cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5439/1973366
title: rain (a1)

description: A rain gauge (RAIN) gathers and measures the amount of liquid precipitation over a set period of time. ARM uses many types of rain gauges at its climate research facilities. The tipping bucket rain gauge consists of a funnel that collects and directs precipitation into a seesaw container that tips after a pre-set amount of liquid enters. When the seesaw tips, it sends an electrical signal that is counted by a recording device. The weighing precipitation gauge consists of a storage container, which is weighted to record the mass of precipitation. Some models measure the mass of precipitation using a pen on a rotating drum or by using a vibrating wire attached to a data logger. The advantage of this type of rain gauge is that it can measure all forms of precipitation. Disdrometers are used with rain gauges to add details about the precipitation being gathered. They are capable of measuring the velocity and size distribution of precipitation, measurements important for studying the evolution of water droplets.
DOI: 10.5439/1973367
title: rain (b1)

description: A rain gauge (RAIN) gathers and measures the amount of liquid precipitation over a set period of time. ARM uses many types of rain gauges at its climate research facilities. The tipping bucket rain gauge consists of a funnel that collects and directs precipitation into a seesaw container that tips after a pre-set amount of liquid enters. When the seesaw tips, it sends an electrical signal that is counted by a recording device. The weighing precipitation gauge consists of a storage container, which is weighted to record the mass of precipitation. Some models measure the mass of precipitation using a pen on a rotating drum or by using a vibrating wire attached to a data logger. The advantage of this type of rain gauge is that it can measure all forms of precipitation. Disdrometers are used with rain gauges to add details about the precipitation being gathered. They are capable of measuring the velocity and size distribution of precipitation, measurements important for studying the evolution of water droplets.
DOI: 10.5439/1973368
title: aosmet (01)

description: AOSMET are standard meteorological measurements of the ambient temperature, pressure, relative humidity, and wind speed and direction intended solely for analyzing the data from the AOS instruments.
DOI: 10.5439/1973369
title: aoppsap1flynn1h (s1)

description: aoppsap1flynn1h
DOI: 10.5439/1973370
title: aoppsap1flynn1m (s1)

description: aoppsap1flynn1m
DOI: 10.5439/1973374
title: mfr3m (b1)

description: The multifilter radiometer (MFR) is simply the head from a multifilter rotating shadowband radiometer (MFRSR) mounted on a tower pointing at the surface. Like the MFRSR, it has six narrowband channels (415, 500, 615, 673, 870, and 940 nm) and one broadband channel. Pointing the head at the surface provides spectral measurements of reflected irradiance. Measurements are taken every 20 seconds, with no averaging performed.
DOI: 10.5439/1973375
title: aoshumidgr (01)

description: The aerosol observing system (AOS) is the primary platform for in situ aerosol measurements at Earth’s surface. Each AOS has a different complement of instruments, which are selected to ensure the best measurements at each deployment site. The AOS measures aerosol optical properties to better understand how particles interact with solar radiation and influence the Earth's radiation balance. These measurements are useful for calculating parameters used in radiative forcing calculations, such as the aerosol single scattering albedo, asymmetry parameter, mass scattering efficiency, and hygroscopic growth. Measurements made with the AOS form a long-term record at the ARM fixed sites. Shorter measurement records are available from mobile deployments in a wide variety of geographical regions. These measurements are valuable for: identifying long-term changes in aerosol properties identifying atmospheric processes controlling aerosol life cycle and their influence on clouds providing experimental data for developing and testing atmospheric climate models.
DOI: 10.6084/m9.figshare.22826417
title: Annual dry season crop calendar of paddy in Indonesia from 2001 – 2021 with 1-km spatial resolution

description: This dataset contains the annual dry season crop calendar of paddy in Indonesia from 2001 - 2021 with 1-km spatial resolution. Developed using model-based approach using the MODIS-Terra dataset (MOD13A2). This dataset includes the information about the Start of Season (SOS) or the planting dates, the Peak of Season (POS), and the End of Season (EOS) or harvesting dates as well as the total number of cropping season each year which includes both the wet and the dry cropping season. This dataset offers information at a finer spatiotemporal resolution to enable studies on agricultural fields under climate change.
DOI: 10.6084/m9.figshare.22826417.v1
title: Annual dry season crop calendar of paddy in Indonesia from 2001 – 2021 with 1-km spatial resolution

