diff --git a/.coveragerc b/.coveragerc
index fb5ff5e650..2ac706c0b3 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -5,11 +5,3 @@ omit =
     versioneer.py
     setup.py
     act/plotting/histogramdisplay.py
-    act/discovery/get_arm.py
-    act/discovery/arm.py
-    act/discovery/airnow.py
-    act/discovery/asos.py
-    act/discovery/cropscape.py
-    act/discovery/neon.py
-    act/discovery/noaapsl.py
-    act/discovery/surfrad.py
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index 9f8b0b3b90..b06bfb4150 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -278,6 +278,18 @@ the tools documentation for details on this process.
 - https://flake8.pycqa.org/en/latest/
 - https://www.pylint.org/
 
+Naming Convenction
+----------------------------------------
+
+Discovery
+~~~~~~~~~
+When adding discovery modules or functions please adhere to the following
+* Filenames should just include the name of the organization (arm) or portal (airnow) and no other filler words like get or download
+* Functions should follow [get/download]_[org/portal]_[data/other description].  If it is getting data but not downloading a file, it should start with get, like get_asos_data. If it downloads a file, it should start with download.  The other description can vary depending on what you are retrieving.  Please check out the existing functions for ideas.
+
+Discovery
+~~~~~~~~~
+Similarly, for the io modules, the names should not have filler and just be the organization or portal name.  The functions should clearly indicate what it is doing like read_arm_netcdf instead of read_netcdf if the function is specific to ARM files.
 
 Adding Secrets and Environment Variables
 ----------------------------------------
diff --git a/act/discovery/__init__.py b/act/discovery/__init__.py
index 6bc9066d4c..c0a5fab10d 100644
--- a/act/discovery/__init__.py
+++ b/act/discovery/__init__.py
@@ -8,15 +8,14 @@
 
