From 4b1ca83c393db2fda1c200b1b427879506c48e31 Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Fri, 1 Nov 2024 04:56:49 +0000 Subject: [PATCH] Updated datasets 2024-11-01 UTC --- aws_open_datasets.json | 696 +++++++++++++++++++++-------------------- aws_open_datasets.tsv | 115 +++---- gee_catalog.json | 166 +++++----- gee_catalog.tsv | 166 +++++----- nasa_cmr_catalog.json | 631 +++++++++++++++++++++++++------------ nasa_cmr_catalog.tsv | 67 ++-- 6 files changed, 1055 insertions(+), 786 deletions(-) diff --git a/aws_open_datasets.json b/aws_open_datasets.json index 38d4539..08b55d6 100644 --- a/aws_open_datasets.json +++ b/aws_open_datasets.json @@ -24447,9 +24447,9 @@ }, { "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/", @@ -24468,9 +24468,9 @@ }, { "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/", @@ -24566,10 +24566,10 @@ }, { "Name": "OpenAQ", - "Description": "SNS topic for new objects in the openaq-data-archive bucket", - "ARN": "arn:aws:sns:us-east-1:817926761842:openaq-data-archive-object_created", + "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": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://openaq.org", "Contact": "info@openaq.org", "ManagedBy": "[OpenAQ](https://openaq.org)", @@ -24590,10 +24590,10 @@ }, { "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": "SNS topic for new objects in the openaq-data-archive bucket", + "ARN": "arn:aws:sns:us-east-1:817926761842:openaq-data-archive-object_created", "Region": "us-east-1", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://openaq.org", "Contact": "info@openaq.org", "ManagedBy": "[OpenAQ](https://openaq.org)", @@ -24614,8 +24614,8 @@ }, { "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", @@ -24631,7 +24631,7 @@ "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, @@ -24640,8 +24640,8 @@ }, { "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", @@ -24657,7 +24657,7 @@ "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, @@ -25151,8 +25151,8 @@ }, { "Name": "Oxford Nanopore Technologies Benchmark Datasets", - "Description": "Oxford Nanopore Open Datasets", - "ARN": "arn:aws:s3:::ont-open-data", + "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/", @@ -25179,8 +25179,8 @@ }, { "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": "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/", @@ -25207,8 +25207,8 @@ }, { "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": "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/", @@ -25235,8 +25235,8 @@ }, { "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": "Oxford Nanopore Open Datasets", + "ARN": "arn:aws:s3:::ont-open-data", "Region": "eu-west-1", "Type": "S3 Bucket", "Documentation": "https://labs.epi2me.io/dataindex/", @@ -25458,8 +25458,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2016", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2016", + "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/", @@ -25490,8 +25490,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "machine learning models", - "ARN": "arn:aws:s3:::pacific-sound-models", + "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/", @@ -25522,8 +25522,8 @@ }, { "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 2021", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2021", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -25554,8 +25554,8 @@ }, { "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 2016", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2016", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -25586,8 +25586,8 @@ }, { "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 2019", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2019", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -25618,8 +25618,8 @@ }, { "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/", @@ -25650,8 +25650,8 @@ }, { "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 2023", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2023", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -25682,8 +25682,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2021", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2021", + "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/", @@ -25714,8 +25714,8 @@ }, { "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 2025", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2025", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -25746,8 +25746,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2019", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2019", + "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/", @@ -25778,8 +25778,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2018", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2018", + "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/", @@ -25810,8 +25810,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "decimated 16 kHz audio recordings", - "ARN": "arn:aws:s3:::pacific-sound-16khz", + "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/", @@ -25842,8 +25842,8 @@ }, { "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 2022", + "ARN": "arn:aws:s3:::pacific-sound-256khz-2022", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://docs.mbari.org/pacific-sound/", @@ -25874,8 +25874,8 @@ }, { "Name": "Pacific Ocean Sound Recordings", - "Description": "original 256 kHz audio recordings year 2017", - "ARN": "arn:aws:s3:::pacific-sound-256khz-2017", + "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/", @@ -25927,8 +25927,8 @@ }, { "Name": "Pancreatic Cancer Organoid Profiling", - "Description": "RNA-Seq Gene Expression Quantification", - "ARN": "arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-open", + "Description": "WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation,RNA-Seq Splice Junction Quantification", + "ARN": "arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-controlled", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1", @@ -25947,14 +25947,14 @@ ], "Explore": null, "RequesterPays": null, - "ControlledAccess": null, + "ControlledAccess": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1", "AccountRequired": null, "Host": null }, { "Name": "Pancreatic Cancer Organoid Profiling", - "Description": "WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation,RNA-Seq Splice Junction Quantification", - "ARN": "arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-controlled", + "Description": "RNA-Seq Gene Expression Quantification", + "ARN": "arn:aws:s3:::gdc-organoid-pancreatic-phs001611-2-open", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1", @@ -25973,7 +25973,7 @@ ], "Explore": null, "RequesterPays": null, - "ControlledAccess": "https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1", + "ControlledAccess": null, "AccountRequired": null, "Host": null }, @@ -26097,8 +26097,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Nodal Seismometer Sweep Data", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac_sweep/", + "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time MATLAB Files", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/Resampled/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -26114,7 +26114,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac_sweep%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2FResampled%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26123,8 +26123,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Nodal Seismometer Field Notes and Metadata", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_metadata/", + "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", @@ -26140,7 +26140,7 @@ "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&prefix=DAS%2FSEG-Y%2FDASH%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26149,8 +26149,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Nodal Seismometer Continuous Data", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac/", + "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", @@ -26166,7 +26166,7 @@ "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=DAS%2FSEG-Y%2FDASV%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26175,8 +26175,8 @@ }, { "Name": "PoroTomo", - "Description": "HSDS PoroTomo domains", - "ARN": "arn:aws:s3:::nrel-pds-hsds/nrel/porotomo/", + "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", @@ -26192,7 +26192,7 @@ "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)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26201,8 +26201,8 @@ }, { "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 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", @@ -26218,7 +26218,7 @@ "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%2FH5%2FDASH%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26227,8 +26227,8 @@ }, { "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 Nodal Seismometer Sweep Data", + "ARN": "arn:aws:s3:::nrel-pds-porotomo/Nodal/nodal_sac_sweep/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/openEDI/documentation/blob/master/PoroTomo/PoroTomo.md", @@ -26244,7 +26244,7 @@ "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=Nodal%2Fnodal_sac_sweep%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26253,8 +26253,8 @@ }, { "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 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", @@ -26270,7 +26270,7 @@ "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_metadata%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26279,8 +26279,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Horizontal Distributed Acoustic Sensing (DASH) Data Resampled in Time MATLAB Files", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/DAS/SEG-Y/DASH/Resampled/", + "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", @@ -26296,7 +26296,7 @@ "geospatial" ], "Explore": [ - "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=DAS%2FSEG-Y%2FDASH%2FResampled%2F)" + "[Browse Dataset](https://data.openei.org/s3_viewer?bucket=nrel-pds-porotomo&prefix=Nodal%2Fnodal_sac%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26305,8 +26305,8 @@ }, { "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": "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", @@ -26322,7 +26322,7 @@ "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-hsds&prefix=nrel%2Fporotomo%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26331,8 +26331,8 @@ }, { "Name": "PoroTomo", - "Description": "PoroTomo Datasets", - "ARN": "arn:aws:s3:::nrel-pds-porotomo/", + "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", @@ -26348,7 +26348,7 @@ "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-porotomo&prefix=DAS%2FH5%2FDASV%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -26405,8 +26405,8 @@ }, { "Name": "Prefeitura Municipal de S\u00e3o Paulo (PMSP) LiDAR Point Cloud", - "Description": "S\u00e3o Paulo city's 3D LiDAR - Entwine Point Tiles", - "ARN": "arn:aws:s3:::ept-m3dc-pmsp", + "Description": "S\u00e3o Paulo city's 3D LiDAR - LAZ Files", + "ARN": "arn:aws:s3:::laz-m3dc-pmsp", "Region": "sa-east-1", "Type": "S3 Bucket", "Documentation": "https://github.com/geoinfo-smdu/M3DC", @@ -26432,8 +26432,8 @@ }, { "Name": "Prefeitura Municipal de S\u00e3o Paulo (PMSP) LiDAR Point Cloud", - "Description": "S\u00e3o Paulo city's 3D LiDAR - LAZ Files", - "ARN": "arn:aws:s3:::laz-m3dc-pmsp", + "Description": "S\u00e3o Paulo city's 3D LiDAR - Entwine Point Tiles", + "ARN": "arn:aws:s3:::ept-m3dc-pmsp", "Region": "sa-east-1", "Type": "S3 Bucket", "Documentation": "https://github.com/geoinfo-smdu/M3DC", @@ -26485,10 +26485,10 @@ }, { "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)", @@ -26517,7 +26517,7 @@ "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, @@ -26526,10 +26526,10 @@ }, { "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)", @@ -26558,7 +26558,7 @@ "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, @@ -26919,8 +26919,8 @@ }, { "Name": "REDASA COVID-19 Open Data", - "Description": "For all the questions curated during the REDASA project, we created a Kendra index The documents available in this S3 bucket were surfaced by the Kendra index as being relevant to the research medical question", - "ARN": "arn:aws:s3:::pansurg-curation-workflo-kendraqueryresults50d0eb-open-data", + "Description": "An S3 bucket that contains the final curation data in GroundTruth format", + "ARN": "arn:aws:s3:::pansurg-curation-final-curations-open-data", "Region": "eu-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/PanSurg/redasa-sample-data/blob/master/open-data.md", @@ -26945,8 +26945,8 @@ }, { "Name": "REDASA COVID-19 Open Data", - "Description": "This is the raw data repository containing a common crawl of CORD-19 papers and other sources identified by the REDASA Project", - "ARN": "arn:aws:s3:::pansurg-curation-raw-open-data", + "Description": "For all the questions curated during the REDASA project, we created a Kendra index The documents available in this S3 bucket were surfaced by the Kendra index as being relevant to the research medical question", + "ARN": "arn:aws:s3:::pansurg-curation-workflo-kendraqueryresults50d0eb-open-data", "Region": "eu-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/PanSurg/redasa-sample-data/blob/master/open-data.md", @@ -26971,8 +26971,8 @@ }, { "Name": "REDASA COVID-19 Open Data", - "Description": "An S3 bucket that contains the final curation data in GroundTruth format", - "ARN": "arn:aws:s3:::pansurg-curation-final-curations-open-data", + "Description": "This is the raw data repository containing a common crawl of CORD-19 papers and other sources identified by the REDASA Project", + "ARN": "arn:aws:s3:::pansurg-curation-raw-open-data", "Region": "eu-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/PanSurg/redasa-sample-data/blob/master/open-data.md", @@ -27339,10 +27339,10 @@ }, { "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/)", @@ -27361,10 +27361,10 @@ }, { "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/)", @@ -27408,8 +27408,8 @@ }, { "Name": "SILAM Air Quality", - "Description": "Surface Zarr files", - "ARN": "arn:aws:s3:::fmi-opendata-silam-surface-zarr", + "Description": "Surface NetCDF files", + "ARN": "arn:aws:s3:::fmi-opendata-silam-surface-netcdf", "Region": "eu-west-1", "Type": "S3 Bucket", "Documentation": "http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3", @@ -27426,7 +27426,7 @@ "meteorological" ], "Explore": [ - "[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://fmi-opendata-silam-surface-netcdf.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -27435,8 +27435,8 @@ }, { "Name": "SILAM Air Quality", - "Description": "Surface NetCDF files", - "ARN": "arn:aws:s3:::fmi-opendata-silam-surface-netcdf", + "Description": "Surface Zarr files", + "ARN": "arn:aws:s3:::fmi-opendata-silam-surface-zarr", "Region": "eu-west-1", "Type": "S3 Bucket", "Documentation": "http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3", @@ -27453,7 +27453,7 @@ "meteorological" ], "Explore": [ - "[Browse Bucket](https://fmi-opendata-silam-surface-netcdf.s3.amazonaws.com/index.html)" + "[Browse Bucket](https://fmi-opendata-silam-surface-zarr.s3.amazonaws.com/index.html)" ], "RequesterPays": null, "ControlledAccess": null, @@ -27662,10 +27662,10 @@ }, { "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/)", @@ -27679,7 +27679,9 @@ "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, @@ -27687,10 +27689,10 @@ }, { "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/)", @@ -27704,9 +27706,7 @@ "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, @@ -27838,8 +27838,8 @@ }, { "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", @@ -27865,7 +27865,7 @@ "transcriptomics" ], "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, @@ -27874,8 +27874,8 @@ }, { "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", @@ -27901,7 +27901,7 @@ "transcriptomics" ], "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, @@ -27910,8 +27910,8 @@ }, { "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", @@ -27937,7 +27937,7 @@ "transcriptomics" ], "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, @@ -28059,10 +28059,10 @@ }, { "Name": "Sentinel-1 Precise Orbit Determination (POD) Products", - "Description": "Sentinel-1 Orbits bucket", - "ARN": "arn:aws:s3:::s1-orbits", + "Description": "Notifications for new data", + "ARN": "arn:aws:sns:us-west-2:211125554030:s1-orbits-object_created", "Region": "us-west-2", - "Type": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://s1-orbits.s3.us-west-2.amazonaws.com/README.html", "Contact": "https://asf.alaska.edu/asf/contact-us/", "ManagedBy": "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)", @@ -28078,9 +28078,7 @@ "sentinel-1", "synthetic aperture radar" ], - "Explore": [ - "[AWS S3 Explorer](https://s1-orbits.s3.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -28088,10 +28086,10 @@ }, { "Name": "Sentinel-1 Precise Orbit Determination (POD) Products", - "Description": "Notifications for new data", - "ARN": "arn:aws:sns:us-west-2:211125554030:s1-orbits-object_created", + "Description": "Sentinel-1 Orbits bucket", + "ARN": "arn:aws:s3:::s1-orbits", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://s1-orbits.s3.us-west-2.amazonaws.com/README.html", "Contact": "https://asf.alaska.edu/asf/contact-us/", "ManagedBy": "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)", @@ -28107,7 +28105,9 @@ "sentinel-1", "synthetic aperture radar" ], - "Explore": null, + "Explore": [ + "[AWS S3 Explorer](https://s1-orbits.s3.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -28168,10 +28168,10 @@ }, { "Name": "Sentinel-2", - "Description": "New scene notifications for L2A, can subscribe with Lambda", - "ARN": "arn:aws:sns:eu-central-1:214830741341:SentinelS2L2A", + "Description": "Zipped archives for each L1C product with 3 day retention period, in Requester Pays bucket", + "ARN": "arn:aws:s3:::sentinel-s2-l1c-zips", "Region": "eu-central-1", - "Type": "SNS Topic", + "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/)", @@ -28188,17 +28188,17 @@ "stac" ], "Explore": null, - "RequesterPays": null, + "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "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": "S3 Inventory files for L2A and CSV", + "ARN": "arn:aws:s3:::sentinel-inventory/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/)", @@ -28222,8 +28222,8 @@ }, { "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", + "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).", @@ -28241,7 +28241,9 @@ "disaster response", "stac" ], - "Explore": null, + "Explore": [ + "[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)" + ], "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, @@ -28249,8 +28251,8 @@ }, { "Name": "Sentinel-2", - "Description": "Zipped archives for each L1C product with 3 day retention period, in Requester Pays bucket", - "ARN": "arn:aws:s3:::sentinel-s2-l1c-zips", + "Description": "S3 Inventory files for L1C and CSV", + "ARN": "arn:aws:s3:::sentinel-inventory/sentinel-s2-l1c", "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).", @@ -28269,15 +28271,15 @@ "stac" ], "Explore": null, - "RequesterPays": true, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Sentinel-2", - "Description": "S3 Inventory files for L2A and CSV", - "ARN": "arn:aws:s3:::sentinel-inventory/sentinel-s2-l2a", + "Description": "Level 1C scenes and metadata, in Requester Pays S3 bucket", + "ARN": "arn:aws:s3:::sentinel-s2-l1c", "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).", @@ -28295,18 +28297,23 @@ "disaster response", "stac" ], - "Explore": null, - "RequesterPays": null, + "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, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "Sentinel-2", - "Description": "S3 Inventory files for L1C and CSV", - "ARN": "arn:aws:s3:::sentinel-inventory/sentinel-s2-l1c", + "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/)", @@ -28330,10 +28337,10 @@ }, { "Name": "Sentinel-2", - "Description": "Level 1C scenes and metadata, in Requester Pays S3 bucket", - "ARN": "arn:aws:s3:::sentinel-s2-l1c", - "Region": "eu-central-1", - "Type": "S3 Bucket", + "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", "ManagedBy": "[Sinergise](https://www.sinergise.com/)", @@ -28349,21 +28356,16 @@ "disaster response", "stac" ], - "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, + "Explore": null, + "RequesterPays": null, "ControlledAccess": null, "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", + "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).", @@ -28381,9 +28383,7 @@ "disaster response", "stac" ], - "Explore": [ - "[STAC V1.0.0 endpoint](https://sentinel-s2-l2a-stac.s3.amazonaws.com/)" - ], + "Explore": null, "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, @@ -28961,6 +28961,36 @@ "AccountRequired": null, "Host": null }, + { + "Name": "SocialGene RefSeq Databases", + "Description": "SocialGene 2023_v041 Data and Database Dumps", + "ARN": "arn:aws:s3:::socialgene-open-data", + "Region": "us-east-2", + "Type": "S3 Bucket", + "Documentation": "https://socialgene.github.io/precomputed_databases/2023_v0.4.1/aws/aws", + "Contact": "https://github.com/socialgene/socialgene.github.io/issues", + "ManagedBy": "University of Wisconsin-Madison", + "UpdateFrequency": "This database is currently what was published in our 2024 paper introducing SocialGene, but an updated database may be added in the future.", + "License": "Where applicable, SocialGene data is released under CC0 (https://creativecommons.org/public-domain/cc0/), some individual components of the database may have their own license (see https://socialgene.github.io/precomputed_databases/2023_v0.4.1/general)", + "Tags": [ + "metagenomics", + "genomic", + "bioinformatics", + "microbiome", + "chemical biology", + "pharmaceutical", + "graph", + "protein", + "amino acid" + ], + "Explore": [ + "[Browse Bucket](https://socialgene-open-data.s3.amazonaws.com/)" + ], + "RequesterPays": null, + "ControlledAccess": null, + "AccountRequired": null, + "Host": null + }, { "Name": "Sofar Spotter Archive", "Description": "Hourly position, wave spectra and bulk wave parameters from global free drifting Spotter buoys", @@ -28992,8 +29022,8 @@ }, { "Name": "Software Heritage Graph Dataset", - "Description": "S3 Inventory