description: This dataset contains the annual dry season crop calendar of paddy in Indonesia from 2001 - 2021 with 1-km spatial resolution. Developed using model-based approach using the MODIS-Terra dataset (MOD13A2). This dataset includes the information about the Start of Season (SOS) or the planting dates, the Peak of Season (POS), and the End of Season (EOS) or harvesting dates as well as the total number of cropping season each year which includes both the wet and the dry cropping season. This dataset offers information at a finer spatiotemporal resolution to enable studies on agricultural fields under climate change.
DOI: 10.5439/1973480
title: TRACER CLAMPS2 MWR TROPoe Retrievals

description: These files contain 24 hour periods of retrieved thermodynamic profiles derived from observations collected by the CLAMPS2 Atmospheric Emitted Radiance Interferometer (AERI) and microwave radiometer (MWR). The TROPoe algorithm is a python equivalent to the AERIoe algorithm (see Turner and Loehnert, 2014; Turner and Blumberg 2019). These data were collected during the TRACER-CUBIC project. MWR only and combined AERI and MWR retrievals are available. <strong>During this deployment the AERI had high noise so AERI only retrievals are unavailable and the combined AERI and MWR retrievals should be used with extreme caution.</strong>
DOI: 10.6084/m9.figshare.22826426
title: Differentially expressed genes

description: Differentially expressed genes in response to cadmium <br>
DOI: 10.6084/m9.figshare.22826426.v1
title: Differentially expressed genes

description: Differentially expressed genes in response to cadmium <br>
DOI: 10.6084/m9.figshare.21282675
title: ARF_Sites_Pixels_LiDAR_Covariates.xlsx

description: LiDAR-derived covariate data (at the pixel level) for a tree species classification in the Abitibi River Forest (ARF) in the District of Cochrane in northern Ontario, Canada. <br> Covariates were generated from LiDAR data obtained from Land Information Ontario (LIO), for data from the Ontario Ministry of Natural Resources and Forestry (MNRF). LiDAR data was relative to 2016 - 2017. <br> Contains information licensed under the Open Government Licence - Ontario
DOI: 10.6084/m9.figshare.21282675.v1
title: ARF_Sites_Pixels_LiDAR_Covariates.xlsx

description: LiDAR-derived covariate data (at the pixel level) for a tree species classification in the Abitibi River Forest (ARF) in the District of Cochrane in northern Ontario, Canada. <br> Covariates were generated from LiDAR data obtained from Land Information Ontario (LIO), for data from the Ontario Ministry of Natural Resources and Forestry (MNRF). LiDAR data was relative to 2016 - 2017. <br> Contains information licensed under the Open Government Licence - Ontario
DOI: 10.6084/m9.figshare.21128035.v4
title: Data for "An investigation on spatiotemporal sizes of specific types of lightning flashes"

description: This dataset is for a manuscript titled "An investigation on spatiotemporal sizes of specific types of lightning flashes". The figures of our results are included in 'Figure **.fig', and the data are included in 'Flash**Lightning.mat'. These files can be opened by MATLAB R2021a(or later). The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citations.
DOI: 10.6084/m9.figshare.22357813
title: Dataset for article "Effects of Breaking and Non-breaking Surface Wave Mixing on the Changjiang River Plume Dynamics in Summer"

description: Observed dataset used in "Effects of Breaking and Non-breaking Surface Wave Mixing on the Changjiang River Plume Dynamics in Summer".
DOI: 10.6084/m9.figshare.22357813.v1
title: Dataset for article "Effects of Breaking and Non-breaking Surface Wave Mixing on the Changjiang River Plume Dynamics in Summer"

description: Observed dataset used in "Effects of Breaking and Non-breaking Surface Wave Mixing on the Changjiang River Plume Dynamics in Summer".
DOI: 10.6084/m9.figshare.22357813.v2
title: Dataset for article "Effects of Breaking and Non-breaking Surface Wave Mixing on the Changjiang River Plume Dynamics in Summer"

description: Observed dataset used in "Effects of Breaking and Non-breaking Surface Wave Mixing on the Changjiang River Plume Dynamics in Summer".
DOI: 10.26179/rdm9-4798
title: Glaciology and geophysical survey of Law Dome, 1970
DOI: 10.5281/zenodo.7935303
title: Sentinel2 RGB chips over Colombia (NE) with ESA World Cover for Learning with Label Proportions

description: <strong>Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering<br> parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca.</strong> We use the communes administrative division defined by DANE (Departamento Administrativo<br> Nacional de Estadística) under "municipios" in the MGN2021 at <br> https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/ <strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels see https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py <strong>labels: ESA WorldCover 10m V100</strong><br> labels mapped to the interval [1,11] according to the following map<br> { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }<br> pixel value zero is reserved for invalid data.<br> see https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100<br> <br> see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
DOI: 10.5281/zenodo.7935302
title: Sentinel2 RGB chips over Colombia (NE) with ESA World Cover for Learning with Label Proportions