 __getattr__, __dir__, __all__ = lazy.attach(
     __name__,
-    submodules=['get_armfiles', 'get_cropscape', 'get_airnow', 'get_noaapsl', 'get_neon', 'get_surfrad'],
+    submodules=['arm', 'cropscape', 'airnow', 'noaapsl', 'neon', 'surfrad'],
     submod_attrs={
-        'get_arm': ['download_arm_data'],
-        'get_armfiles': ['download_data', 'download_arm_data', 'get_arm_doi'],
-        'get_asos': ['get_asos'],
-        'get_airnow': ['get_airnow_bounded_obs', 'get_airnow_obs', 'get_airnow_forecast'],
-        'get_cropscape': ['croptype'],
-        'get_noaapsl': ['download_noaa_psl_data'],
-        'get_neon': ['get_site_products', 'get_product_avail', 'download_neon_data'],
-        'get_surfrad': ['download_surfrad']
+        'arm': ['download_arm_data', 'get_arm_doi'],
+        'asos': ['get_asos_data'],
+        'airnow': ['get_airnow_bounded_obs', 'get_airnow_obs', 'get_airnow_forecast'],
+        'cropscape': ['get_crop_type'],
+        'noaapsl': ['download_noaa_psl_data'],
+        'neon': ['get_neon_site_products', 'get_neon_product_avail', 'download_neon_data'],
+        'surfrad': ['download_surfrad_data']
     },
 )
diff --git a/act/discovery/get_airnow.py b/act/discovery/get_airnow.py
deleted file mode 100644
index b8b8937a8b..0000000000
--- a/act/discovery/get_airnow.py
+++ /dev/null
@@ -1,261 +0,0 @@
-
-"""
-Function for getting EPA data from AirNow data portal
-
-"""
-
-import pandas as pd
-import numpy as np
-import xarray as xr
-import warnings
-
-
-def get_airnow_forecast(token, date, zipcode=None, latlon=None, distance=25):
-    """
-    This tool will get current or historical AQI values and categories for a
-    reporting area by either Zip code or Lat/Lon coordinate.
-    https://docs.airnowapi.org/
-
-    Parameters
-    ----------
-    token : str
-        The access token for accesing the AirNowAPI web server
-    date : str
-        The date of the data to be acquired. Format is YYYY-MM-DD
-    zipcode : str
-        The zipcode of the location for the data request.
-        If zipcode is not defined then a latlon coordinate must be defined.
-    latlon : array
-        The latlon coordinate of the loaction for the data request.
-        If latlon is not defined then a zipcode must be defined.
-    distance : int
-        If no reporting are is associated with the specified zipcode or latlon,
-        return a forcast from a nearby reporting area with this distance (in miles).
-        Default is 25 miles
-
-    Returns
-    -------
-    ds : xarray.Dataset
-        Returns an Xarray dataset object
-
-    Example
-    -------
-    act.discovery.get_AirNow_forecast(token='XXXXXX', zipcode='60440', date='2012-05-31')
-
-    """
-    message = 'API will be changing from act.discovery.get_airnow to act.discovery.airnow'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    # default beginning of the query url
-    query_url = ('https://airnowapi.org/aq/forecast/')
-
-    # checking is either a zipcode or latlon coordinate is defined
-    # if neither is defined then error is raised
-    if (zipcode is None) and (latlon is None):
-        raise NameError("Zipcode or latlon must be defined")
-
-    if zipcode:
-        url = (query_url + ('zipcode/?' + 'format=text/csv' + '&zipCode='
-                            + str(zipcode) + '&date=' + str(date)
-                            + '&distance=' + str(distance)
-                            + '&API_KEY=' + str(token)))
-
-    if latlon:
-        url = (query_url + ('latLong/?' + 'format=text/csv'
-                            + '&latitude=' + str(latlon[0]) + '&longitude='
-                            + str(latlon[1]) + '&date=' + str(date)
-                            + '&distance=' + str(distance)
-                            + '&API_KEY=' + str(token)))
-
-    df = pd.read_csv(url)
-
-    # converting to xarray dataset object
-    ds = df.to_xarray()
-
-    return ds
-
-
-def get_airnow_obs(token, date=None, zipcode=None, latlon=None, distance=25):
-    """
-    This tool will get current or historical observed AQI values and categories for a
-    reporting area by either Zip code or Lat/Lon coordinate.
-    https://docs.airnowapi.org/
-
-    Parameters
-    ----------
-    token : str
-        The access token for accesing the AirNowAPI web server
-    date : str
-        The date of the data to be acquired. Format is YYYY-MM-DD
-        Default is None which will pull most recent observations
-    zipcode : str
-        The zipcode of the location for the data request.
-        If zipcode is not defined then a latlon coordinate must be defined.
-    latlon : array
-        The latlon coordinate of the loaction for the data request.
-        If latlon is not defined then a zipcode must be defined.
-    distance : int
-        If no reporting are is associated with the specified zipcode or latlon,
-        return a forcast from a nearby reporting area with this distance (in miles).
-        Default is 25 miles
-
-    Returns
-    -------
-    ds : xarray.Dataset
-        Returns an xarray dataset object
-
-    Example
-    -------
-    act.discovery.get_AirNow_obs(token='XXXXXX', date='2021-12-01', zipcode='60440')
-    act.discovery.get_AirNow_obs(token='XXXXXX', latlon=[45,-87])
-
-    """
-
-    message = 'API will be changing from act.discovery.get_airnow to act.discovery.airnow'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    # default beginning of the query url
-    query_url = ('https://www.airnowapi.org/aq/observation/')
-
-    # checking is either a zipcode or latlon coordinate is defined
-    # if neither is defined then error is raised
-    if (zipcode is None) and (latlon is None):
-        raise NameError("Zipcode or latlon must be defined")
-
-    # setting the observation type to either current or historical based on the date
-    if date is None:
-        obs_type = 'current'
-        if zipcode:
-            url = (query_url + ('zipCode/' + str(obs_type) + '/?' + 'format=text/csv'
-                                + '&zipCode=' + str(zipcode) + '&distance=' + str(distance)
-                                + '&API_KEY=' + str(token)))
-        if latlon:
-            url = (query_url + ('latLong/' + str(obs_type) + '/?' + 'format=text/csv'
-                                + '&latitude=' + str(latlon[0])
-                                + '&longitude=' + str(latlon[1]) + '&distance='
-                                + str(distance) + '&API_KEY=' + str(token)))
-    else:
-        obs_type = 'historical'
-        if zipcode:
-            url = (query_url + ('zipCode/' + str(obs_type) + '/?' + 'format=text/csv'
-                                + '&zipCode=' + str(zipcode) + '&date=' + str(date)
-                                + 'T00-0000&distance=' + str(distance) + '&API_KEY=' + str(token)))
-        if latlon:
-            url = (query_url + ('latLong/' + str(obs_type) + '/?' + 'format=text/csv'
-                                + '&latitude=' + str(latlon[0])
-                                + '&longitude=' + str(latlon[1]) + '&date='
-                                + str(date) + 'T00-0000&distance=' + str(distance)
-                                + '&API_KEY=' + str(token)))
-
-    df = pd.read_csv(url)
-
-    # converting to xarray
-    ds = df.to_xarray()
-
-    return ds
-
-
-def get_airnow_bounded_obs(token, start_date, end_date, latlon_bnds, parameters='OZONE,PM25', data_type='B',
-                           mon_type=0):
-    """
-    Get AQI values or data concentrations for a specific date and time range and set of
-    parameters within a geographic area of intrest
-    https://docs.airnowapi.org/
-
-    Parameters
-    ----------
-    token : str
-        The access token for accesing the AirNowAPI web server
-    start_date : str
-        The start date and hour (in UTC) of the data request.
-        Format is YYYY-MM-DDTHH
-    end_date : str
-        The end date and hour (in UTC) of the data request.
-        Format is YYYY-MM-DDTHH
-    latlon_bnds : str
-        Lat/Lon bounding box of the area of intrest.
-        Format is 'minX,minY,maxX,maxY'
-    parameters : str
-        Parameters to return data for. Options are:
-        Ozone, PM25, PM10, CO, NO2, SO2
-        Format is 'PM25,PM10'
-    mon_type : int
-        The type of monitor to be returned. Default is 0
-        0-Permanent, 1-Mobile onlt, 2-Permanent & Mobile
-    data_type : char
-        The type of data to be returned.
-        A-AQI, C-Concentrations, B-AQI & Concentrations
-
-    Returns
-    -------
-    ds : xarray.Dataset
-        Returns an xarray dataset object
-
-    """
-
-    message = 'API will be changing from act.discovery.get_airnow to act.discovery.airnow'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    verbose = 1
-    inc_raw_con = 1
-
-    url = ('https://www.airnowapi.org/aq/data/?startDate=' + str(start_date)
-           + '&endDate=' + str(end_date) + '&parameters=' + str(parameters)
-           + '&BBOX=' + str(latlon_bnds) + '&dataType=' + str(data_type)
-           + '&format=text/csv' + '&verbose=' + str(verbose)
-           + '&monitorType=' + str(mon_type) + '&includerawconcentrations='
-           + str(inc_raw_con) + '&API_KEY=' + str(token))
-
-    # Set Column names
-    names = ['latitude', 'longitude', 'time', 'parameter', 'concentration', 'unit',
-             'raw_concentration', 'AQI', 'category', 'site_name', 'site_agency', 'aqs_id', 'full_aqs_id']
-
-    # Read data into CSV
-    df = pd.read_csv(url, names=names)
-
-    # Each line is a different time or site or variable so need to parse out
-    sites = df['site_name'].unique()
-    times = df['time'].unique()
-    variables = list(df['parameter'].unique()) + ['AQI', 'category', 'raw_concentration']
-    latitude = [list(df['latitude'].loc[df['site_name'] == s])[0] for s in sites]
-    longitude = [list(df['longitude'].loc[df['site_name'] == s])[0] for s in sites]
-    aqs_id = [list(df['aqs_id'].loc[df['site_name'] == s])[0] for s in sites]
-
-    # Set up the dataset ahead of time
-    ds = xr.Dataset(
-        data_vars={
-            'latitude': (['sites'], latitude),
-            'longitude': (['sites'], longitude),
-            'aqs_id': (['sites'], aqs_id)
-        },
-        coords={
-            'time': (['time'], times),
-            'sites': (['sites'], sites)
-        }
-    )
-
-    # Set up emtpy data with nans
-    data = np.empty((len(variables), len(times), len(sites)))
-    data[:] = np.nan
-
-    # For each variable, pull out the data from specific sites and times
-    for v in range(len(variables)):
-        for t in range(len(times)):
-            for s in range(len(sites)):
-                if variables[v] in ['AQI', 'category', 'raw_concentration']:
-                    result = df.loc[(df['time'] == times[t]) & (df['site_name'] == sites[s])]
-                    if len(result[variables[v]]) > 0:
-                        data[v, t, s] = list(result[variables[v]])[0]
-                        atts = {'units': ''}
-                else:
-                    result = df.loc[(df['time'] == times[t]) & (df['site_name'] == sites[s]) & (df['parameter'] == variables[v])]
-                    if len(result['concentration']) > 0:
-                        data[v, t, s] = list(result['concentration'])[0]
-                        atts = {'units': list(result['unit'])[0]}
-
-        # Add variables to the dataset
-        ds[variables[v]] = xr.DataArray(data=data[v, :, :], dims=['time', 'sites'], attrs=atts)
-
-    times = pd.to_datetime(times)
-    ds = ds.assign_coords({'time': times})
-    return ds
diff --git a/act/discovery/get_arm.py b/act/discovery/get_arm.py
deleted file mode 100644
index 9942ad8151..0000000000
--- a/act/discovery/get_arm.py
+++ /dev/null
@@ -1,219 +0,0 @@
-"""
-Script for downloading data from ARM's Live Data Webservice
-
-"""
-
-import argparse
-import json
-import os
-import sys
-from datetime import timedelta
-import requests
-import textwrap
-import warnings
-
-try:
-    from urllib.request import urlopen
-except ImportError:
-    from urllib import urlopen
-
-from act.utils import date_parser
-
-
-def download_arm_data(username, token, datastream, startdate, enddate, time=None, output=None):
-    """
-    This tool will help users utilize the ARM Live Data Webservice to download
-    ARM data.
-
-    Parameters
-    ----------
-    username : str
-        The username to use for logging into the ADC archive.
-    token : str
-        The access token for accessing the ADC archive.
-    datastream : str
-        The name of the datastream to acquire.
-    startdate : str
-        The start date of the data to acquire. Formats accepted are
-        YYYY-MM-DD, DD.MM.YYYY, DD/MM/YYYY, YYYYMMDD, YYYY/MM/DD or
-        any of the previous formats with THH:MM:SS added onto the end
-        (ex. 2020-09-15T12:00:00).
-    enddate : str
-        The end date of the data to acquire. Formats accepted are
-        YYYY-MM-DD, DD.MM.YYYY, DD/MM/YYYY, YYYYMMDD or YYYY/MM/DD, or
-        any of the previous formats with THH:MM:SS added onto the end
-        (ex. 2020-09-15T13:00:00).
-    time: str or None
-        The specific time. Format is HHMMSS. Set to None to download all files
-        in the given date interval.
-    output : str
-        The output directory for the data. Set to None to make a folder in the
-        current working directory with the same name as *datastream* to place
-        the files in.
-
-    Returns
-    -------
-    files : list
-        Returns list of files retrieved
-
-    Notes
-    -----
-    This programmatic interface allows users to query and automate
-    machine-to-machine downloads of ARM data. This tool uses a REST URL and
-    specific parameters (saveData, query), user ID and access token, a
-    datastream name, a start date, and an end date, and data files matching
-    the criteria will be returned to the user and downloaded.
-
-    By using this web service, users can setup cron jobs and automatically
-    download data from /data/archive into their workspace. This will also
-    eliminate the manual step of following a link in an email to download data.
-    All other data files, which are not on the spinning
-    disk (on HPSS), will have to go through the regular ordering process.
-    More information about this REST API and tools can be found on `ARM Live
-    <https://adc.arm.gov/armlive/#scripts>`_.
-
-    To login/register for an access token click `here
-    <https://adc.arm.gov/armlive/livedata/home>`_.
-
-    Author: Michael Giansiracusa
-    Email: giansiracumt@ornl.gov
-
-    Examples
-    --------
-    This code will download the netCDF files from the sgpmetE13.b1 datastream
-    and place them in a directory named sgpmetE13.b1. The data from 14 Jan to
-    20 Jan 2017 will be downloaded. Replace *userName* and *XXXXXXXXXXXXXXXX*
-    with your username and token for ARM Data Discovery. See the Notes for
-    information on how to obtain a username and token.
-
-    .. code-block:: python
-
-        act.discovery.download_data(
-            "userName", "XXXXXXXXXXXXXXXX", "sgpmetE13.b1", "2017-01-14", "2017-01-20"
-        )
-
-    """
-    message = 'API will be changing from act.discovery.get_arm to act.discovery.arm'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    # default start and end are empty
-    start, end = '', ''
-    # start and end strings for query_url are constructed
-    # if the arguments were provided
-    if startdate:
-        start_datetime = date_parser(startdate, return_datetime=True)
-        start = start_datetime.strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3] + 'Z'
-        start = f'&start={start}'
-    if enddate:
-        end_datetime = date_parser(enddate, return_datetime=True)
-        # If the start and end date are the same, and a day to the end date
-        if start_datetime == end_datetime:
-            end_datetime += timedelta(hours=23, minutes=59, seconds=59)