files", - "ARN": "arn:aws:s3:::softwareheritage-inventory", + "Description": "Software Heritage Graph Dataset", + "ARN": "arn:aws:s3:::softwareheritage", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://docs.softwareheritage.org/devel/swh-dataset/graph/athena.html", @@ -29016,8 +29046,8 @@ }, { "Name": "Software Heritage Graph Dataset", - "Description": "Software Heritage Graph Dataset", - "ARN": "arn:aws:s3:::softwareheritage", + "Description": "S3 Inventory files", + "ARN": "arn:aws:s3:::softwareheritage-inventory", "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://docs.softwareheritage.org/devel/swh-dataset/graph/athena.html", @@ -29393,8 +29423,8 @@ }, { "Name": "Sup3rCC", - "Description": "Sup3rCC Generative Models", - "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/models/", + "Description": "Sup3rCC", + "ARN": "arn:aws:s3:::nrel-pds-sup3rcc/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/NREL/sup3r", @@ -29410,7 +29440,7 @@ "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)" ], "RequesterPays": null, "ControlledAccess": null, @@ -29419,8 +29449,8 @@ }, { "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", @@ -29436,7 +29466,7 @@ "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, @@ -29680,9 +29710,9 @@ }, { "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", @@ -29697,7 +29727,9 @@ "geospatial", "disaster response" ], - "Explore": null, + "Explore": [ + "[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -29705,9 +29737,9 @@ }, { "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", @@ -29722,9 +29754,7 @@ "geospatial", "disaster response" ], - "Explore": [ - "[Browse Map](https://elevation-tiles-prod.s3.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, @@ -29753,8 +29783,8 @@ }, { "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", @@ -29772,14 +29802,14 @@ ], "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 }, { "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", @@ -29797,7 +29827,7 @@ ], "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 }, @@ -29986,8 +30016,8 @@ }, { "Name": "The Singapore Nanopore Expression Data Set", - "Description": "Nanopore long read RNA Seq data from the Singapore Nanopore Expression Project (SG-NEx) The data includes raw signal data (blow5), converted from raw signal data (fast5)", - "ARN": "arn:aws:s3:::sg-nex-data-blow5", + "Description": "Nanopore long read RNA Seq data and matched short read RNA-Seq from the Singapore Nanopore Expression Project (SG-NEx) The data includes raw signal data (fast5), basecalled reads (fastq), aligned reads (bam), processed data for RNA modification detection (json), reference genome annotation files (gtf and fa) and sample metadata (txt)", + "ARN": "arn:aws:s3:::sg-nex-data", "Region": "ap-southeast-1", "Type": "S3 Bucket", "Documentation": "https://github.com/GoekeLab/sg-nex-data", @@ -30009,7 +30039,7 @@ "bam" ], "Explore": [ - "[Browse Bucket](http://sg-nex-data-blow5.s3-website-ap-southeast-1.amazonaws.com/)" + "[Browse Bucket](http://sg-nex-data.s3-website-ap-southeast-1.amazonaws.com/)" ], "RequesterPays": null, "ControlledAccess": null, @@ -30018,8 +30048,8 @@ }, { "Name": "The Singapore Nanopore Expression Data Set", - "Description": "Nanopore long read RNA Seq data and matched short read RNA-Seq from the Singapore Nanopore Expression Project (SG-NEx) The data includes raw signal data (fast5), basecalled reads (fastq), aligned reads (bam), processed data for RNA modification detection (json), reference genome annotation files (gtf and fa) and sample metadata (txt)", - "ARN": "arn:aws:s3:::sg-nex-data", + "Description": "Nanopore long read RNA Seq data from the Singapore Nanopore Expression Project (SG-NEx) The data includes raw signal data (blow5), converted from raw signal data (fast5)", + "ARN": "arn:aws:s3:::sg-nex-data-blow5", "Region": "ap-southeast-1", "Type": "S3 Bucket", "Documentation": "https://github.com/GoekeLab/sg-nex-data", @@ -30041,7 +30071,7 @@ "bam" ], "Explore": [ - "[Browse Bucket](http://sg-nex-data.s3-website-ap-southeast-1.amazonaws.com/)" + "[Browse Bucket](http://sg-nex-data-blow5.s3-website-ap-southeast-1.amazonaws.com/)" ], "RequesterPays": null, "ControlledAccess": null, @@ -30356,8 +30386,8 @@ }, { "Name": "USGS 3DEP LiDAR Point Clouds", - "Description": "A Requester Pays Bucket of Raw LAZ 14 3DEP data Data in this bucket is more complete in coverage than the EPT bucket, but it is not a complete 3DEP mirror Some resources in this bucket also have incomplete and missing coordinate system information, which is why they might not be mirrored into the EPT bucket", - "ARN": "arn:aws:s3:::usgs-lidar", + "Description": "Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs-lidar`` bucket", + "ARN": "arn:aws:s3:::usgs-lidar-public", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/hobu/usgs-lidar/", @@ -30374,16 +30404,18 @@ "lidar", "stac" ], - "Explore": null, - "RequesterPays": true, + "Explore": [ + "[STAC Catalog](https://usgs-lidar-stac.s3-us-west-2.amazonaws.com/ept/catalog.json)" + ], + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "Name": "USGS 3DEP LiDAR Point Clouds", - "Description": "Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs-lidar`` bucket", - "ARN": "arn:aws:s3:::usgs-lidar-public", + "Description": "A Requester Pays Bucket of Raw LAZ 14 3DEP data Data in this bucket is more complete in coverage than the EPT bucket, but it is not a complete 3DEP mirror Some resources in this bucket also have incomplete and missing coordinate system information, which is why they might not be mirrored into the EPT bucket", + "ARN": "arn:aws:s3:::usgs-lidar", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/hobu/usgs-lidar/", @@ -30400,10 +30432,8 @@ "lidar", "stac" ], - "Explore": [ - "[STAC Catalog](https://usgs-lidar-stac.s3-us-west-2.amazonaws.com/ept/catalog.json)" - ], - "RequesterPays": null, + "Explore": null, + "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -30431,8 +30461,8 @@ }, { "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-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", @@ -30459,10 +30489,10 @@ }, { "Name": "USGS Landsat", - "Description": "Scenes and metadata", - "ARN": "arn:aws:s3:::usgs-landsat/collection02/", + "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": "S3 Bucket", + "Type": "SNS Topic", "Documentation": "https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-data-access", "Contact": "https://answers.usgs.gov/", "ManagedBy": "[United States Geological Survey](https://www.usgs.gov)", @@ -30479,18 +30509,16 @@ "stac", "cog" ], - "Explore": [ - "[STAC Catalog](https://landsatlook.usgs.gov/stac-server/collections)" - ], - "RequesterPays": true, + "Explore": null, + "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, "Host": null }, { "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, 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", @@ -30517,10 +30545,10 @@ }, { "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": "Scenes and metadata", + "ARN": "arn:aws:s3:::usgs-landsat/collection02/", "Region": "us-west-2", - "Type": "SNS Topic", + "Type": "S3 Bucket", "Documentation": "https://www.usgs.gov/core-science-systems/nli/landsat/landsat-commercial-cloud-data-access", "Contact": "https://answers.usgs.gov/", "ManagedBy": "[United States Geological Survey](https://www.usgs.gov)", @@ -30537,8 +30565,10 @@ "stac", "cog" ], - "Explore": null, - "RequesterPays": null, + "Explore": [ + "[STAC Catalog](https://landsatlook.usgs.gov/stac-server/collections)" + ], + "RequesterPays": true, "ControlledAccess": null, "AccountRequired": null, "Host": null @@ -30622,8 +30652,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2022_01", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-01/", + "Description": "UniProt 2021_01", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-01/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -30652,8 +30682,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2021_04", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-04/", + "Description": "UniProt 2021_02", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-02/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -30712,8 +30742,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2021_01", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-01/", + "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", @@ -30742,8 +30772,8 @@ }, { "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", @@ -30772,8 +30802,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2021_02", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2021-02/", + "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", @@ -30892,8 +30922,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2023_02", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2023-02/", + "Description": "UniProt 2024_05", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-05/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -30922,8 +30952,8 @@ }, { "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", @@ -30952,8 +30982,8 @@ }, { "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", @@ -30982,8 +31012,8 @@ }, { "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", @@ -31012,8 +31042,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2024_01", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-01/", + "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", @@ -31042,8 +31072,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2024_02", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-02/", + "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", @@ -31072,8 +31102,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2024_03", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-03/", + "Description": "UniProt 2024_02", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-02/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31102,8 +31132,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2024_05", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-05/", + "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", @@ -31132,8 +31162,8 @@ }, { "Name": "UniProt", - "Description": "UniProt 2022_05", - "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2022-05/", + "Description": "UniProt 2024_03", + "ARN": "arn:aws:s3:::aws-open-data-uniprot-rdf/2024-03/", "Region": "eu-west-3", "Type": "S3 Bucket", "Documentation": "https://www.uniprot.org/help/about", @@ -31282,8 +31312,8 @@ }, { "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": "Elevation datsets (primarily lidar based) 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 COGs - STATEWIDE__cm_, 2) By Acquisition Year - _cm_ Individual tiles are also available as lossless COGs under the /_Tiles subfolder", + "ARN": "arn:aws:s3:::vtopendata-prd/Elevation", "Region": "us-east-2", "Type": "S3 Bucket", "Documentation": "https://vcgi.vermont.gov/data-and-programs/", @@ -31307,8 +31337,8 @@ }, { "Name": "Vermont Open Geospatial on AWS", - "Description": "Elevation datsets (primarily lidar based) 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 COGs - STATEWIDE__cm_, 2) By Acquisition Year - _cm_ Individual tiles are also available as lossless COGs under the /_Tiles subfolder", - "ARN": "arn:aws:s3:::vtopendata-prd/Elevation", + "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/", @@ -31625,8 +31655,8 @@ }, { "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) - 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", @@ -31641,7 +31671,7 @@ "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%2F9k_airfoils%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -31650,8 +31680,8 @@ }, { "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 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", @@ -31666,7 +31696,7 @@ "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=aerodynamic_shapes%2F2D%2F2k_airfoils%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -31675,8 +31705,8 @@ }, { "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 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", @@ -31691,7 +31721,7 @@ "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=wind_plant_power%2Ffloris%2F)" ], "RequesterPays": null, "ControlledAccess": null, @@ -31830,9 +31860,9 @@ }, { "Name": "YouTube 8 Million - Data Lakehouse Ready", - "Description": "Replica of the two locations above in us-east-1", - "ARN": "arn:aws:s3:::aws-roda-ml-datalake-us-east-1/", - "Region": "us-east-1", + "Description": "Lakehouse ready YT8M as Glue Parquet files Install instructions here", + "ARN": "arn:aws:s3:::aws-roda-ml-datalake/yt8m_ods/", + "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install.md", "Contact": "https://github.com/aws-samples/data-lake-as-code/issues", @@ -31855,8 +31885,8 @@ }, { "Name": "YouTube 8 Million - Data Lakehouse Ready", - "Description": "Lakehouse ready YT8M as Glue Parquet files Install instructions here", - "ARN": "arn:aws:s3:::aws-roda-ml-datalake/yt8m_ods/", + "Description": "Original YT8M *tfrecords File structure info can be found here", + "ARN": "arn:aws:s3:::aws-roda-ml-datalake/yt8m/", "Region": "us-west-2", "Type": "S3 Bucket", "Documentation": "https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install.md", @@ -31880,9 +31910,9 @@ }, { "Name": "YouTube 8 Million - Data Lakehouse Ready", - "Description": "Original YT8M *tfrecords File structure info can be found here", - "ARN": "arn:aws:s3:::aws-roda-ml-datalake/yt8m/", - "Region": "us-west-2", + "Description": "Replica of the two locations above in us-east-1", + "ARN": "arn:aws:s3:::aws-roda-ml-datalake-us-east-1/", + "Region": "us-east-1", "Type": "S3 Bucket", "Documentation": "https://github.com/aws-samples/data-lake-as-code/blob/roda-ml/docs/roda_install.md", "Contact": "https://github.com/aws-samples/data-lake-as-code/issues", @@ -31953,8 +31983,8 @@ }, { "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", @@ -31980,8 +32010,8 @@ }, { "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": "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", @@ -32034,8 +32064,8 @@ }, { "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", @@ -32061,8 +32091,8 @@ }, { "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", @@ -32164,10 +32194,10 @@ }, { "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)", @@ -32181,20 +32211,18 @@ "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": "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)", @@ -32208,18 +32236,20 @@ "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": "nuScenes", - "Description": "nuScenes Dataset", - "ARN": "arn:aws:s3:::motional-nuscenes", + "Description": "Globally cached distribution of the nuScenes Dataset Web frontend is available to browse the dataset", + "ARN": null, "Region": "ap-northeast-1", - "Type": "S3 Bucket", + "Type": "CloudFront Distribution", "Documentation": "https://www.nuscenes.org", "Contact": "https://www.nuscenes.org", "ManagedBy": "[Motional, Inc.](https://motional.com)", @@ -32234,20 +32264,18 @@ "transportation", "urban" ], - "Explore": [ - "[Browse Bucket](https://motional-nuscenes.s3.ap-northeast-1.amazonaws.com/index.html)" - ], + "Explore": null, "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, - "Host": null + "Host": "https://d36yt3mvayqw5m.cloudfront.net" }, { "Name": "nuScenes", - "Description": "Globally cached distribution of the nuScenes Dataset Web frontend is available to browse the dataset", - "ARN": null, + "Description": "nuScenes Dataset", + "ARN": "arn:aws:s3:::motional-nuscenes", "Region": "ap-northeast-1", - "Type": "CloudFront Distribution", + "Type": "S3 Bucket", "Documentation": "https://www.nuscenes.org", "Contact": "https://www.nuscenes.org", "ManagedBy": "[Motional, Inc.](https://motional.com)", @@ -32262,18 +32290,20 @@ "transportation", "urban" ], - "Explore": null, + "Explore": [ + "[Browse Bucket](https://motional-nuscenes.s3.ap-northeast-1.amazonaws.com/index.html)" + ], "RequesterPays": null, "ControlledAccess": null, "AccountRequired": null, - "Host": "https://d36yt3mvayqw5m.cloudfront.net" + "Host": null }, { "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", @@ -32294,10 +32324,10 @@ }, { "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", diff --git a/aws_open_datasets.tsv b/aws_open_datasets.tsv index 714d1e2..c6b8246 100644 --- a/aws_open_datasets.tsv +++ b/aws_open_datasets.tsv @@ -897,15 +897,15 @@ 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 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 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 -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)'] +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 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 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 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 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 @@ -924,10 +924,10 @@ Orcasound - bioacoustic data for marine conservation Labeled audio data for ML m 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 -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 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 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 +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 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 @@ -935,45 +935,45 @@ PALSAR-2 ScanSAR Tropical Cycolne Mocha (L2.1) PALSAR-2 ScanSAR L22 arn:aws:s3:: 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 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)'] 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001611.v1.p1 +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 PersonPath22 Source data arn:aws:s3:::tracking-dataset-eccv-2022 us-east-2 S3 Bucket https://amazon-science.github.io/tracking-dataset/personpath22.html Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 [Amazon Web Services](https://aws.amazon.com/) Periodically Creative Commons Attribution-NonCommercial 4.0 International Public License (CC amazon.science, computer vision Phrase Clustering Dataset (PCD) Phsrase Clustering Dataset (PCD) arn:aws:s3:::amazon-phrase-clustering us-west-2 S3 Bucket https://amazon-phrase-clustering.s3.amazonaws.com/readme.md Post any questions to [re:Post](https://repost.aws/tags/questions/TApd0Wl5P8S9O6 [Amazon](https://www.amazon.com/) Not updated This data is available for anyone to use under the terms of the CDLA-permissive amazon.science, natural language processing, json ['[phrase-clustering-dataset.json](https://amazon-phrase-clustering.s3.amazonaws.com/phrase-clustering-dataset.json)'] Physionet https://s3amazonawscom/physionet-pds/indexhtml arn:aws:s3:::physionet-pds us-east-1 S3 Bucket https://physionet.org/ contact@physionet.org [MIT Laboratory for Computational Physiology](https://lcp.mit.edu/) Not updated PhysioBank databases are made available under the ODC Public Domain Dedication a aws-pds, biology, life sciences 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 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 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 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 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 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 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 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 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 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 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 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)'] 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 +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 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 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)'] +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)'] 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)'] @@ -986,9 +986,9 @@ RACECAR Dataset The RACECAR dataset is the first open dataset for full-scale and 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/)'] +REDASA COVID-19 Open Data An S3 bucket that contains the final curation data in GroundTruth format arn:aws:s3:::pansurg-curation-final-curations-open-data eu-west-2 S3 Bucket https://github.com/PanSurg/redasa-sample-data/blob/master/open-data.md redasa-open-data@imperial.ac.uk REDASA Consortium, Imperial College London, UK Yearly updates CC-BY-4.0 aws-pds, COVID-19, coronavirus, life sciences, information retrieval, natural language processing, text analysis REDASA COVID-19 Open Data For all the questions curated during the REDASA project, we created a Kendra ind arn:aws:s3:::pansurg-curation-workflo-kendraqueryresults50d0eb-open-data eu-west-2 S3 Bucket https://github.com/PanSurg/redasa-sample-data/blob/master/open-data.md redasa-open-data@imperial.ac.uk REDASA Consortium, Imperial College London, UK Yearly updates CC-BY-4.0 aws-pds, COVID-19, coronavirus, life sciences, information retrieval, natural language processing, text analysis REDASA COVID-19 Open Data This is the raw data repository containing a common crawl of CORD-19 papers and arn:aws:s3:::pansurg-curation-raw-open-data eu-west-2 S3 Bucket https://github.com/PanSurg/redasa-sample-data/blob/master/open-data.md redasa-open-data@imperial.ac.uk REDASA Consortium, Imperial College London, UK Yearly updates CC-BY-4.0 aws-pds, COVID-19, coronavirus, life sciences, information retrieval, natural language processing, text analysis -REDASA COVID-19 Open Data An S3 bucket that contains the final curation data in GroundTruth format arn:aws:s3:::pansurg-curation-final-curations-open-data eu-west-2 S3 Bucket https://github.com/PanSurg/redasa-sample-data/blob/master/open-data.md redasa-open-data@imperial.ac.uk REDASA Consortium, Imperial College London, UK Yearly updates CC-BY-4.0 aws-pds, COVID-19, coronavirus, life sciences, information retrieval, natural language processing, text analysis RSNA Abdominal Trauma Detection (RSNA-ABT) Zip archive containing DCM and CSV files arn:aws:s3:::abdominal-trauma-detection us-west-2 S3 Bucket https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Abdominal-Trauma-Detection informatics@rsna.org Radiological Society of North America (https://www.rsna.org/) The dataset may be updated with additional or corrected data