description: <strong>Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering<br> parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca.</strong> We use the communes administrative division defined by DANE (Departamento Administrativo<br> Nacional de Estadística) under "municipios" in the MGN2021 at <br> https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/ <strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels see https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py <strong>labels: ESA WorldCover 10m V100</strong><br> labels mapped to the interval [1,11] according to the following map<br> { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }<br> pixel value zero is reserved for invalid data.<br> see https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100<br> <br> see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
DOI: 10.5281/zenodo.7935237
title: Sentinel2 RGB chips over BENELUX with ESA World Cover for Learning with Label Proportions

description: <strong>Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg</strong> We use the communes administrative division which is standardized across Europe by EUROSTAT at:<br> https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units<br> This is roughly equivalent to the notion municipalities in most countries. From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders<br> are taken from NUTS_RG_01M_2021_3035.shp. <strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels see https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py <strong>labels: ESA WorldCover 10m V100</strong><br> labels mapped to the interval [1,11] according to the following map<br> { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }<br> pixel value zero is reserved for invalid data.<br> see https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100<br> <br> see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
DOI: 10.5281/zenodo.7935236
title: Sentinel2 RGB chips over BENELUX with ESA World Cover for Learning with Label Proportions

description: <strong>Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg</strong> We use the communes administrative division which is standardized across Europe by EUROSTAT at:<br> https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units<br> This is roughly equivalent to the notion municipalities in most countries. From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders<br> are taken from NUTS_RG_01M_2021_3035.shp. <strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels see https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py <strong>labels: ESA WorldCover 10m V100</strong><br> labels mapped to the interval [1,11] according to the following map<br> { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }<br> pixel value zero is reserved for invalid data.<br> see https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100<br> <br> see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
DOI: 10.6084/m9.figshare.22227124
title: CrudeOilTrainPictureLoc_Flickr_2012-2022_shapefile

description: Crude oil train pictures parsed from Flickr website with the keyword 'crude oil train' between year 2012 and 2022.
DOI: 10.15468/dl.t69g62
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((125.02682 2.74675,123.62157 1.58463,120.32649 1.6815,118.92123 -0.40191,118.24283 -3.64607,119.16352 -6.25276,120.71414 -6.78234,124.25151 -5.33679,123.42774 -2.72682,124.39688 -1.08024,126.04442 1.87523,125.02682 2.74675))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ailurops ursinus (Temminck, 1824)"
 ]
}

 The dataset includes 79 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032009-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gemhr3
title: Occurrence Download

description: A dataset containing 210 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chiton albolineatus Broderip & G.B.Sowerby I, 1829"
 ]
}

 The dataset includes 210 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008055-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.0f4yfc
title: Occurrence Download

description: A dataset containing 784 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "Geometry POLYGON ((-79.1015625 -0.08789059053082422, -79.1015625 10.401377554543553, -72.333984375 10.401377554543553, -72.333984375 -0.08789059053082422, -79.1015625 -0.08789059053082422))",
 "TaxonKey is Rupicola peruvianus (Latham, 1790)"
 ]
}

 The dataset includes 784 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004328-170826194755519/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hbm5tu
title: Occurrence Download

description: A dataset containing 31724 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rubus phoenicolasius Maxim."
 ]
}

 The dataset includes 31724 records from 157 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0138301-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vwgzrn
title: Occurrence Download

description: A dataset containing 1297278 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "HasCoordinate is true",
 "TaxonKey is Tracheophyta",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1297278 records from 211 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007376-191105090559680/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.addj92
title: Occurrence Download

description: A dataset containing 952 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-135.00949 -40.49138,10.00949 -40.49138,10.00949 17.49138,-135.00949 17.49138,-135.00949 -40.49138))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pteronura brasiliensis Zimmermann, 1780"
 ]
}

 The dataset includes 952 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025552-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gz67ny
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Deyeuxia ligulata Kunth"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008053-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.udfkvs
title: Occurrence Download

description: A dataset containing 5630 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Melanotis caerulescens (Swainson, 1827)",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5630 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004329-150316153013904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kxsyvh
title: Occurrence Download

description: A dataset containing 2407 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Carcharhinus longimanus (Poey, 1861)"
 ]
}

 The dataset includes 2407 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025561-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.