-        end = end_datetime.strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3] + 'Z'
-        end = f'&end={end}'
-    # build the url to query the web service using the arguments provided
-    query_url = (
-        'https://adc.arm.gov/armlive/livedata/query?' + 'user={0}&ds={1}{2}{3}&wt=json'
-    ).format(':'.join([username, token]), datastream, start, end)
-
-    # get url response, read the body of the message,
-    # and decode from bytes type to utf-8 string
-    response_body = urlopen(query_url).read().decode('utf-8')
-    # if the response is an html doc, then there was an error with the user
-    if response_body[1:14] == '!DOCTYPE html':
-        raise ConnectionRefusedError('Error with user. Check username or token.')
-
-    # parse into json object
-    response_body_json = json.loads(response_body)
-
-    # construct output directory
-    if output:
-        # output files to directory specified
-        output_dir = os.path.join(output)
-    else:
-        # if no folder given, add datastream folder
-        # to current working dir to prevent file mix-up
-        output_dir = os.path.join(os.getcwd(), datastream)
-
-    # not testing, response is successful and files were returned
-    if response_body_json is None:
-        print('ARM Data Live Webservice does not appear to be functioning')
-        return []
-
-    num_files = len(response_body_json['files'])
-    file_names = []
-    if response_body_json['status'] == 'success' and num_files > 0:
-        for fname in response_body_json['files']:
-            if time is not None:
-                if time not in fname:
-                    continue
-            # construct link to web service saveData function
-            save_data_url = (
-                'https://adc.arm.gov/armlive/livedata/' + 'saveData?user={0}&file={1}'
-            ).format(':'.join([username, token]), fname)
-            output_file = os.path.join(output_dir, fname)
-            # make directory if it doesn't exist
-            if not os.path.isdir(output_dir):
-                os.makedirs(output_dir)
-            # create file and write bytes to file
-            with open(output_file, 'wb') as open_bytes_file:
-                data = urlopen(save_data_url).read()
-                if 'This data file is not available' in str(data):
-                    print(fname + ' is not available for download')
-                    continue
-                else:
-                    print(f'[DOWNLOADING] {fname}')
-                    open_bytes_file.write(data)
-            file_names.append(output_file)
-        # Get ARM DOI and print it out
-        doi = get_arm_doi(datastream, start_datetime.strftime('%Y-%m-%d'), end_datetime.strftime('%Y-%m-%d'))
-        print('\nIf you use these data to prepare a publication, please cite:\n')
-        print(textwrap.fill(doi, width=80))
-        print('')
-    else:
-        print(
-            'No files returned or url status error.\n' 'Check datastream name, start, and end date.'
-        )
-
-    return file_names
-
-
-def get_arm_doi(datastream, startdate, enddate):
-    """
-    This function will return a citation with DOI, if available, for specified
-    datastream and date range
-
-    Parameters
-    ----------
-    datastream : str
-        The name of the datastream to get a DOI for.  This must be ARM standard names
-    startdate : str
-        Start date for the citation in the format YY-MM-DD
-    enddate : str
-        End date for the citation in the format YY-MM-DD
-
-    Returns
-    -------
-    doi : str
-        Returns the citation as a string
-
-    """
-
-    message = 'API will be changing from act.discovery.get_arm to act.discovery.arm'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    # Get the DOI information
-    doi_url = 'https://adc.arm.gov/citationservice/citation/datastream?id=' + datastream + '&citationType=apa'
-    doi_url += '&startDate=' + startdate
-    doi_url += '&endDate=' + enddate
-    try:
-        doi = requests.get(url=doi_url)
-    except ValueError as err:
-        return "Webservice potentially down or arguments are not valid: " + err
-
-    if len(doi.text) > 0:
-        doi = doi.json()['citation']
-    else:
-        doi = 'Please check your arguments. No DOI Found'
-
-    return doi
diff --git a/act/discovery/get_armfiles.py b/act/discovery/get_armfiles.py
deleted file mode 100644
index a76b661d85..0000000000
--- a/act/discovery/get_armfiles.py
+++ /dev/null
@@ -1,219 +0,0 @@
-"""
-Script for downloading data from ARM's Live Data Webservice
-
-"""
-
-import argparse
-import json
-import os
-import sys
-from datetime import timedelta
-import requests
-import textwrap
-import warnings
-
-try:
-    from urllib.request import urlopen
-except ImportError:
-    from urllib import urlopen
-
-from act.utils import date_parser
-
-
-def download_data(username, token, datastream, startdate, enddate, time=None, output=None):
-    """
-    This tool will help users utilize the ARM Live Data Webservice to download
-    ARM data.
-
-    Parameters
-    ----------
-    username : str
-        The username to use for logging into the ADC archive.
-    token : str
-        The access token for accessing the ADC archive.
-    datastream : str
-        The name of the datastream to acquire.
-    startdate : str
-        The start date of the data to acquire. Formats accepted are
-        YYYY-MM-DD, DD.MM.YYYY, DD/MM/YYYY, YYYYMMDD, YYYY/MM/DD or
-        any of the previous formats with THH:MM:SS added onto the end
-        (ex. 2020-09-15T12:00:00).
-    enddate : str
-        The end date of the data to acquire. Formats accepted are
-        YYYY-MM-DD, DD.MM.YYYY, DD/MM/YYYY, YYYYMMDD or YYYY/MM/DD, or
-        any of the previous formats with THH:MM:SS added onto the end
-        (ex. 2020-09-15T13:00:00).
-    time: str or None
-        The specific time. Format is HHMMSS. Set to None to download all files
-        in the given date interval.
-    output : str
-        The output directory for the data. Set to None to make a folder in the
-        current working directory with the same name as *datastream* to place
-        the files in.
-
-    Returns
-    -------
-    files : list
-        Returns list of files retrieved
-
-    Notes
-    -----
-    This programmatic interface allows users to query and automate
-    machine-to-machine downloads of ARM data. This tool uses a REST URL and
-    specific parameters (saveData, query), user ID and access token, a
-    datastream name, a start date, and an end date, and data files matching
-    the criteria will be returned to the user and downloaded.
-
-    By using this web service, users can setup cron jobs and automatically
-    download data from /data/archive into their workspace. This will also
-    eliminate the manual step of following a link in an email to download data.
-    All other data files, which are not on the spinning
-    disk (on HPSS), will have to go through the regular ordering process.
-    More information about this REST API and tools can be found on `ARM Live
-    <https://adc.arm.gov/armlive/#scripts>`_.
-
-    To login/register for an access token click `here
-    <https://adc.arm.gov/armlive/livedata/home>`_.
-
-    Author: Michael Giansiracusa
-    Email: giansiracumt@ornl.gov
-
-    Examples
-    --------
-    This code will download the netCDF files from the sgpmetE13.b1 datastream
-    and place them in a directory named sgpmetE13.b1. The data from 14 Jan to
-    20 Jan 2017 will be downloaded. Replace *userName* and *XXXXXXXXXXXXXXXX*
-    with your username and token for ARM Data Discovery. See the Notes for
-    information on how to obtain a username and token.
-
-    .. code-block:: python
-
-        act.discovery.download_data(
-            "userName", "XXXXXXXXXXXXXXXX", "sgpmetE13.b1", "2017-01-14", "2017-01-20"
-        )
-
-    """
-    message = 'act.discovery.get_armfiles.download_data will be retired in version 2.0.0.  Please use act.discovery.arm.download_arm_data instead.'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    # default start and end are empty
-    start, end = '', ''
-    # start and end strings for query_url are constructed
-    # if the arguments were provided
-    if startdate:
-        start_datetime = date_parser(startdate, return_datetime=True)
-        start = start_datetime.strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3] + 'Z'
-        start = f'&start={start}'
-    if enddate:
-        end_datetime = date_parser(enddate, return_datetime=True)
-        # If the start and end date are the same, and a day to the end date
-        if start_datetime == end_datetime:
-            end_datetime += timedelta(hours=23, minutes=59, seconds=59)
-        end = end_datetime.strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3] + 'Z'
-        end = f'&end={end}'
-    # build the url to query the web service using the arguments provided
-    query_url = (
-        'https://adc.arm.gov/armlive/livedata/query?' + 'user={0}&ds={1}{2}{3}&wt=json'
-    ).format(':'.join([username, token]), datastream, start, end)
-
-    # get url response, read the body of the message,
-    # and decode from bytes type to utf-8 string
-    response_body = urlopen(query_url).read().decode('utf-8')
-    # if the response is an html doc, then there was an error with the user
-    if response_body[1:14] == '!DOCTYPE html':
-        raise ConnectionRefusedError('Error with user. Check username or token.')
-
-    # parse into json object
-    response_body_json = json.loads(response_body)
-
-    # construct output directory
-    if output:
-        # output files to directory specified
-        output_dir = os.path.join(output)
-    else:
-        # if no folder given, add datastream folder
-        # to current working dir to prevent file mix-up
-        output_dir = os.path.join(os.getcwd(), datastream)
-
-    # not testing, response is successful and files were returned
-    if response_body_json is None:
-        print('ARM Data Live Webservice does not appear to be functioning')
-        return []
-
-    num_files = len(response_body_json['files'])
-    file_names = []
-    if response_body_json['status'] == 'success' and num_files > 0:
-        for fname in response_body_json['files']:
-            if time is not None:
-                if time not in fname:
-                    continue
-            # construct link to web service saveData function
-            save_data_url = (
-                'https://adc.arm.gov/armlive/livedata/' + 'saveData?user={0}&file={1}'
-            ).format(':'.join([username, token]), fname)
-            output_file = os.path.join(output_dir, fname)
-            # make directory if it doesn't exist
-            if not os.path.isdir(output_dir):
-                os.makedirs(output_dir)
-            # create file and write bytes to file
-            with open(output_file, 'wb') as open_bytes_file:
-                data = urlopen(save_data_url).read()
-                if 'This data file is not available' in str(data):
-                    print(fname + ' is not available for download')
-                    continue
-                else:
-                    print(f'[DOWNLOADING] {fname}')
-                    open_bytes_file.write(data)
-            file_names.append(output_file)
-        # Get ARM DOI and print it out
-        doi = get_arm_doi(datastream, start_datetime.strftime('%Y-%m-%d'), end_datetime.strftime('%Y-%m-%d'))
-        print('\nIf you use these data to prepare a publication, please cite:\n')
-        print(textwrap.fill(doi, width=80))
-        print('')
-    else:
-        print(
-            'No files returned or url status error.\n' 'Check datastream name, start, and end date.'
-        )
-
-    return file_names
-
-
-def get_arm_doi(datastream, startdate, enddate):
-    """
-    This function will return a citation with DOI, if available, for specified
-    datastream and date range
-
-    Parameters
-    ----------
-    datastream : str
-        The name of the datastream to get a DOI for.  This must be ARM standard names
-    startdate : str
-        Start date for the citation in the format YY-MM-DD
-    enddate : str
-        End date for the citation in the format YY-MM-DD
-
-    Returns
-    -------
-    doi : str
-        Returns the citation as a string
-
-    """
-
-    message = 'API will be changing from act.discovery.get_armfiles to act.discovery.arm'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    # Get the DOI information
-    doi_url = 'https://adc.arm.gov/citationservice/citation/datastream?id=' + datastream + '&citationType=apa'
-    doi_url += '&startDate=' + startdate
-    doi_url += '&endDate=' + enddate
-    try:
-        doi = requests.get(url=doi_url)
-    except ValueError as err:
-        return "Webservice potentially down or arguments are not valid: " + err
-
-    if len(doi.text) > 0:
-        doi = doi.json()['citation']
-    else:
-        doi = 'Please check your arguments. No DOI Found'
-
-    return doi
diff --git a/act/discovery/get_asos.py b/act/discovery/get_asos.py
deleted file mode 100644
index 4cad5def04..0000000000
--- a/act/discovery/get_asos.py
+++ /dev/null
@@ -1,288 +0,0 @@
-"""
-Script for downloading ASOS data from the Iowa Mesonet API
-
-"""
-
-import json
-import time
-import warnings
-from datetime import datetime
-
-import numpy as np
-import pandas as pd
-import xarray as xr
-from six import StringIO
-
-try:
-    from urllib.request import urlopen
-except ImportError:
-    from urllib2 import urlopen
-
-
-def get_asos(time_window, lat_range=None, lon_range=None, station=None):
-    """
-    Returns all of the station observations from the Iowa Mesonet from either
-    a given latitude and longitude window or a given station code.
-
-    Parameters
-    ----------
-    time_window: tuple
-        A 2 member list or tuple containing the start and end times. The
-        times must be python datetimes.
-    lat_range: tuple
-        The latitude window to grab all of the ASOS observations from.
-    lon_range: tuple
-        The longitude window to grab all of the ASOS observations from.
-    station: str
-        The station ID to grab the ASOS observations from.
-
-    Returns
-    -------
-    asos_ds: dict of xarray.Datasets
-        A dictionary of ACT datasets whose keys are the ASOS station IDs.
-
-    Examples
-    --------
-    If you want to obtain timeseries of ASOS observations for Chicago O'Hare
-    Airport, simply do::
-
-        $ time_window = [datetime(2020, 2, 4, 2, 0), datetime(2020, 2, 10, 10, 0)]
-        $ station = "KORD"
-        $ my_asoses = act.discovery.get_asos(time_window, station="ORD")
-    """
-
-    message = 'API will be changing from act.discovery.get_asos.get_asos to act.discovery.asos.get_asos_data'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    # First query the database for all of the JSON info for every station
-    # Only add stations whose lat/lon are within the Grid's boundaries
-    regions = """AF AL_ AI_ AQ_ AG_ AR_ AK AL AM_
-        AO_ AS_ AR AW_ AU_ AT_
-        AZ_ BA_ BE_ BB_ BG_ BO_ BR_ BF_
-        BT_ BS_ BI_ BM_ BB_ BY_ BZ_ BJ_ BW_ AZ CA CA_AB
-        CA_BC CD_ CK_ CF_ CG_ CL_ CM_ CO CO_ CN_ CR_ CT
-        CU_ CV_ CY_ CZ_ DE DK_ DJ_ DM_ DO_
-        DZ EE_ ET_ FK_ FM_ FJ_ FI_ FR_ GF_ PF_
-        GA_ GM_ GE_ DE_ GH_ GI_ KY_ GB_ GR_ GL_ GD_
-        GU_ GT_ GN_ GW_ GY_ HT_ HN_ HK_ HU_ IS_ IN_
-        ID_ IR_ IQ_ IE_ IL_ IT_ CI_ JM_ JP_
-        JO_ KZ_ KE_ KI_ KW_ LA_ LV_ LB_ LS_ LR_ LY_
-        LT_ LU_ MK_ MG_ MW_ MY_ MV_ ML_ CA_MB
-        MH_ MR_ MU_ YT_ MX_ MD_ MC_ MA_ MZ_ MM_ NA_ NP_
-        AN_ NL_ CA_NB NC_ CA_NF NF_ NI_
-        NE_ NG_ MP_ KP_ CA_NT NO_ CA_NS CA_NU OM_
-        CA_ON PK_ PA_ PG_ PY_ PE_ PH_ PN_ PL_
-        PT_ CA_PE PR_ QA_ CA_QC RO_ RU_RW_ SH_ KN_
-        LC_ VC_ WS_ ST_ CA_SK SA_ SN_ RS_ SC_
-        SL_ SG_ SK_ SI_ SB_ SO_ ZA_ KR_ ES_ LK_ SD_ SR_
-        SZ_ SE_ CH_ SY_ TW_ TJ_ TZ_ TH_