on a need-to-update You may access and use these de-identified imaging datasets and annotations (“th aws-pds, radiology, medical imaging, medical image computing, machine learning, computer vision, csv, labeled, computed tomography, x-ray tomography https://mira.rsna.org/dataset/5 RSNA Cervical Spine Fracture Detection (RSNA-CSF) Dataset Zip archive containing DCM and CSV files arn:aws:s3:::cervical-spine-fracture us-west-2 S3 Bucket https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Cervical-Spine-Fracture-Dete informatics@rsna.org [Radiological Society of North America](https://www.rsna.org/) The dataset may be updated with additional or corrected data on a need-to-update You may access and use these de-identified imaging datasets and annotations (“th aws-pds, radiology, medical imaging, medical image computing, machine learning, computer vision, csv, labeled, computed tomography, x-ray tomography https://mira.rsna.org/dataset/4 RSNA Intracranial Hemorrhage Detection Zip archive containing DCM and CSV files arn:aws:s3:::intracranial-hemorrhage us-west-2 S3 Bucket https://github.com/RSNA/AI-Challenge-Data informatics@rsna.org Radiological Society of North America (https://www.rsna.org/) The dataset may be updated with additional or corrected data on a need-to-update You may access and use these de-identified imaging datasets and annotations (“th aws-pds, radiology, medical imaging, medical image computing, machine learning, computer vision, csv, labeled, computed tomography, x-ray tomography https://mira.rsna.org/dataset/2 @@ -1001,11 +1001,11 @@ Reference Elevation Model of Antarctica (REMA) REMA DEM Mosaics arn:aws:s3:::pgc 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 +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 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 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 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 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 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)'] @@ -1014,32 +1014,32 @@ 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 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)'] +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 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) 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 ['[Browse Bucket](https://sea-ad-spatial-transcriptomics.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 ['[Browse Bucket](https://sea-ad-single-cell-profiling.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 ['[Browse Bucket](https://sea-ad-quantitative-neuropathology.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 ['[Browse Bucket](https://sea-ad-spatial-transcriptomics.s3.amazonaws.com/index.html)'] 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 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 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 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 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 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 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 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 @@ -1062,9 +1062,10 @@ Shopping Humor Generation Shopping humor generation dataset arn:aws:s3:::shoppin SiPeCaM (Sitios Permanentes de la Calibración y Monitoreo de la Biodiversidad) Audio, video and image representation of mexican fauna arn:aws:s3:::sipecam-open-data us-west-2 S3 Bucket https://github.com/CONABIO/sipecam-open-data/blob/main/docs/SiPeCaM.md [mschmidt@conabio.gob.mx](mschmidt@conabio.gob.mx) [Conabio](https://monitoreo.conabio.gob.mx/) SiPeCaM offers data releases once the biologist recollect the data from the prot Attribution 4.0 International (CC BY 4.0) aws-pds, biodiversity, biology, ecosystems, image processing, multimedia, wildlife Single-Cell Atlas of Human Blood During Healthy Aging Raw sequencing data (fastqgz), TCR/BCR clonotype tables (csv), normalized coun arn:aws:s3:::single.cell.human.blood.atlas.opendata.sagebase.org us-east-1 S3 Bucket https://www.synapse.org/#!Synapse:syn49637038/ martyomov@wustl.edu Sage Bionetworks Never [CC BY] aws-pds, protein, single-cell transcriptomics https://www.synapse.org/#!Synapse:syn49637038/ False Smithsonian Open Access Smithsonian Open Access Media and Metadata arn:aws:s3:::smithsonian-open-access us-west-2 S3 Bucket http://edan.si.edu/openaccess/docs/ openaccess@si.edu [SI](http://www.si.edu/) New / updated metadata and image files will be pushed weekly. CC0 aws-pds, art, history, culture, museum, encyclopedic +SocialGene RefSeq Databases SocialGene 2023_v041 Data and Database Dumps arn:aws:s3:::socialgene-open-data us-east-2 S3 Bucket https://socialgene.github.io/precomputed_databases/2023_v0.4.1/aws/aws https://github.com/socialgene/socialgene.github.io/issues University of Wisconsin-Madison This database is currently what was published in our 2024 paper introducing Soci Where applicable, SocialGene data is released under CC0 (https://creativecommons metagenomics, genomic, bioinformatics, microbiome, chemical biology, pharmaceutical, graph, protein, amino acid ['[Browse Bucket](https://socialgene-open-data.s3.amazonaws.com/)'] 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)'] -Software Heritage Graph Dataset S3 Inventory files arn:aws:s3:::softwareheritage-inventory us-east-1 S3 Bucket https://docs.softwareheritage.org/devel/swh-dataset/graph/athena.html aws@softwareheritage.org Software Heritage Data is updated yearly Creative Commons Attribution 4.0 International.By accessing the dataset, you agr aws-pds, source code, open source software, free software, digital preservation Software Heritage Graph Dataset Software Heritage Graph Dataset arn:aws:s3:::softwareheritage us-east-1 S3 Bucket https://docs.softwareheritage.org/devel/swh-dataset/graph/athena.html aws@softwareheritage.org Software Heritage Data is updated yearly Creative Commons Attribution 4.0 International.By accessing the dataset, you agr aws-pds, source code, open source software, free software, digital preservation +Software Heritage Graph Dataset S3 Inventory files arn:aws:s3:::softwareheritage-inventory us-east-1 S3 Bucket https://docs.softwareheritage.org/devel/swh-dataset/graph/athena.html aws@softwareheritage.org Software Heritage Data is updated yearly Creative Commons Attribution 4.0 International.By accessing the dataset, you agr aws-pds, source code, open source software, free software, digital preservation Solar Dynamics Observatory (SDO) Machine Learning Dataset The v1 dataset includes AIA observations 2010-2018 and v2 includes AIA observati arn:aws:s3:::gov-nasa-hdrl-data1/contrib/fdl-sdoml/ us-west-2 S3 Bucket https://github.com/SDOML/sdoml.github.io Meng Jin (jinmeng@lmsal.com) and Paul Wright (paul@pauljwright.co.uk) [NASA](http://www.nasa.gov/) N/A (The IDL/Python scripts for generating the datasets are published online, wh There are no restrictions on the use of this data. aws-pds, machine learning, NASA SMD AI 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/)'] Sophos/ReversingLabs 20 Million malware detection dataset Sophos/ReversingLabs 20 million sample dataset arn:aws:s3:::sorel-20m/ us-west-2 S3 Bucket https://github.com/sophos-ai/SOREL-20M/blob/master/README.md sorel-dataset@sophos.com Sophos AI At most annually See the [Terms of Use](https://github.com/sophos-ai/SOREL-20M/blob/master/Terms% aws-pds, cyber security, deep learning, labeled, machine learning @@ -1079,8 +1080,8 @@ Sub-Meter Canopy Tree Height of California in 2020 by CTrees.org Cloud-optimized 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 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 - 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 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 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)'] +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)'] 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 @@ -1090,11 +1091,11 @@ 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 - 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)'] +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 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 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 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 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 @@ -1102,8 +1103,8 @@ The Human Sleep Project The Human Sleep Project (HSP) sleep physiology dataset i The Klarna Product-Page Dataset Bucket containing the two datasets (one in the MHTML and one in the WTL snapshot arn:aws:s3:::klarna-research-public-datasets/ eu-west-1 S3 Bucket https://github.com/klarna/product-page-dataset https://github.com/klarna/product-page-dataset/issues, stefan.magureanu@klarna.c Web Automation Research, Klarna The dataset is not expected to update frequently. CC BY-NC-SA aws-pds, internet, natural language processing, computer vision, commerce, deep learning, machine learning, information retrieval, graph The MIT Supercloud Dataset The MIT Supercloud Dataset arn:aws:s3:::mit-supercloud-dataset us-west-2 S3 Bucket https://github.com/MIT-AI-Accelerator/datacenter-challenge mit-dcc@mit.edu Siddharth Samsi Data will be updated annually http://creativecommons.org/licenses/by-nc-nd/4.0/ datacenter, HPC, cloud computing, workload analysis, energy, aws-pds The Massively Multilingual Image Dataset (MMID) Images for words in various languages, packaged by in tar archives by each lang arn:aws:s3:::mmid-pds us-east-1 S3 Bucket https://multilingual-images.org/doc.html mmid-users@googlegroups.com [Penn NLP](https://github.com/penn-nlp) Language data is added as it is ready for distribution. See citation instructions at http://multilingual-images.org aws-pds, computer vision, machine learning, machine translation, natural language processing -The Singapore Nanopore Expression Data Set Nanopore long read RNA Seq data from the Singapore Nanopore Expression Project ( arn:aws:s3:::sg-nex-data-blow5 ap-southeast-1 S3 Bucket https://github.com/GoekeLab/sg-nex-data [SG-NEx team](https://github.com/GoekeLab/sg-nex-data) The Genome Institute of Singapore (https://www.a-star.edu.sg/gis) Datasets will be updated periodically as additional data are generated. [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) aws-pds, genomic, transcriptomics, life sciences, long read sequencing, short read sequencing, bioinformatics, fast5, fasta, fastq, bam ['[Browse Bucket](http://sg-nex-data-blow5.s3-website-ap-southeast-1.amazonaws.com/)'] The Singapore Nanopore Expression Data Set Nanopore long read RNA Seq data and matched short read RNA-Seq from the Singapor arn:aws:s3:::sg-nex-data ap-southeast-1 S3 Bucket https://github.com/GoekeLab/sg-nex-data [SG-NEx team](https://github.com/GoekeLab/sg-nex-data) The Genome Institute of Singapore (https://www.a-star.edu.sg/gis) Datasets will be updated periodically as additional data are generated. [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) aws-pds, genomic, transcriptomics, life sciences, long read sequencing, short read sequencing, bioinformatics, fast5, fasta, fastq, bam ['[Browse