_json Abstract The Cascadia Tsunami Deposit Database contains data on the location and sedimentological properties of tsunami deposits found along the Cascadia margin. Data have been compiled from 52 studies, documenting 59 sites from northern California to Vancouver Island, British Columbia that contain known or potential tsunami deposits. Bibliographical references are provided for all sites included in the database. Cascadia tsunami deposits are usually seen as anomalous sand layers in coastal marsh or lake sediments. The studies cited in the database use numerous criteria based on sedimentary characteristics to distinguish tsunami deposits from sand layers deposited by other processes, such as river flooding and storm surges. Several studies cited in the database contain evidence for more than one tsunami at a site. Data categories include age, thickness, layering, grainsize, and other sedimentological characteristics of Cascadia tsunami deposits. The database documents the variability observed in tsunami deposits found along the Cascadia margin. proprietary -USGS_OFR-97-792 500,000 Year-old Stable Isotopic Record from Devils Hole, USGS OFR-97-792 ALL STAC Catalog 1970-01-01 -116.3, 36.42, -116.3, 36.42 https://cmr.earthdata.nasa.gov/search/concepts/C2231554597-CEOS_EXTRA.umm_json Devils Hole is a tectonically formed cave developed in the discharge zone of a regional aquifer in south-central Nevada. (See Riggs, et al., 1994.) The walls of this subaqueous cavern are coated with dense vein calcite which provides an ideal material for precise uranium-series dating via thermal ionization mass spectrometry (TIMS). Devils Hole Core DH-11 is a 36-cm-long core of vein calcite from which we obtained an approximately 500,000-year-long continuous record of paleotemperature and other climatic proxies. Data from this core were recently used by Winograd and others (1997) to discuss the length and stability of the last four interglaciations. These data are given in table 1 (http://pubs.usgs.gov/of/1997/ofr97-792/) These records have provided information that has posed several challenges to the orbital theory of the causation of the Pleistocene glaciations, suggested insights regarding the duration of current Holocene climate, provided a new chronology for the Vostok, Antarctica, ice core paleotemperature record, and yielded insights on the age of the groundwater in the principal aquifer of southern Nevada (http://pubs.usgs.gov/of/2002/ofr02-266/) Carbon and oxygen stable isotopic ratios were measured on 285 samples cut at regular intervals inward from the free face of the core (as reported in Winograd et al. ,1992, and in Coplen et al., 1994). Table 1 lists only 284 samples because a sample taken at 114.28 mm was eliminated when post-1994 reanalysis of its delta 18O value indicated an error in the earlier determination. Carbon isotopic ratios are reported in per mill relative to VPDB, defined by assigning a delta 13C of +1.95 per mill to the reference material NBS 19 calcite. Oxygen isotopic ratios are reported relative to VSMOW reference water on a scale normalized such that SLAP reference water is -55.5 per mill relative to VSMOW reference water. The oxygen isotopic fractionation factors employed in this determination are those listed in Coplen and others (1983). The delta 18O value of the isotopic reference material NBS 19 on this scale is +28.65 per mill. The ± 1 sd (standard deviation) error for the delta 18O and delta 13C analyses is ±0.07 and 0.05 per mill, respectively. Ages were estimated by linear interpolation between age control points taken at key intervals in the core and analyzed by TIMS 230Th-234U-238U dating. The age estimates in Table 1 are based on the original 21 control points (see Table 2 in Ludwig, et al., 1992, and Figure 2 in Winograd, et al., 1992) as well as for the recently obtained TIMS age of 143.8±0.9 ka (2 sd analytical error) at 51.5 mm (Winograd, et al., 1997). The later sample was taken specifically for additional control in a critical portion of the core. Errors in the ages vary but are bounded by the errors in the appropriate control points. (See Table 2 in Ludwig, et al., 1992.) proprietary USGS_OFR-97-792 500,000 Year-old Stable Isotopic Record from Devils Hole, USGS OFR-97-792 CEOS_EXTRA STAC Catalog 1970-01-01 -116.3, 36.42, -116.3, 36.42 https://cmr.earthdata.nasa.gov/search/concepts/C2231554597-CEOS_EXTRA.umm_json Devils Hole is a tectonically formed cave developed in the discharge zone of a regional aquifer in south-central Nevada. (See Riggs, et al., 1994.) The walls of this subaqueous cavern are coated with dense vein calcite which provides an ideal material for precise uranium-series dating via thermal ionization mass spectrometry (TIMS). Devils Hole Core DH-11 is a 36-cm-long core of vein calcite from which we obtained an approximately 500,000-year-long continuous record of paleotemperature and other climatic proxies. Data from this core were recently used by Winograd and others (1997) to discuss the length and stability of the last four interglaciations. These data are given in table 1 (http://pubs.usgs.gov/of/1997/ofr97-792/) These records have provided information that has posed several challenges to the orbital theory of the causation of the Pleistocene glaciations, suggested insights regarding the duration of current Holocene climate, provided a new chronology for the Vostok, Antarctica, ice core paleotemperature record, and yielded insights on the age of the groundwater in the principal aquifer of southern Nevada (http://pubs.usgs.gov/of/2002/ofr02-266/) Carbon and oxygen stable isotopic ratios were measured on 285 samples cut at regular intervals inward from the free face of the core (as reported in Winograd et al. ,1992, and in Coplen et al., 1994). Table 1 lists only 284 samples because a sample taken at 114.28 mm was eliminated when post-1994 reanalysis of its delta 18O value indicated an error in the earlier determination. Carbon isotopic ratios are reported in per mill relative to VPDB, defined by assigning a delta 13C of +1.95 per mill to the reference material NBS 19 calcite. Oxygen isotopic ratios are reported relative to VSMOW reference water on a scale normalized such that SLAP reference water is -55.5 per mill relative to VSMOW reference water. The oxygen isotopic fractionation factors employed in this determination are those listed in Coplen and others (1983). The delta 18O value of the isotopic reference material NBS 19 on this scale is +28.65 per mill. The ± 1 sd (standard deviation) error for the delta 18O and delta 13C analyses is ±0.07 and 0.05 per mill, respectively. Ages were estimated by linear interpolation between age control points taken at key intervals in the core and analyzed by TIMS 230Th-234U-238U dating. The age estimates in Table 1 are based on the original 21 control points (see Table 2 in Ludwig, et al., 1992, and Figure 2 in Winograd, et al., 1992) as well as for the recently obtained TIMS age of 143.8±0.9 ka (2 sd analytical error) at 51.5 mm (Winograd, et al., 1997). The later sample was taken specifically for additional control in a critical portion of the core. Errors in the ages vary but are bounded by the errors in the appropriate control points. (See Table 2 in Ludwig, et al., 1992.) proprietary +USGS_OFR-97-792 500,000 Year-old Stable Isotopic Record from Devils Hole, USGS OFR-97-792 ALL STAC Catalog 1970-01-01 -116.3, 36.42, -116.3, 36.42 https://cmr.earthdata.nasa.gov/search/concepts/C2231554597-CEOS_EXTRA.umm_json Devils Hole is a tectonically formed cave developed in the discharge zone of a regional aquifer in south-central Nevada. (See Riggs, et al., 1994.) The walls of this subaqueous cavern are coated with dense vein calcite which provides an ideal material for precise uranium-series dating via thermal ionization mass spectrometry (TIMS). Devils Hole Core DH-11 is a 36-cm-long core of vein calcite from which we obtained an approximately 500,000-year-long continuous record of paleotemperature and other climatic proxies. Data from this core were recently used by Winograd and others (1997) to discuss the length and stability of the last four interglaciations. These data are given in table 1 (http://pubs.usgs.gov/of/1997/ofr97-792/) These records have provided information that has posed several challenges to the orbital theory of the causation of the Pleistocene glaciations, suggested insights regarding the duration of current Holocene climate, provided a new chronology for the Vostok, Antarctica, ice core paleotemperature record, and yielded insights on the age of the groundwater in the principal aquifer of southern Nevada (http://pubs.usgs.gov/of/2002/ofr02-266/) Carbon and oxygen stable isotopic ratios were measured on 285 samples cut at regular intervals inward from the free face of the core (as reported in Winograd et al. ,1992, and in Coplen et al., 1994). Table 1 lists only 284 samples because a sample taken at 114.28 mm was eliminated when post-1994 reanalysis of its delta 18O value indicated an error in the earlier determination. Carbon isotopic ratios are reported in per mill relative to VPDB, defined by assigning a delta 13C of +1.95 per mill to the reference material NBS 19 calcite. Oxygen isotopic ratios are reported relative to VSMOW reference water on a scale normalized such that SLAP reference water is -55.5 per mill relative to VSMOW reference water. The oxygen isotopic fractionation factors employed in this determination are those listed in Coplen and others (1983). The delta 18O value of the isotopic reference material NBS 19 on this scale is +28.65 per mill. The ± 1 sd (standard deviation) error for the delta 18O and delta 13C analyses is ±0.07 and 0.05 per mill, respectively. Ages were estimated by linear interpolation between age control points taken at key intervals in the core and analyzed by TIMS 230Th-234U-238U dating. The age estimates in Table 1 are based on the original 21 control points (see Table 2 in Ludwig, et al., 1992, and Figure 2 in Winograd, et al., 1992) as well as for the recently obtained TIMS age of 143.8±0.9 ka (2 sd analytical error) at 51.5 mm (Winograd, et al., 1997). The later sample was taken specifically for additional control in a critical portion of the core. Errors in the ages vary but are bounded by the errors in the appropriate control points. (See Table 2 in Ludwig, et al., 1992.) proprietary USGS_OFR00-45_1.0 Bedrock Geologic Map of the Hubbard Brook Experimental Forest, Grafton County, New Hampshire, USGS/OFR 00-45 CEOS_EXTRA STAC Catalog 1998-01-01 2000-12-31 -71.875, 43.875, -71.625, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2231550787-CEOS_EXTRA.umm_json Our mapping study was funded by the USGS Toxic Substances Hydrology Program and was undertaken for the following reasons: 1) to ascertain whether the area might have a greater number of mappable lithologic units than shown on Barton's (1997) map, and to verify the stratigraphically higher formations shown on the map; 2) to have sufficient data to draw geologic cross- sections through the Mirror Lake research site; 3) to gather more data on brittle fracture distribution and orientation; and 4) to assess the degree to which the subsurface lithologies, ductile structures, and fractures observed at the two Mirror Lake well fields correlate with the geology of the surrounding region. The bedrock geology of the Hubbard Brook Experimental Forest, Grafton County, New Hampshire is described in this report of new field investigation. The database includes contacts of bedrock geologic units, faults, folds, and other structural geologic information, as well as the base maps on which the mapped geological features are registered. This report supersedes Barton (1997). Data were originally collected in UTM coordinates, zone 19, NAD 1927, and reprojected to geographic coordinates (Lat/Long), NAD 1983. The database is accompanied by two large format color maps, a readme.txt file, and a explanatory pamphlet. proprietary USGS_OFR00-462 Archive of Chirp Subbottom Data Collected During USGS Cruise MGNM 00014, Central South Carolina, 13-30 March, 2000, USGS/OFR 00-462 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -79.17, 33.25, -78.5, 33.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231554800-CEOS_EXTRA.umm_json In November 1999, the U. S. Geological Survey, in cooperation with Coastal Carolina University, began a program to produce geologic maps of the nearshore regime off northern South Carolina, utilizing high resolution sidescan sonar, interferometric (direct phase methods) swath bathymetry, and seismic subbottom profiling systems. The study areas extends from the ~7m isobath to about 10km offshore (water depths <12m). The goals of the investigation are to determine regional scale sand resource availability needed for planned beach nourishment programs, to investigate the roles that the inner shelf morphology and geologic framework play in the evolution of this coastal region, and to provide baseline geologic maps for use in proposed biologic habitat studies. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS MGNM 00014 cruise. The coverage is the nearshore of central South Carolina. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary USGS_OFR00-463 Archive of Boomer Subbottom Data Collected During USGS Cruise MGNM 00014, Central South Carolina, 13-30 March, 2000, USGS, OFR 00-463 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -79.17, 33.25, -78.5, 33.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231555400-CEOS_EXTRA.umm_json In November 1999, the U. S. Geological Survey, in cooperation with Coastal Carolina University, began a program to produce geologic maps of the nearshore regime off northern South Carolina, utilizing high resolution sidescan sonar, interferometric (direct phase methods) swath bathymetry, and seismic subbottom profiling systems. The study areas extends from the ~7m isobath to about 10km offshore (water depths <12m). The goals of the investigation are to determine regional scale sand resource availability needed for planned beach nourishment programs, to investigate the roles that the inner shelf morphology and geologic framework play in the evolution of this coastal region, and to provide baseline geologic maps for use in proposed biologic habitat studies. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS MGNM 00014 cruise. The coverage is the nearshore of central South Carolina. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary @@ -15864,8 +15844,8 @@ USGS_OFR_2004_1038 Inventory of Significant Mineral Deposit Occurrences in the H USGS_OFR_2004_1039 Location, Age, and Tectonic Significance of the Western Idaho Suture Zone CEOS_EXTRA STAC Catalog 1970-01-01 -118, 43, -112, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231552012-CEOS_EXTRA.umm_json The Western Idaho Suture Zone (WISZ) represents the boundary between crust overlying Proterozoic North American lithosphere and Late Paleozoic and Mesozoic intraoceanic crust accreted during Cretaceous time. Highly deformed plutons constituted of both arc and sialic components intrude the WISZ and in places are thrust over the accreted terranes. Pronounced variations in Sr, Nd, and O isotope ratios and in major and trace element composition occur across the suture zone in Mesozoic plutons. The WISZ is located by an abrupt west to east increase in initial 87Sr/86Sr ratios, traceable for over 300 km from eastern Washington near Clarkston, east along the Clearwater River thorough a bend to the south of about 110° from Orofino Creek to Harpster, and extending south-southwest to near Ola, Idaho, where Columbia River basalts conceal its extension to the south. K-Ar and 40Ar/39Ar apparent ages of hornblende and biotite from Jurassic and Early Cretaceous plutons in the accreted terranes are highly discordant within about 10 km of the WISZ, exhibiting patterns of thermal loss caused by deformation, subsequent batholith intrusion, and rapid rise of the continental margin. Major crustal movements within the WISZ commenced after about 135 Ma, but much of the displacement may have been largely vertical, during and following emplacement of batholith-scale silicic magmas. Deformation continued until at least 85 Ma and probably until 74 Ma, progressing from south to north. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1049_1.0 Geologic and Bathymetric Reconnaissance Overview of the San Pedro Shelf Region, Southern California CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 -118.33333, 33.46667, -117.83333, 33.78333 https://cmr.earthdata.nasa.gov/search/concepts/C2231548808-CEOS_EXTRA.umm_json This report presents a series of maps that describe the bathymetry and late Quaternary geology of the San Pedro shelf area as interpreted from seismic-reflection profiles and 3.5-kHz and multibeam bathymetric data. Some of the seismic-reflection profiles were collected with Uniboom and 120-kJ sparker during surveys conducted by the U.S. Geological Survey (USGS) in 1973 (K-2-73-SC), 1978 (S-2-78-SC), and 1979 (S-2a-79-SC). The remaining seismic-reflection profiles were collected in 2000 using Geopulse boomer and minisparker during USGS cruise A-1-00-SC. The report consists of seven oversized sheets: 1. Map of 1978 and 1979 uniboom seismic-reflection and 3.5-kHz tracklines used to map faults and folds on San Pedro Shelf. 2. Maps of multibeam shaded bathymetric relief with faults and folds, and bathymetric contours. 3. Isopach map of unconsolidated sediment, seismic-reflection profile across the San Pedro shelf, seismic-reflection profile across San Gabriel paleo-valley. 4. Seismic-reflection profiles across the Palos Verdes Fault Zone. 5. Geologic map and samples of Uniboom and 120-kJ sparker seismic-reflection profiles used to make the map. 6. Map showing thickness of uppermost (Holocene?) sediment layer. 7. Map of San Gabriel Canyon paleo-valley and associated drainage basins. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1054 Assessment of Hazards Associated with the Bluegill Landslide, South-Central Idaho CEOS_EXTRA STAC Catalog 1970-01-01 -117.59, 41.64, -110.7, 49.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231554051-CEOS_EXTRA.umm_json The Bluegill landslide, located in south-central Idaho, is part of a larger landslide complex that forms an area in the Salmon Falls Creek drainage named Sinking Canyon. The landslide is on public property administered by the U.S. Bureau of Land Management (BLM). As part of ongoing efforts to address possible public safety concerns, the BLM requested that the U.S. Geological Survey (USGS) conduct a preliminary hazard assessment of the landslide, examine possible mitigation options, and identify alternatives for further study and monitoring of the landslide. This report presents the findings of that assessment based on a field reconnaissance of the landslide on September 24, 2003, a review of data and information provided by BLM and researchers from Idaho State University, and information collected from other sources. [Summary provided by the USGS.] proprietary -USGS_OFR_2004_1058 2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory ALL STAC Catalog 2002-01-01 -168, 46, -126, 76 https://cmr.earthdata.nasa.gov/search/concepts/C2231549438-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO) tracks activity at the more than 40 historically active volcanoes of the Aleutian Arc. As of December 31, 2002, 24 of these volcanoes are monitored with short-period seismometer networks. AVO's monitoring program also includes daily analysis of satellite imagery supported by occasional over flights and compilation of pilot reports, observations of local residents, and observations of mariners. In 2002, AVO responded to eruptive activity or suspect volcanic activity at 6 volcanic centers in Alaska - Wrangell, the Katmai Group, Veniaminof, Shishaldin, Emmons Lake (Hague), and Great Sitkin volcanoes. In addition to responding to eruptive activity at Alaskan volcanoes, AVO also disseminated information on behalf of the Kamchatkan Volcanic Eruption Response Team (KVERT) about activity at 5 Russian volcanoes - Sheveluch, Klyuchevskoy, Bezymianny, Karymsky, and Chikurachki. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1058 2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory CEOS_EXTRA STAC Catalog 2002-01-01 -168, 46, -126, 76 https://cmr.earthdata.nasa.gov/search/concepts/C2231549438-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO) tracks activity at the more than 40 historically active volcanoes of the Aleutian Arc. As of December 31, 2002, 24 of these volcanoes are monitored with short-period seismometer networks. AVO's monitoring program also includes daily analysis of satellite imagery supported by occasional over flights and compilation of pilot reports, observations of local residents, and observations of mariners. In 2002, AVO responded to eruptive activity or suspect volcanic activity at 6 volcanic centers in Alaska - Wrangell, the Katmai Group, Veniaminof, Shishaldin, Emmons Lake (Hague), and Great Sitkin volcanoes. In addition to responding to eruptive activity at Alaskan volcanoes, AVO also disseminated information on behalf of the Kamchatkan Volcanic Eruption Response Team (KVERT) about activity at 5 Russian volcanoes - Sheveluch, Klyuchevskoy, Bezymianny, Karymsky, and Chikurachki. [Summary provided by the USGS.] proprietary +USGS_OFR_2004_1058 2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory ALL STAC Catalog 2002-01-01 -168, 46, -126, 76 https://cmr.earthdata.nasa.gov/search/concepts/C2231549438-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO) tracks activity at the more than 40 historically active volcanoes of the Aleutian Arc. As of December 31, 2002, 24 of these volcanoes are monitored with short-period seismometer networks. AVO's monitoring program also includes daily analysis of satellite imagery supported by occasional over flights and compilation of pilot reports, observations of local residents, and observations of mariners. In 2002, AVO responded to eruptive activity or suspect volcanic activity at 6 volcanic centers in Alaska - Wrangell, the Katmai Group, Veniaminof, Shishaldin, Emmons Lake (Hague), and Great Sitkin volcanoes. In addition to responding to eruptive activity at Alaskan volcanoes, AVO also disseminated information on behalf of the Kamchatkan Volcanic Eruption Response Team (KVERT) about activity at 5 Russian volcanoes - Sheveluch, Klyuchevskoy, Bezymianny, Karymsky, and Chikurachki. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1064 Coastal Vulnerability Assessment of Cape Hatteras National Seashore (CAHA) to Sea-Level Rise CEOS_EXTRA STAC Catalog 1970-01-01 -80, 33, -76, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231549408-CEOS_EXTRA.umm_json A coastal vulnerability index (CVI) was used to map the relative vulnerability of the coast to future sea-level rise within Cape Hatteras National Seashore (CAHA) in North Carolina. The CVI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level rise, historical shoreline change rates, mean tidal range, and mean significant wave height. The rankings for each variable were combined and an index value was calculated for 1-minute grid cells covering the park. The CVI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural vulnerability to the effects of sea-level rise. The CVI provides an objective technique for evaluation and long-term planning by scientists and park managers. Cape Hatteras National Seashore consists of stable and washover dominated segments of barrier beach backed by wetland and marsh. The areas within Cape Hatteras that are likely to be most vulnerable to sea-level rise are those with the highest occurrence of overwash and the highest rates of shoreline change. [Summary provided by the USGS.] proprietary USGS_OFR_2004_1067 Digital Database of Selected Aggregate and Related Resources in Ada, Boise, Canyon, Elmore, Gem, and Owyhee Counties, Southwestern Idaho CEOS_EXTRA STAC Catalog 1934-01-01 2003-12-31 -117.01154, 42.29952, -115.10053, 44.17547 https://cmr.earthdata.nasa.gov/search/concepts/C2231549777-CEOS_EXTRA.umm_json "The U.S. Geological Survey (USGS) compiled a database of aggregate sites and geotechnical sample data for six counties - Ada, Boise, Canyon, Elmore, Gem, and Owyhee - in southwest Idaho as part of a series of studies in support of the Bureau of Land Management (BLM) planning process. Emphasis is placed on sand and gravel sites in deposits of the Boise River, Snake River, and other fluvial systems and in Neogene lacustrine deposits. Data were collected primarily from unpublished Idaho Transportation Department (ITD) records and BLM site descriptions, published Army Corps of Engineers (ACE) records, and USGS sampling data. The results of this study provides important information needed by land-use planners and resource managers, particularly in the BLM, to anticipate and plan for demand and development of sand and gravel and other mineral material resources on public lands in response to the urban growth in southwestern Idaho. The aggregate database combines two data sets - site information and geotechnical sample data - into an integrated spatial database with 82 unique fields. The material source site data set includes information on 680 sites, and the geotechnical data set consists of selected information from 2,723 laboratory analyses of samples collected from many, but not all, of the sites. The 680 aggregate sites are divided into six classes: sand & gravel (614); rock quarry (43); cinder quarry (9); placer tailings (8); talus (4); and mine waste rock (2). Most importantly, the aggregate database includes detailed location information allowing individual sites to be located at least within a section and most often within a small parcel of a section. Additional information includes, but is not limited to: lithology-mineralogy or geologic formation (if known); surface ownership; size; production; permitting; agency; and number of samples. Geotechnical data include: lab number and test date; field parameters including sample location, type of material, and size; and the results of geotechnical analyses - gradation (grain size distribution), Los Angeles (LA) Degradation, sand equivalent, absorption, density, and several other tests. Ninety-five percent of the 2,723 geotechnical sample records include gradation data, and 72 percent of the samples have sand equivalent data. However, LA Degradation, absorption, and bulk density data are reported only in about 30 percent of the sample records. Large volumes of geotechnical data reside in a variety of accessible but little-used archives maintained by local and county highway districts, state transportation bureaus, and federal engineering, construction and transportation agencies. Integration of good quality geotechnical lithogeochemical information, particularly in digital form suitable for geospatial analysis, can produce profoundly superior databases that may allow more accurate and reliable ""expert"" decision making and improved land use planning. The database that accompanies this report, structured for direct import into geographic information system (GIS) software, is the first step toward producing such an integrated geologic-geotechnical spatial database. [Summary provided by the USGS.]" proprietary USGS_OFR_2004_1069 A 30-Year Record of Surface Mass Balance (1966-95) and Motion and Surface Altitude (1975-95) at Wolverine Glacier, Alaska ALL STAC Catalog 1966-04-01 1995-12-31 -156, 57, -144, 66 https://cmr.earthdata.nasa.gov/search/concepts/C2231554448-CEOS_EXTRA.umm_json Scientific measurements at Wolverine Glacier, on the Kenai Peninsula in south-central Alaska, began in April 1966. At three long-term sites in the research basin, the measurements included snow depth, snow density, heights of the glacier surface and stratigraphic summer surfaces on stakes, and identification of the surface materials. Calculations of the mass balance of the surface strata-snow, new firn, superimposed ice, and old firn and ice mass at each site were based on these measurements. Calculations of fixed-date annual mass balances for each hydrologic year (October 1 to September 30), as well as net balances and the dates of minimum net balance measured between time-transgressive summer surfaces on the glacier, were made on the basis of the strata balances augmented by air temperature and precipitation recorded in the basin. From 1966 through 1995, the average annual balance at site A (590 meters altitude) was -4.06 meters water equivalent; at site B (1,070 meters altitude), was -0.90 meters water equivalent; and at site C (1,290 meters altitude), was +1.45 meters water equivalent. Geodetic determination of displacements of the mass balance stake, and glacier surface altitudes was added to the data set in 1975 to detect the glacier motion responses to variable climate and mass balance conditions. The average surface speed from 1975 to 1996 was 50.0 meters per year at site A, 83.7 meters per year at site B, and 37.2 meters per year at site C. The average surface altitudes were 594 meters at site A, 1,069 meters at site B, and 1,293 meters at site C; the glacier surface altitudes rose and fell over a range of 19.4 meters at site A, 14.1 meters at site B, and 13.2 meters at site C. [Summary provided by the USGS.] proprietary @@ -15908,8 +15888,8 @@ USGS_OFR_2005_1070_1.0 Molokai Benthic Habitat Mapping CEOS_EXTRA STAC Catalog 1 USGS_OFR_2005_1132_1.0 Ground-Magnetic Studies of the Amargosa Desert Region, California and Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231555068-CEOS_EXTRA.umm_json High-resolution aeromagnetic surveys of the Amargosa Desert region, California and Nevada, exhibit a diverse array of magnetic anomalies reflecting a wide range of mid- and upper-crustal lithologies. In most cases, these anomalies can be interpreted in terms of exposed rocks and sedimentary deposits. More difficult to explain are linear magnetic anomalies situated over lithologies that typically have very low magnetizations. Aeromagnetic anomalies are observed, for example, over thick sections of Quaternary alluvial deposits and spring deposits associated with past or modern ground-water discharge in Ash Meadows, Pahrump Valley, and Furnace Creek Wash. Such deposits are typically considered nonmagnetic. To help determine the source of these aeromagnetic anomalies, we conducted ground-magnetic studies at five areas: near Death Valley Junction, at Point of Rocks Spring, at Devils Hole, at Fairbanks Spring, and near Travertine Springs. Depth-to-source calculations show that the sources of these anomalies lie within the Tertiary and Quaternary sedimentary section. We conclude that they are caused by discrete volcanic units lying above the pre-Tertiary basement. At Death Valley Junction and Travertine Springs, these concealed volcanic units are probably part of the Miocene Death Valley volcanic field exposed in the nearby Greenwater Range and Black Mountains. The linear nature of the aeromagnetic anomalies suggests that these concealed volcanic rocks are bounded and offset by near-surface faults. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1135_1.0 Modified Mercalli Intensity Maps for the 1906 San Francisco Earthquake Plotted in ShakeMap Format CEOS_EXTRA STAC Catalog 1906-04-18 1906-04-18 -124, 34, -120, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231554244-CEOS_EXTRA.umm_json This website presents Modified Mercalli Intensity maps for the great San Francisco earthquake of April 18, 1906. These new maps combine two important developments. First, we have re-evaluated and relocated the damage and shaking reports compiled by Lawson (1908). These reports yield intensity estimates for more than 600 sites and constitute the largest set of intensities ever compiled for a single earthquake. Second, we use the recent ShakeMap methodology to map these intensities. The resulting MMI intensity maps are remarkably detailed and eloquently depict the enormous power and damage potential of this great earthquake. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1144 Huminite Reflectance Measurements of Paleocene and Upper Cretaceous Coals from Borehole Cuttings, Zavala and Dimmit Counties, South Texas CEOS_EXTRA STAC Catalog 1970-01-01 -107.31, 25.19, -92.85, 37.14 https://cmr.earthdata.nasa.gov/search/concepts/C2231553355-CEOS_EXTRA.umm_json The reflectance of huminite in 19 cuttings samples was determined in support of ongoing investigations into the coal bed methane potential of subsurface Paleocene and Upper Cretaceous coals of South Texas. Coal cuttings were obtained from the Core Research Center of the Bureau of Economic Geology, The University of Texas at Austin. Geophysical logs, mud-gas logs, driller's logs, completion cards, and scout tickets were used to select potentially coal-bearing sample suites and to identify specific sample depths. Reflectance measurements indicate coals of subbituminous rank are present in a wider area in South Texas than previously recognized. [Summary provided by the USGS.] proprietary -USGS_OFR_2005_1148_1.0 Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004 CEOS_EXTRA STAC Catalog 1970-01-01 -80.82, 39.43, -74.41, 42.56 https://cmr.earthdata.nasa.gov/search/concepts/C2231550432-CEOS_EXTRA.umm_json Recent construction for Interstate Highway 99 (I?99) exposed pyrite and associated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative weathering along a 40-60-m deep roadcut through a 270-m long section of the Ordovician Bald Eagle Formation at Skytop, near State College, Centre County, Pennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and limestone in Blair and Centre Counties were prospected in the past; however, these deposits generally were not viable as commercial mines. The pyritic sandstone from the roadcut was crushed and used locally as road base and fill for adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps and runoff from the exposed cut and crushed sandstone raised concerns about surface- and ground-water contamination and prompted a halt in road construction and the beginning of costly remediation. Mineralized sandstones from the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt. Pb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the cut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net acidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t). Primary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. % Fe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale veinlets and disseminated grains in sandstone, as needles, and in a locally massive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and Ni-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or less) explain the primary source of trace metals in the rock and in associated secondary minerals and seepage. Wet/dry cycles associated with intermittent rainfall promoted oxidative weathering and dissolution of primary sulfides and their oxidation products. Resulting sulfate solutions evaporated during dry periods to form intermittent ?blooms? of soluble, yellow and white efflorescent sulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and other surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor Zn. They readily dissolved in deionized water in the laboratory to form solutions with pH <2.5, consistent with field observations. In addition to elevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at the base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and Ni. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester metals and acidity between rainfall events. Episodic salt dissolution then contributes pulses of contamination including acid to surface runoff and ground water. The Skytop experience highlights the need to understand dynamic interactions of mineralogy and hydrology in order to avoid potentially negative environmental impacts associated with excavation in sulfidic rocks. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1148_1.0 Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004 ALL STAC Catalog 1970-01-01 -80.82, 39.43, -74.41, 42.56 https://cmr.earthdata.nasa.gov/search/concepts/C2231550432-CEOS_EXTRA.umm_json Recent construction for Interstate Highway 99 (I?99) exposed pyrite and associated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative weathering along a 40-60-m deep roadcut through a 270-m long section of the Ordovician Bald Eagle Formation at Skytop, near State College, Centre County, Pennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and limestone in Blair and Centre Counties were prospected in the past; however, these deposits generally were not viable as commercial mines. The pyritic sandstone from the roadcut was crushed and used locally as road base and fill for adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps and runoff from the exposed cut and crushed sandstone raised concerns about surface- and ground-water contamination and prompted a halt in road construction and the beginning of costly remediation. Mineralized sandstones from the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt. Pb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the cut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net acidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t). Primary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. % Fe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale veinlets and disseminated grains in sandstone, as needles, and in a locally massive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and Ni-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or less) explain the primary source of trace metals in the rock and in associated secondary minerals and seepage. Wet/dry cycles associated with intermittent rainfall promoted oxidative weathering and dissolution of primary sulfides and their oxidation products. Resulting sulfate solutions evaporated during dry periods to form intermittent ?blooms? of soluble, yellow and white efflorescent sulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and other surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor Zn. They readily dissolved in deionized water in the laboratory to form solutions with pH <2.5, consistent with field observations. In addition to elevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at the base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and Ni. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester metals and acidity between rainfall events. Episodic salt dissolution then contributes pulses of contamination including acid to surface runoff and ground water. The Skytop experience highlights the need to understand dynamic interactions of mineralogy and hydrology in order to avoid potentially negative environmental impacts associated with excavation in sulfidic rocks. [Summary provided by the USGS.] proprietary +USGS_OFR_2005_1148_1.0 Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004 CEOS_EXTRA STAC Catalog 1970-01-01 -80.82, 39.43, -74.41, 42.56 https://cmr.earthdata.nasa.gov/search/concepts/C2231550432-CEOS_EXTRA.umm_json Recent construction for Interstate Highway 99 (I?99) exposed pyrite and associated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative weathering along a 40-60-m deep roadcut through a 270-m long section of the Ordovician Bald Eagle Formation at Skytop, near State College, Centre County, Pennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and limestone in Blair and Centre Counties were prospected in the past; however, these deposits generally were not viable as commercial mines. The pyritic sandstone from the roadcut was crushed and used locally as road base and fill for adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps and runoff from the exposed cut and crushed sandstone raised concerns about surface- and ground-water contamination and prompted a halt in road construction and the beginning of costly remediation. Mineralized sandstones from the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt. Pb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the cut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net acidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t). Primary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. % Fe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale veinlets and disseminated grains in sandstone, as needles, and in a locally massive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and Ni-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or less) explain the primary source of trace metals in the rock and in associated secondary minerals and seepage. Wet/dry cycles associated with intermittent rainfall promoted oxidative weathering and dissolution of primary sulfides and their oxidation products. Resulting sulfate solutions evaporated during dry periods to form intermittent ?blooms? of soluble, yellow and white efflorescent sulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and other surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor Zn. They readily dissolved in deionized water in the laboratory to form solutions with pH <2.5, consistent with field observations. In addition to elevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at the base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and Ni. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester metals and acidity between rainfall events. Episodic salt dissolution then contributes pulses of contamination including acid to surface runoff and ground water. The Skytop experience highlights the need to understand dynamic interactions of mineralogy and hydrology in order to avoid potentially negative environmental impacts associated with excavation in sulfidic rocks. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1153_1.0 Multibeam Bathymetry and Backscatter Data: Northeastern Channel Islands Region, Southern California CEOS_EXTRA STAC Catalog 2004-08-06 2004-08-15 -119.72, 33.88, -119.03, 34.33 https://cmr.earthdata.nasa.gov/search/concepts/C2231553010-CEOS_EXTRA.umm_json The U.S. Geological Survey (USGS) in cooperation with the Minerals Management Service (MMS) conducted multibeam mapping in the eastern Santa Barbara Channel and northeastern Channel Islands region from August 8 to15, 2004 aboard the R/V Maurice Ewing. The survey was directed and funded by the Minerals Management Service, which is interested in maps of hard bottom habitats, particularly natural outcrops, that support reef communities in areas affected by oil and gas activity. The maps are also useful to biologists studying fish that use the platforms and the sea floor beneath them as habitat. The survey collected bathymetry and corrected, co-registered acoustic backscatter using a Kongsberg Simrad EM1002 multibeam echosounder that was mounted on the hull of the R/V Maurice Ewing. Three main regions were mapped during the survey including: (1) the Eastern Santa Barbara Channel adjacent to an area previously mapped with multibeam-sonar by the Monterey Bay Aquarium Research Institute (see the MBARI Santa Barbara Basin Multibeam Survey web page), (2) the Footprint area south of Anacapa Island, which has been studied extensively by rockfish biologists and is considered a good site for a marine protected area, and (3) part of the submarine canyons along the continental slope south of Port Hueneme. These data will be used to support a number of new and ongoing projects including, habitat mapping, shelf and slope processes, and offshore hazards and resources. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1164_1.0 An Assessment of Volcanic Threat and Monitoring Capabilities in the United States: Framework for a National Volcano Early Warning System CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231551822-CEOS_EXTRA.umm_json A National Volcano Early Warning System NVEWS is being formulated by the Consortium of U.S. Volcano Observatories (CUSVO) to establish a proactive, fully integrated, national-scale monitoring effort that ensures the most threatening volcanoes in the United States are properly monitored in advance of the onset of unrest and at levels commensurate with the threats posed. Volcanic threat is the combination of hazards (the destructive natural phenomena produced by a volcano) and exposure (people and property at risk from the hazards). The United States has abundant volcanoes, and over the past 25 years the Nation has experienced a diverse range of the destructive phenomena that volcanoes can produce. Hazardous volcanic activity will continue to occur, and because of increasing population, increasing development, and expanding national and international air traffic over volcanic regions the exposure of human life and enterprise to volcano hazards is increasing. Fortunately, volcanoes exhibit precursory unrest that if detected and analyzed in time allows eruptions to be anticipated and communities at risk to be forewarned with reliable information in sufficient time to implement response plans and mitigation measures. In the 25 years since the cataclysmic eruption of Mount St. Helens, scientific and technological advances in volcanology have been used to develop and test models of volcanic behavior and to make reliable forecasts of expected activity a reality. Until now, these technologies and methods have been applied on an ad hoc basis to volcanoes showing signs of activity. However, waiting to deploy a robust, modern monitoring effort until a hazardous volcano awakens and an unrest crisis begins is socially and scientifically unsatisfactory because it forces scientists, civil authorities, citizens, and businesses into playing catch up with the volcano, trying to get instruments and civil-defense measures in place before the unrest escalates and the situation worsens. Inevitably, this manner of response results in our missing crucial early stages of the volcanic unrest and hampers our ability to accurately forecast events. Restless volcanoes do not always progress to eruption; nevertheless, monitoring is necessary in such cases to minimize either over-reacting, which costs money, or under-reacting, which may cost lives. [Summary provided by the USGS.] proprietary USGS_OFR_2005_1176 Flooding of the Androscoggin River during December 18-19, 2003, in Canton, Maine CEOS_EXTRA STAC Catalog 2003-12-18 2003-12-19 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C2231550802-CEOS_EXTRA.umm_json The Androscoggin River flooded the town of Canton, Maine in December 2003, resulting in damage to and (or) evacuation of 44 homes. Streamflow records at the U.S. Geological Survey (USGS) streamflow-gaging stations at Rumford (USGS station identification number 01054500) and Auburn (01059000) were used to estimate the peak streamflow for the Androscoggin in the town of Canton for this flood (December 18-19, 2003). The estimated peak flood streamflow at Canton was approximately 39,800 ft3/s, corresponding to an estimated recurrence interval of 4.4 years; however, an ice jam downstream from Canton Point on December 18-19 obstructed river flow resulting in a high-water elevation commensurate with an open-water flood approximately equal to a 15-year event. The high water-surface elevations attained during the December 18-19 flood event in Canton were higher than the expected open-water flood water-surface elevations; this verified the assumption that the water-surface elevation was augmented due to the downstream ice jam. The change in slope of the riverbed from upstream of Canton to the impoundment at the downstream corporate limits, and the river bend near Stevens Island are principal factors in ice-jam formation near Canton. The U.S. Army Corps of Engineers Ice Jam Database indicates five ice-jam-related floods (including December 2003) for the town of Canton: March 13, 1936; January 1978; March 12, 1987; January 29, 1996; and December 18-19, 2003. There have been more ice-jam-related flood events in Canton than these five documented events, but the exact number and nature of ice jams in Canton cannot be determined without further research. proprietary @@ -16019,10 +15999,10 @@ USGS_OFR_99_438_1.0 Digital geologic map of part of the Thompson Falls 1:100,000 USGS_OFR_Acid_Deposition Acid Deposition Sensitivity of the Southern Appalachian Assessment Area ALL STAC Catalog 1970-01-01 -87, 31, -77, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231550930-CEOS_EXTRA.umm_json The Acid Deposition Sensitivity of the Southern Appalachian Assessment Area is a project that studies areas having various susceptibilities to acid deposition from air pollution which are designated on a three tier ranking in the region of the Southern Appalachian Assessment (SAA). The assessment is being conducted by Federal agencies that are members of the Southern Appalachian Man and Biosphere (SAMAB) Cooperative. Sensitivities to acid deposition, ranked high, medium, and low are assigned on the basis of bedrock compositions and their associated soils, and their capacities to neutralize acid precipitation. The data is available in Arc/Info export format. [Summary provided by the USGS] proprietary USGS_OFR_Acid_Deposition Acid Deposition Sensitivity of the Southern Appalachian Assessment Area CEOS_EXTRA STAC Catalog 1970-01-01 -87, 31, -77, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2231550930-CEOS_EXTRA.umm_json The Acid Deposition Sensitivity of the Southern Appalachian Assessment Area is a project that studies areas having various susceptibilities to acid deposition from air pollution which are designated on a three tier ranking in the region of the Southern Appalachian Assessment (SAA). The assessment is being conducted by Federal agencies that are members of the Southern Appalachian Man and Biosphere (SAMAB) Cooperative. Sensitivities to acid deposition, ranked high, medium, and low are assigned on the basis of bedrock compositions and their associated soils, and their capacities to neutralize acid precipitation. The data is available in Arc/Info export format. [Summary provided by the USGS] proprietary USGS_OFR_aqbound_1.0 Digital boundaries of the Antlers aquifer in southeastern Oklahoma CEOS_EXTRA STAC Catalog 1992-01-01 1992-12-31 -97.4976, 33.7288, -94.4684, 34.3644 https://cmr.earthdata.nasa.gov/search/concepts/C2231550862-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digitized aquifer boundaries of the Antlers aquifer in southeastern Oklahoma. The Early Cretaceous-age Antlers Sandstone is an important source of water in an area that underlies about 4,400-square miles of all or part of Atoka, Bryan, Carter, Choctaw, Johnston, Love, Marshall, McCurtain, and Pushmataha Counties. The Antlers aquifer consists of sand, clay, conglomerate, and limestone in the outcrop area. The upper part of the Antlers aquifer consists of beds of sand, poorly cemented sandstone, sandy shale, silt, and clay. The Antlers aquifer is unconfined where it outcrops in about an 1,800-square-mile area. The data set includes the outcrop area of the Antlers Sandstone in Oklahoma and areas where the Antlers is overlain by alluvial and terrace deposits and a few small thin outcrops of the Goodland Limestone. Most of the aquifer boundary lines were extracted from published digital geology data sets. Some of the lines were interpolated in areas where the Antlers aquifer is overlain by alluvial and terrace deposits near streams and rivers. The interpolated lines are very similar to the aquifer boundaries published in a ground-water modeling report for the Antlers aquifer. The maps from which this data set was derived were scanned or digitized from maps published at a scale of 1:250,000. This data set is one of four digital map data sets being published together for this aquifer. The four data sets are: aqbound - aquifer boundaries cond - hydraulic conductivity recharg - aquifer recharge wlelev - water-level elevation contours proprietary -USGS_P-11_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province ALL STAC Catalog 1990-12-01 1990-12-01 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 11 (Central Coastal) are listed here by play number, type, and name: Number Type Name 1101 conventional Point Arena Oil 1102 conventional Point Reyes Oil 1103 conventional Pescadero Oil 1104 conventional La Honda Oil 1105 conventional Bitterwater Oil 1106 conventional Salinas Oil 1107 conventional Western Cuyama Basin 1109 conventional Cox Graben proprietary USGS_P-11_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 11 (Central Coastal) are listed here by play number, type, and name: Number Type Name 1101 conventional Point Arena Oil 1102 conventional Point Reyes Oil 1103 conventional Pescadero Oil 1104 conventional La Honda Oil 1105 conventional Bitterwater Oil 1106 conventional Salinas Oil 1107 conventional Western Cuyama Basin 1109 conventional Cox Graben proprietary -USGS_P-11_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province ALL STAC Catalog 1996-01-01 1996-12-31 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231551956-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 11 (Central Coastal) are listed here by play number and name: Number Name 1101 Point Arena Oil 1102 Point Reyes Oil 1103 Pescadero Oil 1104 La Honda Oil 1105 Bitterwater Oil 1106 Salinas Oil 1107 Western Cuyama Basin 1109 Cox Graben proprietary +USGS_P-11_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province ALL STAC Catalog 1990-12-01 1990-12-01 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 11 (Central Coastal) are listed here by play number, type, and name: Number Type Name 1101 conventional Point Arena Oil 1102 conventional Point Reyes Oil 1103 conventional Pescadero Oil 1104 conventional La Honda Oil 1105 conventional Bitterwater Oil 1106 conventional Salinas Oil 1107 conventional Western Cuyama Basin 1109 conventional Cox Graben proprietary USGS_P-11_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231551956-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 11 (Central Coastal) are listed here by play number and name: Number Name 1101 Point Arena Oil 1102 Point Reyes Oil 1103 Pescadero Oil 1104 La Honda Oil 1105 Bitterwater Oil 1106 Salinas Oil 1107 Western Cuyama Basin 1109 Cox Graben proprietary +USGS_P-11_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province ALL STAC Catalog 1996-01-01 1996-12-31 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231551956-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 11 (Central Coastal) are listed here by play number and name: Number Name 1101 Point Arena Oil 1102 Point Reyes Oil 1103 Pescadero Oil 1104 La Honda Oil 1105 Bitterwater Oil 1106 Salinas Oil 1107 Western Cuyama Basin 1109 Cox Graben proprietary USGS_P1650-a_1.0 Atlas of Relations Between Climatic Parameters and Distributions of Important Trees and Shrubs in North America CEOS_EXTRA STAC Catalog 1970-01-01 -170, 20, -80, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552968-CEOS_EXTRA.umm_json This atlas explores the continental-scale relations between the geographic ranges of woody plant species and climate in North America. A 25-km equal-area grid of modern climatic and bioclimatic parameters was constructed from instrumental weather records. The geographic distributions of selected tree and shrub species were digitized, and the presence or absence of each species was determined for each cell on the 25-km grid, thus providing a basis for comparing climatic data and species' distributions. The relations between climate and plant distributions are explored in graphical and tabular form. The results of this effort are primarily intended for use in biogeographic, paleoclimatic, and global-change research. These web pages provide access to the text, digital representations of figures, and supplemental data files from USGS Professional Paper 1650, chapters A and B. A printed set of these volumes can be ordered from the USGS at a cost of US$63.00. To order, please call or write: USGS Information Services Box 25286 Denver Federal Center Denver, CO 80225 Tel: 303-202-4700; Fax: 303-202-4693 [Summary provided by the USGS.] proprietary USGS_PA_DIGIT_1.0 Digital drainage basin boundaries of named streams in Pennsylvania CEOS_EXTRA STAC Catalog 1970-01-01 -76.4304, 39.7151, -74.6865, 42.0007 https://cmr.earthdata.nasa.gov/search/concepts/C2231548560-CEOS_EXTRA.umm_json "In 1989, the Pennsylvania Department of Environmental Resources (PaDER), in cooperation with the U.S. Geological Survey, Water Resources Division (USGS published the Pennsylvania (PA) Gazetteer of Streams. This publication contains information related to named streams in Pennsylvania. Drainage basin boundaries are delineated on the 7.5-minute series topographic paper quadrangle maps for PA and parts of the bordering states of New York, Maryland, Ohio, West Virginia, and Delaware. These boundaries enclose catchment areas for named streams officially recognized by the Board on Geographic Names and other unofficially named streams that flow through named hollows, using the hollow name, e.g. ""Smith Hollow"". This was done in an effort to name as many of the 64,000 streams as possible. In 1991, work began by USGS to put these drainage basin boundaries into digital form for use in a geographic information system (GIS). Digitizing started with USGS in Lemoyne, PA., but expanded with assistance by PaDER and the Natural Resource Conservation Service (NRCS), formerly the U.S Department of Agriculture, Soil Conservation Service (SCS). USGS performed all editing, attributing, and edgematching. There are 878, 7.5-minute quadrangle maps in PA. This documentation applies to only those maps in the Delaware River basin (164). Parts of the Delaware River drainage originate outside the PA border. At this time, no effort is being made by USGS to include those named stream basins. [Summary provided by the USGS.]" proprietary USGS_PONTCHARTRAIN Geologic Framework and Processes of the Lake Pontchartrain Basin CEOS_EXTRA STAC Catalog 1970-01-01 -91, 29, -89, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2231549642-CEOS_EXTRA.umm_json Lake Pontchartrain and adjacent lakes in Louisiana form one of the larger estuaries in the Gulf Coast region. The estuary drains the Pontchartrain Basin (at right), an area of over 12,000 km2 situated on the eastern side of the Mississippi River delta plain. In Louisiana, nearly one-third of the State population lives within the 14 parishes of the basin. Over the past 60 years, rapid growth and development within the basin, along with natural processes, have resulted in significant environmental degradation and loss of critical habitat in and around Lake Pontchartrain. Human activities associated with pollutant discharge and surface drainage have greatly affected the water quality in the lake. This change is evident in the bottom sediments, which record the historic health of the lake. Also, land-altering activities such as logging, dredging, and flood control in and around the lake, lead to shoreline erosion and loss of wetlands.The effects of pollution, shoreline erosion and wetland loss on the lake and surrounding areas have become a major public concern. To better understand the basin's origin and the processes driving its development and degradation requires a wide-ranging study involving many organizations and personnel. When the U.S. Geological Survey began the study of Lake Pontchartrain in 1994, information on four topics was needed: -Geologic Framework, or how the various sedimentary layers that make up the basin are put together -Sediment Characterization, that is, what are the sediments made of, where did they come from, and what kinds of pollutants do they contain -Shoreline and Wetland Change over time -what are the processes that control Water Circulation [Summary provided by the USGS.] proprietary @@ -16035,8 +16015,8 @@ USGS_SESC_ExtinctFish Extinct North American Freshwater Fishes CEOS_EXTRA STAC C USGS_SESC_ImperiledFish American Fisheries Society Imperiled Freshwater and Diadromous Fishes of North America CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551557-CEOS_EXTRA.umm_json About: This website presents the 2008 American Fisheries Society Endangered Species Committee list of imperiled North American freshwater and diadromous fishes. The committee considered continental fishes native to Canada, Mexico, and the United States, evaluated their conservation status and determined the major threats impacting these taxa. We use the terms taxon (singular) or taxa (plural) to include named species, named subspecies, undescribed forms, and distinct populations as characterized by unique morphological, genetic, ecological, or other attributes warranting taxonomic recognition. Undescribed taxa are included, based on the above diagnostic criteria in combination with known geographic distributions and documentation deemed of scientific merit, as evidenced from publication in peer-reviewed literature, conference abstracts, unpublished theses or dissertations, or information provided by recognized taxonomic experts. Although we did not independently evaluate the taxonomic validity of undescribed taxa, the committee adopted a conservative approach to recognize them on the basis of prevailing evidence which suggests that these forms are sufficiently distinct to warrant conservation and management actions. Summary: This is the third compilation of imperiled (i.e., endangered, threatened, vulnerable) plus extinct freshwater and diadromous fishes of North America prepared by the American Fisheries Society's Endangered Species Committee. Since the last revision in 1989, imperilment of inland fishes has increased substantially. This list includes 700 extant taxa representing 133 genera and 36 families, a 92% increase over the 364 listed in 1989. The increase reflects the addition of distinct populations, previously non-imperiled fishes, and recently described or discovered taxa. Approximately 39% of described fish species of the continent are imperiled. There are 230 vulnerable, 190 threatened, and 280 endangered extant taxa; 61 taxa are presumed extinct or extirpated from nature. Of those that were imperiled in 1989, most (89%) are the same or worse in conservation status; only 6% have improved in status, and 5% were delisted for various reasons. Habitat degradation and nonindigenous species are the main threats to at-risk fishes, many of which are restricted to small ranges. Documenting the diversity and status of rare fishes is a critical step in identifying and implementing appropriate actions necessary for their protection and management. Maps: In collaboration with the World Wildlife Fund, the committee developed a map of freshwater ecoregions that combines spatial and faunistic information derived from Maxwell and others (1995), Abell and others (2000; 2008), U.S. Geological Survey Hydrologic Unit Code maps (Watermolen 2002), Atlas of Canada (2003), and Commission for Environmental Cooperation (2007). Eighty ecoregions were identified based on physiography and faunal assemblages of the Atlantic, Arctic, and Pacific basins. Each taxon on the list was assigned to one or more ecoregions that circumscribes its native distribution. A variety of sources were used to obtain distributional information, most notably Lee and others (1980), Hocutt and Wiley (1986), Page and Burr (1991), Behnke (2002), Miller and others (2005), numerous state and provincial fish books for the United States and Canada, and the primary literature, including original taxonomic descriptions. Taxa were also associated with the states or provinces where they naturally occur or occurred in the past. proprietary USGS_SESC_ImperiledFreshwaterOrganisms Imperiled Freshwater Organisms of North America CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231549663-CEOS_EXTRA.umm_json This website provides access to maps and lists of imperiled freshwater organisms of North America as determined by the American Fisheries Society (AFS) Endangered Species Committee (ESC). At this website, one can view lists of animals by freshwater ecoregion, by state or province boundary, and plot distributions of these same creatures by ecoregions or political boundaries. Both the AFS and U.S. Geological Survey (USGS) have a long standing commitment to the advancement of aquatic sciences and sharing that information with the public. Since 1972, the ESC has been tracking the status of imperiled fishes and aquatic invertebrates in North America. Recently, the fish (2008) and crayfish (2007) subcommittees provided revised status lists of at-risk taxa, and the mussel and snail subcommittees are in the process of completing similar revisions. Historically, the revised AFS lists of imperiled fauna have been published in Fisheries. With rapid advances in technology and information transfer, there is a growing need to provide to stakeholders immediate and dynamic data on imperiled resources. The USGS is a leader in aquatic resource research that effectively disseminates results from those studies to the public through print and internet media. A Memorandum Of Understanding formally establishes an agreement between the AFS and USGS to create this website that will serve as a conduit for information exchange about imperiled aquatic organisms of North America. proprietary USGS_SESC_SnailStatus American Fisheries Society List of Freshwater Snails from Canada and the United States CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551686-CEOS_EXTRA.umm_json About: This website presents the 2013 American Fisheries Society Endangered Species Committee list of freshwater snails (Gastropods) of United States and Canada. The committee evaluated the conservation status and determined the major threats impacting these taxa. Summary: This is the first conservation status review for freshwater snails (gastropods) of Canada and the United States by the American Fisheries Society's Endangered Species Freshwater Gastropod Subcommittee. The goals of this contribution are to provide: 1) a current and comprehensive taxonomic authority list for all native freshwater gastropods of Canada and the United States, 2) provincial and state distributions as presently understood, 3) a conservation assessment, and, 4) references on their biology, distribution and conservation. Freshwater gastropods occupy every type of aquatic habitat ranging from subterranean aquifers to brawling montane headwater creeks. Gastropods are ubiquitous invertebrates and frequently dominate aquatic invertebrate biomass. Of the 703 gastropods documented by Johnson et al. (2013), 74% are imperiled or extinct (278 endangered, 102 threatened, 73 vulnerable, and 67 are considered extinct); only 157 species are considered stable. Map queries display species distributions in provinces and states in which they are believed to occur or occurred in the past, but considerable fieldwork is required to determine exact geographic limits of species. We hope this list stimulates a surge in the study of freshwater gastropods. Supporting Literature: Supporting literature for the North American freshwater gastropods assessment are organized alphabetically by state and province, followed by national, regional, and other general references. This literature compilation is not comprehensive, but offers considerable information for individuals interested in freshwater snails. Recovery Examples: Although the gastropod fauna of Canada and the United States is beleaguered by multiple forms of habitat loss, the fauna is resilient and capable of remarkable recovery when suitable habitat is available. Three examples of recovery demonstrate the inherent reviving potential of freshwater gastropods. Images of the incredible diversity of freshwater snails are presented in plates and photo gallery. Maps: Each species on the list was assigned to one or more states or provinces that circumscribe its native distribution. Mapped distributions indicate where taxa naturally occur or occurred in the past. Resources used to obtain distributional information include state and regional publications. proprietary -USGS_SESC_SturgeonBiblio_3 A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552134-CEOS_EXTRA.umm_json "This functional bibliography is meant to be a complete and comprehensive bibliography of all discoverable reports containing information on the Gulf Sturgeon (GS). This bibliography contains all known reports presenting, documenting, summarizing, listing, or interpreting information on the GS through 31 December 2013. Report citations are organized into four sections. Section I includes published scientific journal articles, books, dissertations and theses, published and unpublished technical reports, published harvest prohibitions, and online articles reporting substantive scientific information. Section II includes newspaper, newsletter, magazine, book, agency news releases, and online articles reporting on GS occurrences, mortalities, captures, jumping, boat collisions, aquaculture, historical photographs, and other largely non-scientific or anecdotal issues. Section III consists of books, theses, ecotour-guides, media articles, editorials, and blogs reporting a mix of anecdotal information, historical information, and opinion on GS conservation, habitat issues, exploitation, aquaculture, and human interaction - but presenting very limited or no substantive scientific information. Section IV includes videos, films and audio recordings documenting GS life history and behavior. Each reference includes a bibliographic citation, as well as a brief annotation of key topics in brackets, where possible. The names of journals, theses, dissertations, and books are given in bold within each citation, and relevant page numbers are noted in parentheses at the end of citations, where applicable. Newspaper and magazine article titles are placed within parentheses. Key topic annotations are inserted in bracketed italics on a separate line. If the reference reports GS information under a different common or scientific name (e.g., Atlantic Sturgeon, Common Sturgeon, Sturgeon, Sea Sturgeon, Acipenser oxyrinchus oxyrinchus, Acipenser oxyrhynchus or Acipenser sturio), a notation to that effect is given within the key words annotation line, e.g., [Reported as ""Atlantic sturgeon""]. A small number of reports could not be obtained. These include historical reports from newspapers and magazines long out of circulation. In these limited cases, titles are still provided to substantiate their existence. Other reports that are no longer readily available, but which have been obtained during preparation of this bibliography, have been archived in hardcopy and/or as scanned pdf files at USGS, SESC. Copies of such hard to obtain reports, if non-copyrighted, may be available upon request from USGS corresponding author, via email: mrandall@usgs.gov." proprietary USGS_SESC_SturgeonBiblio_3 A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi. CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552134-CEOS_EXTRA.umm_json "This functional bibliography is meant to be a complete and comprehensive bibliography of all discoverable reports containing information on the Gulf Sturgeon (GS). This bibliography contains all known reports presenting, documenting, summarizing, listing, or interpreting information on the GS through 31 December 2013. Report citations are organized into four sections. Section I includes published scientific journal articles, books, dissertations and theses, published and unpublished technical reports, published harvest prohibitions, and online articles reporting substantive scientific information. Section II includes newspaper, newsletter, magazine, book, agency news releases, and online articles reporting on GS occurrences, mortalities, captures, jumping, boat collisions, aquaculture, historical photographs, and other largely non-scientific or anecdotal issues. Section III consists of books, theses, ecotour-guides, media articles, editorials, and blogs reporting a mix of anecdotal information, historical information, and opinion on GS conservation, habitat issues, exploitation, aquaculture, and human interaction - but presenting very limited or no substantive scientific information. Section IV includes videos, films and audio recordings documenting GS life history and behavior. Each reference includes a bibliographic citation, as well as a brief annotation of key topics in brackets, where possible. The names of journals, theses, dissertations, and books are given in bold within each citation, and relevant page numbers are noted in parentheses at the end of citations, where applicable. Newspaper and magazine article titles are placed within parentheses. Key topic annotations are inserted in bracketed italics on a separate line. If the reference reports GS information under a different common or scientific name (e.g., Atlantic Sturgeon, Common Sturgeon, Sturgeon, Sea Sturgeon, Acipenser oxyrinchus oxyrinchus, Acipenser oxyrhynchus or Acipenser sturio), a notation to that effect is given within the key words annotation line, e.g., [Reported as ""Atlantic sturgeon""]. A small number of reports could not be obtained. These include historical reports from newspapers and magazines long out of circulation. In these limited cases, titles are still provided to substantiate their existence. Other reports that are no longer readily available, but which have been obtained during preparation of this bibliography, have been archived in hardcopy and/or as scanned pdf files at USGS, SESC. Copies of such hard to obtain reports, if non-copyrighted, may be available upon request from USGS corresponding author, via email: mrandall@usgs.gov." proprietary +USGS_SESC_SturgeonBiblio_3 A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552134-CEOS_EXTRA.umm_json "This functional bibliography is meant to be a complete and comprehensive bibliography of all discoverable reports containing information on the Gulf Sturgeon (GS). This bibliography contains all known reports presenting, documenting, summarizing, listing, or interpreting information on the GS through 31 December 2013. Report citations are organized into four sections. Section I includes published scientific journal articles, books, dissertations and theses, published and unpublished technical reports, published harvest prohibitions, and online articles reporting substantive scientific information. Section II includes newspaper, newsletter, magazine, book, agency news releases, and online articles reporting on GS occurrences, mortalities, captures, jumping, boat collisions, aquaculture, historical photographs, and other largely non-scientific or anecdotal issues. Section III consists of books, theses, ecotour-guides, media articles, editorials, and blogs reporting a mix of anecdotal information, historical information, and opinion on GS conservation, habitat issues, exploitation, aquaculture, and human interaction - but presenting very limited or no substantive scientific information. Section IV includes videos, films and audio recordings documenting GS life history and behavior. Each reference includes a bibliographic citation, as well as a brief annotation of key topics in brackets, where possible. The names of journals, theses, dissertations, and books are given in bold within each citation, and relevant page numbers are noted in parentheses at the end of citations, where applicable. Newspaper and magazine article titles are placed within parentheses. Key topic annotations are inserted in bracketed italics on a separate line. If the reference reports GS information under a different common or scientific name (e.g., Atlantic Sturgeon, Common Sturgeon, Sturgeon, Sea Sturgeon, Acipenser oxyrinchus oxyrinchus, Acipenser oxyrhynchus or Acipenser sturio), a notation to that effect is given within the key words annotation line, e.g., [Reported as ""Atlantic sturgeon""]. A small number of reports could not be obtained. These include historical reports from newspapers and magazines long out of circulation. In these limited cases, titles are still provided to substantiate their existence. Other reports that are no longer readily available, but which have been obtained during preparation of this bibliography, have been archived in hardcopy and/or as scanned pdf files at USGS, SESC. Copies of such hard to obtain reports, if non-copyrighted, may be available upon request from USGS corresponding author, via email: mrandall@usgs.gov." proprietary USGS_SIR-5079_MSRiverFloodMaps Development of flood-inundation maps for the Mississippi River in Saint Paul, Minnesota CEOS_EXTRA STAC Catalog 1970-01-01 -93.15028, 44.90479, -92.999855, 44.97016 https://cmr.earthdata.nasa.gov/search/concepts/C2231549022-CEOS_EXTRA.umm_json Digital flood-inundation maps for a 6.3-mile reach of the Mississippi River in Saint Paul, Minnesota, were developed through a multi-agency effort by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers and in collaboration with the National Weather Service. The inundation maps, which can be accessed through the U.S. Geological Survey Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ and the National Weather Service Advanced Hydrologic Prediction Service site at http://water.weather.gov/ahps/inundation.php , depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the U.S. Geological Survey streamgage at the Mississippi River at Saint Paul (05331000). The National Weather Service forecasted peak-stage information at the streamgage may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the Mississippi River by means of a one-dimensional step-backwater model. The hydraulic model was calibrated using the most recent stage-discharge relation at the Robert Street location (rating curve number 38.0) of the Mississippi River at Saint Paul (streamgage 05331000), as well as an approximate water-surface elevation-discharge relation at the Mississippi River at South Saint Paul (U.S. Army Corps of Engineers streamgage SSPM5). The model also was verified against observed high-water marks from the recent 2011 flood event and the water-surface profile from existing flood insurance studies. The hydraulic model was then used to determine 25 water-surface profiles for flood stages at 1-foot intervals ranging from approximately bankfull stage to greater than the highest recorded stage at streamgage 05331000. The simulated water-surface profiles were then combined with a geographic information system digital elevation model, derived from high-resolution topography data, to delineate potential areas flooded and to determine the water depths within the inundated areas for each stage at streamgage 05331000. The availability of these maps along with information regarding current stage at the U.S. Geological Survey streamgage and forecasted stages from the National Weather Service provides enhanced flood warning and visualization of the potential effects of a forecasted flood for the city of Saint Paul and its residents. The maps also can aid in emergency management planning and response activities, such as evacuations and road closures, as well as for post-flood recovery efforts. proprietary USGS_SOFIA_75_29_flows Baseline hydrologic data collection along the I-75 - State Road 29 corridor in the Big Cypress National Preserve CEOS_EXTRA STAC Catalog 2005-11-01 2009-09-30 -81.325, 25.75, -80.75, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549536-CEOS_EXTRA.umm_json The objectives of this study are to develop and continue a program of surface water flow monitoring across I-75 and SR 29 in the I-75 corridor from Snake Road west to SR 29 and SR 29 from I-75 south to USGS site 02291000 Barron River near Everglades, Florida. Quarterly discharge measurements will be made along both reaches to assess hydrologic flow patterns and evaluate the feasibility of creating a stage-discharge/index-velocity relationship for this area. Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area. proprietary USGS_SOFIA_75_29_hydro_data Hydrologic Data Collected along I-75/SR29 corridor in Big Cypress National Preserve CEOS_EXTRA STAC Catalog 2005-11-01 2009-09-30 -81.325, 25.75, -80.75, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549847-CEOS_EXTRA.umm_json The location of each site is shown on a Google Map. Data are available as a Google Map with links to Station Information and Data for each site. Data are available for 58 sites along I-75 and for 28 sites along State Road 29 in Big Cypress National Preserve. Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area. proprietary @@ -16051,8 +16031,8 @@ USGS_SOFIA_CarbonFlux Carbon Flux and Greenhouse Gasses of Restored and Degraded USGS_SOFIA_Ding_Darling_baseline Ding Darling National Wildlife Refuge - Greater Everglades Baseline Information and Response to CERP CEOS_EXTRA STAC Catalog 2009-10-01 2014-09-30 -82.5, 26.3, -81.6, 27 https://cmr.earthdata.nasa.gov/search/concepts/C2231549274-CEOS_EXTRA.umm_json The greater Everglades Restoration program includes a management plan for the C-43 Canal, or Caloosahatchee River. This plan affects the quantity, quality, and timing of freshwater releases at control structure S-79 at Franklin Locks. Freshwater contributions are from Lake Okeechobee, and farming runoff along the canal from Lake Okeechobee to the town of Alva. This study will provide basic information on the effects on the quality of water entering J. N. Ding Darling National Wildlife Refuge as the result of freshwater releases at control structure S-79 proprietary USGS_SOFIA_EDEN_grid_shapefile_v02 EDEN Grid Shapefile CEOS_EXTRA STAC Catalog 1970-01-01 -81.51, 24.7, -79.9, 27.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231549862-CEOS_EXTRA.umm_json This shapefile serves as a net (fishnet or grid) to be placed over the South Florida study area to allow for sampling within the 400 meter cells (grid cells or polygons). The origin and extent of the Everglades Depth Estimation Network (EDEN) grid were selected to cover not only existing Airborne Height Finder (AHF) data and current regions of interest for Everglades restoration, but to cover a rectangular area that includes all landscape units (USACE, 2004) and conservation areas in place at the time of its development. This will allow for future expansion of analyses throughout the Greater Everglades region should resources allow and scientific or management questions require it. Combined with the chosen extent, the 400m cell resolution produces a grid that is 675 rows and 375 columns.. The shapefile contains the 253125 grid cells described above. Some characteristics of this grid, such as the size of its cells, its origin, the area of Florida it is designed to represent, and individual grid cell identifiers, could not be changed once the grid database was developed. These characteristics were selected to design as robust a grid as possible and to ensure the grid’s long-term utility. proprietary USGS_SOFIA_EDEN_proj Everglades Depth Estimation Network (EDEN) CEOS_EXTRA STAC Catalog 1999-01-01 2008-10-28 -81.3, 25, -80.16, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231550596-CEOS_EXTRA.umm_json The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring, ground elevation modeling, and water-surface modeling that provides scientists and managers with current (1999-present), on-line water-depth information for the entire freshwater portion of the Greater Everglades. Presented on a 400-square-meter grid spacing, EDEN offers a consistent and documented dataset that can be used by scientists and managers to:1) guide large-scale field operations, 2) integrate hydrologic and ecological responses, and 3) support biological and ecological assessments that measure ecosystem responses to the implementation of the comprehensive Everglades Restoration plan (CERP) from the U.S. Army Corps of Engineers in 1999. Research has shown that relatively high-resolution data are needed to explicitly represent variations in the Everglades topography and vegetation that are important for landscape analysis and modeling. The EDEN project will provide a better representation of water depths if elevation variation within each 400-meter grid cell can be taken into account. The EDEN network provides a framework to integrate data collected by other agencies in a common quality-assured database. In addition to real-time network, collaboration among agencies will provide the EDEN project with valuable historic vegetation and water-depth data. This is the first time these data have been compiled and analyzed as a collective set. proprietary -USGS_SOFIA_Eco_hist_db_2008_present_2 2008 - Present Ecosystem History of South Florida's Estuaries Database version 2 ALL STAC Catalog 2008-03-16 2012-09-30 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549653-CEOS_EXTRA.umm_json The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - location information. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain basic location information. proprietary USGS_SOFIA_Eco_hist_db_2008_present_2 2008 - Present Ecosystem History of South Florida's Estuaries Database version 2 CEOS_EXTRA STAC Catalog 2008-03-16 2012-09-30 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549653-CEOS_EXTRA.umm_json The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - location information. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain basic location information. proprietary +USGS_SOFIA_Eco_hist_db_2008_present_2 2008 - Present Ecosystem History of South Florida's Estuaries Database version 2 ALL STAC Catalog 2008-03-16 2012-09-30 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549653-CEOS_EXTRA.umm_json The 2008 - Present Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), and modern monitoring site survey information (water chemistry, floral and faunal data, etc.). Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - location information. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain basic location information. proprietary USGS_SOFIA_Ever_hydr_FB_dynam Interrelationships of Everglades Hydrology and Florida Bay Dynamics CEOS_EXTRA STAC Catalog 1850-01-01 2004-12-31 -80.89015, 25.1004, -80.39827, 25.471722 https://cmr.earthdata.nasa.gov/search/concepts/C2231554284-CEOS_EXTRA.umm_json This interdisciplinary synthesis project is designed to identify and document the interrelation of Everglades’ hydrology and tidal dynamics of Florida Bay on ecosystem response to past environmental changes, both natural and human imposed. The project focuses on integrating historical, hydrological, and ecological findings of scientific investigations within the Southern Inland and Coastal System (SICS), which encompasses the transition zone between the wetlands of Taylor Slough and C-111 canal and nearshore embayments of Florida Bay. In the ecological component, hindcast simulations of historical flow events are being developed for ecological analyses. The Across Trophic Level System Simulation (ATLSS) ecological modeling team is collaborating with the SICS hydrologic modeling team to develop the necessary hydrologic inputs for refined indicator species models. The interconnected freshwater wetland and coastal marine ecosystems of south Florida have undergone numerous human disturbances, including the introduction of exotic species and the alteration of wetland hydroperiods, landscape characteristics, and drainage patterns through implementation of the extensive canal and road system and the expansion of agricultural activity. In this project, collaborative efforts are focused on documenting the impact of past hydrological and ecological changes along the southern Everglades interface with Florida Bay by reconstructing past hydroperiods and investigating the correlation of human-imposed and natural impacts on hydrological changes with shifts in biotic species. The primary objectives are to identify the historical effects of past management practices, to integrate refined hydrological and ecological modeling efforts at indicator species levels to identify cause-and-effect relationships, and to produce a report that documents findings that link hydrological and ecological changes to management practices, wherever evident. proprietary USGS_SOFIA_Fbbslmap Florida Bay Bottom Salinity Maps CEOS_EXTRA STAC Catalog 1994-11-01 1996-12-31 -81.167, 24.83, -80.33, 25.33 https://cmr.earthdata.nasa.gov/search/concepts/C2231549334-CEOS_EXTRA.umm_json The maps show the bottom salinity for Florida Bay at 5ppt salinity intervals approximately every other month beginning in November 1994 through December 1996. Recent algal blooms and seagrass mortality have raised concerns about the water quality of Florida Bay, particularly its nutrient content (nitrogen and phosphorous), hypersalinity, and turbidity. Water quality is closely tied to sediment transport processes because resuspension of sediments increases turbidity, releases stored nutrients, and facilitates sediment export to the reef tract. The objective of this research is to provide a better understanding of how and when sediments within Florida Bay are resuspended and deposited, to define the spatial distribution of the potential for resuspension, to delineate patterns of potential bathymetric change, and to predict the impacts of storms or seagrass die-off on bathymetry and circulation within the bay. By combining these results with the findings of other research being conducted in Florida Bay, we hope to quantify sediment export from the bay, better define the nutrient input during resuspension events, and assist in modeling circulation and water quality. Results will enable long-term sediment deposition and erosion in various regions of the bay to be integrated with data on the anticipated sea-level rise to predict future water depths and volumes. Results from this project, together with established sediment production rates, will provide the basis for a sediment budget for Florida Bay. proprietary USGS_SOFIA_Fbbtypes Florida Bay Bottom Types map - USGS_SOFIA_Fbbtypes CEOS_EXTRA STAC Catalog 1996-01-01 1997-01-31 -81.25, 24.75, -80.25, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231553376-CEOS_EXTRA.umm_json The map shows the bottom types for Florida Bay that resulted from site surveys and boat transects (summer 1996-January 1997) compared with aerial photographs (December 1994-January 1995) and SPOT satellite imagery (1987). The purpose of this map is to describe the bottom types found within Florida Bay for use in 1) assessing bottom friction associated with sediment and benthic communities and 2) providing a very general description for other research needs. For these purposes, two descriptors were considered particularly important: density of seagrass cover and sediment texture. Seagrass estimates are visual estimates of the amount of seagrass cover including both number of plants and leaf length. Therefore, seagrass cover may be greater in areas with long leaves than in areas with short blades, even though the number of shoots may be the same. Seagrass cover is a different measure than density (Zieman et al., 1989 or Durako et al., 1996). It is used here to more accurately reflect hydrodynamic influence than the standing crop of seagrass. The use and definitions of dense, intermediate and sparse seagrass cover are similar to those used by Scoffin (1970). This map and associated descriptions are not meant to assess ecologic communities or detail sedimentological facies. The resolution of the map has been selected in an effort to define broad regions for use in modeling efforts. For these purposes, small-scale changes in bottom type (e.g. small seagrass patches) are not delineated. proprietary @@ -16076,12 +16056,12 @@ USGS_SOFIA_MeHg_degrad_rates Methylmercury Degradation Rates CEOS_EXTRA STAC Cat USGS_SOFIA_SF_CIR_DOQs Color Infrared Digital Orthophoto Quadrangles for the South Florida Ecosystem Area CEOS_EXTRA STAC Catalog 1994-01-01 1999-12-31 -82.2, 24.6, -80.1, 27.8 https://cmr.earthdata.nasa.gov/search/concepts/C2231553946-CEOS_EXTRA.umm_json The digital orthophoto quadrangles (DOQ's) produced by the USGS for the South Florida Ecosystem Initiative iare color-infrared, 1-meter ground resolution quadrangle images covering 3.75 minutes of latitude by 3.75 minutes of longitude at a map scale of 12,000. Orthophotos combine the image characteristics of a photograph with the geometric qualities of a map. The primary digital orthophotoquadrangle (DOQ) is a 1-meter ground resolution, quarter-quadrangle (3.75 minutes of latitude by 3.75 minutes of longitude) image cast on the Universal Transverse Mercator projection (UTM) on the North American Datum of 1983 (NAD83). The geographic extent of the DOQ is equivalent to a quarter-quadrangle plus the overedge ranges from a minimum of 50 meters to a maximum of 300 meters beyond the extremes of the primary and secondary corner points. The overedge is included to facilitate tonal matching for mosaicking and for the placement of the NAD83 and secondary datum corner ticks. The normal orientation of data is by lines (rows) and samples (columns). Each line contains a series of pixels ordered from west to east with the order of the lines from north to south. The radiometric image brightness values are stored as 256 gray levels, ranging from 0 to 255. The standard, uncompressed gray scale DOQ format contains an ASCII header followed by a series of 8-bit image data lines. The keyword-based, ASCII header may vary in the number of data entries. The header is affixed to the beginning of the image and is composed of strings of 80 characters with an asterisk (*) as character 79 and an invisible newline character as character 80. Each keyword string contains information for either identification, display, or registration of the image. Additional strings of blanks are added to the header so that the length of a header line equals the number of bytes in a line of image data. The header line will be equal in length to the length of an image line. If the sum of the byte count of the header is less than the sample count of one DOQ image line, then the remainder of the header is padded with the requisite number of 80 character blank entries, each terminated with an asterisk and newline character. The objective of this project was to provide color infrared (CIR) digital orthophoto coverage for the entire south Florida ecosystem area. The main advantage of a digital orthophoto is that it gives a measurable image free of distortion. Therefore, the digital orthophotos for the ecosystem provide multi-use base images for identifying natural and manmade features and for determining their extent and boundaries; the images can also be used for the interpretation and classification of these areas. proprietary USGS_SOFIA_SnailKites_AppleSnails Comprehensive Monitoring Plan for Snail Kites and Apple Snails in the Greater Everglades CEOS_EXTRA STAC Catalog 2010-01-01 2015-12-31 -81.6, 25, -80.6, 27.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231554049-CEOS_EXTRA.umm_json The endangered snail kite (Rostrhamus sociabilis) is a wetland-dependent raptor feeding almost exclusively on a single species of aquatic snail, the Florida apple snail (Pomacea paludosa). The viability of the kite population is dependent on the hydrologic conditions (both short-term and long-term) that (1) maintain sufficient abundances and densities of apple snails, and (2) provide suitable conditions for snail kite foraging and nesting, which include specific vegetative community compositions. Many wetlands comprising its range are no longer sustained by the natural processes under which they evolved (USFWS 1999, RECOVER 2005), and not necessarily characteristic of the historical ecosystems that once supported the kite population (Bennetts and Kitchens 1999, Martin et al. 2008). Natural resource managers currently lack a fully integrative approach to managing hydrology and vegetative communities with respect to the apple snail and snail kite populations. At this point in time the kite population is approximately 1,218 birds (Cattau et al 2012), down from approximately 4000 birds in 1999. It is imperative to improve our understanding hydrological conditions effecting kite reproduction and recruitment. Water Conservation area 3-A, WCA3A, is one of the 'most critical' wetlands comprising the range of the kite in Florida (see Bennetts and Kitchens 1997, Mooij et al. 2002, Martin et al. 2006, 2008). Snail kite reproduction in WCA3A sharply decreased after 1998 (Martin et al. 2008), and alarmingly, no kites were fledged there in 2001, 2005, 2007, or 2008. Bowling (20098) found that juvenile movement probabilities away (emigrating) from WCA3A were significantly higher for the few kites that did fledge there in recent years (i.e. 2003, 2004, 2006) compared to those that fledged there in the 1990s. The paucity of reproduction in and the high probability of juveniles emigrating from WCA3A are likely indicative of habitat degradation (Bowling 20098, Martin et al. 2008), which may stem, at least in part, from a shift in water management regimes (Zweig and Kitchens 2008). Given the recent demographic trends in snail kite population, the need for a comprehensive conservation strategy is imperative; however, information gaps currently preclude our ability to simultaneously manage the hydrology in WCA3A with respect to vegetation, snails, and kites. While there have been significant efforts in filling critical information gaps regarding snail kite demography (e.g., Martin et al. 2008) and variation in apple snail density to water management issues (e.g., Darby et al. 2002, Karunaratne et al. 2006, Darby et al. 2008), there is surprisingly very little information relevant for management that directly links variation in apple snail density with the demography and behavior of snail kites (but see Bennetts et al. 2006). The U.S. Fish and Wildlife Service (USFWS), the U. S. Army Corps of Engineers, and the Florida Fish and Wildlife Conservation Commission (FWC) have increasingly sought information pertaining to the potential effects of specific hydrological management regimes with respect to the apple snail and snail kite populations, as well as the vegetative communities that support them. proprietary USGS_SOFIA_YY_Males Development of YY male technology to control non-native fishes in the Greater Everglades CEOS_EXTRA STAC Catalog 2009-10-01 -81, 25, -80, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231552421-CEOS_EXTRA.umm_json Dozens of non-native fish species have established throughout south Florida (including Everglades National Park, Big Cypress National Preserve, Biscayne National Park and various state and private lands). Thus far, research on these species has focused on documenting their distributions, natural history, and physiological tolerances. Research is beginning to emerge on interactions of native species with non-natives, although it is only in the early stages. Research on control of non-native fishes in South Florida is also lacking, although it is potentially the most important and useful to natural resource managers. At present, the only management techniques available to control non-native fishes are physical removal, dewatering or ichthyocides. Unfortunately, all of these methods negatively impact native fauna as well as the targeted non-native fishes and require a great deal of effort (and therefore, funding). Herein, we propose a research program focused on applying a genetic technique common in aquaculture to control of non-native fishes. This proposal focuses on developing a technique (YY supermales) to control a non-native fish in South Florida (African jewelfish Hemichromis letourneuxi). However, the concept can be applied to a wide variety of species, including other fishes (e.g., brown hoplo Hoplosternum littorale), invasive applesnails (Pomacea spp.), the Australian red claw crayfish (Cherax spp.) and the green mussel (Perna veridis). proprietary -USGS_SOFIA_aerial-photos Aerial Photos of the 1940s ALL STAC Catalog 1940-02-14 1940-08-21 -81.9, 24.41, -79.98, 26.22 https://cmr.earthdata.nasa.gov/search/concepts/C2231554384-CEOS_EXTRA.umm_json The images are available as .jpeg and as georeferenced .tiff files. With the exception of three images, all images are subset to 7500 pixels square. Individual photos can be selected from the 1940 flight lines image at http://sofia.usgs.gov/exchange/aerial-photos/40s_flight.html The numbering scheme for the aerial photos is an identification number consisting of the flight number followed by the photo or frame number. A foundation for Everglades research must include a clear understanding of the pre-drainage south Florida landscape. Knowledge of the spatial organization and structure of pre-drainage landscape communities such as mangrove forests, marshes, sloughs, wet prairies. And pinelands, is essential to provide potential endpoints, restoration goals and performance measures to gauge restoration success. Information contained in historical aerial photographs of the Everglades can aid in this endeavor. The earliest known aerial photographs are from the mid-to-late 1920s and resulted in the production of what are called T-sheets (Topographic sheets) for the coasts and shorelines of far south Florida. The position of the boundary between differing vegetation communities (the ecotone) can be accurately measured. If followed through time, changes in the position of these ecotones could potentially be used to judge effects of drainage on the Everglades ecosystem and to monitor restoration success. The Florida Integrated Science Center (FISC), a center of the U.S. Geological Survey's (USGS) Biological Resources Discipline (BRD), in collaboration with the Eastern Region Geography (ERG) of the Geography Discipline has created digital files of existing 1940 (1:40,000-scale) Black and White aerial photography for the South Florida region. These digital files are available through the SOFIA web site at http://sofia.usgs.gov/exchange/aerial-photos/index.html proprietary USGS_SOFIA_aerial-photos Aerial Photos of the 1940s CEOS_EXTRA STAC Catalog 1940-02-14 1940-08-21 -81.9, 24.41, -79.98, 26.22 https://cmr.earthdata.nasa.gov/search/concepts/C2231554384-CEOS_EXTRA.umm_json The images are available as .jpeg and as georeferenced .tiff files. With the exception of three images, all images are subset to 7500 pixels square. Individual photos can be selected from the 1940 flight lines image at http://sofia.usgs.gov/exchange/aerial-photos/40s_flight.html The numbering scheme for the aerial photos is an identification number consisting of the flight number followed by the photo or frame number. A foundation for Everglades research must include a clear understanding of the pre-drainage south Florida landscape. Knowledge of the spatial organization and structure of pre-drainage landscape communities such as mangrove forests, marshes, sloughs, wet prairies. And pinelands, is essential to provide potential endpoints, restoration goals and performance measures to gauge restoration success. Information contained in historical aerial photographs of the Everglades can aid in this endeavor. The earliest known aerial photographs are from the mid-to-late 1920s and resulted in the production of what are called T-sheets (Topographic sheets) for the coasts and shorelines of far south Florida. The position of the boundary between differing vegetation communities (the ecotone) can be accurately measured. If followed through time, changes in the position of these ecotones could potentially be used to judge effects of drainage on the Everglades ecosystem and to monitor restoration success. The Florida Integrated Science Center (FISC), a center of the U.S. Geological Survey's (USGS) Biological Resources Discipline (BRD), in collaboration with the Eastern Region Geography (ERG) of the Geography Discipline has created digital files of existing 1940 (1:40,000-scale) Black and White aerial photography for the South Florida region. These digital files are available through the SOFIA web site at http://sofia.usgs.gov/exchange/aerial-photos/index.html proprietary +USGS_SOFIA_aerial-photos Aerial Photos of the 1940s ALL STAC Catalog 1940-02-14 1940-08-21 -81.9, 24.41, -79.98, 26.22 https://cmr.earthdata.nasa.gov/search/concepts/C2231554384-CEOS_EXTRA.umm_json The images are available as .jpeg and as georeferenced .tiff files. With the exception of three images, all images are subset to 7500 pixels square. Individual photos can be selected from the 1940 flight lines image at http://sofia.usgs.gov/exchange/aerial-photos/40s_flight.html The numbering scheme for the aerial photos is an identification number consisting of the flight number followed by the photo or frame number. A foundation for Everglades research must include a clear understanding of the pre-drainage south Florida landscape. Knowledge of the spatial organization and structure of pre-drainage landscape communities such as mangrove forests, marshes, sloughs, wet prairies. And pinelands, is essential to provide potential endpoints, restoration goals and performance measures to gauge restoration success. Information contained in historical aerial photographs of the Everglades can aid in this endeavor. The earliest known aerial photographs are from the mid-to-late 1920s and resulted in the production of what are called T-sheets (Topographic sheets) for the coasts and shorelines of far south Florida. The position of the boundary between differing vegetation communities (the ecotone) can be accurately measured. If followed through time, changes in the position of these ecotones could potentially be used to judge effects of drainage on the Everglades ecosystem and to monitor restoration success. The Florida Integrated Science Center (FISC), a center of the U.S. Geological Survey's (USGS) Biological Resources Discipline (BRD), in collaboration with the Eastern Region Geography (ERG) of the Geography Discipline has created digital files of existing 1940 (1:40,000-scale) Black and White aerial photography for the South Florida region. These digital files are available through the SOFIA web site at http://sofia.usgs.gov/exchange/aerial-photos/index.html proprietary USGS_SOFIA_analysis_hist_wq Analysis of Historic Water Quality Data CEOS_EXTRA STAC Catalog 1960-01-01 2005-09-30 -81.55, 25.11, -80.125, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231553759-CEOS_EXTRA.umm_json "The Big Cypress National Preserve (BICY), the Everglades National Park (EVER), and Loxahatchee National Wildlife Refuge (LOX) are water-dominated ecosystems that are susceptible to water-quality impacts. A comprehensive analysis of historical water-quality and ancillary data is needed to direct the restoration of the Everglades and the adoption of water-quality standards in BICY, EVER, and LOX because of their designations as Outstanding Florida Waters. Big Cypress National Preserve (BICY), Everglades National Park (EVER)), and Loxahatchee National Wildlife Refuge (LOX) maintain separate networks of hydrologic monitoring stations (hydrostations) for measuring the stage and quality of surface water throughout their units. The data collected at these sites provides a historical baseline for assessing hydrologic conditions and making a wide range of management decisions (both internally and externally). Surface-water stage data is relatively straight-forward to analyze, both in real time and relative to historic conditions, and has typically been conducted by in-house hydrology staff at both units. Analysis of surface water-quality data is generally regarded as being more complex because of the subtleness of trends, absence of continuous data (bi-monthly for BICY and monthly for EVER), and dependence on surface water depth and season. Collection and analysis of water-quality samples at BICY, EVER, and LOX are done under cooperative agreements with the South Florida Water Management District (SFWMD). Under these agreements, the Park Service collects the samples in the field and the SFWMD provides sampling equipment and laboratory analyses. EVER has been sampling water quality on a monthly basis at 9 ""internal marsh"" stations since 1984 as part of this program. BICY has been sampling water quality on a monthly basis at 10 ""internal"" stations since 1995 as part of this agreement, with water quality data at these sites extending as far back to 1988 (but not as part of the agreement). Water-quality data collected at the BICY and EVER stations has been archived and reported for short-time intervals (yearly and bi-yearly), but an analysis that covers all sampled parameters, extends over the full period of record, and provides comparisons between the two parks has yet to be performed. Water-quality data have been collected at 14 internal marsh sites in LOX by the U.S. Fish and Wildlife Service for over 10 years. These samples have been analyzed by SFWMD laboratory. In 2000, a study was begun by the U.S. Geological Survey to gather, edit, and interpret selected water-quality data from a variety of sources to improve the understanding of changes in water-quality in areas impacted by human activities or in more remote and relatively unimpacted areas of the Everglades and Big Cypress Swamp. One purpose is to look for long-term trends and possibly relate the trends to human or natural influences on water quality such as agriculture, drought, hurricanes, changes in water management, etc. Another purpose is to interpret data from the most remote and unimpacted areas to discern, if possible, what the natural background concentrations are for water-quality constituents that have sufficient data. An attempt will be made to find correlations between available water-quality, physical, and meteorological parameters. Such analyses of water-quality and ancillary data may assist in establishing water-quality standards appropriate for the designation as Outstanding Florida Waters in both the Everglades National Park and the Big Cypress National Preserve. Ancillary data such as precipitation, water-level, water flow, dates of major storms, and beginning and ending dates of water-control effects will be studied to relate their timing to any noticeable changes in water quality. The initial study area was in BICY and EVER; the study area was extended into LOX in 2003." proprietary USGS_SOFIA_asr_data_lake_okee Aquifer Storage and Recovery Data (Lake Okeechobee) CEOS_EXTRA STAC Catalog 1999-08-01 2000-05-31 -81.08, 26.35, -80.28, 27.2 https://cmr.earthdata.nasa.gov/search/concepts/C2231554472-CEOS_EXTRA.umm_json The objective of this project was to determine geochemically significant water-quality characteristics of possible aquifer storage and recovery (ASR) source and receiving waters north of Lake Okeechobee and at a site along the Hillsboro Canal. The data from this study will be combined with similar information on the detailed composition of aquifer materials in ASR receiving zones to develop geochemical models. Such models are needed to evaluate the possible chemical reactions that may change the physical properties of the aquifer matrix and/or the quality of injected water prior to recovery. proprietary -USGS_SOFIA_atlss_prog Across Trophic Level System Simulation (ATLSS) Program CEOS_EXTRA STAC Catalog 1996-01-01 -81.30333, 24.696152, -80.26212, 25.847113 https://cmr.earthdata.nasa.gov/search/concepts/C2231554119-CEOS_EXTRA.umm_json The ATLSS (Across Trophic Level System Simulation) program addresses CERP’s (Comprehensive Everglades Restoration Plan) need for quantitative projections of effects of scenarios on biota of the Greater Everglades and can provide guidance to monitoring in an adaptive assessment framework. It does this through creating a suite of models for selected Everglades biota, which can translate the hydrologic scenarios into effects on habitat and demographic variables of populations. ATLSS is constructed as a multimodel, meaning that it includes a collection of linked models for various physical and biotic systems components of the Greater Everglades. The ATLSS models are all linked through a common framework of vegetative, topographic, and land use maps that allow for the necessary interaction between spatially explicit information on physical processes and the dynamics of organism response across the landscape. Currently, two important new developments are taking place. First the ATLSS models will soon migrate to a Web-based availability, so that they can be run remotely for various hydrologic scenarios and a set of different assumptions. Second, a vegetation succession model is being completed, which will allow projection of changes in vegetation types across the Everglades landscape as a function of changing hydrology, fire frequency, and nutrient loading. An essential component of restoration planning in South Florida has been the development and use of computer simulation models for the major physical processes driving the system, notably models of hydrology incorporating effects of alternative human control systems and non controlled inputs such as rainfall. The USGS’s ATLSS (Across Trophic Level System Simulation) Program utilizes the outputs of such physical system models as inputs to a variety of ecological models that compare the relative impacts of alternative hydrologic scenarios on the biotic components of South Florida. The immediate objective of ATLSS is to provide a rational, scientific basis for ranking the water management scenarios as part of to the planning process for Everglades restoration. The longer term goals of ATLSS are to help achieve a better understanding of components of the Everglades ecosystem, to provide an integrative tool for empirical studies, and to provide a framework monitoring and adaptive management schemes. The ATLSS Program coordinates and integrates the work of modelers and empirical ecologists at many universities and research centers. The ongoing goals in the ATLSS Program have been to produce models capable of projecting and comparing the effects of alternative hydrologic scenarios on various trophic components of the Everglades. The methodology involves: 1) a landscape structure; 2) a high resolution topography to estimate high resolution water depth across the landscape; 3) models to calculate spatially explicit species indices (SESI) for breeding and foraging success measures across the landscape; 4) spatially explicit individual-based (SEIB) computer simulation models of selected species populations; and 5) ability to plug into variety of visualization and evaluation tools to aid model development, validation, and comparison to field data. Included in this are numerous sub-projects for different species, vegetation succession, analysis of alternative approaches to developing high resolution, models which deal with estuarine systems, methods to allow users from a variety of agencies to access and run the models, and methods to enhance the computational efficiency of the simulations. The continuing general objective is to provide a flexible, efficient collection of methods, utilizing the best current science, to evaluate the relative impacts of alternative hydrologic plans on the biotic systems of South Florida. This is done in a spatially-explicit manner which allows different stakeholders to evaluate the impacts based upon their own criteria for the locations and biotic systems under consideration. There are four projects under the ATLSS program: 1. ATLSS Model Use in CERP Evaluations, Model Testing and Extension to Web-Based Interface 2. Development of an Internet Based GIS to Visualize ATLSS Datasets for Resource Managers 3. Spatial Decision Support for Biodiversity and Indicator Species Responses to CERP Project Activities 4. Integrating Wading Bird Empirical Data into a Model of Wading Bird Foraging Success as a Function of Hydrologic Conditions There are several submodels within the ATLSS Project, including: Alligators, Cape Sable Seaside Sparrows, Crayfish, Deer, Fish, Florida Panthers, Hydrology, Snail Kite, Landscape/Vegetation, and Wading Birds. Models currently available are: ATLSS SESI models: Cape sable seaside sparrow breeding potential index (Version 1.1) Snail kite breeding potential index (Version 1.1) Long-legged wading bird foraging condition index (Version 1.1) Short-legged wading bird foraging condition index (Version 1.1) Empirically-based fish biomass index (Version 1.1) White-tailed deer breeding potential index (Version 1.1) American alligator breeding potential index (Version 1.1) Everglades and slough crayfish (Version 1.1) Apple snail SESI model (Version 1.1) Spatially Explicit Demographic Models: Cape sable seaside sparrow demographic model (SIMSPAR - Version 1.3) Snail kite demographic model (EVERKITE - Version 3.1) Alligator demographic model (Version 1.1) Spatially Explicit Functional Group Models: Freshwater fish dynamics (ALFISH - Version 3.1.17) GIS Animal Tracking Tool: Florida panther tracking tool (PANTRACK - Version 1.1) Landscape Models: High Resolution Topography (HRT - Version 1.4.8) Vegetation productivity (HTDAM - Version 1.1) High Resolution Hydrology (HRH - Version 1.4.8) proprietary USGS_SOFIA_atlss_prog Across Trophic Level System Simulation (ATLSS) Program ALL STAC Catalog 1996-01-01 -81.30333, 24.696152, -80.26212, 25.847113 https://cmr.earthdata.nasa.gov/search/concepts/C2231554119-CEOS_EXTRA.umm_json The ATLSS (Across Trophic Level System Simulation) program addresses CERP’s (Comprehensive Everglades Restoration Plan) need for quantitative projections of effects of scenarios on biota of the Greater Everglades and can provide guidance to monitoring in an adaptive assessment framework. It does this through creating a suite of models for selected Everglades biota, which can translate the hydrologic scenarios into effects on habitat and demographic variables of populations. ATLSS is constructed as a multimodel, meaning that it includes a collection of linked models for various physical and biotic systems components of the Greater Everglades. The ATLSS models are all linked through a common framework of vegetative, topographic, and land use maps that allow for the necessary interaction between spatially explicit information on physical processes and the dynamics of organism response across the landscape. Currently, two important new developments are taking place. First the ATLSS models will soon migrate to a Web-based availability, so that they can be run remotely for various hydrologic scenarios and a set of different assumptions. Second, a vegetation succession model is being completed, which will allow projection of changes in vegetation types across the Everglades landscape as a function of changing hydrology, fire frequency, and nutrient loading. An essential component of restoration planning in South Florida has been the development and use of computer simulation models for the major physical processes driving the system, notably models of hydrology incorporating effects of alternative human control systems and non controlled inputs such as rainfall. The USGS’s ATLSS (Across Trophic Level System Simulation) Program utilizes the outputs of such physical system models as inputs to a variety of ecological models that compare the relative impacts of alternative hydrologic scenarios on the biotic components of South Florida. The immediate objective of ATLSS is to provide a rational, scientific basis for ranking the water management scenarios as part of to the planning process for Everglades restoration. The longer term goals of ATLSS are to help achieve a better understanding of components of the Everglades ecosystem, to provide an integrative tool for empirical studies, and to provide a framework monitoring and adaptive management schemes. The ATLSS Program coordinates and integrates the work of modelers and empirical ecologists at many universities and research centers. The ongoing goals in the ATLSS Program have been to produce models capable of projecting and comparing the effects of alternative hydrologic scenarios on various trophic components of the Everglades. The methodology involves: 1) a landscape structure; 2) a high resolution topography to estimate high resolution water depth across the landscape; 3) models to calculate spatially explicit species indices (SESI) for breeding and foraging success measures across the landscape; 4) spatially explicit individual-based (SEIB) computer simulation models of selected species populations; and 5) ability to plug into variety of visualization and evaluation tools to aid model development, validation, and comparison to field data. Included in this are numerous sub-projects for different species, vegetation succession, analysis of alternative approaches to developing high resolution, models which deal with estuarine systems, methods to allow users from a variety of agencies to access and run the models, and methods to enhance the computational efficiency of the simulations. The continuing general objective is to provide a flexible, efficient collection of methods, utilizing the best current science, to evaluate the relative impacts of alternative hydrologic plans on the biotic systems of South Florida. This is done in a spatially-explicit manner which allows different stakeholders to evaluate the impacts based upon their own criteria for the locations and biotic systems under consideration. There are four projects under the ATLSS program: 1. ATLSS Model Use in CERP Evaluations, Model Testing and Extension to Web-Based Interface 2. Development of an Internet Based GIS to Visualize ATLSS Datasets for Resource Managers 3. Spatial Decision Support for Biodiversity and Indicator Species Responses to CERP Project Activities 4. Integrating Wading Bird Empirical Data into a Model of Wading Bird Foraging Success as a Function of Hydrologic Conditions There are several submodels within the ATLSS Project, including: Alligators, Cape Sable Seaside Sparrows, Crayfish, Deer, Fish, Florida Panthers, Hydrology, Snail Kite, Landscape/Vegetation, and Wading Birds. Models currently available are: ATLSS SESI models: Cape sable seaside sparrow breeding potential index (Version 1.1) Snail kite breeding potential index (Version 1.1) Long-legged wading bird foraging condition index (Version 1.1) Short-legged wading bird foraging condition index (Version 1.1) Empirically-based fish biomass index (Version 1.1) White-tailed deer breeding potential index (Version 1.1) American alligator breeding potential index (Version 1.1) Everglades and slough crayfish (Version 1.1) Apple snail SESI model (Version 1.1) Spatially Explicit Demographic Models: Cape sable seaside sparrow demographic model (SIMSPAR - Version 1.3) Snail kite demographic model (EVERKITE - Version 3.1) Alligator demographic model (Version 1.1) Spatially Explicit Functional Group Models: Freshwater fish dynamics (ALFISH - Version 3.1.17) GIS Animal Tracking Tool: Florida panther tracking tool (PANTRACK - Version 1.1) Landscape Models: High Resolution Topography (HRT - Version 1.4.8) Vegetation productivity (HTDAM - Version 1.1) High Resolution Hydrology (HRH - Version 1.4.8) proprietary +USGS_SOFIA_atlss_prog Across Trophic Level System Simulation (ATLSS) Program CEOS_EXTRA STAC Catalog 1996-01-01 -81.30333, 24.696152, -80.26212, 25.847113 https://cmr.earthdata.nasa.gov/search/concepts/C2231554119-CEOS_EXTRA.umm_json The ATLSS (Across Trophic Level System Simulation) program addresses CERP’s (Comprehensive Everglades Restoration Plan) need for quantitative projections of effects of scenarios on biota of the Greater Everglades and can provide guidance to monitoring in an adaptive assessment framework. It does this through creating a suite of models for selected Everglades biota, which can translate the hydrologic scenarios into effects on habitat and demographic variables of populations. ATLSS is constructed as a multimodel, meaning that it includes a collection of linked models for various physical and biotic systems components of the Greater Everglades. The ATLSS models are all linked through a common framework of vegetative, topographic, and land use maps that allow for the necessary interaction between spatially explicit information on physical processes and the dynamics of organism response across the landscape. Currently, two important new developments are taking place. First the ATLSS models will soon migrate to a Web-based availability, so that they can be run remotely for various hydrologic scenarios and a set of different assumptions. Second, a vegetation succession model is being completed, which will allow projection of changes in vegetation types across the Everglades landscape as a function of changing hydrology, fire frequency, and nutrient loading. An essential component of restoration planning in South Florida has been the development and use of computer simulation models for the major physical processes driving the system, notably models of hydrology incorporating effects of alternative human control systems and non controlled inputs such as rainfall. The USGS’s ATLSS (Across Trophic Level System Simulation) Program utilizes the outputs of such physical system models as inputs to a variety of ecological models that compare the relative impacts of alternative hydrologic scenarios on the biotic components of South Florida. The immediate objective of ATLSS is to provide a rational, scientific basis for ranking the water management scenarios as part of to the planning process for Everglades restoration. The longer term goals of ATLSS are to help achieve a better understanding of components of the Everglades ecosystem, to provide an integrative tool for empirical studies, and to provide a framework monitoring and adaptive management schemes. The ATLSS Program coordinates and integrates the work of modelers and empirical ecologists at many universities and research centers. The ongoing goals in the ATLSS Program have been to produce models capable of projecting and comparing the effects of alternative hydrologic scenarios on various trophic components of the Everglades. The methodology involves: 1) a landscape structure; 2) a high resolution topography to estimate high resolution water depth across the landscape; 3) models to calculate spatially explicit species indices (SESI) for breeding and foraging success measures across the landscape; 4) spatially explicit individual-based (SEIB) computer simulation models of selected species populations; and 5) ability to plug into variety of visualization and evaluation tools to aid model development, validation, and comparison to field data. Included in this are numerous sub-projects for different species, vegetation succession, analysis of alternative approaches to developing high resolution, models which deal with estuarine systems, methods to allow users from a variety of agencies to access and run the models, and methods to enhance the computational efficiency of the simulations. The continuing general objective is to provide a flexible, efficient collection of methods, utilizing the best current science, to evaluate the relative impacts of alternative hydrologic plans on the biotic systems of South Florida. This is done in a spatially-explicit manner which allows different stakeholders to evaluate the impacts based upon their own criteria for the locations and biotic systems under consideration. There are four projects under the ATLSS program: 1. ATLSS Model Use in CERP Evaluations, Model Testing and Extension to Web-Based Interface 2. Development of an Internet Based GIS to Visualize ATLSS Datasets for Resource Managers 3. Spatial Decision Support for Biodiversity and Indicator Species Responses to CERP Project Activities 4. Integrating Wading Bird Empirical Data into a Model of Wading Bird Foraging Success as a Function of Hydrologic Conditions There are several submodels within the ATLSS Project, including: Alligators, Cape Sable Seaside Sparrows, Crayfish, Deer, Fish, Florida Panthers, Hydrology, Snail Kite, Landscape/Vegetation, and Wading Birds. Models currently available are: ATLSS SESI models: Cape sable seaside sparrow breeding potential index (Version 1.1) Snail kite breeding potential index (Version 1.1) Long-legged wading bird foraging condition index (Version 1.1) Short-legged wading bird foraging condition index (Version 1.1) Empirically-based fish biomass index (Version 1.1) White-tailed deer breeding potential index (Version 1.1) American alligator breeding potential index (Version 1.1) Everglades and slough crayfish (Version 1.1) Apple snail SESI model (Version 1.1) Spatially Explicit Demographic Models: Cape sable seaside sparrow demographic model (SIMSPAR - Version 1.3) Snail kite demographic model (EVERKITE - Version 3.1) Alligator demographic model (Version 1.1) Spatially Explicit Functional Group Models: Freshwater fish dynamics (ALFISH - Version 3.1.17) GIS Animal Tracking Tool: Florida panther tracking tool (PANTRACK - Version 1.1) Landscape Models: High Resolution Topography (HRT - Version 1.4.8) Vegetation productivity (HTDAM - Version 1.1) High Resolution Hydrology (HRH - Version 1.4.8) proprietary USGS_SOFIA_avian_ecology_spoonbills Avian Ecology of the Greater Everglades (Roseate Spoonbill and Limpkins) CEOS_EXTRA STAC Catalog 2002-10-01 2005-09-30 -81.25, 24.875, -80.375, 25.375 https://cmr.earthdata.nasa.gov/search/concepts/C2231549705-CEOS_EXTRA.umm_json "The primary objectives of our research are to (1) quantify the changes in spatial distribution and success of nesting spoonbills relative to hydrologic patterns, (2) test hypotheses about the causal mechanisms for observed changes, (3) establish a science-based criteria for nesting distribution and success to be used as a performance measure for hydrologic restoration, and (4) estimate demographic parameters. To meet these objectives, we will use a combined field/modeling approach. Based on previous and concurrent research, hypothesized relationships between hydrology, fish populations, and spoonbill nesting distribution and success will be expressed in a simple, but spatially explicit, conceptual model. Field data will be collected and compared with predicted responses to monitor changes in spoonbill nesting as hydrologic restoration is implemented, and to test the hypothesized mechanisms for observed changes. Variation of hydrologic conditions among years and locations is a virtual certainty; thus we will treat this variation in a quasi-experimental framework where the variation in wet and dry season conditions constitutes a series of ""natural experiments"". Our project is designed to evaluate the effect of hydrologic restoration on the nesting distribution and success of Roseate Spoonbills (Ajaia ajaia) in Florida Bay and surrounding mangrove estuarine habitats. This project is further designed to test hypotheses about the causal mechanisms of observed changes. The Everglades ecosystem has suffered extensive degradation over the past century, including an 85-90% decrease in the numbers of wading birds. Previous monitoring of Roseate Spoonbills in Florida Bay over the past 50 years has shown that this species responds markedly to changes in hydrology and corresponding changes in prey abundance and availability. Shifts in nesting distribution and declines in nest success have been attributed to declines in prey populations as a direct result of water management. Consequently, the re-establishment of spoonbill colonies in northeast Florida Bay is one change predicted under a conceptual model of the mangrove estuarine transition zone of Florida Bay. Changes in nesting distribution and success will further be used as a performance measure for success of restoration efforts and will be incorporated in a model linking mangrove fish populations and spoonbills to alternative hydrologic scenarios." proprietary USGS_SOFIA_ba_geologic_data Biscayne Aquifer geologic data CEOS_EXTRA STAC Catalog 1998-01-01 2005-12-31 -80.6, 25.5, -80.3, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231550961-CEOS_EXTRA.umm_json This report from which the data is taken identifies and characterizes candidate ground-water flow zones in the upper part of the shallow, eogenetic karst limestone of the Biscayne aquifer using GPR, cyclostratigraphy, borehole geophysical logs, continuously drilled cores, and paleontology. About 60 mi of GPR profiles were acquired and are used to calculate the depth to shallow geologic contacts and hydrogeologic units, image karst features, and produce a qualitative perspective of the porosity distribution within the upper part of the karstic Biscayne aquifer in the Lake Belt area of north-central Miami-Dade County. . Descriptions of lithology, rock fabric, cyclostratigraphy, and depositional environments of 50 test coreholes were linked to geophysical data to provide a more refined hydrogeologic framework for the upper part of the Biscayne aquifer. Interpretation of depositional environments was constrained by analysis of depositional textures and molluscan and benthic foraminiferal paleontology. Digital borehole images were used to help quantify large-scale vuggy porosity. Preliminary heat-pulse flowmeter data were coupled with the digital borehole image data to identify potential ground-water flow zones. The objectives of this cooperative project were to identify and characterize candidate ground-water flow zones in the upper part of the shallow, eogenetic karst limestone of the Biscayne aquifer using ground-penetrating radar, cyclostratigraphy, borehole geophysical logs, continuously drilled cores and paleontology. In 1998, the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District (SFWMD), initiated a study to provide a regional-scale hydrogeologic framework of a shallow semiconfining unit within the Biscayne aquifer of southeastern Florida. Initially, the primary objective was to characterize and delineate a low-permeability zone in the upper part of the Biscayne aquifer that spans the base of the Miami Limestone and uppermost part of the Fort Thompson Formation. Delineation of this zone was to aid development of a conceptual hydrogeologic model to be used as input into the SFWMD Lake Belt ground-water model. The approximate area encompassed by the conceptual hydrogeologic model is shown as the study area at http://sofia.usgs.gov/exchange/cunningham/bbwelllocation.html. Subsequent analysis of the preliminary data suggested hydraulic compartmentalization occurred within the Biscayne aquifer, and that there was a need to characterize and delineate ground-water flow zones and relatively low-permeability zones within the upper part of the Biscayne aquifer. Consequently, preliminary results suggested that the historical understanding of the porosity and preferential pathways for Biscayne aquifer ground-water flow required considerable revision. This project was carried out in cooperation with the South Florida Water Management District (SFWMD). proprietary USGS_SOFIA_bbcw_geophysical Biscayne Bay Coastal Wetlands Geophysical Data CEOS_EXTRA STAC Catalog 2004-01-01 -80.4, 25.4, -80.3, 25.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231549059-CEOS_EXTRA.umm_json The objectives of this data acquisition project were to complete the downhole geophysical logging including video and flowmeter logging of two core holes (9A and 11A), which are the deepest wells at monitor well sites 0009AB and 0011AB. The goal of the Comprehensive Everglades Restoration Plan Biscayne Bay Coastal Wetlands Project (BBCWP) is to rehydrate wetlands and reduce point-source discharge to Biscayne Bay. The BBCWP will replace lost overland flow and partially compensate for the reduction in ground-water seepage by redistributing, through a spreader system, available surface water entering the area from regional canals. The proposed redistribution of freshwater flow across a broad front is expected to restore or enhance freshwater wetlands, tidal wetlands, and near shore bay habitat. A critical component of the BBCWP is the development of a realistic representation of ground-water flow within the karst Biscayne aquifer. Mapping these ground-water flow units is key to the development of models that simulate ground-water flow from the Everglades and urban areas through the coastal wetlands to Biscayne Bay. Because there is little detailed hydrogeologic data of the Surficial aquifer (to depth) in this area, the Biscayne Bay Coastal Wetlands Project Delivery Team installed two monitor-well sites and collected the necessary detailed hydrogeologic data. The L-31 North Canal Seepage Management Pilot Project is intended to curtail easterly seepage emanating from within Everglades National Park (ENP). The pilot project is examining various seepage management technologies as well as operational changes that could be implemented to reduce the water losses from ENP. This project is in close proximity to Biscayne Bay so an effort has been made to combine ongoing work efforts at the two project areas. The distribution of seepage into the L-31 North Canal and beneath it is not known with any degree of certainty today. A canal draw down experiment was conducted to provide additional field data that will be utilized to refine seepage estimates in the study area as well as determine aquifer parameters in the study area. This project was funded by the USGS Florida Integrated Science Center and the South Florida Water Management District (SFWMD). proprietary @@ -16098,8 +16078,8 @@ USGS_SOFIA_chron_isotope_geochem_FL_Keys Chronology and Isotope Geochemistry of USGS_SOFIA_coastal_ever_tjslll_04 Coastal Everglades Wetlands: Hydrology, Vegetation and Sediment Dynamics CEOS_EXTRA STAC Catalog 2002-10-01 2009-12-31 -81.75, 25, -80.25, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231550711-CEOS_EXTRA.umm_json This project has three objectives (tasks): 1) operate and maintain the Mangrove Hydrology sampling network; 2) study the dynamics of coastal vegetation (mangroves, marshes) in relation to sea-level, fire, disturbance and restoration; and, 3) measure rates of sediment surface elevation change and soil accretion or loss in coastal mangrove forests and brackish marshes of the Everglades and determine how sediment elevation varies in relation to hydrology (i.e. the restoration). The objective of this project is to conduct integrated studies to develop an understanding of how hydrologic parameters, disturbance, sediment, and global change (e.g. sea level) influence ecological systems in coastal wetlands. Hydrological factors studied include surface and groundwater stage and conductivity, surface water flow, nutrient concentration and suspended sediment. Fire, freeze, hurricanes and lightning strikes are among the disturbances that are important in coastal wetlands. Sediment elevation changes in coastal wetlands as a function of plant growth and decomposition, accretion or erosion due to tides and surface water flows, fire (in freshwater peats) and hurricanes. Both positive and negative feedbacks on sediment elevation have been discovered. Sea level has increased almost 30cm in the past century. The influence of continued sea level rise on CERP for restoring coastal areas is unknown at present. These questions have been addressed by the development of an integrated network of sampling and measurement sites where instrumentation is collocated. Many sites have surface and ground water sampling wells, sediment elevations tables and permanent vegetation plots. Transects, with both permanent plots and hydrology sampling wells, have been established across the mangrove - marsh ecotone to examine the influence of hydrology and fires (both partly controllable), freezes and sea level (not manageable) on the position of the ecotone. proprietary USGS_SOFIA_coastal_grads Coastal Gradients of Flow, Salinity, and Nutrients CEOS_EXTRA STAC Catalog 2003-01-01 2010-12-31 -81.125, 25.08, -80.08, 25.67 https://cmr.earthdata.nasa.gov/search/concepts/C2231552103-CEOS_EXTRA.umm_json Ten monitoring stations will be operated and maintained along the southwest coast of ENP, the Everglades wetlands, and along the coastlines of northeastern Florida Bay and northwest Barnes Sound. Data collected at these 10 stations will include water level, velocity, salinity, and temperature. Three stations (Upstream North River, North River, and West Highway Creek) will also include automatic samplers for the collection of water samples and determination of Total Nutrients (TN and TP). These 10 stations will complement information currently being generated through an existing network of 20 hydrologic monitoring stations of on-going USGS projects. By combining data collected from the ten monitoring stations and the existing monitoring network, information will be available across 9 generalized coastal gradients or transects. Data collected at all flow sites will be transmitted in near real time (every 1 or 4 hours) by way of satellite telemetry to the automated data processing system (ADAPS) database in the USGS Center for Water and Restoration Studies (CWRS) in Miami and available for CERP purposes. In addition to data from monitoring stations described above, salinity surveys will be performed along these 9 generalized transects, and these will include salinity, temperature, and GPS data from boat-mounted systems. Surveys will be performed regularly on a quarterly basis and twice following hydrologic events, totaling a maximum of 6 surveys per year. The Water Resources Development Act (WRDA) of 2000 authorized the Comprehensive Everglades Restoration Plan (CERP) as a framework for modifications and operational changes to the Central and Southern Florida Project needed to restore the south Florida ecosystem. Provisions within WRDA 2000 provide for specific authorization for an adaptive assessment and monitoring program. A Monitoring and Assessment Plan (MAP) has been developed as the primary tool to assess the system-wide performance of the CERP by the REstoration, COordination and VERification (RECOVER) program. The MAP presents the monitoring and supporting enhancement of scientific information and technology needed to measure the responses of the South Florida ecosystem. The MAP also presents the system-wide performance measures representative of the natural and human systems found in South Florida that will be evaluated to help determine the success of CERP. These system-wide performance measures address the responses of the South Florida ecosystem that the CERP is explicitly designed to improve, correct, or otherwise directly affect. A separate Performance Measure Documentation Report being prepared by RECOVER provides the scientific, technical, and legal basis for the performance measures. This project is intended to support the Greater Everglades (GE) Wetlands module of the MAP and is directly linked to the monitoring or supporting enhancement component In 2003, CERP MAP funding through the South Florida Water Management District established 10 monitoring stations as part of the Coastal Gradients Network. The purpose of this MAP project with the USACE is to continue operation of these 10 stations for the MAP activities. proprietary USGS_SOFIA_coastal_grads_salsurveys Coastal Gradients Salinity Surveys CEOS_EXTRA STAC Catalog 2003-12-11 -81, 25.16, -80.38, 25.57 https://cmr.earthdata.nasa.gov/search/concepts/C2231553403-CEOS_EXTRA.umm_json Ten monitoring stations were operated and maintained along the southwest coast of ENP, the Everglades wetlands, and along the coastlines of northeastern Florida Bay and northwest Barnes Sound. Data collected at these 10 stations includes water level, velocity, salinity, and temperature. These 10 stations will complement information currently being generated through an existing network of 20 hydrologic monitoring stations of on-going USGS projects. The Water Resources Development Act (WRDA) of 2000 authorized the Comprehensive Everglades Restoration Plan (CERP) as a framework for modifications and operational changes to the Central and Southern Florida Project needed to restore the south Florida ecosystem. Provisions within WRDA 2000 provide for specific authorization for an adaptive assessment and monitoring program. A Monitoring and Assessment Plan (MAP) has been developed as the primary tool to assess the system-wide performance of the CERP by the REstoration, COordination and VERification (RECOVER) program. The MAP presents the monitoring and supporting enhancement of scientific information and technology needed to measure the responses of the South Florida ecosystem. The MAP also presents the system-wide performance measures representative of the natural and human systems found in South Florida that will be evaluated to help determine the success of CERP. These system-wide performance measures address the responses of the South Florida ecosystem that the CERP is explicitly designed to improve, correct, or otherwise directly affect. A separate Performance Measure Documentation Report being prepared by RECOVER provides the scientific, technical, and legal basis for the performance measures. This project is intended to support the Greater Everglades (GE) Wetlands module of the MAP and is directly linked to the monitoring or supporting enhancement component In 2003, CERP MAP funding through the South Florida Water Management District established 10 monitoring stations as part of the Coastal Gradients Network. The purpose of this MAP project with the USACE is to continue operation of these 10 stations for the MAP activities. proprietary -USGS_SOFIA_coupled_sw-gw_model A Coupled Surface Water and Ground-Water Model to Simulate Past, Present, and Future Hydrologic Conditions in DOI Managed Lands ALL STAC Catalog 1995-01-01 2009-09-30 -81.56, 25.02, -80, 25.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231553820-CEOS_EXTRA.umm_json This project has two objectives: 1) update and reconfigure the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) modeling code to include all version modifications and enhancements in order to provide easier transition for coupling of models and 2) to develop a comprehensive model by using the established USGS Tides and Inflows to the Mangrove Ecotone (TIME) model application of the southern Everglades and linking it to a coupled surface and ground water model application of Biscayne Bay that is currently in development. The Comprehensive Everglades Restoration Plan (CERP) aims to reestablish predevelopment natural flows in the Everglades system and surrounding areas including Biscayne Bay. The changes proposed within this plan may cause significant alterations to the hydrologic conditions that exist in both Everglades National Park (ENP) and Biscayne National Park (BNP). System-wide, there are water management, water supply, and environmental concerns regarding the impact of wetland restoration on groundwater flow between the ENP and BNP and along the L-31 and C-111 canals. For example, restoration of wetlands may lead to increases in coastal ground-water levels and cause offshore springs in Biscayne Bay to become reestablished as a significant site of freshwater discharge in BNP. Accordingly, the CERP restoration activities may increase the rate of coastal groundwater discharge and aid transport of anthropogenic contaminants into the offshore marine ecosystem. Under this scenario, there is significant potential for habitat deterioration of many different threatened or endangered species of plants and animals that reside along the coastline of Biscayne Bay, in the Bay, or on the coral reef tract. In contrast to a surface water system which has been extensively compartmentalized and channelized, the Biscayne aquifer which flows under both ENP and BNP is continuous and not as amenable to partial domain simulation. A comprehensive model is needed to reliably and credibly assess the effects of groundwater flow and transport on both parks. Hydrologic conditions should be evaluated prior to substantial water delivery changes in order to protect these sensitive ecosystems. A numerical model that can simulate salinity and surface and ground-water flow patterns under different hydrologic conditions is an essential part of this effort. The USGS developed a coupled surface-water/ground-water numerical code known as the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) to represent the surface water and ground-water hydrologic conditions in south Florida, specifically in the Everglades. The FTLOADDS code combines the two-dimensional hydrodynamic surface-water model SWIFT2D to simulate variable density overland flow (Schaffranek, 2004; Swain, 2005), the three-dimensional ground-water model SEAWAT to simulate fully-saturated variable-density groundwater flow (Guo and Langevin, 2002), and accounts for leakage and salt flux between the surface water and ground water (Langevin and others, 2005). The code was then applied to two major testing regions: 1) the Southern Inland and Coastal Systems (SICS) model domain (Swain and others, 2004) and 2) the Tides and Inflows in the Mangroves of the Everglades (TIME) model domain. The first application used code versions 1.0 and 1.1 which only utilized the SWIFT2D surface-water code. Later applications in the SICS area used version 2.1 (Langevin and others, 2005) where SWIFT2D was coupled to the SEAWAT groundwater model code. The second domain, TIME (Wang and others, 2007), utilizes the enhanced version 2.2 code, which includes enhancements to the wetting and drying routines, changes to the frictional resistance terms applications, and calculations of evapotranspiration. In 2006, FTLOADDS was modified again to represent Biscayne Bay and surrounding areas. This will provide one large sub-regional model that will give an integrated comprehensive assessment of how different scenarios will affect water flows in both Everglades National Park and Biscayne National Park. Once calibrated, additional simulations will be performed to estimate predevelopment hydrologic conditions and to predict hydrologic conditions under one or more of the proposed restoration alternatives, using inputs from the Natural Systems Model (NSM) (SFWMD, 1997A) and the South Florida Water Management Model (SFWMM) (MacVicar and others, 1984, SFWMD, 1997B). proprietary USGS_SOFIA_coupled_sw-gw_model A Coupled Surface Water and Ground-Water Model to Simulate Past, Present, and Future Hydrologic Conditions in DOI Managed Lands CEOS_EXTRA STAC Catalog 1995-01-01 2009-09-30 -81.56, 25.02, -80, 25.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231553820-CEOS_EXTRA.umm_json This project has two objectives: 1) update and reconfigure the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) modeling code to include all version modifications and enhancements in order to provide easier transition for coupling of models and 2) to develop a comprehensive model by using the established USGS Tides and Inflows to the Mangrove Ecotone (TIME) model application of the southern Everglades and linking it to a coupled surface and ground water model application of Biscayne Bay that is currently in development. The Comprehensive Everglades Restoration Plan (CERP) aims to reestablish predevelopment natural flows in the Everglades system and surrounding areas including Biscayne Bay. The changes proposed within this plan may cause significant alterations to the hydrologic conditions that exist in both Everglades National Park (ENP) and Biscayne National Park (BNP). System-wide, there are water management, water supply, and environmental concerns regarding the impact of wetland restoration on groundwater flow between the ENP and BNP and along the L-31 and C-111 canals. For example, restoration of wetlands may lead to increases in coastal ground-water levels and cause offshore springs in Biscayne Bay to become reestablished as a significant site of freshwater discharge in BNP. Accordingly, the CERP restoration activities may increase the rate of coastal groundwater discharge and aid transport of anthropogenic contaminants into the offshore marine ecosystem. Under this scenario, there is significant potential for habitat deterioration of many different threatened or endangered species of plants and animals that reside along the coastline of Biscayne Bay, in the Bay, or on the coral reef tract. In contrast to a surface water system which has been extensively compartmentalized and channelized, the Biscayne aquifer which flows under both ENP and BNP is continuous and not as amenable to partial domain simulation. A comprehensive model is needed to reliably and credibly assess the effects of groundwater flow and transport on both parks. Hydrologic conditions should be evaluated prior to substantial water delivery changes in order to protect these sensitive ecosystems. A numerical model that can simulate salinity and surface and ground-water flow patterns under different hydrologic conditions is an essential part of this effort. The USGS developed a coupled surface-water/ground-water numerical code known as the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) to represent the surface water and ground-water hydrologic conditions in south Florida, specifically in the Everglades. The FTLOADDS code combines the two-dimensional hydrodynamic surface-water model SWIFT2D to simulate variable density overland flow (Schaffranek, 2004; Swain, 2005), the three-dimensional ground-water model SEAWAT to simulate fully-saturated variable-density groundwater flow (Guo and Langevin, 2002), and accounts for leakage and salt flux between the surface water and ground water (Langevin and others, 2005). The code was then applied to two major testing regions: 1) the Southern Inland and Coastal Systems (SICS) model domain (Swain and others, 2004) and 2) the Tides and Inflows in the Mangroves of the Everglades (TIME) model domain. The first application used code versions 1.0 and 1.1 which only utilized the SWIFT2D surface-water code. Later applications in the SICS area used version 2.1 (Langevin and others, 2005) where SWIFT2D was coupled to the SEAWAT groundwater model code. The second domain, TIME (Wang and others, 2007), utilizes the enhanced version 2.2 code, which includes enhancements to the wetting and drying routines, changes to the frictional resistance terms applications, and calculations of evapotranspiration. In 2006, FTLOADDS was modified again to represent Biscayne Bay and surrounding areas. This will provide one large sub-regional model that will give an integrated comprehensive assessment of how different scenarios will affect water flows in both Everglades National Park and Biscayne National Park. Once calibrated, additional simulations will be performed to estimate predevelopment hydrologic conditions and to predict hydrologic conditions under one or more of the proposed restoration alternatives, using inputs from the Natural Systems Model (NSM) (SFWMD, 1997A) and the South Florida Water Management Model (SFWMM) (MacVicar and others, 1984, SFWMD, 1997B). proprietary +USGS_SOFIA_coupled_sw-gw_model A Coupled Surface Water and Ground-Water Model to Simulate Past, Present, and Future Hydrologic Conditions in DOI Managed Lands ALL STAC Catalog 1995-01-01 2009-09-30 -81.56, 25.02, -80, 25.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231553820-CEOS_EXTRA.umm_json This project has two objectives: 1) update and reconfigure the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) modeling code to include all version modifications and enhancements in order to provide easier transition for coupling of models and 2) to develop a comprehensive model by using the established USGS Tides and Inflows to the Mangrove Ecotone (TIME) model application of the southern Everglades and linking it to a coupled surface and ground water model application of Biscayne Bay that is currently in development. The Comprehensive Everglades Restoration Plan (CERP) aims to reestablish predevelopment natural flows in the Everglades system and surrounding areas including Biscayne Bay. The changes proposed within this plan may cause significant alterations to the hydrologic conditions that exist in both Everglades National Park (ENP) and Biscayne National Park (BNP). System-wide, there are water management, water supply, and environmental concerns regarding the impact of wetland restoration on groundwater flow between the ENP and BNP and along the L-31 and C-111 canals. For example, restoration of wetlands may lead to increases in coastal ground-water levels and cause offshore springs in Biscayne Bay to become reestablished as a significant site of freshwater discharge in BNP. Accordingly, the CERP restoration activities may increase the rate of coastal groundwater discharge and aid transport of anthropogenic contaminants into the offshore marine ecosystem. Under this scenario, there is significant potential for habitat deterioration of many different threatened or endangered species of plants and animals that reside along the coastline of Biscayne Bay, in the Bay, or on the coral reef tract. In contrast to a surface water system which has been extensively compartmentalized and channelized, the Biscayne aquifer which flows under both ENP and BNP is continuous and not as amenable to partial domain simulation. A comprehensive model is needed to reliably and credibly assess the effects of groundwater flow and transport on both parks. Hydrologic conditions should be evaluated prior to substantial water delivery changes in order to protect these sensitive ecosystems. A numerical model that can simulate salinity and surface and ground-water flow patterns under different hydrologic conditions is an essential part of this effort. The USGS developed a coupled surface-water/ground-water numerical code known as the Flow and Transport in a Linked Overland/Aquifer Density-Dependent System (FTLOADDS) to represent the surface water and ground-water hydrologic conditions in south Florida, specifically in the Everglades. The FTLOADDS code combines the two-dimensional hydrodynamic surface-water model SWIFT2D to simulate variable density overland flow (Schaffranek, 2004; Swain, 2005), the three-dimensional ground-water model SEAWAT to simulate fully-saturated variable-density groundwater flow (Guo and Langevin, 2002), and accounts for leakage and salt flux between the surface water and ground water (Langevin and others, 2005). The code was then applied to two major testing regions: 1) the Southern Inland and Coastal Systems (SICS) model domain (Swain and others, 2004) and 2) the Tides and Inflows in the Mangroves of the Everglades (TIME) model domain. The first application used code versions 1.0 and 1.1 which only utilized the SWIFT2D surface-water code. Later applications in the SICS area used version 2.1 (Langevin and others, 2005) where SWIFT2D was coupled to the SEAWAT groundwater model code. The second domain, TIME (Wang and others, 2007), utilizes the enhanced version 2.2 code, which includes enhancements to the wetting and drying routines, changes to the frictional resistance terms applications, and calculations of evapotranspiration. In 2006, FTLOADDS was modified again to represent Biscayne Bay and surrounding areas. This will provide one large sub-regional model that will give an integrated comprehensive assessment of how different scenarios will affect water flows in both Everglades National Park and Biscayne National Park. Once calibrated, additional simulations will be performed to estimate predevelopment hydrologic conditions and to predict hydrologic conditions under one or more of the proposed restoration alternatives, using inputs from the Natural Systems Model (NSM) (SFWMD, 1997A) and the South Florida Water Management Model (SFWMM) (MacVicar and others, 1984, SFWMD, 1997B). proprietary USGS_SOFIA_dade_biscayne_limit_west_arc Approximate Western Limit of the Biscayne Aquifer in Dade County, USGS WRIR 90-4108, figure 16 CEOS_EXTRA STAC Catalog 1939-01-01 1985-12-31 -80.874054, 25.422379, -80.652664, 25.98292 https://cmr.earthdata.nasa.gov/search/concepts/C2231550143-CEOS_EXTRA.umm_json The map shows the approxiamte western limit of the Biscayne aquifer in Miami-Dade County. Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system. proprietary USGS_SOFIA_dade_config_base_biscayne_arc Configuration of the Base of the Biscayne Aquifer in Dade County, USGS WRIR 90-4108, figure 16 CEOS_EXTRA STAC Catalog 1939-01-01 1985-12-31 -80.858925, 25.187017, -80.11909, 25.986544 https://cmr.earthdata.nasa.gov/search/concepts/C2231549896-CEOS_EXTRA.umm_json The map shows the altitude below sea level of the base of the Biscayne aquifer in Miami-Dade County. The contour interval is 10 feet. Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system. proprietary USGS_SOFIA_dade_config_base_glime_arc Configuration of the Base of the Gray Limestone Aquifer in Dade County, Fl, USGS WRIR 90-4108, figure 15 CEOS_EXTRA STAC Catalog 1939-01-01 1985-12-31 -80.85567, 25.2942, -80.331, 25.994343 https://cmr.earthdata.nasa.gov/search/concepts/C2231554187-CEOS_EXTRA.umm_json Contours of the altitude below sea level of the base of the highly permeable gray limestone aquifer in the Tamiami Formation are shown in this map. The aquifer, as mapped, includes all intervals of the gray limestone that are at least 10 ft. thick and have an estimated hydraulic conductivity of at least 100ft/d. The contour interval is 10 feet. Southeastern Florida is underlain by geologic units of varying permeability from land surface to depths between 150 and 400 ft. These units form an unconfined aquifer system that is the source of most of the potable water used in the area. This body of geologic units is called the surficial aquifer system. In parts of Dade, Broward, and Palm Beach Counties, a highly permeable part of that aquifer system has been named the Biscayne aquifer (Parker, 1951; Parker and others, 1955). Adjacent to or underlying the Biscayne aquifer are less-permeable but potentially important water-bearing units that also are part of the surficial aquifer system. Most previous hydrogeologic investigations in southeastern Florida concentrated on the populated coastal area. Drilling and monitoring activities were commonly restricted to zones used for water supply or to overlying zones. Hence, information on the characteristics of the western or deeper parts of the Biscayne aquifer and of sediments below the Biscayne aquifer in the surficial aquifer system was insufficient for present needs. Continuing increases in the demand for water from the surficial aquifer system in the highly populated coastal area of southeastern Florida and attendant concerns for the protection and management of the water supply have resulted in a study by the U.S. Geological Survey (USGS), in cooperation with the South Florida Water Management District, to define the extent of the surficial aquifer system and its regional hydrogeologic characteristics. The overall objectives of the regional study are to determine the geologic framework of the surficial aquifer system, the areal and vertical water-quality distribution, factors that affect water quality, the hydraulic characteristics of the components of the surficial aquifer system, and to describe ground-water flow in the aquifer system. proprietary @@ -16109,8 +16089,8 @@ USGS_SOFIA_dawmet Ecosystem History: Terrestrial and Fresh-Water Ecosystems of s USGS_SOFIA_discharge_tamiami_canal Discharge Data (Tamiami Canal) CEOS_EXTRA STAC Catalog 1986-01-01 2001-12-31 -81.5, 25.75, -80.5, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231553115-CEOS_EXTRA.umm_json The data are from the following four stations: Station 02288800 - Tamiami Canal Outlets, Monroe to Carnestown; Station 02288900 - Tamiami Canal Outlets, 40-Mile Bend to Monroe, near Miami, FL; Station 02289040 - Tamiami Canal Outlets, Levee 67A to 40-Mile Bend, near Miami, FL; Station 02289060 - Tamiami Canal Outlets, Levee 30 to Levee 67A, near Miami, FL. The data were compiled from records from 1986 to 1999 in the USGS Ft. Lauderdale, FL office of the Water Resources Discipline in 2000. Each station has numerous individual flow measurements at gages that were used in the calculation of the mean flow for each station. The data were collected by USGS personnel and the gages are maintained and operated by USGS Ft. Lauderdale office personnel. Canals are a major water-delivery component of the south Florida ecosystem. They interact with surrounding flow systems and waterbodies, either directly through structure discharges and levee overflows or indirectly through levee seepage and leakage, and thereby quantitatively affect wetland hydroperiods as well as estuarine salinities. Knowledge of this flow interaction, as well as timing, extent, and duration of inundation that it contributes to, is needed to identify and eliminate any potential adverse effects of altered flow conditions and transported constituents on vegetation and biota. Comprehensive analytical tools and methods are needed to assess the effects of nutrient and contaminant loads from agricultural and urban run-off entering canals and thereby conveyed into connected wetlands and other adjoining coastal ecosystems. These data from the individual gages were transferred to electronic form to provide a better understanding of the distribution of flow from north to south under the Tamiami Trail to aid in decisions about future changes to flow along the Trail. proprietary USGS_SOFIA_dk_merc_cycl_bio Mercury Cycling and Bioaccumulation CEOS_EXTRA STAC Catalog 2000-10-01 2006-12-31 -81.33137, 24.67165, -80.22201, 25.890877 https://cmr.earthdata.nasa.gov/search/concepts/C2231550667-CEOS_EXTRA.umm_json This proposal identifies work elements that are logical extensions, and which build off, our previous work. Our overall scientific objective is to provide a complete understanding of the external factors (such as atmospheric mercury and sulfate runoff loads) and internal factors (such as hydroperiod maintenance and water chemistry) that result in the formation and bioaccumulation of MeHg in south Florida ecosystems, and to conduct this research is such a way that it will be directly useable by land and water resource managers. More specifically, we will seek to achieve the following subobjectives (1) Extend our mesocosms studies to provide a more omprehensive examination of the newly discovered 'new versus old' mercury effect by conducting studies under differing hydrologic conditions and sub-ecosystem settings so that our experimental results will be more generally applicable to the greater south Florida ecosystem including the STA’s that have been recently constructed and are yielding very high levels of methylmercury but the cause is currently unknown; (2) Seek to further identify the mechanisms that result in extremely high levels of MeHg after natural drying and rewetting cycles in the Everglades and which have major implications for the Restoration Plan; (3) Further our studies on the production of methylmercury in south Florida estuaries and tidal marshes by conducting mass-balance studies of tidal marshes; (4) Begin to partner with wildlife toxicologists funded by the State of Florida to unravel the complexities surrounding methylmercury exposure and effects to higher order wildlife in south Florida; and , (5) Continue to participate with mercury ecosystem modelers who are funded by the State of Florida and the USEPA to evaluate the overall ecological effects of reducing mercury emissions and the risks associated with methylmercury exposure. Although ecological impacts from phosphorous contamination have become synonymous with water quality in south Florida, especially for Everglades restoration, there are several other contaminants presently entering the Everglades that may be of equal or greater impact, including: pesticides, herbicides, polycyclic aromatic hydrocarbons, and trace metals. This project focuses on mercury, a sparingly soluble trace metal that is principally derived from atmospheric sources and affects the entire south Florida ecosystem. Mercury interacts with another south Florida contaminant, sulfur, that is derived from agricultural runoff, and results in a problem with potentially serious toxicological impacts for all the aquatic food webs (marine and freshwater) in the south Florida ecosystem. The scientific focus of this project is to examine the complex interactions of these contaminants (synergistic and antagonistic), ecosystem responses to variations in contaminant loading (time and space dimensions), and how imminent ecosystem restoration steps may affect existing contaminant pools. The Everglades restoration program is prescribing ecosystem-wide changes to some of the physical, hydrological and chemical components of this ecosystem. However, it remains uncertain what overall effects will occur as these components react to the perturbations (especially the biological and chemical components) and toward what type of 'new ecosystem' the Everglades will evolve. The approaches used by this study have been purposefully chosen to yield results that should be directly useable by land management and restoration decision makers. Presently, we are addressing several major questions surrounding the mercury research field, and the Everglades Restoration program: (l) What, if any, ecological benefit to the Everglades would be realized if mercury emissions reductions would be enacted, and over what time scales (years or tens of years) would improvements be realized? (2) What is the role of old mercury (previously deposited and residing in soils and sediment) versus new mercury (recent deposition) in fueling the mercury problem? (3) In the present condition, is controlling sulfur or mercury inputs more important for reducing the mercury problem in the Everglades? (4) Does sulfur loading have any additional ecological impacts that have not been realized previously (e.g., toxicity to plant and animals) that may be contributing to an overall decreased ecological health? (5) Commercial fisheries in the Florida Bay are contaminated with mercury, is this mercury derived from Everglades runoff or atmospheric runoff? (6) What is the precise role of carbon (the third member of the 'methylmercury axis of evil', along with sulfur and mercury), and do we have to be concerned with high levels of natural carbon mobilization from agricultural runoff as well? (7) Hundreds of millions of dollars are being, or have been spent, on STA construction to reduce phosphorus loading to the Everglades, however, recently constructed STAs have yielded the highest known concentration of toxic methylmercury; can STA operations be altered to reduce methylmercury production and maintain a high level of phosphorus retention over extended periods of time? The centerpiece of our research continues to be the use of environmental chambers (enclosures or mesocosms), inside which we conduct dosing experiments using sulfate, dissolved organic carbon and mercury isotopic tracers. The goal of the mesocosm experiments is to quantify the in situ ecological response to our chemical dosing, and to also determine the ecosystem recovery time to the doses. proprietary USGS_SOFIA_eco_assess_risk_toxics Ecological Risk Assessment of Toxic Substances in the Greater Everglades Ecosystem: Wildlife Effects and Exposure Assessment CEOS_EXTRA STAC Catalog 2000-10-01 2004-09-30 -81.125, 25.125, -80.125, 26.75 https://cmr.earthdata.nasa.gov/search/concepts/C2231551985-CEOS_EXTRA.umm_json This project will be carried out in several locations throughout those areas critical to the South Florida Restoration Initiative. These areas include: 1) Water Conservation Areas 1, 2, and 3 of the Central Everglades, 2) Everglades National Park, 3) Loxahatchee National Wildlife Refuge, 4) Big Cypress National Preserve, 5) multiple Miami Metropolitan area canals and drainages, and 6) restoration related STA’s (STA’s 1-6) adjacent to the Everglades. Specific site selections will be based upon consideration of USACE restoration plans and upon discussions with other place-based and CESI approved projects. The overall objectives are characterize the exposure of wildlife to contaminants within the aquatic ecosystems of South Florida, through a multi-stage process: a) screening of biota to identify hazards/contaminants posing risk, and b) evaluation of the potential effects of those contaminants on appropriate animal/wildlife receptors. This project will focus upon each of these stages/needs, with an emphasis on understanding the effects of contaminants on alligators, fishes, birds, amphibians and macroinvertebrates. Historically, little consideration has been given to environmental chemical stressors/contaminants within the ecosystem restoration efforts for the Greater Everglades Ecosystem. The restoration is primarily guided by determining and restoring the historical relationships between ecosystem function and hydrology. The restoration plan was formulated to restore the natural hydrology and therefore, the resultant landscape patterns, bio-diversity and wildlife abundance. However, additional efforts need to consider the role that chemical contaminants such as pesticides and other inorganic/organic contaminants play in the structure and function of the resultant South Florida ecosystems. Indeed, the current level of agriculture and expanding urbanization and development necessitate that more emphasis be placed on chemical contaminants within this sensitive ecosystem and the current restoration efforts. The primary goal of the proposed project, therefore, is to develop an improved understanding of the exposure/fate (i.e. degradation, metabolism, dissipation, accumulation and transport) and potential ecological effects produced as a result of chemical stressors and their interactions in South Florida freshwater and wetland ecosystems. The overall objectives are to evaluate the risk posed by contaminants to biota within the aquatic ecosystems of South Florida, through a multi-stage process: a) screening of biota to identify hazards/contaminants posing risk and b) evaluation of the potential effects of those contaminants on appropriate animal/wildlife receptors. This project will focus upon each of these stages/needs, with an emphasis on understanding the effects of contaminants on alligators, fishes, birds, amphibians and macroinvertebrates. The specific objectives of this project are to: 1. Assess current exposure and potential adverse effects for appropriate receptors/species within the South Florida ecosystems with some emphasis on DOI trust species. These efforts will determine whether natural populations are significantly exposed to a variety of chemical stressors/contaminants, such as mercury, chlorinated hydrocarbon pesticides, historic and/or current use agricultural chemicals, and/or mixtures, as well as document lethal and non-lethal adverse effects in multiple health, physiologic and/or endocrine endpoints. 2. Assess exposure and potential adverse effects for appropriate species within South Florida as a function of restoration implementation. proprietary -USGS_SOFIA_eco_hist_db1995-2007_version 7 1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7 ALL STAC Catalog 1994-09-27 2007-04-03 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231554288-CEOS_EXTRA.umm_json The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, etc.), and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain either percent abundance data or actual counts of the distribution of mollusks, ostracodes, forams, and pollen within the cores collected in the estuaries. For some cores dinocyst or diatom data may be available. proprietary USGS_SOFIA_eco_hist_db1995-2007_version 7 1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7 CEOS_EXTRA STAC Catalog 1994-09-27 2007-04-03 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231554288-CEOS_EXTRA.umm_json The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, etc.), and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain either percent abundance data or actual counts of the distribution of mollusks, ostracodes, forams, and pollen within the cores collected in the estuaries. For some cores dinocyst or diatom data may be available. proprietary +USGS_SOFIA_eco_hist_db1995-2007_version 7 1995 - 2007 Ecosystem History of South Florida's Estuaries Database version 7 ALL STAC Catalog 1994-09-27 2007-04-03 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231554288-CEOS_EXTRA.umm_json The 1995 - 2007 Ecosystem History of South Florida's Estuaries Database contains listings of all sites (modern and core), modern monitoring site survey information (water chemistry, floral and faunal data, etc.), and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Data are available for modern sites and cores in the general areas of Florida Bay, Biscayne Bay, and the southwest (Florida) coastal mangrove estuaries. Specific sites in the Florida Bay area include Taylor Creek, Bob Allen Key, Russell Bank, Pass Key, Whipray Basin, Rankin Bight, park Key, and Mud Creek core). Specific Biscayne Bay sites include Manatee Bay, Featherbed Bank, Card bank, No Name Bank, Middle Key, Black Point North, and Chicken Key. Sites on the southwest coast include Alligator Bay, Big Lostmans Bay, Broad River Bay, Roberts River mouth, Tarpon Bay, Lostmans River First and Second Bays, Harney River, Shark River near entrance to Ponce de Leon Bay, and Shark River channels. Modern field data contains (1) general information about the site, description, latitude and longitude, date of data collection, (2) water chemistry information, and (3) descriptive text of fauna and flora observed at the site. Core data contain either percent abundance data or actual counts of the distribution of mollusks, ostracodes, forams, and pollen within the cores collected in the estuaries. For some cores dinocyst or diatom data may be available. proprietary USGS_SOFIA_eco_hist_db_version 3 Ecosystem History of South Florida Estuaries Data CEOS_EXTRA STAC Catalog 1994-02-24 2008-03-20 -81.83, 24.75, -80, 26.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552167-CEOS_EXTRA.umm_json The Ecosystem History Access Database contains listings of all sites (modern and core), modern monitoring site survey information, and published core data. Two general types of data are contained within this database: 1) Modern Field Data and 2) Core data - primarily faunal assemblages. Scientists over the past few decades have noticed that the South Florida ecosystem has become increasingly stressed. The purposes of the ecosystem history projects (started in 1995) are to determine what south Florida's estuaries have looked like over time, how they have changed, and what is the rate and frequency of change. To accomplish this, shallow sediment cores are collected within the bays, and the faunal and floral remains, sediment geochemistry, and shell biochemistry are analyzed. Modern field data are collected from the same region as the cores and serve as proxies to allow accurate interpretation of past depositional environments. The USGS South Florida Ecosystem History Project is designed to integrate studies from a number of researchers compiling data from terrestrial, marine, and freshwater ecosystems within south Florida. The project is divided into 3 regions: Biscayne Bay and the Southeast coast, Florida Bay and the Southwest coast, and Terrestrial and Freshwater Ecosystems of Southern Florida. The purpose of the projects is to provide information about the ecosystem's recent history based on analyses of paleontology, geochemistry, hydrology, and sedimentology of cores taken from the south Florida region. Data generated from the South Florida Ecosystem History project will be integrated to provide biotic reconstructions for the area at selected time slices and will be useful in testing ecological models designed to predict floral and faunal response to changes in environmental parameters. Biscayne Bay is of interest to scientists because of the rapid urbanization that has occurred in the Miami area and includes Biscayne National Park. Dredging, propeller scars, and changes in freshwater input have altered parts of Biscayne Bay. Currently, the main freshwater input to Biscayne Bay is through the canal system, but many scientists believe subsurface springs used to introduce fresh groundwater into the Bay ecosystem. Study of the modern environment and core sediments from Biscayne Bay will provide important information on past salinity and seagrass coverage which will be useful for predicting future change within the Bay. Plant and animal communities in the South Florida ecosystem have undergone striking changes over the past few decades. In particular, Florida Bay has been plagued by seagrass die-offs, algal blooms, and declining sponge and shellfish populations. These alterations in the ecosystem have traditionally been attributed to human activities and development in the region. Scientists at the U.S. Geological Survey (USGS) are studying the paleoecological changes taking place in Florida Bay in hopes of understanding the physical environment to aid in the restoration process. As in Biscayne Bay, scientists must first determine which changes are part of the natural variation in Florida Bay and which resulted from human activities. To answer this question, scientists are studying both modern samples and piston cores that reveal changes over the past 150-600 years. These two types of data complement each other by providing information about the current state of the Bay, changes that occurred over time, and patterns of change. Terrestrial ecosystems of South Florida have undergone numerous human disturbances, ranging from alteration of the hydroperiod, fire history, and drainage patterns through implementation of the canal system to expansion of the agricultural activity to the introduction of exotic species such as Melalueca, Australian pine, and the Pepper Tree. Over historical time, dramatic changes in the ecosystem have been documented and these changes attributed to various human activities. However, cause-and-effect relationships between specific biotic and environmental changes have not been established scientifically. One part of the South Florida Ecosystem History group of project is designed to document changes in the terrestrial ecosystem quantitatively, to date any changes and determine whether they resulted from documented human activities, and to establish the baseline level of variability in the South Florida ecosystem to estimate whether the observed changes are greater than what would occur naturally. Specific goals of this part of the project are to 1) document the patterns of floral and faunal changes at sites throughout southern Florida over the last 150 years, 2) determine whether the changes occurred throughout the region or whether they were localized, 3) examine the floral and faunal history of the region over the last few millennia, 4) determine the baseline level of variability in the communities prior to significant human activity in the region, and 5) determine whether the fire frequency, extent, and influence can be quantified, and if so, document the fire history for sites in the region. proprietary USGS_SOFIA_eco_hist_swcoast_srs_04 Ecosystem History of the Southwest Coast-Shark River Slough Outflow Area CEOS_EXTRA STAC Catalog 2003-10-01 2008-09-30 -81.75, 25, -80.83, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231554376-CEOS_EXTRA.umm_json The objectives of this project are to document impacts of changes in salinity, water quality, coastal plant and animal communities and other critical ecosystem parameters on a subdecadal-centennial scale in the southwest coastal region (from Whitewater Bay, north to the 10,000 Islands), and to correlate these changes with natural events and resource management practices. Emphasis will be placed on 1) determining the amount, timing and sources of freshwater influx (groundwater vs. runoff) into the coastal ecosystem prior to and since significant anthropogenic alteration of flow; and 2) determining whether the rate of mangrove and brackish marsh migration inland has increased since 20th century water diversion and what role sealevel rise might play in the migration. First, the environmental preferences and distributions of modern fauna and flora are established through analyses of modern samples in south Florida estuaries and coastal systems. Much of these data have already been obtained through project work conducted in Florida Bay and the terrestrial Everglades starting in 1995. These modern data are used as proxies for interpreting the historical data from Pb-210 and C-14 dated sediment cores based on assemblage analysis. On the basis of USGS data obtained from cores in Florida Bay and Biscayne Bay, the temporal span of the cores should be at a minimum the last 150 years; this is in agreement with University of Miami data showing sedimentation rates in Whitewater Bay to be approximately 1cm/year. For the estuarine/coastal ecosystems, a multidisciplinary, multiproxy approach will be utilized on cores from a transect from Whitewater Bay north to 10,000 Islands. Biochemical analyses of shells and chemical analyses of sediments will be used to refine data on salinity and nutrient supply, and isotopic analyses of shells will determine sources of water influx into the system. Nutrient analyses will be conducted to determine historical patterns of nutrient influx. To examine the inland migration of the mangrove/coastal marsh ecotone, transects from the mouth of the Shark and Harney Rivers inland into Shark River slough will be taken. These cores will be evaluated for floral remains, nutrients, charcoal, and if present, faunal remains. This project will provide 1) baseline data for restoration managers and hydrologic modelers on the amount and sources of freshwater influx into the southwest coastal zone and the quality of the water, 2) the relative position of the coastal marsh-mangrove ecotone at different periods in the past, and 3) data to test probabilities of system response to restoration changes. One of the primary goals of the Central Everglades Restoration Plan (CERP) is to restore the natural flow of water through the terrestrial Everglades and into the coastal zones. Historically, Shark River Slough, which flows through the central portion of the Everglades southwestward, was the primary flow path through the Everglades Ecosystem. However, this flow has been dramatically reduced over the last century as construction of canals, water conservation areas and the Tamiami Trail either retained or diverted flow from Shark River Slough. The reduction in flow and changes in water quality through Shark River have had a profound effect on the freshwater marshes and the associated coastal ecosystems. Additionally, the flow reduction may have shifted the balance of fresh to salt-water inflow along coastal zones, resulting in an acceleration of the rate of inland migration of mangroves into the freshwater marshes. For successful restoration to occur, it is critical to understand how CERP and the natural patterns of freshwater flow, precipitation, and sea level rise will affect the future maintenance of the mangrove-freshwater marsh ecotone and the coastal environment. proprietary USGS_SOFIA_eden_dem_cm_nov07_nc Everglades Depth Estimation Network (EDEN) November 2007 Digital Elevation Model for use with EDENapps CEOS_EXTRA STAC Catalog 1995-01-01 2007-12-31 -81.36353, 25.229605, -80.22176, 26.683613 https://cmr.earthdata.nasa.gov/search/concepts/C2231551925-CEOS_EXTRA.umm_json This is the 1st release of the third version of an Everglades Depth Estimation Network (EDEN) digital elevation model (DEM) generated from certified airborne height finder (AHF) and airboat collected ground surface elevations for the Greater Everglades Region. This version includes all data collected and certified by the USGS prior to the conclusion of the AHF collection process. It differs from the previous elevation model (EDEN_EM_JAN07) in that the modeled area of WCA3N (all the WCA3A area north of I-75) is increased while the modeled area of the Big Cypress National Preserve (BNCP) has been both refined and reduced to the region where standard error of cross-validation points falls below 0.16 meters. EDEN offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to Comprehensive Everglades Restoration Plan. To produce historic and near-real time maps of water depths, the EDEN requires a system-wide DEM of the ground surface. This file is a modification of the eden dem released in October of 2007 (i.e., eden_em_oct07) in which the elevation values have been converted from meters (m) to centimeters(cm) for use by EDEN applications software. This file is intended specifically for use in the EDEN applications software. Aside from this difference in horizontal units, the following documentation applies. These data were specifically created for the development of water depth information using interpolated water surfaces from the EDEN stage data network. proprietary @@ -16248,8 +16228,8 @@ USGS_cir89_Version 1.0 Color-infrared composite of Landsat data for the Sarcobat USGS_cira92_Version 1.0 Color-infrared composite of Landsat data for the Death Valley regional flow system, Nevada and California, 1992 CEOS_EXTRA STAC Catalog 1992-06-01 1992-06-13 -117.550385, 35.378323, -115.251015, 37.653557 https://cmr.earthdata.nasa.gov/search/concepts/C2231551442-CEOS_EXTRA.umm_json "This data set was created to determine phreatophyte boundaries for use in the report, ""Ground-water discharge determined from estimates of evapotranspiration, Death Valley regional flow system, Nevada and California"". The raster-based, color-infrared composite was derived from Landsat Thematic Mapper imagery data acquired during June 1992 for the Death Valley regional flow system. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite. The data set was used in determining phreatophyte boundaries for a ground-water evapotranspiration study. The raster-based, color-infrared composite (CIR) was derived from Landsat Thematic Mapper (TM) imagery data acquired during June 1992 for the Death Valley ground-water flow system, Nevada and California. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite (Beverley and Penton, 1989). TM channels 2, 3, and 4 are used in the classification process. The wavelengths of these channels correspond to those used for a CIR composite. The data range of each channel is divided into eight divisions. The 512 possible combinations are then reduced to 256. A color table of red, green, and blue values is created for display of the image. Sixteen possible color values exist for each color. These values are scaled between 0 and 255. The image is reduced from more than 16 million colors to 256 colors." proprietary USGS_cont1992 1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.652695, 34.364513, -116.55357, 35.081955 https://cmr.earthdata.nasa.gov/search/concepts/C2231553864-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River Basin. The U.S. Geological Survey, in cooperation with the Mojave Water Agency, constructed a water-table map of the Mojave River ground-water basin for ground-water levels measured in November 1992. Water-level data were collected from approximately 300 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,200 to 1,600 feet above sea level. [Summary provided by the USGS.] proprietary USGS_cont1992 1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.652695, 34.364513, -116.55357, 35.081955 https://cmr.earthdata.nasa.gov/search/concepts/C2231553864-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River Basin. The U.S. Geological Survey, in cooperation with the Mojave Water Agency, constructed a water-table map of the Mojave River ground-water basin for ground-water levels measured in November 1992. Water-level data were collected from approximately 300 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,200 to 1,600 feet above sea level. [Summary provided by the USGS.] proprietary -USGS_cont1994 1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.07194, 34.095333, -115.98976, 34.64026 https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.] proprietary USGS_cont1994 1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.07194, 34.095333, -115.98976, 34.64026 https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.] proprietary +USGS_cont1994 1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.07194, 34.095333, -115.98976, 34.64026 https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.] proprietary USGS_cont1996 1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.63461, 34.109745, -115.98707, 35.31552 https://cmr.earthdata.nasa.gov/search/concepts/C2231555091-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins. The U.S. Geological Survey constructed a water-table map of the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins for ground-water levels measured during the period January-September 1996. Water-level data were collected from 632 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:175,512. The contour interval ranges from 3,400 to 1,550 feet above sea level. [Summary provided by the USGS.] proprietary USGS_cont1996 1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.63461, 34.109745, -115.98707, 35.31552 https://cmr.earthdata.nasa.gov/search/concepts/C2231555091-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins. The U.S. Geological Survey constructed a water-table map of the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins for ground-water levels measured during the period January-September 1996. Water-level data were collected from 632 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:175,512. The contour interval ranges from 3,400 to 1,550 feet above sea level. [Summary provided by the USGS.] proprietary USGS_erf1_Version 1.2, August 01, 1999 ERF1 -- Enhanced River Reach File 1.2 CEOS_EXTRA STAC Catalog 1999-01-07 1999-01-07 -127.8169, 23.247017, -65.55541, 48.19323 https://cmr.earthdata.nasa.gov/search/concepts/C2231552175-CEOS_EXTRA.umm_json ERF1 was designed to be a digital data base of river reaches capable of supporting regional and national water-quality and river-flow modeling and transport investigations in the water-resources community. ERF1 has been recently used at the U.S. Geological Survey to support interpretations of stream water-quality monitoring network data (see Alexander and others, 1996; Smith and others, 1995). In these analyses, the reach network has been used to determine flow pathways between the sources of point and nonpoint pollutants (e.g., fertilizer use, municipal wastewater discharges) and downstream water-quality monitoring locations in support of predictive water-quality models of stream nutrient transport. The digital data set ERF1 includes enhancements to the U.S. Environmental Protection Agency's River Reach File 1 (RF1)to ensure the hydrologic integrity of the digital reach traces and to quantify the time of travel of river reaches and reservoirs [see U.S.EPA (1996) for a description of the original RF1]. Any use of trade, product, or firm names is for descriptive proprietary @@ -16316,8 +16296,8 @@ USM_pCO2_0 University of Southern Mississippi (USM) - partial pressure of carbon US_FOREST_FRAGMENTATION Forest Fragmentation in the United States CEOS_EXTRA STAC Catalog 1970-01-01 -128, 24, -65, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231549003-CEOS_EXTRA.umm_json "National Land Cover Data (NLCD) was reclassified into three categories: forest, other natural (e.g., grassland and wetland), and anthropogenic use (e.g., agricultural and urban). Three new grids were created, one for each edge type (forest, forest, forest natural, and forest anthropogenic). The values in these grids were calculated as the number of edges with the appropriate type in the window divided by the total number of forest edges, regardless of neighbor. These grids represented forest connectivity (forest forest edges), naturally caused forest fragmentation (forest natural edges), and human-caused forest fragmentation (forest anthropogenic edges). In the map, forest connectivity is displayed in green, natural fragmentation in blue, and human fragmentation in red. Pure green identifies areas where most or all forest edges are shared by another forest pixel. Pure red areas are where forest edges are largely shared with human land use. Pure blue areas show where most or all forest edges are shared with another natural land cover type. Different mixes of the three edge types can produce other colors. Two common examples in the map are yellow and cyan. Yellow identifies areas with roughly equal amounts of forest connectivity and anthropogenic fragmentation. Cyan is where forest connectivity and natural fragmentation are approximately equal. Black represents areas with no forest in the window, and white represents ignored areas, mostly water, as well as state boundaries. With few exceptions, forest fragmentation by other natural land cover types is confined to the western United States, while most human-caused forest fragmentation is in the East and Midwest. The yellow and red areas around Yellowstone in northwest Wyoming are a result of the wildfires in 1988. The burned areas are classified as ""transitional"" in the NLCD, which are treated as anthropogenic use. The Mississippi River valley was largely forested at one time but has been almost entirely converted to agricultural use, resulting in a display of black and red. Las Vegas, Nevada, is visible as a patch of red in the Mojave Desert due to an ""urban forest"" effect from trees planted by residents. Riparian corridors are highly visible in arid and developed areas, especially the West and Midwest. In arid areas, climate often confines trees to riparian zones that are displayed in shades of blue. In the intensely farmed Midwest, intact and restored riparian vegetation is depicted in yellow or red. Southern Atlantic coastal plain riparian zones are wider; forest is better connected and is shown in green." proprietary US_MODIS_NDVI_1299_3 MODIS NDVI Data, Smoothed and Gap-filled, for the Conterminous US: 2000-2015 ORNL_CLOUD STAC Catalog 2000-01-01 2015-12-31 -129.89, 20.85, -62.56, 50.56 https://cmr.earthdata.nasa.gov/search/concepts/C2764637520-ORNL_CLOUD.umm_json This data set provides Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, smoothed and gap-filled, for the conterminous US for the period 2000-01-01 through 2015-12-31. The data were generated using the NASA Stennis Time Series Product Tool (TSPT) to generate NDVI data streams from the Terra satellite (MODIS MOD13Q1 product) and Aqua satellite (MODIS MYD13Q1 product) instruments. TSPT produces NDVI data that are less affected by clouds and bad pixels. proprietary US_MODIS_Veg_Parameters_1539_1 MODIS-derived Vegetation and Albedo Parameters for Agroecosystem-Climate Modeling ORNL_CLOUD STAC Catalog 2003-01-01 2010-12-31 -139.05, 15.15, -51.95, 49.15 https://cmr.earthdata.nasa.gov/search/concepts/C2517700524-ORNL_CLOUD.umm_json This dataset provides MODIS-derived leaf area index (LAI), stem area index (SAI), vegetation area fraction, dominant landcover category, and albedo parameters for the continental US (CONUS), parts of southern Canada, and Mexico at 30 km resolution. The data cover the period 2003-2010 and were developed to be used as surface input data for regional agroecosystem-climate models. MODIS Collection 5 products used to derive these parameters included the Terra yearly water mask, vegetation continuous field products, the combined Terra and Aqua yearly land-cover category (LCC) (MCD12Q1), 8-day composites for LAI (MCD15A2), and albedo parameter (MCD43B1) products. Please note that the MODIS Version 5 land data products used in this dataset have been superseded by Version 6 data products. proprietary -UTC_1990countyboundaries 1990 County Boundaries of the United States ALL STAC Catalog 1972-01-01 1990-12-31 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231550562-CEOS_EXTRA.umm_json This data set portrays the 1990 State and county boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The data set was created by extracting county polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then merged into a single file and the boundaries were modified to what they were in 1990. This is a revised version of the March 2000 data set. proprietary UTC_1990countyboundaries 1990 County Boundaries of the United States CEOS_EXTRA STAC Catalog 1972-01-01 1990-12-31 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231550562-CEOS_EXTRA.umm_json This data set portrays the 1990 State and county boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The data set was created by extracting county polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then merged into a single file and the boundaries were modified to what they were in 1990. This is a revised version of the March 2000 data set. proprietary +UTC_1990countyboundaries 1990 County Boundaries of the United States ALL STAC Catalog 1972-01-01 1990-12-31 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231550562-CEOS_EXTRA.umm_json This data set portrays the 1990 State and county boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The data set was created by extracting county polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then merged into a single file and the boundaries were modified to what they were in 1990. This is a revised version of the March 2000 data set. proprietary UTC_TNgeologicmaps Geologic Maps of Tennessee CEOS_EXTRA STAC Catalog 1966-01-01 1966-12-31 -90.31191, 34.983253, -81.64822, 36.679295 https://cmr.earthdata.nasa.gov/search/concepts/C2231549514-CEOS_EXTRA.umm_json This data set is a digital representation of the printed 1:250,000 geologic maps from the Tennessee Department of Environment and Conservation, Division of Geology. The coverage was designed primarily to provide a more detailed geologic base than the 1:2,500,000 King and Beikman (1974). 1:24,000 scale coverage of the state is available for about 40 percent of the state. Formation names and geologic unit codes used in the coverage are from the Tennessee Division of Geology published maps and may not conform to USGS nomenclature. The Tennessee Division of Geology can be contacted at (615) 532-1500. proprietary UTC_TRIfacilities Facilities in the Toxic Release Inventory CEOS_EXTRA STAC Catalog 1997-12-31 -127.61431, 23.24277, -65.505165, 51.523094 https://cmr.earthdata.nasa.gov/search/concepts/C2231553589-CEOS_EXTRA.umm_json This data set is a subset of the U.S. Environmental Protection Agency (USEPA) Envirofacts point data set which includes facilities included in the the Toxic Release Inventory. Information on total pounds of volatile organic compounds released in 1995 (from USEPA's Toxic Release Inventory CD-ROM) has been included. This data set is designed to locate or plot manufacturing facilities included in the Toxic Release Inventory and display or analysis of volatile organic compounds releases in pounds per year. The following are the volatile organic compounds (VOC's) selected to calculate the total releases at each facility. Not all of these chemicals actually appear in the TRI data set, but this list was used to select releases to sum for each facility. CAS-ID Chemical name > ---------- ---------------------------- > 1 630-20-6 1,1,1,2-Tetrachloroethane > 2 71-55-6 1,1,1-Trichloroethane > 3 79-34-5 1,1,2,2-Tetrachloroethane > 4 76-13-1 1,1,2-Trichloro-1,2,2-trifluoroethane > 5 79-00-5 1,1,2-Trichloroethane > 6 75-34-3 1,1-Dichloroethane > 7 75-35-4 1,1-Dichloroethene > 8 563-58-6 1,1-Dichloropropene > 9 87-61-6 1,2,3-Trichlorobenzene > 10 96-18-4 1,2,3-Trichloropropane > 11 120-82-1 1,2,4-Trichlorobenzene > 12 95-63-6 1,2,4-Trimethylbenzene > 13 96-12-8 1,2-Dibromo-3-chloropropane > 14 106-93-4 1,2-Dibromoethane > 15 95-50-1 1,2-Dichlorobenzene > 16 107-06-2 1,2-Dichloroethane > 17 78-87-5 1,2-Dichloropropane > 18 108-67-8 1,3,5-Trimethylbenzene > 19 541-73-1 1,3-Dichlorobenzene > 20 142-28-9 1,3-Dichloropropane > 21 106-46-7 1,4-Dichlorobenzene > 22 95-49-8 1-Chloro-2-methylbenzene > 23 106-43-4 1-Chloro-4-methylbenzene > 24 594-20-7 2,2-Dichloropropane > 25 71-43-2 Benzene > 26 108-86-1 Bromobenzene > 27 74-97-5 Bromochloromethane > 28 75-27-4 Bromodichloromethane > 29 74-83-9 Bromomethane > 30 108-90-7 Chlorobenzene > 31 75-00-3 Chloroethane > 32 75-01-4 Chloroethene > 33 74-87-3 Chloromethane > 34 124-48-1 Dibromochloromethane > 35 74-95-3 Dibromomethane > 36 75-71-8 Dichlorodifluoromethane > 37 75-09-2 Dichloromethane > 38 1330-20-7 Dimethylbenzenes > 39 100-42-5 Ethenylbenzene > 40 100-41-4 Ethylbenzene > 41 87-68-3 Hexachlorobutadiene > 42 98-82-8 Isopropylbenzene > 43 1634-04-4 Methyl tert-butyl ether > 44 108-88-3 Methylbenzene > 45 91-20-3 Naphthalene > 46 127-18-4 Tetrachloroethene > 47 56-23-5 Tetrachloromethane > 48 75-25-2 Tribromomethane > 49 79-01-6 Trichloroethene > 50 75-69-4 Trichlorofluoromethane > 51 67-66-3 Trichloromethane > 52 156-59-2 cis-1,2-Dichloroethene > 53 10061-01-5 cis-1,3-Dichloropropene > 54 104-51-8 n-Butylbenzene > 55 103-65-1 n-Propylbenzene > 56 99-87-6 p-Isopropyltoluene > 57 135-98-8 sec-Butylbenzene > 58 98-06-6 tert-Butylbenzene > 59 156-60-5 trans-1,2-Dichloroethene > 60 10061-02-6 trans-1,3-Dichloropropene Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology. proprietary UTC_USdams Major Dams in the United States CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -162.93422, 18.016077, -66.01461, 68.06759 https://cmr.earthdata.nasa.gov/search/concepts/C2231555196-CEOS_EXTRA.umm_json "This data set portrays major dams of the United States, including Puerto Rico and the U.S. Virgin Islands. The data set was created by extracting dams 50 feet or more in height, or with a normal storage capacity of 5,000 acre- feet or more, or with a maximum storage capacity of 25,000 acre-feet or more, from the 75,187 dams in the U.S. Army Corps of Engineers National Inventory of Dams. These data are intended for geographic display and analysis at the national level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:2,000,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. In the online, interactive National Atlas of the United States, at scales smaller than 1:4,850,000 the data is thinned for display purposes. For scales between 1: 4,850,000 and 1:22,000,000, dams are only shown if they have a height of 500 feet or more, or a normal storage capacity of 50,000 acre-feet or more, or a maximum storage capacity of 250,000 acre-feet or more (1173 dams). At scales smaller than 1:22,000,000, dams are only shown if they have a height of 5000 feet or more, or a normal storage capacity of 500,000 acre-feet or more, or a maximum storage capacity of 2,500,000 acre-feet or more (240 dams). The dams in this file were selected from the National Inventory of Dams (NID). First, a subset of the attributes contained in the NID was selected based on input from the Army Corps of Engineers. Using an ArcView query, the dams with a height of 50 feet or more were selected, along with the dams with a normal storage capacity of 5,000 acre-feet or more, and those with a maximum storage capacity of 25,000 acre-feet or more. (The International Committee on Large Dams considers dams over 50 feet to be large dams. The USGS Water Resources Division considers large reservoirs to be those with a normal storage capacity of 5,000 acre-feet or more, or with a maximum storage capacity of 25,000 acre-feet or more.) The resulting data set was converted to an ArcView shape file using the ""Convert to Shapefile"" command. 33 dams that fell outside the 50 States were deleted (1 in Guam, 1 in the Trust Territories, and 31 in Puerto Rico), and 78 dams without coordinates were also deleted. Several misspelled county names were corrected, and the entries in the FIPS_cnty (County FIPS) field were cleaned up. For all dams with a valid county name but no County FIPS, the FIPS code was added based on the listed county name. If two county names were given, the FIPS code used was for the first one listed, or for the county in the listed State. Where the county name was invalid or missing, the county was determined by comparing the dam location to the National Atlas counties file. If the dam fell on a State line, the county name and FIPS code used were those appropriate for the listed State. The shape file was converted to an Arc/Info coverage and then converted to NAD 83 for display purposes. The result was then converted back to shapefile format." proprietary @@ -16626,8 +16606,8 @@ VMS_Bathy_Processing_1 Acoustic depth soundings collected on Australian Antarcti VMS_Benthic_Photography_1 High resolution still photographs of the seafloor across the Mertz Glacier Region AU_AADC STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314096-AU_AADC.umm_json Geoscience Australia and the Australian Antarctic Division conducted a benthic community survey using underwater still photographs on the shelf around the Mertz Glacier region. The purpose of the work was to collect high resolution still photographs of the seafloor across the shelf to address three main objectives: 1. to investigate benthic community composition in the area previously covered by the Mertz Glacier tongue and to the east, an area previously covered by fast ice 2. to investigate benthic community composition (or lack thereof) in areas of known iceberg scours 3. to investigate the lateral extent of cold water coral communities in canyons along the shelf break. Benthic photos were captured using a Canon EOS 20D SLR 8 megapixel stills camera fitted with a Canon EF 35mm f1.4 L USM lens in a 2500m rated flat port anodised aluminium housing. Two Canon 580EX Speedlight strobes were housed in 6000m rated stainless steel housings with hemispherical acrylic domes. The camera and strobes were powered with a 28V 2.5Ah cyclone SLA battery pack fitted in the camera housing and connected using Brantner Wetconn series underwater connectors. The results were obtained with 100 ASA and a flash compensation value of +2/3 of a stop. The focus was set manually to 7m and the image was typically exposed at f2.8 and a shutter speed of 1/60 sec. The interval between photos was set to 10 or 15 seconds. The camera was fitted to either the CTD frame or the beam trawl frame and lowered to approximately 4-5 m from the bottom. Two laser pointers, set 50 cm apart, were used for scale. The camera was deployed at 93 stations, 7 using the beam trawl frame and 86 using the CTD frame. The stations were named by: 1. Camera deployment frame (e.g. CTD or beam trawl, BT) 2. Frame sequence number (e.g. CTD53) 3. Instrument (e.g. camera = CAM) 4. Sequence of camera deployments through the survey overall (e.g. first deployment = CAM01, second deployment = CAM02 etc). For example, BT5_CAM16 is the sixteenth camera deployment of the survey overall, and was the fifth deployment using the beam trawl frame. From the 93 stations, there were 75 successful camera deployments. There were no photos captured at 9 stations. This was due to the camera or strobes malfunctioning, the camera being too far from the bottom, or the camera or strobes being in the mud at the bottom. The photos at a further 9 stations are considered poor due to the camera being out of focus, the camera being a little too far from the bottom or because very few photos were captured of the bottom. The benthic photo will be used to document the fauna and communities associated with representative habitats in the study area. The post-cruise analysis of the benthic photos will involve recording seabed geology and biology (class or order, and whatever is significant for the habitat) for each image proprietary VMS_FRRF_1 2010/11 VMS - Fast Repetition Rate Fluorometer (FRRF) sampling on the Aurora Australis AU_AADC STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314029-AU_AADC.umm_json FRRF deployments were conducted at 22 sites in conjunction with ship stop times when the CTD was deployed. See event log for locations. Some underway FRRF sampling was conducted on the return voyage. This work was conducted as part of the VMS (Voyage Marine Science) voyage of the Aurora Australis in the 2010-2011 season. A report providing further details about the FRRF work is available as part of the download file. The download file also contains a word document (also included in the download file for metadata record ASAC_1307) explaining the data columns in the excel spreadsheets. proprietary VMS_FRRF_1 2010/11 VMS - Fast Repetition Rate Fluorometer (FRRF) sampling on the Aurora Australis ALL STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314029-AU_AADC.umm_json FRRF deployments were conducted at 22 sites in conjunction with ship stop times when the CTD was deployed. See event log for locations. Some underway FRRF sampling was conducted on the return voyage. This work was conducted as part of the VMS (Voyage Marine Science) voyage of the Aurora Australis in the 2010-2011 season. A report providing further details about the FRRF work is available as part of the download file. The download file also contains a word document (also included in the download file for metadata record ASAC_1307) explaining the data columns in the excel spreadsheets. proprietary -VMS_Genomics_1 2010/11 VMS Geonomics sampling - data collected from the VMS (Voyage Marine Science) voyage of the Aurora Australis AU_AADC STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314097-AU_AADC.umm_json Purpose of future metagenomic (DNA), metaproteomic (protein) and metatranscriptomic (RNA) analysis: For each sample, two drums (~200L each) of seawater were collected. Samples were taken from CTD sites, and surface samples (2m depth) taken at each of these sites. At most of these CTD sites, a deeper sample was taken according to the location of the DCM at that site. The 200L seawater is pumped through a 20 micron mesh to remove the largest particles, then the biomass is collected on three consecutive filters corresponding to decreasing pore size (3.0 microns, 0.8 microns, 0.1 microns). This is repeated for each sample using the second 200L of seawater to generate duplicates for each sample. The overall aim is to determine the identity of microbes present in the Southern Ocean, and what microbial metabolic processes are in operation. In other words: who is there, and what they are doing. Special emphasis was placed on the SR3 transect. Samples were collected as below. For each sample, a total of six filters were obtained (3x pore sizes, 2x replicates). Each filter is stored in a storage buffer in a 50mL tube, and placed at -80 degrees C for the remainder of the voyage. proprietary VMS_Genomics_1 2010/11 VMS Geonomics sampling - data collected from the VMS (Voyage Marine Science) voyage of the Aurora Australis ALL STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314097-AU_AADC.umm_json Purpose of future metagenomic (DNA), metaproteomic (protein) and metatranscriptomic (RNA) analysis: For each sample, two drums (~200L each) of seawater were collected. Samples were taken from CTD sites, and surface samples (2m depth) taken at each of these sites. At most of these CTD sites, a deeper sample was taken according to the location of the DCM at that site. The 200L seawater is pumped through a 20 micron mesh to remove the largest particles, then the biomass is collected on three consecutive filters corresponding to decreasing pore size (3.0 microns, 0.8 microns, 0.1 microns). This is repeated for each sample using the second 200L of seawater to generate duplicates for each sample. The overall aim is to determine the identity of microbes present in the Southern Ocean, and what microbial metabolic processes are in operation. In other words: who is there, and what they are doing. Special emphasis was placed on the SR3 transect. Samples were collected as below. For each sample, a total of six filters were obtained (3x pore sizes, 2x replicates). Each filter is stored in a storage buffer in a 50mL tube, and placed at -80 degrees C for the remainder of the voyage. proprietary +VMS_Genomics_1 2010/11 VMS Geonomics sampling - data collected from the VMS (Voyage Marine Science) voyage of the Aurora Australis AU_AADC STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314097-AU_AADC.umm_json Purpose of future metagenomic (DNA), metaproteomic (protein) and metatranscriptomic (RNA) analysis: For each sample, two drums (~200L each) of seawater were collected. Samples were taken from CTD sites, and surface samples (2m depth) taken at each of these sites. At most of these CTD sites, a deeper sample was taken according to the location of the DCM at that site. The 200L seawater is pumped through a 20 micron mesh to remove the largest particles, then the biomass is collected on three consecutive filters corresponding to decreasing pore size (3.0 microns, 0.8 microns, 0.1 microns). This is repeated for each sample using the second 200L of seawater to generate duplicates for each sample. The overall aim is to determine the identity of microbes present in the Southern Ocean, and what microbial metabolic processes are in operation. In other words: who is there, and what they are doing. Special emphasis was placed on the SR3 transect. Samples were collected as below. For each sample, a total of six filters were obtained (3x pore sizes, 2x replicates). Each filter is stored in a storage buffer in a 50mL tube, and placed at -80 degrees C for the remainder of the voyage. proprietary VNP01_NRT_2 VIIRS/NPP Raw Radiances in Counts 6-Min L1A Swath NRT LANCEMODIS STAC Catalog 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208439292-LANCEMODIS.umm_json VIIRS/NPP Raw Radiances in Counts 6-Min L1A Swath - NRT product contains the unpacked, raw VIIRS science, calibration and engineering data; the extracted ephemeris and attitude data from the spacecraft diary packets; and the raw ADCS and bus-critical spacecraft telemetry data from those packets, with a few critical fields extracted. The shortname for this product is VNP01_NRT. For more information download VIIRS Level 1 Product User's Guide at https://oceancolor.gsfc.nasa.gov/docs/format/VIIRS_Level-1_DataProductUsersGuide.pdf file_naming_convention = VNP01_NRT.AYYYYDDD.HHMM.CCC.nc AYYYYDDD = Acquisition Year and Day of Year HHMM = Acquisition Hour and Minute CCC = Collection number nc = NetCDF5 proprietary VNP02DNB_2 VIIRS/NPP Day/Night Band 6-Min L1B Swath 750 m LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105091380-LAADS.umm_json The VIIRS/NPP Day/Night Band 6-Min L1B Swath 750 m product, short-name VNP02DNB, is a panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan). The panchromatic DNB’s spectral wavelength ranges from 0.5 µm to 0.9 µm. It facilitates measuring night lights, reflected solar/lunar lights with a large dynamic range between a low of a quarter moon illumination to the brightest daylight. More information is available at product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP02DNB/ proprietary VNP02DNB_NRT_2 VIIRS/NPP Day/Night Band 6-Min L1B Swath 750m NRT LANCEMODIS STAC Catalog 2022-01-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208367854-LANCEMODIS.umm_json The VIIRS/NPP Day/Night Band 6-Min L1B Swath SDR 750m Near Real Time (NRT) product, short-name VNP02DNB_NRT is among the VIIRS Level 1 and Level 2 swath products that are generated from the processing of 6 minutes of VIIRS data acquired during the S-NPP satellite overpass. The Day/Night band (DNB) is a panchromatic channel covering the wavelengths from 500 nm to 900 nm, and sensitive to visible and near-infrared from daylight down to the low-level radiation observed at night. The VIIRS DNB is much improved from previous products due in large part to its complicated continuous on-board calibration. In addition, new-moon Earth observations are used to estimate and remove stray light. These corrections are a first of its kind to provide on-orbit radiometric calibration. The corrections made to the DNB data are provided by the NASA VIIRS Characterization Support Team and are likely to continue to evolve given this new methodology. The spatial resolution of the instrument at viewing nadir is approximately 750 m for the DNB and the Moderate-resolution Bands and and 375m for the Imagery bands. The DNB is aggregated to maintain nearly constant horizontal spatial resolution across the swath. As the DNB is sensitive to nighttime radiation over the full lunar cycle, the incoming solar and lunar radiation must be properly modeled to calculate the reflectance. However, the DNB is sensitive to more sources of radiation than just the sun and moon. proprietary @@ -16860,8 +16840,8 @@ Vulcan_V3_Annual_Emissions_1741_1 Vulcan: High-Resolution Annual Fossil Fuel CO2 Vulcan_V3_Hourly_Emissions_1810_1 Vulcan: High-Resolution Hourly Fossil Fuel CO2 Emissions in USA, 2010-2015, Version 3 ORNL_CLOUD STAC Catalog 2010-01-01 2016-01-01 -165.21, 22.86, -65.31, 73.75 https://cmr.earthdata.nasa.gov/search/concepts/C2516155224-ORNL_CLOUD.umm_json The Vulcan version 3.0 hourly dataset quantifies hourly emissions at a 1-km resolution for the 2010-2015 time period. Estimates are provided of hourly carbon dioxide (CO2) emissions from the combustion of fossil fuels (FF) and CO2 emissions from cement production for the conterminous United States and the state of Alaska. Referred to as FFCO2, the emissions from Vulcan are categorized into 10 source sectors including; residential, commercial, industrial, electricity production, onroad, nonroad, commercial marine vessel, airport, rail, and cement. Files for hourly total emissions are also available. Data are represented in space on a 1 km x 1 km grid as hourly totals for 2010-2015. This dataset provides the first bottom-up U.S.-wide FFCO2 emissions data product at 1 km2/hourly for multiple years and is designed to be used as emission estimates in atmospheric transport modeling, policy, mapping, and other data analyses and applications. proprietary WACS2_0 Western Atlantic Climate Study II OB_DAAC STAC Catalog 2014-05-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360697-OB_DAAC.umm_json Sea spray aerosol (SSA) impacts the Earth’s radiation budget indirectly by altering cloud properties including albedo, lifetime, and extent, and directly by scattering solar radiation. Characterization of the properties of SSA in its freshly emitted state is needed for accurate model calculations of climate impacts. In addition, simultaneous measurements of surface seawater are required to assess the impact of ocean properties on sea spray aerosol and to develop accurate parameterizations of the SSA number production flux for use in regional and global scale models.Sea spray aerosol (SSA) impacts the Earth’s radiation budget indirectly by altering cloud properties including albedo, lifetime, and extent, and directly by scattering solar radiation. Characterization of the properties of SSA in its freshly emitted state is needed for accurate model calculations of climate impacts. In addition, simultaneous measurements of surface seawater are required to assess the impact of ocean properties on sea spray aerosol and to develop accurate parameterizations of the SSA number production flux for use in regional and global scale models. proprietary WAF_DEALIASED_SASS_L2_1 SEASAT SCATTEROMETER DEALIASED OCEAN WIND VECTORS (Wentz et al.) POCLOUD STAC Catalog 1978-07-07 1978-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617197640-POCLOUD.umm_json Contains Seasat-A Scatterometer (SASS) wind vector measurements for the entire Seasat mission, from July 1978 until October 1978. The data are global and presented chronologically in by swath. Each record contains data binned in 100 km cells. No wind vectors are computed for the cells along the left and right edges of the swath. Wind direction ambiguities are resolved using a global weather prediction model. This complete dataset is the result of the reprocessing efforts on behalf of Frank Wentz, Robert Atlas, and Michael Freilich. proprietary -WARd0002_108 Administration Division Maps Of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232846827-CEOS_EXTRA.umm_json Administration division of Poland created on a basis of digitization with manual generalisation proper for specific scales. Projection Albers; points and polygons; ARC/INFO and SINUS systems proprietary WARd0002_108 Administration Division Maps Of Poland ALL STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232846827-CEOS_EXTRA.umm_json Administration division of Poland created on a basis of digitization with manual generalisation proper for specific scales. Projection Albers; points and polygons; ARC/INFO and SINUS systems proprietary +WARd0002_108 Administration Division Maps Of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232846827-CEOS_EXTRA.umm_json Administration division of Poland created on a basis of digitization with manual generalisation proper for specific scales. Projection Albers; points and polygons; ARC/INFO and SINUS systems proprietary WARd0004_108 Land Use Division Maps of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232848834-CEOS_EXTRA.umm_json Land use map of Poland acquisited form interpreted Landsat TM, MSS images by digitization. 24 classes of land use grouped in subjects (agriculture, grass lands, settlements and communication areas, forests, surface waters, industry, not used areas). Vector and raster format; projection Albers; ARC/INFO and SINUS systems proprietary WARd0005_108 Geomorphology Forms of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232848304-CEOS_EXTRA.umm_json Geomorphological forms of Poland created within Central Scientific Programme 10.4/1989. Digitized from the map of relief types in Poland; Scale 1:1 000 000. proprietary WARd0006_108 Hunting Unit Border Maps of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232849207-CEOS_EXTRA.umm_json Borders of hunting units digitized from the maps prepared by Polish Hunting Association within Central Scientific Programme 10.4/1989. proprietary @@ -16888,8 +16868,8 @@ WENTZ_NIMBUS-7_SMMR_L2_1 NIMBUS-7 SMMR GLOBAL AIR-SEA PARAMETERS IN SWATH (Wentz WENTZ_SASS_SIGMA0_L2_1 SEASAT SCATTEROMETER BINNED 50KM SIGMA-0 DATA (Wentz) POCLOUD STAC Catalog 1978-07-07 1978-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617197621-POCLOUD.umm_json Contains Seasat-A Scatterometer (SASS) Sigma-0 measurements for the entire Seasat mission, from July 1978 until October 1978, produced by Frank Wentz at Remote Sensing Systems. The data are presented chronologically by swath and consist of the forward and aft values, binned in 50 km cells. For each cell there are 17 parameters including time, location, incidence angle, sigma-0, instrument corrections, and data quality. proprietary WHITECAPS_0 Influence of Whitecaps on Aerosol and Ocean-Color Remote Sensing OB_DAAC STAC Catalog 2011-02-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360700-OB_DAAC.umm_json The influence of whitecaps on ocean color and aerosol remote sensing from space were invistigated onboard the R/V Melville (MV1102) from Cape Town, South Africa to Valparaiso, Chile from February 2, 2011 to March 14, 2011. Satellite imagery has revealed relatively large amounts of aerosols and particulate organic and inorganic carbon in the Southern oceans, but it is not clear whether this is real or the result of not taking into account properly whitecap effects in the retrieval algorithms. By measuring whitecap optical properties and profiles of marine reflectance and backscattering and absorption coefficients, a bulk whitecap reflectance model will be developed. The measurements will allow comparisons of the aerosol optical thickness and marine reflectance one should retrieve (i.e., in the absence of whitecaps and bubbles) with the satellite-derived estimates. The parameters/variables that will be measured include whitecap coverage, surface reflectance, aerosol optical thickness, in situ profiles of marine reflectance, backscattering and attenuation coefficients, and particle size distribution, and absorption and backscattering coefficients and HPLC pigments from water samples. The backscattering and absorption measurements from water samples will characterize conditions without whitecaps. Cruise information can be found in the R2R repository: https://www.rvdata.us/search/cruise/MV1102. proprietary WILKS_2018_Chatham_sedimenttraps_specieslist_3 Diatom and coccolithophore assemblages from archival sediment trap samples of the Subtropical and Subantarctic Southwest Pacific AU_AADC STAC Catalog 1996-06-17 1997-05-07 174.90234, -45.39845, 179.73633, -40.71396 https://cmr.earthdata.nasa.gov/search/concepts/C1459701888-AU_AADC.umm_json "This spreadsheet contains species lists and counts from four sediment trap records. The sediment traps were deployed for ~1 year north and south of the Chatham Rise, New Zealand, between 1996 and 1997. Sheets 1a and 1b refer to North Chatham Rise (NCR). 1a = the 300m trap. 1b = the 1000m trap. Sheets 2a and 2b are for the South Chatham Rise traps (SCR). 2a= 300m, 2b= 1000m. Counting was undertaken on 1/16th splits. Material was cleaned of organics before diatom counting under light microscopy. Coccolith counting on uncleaned material was only undertaken at the 300m traps. Radiolarians and silicoflagellates were counted but not identified. Diatoms and coccoliths were counted along non-overlapping transects until 300 specimens had been counted per sample, or until 10 transects had been made. This dataset includes counts of diatom, coccolithophores, radiolarians and silicoflagellates for four sediment trap records- North Chatham Rise (NCR) and South Chatham Rise (SCR) at two trap depths each (300 m and 1000 m). It is intended as supplementary material to Wilks et al. 2018 (submitted) ""Diatom and coccolithophore assemblages from archival sediment trap samples of the Subtropical and Subantarctic Southwest Pacific."" Numbers are raw count per sample cup. Authorities are given. Coordinates of traps given in degrees, minutes and seconds." proprietary -WIND_3DP 3-D Plasma and Energetic Particle Investigation on WIND SCIOPS STAC Catalog 1994-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214615352-SCIOPS.umm_json The main purpose of the Wind spacecraft is to measure the incoming solar wind, magnetic fields and particles, although early on it will also observe the Earth's foreshock region. Wind, together with Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative scientific satellite project designated the International Solar Terrestrial Physics (ISTP) program which aims at gaining improved understanding of the physics of solar terrestrial relations. This experiment is designed to measure the full three-dimensional distribution of suprathermal electrons and ions at energies from a few eV to over several hundred keV on the WIND spacecraft. Its high sensitivity, wide dynamic range, and good energy and angular resolution make it especially capable of detecting and characterizing the numerous populations of particles that are present in interplanetary space at energies above the bulk of the solar wind particles and below the energies typical of most cosmic rays. Data consists of ion moments, energy spectra, electron spectra, electron and ion omni directional energy spectra. Data are available from SSL at University of California, Berkeley (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/) proprietary WIND_3DP 3-D Plasma and Energetic Particle Investigation on WIND ALL STAC Catalog 1994-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214615352-SCIOPS.umm_json The main purpose of the Wind spacecraft is to measure the incoming solar wind, magnetic fields and particles, although early on it will also observe the Earth's foreshock region. Wind, together with Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative scientific satellite project designated the International Solar Terrestrial Physics (ISTP) program which aims at gaining improved understanding of the physics of solar terrestrial relations. This experiment is designed to measure the full three-dimensional distribution of suprathermal electrons and ions at energies from a few eV to over several hundred keV on the WIND spacecraft. Its high sensitivity, wide dynamic range, and good energy and angular resolution make it especially capable of detecting and characterizing the numerous populations of particles that are present in interplanetary space at energies above the bulk of the solar wind particles and below the energies typical of most cosmic rays. Data consists of ion moments, energy spectra, electron spectra, electron and ion omni directional energy spectra. Data are available from SSL at University of California, Berkeley (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/) proprietary +WIND_3DP 3-D Plasma and Energetic Particle Investigation on WIND SCIOPS STAC Catalog 1994-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214615352-SCIOPS.umm_json The main purpose of the Wind spacecraft is to measure the incoming solar wind, magnetic fields and particles, although early on it will also observe the Earth's foreshock region. Wind, together with Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative scientific satellite project designated the International Solar Terrestrial Physics (ISTP) program which aims at gaining improved understanding of the physics of solar terrestrial relations. This experiment is designed to measure the full three-dimensional distribution of suprathermal electrons and ions at energies from a few eV to over several hundred keV on the WIND spacecraft. Its high sensitivity, wide dynamic range, and good energy and angular resolution make it especially capable of detecting and characterizing the numerous populations of particles that are present in interplanetary space at energies above the bulk of the solar wind particles and below the energies typical of most cosmic rays. Data consists of ion moments, energy spectra, electron spectra, electron and ion omni directional energy spectra. Data are available from SSL at University of California, Berkeley (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/) proprietary WIR_98_4105 Major-Ion, Nutrient, and Trace-Element Concentrations in the Steamboat Creek Basin CEOS_EXTRA STAC Catalog 1996-09-09 1996-09-13 -122.7, 42.3, -122.5, 43.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231554333-CEOS_EXTRA.umm_json In September 1996, a water-quality study was done by the U.S. Geological Survey, in coordination with the U.S. Forest Service, in headwater streams of Steamboat Creek, a tributary to the North Umpqua River Basin in southwestern Oregon. Field measurements were made in and surface-water and bottom-sediment samples were collected from three tributaries of Steamboat Creek-Singe Creek, City Creek, and Horse Heaven Creek-and at one site in Steamboat Creek upstream from where the three tributaries flow into Steamboat Creek. Water samples collected in Singe Creek had larger concentrations of most major-ion constituents and smaller concentrations of most nutrient constitu ents than was observed in the other three creeks. City Creek, Horse Heaven Creek, and Steamboat Creek had primarily calcium bicarbonate water, whereas Singe Creek had primarily a calcium sulfate water; the calcium sulfate water detected in Singe Creek, along with the smallest observed alkalinity and pH values, suggests that Singe Creek may be receiving naturally occurring acidic water. Of the 18 trace elements analyzed in filtered water samples, only 6 were detected-aluminum, barium, cobalt, iron, manganese, and zinc. All six of the trace elements were detected in Singe Creek, at concentrations generally larger than those observed in the other three creeks. Of the detected trace elements, only iron and zinc have chronic toxicity criteria established by the U.S. Environmental Protection Agency (USEPA) for the protection of aquatic life; none exceeded the USEPA criterion. Bottom-sediment concentrations of antimony, arsenic, cadmium, copper, lead, mercury, zinc, and organic carbon were largest in City Creek. In City Creek and Horse Heaven Creek, concentrations for 11 constituents--antimony, arsenic, cadmium, copper, lead, manganese (Horse Heaven Creek only), mercury, selenium, silver, zinc, and organic carbon (City Creek only)--exceeded concentrations considered to be enriched in streams of the nearby Willamette River Basin, whereas in Steamboat Creek only two trace elements--antimony and nickel--exceeded Willamette River enriched concentrations. Bottom-sediment concentrations for six of these constituents in City Creek and Horse Heaven Creek--arsenic, cadmium, copper, lead, mercury, and zinc--also exceeded interim Canadian threshold effect level (TEL) concentrations established for the protection of aquatic life, whereas only four constituents between Singe Creek and Steamboat Creek--arsenic, chromium, copper (Singe Creek only), and nickel--exceeded the TEL concentrations. The data set checked for the concentrations of major ions, nutrients, and trace elements in water and bottom sediments collected in the four tributaries during the low-flow conditions of September 9-13, 1996. Stream-water chemistry results were contrasted, and trace-element concentrations were compared with U.S. Environmental Protection Agency chronic aquatic life toxicity criteria. Bottom-sediment trace-element results were also contrasted and compared with concentrations considered to be enriched in streams of the nearby Willamette River Basin and to interim Canadian threshold level (TEL) concentrations established for the protection of aquatic life. The area of study was Headwater streams of Steamboat Creek, a tributary to the north Umpqua River Basin in southwestern Oregon Field measurements and surface-water and bottom-sediment samples at each of the four sites included streamflow, stream temperature, specific conductance, dissolved oxygen, pH, alkalinity, major ions in filtered water (8 constituents), low-level concentrations of trace elements in filtered water (18 elements), and trace elements and carbon in bottom sediment (47 elements). Stream temperature, specific conductance, dissolved oxygen, and pH were measured using a calibrated Hydrolab multiparameter unit. Because stream widths were less than 8 feet, field measurements were made only near the center of flow at 1 foot or less below water surface. The Hydrolab unit was calibrated at each site before and after sampling. Stream temperatures were recorded to the nearest 0.1 degree Centigrade; specific conductance to the nearest 1 microsiemen per centimeter at 25 degrees Centigrade ; dissolved oxygen to the nearest 0.1 milligrams per liter; and pH to the nearest 0.1 pH units. Measurements of streamflow were made in accordance with standard USGS procedures (Rantz and others, 1982). Alkalinity measurements were made on filtered water samples using an incremental titration method (Shelton, 1994), and results were reported to the nearest 1 milligram per liter as calcium carbonate (CaCO3). Water samples were collected using 1-liter narrow-mouth acid-rinsed polyethylene bottles from a minimum of eight verticals in the cross section, suing an equal-width-increment method described by Edwards and Glysson (1988), and composited into a 8-liter polyethylene acid-rinsed churn splitter. Sample and compositing containers were prerinsed with native water prior to sample collection. Water samples were collected using clean procedures as outlined by Horowitz and others (1994). Samples were chilled on ice from time of sample collection until analysis, except when samples were processed. Processing of the field samples was accomplished either in the mobile field laboratory or in an area suitably clean for carrying out the filtering and preservation procedures. Samples for major ions, nutrients, and trace elements in filtered water (operationally defined as dissolved) were passed through 0.45 micrometer pore-size capsule filters into polyethylene bottles using procedures outlined by Horowitz and others (1994). Filtered-water trace-element samples were preserved with 0.5 milliliter of ultra-pure nitric acid per 250 mL of sample; nutrient samples were placed in dark brown polyethylene bottles and were chilled for preservation. All chemical samples were shipped to the USGS National Water Quality Laboratory (NWQL) in Arvada, Colorado, for analysis according to methods outlined by Fishman (1993). The information for this metadata was taken from the Online Publications of the Oregon District at http://oregon.usgs.gov/pubs_dir/online_list.html . proprietary WISPMAWSON04-05_1 A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season AU_AADC STAC Catalog 2004-12-10 2005-04-25 62.18384, -67.68587, 63.40759, -67.47282 https://cmr.earthdata.nasa.gov/search/concepts/C1214314124-AU_AADC.umm_json Very little information is known about the distribution and abundance of Wilson's storm petrels at the regional and local scales. This dataset contains locations of Wilson's storm petrel nests, mapped in the Mawson region during 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile(ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in description of the shapefile. A text file also provide the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary WISPMAWSON04-05_1 A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season ALL STAC Catalog 2004-12-10 2005-04-25 62.18384, -67.68587, 63.40759, -67.47282 https://cmr.earthdata.nasa.gov/search/concepts/C1214314124-AU_AADC.umm_json Very little information is known about the distribution and abundance of Wilson's storm petrels at the regional and local scales. This dataset contains locations of Wilson's storm petrel nests, mapped in the Mawson region during 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile(ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in description of the shapefile. A text file also provide the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary @@ -16914,12 +16894,12 @@ WV03_SWIR_L1B_1 WorldView-3 Level 1B Shortwave Infrared 8-Band Satellite Imagery WV04_MSI_L1B_1 WorldView-4 Level 1B Multispectral 4-Band Satellite Imagery CSDA STAC Catalog 2016-12-01 2019-01-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497446902-CSDA.umm_json The WorldView-4 Multispectral 4-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the SpaceView-110 camera across the global land surface from December 2016 to January 2019. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The multispectral imagery has a spatial resolution of 1.24m at nadir and has a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a Maxar End User License Agreement for Worldview 4 imagery and investigators must be approved by the CSDA Program. proprietary WV04_Pan_L1B_1 WorldView-4 Level 1B Panchromatic Satellite Imagery CSDA STAC Catalog 2016-12-01 2019-01-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497439327-CSDA.umm_json The WorldView-4 Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the WorldView-110 camera across the global land surface from December 2016 to January 2019. This data product includes panchromatic imagery with a spatial resolution of 0.31m at nadir and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a Maxar End User License Agreement for Worldview 4 imagery and investigators must be approved by the CSDA Program. proprietary WV_LCC_SC_FSCA_1 Land Cover Classification, Snow Cover, and Fractional Snow-Covered Area Maps from Maxar WorldView Satellite Images V001 NSIDC_ECS STAC Catalog 2015-05-20 2019-05-05 -121.203708, 38.867847, -108.032283, 48.672717 https://cmr.earthdata.nasa.gov/search/concepts/C2695676729-NSIDC_ECS.umm_json This data set includes: (1) fine-scale snow and land cover maps from two mountainous study sites in the Western U.S., produced using machine-learning models trained to extract land cover data from WorldView-2 and WorldView-3 stereo panchromatic and multispectral images; (2) binary snow maps derived from the land cover maps; and (3) 30 m and 465 m fractional snow-covered area (fSCA) maps, produced via downsampling of the binary snow maps. The land cover classification maps feature between three and six classes common to mountainous regions and integral for accurate stereo snow depth mapping: illuminated snow, shaded snow, vegetation, exposed surfaces, surface water, and clouds. Also included are Landsat and MODSCAG fSCA map products. The source imagery for these data are the Maxar WorldView-2 and Maxar WorldView-3 Level-1B 8-band multispectral images, orthorectified and converted to top-of-atmosphere reflectance. These Level-1B images are available under the NGA NextView/EnhancedView license. proprietary -WYGISC_HYDRO100K 1:100,000-scale Hydrography for Wyoming (enhanced DLGs) SCIOPS STAC Catalog 1970-01-01 -111.36555, 40.944794, -103.783806, 44.99391 https://cmr.earthdata.nasa.gov/search/concepts/C1214614313-SCIOPS.umm_json The purpose of this data layer was to provide a base layer of water features at a statewide level for riparian/aquatic species distribution modeling for the Wyoming Gap Analysis project. However the data may also be used for a variety of other natural resources management/biological studies at the appropriate scale. Hydrographic features for Wyoming at 1:100,000-scale, including perennial and intermittent designations and Strahler stream order attributes for streams. Does not include man-made ditches, canals and aqueducts. The data was originally produced by USGS, a Digital Line Graph (DLG) product, though this product was enhanced (edgematched, some linework and attributes corrected, stream order attribute added). A subset of this dataset is also available for distribution, including only major streams (order 4 to 7) and major lakes and reservoirs. In order to reduce the size of this subset, the line segments were dissolved to remove unncessary segments. Both datasets are available in Arc export file and shapefile format for download Statewide and tiled data: there is one export file, which when imported into ARC/INFO, will contain one coverage with both polygon (lakes, reservoirs) and line (streams) topology and two feature attribute files (.PAT and .AAT) along with three additional attribute files containing descriptive information. In shapefile format, there will be two shapefiles (polygons and lines separated), with all attribute files in Dbase format. proprietary WYGISC_HYDRO100K 1:100,000-scale Hydrography for Wyoming (enhanced DLGs) ALL STAC Catalog 1970-01-01 -111.36555, 40.944794, -103.783806, 44.99391 https://cmr.earthdata.nasa.gov/search/concepts/C1214614313-SCIOPS.umm_json The purpose of this data layer was to provide a base layer of water features at a statewide level for riparian/aquatic species distribution modeling for the Wyoming Gap Analysis project. However the data may also be used for a variety of other natural resources management/biological studies at the appropriate scale. Hydrographic features for Wyoming at 1:100,000-scale, including perennial and intermittent designations and Strahler stream order attributes for streams. Does not include man-made ditches, canals and aqueducts. The data was originally produced by USGS, a Digital Line Graph (DLG) product, though this product was enhanced (edgematched, some linework and attributes corrected, stream order attribute added). A subset of this dataset is also available for distribution, including only major streams (order 4 to 7) and major lakes and reservoirs. In order to reduce the size of this subset, the line segments were dissolved to remove unncessary segments. Both datasets are available in Arc export file and shapefile format for download Statewide and tiled data: there is one export file, which when imported into ARC/INFO, will contain one coverage with both polygon (lakes, reservoirs) and line (streams) topology and two feature attribute files (.PAT and .AAT) along with three additional attribute files containing descriptive information. In shapefile format, there will be two shapefiles (polygons and lines separated), with all attribute files in Dbase format. proprietary -WYGISC_HYDRO24K 1:24,000-scale Hydrography for ortions Wyoming, various sources SCIOPS STAC Catalog 1967-01-01 1971-12-31 -111, 41, -104, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214614312-SCIOPS.umm_json "The purpose of this data layer is to provide a base layer of hydrography at the watershed scale for GIS display and analysis. The hydrography described by this metadata, including streams, lakes, reservoirs and"" ditches, came from three different sources, all at 1:24,000-scale:"" -USGS Digital Line Graphs -USFS Cartographic Feature File -digitized by Wyoming Water Resources Center off of paper topographic maps" proprietary +WYGISC_HYDRO100K 1:100,000-scale Hydrography for Wyoming (enhanced DLGs) SCIOPS STAC Catalog 1970-01-01 -111.36555, 40.944794, -103.783806, 44.99391 https://cmr.earthdata.nasa.gov/search/concepts/C1214614313-SCIOPS.umm_json The purpose of this data layer was to provide a base layer of water features at a statewide level for riparian/aquatic species distribution modeling for the Wyoming Gap Analysis project. However the data may also be used for a variety of other natural resources management/biological studies at the appropriate scale. Hydrographic features for Wyoming at 1:100,000-scale, including perennial and intermittent designations and Strahler stream order attributes for streams. Does not include man-made ditches, canals and aqueducts. The data was originally produced by USGS, a Digital Line Graph (DLG) product, though this product was enhanced (edgematched, some linework and attributes corrected, stream order attribute added). A subset of this dataset is also available for distribution, including only major streams (order 4 to 7) and major lakes and reservoirs. In order to reduce the size of this subset, the line segments were dissolved to remove unncessary segments. Both datasets are available in Arc export file and shapefile format for download Statewide and tiled data: there is one export file, which when imported into ARC/INFO, will contain one coverage with both polygon (lakes, reservoirs) and line (streams) topology and two feature attribute files (.PAT and .AAT) along with three additional attribute files containing descriptive information. In shapefile format, there will be two shapefiles (polygons and lines separated), with all attribute files in Dbase format. proprietary WYGISC_HYDRO24K 1:24,000-scale Hydrography for ortions Wyoming, various sources ALL STAC Catalog 1967-01-01 1971-12-31 -111, 41, -104, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214614312-SCIOPS.umm_json "The purpose of this data layer is to provide a base layer of hydrography at the watershed scale for GIS display and analysis. The hydrography described by this metadata, including streams, lakes, reservoirs and"" ditches, came from three different sources, all at 1:24,000-scale:"" -USGS Digital Line Graphs -USFS Cartographic Feature File -digitized by Wyoming Water Resources Center off of paper topographic maps" proprietary -WYGISC_LANDUSE Agricultural Land Use of Wyoming SCIOPS STAC Catalog 1980-01-01 1982-12-31 -111.09, 40.95, -103.88, 45.107 https://cmr.earthdata.nasa.gov/search/concepts/C1214614317-SCIOPS.umm_json The purpose of this data layer is to provide a digital layer showing areas of agriculture and agricultural chemical use in Wyoming. This layer was designed to be applied in the Wyoming Ground-Water Vulnerability Mapping Project. This dataset represents croplands of Wyoming as interpreted from 1:58,200-scale National High Altitude Program (NHAP) color infrared aerial photography. The photos, which were taken in 1980-1982, were interpreted and land use designations were hand-drawn onto plots produced at the same scale as the photos, using a light table. The plots were then digitized as polygons into ARC/INFO 7.0.2. Valid polygons include irrigated croplands, non-irrigated croplands, urban lands, golf-courses, and non-agricultural lands. Golf courses boundaries, which have changed recently, were later updated with 1994 NAPP photos. proprietary +WYGISC_HYDRO24K 1:24,000-scale Hydrography for ortions Wyoming, various sources SCIOPS STAC Catalog 1967-01-01 1971-12-31 -111, 41, -104, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214614312-SCIOPS.umm_json "The purpose of this data layer is to provide a base layer of hydrography at the watershed scale for GIS display and analysis. The hydrography described by this metadata, including streams, lakes, reservoirs and"" ditches, came from three different sources, all at 1:24,000-scale:"" -USGS Digital Line Graphs -USFS Cartographic Feature File -digitized by Wyoming Water Resources Center off of paper topographic maps" proprietary WYGISC_LANDUSE Agricultural Land Use of Wyoming ALL STAC Catalog 1980-01-01 1982-12-31 -111.09, 40.95, -103.88, 45.107 https://cmr.earthdata.nasa.gov/search/concepts/C1214614317-SCIOPS.umm_json The purpose of this data layer is to provide a digital layer showing areas of agriculture and agricultural chemical use in Wyoming. This layer was designed to be applied in the Wyoming Ground-Water Vulnerability Mapping Project. This dataset represents croplands of Wyoming as interpreted from 1:58,200-scale National High Altitude Program (NHAP) color infrared aerial photography. The photos, which were taken in 1980-1982, were interpreted and land use designations were hand-drawn onto plots produced at the same scale as the photos, using a light table. The plots were then digitized as polygons into ARC/INFO 7.0.2. Valid polygons include irrigated croplands, non-irrigated croplands, urban lands, golf-courses, and non-agricultural lands. Golf courses boundaries, which have changed recently, were later updated with 1994 NAPP photos. proprietary +WYGISC_LANDUSE Agricultural Land Use of Wyoming SCIOPS STAC Catalog 1980-01-01 1982-12-31 -111.09, 40.95, -103.88, 45.107 https://cmr.earthdata.nasa.gov/search/concepts/C1214614317-SCIOPS.umm_json The purpose of this data layer is to provide a digital layer showing areas of agriculture and agricultural chemical use in Wyoming. This layer was designed to be applied in the Wyoming Ground-Water Vulnerability Mapping Project. This dataset represents croplands of Wyoming as interpreted from 1:58,200-scale National High Altitude Program (NHAP) color infrared aerial photography. The photos, which were taken in 1980-1982, were interpreted and land use designations were hand-drawn onto plots produced at the same scale as the photos, using a light table. The plots were then digitized as polygons into ARC/INFO 7.0.2. Valid polygons include irrigated croplands, non-irrigated croplands, urban lands, golf-courses, and non-agricultural lands. Golf courses boundaries, which have changed recently, were later updated with 1994 NAPP photos. proprietary WaterBalance_Daily_Historical_GRIDMET_1.5 Daily Historical Water Balance Products for the CONUS LPCLOUD STAC Catalog 1980-01-01 2023-12-31 -131.70607, 21.115301, -60.530453, 55.457306 https://cmr.earthdata.nasa.gov/search/concepts/C2674694066-LPCLOUD.umm_json This dataset provides daily historical Water Balance Model outputs from a Thornthwaite-type, single bucket model. Climate inputs to the model are from GridMet daily temperature and precipitation for the Continental United States (CONUS). The Water Balance Model output variables include the following: Potential Evapotranspiration (PET, mm), Actual Evapotranspiration (AET, mm), Moisture Deficit (Deficit, mm), Soil Water (soilwater, mm), Runoff (mm), Rain (mm), and Accumulated Snow Water Equivalent (accumswe, mm). The dataset covers the period from January 1 to December 31 for years 1980 through 2023 for the CONUS. Water Balance Model variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant NetCDF file format. proprietary WaterBalance_Monthly_Historical_GRIDMET_1.5 Monthly Historical Water Balance Products for the CONUS LPCLOUD STAC Catalog 1980-01-01 2023-12-31 -131.70607, 21.115301, -60.530453, 55.457306 https://cmr.earthdata.nasa.gov/search/concepts/C2674700048-LPCLOUD.umm_json This dataset provides daily historical Water Balance Model outputs from a Thornthwaite-type, single bucket model. Climate inputs to the model are from GridMet daily temperature and precipitation for the Continental United States (CONUS). The Water Balance Model output variables include the following: Potential Evapotranspiration (PET, mm), Actual Evapotranspiration (AET, mm), Moisture Deficit (Deficit, mm), Soil Water (soilwater, mm), Runoff (mm), Rain (mm), and Accumulated Snow Water Equivalent (accumswe, mm). The dataset covers the period from January 1 to December 31 for years 1980 through 2023 for the CONUS. Water Balance Model variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant NetCDF file format. proprietary WebbRosenzweig_548_1 Global Soil Texture and Derived Water-Holding Capacities (Webb et al.) ORNL_CLOUD STAC Catalog 1950-01-01 1996-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216863033-ORNL_CLOUD.umm_json A standardized global data set of soil horizon thicknesses and textures (particle size distributions). proprietary @@ -16929,20 +16909,20 @@ West_Soil_Carbon_1238_1 Soil Carbon Estimates in 20-cm Layers to 1-meter Depth, Western USA Live Fuel Moisture_1 Western USA Live Fuel Moisture MLHUB STAC Catalog 2020-01-01 2023-01-01 -123.5313889, 28.3, -93.8227778, 48.4136111 https://cmr.earthdata.nasa.gov/search/concepts/C2781412788-MLHUB.umm_json "This data contains manually collected live fuel moisture measurements in the western United States and remotely-sensed variables. Live fuel moisture represents the mass of water in live vegetation elements like leaves, needles, and twigs divided by its oven-dried mass. It is represented in percentages. Higher the live fuel moisture, wetter the vegetation elements, and vice versa. Live fuel moisture measurements were collected by the United States Forest Service and are available from the [National Fuel Moisture Database](https://www.wfas.net/index.php/national-fuel-moisture-database-moisture-drought-103). Each row of the data corresponds to one unique ground measurement of live fuel moisture (column named ""percent(t)"") matched with various remotely-sensed observables that may be used to predict live fuel moisture. The live fuel moisture is sampled for representative species within a 5-acre plot (or 20,000 m2) centered at the location described by the columns ""latitude"" and ""longitude"" on the day described by the column ""date"". All other columns represent remotely-sensed observables from satellites (e.g., Sentinel-1 and Landsat-8) or maps (e.g., soil texture). Temporally varying remotely-sensed observables are interpolated to 15-day periods and are provided for the date closest to the day of ground-measurement as well as for 6 fortnights preceding the day of live fuel moisture measurement. The time series of satellite data may allow for greater predictability of live fuel moisture." proprietary Western_Gulf_of_Maine_0 Observations from the Western Gulf of Maine OB_DAAC STAC Catalog 2006-02-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360698-OB_DAAC.umm_json Observations from the Western Gulf of Maine proprietary Wetland_Soil_CarbonStocks_WA_2249_1 Soil Organic Carbon and Wetland Intrinsic Potential, Hoh River Watershed, WA, 2012-13 ORNL_CLOUD STAC Catalog 2012-01-01 2022-06-29 -124.54, 47.57, -123.83, 47.9 https://cmr.earthdata.nasa.gov/search/concepts/C2951683862-ORNL_CLOUD.umm_json This dataset contains estimates of soil organic carbon stocks and wetland intrinsic potential (WIP) across the Hoh River Watershed in the Olympic Peninsula, WA, USA in 2012-2013. Estimates were derived from an equation based on wetland intrinsic potential and geology type (Stewart et al., 2023). Wetland intrinsic potential estimates the likelihood that that an area is a wetland using a random forest model built on vegetation, hydrology, and soil data (Halabisky et al., 2022). SOC estimates at 1 m and 30 cm, SOC standard deviations, and WIP are presented in Cloud-Optimized GeoTIFF (*.tif) format at 4-m resolution. Also included are 36 field observations of SOC collected from 2020-08-01 to 2022-06-29. These are contained in a comma separated (*.csv) file. proprietary -Wetland_VegClassification_PAD_2069_1 ABoVE: Wetland Vegetation Classification for Peace-Athabasca Delta, Canada, 2019 ALL STAC Catalog 2019-07-15 2019-09-15 -112.11, 58.21, -110.83, 59.14 https://cmr.earthdata.nasa.gov/search/concepts/C2308233855-ORNL_CLOUD.umm_json This dataset contains land cover classification focused on water and wetland vegetation communities over the Peace-Athabasca Delta, Canada. Four classification maps with 5-m resolution were derived various combinations of Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) acquired in July and September 2019, and a historical LiDAR archive data. The maps include 10 land cover classes, including open water, emergent aquatic vegetation types, terrestrial vegetation, and forest. Based on field data, the best performing model, which combined all three data sources, achieved an overall accuracy of 93.5%. The land cover maps are provided in GeoTIFF format along with polygons of AVIRIS-NG, UAVSAR, and LiDAR footprints in shapefile and KML formats. proprietary Wetland_VegClassification_PAD_2069_1 ABoVE: Wetland Vegetation Classification for Peace-Athabasca Delta, Canada, 2019 ORNL_CLOUD STAC Catalog 2019-07-15 2019-09-15 -112.11, 58.21, -110.83, 59.14 https://cmr.earthdata.nasa.gov/search/concepts/C2308233855-ORNL_CLOUD.umm_json This dataset contains land cover classification focused on water and wetland vegetation communities over the Peace-Athabasca Delta, Canada. Four classification maps with 5-m resolution were derived various combinations of Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) acquired in July and September 2019, and a historical LiDAR archive data. The maps include 10 land cover classes, including open water, emergent aquatic vegetation types, terrestrial vegetation, and forest. Based on field data, the best performing model, which combined all three data sources, achieved an overall accuracy of 93.5%. The land cover maps are provided in GeoTIFF format along with polygons of AVIRIS-NG, UAVSAR, and LiDAR footprints in shapefile and KML formats. proprietary +Wetland_VegClassification_PAD_2069_1 ABoVE: Wetland Vegetation Classification for Peace-Athabasca Delta, Canada, 2019 ALL STAC Catalog 2019-07-15 2019-09-15 -112.11, 58.21, -110.83, 59.14 https://cmr.earthdata.nasa.gov/search/concepts/C2308233855-ORNL_CLOUD.umm_json This dataset contains land cover classification focused on water and wetland vegetation communities over the Peace-Athabasca Delta, Canada. Four classification maps with 5-m resolution were derived various combinations of Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) acquired in July and September 2019, and a historical LiDAR archive data. The maps include 10 land cover classes, including open water, emergent aquatic vegetation types, terrestrial vegetation, and forest. Based on field data, the best performing model, which combined all three data sources, achieved an overall accuracy of 93.5%. The land cover maps are provided in GeoTIFF format along with polygons of AVIRIS-NG, UAVSAR, and LiDAR footprints in shapefile and KML formats. proprietary WhitePhenoregions_799_1 Phenoregions For Monitoring Vegetation Responses to Climate Change ORNL_CLOUD STAC Catalog 1982-01-01 1999-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784383305-ORNL_CLOUD.umm_json The overall purpose in this research was to identify the regions of the world best suited for long-term monitoring of biospheric responses to climate change, i.e., monitoring land surface phenology. The user is referred to White et al. [2005] for further details. Using global 8 km 1982 to 1999 Normalized Difference Vegetation Index (NDVI) data and an eight-element monthly climatology, we identified pixels consistently dominated by annual cycles and then created phenologically and climatically self-similar clusters, which we term phenoregions. We then ranked and screened each phenoregion as a function of landcover homogeneity and consistency, evidence of human impacts, and political diversity.This dataset contains material providing users with direct access to data used to construct the figures in White et al. [2005]. Users are referred to this reference for additional information. Data files include ASCII and binary versions of the image files for the 500 elemental phenoregions and the 136 final monitoring phenoregions (shown in figure below) and a corresponding .jpg map. Also included are the classification data in tabular ACSII format for each of the 500 elemental phenoregions.Selected monitoring phenoregions. Phenoregions with fewer than 100 pixels or dominated by crop, urban or barren landcover removed. The 136 remaining phenoregions are those passing the screening factors in Table 1 and are shown with normalized rankings by landcover. (From White et al., 2005) proprietary WhiteSpruce_Leaf_Traits_Alaska_2124_1 ABoVE: White Spruce Photosynthetic and Leaf Traits, Alaska and New York, 2017 ORNL_CLOUD STAC Catalog 2017-06-19 2017-07-20 -149.75, 41.4, -74.02, 67.99 https://cmr.earthdata.nasa.gov/search/concepts/C2636355463-ORNL_CLOUD.umm_json This dataset provides measurements of gas exchange (light response curves, Kok curves and ACi curves), leaf traits (carbon, nitrogen, and specific leaf area), leaf pigments (Chlorophyll a, Chlorophyll b and Carotenoids), the photochemical reflectance index (PRI), and average photosynthetic photon flux density as collected from hemispherical photographs. Data were collected on white spruce trees (Picea glauca (Moench) Voss) growing at the northern edge of the species' distribution in Alaska and at the southern edge of the species' distribution in Black Rock Forest (BRF), New York. Measurements were taken at high and low canopy positions on each tree at both sites during the 2017 growing season (2017-06-19 to 2017-07-20). Gas exchange, leaf trait, pigment and spectral measurements were obtained using a portable photosynthesis system (LI-6800, LI-COR, Lincoln, NE). Photochemical reflectance index was determined using a spectroradiometer, and hemispherical photographs were taken with a digital camera. These data were collected to better understand how vertical canopy gradients in photosynthetic physiology change from the southernmost to the northernmost range extremes of white spruce. The data are provided in comma-separated value (CSV) format. proprietary WhiteSpruce_Leaf_Traits_Alaska_2124_1 ABoVE: White Spruce Photosynthetic and Leaf Traits, Alaska and New York, 2017 ALL STAC Catalog 2017-06-19 2017-07-20 -149.75, 41.4, -74.02, 67.99 https://cmr.earthdata.nasa.gov/search/concepts/C2636355463-ORNL_CLOUD.umm_json This dataset provides measurements of gas exchange (light response curves, Kok curves and ACi curves), leaf traits (carbon, nitrogen, and specific leaf area), leaf pigments (Chlorophyll a, Chlorophyll b and Carotenoids), the photochemical reflectance index (PRI), and average photosynthetic photon flux density as collected from hemispherical photographs. Data were collected on white spruce trees (Picea glauca (Moench) Voss) growing at the northern edge of the species' distribution in Alaska and at the southern edge of the species' distribution in Black Rock Forest (BRF), New York. Measurements were taken at high and low canopy positions on each tree at both sites during the 2017 growing season (2017-06-19 to 2017-07-20). Gas exchange, leaf trait, pigment and spectral measurements were obtained using a portable photosynthesis system (LI-6800, LI-COR, Lincoln, NE). Photochemical reflectance index was determined using a spectroradiometer, and hemispherical photographs were taken with a digital camera. These data were collected to better understand how vertical canopy gradients in photosynthetic physiology change from the southernmost to the northernmost range extremes of white spruce. The data are provided in comma-separated value (CSV) format. proprietary Wildfire_Effects_Spruce_Field_1595_1 ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017 ORNL_CLOUD STAC Catalog 2017-07-26 2017-07-28 -152.42, 65.1, -151.95, 65.23 https://cmr.earthdata.nasa.gov/search/concepts/C2162141870-ORNL_CLOUD.umm_json This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska, in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015. proprietary Wildfire_Effects_Spruce_Field_1595_1 ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017 ALL STAC Catalog 2017-07-26 2017-07-28 -152.42, 65.1, -151.95, 65.23 https://cmr.earthdata.nasa.gov/search/concepts/C2162141870-ORNL_CLOUD.umm_json This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska, in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015. proprietary Wildfire_Impacts_Boreal_Ecosys_2359_1 Impacts of Wildfires on Boreal Forest Ecosystem Carbon Dynamics ORNL_CLOUD STAC Catalog 1986-01-01 2020-12-31 -166, 43.5, -53, 70 https://cmr.earthdata.nasa.gov/search/concepts/C3234724704-ORNL_CLOUD.umm_json This dataset contains simulations of net primary production (NPP), heterotrophic respiration (RH), net ecosystem production (NEP), and soil temperature data in North American boreal forests for the period 1986-2020. Data sources included historical fire sources and Landsat data. The delta Normalized Burn Ratio (dNBR), which can be used to represent burn severity for a fire, was calculated for each individual fire over the time period. The interactions between canopy, fire and soil thermal dynamics were modelled using a soil surface energy balance model incorporated into a previous Terrestrial Ecosystem Model (TEM). Using the revised TEM, two regional simulations were conducted with and without fire disturbance. Fire polygons were dissected into each unit with unique fire history and then intersected with each grid cell to measure fire impacts. The output values for each grid cell are the area-weighted mean of each fire polygon and unburned area within the cell. Two extra simulations without a canopy energy balance scheme were also conducted to quantify the impact of the canopy. Soil temperature was simulated with and without the canopy energy balance scheme in the model in addition to considering fire impacts. proprietary -Wildfires_2014_NWT_Canada_1307_1 ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014 ORNL_CLOUD STAC Catalog 1997-07-07 2015-07-15 -121.6, 60.33, -110.68, 64.25 https://cmr.earthdata.nasa.gov/search/concepts/C2170968584-ORNL_CLOUD.umm_json This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites. proprietary Wildfires_2014_NWT_Canada_1307_1 ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014 ALL STAC Catalog 1997-07-07 2015-07-15 -121.6, 60.33, -110.68, 64.25 https://cmr.earthdata.nasa.gov/search/concepts/C2170968584-ORNL_CLOUD.umm_json This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites. proprietary -Wildfires_Date_of_Burning_1559_1.1 ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019 ALL STAC Catalog 2001-01-01 2019-12-31 -178.84, 41.75, -53.83, 70.16 https://cmr.earthdata.nasa.gov/search/concepts/C2162122340-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021. proprietary +Wildfires_2014_NWT_Canada_1307_1 ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014 ORNL_CLOUD STAC Catalog 1997-07-07 2015-07-15 -121.6, 60.33, -110.68, 64.25 https://cmr.earthdata.nasa.gov/search/concepts/C2170968584-ORNL_CLOUD.umm_json This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites. proprietary Wildfires_Date_of_Burning_1559_1.1 ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019 ORNL_CLOUD STAC Catalog 2001-01-01 2019-12-31 -178.84, 41.75, -53.83, 70.16 https://cmr.earthdata.nasa.gov/search/concepts/C2162122340-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021. proprietary -Wildfires_NWT_Canada_1548_1 ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016 ALL STAC Catalog 2015-05-20 2016-08-08 -135.54, 59.93, -106.76, 68.33 https://cmr.earthdata.nasa.gov/search/concepts/C2162122286-ORNL_CLOUD.umm_json This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites. proprietary +Wildfires_Date_of_Burning_1559_1.1 ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019 ALL STAC Catalog 2001-01-01 2019-12-31 -178.84, 41.75, -53.83, 70.16 https://cmr.earthdata.nasa.gov/search/concepts/C2162122340-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021. proprietary Wildfires_NWT_Canada_1548_1 ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016 ORNL_CLOUD STAC Catalog 2015-05-20 2016-08-08 -135.54, 59.93, -106.76, 68.33 https://cmr.earthdata.nasa.gov/search/concepts/C2162122286-ORNL_CLOUD.umm_json This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites. proprietary +Wildfires_NWT_Canada_1548_1 ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016 ALL STAC Catalog 2015-05-20 2016-08-08 -135.54, 59.93, -106.76, 68.33 https://cmr.earthdata.nasa.gov/search/concepts/C2162122286-ORNL_CLOUD.umm_json This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites. proprietary Wildfires_NWT_Canada_2018_1703_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2018 ORNL_CLOUD STAC Catalog 2018-08-12 2018-08-22 -117.43, 60.45, -113.42, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2143403376-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites. Additional measurements included soil moisture, collected for validation of the UAVSAR airborne collection, and depth to frozen ground at the nine unburned sites. This 2018 fieldwork completes four years of field sampling at the wildfire areas. proprietary Wildfires_NWT_Canada_2018_1703_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2018 ALL STAC Catalog 2018-08-12 2018-08-22 -117.43, 60.45, -113.42, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2143403376-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites. Additional measurements included soil moisture, collected for validation of the UAVSAR airborne collection, and depth to frozen ground at the nine unburned sites. This 2018 fieldwork completes four years of field sampling at the wildfire areas. proprietary Wildfires_NWT_Canada_2019_1900_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2019 ORNL_CLOUD STAC Catalog 2018-08-16 2019-09-05 -117.43, 60.92, -113.02, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2445465291-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2019 from 11 study areas, which contained 28 sites that were burned by wildfires in 2014 and 2015, and 14 unburned sites in the Northwest Territories (NWT), Canada. Burn sites included peatland and upland. These field data include vegetation inventories, ground cover, as well as diameter and height for trees and shrubs in the unburned sites. Similar data were collected for the unburned sites in the years 2015-18 and are available in related separate datasets. In 2019, the focus was on woody and non-woody seedling/sprouting regrowth data in the burned sites. Additional measurements collected at all sites included total peat depth, soil moisture, and active layer thickness (ALT). Soil moisture and ALT were collected for validation of the UAVSAR airborne collection and Radarsat-2 overpasses. This 2019 fieldwork completes five years of field sampling at the wildfire areas. proprietary @@ -16961,10 +16941,10 @@ XAERDT_L2_ABI_G16_1 ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km LA XAERDT_L2_ABI_G16_1 ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859273114-LAADS.umm_json The ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G16 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-16 has been serving in the operational GOES-East position (near -75°W) since December 18, 2017. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G16 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G16 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G16 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_ABI_G17_1 ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859265967-LAADS.umm_json The ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G17 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-17 served in the operational GOES-West position (near -137°W), from February 12, 2019, through January 4, 2023. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G17 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G17 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G17 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_ABI_G17_1 ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859265967-LAADS.umm_json The ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G17 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-17 served in the operational GOES-West position (near -137°W), from February 12, 2019, through January 4, 2023. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G17 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G17 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G17 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary -XAERDT_L2_AHI_H08_1 AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859255251-LAADS.umm_json The AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H08 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-8 platform served in the operational Himawari position (near 140.7°E) between October 2014 and 13 December 2022. Himawari-9 replaced Himawari-8 and is currently operational. The Himawari-8/AHI collection record spans from January 2019 through 12th December 2022. The final 19 days of 2022 (December 13 through 31) are served by L2 products derived from the Himawari-9/AHI instrument. The XAERDT_L2_AHI_H08 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H08 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H08 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_AHI_H08_1 AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859255251-LAADS.umm_json The AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H08 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-8 platform served in the operational Himawari position (near 140.7°E) between October 2014 and 13 December 2022. Himawari-9 replaced Himawari-8 and is currently operational. The Himawari-8/AHI collection record spans from January 2019 through 12th December 2022. The final 19 days of 2022 (December 13 through 31) are served by L2 products derived from the Himawari-9/AHI instrument. The XAERDT_L2_AHI_H08 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H08 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H08 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary -XAERDT_L2_AHI_H09_1 AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2022-12-13 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859261579-LAADS.umm_json The AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H09 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-9 platform currently serves in the operational Himawari position (near 140.7°E) since it was launched November 2, 2016, and replaces Himawari-8. The Himawari-9/AHI collection record spans from 13th December 2022 through 31st December 2022. The XAERDT_L2_AHI_H09 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H09 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H09 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary +XAERDT_L2_AHI_H08_1 AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859255251-LAADS.umm_json The AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H08 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-8 platform served in the operational Himawari position (near 140.7°E) between October 2014 and 13 December 2022. Himawari-9 replaced Himawari-8 and is currently operational. The Himawari-8/AHI collection record spans from January 2019 through 12th December 2022. The final 19 days of 2022 (December 13 through 31) are served by L2 products derived from the Himawari-9/AHI instrument. The XAERDT_L2_AHI_H08 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H08 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H08 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_AHI_H09_1 AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2022-12-13 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859261579-LAADS.umm_json The AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H09 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-9 platform currently serves in the operational Himawari position (near 140.7°E) since it was launched November 2, 2016, and replaces Himawari-8. The Himawari-9/AHI collection record spans from 13th December 2022 through 31st December 2022. The XAERDT_L2_AHI_H09 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H09 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H09 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary +XAERDT_L2_AHI_H09_1 AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2022-12-13 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859261579-LAADS.umm_json The AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H09 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-9 platform currently serves in the operational Himawari position (near 140.7°E) since it was launched November 2, 2016, and replaces Himawari-8. The Himawari-9/AHI collection record spans from 13th December 2022 through 31st December 2022. The XAERDT_L2_AHI_H09 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H09 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H09 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_MODIS_Aqua_1 MODIS/Aqua Dark Target Aerosol 5-Min L2 Swath 10 km LAADS STAC Catalog 2019-01-01 2023-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859238768-LAADS.umm_json The MODIS/Aqua Dark Target Aerosol 5-Min L2 Swath 10 km product, short-name XAERDT_L2_MODIS_Aqua is provided at 10-km spatial resolution (at-nadir) and a 5-minute cadence that typically yields about 140 granules over the daylit hours of a 24-hour period. The Aqua/MODIS L2 collection record spans from January 2019 through December 2022. The XAERDT_L2_MODIS_Aqua product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_MODIS_Aqua product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_MODIS_Aqua Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_MODIS_Terra_1 MODIS/Terra Dark Target Aerosol 5-Min L2 Swath 10 km LAADS STAC Catalog 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859248304-LAADS.umm_json The MODIS/Terra Dark Target Aerosol 5-Min L2 Swath 10 km product, short-name XAERDT_L2_MODIS_Terra is provided at 10-km spatial resolution (at-nadir) and a 5-minute cadence that typically yields about 140 granules over the daylit hours of a 24-hour period. The Terra/MODIS L2 collection record spans from January 2019 through December 2022. The XAERDT_L2_MODIS_Terra product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_MODIS_Terra product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_MODIS_Terra Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary XAERDT_L2_VIIRS_NOAA20_1 VIIRS/NOAA20 Dark Target Aerosol L2 6-Min Swath 6 km LAADS STAC Catalog 2019-01-01 2023-05-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859228520-LAADS.umm_json The VIIRS/NOAA20 L2 Dark Target Aerosol 6-Min L2 Swath 6 km product, short-name XAERDT_L2_VIIRS_NOAA20 is provided at 6-km spatial resolution (at-nadir) and a 6-minute cadence that typically yields about 130 granules over the daylit hours of a 24-hour period. The NOAA20/VIIRS L2 collection record spans from January 2019 through December 2022. The XAERDT_L2_VIIRS_NOAA20 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_VIIRS_NOAA20 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_VIIRS_NOAA20 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary @@ -17012,30 +16992,30 @@ accum-measurements-domec-traverse-1982_1 Accumulation Measurements from Pioneers accumulation-movement-markers-mirny-domec_1 Detailed Notes on Accumulation/Movement Markers, Mirny-Dome C AU_AADC STAC Catalog 1977-01-01 1978-12-31 124.5, -78.5, 93, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311711-AU_AADC.umm_json Detailed notes about each of the markers used for movement (and accumulation) measurements along the Mirny-Dome C traverse line. Includes processing notes from the JMR position analysis. These documents have been archived in the records store at the Australian Antarctic Division. proprietary accumulation_lawdome_1960_1 Accumulation Measurements, Law Dome 1959-1960 ALL STAC Catalog 1959-01-01 1960-12-31 110, -67, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214305674-AU_AADC.umm_json A collection of information on the position and measurements of snow accumulation via accumulation stakes placed on Law Dome in 1959, and measured over 1959 and 1960. These documents have been archived at the Australian Antarctic Division. proprietary accumulation_lawdome_1960_1 Accumulation Measurements, Law Dome 1959-1960 AU_AADC STAC Catalog 1959-01-01 1960-12-31 110, -67, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214305674-AU_AADC.umm_json A collection of information on the position and measurements of snow accumulation via accumulation stakes placed on Law Dome in 1959, and measured over 1959 and 1960. These documents have been archived at the Australian Antarctic Division. proprietary -aces1am_1 ACES Aircraft and Mechanical Data ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977826980-GHRC_DAAC.umm_json The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). proprietary aces1am_1 ACES Aircraft and Mechanical Data GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977826980-GHRC_DAAC.umm_json The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). proprietary -aces1cont_1 ACES CONTINUOUS DATA V1 ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847043-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels. proprietary +aces1am_1 ACES Aircraft and Mechanical Data ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977826980-GHRC_DAAC.umm_json The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). proprietary aces1cont_1 ACES CONTINUOUS DATA V1 GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847043-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels. proprietary -aces1efm_1 ACES ELECTRIC FIELD MILL V1 ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847178-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data from Electric Field Mills, which yield information about the atmospheric electrical fields above the instruments. proprietary +aces1cont_1 ACES CONTINUOUS DATA V1 ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847043-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels. proprietary aces1efm_1 ACES ELECTRIC FIELD MILL V1 GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847178-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data from Electric Field Mills, which yield information about the atmospheric electrical fields above the instruments. proprietary -aces1log_1 ACES LOG DATA ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977853903-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight. proprietary +aces1efm_1 ACES ELECTRIC FIELD MILL V1 ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847178-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data from Electric Field Mills, which yield information about the atmospheric electrical fields above the instruments. proprietary aces1log_1 ACES LOG DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977853903-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight. proprietary +aces1log_1 ACES LOG DATA ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977853903-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight. proprietary aces1time_1 ACES TIMING DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977855412-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August or 2002, ACES researchers overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform. proprietary aces1time_1 ACES TIMING DATA ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977855412-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August or 2002, ACES researchers overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform. proprietary -aces1trig_1 ACES TRIGGERED DATA ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977858342-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued. proprietary aces1trig_1 ACES TRIGGERED DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977858342-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued. proprietary +aces1trig_1 ACES TRIGGERED DATA ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977858342-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued. proprietary acoustic_charts_v6_1994_95_1 Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS) AU_AADC STAC Catalog 1995-02-06 1995-04-12 60, -69.393, 147.473, -42.882 https://cmr.earthdata.nasa.gov/search/concepts/C1214311712-AU_AADC.umm_json Acoustic sounder charts were collected at six locations during Australian Antarctic Division Voyage 6 1994/95 (BANGSS) using the Kongsberg EA200 Echo Sounder on the Aurora Australis. BANGSS is an acronym for Big ANtarctic Geological and Seismic Survey. The voyage began on 6 February 1995 and finished on 12 April 1995. Each chart is labelled with information about when and where the data was collected: date, time, latitude and longitude. The charts provide a profile of the sea floor and have a time axis with numbers in the following format. the first two digits are the day the next two digits are the month the next five digits are the time (UTC) the last ten digits are the maximum value on the depth axis eg 2402005 360000000500 means 24 February 5:36 UTC and the maximum value on the depth axis is 500 metres See a Related URL for a link to information about the voyage including the voyage report. proprietary acoustic_charts_v6_1994_95_1 Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS) ALL STAC Catalog 1995-02-06 1995-04-12 60, -69.393, 147.473, -42.882 https://cmr.earthdata.nasa.gov/search/concepts/C1214311712-AU_AADC.umm_json Acoustic sounder charts were collected at six locations during Australian Antarctic Division Voyage 6 1994/95 (BANGSS) using the Kongsberg EA200 Echo Sounder on the Aurora Australis. BANGSS is an acronym for Big ANtarctic Geological and Seismic Survey. The voyage began on 6 February 1995 and finished on 12 April 1995. Each chart is labelled with information about when and where the data was collected: date, time, latitude and longitude. The charts provide a profile of the sea floor and have a time axis with numbers in the following format. the first two digits are the day the next two digits are the month the next five digits are the time (UTC) the last ten digits are the maximum value on the depth axis eg 2402005 360000000500 means 24 February 5:36 UTC and the maximum value on the depth axis is 500 metres See a Related URL for a link to information about the voyage including the voyage report. proprietary acoustic_doppler_current_profiler_data_-_2010 Acoustic Doppler Current Profiler Data - 2010 SCIOPS STAC Catalog 2010-08-21 2010-09-17 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602088-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Two files are included: A matlab file and a .zip file containing ascii files for each deployement. 2.) ascii format. The .mat file sos2010_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format. 
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2010dt_ascii.zip. 
The first line of each file gives the center depth of the ADCP bins in meters. 
Note that both the bin depths as well as the number of bins may change
between deployments.

It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec, 
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5) 
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins 
- N values of meridional velocity, positive northward

""Bad"" data are marked with the flag value 999.99." proprietary acoustic_doppler_current_profiler_data_-_2010 Acoustic Doppler Current Profiler Data - 2010 ALL STAC Catalog 2010-08-21 2010-09-17 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602088-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Two files are included: A matlab file and a .zip file containing ascii files for each deployement. 2.) ascii format. The .mat file sos2010_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format. 
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2010dt_ascii.zip. 
The first line of each file gives the center depth of the ADCP bins in meters. 
Note that both the bin depths as well as the number of bins may change
between deployments.

It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec, 
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5) 
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins 
- N values of meridional velocity, positive northward

""Bad"" data are marked with the flag value 999.99." proprietary acoustic_doppler_current_profiler_data_-_2011 Acoustic Doppler Current Profiler Data - 2011 SCIOPS STAC Catalog 2011-08-22 2011-09-13 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214600594-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Three files are included: A matlab file and .zip file and .tar files containing ascii files for each deployement. 1.) Matlab format. The .mat file sos2011_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format. 
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2011dt_ascii.zip or sos2011dt_asc.tar. 
The first line of each file gives the center depth of the ADCP bins in meters. 
Note that both the bin depths as well as the number of bins may change
between deployments.

It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec, 
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5) 
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins 
- N values of meridional velocity, positive northward

""Bad"" data are marked with the flag value 999.99." proprietary acoustic_doppler_current_profiler_data_-_2011 Acoustic Doppler Current Profiler Data - 2011 ALL STAC Catalog 2011-08-22 2011-09-13 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214600594-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Three files are included: A matlab file and .zip file and .tar files containing ascii files for each deployement. 1.) Matlab format. The .mat file sos2011_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format. 
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2011dt_ascii.zip or sos2011dt_asc.tar. 
The first line of each file gives the center depth of the ADCP bins in meters. 
Note that both the bin depths as well as the number of bins may change
between deployments.

It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec, 
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5) 
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins 
- N values of meridional velocity, positive northward

""Bad"" data are marked with the flag value 999.99." proprietary -active_layer_arcss_grid_atqasuk_alaska_2010 Active Layer ARCSS grid Atqasuk, Alaska 2010 ALL STAC Catalog 2010-07-10 2010-08-16 -156, 70, -158, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214602289-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary active_layer_arcss_grid_atqasuk_alaska_2010 Active Layer ARCSS grid Atqasuk, Alaska 2010 SCIOPS STAC Catalog 2010-07-10 2010-08-16 -156, 70, -158, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214602289-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary +active_layer_arcss_grid_atqasuk_alaska_2010 Active Layer ARCSS grid Atqasuk, Alaska 2010 ALL STAC Catalog 2010-07-10 2010-08-16 -156, 70, -158, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214602289-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary active_layer_arcss_grid_atqasuk_alaska_2011 Active Layer ARCSS grid Atqasuk, Alaska 2011 ALL STAC Catalog 2011-06-17 2011-08-12 -157, 70, -156, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600393-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2011 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary active_layer_arcss_grid_atqasuk_alaska_2011 Active Layer ARCSS grid Atqasuk, Alaska 2011 SCIOPS STAC Catalog 2011-06-17 2011-08-12 -157, 70, -156, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600393-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2011 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary -active_layer_arcss_grid_atqasuk_alaska_2012 Active Layer ARCSS grid Atqasuk, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214601993-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2012 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary active_layer_arcss_grid_atqasuk_alaska_2012 Active Layer ARCSS grid Atqasuk, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214601993-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2012 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary +active_layer_arcss_grid_atqasuk_alaska_2012 Active Layer ARCSS grid Atqasuk, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214601993-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2012 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary active_layer_arcss_grid_barrow_alaska_2010 Active Layer ARCSS grid Barrow, Alaska 2010 ALL STAC Catalog 2010-06-30 2010-08-11 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600590-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary active_layer_arcss_grid_barrow_alaska_2010 Active Layer ARCSS grid Barrow, Alaska 2010 SCIOPS STAC Catalog 2010-06-30 2010-08-11 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600590-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary active_layer_arcss_grid_barrow_alaska_2011 Active Layer ARCSS grid Barrow, Alaska 2011 ALL STAC Catalog 2011-06-14 2011-07-25 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600390-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass. proprietary @@ -17046,10 +17026,10 @@ active_layer_nims_grid_atqasuk_alaska_2011 Active Layer NIMS grid Atqasuk, Alask active_layer_nims_grid_atqasuk_alaska_2011 Active Layer NIMS grid Atqasuk, Alaska 2011 SCIOPS STAC Catalog 2011-06-05 2011-08-12 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600341-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary active_layer_nims_grid_atqasuk_alaska_2012 Active Layer NIMS grid Atqasuk, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600318-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2012 summer field season. UTEP SEL's CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary active_layer_nims_grid_atqasuk_alaska_2012 Active Layer NIMS grid Atqasuk, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600318-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2012 summer field season. UTEP SEL's CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary -active_layer_nims_grid_barrow_alaska_2011 Active Layer NIMS grid Barrow, Alaska 2011 ALL STAC Catalog 2011-06-14 2011-08-09 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214602385-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary active_layer_nims_grid_barrow_alaska_2011 Active Layer NIMS grid Barrow, Alaska 2011 SCIOPS STAC Catalog 2011-06-14 2011-08-09 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214602385-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary -active_layer_nims_grid_barrow_alaska_2012 Active Layer NIMS grid Barrow, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600541-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2012 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary +active_layer_nims_grid_barrow_alaska_2011 Active Layer NIMS grid Barrow, Alaska 2011 ALL STAC Catalog 2011-06-14 2011-08-09 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214602385-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary active_layer_nims_grid_barrow_alaska_2012 Active Layer NIMS grid Barrow, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600541-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2012 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary +active_layer_nims_grid_barrow_alaska_2012 Active Layer NIMS grid Barrow, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600541-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2012 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary ada968fd392d49fbbb07ac84eeb23ac6_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Zachariae Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-06-24 2017-08-10 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142710-FEDEO.umm_json This dataset contains an optical ice velocity time series and seasonal product of the Zachariae Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The product was generated by S[&]T Norway. proprietary adaptive_long-term_fasting_in_land_and_ice-bound_polar_bears_data_table Adaptive long-term fasting in land and ice-bound polar bears: Data Table SCIOPS STAC Catalog 2008-01-01 2011-12-31 -155, 70, -122, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1214602399-SCIOPS.umm_json The datasets in the data table have been collected as part of a project to understand how reduced sea ice cover in the Arctic will impact polar bear populations. Bears that stay ashore in summer have almost no access to food and tend to be inactive. Those that stay on the ice, however, have continued access to prey and make extensive movements. Over a three year period, scientists from the University of Wyoming and the U. S. Geological Service followed the movements of bears in both habitats and monitored their body temperature, muscle condition, blood chemistry, and metabolism. The physiological data will be added to spatially-explicit individual-based population models to predict population response to reduced ice cover. proprietary adaptive_long-term_fasting_in_land_and_ice-bound_polar_bears_data_table Adaptive long-term fasting in land and ice-bound polar bears: Data Table ALL STAC Catalog 2008-01-01 2011-12-31 -155, 70, -122, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1214602399-SCIOPS.umm_json The datasets in the data table have been collected as part of a project to understand how reduced sea ice cover in the Arctic will impact polar bear populations. Bears that stay ashore in summer have almost no access to food and tend to be inactive. Those that stay on the ice, however, have continued access to prey and make extensive movements. Over a three year period, scientists from the University of Wyoming and the U. S. Geological Service followed the movements of bears in both habitats and monitored their body temperature, muscle condition, blood chemistry, and metabolism. The physiological data will be added to spatially-explicit individual-based population models to predict population response to reduced ice cover. proprietary @@ -17060,29 +17040,29 @@ adpe-aat-census_1 Adelie penguin census from records from 1931 to 2007 AAT regio adu_birp Animal Demography Unit - The Birds in Reserves Project (BIRP) CEOS_EXTRA STAC Catalog 1906-02-05 2007-05-20 16.46, -34.77, 32.86, -22.61 https://cmr.earthdata.nasa.gov/search/concepts/C2232477691-CEOS_EXTRA.umm_json BIRP is a joint project of BirdLife South Africa (BLSA), and the Animal Demography Unit (ADU), based at the University of Cape Town (UCT). The basic purpose of BIRP is to compile a comprehensive catalogue of the species of birds which occur and breed in South Africa’s many protected areas. A database of this kind will help to identify the species which are as yet not adequately protected and will also provide the managers of protected areas with information useful in setting management policies. proprietary adu_cwac Animal Demography Unit - Coordinated Waterbird Counts (CWAC) CEOS_EXTRA STAC Catalog 1983-07-15 2006-09-30 16.46, -34.72, 32.88, -22.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232477679-CEOS_EXTRA.umm_json The Coordinated Waterbird Counts (CWAC) project was launched in 1992. The objective of CWAC is to monitor South Africa's waterbird populations and the conditions of the wetlands which are important for waterbirds. This is being done by means of a programme of regular mid-summer and mid-winter censuses at a large number of South African wetlands. Regular six-monthly counts are conducted; however, we do encourage counters to survey their wetlands on a more regular basis as this provides better data. CWAC currently monitors over 400 wetlands around the country on a regular basis, and furthermore curates waterbird data for close to 600 wetlands. proprietary adu_safring Animal Demography Unit - South African Bird Ringing Unit (SAFRING) CEOS_EXTRA STAC Catalog 1899-12-30 2004-12-31 -76.33, -71.9, 73.5, 72.25 https://cmr.earthdata.nasa.gov/search/concepts/C2232477669-CEOS_EXTRA.umm_json The South African Bird Ringing Unit (SAFRING) administers bird ringing in southern Africa, supplying rings, ringing equipment and services to volunteer and professional ringers in South Africa and neighbouring countries. All ringing records are curated by SAFRING, which is an essential arm of the Animal Demography Unit. Contact is maintained by the SAFRING Project Coordinator with all ringers (banders in North American or Australian terminology). The Bird Ringing Scheme in South Africa was initiated in 1948, so 1998 saw the 50th anniversary of the scheme. During this period over 1.7 million birds of 852 species were ringed. There have been a total of 16 800 ring recoveries since the inception of the scheme. This gives an overall recovery rate for rings in southern Africa of marginally less than 1%, averaged across all species. This probability varies enormously across species. proprietary -aerial_casa_2010_11_1 Aerial photography flown for the Australian Antarctic Division from CASA 212-400 aircraft, 2010-11 AU_AADC STAC Catalog 2011-01-02 2011-02-06 89.17, -72.37, 112.42, -65.69 https://cmr.earthdata.nasa.gov/search/concepts/C1214305645-AU_AADC.umm_json Digital aerial photography was flown by a contractor for the Australian Antarctic Division (AAD) from CASA 212-400 aircraft during the 2010-11 season. Photographs were taken for various projects or needs: Whales project requested by Natalie Kelly (Science Branch AAD and CSIRO); Cronk Islands, Knox Coast, Wilkes Coast - requested by Colin Southwell (Science Branch AAD, AAS project 2722) - the coverage also includes Bailey Peninsula and part of Clark Peninsula; Frazier Islands - requested by Ian Hay (Strategies Branch AAD, AAS project 3154); Aurora Basin - taken on the return flight from Dome C to Casey of Aurora Basin GC41 position 71 degrees 36'10''S, 111 degrees 15'46''E; Wilkins Aerodrome - to photograph runway and melt; Casey, Wilkes - requested by Gill Slocum (Strategies Branch AAD). The photographs were taken between 2 January 2011 and 6 February 2011. In most cases the images were georeferenced in the camera using the aircraft GPS. Vertical photographs were taken with an in floor camera system using a Nikon D200 digital camera and oblique photographs were taken using a handheld Nikon D700 digital camera in the cockpit. The set of images is too big for download but the images are available upon request from the Australian Antarctic Data Centre. Data extracted from the exif information of the images are available for download as csv files and, in some cases, shapefiles. These data include file name, date, camera, focal length, latitude, longitude and altitude. The images of the Cronk Islands and the Frazier Islands can be viewed in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see a Related URL below. The Film/Digital Series are ANTD1260 (Cronk Islands and Frazier Islands 2 January 2011) and ANTD1261 (Frazier Islands 23 January 2011). proprietary aerial_casa_2010_11_1 Aerial photography flown for the Australian Antarctic Division from CASA 212-400 aircraft, 2010-11 ALL STAC Catalog 2011-01-02 2011-02-06 89.17, -72.37, 112.42, -65.69 https://cmr.earthdata.nasa.gov/search/concepts/C1214305645-AU_AADC.umm_json Digital aerial photography was flown by a contractor for the Australian Antarctic Division (AAD) from CASA 212-400 aircraft during the 2010-11 season. Photographs were taken for various projects or needs: Whales project requested by Natalie Kelly (Science Branch AAD and CSIRO); Cronk Islands, Knox Coast, Wilkes Coast - requested by Colin Southwell (Science Branch AAD, AAS project 2722) - the coverage also includes Bailey Peninsula and part of Clark Peninsula; Frazier Islands - requested by Ian Hay (Strategies Branch AAD, AAS project 3154); Aurora Basin - taken on the return flight from Dome C to Casey of Aurora Basin GC41 position 71 degrees 36'10''S, 111 degrees 15'46''E; Wilkins Aerodrome - to photograph runway and melt; Casey, Wilkes - requested by Gill Slocum (Strategies Branch AAD). The photographs were taken between 2 January 2011 and 6 February 2011. In most cases the images were georeferenced in the camera using the aircraft GPS. Vertical photographs were taken with an in floor camera system using a Nikon D200 digital camera and oblique photographs were taken using a handheld Nikon D700 digital camera in the cockpit. The set of images is too big for download but the images are available upon request from the Australian Antarctic Data Centre. Data extracted from the exif information of the images are available for download as csv files and, in some cases, shapefiles. These data include file name, date, camera, focal length, latitude, longitude and altitude. The images of the Cronk Islands and the Frazier Islands can be viewed in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see a Related URL below. The Film/Digital Series are ANTD1260 (Cronk Islands and Frazier Islands 2 January 2011) and ANTD1261 (Frazier Islands 23 January 2011). proprietary -aerial_mosaics_macquarie_2017_2 Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017 AU_AADC STAC Catalog 2017-01-15 2017-02-15 158.874, -54.506, 158.954, -54.483 https://cmr.earthdata.nasa.gov/search/concepts/C1437176029-AU_AADC.umm_json One vertical and two oblique mosaics of The Isthmus at Macquarie Island were created from aerial photographs taken with a UAV (Unmanned Aerial Vehicle) during the course of Australian Antarctic Science Project 4340 in January and February 2017. The oblique mosaics include Wireless Hill and the northern end of the island's plateau. One oblique mosaic is a view from the eastern side of The Isthmus and the other is a view from the western side of The Isthmus. The photographs were taken by Murray Hamilton of the University of Adelaide using a DJI Phantom 3 Advanced UAV (under Monash University's Operators Certificate) which he was using to make temperature and humidity observations. They were taken when the UAV was waiting to descend and measure a temperature profile. The measuring instrument needed some time for the temperature to equilibrate after a rapid ascent. The photographs were taken by rotating the craft, taking snapshots every few tens of degrees. Hugin software was used to create the mosaics. The photographs for the vertical mosaic were taken on 15 January 2017 and the photographs for the oblique mosaics were taken on 7 February 2017 (view from east) and 15 February 2017 (view from west). The vertical mosaic was produced at the request of the Building Services Supervisor at the station. proprietary +aerial_casa_2010_11_1 Aerial photography flown for the Australian Antarctic Division from CASA 212-400 aircraft, 2010-11 AU_AADC STAC Catalog 2011-01-02 2011-02-06 89.17, -72.37, 112.42, -65.69 https://cmr.earthdata.nasa.gov/search/concepts/C1214305645-AU_AADC.umm_json Digital aerial photography was flown by a contractor for the Australian Antarctic Division (AAD) from CASA 212-400 aircraft during the 2010-11 season. Photographs were taken for various projects or needs: Whales project requested by Natalie Kelly (Science Branch AAD and CSIRO); Cronk Islands, Knox Coast, Wilkes Coast - requested by Colin Southwell (Science Branch AAD, AAS project 2722) - the coverage also includes Bailey Peninsula and part of Clark Peninsula; Frazier Islands - requested by Ian Hay (Strategies Branch AAD, AAS project 3154); Aurora Basin - taken on the return flight from Dome C to Casey of Aurora Basin GC41 position 71 degrees 36'10''S, 111 degrees 15'46''E; Wilkins Aerodrome - to photograph runway and melt; Casey, Wilkes - requested by Gill Slocum (Strategies Branch AAD). The photographs were taken between 2 January 2011 and 6 February 2011. In most cases the images were georeferenced in the camera using the aircraft GPS. Vertical photographs were taken with an in floor camera system using a Nikon D200 digital camera and oblique photographs were taken using a handheld Nikon D700 digital camera in the cockpit. The set of images is too big for download but the images are available upon request from the Australian Antarctic Data Centre. Data extracted from the exif information of the images are available for download as csv files and, in some cases, shapefiles. These data include file name, date, camera, focal length, latitude, longitude and altitude. The images of the Cronk Islands and the Frazier Islands can be viewed in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see a Related URL below. The Film/Digital Series are ANTD1260 (Cronk Islands and Frazier Islands 2 January 2011) and ANTD1261 (Frazier Islands 23 January 2011). proprietary aerial_mosaics_macquarie_2017_2 Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017 ALL STAC Catalog 2017-01-15 2017-02-15 158.874, -54.506, 158.954, -54.483 https://cmr.earthdata.nasa.gov/search/concepts/C1437176029-AU_AADC.umm_json One vertical and two oblique mosaics of The Isthmus at Macquarie Island were created from aerial photographs taken with a UAV (Unmanned Aerial Vehicle) during the course of Australian Antarctic Science Project 4340 in January and February 2017. The oblique mosaics include Wireless Hill and the northern end of the island's plateau. One oblique mosaic is a view from the eastern side of The Isthmus and the other is a view from the western side of The Isthmus. The photographs were taken by Murray Hamilton of the University of Adelaide using a DJI Phantom 3 Advanced UAV (under Monash University's Operators Certificate) which he was using to make temperature and humidity observations. They were taken when the UAV was waiting to descend and measure a temperature profile. The measuring instrument needed some time for the temperature to equilibrate after a rapid ascent. The photographs were taken by rotating the craft, taking snapshots every few tens of degrees. Hugin software was used to create the mosaics. The photographs for the vertical mosaic were taken on 15 January 2017 and the photographs for the oblique mosaics were taken on 7 February 2017 (view from east) and 15 February 2017 (view from west). The vertical mosaic was produced at the request of the Building Services Supervisor at the station. proprietary -aerial_photo_sea_ice_1 Aerial photographs of sea ice flown by the Australian Antarctic Division ALL STAC Catalog 2003-09-10 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1214305646-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05; and Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 SIPEX: 29 Aug 2007 to 16 Oct 2007 SIPEX II: 25 Sep 2012 to 6 Nov 2012 The child records include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. proprietary +aerial_mosaics_macquarie_2017_2 Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017 AU_AADC STAC Catalog 2017-01-15 2017-02-15 158.874, -54.506, 158.954, -54.483 https://cmr.earthdata.nasa.gov/search/concepts/C1437176029-AU_AADC.umm_json One vertical and two oblique mosaics of The Isthmus at Macquarie Island were created from aerial photographs taken with a UAV (Unmanned Aerial Vehicle) during the course of Australian Antarctic Science Project 4340 in January and February 2017. The oblique mosaics include Wireless Hill and the northern end of the island's plateau. One oblique mosaic is a view from the eastern side of The Isthmus and the other is a view from the western side of The Isthmus. The photographs were taken by Murray Hamilton of the University of Adelaide using a DJI Phantom 3 Advanced UAV (under Monash University's Operators Certificate) which he was using to make temperature and humidity observations. They were taken when the UAV was waiting to descend and measure a temperature profile. The measuring instrument needed some time for the temperature to equilibrate after a rapid ascent. The photographs were taken by rotating the craft, taking snapshots every few tens of degrees. Hugin software was used to create the mosaics. The photographs for the vertical mosaic were taken on 15 January 2017 and the photographs for the oblique mosaics were taken on 7 February 2017 (view from east) and 15 February 2017 (view from west). The vertical mosaic was produced at the request of the Building Services Supervisor at the station. proprietary aerial_photo_sea_ice_1 Aerial photographs of sea ice flown by the Australian Antarctic Division AU_AADC STAC Catalog 2003-09-10 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1214305646-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05; and Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 SIPEX: 29 Aug 2007 to 16 Oct 2007 SIPEX II: 25 Sep 2012 to 6 Nov 2012 The child records include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. proprietary +aerial_photo_sea_ice_1 Aerial photographs of sea ice flown by the Australian Antarctic Division ALL STAC Catalog 2003-09-10 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1214305646-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05; and Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 SIPEX: 29 Aug 2007 to 16 Oct 2007 SIPEX II: 25 Sep 2012 to 6 Nov 2012 The child records include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. proprietary aerial_photo_sea_ice_ARISE_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the ARISE voyage in 2003 AU_AADC STAC Catalog 2003-09-10 2003-10-31 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611591-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE), 10 Sep 2003 to 31 Oct 2003. The Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs. The ARISE aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. The collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. Select ARISE from the Aerial Photography Series picklist. Preview images of the photos may be viewed using this search. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. The flight lines and photo centres for ARISE are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles. proprietary aerial_photo_sea_ice_ARISE_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the ARISE voyage in 2003 ALL STAC Catalog 2003-09-10 2003-10-31 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611591-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE), 10 Sep 2003 to 31 Oct 2003. The Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs. The ARISE aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. The collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. Select ARISE from the Aerial Photography Series picklist. Preview images of the photos may be viewed using this search. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. The flight lines and photo centres for ARISE are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles. proprietary aerial_photo_sea_ice_ISPOL_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the ISPOL voyage in 2004 AU_AADC STAC Catalog 2004-11-06 2005-01-19 -58.2, -69.67, -55.2, -67.57 https://cmr.earthdata.nasa.gov/search/concepts/C1292611592-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the voyage, Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05, 6 Nov 2004 to 19 Jan 2005. Flights were conducted around the edges of a triangular array of drifting buoys each transmitting GPS location. Flights throughout the experiment show changes in the surface properties (floe size, extent of surface flooding) with time. See the metadata record 'AAD buoy data collected during ISPOL 2004, Western Weddell Sea' for more information on the ISPOL project. The Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs. The ISPOL aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. The collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. Select ISPOL from the Aerial Photography Series picklist. Preview images of the photos may be viewed using this search. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. The flight lines and photo centres for ISPOL are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles. proprietary aerial_photo_sea_ice_ISPOL_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the ISPOL voyage in 2004 ALL STAC Catalog 2004-11-06 2005-01-19 -58.2, -69.67, -55.2, -67.57 https://cmr.earthdata.nasa.gov/search/concepts/C1292611592-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the voyage, Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05, 6 Nov 2004 to 19 Jan 2005. Flights were conducted around the edges of a triangular array of drifting buoys each transmitting GPS location. Flights throughout the experiment show changes in the surface properties (floe size, extent of surface flooding) with time. See the metadata record 'AAD buoy data collected during ISPOL 2004, Western Weddell Sea' for more information on the ISPOL project. The Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs. The ISPOL aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. The collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. Select ISPOL from the Aerial Photography Series picklist. Preview images of the photos may be viewed using this search. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. The flight lines and photo centres for ISPOL are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles. proprietary -aerial_photo_sea_ice_SIPEX_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007 ALL STAC Catalog 2007-08-29 2007-10-16 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611658-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: SIPEX: 29 Aug 2007 to 16 Oct 2007 The Related URLs in this metadata record include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. Some of the SIPEX aerial photographs were taken at ice stations. Refer to the metadata record 'An integrated study of processes linking sea ice and biological ecosystem elements off East Antarctica during winter', Entry ID: ASAC_2767, for information about the ice stations. The metadata record 'RAPPLS Surveys (Radar, Aerial Photography, Pyrometer, and Laser Scanning system) made during the SIPEX II voyage of the Aurora Australis, 2012', Entry ID: SIPEX_II_RAPPLS, describes the aerial photography conducted on SIPEX II, 13 Sep 2012 to 15 Nov 2012. proprietary aerial_photo_sea_ice_SIPEX_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007 AU_AADC STAC Catalog 2007-08-29 2007-10-16 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611658-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: SIPEX: 29 Aug 2007 to 16 Oct 2007 The Related URLs in this metadata record include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. Some of the SIPEX aerial photographs were taken at ice stations. Refer to the metadata record 'An integrated study of processes linking sea ice and biological ecosystem elements off East Antarctica during winter', Entry ID: ASAC_2767, for information about the ice stations. The metadata record 'RAPPLS Surveys (Radar, Aerial Photography, Pyrometer, and Laser Scanning system) made during the SIPEX II voyage of the Aurora Australis, 2012', Entry ID: SIPEX_II_RAPPLS, describes the aerial photography conducted on SIPEX II, 13 Sep 2012 to 15 Nov 2012. proprietary +aerial_photo_sea_ice_SIPEX_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007 ALL STAC Catalog 2007-08-29 2007-10-16 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611658-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: SIPEX: 29 Aug 2007 to 16 Oct 2007 The Related URLs in this metadata record include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. Some of the SIPEX aerial photographs were taken at ice stations. Refer to the metadata record 'An integrated study of processes linking sea ice and biological ecosystem elements off East Antarctica during winter', Entry ID: ASAC_2767, for information about the ice stations. The metadata record 'RAPPLS Surveys (Radar, Aerial Photography, Pyrometer, and Laser Scanning system) made during the SIPEX II voyage of the Aurora Australis, 2012', Entry ID: SIPEX_II_RAPPLS, describes the aerial photography conducted on SIPEX II, 13 Sep 2012 to 15 Nov 2012. proprietary aerial_photo_sea_ice_shapefiles_1 Flight lines and photo centres of aerial photographs of sea ice flown by the Australian Antarctic Division on the ARISE and ISPOL voyages in 2003 and 2004 AU_AADC STAC Catalog 2003-09-10 2005-01-19 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611653-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05. Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 The ARISE and ISPOL aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. proprietary -aerial_photographs_from_columbia_glacier_1976-2010 Aerial Photographs from Columbia Glacier, 1976-2010 ALL STAC Catalog 1976-07-24 2011-06-15 -146.895, 61.22, -146.895, 61.22 https://cmr.earthdata.nasa.gov/search/concepts/C1214600568-SCIOPS.umm_json

Aerial stereophotography missions were flown at least once every year over the Columbia Glacier in 1976-2010, and documented further in the Aerial Inventory. Flight data include all existing scans of the large format diapositives and their derived data products from 2002-2010.

This dataset consists of scanned aerial diapositives in high resolution from a photogrammetric scanner and low resolution JPEG previews. The data are collected into TAR files by year. Data gathered during 2002-2003 are collected into TAR files by day and part (e.g. 20020826_01.tar).

proprietary aerial_photographs_from_columbia_glacier_1976-2010 Aerial Photographs from Columbia Glacier, 1976-2010 SCIOPS STAC Catalog 1976-07-24 2011-06-15 -146.895, 61.22, -146.895, 61.22 https://cmr.earthdata.nasa.gov/search/concepts/C1214600568-SCIOPS.umm_json

Aerial stereophotography missions were flown at least once every year over the Columbia Glacier in 1976-2010, and documented further in the Aerial Inventory. Flight data include all existing scans of the large format diapositives and their derived data products from 2002-2010.

This dataset consists of scanned aerial diapositives in high resolution from a photogrammetric scanner and low resolution JPEG previews. The data are collected into TAR files by year. Data gathered during 2002-2003 are collected into TAR files by day and part (e.g. 20020826_01.tar).

proprietary +aerial_photographs_from_columbia_glacier_1976-2010 Aerial Photographs from Columbia Glacier, 1976-2010 ALL STAC Catalog 1976-07-24 2011-06-15 -146.895, 61.22, -146.895, 61.22 https://cmr.earthdata.nasa.gov/search/concepts/C1214600568-SCIOPS.umm_json

Aerial stereophotography missions were flown at least once every year over the Columbia Glacier in 1976-2010, and documented further in the Aerial Inventory. Flight data include all existing scans of the large format diapositives and their derived data products from 2002-2010.

This dataset consists of scanned aerial diapositives in high resolution from a photogrammetric scanner and low resolution JPEG previews. The data are collected into TAR files by year. Data gathered during 2002-2003 are collected into TAR files by day and part (e.g. 20020826_01.tar).

proprietary aerial_rpa_nov2016_1 Aerial photographs of Davis and Heidemann Valley taken with Remotely Piloted Aircraft, November 2016 ALL STAC Catalog 2016-11-07 2016-11-20 77.9619, -68.5811, 78.0131, -68.5731 https://cmr.earthdata.nasa.gov/search/concepts/C1367275166-AU_AADC.umm_json The Australian Antarcic Division (AAD) contracted Helicopter Resources to fly remotely piloted aircraft (RPA) on Voyage 1 2016/17. The RPA were used to take aerial photographs for sea ice reconnaisance from the RSV Aurora Australis, aerial photographs of Davis, aerial photographs for building roof inspections at Davis and aerial photographs of part of Heidemann Valley. Video was also recorded from the RSV Aurora Australis and of Heidemann Valley. The flights over Heidemann Valley were done at the request of the AAD's Antarctic Modernisation Taskforce. The roof inspections were done at the request of the AAD's Infrastructure section. The following can be downloaded or requested from this metadata record by AAD staff only (see Related URLs): 1 A report prepared by Doug Thost, the chief RPA pilot; 2 The aerial photographs of Davis and Heidemann Valley; and 3 Some panoramas created from aerial photographs taken at Davis. The AAD's Multimedia section have a copy of the videos. The AAD's Infrastructure section have a copy of the aerial photographs taken for roof inspections. See the report for further details. proprietary aerial_rpa_nov2016_1 Aerial photographs of Davis and Heidemann Valley taken with Remotely Piloted Aircraft, November 2016 AU_AADC STAC Catalog 2016-11-07 2016-11-20 77.9619, -68.5811, 78.0131, -68.5731 https://cmr.earthdata.nasa.gov/search/concepts/C1367275166-AU_AADC.umm_json The Australian Antarcic Division (AAD) contracted Helicopter Resources to fly remotely piloted aircraft (RPA) on Voyage 1 2016/17. The RPA were used to take aerial photographs for sea ice reconnaisance from the RSV Aurora Australis, aerial photographs of Davis, aerial photographs for building roof inspections at Davis and aerial photographs of part of Heidemann Valley. Video was also recorded from the RSV Aurora Australis and of Heidemann Valley. The flights over Heidemann Valley were done at the request of the AAD's Antarctic Modernisation Taskforce. The roof inspections were done at the request of the AAD's Infrastructure section. The following can be downloaded or requested from this metadata record by AAD staff only (see Related URLs): 1 A report prepared by Doug Thost, the chief RPA pilot; 2 The aerial photographs of Davis and Heidemann Valley; and 3 Some panoramas created from aerial photographs taken at Davis. The AAD's Multimedia section have a copy of the videos. The AAD's Infrastructure section have a copy of the aerial photographs taken for roof inspections. See the report for further details. proprietary aerial_surveys_vestfold_2017-18_1 Aerial surveys of Davis station and an area of the Vestfold Hills to the north-east of the station 2017/18 AU_AADC STAC Catalog 2017-11-19 2018-01-31 77.8923, -68.6067, 78.2235, -68.4809 https://cmr.earthdata.nasa.gov/search/concepts/C1542262550-AU_AADC.umm_json "Three aerial surveys were flown by Helicopter Resources Pty Ltd for the Australian Antarctic Division's Antarctic Modernisation Taskforce during the 2017/18 field season. The photography was done from a helicopter and covered Davis station and an area of the Vestfold Hills to the north-east of the station. The first survey conducted on 19 November 2017 covered an inner higher resolution area with flying heights approximately 300 to 400 metres above sea level. The second survey conducted on 20 November 2017 covered a more extensive area at lower resolution with flying heights approximately 800 metres above sea level. The third survey was conducted on 31 January 2018 over a similar area to the first survey with flying heights approximately 300 to 400 metres above sea level. The report on the third survey states ""As a general comment, in comparison to Survey 1, this survey was flown more accurately, in better lighting conditions, with less snow cover, and by all statistical metrics has resulted in a higher quality survey overall."" The spatial extents given in this metadata record are for the second survey. For each survey there is zip file with a report and the following products generated from the survey data: (i) an orthophoto; (ii) a Digital Surface Model (DSM); and (iii) contours generated from the DSM. The products are stored in the UTM zone 44S coordinate system, based on the horizontal datum ITRF2000. Elevations are in metres above Mean Sea Level. There is also a separate zip file with the aerial photographs from the three surveys and a spreadsheet with latitude and longitude for each photo centre. Ground control points were used to constrain the DSM for each survey. One metre by one metre cross markers were set out across the survey area prior to the aerial surveys being flown. The centre of each cross was surveyed by Australian Defence Force surveyors Sam Kelly and Warwick Cox. Some permanent survey marks were used as an independent check of the overall accuracy of the DSM." proprietary aerial_surveys_vestfold_2017-18_1 Aerial surveys of Davis station and an area of the Vestfold Hills to the north-east of the station 2017/18 ALL STAC Catalog 2017-11-19 2018-01-31 77.8923, -68.6067, 78.2235, -68.4809 https://cmr.earthdata.nasa.gov/search/concepts/C1542262550-AU_AADC.umm_json "Three aerial surveys were flown by Helicopter Resources Pty Ltd for the Australian Antarctic Division's Antarctic Modernisation Taskforce during the 2017/18 field season. The photography was done from a helicopter and covered Davis station and an area of the Vestfold Hills to the north-east of the station. The first survey conducted on 19 November 2017 covered an inner higher resolution area with flying heights approximately 300 to 400 metres above sea level. The second survey conducted on 20 November 2017 covered a more extensive area at lower resolution with flying heights approximately 800 metres above sea level. The third survey was conducted on 31 January 2018 over a similar area to the first survey with flying heights approximately 300 to 400 metres above sea level. The report on the third survey states ""As a general comment, in comparison to Survey 1, this survey was flown more accurately, in better lighting conditions, with less snow cover, and by all statistical metrics has resulted in a higher quality survey overall."" The spatial extents given in this metadata record are for the second survey. For each survey there is zip file with a report and the following products generated from the survey data: (i) an orthophoto; (ii) a Digital Surface Model (DSM); and (iii) contours generated from the DSM. The products are stored in the UTM zone 44S coordinate system, based on the horizontal datum ITRF2000. Elevations are in metres above Mean Sea Level. There is also a separate zip file with the aerial photographs from the three surveys and a spreadsheet with latitude and longitude for each photo centre. Ground control points were used to constrain the DSM for each survey. One metre by one metre cross markers were set out across the survey area prior to the aerial surveys being flown. The centre of each cross was surveyed by Australian Defence Force surveyors Sam Kelly and Warwick Cox. Some permanent survey marks were used as an independent check of the overall accuracy of the DSM." proprietary -aerosol-data-davos-wolfgang_1.0 Aerosol Data Davos Wolfgang ALL STAC Catalog 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary aerosol-data-davos-wolfgang_1.0 Aerosol Data Davos Wolfgang ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary -aerosol-data-weissfluhjoch_1.0 Aerosol Data Weissfluhjoch ALL STAC Catalog 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary +aerosol-data-davos-wolfgang_1.0 Aerosol Data Davos Wolfgang ALL STAC Catalog 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary aerosol-data-weissfluhjoch_1.0 Aerosol Data Weissfluhjoch ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary +aerosol-data-weissfluhjoch_1.0 Aerosol Data Weissfluhjoch ALL STAC Catalog 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary aerosol_properties_725_1 SAFARI 2000 Physical and Chemical Properties of Aerosols, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-17 2000-09-13 5, -35, 60, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2789011485-ORNL_CLOUD.umm_json SAFARI 2000 provided an opportunity to study aerosol particles produced by savanna burning. We used analytical transmission electron microscopy (TEM), including energy-dispersive X-ray spectrometry (EDS) and electron energy-loss spectroscopy (EELS), to study aerosol particles from several smoke and haze samples and from a set of cloud samples. These aerosol particle samples were collected using the University of Washington Convair CV-580 research aircraft (Posfai et al., 2003). proprietary aes5davg_236_1 BOREAS AES Five-day Averaged Surface Meteorological and Upper Air Data ORNL_CLOUD STAC Catalog 1976-01-01 1997-01-01 -107.87, 52.17, -97.83, 57.35 https://cmr.earthdata.nasa.gov/search/concepts/C2807614663-ORNL_CLOUD.umm_json Contains 5-day averages of hourly and daily data from 23 meteorological stations across Canada along with full-resolution upper air measurements from 1 station in The Pas, Manitoba. proprietary aes_upl1_238_1 BOREAS AFM-05 Level-1 Upper Air Network Data, R1 ORNL_CLOUD STAC Catalog 1993-08-16 1996-10-22 -111, 50.09, -93.5, 59.98 https://cmr.earthdata.nasa.gov/search/concepts/C2812433046-ORNL_CLOUD.umm_json Contains basic upper-air parameters collected by the AFM-05 team from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region. proprietary @@ -17102,8 +17082,8 @@ afm4toas_498_1 BOREAS AFM-04 Twin Otter Aircraft Sounding Data ORNL_CLOUD STAC C afm4tofx_497_1 BOREAS AFM-04 Twin Otter Aircraft Flux Data ORNL_CLOUD STAC Catalog 1994-05-25 1994-09-19 -104.81, 53.79, -98.4, 55.95 https://cmr.earthdata.nasa.gov/search/concepts/C2808092173-ORNL_CLOUD.umm_json Measurements in the boundary layer of the fluxes of sensible and latent heat, momentum, ozone, methane, and carbon dioxide, plus supporting meteorological parameters such as temperature, humidity, and wind speed and direction. proprietary afm6gifs_433_1 BOREAS AFM-06 NOAA/ETL 35 GHz Cloud/Turbulence Radar GIF Images ORNL_CLOUD STAC Catalog 1994-07-16 1994-08-08 -110.05, 50.57, -94.08, 59.34 https://cmr.earthdata.nasa.gov/search/concepts/C2928013317-ORNL_CLOUD.umm_json The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environmental Technology Laboratory (NOAA/ETL) operated a 35 GHz cloud-sensing radar in the Northern Study Area (NSA) near the Old Jack Pine (OJP) tower from 16-Jul-1994 to 08-Aug-1994. proprietary african_woody_savanna_850_1 Characteristics of African Savanna Biomes for Determining Woody Cover ORNL_CLOUD STAC Catalog 1981-01-01 2003-12-31 -15.84, -27.75, 37.24, 16.76 https://cmr.earthdata.nasa.gov/search/concepts/C2784383572-ORNL_CLOUD.umm_json This data set includes the soil and vegetation characteristics, herbivore estimates, and precipitation measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover but perceptions differ on which of these are the primary drivers of savanna structure. Analyses of data from 854 sites across Africa (Figure 1) showed that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered stable systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are unstable systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics (Sankaran et al., 2005).This data set includes the site characteristics and measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. The data are provided in two formats, *.xls and *.csv. See the data format section below for more information. A companion document composed of the supplemental documentation and figures provided with Sankaran et al., 2005 is also included (ftp://daac.ornl.gov/data/global_vegetation/african_woody_savanna/comp/Woody_Cover.pdf). proprietary -agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0 Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis ALL STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081749-ENVIDAT.umm_json "Supplementary material for the publication "" Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using material substance and energy flow analysis"" Burg, V., b, Rolli, C., Schnorf, V., Scharfy, D., Anspach, V., Bowman, G. Today's agro-food system is typically based on linear fluxes (e.g. mineral fertilizers importation), when a circular approach should be privileged. The production of biogas as a renewable energy source and digestate, used as an organic fertilizer, is essential for the circular economy in the agricultural sector. This study investigates the current utilization of wet biomass in agricultural anaerobic digestion plants in Switzerland in terms of mass, nutrients, and energy flows, to see how biomass use contributes to circular economy and climate change mitigation through the substitution effect of mineral fertilizers and fossil fuels. We quantify the system and its benefits in details and examine future developments of agricultural biogas plants using different scenarios. Our results demonstrate that agricultural anaerobic digestion could be largely increased, as it could provide ten times more biogas by 2050, while saving significant amounts of mineral fertilizer and GHG emissions." proprietary agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0 Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081749-ENVIDAT.umm_json "Supplementary material for the publication "" Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using material substance and energy flow analysis"" Burg, V., b, Rolli, C., Schnorf, V., Scharfy, D., Anspach, V., Bowman, G. Today's agro-food system is typically based on linear fluxes (e.g. mineral fertilizers importation), when a circular approach should be privileged. The production of biogas as a renewable energy source and digestate, used as an organic fertilizer, is essential for the circular economy in the agricultural sector. This study investigates the current utilization of wet biomass in agricultural anaerobic digestion plants in Switzerland in terms of mass, nutrients, and energy flows, to see how biomass use contributes to circular economy and climate change mitigation through the substitution effect of mineral fertilizers and fossil fuels. We quantify the system and its benefits in details and examine future developments of agricultural biogas plants using different scenarios. Our results demonstrate that agricultural anaerobic digestion could be largely increased, as it could provide ten times more biogas by 2050, while saving significant amounts of mineral fertilizer and GHG emissions." proprietary +agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0 Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis ALL STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081749-ENVIDAT.umm_json "Supplementary material for the publication "" Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using material substance and energy flow analysis"" Burg, V., b, Rolli, C., Schnorf, V., Scharfy, D., Anspach, V., Bowman, G. Today's agro-food system is typically based on linear fluxes (e.g. mineral fertilizers importation), when a circular approach should be privileged. The production of biogas as a renewable energy source and digestate, used as an organic fertilizer, is essential for the circular economy in the agricultural sector. This study investigates the current utilization of wet biomass in agricultural anaerobic digestion plants in Switzerland in terms of mass, nutrients, and energy flows, to see how biomass use contributes to circular economy and climate change mitigation through the substitution effect of mineral fertilizers and fossil fuels. We quantify the system and its benefits in details and examine future developments of agricultural biogas plants using different scenarios. Our results demonstrate that agricultural anaerobic digestion could be largely increased, as it could provide ten times more biogas by 2050, while saving significant amounts of mineral fertilizer and GHG emissions." proprietary air_methane_lawdome_1 Dated Readings For Air Composition And Methane From Law Dome Ice Core AU_AADC STAC Catalog 1988-01-01 1993-12-31 112.8, -66.771, 112.81, -66.77 https://cmr.earthdata.nasa.gov/search/concepts/C1214311761-AU_AADC.umm_json "This work was completed as part of ASAC project 757 (ASAC_757). This file comprises three main records compiled for publication in the following: V. Morgan, M. Delmotte, T. van Ommen, J. Jouzel, J. Chappellaz, S. Woon, V. Masson-Delmotte and D. Raynaud. Relative Timing of Deglacial Climate Events in Antarctica and Greenland, Science, 13 September 2002, Vol 297 (5588), pp. 1862-1864, DOI: 10.1126/science.1074257. Supporting Material - http://www.sciencemag.org/cgi/content/full/sci;297/5588/1862/DC1 Law Dome is a small (200 km in diameter) ice sheet located at the edge of the Indian Ocean sector of East Antarctica. The core site, near the summit of Law Dome (66 degrees 46'S, 112 degrees 48'E), is characterised by a high rate of accumulation (late Holocene average, 0.68 m ice equivalent per year) that results in an ice core with a highly tapered time scale in which the Holocene represents some 93% of the ice thickness of 1200 m. However, the full Law Dome isotopic record generally matches the long records from Vostok and Byrd to at least 80 ka, indicating that the record is continuous and undisturbed over this period. The Law Dome record is suited to gas-synchronisation studies because the high accumulation rate and consequent rapid burial give a small age difference (Delta age) between trapped air and the older enclosing ice. Derivation of an age scale for the Law Dome core, is based upon a Dansgaard- Johnsen flow model (S1) matched to the observed layer thinning (S2). Continuously sampled seasonal cycles down to ~1/3 ice-thickness (~1ky) and spot measurements of seasonal layers to ~85% ice-thickness (~4 ky) constrain the ice-flow model through this period in which mean accumulation is assumed to be free of large trends. Chronological control in the lower portion of the ice-sheet prior to 4 ky is through ties to other records. For the period of discussion, namely 10 ky to 17 ky, ties at 9.6 ky, 11.0 ky, 11.6 ky, 12.5 ky, 12.8 ky, 14.3 ky and 16.3 ky, are obtained by matching air composition changes with those of GRIP. The 9.6 ky tie is obtained by matching to d18O of air in GRIP (S3) and GISP2 (S4) data, and the remainder synchronise with the Byrd and GRIP CH4 records (S5). Dust concentration data also provide additional constraints on the 16.3 ky tie. Beyond 16.3 ky control is by a tie at 32 ky (based on both dust and d18Oice matched to the Byrd ice core (S6) on the GRIP timescale (S5)). The mean temporal resolution of the LD isotope data is ~24y through this period. The air-composition age-ties require Delta age computations for sequencing events within the LD record and for synchronisation of the chronology with GRIP. The high accumulation at DSS results in a particularly small Delta age value. The modern difference between ice-age and gas-age is 60 plus or minus 2 years for methane (S7). Note that at such low Delta age values, the diffusive mixing time from the free atmosphere down to seal-off depth becomes significant and must be accounted for; in the case of CH4 this is ~8 years (S7). The absolute chronology derived for the LD record has contributions from both the LD and GRIP Delta age errors, but the relative timing between the LD CH4 and water isotope (d18Oice) signals is only uncertain to within the small errors associated with LD Delta age. While the present-day trapping age at LD is small, lower temperatures and accumulation rates during the deglaciation lead to longer trapping times. To estimate Delta age under past conditions, we use a model (S8) to compute trapping age from accumulation and temperature (this model agrees with precise experimentally determined present day values). Since we have no direct indicators for palaeoaccumulation and palaeotemperature, we adopt two scenarios that use alternative estimation methods. Estimation of palaeotemperature from the isotope data in both scenarios is by application of a calibration slope, ""Beta ppt/degrees C"". For the young chronology, which has minimum Delta age, the commonly applied spatial slope of Beta=0.67 ppt/degrees C is used, giving relatively warm temperatures. The default chronology uses a long-term temporal calibration (S9) for Law Dome, Beta=0.44 ppt/degrees C. This estimate, which is seasonally derived, gives greater temperature sensitivity for isotopic changes than the spatial slope. The use of this lower value for Beta is supported by direct comparisons between annual averages in d18O and temperature at the site and elsewhere on Law Dome. Over several years to a few decades, these yield coefficients of typically ~0.33 ppt/degrees C. We adopt the value 0.44 ppt/degrees C as a conservative choice, based on a longer-term calibration and because the incorporation of seasonal sea-ice variations may better capture glacial-to- Holocene environmental shifts. Estimation of palaeoaccumulation for the young chronology is via the commonly applied method (see, e.g. S5) that scales modern accumulation-rate using the derivative of saturation vapour-pressure versus temperature relationship (also using Beta=0.67 ppt/degrees C). This method explicitly assumes no non-thermodynamic changes to moisture transport during climate variations (such as, e.g., atmospheric circulation changes) that may be important at this near-coastal location. Our alternative palaeoaccumulation estimate used for the default chronology assumes that the flow-model is correct and infers accumulation from the known age-intervals between the gas ties. This leads to considerably larger changes in accumulation which may nonetheless be understandable given the distinctively high Holocene precipitation regime that prevails at Law Dome. In addition, dust concentration data show a larger LGM to Holocene decrease at LD than Vostok. If relative flux changes at the two sites are similar, then the exaggerated dilution at LD is consistent with a large interglacial accumulation shift. Trapped gas measurements were made in France: CH4 measurements at LGGE, Grenoble and d18Oair measurements at LSCE, Saclay. Both analyses were conducted using a wet extraction procedure to release the air of the ice and followed by an injection into a gas chromatograph (CH4 measurement) or by a mass spectrometer isotopic analysis (d18Oair measurements). Both analyses were conducted using established procedures (S10,S11). The methane analytical uncertainty is plus or minus 20 ppbv with values were obtained on a single measurement (in which the sample was exhausted) and are presented on the LGGE scale which differs slightly from the NOAA scale but is well calibrated against it: LGGE = 1.02*NOAA (S12). The d18Oair values arise from means of duplicate measurements (except for one point with an obvious experimental problem, 1127.492 m depth). The analytical precision for d18Oair is around 0.05 ppt with a mean reproducibility of about 0.1 ppt. d18Oice measurements were made in Hobart and have an analytical precision of approximately 0.1 ppt. The results are expressed using the conventional reference of VSMOW (Vienna Standard Mean Ocean Water). Supporting References and Notes S1. W. Dansgaard, S. J. Johnsen, J. Glaciol., 8, 215 (1969). S2. V. Morgan et al., J. Glaciol., 43, 3 (1997). S3. M. Cross, (Compiler) Greenland summit ice cores CD-ROM. Boulder, CO: National Snow and Ice Data Center in association with the World Data Center for Paleoclimatology at NOAA-NGDC, and the Institute of Arctic and Alpine Research (1997). S4. M. Bender et al., Nature 372, 663-666 (1994). S5. T. Blunier, et al., Nature 394, 739 (1998). S6. S. J. Johnsen, W. Dansgaard, H. B. Clausen, C. C. Langway, Nature, 235, 429 (1972). S7. D. M. Etheridge et al., J. Geophys. Res., 101, 4115 (1996). S8. J.-M. Barnola, P. Pimienta, D. Raynaud, Y. S. Korotkevich, Tellus Ser. B, 43, 83 (1991). S9. T. D. van Ommen, V. Morgan, J. Geophys. Res., 102, 9351 (1997). S10. J. Chappellaz, et al., J. Geophys. Res., 102, 15987, (1997). S11. B. Malaize, Analyse isotopique de l'oxygene de l'air piege dans les glaces de l'Antarctique et du Groenland: correlation inter-hemispheriques et effet Dole, PhD thesis, University Paris 6, (1998). S12. T. Sowers et al, J. Geophys. Res., 102, 26527, (1997)." proprietary air_sea_gas_exchange_xdeg_1208_1 ISLSCP II Air-Sea Carbon Dioxide Gas Exchange ORNL_CLOUD STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2785340637-ORNL_CLOUD.umm_json This data set contains the calculated net ocean-air carbon dioxide (CO2) flux and sea-air CO2 partial pressure (pCO2) difference. The estimates are based on approximately one million measurements made for the pCO2 in surface waters of the global ocean since the International Geophysical Year, 1956-1959. Only the ocean water pCO2 values measured using direct gas-seawater equilibration methods were used. The results represent the climatological distributions under non-El Nino conditions. Since the measurements were made in different years, during which the atmospheric pCO2 was increasing, they were corrected to a single reference year (arbitrarily chosen to be 1995) on the basis of the following assumptions: -Surface waters in subtropical gyres mix vertically at slow rates with subsurface waters due to the presence of strong stratification at the base of the mixed layer. This will allow a long contact time with the atmosphere to exchange CO2. Therefore, their CO2 chemistry tends to follow the atmospheric CO2 increase. Accordingly, the pCO2 measured in a given month and year is corrected to the same month of the reference year 1995 using changes in the atmospheric CO2 concentration occurred during this period.-Oceanic pCO2 measurements made after the beginning of 1979 have been corrected to 1995 using the atmospheric CO2 concentration data from the GLOBALVIEW-CO2 database (2000), in which the zonal mean atmospheric concentrations (for each 0.05 in sine of latitude) within the planetary boundary layer are summarized for each month since 1979 to 2000.-Pre-1979 oceanic pCO2 data were corrected to 1979 using the annual mean trend for the global mean atmospheric CO2 concentration constructed from the Mauna Loa data of Keeling and Whorf (2000), and then from 1979 to 1995 using the GLOBALVIEW-CO2 database. -Measurements for pCO2 made in the following areas have been corrected for the time of observation; 45 degrees N, 50 degrees S, in the Atlantic Ocean, north of 50 degrees S in the Indian Ocean, 40 degrees N, 50 degrees S in the western Pacific west of the date line, and 40 degrees N, 60 degrees S, in the eastern Pacific east of the date line. proprietary air_temperature_observations_in_the_arctic_1979-2004 Air Temperature Observations in the Arctic 1979-2004 ALL STAC Catalog 1979-01-01 2005-12-01 -180, 14.5, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600622-SCIOPS.umm_json The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979-2004 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, i.e. as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called IABP/POLES. Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides 12-hour fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979-2004. proprietary @@ -17121,8 +17101,8 @@ alaskan_air_ground_snow_and_soil_temperatures__1998-2005 Alaskan Air Ground Snow albedo_line_snow_depths Albedo Line Snow Depths ALL STAC Catalog 2009-04-27 2009-04-28 -157, 71, -156, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214600343-SCIOPS.umm_json Snow depth measurements recorded every half meter along the transects used for albedo measurements using a GPS magnaprobe. Included in the file are latitude, longitude, and snow depth. The first set of columns are at the south site, the second set are at the north site. Note that the south site was surveyed first along the line every half meter, and then a large dune field north of the line was extensively surveyed. Data Citation: Eicken, H., R. Gradinger, T. Heinrichs, M. Johnson, A. Lovecraft, and M. Sturm. (Nov. 29, 2009, Updated May 9, 2012). Albedo Line Snow Depths (SIZONET). UCAR/NCAR - CISL - ACADIS. http://dx.doi.org/10.5065/D6057CV2 proprietary albedo_line_snow_depths Albedo Line Snow Depths SCIOPS STAC Catalog 2009-04-27 2009-04-28 -157, 71, -156, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214600343-SCIOPS.umm_json Snow depth measurements recorded every half meter along the transects used for albedo measurements using a GPS magnaprobe. Included in the file are latitude, longitude, and snow depth. The first set of columns are at the south site, the second set are at the north site. Note that the south site was surveyed first along the line every half meter, and then a large dune field north of the line was extensively surveyed. Data Citation: Eicken, H., R. Gradinger, T. Heinrichs, M. Johnson, A. Lovecraft, and M. Sturm. (Nov. 29, 2009, Updated May 9, 2012). Albedo Line Snow Depths (SIZONET). UCAR/NCAR - CISL - ACADIS. http://dx.doi.org/10.5065/D6057CV2 proprietary ali_etm_tandem_821_1 SAFARI 2000 ALI/ETM+ Tandem Image Pair for Skukuza, South Africa, May 2001 ORNL_CLOUD STAC Catalog 2001-05-30 2001-05-30 30.76, -25.5, 33.12, -23.59 https://cmr.earthdata.nasa.gov/search/concepts/C2789740161-ORNL_CLOUD.umm_json A tandem pair of Advanced Land Imager (ALI) and Landsat Enhanced Thematic Mapper Plus (ETM+) scenes covering the same part of Kruger National Park (KNP), South Africa (including the Skukuza tower site and rest camp), were acquired about a minute apart on May 30, 2001. The ALI is one of three instruments aboard NASA's first New Millennium Program Earth Observing 1 (EO-1) satellite. ALI is a technology validation testbed that employs novel wide-angle optics and a highly integrated multispectral and panchromatic spectroradiometer.The tandem pair was produced to evaluate the differences between ALI and ETM+ and determine if technology similar to that of the ALI is suitable for future land imaging that will continue the observations begun by the Landsat satellites in 1972.The ALI and ETM+ images are false color composites combining shortwave infrared, near infrared, and visible wavelengths, displayed as red, green, and blue, respectively. Dense vegetation appears green. The similarity of the images demonstrates the ability of the ALI to produce data comparable to ETM+. Several SAFARI 2000 field campaigns conducted in KNP provided ground-based data needed to evaluate measurements from the satellite sensors.Each band is stored as an individual binary file. A metadata file accompanies each set of ALI and ETM+ band files to document the path and row number, sample and line counts, band file names, and sun azimuth and elevation angles. There is also a calibration parameter file that was used for 1R processing. proprietary -allADCP_GB Acoustic Doppler Current Profiler (ADCP) observations, Georges Bank area, April-June 1995, GLOBEC. SCIOPS STAC Catalog 1995-04-25 1995-06-16 -68, 40.5, -67, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214155092-SCIOPS.umm_json Acoustic Doppler Current Profiler (ADCP) observations, were collected from the R/V Seward Johnson on two cruises to the Georges Bank region, April-June 1995. Three different ADCP units were used: two broadband at 150 and 600 kHz, and one narrowband at 150 kHz. The broadband 150 kHz unit was used at anchor stations with data reported at hourly intervals. The broad-band 600 kHz and narrow-band 150 kHz units collected data in the along track mode with data reported at five minute intervals. For each time interval, the u and v components of currents are reported at uniform depth intervals throughout the water column. Ship cruise dates R/V Seward Johnson 9506 1995 04 25 1995 05 02 R/V Seward Johnson 9508 1995 06 06 1995 06 16 proprietary allADCP_GB Acoustic Doppler Current Profiler (ADCP) observations, Georges Bank area, April-June 1995, GLOBEC. ALL STAC Catalog 1995-04-25 1995-06-16 -68, 40.5, -67, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214155092-SCIOPS.umm_json Acoustic Doppler Current Profiler (ADCP) observations, were collected from the R/V Seward Johnson on two cruises to the Georges Bank region, April-June 1995. Three different ADCP units were used: two broadband at 150 and 600 kHz, and one narrowband at 150 kHz. The broadband 150 kHz unit was used at anchor stations with data reported at hourly intervals. The broad-band 600 kHz and narrow-band 150 kHz units collected data in the along track mode with data reported at five minute intervals. For each time interval, the u and v components of currents are reported at uniform depth intervals throughout the water column. Ship cruise dates R/V Seward Johnson 9506 1995 04 25 1995 05 02 R/V Seward Johnson 9508 1995 06 06 1995 06 16 proprietary +allADCP_GB Acoustic Doppler Current Profiler (ADCP) observations, Georges Bank area, April-June 1995, GLOBEC. SCIOPS STAC Catalog 1995-04-25 1995-06-16 -68, 40.5, -67, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214155092-SCIOPS.umm_json Acoustic Doppler Current Profiler (ADCP) observations, were collected from the R/V Seward Johnson on two cruises to the Georges Bank region, April-June 1995. Three different ADCP units were used: two broadband at 150 and 600 kHz, and one narrowband at 150 kHz. The broadband 150 kHz unit was used at anchor stations with data reported at hourly intervals. The broad-band 600 kHz and narrow-band 150 kHz units collected data in the along track mode with data reported at five minute intervals. For each time interval, the u and v components of currents are reported at uniform depth intervals throughout the water column. Ship cruise dates R/V Seward Johnson 9506 1995 04 25 1995 05 02 R/V Seward Johnson 9508 1995 06 06 1995 06 16 proprietary alnus-glutinosa-orientus-ishidae-flavescence-doree_1.0 Alnus glutinosa (L.) Gaertn. and Orientus ishidae (Matsumura, 1902) share phytoplasma genotypes linked to the “Flavescence dorée” epidemics ENVIDAT STAC Catalog 2021-01-01 2021-01-01 8.4484863, 45.8115721, 9.4372559, 46.4586735 https://cmr.earthdata.nasa.gov/search/concepts/C2789814963-ENVIDAT.umm_json Flavescence dorée (FD) is a grapevine disease caused by associated phytoplasmas (FDp), which are epidemically spread by their main vector Scaphoideus titanus. The possible roles of alternative and secondary FDp plant hosts and vectors have gained interest to better understand the FDp ecology and epidemiology. A survey conducted in the surroundings of a vineyard in the Swiss Southern Alps aimed at studying the possible epidemiological role of the FDp secondary vector Orientus ishidae and the FDp host plant Alnus glutinosa is reported. Data used for the publication. Insects were captured by using a sweeping net (on common alder trees) and yellow sticky traps (Rebell Giallo, Andermatt Biocontrol AG, Switzerland) placed in the vineyard canopy. Insects were later determined and selected for molecular analyses. Grapevines and common alder samples were collected using the standard techniques. The molecular analyses were conducted in order to identify samples infected by the Flavescence dorée phytoplasma (16SrV-p) and the Bois Noir phytoplasma (16SrXII-p). A selection of the infected sampled were further characterized by map genotype and sequenced in order to compare the genotypes in insects, grapevines and common alder trees. proprietary alos-prism-l1c_8.0 ALOS PRISM L1C ESA STAC Catalog 2006-08-01 2011-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2619280661-ESA.umm_json "This collection provides access to the ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) OB1 L1C data acquired by ESA stations (Kiruna, Maspalomas, Matera, Tromsoe) in the _$$ADEN zone$$ https://earth.esa.int/eogateway/documents/20142/37627/Information-on-ALOS-AVNIR-2-PRISM-Products-for-ADEN-users.pdf , in addition to worldwide data requested by European scientists. The ADEN zone was the area belonging to the European Data node and covered both the European and African continents, a large part of Greenland and the Middle East. The full mission archive is included in this collection, though with gaps in spatial coverage outside of the; with respect to the L1B collection, only scenes acquired in sensor mode, with Cloud Coverage score lower than 70% and a sea percentage lower than 80% are published: • Time window: from 2006-08-01 to 2011-03-31 • Orbits: from 2768 to 27604 • Path (corresponds to JAXA track number): from 1 to 665 • Row (corresponds to JAXA scene centre frame number): from 310 to 6790. The L1C processing strongly improve accuracy compared to L1B1 from several tenths of meters in L1B1 (~40 m of northing geolocation error for Forward views and ~10-20 m for easting errors) to some meters in L1C scenes (< 10 m both in north and easting errors). The collection is composed by only PSM_OB1_1C EO-SIP product type, with PRISM sensor operating in OB1 mode and having the three views (Nadir, Forward and Backward) at 35km width. The most part of the products contains all the three views, but the Nadir view is always available and is used for the frame number identification. All views are packaged together; each view, in CEOS format, is stored in a directory named according to the JAXA view ID naming convention." proprietary alos.prism.l1c.european.coverage.cloud.free_12.0 ALOS PRISM L1C European Coverage Cloud Free ESA STAC Catalog 2007-03-26 2011-03-31 -25, 27, 46, 72 https://cmr.earthdata.nasa.gov/search/concepts/C3325394222-ESA.umm_json This collection is composed of a subset of ALOS-1 PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) OB1 L1C products from the _$$ALOS PRISM L1C collection$$ https://earth.esa.int/eogateway/catalog/alos-prism-l1c (DOI: 10.57780/AL1-ff3877f) which have been chosen so as to provide a cloud-free coverage over Europe. 70% of the scenes contained within the collection have a cloud cover percentage of 0%, while the remaining 30% of the scenes have a cloud cover percentage of no more than 20%. The collection is composed of PSM_OB1_1C EO-SIP products, with the PRISM sensor operating in OB1 mode with three views (Nadir, Forward and Backward) at 35 km width. proprietary @@ -17134,8 +17114,8 @@ ames_sunphotometer_643_1 SAFARI 2000 Airborne Sunphotometer Aerosol Optical Dept amount_of_dead_wood-214_1.0 Amount of dead wood ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814565-ENVIDAT.umm_json Wood volume of all deadwood recorded according to the NFI3 method. For standing trees and shrubs starting at 12 cm dbh, the volume of stemwood reduced due to stem breakage is recorded, and for lying deadwood the merchantable wood ( starting at 7 cm in diameter). Heaps of branches are not included. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary amphibian-and-landscape-data-swiss-lowlands_1.0 Amphibian and urban-rural landscape data Swiss Lowlands ENVIDAT STAC Catalog 2022-01-01 2022-01-01 7.7124023, 47.0776041, 9.0637207, 47.7983967 https://cmr.earthdata.nasa.gov/search/concepts/C2789814582-ENVIDAT.umm_json "The data includes (1) amphibian occurrence data (2017-2019) for ten species across the cantons of Aargau and Zürich gathered from the Coordination Center for the Protection of Amphibians and Reptiles of Switzerland (http://www.karch.ch), (2) amphibian whole-life cycle environmental predictors (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related), and (3) local urban ""green"" and ""grey"" landcover data which can be used to identify opportunities for Blue-Green Infrastructure (through green or grey transitions) in support of regional landscape connectivity." proprietary amphibian-data-aargau_1.0 Amphibian observation and pond data (Aargau, Switzerland) ENVIDAT STAC Catalog 2021-01-01 2021-01-01 7.7, 47.15, 8.46, 47.62 https://cmr.earthdata.nasa.gov/search/concepts/C2789814599-ENVIDAT.umm_json In the canton of Aargau, hundreds of new ponds have been constructed since the 1990s to benefit declining amphibian populations. This dataset consists of monitoring data for all 12 pond-breeding amphibian species in the canton of Aargau from 1999 to 2019 in 856 ponds, and environmental variables that describe the ponds and the landscape surrounding the ponds. Species observation data is detection/non-detection data from repeat visits during survey years, during which all potentially suitable ponds in an area were surveyed. Environmental variables describing the ponds are whether the pond has been newly constructed since 1991 or not, pond age (if constructed), elevation a.s.l., the water surface area, and whether the water table fluctuates or not. Environmental variables describing the surroundings of the ponds are the percent area of forest within a circular buffer of radius 100m around the pond, the area of large (width ≥6m) roads within a circular buffer of radius 1km around the pond, as well as structural and potential population connectivity, quantified by three different metrics each. The canton of Aargau is the owner of the monitoring data; the original datafile is only disclosed upon request and in consultation with the canton of Aargau. The edited dataset contains cleaned observation data for the 12 amphibian species, as well as compiled and edited covariate data and code to fit dynamic occupancy models. proprietary -amprimpacts_1 Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS GHRC_DAAC STAC Catalog 2020-01-18 2023-03-02 -124.153, 26.507, -64.366, 49.31 https://cmr.earthdata.nasa.gov/search/concepts/C2004708841-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor, and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from January 18, 2020, through March 2, 2023, in netCDF-4 format. proprietary amprimpacts_1 Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS ALL STAC Catalog 2020-01-18 2023-03-02 -124.153, 26.507, -64.366, 49.31 https://cmr.earthdata.nasa.gov/search/concepts/C2004708841-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor, and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from January 18, 2020, through March 2, 2023, in netCDF-4 format. proprietary +amprimpacts_1 Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS GHRC_DAAC STAC Catalog 2020-01-18 2023-03-02 -124.153, 26.507, -64.366, 49.31 https://cmr.earthdata.nasa.gov/search/concepts/C2004708841-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor, and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from January 18, 2020, through March 2, 2023, in netCDF-4 format. proprietary amprtbcp_2 AMPR BRIGHTNESS TEMPERATURE CAPE EXPERIMENT GHRC_DAAC STAC Catalog 1991-07-21 1991-08-16 -83.2024, 0, 12.6618, 38.1879 https://cmr.earthdata.nasa.gov/search/concepts/C1977858384-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Precipitation/Electrification Experiment (CaPE). AMPR data werecollected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) during the time period of July 21, 1991 - Aug. 16, 1991. CaPE took place in centralFlorida between 43 N - 25.5 N latitude and 86 W - 69 W longitude. proprietary amprtbcx1_2 AMPR BRIGHTNESS TEMPERATURE CAMEX-1 GHRC_DAAC STAC Catalog 1993-09-26 1993-10-05 -83.8511, 23.9917, -68.2377, 42.6325 https://cmr.earthdata.nasa.gov/search/concepts/C1977858400-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiments (CAMEX-1) conducted at Wallops Island, VA. AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) during the time period of September 26 - October 5, 1993. The geographic domain of the CAMEX region was between 25.5N - 43N latitude and 70W - 83W longitude. proprietary amprtbcx2_2 AMPR BRIGHTNESS TEMPERATURE CAMEX-2 GHRC_DAAC STAC Catalog 1995-08-23 1995-08-30 -78.907, 30.0262, -72.3661, 41.0703 https://cmr.earthdata.nasa.gov/search/concepts/C1977858440-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiment 2 (CAMEX-2). AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) during the time period of August 23 - August 30, 1995. The geographic domain of the CAMEX-2 region was between 25.5 N - 43 N latitude and 83 W - 70 W longitude. proprietary @@ -17150,10 +17130,10 @@ ams_cs93_403_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological D ams_cs94_404_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological Data: 1994 ORNL_CLOUD STAC Catalog 1994-01-01 1994-12-31 -108.52, 50.95, -94.7, 58.18 https://cmr.earthdata.nasa.gov/search/concepts/C2808090015-ORNL_CLOUD.umm_json Contains data from 1994 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. proprietary ams_cs95_405_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological Data: 1995 ORNL_CLOUD STAC Catalog 1995-01-01 1995-12-31 -108.52, 50.95, -94.7, 58.18 https://cmr.earthdata.nasa.gov/search/concepts/C2808090046-ORNL_CLOUD.umm_json Contains data from 1995 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. proprietary ams_cs96_406_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological Data: 1996 ORNL_CLOUD STAC Catalog 1996-01-01 1996-12-31 -108.52, 50.95, -94.7, 58.18 https://cmr.earthdata.nasa.gov/search/concepts/C2808090091-ORNL_CLOUD.umm_json Contains data from 1996 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. proprietary -amsua15sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 ALL STAC Catalog 1998-08-03 -180, -90, 180, 89.756 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. proprietary amsua15sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 GHRC_DAAC STAC Catalog 1998-08-03 -180, -90, 180, 89.756 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. proprietary -amsua16sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16 ALL STAC Catalog 2001-05-27 2009-07-30 -180, -89.91, 180, 89.73 https://cmr.earthdata.nasa.gov/search/concepts/C1979956366-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit. proprietary +amsua15sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 ALL STAC Catalog 1998-08-03 -180, -90, 180, 89.756 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. proprietary amsua16sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16 GHRC_DAAC STAC Catalog 2001-05-27 2009-07-30 -180, -89.91, 180, 89.73 https://cmr.earthdata.nasa.gov/search/concepts/C1979956366-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit. proprietary +amsua16sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16 ALL STAC Catalog 2001-05-27 2009-07-30 -180, -89.91, 180, 89.73 https://cmr.earthdata.nasa.gov/search/concepts/C1979956366-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit. proprietary amsua17sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-17 ALL STAC Catalog 2002-07-21 2003-12-13 -180, -89.575, 180, 89.629 https://cmr.earthdata.nasa.gov/search/concepts/C1979975136-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Third Advanced Microwave Sounding Unit-A was launched on NOAA-17 on 24 June 2002 from Vandenberg AFB, California on a Titan II booster. proprietary amsua17sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-17 GHRC_DAAC STAC Catalog 2002-07-21 2003-12-13 -180, -89.575, 180, 89.629 https://cmr.earthdata.nasa.gov/search/concepts/C1979975136-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Third Advanced Microwave Sounding Unit-A was launched on NOAA-17 on 24 June 2002 from Vandenberg AFB, California on a Titan II booster. proprietary anezet-analysing-net-zero-transformations_1.0 ANEZET: Analysing Net-Zero Transformations ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081289-ENVIDAT.umm_json We have analysed past transformations in Switzerland in four environmental domains, with the aim to draw conclusions for current challenges, such as the net‐zero transformation. The data comprise transcripts of interviews with experts in the field of biodiversity, forests, landscape and natural hazard research. proprietary @@ -17165,8 +17145,8 @@ antarctic_circumpolar_current_fronts_1 Fronts of the Antarctic Circumpolar Curre antarctic_single_frames USGS Antarctic Single Frame Records USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567558-USGS_LTA.umm_json Antarctic Single Frame Records are a collection of aerial photographs over Antarctica from the United States Antarctic Resource Center (USARC) and the British Antarctic Survey (BAS) dating from 1946 to 2000. The Antarctic Single Frame Records collection includes black-and-white, natural color and color infrared images with a photographic scale ranging from 1:1,000 to 1:64,000. proprietary anthropogenic-change-and-net-n-mineralization_1.0 Anthropogenic change and soil net N mineralization ENVIDAT STAC Catalog 2020-01-01 2020-01-01 158.90625, -54.9776137, -132.1875, 61.2702328 https://cmr.earthdata.nasa.gov/search/concepts/C2789814650-ENVIDAT.umm_json This dataset contains all data on which the following publication below is based. Paper Citation: Risch Anita C., Zimmermann, Stefan, Moser, Barbara, Schütz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology Please cite this paper together with the citation for the datafile. We assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth’s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000). proprietary aoci0bil_281_1 BOREAS Level-0 AOCI Imagery: Digital Counts in BIL Format ORNL_CLOUD STAC Catalog 1994-07-21 1994-07-21 -105.91, 52.98, -104.93, 54.46 https://cmr.earthdata.nasa.gov/search/concepts/C2927616228-ORNL_CLOUD.umm_json The level-0 AOCI imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas. The AOCI was the only remote sensing instrument flown with wavelength bands specific to the investigation of various aquatic parameters such as chlorophyll content and turbidity. proprietary -apr3cpex_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX ALL STAC Catalog 2017-05-27 2017-06-24 -96.0262, 16.8091, -69.2994, 28.9042 https://cmr.earthdata.nasa.gov/search/concepts/C2409563129-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment (CPEX) aircraft field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 27, 2017 through June 24, 2017 in a HDF-5 file, with associated browse imagery in JPG format. proprietary apr3cpex_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX GHRC_DAAC STAC Catalog 2017-05-27 2017-06-24 -96.0262, 16.8091, -69.2994, 28.9042 https://cmr.earthdata.nasa.gov/search/concepts/C2409563129-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment (CPEX) aircraft field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 27, 2017 through June 24, 2017 in a HDF-5 file, with associated browse imagery in JPG format. proprietary +apr3cpex_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX ALL STAC Catalog 2017-05-27 2017-06-24 -96.0262, 16.8091, -69.2994, 28.9042 https://cmr.earthdata.nasa.gov/search/concepts/C2409563129-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment (CPEX) aircraft field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 27, 2017 through June 24, 2017 in a HDF-5 file, with associated browse imagery in JPG format. proprietary apr3cpexaw_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW ALL STAC Catalog 2021-08-20 2021-09-04 -80.7804, 11.8615, -45.6417, 34.046 https://cmr.earthdata.nasa.gov/search/concepts/C2269541013-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment – Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. These data files are available from August 20, 2021 through September 4, 2021 in a MatLab file, with associated browse files in JPEG format. proprietary apr3cpexaw_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW GHRC_DAAC STAC Catalog 2021-08-20 2021-09-04 -80.7804, 11.8615, -45.6417, 34.046 https://cmr.earthdata.nasa.gov/search/concepts/C2269541013-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment – Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. These data files are available from August 20, 2021 through September 4, 2021 in a MatLab file, with associated browse files in JPEG format. proprietary apr3cpexcv_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-CV ALL STAC Catalog 2022-09-02 2022-09-30 -89.6733315, 1.7593585, -14.8189435, 39.1985524 https://cmr.earthdata.nasa.gov/search/concepts/C2708951073-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-CV dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross-section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment – Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign will be based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX – Aerosols and Winds (CPEX-AW) and was conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. These data files are available from September 2, 2022, through September 30, 2022, in netCDF-4 format, with associated browse imagery in JPG format. proprietary @@ -17185,8 +17165,8 @@ aspas_asmas_aat_3 Antarctic Specially Protected Areas and Antarctic Specially Ma asrb-dav_1.0 ASRB_DAV: Shortwave and longwave radiation measurements (2 min) in Davos Dorf ENVIDAT STAC Catalog 2017-01-01 2017-01-01 9.84827, 46.81277, 9.84827, 46.81277 https://cmr.earthdata.nasa.gov/search/concepts/C2789814851-ENVIDAT.umm_json Incoming and outgoing shortwave and longwave 2 min radiation measurements in Davos Dorf, CH. ### References 1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019. 2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf. proprietary asrb-vf_1.0 ASRB_WFJVF: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos ENVIDAT STAC Catalog 2016-01-01 2016-01-01 9.809204, 46.829631, 9.809204, 46.829631 https://cmr.earthdata.nasa.gov/search/concepts/C2789814947-ENVIDAT.umm_json Incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch research site, Davos, CH. The experimental site at the Weissfluhjoch (WFJ, 46.83 N, 9.81 E) is located at an altitude of 2540 m in the Swiss Alps near Davos. During the winter months, almost all precipitation falls as snow at this altitude. As a consequence, a continuous seasonal snow cover builds up every winter, with a maximum snow height ranging from 153–366 cm over the period 1934–2012. The measurement site is located in an almost flat part of a southeast oriented slope. ### References 1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019. 2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf. proprietary asrb-wfj_1.0 ASRB_WFJ: Shortwave and longwave radiation measurements (2 min) at the Weissfluhjoch research site, Davos ENVIDAT STAC Catalog 2017-01-01 2017-01-01 9.809204, 46.829631, 9.809204, 46.829631 https://cmr.earthdata.nasa.gov/search/concepts/C2789814987-ENVIDAT.umm_json Corrected incoming and outgoing shortwave and longwave 2 min radiation measurements at the Weissfluhjoch summit, Davos, CH. ### References 1. Marty, C., Philipona, R., Frohlich, C., Ohmura, A.. Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network. 2002. Theoretical and Applied Climatology. Volume 72. Issue 3-4. 137-155. http://dx.doi.org/10.1007/s007040200019. 10.1007/s007040200019. 2. Christoph Marty. Surface Radiation, Cloud Forcing and Greenhouse Effect in the Alps. 2000. Institute fuer Klimaforschung ETH. Zuercher Klima-Schriften. Volume 79. http://e-collection.library.ethz.ch/eserv/eth:23491/eth-23491-01.pdf. proprietary -aster_1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre ALL STAC Catalog 2000-10-08 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214313130-AU_AADC.umm_json Advanced Spaceborne Thermal Emission and Reflection Radiometer. Level 1A and level 1B data. The L1A data are reconstructed, unprocessed instrument data at full resolution. It consists of the image data, the radiometric coefficients, the geometric coefficients and other auxiliary data without applying the coefficients to the image data. The L1B data have these coefficents applied for radiometric calibration and geometric resampling. There are approximately 2500 scenes available. Of these, over 3/5 of theme are level 1B data. Search the Satellite Image Catalogue for more information using the link included. proprietary aster_1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre AU_AADC STAC Catalog 2000-10-08 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214313130-AU_AADC.umm_json Advanced Spaceborne Thermal Emission and Reflection Radiometer. Level 1A and level 1B data. The L1A data are reconstructed, unprocessed instrument data at full resolution. It consists of the image data, the radiometric coefficients, the geometric coefficients and other auxiliary data without applying the coefficients to the image data. The L1B data have these coefficents applied for radiometric calibration and geometric resampling. There are approximately 2500 scenes available. Of these, over 3/5 of theme are level 1B data. Search the Satellite Image Catalogue for more information using the link included. proprietary +aster_1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster) satellite image data held by the Australian Antarctic Data Centre ALL STAC Catalog 2000-10-08 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214313130-AU_AADC.umm_json Advanced Spaceborne Thermal Emission and Reflection Radiometer. Level 1A and level 1B data. The L1A data are reconstructed, unprocessed instrument data at full resolution. It consists of the image data, the radiometric coefficients, the geometric coefficients and other auxiliary data without applying the coefficients to the image data. The L1B data have these coefficents applied for radiometric calibration and geometric resampling. There are approximately 2500 scenes available. Of these, over 3/5 of theme are level 1B data. Search the Satellite Image Catalogue for more information using the link included. proprietary aster_global_dem ASTER Global DEM USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567908-USGS_LTA.umm_json ASTER is capable of collecting in-track stereo using nadir- and aft-looking near infrared cameras. Since 2001, these stereo pairs have been used to produce single-scene (60- x 60-kilomenter (km)) digital elevation models (DEM) having vertical (root-mean-squared-error) accuracies generally between 10- and 25-meters (m). The methodology used by Japan's Sensor Information Laboratory Corporation (SILC) to produce the ASTER GDEM involves automated processing of the entire ASTER Level-1A archive. Stereo-correlation is used to produce over one million individual scene-based ASTER DEMs, to which cloud masking is applied to remove cloudy pixels. All cloud-screened DEMS are stacked and residual bad values and outliers are removed. Selected data are averaged to create final pixel values, and residual anomalies are corrected before partitioning the data into 1 degree (°) x 1° tiles. The ASTER GDEM covers land surfaces between 83°N and 83°S and is comprised of 22,702 tiles. Tiles that contain at least 0.01% land area are included. The ASTER GDEM is distributed as Geographic Tagged Image File Format (GeoTIFF) files with geographic coordinates (latitude, longitude). The data are posted on a 1 arc-second (approximately 30–m at the equator) grid and referenced to the 1984 World Geodetic System (WGS84)/ 1996 Earth Gravitational Model (EGM96) geoid. proprietary atlas_buildings_gis_1 Differential GPS survey of the Atlas Cove ANARE Station ruins on Heard Island AU_AADC STAC Catalog 2000-01-01 2000-02-28 73.3, -53.1, 73.5, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214313143-AU_AADC.umm_json Alistair Grinbergs (Heritage Officer) was on Heard island in January and February 2000) as part of the 2000 ANARE, to make an assessment of the heritage value of the old ANARE station ruins. This GPS survey data of the corners of buildings and other artefacts will form part of the record of the station site, together with drawings and other measurements. The assessment will be used to formulate a conservation management plan for the site. proprietary atlas_cove_photos_1 Atlas Cove Terrestrial Photos - historic ANARE Base AU_AADC STAC Catalog 2008-03-26 2008-03-26 73.391, -53.02, 73.394, -53.018 https://cmr.earthdata.nasa.gov/search/concepts/C1214313131-AU_AADC.umm_json Photographs and photo locations of the historic Australian National Antarctic Research Expedition (ANARE) base at Atlas Cove on Heard Island. The station was established 11 December 1947 and was closed down on 9 March 1955. Photos were taken in March of 2008 by Kerry Steinberner during a visit to Heard Island. The map used to locate the images is described in the following metadata record: Topographic Survey at Atlas Cove, Heard Island, November 2000 [atlas_survey2000_gis] The images include shots of the remains of ANARE buildings, vehicles, tanks, debris, fences, artefacts and flora. The dataset includes a copy of the images, an excel spreadsheet cataloguing the images, and shapefiles showing the image locations. proprietary @@ -17315,14 +17295,14 @@ brdpier0006 Demography and Movements of the Endangered Akepa and Hawaii Creeper brdpier0008 Determining age and sex of Oma'o (Myadestes obscurus) CEOS_EXTRA STAC Catalog 1976-01-01 1982-12-31 -155, 19, -155, 19 https://cmr.earthdata.nasa.gov/search/concepts/C2231549047-CEOS_EXTRA.umm_json Methods to determine the age and sex of 'Oma'o (Myadestes obscurus) were developed on the basis of 66 museum speciments and 149 live 'Oma'o captured in mist nets on the island of Hawaii. 'Oma'o in juvenile plumage are heavily spotted with scalloped greater coverts and tertials and are easily distinguished from adults. Birds in their first prebasic plumage usually retain one or more scallped wing coverts or tertials. Wing lengths of adult and immature male 'Oma'o were significantly longer than those of females, but only 80% of adult specimens were accurately sexed by wing length. Geographic Description: Island of Hawaii, Keauhou Ranch (19.50, -155.33; 1800 m elevation) and Kilauea Forest (19.52, -155.32; 1600-1650 m). 1.5.2 Bounding Rectangle Coordinates Methodology: Recorded plumage characteristics and exteral measurements of 55 'Oma'o specimens at the Bernice P. Bishop Museum and 11 'Oma'o specimens loaned by the American Museum of Natural History. 'Oma'op juvenal plumage are dark and below and are easily distinguished from adults. The feathers of the breast, belly, and flanks are buffy white in the center but are broadly bordered with blackish brown, giving the feathers a scalloped pattern (Berger 1981, Pratt 1982). proprietary brdpier0009 Diets of Owls and Feral Cats in Hawaii CEOS_EXTRA STAC Catalog 1990-01-01 1994-12-31 -155, 19, -155, 19 https://cmr.earthdata.nasa.gov/search/concepts/C2231549329-CEOS_EXTRA.umm_json "The feral house cat (Felis catus), Hawaiian Short-eared Owl or Pueo (Asio flammeus sandwichensis), and Common Barn Owl (Tyto alba) are important predators of birds and introduced rodents in Hawai'i. Cat scats from the island of Hawai'i (n=87), Pueo pellets from Hawai'i, Kaua'i, and Kaho'olawe (n=36), and Barn Owl pellets from Hawai'i, O'ahu and Kaho'olawe (n=301) were examined to determine the incidence of rodent, bird and insect remains in the diets of these predators. Rodents were the main prey of cats, Pueo, and Barn Owls, but the incidence of bird remains in diets of all three predator species was high relative to studies conducted elsewhere in the world. Geographic Description: All cat scats were collected in dry mamane (Sophora chrysophylla)-naio (Myoporum sandwichensis) forests on the western and eastern slopes of Mauna Kea above 2,000 m elevation. Pueo pellets were collected in dry forests on Mauna Kea (n=13), from Kaumana Gulch on Kaho'olawe (n=21), and from the Alakai Swamp on Kaua'i (n=2). Barn Owl pellets were collected at roosts and nests at Kakalau Forest National Wildlife Refuge on Hawai'i (n=207), near the Pu'u La'au cabin on Mauna Kea (n= 73), on O'ahu (n=19), at Ahupi Beach on Kaho'olawe (n=1). Acumulations of Barn Owl pellets were found below roosting sites, whereas single Pueo pellets were found below tall trees or on open ground (Mauna Kea), or on cliff faces on Kaho'olawe. On Kaua'i, Pueo pellets were found in an open bog near the remains of a recent Pacific Golden Plover kill. 1.5.2 Bounding Rectangle Coordinates Methodology: Determined predator diets from analysis of 87 cat scats, 36 Pueo pellets, and 301 Barn Owl pellets. All cat scats were collected in dry mamane-naio forests. Size, appearance, and consistency were used to determine the source of scats and pellets. Cat scats were smaller than pellets and had tapered ends with fewer bones distributed through them. Pueo pellets were smaller than Barn Owl pellets and had a uniformly cylindrical shape. They fit Mikkola's (1983) description as ""elongated, roughly cylindrical dark gray and formed from a tightly-massed conglomeration of fur or feathers with a central core of mammal and bird bones.""" proprietary brdwerc0002 Comparison of the Sedimentary Record of Fire with the Tree-Ring Record Within and Near Giant Sequoia Groves, Sierra Nevada, California CEOS_EXTRA STAC Catalog 1997-09-18 1998-09-30 -123, 35, -117, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231553360-CEOS_EXTRA.umm_json "The larger Sierra Nevada Global Change Research Program (SNGCRP) seeks to understand past, present, and possible future changes in Sierran forest structure, composition, and dynamics resulting from changing management practices and anticipated global climate change. Within the larger program, this project (""Comparison of the sedimentary record of fire with the tree-ring record within and near giant sequoia groves, Sierra Nevada, California"") will use high precision carbon dating of charcoal and pollen in sediment cores in order to (1) develop a 10,000-year record of fire history in the southern and central Sierra Nevada, calibrated against multi-millennial, annual-resolution fire histories from tree rings at the same sites, and (2) develop detailed descriptions of changes in forest composition over the last few millennia, to be compared with climate and fire histories developed by other SNGCRP projects. This work will provide data for calibration and testing of fire spread and forest dynamics models currently being developed by other global change research projects, and will provide baseline data on past disturbance regimes, their variability, and consequent forest response. These objectives will be achieved by analyzing four sediment cores. The cores have already been collected from meadows adjacent to sites with multi-millennial, annual-resolution fire histories developed from giant sequoia tree rings: Giant Forest (Sequoia National Park), Mountain Home Grove (Mountain Home State Forest), Mariposa Grove (Yosemite National Park), and Big Stump Grove (Kings Canyon National Park)." proprietary -breeding_success_BI_1 Adelie penguin breeding success for Bechervaise Island, Mawson ALL STAC Catalog 1990-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214313363-AU_AADC.umm_json Adelie penguin breeding success records for Bechervaise Island, Mawson since 1990-91. Data include counts of occupied nests and chick counts when either 2/3 of the nests have creched or when all nests have creched. Breeding success values are calculated as the number of chicks per occupied nest. Breeding Success = the number of chicks raised to fledging per nest with eggs Breeding success is calculated from four different whole island counts: 1) the number of incubating nests (i.e. the number of nest with eggs) - 'incubating nest count' 2) the number of brooding nests (i.e. the number of nests brooding chicks) - 'brooding chick count' 3) the number of chicks present when 2/3 of the nests have creched their chicks - '2/3-creche count' 4) the number of chicks present when all the nests have creche their chicks - 'fully-creche count' Each colony on the island is manually counted by field observers, using 'counters', three times each. Counts within 10% of each other are used to average the number of nests or chicks for each colony and then in later calculations to determine breeding success. Incubating nest counts are conducted on or about 2nd December; Brooding chick counts are conducted on or about the 7th January; 2/3-creche counts on or about the 19th January; and Fully-creche chick counts on or about 26th January. Whole island 2/3-creche and fully-creche chick count dates are determined from calculating when 2/3 and all study nests in the census area (study colonies) have creche their chicks. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year Breeding success Occupied nests proprietary breeding_success_BI_1 Adelie penguin breeding success for Bechervaise Island, Mawson AU_AADC STAC Catalog 1990-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214313363-AU_AADC.umm_json Adelie penguin breeding success records for Bechervaise Island, Mawson since 1990-91. Data include counts of occupied nests and chick counts when either 2/3 of the nests have creched or when all nests have creched. Breeding success values are calculated as the number of chicks per occupied nest. Breeding Success = the number of chicks raised to fledging per nest with eggs Breeding success is calculated from four different whole island counts: 1) the number of incubating nests (i.e. the number of nest with eggs) - 'incubating nest count' 2) the number of brooding nests (i.e. the number of nests brooding chicks) - 'brooding chick count' 3) the number of chicks present when 2/3 of the nests have creched their chicks - '2/3-creche count' 4) the number of chicks present when all the nests have creche their chicks - 'fully-creche count' Each colony on the island is manually counted by field observers, using 'counters', three times each. Counts within 10% of each other are used to average the number of nests or chicks for each colony and then in later calculations to determine breeding success. Incubating nest counts are conducted on or about 2nd December; Brooding chick counts are conducted on or about the 7th January; 2/3-creche counts on or about the 19th January; and Fully-creche chick counts on or about 26th January. Whole island 2/3-creche and fully-creche chick count dates are determined from calculating when 2/3 and all study nests in the census area (study colonies) have creche their chicks. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year Breeding success Occupied nests proprietary +breeding_success_BI_1 Adelie penguin breeding success for Bechervaise Island, Mawson ALL STAC Catalog 1990-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214313363-AU_AADC.umm_json Adelie penguin breeding success records for Bechervaise Island, Mawson since 1990-91. Data include counts of occupied nests and chick counts when either 2/3 of the nests have creched or when all nests have creched. Breeding success values are calculated as the number of chicks per occupied nest. Breeding Success = the number of chicks raised to fledging per nest with eggs Breeding success is calculated from four different whole island counts: 1) the number of incubating nests (i.e. the number of nest with eggs) - 'incubating nest count' 2) the number of brooding nests (i.e. the number of nests brooding chicks) - 'brooding chick count' 3) the number of chicks present when 2/3 of the nests have creched their chicks - '2/3-creche count' 4) the number of chicks present when all the nests have creche their chicks - 'fully-creche count' Each colony on the island is manually counted by field observers, using 'counters', three times each. Counts within 10% of each other are used to average the number of nests or chicks for each colony and then in later calculations to determine breeding success. Incubating nest counts are conducted on or about 2nd December; Brooding chick counts are conducted on or about the 7th January; 2/3-creche counts on or about the 19th January; and Fully-creche chick counts on or about 26th January. Whole island 2/3-creche and fully-creche chick count dates are determined from calculating when 2/3 and all study nests in the census area (study colonies) have creche their chicks. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year Breeding success Occupied nests proprietary brok_5k_gis_1 Broknes Peninsula 1:5000 Topographic GIS Dataset AU_AADC STAC Catalog 1994-11-03 1994-11-17 76.2, -69.4333, 76.4333, -69.3333 https://cmr.earthdata.nasa.gov/search/concepts/C1214313345-AU_AADC.umm_json Broknes Peninsula, Larsemann Hills, 1:5000 GIS dataset. This dataset has been superseded by the datasets described by the metadata records: 'Larsemann Hills - Mapping from aerial photography captured February 1998' and 'Larsemann Hills - Mapping from Landsat 7 imagery captured January 2000'. These data have been archived as they have been superseded. proprietary broknes_lake_catchments_gis_1 Lake catchments on Broknes, Larsemann Hills AU_AADC STAC Catalog 1997-05-06 2001-08-14 76.285, -69.4193, 76.42, -69.3698 https://cmr.earthdata.nasa.gov/search/concepts/C1214313378-AU_AADC.umm_json Catchment boundaries of the the lakes on Broknes, Larsemann Hills. These catchments were generated using the FLOWDIRECTION and BASINS routines in the GRID module of ArcInfo GIS. proprietary -bromwich_0337948_1 A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5 ALL STAC Catalog 1979-01-01 2002-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214586989-SCIOPS.umm_json This 3-year project (June 2004-May 2007) was funded by the National Science Foundation's Office of Polar Programs (Glaciology). We employed the Polar MM5 to model variability and change in the surface mass balance (the net accumulation of moisture) over Antarctica in recent decades. Available here are annually and seasonally resolved grids of atmospheric data simulated by Polar MM5 for the period Jan 1979-Aug 2002. The ERA-40 dataset provided the initial and boundary conditions for the simulations. The burden of validating the data provided is the responsibility of anyone choosing to download it. MODEL CONFIGURATION: The Polar MM5 simulations were performed on a 121 x 121 polar stereographic grid covering the Antarctic and centered over the South Pole. The model resolution is 60-km in each horizontal direction. Vertically, the domain contains 32 sigma levels ranging from the surface to 10 hPa. Atmospheric data (U,V,T,Q,P) and sea surface temperatures were provided by ERA-40. 25-km resolution daily sea ice concentration grids were provided by the National Snow and Ice Data Center to determine fractional ice coverage over ocean gridpoints. The model topography was interpolated from the 1-km resolution digital elevation model of Liu et al. (2001). Images of the model domain, topography and land use specifications can be found here. More information on the physics in Polar MM5 can be found on the Polar MM5 Webpage, http://polarmet.mps.ohio-state.edu/PolarMet/pmm5.html Please reference the following publication if you use the data in a publication: Monaghan, A. J., D. H. Bromwich, and S.-H. Wang, 2006: Recent trends in Antarctic snow accumulation from Polar MM5. Philosophical Trans. Royal. Soc. A, 364, 1683-1708. proprietary bromwich_0337948_1 A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5 SCIOPS STAC Catalog 1979-01-01 2002-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214586989-SCIOPS.umm_json This 3-year project (June 2004-May 2007) was funded by the National Science Foundation's Office of Polar Programs (Glaciology). We employed the Polar MM5 to model variability and change in the surface mass balance (the net accumulation of moisture) over Antarctica in recent decades. Available here are annually and seasonally resolved grids of atmospheric data simulated by Polar MM5 for the period Jan 1979-Aug 2002. The ERA-40 dataset provided the initial and boundary conditions for the simulations. The burden of validating the data provided is the responsibility of anyone choosing to download it. MODEL CONFIGURATION: The Polar MM5 simulations were performed on a 121 x 121 polar stereographic grid covering the Antarctic and centered over the South Pole. The model resolution is 60-km in each horizontal direction. Vertically, the domain contains 32 sigma levels ranging from the surface to 10 hPa. Atmospheric data (U,V,T,Q,P) and sea surface temperatures were provided by ERA-40. 25-km resolution daily sea ice concentration grids were provided by the National Snow and Ice Data Center to determine fractional ice coverage over ocean gridpoints. The model topography was interpolated from the 1-km resolution digital elevation model of Liu et al. (2001). Images of the model domain, topography and land use specifications can be found here. More information on the physics in Polar MM5 can be found on the Polar MM5 Webpage, http://polarmet.mps.ohio-state.edu/PolarMet/pmm5.html Please reference the following publication if you use the data in a publication: Monaghan, A. J., D. H. Bromwich, and S.-H. Wang, 2006: Recent trends in Antarctic snow accumulation from Polar MM5. Philosophical Trans. Royal. Soc. A, 364, 1683-1708. proprietary -brownbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands ALL STAC Catalog 1997-02-01 2000-02-05 110.54, -66.281, 110.548, -66.279 https://cmr.earthdata.nasa.gov/search/concepts/C1214308318-AU_AADC.umm_json This dataset is a Digital Elevation Model (DEM) of Brown Bay, Windmill Islands and contours and bathymetric contours derived from the DEM. The data are stored in a UTM zone 49 projection. They were created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA ARE NOT FOR NAVIGATION PURPOSES. proprietary +bromwich_0337948_1 A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5 ALL STAC Catalog 1979-01-01 2002-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214586989-SCIOPS.umm_json This 3-year project (June 2004-May 2007) was funded by the National Science Foundation's Office of Polar Programs (Glaciology). We employed the Polar MM5 to model variability and change in the surface mass balance (the net accumulation of moisture) over Antarctica in recent decades. Available here are annually and seasonally resolved grids of atmospheric data simulated by Polar MM5 for the period Jan 1979-Aug 2002. The ERA-40 dataset provided the initial and boundary conditions for the simulations. The burden of validating the data provided is the responsibility of anyone choosing to download it. MODEL CONFIGURATION: The Polar MM5 simulations were performed on a 121 x 121 polar stereographic grid covering the Antarctic and centered over the South Pole. The model resolution is 60-km in each horizontal direction. Vertically, the domain contains 32 sigma levels ranging from the surface to 10 hPa. Atmospheric data (U,V,T,Q,P) and sea surface temperatures were provided by ERA-40. 25-km resolution daily sea ice concentration grids were provided by the National Snow and Ice Data Center to determine fractional ice coverage over ocean gridpoints. The model topography was interpolated from the 1-km resolution digital elevation model of Liu et al. (2001). Images of the model domain, topography and land use specifications can be found here. More information on the physics in Polar MM5 can be found on the Polar MM5 Webpage, http://polarmet.mps.ohio-state.edu/PolarMet/pmm5.html Please reference the following publication if you use the data in a publication: Monaghan, A. J., D. H. Bromwich, and S.-H. Wang, 2006: Recent trends in Antarctic snow accumulation from Polar MM5. Philosophical Trans. Royal. Soc. A, 364, 1683-1708. proprietary brownbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands AU_AADC STAC Catalog 1997-02-01 2000-02-05 110.54, -66.281, 110.548, -66.279 https://cmr.earthdata.nasa.gov/search/concepts/C1214308318-AU_AADC.umm_json This dataset is a Digital Elevation Model (DEM) of Brown Bay, Windmill Islands and contours and bathymetric contours derived from the DEM. The data are stored in a UTM zone 49 projection. They were created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA ARE NOT FOR NAVIGATION PURPOSES. proprietary +brownbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands ALL STAC Catalog 1997-02-01 2000-02-05 110.54, -66.281, 110.548, -66.279 https://cmr.earthdata.nasa.gov/search/concepts/C1214308318-AU_AADC.umm_json This dataset is a Digital Elevation Model (DEM) of Brown Bay, Windmill Islands and contours and bathymetric contours derived from the DEM. The data are stored in a UTM zone 49 projection. They were created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA ARE NOT FOR NAVIGATION PURPOSES. proprietary bryophyte-observer-bias_1.0 Greater observer expertise leads to higher estimates of bryophyte species richness ENVIDAT STAC Catalog 2024-01-01 2024-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081769-ENVIDAT.umm_json This dataset contains bryophyte species count data and information about the observers bryophyte expertise for 2332 relevés conducted from 2011 to 2021 on 10-m2 plots in a long-term monitoring program in Switzerland. Plots were situated in raised bogs and fens of national importance, which were distributed across the whole country. The majority of the plots is represented by two relevés as sites are revisited every six years. The dataset was used in the paper mentioned below to test if species richness estimates differed among categories of observer expertise. Moser T, Boch S, Bedolla A, Ecker KT, Graf UH, Holderegger R, Küchler H, Pichon NA, Bergamini A (2024) Greater observer expertise leads to higher estimates of bryophyte species richness. _Journal of Vegetation Science_. (submitted) proprietary bunger_east_sat_1 Bunger Hills East Satellite Image Map 1:50 000 AU_AADC STAC Catalog 1992-06-01 1992-06-30 101, -66, 102, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313379-AU_AADC.umm_json Satellite image map of Bunger Hills East/Wilkes Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG Commercial (now Geoscience Australia), in Australia, in 1992. The map is at a scale of 1:50000, and was produced from four multispectral space imagery SPOT 1 scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and gives some historical text information. The map has both geographical and UTM co-ordinates. proprietary bunger_geology_gis_1 Bunger Hills - Denman Glacier Bedrock Geology GIS Dataset AU_AADC STAC Catalog 1980-01-01 1997-12-31 98, -67.5, 102, -65.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214313380-AU_AADC.umm_json Bunger Hills - Denman Glacier Bedrock Geology GIS Dataset. For additional information, see the published map 'Bunger Hills - Denman Glacier Bedrock Geology', published in 1994, and available at the provided URL. proprietary @@ -17331,14 +17311,14 @@ bunger_hills_spot5_dem_gis_1 Bunger Hills SPOT5 DEM (Digital Elevation Model) AU bunger_west_sat_1 Bunger Hills West Satellite Image Map 1:50 000 AU_AADC STAC Catalog 1992-06-01 1992-06-30 100, -66, 101, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313353-AU_AADC.umm_json Satellite image map of Bunger Hills West/Wilkes Land, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG Commercial (now Geoscience Australia), in Australia, in 1992. The map is at a scale of 1:50000, and was produced from four multispectral space imagery SPOT 1 scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases and gives some historical text information. The map has both geographical and UTM co-ordinates. proprietary burning_emissions_752_1 SAFARI 2000 Biomass Burning Emissions, Selected Sites, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-11-24 2001-01-16 10, -35, 50, 0 https://cmr.earthdata.nasa.gov/search/concepts/C2789024214-ORNL_CLOUD.umm_json Biomass burning is a major source for gaseous and particulate atmospheric pollution over southern Africa and globally. The purpose of this study was to quantify biomass burning emissions in an attempt to better understand and predict associated environmental impacts. Sixty biomass burning experiments were carried out November 2000-January 2001 in three regions of southern Africa that are representative of major regional ecosystem types: Etosha National Park (Namibia), Kruger National Park (South Africa), and woodland sites in Zambia and Malawi. proprietary bvoc_flux_759_1 SAFARI 2000 BVOC Measurements at Skukuza and Maun Flux Towers, Wet Season 2001 ORNL_CLOUD STAC Catalog 2001-02-01 2001-02-12 23.55, -19.9, 23.55, -19.9 https://cmr.earthdata.nasa.gov/search/concepts/C2780105326-ORNL_CLOUD.umm_json Biogenic volatile organic compound (BVOC) emissions were measured in a Colophospermum mopane woodland near Maun, Botswana, and in a Combretum-Acacia savanna in Kruger National Park, 13 km from Skukuza, Republic of South Africa (RSA) during the 2001 wet season campaign of SAFARI 2000. In addition, relaxed eddy accumulation (REA) measurements of BVOC fluxes were made on flux towers at these sites, where net CO2 emissions were also measured simultaneously. proprietary -c05fa2267e6e03d0e5b9bb6429fdbb974a8194a1 3 year daily average solar exposure map Mali 3Km GRAS August 2008-2011 SCIOPS STAC Catalog 2008-03-01 2011-03-31 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214604040-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for August. proprietary c05fa2267e6e03d0e5b9bb6429fdbb974a8194a1 3 year daily average solar exposure map Mali 3Km GRAS August 2008-2011 ALL STAC Catalog 2008-03-01 2011-03-31 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214604040-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for August. proprietary +c05fa2267e6e03d0e5b9bb6429fdbb974a8194a1 3 year daily average solar exposure map Mali 3Km GRAS August 2008-2011 SCIOPS STAC Catalog 2008-03-01 2011-03-31 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214604040-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for August. proprietary c0b9f42f-640a-44e0-9080-7e80081942c9_NA MERIS - Water Parameters - North Sea, Daily FEDEO STAC Catalog 2005-04-22 2010-03-18 -6.10393, 49.9616, 11.4301, 61.9523 https://cmr.earthdata.nasa.gov/search/concepts/C2207458012-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides daily maps. proprietary c183044b88734442b6d37f5c4f6b0092_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ensemble product), Version 2.6 FEDEO STAC Catalog 2002-01-01 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143201-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily, monthly and yearly gridded aerosol products from the AATSR instrument on the ENVISAT satellite. The data is an uncertainty-weighted ensemble of the outputs of three separate algorithms (the SU, ADV, and ORAC algorithms.) This product is version 2.6 of the ensemble product. Data is provided for the period 2002 to 2012. In the early period, it also contains data from the ATSR-2 instrument on the ERS-2 satellite. A separate ATSR-2 product covering the period 1995-2001 is also available, and together these form a continuous timeseries from 1995-2012.For further details about these data products please see the documentation. proprietary c241e665-5175-4c26-b0cd-f0dfee32afdb Earthquakes events from ANSS 1970-March 2011 CEOS_EXTRA STAC Catalog 1970-01-02 2011-04-01 -180, -58, 180, 85.03594 https://cmr.earthdata.nasa.gov/search/concepts/C2232847370-CEOS_EXTRA.umm_json This dataset includes earthqakes events with magnitudes higher than 5.0 as reported by the Advanced national Seismic System (ANSS) Catalogue over the period 1970 - March 2011. UNEP/GRID-Europe processed the intensity buffer of each event following a methodology developped in GRAVITY I and II (http://www.grid.unep.ch/product/publication/download/ew_gravity1.pdf and http://www.grid.unep.ch/product/publication/download/ew_gravity2.pdf). Credit: Earthquakes events (USGS/ANSS), Intensity buffers UNEP/GRID-Europe. Attributes descriptions: EV_ID: Event ID ISO3YEAR: Country and year ISO3: Country ISO3 ID_NAT: Event ID and ISO3 ID_CAT: ANSS ID YEAR: Year START_DATE: Year, Month and Day (YYYYMMDD) MAG: Earthquake magnitude DEPTH: Earthquake depth (kilometer) RADIUS_M: Buffer radius following Gravity I and II methodology (meter) LATITUDE: Latitude (decimal degrees) proprietary c2af8764c84744de87a69db7fecf7af9_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.1 FEDEO STAC Catalog 1991-08-05 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142704-FEDEO.umm_json The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.The v06.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 proprietary -c2d1361c3fcbc6c7b60b35791f7bbc45bf8079dc 3 year daily average solar exposure map Mali 3Km GRAS January 2008-2011 ALL STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603977-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for January. proprietary c2d1361c3fcbc6c7b60b35791f7bbc45bf8079dc 3 year daily average solar exposure map Mali 3Km GRAS January 2008-2011 SCIOPS STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603977-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for January. proprietary +c2d1361c3fcbc6c7b60b35791f7bbc45bf8079dc 3 year daily average solar exposure map Mali 3Km GRAS January 2008-2011 ALL STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603977-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for January. proprietary c4_percent_1deg_932_1 ISLSCP II C4 Vegetation Percentage ORNL_CLOUD STAC Catalog 1993-01-01 1998-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784880272-ORNL_CLOUD.umm_json The photosynthetic composition (C3 or C4) of vegetation on the land surface is essential for accurate simulations of biosphere-atmosphere exchanges of carbon, water, and energy. C3 and C4 plants have different responses to light, temperature, CO2, and nitrogen; they also differ in physiological functions like stomatal conductance and isotope fractionation. A fine-scale distribution of these plant types is essential for earth science modeling.The C4 percentage is determined from data sets that describe the continuous distribution of plant growth forms (i.e., the percent of a grid cell covered by herbaceous or woody vegetation), climate classifications, the fraction of a grid cell covered in croplands, and national crop type harvest area statistics. The staff from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II have made the original data set consistent with the ISLSCP-2 land/water mask. This data set contains a single file in ArcInfo ASCIIGRID format.This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets. proprietary c4a7495d-6275-4169-8ceb-59cfaa2dd09b_NA METOP GOME-2 - Water Vapour (H2O) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207458016-FEDEO.umm_json The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational H2O total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV/VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The total H2O column is retrieved from GOME solar backscattered measurements in the red wavelength region (614-683.2 nm), using the Differential Optical Absorption Spectroscopy (DOAS) method. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/ proprietary c4aaero_1 CAMEX-4 AEROSONDE V1 GHRC_DAAC STAC Catalog 2001-08-19 2001-09-10 -81.4325, 30.2039, -80.649, 30.5738 https://cmr.earthdata.nasa.gov/search/concepts/C1979080632-GHRC_DAAC.umm_json The CAMEX-4 Aerosonde dataset contains temperature, humidity, and atmospheric pressure measurements collected to study the boundary layer below levels where traditional hurricane reconnasissance aircaft fly. The Aerosonde is an unmanned aerial vehicle with a wingspan of 2.9 meters (~9 feet) weighing approximately 14 kg (~31 lbs). Carrying a payload of air pressure, temperature and humidity probes, the aircraft can fly at altitudes from near the surface to 21,000 feet at speeds of 50-95 mph for periods of up to 30 hours. Controlled by dual computers and navigated by GPS, the Aerosonde is designed to economically collect meteorological data over a wide area. proprietary @@ -17390,8 +17370,8 @@ calibgas_500_1 BOREAS Calibration Gas Standards ORNL_CLOUD STAC Catalog 1994-05- canopychem_422_1 Seedling Canopy Chemistry, 1992-1993 (ACCP) ORNL_CLOUD STAC Catalog 1992-11-06 1993-03-15 -122.05, 37.4, -122.05, 37.4 https://cmr.earthdata.nasa.gov/search/concepts/C2776831590-ORNL_CLOUD.umm_json The nitrogen and chlorophyll concentrations of constructed Douglas-fir and bigleaf maple seedling canopies were determined. Canopy reflectance spectra were measured before foliage samples were collected. proprietary canopyspec_423_1 Seedling Canopy Reflectance Spectra, 1992-1993 (ACCP) ORNL_CLOUD STAC Catalog 1992-11-06 1993-03-15 -122.05, 37.4, -122.05, 37.4 https://cmr.earthdata.nasa.gov/search/concepts/C2776849767-ORNL_CLOUD.umm_json The reflectance spectra of Douglas-fir and bigleaf maple seedling canopies were measured. Canopies varied in fertilizer treatment and leaf area density respectively. proprietary capeden_management_gis_1 Cape Denison Management Zone GIS Dataset AU_AADC STAC Catalog 2004-01-01 2004-12-31 142.651, -67.014, 142.691, -67.003 https://cmr.earthdata.nasa.gov/search/concepts/C1214313393-AU_AADC.umm_json This GIS dataset is comprised of the boundary of the Visual Protection Zone at Cape Denison, Antarctica. The data were created for the Management Plan for Historic Site and Monument No 77 and Antarctic Specially Managed Area (ASMA) No 3 produced by the Australian Antarctic Division in 2004. The data are formatted according to the SCAR Feature Catalogue and are available for download (see Related URLS). proprietary -capeden_sat_ikonos_1 A georeferenced high resolution satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast acquired on 26, 31 January 2001 ALL STAC Catalog 2001-01-26 2001-01-31 142.5153, -67.0697, 143.03, -66.9478 https://cmr.earthdata.nasa.gov/search/concepts/C1214313394-AU_AADC.umm_json The following was done by a contractor for the Australian Antarctic Division: A satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast was created by combining parts of three satellite images acquired by the Ikonos satellite. Two of the images were acquired on 26 January 2001 and the third image was acquired on 31 January 2001. The multispectral component of the mosaic was then (i) pan sharpened to increase the resolution from 4 metres to 1 metre; and (ii) georeferenced. See the Quality section for details about the satellite images and the georeferencing. The georeferenced mosaic is stored in two parts. See the Satellite Image Catalogue entries in Related URLs for details. Three satellite image maps were produced from the georeferenced mosaic. See the SCAR Map Catalogue entries in Related URLs for details. proprietary capeden_sat_ikonos_1 A georeferenced high resolution satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast acquired on 26, 31 January 2001 AU_AADC STAC Catalog 2001-01-26 2001-01-31 142.5153, -67.0697, 143.03, -66.9478 https://cmr.earthdata.nasa.gov/search/concepts/C1214313394-AU_AADC.umm_json The following was done by a contractor for the Australian Antarctic Division: A satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast was created by combining parts of three satellite images acquired by the Ikonos satellite. Two of the images were acquired on 26 January 2001 and the third image was acquired on 31 January 2001. The multispectral component of the mosaic was then (i) pan sharpened to increase the resolution from 4 metres to 1 metre; and (ii) georeferenced. See the Quality section for details about the satellite images and the georeferencing. The georeferenced mosaic is stored in two parts. See the Satellite Image Catalogue entries in Related URLs for details. Three satellite image maps were produced from the georeferenced mosaic. See the SCAR Map Catalogue entries in Related URLs for details. proprietary +capeden_sat_ikonos_1 A georeferenced high resolution satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast acquired on 26, 31 January 2001 ALL STAC Catalog 2001-01-26 2001-01-31 142.5153, -67.0697, 143.03, -66.9478 https://cmr.earthdata.nasa.gov/search/concepts/C1214313394-AU_AADC.umm_json The following was done by a contractor for the Australian Antarctic Division: A satellite image mosaic of Cape Denison, Mackellar Islands and part of the George V Land Coast was created by combining parts of three satellite images acquired by the Ikonos satellite. Two of the images were acquired on 26 January 2001 and the third image was acquired on 31 January 2001. The multispectral component of the mosaic was then (i) pan sharpened to increase the resolution from 4 metres to 1 metre; and (ii) georeferenced. See the Quality section for details about the satellite images and the georeferencing. The georeferenced mosaic is stored in two parts. See the Satellite Image Catalogue entries in Related URLs for details. Three satellite image maps were produced from the georeferenced mosaic. See the SCAR Map Catalogue entries in Related URLs for details. proprietary carabid-beetles-in-forests_2.0 Carabid beetles in forests ENVIDAT STAC Catalog 2019-01-01 2019-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814572-ENVIDAT.umm_json Carabidae data from all historic up to the recent projects (21.10.2019) of WSL, collected with various methods in forests of different types. Version 2 ('FIDO_global_extract 2019-11-22_18-11-24 WSL-Forest-Carabidae') contains additional data field PROJ_FALLENBEZEICHNUNG. Data are provided on request to contact person against bilateral agreement. proprietary casey_alk_clones_1 Alkane mono-oxygenase genes from marine sediment near Casey AU_AADC STAC Catalog 2001-12-01 2001-12-25 110.3, -66.35, 110.35, -66.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214313396-AU_AADC.umm_json This dataset consists of 67 DNA sequences of the alkane mono-oxygenase gene. The sequence data are in FASTA format which can be opened with word-processing as well as sequence analysis software. The clone library was created using the primers described by Kloos et al. (2006, Journal Microbiological Methods 66:486-496) F: AAYACNGCNCAYGARCTNGGNCAYAA and R:GCRTGRTGRTCNGARTGNCGYTG. The library was created from a marine sediment sample that was part of the SRE4 marine biodegradation experiment in O'Brien bay near Casey station. The sample used was from the sediment exposed to Special Antarctic Blend diesel 5-weeks after the time of deployment. These data were collected as part of AAS project 2672 - Pathways of alkane biodegradation in antarctic and subantarctic soils and sediments. proprietary casey_alk_clones_1 Alkane mono-oxygenase genes from marine sediment near Casey ALL STAC Catalog 2001-12-01 2001-12-25 110.3, -66.35, 110.35, -66.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214313396-AU_AADC.umm_json This dataset consists of 67 DNA sequences of the alkane mono-oxygenase gene. The sequence data are in FASTA format which can be opened with word-processing as well as sequence analysis software. The clone library was created using the primers described by Kloos et al. (2006, Journal Microbiological Methods 66:486-496) F: AAYACNGCNCAYGARCTNGGNCAYAA and R:GCRTGRTGRTCNGARTGNCGYTG. The library was created from a marine sediment sample that was part of the SRE4 marine biodegradation experiment in O'Brien bay near Casey station. The sample used was from the sediment exposed to Special Antarctic Blend diesel 5-weeks after the time of deployment. These data were collected as part of AAS project 2672 - Pathways of alkane biodegradation in antarctic and subantarctic soils and sediments. proprietary @@ -17437,11 +17417,11 @@ chlorophyll_65-02_1 Long-term variation of surface phytoplankton chlorophyll a i chm-hp-4rtm_1.0 Forest canopy structure data for radiation and snow modelling (CH/FIN) ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.871859, 46.845432, 26.6365886, 67.366827 https://cmr.earthdata.nasa.gov/search/concepts/C2789814990-ENVIDAT.umm_json This dataset contains forest canopy structure data acquired in a spruce forest at Laret, Switzerland, and a pine forest at Sodankylä, Finland. Data include: * Hemispherical photographs taken at transect intersection points of 13 experimental plots (40x40m each) * a Canopy Height Model (tree height map) derived by rasterizing airborne LiDAR data, encompassing the entire simulation domain at Laret (150'000 m2) These data provide the necessary basis for creating canopy structure datasets to be used as input to the forest snow snow model FSM2. These datasets, the model input derivatives and the radiation and snow modelling are described in detail in the following publication: _Mazzotti, G., Webster, C., Essery, R., and Jonas, T. (2021) Improving the physical representation of forest snow processes in coarse-resolution models: lessons learned from upscaling hyper-resolution simulations. Water Resources Research 57, e2020WR029064. [doi: 10.1029/2020WR029064](https://doi.org/10.1029/2020WR029064)_ This publication must be cited when using the data. ### See also: For additional information on the FSM2 model, see the corresponding [GitHub repository](https://github.com/GiuliaMazzotti/FSM2/tree/hyres_enhanced_canopy) The datasets and the model have also been used in _Mazzotti et al. (2020) Process-level evaluation of a hyper-resolution forest snow model using distributed multi-sensor observations. [doi: 10.1029/2020WR027572](https://doi.org/10.1029/2020WR027572) proprietary climate-change-scenarios-at-hourly-resolution_1.0 Dataset for: Climate change scenarios at hourly time-step over Switzerland from an enhanced temporal downscaling approach ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814547-ENVIDAT.umm_json In fall 2019, a new set of climate change scenarios has been released for Switzerland, the CH2018 dataset (www.climate-scenarios.ch). The data are provided at daily resolution. We produced from the CH2018 dataset a new set of climate change scenarios temporally downscaled at hourly resolution. In addition, we extended this dataset integrating the meteorological stations from the Inter-Cantonal Measurement and Information System (IMIS) network, an alpine network of automatic meteorological stations operated by the WSL Institute for Snow and Avalanche Research SLF. The extension to the IMIS network is obtained using a Quantile Mapping approach in order to perform a spatial transfer of the CH2018 scenarios from the location of the MeteoSwiss stations to the location of the IMIS stations. The temporal downscaling is performed using an enhanced Delta-Change approach. This approach is based on objective criteria for assessing the quality of the determined delta and downscaled time series. In addition, this method also fixes a flaw of common quantile mapping methods (such as used in the CH2018 dataset for spatial downscaling) related to the decrease of correlation between different variables. The idea behind the delta change approach is to take the main seasonal signal (and mean) from climate change scenarios at daily resolution and to map it to a historical time series at hourly resolution in order to modify the historical time series. The obtained time series exhibit the same seasonal signal as the original climate change time series, while it keeps the sub-daily cycle from the historical time series. The applied methods (Quantile Mapping and Delta-Change) have limitations in correctly representing statistically extreme events and changes in the frequency of discontinuous events such as precipitation. In addition, the sub-daily cycle in the data is inherited from the historical time series, so there is no information of the climate change signal in this sub-daily cycle. A careful reading of the paper accompanying the dataset is necessary to understand the limitations and scope of application of this new dataset. This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode). proprietary climate_iceberg_1 Antarctic CRC and Australian Antarctic Division Climate Data Set - Australian iceberg observations AU_AADC STAC Catalog 1978-12-13 2001-03-20 -160, -70, 45, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214313409-AU_AADC.umm_json This dataset contains iceberg observations collected routinely on Australian National Antarctic Research Expeditions (ANARE) by Antarctic expeditioners on a volunteer basis. The observations were made each austral summer from the 1978/1979 season until the 2000/2001 season. Data included voyage number, date, time, latitude, longitude, sea ice concentration, water temperature, total icebergs, number of icebergs in each width category, the width to height ratio of selected larger tabular icebergs. It was been compiled and presented on the web by the Glaciology program of the Antarctic CRC (now ACE CRC). proprietary -climate_pressure_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air pressure AU_AADC STAC Catalog 1901-01-01 1998-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313319-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air pressure for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary climate_pressure_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air pressure ALL STAC Catalog 1901-01-01 1998-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313319-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air pressure for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary +climate_pressure_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air pressure AU_AADC STAC Catalog 1901-01-01 1998-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313319-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air pressure for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary climate_sea_ice_1 Antarctic CRC and Australian Antarctic Division Climate Data Set - Northern extent of Antarctic sea ice AU_AADC STAC Catalog 1973-01-18 1996-12-19 -180, -80, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214313423-AU_AADC.umm_json This dataset contains the digitisation of one U.S. Navy/NOAA Joint Ice Facility sea ice extent and concentration map monthly to give the latitude and longitude of the northern extent of the Antarctic sea ice. Maps were produced weekly, but have been digitised monthly, since distribution began in January 1973 (except August 1985), until December 1996. Maps were digitised at each 10 degrees of longitude, and the longitude, distance from the south pole to the northern edge of the sea ice at that longitude, and latitude of that edge is given, as well as the mean distance and latitude for that map. Summary tabulations (sea ice northern extent latitudes at each 10 degree of longitude each year, grouped by month) and mean monthly sea ice extent statistics are also available. proprietary -climate_temps_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures ALL STAC Catalog 1901-01-01 2002-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313410-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary climate_temps_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures AU_AADC STAC Catalog 1901-01-01 2002-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313410-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary +climate_temps_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures ALL STAC Catalog 1901-01-01 2002-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313410-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary climatological-snow-data-1998-2022-oshd_1.0 Climatological snow data since 1998, OSHD ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081762-ENVIDAT.umm_json This dataset comprises the climatology on gridded data of snow water equivalent and snow melt runoff spanning 1998-2022, with a spatial resolution of 1 km and daily temporal resolution. This data was produced with the conceptual OSHD model (Temperature Index Model). proprietary climwat CLIMWAT, A Climatic Database CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232283619-CEOS_EXTRA.umm_json CLIMWAT is a climatic database to be used in combination with the computer program CROPWAT and allows the ready calculation of crop water requirements, irrigation supply and irrigation scheduling for various crops for a range of climatological stations worldwide. The CLIMWAT database includes data from a total of 3262 meteorological stations from 144 countries. CLIMWAT is published as Irrigation and Drainage paper No 49 in 1994 and includes a Manual with description of the use of the database with CROPWAT The data are contained in five diskettes included in the publication and can be ordered as FAO Irrigation and Drainage Paper 49 through the FAO Sales and Marketing Group. [Summary provided by the FAO.] proprietary cmar_wh CSIRO Marine Data Warehouse (OBIS Australia) CEOS_EXTRA STAC Catalog 1978-02-05 1997-08-30 114, -44, 155, -8 https://cmr.earthdata.nasa.gov/search/concepts/C2226653616-CEOS_EXTRA.umm_json The CSIRO Marine Data Warehouse is a repository for biological and other marine survey data collected by CSIRO Division of Marine and Atmospheric Research (CMAR), Australia. It contains field (observational) data from numerous research trawls and other fisheries-related surveys conducted in waters around Australia by the Division since the late 1970s. At time of writing (April 2006) the database is serving approximately 106,000 species-level records to OBIS. Multiple species records and those of taxa not identified to species level are presently excluded. Associated data include species counts and/or weights in some but not all cases. proprietary @@ -17758,12 +17738,12 @@ fife_AF_filtr_nae_6_1 Aircraft Flux-Filtered: NRCC (FIFE) ORNL_CLOUD STAC Catalo fife_AF_filtr_nae_6_1 Aircraft Flux-Filtered: NRCC (FIFE) ALL STAC Catalog 1987-06-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968516479-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_filtr_ncar_8_1 Aircraft Flux-Filtered: Univ. Col. (FIFE) ALL STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968522986-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_filtr_ncar_8_1 Aircraft Flux-Filtered: Univ. Col. (FIFE) ORNL_CLOUD STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968522986-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary -fife_AF_filtr_wyo_7_1 Aircraft Flux-Filtered: U of Wy. (FIFE) ALL STAC Catalog 1987-08-11 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968521064-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_filtr_wyo_7_1 Aircraft Flux-Filtered: U of Wy. (FIFE) ORNL_CLOUD STAC Catalog 1987-08-11 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968521064-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary -fife_AF_raw_nae_9_1 Aircraft Flux-Raw: NRCC (FIFE) ORNL_CLOUD STAC Catalog 1987-06-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968531540-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary +fife_AF_filtr_wyo_7_1 Aircraft Flux-Filtered: U of Wy. (FIFE) ALL STAC Catalog 1987-08-11 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968521064-ORNL_CLOUD.umm_json Filtered boundary layer fluxes recorded on aircraft flights over the Konza proprietary fife_AF_raw_nae_9_1 Aircraft Flux-Raw: NRCC (FIFE) ALL STAC Catalog 1987-06-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968531540-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary -fife_AF_raw_ncar_11_1 Aircraft Flux-Raw: Univ. Col. (FIFE) ORNL_CLOUD STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968534531-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary +fife_AF_raw_nae_9_1 Aircraft Flux-Raw: NRCC (FIFE) ORNL_CLOUD STAC Catalog 1987-06-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968531540-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary fife_AF_raw_ncar_11_1 Aircraft Flux-Raw: Univ. Col. (FIFE) ALL STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968534531-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary +fife_AF_raw_ncar_11_1 Aircraft Flux-Raw: Univ. Col. (FIFE) ORNL_CLOUD STAC Catalog 1987-05-26 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968534531-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary fife_AF_raw_wyo_10_1 Aircraft Flux-Raw: U of Wy. (FIFE) ALL STAC Catalog 1987-08-11 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968533497-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary fife_AF_raw_wyo_10_1 Aircraft Flux-Raw: U of Wy. (FIFE) ORNL_CLOUD STAC Catalog 1987-08-11 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2968533497-ORNL_CLOUD.umm_json Raw (unmodified) boundary layer fluxes recorded on aircraft flights over Konza proprietary fife_atmos_brut_drv_14_1 Atmos. Profile: Std. Press. Level (FIFE) ORNL_CLOUD STAC Catalog 1987-05-26 1989-08-12 -96.56, 39.12, -96.56, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2978502225-ORNL_CLOUD.umm_json Derived (5mb interval) radiosonde observations from Wilf Brutsaert's data proprietary @@ -17835,8 +17815,8 @@ fife_sur_met_hday_met_39_1 Historic Daily Meteorology Data (FIFE) ORNL_CLOUD STA fife_sur_met_hmon_met_40_1 Historic Monthly Meteorology Data (FIFE) ORNL_CLOUD STAC Catalog 1858-01-01 1989-12-01 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2980078028-ORNL_CLOUD.umm_json Manhattan, KS. average rainfall measurements for every month since January 1858 proprietary fife_sur_met_ncdc_sur_122_1 Surface Meteorology Data: NCDC (FIFE) ORNL_CLOUD STAC Catalog 1988-10-01 1989-10-31 -97.87, 37.62, -95.48, 40.85 https://cmr.earthdata.nasa.gov/search/concepts/C2980787683-ORNL_CLOUD.umm_json NCDC surface meteorology data for 1989 proprietary fife_sur_met_noaa_sur_58_1 NOAA Regional Surface Data (FIFE) ORNL_CLOUD STAC Catalog 1985-07-02 1988-10-23 -97, 39, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2980450611-ORNL_CLOUD.umm_json Hourly surface weather reports collected by NESDIS for stations near FIFE proprietary -fife_sur_met_rain_30m_2_1 30 Minute Rainfall Data (FIFE) ORNL_CLOUD STAC Catalog 1987-05-29 1987-10-26 -96.6, 39.08, -96.55, 39.11 https://cmr.earthdata.nasa.gov/search/concepts/C2977893818-ORNL_CLOUD.umm_json 30 minute rainfall data for the Konza Prairie proprietary fife_sur_met_rain_30m_2_1 30 Minute Rainfall Data (FIFE) ALL STAC Catalog 1987-05-29 1987-10-26 -96.6, 39.08, -96.55, 39.11 https://cmr.earthdata.nasa.gov/search/concepts/C2977893818-ORNL_CLOUD.umm_json 30 minute rainfall data for the Konza Prairie proprietary +fife_sur_met_rain_30m_2_1 30 Minute Rainfall Data (FIFE) ORNL_CLOUD STAC Catalog 1987-05-29 1987-10-26 -96.6, 39.08, -96.55, 39.11 https://cmr.earthdata.nasa.gov/search/concepts/C2977893818-ORNL_CLOUD.umm_json 30 minute rainfall data for the Konza Prairie proprietary fife_sur_met_rain_day_29_1 Daily Rainfall Data (FIFE) ORNL_CLOUD STAC Catalog 1982-04-27 1989-12-30 -96.61, 39.07, -96.55, 39.11 https://cmr.earthdata.nasa.gov/search/concepts/C2980036855-ORNL_CLOUD.umm_json Daily rainfall data, by site & date proprietary fife_sur_refl_gem_helo_38_1 Gemma Helicopter Data (FIFE) ORNL_CLOUD STAC Catalog 1989-08-04 1989-08-12 -96.61, 38.98, -96.45, 39.19 https://cmr.earthdata.nasa.gov/search/concepts/C2980074218-ORNL_CLOUD.umm_json Spectral reflected radiances measured with Russian GEMMA spectrometer from a helicopter proprietary fife_sur_refl_irt_grnd_72_1 Radiant Temperature Ground Data (FIFE) ORNL_CLOUD STAC Catalog 1989-06-15 1989-08-11 -96.55, 39.05, -96.54, 39.09 https://cmr.earthdata.nasa.gov/search/concepts/C2980521154-ORNL_CLOUD.umm_json Surface temperatures collected w/ Everest Infrared Temperature Transducer proprietary @@ -17967,8 +17947,8 @@ geodata_0290 Administrative Boundaries - First Level (ESRI) CEOS_EXTRA STAC Cata geodata_0290 Administrative Boundaries - First Level (ESRI) ALL STAC Catalog 1998-01-01 1998-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848662-CEOS_EXTRA.umm_json The sub Country Administrative Units 1998 GeoDataset represents a small-scale political map of the world. The data are generalized and were designed for display at scales to about 1:10,000,000. The data were generalized from ESRI's ArcWorld Supplement Map data. Country codes are from U.S. Federal Information Processing Standards (FIPS) version 10-4. proprietary geodata_0291 Dams CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848575-CEOS_EXTRA.umm_json Construction of reservoirs became a worldwide activity in the second half of the twentieth century. The total storage capacity of the large reservoirs is more than 100 million cubic meters, which makes up more than 95% of water accumulated in all the reservoirs of the world. The total area of the more than 60,000 reservoirs that have been built in the last 50 years exceeds more than 100,000 square kilometers. This is an area equivalent to 11 water bodies the size of the Sea of Azov or five the size of Lake Superior. These man-made lakes affect natural and economic conditions over an area of 1.5 million square kilometers. Many of the world's large rivers, such as the Volga, Angara, Missouri, Colorado, and Parana Rivers, have been transformed into cascades of reservoirs. Construction and use of reservoirs cause inevitable changes in the environment, both positive and negative. Environmental changes can include overflowing and swamping; transformation of coasts; changes of soil, vegetation, and fauna; and changes of reproduction and habitat conditions of various aquatic organisms, especially fish and blue-green algae. The impact of reservoirs on the environment is diverse and contradictory. proprietary geodata_0295 Global Vegetation Index 1983-1990 CEOS_EXTRA STAC Catalog 1991-01-01 1991-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848459-CEOS_EXTRA.umm_json "The NOAA/GVI (Global Vegetation Index; see reference pg. 3) Eight-Year Mean Maximum data set was developed in the following manner. First, eight years of NOAA/GVI Monthly Maximum data were obtained from GRID's Geneva archive of these data*. At GRID-Nairobi, an analyst then used these data files (12 per year) to calculate yearly mean maximum images, and the eight yearly mean images were averaged in their turn, in order to create a single eight-year mean maximum image. The original idea had been to produce an eight-year :hp2.maximum:ehp2. value image, but this was abandoned due to the accretion of ""noise"" from spurious maximum-value pixels in the individual data files (UNEP/GRID, 1990). * - GRID-Geneva has compiled an archive of NOAA/GVI Weekly data from the U.S. National Oceanic and Atmospheric Administration / National Environmental Satellite Data and Information Service / National Climate Data Center / Satellite Data Services Division (or the NOAA / NESDIS / NCDC / SDSD). This collection covers the period from April 1982 to present. At GRID-Geneva, the Weekly data are used to create Monthly, Seasonal and Annual Maximum images, in addition to the archived NOAA/GVI Weekly data. " proprietary -geodata_0331 Agriculture Value Added - Percent of GDP ALL STAC Catalog 1960-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848745-CEOS_EXTRA.umm_json Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files. proprietary geodata_0331 Agriculture Value Added - Percent of GDP CEOS_EXTRA STAC Catalog 1960-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848745-CEOS_EXTRA.umm_json Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files. proprietary +geodata_0331 Agriculture Value Added - Percent of GDP ALL STAC Catalog 1960-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848745-CEOS_EXTRA.umm_json Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files. proprietary geodata_0335 Industry Value Added - Percent of GDP CEOS_EXTRA STAC Catalog 1960-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848389-CEOS_EXTRA.umm_json Industry corresponds to ISIC divisions 10-45 and includes manufacturing (ISIC divisions 15-37). It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files. proprietary geodata_0337 Fish Catch - Total CEOS_EXTRA STAC Catalog 1960-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848427-CEOS_EXTRA.umm_json CAPTURE PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is excluded. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals proprietary geodata_0344 Energy Production - Total (IEA) CEOS_EXTRA STAC Catalog 1971-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849265-CEOS_EXTRA.umm_json Total energy production is the production of primary energy, from, the total of all energy sources : hard coal, lignite/brown coal, peat, crude oil, NGLs, natural gas, combustible renewables and wastes, nuclear, hydro, geothermal, solar and the heat from heat pumps that is extracted from the ambient environment. Production is calculated after removal of impurities (e.g. sulphur from natural). proprietary @@ -18101,8 +18081,8 @@ geodata_1649 Irrigated Areas (Europe) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12 geodata_1650 Irrigated Areas (South America) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -122.85, -55.78, -18.14, 30.46 https://cmr.earthdata.nasa.gov/search/concepts/C2232847182-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary geodata_1651 Irrigated Areas - geodata_1651 CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -178.97, 8.56, -11.98, 87.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232848914-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary geodata_1652 Irrigated Areas (Middle East) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 29.09, 9.24, 63.99, 38.67 https://cmr.earthdata.nasa.gov/search/concepts/C2232846636-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary -geodata_1672 Agricultural Area ALL STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848984-CEOS_EXTRA.umm_json "Agricultural area, this category is the sum of areas under a) arable land - land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for Arable land are not meant to indicate the amount of land that is potentially cultivable; (b) permanent crops - land cultivated with long-term crops which do not have to be replanted for several years (such as cocoa and coffee); land under trees and shrubs producing flowers, such as roses and jasmine; and nurseries (except those for forest trees, which should be classified under ""forest""); and (c) permanent meadows and pastures - land used permanently (five years or more) to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land)." proprietary geodata_1672 Agricultural Area CEOS_EXTRA STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848984-CEOS_EXTRA.umm_json "Agricultural area, this category is the sum of areas under a) arable land - land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for Arable land are not meant to indicate the amount of land that is potentially cultivable; (b) permanent crops - land cultivated with long-term crops which do not have to be replanted for several years (such as cocoa and coffee); land under trees and shrubs producing flowers, such as roses and jasmine; and nurseries (except those for forest trees, which should be classified under ""forest""); and (c) permanent meadows and pastures - land used permanently (five years or more) to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land)." proprietary +geodata_1672 Agricultural Area ALL STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848984-CEOS_EXTRA.umm_json "Agricultural area, this category is the sum of areas under a) arable land - land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for Arable land are not meant to indicate the amount of land that is potentially cultivable; (b) permanent crops - land cultivated with long-term crops which do not have to be replanted for several years (such as cocoa and coffee); land under trees and shrubs producing flowers, such as roses and jasmine; and nurseries (except those for forest trees, which should be classified under ""forest""); and (c) permanent meadows and pastures - land used permanently (five years or more) to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land)." proprietary geodata_1685 Land Area CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846605-CEOS_EXTRA.umm_json Land area is the total area of the country excluding area under inland water bodies. Possible variations in the data may be due to updating and revisions of the country data and not necessarily to any change of area. proprietary geodata_1706 Consumption of Ozone-Depleting Substances - Methyl Bromide CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849036-CEOS_EXTRA.umm_json Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. Methyl bromide (CH3Br) is used as a fumigant for high-value crops, pest control, and quarantine treatment of agricultural commodities awaiting export. Total world annual consumption is about 70,000 tonnes, most of it in the industrialized countries. It takes about 0.7 years to break down. proprietary geodata_1708 Consumption of Ozone-Depleting Substances - Hydrochlorofluorocarbons (HCFCs) CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847289-CEOS_EXTRA.umm_json Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. Hydrochlorofluorocarbons (HCFCs) were developed as the first major replacement for CFCs. While much less destructive than CFCs, HCFCs also contribute to ozone depletion. They have an atmospheric lifetime of about 1.4 to 19.5 years. proprietary @@ -18187,8 +18167,8 @@ geodata_2128 Cadmium (Cd) Consumption CEOS_EXTRA STAC Catalog 1999-01-01 2006-12 geodata_2129 Lead (Pb) Production CEOS_EXTRA STAC Catalog 2003-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847966-CEOS_EXTRA.umm_json Lead production refers to World mine production (metal content). proprietary geodata_2130 Lead (Pb) Consumption CEOS_EXTRA STAC Catalog 2003-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848445-CEOS_EXTRA.umm_json Lead Consumption refers to World refined lead consumption proprietary geodata_2131 Mercury (Hg) Production CEOS_EXTRA STAC Catalog 1999-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848487-CEOS_EXTRA.umm_json World metal production (primary metal) proprietary -geodata_2134 Agricultural Area Irrigated CEOS_EXTRA STAC Catalog 2001-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848664-CEOS_EXTRA.umm_json Agricultural area irrigated, part of the full or partial control irrigated Agricultural land which is actually irrigated in a given year. Often, part of the equipped area is not irrigated for various reasons, such as lack of water, absence of farmers, land degradation, damage, organizational problems etc. proprietary geodata_2134 Agricultural Area Irrigated ALL STAC Catalog 2001-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848664-CEOS_EXTRA.umm_json Agricultural area irrigated, part of the full or partial control irrigated Agricultural land which is actually irrigated in a given year. Often, part of the equipped area is not irrigated for various reasons, such as lack of water, absence of farmers, land degradation, damage, organizational problems etc. proprietary +geodata_2134 Agricultural Area Irrigated CEOS_EXTRA STAC Catalog 2001-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848664-CEOS_EXTRA.umm_json Agricultural area irrigated, part of the full or partial control irrigated Agricultural land which is actually irrigated in a given year. Often, part of the equipped area is not irrigated for various reasons, such as lack of water, absence of farmers, land degradation, damage, organizational problems etc. proprietary geodata_2135 Country Area CEOS_EXTRA STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848571-CEOS_EXTRA.umm_json Country area, area of the country including area under inland water bodies, but excluding offshore territorial waters. Possible variations in the data may be due to updating and revisions of the country data and not necessarily to any change of area. proprietary geodata_2136 Forest Area CEOS_EXTRA STAC Catalog 1990-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848786-CEOS_EXTRA.umm_json Forest area is the land spanning more than 0.5 hectares with trees higher than 5 metres and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use. Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 metres (m) in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate. Includes: areas with bamboo and palms provided that height and canopy cover criteria are met; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry or protective purposes, such as: rubber-wood plantations and cork, oak stands. Excludes: tree stands in agricultural production systems, for example in fruit plantations and agroforestry systems. The term also excludes trees in urban parks and gardens. proprietary geodata_2169 Consumption of Ozone-Depleting Substances - All CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849311-CEOS_EXTRA.umm_json Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. Hydrochlorofluorocarbons (HCFCs) were developed as the first major replacement for CFCs. While much less destructive than CFCs, HCFCs also contribute to ozone depletion. They have an atmospheric lifetime of about 1.4 to 19.5 years. proprietary @@ -18208,10 +18188,10 @@ geodata_2207 Livestock Production Index Base 1999-2001 - Total CEOS_EXTRA STAC C geodata_2208 Cereals - Area Harvested CEOS_EXTRA STAC Catalog 1961-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849210-CEOS_EXTRA.umm_json Cereals also includes other cereals such as mixed grains and buckwheat. Crop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption). When the production data available refers to a production period falling into two successive calendar years and it is not possible to allocate the relative production to each of them, it is usual to refer production data to that year into which the bulk of the production falls. Crop production data are stored in tonnes (T). proprietary geodata_2215 Hazardous Pesticides - Exports CEOS_EXTRA STAC Catalog 2007-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847283-CEOS_EXTRA.umm_json Refers to the value of the type of pesticide (put up in forms or packings for retail sale or as preparations or articles), provided to (exports) or received (imported) from the rest of the world. Differences between figures given for total exports and total imports at the world level may be due to several factors, e.g. the time lag between the dispatch of goods from exporting country and their arrival in the importing country; the use of different classification of the same product by different countries; or the fact that some countries supply data on general trade while others give data on special trade. proprietary geodata_2216 Hazardous Pesticides - Imports CEOS_EXTRA STAC Catalog 2007-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847501-CEOS_EXTRA.umm_json Refers to the value of the type of pesticide (put up in forms or packings for retail sale or as preparations or articles), provided to (exports) or received (imported) from the rest of the world. Differences between figures given for total exports and total imports at the world level may be due to several factors, e.g. the time lag between the dispatch of goods from exporting country and their arrival in the importing country; the use of different classification of the same product by different countries; or the fact that some countries supply data on general trade while others give data on special trade. proprietary -geodata_2217 Agricultural Area Certified Organic CEOS_EXTRA STAC Catalog 2003-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847551-CEOS_EXTRA.umm_json Land area exclusively dedicated to organic agriculture and managed by applying organic agriculture methods. It refers to the land area fully converted to organic agriculture. It is the portion of land area (including arable lands, pastures or wild areas) managed (cultivated) or wild harvested in accordance with specific organic standards or technical regulations and that has been inspected and approved by a certification body. proprietary geodata_2217 Agricultural Area Certified Organic ALL STAC Catalog 2003-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847551-CEOS_EXTRA.umm_json Land area exclusively dedicated to organic agriculture and managed by applying organic agriculture methods. It refers to the land area fully converted to organic agriculture. It is the portion of land area (including arable lands, pastures or wild areas) managed (cultivated) or wild harvested in accordance with specific organic standards or technical regulations and that has been inspected and approved by a certification body. proprietary -geodata_2222 Adjusted Human Water Security Threat ALL STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849396-CEOS_EXTRA.umm_json Rivers maintain unique biotic resources and provide critical water supplies to people. The Earth's limited supplies of fresh water and irreplaceable biodiversity are vulnerable to human mismanagement of watersheds and waterways. Multiple environmental stressors, such as agricultural runoff, pollution and invasive species, threaten rivers that serve 80 percent of the world’s population. These same stressors endanger the biodiversity of 65 percent of the world’s river habitats putting thousands of aquatic wildlife species at risk. Efforts to abate fresh water degradation through highly engineered solutions are effective at reducing the impact of threats but at a cost that can be an economic burden and often out of reach for developing nations. proprietary +geodata_2217 Agricultural Area Certified Organic CEOS_EXTRA STAC Catalog 2003-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847551-CEOS_EXTRA.umm_json Land area exclusively dedicated to organic agriculture and managed by applying organic agriculture methods. It refers to the land area fully converted to organic agriculture. It is the portion of land area (including arable lands, pastures or wild areas) managed (cultivated) or wild harvested in accordance with specific organic standards or technical regulations and that has been inspected and approved by a certification body. proprietary geodata_2222 Adjusted Human Water Security Threat CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849396-CEOS_EXTRA.umm_json Rivers maintain unique biotic resources and provide critical water supplies to people. The Earth's limited supplies of fresh water and irreplaceable biodiversity are vulnerable to human mismanagement of watersheds and waterways. Multiple environmental stressors, such as agricultural runoff, pollution and invasive species, threaten rivers that serve 80 percent of the world’s population. These same stressors endanger the biodiversity of 65 percent of the world’s river habitats putting thousands of aquatic wildlife species at risk. Efforts to abate fresh water degradation through highly engineered solutions are effective at reducing the impact of threats but at a cost that can be an economic burden and often out of reach for developing nations. proprietary +geodata_2222 Adjusted Human Water Security Threat ALL STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849396-CEOS_EXTRA.umm_json Rivers maintain unique biotic resources and provide critical water supplies to people. The Earth's limited supplies of fresh water and irreplaceable biodiversity are vulnerable to human mismanagement of watersheds and waterways. Multiple environmental stressors, such as agricultural runoff, pollution and invasive species, threaten rivers that serve 80 percent of the world’s population. These same stressors endanger the biodiversity of 65 percent of the world’s river habitats putting thousands of aquatic wildlife species at risk. Efforts to abate fresh water degradation through highly engineered solutions are effective at reducing the impact of threats but at a cost that can be an economic burden and often out of reach for developing nations. proprietary geodata_2223 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2000 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849273-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary geodata_2224 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2001 CEOS_EXTRA STAC Catalog 2001-01-01 2001-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849194-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary geodata_2225 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2003 CEOS_EXTRA STAC Catalog 2003-01-01 2003-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849086-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary @@ -18330,18 +18310,18 @@ gom_bathymetry Digital Bathymetric Data for the Gulf of Maine CEOS_EXTRA STAC Ca gomc_156 Adopt-a-Tide Pool SCIOPS STAC Catalog 1990-01-01 -70.923, 42.489, -70.763, 42.577 https://cmr.earthdata.nasa.gov/search/concepts/C1214586152-SCIOPS.umm_json Salem Sound Coastwatch trains volunteers to monitor tide pools through the Adopt-A-Tide pool program. Volunteers will help us focus special attention on local tide pools and catalog the diversity of both native and invasive species. This information will be passed on to scientists working on strategies to address marine invasive species. Waterbody or Watershed Names: Salem Sound proprietary gomc_156 Adopt-a-Tide Pool ALL STAC Catalog 1990-01-01 -70.923, 42.489, -70.763, 42.577 https://cmr.earthdata.nasa.gov/search/concepts/C1214586152-SCIOPS.umm_json Salem Sound Coastwatch trains volunteers to monitor tide pools through the Adopt-A-Tide pool program. Volunteers will help us focus special attention on local tide pools and catalog the diversity of both native and invasive species. This information will be passed on to scientists working on strategies to address marine invasive species. Waterbody or Watershed Names: Salem Sound proprietary gomc_162 Circulation and Contaminant Transport in Massachusetts Coastal Waters CEOS_EXTRA STAC Catalog 1977-01-01 -70.95037, 42.09017, -70.26193, 42.61774 https://cmr.earthdata.nasa.gov/search/concepts/C2231548638-CEOS_EXTRA.umm_json U.S. Geological Survey studies show that the concentrations of metals in surface sediments of Boston Harbor are decreasing with time. This conclusion is supported by analysis of (1) surface sediments collected at monitoring stations in the outer harbor between 1977 and 1993, (2) sediment cores from depositional areas of the harbor, and (3) historical data from a contaminated-sediment data base, which includes information on metal and organic contaminants and sediment texture. During the 16 years of the continuing study, chromium, lead, mercury, silver, and zinc concentrations in surface sediments have decreased by about 50 percent. Although these trends are indeed encouraging, concentrations of some metals in harbor sediments are still above levels considered toxic to certain bottom-dwelling organisms. Type: Bay Waterbody or Watershed Names: Boston Harbor proprietary -gomc_219 2001 Long Island Sound Study Ambient Water Quality and Monitoring Program SCIOPS STAC Catalog 1970-01-01 -74.3, 40.5, -71.75, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214585922-SCIOPS.umm_json The Interstate Environmental Commission is a joint agency of the States of New York, New Jersey, and Connecticut. The IEC was established in 1936 under a Compact between New York and New Jersey and approved by Congress. The State of Connecticut joined the Commission in 1941. Waterbody or Watershed Names: Long Island Sound proprietary gomc_219 2001 Long Island Sound Study Ambient Water Quality and Monitoring Program ALL STAC Catalog 1970-01-01 -74.3, 40.5, -71.75, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214585922-SCIOPS.umm_json The Interstate Environmental Commission is a joint agency of the States of New York, New Jersey, and Connecticut. The IEC was established in 1936 under a Compact between New York and New Jersey and approved by Congress. The State of Connecticut joined the Commission in 1941. Waterbody or Watershed Names: Long Island Sound proprietary +gomc_219 2001 Long Island Sound Study Ambient Water Quality and Monitoring Program SCIOPS STAC Catalog 1970-01-01 -74.3, 40.5, -71.75, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214585922-SCIOPS.umm_json The Interstate Environmental Commission is a joint agency of the States of New York, New Jersey, and Connecticut. The IEC was established in 1936 under a Compact between New York and New Jersey and approved by Congress. The State of Connecticut joined the Commission in 1941. Waterbody or Watershed Names: Long Island Sound proprietary gomc_323 ACAP Saint John's Community Environmental Monitoring Program (CEMP) ALL STAC Catalog 1992-01-01 -66.25, 45, -65.25, 46 https://cmr.earthdata.nasa.gov/search/concepts/C1214585928-SCIOPS.umm_json Parameters measured included: ammonia nitrogen, orthophosphate, dissolved oxygen, pH, turbidity, salinity, faecal coliform. proprietary gomc_323 ACAP Saint John's Community Environmental Monitoring Program (CEMP) SCIOPS STAC Catalog 1992-01-01 -66.25, 45, -65.25, 46 https://cmr.earthdata.nasa.gov/search/concepts/C1214585928-SCIOPS.umm_json Parameters measured included: ammonia nitrogen, orthophosphate, dissolved oxygen, pH, turbidity, salinity, faecal coliform. proprietary -gomc_40 Air Quality Monitoring In New Brunswick SCIOPS STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C1214586182-SCIOPS.umm_json We know that air pollution can have an effect on the health of our environment and on human health. People who have respiratory difficulties are particularly sensitive to poor air quality. Children are frequently affected because of their physiology and because they tend to be more active outdoors. Monitoring air quality in New Brunswick helps us to better understand the sources, movements and effects of various substances in the air we breathe. The data we collect helps us to control sources of air pollution within our province, and to negotiate with governments in other jurisdictions for controls on air pollution that crosses borders. The more we know, the more effectively we can work to protect and enhance our air quality and our environment. proprietary gomc_40 Air Quality Monitoring In New Brunswick ALL STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C1214586182-SCIOPS.umm_json We know that air pollution can have an effect on the health of our environment and on human health. People who have respiratory difficulties are particularly sensitive to poor air quality. Children are frequently affected because of their physiology and because they tend to be more active outdoors. Monitoring air quality in New Brunswick helps us to better understand the sources, movements and effects of various substances in the air we breathe. The data we collect helps us to control sources of air pollution within our province, and to negotiate with governments in other jurisdictions for controls on air pollution that crosses borders. The more we know, the more effectively we can work to protect and enhance our air quality and our environment. proprietary +gomc_40 Air Quality Monitoring In New Brunswick SCIOPS STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C1214586182-SCIOPS.umm_json We know that air pollution can have an effect on the health of our environment and on human health. People who have respiratory difficulties are particularly sensitive to poor air quality. Children are frequently affected because of their physiology and because they tend to be more active outdoors. Monitoring air quality in New Brunswick helps us to better understand the sources, movements and effects of various substances in the air we breathe. The data we collect helps us to control sources of air pollution within our province, and to negotiate with governments in other jurisdictions for controls on air pollution that crosses borders. The more we know, the more effectively we can work to protect and enhance our air quality and our environment. proprietary gone-wild-grapevines-in-forests_1.0 Gone-wild grapevines in forests may act as a potential habitat for “Flavescence dorée” phytoplasma vectors and inoculum ENVIDAT STAC Catalog 2023-01-01 2023-01-01 8.4347534, 45.8809865, 9.2422485, 46.5159373 https://cmr.earthdata.nasa.gov/search/concepts/C3226082143-ENVIDAT.umm_json Dataset used to test the potential role of gone-wild grapevines (GWGV) in forests of Southern Switzerland as a source of Flavescence dorée phytoplasma (FDp) inoculum and as a habitat for its main and alternative vectors, Scaphoideus titanus and Orientus ishidae. In the first phase, GWGV were located and sampled to test their FDp status. In addition, a set of chromotropic traps were placed to monitor the presence and abundance of FDp vectors. In the second phase, wood from GWGV in forests was collected and placed in cages to test the potential oviposition activity by FDp vectors. The results showed that GWGV in forests are a reservoir of FDp and that they can sustain the whole life cycle of both S.titanus and O.ishidae. Eventually, the need to adapt the current FD management strategies are highlighted. proprietary gov.noaa.ncdc:C00842_Version 1.2 Blended 6-Hourly Sea Surface Wind Vectors and Wind Stress on a Global 0.25 Degree Grid (1987-2011) NOAA_NCEI STAC Catalog 1987-07-09 2011-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093688-NOAA_NCEI.umm_json The Blended Global Sea Surface Winds products contain ocean surface wind vectors and wind stress on a global 0.25 degree grid, in multiple time resolutions of 6-hourly and monthly, with an 11-year (1995-2005) monthly climatology. Daily files from a direct average of the 6-hourly data were also produced but are not included in this archive. The period of record is July 9, 1987 to September 30, 2011 for product Version 1.2, released in July 2007. Wind speeds were generated by blending available and selected microwave and scatterometer observations using a Simple spatiotemporally weighted Interpolation (SI) method. The following satellite retrieval datasets from Remote Sensing Systems (RSS) were used for Version 1.2: SSMI Version 6, TMI Version 4, QSCAT Version 3a, and AMSRE Version 5 (updated using the SSMI rain rate). The wind directions are from the NCEP-DOE Reanalysis 2 (NRA-2). The model wind directions are interpolated onto the blended wind speed grids. The 6-hourly satellite-scaled global 0.25-degree grid wind stresses are computed as: taux_s = -[(w_s/w_m)**2]*taux_m tauy_s = -[(w_s/w_m)**2]*tauy_m where 's' indicates satellite-scaled values and 'm' indicates NRA-2 model values interpolated to the satellite grid. Files are in netCDF format and available to users via FTP and THREDDS. A near real-time (NRT) variant of the product is generated quasi-daily to satisfy the needs of real-time users. The publicly available NRT data were replaced by the delayed-mode research quality data on a monthly basis through the end of September 2011, at which time the Seawinds production was impacted by the loss of data from the AMSR-E instrument failure. Production of the delayed-mode research products ends with the loss of AMSR-E in Version 1.2; a future version will extend beyond September 2011. The NRT products are continued after September 2011; however, this archive only includes the delayed-mode research products as the NRT data have a lower maturity rating removing the basis for archiving those data. proprietary gov.noaa.ncdc:C01381_Not Applicable AVHRR/HIRS Longwave Radiation Budget Data (RBUD) NOAA_NCEI STAC Catalog 2000-03-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093896-NOAA_NCEI.umm_json Radiation Budget Data - The Radiation Budget product suite is produced from the primary morning and afternoon Polar Orbiters. Product shows a measure of the longwave radiation emitted (W/m^2) by the earth-atmosphere system to space. The observations are displayed on a one degree equal area map for the day and night. The products are: GAC long wave, HIRS long wave, longwave histogram, annual mean, monthly mean, and seasonal mean. This is a NESDIS legacy product and the file naming pattern is as follows: NPR.RBSD.[SatelliteID].D[YYDDD] or NPR.RBMD.[SatelliteID].D[YYDDD] proprietary gov.noaa.ncdc:C01560_V3 Blended Global Biomass Burning Emissions Product - Extended (GBBEPx) from Multiple Satellites NOAA_NCEI STAC Catalog 2018-01-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107094570-NOAA_NCEI.umm_json The Blended Global Biomass Burning Emissions Product version 3 (GBBEPx V3) system produces global biomass burning emissions. The product contains daily global biomass burning emissions (PM2.5, BC, CO, CO2, OC, and SO2) blended fire observations from MODIS Quick Fire Emission Dataset (QFED), VIIRS (NPP and JPSS-1) fire emissions, and Global Biomass Burning Emission Product from Geostationary satellites (GBBEP-Geo), which are in a grid cell of 0.25 × 0.3125 degree and 0.1 x 0.1 degree. It also produces hourly emissions from geostationary satellites, which is at individual fire pixels. The product output also include fire detection record in a HMS format, quality flag in biomass burning emissions, spatial pattern of PM2.5 emissions, and statistic PM2.5 information at continental scale. In Version3, daily biomass burning emissions at a FV3 C384 grid in binary format and daily biomass burning emissions at a 0.1 x 0.1 degree grid that include all the emissions species are added as new output. proprietary -gov.noaa.ncdc:C01598_Beta4 Adaptive Ecosystem Climatology Beta 4 Model and Satellite Climatology NOAA_NCEI STAC Catalog 1980-01-01 2012-12-31 -98, 18.091, -77.36, 30.73 https://cmr.earthdata.nasa.gov/search/concepts/C2107094643-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary gov.noaa.ncdc:C01598_Beta4 Adaptive Ecosystem Climatology Beta 4 Model and Satellite Climatology ALL STAC Catalog 1980-01-01 2012-12-31 -98, 18.091, -77.36, 30.73 https://cmr.earthdata.nasa.gov/search/concepts/C2107094643-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary +gov.noaa.ncdc:C01598_Beta4 Adaptive Ecosystem Climatology Beta 4 Model and Satellite Climatology NOAA_NCEI STAC Catalog 1980-01-01 2012-12-31 -98, 18.091, -77.36, 30.73 https://cmr.earthdata.nasa.gov/search/concepts/C2107094643-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary gov.noaa.ncdc:C01599_beta6 Adaptive Ecosystem Climatology Beta 6 Satellite Climatology ALL STAC Catalog 1980-01-01 2012-12-31 -135, 22.9276, -62.987, 53 https://cmr.earthdata.nasa.gov/search/concepts/C2107094649-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary gov.noaa.ncdc:C01599_beta6 Adaptive Ecosystem Climatology Beta 6 Satellite Climatology NOAA_NCEI STAC Catalog 1980-01-01 2012-12-31 -135, 22.9276, -62.987, 53 https://cmr.earthdata.nasa.gov/search/concepts/C2107094649-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary gov.noaa.ngdc.mgg.photos:12_Not Applicable April 1906 San Francisco, USA Images NOAA_NCEI STAC Catalog 1906-04-18 1906-04-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105705777-NOAA_NCEI.umm_json The 1906 San Francisco earthquake was the largest event (magnitude 8.3) to occur in the conterminous United States in the 20th Century. Recent estimates indicate that as many as 3,000 people lost their lives in the earthquake and ensuing fire. In terms of 1906 dollars, the total property damage amounted to about $24 million from the earthquake and $350 million from the fire. The fire destroyed 28,000 buildings in a 520-block area of San Francisco. proprietary @@ -18358,15 +18338,15 @@ gov.noaa.ngdc.mgg.photos:32_Not Applicable April 1968 Southeast of Hawaii, USA I gov.noaa.ngdc.mgg.photos:36_Not Applicable April 1981 Westmorland, Calipatria, USA Images NOAA_NCEI STAC Catalog 1981-04-26 1981-04-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105705807-NOAA_NCEI.umm_json Magnitude 6.3. Damage $1-$3 million. Subsidence was reported on several rural roads in the area. Liquefaction caused scores of mudpots, and oozing soil in nearby fields. One country road west of Westmorland collapsed, producing a 2-foot drop-off. In rural areas, unreinforced, concrete-lined irrigation canals were broken. proprietary gov.noaa.ngdc.mgg.photos:4_Not Applicable April 1965 Seattle, USA Images NOAA_NCEI STAC Catalog 1965-04-29 1965-04-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105705734-NOAA_NCEI.umm_json The magnitude 6.5 earthquake killed 7 and caused 12.5 million in property damage. proprietary gov.noaa.ngdc.mgg.photos:52_Not Applicable April 2007 Solomon Islands, Papua New Guinea Images NOAA_NCEI STAC Catalog 2007-04-01 2007-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105705809-NOAA_NCEI.umm_json An earthquake measuring 8.1 struck 345 kilometers northwest of the Solomon Islands' capital Honiara at 0740 local time on 2 April. (2040 GMT 1 April). The earthquake created a tsunami causing significant damage in the Solomon Islands. Large tsunami waves (reports range from 2m to 10m) are reported to have struck the islands in the Western Province area of Solomon Islands and some parts of Papua New Guinea. Affected areas include Gizo, Simbo, Ranogga, Shortlands, Munda, Noro, Vella la Vella, Kolombangarra and parts of the southern coast of Choiseul. At least 34 were killed and several dozen missing. 5,500 people are thought to have been displaced in total. The Ministry of Health and Medical Services (MHMS) estimates that up to 50,000 people may be affected out of a total population of 100,000 in Western and Choiseul provinces. proprietary -gov.noaa.nodc:0000015_Not Applicable Alkalinity, dissolved oxygen, nutrients, pH, phosphate, salinity, silicate, and temperature collected by bottle from multiple cruises in the Southern Oceans from 1/15/1958 - 3/2/1990 (NCEI Accession 0000015) ALL STAC Catalog 1958-01-15 1990-03-02 6.05, -70.233333, -47.033333, -26.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089372155-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0000015_Not Applicable Alkalinity, dissolved oxygen, nutrients, pH, phosphate, salinity, silicate, and temperature collected by bottle from multiple cruises in the Southern Oceans from 1/15/1958 - 3/2/1990 (NCEI Accession 0000015) NOAA_NCEI STAC Catalog 1958-01-15 1990-03-02 6.05, -70.233333, -47.033333, -26.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089372155-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:0000015_Not Applicable Alkalinity, dissolved oxygen, nutrients, pH, phosphate, salinity, silicate, and temperature collected by bottle from multiple cruises in the Southern Oceans from 1/15/1958 - 3/2/1990 (NCEI Accession 0000015) ALL STAC Catalog 1958-01-15 1990-03-02 6.05, -70.233333, -47.033333, -26.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089372155-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0000028_Not Applicable Benthic species - TAXA counts, identities, and wet weights collected by sediment grab from multiple cruises in Prince William Sound, Alaska, from 10/22/1985 - 8/31/1988 (NCEI Accession 0000028) NOAA_NCEI STAC Catalog 1985-10-22 1998-08-31 -146.597, 61.0802, -146.2983, 61.13 https://cmr.earthdata.nasa.gov/search/concepts/C2089372272-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0000029_Not Applicable 1990, 1991, 1992 and 1995 CRETM/LMER Zooplankton Data Sets (NCEI Accession 0000029) NOAA_NCEI STAC Catalog 1990-09-26 1995-05-26 -124.041667, 0.766667, -16.25, 46.263167 https://cmr.earthdata.nasa.gov/search/concepts/C2089372282-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0000029_Not Applicable 1990, 1991, 1992 and 1995 CRETM/LMER Zooplankton Data Sets (NCEI Accession 0000029) ALL STAC Catalog 1990-09-26 1995-05-26 -124.041667, 0.766667, -16.25, 46.263167 https://cmr.earthdata.nasa.gov/search/concepts/C2089372282-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0000035_Not Applicable 1996 - Early 1998 CRETM/LMER Phytoplankton Data (NCEI Accession 0000035) ALL STAC Catalog 1996-07-09 1998-03-06 -124.003, 46.179833, -123.183167, 46.261667 https://cmr.earthdata.nasa.gov/search/concepts/C2089372325-NOAA_NCEI.umm_json Pump cast sampling, and associated CTD casts took place from a fixed vessel during one 28-35 day cruise per year in 1990, 1991, 1992, 1995, and 1996. In 1997 there were 2 week cruises in May, July, and October. proprietary gov.noaa.nodc:0000035_Not Applicable 1996 - Early 1998 CRETM/LMER Phytoplankton Data (NCEI Accession 0000035) NOAA_NCEI STAC Catalog 1996-07-09 1998-03-06 -124.003, 46.179833, -123.183167, 46.261667 https://cmr.earthdata.nasa.gov/search/concepts/C2089372325-NOAA_NCEI.umm_json Pump cast sampling, and associated CTD casts took place from a fixed vessel during one 28-35 day cruise per year in 1990, 1991, 1992, 1995, and 1996. In 1997 there were 2 week cruises in May, July, and October. proprietary -gov.noaa.nodc:0000052_Not Applicable 1988 Resurrection Bay Zooplankton Data Set from 01 March 1988 to 28 June 1988 (NCEI Accession 0000052) ALL STAC Catalog 1988-03-01 1988-06-28 -149.4083, 59.9117, -149.3583, 60.02 https://cmr.earthdata.nasa.gov/search/concepts/C2089372461-NOAA_NCEI.umm_json Zooplantkon and beach tar data were collected using plankton net casts in the Gulf of Alaska from the ALPHA HELIX. Data were collected from 01 March 1988 to 28 June 1988 by University of Alaska in Fairbanks; Institute of Marine Science with support from the Gulf of Alaska - 1 (GAK-1) project. proprietary gov.noaa.nodc:0000052_Not Applicable 1988 Resurrection Bay Zooplankton Data Set from 01 March 1988 to 28 June 1988 (NCEI Accession 0000052) NOAA_NCEI STAC Catalog 1988-03-01 1988-06-28 -149.4083, 59.9117, -149.3583, 60.02 https://cmr.earthdata.nasa.gov/search/concepts/C2089372461-NOAA_NCEI.umm_json Zooplantkon and beach tar data were collected using plankton net casts in the Gulf of Alaska from the ALPHA HELIX. Data were collected from 01 March 1988 to 28 June 1988 by University of Alaska in Fairbanks; Institute of Marine Science with support from the Gulf of Alaska - 1 (GAK-1) project. proprietary +gov.noaa.nodc:0000052_Not Applicable 1988 Resurrection Bay Zooplankton Data Set from 01 March 1988 to 28 June 1988 (NCEI Accession 0000052) ALL STAC Catalog 1988-03-01 1988-06-28 -149.4083, 59.9117, -149.3583, 60.02 https://cmr.earthdata.nasa.gov/search/concepts/C2089372461-NOAA_NCEI.umm_json Zooplantkon and beach tar data were collected using plankton net casts in the Gulf of Alaska from the ALPHA HELIX. Data were collected from 01 March 1988 to 28 June 1988 by University of Alaska in Fairbanks; Institute of Marine Science with support from the Gulf of Alaska - 1 (GAK-1) project. proprietary gov.noaa.nodc:0000064_Not Applicable Arabian Sea Biogeochemistry from 27 August 1994 to 19 December 1994 (NCEI Accession 0000064) NOAA_NCEI STAC Catalog 1994-08-27 1994-12-19 56.5529, 7.7811, 67.3194, 26.0221 https://cmr.earthdata.nasa.gov/search/concepts/C2089372546-NOAA_NCEI.umm_json Arabesque was a multidisciplinary oceanographic research project focused on the Arabian Sea and Northwest Indian Ocean during the monsoon and intermonsoon season in 1994. proprietary gov.noaa.nodc:0000085_Not Applicable Benthic taxonomy and benthic biomass data collected by the R/V Alpha Helix in support of the ISHTAR Project in the Bering and Chukchi Seas, 1984-1990 (NCEI Accession 0000085) NOAA_NCEI STAC Catalog 1984-06-19 1990-06-21 -175.00118, 60.014, -163.75, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2089372672-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0000103_Not Applicable Bering Sea Inner Front zooplankton data sets collected with CalVet net on four cruises from 6/3/1997 - 9/1/1998 (NCEI Accession 0000103) NOAA_NCEI STAC Catalog 1997-06-01 1998-09-01 -168.745, 55.0372, -159.994, 59.1733 https://cmr.earthdata.nasa.gov/search/concepts/C2089372740-NOAA_NCEI.umm_json Zooplankton and other data were collected using CalVet net in Bering sea from ALPHA HELIX. Data were collected from 01 June 1997 to 01 September 1998 by University of Alaska in Fairbanks with support from the Inner Front project. proprietary @@ -18391,8 +18371,8 @@ gov.noaa.nodc:0000411_Not Applicable Aquatic vegetation were photographed from a gov.noaa.nodc:0000422_Not Applicable An Eighteen-Year Time-Series of Chlorophyll Monthly Averages from Kaneohe Bay, Oahu, Hawaii, 1982 - 2001 (NCEI Accession 0000422) NOAA_NCEI STAC Catalog 1982-06-01 2001-01-31 -157.78, 21.41, -157.78, 24.41 https://cmr.earthdata.nasa.gov/search/concepts/C2089374869-NOAA_NCEI.umm_json Chlorophyll data were collected from a sewage outfall site in Kaneohe Bay, Hawaii, from 1982 to 2001. The purpose of the project was to study the responses of the ecosystem to the sewage diversion from the inner bay to an offshore, deep water location and to continue monitoring the location to denote changes associated with natural environmental and anthropogenic forcing on the primary productivity. Data were submitted by the University of Hawaii at Manoa and funding was provided by the Environmental Protective Agency (EPA). proprietary gov.noaa.nodc:0000425_Not Applicable Biological, chemical, geological, and other data were collected from the R/V KITTIWAKE at 100 sites in Puget Sound from 01 June 1998 to 01 July 1998 as part of a three-year study of toxins (NCEI Accession 0000425) NOAA_NCEI STAC Catalog 1998-06-01 1998-07-01 -122.3, 47.3, -122.3, 47.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089374887-NOAA_NCEI.umm_json Biological, chemical, geological, and other data were collected from the R/V Kittiwait from 01 June 1998 to 01 July 1998. Data were submitted by the Washington State Department of Ecology (WADOE) as part of a 3 year, 100 site, study of toxins in the Puget Sound. Biological data include infauna surveys, amphipod bioassays, and percent urchin fertilization. Chemical data include results of tests for toxins by cytochrome and microtoxology. Geological data include determination of grain fractions. proprietary gov.noaa.nodc:0000447_Not Applicable Benthic data from bottom grabs from Prince William Sound in support of Exxon Valdez Oil Spill Restoration Project from the R/V DAVIDSON and R/V BIG VALLEY from 03 July 1990 to 25 June of 1991 (NCEI Accession 0000447) NOAA_NCEI STAC Catalog 1990-07-03 1991-06-25 -147.08803, 60.273, -146.92303, 60.332 https://cmr.earthdata.nasa.gov/search/concepts/C2089375015-NOAA_NCEI.umm_json Benthic samples and other data were collected from the R/V DAVIDSON and R/V BIG VALLEY from the Prince William Sound from 03 July 1990 to 25 June of 1991 . Data were collected as part of the Exxon Valdez Oil Spill Restoration Project. Data were collected by the University of Alaska - Fairbanks / Institute of Marine Science (UAK/IMS) with bottom grab sampler and include taxonomic identities and taxonomic counts of benthic animals. proprietary -gov.noaa.nodc:0000501_Not Applicable A unified, long-term, Caribbean-wide initiative to identity the factors responsible for sustaining mangrove wetland, seagrass meadow, and coral reef productivity, February 1993 - October 1998 (NCEI Accession 0000501) NOAA_NCEI STAC Catalog 1993-02-12 1998-10-15 -90.583333, 9.583333, -59.633333, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089375341-NOAA_NCEI.umm_json The Caribbean Coastal Marine Productivity (CARICOMP) Program is a Caribbean-wide research and monitoring network of 27 marine laboratories, parks, and reserves in 17 countries. This data set includes data collected from 42 stations at 29 sites in the Caribbean from 1993 to 1998. Line transects were used to determine the abundance of hard and soft corals, algae, sponges, urchins, and biotic material such as substrate type. proprietary gov.noaa.nodc:0000501_Not Applicable A unified, long-term, Caribbean-wide initiative to identity the factors responsible for sustaining mangrove wetland, seagrass meadow, and coral reef productivity, February 1993 - October 1998 (NCEI Accession 0000501) ALL STAC Catalog 1993-02-12 1998-10-15 -90.583333, 9.583333, -59.633333, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089375341-NOAA_NCEI.umm_json The Caribbean Coastal Marine Productivity (CARICOMP) Program is a Caribbean-wide research and monitoring network of 27 marine laboratories, parks, and reserves in 17 countries. This data set includes data collected from 42 stations at 29 sites in the Caribbean from 1993 to 1998. Line transects were used to determine the abundance of hard and soft corals, algae, sponges, urchins, and biotic material such as substrate type. proprietary +gov.noaa.nodc:0000501_Not Applicable A unified, long-term, Caribbean-wide initiative to identity the factors responsible for sustaining mangrove wetland, seagrass meadow, and coral reef productivity, February 1993 - October 1998 (NCEI Accession 0000501) NOAA_NCEI STAC Catalog 1993-02-12 1998-10-15 -90.583333, 9.583333, -59.633333, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089375341-NOAA_NCEI.umm_json The Caribbean Coastal Marine Productivity (CARICOMP) Program is a Caribbean-wide research and monitoring network of 27 marine laboratories, parks, and reserves in 17 countries. This data set includes data collected from 42 stations at 29 sites in the Caribbean from 1993 to 1998. Line transects were used to determine the abundance of hard and soft corals, algae, sponges, urchins, and biotic material such as substrate type. proprietary gov.noaa.nodc:0000504_Not Applicable Bacteria, plankton, and trace metal, and other data from bottle and CTD casts in the Antarctic from the NATHANIEL B. PALMER and ROGER REVELLE in support of the US Joint Global Ocean Flux Study / Antarctic Environments Southern Ocean Process Study (JGOFS /AESOPS) from 1996-10-17 to 1998-03-15 (NCEI Accession 0000504) NOAA_NCEI STAC Catalog 1996-10-17 1998-03-15 163.34, -78.05, -165.91, -52.95 https://cmr.earthdata.nasa.gov/search/concepts/C2089375350-NOAA_NCEI.umm_json Phytoplankton and other data were collected in the Antarctic from the NATHANIEL B. PALMER and ROGER REVELL from 17 October 1996 to 15 March 1998. Bottle data include enumeration and counts of bacteria, picoplankton, nanoplankton and nano microplankton. Bottle data also include concentrations of trace metals. CTD data include conductivity, temperature, and salinity profiles. Data were collected in support of the US Joint Global Ocean Flux Study / Antarctic Environments Southern Ocean Process Study (JGOFS/AESOPS). proprietary gov.noaa.nodc:0000525_Not Applicable Chlorophyll and brevetoxin data from the ECOHAB project along the west coast of Florida from 1999-2000 (NCEI Accession 0000525) NOAA_NCEI STAC Catalog 1999-09-10 2000-09-29 -87.23565, 25.44867, -81.71588, 30.39237 https://cmr.earthdata.nasa.gov/search/concepts/C2089375484-NOAA_NCEI.umm_json Water and sediment samples were collected on annual ECOHAB Process cruises and on isolated Mote transects (10/13/99 and 10/20/99). Samples will be analyzed for brevetoxin using a competetive ELISA assay (Naar and Baden, in progress) as well as a receptor-binding assay (VanDolah et al., 1994), and have been analyzed for chlorophyll a (water only) using the Welschmeyer (1994) non-acidification technique. (To be updated when data has been analyzed.) proprietary gov.noaa.nodc:0000599_Not Applicable Aids to Navigation (ATONS) GIS data from the Gulf of Mexico and coastal waters of Alabama, Florida, Louisiana, Mississippi and Texas as of 1999-10-21 (NCEI Accession 0000599) NOAA_NCEI STAC Catalog 1999-01-01 1999-10-21 -98.320706, 17.398031, -61.876841, 32.288483 https://cmr.earthdata.nasa.gov/search/concepts/C2089376009-NOAA_NCEI.umm_json "This accession contains a GIS database of Aids to Navigation in the Gulf of Mexico and coastal waters of Alabama, Florida, Louisiana, Mississippi and Texas. These data were compiled on 1999-10-21. The term ""Aids to Navigation"" (ATONS or AIDS) refers to a device outside of a vessel used to assist mariners in determining their position or safe course, or to warn them of obstructions. AIDS to navigation include lighthouses, lights, buoy, sound signals, landmarks, racons, radio beacons, LORAN, and omega. These include AIDS which are installed and maintained by the Coast Guard as well as privately installed and maintained aids (permit required). This does not include unofficial AIDS (illegal) such as stakes, PVC pipes, and such placed without permission. Each USCG District Headquarters is responsible for updating their database on an ""as needed"" basis. When existing AIDS are destroyed or relocated and new AIDS are installed the database is updated. Each AID is assigned an official ""light listing number"". The light list is a document listing the current status of ATONS and it is published and distributed on a regular basis. Interim changes to the light list are published in local Notices to Mariners which are the official means which navigators are supposed to keep their charts current. In addition, the USCG broadcasts Notices to Mariners on the marine band radio as soon as changes of the status of individual AIDS are reported. The light list number and local Notices to Mariners reports are suggested ways to keep the database current on a regular or even ""real time"" basis. However, annual (or more frequent) updates of the entire dataset may be obtained from each USCG District Headquarters. Geographic Information System (GIS) software is required to display the data in this NCEI accession." proprietary @@ -18430,21 +18410,21 @@ gov.noaa.nodc:0001344_Not Applicable Chlorophyll and Plankton data from the Indi gov.noaa.nodc:0001410_Not Applicable Bathymetric Survey of the West Florida Shelf, Gulf of Mexico 2001 (NCEI Accession 0001410) NOAA_NCEI STAC Catalog 2001-09-03 2001-10-12 -86.71, 28.04, -84.61, 30.06 https://cmr.earthdata.nasa.gov/search/concepts/C2089376038-NOAA_NCEI.umm_json A zone of deep-water reefs is thought to extend from the mid and outer shelf south of Mississippi and Alabama to at least the northwestern Florida shelf off Panama City, Florida. Reefs off Mississippi and Alabama are found in water depths of 60 to 120 m (Ludwick and Walton, 1957, Gardner et al., in press) and were the focus of a multibeam echosounder mapping survey by the U.S. Geological Survey (USGS) in 2000 (Gardner et al., 2000, in press). It is critical to determine the accurate geomorphology and type of the reefs that occur because of their importance as benthic habitats for fisheries. These data are ArcInfo GRID and XYZ ASCII format data generated from a U.S. Geological Survey multibeam sonar survey of the West Florida Shelf, Gulf of Mexico. The data include high-resolution bathymetry and calibrated acoustic backscatter. File types include arc files .dat, .nit, and .adf. Documentation is included as metadata .txt files. Because the area is so large (i.e., the file sizes are very large), the area was subdivided into North, Central, and South regions as reflected in the data subdirectories for this accession. proprietary gov.noaa.nodc:0001419_Not Applicable Assessment of Nonindigenous Species on Coral Reefs in the Hawaiian Islands, with Emphasis on Introduced Invertebrates, November 2, 2002 - November 5, 2003 (NCEI Accession 0001419) NOAA_NCEI STAC Catalog 2002-11-02 2003-11-05 -159.65, 19.5, -155.83, 21.96 https://cmr.earthdata.nasa.gov/search/concepts/C2089376077-NOAA_NCEI.umm_json Coral reefs on the islands of Kauai, Molokai, Maui, Hawaii and Oahu were surveyed for the presence and impact of marine nonindigenous and cryptogenic species (NIS) using a rapid assessment method that standardized search effort for approximately 312 m2 at each site. A total of 41 sites were surveyed by three investigators for a total of approximately 120 hours search time on the five islands. Algae, invertebrate, and fish taxa were identified on site or returned to laboratory for identity confirmation. Only 26 NIS, comprised of three species of algae, 19 invertebrates, and four fishes were recorded from a total of 486 total taxa on the entire study, and 17 of the NIS occurred at only one or two sites. The most NIS that occurred at any site was six, and 21 of the sites had less than three. If the three species of fish that were introduced in the 1950s and known to occur throughout Hawaii are excluded, over half the sites had less than two NIS. proprietary gov.noaa.nodc:0001624_Not Applicable Bottle and Pumpcast data collected by CTD casts from the R/V Knorr during cruises 2 through 5 of the 1988 Black Sea Oceanographic Expedition (NCEI Accession 0001624) NOAA_NCEI STAC Catalog 1988-05-14 1988-07-29 28, 41, 42, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2089372426-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:0001746_Not Applicable ALINE time series (NCEI Accession 0001746) NOAA_NCEI STAC Catalog 1989-01-01 2001-01-01 141, 37, 150, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2089372824-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0001746_Not Applicable ALINE time series (NCEI Accession 0001746) ALL STAC Catalog 1989-01-01 2001-01-01 141, 37, 150, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2089372824-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:0001746_Not Applicable ALINE time series (NCEI Accession 0001746) NOAA_NCEI STAC Catalog 1989-01-01 2001-01-01 141, 37, 150, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2089372824-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0001756_Not Applicable Assessment of economic benefits and costs of marine managed areas in Hawaii, 1998 - 2003 (NCEI Accession 0001756) NOAA_NCEI STAC Catalog 1998-01-01 2003-12-31 -158.9, 18.8, -154.9, 22.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089372862-NOAA_NCEI.umm_json "This dataset combines the research results from a number of papers carried out under the study ""Assessment of Economic Benefits and Costs of Marine Managed Areas in Hawaii"". The studies included a paper on the fisheries benefits of MMAs (Friedlander and Cesar, 2004), a write-up of the recreational survey at the MMA sites (Van Beukering and Cesar, 2004), a background on the institutional/regulatory framework on MMAs in Hawaii (Cesar, 2004), a paper on the economic value and cost-benefit analysis of management options for MMAs (Van Beukering and Cesar, 2004) and a paper on the international experience of sustainable financing of MMAs (Cesar and van Beukering, 2004). This dataset is basically a set of MS Word documents with mostly social-economic data embedded within tables. The habitat and fish data in this dataset are drawn from other datasets already in the NOAA archives, the NOAA Benthic Habitat Maps and the Coral Reef Assessment and Monitoring Program (CRAMP), respectively." proprietary -gov.noaa.nodc:0001941_Not Applicable Aerial surveys of bowhead and beluga whales along with incidental sighting of other marine mammals in the Bering, Beaufort and Chukchi Seas for the Bowhead Whale Aerial Survey Project (BWASP), 1979 - 2004 (NCEI Accession 0001941) ALL STAC Catalog 1979-04-01 2004-10-18 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373265-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary gov.noaa.nodc:0001941_Not Applicable Aerial surveys of bowhead and beluga whales along with incidental sighting of other marine mammals in the Bering, Beaufort and Chukchi Seas for the Bowhead Whale Aerial Survey Project (BWASP), 1979 - 2004 (NCEI Accession 0001941) NOAA_NCEI STAC Catalog 1979-04-01 2004-10-18 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373265-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary +gov.noaa.nodc:0001941_Not Applicable Aerial surveys of bowhead and beluga whales along with incidental sighting of other marine mammals in the Bering, Beaufort and Chukchi Seas for the Bowhead Whale Aerial Survey Project (BWASP), 1979 - 2004 (NCEI Accession 0001941) ALL STAC Catalog 1979-04-01 2004-10-18 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373265-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary gov.noaa.nodc:0002013_Not Applicable A mesoscale hydrographic survey off Northwest Africa to examine the horizontal mixing by eddies, March - April 2003 (NCEI Accession 0002013) NOAA_NCEI STAC Catalog 2003-03-26 2003-04-16 -31.5, 6.6, -25, 11 https://cmr.earthdata.nasa.gov/search/concepts/C2089373546-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0002013_Not Applicable A mesoscale hydrographic survey off Northwest Africa to examine the horizontal mixing by eddies, March - April 2003 (NCEI Accession 0002013) ALL STAC Catalog 2003-03-26 2003-04-16 -31.5, 6.6, -25, 11 https://cmr.earthdata.nasa.gov/search/concepts/C2089373546-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:0002170_Not Applicable 22 Real-time XBT replacements assembled by Canada Department of Fisheries and Oceans (DFO) for the Global Temperature-Salinity Profile Program (GTSPP), dates ranging from 05/26/2004 to 05/27/2004 (NCEI Accession 0002170) ALL STAC Catalog 2004-05-27 2004-05-27 9.106, 31.684, 33.058, 44.043 https://cmr.earthdata.nasa.gov/search/concepts/C2089373990-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0002170_Not Applicable 22 Real-time XBT replacements assembled by Canada Department of Fisheries and Oceans (DFO) for the Global Temperature-Salinity Profile Program (GTSPP), dates ranging from 05/26/2004 to 05/27/2004 (NCEI Accession 0002170) NOAA_NCEI STAC Catalog 2004-05-27 2004-05-27 9.106, 31.684, 33.058, 44.043 https://cmr.earthdata.nasa.gov/search/concepts/C2089373990-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:0002170_Not Applicable 22 Real-time XBT replacements assembled by Canada Department of Fisheries and Oceans (DFO) for the Global Temperature-Salinity Profile Program (GTSPP), dates ranging from 05/26/2004 to 05/27/2004 (NCEI Accession 0002170) ALL STAC Catalog 2004-05-27 2004-05-27 9.106, 31.684, 33.058, 44.043 https://cmr.earthdata.nasa.gov/search/concepts/C2089373990-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0002192_Not Applicable A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico from 1999 to 2002 (NCEI Accession 0002192) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-25 -96.01, 23.49, -85.47, 29.38 https://cmr.earthdata.nasa.gov/search/concepts/C2089374092-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary gov.noaa.nodc:0002192_Not Applicable A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico from 1999 to 2002 (NCEI Accession 0002192) ALL STAC Catalog 1999-09-01 2002-08-25 -96.01, 23.49, -85.47, 29.38 https://cmr.earthdata.nasa.gov/search/concepts/C2089374092-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary -gov.noaa.nodc:0002193_Not Applicable A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002193) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374098-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary gov.noaa.nodc:0002193_Not Applicable A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002193) ALL STAC Catalog 1999-09-01 2002-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374098-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary -gov.noaa.nodc:0002196_Not Applicable Acoustic doppler current meter data collected in support of the Minerals Management Service-supported Deep Water Program in the the Gulf of Mexico, 1999 - 2003 (NCEI Accession 0002196) NOAA_NCEI STAC Catalog 1999-09-01 2003-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374197-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary +gov.noaa.nodc:0002193_Not Applicable A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002193) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374098-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary gov.noaa.nodc:0002196_Not Applicable Acoustic doppler current meter data collected in support of the Minerals Management Service-supported Deep Water Program in the the Gulf of Mexico, 1999 - 2003 (NCEI Accession 0002196) ALL STAC Catalog 1999-09-01 2003-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374197-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary +gov.noaa.nodc:0002196_Not Applicable Acoustic doppler current meter data collected in support of the Minerals Management Service-supported Deep Water Program in the the Gulf of Mexico, 1999 - 2003 (NCEI Accession 0002196) NOAA_NCEI STAC Catalog 1999-09-01 2003-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374197-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary gov.noaa.nodc:0002198_Not Applicable A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002198) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-01 -96, 23.49, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374298-NOAA_NCEI.umm_json A research program has been initiated by the Minerals Management Service (Contract No.1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology. Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation. proprietary gov.noaa.nodc:0002198_Not Applicable A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002198) ALL STAC Catalog 1999-09-01 2002-08-01 -96, 23.49, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374298-NOAA_NCEI.umm_json A research program has been initiated by the Minerals Management Service (Contract No.1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology. Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation. proprietary gov.noaa.nodc:0002199_Not Applicable Biological, chemical, and physical data from CTD/XCTD from five Japanese R/Vs in the North Pacific Ocean and other marginal basins from 1993 to 2003 (NCEI Accession 0002199) NOAA_NCEI STAC Catalog 1993-01-01 2003-12-31 179, 20, 130, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2089374415-NOAA_NCEI.umm_json The Japan Meteorological Agency (JMA) has been carrying out oceanographic and marine meteorological observations on board research vessels, at the coastal water temperature observation stations and by ocean data buoys, for the purposes of the better understanding of dynamical processes of the oceanic general circulation affecting climate change, prevention and mitigation of natural disasters, and contributing to international cooperative activities. This Data Report contains the data obtained from the observations made by JMA in 2003 together with the explanations. The observations include the followings: 1. Oceanographic and Marine Meteorological Observations on board Research Vessels Oceanographic observations are conducted in the seas adjacent to Japan and in the western North Pacific on board five vessels: Ryofu Maru, Keifu Maru, Kofu Maru, Chofu Maru and Seifu Maru. 2. Coastal Water Temperature Observations JMA has carried out water temperature observations at the coastal stations. Historical time series of 10 day and monthly mean temperatures, daily observations and hourly observations are available in this CD-ROM. 3. Ocean Data Buoy Observations Operational ocean data buoy observations have been made to obtain marine meteorological and oceanographic observations in the seas around Japan. Correspondence relating to this Data Report may be directed to: Marine Division Climate and Marine Department Japan Meteorological Agency 1-3-4 Otemachi, Chiyoda-ku, Tokyo, 100-8122 JAPAN Facsimile: +81-3-3211-6908 E-mail: seadata@hq.kishou.go.jp proprietary @@ -18460,16 +18440,16 @@ gov.noaa.nodc:0002650_Not Applicable A survey of the marine biota of the island gov.noaa.nodc:0002805_Not Applicable Chlorophyll data collected from the old outfall site in the south sector of Kaneohe Bay, Oahu, Hawaii, February 2001 to May 2004 (NCEI Accession 0002805) NOAA_NCEI STAC Catalog 2001-02-07 2004-05-26 -157.77, 21.41, -157.77, 21.41 https://cmr.earthdata.nasa.gov/search/concepts/C2089376053-NOAA_NCEI.umm_json Kaneohe Bay received increasing amounts of sewage from the 1950s through 1977. Most sewage was diverted from the bay in 1977 and early 1978. Data were collected beginning in September 1976 and continued until June 1979. The time series was re-established in June 1982 and continued to December 2005, when it was terminated. The sampling was at 1 m depth in the south sector of Kaneohe Bay, Oahu near the old outfall that ceased in 1977. Previous NODC Accessions 0000396 (1976-1979) and 0000422 (1982-1/2001) contained monthly averages of chlorophyll a, based on weekly to bi-weekly samples. This data set has the weekly to bi-weekly chlorophyll a, pheo, water temperature, secchi depth, and sample site depth. Additional data were taken from June 2004 - December 2005 and these will be available in a separate data set. proprietary gov.noaa.nodc:0013170_Not Applicable Chemical and biological data collected as part of the CArbon Retention In A Colored Ocean (CARIACO) program in the Cariaco Basin off the coast of Venezuela, January 17, 2005 - January 16, 2006 (NCEI Accession 0013170) NOAA_NCEI STAC Catalog 2005-01-17 2006-01-16 -65.56, 10.45, -64.65, 10.66 https://cmr.earthdata.nasa.gov/search/concepts/C2089372614-NOAA_NCEI.umm_json Chemical and biological data were collected using bottle casts on the continental shelf of Venezuela from the HERMANO GINES from January 17, 2005 to January 16, 2006. Data were collected and submitted by Dr. Mary Scranton of Stony Brook University with support from the CArbon Retention In A Colored Ocean (CARIACO) program. proprietary gov.noaa.nodc:0014123_Not Applicable Chemical and physical profile data collected from CTD casts from 01 January 2003 to 01 October 2005 aboard the F. G. WALTON SMITH in the Straits of Florida (NCEI Accession 0014123) NOAA_NCEI STAC Catalog 2003-01-01 2005-10-01 -81.299667, 23.249833, -79.017833, 25.627167 https://cmr.earthdata.nasa.gov/search/concepts/C2089372909-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:0014906_Not Applicable Aerial sightings of bowhead whales and other marine mammals by the US Department of the Interior's Minerals Management Service, 1979 - 2006, in the Bering, Chukchi and Beaufort Seas (NCEI Accession 0014906) ALL STAC Catalog 1979-04-01 2006-10-31 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373613-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary gov.noaa.nodc:0014906_Not Applicable Aerial sightings of bowhead whales and other marine mammals by the US Department of the Interior's Minerals Management Service, 1979 - 2006, in the Bering, Chukchi and Beaufort Seas (NCEI Accession 0014906) NOAA_NCEI STAC Catalog 1979-04-01 2006-10-31 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373613-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary +gov.noaa.nodc:0014906_Not Applicable Aerial sightings of bowhead whales and other marine mammals by the US Department of the Interior's Minerals Management Service, 1979 - 2006, in the Bering, Chukchi and Beaufort Seas (NCEI Accession 0014906) ALL STAC Catalog 1979-04-01 2006-10-31 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373613-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary gov.noaa.nodc:0033380_Not Applicable Assessment of invasiveness of the Orange Keyhole Sponge Mycale Armata in Kaneohe Bay, Oahu, Hawaii, based on surveys in 2005 - 2006, Year 2 of Hawaii Coral Reef Initiative (NCEI Accession 0033380) NOAA_NCEI STAC Catalog 2005-01-02 2006-03-31 -157.85, 21.41, -157.76, 21.51 https://cmr.earthdata.nasa.gov/search/concepts/C2089374745-NOAA_NCEI.umm_json The purpose of this study was to determine Mycale armata's distribution, abundance throughout the bay, its growth rates on permanent quadrats, and whether mechanical removal would be an effective management technique for its control. The study utilized both quadrat surveys and manta tow boards for data collection. Data files are in Excel, PDF, MS Word, and JPEG image formats. proprietary gov.noaa.nodc:0038513_Not Applicable Chemical and biological data collected as part of the CArbon Retention In A Colored Ocean (CARIACO) program in the Cariaco Basin off the coast of Venezuela, May 23, 2005 - November 11, 2006 (NCEI Accession 0038513) NOAA_NCEI STAC Catalog 2005-05-23 2006-11-11 -65.58727, 10.49568, -64.5845, 10.71638 https://cmr.earthdata.nasa.gov/search/concepts/C2089375332-NOAA_NCEI.umm_json Chemical and biological data were collected using bottle casts on the continental shelf of Venezuela from the HERMANO GINES from May 23, 2005 to November 11, 2006. Data were collected and submitted by Dr. Mary Scranton of Stony Brook University with support from the CArbon Retention In A Colored Ocean (CARIACO) program. proprietary gov.noaa.nodc:0040205_Not Applicable Carbon dioxide from surface underway survey in global oceans from 1968 to 2006 (Version 1.0) (NCEI Accession 0040205) NOAA_NCEI STAC Catalog 1966-01-01 2006-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089375975-NOAA_NCEI.umm_json More than 3 million measurements of surface water partial pressure of CO2 obtained over the global oceans during 1968 to 2006 are listed in the Lamont-Doherty Earth Observatory database, which includes open ocean and coastal water measurements. The data assembled include only those measured by equilibrator CO2 analyzer systems and have been quality-controlled based on the stability of the system performance, the reliability of calibrations for CO2 analysis, and the internal consistency of data. Versions up to 2007 are included in this dataset proprietary gov.noaa.nodc:0043167_Not Applicable Aurora 1993 XBT's temperature measurements collected using XBT from Aurora Australis in the Tasman Sea during 1993 (NCEI Accession 0043167) NOAA_NCEI STAC Catalog 1993-01-05 1993-10-08 61.52, -68.93, 159, -42.83 https://cmr.earthdata.nasa.gov/search/concepts/C2089372431-NOAA_NCEI.umm_json Temperature data received at NODC on April 14, 2008 by Tim Boyer placed on the FTP server by Ann Thresher, CSIRO (COMMONWEALTH SCIENTIFIC AND INDUSTRIAL RESEARCH ORGANIZATION) for XBT/CTD comparisons proprietary gov.noaa.nodc:0045502_Not Applicable Carbon dioxide, temperature, salinity, and atmospheric pressure from surface underway survey in the North Pacific from January 1998 to January 2004 (NCEI Accession 0045502) NOAA_NCEI STAC Catalog 1998-01-01 2004-01-01 -100, -10, 120, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2089372737-NOAA_NCEI.umm_json Sea surface pCO2, sea surface temperature, sea surface salinity, and atmospheric pressure measurements collected in the North Pacific as part of the NOAA Office of Climate Observations (OCO) and U.S. Carbon Cycle Science Programs. proprietary gov.noaa.nodc:0045505_Not Applicable AOML VOS pCO2. temperature, salinity, and other underway measurements collected using in the Pacific and Atlantic from 2007 to 2008 (NCEI Accession 0045505) NOAA_NCEI STAC Catalog 2007-04-06 2008-01-15 -90, -40, -20, 20 https://cmr.earthdata.nasa.gov/search/concepts/C2089372759-NOAA_NCEI.umm_json AOML pCO2 underway measurements collected using in the Pacific and Atlantic from 2007 to 2008 proprietary -gov.noaa.nodc:0046934_Not Applicable Acropora Spatial Survey Data of the Upper Florida Keys National Marine Sanctuary, 2005 - 2007 (NCEI Accession 0046934) ALL STAC Catalog 2005-01-01 2007-12-31 -81.41079, 24.54466, -80.19632, 25.29129 https://cmr.earthdata.nasa.gov/search/concepts/C2089373092-NOAA_NCEI.umm_json These data were collected by the NOAA Southeast Fisheries Science Center to document the presence or absence of Acropora spp at shallow reef sites in the Upper Florida Keys (USA). The presence or absence of acroporid corals was marked by handheld GPS during snorkel or tow surveys of shallow water (<5m) reef habitats in the Upper Florida Keys National Marine Sanctuary. The data are in GIS shape and layer files with associated attribute files, metadata files, and additional .pdf file outputs of the GIS data layers. proprietary gov.noaa.nodc:0046934_Not Applicable Acropora Spatial Survey Data of the Upper Florida Keys National Marine Sanctuary, 2005 - 2007 (NCEI Accession 0046934) NOAA_NCEI STAC Catalog 2005-01-01 2007-12-31 -81.41079, 24.54466, -80.19632, 25.29129 https://cmr.earthdata.nasa.gov/search/concepts/C2089373092-NOAA_NCEI.umm_json These data were collected by the NOAA Southeast Fisheries Science Center to document the presence or absence of Acropora spp at shallow reef sites in the Upper Florida Keys (USA). The presence or absence of acroporid corals was marked by handheld GPS during snorkel or tow surveys of shallow water (<5m) reef habitats in the Upper Florida Keys National Marine Sanctuary. The data are in GIS shape and layer files with associated attribute files, metadata files, and additional .pdf file outputs of the GIS data layers. proprietary +gov.noaa.nodc:0046934_Not Applicable Acropora Spatial Survey Data of the Upper Florida Keys National Marine Sanctuary, 2005 - 2007 (NCEI Accession 0046934) ALL STAC Catalog 2005-01-01 2007-12-31 -81.41079, 24.54466, -80.19632, 25.29129 https://cmr.earthdata.nasa.gov/search/concepts/C2089373092-NOAA_NCEI.umm_json These data were collected by the NOAA Southeast Fisheries Science Center to document the presence or absence of Acropora spp at shallow reef sites in the Upper Florida Keys (USA). The presence or absence of acroporid corals was marked by handheld GPS during snorkel or tow surveys of shallow water (<5m) reef habitats in the Upper Florida Keys National Marine Sanctuary. The data are in GIS shape and layer files with associated attribute files, metadata files, and additional .pdf file outputs of the GIS data layers. proprietary gov.noaa.nodc:0049902_Not Applicable Biological dataset collected from bottle casts from the R/V LAURENCE M. GOULD and the R/V NATHANIEL B. PALMER in the Southern Drake Passage and Scotia Sea in support of National Science Foundation projects OPP 03-30443 and ANT 04-44134 from 15 February 2004 to 09 August 2006 (NCEI Accession 0049902) NOAA_NCEI STAC Catalog 2004-02-15 2006-08-09 -64.9884, -64.675, -52.8742, -54.8127 https://cmr.earthdata.nasa.gov/search/concepts/C2089373417-NOAA_NCEI.umm_json Ocean biology data were collected in Southern Drake Passage and Scotia Sea during two research cruises supported by NSF awards. These two cruises, namely LMG0402 and NBP0606, were conducted during Februay to March 2004 and July to August 2006, respectively. Dataset includes concentration of pigments in phytoplankton, particulate organic matter concentration, macronutrients, primary productivity and microbial biomass and productivity. proprietary gov.noaa.nodc:0051848_Not Applicable Biomass measurements collected in the Pacific Ocean using a net from various platform from 1950 - 1961 (NCEI Accession 0051848) NOAA_NCEI STAC Catalog 1950-05-14 1961-07-29 -170, 0, -135, 30 https://cmr.earthdata.nasa.gov/search/concepts/C2089373644-NOAA_NCEI.umm_json Zooplankton biomass data collected from Pacific Ocean in 1950 - 1961 years received from NMFS proprietary gov.noaa.nodc:0053277_Not Applicable Biomass measurements collected using net in the North and South Atlantic from several platforms from 1950 to 989 (NCEI Accession 0053277) NOAA_NCEI STAC Catalog 1950-01-01 1989-12-31 -86.367, -42.78, 14.175, 53.683 https://cmr.earthdata.nasa.gov/search/concepts/C2089373850-NOAA_NCEI.umm_json Zooplankton biomass data collected by Institute of Biology of the Southern Seas from the Atlantic Ocean in 1950-1989 years and received from the NMFS. proprietary @@ -18477,8 +18457,8 @@ gov.noaa.nodc:0057319_Not Applicable Arctic Freshwater Switchyard Project: Sprin gov.noaa.nodc:0058268_Not Applicable Beaufort Gyre hydrographic data: Temperature, salinity and transmissivity data from the Louis S St. Laurent in the Arctic Ocean, 2003 - 2008 (NCEI Accession 0058268) NOAA_NCEI STAC Catalog 2003-10-11 2008-10-20 -150, 75, -140, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2089374751-NOAA_NCEI.umm_json The major goal of the observational program is to determine the variability of different components of the Beaufort Gyre fresh water (ocean and sea ice) system and to assess the partial concentrations of fresh water of different origin (rivers, Pacific Ocean, precipitation, ice/snow melt, etc). Using moorings, drifting buoys, shipboard, and remote sensing measurements we have been measuring time series of temperature, salinity, currents, geochemical tracers, sea ice draft, and sea level since August 2003, to determine freshwater content and freshwater fluxes in the Beaufort Gyre during a complete seasonal cycle and beyond. proprietary gov.noaa.nodc:0058858_Not Applicable Abundance data for the copepod species Calanus pacificus and Metridia pacifica collected at a fixed station in Dabob Bay, Hood Canal, Puget Sound, Washington during six cruises aboard the CLIFFORD A. BARNES, October 2006 - April 2008 (NCEI Accession 0058858) NOAA_NCEI STAC Catalog 2006-10-12 2008-04-15 -122.835, 47.769, -122.835, 47.769 https://cmr.earthdata.nasa.gov/search/concepts/C2089374860-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0058858_Not Applicable Abundance data for the copepod species Calanus pacificus and Metridia pacifica collected at a fixed station in Dabob Bay, Hood Canal, Puget Sound, Washington during six cruises aboard the CLIFFORD A. BARNES, October 2006 - April 2008 (NCEI Accession 0058858) ALL STAC Catalog 2006-10-12 2008-04-15 -122.835, 47.769, -122.835, 47.769 https://cmr.earthdata.nasa.gov/search/concepts/C2089374860-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:0061208_Not Applicable Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208) ALL STAC Catalog 2005-11-13 2007-05-23 -93.58, 27.85, -92.45, 28.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089375074-NOAA_NCEI.umm_json The most active hurricane season on record in the Atlantic and Gulf of Mexico occurred in 2005, fueled by higher than normal sea-surface temperatures. Eleven tropical cyclones entered the Gulf of Mexico in 2005, including Hurricane Rita. Hurricane Rita was a Category 3 storm when it passed near the shelf edge banks on September 23, 2005. Several sensitive habitats within the northwestern Gulf of Mexico were close to the path of Hurricane Rita, including Sonnier, McGrail, Geyer, Bright, and East Flower Garden Banks. Hindcast hydrodynamic models estimated wave heights at 20-m or higher on these banks. This may have left some bank caps exposed, even at ~20- to 30-m depths. The implications for catastrophic damage to benthic community structure prompted the Minerals Management Service to characterize the banks in their post-hurricane state. This study, using the data in NODC Accession 0061208, characterized and compared the benthic habitats of four banks (Sonnier, McGrail, Geyer, and Bright) and recorded possible hurricane damage at these banks and the East Flower Garden Bank (EFGB). At Sonnier, McGrail, Geyer, and Bright Banks, videographic records were collected by SCUBA and ROV in April and May 2007, at four depth ranges to assess benthic cover to the lowest possible taxonomic level: 22-27 m, 30-36.5 m, 45-50 m, and 55-60 m. Video transects were qualitatively assessed for evidence of hurricane damage. To document recovery from Hurricane Rita at the existing long-term monitoring site on the EFGB, repetitive quadrats and perimeter line surveys were conducted in November 2005 and compared to data collected subsequently in June 2006. proprietary gov.noaa.nodc:0061208_Not Applicable Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208) NOAA_NCEI STAC Catalog 2005-11-13 2007-05-23 -93.58, 27.85, -92.45, 28.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089375074-NOAA_NCEI.umm_json The most active hurricane season on record in the Atlantic and Gulf of Mexico occurred in 2005, fueled by higher than normal sea-surface temperatures. Eleven tropical cyclones entered the Gulf of Mexico in 2005, including Hurricane Rita. Hurricane Rita was a Category 3 storm when it passed near the shelf edge banks on September 23, 2005. Several sensitive habitats within the northwestern Gulf of Mexico were close to the path of Hurricane Rita, including Sonnier, McGrail, Geyer, Bright, and East Flower Garden Banks. Hindcast hydrodynamic models estimated wave heights at 20-m or higher on these banks. This may have left some bank caps exposed, even at ~20- to 30-m depths. The implications for catastrophic damage to benthic community structure prompted the Minerals Management Service to characterize the banks in their post-hurricane state. This study, using the data in NODC Accession 0061208, characterized and compared the benthic habitats of four banks (Sonnier, McGrail, Geyer, and Bright) and recorded possible hurricane damage at these banks and the East Flower Garden Bank (EFGB). At Sonnier, McGrail, Geyer, and Bright Banks, videographic records were collected by SCUBA and ROV in April and May 2007, at four depth ranges to assess benthic cover to the lowest possible taxonomic level: 22-27 m, 30-36.5 m, 45-50 m, and 55-60 m. Video transects were qualitatively assessed for evidence of hurricane damage. To document recovery from Hurricane Rita at the existing long-term monitoring site on the EFGB, repetitive quadrats and perimeter line surveys were conducted in November 2005 and compared to data collected subsequently in June 2006. proprietary +gov.noaa.nodc:0061208_Not Applicable Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208) ALL STAC Catalog 2005-11-13 2007-05-23 -93.58, 27.85, -92.45, 28.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089375074-NOAA_NCEI.umm_json The most active hurricane season on record in the Atlantic and Gulf of Mexico occurred in 2005, fueled by higher than normal sea-surface temperatures. Eleven tropical cyclones entered the Gulf of Mexico in 2005, including Hurricane Rita. Hurricane Rita was a Category 3 storm when it passed near the shelf edge banks on September 23, 2005. Several sensitive habitats within the northwestern Gulf of Mexico were close to the path of Hurricane Rita, including Sonnier, McGrail, Geyer, Bright, and East Flower Garden Banks. Hindcast hydrodynamic models estimated wave heights at 20-m or higher on these banks. This may have left some bank caps exposed, even at ~20- to 30-m depths. The implications for catastrophic damage to benthic community structure prompted the Minerals Management Service to characterize the banks in their post-hurricane state. This study, using the data in NODC Accession 0061208, characterized and compared the benthic habitats of four banks (Sonnier, McGrail, Geyer, and Bright) and recorded possible hurricane damage at these banks and the East Flower Garden Bank (EFGB). At Sonnier, McGrail, Geyer, and Bright Banks, videographic records were collected by SCUBA and ROV in April and May 2007, at four depth ranges to assess benthic cover to the lowest possible taxonomic level: 22-27 m, 30-36.5 m, 45-50 m, and 55-60 m. Video transects were qualitatively assessed for evidence of hurricane damage. To document recovery from Hurricane Rita at the existing long-term monitoring site on the EFGB, repetitive quadrats and perimeter line surveys were conducted in November 2005 and compared to data collected subsequently in June 2006. proprietary gov.noaa.nodc:0066319_Not Applicable Benthic data for corals, macroalgae, invertebrates, and non-living bottom types from Fagatele Bay, Pago Pago, and Fagasa, American Samoa, 2004-2008 (NCEI Accession 0066319) NOAA_NCEI STAC Catalog 2004-01-01 2008-08-01 -170.76892, -14.37023, -170.63047, -14.27847 https://cmr.earthdata.nasa.gov/search/concepts/C2089376136-NOAA_NCEI.umm_json This data set was derived from surveys in Fagatele Bay National Marine Sanctuary, Pago Pago (Rainmaker and Aua), and Fagasa (Sita Bay and Cape Larsen) conducted in 2004 and 2007-2008. Parameters include coral, algal, or invertebrate species, coral colony diameter size, and non-living bottom type. Summaries of species identification from sites above and Ofu-Olosega Islands, Ta'u Island, Aunu'u, Manu'a, and Rose Atoll, based on historic surveys back to 1917 are also given in spreadsheets. This is a working list put together by Dr. Charles Birkeland. Fish data were collected by Dr. Alison Green on the same dates and transects and are available in a separate NODC accession. proprietary gov.noaa.nodc:0068364_Not Applicable Benthic data for corals, macroalgae, invertebrates, and non-living bottom types from Fagatele Bay National Marine Sanctuary, South Pacific Ocean, 2007-04-02 to 2008-12-31 (NCEI Accession 0068364) NOAA_NCEI STAC Catalog 2007-04-02 2008-12-31 -170.814, -14.3654, -170.562, -14.1271 https://cmr.earthdata.nasa.gov/search/concepts/C2089372324-NOAA_NCEI.umm_json Benthic transects were repeated at 12 sites around Tutuila at various depths on the reef slopes and flats. Benthic coverage categories include coral species, invertebrates, and non-living substrate type. Annual surveys took place during 2005-2009. The most detailed data are from 2008. The data were provided as spreadsheets and metadata within a PDF document, focusing on the 2008 surveys. A related data set was can be found in NCEI Accession 0066319, which was derived from surveys in Fagatele Bay National Marine Sanctuary, Pago Pago (Rainmaker and Aua), and Fagasa (Sita Bay and Cape Larsen) conducted in 2004 and 2007-2008. Parameters include coral, algal, or invertebrate species, coral colony diameter size, and non-living bottom type. Also in 0066319 are summaries of species identification from sites above and Ofu-Olosega Islands, Ta'u Island, Aunu'u, Manu'a, and Rose Atoll, based on historic surveys back to 1917 are also given in spreadsheets. This is a working list put together by Dr. Charles Birkeland. proprietary gov.noaa.nodc:0068586_Not Applicable Chemical and physical oceanographic profile data collected from CTD casts aboard the SEWARD JOHNSON in the North Atlantic Ocean and Gulf of Mexico from 2010-07-10 to 2010-07-14 in response to the Deepwater Horizon oil spill event (NCEI Accession 0068586) NOAA_NCEI STAC Catalog 2010-07-10 2010-07-14 -83.153333, 24.251833, -79.812, 26.011833 https://cmr.earthdata.nasa.gov/search/concepts/C2089372374-NOAA_NCEI.umm_json Chemical and physical oceanographic profile data were collected aboard the SEWARD JOHNSON in the North Atlantic Ocean and Gulf of Mexico from 2010-07-10 to 2010-07-14 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0068586) proprietary @@ -18626,12 +18606,12 @@ gov.noaa.nodc:0118500_Not Applicable Biological and physical geospatial data fro gov.noaa.nodc:0118680_Not Applicable Biological and chemical data determined in mesocosm experiments by Dauphin Island Sea Lab in June and August of 2011 (NCEI Accession 0118680) NOAA_NCEI STAC Catalog 2011-06-01 2011-09-01 -88.080239, 30.243423, -88.080239, 30.243423 https://cmr.earthdata.nasa.gov/search/concepts/C2089373185-NOAA_NCEI.umm_json Abundances of viruses, prokaryotes, diatoms, dinoflagellates, ciliates and heterotrophic nanoflagellates were determined over time in mesocosm experiments measuring the effects of oil, dispersant and dispersed oil on the microbial loop. Two separate experiments were carried out in June and August 2011. Abundances in the treated mesocosms were compared to a no addition control and a glucose addition control. proprietary gov.noaa.nodc:0118720_Not Applicable Biological, chemical, and physical data collected in Delaware Bay from 1997-09-02 to 1997-10-08 (NCEI Accession 0118720) NOAA_NCEI STAC Catalog 1997-09-02 1997-10-08 -75.6082, 38.5167, -74.723, 40.147 https://cmr.earthdata.nasa.gov/search/concepts/C2089373222-NOAA_NCEI.umm_json This study was based on the sediment quality triad (SQT) approach. A stratified probabilistic sampling design was utilized to characterize the Delaware Bay system in terms of chemical contamination, sediment toxicity (Microtox, amphipod bioassay; sea urchin gamete bioassay; and P450 biomarker) and benthic infaunal community structure. The purpose was to define the extent and magnitude of toxicity and other biological effects associated with contaminants in the Delaware estuary system from the fall line to the mouth of the Bay. This file contains data measured in the Delaware Bay Estuary and adjacent waters during 1997. Samples were collected for water and sediment analyses. proprietary gov.noaa.nodc:0124257_Not Applicable Baseline characterization of benthic and coral communities of the Flower Garden Banks, Texas from 2010-05-01 to 2012-08-31 (NCEI Accession 0124257) NOAA_NCEI STAC Catalog 2010-05-01 2012-08-31 -93.87, 27.82, -93.57, 27.99 https://cmr.earthdata.nasa.gov/search/concepts/C2089375884-NOAA_NCEI.umm_json This study utilized ROV photograph transects to quantify benthic habitat and coral communities among the five habitat types (algal nodule, coralline algal reefs, deep reefs and soft bottom) identified in the Flower Garden Banks National Marine Sanctuary (FGBNMS). ROV surveys were conducted in the mid and lower mesophotic zone of the sanctuary (17-150 m) on both the East Bank and the West Bank. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. During the course of the sanctuary’s management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project is to provide baseline data for all benthic habitats and coral communities. proprietary -gov.noaa.nodc:0125596_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596) NOAA_NCEI STAC Catalog 2009-03-18 2012-12-10 -51.493, -34.504, -44.498, -34.499 https://cmr.earthdata.nasa.gov/search/concepts/C2089376227-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0125596_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596) ALL STAC Catalog 2009-03-18 2012-12-10 -51.493, -34.504, -44.498, -34.499 https://cmr.earthdata.nasa.gov/search/concepts/C2089376227-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:0125596_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596) NOAA_NCEI STAC Catalog 2009-03-18 2012-12-10 -51.493, -34.504, -44.498, -34.499 https://cmr.earthdata.nasa.gov/search/concepts/C2089376227-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0125597_Not Applicable Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597) NOAA_NCEI STAC Catalog 2004-09-27 2016-02-25 -76.84, 26.491, -72.004, 26.516 https://cmr.earthdata.nasa.gov/search/concepts/C2089376235-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:0125597_Not Applicable Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597) ALL STAC Catalog 2004-09-27 2016-02-25 -76.84, 26.491, -72.004, 26.516 https://cmr.earthdata.nasa.gov/search/concepts/C2089376235-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:0127525_Not Applicable Abundance and behavior of parrotfishes (Labridae, Scarinae) in the upper Florida Keys from 2013-06-19 to 2013-07-30 (NCEI Accession 0127525) ALL STAC Catalog 2013-06-19 2013-07-30 -80.38, 25, -80.21, 25.22 https://cmr.earthdata.nasa.gov/search/concepts/C2089376534-NOAA_NCEI.umm_json To better understand the functional roles of parrotfishes on Caribbean coral reefs we documented abundance, habitat preferences, and diets of nine species of parrotfishes (Scarus coelestinus, Scarus coeruleus, Scarus guacamaia, Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) on three high-relief spur-and-groove reefs (Molasses, Carysfort, and Elbow) offshore of Key Largo in the Florida Keys National Marine Sanctuary. On each reef, we conducted fish surveys, behavioral observations, and benthic surveys in three habitat types: high-relief spur and groove (depth 2 - 6 m), low-relief carbonate platform/hardbottom (depth 4 - 12 m), and carbonate boulder/rubble fields (depth 4 - 9 m). In addition, fish surveys were also conducted on a fourth high-relief spur-and-groove reef (French). We estimated parrotfish abundance in each of the three habitat types in order to assess the relative abundance and biomass of different species and to quantify differences in habitat selection. To estimate parrotfish density, we conducted 20 to 30 minute timed swims while towing a GPS receiver on a float on the surface to calculate the amount of area sampled. During a swim the observer would swim parallel with the habitat type being sampled and count and estimate the size to the nearest cm of all parrotfishes greater than or equal to 15 cm in length that were encountered in a 5 m wide swath. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous “turf” algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, or (5) ledge. Dead coral included both convex and concave surfaces on the vertical and horizontal planes of three dimensional coral skeletons (primarily dead Acropora palmata) that were attached to reef substrate. Coral pavement was carbonate reef with little topographic complexity (i.e., flat limestone pavement). Boulder was large remnants of dead mounding corals not clearly attached to the bottom and often partially buried in sand. Coral rubble consisted of small dead coral fragments (generally < 10 cm in any dimension) that could be moved with minimal force. Ledges consisted entirely of the undercut sides of large spurs in the high-relief spur and groove habitat. In order to quantify the relative abundance of different food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the five substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, or ledge) in 0.5 m x 0.5 m photoquadrats. We photographed a total of 8 haphazardly selected quadrats dispersed throughout the study site for each substrate type at each of the three sites (N = 24 quadrats per substrate type, N = 120 quadrats total). Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat). proprietary gov.noaa.nodc:0127525_Not Applicable Abundance and behavior of parrotfishes (Labridae, Scarinae) in the upper Florida Keys from 2013-06-19 to 2013-07-30 (NCEI Accession 0127525) NOAA_NCEI STAC Catalog 2013-06-19 2013-07-30 -80.38, 25, -80.21, 25.22 https://cmr.earthdata.nasa.gov/search/concepts/C2089376534-NOAA_NCEI.umm_json To better understand the functional roles of parrotfishes on Caribbean coral reefs we documented abundance, habitat preferences, and diets of nine species of parrotfishes (Scarus coelestinus, Scarus coeruleus, Scarus guacamaia, Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) on three high-relief spur-and-groove reefs (Molasses, Carysfort, and Elbow) offshore of Key Largo in the Florida Keys National Marine Sanctuary. On each reef, we conducted fish surveys, behavioral observations, and benthic surveys in three habitat types: high-relief spur and groove (depth 2 - 6 m), low-relief carbonate platform/hardbottom (depth 4 - 12 m), and carbonate boulder/rubble fields (depth 4 - 9 m). In addition, fish surveys were also conducted on a fourth high-relief spur-and-groove reef (French). We estimated parrotfish abundance in each of the three habitat types in order to assess the relative abundance and biomass of different species and to quantify differences in habitat selection. To estimate parrotfish density, we conducted 20 to 30 minute timed swims while towing a GPS receiver on a float on the surface to calculate the amount of area sampled. During a swim the observer would swim parallel with the habitat type being sampled and count and estimate the size to the nearest cm of all parrotfishes greater than or equal to 15 cm in length that were encountered in a 5 m wide swath. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous “turf” algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, or (5) ledge. Dead coral included both convex and concave surfaces on the vertical and horizontal planes of three dimensional coral skeletons (primarily dead Acropora palmata) that were attached to reef substrate. Coral pavement was carbonate reef with little topographic complexity (i.e., flat limestone pavement). Boulder was large remnants of dead mounding corals not clearly attached to the bottom and often partially buried in sand. Coral rubble consisted of small dead coral fragments (generally < 10 cm in any dimension) that could be moved with minimal force. Ledges consisted entirely of the undercut sides of large spurs in the high-relief spur and groove habitat. In order to quantify the relative abundance of different food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the five substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, or ledge) in 0.5 m x 0.5 m photoquadrats. We photographed a total of 8 haphazardly selected quadrats dispersed throughout the study site for each substrate type at each of the three sites (N = 24 quadrats per substrate type, N = 120 quadrats total). Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat). proprietary +gov.noaa.nodc:0127525_Not Applicable Abundance and behavior of parrotfishes (Labridae, Scarinae) in the upper Florida Keys from 2013-06-19 to 2013-07-30 (NCEI Accession 0127525) ALL STAC Catalog 2013-06-19 2013-07-30 -80.38, 25, -80.21, 25.22 https://cmr.earthdata.nasa.gov/search/concepts/C2089376534-NOAA_NCEI.umm_json To better understand the functional roles of parrotfishes on Caribbean coral reefs we documented abundance, habitat preferences, and diets of nine species of parrotfishes (Scarus coelestinus, Scarus coeruleus, Scarus guacamaia, Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) on three high-relief spur-and-groove reefs (Molasses, Carysfort, and Elbow) offshore of Key Largo in the Florida Keys National Marine Sanctuary. On each reef, we conducted fish surveys, behavioral observations, and benthic surveys in three habitat types: high-relief spur and groove (depth 2 - 6 m), low-relief carbonate platform/hardbottom (depth 4 - 12 m), and carbonate boulder/rubble fields (depth 4 - 9 m). In addition, fish surveys were also conducted on a fourth high-relief spur-and-groove reef (French). We estimated parrotfish abundance in each of the three habitat types in order to assess the relative abundance and biomass of different species and to quantify differences in habitat selection. To estimate parrotfish density, we conducted 20 to 30 minute timed swims while towing a GPS receiver on a float on the surface to calculate the amount of area sampled. During a swim the observer would swim parallel with the habitat type being sampled and count and estimate the size to the nearest cm of all parrotfishes greater than or equal to 15 cm in length that were encountered in a 5 m wide swath. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous “turf” algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, or (5) ledge. Dead coral included both convex and concave surfaces on the vertical and horizontal planes of three dimensional coral skeletons (primarily dead Acropora palmata) that were attached to reef substrate. Coral pavement was carbonate reef with little topographic complexity (i.e., flat limestone pavement). Boulder was large remnants of dead mounding corals not clearly attached to the bottom and often partially buried in sand. Coral rubble consisted of small dead coral fragments (generally < 10 cm in any dimension) that could be moved with minimal force. Ledges consisted entirely of the undercut sides of large spurs in the high-relief spur and groove habitat. In order to quantify the relative abundance of different food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the five substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, or ledge) in 0.5 m x 0.5 m photoquadrats. We photographed a total of 8 haphazardly selected quadrats dispersed throughout the study site for each substrate type at each of the three sites (N = 24 quadrats per substrate type, N = 120 quadrats total). Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat). proprietary gov.noaa.nodc:0128996_Not Applicable Benthic and biological data in the New York Bight from 2010-06-01 to 2012-05-31 (NCEI Accession 0128996) NOAA_NCEI STAC Catalog 2010-06-01 2012-05-31 -75, 37, -69, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2089376996-NOAA_NCEI.umm_json These data sets show the distribution of key species and habitats, such as seabirds, bathymetry, surficial sediments, deep sea corals, and oceanographic habitats. NOAA’s Biogeography Branch worked with the New York Department of State (DOS) to interpret existing ecological information and create these new data sets. New York plans to integrate this information with other ecological and human use data compiled by others (for example, The Nature Conservancy, Northeast Fisheries Science Center) and apply ecosystem-based management and plan for ocean uses. Many academic, state and federal and non-governmental organization partners contributed to this project with data, analyses and reviews. Project partners included: the University of Alaska, Biology and Wildlife Department; University of Texas, Institute for Geophysics; The Nature Conservancy, Mid-Atlantic Marine Program; the National Marine Fisheries Service (NMFS), Northeast Fisheries Science Center, and the NMFS, Deep-Sea Coral Research and Technology Program. proprietary gov.noaa.nodc:0129395_Not Applicable Chlorophyll accessory pigments collected from NOAA Ship OSCAR ELTON SETTE in North Pacific Ocean from 2008-03-01 to 2011-04-01 (NCEI Accession 0129395) NOAA_NCEI STAC Catalog 2008-03-01 2011-04-01 -158, 26, -158, 36 https://cmr.earthdata.nasa.gov/search/concepts/C2089377189-NOAA_NCEI.umm_json These data represent the chlorophyll accessory pigments measured from discrete depth water samples collected in CTD-mounted 10 liter Niskin bottles as part of NOAA surveys in the central North Pacific Ocean north of Hawaii. Accessory pigments were measured post-survey at the University of Hawaii using HPLC methods. proprietary gov.noaa.nodc:0130065_Not Applicable Chlorophyll A, hydrostatic pressure, and water density measurements collected from New Horizon in Gulf of California and North Pacific Ocean from 2004-07-14 to 2008-08-06 (NCEI Accession 0130065) NOAA_NCEI STAC Catalog 2004-07-14 2008-08-06 -120.5, 20.48, -106.48, 32.52 https://cmr.earthdata.nasa.gov/search/concepts/C2089377812-NOAA_NCEI.umm_json Extracted chlorophyll A, normalized to filtered volume, from suspended particulate material collected via Niskin bottle from the Gulf of California in the summers of 2004, 2005, and 2008, as well as from the Eastern Tropical North Pacific in 2008. proprietary @@ -18643,28 +18623,28 @@ gov.noaa.nodc:0133936_Not Applicable Beluga whales aerial survey conducted by Al gov.noaa.nodc:0133937_Not Applicable Bowhead whale aerial abundance survey conducted by Alaska Fisheries Science Center, National Marine Mammal Laboratory from 2011-04-19 to 2011-06-11 (NCEI Accession 0133937) NOAA_NCEI STAC Catalog 2011-04-19 2011-06-11 -164.42379, 68.987009, -148.41013, 71.974838 https://cmr.earthdata.nasa.gov/search/concepts/C2089379086-NOAA_NCEI.umm_json Aerial photographic surveys for bowhead whales were conducted near Point Barrow, Alaska, from 19 April to 6 June in 2011. Approximately 4,594 photographs containing 6,801 bowhead whale images were obtained (not accounting for resightings). The 2011 field season was very successful: we flew 36 out of 49 available days and conducted 49 flights in that time; we were grounded due to weather on 13 days. The longest period of time that we were grounded due to weather (low ceilings/fog) was three days. This occurred after the migration had slowed down, during a time when few whales passed the ice perches according to the ice-based visual survey. The 2011 migration was steady with several peaks (30 April, 4-5 May, 12 May), and then the migration rate slowed down considerably after 14 May. The photographs taken in 2011 are a significant contribution to the bowhead whale photographic catalogue. They will be used to calculate a population estimate that may be used for comparison with the 2011 ice-based estimate and will provide better precision in estimates of bowhead whale life-history parameters. proprietary gov.noaa.nodc:0137093_Not Applicable Calcification Rates of Crustose Coralline Algae derived from Calcification Accretion Units (CAUs) deployed across American Samoa and the Pacific Remote Island Areas in 2010 and recovered in 2012 (NCEI Accession 0137093) NOAA_NCEI STAC Catalog 2010-01-25 2012-05-17 -176.624, -14.5596, -160.014, 16.7477 https://cmr.earthdata.nasa.gov/search/concepts/C2089379273-NOAA_NCEI.umm_json Laboratory experiments reveal calcification rates of crustose coralline algae are strongly correlated to seawater aragonite saturation state. Predictions of reduced coral calcification rates, due to ocean acidification, suggest that coral reef communities will undergo ecological phase shifts as calcifying organisms are negatively impacted by changing seawater chemistry. The data described here result from the use of calcification accretion units, or CAUs, to assess the current effects of changes in seawater carbonate chemistry on calcification and accretion rates of calcareous and fleshy algae. This effort is a partnership between CREP and Drs. Nicole Price of Bigelow Marine Laboratory and Jen Smith of Scripps Institution of Oceanography, who have extensive knowledge of marine benthic algal community ecology. CAUs are composed of two 10 x 10 centimeter (cm) flat, square, gray PVC plates, stacked 1 cm apart, and are attached to the benthos using stainless steel threaded rods. Calcareous organisms, primarily crustose coralline algae and encrusting corals, recruit to these plates and accrete/calcify carbonate skeletons over 2-3 year deployments. Due to the simple, low-cost design and analysis, statistically robust numbers of calcification plates can easily be deployed, recovered, and processed to provide estimates of net calcification, percent cover, and vertical accretion rates. CAUs have been deployed and replaced at existing, long-term monitoring sites during Pacific RAMP cruises, in accordance with protocols developed by Price et al. 2012. There are typically five CAU sites established at each location CREP visits with five units deployed at each site. The study provides information about Pacific-wide spatial patterns of algal calcification and accretion rates and serves as a basis for detecting changes associated with changing seawater chemistry due to ocean acidification. In conjuction with benthic community composition data (separate dataset), the calcification rates will aid in determining the magnitude of how ocean acidification affects coral reefs in the natural environment. The data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive, accession 0137093. The reef study sites are throughout the Pacific Ocean, in areas with little or no direct local anthropogenic impacts and areas of anthropogenic impact. Pacific RAMP is an ideal platform from which to collect samples over a broad range of benthic ecosystems, oceanic regimes and gradients, to observe ecological impacts of ocean acidification on natural reef systems, outside of the laboratory. Analysis of these data will expand scientists’ capacity for assessing coral reef resilience regarding the effects of ocean acidification outside of controlled laboratory experiments. These data can also be used in comparative analyses across natural gradients, thereby assisting efforts to determine whether key reef-building taxa can acclimatize to changing oceanographic environments. These data will have immediate, direct impacts on predictions of reef resilience in a higher CO2 world and on the design of reef management strategies. proprietary gov.noaa.nodc:0138649_Not Applicable Bottom water temperature, salinity, pH, benthic cover, dissolved inorganic carbon and other data collected from NOAA Ship HI'IALAKAI and other in Northern Marianna Islands from 2014-05-17 to 2014-08-13 (NCEI Accession 0138649) NOAA_NCEI STAC Catalog 2014-05-17 2014-08-13 145.2074, 19.9964, 145.2316, 20.03215 https://cmr.earthdata.nasa.gov/search/concepts/C2089376259-NOAA_NCEI.umm_json These data correspond to that published in the analysis of the following manuscript: I.C. Enochs, Manzello, D.P., Donham, E.M., Kolodziej, G., Okano, R., et al. (in press) Shift from coral to macroalgae dominance on a volcanically acidified reef. Nature Climate Change. https://doi.org/10.1038/nclimate2758 proprietary -gov.noaa.nodc:0138863_Not Applicable Acoustics short-term passive monitoring using sonobuoys in the Bering, Chukchi, and Western Beaufort Seas conducted by Alaska Fisheries Scientific Center, National Marine Mammal Laboratory from 2007-08-01 to 2015-09-28 (NCEI Accession 0138863) ALL STAC Catalog 2007-08-01 2015-09-28 -177.5925, 53.52167, -141.62497, 72.86938 https://cmr.earthdata.nasa.gov/search/concepts/C2089376269-NOAA_NCEI.umm_json The National Marine Mammal Laboratory (NMML) has conducted passive acoustic monitoring in the Bering, Chukchi, and Western Beaufort Seas to determine spatio-temporal distribution of marine mammals as well as environmental and anthropogenic noise. Species and sounds detected on sonobuoys include fin, blue, bowhead, humpback, killer, gray, minke, sperm, beluga, sei, and North Pacific right whales, walrus, ribbon and bearded seals, and seismic airguns. This short-term passive acoustic monitoring was also used to locate vocalizing species of interest for photo-identification, tagging, and behavioral studies. Recordings are available since 2007 in the Bering Sea, since 2010 in the Chukchi and Beaufort Seas, and in 2013 in the Gulf of Alaska. Both omnidirectional and DiFAR sonobuoys have been used. The vast majority of the sonobuoys were deployed opportunistically along the tracks of research cruises funded by the Bureau of Ocean Energy Management (BOEM). In one year (2009), sonobuoys were deployed opportunistically from an aerial survey plane. All sonobuoys were provided by the United States Navy (Naval Operational Logistics Support Center, Naval Surface Warfare Center, Crance Division, and the Office of the Assistant Secretary of the Navy). proprietary gov.noaa.nodc:0138863_Not Applicable Acoustics short-term passive monitoring using sonobuoys in the Bering, Chukchi, and Western Beaufort Seas conducted by Alaska Fisheries Scientific Center, National Marine Mammal Laboratory from 2007-08-01 to 2015-09-28 (NCEI Accession 0138863) NOAA_NCEI STAC Catalog 2007-08-01 2015-09-28 -177.5925, 53.52167, -141.62497, 72.86938 https://cmr.earthdata.nasa.gov/search/concepts/C2089376269-NOAA_NCEI.umm_json The National Marine Mammal Laboratory (NMML) has conducted passive acoustic monitoring in the Bering, Chukchi, and Western Beaufort Seas to determine spatio-temporal distribution of marine mammals as well as environmental and anthropogenic noise. Species and sounds detected on sonobuoys include fin, blue, bowhead, humpback, killer, gray, minke, sperm, beluga, sei, and North Pacific right whales, walrus, ribbon and bearded seals, and seismic airguns. This short-term passive acoustic monitoring was also used to locate vocalizing species of interest for photo-identification, tagging, and behavioral studies. Recordings are available since 2007 in the Bering Sea, since 2010 in the Chukchi and Beaufort Seas, and in 2013 in the Gulf of Alaska. Both omnidirectional and DiFAR sonobuoys have been used. The vast majority of the sonobuoys were deployed opportunistically along the tracks of research cruises funded by the Bureau of Ocean Energy Management (BOEM). In one year (2009), sonobuoys were deployed opportunistically from an aerial survey plane. All sonobuoys were provided by the United States Navy (Naval Operational Logistics Support Center, Naval Surface Warfare Center, Crance Division, and the Office of the Assistant Secretary of the Navy). proprietary +gov.noaa.nodc:0138863_Not Applicable Acoustics short-term passive monitoring using sonobuoys in the Bering, Chukchi, and Western Beaufort Seas conducted by Alaska Fisheries Scientific Center, National Marine Mammal Laboratory from 2007-08-01 to 2015-09-28 (NCEI Accession 0138863) ALL STAC Catalog 2007-08-01 2015-09-28 -177.5925, 53.52167, -141.62497, 72.86938 https://cmr.earthdata.nasa.gov/search/concepts/C2089376269-NOAA_NCEI.umm_json The National Marine Mammal Laboratory (NMML) has conducted passive acoustic monitoring in the Bering, Chukchi, and Western Beaufort Seas to determine spatio-temporal distribution of marine mammals as well as environmental and anthropogenic noise. Species and sounds detected on sonobuoys include fin, blue, bowhead, humpback, killer, gray, minke, sperm, beluga, sei, and North Pacific right whales, walrus, ribbon and bearded seals, and seismic airguns. This short-term passive acoustic monitoring was also used to locate vocalizing species of interest for photo-identification, tagging, and behavioral studies. Recordings are available since 2007 in the Bering Sea, since 2010 in the Chukchi and Beaufort Seas, and in 2013 in the Gulf of Alaska. Both omnidirectional and DiFAR sonobuoys have been used. The vast majority of the sonobuoys were deployed opportunistically along the tracks of research cruises funded by the Bureau of Ocean Energy Management (BOEM). In one year (2009), sonobuoys were deployed opportunistically from an aerial survey plane. All sonobuoys were provided by the United States Navy (Naval Operational Logistics Support Center, Naval Surface Warfare Center, Crance Division, and the Office of the Assistant Secretary of the Navy). proprietary gov.noaa.nodc:0138984_Not Applicable Characterizing pinniped use of offshore oil and gas platforms as haulouts and foraging areas in waters off southern California from 2013-01-01 to 2015-01-31 (NCEI Accession 0138984) NOAA_NCEI STAC Catalog 2013-01-01 2015-01-31 -121, 33, -118, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2089376321-NOAA_NCEI.umm_json California sea lions (Zalophus californianus) and Pacific harbor seals (Phoca vitulina) use offshore oil and gas platforms as resting and foraging areas. Both species are protected by the Marine Mammal Protection Act (1972). The Bureau of Ocean Energy Management (BOEM) is required to collect information from platforms being used by California sea lions and harbor seals (or other pinniped species) with the goal of meeting environmental review and permitting requirements associated with the eventual decommissioning of offshore platforms. Decommissioning requirements are under the jurisdiction of BOEMs sister agency, the Bureau of Safety and Environmental Enforcement (BSEE). However, BOEM provides environmental studies and environmental review support for BSEE actions. To accomplish this goal, BOEM entered an inter-agency agreement with the National Marine Mammal Laboratories' California Current Ecosystem Program (CCEP; AFSC/NOAA) in 2012. Specifically, BOEM funded CCEP to conduct a study (from January 2012 to January 2015) to characterize and quantify California sea lion and Pacific harbor seal use of the platforms, including; abundance, seasonal use patterns, and age and sex class composition of animals on the platforms. Inter- (i.e. spatial) and intra- (i.e. temporal) platform comparisons were examined. proprietary gov.noaa.nodc:0140481_Not Applicable Bristol Bay Beluga hearing sensitivity data collected from 2012-09-02 to 2014-09-03 (NCEI Accession 0140481) NOAA_NCEI STAC Catalog 2012-09-02 2014-09-03 -159, 58.5, -158.2, 59.7 https://cmr.earthdata.nasa.gov/search/concepts/C2089376409-NOAA_NCEI.umm_json Hearing sensitivity data was collected on beluga whales in Bristol Bay with auditory evoked potential (AEP) methods for the frequencies 4, 8, 11.2, 16, 22.5, 32, 45, 54, 80, 100, 128, 150 kHz in 7 belugas in 2012 and 9 in 2014. proprietary -gov.noaa.nodc:0143303_Not Applicable Acoustics long-term passive monitoring using moored autonomous recorders in the Bering, Chukchi, and Western Beaufort Seas conducted by Alaska Fisheries Scientific Center, National Marine Mammal Laboratory from 2007-08-15 to 2015-04-30 (NCEI Accession 0143303) ALL STAC Catalog 2007-08-15 2015-04-30 171.7, 53.63, -0.78, 78.837 https://cmr.earthdata.nasa.gov/search/concepts/C2089376734-NOAA_NCEI.umm_json The National Marine Mammal Laboratory (NMML) has deployed long-term passive acoustic recorders in various locations in Alaskan waters and in the High Arctic to determine spatio-temporal distribution of marine mammals as well as environmental and anthropogenic noise. Following the timing of peak calling among the various long-term recorders may provide some insight into finer-scale movements of cetaceans throughout the Bering, Chukchi, and Beaufort Seas. Changes in ambient noise levels can also be tracked. Recordings are available since 2007 in the Bering and Beaufort Seas, since 2010 in the Chukchi, and from 2008-2012 in Fram Strait. The majority of these recorders were deployed on NMML subsurface moorings, although several have been deployed on the oceanographic moorings of other researchers. Several different types of autonomous passive acoustic recorders have been deployed, most for one year. Recording parameters varied among instrument types and have evolved among projects. The majority of these recorders and deployments were funded by the Bureau of Ocean Energy Management (BOEM); however, several were funded by a grant from the Ocean Acoustics Program (NOAA/S and T). proprietary gov.noaa.nodc:0143303_Not Applicable Acoustics long-term passive monitoring using moored autonomous recorders in the Bering, Chukchi, and Western Beaufort Seas conducted by Alaska Fisheries Scientific Center, National Marine Mammal Laboratory from 2007-08-15 to 2015-04-30 (NCEI Accession 0143303) NOAA_NCEI STAC Catalog 2007-08-15 2015-04-30 171.7, 53.63, -0.78, 78.837 https://cmr.earthdata.nasa.gov/search/concepts/C2089376734-NOAA_NCEI.umm_json The National Marine Mammal Laboratory (NMML) has deployed long-term passive acoustic recorders in various locations in Alaskan waters and in the High Arctic to determine spatio-temporal distribution of marine mammals as well as environmental and anthropogenic noise. Following the timing of peak calling among the various long-term recorders may provide some insight into finer-scale movements of cetaceans throughout the Bering, Chukchi, and Beaufort Seas. Changes in ambient noise levels can also be tracked. Recordings are available since 2007 in the Bering and Beaufort Seas, since 2010 in the Chukchi, and from 2008-2012 in Fram Strait. The majority of these recorders were deployed on NMML subsurface moorings, although several have been deployed on the oceanographic moorings of other researchers. Several different types of autonomous passive acoustic recorders have been deployed, most for one year. Recording parameters varied among instrument types and have evolved among projects. The majority of these recorders and deployments were funded by the Bureau of Ocean Energy Management (BOEM); however, several were funded by a grant from the Ocean Acoustics Program (NOAA/S and T). proprietary +gov.noaa.nodc:0143303_Not Applicable Acoustics long-term passive monitoring using moored autonomous recorders in the Bering, Chukchi, and Western Beaufort Seas conducted by Alaska Fisheries Scientific Center, National Marine Mammal Laboratory from 2007-08-15 to 2015-04-30 (NCEI Accession 0143303) ALL STAC Catalog 2007-08-15 2015-04-30 171.7, 53.63, -0.78, 78.837 https://cmr.earthdata.nasa.gov/search/concepts/C2089376734-NOAA_NCEI.umm_json The National Marine Mammal Laboratory (NMML) has deployed long-term passive acoustic recorders in various locations in Alaskan waters and in the High Arctic to determine spatio-temporal distribution of marine mammals as well as environmental and anthropogenic noise. Following the timing of peak calling among the various long-term recorders may provide some insight into finer-scale movements of cetaceans throughout the Bering, Chukchi, and Beaufort Seas. Changes in ambient noise levels can also be tracked. Recordings are available since 2007 in the Bering and Beaufort Seas, since 2010 in the Chukchi, and from 2008-2012 in Fram Strait. The majority of these recorders were deployed on NMML subsurface moorings, although several have been deployed on the oceanographic moorings of other researchers. Several different types of autonomous passive acoustic recorders have been deployed, most for one year. Recording parameters varied among instrument types and have evolved among projects. The majority of these recorders and deployments were funded by the Bureau of Ocean Energy Management (BOEM); however, several were funded by a grant from the Ocean Acoustics Program (NOAA/S and T). proprietary gov.noaa.nodc:0143928_Not Applicable Benthic Habitats of the Florida Keys derived from color aerial photography collected between 1991-12 and March 1992 (NCEI Accession 0143928) NOAA_NCEI STAC Catalog 1991-12-01 1998-01-01 -83, 24.25, -80.2, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2089376853-NOAA_NCEI.umm_json This project was a cooperative effort between the National Ocean Service and the Florida Department of Environmental Protection's Florida Marine Research Institute (now called the Fish and Wildlife Research Institute). The goal of the effort was to produce shallow-water (from 0 to approximately 30 m water depth) benthic habitat maps of the Florida Keys and adjacent waters. The maps were generated by expert visual interpretation of 1:48,000 scale color aerial photography and subsequent photogrammetric, stereo, digital compilation of interpreted habitat polygon boundaries from aerial photography. The Minimum mapping unit = 0.4 hectare (4,047 sq m; 1 acre) for all habitat. Patch reefs may be <0.5 ha. The aerial photography was acquired using a NOAA jet from December 1991 through March 1992. The photography was acquired with 60% side and 80% forward overlap to facilitate stereo compilation. Approximately 450 aerial photographs were acquired and used for the mapping project. Ground validation of interpreted habitat polygons was performed by visual verification at actual field sites prior to compilation. Aircraft Inertial Measurement Unit data were used to correct photography positioning in photogrammetric analytical plotters. The analytical solution used in the photogrammetric plotter for positioning was applied to bundles of 30-40 adjacent, overlapping aerial photographs. The stereo positioning of the photography was < 1 m. Digital data for bundles of compiled aerial photographs from the photogrammetric stereo plotter was imported into the ESRI ArcInfo GIS. The GIS was used to merge and edit the vector and attribute features of the 15 bundles to generate a mosaic benthic habitat map of the Florida Keys and adjacent areas covered by the aerial photography. Field validation of digitized habitat features visible in the aerial photography mosaics was performed to ensure correct interpretation. An assessment of the correctness of the interpreted digital map was performed by experts familiar with the the seafloor habitat found in the Florida Keys. proprietary gov.noaa.nodc:0145165_Not Applicable California sea lion and northern fur seal censuses conducted at Channel Islands, California by Alaska Fisheries Science Center from 1969-07-31 to 2015-08-08 (NCEI Accession 0145165) NOAA_NCEI STAC Catalog 1969-07-31 2015-08-08 -120.5, 33, -119, 34.11 https://cmr.earthdata.nasa.gov/search/concepts/C2089377845-NOAA_NCEI.umm_json The National Marine Mammal Laboratories' California Current Ecosystem Program (AFSC/NOAA) initiated and maintains census programs for California sea lions (Zalophus californianus) and northern fur seals (Callorhinus ursinus) at San Miguel and San Nicolas Islands, California. The program documents annual pup births, pup mortality, and temporal patterns in adult and juvenile presence at San Miguel Island. For both species, the database contains field data on the annual number of live pups and dead pups by location. At San Miguel Island, daily counts of adults, pups, and juveniles in a sample area are also available. The data are used to describe population trends and changes in land resource use among the species. proprietary gov.noaa.nodc:0146259_Not Applicable Capture and resight data of California sea lions in Washington State, 1989-02-15 to 2006-06-01 (NCEI Accession 0146259) NOAA_NCEI STAC Catalog 1989-02-15 2006-06-01 -132, 32, -122, 54.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089378578-NOAA_NCEI.umm_json This dataset contains data from the capture and recapture of over 1500 male California sea lions (Zalophus californianus) from Washington between 1989-2006. The data fields include capture data such as time, location, weight, length, and girth for each animal captured. The dataset also includes records of resights of each animal from records collected from observers from California to Vancouver Island, British Columbia, Canada. The dataset also contains information from opportunistic captures of Steller sea lions (Eumetopias jubatus) in the same region. proprietary gov.noaa.nodc:0146680_Not Applicable Benthic Surveys in Vatia, American Samoa: benthic images collected during belt transect surveys from 2015-11-2 to 2015-11-12 (NCEI Accession 0146680) NOAA_NCEI STAC Catalog 2015-11-02 2015-11-12 -170.674, -14.2501, -170.667, -14.2432 https://cmr.earthdata.nasa.gov/search/concepts/C2089378606-NOAA_NCEI.umm_json Jurisdictional managers have expressed concerns that nutrients from the village of Vatia, Tutuila, American Samoa, are having an adverse effect on the coral reef ecosystem in Vatia Bay. Excess nutrient loads promote increases in algal growth that can have deleterious effects on corals, such as benthic algae outcompeting and overgrowing corals. Nitrogen and phosphorus can also directly impact corals by lowering fertilization success, and reducing both photosynthesis and calcification rates. Land-based contributions of nutrients come from a variety of sources; in Vatia the most likely sources are poor wastewater management from piggeries and septic systems. NOAA scientists conducted benthic surveys to establish a baseline against which to compare changes in the algal and coral assemblages in response to nutrient fluxes. The data described here were collected via belt transect surveys of coral demography (adult and juvenile corals) by the NOAA Coral Reef Ecosystem Program (CREP) according to protocols established by the NOAA National Coral Reef Monitoring Program (NCRMP). In 2015 data were collected at 18 stratified randomly selected sites in Vatia Bay. These data include photoquadrat benthic images. proprietary gov.noaa.nodc:0146682_Not Applicable Benthic Surveys in Faga'alu, American Samoa: benthic images collected during belt transect surveys in 2012 and 2015 (NCEI Accession 0146682) NOAA_NCEI STAC Catalog 2012-03-28 2015-11-11 -170.681, -14.2952, -170.673, -14.287 https://cmr.earthdata.nasa.gov/search/concepts/C2089378626-NOAA_NCEI.umm_json The data described herein are part of a NOAA Coral Reef Conservation Program (CRCP) funded project aimed at establishing baseline data for coral demographics and benthic cover and composition via Rapid Ecological Assessment (REA) surveys conducted by the NOAA Coral Reef Ecosystem Program (CREP) at Faga'alu Bay, Tutuila, American Samoa between 2012 and 2015. Photoquadrat benthic images were collected in 2012 and 2015 only, via belt transect surveys of coral demography according to protocols established by CREP in 2012 and by the NOAA National Coral Reef Monitoring Program (NCRMP) in 2015. proprietary gov.noaa.nodc:0147683_Not Applicable Bottom longline analytical data collected in Gulf of Mexico from 1995-01-01 to 2013-12-30 (NCEI Accession 0147683) NOAA_NCEI STAC Catalog 1995-01-01 2013-12-30 -97.3473, 24.3627, -81.5875, 30.3677 https://cmr.earthdata.nasa.gov/search/concepts/C2089378649-NOAA_NCEI.umm_json NOAA NMFS does not approve, recommend, or endorse any proprietary product or proprietary material mentioned in this publication. No reference shall be made to NMFS, or to this publication furnished by NMFS, in any advertising or sales promotion which would indicate or imply that NMFS approves, recommends, or endorses any proprietary product or proprietary material mentioned herein or which has as its purpose any intent to cause directly or indirectly the advertised product to be used or purchased because of this NMFS publication. NMFS is not responsible for any uses of these datasets beyond those for which they were intended, and NMFS makes no claims regarding the accuracy of any data provided by agencies or individuals outside NMFS. Acknowledgment of NOAA NMFS and SEAMAP would be appreciated in products derived or publications generated from this data. proprietary -gov.noaa.nodc:0148759_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759) ALL STAC Catalog 2009-08-11 2016-02-20 -38.146, 66.329, -38.146, 66.329 https://cmr.earthdata.nasa.gov/search/concepts/C2089378741-NOAA_NCEI.umm_json The Helheim Glacier was observed to retreat and speed up during the mid 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary gov.noaa.nodc:0148759_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759) NOAA_NCEI STAC Catalog 2009-08-11 2016-02-20 -38.146, 66.329, -38.146, 66.329 https://cmr.earthdata.nasa.gov/search/concepts/C2089378741-NOAA_NCEI.umm_json The Helheim Glacier was observed to retreat and speed up during the mid 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary +gov.noaa.nodc:0148759_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759) ALL STAC Catalog 2009-08-11 2016-02-20 -38.146, 66.329, -38.146, 66.329 https://cmr.earthdata.nasa.gov/search/concepts/C2089378741-NOAA_NCEI.umm_json The Helheim Glacier was observed to retreat and speed up during the mid 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary gov.noaa.nodc:0148760_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Jakobshavn Glacier Ice Front from 2007-10-13 to 2016-02-14 (NCEI Accession 0148760) NOAA_NCEI STAC Catalog 2007-10-13 2016-02-14 -49.815, 69.222, -49.815, 69.222 https://cmr.earthdata.nasa.gov/search/concepts/C2089378750-NOAA_NCEI.umm_json The Jakobshavn Glacier was observed to retreat and speed up during the late 1990s and early 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary gov.noaa.nodc:0148760_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Jakobshavn Glacier Ice Front from 2007-10-13 to 2016-02-14 (NCEI Accession 0148760) ALL STAC Catalog 2007-10-13 2016-02-14 -49.815, 69.222, -49.815, 69.222 https://cmr.earthdata.nasa.gov/search/concepts/C2089378750-NOAA_NCEI.umm_json The Jakobshavn Glacier was observed to retreat and speed up during the late 1990s and early 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary gov.noaa.nodc:0155488_Not Applicable Bottom Dissolved Oxygen Maps From SEAMAP Summer and Fall Groundfish/Shrimp Surveys from 1982 to 1998 (NCEI Accession 0155488) NOAA_NCEI STAC Catalog 1982-01-01 1998-01-01 -98, 18, -74, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2089380245-NOAA_NCEI.umm_json Bottom dissolved oxygen (DO) data was extracted from environmental profiles acquired during the Southeast Fisheries Science Center Mississippi Laboratories summer groundfish trawl surveys of the Western and North-central Gulf of Mexico from 1982-1998. The data were distributed to hypoxia researchers in near real time and used to generate bottom DO maps as part of the Hypoxia Watch Project (http://www.ncddc.noaa.gov/hypoxia/). The profiles were acquired with a Sea-Bird Model SB9 profiler equipped with pressure, temperature, conductivity, fluorescence, and beam transmission sensors. The data were processed with Sea-Bird software using the standard processing protocol developed by the Mississippi Laboratories. Water temperature, beam transmission, and derived salinity, DO and DO percent saturation, and density were retained in the processed files. SAS software was used to extract the bottom DO and other relevant data (e.g., date, time, position, and station number) and format the data as comma-delimited ASCII files. proprietary gov.noaa.nodc:0155948_Not Applicable CHLOROPHYLL A CONCENTRATION collected from NOAA Ship OSCAR ELTON SETTE in Hawaii EEZ and Palmyra EEZ from 2011-10-20 to 2011-11-17 (NCEI Accession 0155948) NOAA_NCEI STAC Catalog 2011-10-20 2011-11-17 -165.19666, 4.1355, -156.3175, 21.221 https://cmr.earthdata.nasa.gov/search/concepts/C2089376252-NOAA_NCEI.umm_json Water samples were collected from the ocean surface using a bucket and from below the surface using bottles attached to the CTD during a Pacific Islands Fisheries Science Center's Cetacean Research Program's shipboard cetacean survey (Cruise ID: SE 11-08). A minimum of three surface water samples were taken each day, primarily at 0900, 1200, and 1500 hours local ship time. Surface water samples were also collected opportunistically during some cetacean sightings. CTD samples were collected once each morning. The 250ml water samples were filtered onto GF/F filters, placed in 10ml of 90% acetone, refrigerated or frozen for 24 hours, and then analyzed for chlorophyll a concentration using the Turner Designs model 10AU field flourometer. Measurements were recorded in an Excel spreadsheet. proprietary gov.noaa.nodc:0155964_Not Applicable CHLOROPHYLL A CONCENTRATION collected from NOAA Ship OSCAR ELTON SETTE in Hawaii EEZ and Papahanaumokuakea Marine National Monument from 2013-05-08 to 2013-06-03 (NCEI Accession 0155964) NOAA_NCEI STAC Catalog 2013-05-08 2013-06-03 -177, -14.2446, -157.92, 28.79 https://cmr.earthdata.nasa.gov/search/concepts/C2089376312-NOAA_NCEI.umm_json Water samples were collected from the ocean surface using a bucket and from below the surface using bottles attached to the CTD during a Pacific Islands Fisheries Science Center's Cetacean Research Program's shipboard cetacean survey (Cruise ID SE 13-03). A minimum of three surface water samples were taken each day, primarily at 0900, 1200, and 1500 hours local ship time. Surface water samples were also collected opportunistically during some cetacean sightings. CTD samples were collected once each morning. The 250ml water samples were filtered onto GF/F filters, placed in 10ml of 90% acetone, refrigerated or frozen for 24 hours, and then analyzed for chlorophyll a concentration using the Turner Designs model 10AU field flourometer. Measurements were recorded in an Excel spreadsheet. proprietary gov.noaa.nodc:0155998_Not Applicable CHLOROPHYLL A CONCENTRATION collected from NOAA Ship OSCAR ELTON SETTE in Hawaii EEZ, Palmyra EEZ, and American Samoa EEZ from 2012-04-23 to 2012-05-15 (NCEI Accession 0155998) NOAA_NCEI STAC Catalog 2012-04-23 2012-05-15 -169.9633, -14.2446, -157.2218, 19.2698 https://cmr.earthdata.nasa.gov/search/concepts/C2089376410-NOAA_NCEI.umm_json Surface water samples were collected during a Pacific Islands Fisheries Science Center's Cetacean Research Program's shipboard cetacean survey (Cruise ID SE 12-03). A minimum of three surface water samples were taken each day, primarily at 0900, 1200, and 1500 hours local ship time. Samples were also collected opportunistically during some cetacean sightings. The 250ml water samples were filtered onto GF/F filters, placed in 10ml of 90% acetone, refrigerated or frozen for 24 hours, and then analyzed for chlorophyll a concentration using the Turner Designs model 10AU field flourometer. Measurements were recorded in an Excel spreadsheet. proprietary -gov.noaa.nodc:0156424_Not Applicable Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424) ALL STAC Catalog 1950-01-01 1996-12-31 -180, 58, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376812-NOAA_NCEI.umm_json The dataset (called EWG-V) comprises 3D gridded climatological fields of absolute geostrophic velocity inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean using the P-vector method. It provides a climatological velocity field that is dynamically compatible to the EWG (T, S) fields. The EWG-V velocity fields have the annual, and seasonal (winter and summer) means with the same horizontal resolution of 25 km and 90 vertical levels as the EWG temperature and salinity fields. proprietary gov.noaa.nodc:0156424_Not Applicable Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424) NOAA_NCEI STAC Catalog 1950-01-01 1996-12-31 -180, 58, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376812-NOAA_NCEI.umm_json The dataset (called EWG-V) comprises 3D gridded climatological fields of absolute geostrophic velocity inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean using the P-vector method. It provides a climatological velocity field that is dynamically compatible to the EWG (T, S) fields. The EWG-V velocity fields have the annual, and seasonal (winter and summer) means with the same horizontal resolution of 25 km and 90 vertical levels as the EWG temperature and salinity fields. proprietary +gov.noaa.nodc:0156424_Not Applicable Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424) ALL STAC Catalog 1950-01-01 1996-12-31 -180, 58, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376812-NOAA_NCEI.umm_json The dataset (called EWG-V) comprises 3D gridded climatological fields of absolute geostrophic velocity inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean using the P-vector method. It provides a climatological velocity field that is dynamically compatible to the EWG (T, S) fields. The EWG-V velocity fields have the annual, and seasonal (winter and summer) means with the same horizontal resolution of 25 km and 90 vertical levels as the EWG temperature and salinity fields. proprietary gov.noaa.nodc:0156425_Not Applicable Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425) ALL STAC Catalog 1900-01-01 1998-12-31 -180, 45, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376820-NOAA_NCEI.umm_json The dataset (called PHC-V) comprises 3D gridded climatological fields of absolute geostrophic velocity of the Arctic Ocean inverted from the Polar science center Hydrographic Climatology (PHC) temperature and salinity fields (version 3.0) using the P-vector method. It provides climatological annual, seasonal, and monthly mean velocity fields with the same horizontal resolution (1 deg in horizontal, 33 levels in vertical), and dynamical compatibility to the PHC3.0 (T, S) fields. proprietary gov.noaa.nodc:0156425_Not Applicable Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425) NOAA_NCEI STAC Catalog 1900-01-01 1998-12-31 -180, 45, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376820-NOAA_NCEI.umm_json The dataset (called PHC-V) comprises 3D gridded climatological fields of absolute geostrophic velocity of the Arctic Ocean inverted from the Polar science center Hydrographic Climatology (PHC) temperature and salinity fields (version 3.0) using the P-vector method. It provides climatological annual, seasonal, and monthly mean velocity fields with the same horizontal resolution (1 deg in horizontal, 33 levels in vertical), and dynamical compatibility to the PHC3.0 (T, S) fields. proprietary gov.noaa.nodc:0156692_Not Applicable Bioerosion Accretion Replicate (BAR) data covering in situ calcification and bioerosion rates along pH gradients at two volcanically acidified reefs in Papua New Guinea from 2013-01-18 to 2014-11-10 (NCEI Accession 0156692) NOAA_NCEI STAC Catalog 2013-01-18 2014-11-10 150.775, -9.875, 150.925, -9.725 https://cmr.earthdata.nasa.gov/search/concepts/C2089377345-NOAA_NCEI.umm_json "Bioerosion Accretion Replicate (BAR) data covering in situ calcification and bioerosion rates along pH gradients at two volcanically acidified reefs in Papua New Guinea. Methodologies, results, and analysis may be found in ""Enhanced macroboring and depressed calcification drive net dissolution at high-CO2 coral reef"" which is published in the Proceedings of the Royal Society, Series B" proprietary @@ -18674,17 +18654,17 @@ gov.noaa.nodc:0156869_Not Applicable Captive sea turtle rearing inventory, feedi gov.noaa.nodc:0156913_Not Applicable Carbonate Budget data of the Southeast Florida Coral Reef Initiative (SEFCRI) region from 2014-09-29 to 2014-10-17 (NCEI Accession 0156913) NOAA_NCEI STAC Catalog 2014-09-29 2014-10-17 -80.104, 25.6519, -80.077, 26.1636 https://cmr.earthdata.nasa.gov/search/concepts/C2089377484-NOAA_NCEI.umm_json This data set includes census based carbonate budget data that was collected in coral reef habitats located within the SEFCRI region. Surveys (based on Perry et al 2012) were collected over the course of several weeks at four major sites: Emerald, Oakland Ridge, Barracuda, and Pillars. Within each of these sites, six transect surveys (10m each) were conducted to quantify benthic cover, macrobioerosion, and microbioerosion. Ten parrotfish surveys were also conducted to account for parrotfish erosion rates at each site. This carbonate budget data along with other sea water chemistry data collected were used to inform the overall project looking at the sensitivity of the SEFCRI region to OA. We measured ambient seasonal variability across inshore/offshore reef habitats to predict the response of the CaCO3 budget of coral reefs in the SEFCRI region to ocean acidification. This data set includes all of the carbonate budget surveys that were collected to identify the sensitivity of the SEFCRI region to OA. proprietary gov.noaa.nodc:0157022_Not Applicable Carbonate data collected from R/V Hildebrand in the SEFCRI region of the Florida Reef Tract from 2014-05-27 to 2015-09-02 (NCEI Accession 0157022) NOAA_NCEI STAC Catalog 2014-05-27 2015-09-02 -80.1328, 25.5906, -80.077, 26.1636 https://cmr.earthdata.nasa.gov/search/concepts/C2089377840-NOAA_NCEI.umm_json This data set includes seawater chemistry that was collected in coral reef habitats located within the SEFCRI region as well as inlets and outfalls that release nutrient rich and/or sediment laden freshwater to the coastal waters South Florida. Freshwater runoff and riverine inputs are known to be enriched in dissolved inorganic carbon, and diluted lower saline waters are known to have elevated pCO2 (e.g., Manzello et al. 2013) which is why those areas in addition to the reef sites were included in our analyses. This data along with other data collected in the field were used to inform the overall project looking at the sensitivity of the SEFCRI region to OA. We measured ambient seasonal variability across inshore/offshore reef habitats to predict the response of the CaCO3 budget of coral reefs in the SEFCRI region to ocean acidification. This data set includes all of the seawater samples that were collected and analyzed to identify the carbonate chemistry in this region. proprietary gov.noaa.nodc:0157033_Not Applicable Atlantic Ocean Red Snapper Multi-gear CRP Project 2012 (NCEI Accession 0157033) NOAA_NCEI STAC Catalog 2012-07-25 2012-12-04 -81, 31, -76.5, 34 https://cmr.earthdata.nasa.gov/search/concepts/C2089377889-NOAA_NCEI.umm_json This data set contains information useful for red snapper stock assessment. The data set provided has count, weight, length, and location available of caught red snapper, red grouper, and other reef fishes. Catches were greatest in waters off Georgia, and declined with increasing latitude off South Carolina and North Carolina. proprietary -gov.noaa.nodc:0157074_Not Applicable ACOUSTIC TRAVEL TIME collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) from March 1995 to March 1997 (NCEI Accession 0157074) NOAA_NCEI STAC Catalog 1995-03-20 1997-03-28 143.63333, -52.08133, 143.805, -47.99867 https://cmr.earthdata.nasa.gov/search/concepts/C2089378023-NOAA_NCEI.umm_json Inverted echo sounder (IES) data were collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) during March 1995 -- March 1997 conducted south of Australia. The collection, processing and calibration of the IES data are described in the report provided. These are the highest quality versions of the data after the least amount of processing. Also provided are low-passed filtered versions that have been calibrated to a common pressure level in order that the data may be used together more conveniently. The measurements were made under the support of the National Science Foundation grants OCE9204041 and OCE9912320. proprietary gov.noaa.nodc:0157074_Not Applicable ACOUSTIC TRAVEL TIME collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) from March 1995 to March 1997 (NCEI Accession 0157074) ALL STAC Catalog 1995-03-20 1997-03-28 143.63333, -52.08133, 143.805, -47.99867 https://cmr.earthdata.nasa.gov/search/concepts/C2089378023-NOAA_NCEI.umm_json Inverted echo sounder (IES) data were collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) during March 1995 -- March 1997 conducted south of Australia. The collection, processing and calibration of the IES data are described in the report provided. These are the highest quality versions of the data after the least amount of processing. Also provided are low-passed filtered versions that have been calibrated to a common pressure level in order that the data may be used together more conveniently. The measurements were made under the support of the National Science Foundation grants OCE9204041 and OCE9912320. proprietary +gov.noaa.nodc:0157074_Not Applicable ACOUSTIC TRAVEL TIME collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) from March 1995 to March 1997 (NCEI Accession 0157074) NOAA_NCEI STAC Catalog 1995-03-20 1997-03-28 143.63333, -52.08133, 143.805, -47.99867 https://cmr.earthdata.nasa.gov/search/concepts/C2089378023-NOAA_NCEI.umm_json Inverted echo sounder (IES) data were collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) during March 1995 -- March 1997 conducted south of Australia. The collection, processing and calibration of the IES data are described in the report provided. These are the highest quality versions of the data after the least amount of processing. Also provided are low-passed filtered versions that have been calibrated to a common pressure level in order that the data may be used together more conveniently. The measurements were made under the support of the National Science Foundation grants OCE9204041 and OCE9912320. proprietary gov.noaa.nodc:0157087_Not Applicable Behavior of parrotfishes (Labridae, Scarinae) in St. Croix from 2015-07-06 to 2015-07-26 (NCEI Accession 0157087) NOAA_NCEI STAC Catalog 2015-07-06 2015-07-26 -64.813, 17.759, -64.608, 17.787 https://cmr.earthdata.nasa.gov/search/concepts/C2089378063-NOAA_NCEI.umm_json To better understand the functional roles of parrotfishes on coral reefs in the Caribbean this project documented the foraging behavior and diets of six species of parrotfishes (Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) at three locations (Long Reef, Cane Bay, and Buck Island) on the north shore of St. Croix, U. S. Virgin Islands. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous “turf” algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, (5) ledge, or (6) sand. In order to quantify the relative abundance of different substrates and food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the six substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, ledge, and sand) in 0.5 m x 0.5 m photoquadrats. Photographs were taken at 2.5 m intervals on 30 m transects, with a total of 10 haphazardly placed transects sampled at each site. Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat). proprietary gov.noaa.nodc:0157611_Not Applicable Benthic Images from Towed-Diver Surveys in the Main Hawaiian Islands to Assess the Mass Coral Bleaching Event from 2015-11-03 to 2015-11-18 (NCEI Accession 0157611) NOAA_NCEI STAC Catalog 2015-11-03 2015-11-18 -157.9472292, 19.748537, -155.829342, 21.3030689 https://cmr.earthdata.nasa.gov/search/concepts/C2089376905-NOAA_NCEI.umm_json A team from the Pacific Islands Fisheries Science Center (PIFSC), Coral Reef Ecosystem Program (CREP) deployed on a two-week research cruise in November 2015 to evaluate the impacts of the 2015 mass coral bleaching event in the Main Hawaiian Islands via towed-diver surveys. Areas surveyed included south Oahu, west Maui, Lana’i, and west Hawaii island. Over the course of 10 survey days, the team surveyed approximately 90 km of 15-m wide transects at depths ranging from 2 to 10 m. Data provided in this dataset include benthic images that were collected during the towed-diver surveys from a camera that was mounted to the towboard. A downward-facing DSLR camera with strobes collected these photographic quadrat data by capturing an image of the benthos at 15-second intervals during the surveys. Two additional datasets were collected during the surveys and are documented separately. Towed divers recorded visual estimates of percentage of live coral that was pale and bleached, as well as presence/absence data of condition by generic composition. Oceanographic data was collected continuously throughout each survey with a suite of sensors mounted to the towboard recording conductivity, temperature, depth, flourometry (chlorophyll-a), turbidity and dissolved oxygen. proprietary -gov.noaa.nodc:0159386_Not Applicable Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386) ALL STAC Catalog 2002-10-02 2002-10-04 -88.672, 22.203, -84.062, 26.433 https://cmr.earthdata.nasa.gov/search/concepts/C2089377618-NOAA_NCEI.umm_json Airborne eXpendable BathyThermographs (AXBT) data from deployments during field operations to study Hurricane Lili. The data were used in model simulations for Uhlhorn and Shay (2013). This dataset contains water temperature and depth data for this cruise. proprietary gov.noaa.nodc:0159386_Not Applicable Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386) NOAA_NCEI STAC Catalog 2002-10-02 2002-10-04 -88.672, 22.203, -84.062, 26.433 https://cmr.earthdata.nasa.gov/search/concepts/C2089377618-NOAA_NCEI.umm_json Airborne eXpendable BathyThermographs (AXBT) data from deployments during field operations to study Hurricane Lili. The data were used in model simulations for Uhlhorn and Shay (2013). This dataset contains water temperature and depth data for this cruise. proprietary -gov.noaa.nodc:0159419_Not Applicable ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419) ALL STAC Catalog 2013-10-17 2013-10-20 -94.9828, 26.16133, -88, 29.69641 https://cmr.earthdata.nasa.gov/search/concepts/C2089377667-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, October 17-20 2013, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE14-10b was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary +gov.noaa.nodc:0159386_Not Applicable Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386) ALL STAC Catalog 2002-10-02 2002-10-04 -88.672, 22.203, -84.062, 26.433 https://cmr.earthdata.nasa.gov/search/concepts/C2089377618-NOAA_NCEI.umm_json Airborne eXpendable BathyThermographs (AXBT) data from deployments during field operations to study Hurricane Lili. The data were used in model simulations for Uhlhorn and Shay (2013). This dataset contains water temperature and depth data for this cruise. proprietary gov.noaa.nodc:0159419_Not Applicable ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419) NOAA_NCEI STAC Catalog 2013-10-17 2013-10-20 -94.9828, 26.16133, -88, 29.69641 https://cmr.earthdata.nasa.gov/search/concepts/C2089377667-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, October 17-20 2013, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE14-10b was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary +gov.noaa.nodc:0159419_Not Applicable ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419) ALL STAC Catalog 2013-10-17 2013-10-20 -94.9828, 26.16133, -88, 29.69641 https://cmr.earthdata.nasa.gov/search/concepts/C2089377667-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, October 17-20 2013, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE14-10b was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary gov.noaa.nodc:0159850_Not Applicable Burrowing behavior of penaeid shrimps in the Gulf of Mexico from 1984-10-01 to 1985-12-06 (NCEI Accession 0159850) NOAA_NCEI STAC Catalog 1984-10-01 1985-12-06 -94.815127, 29.275417, -94.815127, 29.275417 https://cmr.earthdata.nasa.gov/search/concepts/C2089377792-NOAA_NCEI.umm_json This data set contains hourly visual observations of burrowing behavior in penaeid shrimp. proprietary -gov.noaa.nodc:0161311_Not Applicable A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311) ALL STAC Catalog 1979-01-01 1982-12-31 -88.431, 30.2129, -87.328, 31.0701 https://cmr.earthdata.nasa.gov/search/concepts/C2089378452-NOAA_NCEI.umm_json Digitized maps of Mobile Bay and other coastal areas of Alabama, showing habitat types and species compositions of the vegetation in three broad categories of wetland: swamps, marshes, and submersed grassbeds. All areas in the Alabama Coastal Zone of less than 10 feet elevation above sea level, up to the fork of the Tombigbee and Alabama Rivers, were included in the inventory. Habitat boundary delineations were based on aerial photographs from 1979 and 1980, with transects by boat or foot for field verification in 1980-1982. Dataset includes habitat type classifications based on species compositions, and identifications of dominant species at each location. proprietary gov.noaa.nodc:0161311_Not Applicable A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311) NOAA_NCEI STAC Catalog 1979-01-01 1982-12-31 -88.431, 30.2129, -87.328, 31.0701 https://cmr.earthdata.nasa.gov/search/concepts/C2089378452-NOAA_NCEI.umm_json Digitized maps of Mobile Bay and other coastal areas of Alabama, showing habitat types and species compositions of the vegetation in three broad categories of wetland: swamps, marshes, and submersed grassbeds. All areas in the Alabama Coastal Zone of less than 10 feet elevation above sea level, up to the fork of the Tombigbee and Alabama Rivers, were included in the inventory. Habitat boundary delineations were based on aerial photographs from 1979 and 1980, with transects by boat or foot for field verification in 1980-1982. Dataset includes habitat type classifications based on species compositions, and identifications of dominant species at each location. proprietary +gov.noaa.nodc:0161311_Not Applicable A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311) ALL STAC Catalog 1979-01-01 1982-12-31 -88.431, 30.2129, -87.328, 31.0701 https://cmr.earthdata.nasa.gov/search/concepts/C2089378452-NOAA_NCEI.umm_json Digitized maps of Mobile Bay and other coastal areas of Alabama, showing habitat types and species compositions of the vegetation in three broad categories of wetland: swamps, marshes, and submersed grassbeds. All areas in the Alabama Coastal Zone of less than 10 feet elevation above sea level, up to the fork of the Tombigbee and Alabama Rivers, were included in the inventory. Habitat boundary delineations were based on aerial photographs from 1979 and 1980, with transects by boat or foot for field verification in 1980-1982. Dataset includes habitat type classifications based on species compositions, and identifications of dominant species at each location. proprietary gov.noaa.nodc:0161523_Not Applicable Biological, chemical, physical and time series data collected from station WQB04 by University of Hawai'i at Hilo and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2010-10-23 to 2016-12-31 (NCEI Accession 0161523) NOAA_NCEI STAC Catalog 2010-10-23 2016-12-31 -155.082, 19.7341, -155.082, 19.7341 https://cmr.earthdata.nasa.gov/search/concepts/C2089378474-NOAA_NCEI.umm_json NCEI Accession 0161523 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo collected the data from their in-situ moored station named WQB04: PacIOOS Water Quality Buoy 04 (WQB-04): Hilo Bay, Big Island, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB04 is located in Hilo Bay on the east side of the Big Island. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary gov.noaa.nodc:0162518_Not Applicable ADCP, CTD, and MIDAS data collected from Ewing and Sackett Gulf Deep Banks, Gulf of Mexico on the R/V Pelican in Gulf of Mexico from 2012-11-15 to 2012-11-17 (NCEI Accession 0162518) NOAA_NCEI STAC Catalog 2012-11-15 2012-11-17 -91.20748, 27.49168, -89, 29.0029 https://cmr.earthdata.nasa.gov/search/concepts/C2089380274-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, November 15-17 2012, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE13-14 was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary gov.noaa.nodc:0162518_Not Applicable ADCP, CTD, and MIDAS data collected from Ewing and Sackett Gulf Deep Banks, Gulf of Mexico on the R/V Pelican in Gulf of Mexico from 2012-11-15 to 2012-11-17 (NCEI Accession 0162518) ALL STAC Catalog 2012-11-15 2012-11-17 -91.20748, 27.49168, -89, 29.0029 https://cmr.earthdata.nasa.gov/search/concepts/C2089380274-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, November 15-17 2012, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE13-14 was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary @@ -18692,8 +18672,8 @@ gov.noaa.nodc:0162828_Not Applicable Benthic cover derived from analysis of bent gov.noaa.nodc:0162829_Not Applicable Assessing cryptic reef diversity of colonizing marine invertebrates using Autonomous Reef Monitoring Structures (ARMS) deployed at coral reef sites in Batangas, Philippines from 2012-03-12 to 2015-05-31 (NCEI Accession 0162829) NOAA_NCEI STAC Catalog 2012-03-12 2015-05-31 120.871943, 13.658594, 120.895127, 13.728054 https://cmr.earthdata.nasa.gov/search/concepts/C2089380450-NOAA_NCEI.umm_json Autonomous Reef Monitoring Structures (ARMS) are used by the NOAA Coral Reef Ecosystem Program (CREP) to assess and monitor cryptic reef diversity across the Pacific. Developed in collaboration with the Census of Marine Life (CoML) Census of Coral Reef Ecosystems (CReefs), ARMS are designed to mimic the structural complexity of a reef and attract/collect colonizing marine invertebrates. The key innovation of the ARMS method is that biodiversity is sampled over precisely the same surface area in the exact same manner. Thus, the use of ARMS is a systematic, consistent, and comparable method for monitoring the marine cryptobiota community over time. The data described here were collected by CREP from ARMS moored at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines. Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) from March 2012 to June 2015, and three ARMS units were deployed by SCUBA divers at each survey site. The data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive. Each ARMS unit, constructed in-house by CREP, consisted of 23 cm x 23 cm gray, type 1 PVC plates stacked in alternating series of 4 open and 4 obstructed layers and attached to a base plate of 35 cm x 45 cm, which was affixed to the reef. Upon recovery, each ARMS unit was encapsulated, brought to the surface, and disassembled and processed. Disassembled plates were photographed to document recruited sessile organisms and scraped clean and preserved in 95% ethanol for DNA processing. Recruited motile organisms were sieved into 3 size fractions: 2 mm, 500 µm, and 100 µm. The 500 µm and 100 µm fractions were bulked and also preserved in 95% ethanol for DNA processing. The 2 mm fraction was sorted into morphospecies. The DNA sequencing data are not included in this archival package. proprietary gov.noaa.nodc:0162830_Not Applicable Benthic images collected at coral reef sites in Batangas, Philippines from 2012-03-13 to 2012-03-15 and from 2015-05-24 to 2015-06-03 (NCEI Accession 0162830) NOAA_NCEI STAC Catalog 2012-03-13 2015-06-03 120.872, 13.6586, 120.895, 13.7281 https://cmr.earthdata.nasa.gov/search/concepts/C2089380458-NOAA_NCEI.umm_json Photographs of the seafloor were collected during benthic photo-quadrat surveys conducted by the NOAA Coral Reef Ecosystem Program (CREP) in 2012 and 2015 along transects at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines. Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) over time. The imagery from 2015 has been quantitatively analyzed using image analysis software to derive an estimate of percent benthic cover (archived separately). proprietary gov.noaa.nodc:0162831_Not Applicable Calcification rates of crustose coralline algae (CCA) derived from Calcification Accretion Units (CAUs) deployed at coral reef sites in Batangas, Philippines in 2012 and recovered in 2015 (NCEI Accession 0162831) NOAA_NCEI STAC Catalog 2012-03-13 2015-06-03 120.872, 13.6586, 120.895, 13.7281 https://cmr.earthdata.nasa.gov/search/concepts/C2089380467-NOAA_NCEI.umm_json Laboratory experiments reveal calcification rates of crustose coralline algae (CCA) are strongly correlated to seawater aragonite saturation state. Predictions of reduced coral calcification rates, due to ocean acidification, suggest that coral reef communities will undergo ecological phase shifts as calcifying organisms are negatively impacted by changing seawater chemistry. Calcification accretion units, or CAUs, are used by the NOAA Coral Reef Ecosystem Program (CREP) to assess the current effects of changes in seawater carbonate chemistry on calcification and accretion rates of calcareous and fleshy algae. CAUs, constructed in-house by CREP, are composed of two 10 x 10 cm flat, square, gray PVC plates, stacked 1 cm apart, and are attached to the benthos by SCUBA divers using stainless steel threaded rods. Deployed on the seafloor for a period of time, calcareous organisms, primarily crustose coralline algae and encrusting corals, recruit to these plates and accrete/calcify carbonate skeletons over time. By measuring the change in weight of the CAUs, the reef carbonate accretion rate can be calculated for that time period. The calcification rate data described here were collected by CREP from CAUs moored at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines, in accordance with protocols developed by Price et al. (2012). Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) from March 2012 to June 2015, and five CAUs were deployed at each survey site. In conjunction with benthic community composition data (archived separately), these data serve as a baseline for detecting changes associated with changing seawater chemistry due to ocean acidification within coral reef environments. proprietary -gov.noaa.nodc:0163192_Not Applicable A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192) NOAA_NCEI STAC Catalog 1998-07-12 2005-07-27 -86.2279, 27.4432, -80.1996, 30.7692 https://cmr.earthdata.nasa.gov/search/concepts/C2089380703-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains basic biological information on bonnethead and scalloped hammerhead sharks with specific (by stomach and prey item) diet information for these two species. Data were collected by the NMFS Southeast Fisheries Science Center; Panama City, FL Laboratory in the Northeast Gulf of Mexico and the Atlantic Ocean off the coast of Florida from 1998 to 2005. Data are in comma separated value (CSV) format and include length, sex, and number of prey items. proprietary gov.noaa.nodc:0163192_Not Applicable A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192) ALL STAC Catalog 1998-07-12 2005-07-27 -86.2279, 27.4432, -80.1996, 30.7692 https://cmr.earthdata.nasa.gov/search/concepts/C2089380703-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains basic biological information on bonnethead and scalloped hammerhead sharks with specific (by stomach and prey item) diet information for these two species. Data were collected by the NMFS Southeast Fisheries Science Center; Panama City, FL Laboratory in the Northeast Gulf of Mexico and the Atlantic Ocean off the coast of Florida from 1998 to 2005. Data are in comma separated value (CSV) format and include length, sex, and number of prey items. proprietary +gov.noaa.nodc:0163192_Not Applicable A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192) NOAA_NCEI STAC Catalog 1998-07-12 2005-07-27 -86.2279, 27.4432, -80.1996, 30.7692 https://cmr.earthdata.nasa.gov/search/concepts/C2089380703-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains basic biological information on bonnethead and scalloped hammerhead sharks with specific (by stomach and prey item) diet information for these two species. Data were collected by the NMFS Southeast Fisheries Science Center; Panama City, FL Laboratory in the Northeast Gulf of Mexico and the Atlantic Ocean off the coast of Florida from 1998 to 2005. Data are in comma separated value (CSV) format and include length, sex, and number of prey items. proprietary gov.noaa.nodc:0163212_Not Applicable Acoustic Travel Time and Hydrostatic Pressure in Sermilik Fjord in Southeastern Greenland from 2011-08-23 to 2016-08-11 (NCEI Accession 0163212) ALL STAC Catalog 2011-08-23 2016-08-11 -37.8998, 65.5268, -37.6336, 66.2449 https://cmr.earthdata.nasa.gov/search/concepts/C2089380760-NOAA_NCEI.umm_json These data records are time series of (1) round trip surface to bottom acoustic travel time, (2) bottom pressure and (3) bottom temperature (with the latter internal to the instrument). The first goal in collecting these data was to develop and test non-traditional methods to measure the time-varying 
heat content and vertical temperature profiles in high-latitude seas, shelves, and fjords using pressure-sensor-equipped inverted echo sounders (PIESs). The second goal was to use PIESs to measure icebergs and sea ice. We developed these methods with data collected in Sermilik Fjord in southeastern Greenland from a 1-year pilot deployment with 1 PIES (deployed mid fjord from 2011 to 2012) and data collected in a full deployment with 3 PIESs (deployed on the shelf by the fjord mouth, mid-fjord and in the upper fjord from 2013-2015/2016). The data format is NetCDF with CF-1.6 conventions. proprietary gov.noaa.nodc:0163212_Not Applicable Acoustic Travel Time and Hydrostatic Pressure in Sermilik Fjord in Southeastern Greenland from 2011-08-23 to 2016-08-11 (NCEI Accession 0163212) NOAA_NCEI STAC Catalog 2011-08-23 2016-08-11 -37.8998, 65.5268, -37.6336, 66.2449 https://cmr.earthdata.nasa.gov/search/concepts/C2089380760-NOAA_NCEI.umm_json These data records are time series of (1) round trip surface to bottom acoustic travel time, (2) bottom pressure and (3) bottom temperature (with the latter internal to the instrument). The first goal in collecting these data was to develop and test non-traditional methods to measure the time-varying 
heat content and vertical temperature profiles in high-latitude seas, shelves, and fjords using pressure-sensor-equipped inverted echo sounders (PIESs). The second goal was to use PIESs to measure icebergs and sea ice. We developed these methods with data collected in Sermilik Fjord in southeastern Greenland from a 1-year pilot deployment with 1 PIES (deployed mid fjord from 2011 to 2012) and data collected in a full deployment with 3 PIESs (deployed on the shelf by the fjord mouth, mid-fjord and in the upper fjord from 2013-2015/2016). The data format is NetCDF with CF-1.6 conventions. proprietary gov.noaa.nodc:0163750_Not Applicable Biological, chemical and other data collected from station Humboldt Bay Pier by Humboldt State University and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2012-12-13 to 2018-03-07 (NCEI Accession 0163750) NOAA_NCEI STAC Catalog 2012-12-13 2018-03-07 -124.19652, 40.7775, -124.19652, 40.7775 https://cmr.earthdata.nasa.gov/search/concepts/C2089376545-NOAA_NCEI.umm_json NCEI Accession 0163750 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Humboldt Bay Pier in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary @@ -18735,10 +18715,10 @@ gov.noaa.nodc:0171331_Not Applicable Biological, chemical and other data collect gov.noaa.nodc:0171332_Not Applicable Biological, chemical and other data collected from station Indian River Lagoon - Jensen Beach (IRL-JB) by Florida Atlantic University and assembled by Southeast Coastal Ocean Observing Regional Association (SECOORA) in the Coastal Waters of Florida from 2015-10-07 to 2020-06-18 (NCEI Accession 0171332) NOAA_NCEI STAC Catalog 2015-10-07 2020-06-18 -80.20233, 27.22439, -80.20233, 27.22439 https://cmr.earthdata.nasa.gov/search/concepts/C2089377488-NOAA_NCEI.umm_json NCEI Accession 0171332 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Atlantic University collected the data from their in-situ moored station named Indian River Lagoon - Jensen Beach (IRL-JB) in the Coastal Waters of Florida. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Atlantic University and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary gov.noaa.nodc:0171345_Not Applicable Chemical, meteorological and other data collected from station Pilot's Cove, Apalachicola Bay, by Florida Department of Environmental Protection and assembled by Southeast Coastal Ocean Observing Regional Association (SECOORA) in the Coastal Waters of Florida and Gulf of Mexico from 2015-11-09 to 2020-03-09 (NCEI Accession 0171345) NOAA_NCEI STAC Catalog 2015-11-09 2020-03-09 -85.0277, 29.60139, -85.0277, 29.60139 https://cmr.earthdata.nasa.gov/search/concepts/C2089377631-NOAA_NCEI.umm_json NCEI Accession 0171345 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Department of Environmental Protection collected the data from their in-situ moored station named Pilot's Cove, Apalachicola Bay, in the Coastal Waters of Florida and Gulf of Mexico. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Department of Environmental Protection and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary gov.noaa.nodc:0171346_Not Applicable Chemical, meteorological and other data collected from station Dry Bar, Apalachicola Bay, by Florida Department of Environmental Protection and assembled by Southeast Coastal Ocean Observing Regional Association (SECOORA) in the Coastal Waters of Florida and Gulf of Mexico from 2015-12-01 to 2018-10-10 (NCEI Accession 0171346) NOAA_NCEI STAC Catalog 2015-12-01 2018-10-10 -85.05807, 29.67431, -85.05807, 29.67431 https://cmr.earthdata.nasa.gov/search/concepts/C2089377641-NOAA_NCEI.umm_json NCEI Accession 0171346 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Department of Environmental Protection collected the data from their in-situ moored station named Dry Bar, Apalachicola Bay, in the Coastal Waters of Florida and Gulf of Mexico. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Department of Environmental Protection and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary -gov.noaa.nodc:0172043_Not Applicable ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043) ALL STAC Catalog 2012-11-28 2012-12-19 -94.0863, 25.7961, -87.2228, 28.9733 https://cmr.earthdata.nasa.gov/search/concepts/C2089377986-NOAA_NCEI.umm_json This dataset contains shipboard Acoustic Doppler Current Profiles (ADCP) data from a 75khz profiler, vertical profiles of measurements made from a CTD/Rosette system and continuous data from the Multiple Instrument Data Acquisition System (MIDAS). These ancillary data gives additional information about the physical state of the ocean during the Gulf of Mexico Integrated Spill Response Consortium (GISR) G03 cruise aboard R/V Pelican held from November 28 to December 20, 2012. proprietary gov.noaa.nodc:0172043_Not Applicable ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043) NOAA_NCEI STAC Catalog 2012-11-28 2012-12-19 -94.0863, 25.7961, -87.2228, 28.9733 https://cmr.earthdata.nasa.gov/search/concepts/C2089377986-NOAA_NCEI.umm_json This dataset contains shipboard Acoustic Doppler Current Profiles (ADCP) data from a 75khz profiler, vertical profiles of measurements made from a CTD/Rosette system and continuous data from the Multiple Instrument Data Acquisition System (MIDAS). These ancillary data gives additional information about the physical state of the ocean during the Gulf of Mexico Integrated Spill Response Consortium (GISR) G03 cruise aboard R/V Pelican held from November 28 to December 20, 2012. proprietary -gov.noaa.nodc:0172377_Not Applicable Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377) NOAA_NCEI STAC Catalog 2015-07-21 2016-08-05 -64.9199, 17.63764, -64.47889, 17.82709 https://cmr.earthdata.nasa.gov/search/concepts/C2089378141-NOAA_NCEI.umm_json We collected data on parrotfish abundance, biomass, size structure, and species composition at several sites on the N shore of St. Croix during July and August 2015 and 2016. Surveys were conducted using a method that allowed divers to rapidly survey large areas and quantify habitat assocations of different species. Researchers conducted 20-30 min timed swims towing a float with a GPS receiver, which allowed for the calculation of distance traveled during a swim and therefore the total area sampled. During the timed swim survey, the diver estimated and recorded the size to the nearest cm of all parrotfishes that were at least 10 cm in length that were encountered in a 5-m-wide swath. Because these swims often crossed multiple habitats, the diver recorded the habitat each minute. For each site, the total area of each habitat sampled was then calculated in order to determine habitat- and site-specific densities of each parrotfish species. proprietary +gov.noaa.nodc:0172043_Not Applicable ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043) ALL STAC Catalog 2012-11-28 2012-12-19 -94.0863, 25.7961, -87.2228, 28.9733 https://cmr.earthdata.nasa.gov/search/concepts/C2089377986-NOAA_NCEI.umm_json This dataset contains shipboard Acoustic Doppler Current Profiles (ADCP) data from a 75khz profiler, vertical profiles of measurements made from a CTD/Rosette system and continuous data from the Multiple Instrument Data Acquisition System (MIDAS). These ancillary data gives additional information about the physical state of the ocean during the Gulf of Mexico Integrated Spill Response Consortium (GISR) G03 cruise aboard R/V Pelican held from November 28 to December 20, 2012. proprietary gov.noaa.nodc:0172377_Not Applicable Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377) ALL STAC Catalog 2015-07-21 2016-08-05 -64.9199, 17.63764, -64.47889, 17.82709 https://cmr.earthdata.nasa.gov/search/concepts/C2089378141-NOAA_NCEI.umm_json We collected data on parrotfish abundance, biomass, size structure, and species composition at several sites on the N shore of St. Croix during July and August 2015 and 2016. Surveys were conducted using a method that allowed divers to rapidly survey large areas and quantify habitat assocations of different species. Researchers conducted 20-30 min timed swims towing a float with a GPS receiver, which allowed for the calculation of distance traveled during a swim and therefore the total area sampled. During the timed swim survey, the diver estimated and recorded the size to the nearest cm of all parrotfishes that were at least 10 cm in length that were encountered in a 5-m-wide swath. Because these swims often crossed multiple habitats, the diver recorded the habitat each minute. For each site, the total area of each habitat sampled was then calculated in order to determine habitat- and site-specific densities of each parrotfish species. proprietary +gov.noaa.nodc:0172377_Not Applicable Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377) NOAA_NCEI STAC Catalog 2015-07-21 2016-08-05 -64.9199, 17.63764, -64.47889, 17.82709 https://cmr.earthdata.nasa.gov/search/concepts/C2089378141-NOAA_NCEI.umm_json We collected data on parrotfish abundance, biomass, size structure, and species composition at several sites on the N shore of St. Croix during July and August 2015 and 2016. Surveys were conducted using a method that allowed divers to rapidly survey large areas and quantify habitat assocations of different species. Researchers conducted 20-30 min timed swims towing a float with a GPS receiver, which allowed for the calculation of distance traveled during a swim and therefore the total area sampled. During the timed swim survey, the diver estimated and recorded the size to the nearest cm of all parrotfishes that were at least 10 cm in length that were encountered in a 5-m-wide swath. Because these swims often crossed multiple habitats, the diver recorded the habitat each minute. For each site, the total area of each habitat sampled was then calculated in order to determine habitat- and site-specific densities of each parrotfish species. proprietary gov.noaa.nodc:0172588_Not Applicable Biological, chemical, and other data collected from station Humboldt Bay Pier by Humboldt State University and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2012-12-13 to 2021-06-09 (NCEI Accession 0172588) NOAA_NCEI STAC Catalog 2012-12-13 2021-06-09 -124.19652, 40.7775, -124.19652, 40.7775 https://cmr.earthdata.nasa.gov/search/concepts/C2089378189-NOAA_NCEI.umm_json NCEI Accession 0172588 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Humboldt Bay Pier in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary gov.noaa.nodc:0172612_Not Applicable Biological, chemical and other data collected from station Monterey Bay Commercial Wharf by Moss Landing Marine Laboratory and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2015-05-05 to 2020-01-03 (NCEI Accession 0172612) NOAA_NCEI STAC Catalog 2015-05-05 2020-01-03 -121.88935, 36.60513, -121.88935, 36.60513 https://cmr.earthdata.nasa.gov/search/concepts/C2089378278-NOAA_NCEI.umm_json NCEI Accession 0172612 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Moss Landing Marine Laboratory collected the data from their in-situ moored station named Monterey Bay Commercial Wharf in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Moss Landing Marine Laboratory and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary gov.noaa.nodc:0172613_Not Applicable Biological, chemical and other data collected from station Indian Island by Humboldt State University and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2016-04-05 to 2019-10-28 (NCEI Accession 0172613) NOAA_NCEI STAC Catalog 2016-04-05 2019-10-28 -124.15754, 40.81503, -124.15754, 40.81503 https://cmr.earthdata.nasa.gov/search/concepts/C2089378289-NOAA_NCEI.umm_json NCEI Accession 0172613 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Indian Island in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary @@ -18748,13 +18728,13 @@ gov.noaa.nodc:0175745_Not Applicable Acoustic travel time and bottom pressure da gov.noaa.nodc:0175745_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2011-07-07 to 2016-10-29 (NCEI Accession 0175745) NOAA_NCEI STAC Catalog 2011-07-07 2016-10-29 -51.5, -34.503, -44.5, -34.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089380684-NOAA_NCEI.umm_json "This dataset contains round trip acoustic travel time and abmient bottom pressure from bottom-mounted instruments spaced zonally along 34.5S in the SW Atlantic east of Uruguay July 2011 to October 2016. The data were collected for the Southwest Atlantic meridional overturning circulation (""SAM"") project by the NOAA-Atlantic Oceanographic and Meteorological Laboratory. Both the processed/quality-controlled and the raw data files are available. Format is text." proprietary gov.noaa.nodc:0175783_Not Applicable Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783) NOAA_NCEI STAC Catalog 1992-10-14 2016-12-28 27, -40, 30, -34 https://cmr.earthdata.nasa.gov/search/concepts/C2089380711-NOAA_NCEI.umm_json The Agulhas Current is the western boundary current closing the upper-limb of the Indian Ocean subtropical gyre, and is largely linked with the transfer of warm water from the Indian Ocean to the South Atlantic subtropical gyre. This interbasin water exchange takes place mostly through mesoscale processes that occur when the Agulhas Current retroflects south of Africa between 15°E and 25°E. Estimates of the Agulhas Current are carried out by NOAA/AOML using satellite altimetry as the main dataset, and hydrographic observations. For more information, please visit: http://www.aoml.noaa.gov/phod/indexes/index.php proprietary gov.noaa.nodc:0175783_Not Applicable Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783) ALL STAC Catalog 1992-10-14 2016-12-28 27, -40, 30, -34 https://cmr.earthdata.nasa.gov/search/concepts/C2089380711-NOAA_NCEI.umm_json The Agulhas Current is the western boundary current closing the upper-limb of the Indian Ocean subtropical gyre, and is largely linked with the transfer of warm water from the Indian Ocean to the South Atlantic subtropical gyre. This interbasin water exchange takes place mostly through mesoscale processes that occur when the Agulhas Current retroflects south of Africa between 15°E and 25°E. Estimates of the Agulhas Current are carried out by NOAA/AOML using satellite altimetry as the main dataset, and hydrographic observations. For more information, please visit: http://www.aoml.noaa.gov/phod/indexes/index.php proprietary -gov.noaa.nodc:0175786_Not Applicable Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786) ALL STAC Catalog 1986-04-01 2017-06-27 -89.85889, 29.8917, -87.6955, 30.68067 https://cmr.earthdata.nasa.gov/search/concepts/C2089380737-NOAA_NCEI.umm_json This dataset contains records of Gulf of Mexico (GOM) blue crab (Callinectes sapidus), white shrimp (Litopenaeus setiferus), brown shrimp (Farfantepenaeus aztecus), and fishes which can be used to quantify their population abundances and distributions. The data set contains existing data as a baseline and supplemental data from continued sampling. It contains records of early life stage blue crab, white shrimp, brown shrimp, and fishes (measurements and counts) from beach seine and trawl samples across the north GOM in the central Gulf States that were collected using standardized sampling methods. Data also include habitat assessments such as descriptions, georeferencing information, and abiotic factors (DO, salinity, temperature). proprietary gov.noaa.nodc:0175786_Not Applicable Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786) NOAA_NCEI STAC Catalog 1986-04-01 2017-06-27 -89.85889, 29.8917, -87.6955, 30.68067 https://cmr.earthdata.nasa.gov/search/concepts/C2089380737-NOAA_NCEI.umm_json This dataset contains records of Gulf of Mexico (GOM) blue crab (Callinectes sapidus), white shrimp (Litopenaeus setiferus), brown shrimp (Farfantepenaeus aztecus), and fishes which can be used to quantify their population abundances and distributions. The data set contains existing data as a baseline and supplemental data from continued sampling. It contains records of early life stage blue crab, white shrimp, brown shrimp, and fishes (measurements and counts) from beach seine and trawl samples across the north GOM in the central Gulf States that were collected using standardized sampling methods. Data also include habitat assessments such as descriptions, georeferencing information, and abiotic factors (DO, salinity, temperature). proprietary +gov.noaa.nodc:0175786_Not Applicable Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786) ALL STAC Catalog 1986-04-01 2017-06-27 -89.85889, 29.8917, -87.6955, 30.68067 https://cmr.earthdata.nasa.gov/search/concepts/C2089380737-NOAA_NCEI.umm_json This dataset contains records of Gulf of Mexico (GOM) blue crab (Callinectes sapidus), white shrimp (Litopenaeus setiferus), brown shrimp (Farfantepenaeus aztecus), and fishes which can be used to quantify their population abundances and distributions. The data set contains existing data as a baseline and supplemental data from continued sampling. It contains records of early life stage blue crab, white shrimp, brown shrimp, and fishes (measurements and counts) from beach seine and trawl samples across the north GOM in the central Gulf States that were collected using standardized sampling methods. Data also include habitat assessments such as descriptions, georeferencing information, and abiotic factors (DO, salinity, temperature). proprietary gov.noaa.nodc:0176496_Not Applicable Biological Baseline Studies of Mobile Bay (MESC-CAB 1980-1981): Hydrography, Sediments, Benthic Fauna, Pelagic Fauna, Phytoplankton, and Zooplankton (NCEI Accession 0176496) NOAA_NCEI STAC Catalog 1980-04-03 1981-08-26 -88.17333, 30.23833, -87.85167, 30.61333 https://cmr.earthdata.nasa.gov/search/concepts/C2089376767-NOAA_NCEI.umm_json Data from a monthly survey of Mobile Bay between April 1980 and August 1981. Extant data from the MESC Data Management System include sediment particle size distribution, discrete hydrography, identification and enumeration of benthic fauna, and identification and enumeration of water column biota. proprietary gov.noaa.nodc:0185741_Not Applicable Carbonate Chemistry Dynamics on Southeast Florida coral reefs from 2014-05-27 to 2015-09-03 (NCEI Accession 0185741) NOAA_NCEI STAC Catalog 2014-05-27 2015-09-03 -80.132778, 25.6519, -80.076975, 26.1636 https://cmr.earthdata.nasa.gov/search/concepts/C2089379082-NOAA_NCEI.umm_json These data are from the article “Seasonal carbonate chemistry dynamics on southeast Florida coral reefs: localized acidification hotspots from navigational inlets” published in Frontiers in Marine Science. The data in this package were collected from inlets and reefs along the coast of Southeast Florida. Water was collected bi-monthly from four reefs (Oakland Ridge, Barracuda, Pillars, and Emerald) and three closely-associated inlets (Port Everglades, Bakers Haulover, and Port of Miami). Water samples were collected at these locations either at the surface (~1m depth) or immediately above the benthos measured using a rosette sampler (ECO 55, Seabird). Temperature was recorded at each depth using a CTD (SBE 19V2, Seabird). Turbidity (NTU) was measured at time of water collection. Once collected, water samples were transferred to borosilicate glass bottles, samples were fixed using 200 µL of HgCl2 and sealed using Apiezon grease and a glass stopper. Salinity was measured using a densitometer (DMA 5000M, Anton Paar), while total alkalinity (TA) and dissolved inorganic carbon (DIC) were determined using Apollo SciTech instruments (AS-ALK2 and AS-C3, respectively). All values were measured in duplicate and corrected using certified reference materials following recommendations in Dickson et al. (2007). Aragonite saturation state (ΩArag.), Calcite saturation state (ΩCa), pH (Total scale), and the partial pressure of CO2 (pCO2) were calculated with CO2SYS (Lewis and Wallace, 1998) using the dissociation constants of Mehrbach et al. (1973) as refit by Dickson and Millero (1987) and Dickson (1990). Water samples were reserved for nutrient analyzed at time of collection to determine Total Kjeldahl Nitrogen, Total Phosphorous, and fluorescence of Chlorophyll-a. This research was supported through NOAA’s Coral Reef Conservation Program. proprietary gov.noaa.nodc:0185742_Not Applicable Climatology for NOAA Coral Reef Watch (CRW) Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1 for 1985-01-01 to 2012-12-31 (NCEI Accession 0185742) NOAA_NCEI STAC Catalog 1985-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089379091-NOAA_NCEI.umm_json This package contains a set of 12 monthly mean (MM) climatologies, one for each calendar month, and the maximum monthly mean (MMM) climatology. Each climatology has global coverage at 0.05-degree (5km) spatial resolution. The climatologies were derived from NOAA Coral Reef Watch's (CRW) CoralTemp Version 1.0 product and are based on the 1985-2012 time period of the CoralTemp data. They are used in deriving CRW's Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1. MMs are used to derive the SST Anomaly product, and the MMM is used to derive CRW's Coral Bleaching HotSpot, Degree Heating Week, and Bleaching Alert Area products. proprietary -gov.noaa.nodc:0185753_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753) NOAA_NCEI STAC Catalog 2006-09-01 2012-12-31 -84.5, 43.2, -79.8, 46.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089379102-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate surveys conducted in Saginaw Bay in 2006-2009, and in Lake Huron, including Georgian Bay and North Channel, in 2007 and 2012. These basic benthic survey data provide number of each taxon in each replicate sample (abundance), density, and biomass. proprietary gov.noaa.nodc:0185753_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753) ALL STAC Catalog 2006-09-01 2012-12-31 -84.5, 43.2, -79.8, 46.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089379102-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate surveys conducted in Saginaw Bay in 2006-2009, and in Lake Huron, including Georgian Bay and North Channel, in 2007 and 2012. These basic benthic survey data provide number of each taxon in each replicate sample (abundance), density, and biomass. proprietary +gov.noaa.nodc:0185753_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753) NOAA_NCEI STAC Catalog 2006-09-01 2012-12-31 -84.5, 43.2, -79.8, 46.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089379102-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate surveys conducted in Saginaw Bay in 2006-2009, and in Lake Huron, including Georgian Bay and North Channel, in 2007 and 2012. These basic benthic survey data provide number of each taxon in each replicate sample (abundance), density, and biomass. proprietary gov.noaa.nodc:0186561_Not Applicable 2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561) ALL STAC Catalog 2003-01-01 2003-12-31 -98, 25, -80, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2089380124-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains information, angler experiences, and preferences for recreational fishing in the Gulf of Mexico and South Atlantic. Data were collected by the NMFS Southeast Fisheries Science Center; Miami, FL. Data are in comma separated value (CSV) format and include recreational angler information such as age, gender, income, and target fish. proprietary gov.noaa.nodc:0186561_Not Applicable 2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561) NOAA_NCEI STAC Catalog 2003-01-01 2003-12-31 -98, 25, -80, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2089380124-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains information, angler experiences, and preferences for recreational fishing in the Gulf of Mexico and South Atlantic. Data were collected by the NMFS Southeast Fisheries Science Center; Miami, FL. Data are in comma separated value (CSV) format and include recreational angler information such as age, gender, income, and target fish. proprietary gov.noaa.nodc:0191401_Not Applicable Biogeochemical and microbiological measurements in the Cariaco Basin, a truly marine anoxic system in the southeastern Caribbean Sea, from 1995-11-13 to 2015-11-14 by the CARIACO Ocean Time Series Program (formerly known as CArbon Retention In A Colored Ocean) aboard the B/O Hermano Gines (NCEI Accession 0191401) NOAA_NCEI STAC Catalog 1995-11-13 2015-11-14 -64.66, 10.5, -64.66, 10.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089377738-NOAA_NCEI.umm_json Biogeochemical and microbiological variables were measured by Stony Brook University participants (see Author List) in the CARIACO Ocean Time-Series Program in order to study the microbial cycling of carbon, sulfur, and nitrogen occurring at depths where waters transition from oxic to anoxic to sulfidic. Samples were collected by Nikson bottles from 1995-11-13 to 2015-11-14 in the Cariaco Basin (southeastern Caribbean Sea off northeastern Venezuelan coast) aboard the B/O Hermano Gines, operated by the Fundacion La Salle, Venezuela. proprietary @@ -18763,25 +18743,25 @@ gov.noaa.nodc:0194300_Not Applicable ADCP, CTD, water and sediment chemistry, an gov.noaa.nodc:0204167_Not Applicable Cetacean digital photography and aerial observer data collected by an unmanned aerial vehicle and manned aerial vehicle in the Beaufort Sea for the Arctic Aerial Calibration Experiments (ACEs) from 2015-08-26 to 2015-09-07 (NCEI Accession 0204167) NOAA_NCEI STAC Catalog 2015-08-26 2015-09-07 -159.3, 71, -153.1, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2089379246-NOAA_NCEI.umm_json This dataset includes two comma separated files containing data and metadata from three cetacean observation methods from two platforms, the manned Turbo Commander aircraft and the unmanned ScanEagle. The ACEs' imagery described here was collected and analyzed in order to conduct a 3-way comparison of data and derived statistics from the following: Observers in the manned aircraft; Digital photographs from cameras mounted to the manned aircraft; Digital photographs from cameras mounted to the Unmanned Aerial Vehicle (UAV). The Arctic Aerial Calibration Experiments (ACEs) study was designed to evaluate the ability of UAS technology (i.e., airframe, payloads, sensors, and software) to detect cetaceans, identify individuals to species, estimate group size, identify calves, and estimate density in arctic waters, relative to conventional aerial surveys conducted by human observers in fixed wing aircraft and to photographic strip transect data collected from the manned aircraft. proprietary gov.noaa.nodc:0204646_Not Applicable Benthic cover from automated annotation of benthic images collected at coral reef sites in the Pacific Remote Island Areas and American Samoa from 2018-06-08 to 2018-08-11 (NCEI Accession 0204646) NOAA_NCEI STAC Catalog 2018-06-08 2018-08-11 -176.626077, -14.558022, -159.971695, 6.451465 https://cmr.earthdata.nasa.gov/search/concepts/C2089379357-NOAA_NCEI.umm_json "The coral reef benthic community data described here result from the automated annotation (classification) of benthic images collected during photoquadrat surveys conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD, formerly the Coral Reef Ecosystem Division) as part of NOAA's ongoing National Coral Reef Monitoring Program (NCRMP). SCUBA divers conducted benthic photoquadrat surveys in coral reef habitats according to protocols established by ESD and NCRMP during the ESD-led NCRMP mission to the islands and atolls of the Pacific Remote Island Areas (PRIA) and American Samoa from June 8 to August 11, 2018. Still photographs were collected with a high-resolution digital camera mounted on a pole to document the benthic community composition at predetermined points along transects at stratified random sites surveyed only once as part of Rapid Ecological Assessment (REA) surveys for corals and fish (Ayotte et al. 2015; Swanson et al. 2018) and permanent sites established by ESD and resurveyed every ~3 years for climate change monitoring. Overall, 30 photoquadrat images were collected at each survey site. The benthic habitat images were quantitatively analyzed using the web-based, machine-learning, image annotation tool, CoralNet (https://coralnet.ucsd.edu; Beijbom et al. 2015; Williams et al. 2019). Ten points were randomly overlaid on each image and the machine-learning algorithm ""robot"" identified the organism or type of substrate beneath, with 300 annotations (points) generated per site. Benthic elements falling under each point were identified to functional group (Tier 1: hard coral, soft coral, sessile invertebrate, macroalgae, crustose coralline algae, and turf algae) for coral, algae, invertebrates, and other taxa following Lozada-Misa et al. (2017). These benthic data can ultimately be used to produce estimates of community composition, relative abundance (percentage of benthic cover), and frequency of occurrence." proprietary gov.noaa.nodc:0205786_Not Applicable Assessment of heat stress exposure in the wider Caribbean coral reefs through the regional delineation of degree heating week data from 1985-01-01 to 2017-12-31 (NCEI Accession 0205786) NOAA_NCEI STAC Catalog 1985-01-01 2017-12-31 -97, 8.35, -59.2, 32.7 https://cmr.earthdata.nasa.gov/search/concepts/C2089380033-NOAA_NCEI.umm_json "This data package presents a three-decade (1985-2017) assessment of heat stress exposure in the wider Caribbean coral reefs at the ecoregional and local scales. The main heat stress indicator used was the Degree Heating Weeks (DHW) calculated from daily Sea Surface Temperature ""CoralTemp"" data from CRW-NOAA available from 1985 to the present and from the maximum monthly mean (MMM) version 3.1 at 5 km of the CRW-NOAA program. Different metrics were calculated based on daily DHW and are available in this dataset: a) the maximum value of DHW per pixel for the entire time series b) the frequency of the annual maximum values of DHW ≥ 4 °C- weeks (a predictor of coral ""bleaching risk"") per pixel c) the frequency of the annual maximum values of DHW ≥ 8 °C- weeks (a predictor of bleach-induced mortality or ""mortality risk"") per pixel d) the year in which the maximum of DHW occurred e) the trend of the annual maximum values of DHW per pixel. Based on the spatiotemporal annual maximum DHW, a new regionalization of heat stress was performed by cluster analysis with the K-means algorithm through the unsupervised classification, this new regionalization delimits the Caribbean in 8 Heat Stress Regions (HSR). We summarized spatiotemporal daily data to describe the temporal patterns at an ecoregional scale by calculating the descriptive statistics of the regional DHW on a given day. This dataset represents a new baseline and regionalization of heat stress in the wider Caribbean coral reefs that will enhance conservation and planning efforts underway." proprietary -gov.noaa.nodc:0206155_Not Applicable 2019 Summer Hypoxia Survey of Alabama Shelf CTD Data (2019-06-04 to 2019-08-02) (NCEI Accession 0206155) NOAA_NCEI STAC Catalog 2019-06-04 2019-08-02 -88.418, 29.4782, -88.004, 30.2166 https://cmr.earthdata.nasa.gov/search/concepts/C2089380106-NOAA_NCEI.umm_json Along the Fisheries Oceanography in Coastal Alabama (FOCAL) Transect on the Alabama shelf, a CTD survey was conducted using Seabird SBE 25 Sealogger CTD between 06/04/2019 and 08/02/2019. Data collected measured depth (m), salinity (PSU), temperature (ITS-90, deg C), oxygen (% Saturation), oxygen (mg/L), pH (pH), specific conductance (µS/cm), beam attenuation (1/m), beam transmission (%), density (kg/m3), conductivity (µS/cm), PAR (µmol m-1 s-1), fluorescence (mg/m3), and fluorescence (mg/m3). Data was collected on 2019-06-04, 2019-06-28, 2019-07-02, 2019-07-05, 2019-07-09, 2019-07-16, 2019-07-19, 2019-07-30, and 2019-08-02 during the summer of 2019. proprietary gov.noaa.nodc:0206155_Not Applicable 2019 Summer Hypoxia Survey of Alabama Shelf CTD Data (2019-06-04 to 2019-08-02) (NCEI Accession 0206155) ALL STAC Catalog 2019-06-04 2019-08-02 -88.418, 29.4782, -88.004, 30.2166 https://cmr.earthdata.nasa.gov/search/concepts/C2089380106-NOAA_NCEI.umm_json Along the Fisheries Oceanography in Coastal Alabama (FOCAL) Transect on the Alabama shelf, a CTD survey was conducted using Seabird SBE 25 Sealogger CTD between 06/04/2019 and 08/02/2019. Data collected measured depth (m), salinity (PSU), temperature (ITS-90, deg C), oxygen (% Saturation), oxygen (mg/L), pH (pH), specific conductance (µS/cm), beam attenuation (1/m), beam transmission (%), density (kg/m3), conductivity (µS/cm), PAR (µmol m-1 s-1), fluorescence (mg/m3), and fluorescence (mg/m3). Data was collected on 2019-06-04, 2019-06-28, 2019-07-02, 2019-07-05, 2019-07-09, 2019-07-16, 2019-07-19, 2019-07-30, and 2019-08-02 during the summer of 2019. proprietary +gov.noaa.nodc:0206155_Not Applicable 2019 Summer Hypoxia Survey of Alabama Shelf CTD Data (2019-06-04 to 2019-08-02) (NCEI Accession 0206155) NOAA_NCEI STAC Catalog 2019-06-04 2019-08-02 -88.418, 29.4782, -88.004, 30.2166 https://cmr.earthdata.nasa.gov/search/concepts/C2089380106-NOAA_NCEI.umm_json Along the Fisheries Oceanography in Coastal Alabama (FOCAL) Transect on the Alabama shelf, a CTD survey was conducted using Seabird SBE 25 Sealogger CTD between 06/04/2019 and 08/02/2019. Data collected measured depth (m), salinity (PSU), temperature (ITS-90, deg C), oxygen (% Saturation), oxygen (mg/L), pH (pH), specific conductance (µS/cm), beam attenuation (1/m), beam transmission (%), density (kg/m3), conductivity (µS/cm), PAR (µmol m-1 s-1), fluorescence (mg/m3), and fluorescence (mg/m3). Data was collected on 2019-06-04, 2019-06-28, 2019-07-02, 2019-07-05, 2019-07-09, 2019-07-16, 2019-07-19, 2019-07-30, and 2019-08-02 during the summer of 2019. proprietary gov.noaa.nodc:0207181_Not Applicable Ammonia (NH3) emissions characterization from agricultural soil sources from the NH3_STAT statistical model from 1990-01-01 to 2019-01-01 (NCEI Accession 0207181) NOAA_NCEI STAC Catalog 1990-01-01 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089380670-NOAA_NCEI.umm_json This NCEI accession contains statistical model (NH3_STAT) data. Global ammonia (NH3) emissions into the atmosphere are projected to increase in the coming years with the increased use of synthetic nitrogen fertilizers and cultivation of nitrogen-fixing crops. A statistical model (NH3_STAT) is developed for characterizing atmospheric NH3 emissions from agricultural soil sources, and compared to the performance of other global and regional NH3 models (e.g., EDGAR, MASAGE, MIX and U.S. EPA). The statistical model was developed by expressing a multiple linear regression equation between NH3 emission and the physicochemical variables. The model was evaluated for 2012 NH3 emissions. The results indicate that, in comparison to other data sets, the model provides a lower global NH3 estimate by 57%, (NH3_STAT: 13.9 Tg N yr-1; EDGAR: 33.0 Tg N yr-1). We also performed a region-based analysis (U.S., India, and China) using the NH3_STAT model. For the U.S., our model produces an estimate that is 143% higher in comparison to EPA. Meanwhile, the NH3_STAT model estimate for India shows NH3 emissions between -0.8 and 1.4 times lower when compared to other data sets. A lower estimate is also seen for China, where the model estimates NH3 emissions 0.4-5 times lower than other datasets. The difference in the global estimates is attributed to the lower estimates in major agricultural countries like China and India. The statistical model captures the spatial distribution of global NH3 emissions by utilizing a simplified approach compared to other readily available datasets. Moreover, the NH3_STAT model provides an opportunity to predict future NH3 emissions in a changing world. proprietary gov.noaa.nodc:0208019_Not Applicable Carbonate chemistry data at the Aransas Ship Channel from 2018-03-08 to 2019-08-22 (NCEI Accession 0208019) NOAA_NCEI STAC Catalog 2018-03-08 2019-08-22 -97.050278, 27.838056, -97.050278, 27.838056 https://cmr.earthdata.nasa.gov/search/concepts/C2089380855-NOAA_NCEI.umm_json This dataset includes both hydrographic (salinity, temperature, dissolved oxygen) and carbonate chemistry data collected at the Aransas Ship Channel (Port Aransas, TX) under the funding provided by the National Academy of Sciences Gulf Research Program (Grant# 2000009312) during the period of 03/08/2018-08/22/2019. proprietary gov.noaa.nodc:0208388_Not Applicable Biological, chemical, physical and time series data collected from station WQB-04 by University of Hawai'i at Hilo and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2010-10-23 to 2020-12-31 (NCEI Accession 0208388) NOAA_NCEI STAC Catalog 2010-10-23 2020-12-31 -155.082, 19.7341, -155.082, 19.7341 https://cmr.earthdata.nasa.gov/search/concepts/C2089376817-NOAA_NCEI.umm_json NCEI Accession 0208388 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo collected the data from their in-situ moored station named WQB-04: PacIOOS Water Quality Buoy 04: Hilo Bay, Big Island, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-04 is located in Hilo Bay on the east side of the Big Island. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary gov.noaa.nodc:0209056_Not Applicable Bottom Temperatures from ship mounted temperature probes collected in in North Atlantic from 2015-01-16 to 2019-02-10 (NCEI Accession 0209056) NOAA_NCEI STAC Catalog 2015-01-10 2020-02-10 -76.34258, 35.98645, -66.42055, 44.58673 https://cmr.earthdata.nasa.gov/search/concepts/C2089377982-NOAA_NCEI.umm_json This data set contains bottom temperature data collected by thermistors mounted on lobster boats in the North Atlantic and Stellwagen Bank. The accession consists of one .csv file contains the following variables - the location the temperature was recorded( site), the latitude (degrees N), longitude (degrees E), depth (m) and sea water temperature (degrees C) of each record. This data was collected as part of the Environmental Monitors on Lobster Traps (eMOLT) project - a non-profit collaboration of industry, science and academics devoted to the monitoring of the physical environment of the Gulf of Maine and the Southern New England Shelf. proprietary -gov.noaa.nodc:0209071_Not Applicable ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean from 2009-12-01 to 2010-03-23 (NCEI Accession 0209071) NOAA_NCEI STAC Catalog 2009-12-01 2010-03-23 11.2067, -5.8778, 11.2067, -5.8778 https://cmr.earthdata.nasa.gov/search/concepts/C2089378065-NOAA_NCEI.umm_json This dataset contains ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean in the Congo submarine canyon during ~3 month period from 2009-12-01 to 2010-03-23. Two ADCPs with acoustic frequencies of 300 kHz and 75 kHz were deployed on separate moorings placed in the channel axis 700 m apart and at ~2000 m water depth. They acquired data over a range of ~80 m above the seafloor (300 kHz) and 220 m above the seafloor (75 kHz). Data are in netcdf. proprietary gov.noaa.nodc:0209071_Not Applicable ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean from 2009-12-01 to 2010-03-23 (NCEI Accession 0209071) ALL STAC Catalog 2009-12-01 2010-03-23 11.2067, -5.8778, 11.2067, -5.8778 https://cmr.earthdata.nasa.gov/search/concepts/C2089378065-NOAA_NCEI.umm_json This dataset contains ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean in the Congo submarine canyon during ~3 month period from 2009-12-01 to 2010-03-23. Two ADCPs with acoustic frequencies of 300 kHz and 75 kHz were deployed on separate moorings placed in the channel axis 700 m apart and at ~2000 m water depth. They acquired data over a range of ~80 m above the seafloor (300 kHz) and 220 m above the seafloor (75 kHz). Data are in netcdf. proprietary +gov.noaa.nodc:0209071_Not Applicable ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean from 2009-12-01 to 2010-03-23 (NCEI Accession 0209071) NOAA_NCEI STAC Catalog 2009-12-01 2010-03-23 11.2067, -5.8778, 11.2067, -5.8778 https://cmr.earthdata.nasa.gov/search/concepts/C2089378065-NOAA_NCEI.umm_json This dataset contains ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean in the Congo submarine canyon during ~3 month period from 2009-12-01 to 2010-03-23. Two ADCPs with acoustic frequencies of 300 kHz and 75 kHz were deployed on separate moorings placed in the channel axis 700 m apart and at ~2000 m water depth. They acquired data over a range of ~80 m above the seafloor (300 kHz) and 220 m above the seafloor (75 kHz). Data are in netcdf. proprietary gov.noaa.nodc:0209115_Not Applicable Aragonite Saturation State in Deep Sea Coral Habitats collected from NOAA Ship Nancy Foster in Gulf of Mexico from 2017-08-14 to 2017-08-30 (NCEI Accession 0209115) NOAA_NCEI STAC Catalog 2017-08-14 2017-08-30 -84.90713, 25.66118, -80.02228, 29.18645 https://cmr.earthdata.nasa.gov/search/concepts/C2089378161-NOAA_NCEI.umm_json The dataset contains 17 depth profiles from 20-1000 m depth on the West Florida Shelf. Parameters include aragonite saturation state, total alkalinity, DIC, temperature and salinity. The data were collected using a CTD rosette aboard a NOAA-led research expedition in August 2017 entitled ‘Southeast Deep Coral Initiative: Exploring Deep-Sea Corals Ecosystems of the Southeast US’. The NOAA-led survey explored deep-sea coral habitat along West Florida shelf, using the remotely operated vehicle (ROV) Odysseus aboard NOAA Ship Nancy Foster. The cruise report for the expedition is hosted online here: https://doi.org/10.7289/V5/TM-NOS-NCCOS-244 (Wagner et al 2018). proprietary gov.noaa.nodc:0209162_Not Applicable Biological, chemical, physical and time series data collected from station WQB-05 by University of Hawai'i at Hilo and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2018-03-10 to 2020-12-31 (NCEI Accession 0209162) NOAA_NCEI STAC Catalog 2018-03-10 2020-12-31 -155.8285, 20.02415, -155.8285, 20.02415 https://cmr.earthdata.nasa.gov/search/concepts/C2089378336-NOAA_NCEI.umm_json NCEI Accession 0209162 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo collected the data from their in-situ moored station named WQB-05: PacIOOS Water Quality Buoy 05: Pelekane Bay, Big Island, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-05 is located in Pelekane Bay near Kawaihae Harbor on the west side of the Big Island. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary -gov.noaa.nodc:0209222_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Lake Guardian in Lake Michigan, Great Lakes from 2015-07-20 to 2015-07-29 (NCEI Accession 0209222) ALL STAC Catalog 2015-07-20 2015-07-29 -88.1, 41.6, -84.75, 46.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089378673-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate lake wide surveys conducted in Lake Michigan in 2015. These basic benthic survey data provide the number of each taxon in each replicate sample (abundance), density, and biomass. Similar lake wide surveys were conducted to assess the status of benthic taxa beginning in 1994/1995 and repeated every five years through 2015. proprietary gov.noaa.nodc:0209222_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Lake Guardian in Lake Michigan, Great Lakes from 2015-07-20 to 2015-07-29 (NCEI Accession 0209222) NOAA_NCEI STAC Catalog 2015-07-20 2015-07-29 -88.1, 41.6, -84.75, 46.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089378673-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate lake wide surveys conducted in Lake Michigan in 2015. These basic benthic survey data provide the number of each taxon in each replicate sample (abundance), density, and biomass. Similar lake wide surveys were conducted to assess the status of benthic taxa beginning in 1994/1995 and repeated every five years through 2015. proprietary -gov.noaa.nodc:0209226_Not Applicable Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226) ALL STAC Catalog 2006-01-01 2099-12-31 -82.9771, 24.4437, -80.0646, 26.3438 https://cmr.earthdata.nasa.gov/search/concepts/C2089378705-NOAA_NCEI.umm_json To maximize long term (>10yr) survival of nursery raised Acropora cervicornis corals, a map based tool was created that ranks locations in the Florida Acropora Critical Habitat based on climate vulnerability. Climate vulnerability is defined both in terms of exposure to future heat stress and the coral's sensitivity as resilience. Suitable sites are determined by a number of factors, suitable sites must be within the Acropora critical habitat and within the depth range 5-15m, with either hard bottom or coral present. Those possible locations are ranked based on projected climate change impacts and a resilience metric based on seven different indicators: coral cover, macroalgae cover, bleaching resistance, coral diversity, coral disease, herbivore biomass, and temperature variability. The data is presented as a Google Earth tool (zipped), maps, gridded netCDF files and are accompanied by a guidance document and a .csv file ranking all locations. The Google Earth tool contains five major layers: depth, turbidity, resilience, year of annual severe bleaching, and outplanting score. Bleaching projections included here use climate model data from 2006-2099. proprietary +gov.noaa.nodc:0209222_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Lake Guardian in Lake Michigan, Great Lakes from 2015-07-20 to 2015-07-29 (NCEI Accession 0209222) ALL STAC Catalog 2015-07-20 2015-07-29 -88.1, 41.6, -84.75, 46.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089378673-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate lake wide surveys conducted in Lake Michigan in 2015. These basic benthic survey data provide the number of each taxon in each replicate sample (abundance), density, and biomass. Similar lake wide surveys were conducted to assess the status of benthic taxa beginning in 1994/1995 and repeated every five years through 2015. proprietary gov.noaa.nodc:0209226_Not Applicable Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226) NOAA_NCEI STAC Catalog 2006-01-01 2099-12-31 -82.9771, 24.4437, -80.0646, 26.3438 https://cmr.earthdata.nasa.gov/search/concepts/C2089378705-NOAA_NCEI.umm_json To maximize long term (>10yr) survival of nursery raised Acropora cervicornis corals, a map based tool was created that ranks locations in the Florida Acropora Critical Habitat based on climate vulnerability. Climate vulnerability is defined both in terms of exposure to future heat stress and the coral's sensitivity as resilience. Suitable sites are determined by a number of factors, suitable sites must be within the Acropora critical habitat and within the depth range 5-15m, with either hard bottom or coral present. Those possible locations are ranked based on projected climate change impacts and a resilience metric based on seven different indicators: coral cover, macroalgae cover, bleaching resistance, coral diversity, coral disease, herbivore biomass, and temperature variability. The data is presented as a Google Earth tool (zipped), maps, gridded netCDF files and are accompanied by a guidance document and a .csv file ranking all locations. The Google Earth tool contains five major layers: depth, turbidity, resilience, year of annual severe bleaching, and outplanting score. Bleaching projections included here use climate model data from 2006-2099. proprietary +gov.noaa.nodc:0209226_Not Applicable Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226) ALL STAC Catalog 2006-01-01 2099-12-31 -82.9771, 24.4437, -80.0646, 26.3438 https://cmr.earthdata.nasa.gov/search/concepts/C2089378705-NOAA_NCEI.umm_json To maximize long term (>10yr) survival of nursery raised Acropora cervicornis corals, a map based tool was created that ranks locations in the Florida Acropora Critical Habitat based on climate vulnerability. Climate vulnerability is defined both in terms of exposure to future heat stress and the coral's sensitivity as resilience. Suitable sites are determined by a number of factors, suitable sites must be within the Acropora critical habitat and within the depth range 5-15m, with either hard bottom or coral present. Those possible locations are ranked based on projected climate change impacts and a resilience metric based on seven different indicators: coral cover, macroalgae cover, bleaching resistance, coral diversity, coral disease, herbivore biomass, and temperature variability. The data is presented as a Google Earth tool (zipped), maps, gridded netCDF files and are accompanied by a guidance document and a .csv file ranking all locations. The Google Earth tool contains five major layers: depth, turbidity, resilience, year of annual severe bleaching, and outplanting score. Bleaching projections included here use climate model data from 2006-2099. proprietary gov.noaa.nodc:0209247_Not Applicable Benthic cover derived from structure from motion images collected during marine debris surveys at coral reef sites entangled with derelict fishing nets at Pearl and Hermes Atoll in the Northwestern Hawaiian Islands from 2018-09-24 to 2018-10-03 (NCEI Accession 0209247) NOAA_NCEI STAC Catalog 2018-09-24 2018-10-03 -175.8211335, 27.8274571, -175.7880926, 27.8940486 https://cmr.earthdata.nasa.gov/search/concepts/C2089378869-NOAA_NCEI.umm_json The benthic cover and fishing-net related data described in this dataset are derived from the GIS analysis of benthic orthophotos. The source imagery was collected using a Structure from Motion (SfM) approach during in-water marine debris swim surveys conducted by snorkelers in search of derelict fishing nets. Surveys were conducted by the NOAA Fisheries, Ecosystem Sciences Division (ESD) from September 24 to October 3, 2018 at Pearl and Hermes Atoll during an ESD-led marine debris mission to the Northwestern Hawaiian Islands (NWHI) aboard NOAA Ship Oscar Elton Sette. The lagoon at Pearl and Hermes was surveyed equally across the spatial gradient, from locations where derelict fishing nets are common to locations where derelict fishing nets have never been observed. During the 2018 mission, only a subset of marine debris surveys resulted in a SfM survey. Fishing nets were located during swim surveys and selected for SfM if the net was interacting with coral or hard substrate, the depth of the net was within ~1–4 m of the surface, and the area of the net fit within the 9 sq. meter SFM survey plot. During a SFM survey, a permanent 3 x 3 m plot was established around the center of the fishing net, and the net was photographed using a back and forth swim pattern (“before” photos) for later processing using a SfM approach. The net was then removed, the volume of net removed was estimated and recorded, and the same area was photographed again in the same way (“after” photos). A nearby (>50 m distant) paired control site was also photographed using the same method (“control” photos). The photographs were processed using Agisoft Metashape software to generate orthomosaic images that were analyzed in ArcGIS for benthic cover using a random point approach. The number of points at net-impacted sites were constrained to the net coverage area and were scaled to the net area to ensure an equal point density among replicate net-impact sites. The same number of points were randomly assigned to the 3 × 3 m paired control site. Each point was classified into one of seven benthic categories: turf algae, macroalgae, sand, bare substrate, Porites compressa, sponge, or crustose coralline algae (CCA). The annotated points for each site were converted to percent cover for each benthic category. Fishing net size (sq m) and degree of fouling were also calculated from the orthophotos. Analyses were conducted to compare the benthic composition of net sites to control sites and to determine if fouling or net size contributed to these differences. proprietary -gov.noaa.nodc:0209357_Not Applicable A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357) NOAA_NCEI STAC Catalog 2000-01-01 2020-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089379328-NOAA_NCEI.umm_json This NCEA Accession contains MatLab files for a Toolbox for secondary quality control (2nd QC) on ocean chemistry and hydrographic data. High quality, reference measurements of chemical and physical properties of seawater are of great importance for a wide research community, including the need to validate models and attempts to quantify spatial and temporal variability. Whereas data precision has been improved by technological advances, the data accuracy has improved mainly by the use of certified reference materials (CRMs). However, since CRMs are not available for all variables, and use of CRMs does not guarantee bias-free data, we here present a recently developed Matlab toolbox for performing so-called secondary quality control on oceanographic data by the use of crossover analysis. This method and how it has been implemented in this toolbox is described in detail. This toolbox is developed mainly for use by sea-going scientists as a tool for quickly assessing possible bias in the measurements that can, hopefully, be remedied during the expedition, but also for possible post-cruise adjustment of data to be consistent with previous measurements in the region. proprietary gov.noaa.nodc:0209357_Not Applicable A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357) ALL STAC Catalog 2000-01-01 2020-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089379328-NOAA_NCEI.umm_json This NCEA Accession contains MatLab files for a Toolbox for secondary quality control (2nd QC) on ocean chemistry and hydrographic data. High quality, reference measurements of chemical and physical properties of seawater are of great importance for a wide research community, including the need to validate models and attempts to quantify spatial and temporal variability. Whereas data precision has been improved by technological advances, the data accuracy has improved mainly by the use of certified reference materials (CRMs). However, since CRMs are not available for all variables, and use of CRMs does not guarantee bias-free data, we here present a recently developed Matlab toolbox for performing so-called secondary quality control on oceanographic data by the use of crossover analysis. This method and how it has been implemented in this toolbox is described in detail. This toolbox is developed mainly for use by sea-going scientists as a tool for quickly assessing possible bias in the measurements that can, hopefully, be remedied during the expedition, but also for possible post-cruise adjustment of data to be consistent with previous measurements in the region. proprietary -gov.noaa.nodc:0210577_Not Applicable Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from 2014-07-15 to 2018-11-11 (NCEI Accession 0210577) ALL STAC Catalog 2014-07-15 2018-11-11 -162, 11, -50, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2089380393-NOAA_NCEI.umm_json Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from the World Ocean. ALAMO profiling floats measure temperature, salinity, and pressure and were developed to be air deployed in previously difficult locations, including tropical cyclones and around sea ice. Data files in NetCDF. proprietary +gov.noaa.nodc:0209357_Not Applicable A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357) NOAA_NCEI STAC Catalog 2000-01-01 2020-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089379328-NOAA_NCEI.umm_json This NCEA Accession contains MatLab files for a Toolbox for secondary quality control (2nd QC) on ocean chemistry and hydrographic data. High quality, reference measurements of chemical and physical properties of seawater are of great importance for a wide research community, including the need to validate models and attempts to quantify spatial and temporal variability. Whereas data precision has been improved by technological advances, the data accuracy has improved mainly by the use of certified reference materials (CRMs). However, since CRMs are not available for all variables, and use of CRMs does not guarantee bias-free data, we here present a recently developed Matlab toolbox for performing so-called secondary quality control on oceanographic data by the use of crossover analysis. This method and how it has been implemented in this toolbox is described in detail. This toolbox is developed mainly for use by sea-going scientists as a tool for quickly assessing possible bias in the measurements that can, hopefully, be remedied during the expedition, but also for possible post-cruise adjustment of data to be consistent with previous measurements in the region. proprietary gov.noaa.nodc:0210577_Not Applicable Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from 2014-07-15 to 2018-11-11 (NCEI Accession 0210577) NOAA_NCEI STAC Catalog 2014-07-15 2018-11-11 -162, 11, -50, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2089380393-NOAA_NCEI.umm_json Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from the World Ocean. ALAMO profiling floats measure temperature, salinity, and pressure and were developed to be air deployed in previously difficult locations, including tropical cyclones and around sea ice. Data files in NetCDF. proprietary +gov.noaa.nodc:0210577_Not Applicable Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from 2014-07-15 to 2018-11-11 (NCEI Accession 0210577) ALL STAC Catalog 2014-07-15 2018-11-11 -162, 11, -50, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2089380393-NOAA_NCEI.umm_json Air-Launched Autonomous Micro Observer (ALAMO) profiling float data from the World Ocean. ALAMO profiling floats measure temperature, salinity, and pressure and were developed to be air deployed in previously difficult locations, including tropical cyclones and around sea ice. Data files in NetCDF. proprietary gov.noaa.nodc:0210808_Not Applicable Assessment of coral reef fish and benthic communities in the West Hawaii Habitat Focus Area from 2015-10-13 to 2015-10-23 (NCEI Accession 0210808) NOAA_NCEI STAC Catalog 2015-10-13 2015-10-23 -156.048008, 19.568405, -155.828939, 20.059629 https://cmr.earthdata.nasa.gov/search/concepts/C2089380539-NOAA_NCEI.umm_json This archive package contains data on species composition, density, size, and abundance for coral reef fish as well as coral counts, benthic cover, and macroalga cover in the West Hawaii Habitat Focus Area along the Kona coast of the island of Hawaii. Data provided in this collection were gathered as part of the NOAA Habitat Blueprint initiative with support from the Coral Reef Conservation Program. Data were collected primarily by The Nature Conservancy Hawaii. Data were collected in 2015 using the Belt Transect method. This is the first year in a series of monitoring efforts which have taken place in subsequent years to evaluate the resilience of coral reefs in the Focus Area. This dataset serves as a baseline as it was collected during the 2015 coral bleaching event. The dataset accompanies the NOAA technical report Maynard et al. 2016. proprietary gov.noaa.nodc:0213517_Not Applicable Black Sea High Resolution SST L4 Analysis 0.0625 deg Resolution for 2019-09-18 (NCEI Accession 0213517) NOAA_NCEI STAC Catalog 2019-09-18 2019-09-18 26.375, 38.75, 42.375, 48.8125 https://cmr.earthdata.nasa.gov/search/concepts/C2089376602-NOAA_NCEI.umm_json CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625 deg. x 0.0625 deg. horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black sea. proprietary gov.noaa.nodc:0218215_Not Applicable Circulation, temperature, and water surface elevation from Finite Volume Community Ocean Model (FVCOM) simulations of Lake Superior, Great Lakes region from 2010-01-01 to 2012-12-31 to study the 2010 coastal upwelling event (NCEI Accession 0218215) NOAA_NCEI STAC Catalog 2010-01-01 2012-12-31 -92.08, 46.44, -84.38, 48.79 https://cmr.earthdata.nasa.gov/search/concepts/C2089376983-NOAA_NCEI.umm_json "This dataset contains a three-dimensional (3-D), coupled ice-ocean Finite Volume Community Ocean Model (FVCOM) hydrodynamic simulations of circulation, temperature, and water surface elevation of Lake Superior for the years 2010-2012. The model was validated with temperature observations at National Oceanic and Atmospheric Administration (NOAA) buoys and mooring data from 2010. The upwelling event observed in satellite imagery and at a mooring station was reproduced by the model, in August 2010 along the northwestern coast. FVCOM version 3.1.6 was used for these simulations including custom modifications for wind-wave mixing (Hu and Wang, 2010) and centered-difference time integration. Ice simulations used the unstructured-grid, community ice code (UG-CICE) that was included with FVCOM version 3.1.6 (Chen et al. 2011; Gao et al. 2011). North American Regional Reanalysis (NARR) 32 km data (Mesinger et al. 2006) was used as atmospheric boundary conditions which included heat flux components (i.e., ""heating_on=T"" in the namelist). To convert the NARR forcings to the FVCOM unstructured grid, the interpolation scheme built in to FVCOM (WRF2FVCOM) was used. Details for these simulations can be found in the namelist file ""narr_0913_run.nml"" included in this data archive." proprietary @@ -18794,8 +18774,8 @@ gov.noaa.nodc:0225979_Not Applicable Biological, chemical, physical and time ser gov.noaa.nodc:0226059_Not Applicable Biological, chemical, physical and time series data collected from station WQBKN by University of Hawai'i at Hilo and University of Hawai'i at Mānoa and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2008-08-07 to 2017-01-04 (NCEI Accession 0226059) NOAA_NCEI STAC Catalog 2008-08-07 2017-01-04 -157.865, 21.2887, -157.865, 21.2887 https://cmr.earthdata.nasa.gov/search/concepts/C2089380013-NOAA_NCEI.umm_json NCEI Accession 0226059 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo and University of Hawai'i at Mānoa collected the data from their in-situ moored station named WQBKN: PacIOOS Water Quality Buoy KN (WQB-KN): Kilo Nalu, Oahu, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and University of Hawai'i at Mānoa and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-KN is located at the Kilo Nalu Nearshore Reef Observatory, near Kakaako Waterfront Park and Kewalo Basin off of Ala Moana Boulevard in Honolulu. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary gov.noaa.nodc:0226205_Not Applicable ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30 (NCEI Accession 0226205) ALL STAC Catalog 2020-03-28 2020-03-30 -88.576242, 27.591893, -82.438911, 30.342877 https://cmr.earthdata.nasa.gov/search/concepts/C2089380082-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary gov.noaa.nodc:0226205_Not Applicable ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30 (NCEI Accession 0226205) NOAA_NCEI STAC Catalog 2020-03-28 2020-03-30 -88.576242, 27.591893, -82.438911, 30.342877 https://cmr.earthdata.nasa.gov/search/concepts/C2089380082-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary -gov.noaa.nodc:0231662_Not Applicable ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662) ALL STAC Catalog 2019-07-15 2019-07-15 -124.355093, 44.282964, -124.054485, 44.625023 https://cmr.earthdata.nasa.gov/search/concepts/C2089380691-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary gov.noaa.nodc:0231662_Not Applicable ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662) NOAA_NCEI STAC Catalog 2019-07-15 2019-07-15 -124.355093, 44.282964, -124.054485, 44.625023 https://cmr.earthdata.nasa.gov/search/concepts/C2089380691-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary +gov.noaa.nodc:0231662_Not Applicable ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662) ALL STAC Catalog 2019-07-15 2019-07-15 -124.355093, 44.282964, -124.054485, 44.625023 https://cmr.earthdata.nasa.gov/search/concepts/C2089380691-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary gov.noaa.nodc:0232256_Not Applicable American Samoa Territorial Monitoring Program: Assessment of coral reef benthic and fish communities in American Samoa from 2005-03-10 to 2017-04-21 (NCEI Accession 0232256) NOAA_NCEI STAC Catalog 2005-03-10 2017-04-21 -170.563628, -14.364332, -170.812132, -14.252747 https://cmr.earthdata.nasa.gov/search/concepts/C2089380473-NOAA_NCEI.umm_json The data described here result from coral reef assessments of reef slopes (10m depth) at permanent sites around Tutuila, American Samoa as part of the ongoing American Samoa Coral Reef Monitoring Program (ASCRMP). These surveys were conducted by members of the American Samoa Coral Reef Advisory Group between 2005 and 2017. The data was collected via SCUBA surveys and reports on coral, benthic and fish composition and derived metrics (e.g., benthic cover, coral diversity, fish diversity, fish biomass). proprietary gov.noaa.nodc:0234331_Not Applicable Benthic foraminiferal assemblages, stable isotopes, and short-lived radioisotope measurements from sediment cores collected during the multiple cruises in the northwestern margin of Cuba and Gulf of Mexico from 2010-06-13 to 2017-07-19. (NCEI Accession 0234331) NOAA_NCEI STAC Catalog 2010-06-13 2017-07-19 -97.566, 18.631433, -82.339283, 29.701667 https://cmr.earthdata.nasa.gov/search/concepts/C2089380844-NOAA_NCEI.umm_json This dataset contains a compilation of seafloor surface benthic foraminifera assemblages, baseline stable carbon and oxygen isotope measurements from benthic foraminifera, and short-lived radioisotope measurements from sediment cores collected on multiple cruises and field sampling throughout the Gulf of Mexico and the northwestern margin of Cuba from 2010-06-13 to 2017-07-19. Stable isotope measurements were performed on Cibicidoides spp. The dataset includes the sediment core information such as location, date, and depth; benthic foraminiferal stable carbon and oxygen isotopes; and the total density and diversity calculations using Fisher’s Alpha and Shannon indices from the surface-most sub-sample from each core (typically 0-2 mm). For short-lived radioisotope measurements, samples were analyzed by gamma spectrometry with High-Purity Germanium (HPGe) gamma-ray detectors (Canberra Coaxial Planar configuration) for total 210Pb (46.5 keV), 214Pb (295 keV and 351 keV), and 214Bi (609 keV) activities. The mean activity of the 214Pb (295 keV), 214Pb (351 keV), and 214Bi (609 keV) was used as a proxy for 226Ra activity and therefore the supported 210Pb that is produced in situ. The reported excess 210Pb (210Pbxs) is the difference of the total 210Pb and the supported 210Pb. proprietary gov.noaa.nodc:0237816_Not Applicable Assessing cryptic reef diversity of colonizing marine invertebrates using Autonomous Reef Monitoring Structures (ARMS) deployed at coral reef sites in Kimbe Bay, Papua New Guinea from 2009-09-01 to 2012-09-12 (NCEI Accession 0237816) NOAA_NCEI STAC Catalog 2009-09-01 2012-09-12 150.126428, -5.308874, 150.131315, -5.28353 https://cmr.earthdata.nasa.gov/search/concepts/C2089381360-NOAA_NCEI.umm_json Autonomous Reef Monitoring Structures (ARMS) are used by the NOAA Coral Reef Ecosystem Program (CREP) to assess and monitor cryptic reef diversity across the Pacific. Developed in collaboration with the Census of Marine Life (CoML) Census of Coral Reef Ecosystems (CReefs), ARMS are designed to mimic the structural complexity of a reef and attract/collect colonizing marine invertebrates. The key innovation of the ARMS method is biodiversity is sampled over precisely the same surface area in the exact same manner. Thus, the use of ARMS is a systematic, consistent, and comparable method for monitoring the marine cryptobiota community over time. The data described here were collected by CREP from ARMS units moored at fixed climate survey sites located in Kimbe Bay, Papua New Guinea. Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) from September 2009 to September 2012, and three ARMS units were deployed by SCUBA divers at each survey site. The data can be accessed online via the NOAA National Centers for Environmental Information (NCEI) Ocean Archive. Each ARMS unit, constructed in-house by CREP, consisted of 23 cm x 23 cm gray, type 1 PVC plates stacked in alternating series of 4 open and 4 obstructed layers and attached to a base plate of 35 cm x 45 cm, which was affixed to the reef. Upon recovery, each ARMS unit was encapsulated, brought to the surface, and disassembled and processed. Disassembled plates were photographed to document recruited sessile organisms and scraped clean and preserved in 95% ethanol for DNA processing. Recruited motile organisms were sieved into 3 size fractions: 2 mm, 500 µm, and 100 µm. The 500 µm and 100 µm fractions were bulked and also preserved in 95% ethanol for DNA processing. The 2 mm fraction was sorted into morphospecies. This dataset includes information on the species counted and identified in the 2 mm fraction. proprietary @@ -18807,12 +18787,12 @@ gov.noaa.nodc:6901098_Not Applicable Cloud amount/frequency, NITRATE and other d gov.noaa.nodc:7000052_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX From Prince William Sound (Gulf of Alaska) from 1986-12-15 to 1986-12-18 (NCEI Accession 7000052) NOAA_NCEI STAC Catalog 1986-12-15 1986-12-18 -150, 59, -149, 60.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089381217-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7000422_Not Applicable AIR PRESSURE and Other Data from GOSNOLD From NW Atlantic (limit-40 W) from 1969-10-28 to 1969-10-29 (NCEI Accession 7000422) ALL STAC Catalog 1969-10-28 1969-10-29 -72, 39, -71, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2089383028-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7000422_Not Applicable AIR PRESSURE and Other Data from GOSNOLD From NW Atlantic (limit-40 W) from 1969-10-28 to 1969-10-29 (NCEI Accession 7000422) NOAA_NCEI STAC Catalog 1969-10-28 1969-10-29 -72, 39, -71, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2089383028-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:7000981_Not Applicable A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981) NOAA_NCEI STAC Catalog 1970-06-01 1970-07-01 -29.33, 50.01, -14.2, 55.56 https://cmr.earthdata.nasa.gov/search/concepts/C2089381614-NOAA_NCEI.umm_json Seawater chemistry data were collected using bottle from the USNS KANE in the North Atlantic Ocean. Data were collected from 20 July 1970 to 03 July 1970. The seawater chemistry data includes reactive phosphate, reactive silicate, and nitrate. proprietary gov.noaa.nodc:7000981_Not Applicable A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981) ALL STAC Catalog 1970-06-01 1970-07-01 -29.33, 50.01, -14.2, 55.56 https://cmr.earthdata.nasa.gov/search/concepts/C2089381614-NOAA_NCEI.umm_json Seawater chemistry data were collected using bottle from the USNS KANE in the North Atlantic Ocean. Data were collected from 20 July 1970 to 03 July 1970. The seawater chemistry data includes reactive phosphate, reactive silicate, and nitrate. proprietary +gov.noaa.nodc:7000981_Not Applicable A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981) NOAA_NCEI STAC Catalog 1970-06-01 1970-07-01 -29.33, 50.01, -14.2, 55.56 https://cmr.earthdata.nasa.gov/search/concepts/C2089381614-NOAA_NCEI.umm_json Seawater chemistry data were collected using bottle from the USNS KANE in the North Atlantic Ocean. Data were collected from 20 July 1970 to 03 July 1970. The seawater chemistry data includes reactive phosphate, reactive silicate, and nitrate. proprietary gov.noaa.nodc:7001081_Not Applicable Characteristics of Sediments in the James River Estuary, Virginia, 1968 (NCEI Accession 7001081) NOAA_NCEI STAC Catalog 1966-04-01 1967-08-30 -77, 36.7, -76.15, 37.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089382141-NOAA_NCEI.umm_json This report presents data on the physical and chemical characteristics of bottom sediments in the James River estuary, Virgina. The data were generated as part of a comprehensive study of sedimentation in which the initial objective was to broadly define the distribution of sediment properties. proprietary gov.noaa.nodc:7100000_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship DISCOVERER, JAMES COOK and other platforms from 1964-08-24 to 1971-11-17 (NCEI Accession 7100000) NOAA_NCEI STAC Catalog 1964-08-24 1971-11-17 -155.5, -66.7, 175.2, 50.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089383124-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:7100048_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms From NE Pacific (limit-180) from 1969-08-01 to 1969-08-31 (NCEI Accession 7100048) ALL STAC Catalog 1969-08-01 1969-08-31 -85, 7, -75, 12 https://cmr.earthdata.nasa.gov/search/concepts/C2089383261-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7100048_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms From NE Pacific (limit-180) from 1969-08-01 to 1969-08-31 (NCEI Accession 7100048) NOAA_NCEI STAC Catalog 1969-08-01 1969-08-31 -85, 7, -75, 12 https://cmr.earthdata.nasa.gov/search/concepts/C2089383261-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:7100048_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms From NE Pacific (limit-180) from 1969-08-01 to 1969-08-31 (NCEI Accession 7100048) ALL STAC Catalog 1969-08-01 1969-08-31 -85, 7, -75, 12 https://cmr.earthdata.nasa.gov/search/concepts/C2089383261-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7100165_Not Applicable Chemical, physical, and other data collected using bottle casts from the North Pacific Ocean as a part of the California Cooperative Fisheries Investigation (CALCOFI) project, from 1951-01-06 to 1960-10-31 (NCEI Accession 7100165) NOAA_NCEI STAC Catalog 1951-01-06 1960-10-31 -140, 20, -120, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2089383936-NOAA_NCEI.umm_json Chemical, physical, and other data were collected using bottle casts in the North Pacific Ocean from January 6, 1951 to October 31, 1960. Data were submitted by Scripps Institution of Oceanography as part of the California Cooperative Fisheries Investigation (CALCOFI) project. proprietary gov.noaa.nodc:7100603_Not Applicable Chemical, physical, and other data collected using bottle, BT, current meter, MBT, meteorological sensors, and secchi disk casts in the North Pacific Ocean as part of the California Cooperative Fisheries Investigation (CALCOFI) project, from 1968-01-01 to 1968-12-04 (NCEI Accession 7100603) NOAA_NCEI STAC Catalog 1968-01-01 1968-12-04 -122.9, 36.6, -121.9, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2089381029-NOAA_NCEI.umm_json Chemical, physical, and other data were collected using bottle, BT, current meter, MBT, meteorological sensors, and secchi disk casts from January 1, 1968 to December 4, 1968. Data were submitted by Stanford University; Hopkins Marine Station as part of the California Cooperative Fisheries Investigation (CALCOFI) project. Data were processed by NODC to the NODC standard F004 water physics and chemistry format. Full F004 Format descriptions are available from the NODC homepage at www.nodc.noaa.gov/. The F004 format contains data from measurements and analysis of physical and chemical characteristics of the water column. Chemical parameters that may be recorded are salinity, pH and concentration of oxygen, ammonia, nitrate, phosphate, chlorophyll and suspended solids. Physical parameters that may be recorded include temperature, density (sigma-t), transmissivity and current velocity (east-west and north-south components). Cruise and station information may include environmental conditions of the study site at the time of observation. Data are very sparse prior to 1951. proprietary gov.noaa.nodc:7200096_Not Applicable Cloud amount/frequency, NITRATE and other data from EASTWARD from 1968-02-23 to 1971-11-16 (NCEI Accession 7200096) NOAA_NCEI STAC Catalog 1968-02-23 1971-11-16 -86.4, 11, -61.1, 37.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089383889-NOAA_NCEI.umm_json Not provided proprietary @@ -18824,8 +18804,8 @@ gov.noaa.nodc:7201127_Not Applicable Cloud amount/frequency, NITRATE and other d gov.noaa.nodc:7201380_Not Applicable Cloud amount/frequency, NITRATE and other data from EASTWARD from 1971-07-19 to 1972-11-04 (NCEI Accession 7201380) NOAA_NCEI STAC Catalog 1971-07-19 1972-11-04 -80.7, 30.4, -72.7, 38.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089382013-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7201418_Not Applicable Cloud amount/frequency, NITRATE and other data from PANULIRUS and PANULIRUS II from 1970-01-06 to 1972-11-03 (NCEI Accession 7201418) NOAA_NCEI STAC Catalog 1970-01-06 1972-11-03 -64.9, 31.5, -64.5, 32.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089382040-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7300167_Not Applicable Cloud amount/frequency, NITRATE and other data from ALEJANDRO DE HUMBOLDT and NOAA Ship DAVID STARR JORDAN in the Gulf of California from 1971-04-27 to 1971-05-09 (NCEI Accession 7300167) NOAA_NCEI STAC Catalog 1971-04-27 1971-05-09 -115.9, 22.8, -108, 29.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089382675-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:7300282_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms from 1968-07-01 to 1970-12-31 (NCEI Accession 7300282) NOAA_NCEI STAC Catalog 1968-07-01 1970-12-31 113.9, -46.6, 179.8, -0.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089383549-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7300282_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms from 1968-07-01 to 1970-12-31 (NCEI Accession 7300282) ALL STAC Catalog 1968-07-01 1970-12-31 113.9, -46.6, 179.8, -0.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089383549-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:7300282_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms from 1968-07-01 to 1970-12-31 (NCEI Accession 7300282) NOAA_NCEI STAC Catalog 1968-07-01 1970-12-31 113.9, -46.6, 179.8, -0.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089383549-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7301085_Not Applicable Cloud amount/frequency, NITRATE and other data from BELLOWS from 1973-08-10 to 1973-08-15 (NCEI Accession 7301085) NOAA_NCEI STAC Catalog 1973-08-10 1973-08-15 -89.6, 27, -83, 29.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089381369-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7301177_Not Applicable Cloud amount/frequency, NITRATE and other data from GAUSS, METEOR and other platforms in the North Atlantic Ocean from 1959-11-18 to 1972-03-14 (NCEI Accession 7301177) NOAA_NCEI STAC Catalog 1959-11-18 1972-03-14 -85, 0, 35.9, 71.4 https://cmr.earthdata.nasa.gov/search/concepts/C2089381441-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7400073_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship DISCOVERER, USCGC ROCKAWAY and other platforms from 1969-05-01 to 1969-07-29 (NCEI Accession 7400073) NOAA_NCEI STAC Catalog 1969-05-01 1969-07-29 -59.8, 7.4, -52.6, 17.7 https://cmr.earthdata.nasa.gov/search/concepts/C2089381593-NOAA_NCEI.umm_json Not provided proprietary @@ -18843,17 +18823,17 @@ gov.noaa.nodc:7600769_Not Applicable Cloud amount/frequency, NITRATE and other d gov.noaa.nodc:7601177_Not Applicable Cloud amount/frequency, NITRATE and other data from MURRE II in the NE Pacific from 1975-06-20 to 1976-03-29 (NCEI Accession 7601177) NOAA_NCEI STAC Catalog 1975-06-20 1976-03-29 -135.7, 58, -134.2, 58.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089384847-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7601212_Not Applicable BENTHIC SPECIES and Other Data from KANA KEOKI From Gulf of Mexico from 1974-10-26 to 1974-12-21 (NCEI Accession 7601212) NOAA_NCEI STAC Catalog 1974-10-26 1974-12-21 -100, 17, -81, 31.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089384895-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7601237_Not Applicable Chemical and physical data from thermistor, fluorometer, and bottle casts in the Patuxent River from 1972-10-15 to 1972-10-19 (NCEI Accession 7601237) NOAA_NCEI STAC Catalog 1972-10-15 1972-10-19 -76.7, 38, -76.7, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2089384911-NOAA_NCEI.umm_json "The Patuxent River estuary was investigated over a 25-hour tidal cycle from October 17-18, 1972, during the Patuxent River Cooperative Study (conducted by the University of Maryland). These data were collected as part of a joint investigation by the University of Maryland's Center for Environmental and Estuarine Studies (Chesapeake Biological Lab) and the Institute for Fluid Dynamics and Applied Mathematics (College Park, Maryland). The resulting chemical, physical, and biological data were assembled into a format that could be utilized by investigators, collectively titled the Patuxent River Data Bank. The Patuxent River Data Bank was submitted to NODC on a 9-track, 1600 BPI tape in EBCDIC and contains headers and one data file. Heat concentration (in kilocalories/liter) and instantaneous flux magnitude (in megacalories/square meter/second) were recorded over the tidal cycle. Other data associated with this study are filed under NODC Reference #'s L01574 and L01576; all data are in the Level-A directory under L01574.001. Data associated with marine chemistry include: Dissolved organic carbon (milligrams/liter), Particulate carbon (milligrams/liter), salts (grams/liter), Dissolved oxygen (milligrams/liter), and total particulates (milligrams/liter). Instantaneous flux magnitudes for carbon were measured in grams/liter; for salts, in kilograms/liter; for oxygen, in milligrams/liter; and for total particulates, milligrams/liter. Parameters associated with primary productivity (L505) include: Nitrate +Nitrite conc., Ammonia Nitrogen conc., Total Kjeldahl Nitrogen, Organic Phosphate conc., Total Hydrolyzable Phosphate, Active Chlorophyll-a, and Total Chlorophyll. Nutrients were measured in milligrams/liter; chlorophyll concentrations were measured in micrograms/liter. Instantaneous flux magnitudes were measured in milligrams/square meter/second. Additional data collected during this investigation are filed under NODC Reference #'s L01575 and one tape of Patuxent River Estuary Hydro data ""OLD STUFF""" proprietary -gov.noaa.nodc:7601613_Not Applicable AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613) ALL STAC Catalog 1972-01-01 1974-06-30 -77, 37, -76, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2089384776-NOAA_NCEI.umm_json This entry contains tidal information for Chesapeake Bay. Data was submitted by Saul Berkman, NOS Tides Branch, Oceanographic Division. These data are in NODC format. These data were collected roughly 37-39 degrees N, 75 degrees W (stations were in Baltimore, Bayport VA, Cambridge MD, Cheathem Annex VA, Chesapeake City, MD, Gaskins Point, VA, Hampton Roads, VA, Kiptopeke Beach VA, Lower Marlboro, MD, Old Pt Comfort VA, Portsmouth VA, Solomons MD, Taylor Island MD, Washington DC, and Windmill Point VA. The data are in half-hourly units and includes latitude, longitude, date, time, and tidal height. The documentation describes the record format. Tide heights are referred to North American Datum (NAD) 1929. proprietary gov.noaa.nodc:7601613_Not Applicable AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613) NOAA_NCEI STAC Catalog 1972-01-01 1974-06-30 -77, 37, -76, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2089384776-NOAA_NCEI.umm_json This entry contains tidal information for Chesapeake Bay. Data was submitted by Saul Berkman, NOS Tides Branch, Oceanographic Division. These data are in NODC format. These data were collected roughly 37-39 degrees N, 75 degrees W (stations were in Baltimore, Bayport VA, Cambridge MD, Cheathem Annex VA, Chesapeake City, MD, Gaskins Point, VA, Hampton Roads, VA, Kiptopeke Beach VA, Lower Marlboro, MD, Old Pt Comfort VA, Portsmouth VA, Solomons MD, Taylor Island MD, Washington DC, and Windmill Point VA. The data are in half-hourly units and includes latitude, longitude, date, time, and tidal height. The documentation describes the record format. Tide heights are referred to North American Datum (NAD) 1929. proprietary +gov.noaa.nodc:7601613_Not Applicable AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613) ALL STAC Catalog 1972-01-01 1974-06-30 -77, 37, -76, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2089384776-NOAA_NCEI.umm_json This entry contains tidal information for Chesapeake Bay. Data was submitted by Saul Berkman, NOS Tides Branch, Oceanographic Division. These data are in NODC format. These data were collected roughly 37-39 degrees N, 75 degrees W (stations were in Baltimore, Bayport VA, Cambridge MD, Cheathem Annex VA, Chesapeake City, MD, Gaskins Point, VA, Hampton Roads, VA, Kiptopeke Beach VA, Lower Marlboro, MD, Old Pt Comfort VA, Portsmouth VA, Solomons MD, Taylor Island MD, Washington DC, and Windmill Point VA. The data are in half-hourly units and includes latitude, longitude, date, time, and tidal height. The documentation describes the record format. Tide heights are referred to North American Datum (NAD) 1929. proprietary gov.noaa.nodc:7601642_Not Applicable Bacteria, taxonomic code, and other data collected from G.W. PIERCE in North Atlantic Ocean from sediment sampler; 1976-02-20 to 1976-03-23 (NCEI Accession 7601642) NOAA_NCEI STAC Catalog 1976-02-20 1976-03-23 -75.3, 37.1, -71.9, 39.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089384806-NOAA_NCEI.umm_json Bacteria, taxonomic code, and other data were collected using sediment sampler and other instruments in the North Atlantic Ocean from G.W. PIERCE. Data were collected from 20 February 1976 to 23 March 1976 by Virginia Institute of Marine Science in Gloucester Point with support from the Ocean Continental Shelf - Mid Atlantic (OCS-Mid Atlantic) project. proprietary gov.noaa.nodc:7601772_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship OREGON II in the NW Atlantic from 1976-02-20 to 1976-02-25 (NCEI Accession 7601772) NOAA_NCEI STAC Catalog 1976-02-20 1976-02-25 -74.4, 36.8, -72.6, 38.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089384997-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7617993_Not Applicable Cloud amount/frequency, NITRATE and other data from CAPRICORNE from 1974-07-25 to 1974-08-10 (NCEI Accession 7617993) NOAA_NCEI STAC Catalog 1974-07-25 1974-08-10 -10.3, -2.2, -3.9, 4.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385626-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7617994_Not Applicable Cloud amount/frequency, NITRATE and other data from ONVERSAAGD from 1974-06-29 to 1974-07-11 (NCEI Accession 7617994) NOAA_NCEI STAC Catalog 1974-06-29 1974-07-11 -54.3, 14.8, -53.6, 16 https://cmr.earthdata.nasa.gov/search/concepts/C2089385636-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7617995_Not Applicable Cloud amount/frequency, NITRATE and other data from A. V. HUMBOLDT from 1974-07-28 to 1974-08-17 (NCEI Accession 7617995) NOAA_NCEI STAC Catalog 1974-07-28 1974-08-17 -25, -1.5, -23.4, 1.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385645-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:7700058_Not Applicable AIR PRESSURE and Other Data from YELCHO From Drake Passage from 1976-02-27 to 1976-04-08 (NCEI Accession 7700058) ALL STAC Catalog 1976-02-27 1976-04-08 -70, -90, -50, -70 https://cmr.earthdata.nasa.gov/search/concepts/C2089385730-NOAA_NCEI.umm_json Surface Data was collected aboard the YELCHO. Data collected was part of the First Dynamic Response And Kinematic Experiment (FDRAKE) conducted in 1976, along the Drake passage. Data consists of surface temperature, salinity, and silicate. The data was submitted by the Department of Oceanography, Texas A&M University College Station, Texas. Data are in form of computer printout (13 pages), there are no tapes. The experiment was conducted in two separate legs. The first leg was conducted between February 27-March 13, 1976 and the second leg of the experiment was conducted between March 22-April 8, 1976. proprietary gov.noaa.nodc:7700058_Not Applicable AIR PRESSURE and Other Data from YELCHO From Drake Passage from 1976-02-27 to 1976-04-08 (NCEI Accession 7700058) NOAA_NCEI STAC Catalog 1976-02-27 1976-04-08 -70, -90, -50, -70 https://cmr.earthdata.nasa.gov/search/concepts/C2089385730-NOAA_NCEI.umm_json Surface Data was collected aboard the YELCHO. Data collected was part of the First Dynamic Response And Kinematic Experiment (FDRAKE) conducted in 1976, along the Drake passage. Data consists of surface temperature, salinity, and silicate. The data was submitted by the Department of Oceanography, Texas A&M University College Station, Texas. Data are in form of computer printout (13 pages), there are no tapes. The experiment was conducted in two separate legs. The first leg was conducted between February 27-March 13, 1976 and the second leg of the experiment was conducted between March 22-April 8, 1976. proprietary -gov.noaa.nodc:7700179_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179) NOAA_NCEI STAC Catalog 1919-09-29 1976-04-26 -60, 44, 48, 80.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385848-NOAA_NCEI.umm_json This is German Surface Physical & Chemical Data submitted by Deutsches Hydrographische Institut. This data was collected in the Labrador Sea from January 6, 1974 to August 16, 1974. There is no documentation or description of the source code format. proprietary +gov.noaa.nodc:7700058_Not Applicable AIR PRESSURE and Other Data from YELCHO From Drake Passage from 1976-02-27 to 1976-04-08 (NCEI Accession 7700058) ALL STAC Catalog 1976-02-27 1976-04-08 -70, -90, -50, -70 https://cmr.earthdata.nasa.gov/search/concepts/C2089385730-NOAA_NCEI.umm_json Surface Data was collected aboard the YELCHO. Data collected was part of the First Dynamic Response And Kinematic Experiment (FDRAKE) conducted in 1976, along the Drake passage. Data consists of surface temperature, salinity, and silicate. The data was submitted by the Department of Oceanography, Texas A&M University College Station, Texas. Data are in form of computer printout (13 pages), there are no tapes. The experiment was conducted in two separate legs. The first leg was conducted between February 27-March 13, 1976 and the second leg of the experiment was conducted between March 22-April 8, 1976. proprietary gov.noaa.nodc:7700179_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179) ALL STAC Catalog 1919-09-29 1976-04-26 -60, 44, 48, 80.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385848-NOAA_NCEI.umm_json This is German Surface Physical & Chemical Data submitted by Deutsches Hydrographische Institut. This data was collected in the Labrador Sea from January 6, 1974 to August 16, 1974. There is no documentation or description of the source code format. proprietary +gov.noaa.nodc:7700179_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179) NOAA_NCEI STAC Catalog 1919-09-29 1976-04-26 -60, 44, 48, 80.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385848-NOAA_NCEI.umm_json This is German Surface Physical & Chemical Data submitted by Deutsches Hydrographische Institut. This data was collected in the Labrador Sea from January 6, 1974 to August 16, 1974. There is no documentation or description of the source code format. proprietary gov.noaa.nodc:7700437_Not Applicable Cloud amount/frequency, NITRATE and other data from CHAIN from 1973-03-11 to 1973-07-06 (NCEI Accession 7700437) NOAA_NCEI STAC Catalog 1973-03-11 1973-07-06 -72.6, 26.3, -66.8, 33.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089386094-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:7700455_Not Applicable BENTHIC SPECIES and Other Data from GILLISS and Other Platforms from 1975-10-27 to 1976-08-27 (NCEI Accession 7700455) NOAA_NCEI STAC Catalog 1975-10-27 1976-08-27 -75.3, 37.1, -71.9, 39.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089386131-NOAA_NCEI.umm_json Data was submitted by Dr. Gerald L. Engel. This study was organized to collect data on Parasite Type and Location. Parasite (both ecto- and endo-), and site of infection were looked into. SST, wave, turbidity, gear type (trawl), species, parasite (both ecto- and endo-), and site of infection (i.e. data on parasite type and location) data were collected. The documentation describes instruments employed for sampling, units, and a detailed description of the record format. These studies were part of the Mid-Atlantic Outer Continental Shelf Studies (OCS). These data were collected by the Virginia Institute of Marine Science (VIMS). Special codes employed by VIMS to describe parasite types and location were included as hardcopy. The original information submitted on tape has been converted into the current NODC storage format. proprietary gov.noaa.nodc:7700456_Not Applicable BENTHIC SPECIES and Other Data from GILLISS and Other Platforms from 1976-06-14 to 1976-09-02 (NCEI Accession 7700456) NOAA_NCEI STAC Catalog 1976-06-14 1976-09-02 -75.3, 37.5, -71.9, 39.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089386139-NOAA_NCEI.umm_json "Data submitted by Dr. Gerald L. Engel. The data was collected between June 1976 and September 1976. This study was organized to collect Histopathology and Benthic data. SST, wave, turbidity, gear type (trawl v.s dredge), benthic species counts and weights were collected. These data are ""megabenthic"" species. The documentation describes instruments employed for sampling, units, and a detailed description of the record format. The original data on tape has been converted to current NODC storage format. These studies were part of the Mid-Atlantic Outer Continental Shelf Studies (OCS). These data were collected by the Virginia Institute of Marine Science (VIMS)." proprietary @@ -19083,8 +19063,8 @@ gov.noaa.nodc:9300147_Not Applicable Chlorophyll-a profiles collected by various gov.noaa.nodc:9300152_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship RAINIER in the NE Pacific from 1993-03-23 to 1993-07-31 (NCEI Accession 9300152) NOAA_NCEI STAC Catalog 1993-03-23 1993-07-31 -157.3, 56.7, -133.6, 57.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089387756-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in NE Pacific (limit-180). Data was collected from NOAA Ship RAINIER. The data was collected over a period spanning from March 23, 1993 to July 31, 1993. Data was submitted in a diskette by Capt. Russell Arnold, Pacific Marine Environmental Laboratory, Seattle, WA. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC. F022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. proprietary gov.noaa.nodc:9300161_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX From Chukchi Sea and Others from 1992-07-24 to 1992-10-27 (NCEI Accession 9300161) NOAA_NCEI STAC Catalog 1992-07-24 1992-10-27 -170.4, 53.6, -149.4, 71.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089387773-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in Gulf of Alaska, Chukchi Sea, and NW Pacific (limit-180). Data was collected from cruises HX 163, HX 165 and HX 167 of Ship ALPHA HELIX. The data was collected over a period spanning from July 24, 1992 to october 27, 1992. Data was submitted in one exabyte cassette by Dr. Thomas C. Royer, Institute of Marine Science, University of Alaska, Fairbanks, AK. proprietary gov.noaa.nodc:9300187_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship WHITING in the Gulf of Mexico from 1992-04-02 to 1992-07-14 (NCEI Accession 9300187) NOAA_NCEI STAC Catalog 1992-04-02 1992-07-14 -92.9, 27.4, -91.8, 27.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089387862-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in Gulf of Mexico by SEACATs deployed in the area. Data was collected from NOAA Ship WHITING during 7 casts. The data was collected over a period spanning from April 2, 1992 to July 14, 1992. Data was submitted in one diskette by National Ocean Service, Rockville, MD. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC. F022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. proprietary -gov.noaa.nodc:9300196_Not Applicable Algal Species and other data collected from photographs in Southeast Atlantic Ocean from 1991-06-11 to 1993-03-22 (NCEI Accession 9300196) NOAA_NCEI STAC Catalog 1991-06-11 1993-03-22 -88, 17, -85, 22 https://cmr.earthdata.nasa.gov/search/concepts/C2089387904-NOAA_NCEI.umm_json Algal species and other data were collected using photographs from swimmers/divers in Southeast Atlantic Ocean. Data were collected from 11 June 1991 to 22 March 1993 by the Coral Cay Conservation. proprietary gov.noaa.nodc:9300196_Not Applicable Algal Species and other data collected from photographs in Southeast Atlantic Ocean from 1991-06-11 to 1993-03-22 (NCEI Accession 9300196) ALL STAC Catalog 1991-06-11 1993-03-22 -88, 17, -85, 22 https://cmr.earthdata.nasa.gov/search/concepts/C2089387904-NOAA_NCEI.umm_json Algal species and other data were collected using photographs from swimmers/divers in Southeast Atlantic Ocean. Data were collected from 11 June 1991 to 22 March 1993 by the Coral Cay Conservation. proprietary +gov.noaa.nodc:9300196_Not Applicable Algal Species and other data collected from photographs in Southeast Atlantic Ocean from 1991-06-11 to 1993-03-22 (NCEI Accession 9300196) NOAA_NCEI STAC Catalog 1991-06-11 1993-03-22 -88, 17, -85, 22 https://cmr.earthdata.nasa.gov/search/concepts/C2089387904-NOAA_NCEI.umm_json Algal species and other data were collected using photographs from swimmers/divers in Southeast Atlantic Ocean. Data were collected from 11 June 1991 to 22 March 1993 by the Coral Cay Conservation. proprietary gov.noaa.nodc:9300199_Not Applicable Benthic and tissue toxin data from stations in U.S. coastal waters from 1984-01-01 to 1989-12-31 (NCEI Accession 9300199) NOAA_NCEI STAC Catalog 1984-01-01 1989-12-31 -123, 25, -67, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2089387915-NOAA_NCEI.umm_json The accession contains Benthic and Tissue toxin data from stations in U.S. coastal waters (Coastal Waters of Western U.S. and North American Coastline-North) collected under the National Status and Trends (NS&T) program from 1984-1989. NS&T program for marine environmental quality was designed to define the geographic distribution of contaminant concentrations in tissues of marine organisms and sediments, and documenting biological responses to contamination. Samples have been collected under the original Benthic Surveillance Project (sediment and tissue samples from benthic fish) since 1984. Samples have been collected under the Mussel Watch Project (sediment and bivalves) since 1986. Both programs involved collecting samples from fixed sites on both Atlantic and Pacific coasts. Sites were selected so as not to be in close proximity to a major contamination source, as the programs objective was to quantify contamination over general areas. Chemical data from sediments collected during the benthic surveillance project, 1984-1986, is contained in a single delimited ASCII file (bssed.txt). Additional contaminated sediment data from the mussel watch program, 1986-1989, is contained in a single delimited ASCII file (mwsed.txt). These data do not include tissue analysis for contaminants. Chemicals and related parameters measured in sediments include: DDT. Since 1986, NOAA'S NS&T Program has included a component called the mussel watch project that has annually collected and chemically analyzed mussels and oysters from 177 sites at coastal and estuarine sites. Tissue samples from these mollusks have been analyzed to establish temporal trends of contaminant accumulation. Contaminants analyzed during this project include: polyaromatic hydrocarbons, polychlorinated biphenyls, chlorinated pesticides (such as ddt and its metabolites), aluminum, iron, manganese, silicon, other trace elements, and lipids. Tissue contaminant data from the mussel watch project, years 1986-1989, is contained in a single wordperfect 4.2 file, mollto90.txt. a second file, tbt_90.txt, lists the sum of concentrations of tributyl tin and its breakdown products (dibutyl tin and monobutyl tin) found in bivalve tissue samples. Tributylin (tbt) was previously used as an antifouling agent in paints, but its use on vessels under 75 feet was banned in 1988. A third file, mwsiteyr.txt, lists collection sites. proprietary gov.noaa.nodc:9400001_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship WHITING in the NW Atlantic from 1993-08-29 to 1993-11-21 (NCEI Accession 9400001) NOAA_NCEI STAC Catalog 1993-08-29 1993-11-21 -71.3, 41.4, -70.3, 41.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089387925-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) SEACAT data was collected in NW Atlantic (limit-40 W). Data was collected during 17 casts from NOAA Ship WHITING. The data was collected over a period spanning from August 29, 1993 to November 21, 1993. Data was submitted in a diskette by National Ocean Service, Rockville, MD. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC. F022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. proprietary gov.noaa.nodc:9400010_Not Applicable BAROMETRIC PRESSURE and Other Data from SEAWARD EXPLORER From NW Atlantic (limit-40 W) from 1993-02-06 to 1993-08-28 (NCEI Accession 9400010) NOAA_NCEI STAC Catalog 1993-02-06 1993-08-28 -75.9, 34.5, -73.7, 36.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089388069-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in NW Atlantic (limit-40 W) as part of Physical Oceanography Field Program offshore North Carolina supported by grant MMS #14-35-0001-30599. Data was collected from Ship SEAWARD EXPLORER cruises SE9301, SE9303, and SE9309. The data was collected over a period spanning from February 6, 1993 and August 28, 1993. Data from 146 stations containing 7,614 records was submitted on a tape by Dr. Thomas Berger, Science Applications, Inc., Raleigh NC. Data has been processed and is available in F022-CTD-Hi Resolution file format of NODC. proprietary @@ -19138,8 +19118,8 @@ gov.noaa.nodc:9600151_Not Applicable ABSORPTION, SCATTERING, ATTENUATION COEFFIC gov.noaa.nodc:9700022_Not Applicable Chemical and temperature profile data from CTD casts in the East China Sea, Sea of Japan, and North Pacific Ocean (NCEI Accession 9700022) NOAA_NCEI STAC Catalog 123.066667, 3, 147.033333, 45.583333 https://cmr.earthdata.nasa.gov/search/concepts/C2089387774-NOAA_NCEI.umm_json Chemical and temperature profile data were collected from CTD casts in the East China Sea, Sea of Japan, and North Pacific Ocean. Data were submitted by the Japan Meteorological Agency (JMA). proprietary gov.noaa.nodc:9700025_Not Applicable Chemical, physical, and other data collected using fluorometer, laboratory analysis, visual analysis, and bottle casts from NOAA Ship DAVID STARR JORDAN and NEW HORIZON as part of the California Cooperative Fisheries Investigation (CALCOFI) project, from 1994-01-21 to 1996-04-30 (NCEI Accession 9700025) NOAA_NCEI STAC Catalog 1994-01-21 1996-04-30 -124.3, 29.9, -117.3, 35.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089387805-NOAA_NCEI.umm_json Chemical, physical, and other data were collected from NOAA Ship DAVID STARR JORDAN and NEW HORIZON from January 21, 1994 to April 30, 1996. Data were collected using fluorometer, laboratory analysis, visual analysis, and bottle casts in the Northeast Pacific Ocean. Data were submitted by Scripps Institution of Oceanography (SIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project. proprietary gov.noaa.nodc:9700040_Not Applicable Chemical, physical, and other data collected using bottle casts from NOAA Ship DAVID STARR JORDAN and NEW HORIZON as part of the California Cooperative Fisheries Investigation (CALCOFI) project, from 1995-01-04 to 1996-05-03 (NCEI Accession 9700040) NOAA_NCEI STAC Catalog 1995-01-04 1996-05-03 -124.326667, 30.16, -117.303333, 35.09 https://cmr.earthdata.nasa.gov/search/concepts/C2089387897-NOAA_NCEI.umm_json Chemical, physical, and other data were collected from NOAA Ship DAVID STARR JORDAN and NEW HORIZON from January 4, 1995 to May 3, 1996. Data were collected using bottle casts from the Northeast Pacific Ocean. Data were submitted by Scripps Institution of Oceanography (SIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project. proprietary -gov.noaa.nodc:9700063_Not Applicable AIR PRESSURE and Other Data from NOODIN From Great Lakes from 1995-06-20 to 1996-11-14 (NCEI Accession 9700063) ALL STAC Catalog 1995-06-20 1996-11-14 -91.7, 47, -91.7, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2089388236-NOAA_NCEI.umm_json Conductivity, temperature, depth, pressure, transmissivity, and fluorsecence were collected from the NOODIN from June 20, 1995 to October 26, 1995 and May 30, 1996 to November 14, 1996. Data were submitted by Dr. Elise A. Ralph from the University of Minnesota; Large Lakes Observatory. These data were collected using transmissometer, fluorometer, and CTD casts in the Two Harbors, MN to Port Wing, WI on the Lake Superior. proprietary gov.noaa.nodc:9700063_Not Applicable AIR PRESSURE and Other Data from NOODIN From Great Lakes from 1995-06-20 to 1996-11-14 (NCEI Accession 9700063) NOAA_NCEI STAC Catalog 1995-06-20 1996-11-14 -91.7, 47, -91.7, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2089388236-NOAA_NCEI.umm_json Conductivity, temperature, depth, pressure, transmissivity, and fluorsecence were collected from the NOODIN from June 20, 1995 to October 26, 1995 and May 30, 1996 to November 14, 1996. Data were submitted by Dr. Elise A. Ralph from the University of Minnesota; Large Lakes Observatory. These data were collected using transmissometer, fluorometer, and CTD casts in the Two Harbors, MN to Port Wing, WI on the Lake Superior. proprietary +gov.noaa.nodc:9700063_Not Applicable AIR PRESSURE and Other Data from NOODIN From Great Lakes from 1995-06-20 to 1996-11-14 (NCEI Accession 9700063) ALL STAC Catalog 1995-06-20 1996-11-14 -91.7, 47, -91.7, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2089388236-NOAA_NCEI.umm_json Conductivity, temperature, depth, pressure, transmissivity, and fluorsecence were collected from the NOODIN from June 20, 1995 to October 26, 1995 and May 30, 1996 to November 14, 1996. Data were submitted by Dr. Elise A. Ralph from the University of Minnesota; Large Lakes Observatory. These data were collected using transmissometer, fluorometer, and CTD casts in the Two Harbors, MN to Port Wing, WI on the Lake Superior. proprietary gov.noaa.nodc:9700115_Not Applicable Chemical and temperature profile data from bottle and CTD casts in the Pacific Ocean as part of the Joint Global Ocean Flux Study/Equatorial Pacific Basin Study (JGOFS/EQPAC) project, from 1992-03-19 to 1992-10-21 (NCEI Accession 9700115) NOAA_NCEI STAC Catalog 1992-03-19 1992-10-21 -145.489, -12, -134.9117, 12.0317 https://cmr.earthdata.nasa.gov/search/concepts/C2089388395-NOAA_NCEI.umm_json Chemical and temperature profile data were collected using bottle and CTD casts from the THOMAS THOMPSON in the Pacific Ocean from March 19, 1992 to October 21, 1992. Data were collected three different universities and a institution; Oregon State University, University of Washington, Woods Hole Oceanographic Institution, and University of Maryland; Horn Point Environmental Laboratory as part of the Joint Global Ocean Flux Study/Equatorial Pacific Basin Study (JGOFS/EQPAC) project. proprietary gov.noaa.nodc:9700116_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON From TOGA Area - Pacific (30 N to 30 S) from 1992-03-19 to 1992-10-21 (NCEI Accession 9700116) NOAA_NCEI STAC Catalog 1992-03-19 1992-10-21 -145, -12, -140, 0 https://cmr.earthdata.nasa.gov/search/concepts/C2089388417-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9700205_Not Applicable AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1992-02-02 to 1992-10-21 (NCEI Accession 9700205) NOAA_NCEI STAC Catalog 1992-02-02 1992-10-21 -146.293, -12.864, -104.392, 2.999 https://cmr.earthdata.nasa.gov/search/concepts/C2089388823-NOAA_NCEI.umm_json Not provided proprietary @@ -19151,8 +19131,8 @@ gov.noaa.nodc:9700238_Not Applicable BACTERIA - BACTERIAL DENSITY and Other Data gov.noaa.nodc:9800027_Not Applicable BAROMETRIC PRESSURE and Other Data from LITTLE DIPPER from 1995-03-01 to 1998-02-06 (NCEI Accession 9800027) NOAA_NCEI STAC Catalog 1995-03-01 1998-02-06 -149.5, 59.8, -149.4, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2089385859-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9800037_Not Applicable Chemical, temperature, pressure, and salinity data from bottle and CTD casts in the Arabian Sea as part of the Joint Global Ocean Flux Study / Arabian Sea Process Studies (JGOFS/Arabian) project, from 1995-07-17 to 1995-09-15 (NCEI Accession 9800037) NOAA_NCEI STAC Catalog 1995-07-17 1995-09-15 57.2998, 9.9113, 68.751, 22.527 https://cmr.earthdata.nasa.gov/search/concepts/C2089385946-NOAA_NCEI.umm_json Chemical, temperature, pressure, and salinity data were collected using bottle and CTD casts from the R/V Thomas G. Thompson in the Arabian Sea. Data were collected from July 17, 1995 to September 15, 1995. Data were collected by four different institution; Old Dominion University, Bermuda Biological Station for Research, Virginia Institute of Marine Science, and Woods Hole Oceanographic Institution as part of the Joint Global Ocean Flux Study / Arabian Sea Process Studies (JGOFS/Arabian) project. proprietary gov.noaa.nodc:9800052_Not Applicable BENTHIC SPECIES and Other Data from UNKNOWN and Other Platforms from 1989-01-01 to 1997-12-31 (NCEI Accession 9800052) NOAA_NCEI STAC Catalog 1989-01-01 1997-12-31 -123.6, 47.1, -122.4, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2089386070-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:9800085_Not Applicable AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1995-01-09 to 1995-12-28 (NCEI Accession 9800085) ALL STAC Catalog 1995-01-09 1995-12-28 56.5, 9.9, 68.8, 24.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089386309-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9800085_Not Applicable AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1995-01-09 to 1995-12-28 (NCEI Accession 9800085) NOAA_NCEI STAC Catalog 1995-01-09 1995-12-28 56.5, 9.9, 68.8, 24.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089386309-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:9800085_Not Applicable AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1995-01-09 to 1995-12-28 (NCEI Accession 9800085) ALL STAC Catalog 1995-01-09 1995-12-28 56.5, 9.9, 68.8, 24.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089386309-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9800092_Not Applicable BACTERIA - BACTERIAL DENSITY and Other Data from USS CHAUMONT from 1995-01-09 to 1995-12-26 (NCEI Accession 9800092) NOAA_NCEI STAC Catalog 1995-01-09 1995-12-26 57.3, 9.3, 68.8, 22.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089386381-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9800095_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON from 1995-01-08 to 1995-09-12 (NCEI Accession 9800095) NOAA_NCEI STAC Catalog 1995-01-08 1995-09-12 57.3, 10, 68.8, 22.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089386411-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9800118_Not Applicable Chemical, physical, and other data collected using bottle casts from NOAA Ship DAVID STARR JORDAN, ROGER REVILLE, and NEW HORIZON as part of the California Cooperative Fisheries Investigation from 1996-08-07 to 1997-04-19 (NCEI Accession 9800118) NOAA_NCEI STAC Catalog 1996-08-07 1997-04-19 -124.3, 29.8, -117.3, 35.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089386498-NOAA_NCEI.umm_json Chemical, physical, and other data were collected from NOAA Ship DAVID STARR JORDAN, ROGER REVILLE, and NEW HORIZON from August 7, 1996 to April 19, 1997. Data were collected using bottle casts in the Pacific Ocean. Data were submitted by Scripps Institution of Oceanography (SIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project. proprietary @@ -19168,17 +19148,17 @@ gov.noaa.nodc:9800199_Not Applicable BACTERIA - BACTERIAL DENSITY and Other Data gov.noaa.nodc:9900010_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON From Arabian Sea from 1995-03-18 to 1997-08-13 (NCEI Accession 9900010) NOAA_NCEI STAC Catalog 1995-03-18 1997-08-13 56.5, 10, 68.8, 24.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089387251-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900014_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON From Arabian Sea from 1995-01-09 to 1995-09-12 (NCEI Accession 9900014) NOAA_NCEI STAC Catalog 1995-01-09 1995-09-12 57.3, 10, 68.8, 22.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089387273-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900015_Not Applicable CARBON DIOXIDE - PARTIAL PRESSURE (pCO2) - SEA and Other Data from NOAA Ship DISCOVERER and Other Platforms from 1987-05-19 to 1994-01-07 (NCEI Accession 9900015) NOAA_NCEI STAC Catalog 1987-05-19 1994-01-07 -179.9, -70.3, 179.9, 54.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089387289-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:9900022_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms from 1998-08-01 to 1998-12-31 (NCEI Accession 9900022) ALL STAC Catalog 1998-08-01 1998-12-31 -124.1, 44.6, -124, 44.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089387361-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900022_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms from 1998-08-01 to 1998-12-31 (NCEI Accession 9900022) NOAA_NCEI STAC Catalog 1998-08-01 1998-12-31 -124.1, 44.6, -124, 44.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089387361-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:9900054_Not Applicable Algal species and other data collected using photographs in the South Pacific Ocean from 1992-01-02 to 1992-12-31 (NCEI Accession 9900054) ALL STAC Catalog 1992-01-02 1992-12-31 -170.8, -14.4, -170.6, -14.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089387610-NOAA_NCEI.umm_json Data from a 1992 survey of the American Samoa coral reef ecosystem was received from Dr. Barry Smith of the University of Guam. The digital files replace a paper report submitted to NODC in Fall 1998. This study was part of the American Samoa Coastal Resources Inventory (ASCRI), partly funded by Sea Grant. His component of the study focuses on a systematic inventory of conspicuous marine macro-invertebrates observations. proprietary +gov.noaa.nodc:9900022_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms from 1998-08-01 to 1998-12-31 (NCEI Accession 9900022) ALL STAC Catalog 1998-08-01 1998-12-31 -124.1, 44.6, -124, 44.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089387361-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900054_Not Applicable Algal species and other data collected using photographs in the South Pacific Ocean from 1992-01-02 to 1992-12-31 (NCEI Accession 9900054) NOAA_NCEI STAC Catalog 1992-01-02 1992-12-31 -170.8, -14.4, -170.6, -14.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089387610-NOAA_NCEI.umm_json Data from a 1992 survey of the American Samoa coral reef ecosystem was received from Dr. Barry Smith of the University of Guam. The digital files replace a paper report submitted to NODC in Fall 1998. This study was part of the American Samoa Coastal Resources Inventory (ASCRI), partly funded by Sea Grant. His component of the study focuses on a systematic inventory of conspicuous marine macro-invertebrates observations. proprietary -gov.noaa.nodc:9900094_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-01-01 to 1999-04-29 (NCEI Accession 9900094) NOAA_NCEI STAC Catalog 1999-01-01 1999-04-29 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089387865-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:9900054_Not Applicable Algal species and other data collected using photographs in the South Pacific Ocean from 1992-01-02 to 1992-12-31 (NCEI Accession 9900054) ALL STAC Catalog 1992-01-02 1992-12-31 -170.8, -14.4, -170.6, -14.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089387610-NOAA_NCEI.umm_json Data from a 1992 survey of the American Samoa coral reef ecosystem was received from Dr. Barry Smith of the University of Guam. The digital files replace a paper report submitted to NODC in Fall 1998. This study was part of the American Samoa Coastal Resources Inventory (ASCRI), partly funded by Sea Grant. His component of the study focuses on a systematic inventory of conspicuous marine macro-invertebrates observations. proprietary gov.noaa.nodc:9900094_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-01-01 to 1999-04-29 (NCEI Accession 9900094) ALL STAC Catalog 1999-01-01 1999-04-29 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089387865-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:9900094_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-01-01 to 1999-04-29 (NCEI Accession 9900094) NOAA_NCEI STAC Catalog 1999-01-01 1999-04-29 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089387865-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900119_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-05-01 to 1999-06-30 (NCEI Accession 9900119) NOAA_NCEI STAC Catalog 1999-05-01 1999-06-30 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089388259-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900119_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-05-01 to 1999-06-30 (NCEI Accession 9900119) ALL STAC Catalog 1999-05-01 1999-06-30 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089388259-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900158_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from OCEANUS and Other Platforms from 1993-03-12 to 1993-03-23 (NCEI Accession 9900158) NOAA_NCEI STAC Catalog 1993-03-12 1993-03-23 -67.2, 31.7, -64.1, 36.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089388472-NOAA_NCEI.umm_json Not provided proprietary -gov.noaa.nodc:9900159_Not Applicable 1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159) NOAA_NCEI STAC Catalog 1999-06-16 1999-07-18 -124, 45, -122, 49.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089388479-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900159_Not Applicable 1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159) ALL STAC Catalog 1999-06-16 1999-07-18 -124, 45, -122, 49.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089388479-NOAA_NCEI.umm_json Not provided proprietary +gov.noaa.nodc:9900159_Not Applicable 1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159) NOAA_NCEI STAC Catalog 1999-06-16 1999-07-18 -124, 45, -122, 49.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089388479-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900164_Not Applicable BACTERIA - BACTERIAL DENSITY and Other Data from NATHANIEL B. PALMER from 1996-10-08 to 1997-05-05 (NCEI Accession 9900164) NOAA_NCEI STAC Catalog 1996-10-08 1997-05-05 168.9, -78, -175.9, -74 https://cmr.earthdata.nasa.gov/search/concepts/C2089388517-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900202_Not Applicable BACTERIA - BACTERIAL DENSITY and Other Data from HERMANO GINES from 1995-11-13 to 1997-11-14 (NCEI Accession 9900202) NOAA_NCEI STAC Catalog 1995-11-13 1997-11-14 -64.7, 10.5, -64.7, 10.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089388797-NOAA_NCEI.umm_json Not provided proprietary gov.noaa.nodc:9900218_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from NATHANIEL B. PALMER from 1996-10-18 to 1997-02-08 (NCEI Accession 9900218) NOAA_NCEI STAC Catalog 1996-10-18 1997-02-08 169, -78, -176, -76.4 https://cmr.earthdata.nasa.gov/search/concepts/C2089388860-NOAA_NCEI.umm_json Not provided proprietary @@ -19535,8 +19515,8 @@ gps-derived-data-of-swe-hs-and-lwc-and-corresponding-validation-data_1.0 GPS-der grassland-use-intensity-maps-for-switzerland_1.0 Grassland-use intensity maps for Switzerland ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226082226-ENVIDAT.umm_json A rule-based algorithm [(Schwieder et al., 2022)](https://doi.org/10.1016/j.rse.2021.112795) was used to produce annual maps for 2018–2021 of grassland-management events, i.e. mowing and/or grazing, for Switzerland using Sentinel-2 and Landsat 8 satellite time series. All satellite images were processed with the [FORCE](https://force-eo.readthedocs.io) framework. The resulting maps provide information on the number and timing of grassland-management events at a spatial resolution of 10 m × 10 m for the whole of Switzerland. For the final maps, permanent grasslands were masked using a variety of land-use layers, according to [Huber et al. (2022)](https://doi.org/10.1002/rse2.298) but replacing the crop mask with the agricultural-use data from the cantons. We assessed the detection of management events based on independent reference data, which we acquired from daily time series of publicly available webcams that are widely distributed across Switzerland. We further tested the ecological relevance of the generated intensity measures in relation to nationwide biodiversity data (see [Weber et al., 2023](https://doi.org/10.1002/rse2.372)). The webcam-based reference data used for verification was subsequently added on 14.02.2024. proprietary gravity_wilkes_1964_1 Gravity Survey Results, Wilkes Ice Cap, 1964-65 AU_AADC STAC Catalog 1964-01-01 1966-01-01 110, -68, 115, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214308605-AU_AADC.umm_json The results of a gravity survey done on Wilkes Ice Cap. No information in the papers on how it was done, dates, etc - just the numbers. Even year is unsure (could be 1964 or 1965 season). These documents have been archived at the Australian Antarctic Division. proprietary green-infrastructure-in-european-strategic-spatial-plans-of-urban_1.0 Green infrastructure in strategic spatial plans: Evidence from European urban regions ENVIDAT STAC Catalog 2022-01-01 2022-01-01 -17.4023437, 33.5917433, 34.6289063, 68.4698482 https://cmr.earthdata.nasa.gov/search/concepts/C2789815116-ENVIDAT.umm_json "The present dataset is part of the published scientific paper Grădinaru, S. R., & Hersperger, A. M. (2019). Green infrastructure in strategic spatial plans: Evidence from European urban regions. Urban forestry & urban greening, 40, 17-28. The goal of this research was to conduct a comparative analysis of the integration of green infrastructure concept in strategic spatial plans of European Urban regions. Specifically, the paper has the following objectivs: 1) which principles of GI planning are followed in strategic plans of urban regions? 2) can we identify different approaches to GI integration into strategic planning?. The study focues on a sample consisting of 14 case studies spanning 11 countries. We retrieved the strategic plans from the websites of the planning authorities. The list of the reviewed planning documents can be found in Appendix A of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. The planning documents were read in order to address the protocol items. The answer to the protocol items in each of the first two categories (items 1–11) was documented as text, while the answer for the third category, namely items addressing the planning principles (items 12–36), was coded according to Table 1 of the article. As a result, we provide the folowing outputs: • GI_Dataset_1_Items_1-12.xlsx – available on request o Results of the coding on general aspects regarding the strategic plans of urban regions as well as extracts from each plan to justify the coding option – this data was derived from the coding procedure coresponding to items from 1 to 12 of the protocol. The data was discussed qualitativly in the research paper. • GI_Dataset_2_Items_12-36.csv – freely available o Results of the coding on principles of GI planning followed in strategic plans of urban regions– this data was derived from the coding procedure coresponding to items from 12 to 36 of the protocol. The data served as input for the classifications performed through hierarchical cluster analysis. This data is a detailed version of Appendix C in the paper." proprietary -grinstedSBB-ECM-VIDEO 2km long Surface Conductivity Profile and video recording, Scharffenbergbotnen SCIOPS STAC Catalog 1970-01-01 -11.042684, -74.57969, 11.11278, -74.566 https://cmr.earthdata.nasa.gov/search/concepts/C1214586809-SCIOPS.umm_json Location: Scharffenbergbotnen blue ice area, Heimefrontfjella Electrical Conductivity profile of the surface blue ice (stretching ~2.5km from near the ice fall). At the same time a video recording of the surface ice was made. Positions of the records can be tied together with DGPS. proprietary grinstedSBB-ECM-VIDEO 2km long Surface Conductivity Profile and video recording, Scharffenbergbotnen ALL STAC Catalog 1970-01-01 -11.042684, -74.57969, 11.11278, -74.566 https://cmr.earthdata.nasa.gov/search/concepts/C1214586809-SCIOPS.umm_json Location: Scharffenbergbotnen blue ice area, Heimefrontfjella Electrical Conductivity profile of the surface blue ice (stretching ~2.5km from near the ice fall). At the same time a video recording of the surface ice was made. Positions of the records can be tied together with DGPS. proprietary +grinstedSBB-ECM-VIDEO 2km long Surface Conductivity Profile and video recording, Scharffenbergbotnen SCIOPS STAC Catalog 1970-01-01 -11.042684, -74.57969, 11.11278, -74.566 https://cmr.earthdata.nasa.gov/search/concepts/C1214586809-SCIOPS.umm_json Location: Scharffenbergbotnen blue ice area, Heimefrontfjella Electrical Conductivity profile of the surface blue ice (stretching ~2.5km from near the ice fall). At the same time a video recording of the surface ice was made. Positions of the records can be tied together with DGPS. proprietary gripapr2_1 GRIP AIRBORNE SECOND GENERATION PRECIPITATION RADAR (APR-2) V1 GHRC_DAAC STAC Catalog 2010-08-17 2010-09-22 -97.9192, 11.9008, -56.0457, 34.847 https://cmr.earthdata.nasa.gov/search/concepts/C1979833483-GHRC_DAAC.umm_json The GRIP Airborne Second Generation Precipitation Radar (APR-2) dataset was collected from the Second Generation Airborne Precipitation Radar (APR-2), which is a dual-frequency (13 GHz and 35 GHz), Doppler, dual-polarization radar system. It has a downward looking antenna that performs cross track scans. Additional features include: simultaneous dual-frequency, matched beam operation at 13.4 and 35.6 GHz (same as GPM Dual-Frequency Precipitation Radar), simultaneous measurement of both like- and cross-polarized signals at both frequencies, Doppler operation, and real-time pulse compression (calibrated reflectivity data can be produced for large areas in the field during flight, if necessary). The APR-2 flew on the NASA DC-8 for the Genesis and Rapid Intensification Processes (GRIP) experiment and collected data between Aug 17, 2010 - Sep 22, 2010 and are in HDF-4 format. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes. proprietary gripcaps_1 GRIP CLOUD MICROPHYSICS V1 GHRC_DAAC STAC Catalog 2010-08-13 2010-09-25 -100, 0, -71.5, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1979834641-GHRC_DAAC.umm_json The GRIP Cloud Microphysics dataset was collected during the GRIP campaign from three probes: the Cloud, Aerosol, and Precipitation Spectrometer (CAPS), the Precipitation Imaging Probe (PIP), and the Cloud Droplet Probe (CDP). All are manufactured by Droplet Measurement Technologies in Boulder, CO. The CAPS is a combination of two probes, the Cloud Imaging Probe-Greyscale (CIP-G), and the Cloud and Aerosol Spectrometer (CAS). Images of particles are recorded by the CIP-G and PIP, while the CAS probe measures particle size distribution from 0.55 to 52.5 microns and the CDP measures ice amount. Some ice/liquid water content are derived from the particle size distribution. The major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes. Data was collected 13 Aug 2010 through 25 Sep 2010. proprietary gripdawn_1 GRIP DOPPLER AEROSOL WIND LIDAR (DAWN) V1 GHRC_DAAC STAC Catalog 2010-08-24 2010-09-22 -97.8173, 11.9999, -55.3185, 34.752 https://cmr.earthdata.nasa.gov/search/concepts/C1979834812-GHRC_DAAC.umm_json The GRIP Doppler Aerosol WiNd Lidar (DAWN) Dataset was collected by the Doppler Aerosol WiNd (DAWN), a pulsed lidar, which operated aboard a NASA DC-8 aircraft during the Genesis and Rapid Intensification Processes (GRIP) field campaign. he major goal was to better understand how tropical storms form and develop into major hurricanes. NASA used the DC-8 aircraft, the WB-57 aircraft and the Global Hawk Unmanned Airborne System (UAS), configured with a suite of in situ and remote sensing instruments that were used to observe and characterize the lifecycle of hurricanes. This campaign also capitalized on a number of ground networks and space-based assets, in addition to the instruments deployed on aircraft from Ft. Lauderdale, Florida ( DC-8), Houston, Texas (WB-57), and NASA Dryden Flight Research Center, California (Global Hawk). Data values include Line-of-Sight (LOS) Winds, calculated vertical profiles of horizontal wind velocity, frequency-domain signal energy and time versus latitude and longitude. Instrument details can be found in the dataset documentation. Data was gathered during August 24, 2010 thru September 22, 2010 over the Atlantic Ocean. proprietary @@ -19612,8 +19592,8 @@ heard_bathy_gis_1 Heard and McDonald Islands - Bathymetric data created for 1:1 heard_coast_gis_1 Heard Island Coast GIS Dataset AU_AADC STAC Catalog 1997-04-04 1997-04-04 73.24, -53.21, 73.9, -52.95 https://cmr.earthdata.nasa.gov/search/concepts/C1214313514-AU_AADC.umm_json A coastline of Heard Island and McDonald Islands, created in the AMBIS (Australian Maritime Boundaries Information System) by Geoscience Australia (previously AUSLIG). proprietary heard_dem_radarsat02_1 Heard Island RADARSAT (2002) Digital Elevation Model (DEM) AU_AADC STAC Catalog 2002-01-24 2002-03-13 73.18306, -53.27694, 73.97306, -52.80806 https://cmr.earthdata.nasa.gov/search/concepts/C1214308612-AU_AADC.umm_json "This dataset is a Digital Elevation Model (DEM) of Heard Island derived by interferometric processing from RADARSAT images acquired on 17 February 2002 and 13 March 2002. The DEM was created by a contractor for the Australian Antarctic Data Centre. The cell size is 10 metres. Processing stages included: 1 Detection of a coastline from a RADARSAT image of Heard Island acquired 24 January 2002 and rectified using ground control points provided by a second contractor. 2 Generation of the interferometric SAR (InSAR) DEM using the RADARSAT images acquired on 17 February 2002 and 13 March 2002. 3 Co-registration of the InSAR DEM with a DEM derived from stereoscopic RADARSAT images acquired in March and April of 1997 and described by the metadata record 'Heard Island RADARSAT (1997) DEM'. 4 Merging of the InSAR DEM with the 1997 stereoscopic DEM and the coastline detected in stage 1. The following are available for download from the Related URLs below: 1 The final DEM in ArcInfo interchange or ArcInfo ascii formats. 2 The rectified RADARSAT image of Heard Island acquired 24 January 2002. Rectified using ground control points and subsequently used in processing of the DEM. 3 Contours generated from the DEM and the island polygon (coastline) extracted from the rectified RADARSAT image acquired 24 January 2002. 4 A detailed deport describing the generation of the DEM. 5 A report by Dr Arko Lucieer Centre for Spatial Information Science School of Geography and Environmental Studies University of Tasmania Private Bag 76 Hobart 7001 Tasmania, Australia outlining some errors and artefacts in the DEM. Dr Lucieer produced this report while working for the Australian Antarctic Division. On 3 July 2003 Henk Brolsma (Mapping Officer, Australian Antarctic Division) wrote the following email to the contractor who created the DEM. ""What I'm really interested in are the 20 metre contours for the areas with high coherency. These are the areas where most of the field work takes place and where we have a need for contours with an accuracy better than 50 metres and my reason for using INSAR in the first instance. So can you send me: 1. The 20 metre contours for the areas with high coherency? 2. The zone or line where the INSAR and Stereo Imagery were integrated? This would be very useful for the metadata."" He did not receive a reply to that email and that was the reason why he was reluctant to make the DEM public. Since he won't now get a reply and the DEM is probably better than the 1997 DEM, he considers the 2002 DEM should now be published." proprietary heard_dem_radarsat97_1 Heard Island RADARSAT (1997) Digital Elevation Model (DEM) AU_AADC STAC Catalog 1997-10-24 1997-10-24 73.238, -53.2, 73.89, -52.957 https://cmr.earthdata.nasa.gov/search/concepts/C1214313515-AU_AADC.umm_json A Digital Elevation Model (DEM) of Heard island, with a 50 metre grid interval, and held in UTM Zone 43(WGS-84) coordinates. Heights are referenced to mean sea level. 50 metre contours (including a coastline) were derived. Elevation range 0 - less than 2800m. proprietary -heard_dem_terrasar_1 A Digital Elevation Model of Heard Island derived from TerraSAR satellite imagery AU_AADC STAC Catalog 2009-10-31 2009-11-14 73.185, -53.266, 74.02, -52.931 https://cmr.earthdata.nasa.gov/search/concepts/C1214313516-AU_AADC.umm_json This dataset is a Digital Elevation Model (DEM) of Heard Island derived from TerraSAR_X imagery using radargrammetric methods. At least one pair of ascending images and one pair of descending images were used at each location. The TerraSAR_X stereo pairs were acquired between 31 October 2009 and 14 November 2009. The DEM was created by a contractor for the Australian Antarctic Data Centre. It is in geotiff format stored in a UTM zone 43 projection, horizontal datum WGS84. The cell size is 10 metres. Included with the DEM are some auxiliary files and documentation. This includes: 1 an xml file with metadata; 2 a shapefile detailing the images used for each part of the DEM; 3 a geotiff showing the correlation between the images used at each point in the DEM; 4 a spreadsheet with an accuracy assessment of the DEM using ground control points provided by the Australian Antarctic Data Centre. proprietary heard_dem_terrasar_1 A Digital Elevation Model of Heard Island derived from TerraSAR satellite imagery ALL STAC Catalog 2009-10-31 2009-11-14 73.185, -53.266, 74.02, -52.931 https://cmr.earthdata.nasa.gov/search/concepts/C1214313516-AU_AADC.umm_json This dataset is a Digital Elevation Model (DEM) of Heard Island derived from TerraSAR_X imagery using radargrammetric methods. At least one pair of ascending images and one pair of descending images were used at each location. The TerraSAR_X stereo pairs were acquired between 31 October 2009 and 14 November 2009. The DEM was created by a contractor for the Australian Antarctic Data Centre. It is in geotiff format stored in a UTM zone 43 projection, horizontal datum WGS84. The cell size is 10 metres. Included with the DEM are some auxiliary files and documentation. This includes: 1 an xml file with metadata; 2 a shapefile detailing the images used for each part of the DEM; 3 a geotiff showing the correlation between the images used at each point in the DEM; 4 a spreadsheet with an accuracy assessment of the DEM using ground control points provided by the Australian Antarctic Data Centre. proprietary +heard_dem_terrasar_1 A Digital Elevation Model of Heard Island derived from TerraSAR satellite imagery AU_AADC STAC Catalog 2009-10-31 2009-11-14 73.185, -53.266, 74.02, -52.931 https://cmr.earthdata.nasa.gov/search/concepts/C1214313516-AU_AADC.umm_json This dataset is a Digital Elevation Model (DEM) of Heard Island derived from TerraSAR_X imagery using radargrammetric methods. At least one pair of ascending images and one pair of descending images were used at each location. The TerraSAR_X stereo pairs were acquired between 31 October 2009 and 14 November 2009. The DEM was created by a contractor for the Australian Antarctic Data Centre. It is in geotiff format stored in a UTM zone 43 projection, horizontal datum WGS84. The cell size is 10 metres. Included with the DEM are some auxiliary files and documentation. This includes: 1 an xml file with metadata; 2 a shapefile detailing the images used for each part of the DEM; 3 a geotiff showing the correlation between the images used at each point in the DEM; 4 a spreadsheet with an accuracy assessment of the DEM using ground control points provided by the Australian Antarctic Data Centre. proprietary heard_glacier_gis_1 Heard Island - Glacier extents mapped from satellite imagery and aerial photography. AU_AADC STAC Catalog 1987-01-01 1997-12-31 73.24, -53.21, 73.9, -52.95 https://cmr.earthdata.nasa.gov/search/concepts/C1214308621-AU_AADC.umm_json Abstract from: 'An inventory of present glaciers on Heard Island and their historical variation' by Andrew Ruddell. Heard Island is a large ice-covered volcanic cone situated in the south Indian Ocean. Its location enables unique climatic information to be obtained from a very remote and predominantly maritime region. Past studies show that while some glaciers have undergone major recession since the late 1940s, others, such as large non-calving glaciers, have shown little change in extent. The island is usually cloud covered and this has hampered aerial and ground based surveys. Using SPOT satellite imagery acquired in 1988 and supplemented by aerial photography in 1987 and a digital elevation model derived from 1997 Radarsat imagery, an inventory of glacier extent is provided and this indicates that there are a total of 29 glaciated basins (41 termini), with an area of 257 km2 and an estimated volume of 14.2 km3. The satellite imagery is used to rectify earlier estimates of glacier extent based on aerial photography in 1947 and 1980. Between 1947 and 1988 the glaciated area had decreased by 11% and volume by 12%. Approximately half of this occurred during the 1980s. A variety of historical records have been compiled and these provide evidence of glacier behaviour since the mid-1800s when they were at their greatest extent during the recorded period. The elevation range of a glacier is a good indication of glacier hypsometry and its sensitivity to mass balance and climate variations. Glaciers such as the Gotley are of large elevation range and high mass turnover. Such glaciers show little sensitivity to climate variations as they lose much of their ice through calving into the sea rather than surface melt. Glaciers of low elevation range such as those on the Laurens Peninsula are especially sensitive to climate change. Glaciers of this type indicate that while minor decadal fluctuations have occurred in the period from at least 1902 to the 1950s, the recession of many glaciers during the past 50 years has been unprecedented. The glacier variations correlate with observed temperature records. Observations of occasional volcanic eruptions since the 1880s indicate that most activity is related to lava flows from Mawson Peak and fumerole activity on its upper southwestern slope. This activity appears to have had little effect on the Gotley and Lied glaciers. proprietary heard_ice_gis_1 Heard Island Ice Coverage GIS Dataset AU_AADC STAC Catalog 1991-04-07 1991-09-09 73.24, -53.21, 73.9, -52.95 https://cmr.earthdata.nasa.gov/search/concepts/C1214308622-AU_AADC.umm_json Heard Island, ice layer. This is a polygon dataset stored in the Geographical Information System (GIS). The ice layer shows ice/snow as depicted on the Heard Island satellite image map, published in 1991. The amount of ice/snow is as captured on the SPOT image 9 Jan 1988. proprietary heard_is_sat_1 Heard Island and McDonald Islands Satellite Image Map 1:50000 AU_AADC STAC Catalog 1991-12-01 1991-12-31 73.24, -53.21, 73.9, -52.95 https://cmr.earthdata.nasa.gov/search/concepts/C1214308616-AU_AADC.umm_json Satellite image map of Heard Island and McDonald Islands, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1991. The map is at a scale of 1:50000, and was produced from multispectral space imagery SPOT 1 and SPOT 2 scenes, with some areas of photography. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases, refuges/depots, and gives some historical text information. The map has both geographical and UTM co-ordinates. proprietary @@ -19699,8 +19679,8 @@ inishell-2-0-4_2.0.4 Inishell-2.0.4 ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5 inpe_CPTEC_GLOBAl_FORECAST Global Meteorological Model Analysis and Forecast Images (INPE/CPTEC) CEOS_EXTRA STAC Catalog 1970-01-01 -120, -60, 0, 30 https://cmr.earthdata.nasa.gov/search/concepts/C2227456094-CEOS_EXTRA.umm_json "CPTEC offers global model analysis and forecast images for twelve meteorological parameters. Forecast time steps range from the initial analysis each day out to six days. The user may choose forecasts from the most recent forecast run back to the previous 36 hours. Parameters Forecasted: Mean Sea Level Pressure Temperature at 1000 hPa Relative Humidity at 925 hPa, 850 hPa Vertical p_Velocity at 850 hPa, 500 hPa, 200 hPa Velocity Potential at 925 hPa, 200 hPa Stream Function at 925 hPa, 200 hPa 500/1000 hPa Thickness Advection of Temperature at 925 hPa, 850 hPa, 500 hPa Advection of Vorticity at 925 hPa, 850 hPa, 500 hPa Convergence of Humidity Flux at 925 hPa, 850 hPa Streamlines and Wind Speed at 925 hPa, 850 hPa, 200 hPa Total Precipitation Last 24 Hours All forecast images can be obtained via World Wide Web from the CPTEC Home Page. Link to: ""http://www.cptec.inpe.br/""" proprietary input-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0 Input data for break point detection of Swiss snow depth time series ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815138-ENVIDAT.umm_json Data set consists of monthly mean values for snow depth and days with snow on the ground intended for the use of break detection with ACMANT, Climatol and HOMER. List and coordinates of stations used as well as metadata and break detection results from all three methods is included. ## Columns Monthly means for each hydrological year: Nov, Dec, Jan, Feb, Mar, Apr with May to Oct set to zero proprietary input-data-for-impact-assessment-of-homogenised-snow-series_1.0 Input data for impact assessment of homogenised snow series ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226082287-ENVIDAT.umm_json # Input data for the following research article: Impact assessment of homogenised snow depth series on trends The data consists of separate output files from the following homogenisation methods: * Climatol * HOMER * interpQM The variable is seasonal mean snow depth (HSavg) plot.data is an additional data frame containing trends of HSavg (station, method, value, pvalue, altitude) proprietary -insects_subsaharanAfrica A Checklist of the Insects of Subsaharan Africa SCIOPS STAC Catalog 2000-01-01 13.68, -35.9, 33.98, -21.27 https://cmr.earthdata.nasa.gov/search/concepts/C1214611706-SCIOPS.umm_json "One of the most basic needs for inventorying, exploiting and monitoring the changes in the insect diversity of Africa is a complete list of species which are already know to occur in Africa. Surprisingly, such a basic list does not exist, despite some 250 years of formal scientific description of life on earth. The International Centre of Insect Physiology and Ecology (ICIPE), along with the National Museum of Natural History, is therefore sponsoring the production of the list, which will provide a reliable platform of 'standard' names for species on which many other projects depend. This list, or authority file, will greatly enhance communication both among scientists and between scientists and users of scientific data. The African list will also be a major contribution toward the proposed list of world species (e.g. the Global Biodiversity Information Facility (GBIF) and Species 2000 initiative of DIVERSITAS). A demonstration database is provided for the species of the orders Odonata (dragonflies and damselflies), Ephemeroptera (mayflies), Plecoptera (stoneflies), Megaloptera (alderflies), Hemiptera-Heteroptera (true bugs), Homoptera (cicadas, leafhoppers, planthoppers, scales, and others), and Trichoptera (caddisflies). Invitation to collaboration: Compilation of the checklist is being coordinated by Nearctica (formerly Entomological Information Specialists), because of their experience with Nomina Insecta Nearctica. We are attempting to collaborate with known specialists as contributors and reviewers, but we welcome additional suggestions of collaborators. Inquires can be directed to Scott Miller (miller.scott@nmnh.si.edu). Information was obtained from ""http://entomology.si.edu/""." proprietary insects_subsaharanAfrica A Checklist of the Insects of Subsaharan Africa ALL STAC Catalog 2000-01-01 13.68, -35.9, 33.98, -21.27 https://cmr.earthdata.nasa.gov/search/concepts/C1214611706-SCIOPS.umm_json "One of the most basic needs for inventorying, exploiting and monitoring the changes in the insect diversity of Africa is a complete list of species which are already know to occur in Africa. Surprisingly, such a basic list does not exist, despite some 250 years of formal scientific description of life on earth. The International Centre of Insect Physiology and Ecology (ICIPE), along with the National Museum of Natural History, is therefore sponsoring the production of the list, which will provide a reliable platform of 'standard' names for species on which many other projects depend. This list, or authority file, will greatly enhance communication both among scientists and between scientists and users of scientific data. The African list will also be a major contribution toward the proposed list of world species (e.g. the Global Biodiversity Information Facility (GBIF) and Species 2000 initiative of DIVERSITAS). A demonstration database is provided for the species of the orders Odonata (dragonflies and damselflies), Ephemeroptera (mayflies), Plecoptera (stoneflies), Megaloptera (alderflies), Hemiptera-Heteroptera (true bugs), Homoptera (cicadas, leafhoppers, planthoppers, scales, and others), and Trichoptera (caddisflies). Invitation to collaboration: Compilation of the checklist is being coordinated by Nearctica (formerly Entomological Information Specialists), because of their experience with Nomina Insecta Nearctica. We are attempting to collaborate with known specialists as contributors and reviewers, but we welcome additional suggestions of collaborators. Inquires can be directed to Scott Miller (miller.scott@nmnh.si.edu). Information was obtained from ""http://entomology.si.edu/""." proprietary +insects_subsaharanAfrica A Checklist of the Insects of Subsaharan Africa SCIOPS STAC Catalog 2000-01-01 13.68, -35.9, 33.98, -21.27 https://cmr.earthdata.nasa.gov/search/concepts/C1214611706-SCIOPS.umm_json "One of the most basic needs for inventorying, exploiting and monitoring the changes in the insect diversity of Africa is a complete list of species which are already know to occur in Africa. Surprisingly, such a basic list does not exist, despite some 250 years of formal scientific description of life on earth. The International Centre of Insect Physiology and Ecology (ICIPE), along with the National Museum of Natural History, is therefore sponsoring the production of the list, which will provide a reliable platform of 'standard' names for species on which many other projects depend. This list, or authority file, will greatly enhance communication both among scientists and between scientists and users of scientific data. The African list will also be a major contribution toward the proposed list of world species (e.g. the Global Biodiversity Information Facility (GBIF) and Species 2000 initiative of DIVERSITAS). A demonstration database is provided for the species of the orders Odonata (dragonflies and damselflies), Ephemeroptera (mayflies), Plecoptera (stoneflies), Megaloptera (alderflies), Hemiptera-Heteroptera (true bugs), Homoptera (cicadas, leafhoppers, planthoppers, scales, and others), and Trichoptera (caddisflies). Invitation to collaboration: Compilation of the checklist is being coordinated by Nearctica (formerly Entomological Information Specialists), because of their experience with Nomina Insecta Nearctica. We are attempting to collaborate with known specialists as contributors and reviewers, but we welcome additional suggestions of collaborators. Inquires can be directed to Scott Miller (miller.scott@nmnh.si.edu). Information was obtained from ""http://entomology.si.edu/""." proprietary instm_trawl National Institute of Marine Sciences and Technologies - Trawling Surveys CEOS_EXTRA STAC Catalog 1983-04-16 2006-11-03 5.14, 17.1, 13.37, 38.1 https://cmr.earthdata.nasa.gov/search/concepts/C2232477692-CEOS_EXTRA.umm_json The National Institute of Marine Sciences and Technologies (INSTM) fo Tunisia has four laboratories. Regular trawl surveys are done by the Laboratory of Marine Living Resources to assess the exploitable resource stocks. This dataset consists of 7664 records of 90 families. proprietary intercomparison-of-photogrammetric-platforms_1.0 Photogrammetric snow depth maps from satellite-, airplane-, UAS and terrestrial platforms from the Davos region (Switzerland) ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.7544861, 46.6485877, 10.0428772, 46.844319 https://cmr.earthdata.nasa.gov/search/concepts/C2789815195-ENVIDAT.umm_json "This data set contains the produced snow depth maps as well as the reference data set (manual and snow pole measurements) from our paper ""Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping"". __Abstract.__ Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability of snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas, and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques, as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellites (Pléiades), airplane (Ultracam Eagle M3), Unmanned Aerial System (eBee+ with S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D), were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while Unmanned Aerial Systems (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing, as well as using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements the root mean square error (RMSE) values and the normalized median deviation (NMAD) values were 0.52 m and 0.47 m respectively for the satellite snow depth map, 0.17 m and 0.17 m for the airplane snow depth map, 0.16 m and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with 4 manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 m and 0.38 m for the satellite snow depth map, 0.12 m and 0.11 m for the airplane snow depth map, 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km2), the snow depth map from the satellite yielded a RMSE value of 0.92 m and a NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas." proprietary interview-guide-and-transcripts_1.0 Interview guide and transcripts (CONCUR Aim 2 on Governance) ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815227-ENVIDAT.umm_json This dataset is composed of an interview guide used to conduct 43 in-depth, qualitative, and in-person interviews with planning experts, academics and practitioners, in 14 European urban regions and the corresponding interview transcripts (verbatim). These interviews were conducted in the selected urban regions between March and September 2016. They were first digitally recorded and later thoroughly transcribed. proprietary @@ -19719,8 +19699,8 @@ jornada_albedo_667_1 PROVE Surface albedo of Jornada Experimental Range, New Mex jornada_canopy_brf_668_1 PROVE Vegetation Reflectance of Jornada Experimental Range, New Mexico, 1997 ORNL_CLOUD STAC Catalog 1997-05-23 1997-05-28 -106.75, 32.5, -106.75, 32.5 https://cmr.earthdata.nasa.gov/search/concepts/C2804797176-ORNL_CLOUD.umm_json Directional reflected radiation was measured over plots representing selected canopy components (shrubs and individual plants, bare sand, and background) at the Jornada Experiment Range site near Las Cruces, New Mexico, during the Prototype Validation Experiment (PROVE) in May 1997. proprietary jornada_landcover_lai_665_1 PROVE Land Cover and Leaf Area of Jornada Experimental Range, New Mexico, 1997 ORNL_CLOUD STAC Catalog 1997-05-13 1997-05-31 -106.75, 32.5, -106.75, 32.5 https://cmr.earthdata.nasa.gov/search/concepts/C2804794793-ORNL_CLOUD.umm_json Field measurement of shrubland ecological properties is important for both site monitoring and validation of remote-sensing information. During the PROVE exercise on May 20-30, 1997, we calculated plot-level plant area index, leaf area index, total fractional cover, and green fractional cover. proprietary jornada_mquals_666_1 PROVE MQUALS Reflectance at Jornada Experimental Range, New Mexico, 1997 ORNL_CLOUD STAC Catalog 1997-05-23 1997-05-25 -106.75, 32.5, -106.75, 32.5 https://cmr.earthdata.nasa.gov/search/concepts/C2804795305-ORNL_CLOUD.umm_json This study utilized low flying, aircraft-based radiometers for optical characterization of top-of-the-canopy reflectance at Jornada Experimental Range in New Mexico during the Prototype Validation Experiment (PROVE) in May 1997. The objective was to examine the usefulness of low-flying aircraft for Moderate Resolution Imaging Spectroradiometer (MODIS) validation of land products. proprietary -joughin_0631973 Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers ALL STAC Catalog 1980-01-01 2009-12-31 -124.8, -80.8, -86.7, -73.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600138-SCIOPS.umm_json "This data set includes radar-derived annual accumulation rates over Thwaites Glacier between 1980 and 2009 and a gridded climatology (1985-2009) of snow accumulation over Pine Island and Thwaites Glaciers. The snow radar data were collected between 2009 and 2011 as part of NASA's Operation IceBridge Mission and are available at the NSIDC under ""IceBridge Snow Radar L1B Geolocated Radar Echo Strength Profiles""." proprietary joughin_0631973 Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers SCIOPS STAC Catalog 1980-01-01 2009-12-31 -124.8, -80.8, -86.7, -73.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600138-SCIOPS.umm_json "This data set includes radar-derived annual accumulation rates over Thwaites Glacier between 1980 and 2009 and a gridded climatology (1985-2009) of snow accumulation over Pine Island and Thwaites Glaciers. The snow radar data were collected between 2009 and 2011 as part of NASA's Operation IceBridge Mission and are available at the NSIDC under ""IceBridge Snow Radar L1B Geolocated Radar Echo Strength Profiles""." proprietary +joughin_0631973 Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers ALL STAC Catalog 1980-01-01 2009-12-31 -124.8, -80.8, -86.7, -73.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600138-SCIOPS.umm_json "This data set includes radar-derived annual accumulation rates over Thwaites Glacier between 1980 and 2009 and a gridded climatology (1985-2009) of snow accumulation over Pine Island and Thwaites Glaciers. The snow radar data were collected between 2009 and 2011 as part of NASA's Operation IceBridge Mission and are available at the NSIDC under ""IceBridge Snow Radar L1B Geolocated Radar Echo Strength Profiles""." proprietary kakqimpacts_1 KAKQ NEXRAD IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-03-01 -82.1814, 32.8531, -71.8333, 41.115 https://cmr.earthdata.nasa.gov/search/concepts/C1995580744-GHRC_DAAC.umm_json The KAKQ NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary kalahari_aot_h2o_vapor_719_1 SAFARI 2000 AOT and Column Water Vapor, Kalahari Transect, Wet Season 2000 ORNL_CLOUD STAC Catalog 2000-03-03 2000-03-18 21.72, -24.17, 25.5, -18.65 https://cmr.earthdata.nasa.gov/search/concepts/C2788397022-ORNL_CLOUD.umm_json The data presented here include the aerosol optical thickness (AOT) and column water vapor measurements taken at sites along the Kalahari Transect using a Microtops sunphotometer. Data were collected every 30 minutes at 4 sites that were visited during the SAFARI 2000 Kalahari Wet Season Campaign between March 3, 2000, and March 18, 2000. AOT values are provided at 340-, 440-, 675-, 870-, and 936-nm wavelengths. An estimate of the Angstrom Coefficient is also provided to allow the estimation of AOT at other wavelengths. The purpose of this data collection was primarily for documentation of the conditions at each site and to aid in the correction of remote sensing data, for validation of Earth Observation System (EOS) products such as MODIS and MISR aerosol products, and for modeling of canopy productivity. proprietary kalahari_co2_heat_flux_765_1 SAFARI 2000 Kalahari Transect CO2, Water Vapor, and Heat Flux, Wet Season 2000 ORNL_CLOUD STAC Catalog 2000-03-01 2000-03-19 21.71, -24.16, 23.59, -15.44 https://cmr.earthdata.nasa.gov/search/concepts/C2789074715-ORNL_CLOUD.umm_json Short-term measurements of carbon dioxide, water, and energy fluxes were collected at four locations along a mean annual precipitation gradient in southern Africa during the SAFARI 2000 wet (growing) season campaign of 2000. The purpose of this research was to determine how observed vegetation-atmosphere exchange properties are functionally related to long-term climatic conditions. proprietary @@ -19779,8 +19759,8 @@ labchemistrymetamorphism_1.0 Data set on bromide oxidation by ozone in snow duri labes_1.0 LABES 2 Indicators of the Swiss Landscape Monitoring Program ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226082114-ENVIDAT.umm_json The Swiss Landscape Monitoring Program (LABES) records both the physical and the perceived quality of the landscape with about 30 indicators. The surveys of the physical aspects are largely based on evaluations of data available throughout Switzerland from swisstopo and the Federal Statistical Office (FSO). Another significant part of the data comes from a nationwide population survey on landscape perception. This dataset describes data that have been assembled in the 2020 update of the Swiss Landscape Monitoring Program LABES. proprietary lai_45_1 Leaf Area Index Data (OTTER) ORNL_CLOUD STAC Catalog 1991-05-13 1991-05-15 -123.27, 44.29, -121.33, 44.67 https://cmr.earthdata.nasa.gov/search/concepts/C2804754747-ORNL_CLOUD.umm_json LAI estimates computed from unweighted openness by the CANOPY program from digitized canopy photographs proprietary lake_cc_scenarios_ch2018_1.0 Lake climate change scenarios CH2018 ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226082136-ENVIDAT.umm_json "The dataset ""Lake_climate_change_scenarios_CH2018"" provides simulation-based climate change impact scenarios for perialpine lakes in Switzerland. These transient future scenarios were produced by combining the hydrologic model PREVAH with the hydrodynamic model MIKE11 to simulate daily lake water level (Lake_water_level_scenarios_CH2018.xls) and outflow scenarios (Lake_outflow_scenarios_CH2018.xls) from 1981 to 2099, using the Swiss Climate Change Scenarios CH2018. The future scenarios contain a total of 39 model members for three Representative Concentration Pathways, RCP2.6 (concerted mitigation efforts), RCP4.5 (limited climate mitigation) and RCP8.5 (no climate mitigation measures). These scenarios result from the study titled ""Lower summer lake levels in regulated perialpine lakes, caused by climate change,"" authored by Wechsler et al. in 2023. The dataset emphasizes four specific Swiss lakes, each subject to different degrees of lake level management: an unregulated lake (Lake Walen), a semi-regulated lake (Lake Brienz), and two regulated lakes (Lake Zurich and Lake Thun). In addition, the file (Lake_characteristics.xlsx) includes data used in the modeling process, encompassing the stage-area relation for the four lakes, stage-discharge relations for the unregulated and semi-regulated lakes, and lake level management rules for the two regulated lakes." proprietary -lake_erie_aug_2014_0 2014 Lake Erie measurements ALL STAC Catalog 2014-08-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360418-OB_DAAC.umm_json 2014 Lake Erie measurements. proprietary lake_erie_aug_2014_0 2014 Lake Erie measurements OB_DAAC STAC Catalog 2014-08-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360418-OB_DAAC.umm_json 2014 Lake Erie measurements. proprietary +lake_erie_aug_2014_0 2014 Lake Erie measurements ALL STAC Catalog 2014-08-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360418-OB_DAAC.umm_json 2014 Lake Erie measurements. proprietary lambert_geology_gis_1 Geology of the Lambert Glacier - Prydz Bay Region GIS Dataset AU_AADC STAC Catalog 1980-01-01 1997-12-31 58, -76, 78, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214313571-AU_AADC.umm_json This dataset is the GIS data used for the map 'Geology of the Lambert Glacier - Prydz Bay Region, East Antarctica' published by the Australian Geological Survey Organisation in January 1998. The data is in three formats: ArcInfo interchange, ArcInfo coverage and shapefile. A document is included with further information about the data. The map is available from a URL in this metadata record. proprietary land-use-cover-dynamics-in-austin-metropolitan-area-since-1992_1.0 Land use/cover dynamics in Austin metropolitan area since 1992 ENVIDAT STAC Catalog 2022-01-01 2022-01-01 -97.7014167, 30.3732703, -97.7014167, 30.3732703 https://cmr.earthdata.nasa.gov/search/concepts/C2789815150-ENVIDAT.umm_json The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006–2011 and 2011–2016. The study also aims to understand to what extent the changes in the built environment (in terms of ‘efficient growth’ versus ‘inefficient growth’) from the 1990s to 2016 in the Austin metropolitan area corresponded with ‘compact and efficient growth’ planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 × 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package. proprietary land_cover_data-1km_627_1 SAFARI 2000 Land Cover from AVHRR, 1-km, 1992-1993 (Hansen et al.) ORNL_CLOUD STAC Catalog 1992-01-01 1993-12-31 5, -35, 60, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2788343294-ORNL_CLOUD.umm_json This data set consists of a southern African subset of the 1-km Global Land Cover Data Set Derived from AVHRR developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Both ASCII data and binary image files are available. proprietary @@ -19813,8 +19793,8 @@ latent-reserves-in-the-swiss-nfi_1.0 'Latent reserves' within the Swiss NFI ALL law_dome_1977_1 Law Dome Field Logs And Strain Grid Results, 1977 AU_AADC STAC Catalog 1977-03-16 1977-12-14 110, -70, 114, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311164-AU_AADC.umm_json In 1977 several traverses were carried out over the Law Dome area, primarily to drill new ice cores on the dome. The 1974 drill site (near Cape Folger) was redrilled to add instrumentation for inclination, while additional holes at BHQ (418m) and the dome summit (475m, 2x 30m) were also drilled. In addition to the drilling work, two strain grids were laid out on the ice surface, and the grid laid out in 1974 was remeasured. Notes on the traverse and drilling (but few results) are contained in this record, along with the results of the strain grid surveys. Records for this work have been archived at the Australian Antarctic Division. Logbook(s): Glaciology Log of 1977 Field Work proprietary law_dome_700yr_ion_chem_2 700 Years of Ice Core Major Ion Chemistry Data from Law Dome, Antarctica ALL STAC Catalog 1988-01-01 2000-03-06 112.8, -66.76, 112.86, -66.7 https://cmr.earthdata.nasa.gov/search/concepts/C1214313592-AU_AADC.umm_json A compilation of 700 years of Law Dome major ion chemistry data, recorded from 3 ice cores; DSS97, DSS99, DSS main. This work was completed as part of ASAC project 757 (ASAC_757). Species which have been the subject of publication and could be made available after consultation: Species, Period (AD), Resolution, Comments SO4, 1301-1995, Fine (full) NO3, 1888-1995, Fine (full), full 700 year annuals used by Mayewski solar-polar paper in preparation (Ca,K,Mg,Na,NO3,SO4,Cl), 1301-1995, Annual MSA, 1841-1995, Annual Na, 1301-1995, Fine (full) Na, 1301-1995, Annual non-sea-salt SO4 (nss SO4), 1301-1995, Annual, Uses a calculated SO4 fractionation % to correct the seawater ratio (due to fractionation at the source). Corrected ratio 0.087 (using uEq/L). There are still 'negative' values and some zero's - this data has not been 'cleaned'. If you need to use this, please contact Mark Curran for help. An updated copy of this dataset was submitted to the Australian Antarctic Data Centre in early July of 2012. proprietary law_dome_700yr_ion_chem_2 700 Years of Ice Core Major Ion Chemistry Data from Law Dome, Antarctica AU_AADC STAC Catalog 1988-01-01 2000-03-06 112.8, -66.76, 112.86, -66.7 https://cmr.earthdata.nasa.gov/search/concepts/C1214313592-AU_AADC.umm_json A compilation of 700 years of Law Dome major ion chemistry data, recorded from 3 ice cores; DSS97, DSS99, DSS main. This work was completed as part of ASAC project 757 (ASAC_757). Species which have been the subject of publication and could be made available after consultation: Species, Period (AD), Resolution, Comments SO4, 1301-1995, Fine (full) NO3, 1888-1995, Fine (full), full 700 year annuals used by Mayewski solar-polar paper in preparation (Ca,K,Mg,Na,NO3,SO4,Cl), 1301-1995, Annual MSA, 1841-1995, Annual Na, 1301-1995, Fine (full) Na, 1301-1995, Annual non-sea-salt SO4 (nss SO4), 1301-1995, Annual, Uses a calculated SO4 fractionation % to correct the seawater ratio (due to fractionation at the source). Corrected ratio 0.087 (using uEq/L). There are still 'negative' values and some zero's - this data has not been 'cleaned'. If you need to use this, please contact Mark Curran for help. An updated copy of this dataset was submitted to the Australian Antarctic Data Centre in early July of 2012. proprietary -law_dome_700yr_na_1 700 Year Record of Winter Sodium Concentrations (May June July averages) from Law Dome AU_AADC STAC Catalog 1301-01-01 1995-12-31 112.806946, -66.76972, 112.806946, -66.76972 https://cmr.earthdata.nasa.gov/search/concepts/C1214311149-AU_AADC.umm_json This file is a 700 year record of winter sodium concentrations (May June July averages) from Law Dome. This was calculated by dividing each annual cycle into 12 even time bins (nominally months) and taking the average concentrations for bins 5, 6 and 7 (nominally May, june and July). More detail can be found in the publication listed below. For further information regarding this data set please contact Mark Curran at the address below. proprietary law_dome_700yr_na_1 700 Year Record of Winter Sodium Concentrations (May June July averages) from Law Dome ALL STAC Catalog 1301-01-01 1995-12-31 112.806946, -66.76972, 112.806946, -66.76972 https://cmr.earthdata.nasa.gov/search/concepts/C1214311149-AU_AADC.umm_json This file is a 700 year record of winter sodium concentrations (May June July averages) from Law Dome. This was calculated by dividing each annual cycle into 12 even time bins (nominally months) and taking the average concentrations for bins 5, 6 and 7 (nominally May, june and July). More detail can be found in the publication listed below. For further information regarding this data set please contact Mark Curran at the address below. proprietary +law_dome_700yr_na_1 700 Year Record of Winter Sodium Concentrations (May June July averages) from Law Dome AU_AADC STAC Catalog 1301-01-01 1995-12-31 112.806946, -66.76972, 112.806946, -66.76972 https://cmr.earthdata.nasa.gov/search/concepts/C1214311149-AU_AADC.umm_json This file is a 700 year record of winter sodium concentrations (May June July averages) from Law Dome. This was calculated by dividing each annual cycle into 12 even time bins (nominally months) and taking the average concentrations for bins 5, 6 and 7 (nominally May, june and July). More detail can be found in the publication listed below. For further information regarding this data set please contact Mark Curran at the address below. proprietary law_dome_gravity_1964_1968_1 Gravity Measurements on Law Dome, 1964-1968 AU_AADC STAC Catalog 1964-01-01 1968-12-31 110, -68, 115, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311151-AU_AADC.umm_json A compilation of gravity measurements taken on Law Dome from 1964-1968. The hard copy of this document has been archived in the Australian Antarctic Division Records Store. proprietary law_dome_gravity_1971_1 Gravity Observations on Law Dome, 1971-1972 AU_AADC STAC Catalog 1971-01-01 1972-12-31 110, -68, 115, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311152-AU_AADC.umm_json Log of gravity observations made on Law Dome in 1971 and 1972. The hard copy of this document has been archived in the Australian Antarctic Division Records Store. proprietary law_dome_gravity_1981_1 Gravity Measurements on Law Dome, Spring Traverse 1981 AU_AADC STAC Catalog 1981-09-26 1981-12-30 110, -69, 120, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1292615128-AU_AADC.umm_json Gravity measurements taken on Law Dome and Wilkes Land during the spring traverse in 1981. Many readings are taken at the same location at two different times (trip out, and trip back). Physical copies of these documents have been stored in the Australian Antarctic Division records store. proprietary @@ -19823,8 +19803,8 @@ law_dome_met_obs_1981_1 Meteorological Observations, Winter Traverses, Law Dome law_dome_wilkes_land_1984_1 Law Dome/Wilkes Land Traverse Data 1984 AU_AADC STAC Catalog 1984-01-01 1984-12-31 108, -74, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214311153-AU_AADC.umm_json Raw logs for snow accumulation, snow density, gravity and snow pit stratigraphy recorded during 1984 traverse season on Law Dome/Wilkes Land. Copies of these documents have been archived in the records store of the Australian Antarctic Division. proprietary lawdome_1968_season_1 Field and traverse data, Law Dome, 1968 AU_AADC STAC Catalog 1968-01-01 1968-12-31 110, -68, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313593-AU_AADC.umm_json Notes and data observations from field work out of Casey in the 1968 season. Includes data on gravity, accumulation, strain grid measurements, ice core density measurements, levelling, met obs, and echo sounding results. proprietary lawdome_1970_1 Glaciology and geophysical survey of Law Dome, 1970 AU_AADC STAC Catalog 1970-01-01 1970-12-31 110, -68, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1291724265-AU_AADC.umm_json Log books (2) from the 1970 traverses on Law Dome, recording barometric pressure, air temperature, magnetic fields and gravity. Physical copies of these documents have been stored in the Australian Antarctic Division records store. proprietary -lawdome_1979_field_data_1 Accumulation, Air Temperature, Barometric Pressure and Magnetic Readings from Law Dome and Wilkes Land, 1979 ALL STAC Catalog 1979-01-01 1979-12-31 110, -68, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214311166-AU_AADC.umm_json A collection of observations made during the Autumn-Spring 1979 traverses on Law Dome and Wilkes Land. Measurements include accumulation, air temperature, barometric pressure, and magnetic field strength. The data is recorded in four log books and a set of loose pages. Physical copies of these documents have been stored in the Australian Antarctic Division records store. proprietary lawdome_1979_field_data_1 Accumulation, Air Temperature, Barometric Pressure and Magnetic Readings from Law Dome and Wilkes Land, 1979 AU_AADC STAC Catalog 1979-01-01 1979-12-31 110, -68, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214311166-AU_AADC.umm_json A collection of observations made during the Autumn-Spring 1979 traverses on Law Dome and Wilkes Land. Measurements include accumulation, air temperature, barometric pressure, and magnetic field strength. The data is recorded in four log books and a set of loose pages. Physical copies of these documents have been stored in the Australian Antarctic Division records store. proprietary +lawdome_1979_field_data_1 Accumulation, Air Temperature, Barometric Pressure and Magnetic Readings from Law Dome and Wilkes Land, 1979 ALL STAC Catalog 1979-01-01 1979-12-31 110, -68, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214311166-AU_AADC.umm_json A collection of observations made during the Autumn-Spring 1979 traverses on Law Dome and Wilkes Land. Measurements include accumulation, air temperature, barometric pressure, and magnetic field strength. The data is recorded in four log books and a set of loose pages. Physical copies of these documents have been stored in the Australian Antarctic Division records store. proprietary lawdome_1981_traverse_1 Law Dome and Wilkes Land Traverse Logbooks, 1981 AU_AADC STAC Catalog 1981-01-01 1981-12-31 110, -70, 115, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311154-AU_AADC.umm_json Log books for the traverse work carried out on Law Dome and Wilkes Land in 1981. Information recorded includes snow cane accumulation readings, barometric pressure, gravity, temperature, wind, and some oxygen isotope results. Copies of these documents have been archived in the records store of the Australian Antarctic Division. proprietary lawdome_borehole_temp_1987_1 Ice Core Borehole Temperatures, Law Dome 1987 AU_AADC STAC Catalog 1987-01-01 1987-12-31 110.52246, -66.58461, 111.5332, -66.05511 https://cmr.earthdata.nasa.gov/search/concepts/C1214311167-AU_AADC.umm_json A compilation of temperature measurements taken from ice core boreholes on Law Dome in the 1987 season. Includes detailed notes on measuring methodology, and papers on the interpretation of results from the specific equipment used to record the temperatures, as well as calibration work done. A text file of blended borehole temperature readings for the Law Dome DSS (Dome Summit South) site is available for download. A copy of the referenced publication is available to AAD staff. van Ommen, T. D., V. I. Morgan, T. H. Jacka, S. Woon and A. Elcheikh (1999) Near-surface temperatures in the Dome Summit South (Law Dome, East Antarctica) borehole Annals of Glaciology, 29. 141-144. Physical copies of these documents have been stored in the Australian Antarctic Division records store. proprietary lawdome_gravity_1973_74_1 Law Dome Gravity Readings, 1973-1974 AU_AADC STAC Catalog 1973-01-10 1974-02-22 110, -68, 115, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313573-AU_AADC.umm_json Gravity readings on Law Dome for the International Global Aerosol Programme (IGAP) during the 1973/1974 season. The Casey 1973 wintering team included physicists Lyle H Supp (Arizona, USA) and Ian Lawrence McIntosh, who were possibly involved in the collection of these data. The Casey 1974 wintering team included the physicist Gregory Ross Howarth, who may also have been involved. Two geodesists from the US, DL Schneider and HL Edwards were also present, and may also have been involved. These documents are only available in hard copy, and have been archived by the Australian Antarctic Division. proprietary @@ -19988,8 +19968,8 @@ mean-insect-occupancy-1970-2020_1.0 Mean insect occupancy 1970–2020 ENVIDAT ST medical_bibliography_1 A bibliography of polar medicine related articles ALL STAC Catalog 1947-01-01 2007-06-06 60, -90, 160, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311212-AU_AADC.umm_json This bibliography contains a list of publications in medical sciences from Australian National Antarctic Research Expeditions (ANARE) and the Australian Antarctic Program (AAP) from 1947-2007. The bibliography also contains publications related to Australian involvement in the International Biomedical Expedition to the Antarctic (IBEA), 1980-1981. Currently (as at 2007-06-06) the bibliography stands at 285 references, but is updated annually. The publications are divided into the following areas: Clinical medicine Clinical medicine - case reports Telemedicine Dentistry Diving Epidemiology Polar human research - general Physiology Immunology Photobiology Microbiology Psychology and behavioural studies Nutrition Theses Popular articles Miscellaneous IBEA Posters The fields in this dataset are: Author Title Journal Year proprietary medical_bibliography_1 A bibliography of polar medicine related articles AU_AADC STAC Catalog 1947-01-01 2007-06-06 60, -90, 160, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311212-AU_AADC.umm_json This bibliography contains a list of publications in medical sciences from Australian National Antarctic Research Expeditions (ANARE) and the Australian Antarctic Program (AAP) from 1947-2007. The bibliography also contains publications related to Australian involvement in the International Biomedical Expedition to the Antarctic (IBEA), 1980-1981. Currently (as at 2007-06-06) the bibliography stands at 285 references, but is updated annually. The publications are divided into the following areas: Clinical medicine Clinical medicine - case reports Telemedicine Dentistry Diving Epidemiology Polar human research - general Physiology Immunology Photobiology Microbiology Psychology and behavioural studies Nutrition Theses Popular articles Miscellaneous IBEA Posters The fields in this dataset are: Author Title Journal Year proprietary mega-plots_1.0 Towards comparable species richness estimates across plot-based inventories - data ENVIDAT STAC Catalog 2022-01-01 2022-01-01 -14.0625, 33.1375512, 42.1875, 72.1818036 https://cmr.earthdata.nasa.gov/search/concepts/C2789816317-ENVIDAT.umm_json "The data file refers to the data used in Portier et al. ""Plot size matters: towards comparable species richness estimates across plot-based inventories"" (2022) *Ecology and Evolution*. This paper describes a methodoligical framework developed to allow meaningful species richness comparisons across plot-based inventories using different plot sizes. To this end, National Forest Inventory data from Switzerland, Slovakia, Norway and Spain were used. NFI plots were aggregated into mega-plots of larger sizes to build rarefaction curves. The data stored here correspond to the mega-plot level data used in the analyses, including for each country the size of the mega-plots in square meters (A), the corresponding species richness (SR) as well as all enrionmental heterogeneity measures described in the corresponding paper. Mega-plots of country-specific downscaled datasets are also provided. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). Contact details for data requests from all NFIs can be found in the ENFIN website (http://enfin.info/)." proprietary -mendocino_mathison_peak_nff_sr Airborne laser swath mapping (ALSM) data of the San Andreas fault SCIOPS STAC Catalog 2003-02-05 2003-02-11 -123.81387, 39.31092, -123.720085, 39.333496 https://cmr.earthdata.nasa.gov/search/concepts/C1214614580-SCIOPS.umm_json "This airborne laser swath mapping (ALSM) data of the San Andreas fault zone in northern California was acquired by TerraPoint, LLC under contract to the National Aeronautics and Space Administration in collaboration with the United States Geological Survey. The data were acquired by means of LIght Detection And Ranging (LIDAR) using a discrete-return, scanning laser altimeter capable of acquiring up to 4 returns per laser pulse. The data were acquired with a nominal density of 1 laser pulses per square meter achieved with 58% overlap of adjacent data swaths (all areas were mapped at least twice and the data combined to produce final products). The data set consists of 3 parts: (1) the LIDAR point cloud providing the location and elevation of each laser return, along with associated acquisition and classification parameters, (2) a highest-surface digital elevation model (DEM) produced at a 6 foot grid spacing, where each grid cell elevation corresponds to the highest laser return within the cell (cells lacking returns are undefined, usually associated with water or low reflectance surfaces such as fresh asphalt), and (3) a ""bald Earth"" DEM, with vegetation cover and buildings removed, produced at a 6 foot grid spacing by sampling a triangular irregular network (TIN). The TIN was constructed from those returns classified as being from the ground or water based on spatial filtering of the point cloud. Comparison to GPS-established ground control in flat, vegetation-free areas indicates that the DEM vertical accuracy is 17 cm (RMSE for 85 points). Bald Earth elevations under vegetation and for water bodies are less accurate where laser returns from the ground or water are sparse. The highest surface and bald Earth DEMs are distributed as georeferenced geotiff elevation and shaded relief images. The grid cell values in the elevation images are orthometric elevations in international feet referenced to North American Vertical Datum 1988 (NAVD-88) stored as signed floating point values with undefined grid cells set to -99. The shaded relief images are byte values from 0 (shaded) to 255 (illuminated) computed using ENVI 4.0 shaded relief modeling with an illumination azimuth of 225 degrees, illumination elevation of 60 degrees, and a 3x3 kernel size. The images are mosaics based on USGS 7.5 minute quadrangle boundaries. Each mosaic is an east-west strip covering the northern or southern half of adjacent quadrangles. File names include the quadrangle names, a northern (N) or southern (S) half designation, a bald Earth (BE) or highest-surface (FF) designation, and an elevation image (elev) or shaded relief image (SR) designation. FF refers to full-feature indicating vegetation and buildings have not been removed.These data were developed in order to study the geomorphic expression of natural hazards in support of the National Aeronautics and Space Administration (NASA) Solid Earth and Natural Hazards (SENH) Program, the United States Geological Survey (USGS), and the Geology component of the Earthscope Plate Boundary Observatory. Spatial Data Organization Information - Direct Spatial Reference: Raster Raster Object Type: Pixel Row Count: 1285 Column Count: 4398 Vertical Count: 1 Spatial Reference Information - Horizontal Coordinate System Definition - Planar - Map Projection Name: Lambert Conformal Conic Standard Parallel: 38.333333 Standard Parallel: 39.833333 Longitude of Central Meridian: -122.000000 Latitude of Projection Origin: 37.666667 False Easting: 6561666.666667 False Northing: 1640416.666667 Planar Coordinate Encoding Method: row and column Coordinate Representation: Abscissa Resolution: 6.000000 Ordinate Resolution: 6.000000 Distance and Bearing Representation: Planar Distance Units: survey feet Geodetic Model: Horizontal Datum Name: North American Datum of 1983 Ellipsoid Name: Geodetic Reference System 80 Semi-major Axis: 6378137.000000 Denominator of Flattening Ratio: 298.257222" proprietary mendocino_mathison_peak_nff_sr Airborne laser swath mapping (ALSM) data of the San Andreas fault ALL STAC Catalog 2003-02-05 2003-02-11 -123.81387, 39.31092, -123.720085, 39.333496 https://cmr.earthdata.nasa.gov/search/concepts/C1214614580-SCIOPS.umm_json "This airborne laser swath mapping (ALSM) data of the San Andreas fault zone in northern California was acquired by TerraPoint, LLC under contract to the National Aeronautics and Space Administration in collaboration with the United States Geological Survey. The data were acquired by means of LIght Detection And Ranging (LIDAR) using a discrete-return, scanning laser altimeter capable of acquiring up to 4 returns per laser pulse. The data were acquired with a nominal density of 1 laser pulses per square meter achieved with 58% overlap of adjacent data swaths (all areas were mapped at least twice and the data combined to produce final products). The data set consists of 3 parts: (1) the LIDAR point cloud providing the location and elevation of each laser return, along with associated acquisition and classification parameters, (2) a highest-surface digital elevation model (DEM) produced at a 6 foot grid spacing, where each grid cell elevation corresponds to the highest laser return within the cell (cells lacking returns are undefined, usually associated with water or low reflectance surfaces such as fresh asphalt), and (3) a ""bald Earth"" DEM, with vegetation cover and buildings removed, produced at a 6 foot grid spacing by sampling a triangular irregular network (TIN). The TIN was constructed from those returns classified as being from the ground or water based on spatial filtering of the point cloud. Comparison to GPS-established ground control in flat, vegetation-free areas indicates that the DEM vertical accuracy is 17 cm (RMSE for 85 points). Bald Earth elevations under vegetation and for water bodies are less accurate where laser returns from the ground or water are sparse. The highest surface and bald Earth DEMs are distributed as georeferenced geotiff elevation and shaded relief images. The grid cell values in the elevation images are orthometric elevations in international feet referenced to North American Vertical Datum 1988 (NAVD-88) stored as signed floating point values with undefined grid cells set to -99. The shaded relief images are byte values from 0 (shaded) to 255 (illuminated) computed using ENVI 4.0 shaded relief modeling with an illumination azimuth of 225 degrees, illumination elevation of 60 degrees, and a 3x3 kernel size. The images are mosaics based on USGS 7.5 minute quadrangle boundaries. Each mosaic is an east-west strip covering the northern or southern half of adjacent quadrangles. File names include the quadrangle names, a northern (N) or southern (S) half designation, a bald Earth (BE) or highest-surface (FF) designation, and an elevation image (elev) or shaded relief image (SR) designation. FF refers to full-feature indicating vegetation and buildings have not been removed.These data were developed in order to study the geomorphic expression of natural hazards in support of the National Aeronautics and Space Administration (NASA) Solid Earth and Natural Hazards (SENH) Program, the United States Geological Survey (USGS), and the Geology component of the Earthscope Plate Boundary Observatory. Spatial Data Organization Information - Direct Spatial Reference: Raster Raster Object Type: Pixel Row Count: 1285 Column Count: 4398 Vertical Count: 1 Spatial Reference Information - Horizontal Coordinate System Definition - Planar - Map Projection Name: Lambert Conformal Conic Standard Parallel: 38.333333 Standard Parallel: 39.833333 Longitude of Central Meridian: -122.000000 Latitude of Projection Origin: 37.666667 False Easting: 6561666.666667 False Northing: 1640416.666667 Planar Coordinate Encoding Method: row and column Coordinate Representation: Abscissa Resolution: 6.000000 Ordinate Resolution: 6.000000 Distance and Bearing Representation: Planar Distance Units: survey feet Geodetic Model: Horizontal Datum Name: North American Datum of 1983 Ellipsoid Name: Geodetic Reference System 80 Semi-major Axis: 6378137.000000 Denominator of Flattening Ratio: 298.257222" proprietary +mendocino_mathison_peak_nff_sr Airborne laser swath mapping (ALSM) data of the San Andreas fault SCIOPS STAC Catalog 2003-02-05 2003-02-11 -123.81387, 39.31092, -123.720085, 39.333496 https://cmr.earthdata.nasa.gov/search/concepts/C1214614580-SCIOPS.umm_json "This airborne laser swath mapping (ALSM) data of the San Andreas fault zone in northern California was acquired by TerraPoint, LLC under contract to the National Aeronautics and Space Administration in collaboration with the United States Geological Survey. The data were acquired by means of LIght Detection And Ranging (LIDAR) using a discrete-return, scanning laser altimeter capable of acquiring up to 4 returns per laser pulse. The data were acquired with a nominal density of 1 laser pulses per square meter achieved with 58% overlap of adjacent data swaths (all areas were mapped at least twice and the data combined to produce final products). The data set consists of 3 parts: (1) the LIDAR point cloud providing the location and elevation of each laser return, along with associated acquisition and classification parameters, (2) a highest-surface digital elevation model (DEM) produced at a 6 foot grid spacing, where each grid cell elevation corresponds to the highest laser return within the cell (cells lacking returns are undefined, usually associated with water or low reflectance surfaces such as fresh asphalt), and (3) a ""bald Earth"" DEM, with vegetation cover and buildings removed, produced at a 6 foot grid spacing by sampling a triangular irregular network (TIN). The TIN was constructed from those returns classified as being from the ground or water based on spatial filtering of the point cloud. Comparison to GPS-established ground control in flat, vegetation-free areas indicates that the DEM vertical accuracy is 17 cm (RMSE for 85 points). Bald Earth elevations under vegetation and for water bodies are less accurate where laser returns from the ground or water are sparse. The highest surface and bald Earth DEMs are distributed as georeferenced geotiff elevation and shaded relief images. The grid cell values in the elevation images are orthometric elevations in international feet referenced to North American Vertical Datum 1988 (NAVD-88) stored as signed floating point values with undefined grid cells set to -99. The shaded relief images are byte values from 0 (shaded) to 255 (illuminated) computed using ENVI 4.0 shaded relief modeling with an illumination azimuth of 225 degrees, illumination elevation of 60 degrees, and a 3x3 kernel size. The images are mosaics based on USGS 7.5 minute quadrangle boundaries. Each mosaic is an east-west strip covering the northern or southern half of adjacent quadrangles. File names include the quadrangle names, a northern (N) or southern (S) half designation, a bald Earth (BE) or highest-surface (FF) designation, and an elevation image (elev) or shaded relief image (SR) designation. FF refers to full-feature indicating vegetation and buildings have not been removed.These data were developed in order to study the geomorphic expression of natural hazards in support of the National Aeronautics and Space Administration (NASA) Solid Earth and Natural Hazards (SENH) Program, the United States Geological Survey (USGS), and the Geology component of the Earthscope Plate Boundary Observatory. Spatial Data Organization Information - Direct Spatial Reference: Raster Raster Object Type: Pixel Row Count: 1285 Column Count: 4398 Vertical Count: 1 Spatial Reference Information - Horizontal Coordinate System Definition - Planar - Map Projection Name: Lambert Conformal Conic Standard Parallel: 38.333333 Standard Parallel: 39.833333 Longitude of Central Meridian: -122.000000 Latitude of Projection Origin: 37.666667 False Easting: 6561666.666667 False Northing: 1640416.666667 Planar Coordinate Encoding Method: row and column Coordinate Representation: Abscissa Resolution: 6.000000 Ordinate Resolution: 6.000000 Distance and Bearing Representation: Planar Distance Units: survey feet Geodetic Model: Horizontal Datum Name: North American Datum of 1983 Ellipsoid Name: Geodetic Reference System 80 Semi-major Axis: 6378137.000000 Denominator of Flattening Ratio: 298.257222" proprietary met-obs-jmr-stations-1976_1 Meteorological Observations Made At JMR Stations 1976-1977 AU_AADC STAC Catalog 1976-01-01 1977-12-31 124.5, -78.5, 93, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214313660-AU_AADC.umm_json During the Mirny-Dome C traverse in 1976/77, time was spent at a number of cane sites taking JMR measurements, to determine the precise location. During this time, basic meteorological observations of air temperature and pressure were made and recorded. These documents have been archived in the records store at the Australian Antarctic Division. proprietary met_profile_SA_729_1 SAFARI 2000 Upper Air Meteorological Profiles, South Africa, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-01 2000-09-30 -10, -41, 31, -24 https://cmr.earthdata.nasa.gov/search/concepts/C2789021046-ORNL_CLOUD.umm_json The University of Wyoming has a series of balloonborne radiosonde measurements from all around the world, from the surface to 30 km. This data set contains upper air meteorological profiles from 594 radiosonde launches deployed from sites in South Africa. These sonde launches were made to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaign of 2000.Vaisala RS80 sondes were launched from nine sites in South Africa between August 1, 2000 and September 30, 2000. The launch sites were Pietersburg (changed to Polokwane after 2000), Pretoria (Irene), Bethlehem, Springbok, De Aar, Durban, Cape Town, Port Elizabeth, and Gough Island. The parameters measured by the radiosonde instruments include: pressure, air temperature, relative humidity, wind speed, and wind direction. proprietary met_profile_skukuza_728_1 SAFARI 2000 Upper Air Meteorological Profiles, Skukuza, Dry Seasons 1999-2000 ORNL_CLOUD STAC Catalog 1999-08-14 2000-09-23 31.59, -24.97, 31.59, -24.97 https://cmr.earthdata.nasa.gov/search/concepts/C2789020292-ORNL_CLOUD.umm_json Vaisala RS80 sondes were deployed from Skukuza Airport, South Africa, to collect atmospheric sounding profiles of temperature and moisture data from the surface to 30 km. These sonde launches were coordinated to augment the regional sounding network in the region during the SAFARI 2000 Dry Season Campaigns of 1999 and 2000. The radiosondes were launched from Skukuza Airport between August 14-September 3, 1999, and between August 24-September 23, 2000. The radiosonde instrument package RS80 measured the following meteorological parameters: pressure in hecto-Pascals (P), ambient temperature in degrees Celsius (T), and relative humidity in percentage (RH). A hydrostatic equation was applied to the recorded data, after error-checking, to calculate the output parameters: height above sea level in meters, dew point temperature in degrees Celsius, and q (g/kg) which is specific humidity in grams per kilogram. proprietary @@ -20103,8 +20083,8 @@ net-primary-productivity-npp-anomalies-simulated-by-3-pg-model-for-switzerland_1 net_carbon_flux_662_1 Net Carbon Dioxide and Water Fluxes of Global Terrestrial Ecosystems, 1969-1998 ORNL_CLOUD STAC Catalog 1969-01-01 1998-01-01 -162, -45.5, 176, 68.5 https://cmr.earthdata.nasa.gov/search/concepts/C2779678769-ORNL_CLOUD.umm_json The variability of net surface carbon assimilation (Asmax), net ecosystem surface respiration (Rsmax), and net surface evapotranspiration (Etsmax) among and within vegetation types was examined based on a review of studies performed in either a micrometeorological setting or an enclosure setting. proprietary net_increment-80_1.0 Net increment ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815660-ENVIDAT.umm_json Increment including ingrowth minus the mortality. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary net_increment_star-187_1.0 Net increment* ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815851-ENVIDAT.umm_json Increment with ingrowth minus the mortality. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary -newcomb_bay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands ALL STAC Catalog 1997-02-01 2000-02-05 110.512, -66.282, 110.566, -66.256 https://cmr.earthdata.nasa.gov/search/concepts/C1214311215-AU_AADC.umm_json A Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands and terrestrial and bathymetric contours derived from the DEM. The data is stored in a UTM zone 49(WGS-84) projection. Heights are referenced to mean sea level. It was created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA IS NOT FOR NAVIGATION PURPOSES. proprietary newcomb_bay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands AU_AADC STAC Catalog 1997-02-01 2000-02-05 110.512, -66.282, 110.566, -66.256 https://cmr.earthdata.nasa.gov/search/concepts/C1214311215-AU_AADC.umm_json A Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands and terrestrial and bathymetric contours derived from the DEM. The data is stored in a UTM zone 49(WGS-84) projection. Heights are referenced to mean sea level. It was created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA IS NOT FOR NAVIGATION PURPOSES. proprietary +newcomb_bay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands ALL STAC Catalog 1997-02-01 2000-02-05 110.512, -66.282, 110.566, -66.256 https://cmr.earthdata.nasa.gov/search/concepts/C1214311215-AU_AADC.umm_json A Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands and terrestrial and bathymetric contours derived from the DEM. The data is stored in a UTM zone 49(WGS-84) projection. Heights are referenced to mean sea level. It was created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA IS NOT FOR NAVIGATION PURPOSES. proprietary nexeastimpacts_1 NEXRAD Mosaic East IMPACTS V1 GHRC_DAAC STAC Catalog 2019-12-31 2020-02-29 -85, 32.5, -67.525, 46.475 https://cmr.earthdata.nasa.gov/search/concepts/C1995866059-GHRC_DAAC.umm_json The NEXRAD Mosaic East IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) 3D mosaic files created from Level II surveillance data gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Mosaic East dataset is composed of Level II data from 19 NEXRAD sites in the eastern U.S.. These data files are available in netCDF-4 format and contain meteorological and dual-polarization base data quantities including radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio from January 1 through February 29, 2020. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary nexmidwstimpacts_1 NEXRAD Mosaic Midwest IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-02-29 -93, 36, -79.025, 45.975 https://cmr.earthdata.nasa.gov/search/concepts/C1995866123-GHRC_DAAC.umm_json The NEXRAD Mosaic Midwest IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) 3D mosaic files created from Level II surveillance data gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Mosaic Midwest dataset is composed of Level II data from 11 NEXRAD sites in the midwestern U.S. These data files are available in netCDF-4 format and contain meteorological and dual-polarization base data quantities including radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio from January 1 through February 29, 2020. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary niche-partitioning-between-wild-bees-and-honeybees_1.0 Niche partitioning between wild bees and honeybees ENVIDAT STAC Catalog 2021-01-01 2021-01-01 8.4299469, 47.3172277, 8.6949921, 47.4130345 https://cmr.earthdata.nasa.gov/search/concepts/C2789816101-ENVIDAT.umm_json "Cities are socio-ecological systems that filter and select species, thus establishing unique species assemblages and biotic interactions. Urban ecosystems can host richer wild bee communities than highly intensified agricultural areas, specifically in resource-rich urban green spaces such as allotment and family gardens. At the same time, urban beekeeping has boomed in many European cities, raising concerns that the fast addition of a large number of managed bees could deplete the existing floral resources, triggering competition between wild bees and honeybees. The data has been used to investigated the interplay between resource availability and the number of honeybees at local and landscape scales and how this relationship influences wild bee diversity. This dataset contains the raw and processed data supporting the findings from the paper: ""Low resource availability drives feeding niche partitioning between wild bees and honeybees in a European city"". The data contains: 1. Bee trait measurements at the species and individual-level of five functional traits. 2. The values of the feeding niche partitioning (functional dissimilarity to honeybees) 3. The predictors of resource availability and beekeeping intensity at local and landscape scales used in the modelling of the paper for the 23 experimental sites." proprietary @@ -20130,8 +20110,8 @@ number_of_woody_species_from_40_cm_height-144_1.0 Number of woody species (from number_of_woody_species_gt_12_cm_dbh-41_1.0 Number of woody species (>= 12 cm DBH) ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789816627-ENVIDAT.umm_json Number of tree and shrub species starting at 12 cm dbh (diameter at breast height) within the 200 m2 sample plot. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary number_of_young_forest_plants_by_damage-209_1.0 Number of young forest plants by damage ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789816738-ENVIDAT.umm_json Number of regeneration trees starting at 10 cm height up to 11.9 cm dbh with a particular type of damage or with no damage. The attribute is recorded by targeting the next regeneration tree in the centre of the subplot during NFI’s regeneration survey. A regeneration tree may have more than one type of damage, which means it may contribute to the total number of regeneration trees for several different types of damage. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary nutrient-addition-stillberg_1.0 Nutrient addition experiment at the Alpine treeline site Stillberg, Switzerland ENVIDAT STAC Catalog 2023-01-01 2023-01-01 9.867544, 46.7716544, 9.867544, 46.7716544 https://cmr.earthdata.nasa.gov/search/concepts/C3226082769-ENVIDAT.umm_json # Background information The availability of nitrogen (N) and phosphorus (P) is considered to be a major factor limiting growth and productivity in terrestrial ecosystems globally. This project aimed to determine whether the growth stimulation documented in previous short‐term fertilisation trials persisted in a longer‐term study (12 years) in the treeline ecotone, and whether possible negative effects of nutrient addition offset the benefits of any growth stimulation. Over the course of the 12 study years, NPK fertiliser corresponding to 15 or 30 kg N ha−1 a−1 was added annually to plots containing 30‐year‐old *Larix decidua* or 32‐year-old *Pinus uncinata* individuals with an understorey of mainly ericaceous dwarf shrubs. To quantify growth, annual shoot increments of trees and dwarf shrubs as well as radial growth increments of trees were measured. Nutrient concentrations in the soil were also measured and the foliar nutritional status of trees and dwarf shrubs was assessed. # Experimental design Over an elevation gradient of 140 m across the treeline afforestation site Stillberg, 22 locations were chosen that covered the whole range of microenvironmental conditions (*see* Nutrient addition experimental design.png). Half of the blocks included European larch (*L. decidua*) and the other half included mountain pine (*P. uncinata*). Within each block, three plantation quadrats were randomly selected as experimental plots and each plot was assigned to a control (no fertilisation) or to one of two fertiliser dose treatments (15 kg and 30 kg N ha−1 a−1). Treatments were assigned randomly but confined so that the location of fertilised plots within a block was not directly above control plots to avoid nutrient input from drainage. For details about the experiment, *see* Möhl et al (2019). # Data description The available datasets contain climate variables (2004-2016), nutrient isotope measurements (2010 & 2016), shrub growth measurements (2004-2016), soil parameter measurements and annual ring and shoot measurements (2004-2016). All data can be found here: proprietary -nwrc_amphibianslowermiss A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley ALL STAC Catalog 1999-09-05 1999-12-05 -91.95, 31.15, -91.25, 32.4333 https://cmr.earthdata.nasa.gov/search/concepts/C2231550400-CEOS_EXTRA.umm_json Bottomland hardwood forests are floodplain forests distributed along rivers and streams throughout the central and southern United States. The largest bottomland hardwood ecosystem in North America occurred within the Lower Mississippi River Alluvial Valley (LMAV). By the 1980.s, an estimated 80% of the former 10 million ha of bottomland hardwood forest in the LMAV were cleared for flood control efforts, agriculture, and development. Forests are continuing to be cleared today at an alarming rate, and the forests that remain are highly degraded and fragmented. In addition, these forests are subjected to extreme hydrological alterations. Over the past few decades, extensive efforts have begun to reforest marginal agricultural lands within the LMAV. Restoration efforts are limited by the lack of information concerning the habitat needs of bottomland wildlife species. Amphibians are one group of species for which little is known about their population status or habitat requirements in the LMAV. Information concerning the population status of amphibians in the LMAV is especially important since amphibians appear to be declining worldwide. Amphibians may also be important indicators of environmental health because of their sensitivity to land management practices and water quality. Understanding the habitat requirements of amphibians can be a step toward enhancing wildlife populations within the LMAV by providing valuable information for improving land management practices and wetland restoration techniques. To provide an inventory of amphibians at Tensas River and Lake Ophelia National Wildlife Refuges. In addition, to determine amphibian distribution patterns in the LMAV as they relate to landscape habitat features. Research results will be used to develop reports and manuscripts, and to assist land managers in management decisions to benefit amphibian populations. Information was obtained from Janene Lichtenberg for this metadata. proprietary nwrc_amphibianslowermiss A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley CEOS_EXTRA STAC Catalog 1999-09-05 1999-12-05 -91.95, 31.15, -91.25, 32.4333 https://cmr.earthdata.nasa.gov/search/concepts/C2231550400-CEOS_EXTRA.umm_json Bottomland hardwood forests are floodplain forests distributed along rivers and streams throughout the central and southern United States. The largest bottomland hardwood ecosystem in North America occurred within the Lower Mississippi River Alluvial Valley (LMAV). By the 1980.s, an estimated 80% of the former 10 million ha of bottomland hardwood forest in the LMAV were cleared for flood control efforts, agriculture, and development. Forests are continuing to be cleared today at an alarming rate, and the forests that remain are highly degraded and fragmented. In addition, these forests are subjected to extreme hydrological alterations. Over the past few decades, extensive efforts have begun to reforest marginal agricultural lands within the LMAV. Restoration efforts are limited by the lack of information concerning the habitat needs of bottomland wildlife species. Amphibians are one group of species for which little is known about their population status or habitat requirements in the LMAV. Information concerning the population status of amphibians in the LMAV is especially important since amphibians appear to be declining worldwide. Amphibians may also be important indicators of environmental health because of their sensitivity to land management practices and water quality. Understanding the habitat requirements of amphibians can be a step toward enhancing wildlife populations within the LMAV by providing valuable information for improving land management practices and wetland restoration techniques. To provide an inventory of amphibians at Tensas River and Lake Ophelia National Wildlife Refuges. In addition, to determine amphibian distribution patterns in the LMAV as they relate to landscape habitat features. Research results will be used to develop reports and manuscripts, and to assist land managers in management decisions to benefit amphibian populations. Information was obtained from Janene Lichtenberg for this metadata. proprietary +nwrc_amphibianslowermiss A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley ALL STAC Catalog 1999-09-05 1999-12-05 -91.95, 31.15, -91.25, 32.4333 https://cmr.earthdata.nasa.gov/search/concepts/C2231550400-CEOS_EXTRA.umm_json Bottomland hardwood forests are floodplain forests distributed along rivers and streams throughout the central and southern United States. The largest bottomland hardwood ecosystem in North America occurred within the Lower Mississippi River Alluvial Valley (LMAV). By the 1980.s, an estimated 80% of the former 10 million ha of bottomland hardwood forest in the LMAV were cleared for flood control efforts, agriculture, and development. Forests are continuing to be cleared today at an alarming rate, and the forests that remain are highly degraded and fragmented. In addition, these forests are subjected to extreme hydrological alterations. Over the past few decades, extensive efforts have begun to reforest marginal agricultural lands within the LMAV. Restoration efforts are limited by the lack of information concerning the habitat needs of bottomland wildlife species. Amphibians are one group of species for which little is known about their population status or habitat requirements in the LMAV. Information concerning the population status of amphibians in the LMAV is especially important since amphibians appear to be declining worldwide. Amphibians may also be important indicators of environmental health because of their sensitivity to land management practices and water quality. Understanding the habitat requirements of amphibians can be a step toward enhancing wildlife populations within the LMAV by providing valuable information for improving land management practices and wetland restoration techniques. To provide an inventory of amphibians at Tensas River and Lake Ophelia National Wildlife Refuges. In addition, to determine amphibian distribution patterns in the LMAV as they relate to landscape habitat features. Research results will be used to develop reports and manuscripts, and to assist land managers in management decisions to benefit amphibian populations. Information was obtained from Janene Lichtenberg for this metadata. proprietary nymesoimpacts_1 New York State Mesonet IMPACTS GHRC_DAAC STAC Catalog 2020-01-03 2023-03-02 -79.6375, 40.594, -72.1909, 44.9057 https://cmr.earthdata.nasa.gov/search/concepts/C1995873777-GHRC_DAAC.umm_json The New York State Mesonet IMPACTS dataset is browse-only. It consists of temperature, wind, wind direction, mean sea level pressure, precipitation, and snow depth measurements, as well as profiler Doppler LiDAR and Microwave Radiometer (MWR) measurements from the New York State Mesonet network during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The Mesonet network consists of ground weather stations, LiDAR profilers, and microwave radiometer (MWR) profilers. These browse files are available from January 3, 2020, through March 2, 2023, in PNG format. proprietary obrienbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands ALL STAC Catalog 1997-03-31 1997-03-31 110.516, -66.297, 110.54, -66.293 https://cmr.earthdata.nasa.gov/search/concepts/C1214311199-AU_AADC.umm_json A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands. proprietary obrienbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands AU_AADC STAC Catalog 1997-03-31 1997-03-31 110.516, -66.297, 110.54, -66.293 https://cmr.earthdata.nasa.gov/search/concepts/C1214311199-AU_AADC.umm_json A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands. proprietary @@ -20160,8 +20140,8 @@ pedestrian_gentoo_1 Effects of human activity on Gentoo penguins on Macquarie Is pedestrian_king_1 Effects of human activity on King penguins on Macquarie Island AU_AADC STAC Catalog 2002-10-20 2003-03-20 158.76892, -54.78168, 158.96667, -54.47802 https://cmr.earthdata.nasa.gov/search/concepts/C1214311218-AU_AADC.umm_json This project empirically measures the effects of human activity on the behaviour of King penguins on Macquarie Island, under ASAC project 1148. This was achieved by collecting behavioural responses of individual penguins exposed to pedestrian approaches across the breeding stages of incubation and guard. Information produced includes minimum approach guidelines. As of April 2003 all data are stored on Hi-8 digital tape, due to be transformed during 2003 - 2004 into a timecoded tab-delimited text format for analysis using the Observer (Noldus Information Technology 2002). The fields in this dataset are: Sample Date Breeding Phase Approach Colony Focal birds tape number Wide angle tape number Weather Time Windspeed Temperature Precipitation Cloud Pre-approach control Post-approach control Maximum approach distance proprietary pedestrian_royal_1 Effects of human activity on Royal penguins on Macquarie Island AU_AADC STAC Catalog 2002-10-20 2003-03-20 158.76755, -54.78247, 158.95981, -54.47802 https://cmr.earthdata.nasa.gov/search/concepts/C1214311223-AU_AADC.umm_json This project empirically measures the effects of human activity on the behaviour, heart rate and egg-shell surface temperature of Royal penguins on Macquarie Island, as part of ASAC project 1148. This was achieved by collecting behavioural and physiological responses of individual penguins exposed to pedestrian approaches across the breeding stages of incubation, guard, creche and moult. Both single person and group approaches were also investigated. Information produced includes minimum approach guidelines. As of April 2003 all data are stored on Hi-8 digital tape, due to be transformed during 2003 - 2004 into a timecoded tab-delimited text format for analysis using the Observer (Noldus Information Technology 2002). Some notes about some of the fields in this dataset: Temp file refers to whether or not egg shell surface temperature was also recorded for the sample, with the code below refering to the file name. Neighbour refers to the behavioural control file for each sample (neighbouring nests did not recieve an artificial egg, and provide a behavioural control for responses to human approaches without the scientific treatment). Nest refers to the randomly used nest markers for each sample. Heart rate refers to whether heart rate was concurrently recorded with behaviour on the sample (both stored on Hi-8 tape). Stimulus refers to whether single persons or groups of persons (5 -7, recorded within each sample) were used for the human approaches. Environment refers to whether approaches were conducted from colony sections abuting pebbly beach or from poa tussock environs (tussock approaches less than 50 m of the poa / pebbly beach junction). The code system for nest simply refers to the numbered tag placed at the nest (using three colours, g=green, w=white, b=brown) which were used randomly. The fields in this dataset are: Sample Date Breeding Phase Stimulus Type Environment Colony Nest Tape Heart Rate Temp File Neighbour proprietary pfynwald_2016 Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland ENVIDAT STAC Catalog 2016-01-01 2016-01-01 7.61192, 46.30284, 7.61192, 46.30284 https://cmr.earthdata.nasa.gov/search/concepts/C2789816328-ENVIDAT.umm_json To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46° 18' N, 7° 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2°C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years. proprietary -pfynwaldgasexchange_1.0 2013-2020 gas exchange at Pfynwald ENVIDAT STAC Catalog 2021-01-01 2021-01-01 7.6105556, 46.3001905, 7.6163921, 46.3047564 https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.umm_json Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents. proprietary pfynwaldgasexchange_1.0 2013-2020 gas exchange at Pfynwald ALL STAC Catalog 2021-01-01 2021-01-01 7.6105556, 46.3001905, 7.6163921, 46.3047564 https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.umm_json Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents. proprietary +pfynwaldgasexchange_1.0 2013-2020 gas exchange at Pfynwald ENVIDAT STAC Catalog 2021-01-01 2021-01-01 7.6105556, 46.3001905, 7.6163921, 46.3047564 https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.umm_json Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents. proprietary phipsimpacts_1 Particle Habit Imaging and Polar Scattering Probe (PHIPS) IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-18 2023-02-28 -95.243, 33.261, -64.987, 48.237 https://cmr.earthdata.nasa.gov/search/concepts/C1995874351-GHRC_DAAC.umm_json The Particle Habit Imaging and Polar Scattering (PHIPS) Probes IMPACTS dataset consists of cloud particle imagery collected by the Particle Habit Imaging and Polar Scattering (PHIPS) probe onboard the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. PHIPS allows for the measurement of particle shape, size, and habit. The browse files in this dataset contain the post-processed particle-by-particle stereo images (2 images from different angles) collected by PHIPS during the campaign. The files are available from January 18, 2020, through February 28, 2023, in PNG format. proprietary phosphorus-and-nitrogen-leaching-from-beech-forest-soils_1.0 Phosphorus and nitrogen leaching from beech forest soils ENVIDAT STAC Catalog 2021-01-01 2021-01-01 9.927478, 50.3518, 10.26725, 52.838967 https://cmr.earthdata.nasa.gov/search/concepts/C2789816374-ENVIDAT.umm_json Data on dissolved organic and inorganic phosphorus and nitrogen concentrations in leachates and their corresponding fluxes from the litter layer, the Oe/Oa horizon, and the A horizon of two German beech forest sites. Leachate samples were taken in April 2018, July 2018, October 2018, Feb./Mar. 2019, and July 2019 with zero-tension lysimeters after artificial irrigation. Soil samples were taken in July 2019. For more details please refer to the publication. proprietary photo_mosaic_laurens_or_1 Heard Island, Laurens Peninsula, Coastal Orthophoto Mosaic derived from Non-Metric Photography AU_AADC STAC Catalog 1980-01-01 2000-12-31 73.23, -53.05, 73.41, -52.95 https://cmr.earthdata.nasa.gov/search/concepts/C1214311224-AU_AADC.umm_json The orthophoto mosaic is a rectified georeferenced image of the Heard Island, Laurens Peninsula Coastal Area. Distortions due to relief and tilt displacement have been removed. Orthophotos were derived from non-metric cameras (focal length unknown). proprietary @@ -20272,8 +20252,8 @@ rlc_landcover_far_east_690_1 RLC AVHRR-Derived Land Cover, Former Soviet Union, rlc_vector_data_698_1 RLC Selected Infrastructure Data for the Former Soviet Union, 1993 ORNL_CLOUD STAC Catalog 1993-01-01 1993-12-31 25, 23.21, 180, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2810672079-ORNL_CLOUD.umm_json This data set consists of roads, drainage, railroads, utilities, and population center information in readily usable vector format for the land area of the Former Soviet Union. The purpose of this dataset was to create a completely intact vector layer which could be readily used to aid in mapping efforts for the area of the FSU. These five vector data layers were assembled from the Digital Chart of the World (DCW), 1993. Individual record attributes were stored for population centers only. Vector maps for the FSU are in ArcView shapefile format. proprietary rlc_vegetation_1990_700_1 RLC Vegetative Cover of the Former Soviet Union, 1990 ORNL_CLOUD STAC Catalog 1973-01-01 1973-12-31 19.82, 35.17, 170, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2810672200-ORNL_CLOUD.umm_json This dataset is a 1:4 million scale vegetation map for the land area of the Former Soviet Union. Three hundred seventy-three cover classes are distinguished, of which nearly 145 are forest cover-related classes. Stone and Schlesinger (1993) digitized the map Vegetation of the Soviet Union, 1990 (Institute of Geography, 1990). proprietary rlc_world_forest_map_697_1 RLC Generalized Forest Map of the Former Soviet Union, 1-km ORNL_CLOUD STAC Catalog 1998-01-01 1998-12-31 25, 23.21, 180, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2810671823-ORNL_CLOUD.umm_json This data set is the Former Soviet Union (FSU) portion of the Generalized World Forest Map (WCMC, 1998), a 1-kilometer resolution generalized forest cover map for the land area of the Former Soviet Union. There are five forest classes in the original global generalized map. Only two of those classes were distinguished in the geographical portion comprising the FSU. proprietary -robinson_adelie_colonies_1 Adelie penguin colonies in the Robinson Group, Antarctica: surveys conducted during 2005/06 and 2006/07 ALL STAC Catalog 2005-09-30 2007-03-31 63.233334, -67.51667, 63.85, -67.36667 https://cmr.earthdata.nasa.gov/search/concepts/C1214311232-AU_AADC.umm_json GPS surveys of Adelie Penguin colonies in the Robinson Group, Antarctica were carried out by field biologists from the Australian Antarctic Division during the 2005/06 and 2006/07 seasons. In 2005/06 point data were collected representing the presence or absence of colonies on the islands. The data were collected by Matt Low and Lisa Meyer. In 2006/07 polygon data were collected representing the colonies on the islands. The data were collected by Matt Low and Rhonda Pike. The biologists were working on a project led by Dr Colin Southwell of the Australian Antarctic Division. proprietary robinson_adelie_colonies_1 Adelie penguin colonies in the Robinson Group, Antarctica: surveys conducted during 2005/06 and 2006/07 AU_AADC STAC Catalog 2005-09-30 2007-03-31 63.233334, -67.51667, 63.85, -67.36667 https://cmr.earthdata.nasa.gov/search/concepts/C1214311232-AU_AADC.umm_json GPS surveys of Adelie Penguin colonies in the Robinson Group, Antarctica were carried out by field biologists from the Australian Antarctic Division during the 2005/06 and 2006/07 seasons. In 2005/06 point data were collected representing the presence or absence of colonies on the islands. The data were collected by Matt Low and Lisa Meyer. In 2006/07 polygon data were collected representing the colonies on the islands. The data were collected by Matt Low and Rhonda Pike. The biologists were working on a project led by Dr Colin Southwell of the Australian Antarctic Division. proprietary +robinson_adelie_colonies_1 Adelie penguin colonies in the Robinson Group, Antarctica: surveys conducted during 2005/06 and 2006/07 ALL STAC Catalog 2005-09-30 2007-03-31 63.233334, -67.51667, 63.85, -67.36667 https://cmr.earthdata.nasa.gov/search/concepts/C1214311232-AU_AADC.umm_json GPS surveys of Adelie Penguin colonies in the Robinson Group, Antarctica were carried out by field biologists from the Australian Antarctic Division during the 2005/06 and 2006/07 seasons. In 2005/06 point data were collected representing the presence or absence of colonies on the islands. The data were collected by Matt Low and Lisa Meyer. In 2006/07 polygon data were collected representing the colonies on the islands. The data were collected by Matt Low and Rhonda Pike. The biologists were working on a project led by Dr Colin Southwell of the Australian Antarctic Division. proprietary rock_samples_1 Compilation of Rock Samples collected by ANARE AU_AADC STAC Catalog 1954-02-01 1999-11-22 60, -75, 160, -35 https://cmr.earthdata.nasa.gov/search/concepts/C1214313719-AU_AADC.umm_json Rocks from Australian Antarctic Division library This collection turns out to be rather interesting with some of heritage significance. Box 1 is basically odds and ends but includes a bag of coal from the Prince Charles Mountains worthy of display. Boxes 2 and 3 probably all are Phil Law collections. Unfortunately, locality information generally is lacking, but there are some interesting rocks. Box 1. A.Loose samples Two pale grey, rounded specimens, one with round depression. Very light weight (low density). Probably diatomite or radiolarite. Source? Dark grey with some red colours. Fragment of rounded river pebble that has been broken. Very tough, either quartzite or volcanic rock. Source? Scallop (Pecten meridionalis), left valve Tasmania Pink and yellow chert, varnished. One part of outside looks as if it has been fossil wood. Could be recrystallised chert from fossil wood locality. Source? Could be Tasmanian. Two small, dark, angular specimens, quite coarse grained with obvious crystal faces that flash. Specimens are of quartz and galena (PbS). Source? Could be west coast Tasmania such as Zeehan. Three elongate specimens, pale yellow/off white. They fit together to produce original specimen about 20 cm long. These are quite common around coastal Australia where rain soaks through sand, dissolves CaCO3 from surface shell material and redeposits it on the way down, perhaps along the roots of a plant. Goes by various names such as 'fossil roots' (which is wrong), travertine Large lump of black glass. Probably furnace slag but could conceivably be volcanic glass (probably too high density for that). Vesicles (gas bubbles quite common). B. Sample bag A calico bag of Permian coal from the Prince Charles Mountains. Bag is labelled to Assistant Director Science but probably was given to Evlyn Barrett as there is a note inside it suggesting that it is a present. Some specimens are good and could be used for display. Box 2. A note in the box (from me to Knowles Kerry) notes that these rocks were collected by Phil Law. While some cards are there, they are not related to the rocks. Most would appear to be Antarctic. Sample with cellotape, labelled Cape North. Fragment of vein quartz. Pumice. Grey, very light weight. Floats. Product of March 1962 submarine eruption at Protector Shoal in South Sandwich Islands. Rafts of this pumice circulated around Southern Hemisphere for years, slowly disappearing as the material became dispersed, washed onto beaches (small fragments still common on Australian beaches and some on Heard Island) and as fragments rubbed together, ground small chips off and these sank. This sample has some flow structure in it from the original eruption and due to elongation of gas bubbles as it flowed and cooled. It may well be from Heard Island. &It is identical in composition to material collected by Dr Jon Stephenson in 1963 from 'flotsam north of Heard Island' collected during his period on the latter expedition (Stephenson 1964) and identified as having been derived from vast rafts of pumice released in the South Atlantic Ocean during the eruption in the South Sandwich Islands area in 1962 (Gass et al.. 1963). This is probably the same material referred to by Dr Phil Law, who commented (personal communication, 19 August 1993) that he had seen rafts of pumice near Heard Island in January 1963.& (quote from Quilty and Wheller in preparation for Heard Island symposium of 1998). Flat dark grey fragment about 1 cm thick. Otherwise triangular with sharp corners. Rock is phyllite, rather low grade metamorphic rock, originally a shale in which clay has changed to muscovite to generate the good cleavage. Source? Would like to know because I have identical material as a glacial erratic from Kerguelen Plateau. 'Granite' Two fragments - angular, one rounded - of grey granite. Good samples. They are not quite the same material. Angular specimen is probably strictly granodiorite (the difference is important only to geologists). It contains quartz (very pale grey, glassy), two white feldspars (plagioclase-Na-CaAlsSi3O8 - and orthoclase - KalSi3O8) which make up the bulk of the rock in roughly equal proportions and come in two grain sizes - coarse (about 1 cm) and finer (about 2 mm). Dark minerals are biotite (black mica) and hornblende (complex Fe/Mg silicate). Rounded specimen is more uniform in grain and probably has the same pale minerals but they are not so easy to identify. Dark mineral hornblende. Biotite not seen. There also is a brown mineral, sometimes rhomboid in cross section. This probably is sphene. Source of samples? Rauer Island Rocks. (Probably Phil Law's own labelling) Replaced in old plastic bag and in turn in a new thin one. Two glassy (vitreous) grey samples. Monominerallic. Vein quartz. Two flat specimens with marked orientation of very uniform grained constituent minerals. Both high grade metamorphic rocks - amphibolite gneiss. Mineralogy - quartz, amphibole (probably hornblende), plagioclase feldspar. In one the quartz is white and in the other, more yellowish. Rounded specimen with two rock types in it with clear boundary. Pale rock is quartzite and other is amphibolite, probably part of same sequence as other amphibolites. Other rock has great variation in grain size but is otherwise part of the same sequence. Darker part is amphibolite, coarser than in samples described above and with yellowish quartz and orthoclase. This rock seems to be the source of the sand grains as it is more friable than others. Garnet rich sample - Bag 1 One rounded sample contains a significant content of garnet in white 'matrix'. The pale material is quartz/orthoclase and there is a fine grained, high lustre black mineral that could be magnetite (Fe3 O4). Source??? Probably a Law sample. Three specimens in small bag - Bag 2 All are characterised by having quartz veins 1-1.5 cm thick, cutting across the sample and bounded by a layer 1-2 mm thick of a black mineral (amphibole, probably hornblende). Other constituents of the rock are yellowish quartz, traces of garnet and biotite. I couldn't identify any feldspar but would expect it. The rocks, although not labelled with a locality, are very similar to some of those described as from the Rauer Islands but there are some in the Vestfold Hills that are very similar. Metabasalt? - Bag 4 - two samples These look rather like the basalt dykes that are so characteristic of the Vestfold Hills but are they? And who collected them? They probably are Phil Law collections. The dykes were intruded in a series of about 9 episodes from about 2.2 billion to 1.1 billion years. They have been altered since intrusion and while bulk composition changed little, the mineralogy did. They are now very tough rocks that break with highly angular, brittle fractures. Box 3 Judging by the brown sample bag, I suspect these are also Phil Law collections but where from? Brown calico bag - 5 specimens Large specimen is amphibolite gneiss consisting of layers that are amphibole and biotite rich. Also has traces of garnet. Locality? Two pale specimens. Both contain prominent garnet in quartz-feldspar matrix, orthoclase dominating. Metamorphic. Locality? Two small specimens. One is coarser than the other and has obvious garnet with hornblende, biotite, quartz and feldspar. The other is mainly hornblende/quartz but is a surface specimen, somewhat weathered. Brown paper bag (now in plastic bag - 5) Small sample (two almost black specimens). These are different from anything noted above. While the black biotite is the dominant source of the colour, there is also some quartz and I suspect feldspar. There also is quite a deal of very fine acicular mineral. It could be one of several but sillimanite (one of several minerals with the formula Al2SIO3) is a possibility. Largest, dark sample. Amphibolite gneiss. Well banded. Pale bands of quartz-feldspar-muscovite (white mica). Dark bands of hornblende-biotite. Source??? Dominantly pale sample with dark patch. Pale part is quartz-feldspar and the dark is hornblende plus minor acicular mineral (sillimanite?). Thin sample, 6 x 5 cm, 4 mm thick. Details not clear. Too fine grained but probably mainly quartz-feldspar with minor dark mineral (hornblende?). Plastic bag 6. Large flat specimen and one chip off the large block. Low grade metamorphic rock, originally fine sandstone. Source? Plastic bag 7 Rock mainly of coarse K-feldspar and quartz with minor plagioclase. Rock includes layers of brown mica (phlogopite?). Metamorphic. Source? Plastic bag 8. 8A. 3 specimens (2 are counterparts). See also 'Brown paper bag' sample above. Biotite-quartz-sillimanite. 8B. 2 specimens. Beautiful banded gneiss. Bands are pale, dominantly quartz and dark, dominantly biotite with some hornblende. 8C. 2 specimens. Quartz-biotite schist with trace of acicular mineral (sillimanite?) and pyrite. Two remaining specimens. One is of quartz/feldspar(?)/biotite/hornblende-sillimanite? Is feldspar correctly identified? Sieve texture. Other is subrounded boulder, greenish (chlorite?). Patrick G. Quilty AM 22 November 1999 proprietary rockfall-gallery-testing-parde-2016_1.0 Rockfall gallery testing Parde 2016 ENVIDAT STAC Catalog 2018-01-01 2018-01-01 8.698082, 46.6532196, 8.698082, 46.6532196 https://cmr.earthdata.nasa.gov/search/concepts/C2789816316-ENVIDAT.umm_json "Five full-scale field tests were conducted with concrete blocks weighting between 800 and 3200 kg being dropped onto the roof of a gallery structure made from reinforced concrete. The impacts were recorded using high-speed video and acceleration measurements at the falling blocks. The dataset contains the raw data as well as the analyses of the block trajectories, i.e. kinetics and dynamics. Setup of the measurements and the analyses conducted are published in Volkwein, A. ""Durchführung und Auswertung von Steinschlagversuchen auf eine Stahlbetongalerie"", WSL-Berichte, Heft 68, 2018." proprietary root-traits_1.0 Root-traits ENVIDAT STAC Catalog 2019-01-01 2019-01-01 7.6130533, 46.3023351, 7.6130533, 46.3023351 https://cmr.earthdata.nasa.gov/search/concepts/C2789816345-ENVIDAT.umm_json Fine-root traits of Scots pine in response to enhanced soil water availability deriving from long-term irrigation in the Pfynwald Data_Fig.1.xlsx Fine-root biomass of the topsoil (0-10 cm) in the dry and irrigated treatment of the Scots pine forest of the years 2003 to 2016 recorded by soil coring Data_Tab1+2_2005.xlsx Fine-root traits from roots of ingrowth cores from 2005 after two years of growth in the dry and irrigated treatment of the Scots pine forest Data_Tab1+2_2016.xlsx Fine-root traits from roots of ingrowth cores from 2016 after two years of growth, and from roots of the soil-coring sampling from 2016 in the dry and irrigated treatment of the Scots pine forest proprietary @@ -20380,12 +20360,12 @@ sbuparsimpacts_1 SBU Parsivel IMPACTS GHRC_DAAC STAC Catalog 2020-01-01 2023-03- sbuplimpacts_1 SBU Pluvio Precipitation Gauge IMPACTS GHRC_DAAC STAC Catalog 2020-01-07 2023-03-02 -73.138, 40.8556, -72.8714, 40.90712 https://cmr.earthdata.nasa.gov/search/concepts/C1995869760-GHRC_DAAC.umm_json The SBU Pluvio Precipitation Gauge IMPACTS dataset consists of precipitation intensity and precipitation accumulation collected using the OTT Pluvio2 weighing rain gauge during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. NASA’s Earth Venture program funded IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. Data files in this dataset are available in ASCII-CSV format from January 7, 2020, through March 2, 2023. proprietary sbuskylerimpacts_1 SBU X-band Phased Array Radar (SKYLER) IMPACTS GHRC_DAAC STAC Catalog 2022-01-17 2023-02-28 -77.4867, 40.1501, -71.266, 43.695 https://cmr.earthdata.nasa.gov/search/concepts/C2704110186-GHRC_DAAC.umm_json The SBU X-band Phased Array Radar (SKYLER) IMPACTS dataset consists of polarimetric radar data collected by the Stony Brook University (SBU) X-band Phased Array Radar (SKYLER) during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. SKYLER provided detailed observations of cloud and precipitation microphysics, specifically ice and snow processes. These data include reflectivity, mean velocity, spectrum width, linear depolarization ratio, differential reflectivity, differential phase, specific differential phase, co-polarized correlation coefficient, and signal-to-noise ratio. The dataset files are available from January 17, 2022, through February 28, 2023, in netCDF-4 format. proprietary sbusndimpacts_1 SBU Mobile Soundings IMPACTS GHRC_DAAC STAC Catalog 2020-01-18 2023-02-28 -76.980629, 40.4841385, -70.8692093, 43.7849808 https://cmr.earthdata.nasa.gov/search/concepts/C1995869776-GHRC_DAAC.umm_json The SBU Mobile Sounding IMPACTS dataset consists of mobile sounding profiles collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Mobile-sounding profiles were obtained about every three hours during snow events by Stony Brook University (SBU). The sounding measures temperature, humidity, height, and horizontal wind direction and speed in the atmosphere. Atmospheric pressure is calculated from GPS height. Data files are available from January 18, 2020, through February 28, 2023 in netCDF-3 format. proprietary -scarmarbin_1647 Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155436-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1647 Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155436-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary +scarmarbin_1647 Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155436-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1648 Admiralty Bay Benthos Diversity Data Base (ABBED). Cumacea. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155484-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1648 Admiralty Bay Benthos Diversity Data Base (ABBED). Cumacea. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155484-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary -scarmarbin_1649 Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155485-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1649 Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155485-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary +scarmarbin_1649 Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155485-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1651 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 SCIOPS STAC Catalog 1979-01-01 1986-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155486-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1651 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 ALL STAC Catalog 1979-01-01 1986-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155486-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1716 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 - scarmarbin_1716 ALL STAC Catalog 1979-12-27 1980-02-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1221420764-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary @@ -20396,12 +20376,12 @@ scarmarbin_1806 Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (19 scarmarbin_1806 Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (1997). SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155503-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1807 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1994). SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155504-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1807 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1994). ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155504-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary -scarmarbin_1808 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997). ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155505-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_1808 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997). SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155505-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary +scarmarbin_1808 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997). ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155505-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary scarmarbin_987 A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection) ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155566-SCIOPS.umm_json The primary objective of this project is to provide a database of the estimated 25,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. Another objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This datasource provides primary access to the Indo-Pacific Mollusc Dataset using the obis schema. Data in the Indo-Paciffic Mollusc database use names from the Indo-Pacific Mollusc project together with point records from the Academy of Natural Sciences and the Australian Museum. Specimens referenced in this data set may be in the collections of either the Australian Museum or the Academy of Natural Sciences, but may have current identifications in those collections that are junior synonymys (or other junior names) of names in current use in the Indo-Pacific Mollusc database. proprietary scarmarbin_987 A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection) SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155566-SCIOPS.umm_json The primary objective of this project is to provide a database of the estimated 25,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. Another objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This datasource provides primary access to the Indo-Pacific Mollusc Dataset using the obis schema. Data in the Indo-Paciffic Mollusc database use names from the Indo-Pacific Mollusc project together with point records from the Academy of Natural Sciences and the Australian Museum. Specimens referenced in this data set may be in the collections of either the Australian Museum or the Academy of Natural Sciences, but may have current identifications in those collections that are junior synonymys (or other junior names) of names in current use in the Indo-Pacific Mollusc database. proprietary -scarmarbin_ABBED Admiralty Bay Benthos Biodiversity Database [SCAR-MarBIN] ALL STAC Catalog 1906-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155568-SCIOPS.umm_json Admiralty Bay is one of the best studied sites in the maritime Antarctic. The first benthos data has been recorded in 1906 and knowledge is constantly gained by the research activities of permanent stations, Arctowski (Poland, since 1977), and Ferraz (Brazil, since 1984). Admiralty Bay is a protected area within the Antarctic Treaty System, an “Antarctic Specially Managed Area” (ASMA). It was also a reference site under the EASIZ programme, and has been or is currently investigated by several nations : Poland, Brazil, United States, Peru, Ecuador, Germany, The Netherlands, Belgium. ABBED (Admiralty Bay Benthos Biodiversity Database) is a Belgian-Polish initiative, which aims at compiling and linking existing information on Admiralty Bay benthos biodiversity and ecology. This information will be digitized into a database and linked to wider Antarctic marine biodiversity initiatives, such as SCAR-MarBIN, which will disseminate the information through a web portal. Being highly diverse in its content, formats and data providers, ABBED will constitute an extremely interesting case-study for SCAR-MarBIN, allowing to test strategic options which were retained for the development of the network. Moreover, the quality and quantity of data which will be made available to the community will reinforce the status of Admiralty Bay as a true reference point for Antarctic biodiversity research. The project aims at developing an interactive database on the biodiversity of benthic communities of Admiralty Bay, King George Island, for scientific, monitoring, management and conservation purposes. It is intended to be a springboard for promoting future research in this region, by centralizing the relevant information for i.e. scientific programme design. proprietary scarmarbin_ABBED Admiralty Bay Benthos Biodiversity Database [SCAR-MarBIN] SCIOPS STAC Catalog 1906-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155568-SCIOPS.umm_json Admiralty Bay is one of the best studied sites in the maritime Antarctic. The first benthos data has been recorded in 1906 and knowledge is constantly gained by the research activities of permanent stations, Arctowski (Poland, since 1977), and Ferraz (Brazil, since 1984). Admiralty Bay is a protected area within the Antarctic Treaty System, an “Antarctic Specially Managed Area” (ASMA). It was also a reference site under the EASIZ programme, and has been or is currently investigated by several nations : Poland, Brazil, United States, Peru, Ecuador, Germany, The Netherlands, Belgium. ABBED (Admiralty Bay Benthos Biodiversity Database) is a Belgian-Polish initiative, which aims at compiling and linking existing information on Admiralty Bay benthos biodiversity and ecology. This information will be digitized into a database and linked to wider Antarctic marine biodiversity initiatives, such as SCAR-MarBIN, which will disseminate the information through a web portal. Being highly diverse in its content, formats and data providers, ABBED will constitute an extremely interesting case-study for SCAR-MarBIN, allowing to test strategic options which were retained for the development of the network. Moreover, the quality and quantity of data which will be made available to the community will reinforce the status of Admiralty Bay as a true reference point for Antarctic biodiversity research. The project aims at developing an interactive database on the biodiversity of benthic communities of Admiralty Bay, King George Island, for scientific, monitoring, management and conservation purposes. It is intended to be a springboard for promoting future research in this region, by centralizing the relevant information for i.e. scientific programme design. proprietary +scarmarbin_ABBED Admiralty Bay Benthos Biodiversity Database [SCAR-MarBIN] ALL STAC Catalog 1906-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155568-SCIOPS.umm_json Admiralty Bay is one of the best studied sites in the maritime Antarctic. The first benthos data has been recorded in 1906 and knowledge is constantly gained by the research activities of permanent stations, Arctowski (Poland, since 1977), and Ferraz (Brazil, since 1984). Admiralty Bay is a protected area within the Antarctic Treaty System, an “Antarctic Specially Managed Area” (ASMA). It was also a reference site under the EASIZ programme, and has been or is currently investigated by several nations : Poland, Brazil, United States, Peru, Ecuador, Germany, The Netherlands, Belgium. ABBED (Admiralty Bay Benthos Biodiversity Database) is a Belgian-Polish initiative, which aims at compiling and linking existing information on Admiralty Bay benthos biodiversity and ecology. This information will be digitized into a database and linked to wider Antarctic marine biodiversity initiatives, such as SCAR-MarBIN, which will disseminate the information through a web portal. Being highly diverse in its content, formats and data providers, ABBED will constitute an extremely interesting case-study for SCAR-MarBIN, allowing to test strategic options which were retained for the development of the network. Moreover, the quality and quantity of data which will be made available to the community will reinforce the status of Admiralty Bay as a true reference point for Antarctic biodiversity research. The project aims at developing an interactive database on the biodiversity of benthic communities of Admiralty Bay, King George Island, for scientific, monitoring, management and conservation purposes. It is intended to be a springboard for promoting future research in this region, by centralizing the relevant information for i.e. scientific programme design. proprietary schweizerisches-landesforstinventar-2009-2017_1.0 Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009–2017 ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817193-ENVIDAT.umm_json Swiss National Forest Inventory. Results of the fourth survey 2009–2017. The collection of data for the fourth National Forest Inventory (NFI) was carried out from 2009 to 2017, on average eight years after the third survey. The findings about state and development of Swiss forests are described and explained in detail. The report is structured according to the European criteria and indicators for sustainable forest management, namely: forest resources, health and vitality, wood production, biological diversity, protection forest and social economy. Finally, conclusions about sustainability are drawn based on the NFI findings. Keywords: forest area, growing stock, increment, yield, forest structure, forest condition, timber production, biodiversity, protection forest, recreation, sustainability, results National Forest Inventory, Switzerland Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009–2017. In den Jahren 2009 bis 2017 fanden die Erhebungen zum vierten Schweizerischen Landesforstinventar (LFI) statt, im Durchschnitt acht Jahre nach der dritten Erhebung. Die Resultate über den Zustand und die Entwicklung des Schweizer Waldes werden umfassend dargestellt und erläutert. Der Bericht ist thematisch strukturiert nach den europäischen Kriterien und Indikatoren zur nachhaltigen Bewirtschaftung des Waldes: Waldressourcen, Gesundheit und Vitalität, Holzproduktion, biologische Vielfalt, Schutzwald und Sozioökonomie. Eine Bilanz zur Nachhaltigkeit, basierend auf LFI-Ergebnissen, schliesst die Publikation ab. Keywords: Waldfläche, Holzvorrat, Zuwachs, Nutzung, Waldaufbau, Waldzustand, Holzproduktion, Biodiversität, Schutzwald, Erholung, Nachhaltigkeit, Ergebnisse Landesforstinventar, Schweiz Content license: All rights reserved. Copyright © 2020 by WSL, Birmensdorf. proprietary scolytidae_1.0 Scolytidae ENVIDAT STAC Catalog 2019-01-01 2019-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817304-ENVIDAT.umm_json Scolytidae data from all historic up to the recent projects (29.10.2019) of WSL, collected with various methods in forests of different types. Data are provided on request to contact person against bilateral agreement. proprietary scrxsondecpexaw_1 St. Croix Radiosondes CPEX-AW V1 GHRC_DAAC STAC Catalog 2021-08-19 2021-09-14 -65.2209, 17.4441, -64.6749, 18.0047 https://cmr.earthdata.nasa.gov/search/concepts/C2418992215-GHRC_DAAC.umm_json The St. Croix Radiosondes CPEX-AW dataset consists of atmospheric pressure, atmospheric temperature, relative humidity, wind speed, and wind direction measurements. These measurements were taken from the DFM-09 Radiosonde instrument during the Convective Processes Experiment – Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix, U.S. Virgin Islands. Data are available from August 19, 2021 through September 14, 2021 in netCDF and ASCII formats, with associated browse imagery in PNG format. proprietary @@ -20853,8 +20833,8 @@ urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TD_V2_V2 Sentinel-5P Level-3 SO2CBR Daily urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TM_V2_V2 Sentinel-5P Level-3 SO2CBR Monthly Product - V2 FEDEO STAC Catalog 2018-07-01 2025-12-31 -180, -89, 180, 89 https://cmr.earthdata.nasa.gov/search/concepts/C3324213174-FEDEO.umm_json Contains binned Level-2 Sulfur Dioxide (SO2) vertical column products using COvariance-Based Retrieval Algorithm (COBRA) retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html? proprietary urn:eop:VITO:TERRASCOPE_S5P_L3_SO2CBR_TY_V2_V2 Sentinel-5P Level-3 SO2CBR Yearly Product - V2 FEDEO STAC Catalog 2018-07-01 2025-12-31 -180, -89, 180, 89 https://cmr.earthdata.nasa.gov/search/concepts/C3324214083-FEDEO.umm_json Contains binned Level-2 Sulfur Dioxide (SO2) vertical column products using COvariance-Based Retrieval Algorithm (COBRA) retrievals. The L3 binning algorithm weighs individual pixels with the overlap area of the pixel and the Level-3 grid cell. The weighing and count vectors are used to apply this weighted average consistently, see http://stcorp.github.io/harp/doc/html/libharp_product.html? proprietary urn:ogc:def:EOP:VITO:VGT_P_1 Physical products of SPOT VEGETATION (VGT-P) FEDEO STAC Catalog 1998-04-01 2014-05-31 -180, -56, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2207472887-FEDEO.umm_json VGT-P (P= physical) products are adapted for scientific applications requiring highly accurate physical measurements. The data is corrected for system errors (error registration of the different channels, calibration of all the detectors along the line-array detectors for each spectral band) and resampled to predefined geographic projections chosen by the user. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere (TOA). Auxiliary data supplied with the products allow users to process the original reflectance values using their own algorithms. The image products cover all or a part of a VEGETATION segment (data strip over land). The VEGETATION instrument is operational since April 1998, first with VGT1, from March 2003 onwards, with VGT2. More information is available on: https://docs.terrascope.be/#/DataProducts/SPOT-VGT/Level2A/Level2A proprietary -urn:ogc:def:EOP:VITO:VGT_S10_1 10 Days Synthesis of SPOT VEGETATION Images (VGT-S10) FEDEO STAC Catalog 1998-04-01 2014-05-31 -180, -56, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2207472890-FEDEO.umm_json The VGT-S10 are near-global or continental, 10-daily composite images which are synthesised from the 'best available' observations registered in the course of every 'dekad' by the orbiting earth observation system SPOT-VEGETATION. The products provide data from all spectral bands (SWIR, NIR, RED, BLUE), the NDVI and auxiliary data on image acquisition parameters. The VEGETATION system allows operational and near real-time applications, at global, continental and regional scales, in very broad environmentally and socio-economically critical fields. The VEGETATION instrument is operational since April 1998, first with VGT1, from March 2003 onwards, with VGT2. More information is available on: https://docs.terrascope.be/#/DataProducts/SPOT-VGT/Level3/Level3 proprietary urn:ogc:def:EOP:VITO:VGT_S10_1 10 Days Synthesis of SPOT VEGETATION Images (VGT-S10) ALL STAC Catalog 1998-04-01 2014-05-31 -180, -56, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2207472890-FEDEO.umm_json The VGT-S10 are near-global or continental, 10-daily composite images which are synthesised from the 'best available' observations registered in the course of every 'dekad' by the orbiting earth observation system SPOT-VEGETATION. The products provide data from all spectral bands (SWIR, NIR, RED, BLUE), the NDVI and auxiliary data on image acquisition parameters. The VEGETATION system allows operational and near real-time applications, at global, continental and regional scales, in very broad environmentally and socio-economically critical fields. The VEGETATION instrument is operational since April 1998, first with VGT1, from March 2003 onwards, with VGT2. More information is available on: https://docs.terrascope.be/#/DataProducts/SPOT-VGT/Level3/Level3 proprietary +urn:ogc:def:EOP:VITO:VGT_S10_1 10 Days Synthesis of SPOT VEGETATION Images (VGT-S10) FEDEO STAC Catalog 1998-04-01 2014-05-31 -180, -56, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2207472890-FEDEO.umm_json The VGT-S10 are near-global or continental, 10-daily composite images which are synthesised from the 'best available' observations registered in the course of every 'dekad' by the orbiting earth observation system SPOT-VEGETATION. The products provide data from all spectral bands (SWIR, NIR, RED, BLUE), the NDVI and auxiliary data on image acquisition parameters. The VEGETATION system allows operational and near real-time applications, at global, continental and regional scales, in very broad environmentally and socio-economically critical fields. The VEGETATION instrument is operational since April 1998, first with VGT1, from March 2003 onwards, with VGT2. More information is available on: https://docs.terrascope.be/#/DataProducts/SPOT-VGT/Level3/Level3 proprietary urn:ogc:def:EOP:VITO:VGT_S1_1 Global 1 Day Synthesis of SPOT VEGETATION Images (VGT-S1) FEDEO STAC Catalog 1998-04-01 2014-05-31 -180, -56, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2207472898-FEDEO.umm_json VGT-S1 products (daily synthesis) are composed of the 'Best available' ground reflectance measurements of all segments received during one day for the entire surface of the Earth. This is done for each of the images covering the same geographical area. The areas distant from the equator have more overlapping parts so the choice for the best pixel will be out of more data. These products provide data from all spectral bands, the NDVI and auxiliary data on image acquisition parameters. The VEGETATION instrument is operational since April 1998, first with VGT1, from March 2003 onwards, with VGT2. More information is available on: https://docs.terrascope.be/#/DataProducts/SPOT-VGT/Level3/Level3 proprietary usgs_brd_pwrc_bioeco Biological and Ecological Characteristics of Terrestrial Vertebrate Species Residing in Estuaries - usgs_brd_pwrc_bioeco CEOS_EXTRA STAC Catalog 1980-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231549948-CEOS_EXTRA.umm_json The Biomonitoring of Environmental Status and Trends (BEST) program is designed to assess and monitor the effects of environmental contaminants on biological resources, particularly those under the stewardship of the Department of the Interior. BEST examines contaminant issues at national, regional, and local scales, and uses field monitoring techniques and information assessment tools tailored to each scale. As part of this program, the threat of contaminants and other anthropogenic activities to terrestrial vertebrates residing in or near to Atlantic coast estuarine ecosystems is being evaluated by data synthesis and field activities. One of the objectives of this project is to evaluate the relative sensitivity and suitability of various wildlife species for regional contaminant monitoring of estuaries and ecological risk assessment. The purpose of the data is to assess and monitor the effects of environmental contaminants on biological resources, particularly those under the stewardship of the Department of the Interior. BEST examines contaminant issues at national, regional, and local scales, and uses field monitoring techniques and information assessment tools tailored to each scale. As part of this program, the threat of contaminants and other anthropogenic activities to terrestrial vertebrates residing in or near to Atlantic coast estuarine ecosystems is being evaluated by data synthesis and field activities. One of the objectives of this project is to evaluate the relative sensitivity and suitability of various wildlife species for regional contaminant monitoring of estuaries and ecological risk assessment. Information was obtained from http://www.pwrc.usgs.gov/contaminants-online/ and from Dr. Barnett Rattner of the U.S. Geological Survey, Patuxent Wildlife Research Center. proprietary usgs_brd_pwrc_ceetv Contaminant Exposure and Effects - Terrestrial Vertebrates CEOS_EXTRA STAC Catalog 1938-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231550548-CEOS_EXTRA.umm_json The Biomonitoring of Environmental Status and Trends (BEST) program of the Department of the resources under their stewardship. In accordance with the desire of many to continuously monitor the environmental health of our estuaries, much can be learned by summarizing existing temporal, geographic, and phylogenetic contaminant information. To this end, retrospective contamiant exposure and effects data for amphibians, reptiles, birds and mammals residing within 30 km. of the Atlantic, Gulf, Pacific, Alaskan, and Hawaiian coastal estuaries are being assembled through searches of published literature (e.g., Fish and Wildlife Review; BIOSIS) and databases (e.g., US EPA Ecological Incident Information System; USGS Diagnostic and Epizootic Databases), and compilation of summary data from unpublished reports of government natural resource agencies, private conservation groups, and universities. These contaminant vertebrates (CEE-TV) are being summarized using ACCESS in a 120 field format including species, collection time and site coordinates, sample matrix, contaminant concentration, biomarker and bioindicator responses, and source of information. This CEE-TV database (>11,000 records) has been imported into the ARC/INFO geographic information system (GIS), for purposes of examining geographic coverage and trends, and to identify critical data gaps. A preliminary risk assessment has been conducted to identify and characterize contaminants and other stressors potentially affecting terrestrial vertebrates that reside, migrate through or reproduce in these estuaries. The purpose of the Contaminant Exposure and Effects--Terrestrial Vertebrates (CEE-TV) Database is to provide a summary of known contaminant exposure and effects in terrestrial vertebrates in coastal and estuarine habitat. Data Set Credit goes to Jennifer Pearson, Nancy Golden, Lynda Garrett, Jonathan Cohen, Karen Eisenreich, Elise Larsen, Rebecca Kershnar, Roger Hothem. proprietary @@ -20863,10 +20843,10 @@ usgs_global_fiducials USGS Global Fiducials USGS_LTA STAC Catalog 1970-01-01 -1 usgs_nawqa_acf_streamflow Apalachicola-Chatahoochee-Flint River Basin Streamflow Data CEOS_EXTRA STAC Catalog 1992-08-01 1995-09-01 -86, 30, -81, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553691-CEOS_EXTRA.umm_json Surface- and ground-water quality data were collected in the Apalachicola-Chattahoochee-Flint (ACF) River basin from August 1992 to September 1995 as part of the USGS National Water Quality Assessment (NAWQA) program described below. The ACF River basin drains about 19,800 square miles in western Georgia, eastern Alabama, and the Florida panhandle into the Apalachicola Bay, which discharges into the Gulf of Mexico. Data collected as part of this study focused on five major land uses: poultry production in the headwaters of the Chattahoochee River, urban and suburban areas of Metropolitan Atlanta and Columbus, silviculture in the piedmont and fall line hills, and row crop agriculture in the upper coastal plain (clastic hydrogeologic setting) and the lower coastal plain (karst hydrogeologic setting). This description is for the streamflow data. Continuous daily streamflow data is available for the nine surface-water sites, where the most water-quality data collection was performed. These sites are gaged as continuous streamflow sites and include three mainstem integrator sites and six landuse indicator sites for the water years 1992-1995. Streamflow data can be viewed on the screen or downloaded as an RDB file. The user first selects streamflow from the main options menu. The user is asked to complete a form that provides site selection and year of interest. The user then chooses to view or download the table. These data and associated locator maps are accessible on the World Wide Web at the ACF NAWQA home page. Data are presented in manageable tables that are grouped based on land use, site type, and project component. The user can view maps and data tables on the computer screen, or downloaded data tables as tab delimited (RDB) files. Data collected as part of the ACF River basin study are presented by project component: surface-water, ground-water, special studies, streamflow, ancillary, and quality assurance data. The water-quality data are presented by major headings, including water-column, bed-sediment and tissue, and biological. The data are further subdivided into data sets consisting of related constituents. Data tables can be viewed on the users computer screen or retrieved to a users computer as a tab delimited Relational Data Base (RDB) file. To reduce the size of the pesticide, volatile organic compound, bed sediment and tissue, and trace element tables, only those compounds found equal to, or above the minimum reporting limit (MRL) for one or more sites within a group, are shown. The remaining compounds were not detected. A complete list of constituent names and MRL's are available. The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) is designed to describe the status and trends in the quality of the Nation's ground- and surface-water resources and to provide a sound understanding of the natural and human factors that affect the quality of these resources (Leahy and others, 1990). Because much of the public concern over water quality stems from a desire to protect both human health and aquatic life, the NAWQA Program will, in addition to measuring physical and chemical indicators of water-quality, assess the status of aquatic life through surveys of fish, invertebrates, and benthic algae, and habitat conditions (National Research Council, 1990). As an integrated assessment of water quality incorporating physical, chemical, and biological components, the NAWQA Program is ecological in approach. proprietary usgs_nawqa_acf_surfacewater Apalachicola-Chatahoochee-Flint River Basin Surface Water Data CEOS_EXTRA STAC Catalog 1992-08-01 1995-09-01 -86, 30, -81, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553771-CEOS_EXTRA.umm_json Surface- and ground-water quality data were collected in the Apalachicola-Chattahoochee-Flint (ACF) River basin from August 1992 to September 1995 as part of the USGS National Water Quality Assessment (NAWQA) program described below. The ACF River basin drains about 19,800 square miles in western Georgia, eastern Alabama, and the Florida panhandle into the Apalachicola Bay, which discharges into the Gulf of Mexico. Data collected as part of this study focused on five major land uses: poultry production in the headwaters of the Chattahoochee River, urban and suburban areas of Metropolitan Atlanta and Columbus, silviculture in the piedmont and fall line hills, and row crop agriculture in the upper coastal plain (clastic hydrogeologic setting) and the lower coastal plain (karst hydrogeologic setting). This description is for the surface-water sites which are grouped based on six landuse classifications: poultry, suburban, urban, silviculture, agriculture (clastic geology) and agriculure (karst geology), and by site type: main stem and tributary. The data are grouped into three catogories including water column, bed sediment and tissue, and Biological. The data are further subdivided into sets of related constituents. A complete list of constituent names and MRL's is available. The user can view and retrieve these surface-water data sets: Water Column: Field Measurements, Nutrients, Major Ions, Suspended Sediment, Organic Carbon, Turbidity, Pesticides . Bed-Sediment and Tissue: Semivolitile Organic Compounds in Sediment, Organochlorine Compounds in Sediment, Major and Trace Elements in Sediment, Organochlorine Compounds in Tissue, Trace Elements in Tissue. Biological: Algae, Fish, Invertebrates. Physical, chemical, and biological data were collected at 132 stream sites and at 15 locations within 6 reservoirs. The monitoring network is a nested design with a core of fixed monitoring sites (integrator and indicator sites), a group of land-use comparison sites, and a group of mixed land use sites including large tributaries and main stem rivers that provide spatial distribution. Water samples were collected at frequencies varying from hourly to annually, depending on the intended purpose, and were analyzed for nutrients, carbon, pesticides, major ions, and field parameters. These data and associated locator maps are accessible on the World Wide Web at the ACF NAWQA home page. Data are presented in manageable tables that are grouped based on land use, site type, and project component. The user can view maps and data tables on the computer screen, or downloaded data tables as tab delimited (RDB) files. Data collected as part of the ACF River basin study are presented by project component: surface-water, ground-water, special studies, streamflow, ancillary, and quality assurance data. The water-quality data are presented by major headings, including water-column, bed-sediment and tissue, and biological. The data are further subdivided into data sets consisting of related constituents. Data tables can be viewed on the users computer screen or retrieved to a users computer as a tab delimited Relational Data Base (RDB) file. To reduce the size of the pesticide, volatile organic compound, bed sediment and tissue, and trace element tables, only those compounds found equal to, or above the minimum reporting limit (MRL) for one or more sites within a group, are shown. The remaining compounds were not detected. A complete list of constituent names and MRL's are available. The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) is designed to describe the status and trends in the quality of the Nation's ground- and surface-water resources and to provide a sound understanding of the natural and human factors that affect the quality of these resources (Leahy and others, 1990). Because much of the public concern over water quality stems from a desire to protect both human health and aquatic life, the NAWQA Program will, in addition to measuring physical and chemical indicators of water-quality, assess the status of aquatic life through surveys of fish, invertebrates, and benthic algae, and habitat conditions (National Research Council, 1990). As an integrated assessment of water quality incorporating physical, chemical, and biological components, the NAWQA Program is ecological in approach. proprietary usgs_nawqa_acfriver_groundwater Apalachicola-Chatahoochee Flint River Basin Ground Water Data CEOS_EXTRA STAC Catalog 1992-08-01 1995-09-01 -86, 30, -81, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231550128-CEOS_EXTRA.umm_json Surface- and ground-water quality data were collected in the Apalachicola-Chattahoochee-Flint (ACF) River basin from August 1992 to September 1995 as part of the USGS National Water Quality Assessment (NAWQA) program described below. The ACF River basin drains about 19,800 square miles in western Georgia, eastern Alabama, and the Florida panhandle into the Apalachicola Bay, which discharges into the Gulf of Mexico. Data collected as part of this study focused on five major land uses: poultry production in the headwaters of the Chattahoochee River, urban and suburban areas of Metropolitan Atlanta and Columbus, silviculture in the piedmont and fall line hills, and row crop agriculture in the upper coastal plain (clastic hydrogeologic setting) and the lower coastal plain (karst hydrogeologic setting). This description is for the ground-water data. Data for the ground-water component of the ACF River basin study were collected as part of three studies: Study Unit Survey, Land Use Studies (Urban and Agriculture) and Agricultural flow system study. The data are grouped by study component and site type (wells, springs, drains, and pore water) and are subdivided into sets of data consisting of related constituents. A complete list of constituent names and MRL's are available. The user can view and retrieve these ground-water data sets: Field measurements, Nutrients, Organic carbon, Turbidity, Major Ions, Pesticides, Trace elements (collected as part of the Study Unit Survey and Urban Landuse only), Volatile organic compounds, Radionuclides and Stable isotopes. Ground-water quality data were collected at 161 sites within the ACF River basin. These sites included a combination of monitoring and domestic wells, springs and seeps, and subsurface drains. The data are concentrated in the Metropolitan Atlanta (urban land use) area and in the coastal plain (agricultural land use). These data and associated locator maps are accessible on the World Wide Web at the ACF NAWQA home page. Data are presented in manageable tables that are grouped based on land use, site type, and project component. The user can view maps and data tables on the computer screen, or downloaded data tables as tab delimited (RDB) files. Data collected as part of the ACF River basin study are presented by project component: surface-water, ground-water, special studies, streamflow, ancillary, and quality assurance data. The water-quality data are presented by major headings, including water-column, bed-sediment and tissue, and biological. The data are further subdivided into data sets consisting of related constituents. Data tables can be viewed on the users computer screen or retrieved to a users computer as a tab delimited Relational Data Base (RDB) file. To reduce the size of the pesticide, volatile organic compound, bed sediment and tissue, and trace element tables, only those compounds found equal to, or above the minimum reporting limit (MRL) for one or more sites within a group, are shown. The remaining compounds were not detected. A complete list of constituent names and MRL's are available. The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) is designed to describe the status and trends in the quality of the Nation's ground- and surface-water resources and to provide a sound understanding of the natural and human factors that affect the quality of these resources (Leahy and others, 1990). Because much of the public concern over water quality stems from a desire to protect both human health and aquatic life, the NAWQA Program will, in addition to measuring physical and chemical indicators of water-quality, assess the status of aquatic life through surveys of fish, invertebrates, and benthic algae, and habitat conditions (National Research Council, 1990). As an integrated assessment of water quality incorporating physical, chemical, and biological components, the NAWQA Program is ecological in approach. proprietary -usgs_nps_agatefossilbeds Agate Fossil Beds National Monument, Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1995-07-10 1995-08-15 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231549635-CEOS_EXTRA.umm_json "Vegetation field plots at Agate Fossil Beds NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The purpose of the field plots was to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. The field plotting took place in the Agate Fossil Beds National Monument and a 400 meter buffer. Field sampling was done using releve plots. The descriptive plot data were collected for 39 sites whose vegetation represents a full spectrum of alliance types present within Agate Fossil Beds National Monument and its immediate surroundings. Physical description - Attributes collected for each site include: a plot number, a unique plot identification code, community name, field name, state, park name, quad name, map projection, datum, GPS file name, raw UTM coordinates, differentially corrected UTM coordinates, plot survey date, name(s) of surveyors, length, width, photo type, elevation, slope, aspect, topographic position, landform, surface geology, Cowardin System category, hydrology, surface material description, soil texture, soil drainage, leaf phenology, leaf type, and physiognomy. Species - Individual species described at each of 39 plots is listed, one line per species, with the following information: Plot Identification Code, Numeric Species Code, Species Name, Species Cover (0=trace, 1=< 1%, 2=1-5%, 3=5-25%, 4=25-50%, 5=50-75%, 6=75-100%), Plantcode, and Strata Code (T1=emergent, T2=canopy, T3=sub-canopy, S1=tall shrub, S2=short shrub, H=herbaceous, N=non-vascular, V=vinae/liana, E=epiphyte). Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfofield.html""." proprietary usgs_nps_agatefossilbeds Agate Fossil Beds National Monument, Field Plots Data Base for Vegetation Mapping ALL STAC Catalog 1995-07-10 1995-08-15 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231549635-CEOS_EXTRA.umm_json "Vegetation field plots at Agate Fossil Beds NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The purpose of the field plots was to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. The field plotting took place in the Agate Fossil Beds National Monument and a 400 meter buffer. Field sampling was done using releve plots. The descriptive plot data were collected for 39 sites whose vegetation represents a full spectrum of alliance types present within Agate Fossil Beds National Monument and its immediate surroundings. Physical description - Attributes collected for each site include: a plot number, a unique plot identification code, community name, field name, state, park name, quad name, map projection, datum, GPS file name, raw UTM coordinates, differentially corrected UTM coordinates, plot survey date, name(s) of surveyors, length, width, photo type, elevation, slope, aspect, topographic position, landform, surface geology, Cowardin System category, hydrology, surface material description, soil texture, soil drainage, leaf phenology, leaf type, and physiognomy. Species - Individual species described at each of 39 plots is listed, one line per species, with the following information: Plot Identification Code, Numeric Species Code, Species Name, Species Cover (0=trace, 1=< 1%, 2=1-5%, 3=5-25%, 4=25-50%, 5=50-75%, 6=75-100%), Plantcode, and Strata Code (T1=emergent, T2=canopy, T3=sub-canopy, S1=tall shrub, S2=short shrub, H=herbaceous, N=non-vascular, V=vinae/liana, E=epiphyte). Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfofield.html""." proprietary -usgs_nps_agatefossilbedsspatial Agate Fossil Beds National Monument Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1995-07-29 1995-07-29 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231550884-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (July, 1995). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Agate Fossil Beds National Monument was designated as one of the prototype parks. The monument is located in the high Great Plains. It contains prairie, hill, and riverine environments, with vegetation types that include prairie grassland, riverine woodland, and wetlands. The vegetation units were photointerpreted from stereo-paired, natural color photography. Agate Fossil Beds National Monument was created by the National Park Service on June 5, 1965. The park occupies 4.5 square miles of land straddling the Niobrara River in the middle of the Nebraska Panhandle. The park is noted for its history, prehistoric fossils, and scenic quality. Historically, the park was a part of the Agate Springs Ranch, owned by Captain James H. Cook. The park has a collection of ranching and Native American artifacts and memorabilia as a result of its donation from the Ranch. Paleontologically, the park contains a number of Miocene fossil quarries that were excavated through the late 19th century and early 20th century. From a scenic aspect, the park has views of rolling hills, bluffs, and the Niobrara River floodplain. Ranching is also an active part of the landscape. The park is located in the grassy rolling hills of Western Nebraska. The park landscape consists of the east-west trending cap-rocked northern and southern hills, with the treeless Niobrara River floodplain running down the middle of the valley. The city of Harrison is located 23 miles to the north, Mitchell is 34 miles to the south. State Highway 29 runs north-south through the western part of the park. The Vegetation mapping was conducted in Agate Fossil Beds National Moument, Nebraska with a 400 meter buffer. A total of 39 plots were obtained from July 10 through August 15, 1995. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in August of 1997 to assess the initial mapping effort and to refine map class. Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfospatial.html"" and converted to the NASA Directory Interchange Format. Another site to obtain the data is located at Online_Resource: ""ftp://ftp.cbi.usgs.gov/pub/vegmapping/agfo/agfo.exe""." proprietary +usgs_nps_agatefossilbeds Agate Fossil Beds National Monument, Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1995-07-10 1995-08-15 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231549635-CEOS_EXTRA.umm_json "Vegetation field plots at Agate Fossil Beds NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The purpose of the field plots was to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. The field plotting took place in the Agate Fossil Beds National Monument and a 400 meter buffer. Field sampling was done using releve plots. The descriptive plot data were collected for 39 sites whose vegetation represents a full spectrum of alliance types present within Agate Fossil Beds National Monument and its immediate surroundings. Physical description - Attributes collected for each site include: a plot number, a unique plot identification code, community name, field name, state, park name, quad name, map projection, datum, GPS file name, raw UTM coordinates, differentially corrected UTM coordinates, plot survey date, name(s) of surveyors, length, width, photo type, elevation, slope, aspect, topographic position, landform, surface geology, Cowardin System category, hydrology, surface material description, soil texture, soil drainage, leaf phenology, leaf type, and physiognomy. Species - Individual species described at each of 39 plots is listed, one line per species, with the following information: Plot Identification Code, Numeric Species Code, Species Name, Species Cover (0=trace, 1=< 1%, 2=1-5%, 3=5-25%, 4=25-50%, 5=50-75%, 6=75-100%), Plantcode, and Strata Code (T1=emergent, T2=canopy, T3=sub-canopy, S1=tall shrub, S2=short shrub, H=herbaceous, N=non-vascular, V=vinae/liana, E=epiphyte). Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfofield.html""." proprietary usgs_nps_agatefossilbedsspatial Agate Fossil Beds National Monument Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System ALL STAC Catalog 1995-07-29 1995-07-29 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231550884-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (July, 1995). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Agate Fossil Beds National Monument was designated as one of the prototype parks. The monument is located in the high Great Plains. It contains prairie, hill, and riverine environments, with vegetation types that include prairie grassland, riverine woodland, and wetlands. The vegetation units were photointerpreted from stereo-paired, natural color photography. Agate Fossil Beds National Monument was created by the National Park Service on June 5, 1965. The park occupies 4.5 square miles of land straddling the Niobrara River in the middle of the Nebraska Panhandle. The park is noted for its history, prehistoric fossils, and scenic quality. Historically, the park was a part of the Agate Springs Ranch, owned by Captain James H. Cook. The park has a collection of ranching and Native American artifacts and memorabilia as a result of its donation from the Ranch. Paleontologically, the park contains a number of Miocene fossil quarries that were excavated through the late 19th century and early 20th century. From a scenic aspect, the park has views of rolling hills, bluffs, and the Niobrara River floodplain. Ranching is also an active part of the landscape. The park is located in the grassy rolling hills of Western Nebraska. The park landscape consists of the east-west trending cap-rocked northern and southern hills, with the treeless Niobrara River floodplain running down the middle of the valley. The city of Harrison is located 23 miles to the north, Mitchell is 34 miles to the south. State Highway 29 runs north-south through the western part of the park. The Vegetation mapping was conducted in Agate Fossil Beds National Moument, Nebraska with a 400 meter buffer. A total of 39 plots were obtained from July 10 through August 15, 1995. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in August of 1997 to assess the initial mapping effort and to refine map class. Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfospatial.html"" and converted to the NASA Directory Interchange Format. Another site to obtain the data is located at Online_Resource: ""ftp://ftp.cbi.usgs.gov/pub/vegmapping/agfo/agfo.exe""." proprietary +usgs_nps_agatefossilbedsspatial Agate Fossil Beds National Monument Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1995-07-29 1995-07-29 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231550884-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (July, 1995). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Agate Fossil Beds National Monument was designated as one of the prototype parks. The monument is located in the high Great Plains. It contains prairie, hill, and riverine environments, with vegetation types that include prairie grassland, riverine woodland, and wetlands. The vegetation units were photointerpreted from stereo-paired, natural color photography. Agate Fossil Beds National Monument was created by the National Park Service on June 5, 1965. The park occupies 4.5 square miles of land straddling the Niobrara River in the middle of the Nebraska Panhandle. The park is noted for its history, prehistoric fossils, and scenic quality. Historically, the park was a part of the Agate Springs Ranch, owned by Captain James H. Cook. The park has a collection of ranching and Native American artifacts and memorabilia as a result of its donation from the Ranch. Paleontologically, the park contains a number of Miocene fossil quarries that were excavated through the late 19th century and early 20th century. From a scenic aspect, the park has views of rolling hills, bluffs, and the Niobrara River floodplain. Ranching is also an active part of the landscape. The park is located in the grassy rolling hills of Western Nebraska. The park landscape consists of the east-west trending cap-rocked northern and southern hills, with the treeless Niobrara River floodplain running down the middle of the valley. The city of Harrison is located 23 miles to the north, Mitchell is 34 miles to the south. State Highway 29 runs north-south through the western part of the park. The Vegetation mapping was conducted in Agate Fossil Beds National Moument, Nebraska with a 400 meter buffer. A total of 39 plots were obtained from July 10 through August 15, 1995. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in August of 1997 to assess the initial mapping effort and to refine map class. Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfospatial.html"" and converted to the NASA Directory Interchange Format. Another site to obtain the data is located at Online_Resource: ""ftp://ftp.cbi.usgs.gov/pub/vegmapping/agfo/agfo.exe""." proprietary usgs_nps_congareeswamp Congaree Swamp National Monument Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1996-06-01 1996-09-01 -80.85, 33.75, -80.67083, 33.84167 https://cmr.earthdata.nasa.gov/search/concepts/C2231552960-CEOS_EXTRA.umm_json "Vegetation field plots at Congaree Swamp National Monument were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The vegetation plots were used to describe the vegetation in and around Congaree Swamp National Monument and to assist in developing a final mapping classification system. On June 30, 1983, Congaree Swamp National Monument became an International Biosphere Reserve. Congaree is noted for containing one of the last significant stands of old growth bottomland hardwood forest, over 11,000 acres in all. The Monument contains over 90 species of trees, 16 of which hold state records for size. Included in this list of records is a national record sweet gum with a basal circumference of nearly 20 feet. Congaree Swamp National Monument is located approximately 15 miles southeast of Columbia, the state capitol of South Carolina. Old Bluff Highway (old Highway 48) lies just north of the Monument boundary. The eastern boundary is located just northwest of the confluence of the Congaree and Wateree Rivers. The Monument extends west to where Cedar Creek and Myers Creek join. The methods used for the sampling and analysis of vegetation data and the development of the classification generally followed the standards Doutline in the Field Methods for Vegetation Mapping document ""http://biology.usgs.gov/npsveg/fieldmethods/index.html"" produced for the USGS-NPS Vegetation Mapping project. This process began with the development of a provisional list of twenty-five vegetation types from teh International Classification of Ecological Communities (ICEC) that were thought to have a high likelihood of being in the park based on an initial field visit on 13-14 June, 1996. One hundred twenty-eight plots were sampled by two two-person field teams in July, August, and September of 1996. In a devation from the methodology outlined in the Field Methods document, initial sample points were selected in order to have plots in each of the aerial photograph signature types. The gradsect approach was rejected because there appeared to be no potential for stratifying sampling of the park based on slope, aspect, elevation, soil or other natural features due to a lack of available information. Furthermore, because of isolation from roads and trails of many portions of the park, it was deemed not feasible to use a transect to establish plot locations. After sampling, plots were tentatively assigned to the ICEC at the alliance level and our goal was to have at least five plots in each of the twenty-five provisional vegetation types. TIme limitations precluded the ability of the field teams to install ten plots in each of the expected vegetation types as recommended in the Field Methods document. The information for the metadata came from ""http://biology.usgs.gov/npsveg/cosw/metacoswfield.html""" proprietary usgs_nps_congareeswampspatial Congaree Swamp National Monument Spatial Vegetation Data; Cover Type/Association Level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1996-04-27 1996-04-27 -80.85, 33.75, -80.67083, 33.84167 https://cmr.earthdata.nasa.gov/search/concepts/C2231550252-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (April, 1996). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Congaree Swamp National Monument was designated as one of the prototype parks. Congaree Swamp National Monument, established in 1976, was designated as one of the prototypes within the National Park System. The park contains approximately 22,200 acres (34 square miles). Congaree Swamp National Monument is located approximately 15 miles southeast of Columbia, the state capitol of South Carolina. The Congaree River, draining over 8,000 square miles of Piedmont land to the northwest, forms the southern border. On June 30, 1983, Congaree Swamp National Monument became an International Biosphere Reserve. Congaree is noted for containing one of the last significant stands of old growth bottomland hardwood forest, over 11,000 acres in all. The Monument contains over 90 species of trees, 16 of which hold state records for size. Included in this list of records is a national record sweet gum with a basal circumference of nearly 20 feet. Congaree Swamp National Monument is located approximately 15 miles southeast of Columbia, the state capitol of South Carolina. Old Bluff Highway (old Highway 48) lies just north of the Monument boundary. The eastern boundary is located just northwest of the confluence of the Congaree and Wateree Rivers. The Monument extends west to where Cedar Creek and Myers Creek join. The normal process in vegetation mapping is to conduct an initial field reconnaissance, map the vegetation units through photointerpretation, and then conduct a field verification. The field reconnaissance visit serves two major functions. First, the photointerpreter keys the signature on the aerial photos to the vegetation on the ground at each signature site. Second, the photointerpreter becomes familiar with the flora, vegetation communities and local ecology that occur in the study area. Park and/or TNC field biologists that are familiar with the local vegetation and ecology of the park are present to help the photointerpreter understand these elements and their relationship with the geography of the park. Upon completion of the field reconnaissance, photo interpreters delineate vegetation units on mylar that overlay the 9x9 aerial photos. This effort is conducted in accordance with the TNC vegetation classification and criteria for defining each community or alliance. The initial mapping is then followed by a field verification session, whose purpose is to verify that the vegetation units were mapped correctly. Any PI related questions are also addressed during the visit. The vegetation mapping at Congaree Swamp National Monument in general followed the normal mapping procedure as described in the above paragraph with two major exceptions: 1) Preliminary delineations for most of the park, including a set of Focused Transect overlays that were labeled with an initial PI signature commenced prior to the field reconnaissance visit. 2) A TNC classification did not exist at the time the initial delineations began. TNC ecologist and AIS photo interpreters worked together to develop an interim signature key which addressed what was known at the time. At that time, no comprehensive study containing plot data was available to create an interim classification. From the onset of the Vegetation Inventory and Mapping Program, a standardized program-wide mapping criteria has been used. The mapping criteria contains a set of documented working decision rules used to facilitate the maintenance of accuracy and consistency of the photointerpretation. This criteria assists the user in understanding the characteristics, definition and context for each vegetation community. The mapping criteria for Congaree Swamp National Monument was composed of four parts: The standardized program-wide general mapping criteria A park specific mapping criteria A working photo signature key The TNC classification, key and descriptions The following sections detail the mapping criteria used during the photointerpretation of Congaree Swamp. General Mapping Criteria The mapping criteria at Congaree Swamp are a modified version from previously mapped parks. The criteria differs primarily in that the height and density variables were not mapped at Congaree Swamp. Instead, two additional variables were addressed: pre-hurricane Hugo community types and areas of pine that have been logged since the time of the 1976 aerial photography. These two categories will be addressed in the Park Specific Mapping Criteria section of this report. Since forest densities within the Monument are nearly always greater than 60%, it served little or no purpose in addressing this element as a separate attribute in the database. In addition it was also determined that height categories are extremely difficult to map in the Monument due to variability of the tree emergent layer, and lack of any significant reference points that help in determining canopy heights. Alliance / Community Associations The assignment of alliance and community association to the vegetation is based on criteria formulated by the field effort and classification development. In the case of Congaree Swamp National Monument, TNC provided AIS with a tentative community classification in April 1998. A final vegetation classification, key, and descriptions of each alliance and community, was provided in October 1998. In addition, TNC provided AIS with detailed plot data showing how the communities were developed in the Monument. The information for the metadata came from ""http://biology.usgs.gov/npsveg/cosw/metacoswspatial.html"" and was converted to the NASA Directory Interchange Format." proprietary usgs_nps_d_microbialcontam Microbial Contamination of Water Resources in the Chatahoochee River National Recreation Area, Georgia CEOS_EXTRA STAC Catalog 1999-03-01 2000-04-01 -86, 30, -81, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231549590-CEOS_EXTRA.umm_json The study area is the watershed for the Chattahoochee River from Buford Dam to just downstream of the mouth of Peachtree Creek. This study area includes the entire Chattahoochee River National Recreation Area, much of Metropolitan Atlanta, and extends downstream of two major wastewater treatment plant outfalls for the City of Atlanta and Cobb County. The 2-year study is for fiscal years 1999 and 2000. There are six months of microbial sampling in each fiscal year spanning from April 1, 1999 through March 30, 2000. This study measures fecal-indicator bacteria (fecal coliform, E. coli, and enterococci) every five days from April 1, 1999 to September 30, 1999 and every 8 days from October 1, 1999 to March 30, 2000 at three main stem Chattahoochee River sites. The five-day and eight-day sampling intervals will ensure mid week and weekend flow conditions are sampled. Indicator bacteria samples will also be collected during one 26-hour period to look at diel fluctuations. Another indicator bacteria (Clostridium perfringens), F-specific coliphages, somatic coliphages, and chemical sewage tracers will be measured as part of several synoptic surveys at 3 fixed sites and 9 synoptic sites. The 2-year project investigates the existence, severity, and extent of microbial contamination in the Chattahoochee River and 8 major tributaries within the Chattahoochee River National Recreation Area (CRNRA). High levels of fecal-indicator bacteria are the principal basis for impairment of streams in the CRNRA. Three data-collection activities include: 1.Fixed interval: Sample fecal-indicator bacteria and predictor variables (stream stage, stream flow, turbidity, and field water-quality parameters) every 5 days from April 1 to September 30, 1999 and every 8 days from October 1, 1999 to March 30, 2000 at 3 Chattahoochee River sites. (view map) 2.Synoptic surveys: Sample fecal-indicator bacteria, Clostridium perfringens, viruses, predictor variables, and chemical sewage tracers at 4 Chattahoochee River sites and 8 tributary sites during critical seasons and hydrologic conditions. 3.Diel samples: Sample fecal-indicator bacteria and predictor variables every 2 hours for one 26-hour period (August 4-5, 1999) at the Chattahoochee River at Atlanta, which is downstream of the CRNRA. Four proposed main stem sampling sites in downstream order on the Chattahoochee River include: 1.Chattahoochee River at Settles Bridge Road near Suwanee 2.Chattahoochee River at Johnsons Ferry Road near Atlanta 3.Chattahoochee River at Atlanta (Paces Ferry Road; downstream from Palisades Unit) 4.Chattahoochee River at State Highway 280, near Atlanta (Synoptic site only; downstream from all of the CRNRA, much of Metropolitan Atlanta, and 2 major wastewater treatment outfalls for the City of Atlanta and Cobb County; will provide microbial data for a Chattahoochee River site directly affected by point sources of wastewater effluent) Eight proposed tributary sampling sites within the CRNRA watershed in downstream order include: 1.James Creek near Cumming (James Burgess Road) 2.Suwanee Creek near Suwanee (at US Route 23, Buford Hwy) 3.Johns Creek near Warsaw (Buice Road) 4.Crooked Creek near Norcross (Spalding Road) 5.Big Creek near Roswell (below Water Works intake) 6.Willeo Creek near Roswell (State Route 120) 7.Sope Creek near Marietta (Lower Roswell Road) 8.Rottenwood Creek near Smyrna (Interstate Parkway North) In general, fecal-indicator bacteria are used to assess the public-health acceptability of water. The concentration of indicator bacteria is a measure of water safety for body-contact recreation or for consumption (Myers and Sylvester, 1997). Indicator bacteria do not typically cause diseases (pathogenic), but they indicate the possible presence of pathogenic organisms. Escherichia coli (E. coli) and enterococci are currently the preferred fecal indicators for recreational freshwaters because they are superior to fecal coliforms and fecal streptococci as predictors of swimming-associated gastroenteritis (Cabelli, 1977; Dufour, 1984); however fecal coliforms are still used by many states including Georgia to monitor recreational waters. Most historical indicator bacteria data for surface water within the CRNRA are fecal coliform counts collected once a month on a mid-weekday during normal working hours. This study proposes to measure fecal coliform using the membrane filter technique (preferred over the broth technique used by Georgia EPD),E. coli, and enterococci every five days during the recreation season at three main stem sites. The five-day cycle will ensure mid week and weekend flow conditions are sampled. All samples will be collected using USGS protocols for bacteria and equal width interval (EWI) sampling. Clostridium perfringens (C. perfringens) is another indicator bacteria that is present in large numbers in human and animal wastes, and its spores are more resistant to disinfection and environmental stresses than are most other bacteria. It is also a sensitive indicator of microorganisms that enter streams from point sources (Sorenson and others, 1989). It must be analyzed under anaerobic conditions in a laboratory and is best attempted by a biologist or highly trained technician. This study proposes to measure C. perfringens at 4 main stem and 8 tributary sites as part of synoptic surveys during critical seasons and hydrologic conditions. Because monitoring of enteric viruses is recognized as being difficult,time consuming, and expensive, some researchers advocate the use of coliphage for routine viral monitoring. Coliphages are bacteriophages that infect and replicate in coliform bacteria. Although somatic and Fecal-Specific coliphages are not consistently found in feces, they are found in high numbers in sewage and are thought to be reliable indicators of the sewage contamination of waters (International Association on Water Pollution Research and Control, 1991). Coliphage is also recognized to be representative of the survival transport of viruses in the environment. However, to date, they have not been found to correlate with the presence of pathogenic viruses. This study proposes to measure enteric viruses at 4 main stem and 8 tributary sites as part of synoptic surveys during critical seasons and hydrologic conditions. proprietary @@ -20884,23 +20864,23 @@ usgs_npwrc_acutetoxicity_Version 06JUL2000 Acute Toxicity of Fire-Control Chemic usgs_npwrc_alpha_Version 16MAY2000 Alpha Status, Dominance, and Division of Labor in Wolf Packs. CEOS_EXTRA STAC Catalog 1986-01-01 1998-12-31 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231552683-CEOS_EXTRA.umm_json "The prevailing view of a wolf (Canis lupus) pack is that of a group of individuals ever vying for dominance but held in check by the ""alpha"" pair, the alpha male and the alpha female. Most research on the social dynamics of wolf packs, however, has been conducted on non-natural assortments of captive wolves. Here I describe the wolf-pack social order as it occurs in nature, discuss the alpha concept and social dominance and submission, and present data on the precise relationships among members in free-living packs based on a literature review and 13 summers of observations of wolves on Ellesmere Island, Northwest Territories, Canada. I conclude that the typical wolf pack is a family, with the adult parents guiding the activities of the group in a division-of-labor system in which the female predominates primarily in such activities as pup care and defense and the male primarily during foraging and food-provisioning and the travels associated with them." proprietary usgs_npwrc_canvasbacks_Version 13NOV2001 Influence of Age and Selected Environmental Factors on Reproductive Performance of Canvasbacks CEOS_EXTRA STAC Catalog 1974-01-01 1980-01-01 -102.5, 48, -95, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2231549601-CEOS_EXTRA.umm_json Age, productivity, and other factors affecting breeding performance of canvasbacks (Aythya valisineria) are poorly understood. Consequently, we tested whether reproductive performance of female canvasbacks varied with age and selected environmental factors in southwestern Manitoba from 1974 to 1980. Neither clutch size, nest parasitism, nest success, nor the number of ducklings/brood varied with age. Return rates, nest initiation dates, renesting, and hen success were age-related. Return rates averaged 21% for second-year (SY) and 69% for after-second-year (ASY) females (58% for third-year and 79% for after-third-year females). Additionally, water conditions and spring temperatures influenced chronology of arrival, timing of nesting, and reproductive success. Nest initiation by birds of all ages was affected by minimum April temperatures. Clutch size was higher in nests initiated earlier. Interspecific nest parasitism did not affect clutch size, nest success, hen success, or hatching success. Nest success was lower in dry years (17%) than in moderately wet (54%) or wet (60%) years. Nests per female were highest during wet years. No nests of SY females were found in dry years. In years of moderate to good wetland conditions, females of all ages nested. Predation was the primary factor influencing nest success. Hen success averaged 58% over all years. The number of ducklings surviving 20 days averaged 4.7/brood. Because SY females have lower return rates and hen success than ASY females, especially during drier years, management to increase canvasback populations might best be directed to increasing first year recruitment (no. of females returning to breed) and to increasing overall breeding success by reducing predation and enhancing local habitat conditions during nesting. proprietary usgs_npwrc_ducks_Version 07JAN98 Assessing Breeding Populations of Ducks by Ground Counts. CEOS_EXTRA STAC Catalog 1952-01-01 1959-12-31 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231554819-CEOS_EXTRA.umm_json Waterfowl inventories taken during the breeding season are recognized as a basic technique in assessing the number of ducks per unit area. That waterfowl censusing is still an inexact technology leading to divergent interpretations of results is also recognized. The inexactness stems from a wide spectrum of factors that include weather, breeding phenology, asynchronous nesting periods, vegetative growth, species present and their daily activity, previous field experience of personnel, plus others (Stewart et al., 1958; Diem and Lu, 1960; Crissey, 1963a). In spite of the possible errors, accurate estimates are necessary to our understanding of production rates of all North American breeding waterfowl. Statistically adequate censuses of breeding pairs and accurate predictions of young produced per pair still remain as two of the primary statistics in determining yearly recruitment rate of species breeding in particular units of pond habitats. Without precise breeding pair and production data, the problems involved in describing the reproductive potential of any species and its environmental or density-dependent limiting factors cannot be adequately resolved. The purposes of this paper are to (1) describe methods used to estimate yearly breeding pair abundance on two study areas, one in Manitoba and the other in Saskatchewan; (2) assess the relative consistency, precision, and accuracy of pair counts as related to the breeding biology of duck species; and (3) recommend census methods that can more closely approximate absolute populations breeding in parkland and grassland habitats. proprietary -usgs_npwrc_graywolves_Version 30APR2001 Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear ALL STAC Catalog 1970-01-01 -168, 43.5, -75, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2231553641-CEOS_EXTRA.umm_json We evaluated the accuracy and precision of tooth wear for aging gray wolves (Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or known-minimum-age skulls. Estimates of age using tooth wear and a commercial cementum annuli-aging service were useful for wolves up to 14 years old. The precision of estimates from cementum annuli was greater than estimates from tooth wear, but tooth wear estimates are more applicable in the field. We tended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The commercial service aged young wolves with cementum annuli to within year of actual age, but under estimated ages of wolves 9 years old by 1-3 years. No differences were detected in tooth wear patterns for wild wolves from Alaska, Minnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not appropriate for aging wolves with an underbite that prevented normal wear or severely broken and missing teeth. proprietary usgs_npwrc_graywolves_Version 30APR2001 Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear CEOS_EXTRA STAC Catalog 1970-01-01 -168, 43.5, -75, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2231553641-CEOS_EXTRA.umm_json We evaluated the accuracy and precision of tooth wear for aging gray wolves (Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or known-minimum-age skulls. Estimates of age using tooth wear and a commercial cementum annuli-aging service were useful for wolves up to 14 years old. The precision of estimates from cementum annuli was greater than estimates from tooth wear, but tooth wear estimates are more applicable in the field. We tended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The commercial service aged young wolves with cementum annuli to within year of actual age, but under estimated ages of wolves 9 years old by 1-3 years. No differences were detected in tooth wear patterns for wild wolves from Alaska, Minnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not appropriate for aging wolves with an underbite that prevented normal wear or severely broken and missing teeth. proprietary +usgs_npwrc_graywolves_Version 30APR2001 Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear ALL STAC Catalog 1970-01-01 -168, 43.5, -75, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2231553641-CEOS_EXTRA.umm_json We evaluated the accuracy and precision of tooth wear for aging gray wolves (Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or known-minimum-age skulls. Estimates of age using tooth wear and a commercial cementum annuli-aging service were useful for wolves up to 14 years old. The precision of estimates from cementum annuli was greater than estimates from tooth wear, but tooth wear estimates are more applicable in the field. We tended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The commercial service aged young wolves with cementum annuli to within year of actual age, but under estimated ages of wolves 9 years old by 1-3 years. No differences were detected in tooth wear patterns for wild wolves from Alaska, Minnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not appropriate for aging wolves with an underbite that prevented normal wear or severely broken and missing teeth. proprietary usgs_npwrc_incidentalmarinecatc_Version 11APR2001 Incidental Catch of Marine Birds in the North Pacific High Seas Driftnet Fisheries in 1990. CEOS_EXTRA STAC Catalog 1990-01-01 1990-01-01 -140, 20, 140, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231553439-CEOS_EXTRA.umm_json "The incidental take of marine birds was estimated for the following North Pacific driftnet fisheries in 1990: Japanese squid, Japanese large-mesh, Korean squid, and Taiwanese squid and large-mesh combined. The take was estimated by assuming that the data represented a random sample from an unstratified population of all driftnet fisheries in the North Pacific. Estimates for 13 species or species groups are presented, along with some discussion of inadequacies of the design. About 416,000 marine birds were estimated to be taken incidentally during the 1990 season; 80 % of these were in the Japanese squid fishery. Sooty Shearwaters, Short-tailed Shearwaters, and Laysan Albatrosses were the most common species in the bycatch. Regression models were also developed to explore the relations between bycatch rate of three groups Black-footed Albatross, Laysan Albatross, and ""dark"" shearwatersand various explanatory variables, such as latitude, longitude, month, vessel, sea surface temperature, and net soak time (length of time nets were in the water). This was done for only the Japanese squid fishery, for which the most complete information was available. For modeling purposes, fishing operations for each vessel were grouped into 5-degree blocks of latitude and longitude. Results of model building indicated that vessel had a significant influence on bycatch rates of all three groups. This finding emphasizes the importance of the sample of vessels being representative of the entire fleet. In addition, bycatch rates of all three groups varied spatially and temporally. Bycatch rates for Laysan Albatrosses tended to decline during the fishing season, whereas those for Black-footed Albatrosses and dark shearwaters tended to increase as the season progressed. Bycatch rates were positively related to net soak time for Laysan Albatrosses and dark shearwaters. Bycatch rates of dark shearwaters were lower for higher sea surface temperatures." proprietary -usgs_npwrc_manitobaspiders_Version 16JUL97 A Checklist of Manitoba Spiders (Araneae) with Notes on Geographic Relationships CEOS_EXTRA STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231553142-CEOS_EXTRA.umm_json An annotated list of spider species is compiled from museum collections and several personal collections. This list includes 483 species in 20 families; 139 species are new provincial records. The spider fauna of Manitoba is compared with that of British Columbia, Quebec, and Newfoundland. Manitoba's spider fauna is composed of northern elements (arctic or subarctic species), boreal elements (holarctic or nearctic), and eastern elements (mainly species of the eastern deciduous forest), and a few that are regarded as introductions from abroad. Forty-three species reach the limits of their ranges here. This relatively small province (6.5% of the total land mass of Canada) contains 59% of the Canadian spider families and 37% of the Canadian species. proprietary usgs_npwrc_manitobaspiders_Version 16JUL97 A Checklist of Manitoba Spiders (Araneae) with Notes on Geographic Relationships ALL STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231553142-CEOS_EXTRA.umm_json An annotated list of spider species is compiled from museum collections and several personal collections. This list includes 483 species in 20 families; 139 species are new provincial records. The spider fauna of Manitoba is compared with that of British Columbia, Quebec, and Newfoundland. Manitoba's spider fauna is composed of northern elements (arctic or subarctic species), boreal elements (holarctic or nearctic), and eastern elements (mainly species of the eastern deciduous forest), and a few that are regarded as introductions from abroad. Forty-three species reach the limits of their ranges here. This relatively small province (6.5% of the total land mass of Canada) contains 59% of the Canadian spider families and 37% of the Canadian species. proprietary +usgs_npwrc_manitobaspiders_Version 16JUL97 A Checklist of Manitoba Spiders (Araneae) with Notes on Geographic Relationships CEOS_EXTRA STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231553142-CEOS_EXTRA.umm_json An annotated list of spider species is compiled from museum collections and several personal collections. This list includes 483 species in 20 families; 139 species are new provincial records. The spider fauna of Manitoba is compared with that of British Columbia, Quebec, and Newfoundland. Manitoba's spider fauna is composed of northern elements (arctic or subarctic species), boreal elements (holarctic or nearctic), and eastern elements (mainly species of the eastern deciduous forest), and a few that are regarded as introductions from abroad. Forty-three species reach the limits of their ranges here. This relatively small province (6.5% of the total land mass of Canada) contains 59% of the Canadian spider families and 37% of the Canadian species. proprietary usgs_npwrc_muskoxen_Version 31MAY2000 Lack of Reproduction in Muskoxen and Arctic Hares Caused by Early Winter CEOS_EXTRA STAC Catalog 1998-07-01 1998-07-11 -86.1, 79.5, -85.9, 80.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231549051-CEOS_EXTRA.umm_json A lack of young muskoxen (Ovibos moschatus) and arctic hares (Lepus arcticus) in the Eureka area of Ellesmere Island, Northwest Territories (now Nunavut), Canada, was observed during summer 1998, in contrast to most other years since 1986. Evidence of malnourished muskoxen was also found. Early winter weather and a consequent 50% reduction of the 1997 summer replenishment period appeared to be the most likely cause, giving rise to a new hypothesis about conditions that might cause adverse demographic effects in arctic herbivores. The study area included a 150 km2 region of the Fosheim Peninsula in a 180o arc north of Eureka, Ellesmere Island, Nunavut, Canada (all within about 9 km of 80oN, 86oW). The area, extending from Eureka Sound to Remus Creek and from Slidre Fiord to Eastwind Lake, included shoreline, hills, lowlands, creek bottoms, and the west side of Blacktop Ridge. An associate, Layne Adams, and I spent 1-11 July 1998 in this area on all-terrain vehicles, following a pair of wolves Canis lupus (Mech, 1994). Adams and I also surveyed the surrounding area with binoculars for prey animals, in much the same manner that my assistants and I have practiced for one to six weeks each summer in the same area since 1986 (Mech, 1995, 1997). Because both muskoxen and arctic hares were common residents of the area during most years and were not the focus of our studies, no standardized counts were made. However, general field notes were sufficient to document that during most summers both species and their young were present. proprietary usgs_npwrc_nestingsuccess_Version 26MAR2001 Importance of Individual Species of Predators on Nesting Success of Ducks in the Canadian Prairie Pothole Region CEOS_EXTRA STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231551032-CEOS_EXTRA.umm_json We followed 3094 upland nests of several species of ducks. Clutches in most nests were lost to predation. We related daily nest predation rates to indices of activity of eight egg-eating predators, precipitation during the nesting season, and measures of wetland conditions. Activity indices of red fox (Vulpes vulpes), striped skunk (Mephitis mephitis), and raccoon (Procyon lotor) activity were positively correlated, as were activity indices of coyote (Canis latrans), Franklin's ground squirrel (Spermophilus franklinii), and black-billed magpie (Pica pica). Indices of fox and coyote activity were strongly negatively correlated (r = early-season nests were lower in areas and years in which larger fractions of seasonal wetlands contained water. For late-season nests, a similar relationship held involving semipermanent wetlands. We suspect that the wetland measures, which reflect precipitation during some previous period, also indicate vegetation growth and the abundance of buffer prey, factors that may influence nest predation rates. proprietary usgs_npwrc_purpleloostrife_Version 04JUN99 Avian Use of Purple Loosestrife Dominated Habitat Relative to Other Vegetation Types in a Lake Huron Wetland Complex CEOS_EXTRA STAC Catalog 1994-01-01 1995-12-31 -84.2, 43.3, -82.5, 44.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231555362-CEOS_EXTRA.umm_json Purple loosestrife (Lythrum salicaria), a native of Eurasia, is an introduced perennial plant in North American wetlands that displaces other wetland plants. Although not well studied, purple loosestrife is widely believed to have little value as habitat for birds. To examine the value of purple loosestrife as avian breeding habitat, we conducted early, mid-, and late season bird surveys during two years (1994 and 1995) at 258 18-m (0.1 ha) fixed-radius plots in coastal wetlands of Saginaw Bay, Lake Huron. We found that loosestrife-dominated habitats had higher avian densities, but lower avian diversities than other vegetation types. The six most commonly observed bird species in all habitats combined were Sedge Wren (Cistothorus platensis), Marsh Wren (C. palustris), Yellow Warbler (Dendroica petechia), Common Yellowthroat (Geothylpis trichas), Swamp Sparrow (Melospiza georgiana), and Red-winged Blackbird (Agelaius phoeniceus). Swamp Sparrow densities were highest and Marsh Wren densities were lowest in loosestrife dominated habitats. We observed ten breeding species in loosestrife dominated habitats. We conclude that avian use of loosestrife warrants further quantitative investigation because avian use may be higher than is commonly believed. Received 27 May 1998, accepted 26 Aug. 1998. proprietary usgs_npwrc_saltmam Mammal Checklists of the United States - Salton Sea National Wildlife Refuge CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231552573-CEOS_EXTRA.umm_json Wildlife species in this brochure have been grouped into four categories: Birds, Mammals, Reptiles and Amphibians, and Fish. All mammals listed are considered resident species with the exception of the bats which migrate on a seasonal basis like many of the birds. Families follow that of A Field Guide to the Mammals by Burt and Grossenheider. proprietary -usgsbrdasc00000004 Air quality monitoring protocol - Denali National Park and Preserve ALL STAC Catalog 1992-01-01 1998-01-01 -149, 63, -148, 64 https://cmr.earthdata.nasa.gov/search/concepts/C1214607513-SCIOPS.umm_json Ambient air quality monitoring is important in Denali, to document baseline conditions and to track long term trends. Denali National Park and Preserve is the only National Park in Alaska designated as class I under the Clean Air Act. Geographic Description: Specific coordinates in the Denali National Park and Preserve, Alaska. Denali National Park and Preserve is located in the central Alaska Range, approximately 210 km southwest of Fairbanks, Alaska. Methodology: Denali currently participates in three nationwide air quality monitoring networks: National Atmospheric Deposition Program (NADP), Interagency Monitoring of Protected Visual Environments (IMPROVE), National Park Service Gaseous Pollutant Monitoring Network (ozone monitoring). Air quality monitoring protocols have been written for each network, and approved by the respective network steering committees. Since there is no local control over methodology, the network manuals are the park's guiding documents. This is a compilation of network protocols. proprietary usgsbrdasc00000004 Air quality monitoring protocol - Denali National Park and Preserve SCIOPS STAC Catalog 1992-01-01 1998-01-01 -149, 63, -148, 64 https://cmr.earthdata.nasa.gov/search/concepts/C1214607513-SCIOPS.umm_json Ambient air quality monitoring is important in Denali, to document baseline conditions and to track long term trends. Denali National Park and Preserve is the only National Park in Alaska designated as class I under the Clean Air Act. Geographic Description: Specific coordinates in the Denali National Park and Preserve, Alaska. Denali National Park and Preserve is located in the central Alaska Range, approximately 210 km southwest of Fairbanks, Alaska. Methodology: Denali currently participates in three nationwide air quality monitoring networks: National Atmospheric Deposition Program (NADP), Interagency Monitoring of Protected Visual Environments (IMPROVE), National Park Service Gaseous Pollutant Monitoring Network (ozone monitoring). Air quality monitoring protocols have been written for each network, and approved by the respective network steering committees. Since there is no local control over methodology, the network manuals are the park's guiding documents. This is a compilation of network protocols. proprietary +usgsbrdasc00000004 Air quality monitoring protocol - Denali National Park and Preserve ALL STAC Catalog 1992-01-01 1998-01-01 -149, 63, -148, 64 https://cmr.earthdata.nasa.gov/search/concepts/C1214607513-SCIOPS.umm_json Ambient air quality monitoring is important in Denali, to document baseline conditions and to track long term trends. Denali National Park and Preserve is the only National Park in Alaska designated as class I under the Clean Air Act. Geographic Description: Specific coordinates in the Denali National Park and Preserve, Alaska. Denali National Park and Preserve is located in the central Alaska Range, approximately 210 km southwest of Fairbanks, Alaska. Methodology: Denali currently participates in three nationwide air quality monitoring networks: National Atmospheric Deposition Program (NADP), Interagency Monitoring of Protected Visual Environments (IMPROVE), National Park Service Gaseous Pollutant Monitoring Network (ozone monitoring). Air quality monitoring protocols have been written for each network, and approved by the respective network steering committees. Since there is no local control over methodology, the network manuals are the park's guiding documents. This is a compilation of network protocols. proprietary usgsbrdfcsc_d_seagrass Mapping and Characterizing Seagrass Areas Important to Manatees in Puerto CEOS_EXTRA STAC Catalog 1994-10-01 1995-06-01 -65.75, 18.15, -65.5, 18.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231549769-CEOS_EXTRA.umm_json "The population of manatees in Puerto Rico is the only group of Antillean manatees (Trichechus manatus manatus) managed and protected by the United States. The Manatee Recovery Plan for the Puerto Rico Population of West Indian Manatees includes requirements to identify and manage habitats and develop criteria and biological information important to its recovery. To this end, the Sirenia Project initiated telemetry studies of manatees in Puerto Rico at the U.S. Naval Station Roosevelt Roads (RRNS) in 1992. Concurrently, the Project began gathering information on habitats critical to manatee in eastern Puerto Rico. Computer aided mapping based on the interpretation of aerial photographs and field groundtruthing was used in the current project to define these habitats and map their distribution in the area of high manatee use. Benthic habitats along approximately 32 miles (52 kilometers of RRNS shoreline were mapped. Field assessment and characterization of important seagrass habitats was conducted as a means of identifying seagrass and macroalgae communities, especially in areas with known manatee feeding sites. The purpose of this dataset is to identify and manage manatee habitats and to develop biological information important to the manatees' recovery. Data was obtained during ground truthing in October, 1994 and June, 1995. One hundred and twenty-five sites, many representing questions raised during preliminary habitat delineations were visited, along with sites with characteristic signatures useful for broader interpretations. Transects were made over several areas with rapidly changing benthic communities and confusing signatures. Data recorded at each site included depth (range 0.5-7.1 m), classification, dominant community, subdominant community, and pertinent comments. the locations of all groundtruth sites were plotted onto one Arc Cad layer of mapping information. Groundtruthing was used to field verify and correct the initial delineations made. Improvements were made to the draft classification scheme based on field observations. Sites of questionable draft delineations were located on the water and confirmed or corrected. Known manatee use of the area for resting or feeding was noted. These sites were accurately located on the overlay for inclusion on the maps. Site location (latitude and longitude) was determined with a Garmin 45 GPS and water depth (tape), temperature (hand-held mercury thermometer), and salinity (hand-held temperature compensated refractometer) recorded. In addition, salinity measurements were made at select nearshore locations to assess the influence of drainage creeks and ditches on nearshore water salinity. Underwater video photography and 35 mm photography were used to document observations. A review of vertical images of waters of RRNS was taken on December 17, 1993, for the United States Navy, along with other collateral information, was used to develop a benthic habitat classification system useful for mapping benthic communities in the area. The system developed for this project was similar to that developed for Geographic Information System (GIS) mapping of benthic communities in the Florida Keys National Marine Sanctuary. Clear acetate overlays were placed over the 9"" x 9"" aerial prints and the polygon method of delineation used to outline habitats on the overlays. Computer aided design methods (PC Arc Cad) were used to create a shoreline base map from navigational charts for this region of Puerto Rico. Habitat polygons extending as far from shore as allowed by the resolution of the images were digitized onto the base map. A minimum mapping unit of 0.5 acres was applied based on the scale and quality of the images. Once finalized, maps were printed in both color and black-line. The information for this metadata was partially taken from the document Mapping and Characterizing Seagrass Areas Important to Manatees in Puerto Rico - Benthic Communities Mapping and Assessment. Prepared for the U.S. Department of Interior, National Biological Service, Sirenia Project. Prepared by Curtis Kruer, Senior Biologist, Caribbean Fisheries Consultants, Inc." proprietary usgsbrdfcsc_d_vieques Mapping and Characterization of Nearshore Benthic Habitats around Vieques Island, Puerto Rico CEOS_EXTRA STAC Catalog 1995-09-01 -65.75, 18.15, -65.5, 18.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231553026-CEOS_EXTRA.umm_json "The Vieques Island Mapping Project was initiated in September 1995 as a cooperative effort between NSRR and the Sirenia Project (Military Interdepartmental Purchase Request no. NOO38995MP00012). Caribbean Fisheries Consultants, Inc. was contracted by the Sirenia Project to help produce the desired information in conjunction with Sirenia Project biologists. Products include maps delineating Vieques' benthic habitat and coastal wetlands, an electronic georeferenced habitat map (UTM coordinate system) in a format compatible with ARC/INFO (Environmental Systems Research Institute, Inc.) and a report describing methods used, the classification scheme, and the relationship of these habitats to manatee use of Vieques Island. These map products complement the Navy's Vieques Land Use Management Plan by identifying marine resources targeted for protection in the plan. Objectives include producing maps of the coastal seagrass beds and other bottom habitat (including coral reefs) surrounding the island of Vieques and characterizing the species composition and density of seagrasses in areas frequented by manatees near Vieques. Ground truthing by boat around Vieques Island was conducted from May 14 to May 19 1996 and from October 4 through October 9 1996. The ground truthing was conducted to verify the interpretation of benthic habitat visible in the images, verify accuracy of the shoreline limits, and refine the habitat classification scheme used for the Vieques maps. Three hundred and thirty-two ground truth stations were established around Vieques Island, located on the aerial image overlays, and digitized. These sites are plotted as a layer on the habitat map. The listing of ground truth sites includes site identifier, latitude and longitude, community classification, depteh, dominant community elements, less dominant elements, and other pertinent information. Latitude and longitude were obtained for each station in the field using a Garmin 45 GPS unit. Water depth for each station was determined from a Hummingbird LCR - 400 Video Fathometer with transom mounted transducer. Underwater Hi-8 video and 35 mm photography were used to document observations at selected sites. The habitat classification scheme used is similar to that used by Kruer and others in southern Forida seagrass beds and other benthic habitats in the Florida Keys National Marine Sanctuary and Biscayne National Park. This scheme, also used for benthic habitat mapping at NSRR in 1994/1995 (Kruer 1995), was refined for the Vieques Island mapping project by adding the category ""sand bottom with rock"". Also, mangroves were mapped in interior areas. The information for this metadata was partially taken from the report - Mapping and characterization of Nearshore Benthic Habitats around Vieques Island, Puerto Rico." proprietary usgsbrdnpwrc_d_birds_checklists_Version 12MAY03 Birds Checklists of the United States CEOS_EXTRA STAC Catalog 1996-01-01 -125, 25, -67, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550188-CEOS_EXTRA.umm_json This resource is known as Bird Checklists of the United States. Bird Checklists of the United States. For years, people and groups have developed listings or checklists of birds that occur in a particular region. Information on the distribution or seasonal occurrence of birds in an area, however, can change over time. Bird checklists often are outdated in only a few years after printing, but budget and time constraints prohibit regular updates. The Internet provides new opportunities for the compilation and dissemination of current information on bird distribution. Here we offer bird checklists developed by others that indicate the seasonal occurrence of birds in state, federal, and private management areas, nature preserves, and other areas of special interest in the United States. Bird checklists exist for Great Plains States: Colorado, Iowa, Kansas, Minnesota, Missouri, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Wyoming; East of Great Plains states: Alabama, Arkansas, Connecticut, Delaware, Florida, Georgia, Illinois, Indiana, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Mississippi, New Hampshire, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee, Vermont, Virginia, West Virginia, Wisconsin; and West of Great Plains: Arizona, California, Idaho, Nevada, Oregon, Utah, Washington. It is hoped that these checklists will serve several purposes. First, we hope the checklists will help bird enthusiasts decide where to visit. A visit to these unique areas can be a rewarding experience for both the amateur and expert birdwatcher. Second, we hope that these checklists will provide potential visitors with a guide to birds that might occur in a region during a particular season. The checklists were kept simple to facilitate printing so they can be easily carried into the field. And third, we hope that these checklists will stimulate and encourage visitors to these areas to help improve the accuracy and completeness of the checklists. The information in some checklists already has been updated; these checklists contain more current information than the printed versions. Sightings of birds and other wildlife are an important part of monitoring wildlife use. Visitors are encouraged to share their observations of rare, aberrant, or occasional birds with the staff at these areas. With each checklist, we have included an address for visitors to send information on rare birds so that checklists can be updated. To assist in establishing standards in observation and reporting, we also provide a Record Documentation Form to document supporting details of rare bird observations. The efforts and dedication of the many birders, birding groups, biologists, and resource managers who developed these checklists are acknowledged. The information for this metadata was partially taken from the Northern Prairie Wildlife Research Center website at http://www.npwrc.usgs.gov/resource/birds/chekbird/index.htm proprietary usgsbrdnpwrc_d_ndfleas_Version 16JUL97 Fleas of North Dakota CEOS_EXTRA STAC Catalog 1970-01-01 -104, 46, -96.5, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231553614-CEOS_EXTRA.umm_json The dataset contains distribution maps for the following species of fleas: Aetheca wagneri, Amaradix euphorbi, Amphipsylla sibirica pollionis, Callistopsyllus terinus campestris, Cediopsylla inaequalis inaequalis, Ceratophyllus (Ceratophyllus) idius, Corrodopsylla curvata curvata, Chaetopsylla lotoris, Ctenocephalides canis, Epitedia faceta, Epitedia wenmanni, Euhoplopsyllus glacialis affinis, Eumolpianus eumolpi eumolpi, Foxella ignota albertensis, Hystrichopsylla dippiei dippiei, Megabothris (Megabothris) asio megacolpus, Megabothris (Amegabothris) lucifer, Meringis jamesoni, Myodopsylla insignis, Nearctopsylla genalis hygini, Neopsylla inopina, Nosopsyllus fasciatus, Oropsylla (Oropsylla) arctomys, Opisodasys (Opisodasys) pseudarctomys, Orchopeas caedens, Orchopeas howardi, Peromyscopsylla hamifer, Pleochaetis exilis, Pules irritans, Rhadinopsylla (Actenophthalmus) fraterna, Thrassis bacchi bacchi. The information for this metadata was partially taken from the Northern Prairie Wildlife Research Center website at http://www.npwrc.usgs.gov/resource/insects/ndfleas/ proprietary -usgsbrdnpwrcb00000013_Version 30SEP2002 A Bibliography of Fisheries Biology in North and South Dakota ALL STAC Catalog 1970-01-01 -104, 43, -96, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550006-CEOS_EXTRA.umm_json This bibliography lists as many fisheries biology and related references as possible from North Dakota and South Dakota waters for use by fishery biologists. Selected references from contiguous states sharing river basins with the Dakotas are included. Studies in the Missouri River downstream from Gavins Point Dam are also included. In addition to published fishery and related aquatic studies, attempts were made to list all dissertations and Masters theses in these fields. proprietary usgsbrdnpwrcb00000013_Version 30SEP2002 A Bibliography of Fisheries Biology in North and South Dakota CEOS_EXTRA STAC Catalog 1970-01-01 -104, 43, -96, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550006-CEOS_EXTRA.umm_json This bibliography lists as many fisheries biology and related references as possible from North Dakota and South Dakota waters for use by fishery biologists. Selected references from contiguous states sharing river basins with the Dakotas are included. Studies in the Missouri River downstream from Gavins Point Dam are also included. In addition to published fishery and related aquatic studies, attempts were made to list all dissertations and Masters theses in these fields. proprietary +usgsbrdnpwrcb00000013_Version 30SEP2002 A Bibliography of Fisheries Biology in North and South Dakota ALL STAC Catalog 1970-01-01 -104, 43, -96, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550006-CEOS_EXTRA.umm_json This bibliography lists as many fisheries biology and related references as possible from North Dakota and South Dakota waters for use by fishery biologists. Selected references from contiguous states sharing river basins with the Dakotas are included. Studies in the Missouri River downstream from Gavins Point Dam are also included. In addition to published fishery and related aquatic studies, attempts were made to list all dissertations and Masters theses in these fields. proprietary usgsbrdnpwrcb00000016_Version 16JUL97 American Wildcelery (Vallisneria americana) Ecological Considerations for Restoration CEOS_EXTRA STAC Catalog 1970-01-01 -125, 25, -67, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231548602-CEOS_EXTRA.umm_json The success of vegetation management programs for waterfowl is dependent on knowing the physical and physiological requirements of the target species. Lakes and riverine impoundments that contain an abundance of the American wildcelery plant (Vallisneria americana) have traditionally been favored by canvasback ducks (Aythya valisineria) and other waterfowl species as feeding areas during migration. Information on the ecology of V. americana is summarized to serve as a guide for potential wetland restoration projects. Because of the geographic diversity and wetland conditions in which V. americana is found, we have avoided making hard-and-fast conclusions about the requirements of the plant. Rather, we present as much general information as possible and provide the sources of more specific information. Vallisneria americana is a submersed aquatic plant that has management potential. Techniques are described for transplanting winter buds from one location to another. Management programs that employ these techniques should define objectives clearly and evaluate the water regime carefully before initiating a major effort. proprietary usgsbrdnpwrcd00000002_Version 02MAR98 Ecological Effects of Fire Retardant Chemicals and Fire Suppressant Foams CEOS_EXTRA STAC Catalog 1993-01-01 1998-01-01 -98, 47, -98, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231550534-CEOS_EXTRA.umm_json Laboratory studies with algae, aquatic invertebrates, and fish. Short-term toxicity tests showed that both fire-retardant and foam-suppressant chemicals were very toxic to aquatic organisms including algae, aquatic invertebrates, and fish. Foam-suppressant are more toxic than fire-retardant chemicals. The primary toxicant in fire-retardants is the ammonia component, whereas the nitrite and nitrate components do not seem to contribute much to the toxicity of the formulations. In foam suppressants the primary toxicant is the surfactant component. The most sensitive life-stage for fish is the swim-up stage. Accidental spills of fire-fighting chemicals in streams could cause substantial fish kills depending on the stream size and flow rate. For example, the retardant Fire-Trol GTS-R is prepared for field use by mixing 1.66 pounds per gallon of water to produce 1.1 gallons of slurry, which is equivalent to 198,930 mg/liter. Comparing the concentration of FT GTS-R field mixture to the acute toxicity values for the most sensitive life stage for rainbow trout gives a ratio of 853 in soft water and 1474 in hard water. Applying a safety factor of 100 to this ratio suggests a dilution of 85, 300 in soft water and 147,400 in hard water is needed to lower the chemical concentration in a receiving water to limit adverse effects, i.e., fish kill, in a stream. For rainbow trout, other dilution factors would be 52,100 for Fire-Trol LCG-R, 85,600 for Phos-Chek D75-F, 71,400 for Phos-Chek WD-881, and 50,000 for Silv-ex. Fire-fighting chemicals are very toxic in aquatic environments and fire control managers need to consider protection of aquatic resources, especially if endangered species are present. proprietary usgsbrdnpwrcd00000012_Version 31JUL97 Changes in Breeding Bird Populations in North Dakota: 1967 to 1992-93. CEOS_EXTRA STAC Catalog 1967-01-01 1993-01-01 -104, 46, -97, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231551668-CEOS_EXTRA.umm_json Breeding bird populations in North Dakota were compared using surveys conducted in 1967 and 1992-93. In decreasing order, the five most frequently occurring species were Horned Lark (Eremophia alpestris), Brown-headed Cowbird (Molothrus ater), Western Meadowlark (Sturnella neglecta), Red-winged Blackbird (Agelaius phoeniceus), and Eastern Kingbird (Tyrannus tyrannus). The five most abundant species - Horned Lark, Chestnut-collared Longspur (Calcarius ornatus), Red-winged Blackbird, Western Meadowlark, and Brown-headed Cowbird - accounted for 31-41% of the estimated statewide breeding bird population in the three years. Although species composition remained relatively similar among years, between-year patterns in abundance and frequency varied considerably among species. Data from this survey and the North American Breeding Bird Survey indicated that species exhibiting significant declines were primarily grassland- and wetland-breeding birds, whereas species exhibiting significant increases primarily were those associated with human structures and woody vegetation. Population declines and increases for species with similar habitat associations paralleled breeding habitat changes, providing evidence that factors on the breeding grounds are having a detectable effect on breeding birds in the northern Great Plains. proprietary @@ -20948,8 +20928,8 @@ waddington_0352584 A Unique Opportunity for In-Situ Measurement of Seasonally-Va waldinventursihlwald_1.0 Supplementary Data Sample Plot Inventory Sihlwald ENVIDAT STAC Catalog 2020-01-01 2020-01-01 8.552084, 47.2538697, 8.552084, 47.2538697 https://cmr.earthdata.nasa.gov/search/concepts/C2789818127-ENVIDAT.umm_json # Supplementary Data Sample Plot Inventory Sihlwald The Sihlwald is one of the largest contiguous beech forests in the Swiss Plateau region. In the year 2000, timber harvesting was abandoned. Since 2007 the forest has been under strict protection as a natural forest reserve on an area of 1098 ha and since 2008 as a cantonal nature and landscape conservation area (SVO Sihlwald). Since 2010, it carries the national label ‘Nature discovery park’ (‘Naturerlebnispark’). As part of the national monitoring in nature forest reserves, a sampling inventory (calipering threshold of 7 cm) with 226 plots on an area of 917 ha was carried out in the Sihlwald in autumn and early winter 2017. The aim was to describe the state and development of the forest structure and make comparisons with earlier sampling inventories in the same area from 1981, 1989 and 2003. This dataset contains supplementary tables for the publication by Brändli et al. (2020). The metadata file describes the structure of the tables. proprietary water-availability-of-swiss-forests-during-the-2015-and-2018-droughts_1.0 Water availability of Swiss forests during the 2015 and 2018 droughts ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817096-ENVIDAT.umm_json The Swiss forests' water availability during the 2015 and 2018 droughts was modelled by implementing the mechanistic Soil-Vegetation-Atmosphere-Transport (SVAT) model LWF-Brook90 taking advantage of regionalized depth-resolved soil information and measured soil matric potential and eddy covariance data. Data include 1) csv of soil matrix potential and eddy covariance data, 2) csv of posterior model parameters, 3) geotiffs of plant-available water storage capacity until 1m soil depth and the potential rooting depth, 4) geotiffs of yearly average (2014-2019) of precipitation (P), actual evapotranspiration (ETa), evaporation as the sum of soil, snow and interception evaporation (E), actual transpiration (Ta), runoff (F) and total soil water storage (SWAT), 5) csv of simulated root water uptake aggregated for different soil depths per deciduous and coniferous trees across Switzerland at daily resolution and cumulative root fraction per soil depth for coniferous and deciduous sites, 6) geotiffs of the ratio of actual to potential transpiration (-) as mean of non-drought years 2014, 2016, 2017, 2019 and 2015 and 2018 for the month June, July, August, September and October, 7) geotiff of mean soil matric potential in the rooting zone in August 2018, 8) geotiffs of gravitational water capacity (mm) until 1 m soil depth and the maximum rooting depth (mrd), 9) geotiffs of uncertainties of the available water storage capacity (AWC) until 1m soil depth and the mean maximum rooting depth (mrd), 10) csv of average plant available - (AWC), gravitational (GWC) and residual (RES) water capacity per soil depth layer of the Swiss forest. proprietary water-isotopes-plynlimon_1.0 Stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK ENVIDAT STAC Catalog 2019-01-01 2019-01-01 -3.7631607, 52.418789, -3.6402512, 52.4982845 https://cmr.earthdata.nasa.gov/search/concepts/C2789817232-ENVIDAT.umm_json The data base contains timeseries of stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK. One data set contains weekly stable water isotope data from the Lower Hafren and Tanllwyth catchments, and the other data set contains 7-hourly stable water isotope data from Upper Hafren. Both data sets also include chloride concentrations in precipitation and streamflow. proprietary -wbandimpacts_1 ACHIEVE W-Band Cloud Radar IMPACTS GHRC_DAAC STAC Catalog 2023-01-23 2023-03-01 -72.861, 41.368, -71.655, 42.268 https://cmr.earthdata.nasa.gov/search/concepts/C3247862082-GHRC_DAAC.umm_json The ACHIEVE W-Band Cloud Radar IMPACTS dataset consists of reflectivity, signal-to-noise ratio, and radial velocity data measured during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. ACHIEVE W-Band Cloud Radar data are available from January 23, 2023, through March 1, 2023, in netCDF-4 format. proprietary wbandimpacts_1 ACHIEVE W-Band Cloud Radar IMPACTS ALL STAC Catalog 2023-01-23 2023-03-01 -72.861, 41.368, -71.655, 42.268 https://cmr.earthdata.nasa.gov/search/concepts/C3247862082-GHRC_DAAC.umm_json The ACHIEVE W-Band Cloud Radar IMPACTS dataset consists of reflectivity, signal-to-noise ratio, and radial velocity data measured during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. ACHIEVE W-Band Cloud Radar data are available from January 23, 2023, through March 1, 2023, in netCDF-4 format. proprietary +wbandimpacts_1 ACHIEVE W-Band Cloud Radar IMPACTS GHRC_DAAC STAC Catalog 2023-01-23 2023-03-01 -72.861, 41.368, -71.655, 42.268 https://cmr.earthdata.nasa.gov/search/concepts/C3247862082-GHRC_DAAC.umm_json The ACHIEVE W-Band Cloud Radar IMPACTS dataset consists of reflectivity, signal-to-noise ratio, and radial velocity data measured during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. ACHIEVE W-Band Cloud Radar data are available from January 23, 2023, through March 1, 2023, in netCDF-4 format. proprietary weather-snowpack-danger_ratings-data_1.0 Weather, snowpack and danger ratings data for automated avalanche danger level predictions ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817371-ENVIDAT.umm_json Each set includes the meteorological variables (resampled 24-hour averages) and the profile variables extracted from the simulated profiles for each of the weather stations of the IMIS network in Switzerland, and, the danger ratings for dry-snow conditions assigned to the location of the station. The data set of RF 1 contains the danger ratings published in the official Swiss avalanche bulletin, and the data set of RF 2 is a quality-controlled subset of danger ratings. These data are the basis of the following publication: Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, 2022. proprietary weather-station-wolfgangpass_1.0 Weather Station Davos Wolfgang ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789817645-ENVIDAT.umm_json The dataset contains weather parameters measured at Davos Wolfgang (LON: 9.853594, LAT: 46.835577). proprietary weather_station_klosters_1.0 Weather Station Klosters ENVIDAT STAC Catalog 2019-01-01 2019-01-01 9.880413, 46.869019, 9.880413, 46.869019 https://cmr.earthdata.nasa.gov/search/concepts/C2789817512-ENVIDAT.umm_json A weather station (Lufft WS600) measured meteorological parameters at Klosters (LON: 9.880413, LAT: 46.869019). Detailed information on the specifications can be found [here](https://www.lufft.com/products/compact-weather-sensors-293/ws600-umb-smart-weather-sensor-1832/productAction/outputAsPdf/). proprietary