-        TG_ TO_ TT_ TU TN_ TR_ TM_ UG_ UA_ AE_ UN_ UY_
-        UZ_ VU_ VE_ VN_ VI_ YE_ CA_YT ZM_ ZW_
-        EC_ EG_ FL GA GQ_ HI HR_ IA ID IL IO_ IN KS
-        KH_ KY KM_ LA MA MD ME
-        MI MN MO MS MT NC ND NE NH NJ NM NV NY OH OK
-        OR PA RI SC SV_ SD TD_ TN TX UT VA VT VG_
-        WA WI WV WY"""
-
-    networks = ['AWOS']
-    metadata_list = {}
-    if lat_range is not None and lon_range is not None:
-        lon_min, lon_max = lon_range
-        lat_min, lat_max = lat_range
-        for region in regions.split():
-            networks.append(f'{region}_ASOS')
-
-        site_list = []
-        for network in networks:
-            # Get metadata
-            uri = ('https://mesonet.agron.iastate.edu/' 'geojson/network/%s.geojson') % (network,)
-            data = urlopen(uri)
-            jdict = json.load(data)
-            for site in jdict['features']:
-                lat = site['geometry']['coordinates'][1]
-                lon = site['geometry']['coordinates'][0]
-                if lat >= lat_min and lat <= lat_max:
-                    if lon >= lon_min and lon <= lon_max:
-                        station_metadata_dict = {}
-                        station_metadata_dict['site_latitude'] = lat
-                        station_metadata_dict['site_longitude'] = lat
-                        for my_keys in site['properties']:
-                            station_metadata_dict[my_keys] = site['properties'][my_keys]
-                        metadata_list[site['properties']['sid']] = station_metadata_dict
-                        site_list.append(site['properties']['sid'])
-    elif station is not None:
-        site_list = [station]
-        for region in regions.split():
-            networks.append(f'{region}_ASOS')
-        for network in networks:
-            # Get metadata
-            uri = ('https://mesonet.agron.iastate.edu/' 'geojson/network/%s.geojson') % (network,)
-            data = urlopen(uri)
-            jdict = json.load(data)
-            for site in jdict['features']:
-                lat = site['geometry']['coordinates'][1]
-                lon = site['geometry']['coordinates'][0]
-                if site['properties']['sid'] == station:
-                    station_metadata_dict = {}
-                    station_metadata_dict['site_latitude'] = lat
-                    station_metadata_dict['site_longitude'] = lon
-                    for my_keys in site['properties']:
-                        if my_keys == 'elevation':
-                            station_metadata_dict['elevation'] = (
-                                '%f meter' % site['properties'][my_keys]
-                            )
-                        else:
-                            station_metadata_dict[my_keys] = site['properties'][my_keys]
-                    metadata_list[station] = station_metadata_dict
-
-        # Get station metadata
-    else:
-        raise ValueError('Either both lat_range and lon_range or station must ' + 'be specified!')
-
-    # Get the timestamp for each request
-    start_time = time_window[0]
-    end_time = time_window[1]
-
-    SERVICE = 'http://mesonet.agron.iastate.edu/cgi-bin/request/asos.py?'
-    service = SERVICE + 'data=all&tz=Etc/UTC&format=comma&latlon=yes&'
-
-    service += start_time.strftime('year1=%Y&month1=%m&day1=%d&hour1=%H&minute1=%M&')
-    service += end_time.strftime('year2=%Y&month2=%m&day2=%d&hour2=%H&minute2=%M')
-    asos_ds = {}
-    for stations in site_list:
-        uri = f'{service}&station={stations}'
-        print(f'Downloading: {stations}')
-        data = _download_data(uri)
-        buf = StringIO()
-        buf.write(data)
-        buf.seek(0)
-
-        my_df = pd.read_csv(buf, skiprows=5, na_values='M')
-
-        if len(my_df['lat'].values) == 0:
-            warnings.warn(
-                'No data available at station %s between time %s and %s'
-                % (
-                    stations,
-                    start_time.strftime('%Y-%m-%d %H:%M:%S'),
-                    end_time.strftime('%Y-%m-%d %H:%M:%S'),
-                )
-            )
-        else:
-
-            def to_datetime(x):
-                return datetime.strptime(x, '%Y-%m-%d %H:%M')
-
-            my_df['time'] = my_df['valid'].apply(to_datetime)
-            my_df = my_df.set_index('time')
-            my_df = my_df.drop('valid', axis=1)
-            my_df = my_df.drop('station', axis=1)
-            my_df = my_df.to_xarray()
-
-            my_df.attrs = metadata_list[stations]
-            my_df['lon'].attrs['units'] = 'degree'
-            my_df['lon'].attrs['long_name'] = 'Longitude'
-            my_df['lat'].attrs['units'] = 'degree'
-            my_df['lat'].attrs['long_name'] = 'Latitude'
-
-            my_df['tmpf'].attrs['units'] = 'degrees Fahrenheit'
-            my_df['tmpf'].attrs['long_name'] = 'Temperature in degrees Fahrenheit'
-
-            # Fahrenheit to Celsius
-            my_df['temp'] = (5.0 / 9.0 * my_df['tmpf']) - 32.0
-            my_df['temp'].attrs['units'] = 'degrees Celsius'
-            my_df['temp'].attrs['long_name'] = 'Temperature in degrees Celsius'
-            my_df['dwpf'].attrs['units'] = 'degrees Fahrenheit'
-            my_df['dwpf'].attrs['long_name'] = 'Dewpoint temperature in degrees Fahrenheit'
-
-            # Fahrenheit to Celsius
-            my_df['dwpc'] = (5.0 / 9.0 * my_df['tmpf']) - 32.0
-            my_df['dwpc'].attrs['units'] = 'degrees Celsius'
-            my_df['dwpc'].attrs['long_name'] = 'Dewpoint temperature in degrees Celsius'
-            my_df['relh'].attrs['units'] = 'percent'
-            my_df['relh'].attrs['long_name'] = 'Relative humidity'
-            my_df['drct'].attrs['units'] = 'degrees'
-            my_df['drct'].attrs['long_name'] = 'Wind speed in degrees'
-            my_df['sknt'].attrs['units'] = 'knots'
-            my_df['sknt'].attrs['long_name'] = 'Wind speed in knots'
-            my_df['spdms'] = my_df['sknt'] * 0.514444
-            my_df['spdms'].attrs['units'] = 'm s-1'
-            my_df['spdms'].attrs['long_name'] = 'Wind speed in meters per second'
-            my_df['u'] = -np.sin(np.deg2rad(my_df['drct'])) * my_df['spdms']
-            my_df['u'].attrs['units'] = 'm s-1'
-            my_df['u'].attrs['long_name'] = 'Zonal component of surface wind'
-            my_df['v'] = -np.cos(np.deg2rad(my_df['drct'])) * my_df['spdms']
-            my_df['v'].attrs['units'] = 'm s-1'
-            my_df['v'].attrs['long_name'] = 'Meridional component of surface wind'
-            my_df['mslp'].attrs['units'] = 'mb'
-            my_df['mslp'].attrs['long_name'] = 'Mean Sea Level Pressure'
-            my_df['alti'].attrs['units'] = 'in Hg'
-            my_df['alti'].attrs['long_name'] = 'Atmospheric pressure in inches of Mercury'
-            my_df['vsby'].attrs['units'] = 'mi'
-            my_df['vsby'].attrs['long_name'] = 'Visibility'
-            my_df['vsbykm'] = my_df['vsby'] * 1.60934
-            my_df['vsbykm'].attrs['units'] = 'km'
-            my_df['vsbykm'].attrs['long_name'] = 'Visibility'
-            my_df['gust'] = my_df['gust'] * 0.514444
-            my_df['gust'].attrs['units'] = 'm s-1'
-            my_df['gust'].attrs['long_name'] = 'Wind gust speed'
-            my_df['skyc1'].attrs['long_name'] = 'Sky level 1 coverage'
-            my_df['skyc2'].attrs['long_name'] = 'Sky level 2 coverage'
-            my_df['skyc3'].attrs['long_name'] = 'Sky level 3 coverage'
-            my_df['skyc4'].attrs['long_name'] = 'Sky level 4 coverage'
-            my_df['skyl1'] = my_df['skyl1'] * 0.3048
-            my_df['skyl2'] = my_df['skyl2'] * 0.3048
-            my_df['skyl3'] = my_df['skyl3'] * 0.3048
-            my_df['skyl4'] = my_df['skyl4'] * 0.3048
-            my_df['skyl1'].attrs['long_name'] = 'Sky level 1 altitude'
-            my_df['skyl2'].attrs['long_name'] = 'Sky level 2 altitude'
-            my_df['skyl3'].attrs['long_name'] = 'Sky level 3 altitude'
-            my_df['skyl4'].attrs['long_name'] = 'Sky level 4 altitude'
-            my_df['skyl1'].attrs['long_name'] = 'meter'
-            my_df['skyl2'].attrs['long_name'] = 'meter'
-            my_df['skyl3'].attrs['long_name'] = 'meter'
-            my_df['skyl4'].attrs['long_name'] = 'meter'
-
-            my_df['wxcodes'].attrs['long_name'] = 'Weather code'
-            my_df['ice_accretion_1hr'] = my_df['ice_accretion_1hr'] * 2.54
-            my_df['ice_accretion_1hr'].attrs['units'] = 'cm'
-            my_df['ice_accretion_1hr'].attrs['long_name'] = '1 hour ice accretion'
-            my_df['ice_accretion_3hr'] = my_df['ice_accretion_3hr'] * 2.54
-            my_df['ice_accretion_3hr'].attrs['units'] = 'cm'
-            my_df['ice_accretion_3hr'].attrs['long_name'] = '3 hour ice accretion'
-            my_df['ice_accretion_6hr'] = my_df['ice_accretion_3hr'] * 2.54
-            my_df['ice_accretion_6hr'].attrs['units'] = 'cm'
-            my_df['ice_accretion_6hr'].attrs['long_name'] = '6 hour ice accretion'
-            my_df['peak_wind_gust'] = my_df['peak_wind_gust'] * 0.514444
-            my_df['peak_wind_gust'].attrs['units'] = 'm s-1'
-            my_df['peak_wind_gust'].attrs['long_name'] = 'Peak wind gust speed'
-            my_df['peak_wind_drct'].attrs['drct'] = 'degree'
-            my_df['peak_wind_drct'].attrs['long_name'] = 'Peak wind gust direction'
-            my_df['u_peak'] = -np.sin(np.deg2rad(my_df['peak_wind_drct'])) * my_df['peak_wind_gust']
-            my_df['u_peak'].attrs['units'] = 'm s-1'
-            my_df['u_peak'].attrs['long_name'] = 'Zonal component of surface wind'
-            my_df['v_peak'] = -np.cos(np.deg2rad(my_df['peak_wind_drct'])) * my_df['peak_wind_gust']
-            my_df['v_peak'].attrs['units'] = 'm s-1'
-            my_df['v_peak'].attrs['long_name'] = 'Meridional component of surface wind'
-            my_df['metar'].attrs['long_name'] = 'Raw METAR code'
-            my_df.attrs['_datastream'] = stations
-            buf.close()
-
-            asos_ds[stations] = my_df
-    return asos_ds
-
-
-def _download_data(uri):
-    attempt = 0
-    while attempt < 6:
-        try:
-            data = urlopen(uri, timeout=300).read().decode('utf-8')
-            if data is not None and not data.startswith('ERROR'):
-                return data
-        except Exception as exp:
-            print(f'download_data({uri}) failed with {exp}')
-            time.sleep(5)
-        attempt += 1
-
-    print('Exhausted attempts to download, returning empty data')
-    return ''
diff --git a/act/discovery/get_cropscape.py b/act/discovery/get_cropscape.py
deleted file mode 100644
index 8c7e6ce6e1..0000000000
--- a/act/discovery/get_cropscape.py
+++ /dev/null
@@ -1,112 +0,0 @@
-"""
-Function for getting CropScape data based on an entered lat/lon.
-
-"""
-
-import datetime
-import requests
-import warnings
-
-try:
-    from pyproj import Transformer
-except ImportError:
-    from pyproj.transformer import Transformer
-
-
-def croptype(lat=None, lon=None, year=None):
-    """
-    Function for working with the CropScape API to get a crop type based on
-    the lat,lon, and year entered. The lat/lon is converted to the projection
-    used by CropScape before pased to the API. CropScape - Copyright © Center
-    For Spatial Information Science and Systems 2009 - 2018
-
-    Parameters
-    ----------
-    lat : float
-        Latitude of point to retrieve.
-    lon : float
-        Longitude of point to retrieve.
-    year : int
-        Year to get croptype for.
-
-    Returns
-    -------
-    category : string
-        String of the crop type at that specific lat/lon for the given year.
-
-    References
-    ----------
-    USDA National Agricultural Statistics Service Cropland Data Layer. {YEAR}.
-    Published crop-specific data layer [Online]. Available at https://nassgeodata.gmu.edu/CropScape/
-    (accessed {DATE}; verified {DATE}). USDA-NASS, Washington, DC.
-
-    Examples
-    --------
-    To get the crop type, simply do:
-
-    .. code-block :: python
-
-        type = act.discovery.get_cropscape.croptype(36.8172,-97.1709,'2018')
-
-    """
-
-    message = 'API will be changing from act.discovery.get_cropscape.get_croptype to act.discovery.croptype.get_crop_type'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    # Return if lat/lon are not passed in
-    if lat is None or lon is None:
-        raise RuntimeError('Lat and Lon need to be provided')
-
-    # Set the CropScape Projection
-    outproj = (
-        'PROJCS["NAD_1983_Albers",'
-        'GEOGCS["NAD83",'
-        'DATUM["North_American_Datum_1983",'
-        'SPHEROID["GRS 1980",6378137,298.257222101,'
-        'AUTHORITY["EPSG","7019"]],'
-        'TOWGS84[0,0,0,0,0,0,0],'
-        'AUTHORITY["EPSG","6269"]],'
-        'PRIMEM["Greenwich",0,'
-        'AUTHORITY["EPSG","8901"]],'
-        'UNIT["degree",0.0174532925199433,'
-        'AUTHORITY["EPSG","9108"]],'
-        'AUTHORITY["EPSG","4269"]],'
-        'PROJECTION["Albers_Conic_Equal_Area"],'
-        'PARAMETER["standard_parallel_1",29.5],'
-        'PARAMETER["standard_parallel_2",45.5],'
-        'PARAMETER["latitude_of_center",23],'
-        'PARAMETER["longitude_of_center",-96],'
-        'PARAMETER["false_easting",0],'
-        'PARAMETER["false_northing",0],'
-        'UNIT["meters",1]]'
-    )
-
-    # Set the input projection to be lat/lon
-    inproj = 'EPSG:4326'
-
-    # Get the x/y coordinates for CropScape
-    transformer = Transformer.from_crs(inproj, outproj)
-    x, y = transformer.transform(lat, lon)
-
-    # Build URL
-    url = 'https://nassgeodata.gmu.edu/axis2/services/CDLService/GetCDLValue?'
-    if year is None:
-        now = datetime.datetime.now()
-        year = now.year - 1
-
-    # Add year, lat, and lon as parameters
-    params = {'year': str(year), 'x': str(x), 'y': str(y)}
-
-    try:
-        req = requests.get(url, params=params, timeout=1)
-    except Exception:
-        return
-
-    # Return from the webservice is not convertable to json
-    # So we need to do some text mining
-    text = req.text
-    text = text.split(',')
-    category = [t for t in text if 'category' in t]
-    category = category[0].split(': ')[-1][1:-1]
-
-    return category
diff --git a/act/discovery/get_neon.py b/act/discovery/get_neon.py
deleted file mode 100644
index 91dfb838e4..0000000000
--- a/act/discovery/get_neon.py
+++ /dev/null
@@ -1,173 +0,0 @@
-"""
-Function for downloading data from NSF NEON program
-using their API.
-
-NEON sites can be found through the NEON website
-https://www.neonscience.org/field-sites/explore-field-sites
-
-"""
-
-import json
-import requests
-import os
-import shutil
-import pandas as pd
-import warnings
-
-
-def get_site_products(site_code, print_to_screen=False):
-    """
-    Returns a list of data products available for a NEON site
-    NEON sites can be found through the NEON website
-    https://www.neonscience.org/field-sites/explore-field-sites
-
-    Parameters
-    ----------
-    site : str
-        NEON site identifier. Required variable
-    print_to_screen : boolean
-        If set to True will print to screen
-
-    Returns
-    -------
-    products : list
-        Returns 2D list of data product code and title
-
-    """
-
-    message = 'API will be changing from act.discovery.get_neon.get_site_products to act.discovery.neon.get_neon_site_products'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    # Every request begins with the server's URL
-    server = 'http://data.neonscience.org/api/v0/'
-
-    # Make request, using the sites/ endpoint
-    site_request = requests.get(server + 'sites/' + site_code)
-
-    # Convert to Python JSON object
-    site_json = site_request.json()
-
-    products = {}
-    # View product code and name for every available data product
-    for product in site_json['data']['dataProducts']:
-        if print_to_screen:
-            print(product['dataProductCode'], product['dataProductTitle'])
-        products[product['dataProductCode']] = product['dataProductTitle']
-
-    return products
-
-
-def get_product_avail(site_code, product_code, print_to_screen=False):
-    """
-    Returns a list of data products available for a NEON site
-    NEON sites can be found through the NEON website
-    https://www.neonscience.org/field-sites/explore-field-sites
-
-    Parameters
-    ----------
-    site : str
-        NEON site identifier. Required variable
-    product_code : str
-        NEON product code. Required variable
-    print_to_screen : boolean
-        If set to True will print to screen
-
-    Returns
-    -------
-    dates : list
-        Returns list of available months of data
-
-    """
-
-    message = 'API will be changing from act.discovery.get_neon.get_product_avail to act.discovery.neon.get_neon_product_avail'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    # Every request begins with the server's URL
-    server = 'http://data.neonscience.org/api/v0/'
-
-    # Make request, using the sites/ endpoint
-    site_request = requests.get(server + 'sites/' + site_code)
-
-    # Convert to Python JSON object
-    site_json = site_request.json()
-
-    # View product code and name for every available data product
-    for product in site_json['data']['dataProducts']:
-        if product['dataProductCode'] != product_code:
-            continue
-        if print_to_screen:
-            print(product['availableMonths'])
-        dates = product['availableMonths']
-
-    return dates
-
-
-def download_neon_data(site_code, product_code, start_date, end_date=None, output_dir=None):
-    """
-    Returns a list of data products available for a NEON site.  Please be sure to view the
-    readme files that are downloaded as well as there may be a number of different products.
-
-    If you want more information on the NEON file formats, please see:
-    https://www.neonscience.org/data-samples/data-management/data-formats-conventions
-
-    NEON sites can be found through the NEON website
-    https://www.neonscience.org/field-sites/explore-field-sites
-
-    Please be sure to acknowledge and cite the NEON program and data products appropriately:
-    https://www.neonscience.org/data-samples/data-policies-citation
-
-    Parameters
-    ----------
-    site : str
-        NEON site identifier. Required variable
-    product_code : str
-        NEON product code. Required variable
-    start_date : str
-        Start date of the range to download in YYYY-MM format
-    end_date : str
-        End date of the range to download in YYYY-MM format.
-        If None, will just download data for start_date
-    output_dir : str
-        Local directory to store the data.  If None, will default to
-        [current working directory]/[site_code]_[product_code]
-
-    Returns
-    -------
-    files : list
-        Returns a list of files that were downloaded
-
-    """
-
-    message = 'API will be changing from act.discovery.get_neon to act.discovery.neon'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    # Every request begins with the server's URL
-    server = 'http://data.neonscience.org/api/v0/'
-
-    # Get dates to pass in
-    if end_date is not None:
-        date_range = pd.date_range(start_date, end_date, freq='MS').strftime('%Y-%m').tolist()
-    else:
-        date_range = [start_date]
-
-    # For each month, download data for specified site/product
-    files = []
-    for date in date_range:
-        # Make Request
-        data_request = requests.get(server + 'data/' + product_code + '/' + site_code + '/' + date)
-        data_json = data_request.json()
-
-        if output_dir is None:
-            output_dir = os.path.join(os.getcwd(), site_code + '_' + product_code)
-        if not os.path.exists(output_dir):
-            os.makedirs(output_dir)
-
-        for file in data_json['data']['files']:
-            print('[DOWNLOADING] ', file['name'])
-            output_filename = os.path.join(output_dir, file['name'])
-            with requests.get(file['url'], stream=True) as r:
-                with open(output_filename, 'wb') as f:
-                    shutil.copyfileobj(r.raw, f)
-                    files.append(output_filename)
-
-    return files
diff --git a/act/discovery/get_noaapsl.py b/act/discovery/get_noaapsl.py
deleted file mode 100644
index a76280e235..0000000000
--- a/act/discovery/get_noaapsl.py
+++ /dev/null
@@ -1,173 +0,0 @@
-"""
-Function for downloading data from NOAA PSL Profiler Network
-
-"""
-import json
-from datetime import datetime
-import pandas as pd
-import numpy as np
-import os
-import warnings
-
-try:
-    from urllib.request import urlopen
-except ImportError:
-    from urllib import urlopen
-
-
-def download_noaa_psl_data(site=None, instrument=None, startdate=None, enddate=None,
-                           hour=None, output=None):
-    """
-    Function to download data from the NOAA PSL Profiler Network Data Library
-    https://psl.noaa.gov/data/obs/datadisplay/
-
-    Parameters
-    ----------
-    site : str
-        3 letter NOAA site identifier. Required variable
-    instrument : str
-        Name of the dataset to download.  Options currently include (name prior to -). Required variable
-            'Parsivel' - Parsivel disdrometer data
-            'Pressure', 'Datalogger', 'Net Radiation', 'Temp/RH', 'Solar Radiation' - Surface meteorology/radiation data
-            'Tipping Bucket', 'TBRG', 'Wind Speed', 'Wind Direction' - Surface meteorology/radiation data
-            'Wind Speed and Direction' - Surface meteorology/radiation data
-            'GpsTrimble' - GPS Trimble water vapor data
-            'Radar S-band Moment' - 3 GHz Precipitation Profiler moment data
-            'Radar S-band Bright Band' - 3 GHz Precipitation Profiler bright band data
-            '449RWP Bright Band' - 449 MHz Wind Profiler bright band data
-            '449RWP Wind' - 449 MHz Wind Profiler wind data
-            '449RWP Sub-Hour Wind' - 449 MHz Wind Profiler sub-hourly wind data
-            '449RWP Sub-Hour Temp' - 449 MHz Wind Profiler sub-hourly temperature data
-            '915RWP Wind' - 915 MHz Wind Profiler wind data
-            '915RWP Temp' - 915 MHz Wind Profiler temperature data
-            '915RWP Sub-Hour Wind' - 915 MHz Wind Profiler sub-hourly wind data
-            '915WP Sub-Hour Temp' - 915 MHz Wind Profiler sub-hourly temperature data
-            'Radar FMCW Moment' - FMCW Radar moments data
-            'Radar FMCW Bright Band' - FMCW Radar bright band data
-    startdate : str
-        The start date of the data to acquire. Format is YYYYMMDD. Required variable
-    enddate : str
-        The end date of the data to acquire. Format is YYYYMMDD
-    hour : str
-        Two digit hour of file to dowload if wanting a specific time
-    output : str
-        The output directory for the data. Set to None to make a folder in the
-        current working directory with the same name as *datastream* to place
-        the files in.
-
-    Returns
-    -------
-    files : list
-        Returns list of files retrieved
-
-    """
-
-    message = 'API will be changing from act.discovery.get_noaapsl to act.discovery.noaapsl'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    if (site is None) or (instrument is None) or (startdate is None):
-        raise ValueError('site, instrument, and startdate need to be set')
-
-    datastream = site + '_' + instrument.replace(' ', '_')
-    # Convert dates to day of year (doy) for NOAA folder structure
-    s_doy = datetime.strptime(startdate, '%Y%m%d').timetuple().tm_yday
-    year = datetime.strptime(startdate, '%Y%m%d').year
-    if enddate is None:
-        enddate = startdate
-    e_doy = datetime.strptime(enddate, '%Y%m%d').timetuple().tm_yday
-
-    # Set base URL
-    url = 'https://downloads.psl.noaa.gov/psd2/data/realtime/'
-
-    # Set list of strings that all point to the surface meteorology dataset
-    met_ds = ['Pressure', 'Datalogger', 'Net Radiation', 'Temp/RH',
-              'Solar Radiation', 'Tipping Bucket', 'TBRG', 'Wind Speed',
-              'Wind Direction', 'Wind Speed and Direction']
-
-    # Add to the url depending on which instrument is requested
-    if 'Parsivel' in instrument:
-        url += 'DisdrometerParsivel/Stats/'
-    elif any([d in instrument for d in met_ds]):
-        url += 'CsiDatalogger/SurfaceMet/'
-    elif 'GpsTrimble' in instrument:
-        url += 'GpsTrimble/WaterVapor/'
-    elif 'Radar S-band Moment' in instrument:
-        url += 'Radar3000/PopMoments/'
-    elif 'Radar S-band Bright Band' in instrument:
-        url += 'Radar3000/BrightBand/'
-    elif '449RWP Bright Band' in instrument:
-        url += 'Radar449/BrightBand/'
-    elif '449RWP Wind' in instrument:
-        url += 'Radar449/WwWind/'
-    elif '449RWP Sub-Hour Wind' in instrument:
-        url += 'Radar449/WwWindSubHourly/'
-    elif '449RWP Sub-Hour Temp' in instrument:
-        url += 'Radar449/WwTempSubHourly/'
-    elif '915RWP Wind' in instrument:
-        url += 'Radar915/WwWind/'
-    elif '915RWP Temp' in instrument:
-        url += 'Radar915/WwTemp/'
-    elif '915RWP Sub-Hour Wind' in instrument:
-        url += 'Radar915/WwWindSubHourly/'
-    elif '915RWP Sub-Hour Temp' in instrument:
-        url += 'Radar915/WwTempSubHourly/'
-    elif 'Radar FMCW Moment' in instrument:
-        url += 'RadarFMCW/PopMoments/'
-    elif 'Radar FMCW Bright Band' in instrument:
-        url += 'RadarFMCW/BrightBand/'
-    else:
-        raise ValueError('Instrument not supported')
-
-    # Construct output directory
-    if output:
-        # Output files to directory specified
-        output_dir = os.path.join(output)
-    else:
-        # If no folder given, add datastream folder
-        # to current working dir to prevent file mix-up
-        output_dir = os.path.join(os.getcwd(), datastream)
-
-    if not os.path.isdir(output_dir):
-        os.makedirs(output_dir)
-
-    # Set up doy ranges, taking into account changes for a new year
-    prev_doy = 0
-    if e_doy < s_doy:
-        r = list(range(s_doy, 366)) + list(range(1, e_doy + 1))
-    else:
-        r = list(range(s_doy, e_doy + 1))
-
-    # Set filename variable to return
-    filenames = []
-
-    # Loop through each doy in range
-    for doy in r:
-        # if the previous day is greater than current, assume a new year
-        # i.e. 365 -> 001
-        if prev_doy > doy:
-            year += 1
-        # Add site, year, and 3-digit day to url
-        new_url = url + site + '/' + str(year) + '/' + str(doy).zfill(3) + '/'
-
-        # User pandas to get a list of filenames to download
-        # Exclude the first and last records which are "parent directory" and "nan"
-        files = pd.read_html(new_url, skiprows=[1])[0]['Name']
-        files = list(files[1:-1])
-
-        # Write each file out to a file with same name as online
-        for f in files:
-            if hour is not None:
-                if (str(doy).zfill(3) + str(hour)) not in f and\
-                   (str(doy).zfill(3) + '.' + str(hour)) not in f:
-                    continue
-            output_file = os.path.join(output_dir, f)
-            try:
-                print('Downloading ' + f)
-                with open(output_file, 'wb') as open_bytes_file:
-                    open_bytes_file.write(urlopen(new_url + f).read())
-                filenames.append(output_file)
-            except Exception:
-                pass
-        prev_doy = doy
-
-    return filenames
diff --git a/act/discovery/get_surfrad.py b/act/discovery/get_surfrad.py
deleted file mode 100644
index 1f08925b6e..0000000000
--- a/act/discovery/get_surfrad.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""
-Function for downloading data from
-NOAA Surface Radiation Budget network
-
-"""
-import json
-from datetime import datetime
-import pandas as pd
-import numpy as np
-import os
-import re
-import requests
-import warnings
-
-try:
-    from urllib.request import urlopen
-except ImportError:
-    from urllib import urlopen
-
-
-def download_surfrad(site=None, startdate=None, enddate=None, output=None):
-    """
-    Function to download data from the NOAA Surface Radiation Budget network.
-    https://gml.noaa.gov/grad/surfrad/
-
-    Parameters
-    ----------
-    site : str
-        3 letter NOAA site identifier. Required variable
-        List of sites can be found at https://gml.noaa.gov/grad/surfrad/sitepage.html
-    startdate : str
-        The start date of the data to acquire. Format is YYYYMMDD. Required variable
-    enddate : str
-        The end date of the data to acquire. Format is YYYYMMDD
-    output : str
-        The output directory for the data. Set to None to make a folder in the
-        current working directory with the same name as *datastream* to place
-        the files in.
-
-    Returns
-    -------
-    files : list
-        Returns list of files retrieved
-
-    """
-
-    message = 'API will be changing from act.discovery.get_surfrad.download_surfrad to act.discovery.surfrad.download_surfrad_data'
-    warnings.warn(message, DeprecationWarning, 2)
-
-    if (site is None) or (startdate is None):
-        raise ValueError('site and startdate need to be set')
-
-    site = site.lower()
-    site_dict = {
-        'bnd': 'Bondville_IL',
-        'tbl': 'Boulder_CO',
-        'dra': 'Desert_Rock_NV',
-        'fpk': 'Fort_Peck_MT',
-        'gwn': 'Goodwin_Creek_MS',
-        'psu': 'Penn_State_PA',
-        'sxf': 'Sioux_Falls_SD',
-    }
-    site_name = site_dict[site]
-
-    # Convert dates to day of year (doy) for NOAA folder structure
-    s_doy = datetime.strptime(startdate, '%Y%m%d').timetuple().tm_yday
-    year = datetime.strptime(startdate, '%Y%m%d').year
-    if enddate is None:
-        enddate = startdate
-    e_doy = datetime.strptime(enddate, '%Y%m%d').timetuple().tm_yday
-
-    # Set base URL
-    url = 'https://gml.noaa.gov/aftp/data/radiation/surfrad/'
-
-    # Construct output directory
-    if output:
-        # Output files to directory specified
-        output_dir = os.path.join(output)
-    else:
-        # If no folder given, add datastream folder
-        # to current working dir to prevent file mix-up
-        output_dir = os.path.join(os.getcwd(), site_name + '_surfrad')
-
-    if not os.path.isdir(output_dir):
-        os.makedirs(output_dir)
-
-    # Set up doy ranges, taking into account changes for a new year
-    prev_doy = 0
-    if e_doy < s_doy:
-        r = list(range(s_doy, 366)) + list(range(1, e_doy + 1))
-    else:
-        r = list(range(s_doy, e_doy + 1))
-
-    # Set filename variable to return
-    filenames = []
-
-    # Loop through each doy in range
-    for doy in r:
-        # if the previous day is greater than current, assume a new year
-        # i.e. 365 -> 001
-        if prev_doy > doy:
-            year += 1
-
-        # Add filename to url
-        file = site + str(year)[2:4] + str(doy) + '.dat'
-        new_url = url + site_name + '/' + str(year) + '/' + file
-
-        # Write each file out to a file with same name as online
-        output_file = os.path.join(output_dir, file)
-        try:
-            print('Downloading ' + file)
-            with open(output_file, 'wb') as open_bytes_file:
-                open_bytes_file.write(urlopen(new_url).read())
-            filenames.append(output_file)
-        except Exception:
-            pass
-        prev_doy = doy
-
-    return filenames
diff --git a/act/tests/test_discovery.py b/act/tests/test_discovery.py
index 75b06b1b32..97fc028984 100644
--- a/act/tests/test_discovery.py
+++ b/act/tests/test_discovery.py
@@ -1,15 +1,10 @@
 import glob
 import os
 from datetime import datetime
-
 import numpy as np
 import requests
 from requests.packages.urllib3.exceptions import InsecureRequestWarning
-
 import act
-from act.discovery import get_asos
-
-requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
 
 
 def test_cropType():
@@ -18,8 +13,8 @@ def test_cropType():
     lon = -98.362
     # Try for when the cropscape API is not working
     try:
-        crop = act.discovery.get_cropscape.croptype(lat, lon, year)
-        crop2 = act.discovery.get_cropscape.croptype(lat, lon)
+        crop = act.discovery.cropscape.get_crop_type(lat, lon, year)
+        crop2 = act.discovery.cropscape.get_crop_type(lat, lon)
     except Exception:
         return
 
@@ -33,7 +28,7 @@ def test_cropType():
 
 def test_get_ord():
     time_window = [datetime(2020, 2, 4, 2, 0), datetime(2020, 2, 12, 10, 0)]
-    my_asoses = get_asos(time_window, station='ORD')
+    my_asoses = act.discovery.get_asos_data(time_window, station='ORD')
     assert 'ORD' in my_asoses.keys()
     assert np.all(
         np.equal(
@@ -48,7 +43,7 @@ def test_get_region():
     time_window = [datetime(2020, 2, 4, 2, 0), datetime(2020, 2, 12, 10, 0)]
     lat_window = (41.8781 - 0.5, 41.8781 + 0.5)
     lon_window = (-87.6298 - 0.5, -87.6298 + 0.5)
-    my_asoses = get_asos(time_window, lat_range=lat_window, lon_range=lon_window)
+    my_asoses = act.discovery.get_asos_data(time_window, lat_range=lat_window, lon_range=lon_window)
     asos_keys = [x for x in my_asoses.keys()]
     assert asos_keys == my_keys
 
@@ -69,7 +64,7 @@ def test_get_armfile():
         enddate = startdate
         outdir = os.getcwd() + '/data/'
 
-        results = act.discovery.get_armfiles.download_data(
+        results = act.discovery.arm.download_arm_data(
             username, token, datastream, startdate, enddate, output=outdir
         )
         files = glob.glob(outdir + datastream + '*20200101*cdf')
@@ -82,19 +77,19 @@ def test_get_armfile():
                 os.remove(files[0])
 
         datastream = 'sgpmeetE13.b1'
-        act.discovery.get_armfiles.download_data(
+        act.discovery.arm.download_arm_data(
             username, token, datastream, startdate, enddate, output=outdir
         )
         files = glob.glob(outdir + datastream + '*20200101*cdf')
         assert len(files) == 0
 
         with np.testing.assert_raises(ConnectionRefusedError):
-            act.discovery.get_armfiles.download_data(
+            act.discovery.arm.download_arm_data(
                 username, token + '1234', datastream, startdate, enddate, output=outdir
             )
 
         datastream = 'sgpmetE13.b1'
-        results = act.discovery.get_armfiles.download_data(
+        results = act.discovery.arm.download_arm_data(
             username, token, datastream, startdate, enddate
         )
         assert len(results) == 1
@@ -116,7 +111,7 @@ def test_get_armfile_hourly():
         enddate = '2020-01-01T12:00:00'
         outdir = os.getcwd() + '/data/'
 
-        results = act.discovery.get_armfiles.download_data(
+        results = act.discovery.arm.download_arm_data(
             username, token, datastream, startdate, enddate, output=outdir
         )
         files = glob.glob(outdir + datastream + '*20200101*cdf')
@@ -129,19 +124,19 @@ def test_get_armfile_hourly():
                 os.remove(files[0])
 
         datastream = 'sgpmeetE13.b1'
-        act.discovery.get_armfiles.download_data(
+        act.discovery.arm.download_arm_data(
             username, token, datastream, startdate, enddate, output=outdir
         )
         files = glob.glob(outdir + datastream + '*20200101*cdf')
         assert len(files) == 0
 
         with np.testing.assert_raises(ConnectionRefusedError):
-            act.discovery.get_armfiles.download_data(
+            act.discovery.arm.download_arm_data(
                 username, token + '1234', datastream, startdate, enddate, output=outdir
             )
 
         datastream = 'sgpmetE13.b1'
-        results = act.discovery.get_armfiles.download_data(
+        results = act.discovery.arm.download_arm_data(
             username, token, datastream, startdate, enddate
         )
         assert len(results) == 1
@@ -253,23 +248,23 @@ def test_noaa_psl():
 
 def test_neon():
     site_code = 'BARR'
-    result = act.discovery.get_neon.get_site_products(site_code, print_to_screen=True)
+    result = act.discovery.get_neon_site_products(site_code, print_to_screen=True)
     assert 'DP1.00002.001' in result
     assert result['DP1.00003.001'] == 'Triple aspirated air temperature'
 
     product_code = 'DP1.00002.001'
-    result = act.discovery.get_neon.get_product_avail(site_code, product_code, print_to_screen=True)
+    result = act.discovery.get_neon_product_avail(site_code, product_code, print_to_screen=True)
     assert '2017-09' in result
     assert '2022-11' in result
 
     output_dir = os.path.join(os.getcwd(), site_code + '_' + product_code)
-    result = act.discovery.get_neon.download_neon_data(site_code, product_code, '2022-10', output_dir=output_dir)
+    result = act.discovery.download_neon_data(site_code, product_code, '2022-10', output_dir=output_dir)
     assert len(result) == 20
     assert any('readme' in r for r in result)
     assert any('sensor_position' in r for r in result)
 