Bucket](http://sg-nex-data.s3-website-ap-southeast-1.amazonaws.com/)'] +The Singapore Nanopore Expression Data Set Nanopore long read RNA Seq data from the Singapore Nanopore Expression Project ( arn:aws:s3:::sg-nex-data-blow5 ap-southeast-1 S3 Bucket https://github.com/GoekeLab/sg-nex-data [SG-NEx team](https://github.com/GoekeLab/sg-nex-data) The Genome Institute of Singapore (https://www.a-star.edu.sg/gis) Datasets will be updated periodically as additional data are generated. [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) aws-pds, genomic, transcriptomics, life sciences, long read sequencing, short read sequencing, bioinformatics, fast5, fasta, fastq, bam ['[Browse Bucket](http://sg-nex-data-blow5.s3-website-ap-southeast-1.amazonaws.com/)'] The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset Zip archive containing NifTI files arn:aws:s3:::ucsf-dmi/UCSF_BrainMetastases_v1.zip us-west-1 S3 Bucket https://imagingdatasets.ucsf.edu/dataset/1 dmi-support@ucsf.edu [UCSF Center for Intelligent Imaging](https://intelligentimaging.ucsf.edu/) ad hoc Custom, non-commerical, attribution, no redistribution, no re-identification. F aws-pds, cancer, life sciences, magnetic resonance imaging, medicine, medical imaging, radiology https://imagingdatasets.ucsf.edu/dataset/1 Therapeutically Applicable Research to Generate Effective Treatments (TARGET) Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantificat arn:aws:s3:::gdc-target-phs000218-2-open us-east-1 S3 Bucket https://ocg.cancer.gov/programs/target/ 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, whole genome sequencing, STRIDES 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 @@ -1116,40 +1117,40 @@ UK Biobank Linkage Disequilibrium Matrices Linkage disequilibrium (LD) matrices UK Biobank Pan-Ancestry Summary Statistics Summary statistics from Genome Wide Association Studies (GWASes) of multiple anc arn:aws:s3:::pan-ukb-us-east-1 us-east-1 S3 Bucket https://pan.ukbb.broadinstitute.org ukb.diverse.gwas@gmail.com Analytic and Translational Genetics Unit, Massachusetts General Hospital and the Occasional "CC BY-4.0 (usage may be restricted by UK Biobank, more details on the ""[Download" aws-pds, genetic, genome wide association study, genomic, life sciences, population genetics UK Biobank Pharma Proteomics Project (UKB-PPP) Population-specific GWAS summary statistics per protein measurement, as well as arn:aws:s3:::ukbiobank.opendata.sagebase.org us-east-1 S3 Bucket https://www.synapse.org/#!Synapse:syn51364943/ matthias.arnold@helmholtz-munich.de Sage Bionetworks Never [CC BY] aws-pds, genome wide association study, population genetics https://doi.org/10.7303/syn51364943 False 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)'] -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 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, 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 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 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 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 Unblurred Coadds of the Wide-field Infrared Survey Explorer (unWISE) The unWISE Time-Domain Catalog is based on 'time-resolved' coadds, each of which arn:aws:s3:::nasa-irsa-wise/unwise/ us-west-2 S3 Bucket https://irsa.ipac.caltech.edu/data/WISE/unWISE/overview.html https://irsa.ipac.caltech.edu/docs/help_desk.html NASA/IPAC Infrared Science Archive ([IRSA](https://irsa.ipac.caltech.edu)) at Ca The unWISE dataset is updated periodically to include new data released by NEOWI https://irsa.ipac.caltech.edu/data_use_terms.html aws-pds, astronomy, object detection, parquet, survey False False -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 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 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_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 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 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_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_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 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 2024_05 arn:aws:s3:::aws-open-data-uniprot-rdf/2024-05/ 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_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 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 2024_02 arn:aws:s3:::aws-open-data-uniprot-rdf/2024-02/ 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_03 arn:aws:s3:::aws-open-data-uniprot-rdf/2024-03/ 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 2024_05 arn:aws:s3:::aws-open-data-uniprot-rdf/2024-05/ 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_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 2024_03 arn:aws:s3:::aws-open-data-uniprot-rdf/2024-03/ 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 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 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 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 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 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 @@ -1161,32 +1162,32 @@ Voices Obscured in Complex Environmental Settings (VOiCES) wav audio files, orth 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)'] 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)'] -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 Xiph.Org Test Media Video and imagery data arn:aws:s3:::xiph-media us-east-1 S3 Bucket https://media.xiph.org/aws.html Thomas Daede tdaede@xiph.org [Xiph.org](https://xiph.org/) New videos are added when contributors submit them. Various. Most are under the CC-BY license. License text accompanies each sequenc aws-pds, computer vision, image processing, imaging, media, movies, multimedia, video Yale-CMU-Berkeley (YCB) Object and Model Set Project data files arn:aws:s3:::ycb-benchmarks us-east-1 S3 Bucket http://www.ycbbenchmarks.com/ bcalli@wpi.edu Yale University and Berkeley Yearly Creative Commons Attribution 4.0 International (CC BY 4.0) aws-pds, robotics ['[Browse Bucket](https://ycb-benchmarks.s3.amazonaws.com/index.html)'] -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 YouTube 8 Million - Data Lakehouse Ready Lakehouse ready YT8M as Glue Parquet files Install instructions here arn:aws:s3:::aws-roda-ml-datalake/yt8m_ods/ us-west-2 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 YouTube 8 Million - Data Lakehouse Ready Original YT8M *tfrecords File structure info can be found here arn:aws:s3:::aws-roda-ml-datalake/yt8m/ us-west-2 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 +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 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 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 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 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 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 SnowEx17 Ground Penetrating Radar, Version 2 data set, while the lidar snow depths were sourced from ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1. Data are available between 08 Feb 2017 to 25 Feb 2017 from Grand Mesa, a snow-covered, forested area about 40 miles east of Grand Junction. Parameters include two-way travel (TWT) time, lidar-measured snow depth, calculated snow water equivalent (SWE), calculated snow density, and calculated relative permittivity." proprietary SNEX17_SMP2_1 SnowEx17 Senator Beck SnowMicroPen (SMP) Raw Penetration Force Profiles V001 NSIDC_ECS STAC Catalog 2017-02-06 2017-02-20 -107.752128, 37.856078, -107.6725, 37.940572 https://cmr.earthdata.nasa.gov/search/concepts/C1720458466-NSIDC_ECS.umm_json This data set consists of raw penetration force profiles measured at 8 different snow pits located in Senator Beck, Colorado using the SnowMicroPen (SMP), a digital snow penetrometer. The data files contain force measurements (in Newtons) at various snow depths. proprietary SNEX17_SMP_1 SnowEx17 SnowMicroPen (SMP) Raw Penetration Force Profiles V001 NSIDC_ECS STAC Catalog 2017-02-07 2017-02-25 -108.2291924, 37.856078, -107.6725, 39.02768 https://cmr.earthdata.nasa.gov/search/concepts/C1423026967-NSIDC_ECS.umm_json This data set contains raw penetration force profiles measured at snow pits and along linear transects at Grand Mesa, Colorado using the SnowMicroPen (SMP), a digital snow penetrometer. The data files contain force measurements (in Newtons) at various snow depths. proprietary @@ -15102,13 +15103,21 @@ VJ130P1N_2 VIIRS/JPSS1 Ice Surface Temperature Daily L3 Global 750m EASE-Grid 2. VJ130_2 VIIRS/JPSS1 Ice Surface Temperature 6-Min L2 Swath 750m V002 NSIDC_ECS STAC Catalog 2018-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2317031993-NSIDC_ECS.umm_json This data set reports sea ice surface temperature (IST) derived from radiance data acquired by the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Joint Polar Satellite System's first satellite (JPSS-1). Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique. proprietary VJ130_NRT_2 VIIRS/JPSS1 Ice Surface Temperature 6-Min L2 Swath 750m NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2781422352-LANCEMODIS.umm_json The Visible Infrared Imager Radiometer Suite (VIIRS) Ice Surface Temperature 6-Min L2 Swath 750m is Near Real Time(NRT) (short name VJ130_NRT) product provides surface temperatures retrieved at VIIRS moderate resolution for Arctic and Antarctic Sea Ice, for both day and night. Following the approach used by MODIS, the algorithm converts VIIRS calibrated radiances into brightness temperature and computes IST using a split-window technique. VIIRS flies on board the NOAA-20 - formerly the Joint Polar Satellite System-1 (JPSS-1) satellite. proprietary VJ143IA1N_2 VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 500 m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808108740-LANCEMODIS.umm_json The VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA1N product provides BRDF/Albedo model parameters at 500 meter (m) resolution. The VJ143IA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VJ143IA1N data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. proprietary +VJ143IA1_002 VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310914-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VJ143IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VJ143IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth’s surface. All VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA1 data product provides a total of six SDS layers including: three mandatory quality layers for bands I1, I2, and I3 and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for each imagery band. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary VJ143IA2N_2 VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 500 m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808108845-LANCEMODIS.umm_json The VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA2N product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days to produce 16-day product). The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VJ143IA2N data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name. proprietary +VJ143IA2_002 VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310918-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VJ143IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary VJ143IA3N_2 VIIRS/JPSS1 Albedo Daily L3 Global 500m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808098739-LANCEMODIS.umm_json The VIIRS/JPSS1 Albedo Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA3N product provides albedo values at 500 m resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43IA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VNP43IA3N product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. proprietary +VJ143IA3_002 VIIRS/JPSS1 BRDF/Albedo Albedo Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310922-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary VJ143IA4N_2 VIIRS/JPSS1 Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808108867-LANCEMODIS.umm_json The VIIRS/JPSS1 Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VJ143IA4N product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) product. The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VJ143IA4N product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. proprietary +VJ143IA4_002 VIIRS/JPSS1 BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310926-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VJ143IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary VJ143MA1N_2 VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808131488-LANCEMODIS.umm_json The VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA1N product provides BRDF/Albedo model parameters at 1 km resolution. The VJ143MA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VJ143MA1N data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. proprietary +VJ143MA1_002 VIIRS/JPSS1 BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310930-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VJ143MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VJ143MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M7, and M5 as an RGB image in JPEG format. proprietary VJ143MA2N_2 VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808131412-LANCEMODIS.umm_json The VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA2N product provides BRDF and Albedo quality at 1 km resolution. The VNP43MA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VJ143MA2N data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name. proprietary +VJ143MA2_002 VIIRS/JPSS1 BRDF/Albedo Quality Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310934-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VJ143MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VJ143MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11; as well as land water type class flag; snow BRDF albedo class flag; local solar noon; albedo uncertainty and the platform name. proprietary VJ143MA3N_2 VIIRS/JPSS1 Albedo Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808131352-LANCEMODIS.umm_json The VIIRS/JPSS1 Albedo Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA3N product provides albedo values at 1 km resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VJ143MA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VJ143MA3N product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave infrared (SWIR), and visible (VIS). proprietary +VJ143MA3_002 VIIRS/JPSS1 BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310938-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VJ143MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VJ143MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VJ143MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA3 product provides a total of 36 SDS layers including: BSA; WSA; and mandatory quality layers for nine VIIRS moderate bands M1-M5, M7-M8, and M10-M11; as well as near-infrared (NIR); shortwave; and visible broadbands. A low-resolution image is also available showing retrievals of WSA for band M1 in JPEG format. proprietary VJ143MA4N_2 VIIRS/JPSS1 Nadir BRDF-Adjusted Reflectance Daily L3 Global 1 km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2808131137-LANCEMODIS.umm_json The VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VJ143MA4N product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143MA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VJ143MA4N product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. proprietary +VJ143MA4_002 VIIRS/JPSS1 BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545310943-LPCLOUD.umm_json The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VJ143IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VJ143 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ143 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VJ143 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VJ143MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VJ143MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VJ143MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VJ143MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. proprietary VJ146A1G_NRT_2 VIIRS/JPSS1 Granular Gridded Day Night Band 500m Linear Lat Lon Grid Night NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2781431577-LANCEMODIS.umm_json The Near Real Time (NRT) NOAA-20 - formerly the Joint Polar Satellite System-1 (JPSS-1) Visible Infrared Imaging Radiometer Suite (VIIRS) hourly top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Granular Gridded Day Night Band 500m Linear Lat Lon Grid Night. Known by its short-name, VJ146A1G_NRT, is same as VJ146A1_NRT except that this product is generated hourly, cumulative from start of day through the hour the file is generated for. This product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary VJ146A1_NRT_2 VIIRS/JPSS1 Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2781438623-LANCEMODIS.umm_json The first of two Visible Infrared Imager Radiometer Suite (VIIRS) Day Night Band (DNB) based Near Real Time (NRT) datasets is a daily, top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/JPSS1 Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night NRT. Known by its short-name, VJ146A1_NRT, this product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary VJ201_NRT_2 VIIRS/JPSS2 Raw Radiances in Counts 6-Min L1A Swath NRT LANCEMODIS STAC Catalog 2024-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2837614569-LANCEMODIS.umm_json The Near Real Time (NRT) VIIRS/JPSS2 Raw Radiances in Counts 6-Min L1A Swath, short-name VJ201_NRT data 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. For more information download VIIRS Level 1 Product User's Guide at: https://ladsweb.modaps.eosdis.nasa.gov/archive/Document%20Archive/Science%20Data%20Product%20Documentation/NASA_VIIRS_L1B_UG_August_2021.pdf proprietary @@ -15317,20 +15326,28 @@ VNP43DNBA3_001 VIIRS/NPP DNB BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V00 VNP43DNBA4_001 VIIRS/NPP DNB BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1632561835-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43DNB4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43DNBA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43DNBA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43DNBA3). Researchers can use the BRDF model parameters with a simple polynomial to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43DNBA4 product includes BRDF/Albedo mandatory quality and nadir reflectance for the VIIRS DNB. A low-resolution browse image is also available showing NBAR of the DNB as a red, green, blue (RGB) image in JPEG format. proprietary VNP43IA1N_2 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807589174-LANCEMODIS.umm_json The VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VNP43IA1N product provides BRDF/Albedo model parameters at 500 meter (m) resolution. The VNP43IA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VNP43IA1N data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. proprietary VNP43IA1_001 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1407099489-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 1 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth’s surface. All VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43IA1 data product provides a total of six SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo of the VIIRS imagery bands: I1, I2, and I3. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary +VNP43IA1_002 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314578-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43IA1) Version 2 product provides kernel weights (parameters) at 500 meter (m) resolution. The VNP43IA1 product is produced daily using 16 days of VIIRS data, temporally weighted to the ninth day, which is reflected in the file name. The VNP43IA1 product provides three spectrally dependent kernel weights, also known as model parameters: isotropic (fiso), volumetric (fvol), and geometric (fgeo), which can be used to model anisotropic effects of the Earth’s surface. All VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA1 data product provides a total of six SDS layers including: three mandatory quality layers for bands I1, I2, and I3 and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for each imagery band. Each data product file is provided in HDF-EOS5 format. A low-resolution browse is also available showing BRDF/Albedo parameters for VIIRS imagery bands: I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary VNP43IA2N_2 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 500m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807586151-LANCEMODIS.umm_json The VIIRS/JPSS1 Level 3 16-Day BRDF/Albedo - 500m Near Real Time (NRT), short-name VNP43IA2N product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days to produce 16-day product). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VNP43IA2N data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name. proprietary VNP43IA2_001 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1412449611-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 1 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA2.