-    result = act.discovery.get_neon.download_neon_data(site_code, product_code, '2022-09',
-                                                       end_date='2022-10', output_dir=output_dir)
+    result = act.discovery.download_neon_data(site_code, product_code, '2022-09',
+                                              end_date='2022-10', output_dir=output_dir)
     assert len(result) == 40
     assert any('readme' in r for r in result)
     assert any('sensor_position' in r for r in result)
@@ -291,6 +286,6 @@ def test_arm_doi():
 
 
 def test_download_surfrad():
-    results = act.discovery.download_surfrad(site='tbl', startdate='20230601', enddate='20230602')
+    results = act.discovery.download_surfrad_data(site='tbl', startdate='20230601', enddate='20230602')
     assert len(results) == 2
     assert 'tbl23152.dat' in results[0]
diff --git a/codecov.yml b/codecov.yml
index edde4fd145..37d633bc41 100644
--- a/codecov.yml
+++ b/codecov.yml
@@ -7,14 +7,6 @@ comment: false
 ignore:
   - 'act/tests/*.py'
   - 'act/plotting/histogramdisplay.py'
-  - 'act/discovery/get_arm.py'
-  - 'act/discovery/arm.py'
-  - 'act/discovery/airnow.py'
-  - 'act/discovery/asos.py'
-  - 'act/discovery/cropscape.py'
-  - 'act/discovery/neon.py'
-  - 'act/discovery/noaapsl.py'
-  - 'act/discovery/surfrad.py'
   - 'act/*version*py'
   - 'setup.py'
   - 'versioneer.py'
diff --git a/examples/discovery/plot_airnow.py b/examples/discovery/plot_airnow.py
index 8bacae79b5..24ded44078 100644
--- a/examples/discovery/plot_airnow.py
+++ b/examples/discovery/plot_airnow.py
@@ -9,9 +9,7 @@
 """
 
 import os
-
 import matplotlib.pyplot as plt
-
 import act
 
 # You need an account and token from https://docs.airnowapi.org/ first
diff --git a/examples/discovery/plot_asos_temp.py b/examples/discovery/plot_asos_temp.py
index ef1235e4b7..d145d1212d 100644
--- a/examples/discovery/plot_asos_temp.py
+++ b/examples/discovery/plot_asos_temp.py
@@ -7,14 +7,12 @@
 
 """
 from datetime import datetime
-
 import matplotlib.pyplot as plt
-
 import act
 
 time_window = [datetime(2020, 2, 4, 2, 0), datetime(2020, 2, 10, 10, 0)]
 station = 'KORD'
-my_asoses = act.discovery.get_asos(time_window, station='ORD')
+my_asoses = act.discovery.get_asos_data(time_window, station='ORD')
 
 display = act.plotting.TimeSeriesDisplay(my_asoses['ORD'], subplot_shape=(2,), figsize=(15, 10))
 display.plot('temp', subplot_index=(0,))
diff --git a/examples/discovery/plot_neon.py b/examples/discovery/plot_neon.py
index 43d9d7a61b..610d3a006d 100644
--- a/examples/discovery/plot_neon.py
+++ b/examples/discovery/plot_neon.py
@@ -21,7 +21,7 @@
 
 if token is not None and len(token) > 0:
     # Download ARM data if a username/token are set
-    files = act.discovery.download_data(username, token, 'nsametC1.b1', '2022-10-01', '2022-10-07')
+    files = act.discovery.download_arm_data(username, token, 'nsametC1.b1', '2022-10-01', '2022-10-07')
     ds = act.io.armfiles.read_netcdf(files)
 
     # Download NEON Data
@@ -29,7 +29,7 @@
     # https://www.neonscience.org/field-sites/explore-field-sites
     site_code = 'BARR'
     product_code = 'DP1.00002.001'
-    result = act.discovery.get_neon.download_neon_data(site_code, product_code, '2022-10')
+    result = act.discovery.neon.download_neon_data(site_code, product_code, '2022-10')
 
     # A number of files are downloaded and further explained in the readme file that's downloaded.
     # These are the files we will need for reading 1 minute NEON data
diff --git a/examples/io/plot_surfrad.py b/examples/io/plot_surfrad.py
index 3adccdbb48..473185f24d 100644
--- a/examples/io/plot_surfrad.py
+++ b/examples/io/plot_surfrad.py
@@ -13,7 +13,7 @@
 import matplotlib.pyplot as plt
 
 # Easily download data from SURFRAD
-results = act.discovery.download_surfrad('tbl', startdate='20230601', enddate='20230602')
+results = act.discovery.download_surfrad_data('tbl', startdate='20230601', enddate='20230602')
 print(results)
 
 # But it's easy enough to read form the URLs as well
diff --git a/examples/plotting/plot_ceil.py b/examples/plotting/plot_ceil.py
index ef84721782..d8c03f3a86 100644
--- a/examples/plotting/plot_ceil.py
+++ b/examples/plotting/plot_ceil.py
@@ -21,7 +21,7 @@
     ceil_ds = act.io.armfiles.read_netcdf(act.tests.sample_files.EXAMPLE_CEIL1, engine='netcdf4')
 else:
     # Example to show how easy it is to download ARM data if a username/token are set
-    results = act.discovery.download_data(username, token, 'sgpceilC1.b1', '2022-01-14', '2022-01-19')
+    results = act.discovery.download_arm_data(username, token, 'sgpceilC1.b1', '2022-01-14', '2022-01-19')
     ceil_ds = act.io.armfiles.read_netcdf(results)
 
 # Adjust ceilometer data for plotting
diff --git a/examples/qc/plot_arm_qc.py b/examples/qc/plot_arm_qc.py
index c775fec1ec..8a4f8de373 100644
--- a/examples/qc/plot_arm_qc.py
+++ b/examples/qc/plot_arm_qc.py
@@ -26,7 +26,7 @@
 if username is None or token is None or len(username) == 0 or len(token) == 0:
     results = act.tests.sample_files.EXAMPLE_MFRSR
 else:
-    results = act.discovery.download_data(
+    results = act.discovery.download_arm_data(
         username, token, 'sgpmfrsr7nchE11.b1', '2021-03-29', '2021-03-29'
     )
 print(results)
diff --git a/examples/templates/example_template.py b/examples/templates/example_template.py
index dc15a3337f..6c594ac40e 100644
--- a/examples/templates/example_template.py
+++ b/examples/templates/example_template.py
@@ -10,7 +10,7 @@
 
 # Download and read file or files with the IO and discovery functions
 # within ACT, example:
-results = act.discovery.download_data(
+results = act.discovery.download_arm_data(
     username, token, 'sgpceilC1.b1', '2022-01-14', '2022-01-19')
 ceil_ds = act.io.armfiles.read_netcdf(results)
 
diff --git a/examples/workflows/plot_aerioe_with_cbh.py b/examples/workflows/plot_aerioe_with_cbh.py
index f9240334de..a697a4c967 100644
--- a/examples/workflows/plot_aerioe_with_cbh.py
+++ b/examples/workflows/plot_aerioe_with_cbh.py
@@ -22,9 +22,9 @@
 if username is None or token is None or len(username) == 0 or len(token) == 0:
     pass
 else:
-    results = act.discovery.download_data(username, token, 'sgpaerioe1turnC1.c1', '2022-02-11', '2022-02-11')
+    results = act.discovery.download_arm_data(username, token, 'sgpaerioe1turnC1.c1', '2022-02-11', '2022-02-11')
     aerioe_ds = act.io.armfiles.read_netcdf(results)
-    results = act.discovery.download_data(username, token, 'sgpceilC1.b1', '2022-02-11', '2022-02-11')
+    results = act.discovery.download_arm_data(username, token, 'sgpceilC1.b1', '2022-02-11', '2022-02-11')
     ceil_ds = act.io.armfiles.read_netcdf(results)
 
     # There isn't information content from the AERI above 3 km
diff --git a/examples/workflows/plot_multiple_dataset.py b/examples/workflows/plot_multiple_dataset.py
index 9a0d9fbf01..fc7c0c3f12 100644
--- a/examples/workflows/plot_multiple_dataset.py
+++ b/examples/workflows/plot_multiple_dataset.py
@@ -24,9 +24,9 @@
     met_ds = act.io.armfiles.read_netcdf(act.tests.sample_files.EXAMPLE_MET1)
 else:
     # Download and read data
-    results = act.discovery.download_data(username, token, 'sgpceilC1.b1', '2022-01-01', '2022-01-07')
+    results = act.discovery.download_arm_data(username, token, 'sgpceilC1.b1', '2022-01-01', '2022-01-07')
     ceil_ds = act.io.armfiles.read_netcdf(results)
-    results = act.discovery.download_data(username, token, 'sgpmetE13.b1', '2022-01-01', '2022-01-07')
+    results = act.discovery.download_arm_data(username, token, 'sgpmetE13.b1', '2022-01-01', '2022-01-07')
     met_ds = act.io.armfiles.read_netcdf(results)
 
 # Read in CEIL data and correct it