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary +VNP43IA2_002 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314582-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43IA2) Version 2 product provides BRDF and Albedo quality at 500 meter (m) resolution. The VNP43IA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43IA2 product provides information regarding band quality and days of valid observation within a 16-day period for the VIIRS imagery bands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA2 data product provides a total of 11 SDS layers including: BRDF/Albedo band quality (inversion information) and days of valid observation within a 16-day period for VIIRS imagery bands I1, I2, and I3, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary VNP43IA3N_2 VIIRS/NPP Albedo Daily L3 Global 500m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807588708-LANCEMODIS.umm_json The VIIRS/NPP Albedo Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VNP43IA3N product provides albedo values at 500 m resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43IA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VNP43IA3N product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. proprietary VNP43IA3_001 VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1412449608-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 1 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4) (https://doi.org/10.5067/VIIRS/VNP43IA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands: I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary +VNP43IA3_002 VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314588-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43IA3) Version 2 product provides albedo values at 500 meter (m) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43IA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA3 product provides a total of 9 SDS layers including: BSA, WSA, and mandatory quality layers for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary VNP43IA4N_2 VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 500m SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807591231-LANCEMODIS.umm_json The VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid Near Real Time (NRT), short-name VNP43IA4N product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) product. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43IA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VNP43IA4N product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. proprietary VNP43IA4_001 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1407099497-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) (https://doi.org/10.5067/VIIRS/VNP43IA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3) (https://doi.org/10.5067/VIIRS/VNP43IA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary +VNP43IA4_002 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 500m SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314592-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 500 meter (m) resolution. The VNP43IA4 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43IA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43IA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43IA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43IA4 product includes six SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS imagery bands I1, I2, and I3. A low-resolution browse image is also available showing NBAR bands I1, I2, and I1 as an RGB (red, green, blue) image in JPEG format. proprietary VNP43MA1N_2 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807604939-LANCEMODIS.umm_json The VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA1N product provides BRDF/Albedo model parameters at 1 km resolution. The VNP43MA1N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VNP43MA1N data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. proprietary VNP43MA1_001 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1412449609-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 1 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11. A low resolution browse is also provided showing BRDF/Albedo parameters for bands I1, I2, I1 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status. proprietary +VNP43MA1_002 VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314596-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Model Parameters (VNP43MA1) Version 2 product provides BRDF/Albedo model parameters at 1 kilometer (km) resolution. The VNP43MA1 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA1 data product provides a total of 24 SDS layers including: mandatory quality bands and three multi-dimensional model parameter bands representing fiso, fvol, and fgeo for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11. A low-resolution browse is also provided showing BRDF/Albedo parameters for bands M5, M7, and M5 as an RGB image in JPEG format. proprietary VNP43MA2N_2 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807623096-LANCEMODIS.umm_json The VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA2N product provides BRDF and Albedo quality at 1 km resolution. The VNP43MA2N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VNP43MA2N data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, and the platform name. proprietary VNP43MA2_001 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1412449612-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 1 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) (https://doi.org/10.5067/VIIRS/VNP43MA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3) (https://doi.org/10.5067/VIIRS/VNP43MA3.001). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands: M1-M5, M7-M8, and M10-M11, as well as land water type class flag, snow BRDF albedo class flag, local solar noon, albedo uncertainty and the platform name. proprietary +VNP43MA2_002 VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314601-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo Quality (VNP43MA2) Version 2 product provides BRDF and Albedo quality at 1 kilometer (km) resolution. The VNP43MA2 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43MA2 product gives information regarding band quality and days of valid observation within a 16-day period for nine VIIRS moderate resolution bands and three broadbands. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA2 data product provides a total of 23 SDS layers including: BRDF/Albedo band quality and days of valid observation within a 16-day period for VIIRS moderate resolution bands M1-M5, M7-M8, and M10-M11; as well as land water type class flag; snow BRDF albedo class flag; local solar noon; albedo uncertainty and the platform name. proprietary VNP43MA3N_2 VIIRS/NPP Albedo Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807625522-LANCEMODIS.umm_json The VIIRS/NPP Albedo Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA3N product provides albedo values at 1 km resolution for the bi-hemispherical reflectance white-sky albedos (WSA) and directional hemispherical reflectance black-sky albedos (BSA) at local solar noon. The VNP43MA3N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf The VNP43MA3N product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave infrared (SWIR), and visible (VIS). proprietary VNP43MA3_001 VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1407099488-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 1 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) (https://doi.org/10.5067/VIIRS/VNP43MA1.001) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4) (https://doi.org/10.5067/VIIRS/VNP43MA4.001), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) (https://lpdaac.usgs.gov/documents/194/VNP43_ATBD_V1.pdf). The VNP43MA3 product provides a total of 36 SDS layers including: BSA, WSA, and mandatory quality layers for nine VIIRS moderate bands: M1-M5, M7-M8, and M10-M11, as well as three broadbands: near-infrared (NIR), shortwave, and visible. A low-resolution image is also available showing retrievals of WSA for band I1 in JPEG format. proprietary +VNP43MA3_002 VIIRS/NPP BRDF/Albedo Albedo Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314605-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Bidirectional Reflectance Distribution Function (BRDF) and Albedo (VNP43MA3) Version 2 product provides albedo values at 1 kilometer (km) resolution for the bihemispherical reflectance white-sky albedo (WSA) and directional hemispherical reflectance black-sky albedo (BSA) at local solar noon. The VNP43MA3 product is produced daily using 16 days of VIIRS data and is weighted temporally to the ninth day, which is reflected in the file name. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from (VNP43MA1) to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA3 product provides a total of 36 SDS layers including: BSA; WSA; and mandatory quality layers for nine VIIRS moderate bands M1-M5, M7-M8, and M10-M11; as well as near-infrared (NIR); shortwave; and visible broadbands. A low-resolution image is also available showing retrievals of WSA for band M1 in JPEG format. proprietary VNP43MA4N_2 VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2023-11-20 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2807627777-LANCEMODIS.umm_json The VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 1 km SIN Grid Near Real Time (NRT), short-name VNP43MA4N product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43MA4N product is produced daily using 16-day VIIRS data (i.e., the current day and the previous 15 days). The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1N to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4N), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3N). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD) at https://www.umb.edu/editor_uploads/images/school_for_the_environment_cs/Viirs/VIIRS_ATBD_Apr_Jul2017.pdf. The VNP43MA4N product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. proprietary VNP43MA4_001 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1412449610-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 1 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. Product Maturity Validation at stage 1 has been achieved for the VIIRS BRDF/Albedo product suite. Visit the VIIRS Land Product Quality Assessment website for additional information on validation and product maturity status. proprietary +VNP43MA4_002 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314608-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. proprietary VNP46A1G_NRT_2 VIIRS/NPP Granular Gridded Day Night Band 500m Linear Lat. Lon. Grid Night NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2780764136-LANCEMODIS.umm_json The Near Real Time (NRT) Suomi National Polar-Orbiting Partnership (S-NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) hourly top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Granular Gridded Day Night Band 500m Linear Lat Lon Grid Night. Known by its short-name, VNP46A1G_NRT, is same as VNP46A1_NRT except that this product is generated hourly, cumulative from start of day through the hour the file is generated for. This product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary VNP46A1_1 VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1897815356-LAADS.umm_json The first of two VIIRS DNB-based datasets is a daily, top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Daily Gridded Day Night Band 15 arc-second Linear Lat Lon Grid Night. Known by its short-name, VNP46A1, this product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary VNP46A1_2 VIIRS/NPP Daily Gridded Day Night Band 500 m Linear Lat Lon Grid Night LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2980666614-LAADS.umm_json The VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night product, short-name VNP46A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product. This product is available at 15 arc-second spatial resolution from January 2012 onward. The VNP46A1/VJ146A1 product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary