CivicMemory

Timeline / Data.gov — Climate Datasets

changed Source changed

A new raw object was archived. Both versions are preserved. 2096 line(s) added, 1790 line(s) removed.

Evidence

SourceData.gov — Climate Datasets
AgencyData.gov
URLhttps://api.gsa.gov/technology/datagov/v4/search?q=climate&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY}
Observed by Civic Memory, directly, on 2026-10-04T06:22:52+00:00
Content typeapplication/json
Current object 3e339db7f75f1c32d1edbb01f9b0d1d780e22497ed7ac007e68efe6f7f714f1c download raw metadata
Previous object 7b949ba97fb95a79ba0cb54ca8731dd5ed60afc8caf796764c5f28bcd8fadcde download raw metadata

What changed derived

This diff is not evidence. It was produced by civic-memory.diff_engine 1.1.0 at 2026-10-04T06:22:52+00:00 by normalizing the two archived objects above. The objects are authoritative; this reading of them can be regenerated or deleted without loss. 2096 line(s) added, 1790 line(s) removed.

--- previous
+++ current
@@ -1,11 +1,2051 @@
 {
- "after": "WzE3OTA0NzIyNTA2NzMsNjEuMDA3NTcyLDAsIjAxOGRkZTdmLTFjZmMtNDVhYi05NjliLTUzNjllY2NjYTEzZSJd",
+ "after": "WzE3OTA1NTYxNjI5MzEsNDQuMjEyOTU1LDAsImNkNjM1MWUyLTRhMGMtNGI0ZC04NDkzLWQ2MjNhNjc1M2ViZSJd",
  "results": [
  {
- "_score": 65.40135,
+ "_score": 16.37214,
+ "_sort": [
+ 1791086007831,
+ 16.37214,
+ 0,
+ "1d0740da-1a54-4ccd-8dd4-f64928289359"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Antonio Celis-Murillo",
+ "hasEmail": "mailto:acelis-murillo@usgs.gov"
+ },
+ "description": "The North American Bird Banding Program (NABBP) is administered by the U.S. Geological Survey (USGS) Bird Banding Laboratory (BBL), Eastern Ecological Science Center at the Patuxent Research Refuge (EESC) and in Canada by the Bird Banding Office (BBO), Environment and Climate Change Canada (ECCC). This long-term dataset (1960-2026) contains over 87 million bird banding, encounter and recapture records for 1,096 bird species protected under the Migratory Bird Treaty Act (MBTA).",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P1CCONUX",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6aa98c981ba49b8cd545a29f.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6aa98c981ba49b8cd545a29f",
+ "keyword": [
+ "Africa",
+ "Antarctica",
+ "Asia",
+ "Aves",
+ "Europe",
+ "North America",
+ "Oceania",
+ "South America",
+ "USGS:6aa98c981ba49b8cd545a29f",
+ "bird banding",
+ "capturing (animals)",
+ "migratory species",
+ "natural resource management",
+ "tagging"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-180.0000, -90.0000, 180.0000, 90.0000",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "North American Bird Banding Program Dataset 1960-2026 retrieved 2026-07-15"
+ },
+ "description": "The North American Bird Banding Program (NABBP) is administered by the U.S. Geological Survey (USGS) Bird Banding Laboratory (BBL), Eastern Ecological Science Center at the Patuxent Research Refuge (EESC) and in Canada by the Bird Banding Office (BBO), Environment and Climate Change Canada (ECCC). This long-term dataset (1960-2026) contains over 87 million bird banding, encounter and recapture records for 1,096 bird species protected under the Migratory Bird Treaty Act (MBTA).",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/72443b7a-0bc3-44e5-a834-9aa7616aa916",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/72443b7a-0bc3-44e5-a834-9aa7616aa916/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6aa98c981ba49b8cd545a29f",
+ "keyword": [
+ "Africa",
+ "Antarctica",
+ "Asia",
+ "Aves",
+ "Europe",
+ "North America",
+ "Oceania",
+ "South America",
+ "USGS:6aa98c981ba49b8cd545a29f",
+ "bird banding",
+ "capturing (animals)",
+ "migratory species",
+ "natural resource management",
+ "tagging"
+ ],
+ "last_harvested_date": "2026-10-04T03:53:27.831574",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "north-american-bird-banding-program-dataset-1960-2026-retrieved-2026-07-15",
+ "spatial_centroid": {
+ "lat": -18.0,
+ "lon": -36.0
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -180.0,
+ -90.0
+ ],
+ [
+ -180.0,
+ 90.0
+ ],
+ [
+ 180.0,
+ 90.0
+ ],
+ [
+ 180.0,
+ -90.0
+ ],
+ [
+ -180.0,
+ -90.0
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "North American Bird Banding Program Dataset 1960-2026 retrieved 2026-07-15",
+ "type": "dataset"
+ },
+ {
+ "_score": 10.162827,
+ "_sort": [
+ 1791085807806,
+ 10.162827,
+ 0,
+ "72c5d813-c3aa-428e-b733-2d79ded67d99"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Shelley D. Crausbay",
+ "hasEmail": "mailto:casc-data@usgs.gov"
+ },
+ "description": "This database integrates a list of vegetation transformations that occurred across the Southern and Middle Rockies since 21,000 years ago, the age of occurrence, the type of vegetation switch that occurred, whether the rates of vegetation change peaked at that time, and when applicable, the duration of peak rates of vegetation change.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.21429/2aj3-pg02",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.624b5719d34e21f827635d16.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_624b5719d34e21f827635d16",
+ "keyword": [
+ "USGS:624b5719d34e21f827635d16",
+ "atmospheric and climatic processes",
+ "climate change",
+ "datasets",
+ "external research support",
+ "vegetation"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-114.5969, 35.2875, -103.2169, 48.9706",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Type and speed of vegetation transformations over the past 21,000 years in the Middle and Southern Rockies, U.S.A."
+ },
+ "description": "This database integrates a list of vegetation transformations that occurred across the Southern and Middle Rockies since 21,000 years ago, the age of occurrence, the type of vegetation switch that occurred, whether the rates of vegetation change peaked at that time, and when applicable, the duration of peak rates of vegetation change.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/2a14caa2-6fe8-4a44-9e2d-6b857f4e8c66",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/2a14caa2-6fe8-4a44-9e2d-6b857f4e8c66/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_624b5719d34e21f827635d16",
+ "keyword": [
+ "USGS:624b5719d34e21f827635d16",
+ "atmospheric and climatic processes",
+ "climate change",
+ "datasets",
+ "external research support",
+ "vegetation"
+ ],
+ "last_harvested_date": "2026-10-04T03:50:07.806047",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "type-and-speed-of-vegetation-transformations-over-the-past-21000-years-in-the-middle-and-s",
+ "spatial_centroid": {
+ "lat": 40.76074,
+ "lon": -110.04490000000001
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -114.5969,
+ 35.2875
+ ],
+ [
+ -114.5969,
+ 48.9706
+ ],
+ [
+ -103.2169,
+ 48.9706
+ ],
+ [
+ -103.2169,
+ 35.2875
+ ],
+ [
+ -114.5969,
+ 35.2875
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Type and speed of vegetation transformations over the past 21,000 years in the Middle and Southern Rockies, U.S.A.",
+ "type": "dataset"
+ },
+ {
+ "_score": 22.33466,
+ "_sort": [
+ 1791085361326,
+ 22.33466,
+ 0,
+ "d32329f9-6619-4da0-9190-cfb95d90433c"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Thomas Giambelluca",
+ "hasEmail": "mailto:thomas@hawaii.edu"
+ },
+ "description": "These files contain two datasets. First are vertical fluxes of energy, water vapor and carbon dioxide calculated by the eddy covariance technique using measurements taken at Olaa tower (Flux Data). Second are results of historical and future runs of the Community Land Model (CLM) for the Thurston and Olaa tower sites (CLM Output Data). Output includes time series of energy, water vapor, and carbon dioxide exchanges at each site. The historical runs are forced by gap-filled measured time series at each site. Future data sets were constructed by shifting values in the historical run by increments selected for possible future scenarios. Increments were based on the results of statistical downscaling of future climate by Elison Timm et al. (2015, Statistical downscaling of rainfall changes in Hawai‘i based on the CMIP5 global model projections, Journal of Geophysical Research-Atmospheres 120: 92-112, doi: 10.1002/2014JD022059) and Elison Timm and Fortini (2016, Statistical estimation of future temperature anomalies, data product, http://www.atmos.albany.edu/facstaff/timm/products_data.html).",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P14P4IYZ",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5bf49b8fe4b045bfcae26a41.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5bf49b8fe4b045bfcae26a41",
+ "keyword": [
+ "USGS:5bf49b8fe4b045bfcae26a41",
+ "climate change",
+ "community land model",
+ "ecosystem carbon exchange",
+ "environment",
+ "evapotranspiration",
+ "external research support",
+ "future scenarios",
+ "modeling"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-155.23828, 19.41522, -155.21500, 19.47852",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Ecosystem fluxes and Community Land Model outputs for Thurston and Olaa study sites, Hawai'i"
+ },
+ "description": "These files contain two datasets. First are vertical fluxes of energy, water vapor and carbon dioxide calculated by the eddy covariance technique using measurements taken at Olaa tower (Flux Data). Second are results of historical and future runs of the Community Land Model (CLM) for the Thurston and Olaa tower sites (CLM Output Data). Output includes time series of energy, water vapor, and carbon dioxide exchanges at each site. The historical runs are forced by gap-filled measured time series at each site. Future data sets were constructed by shifting values in the historical run by increments selected for possible future scenarios. Increments were based on the results of statistical downscaling of future climate by Elison Timm et al. (2015, Statistical downscaling of rainfall changes in Hawai‘i based on the CMIP5 global model projections, Journal of Geophysical Research-Atmospheres 120: 92-112, doi: 10.1002/2014JD022059) and Elison Timm and Fortini (2016, Statistical estimation of future temperature anomalies, data product, http://www.atmos.albany.edu/facstaff/timm/products_data.html).",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/e0eddb9f-f99f-4ad6-9956-6c624d32b55b",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/e0eddb9f-f99f-4ad6-9956-6c624d32b55b/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5bf49b8fe4b045bfcae26a41",
+ "keyword": [
+ "USGS:5bf49b8fe4b045bfcae26a41",
+ "climate change",
+ "community land model",
+ "ecosystem carbon exchange",
+ "environment",
+ "evapotranspiration",
+ "external research support",
+ "future scenarios",
+ "modeling"
+ ],
+ "last_harvested_date": "2026-10-04T03:42:41.326032",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "ecosystem-fluxes-and-community-land-model-outputs-for-thurston-and-olaa-study-sites-hawaii",
+ "spatial_centroid": {
+ "lat": 19.440540000000002,
+ "lon": -155.228968
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -155.23828,
+ 19.41522
+ ],
+ [
+ -155.23828,
+ 19.47852
+ ],
+ [
+ -155.215,
+ 19.47852
+ ],
+ [
+ -155.215,
+ 19.41522
+ ],
+ [
+ -155.23828,
+ 19.41522
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Ecosystem fluxes and Community Land Model outputs for Thurston and Olaa study sites, Hawai'i",
+ "type": "dataset"
+ },
+ {
+ "_score": 52.174557,
+ "_sort": [
+ 1791084900911,
+ 52.174557,
+ 0,
+ "7083a342-6827-429a-92e7-2b5a190f404c"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Hongli Feng",
+ "hasEmail": "mailto:hennes65@msu.edu"
+ },
+ "description": "This dataset uses predicted land use changes in predicted future climate change scenarios, in acres per thousand county acres. The data is derived through econometric models which were estimated from historical yield, weather, and market records.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P13S3TCQ",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5c51d6f5e4b0708288fb1051.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5c51d6f5e4b0708288fb1051",
+ "keyword": [
+ "North Dakota",
+ "South Dakota",
+ "USGS:5c51d6f5e4b0708288fb1051",
+ "agriculture",
+ "biota",
+ "climate change",
+ "effects of climate change",
+ "external research support",
+ "geospatial datasets",
+ "land use change"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-104.0630, 42.4885, -96.4394, 49.0000",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Estimation of Climate-driven Acreage Change in Major Crops across North and South Dakota Counties during Historical and Future Periods (1981–2060)"
+ },
+ "description": "This dataset uses predicted land use changes in predicted future climate change scenarios, in acres per thousand county acres. The data is derived through econometric models which were estimated from historical yield, weather, and market records.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/ba68d8eb-dd60-4934-9bb8-4efa95894310",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/ba68d8eb-dd60-4934-9bb8-4efa95894310/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5c51d6f5e4b0708288fb1051",
+ "keyword": [
+ "North Dakota",
+ "South Dakota",
+ "USGS:5c51d6f5e4b0708288fb1051",
+ "agriculture",
+ "biota",
+ "climate change",
+ "effects of climate change",
+ "external research support",
+ "geospatial datasets",
+ "land use change"
+ ],
+ "last_harvested_date": "2026-10-04T03:35:00.911544",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "estimation-of-climate-driven-acreage-change-in-major-crops-across-north-and-south-dakota-c",
+ "spatial_centroid": {
+ "lat": 45.09310000000001,
+ "lon": -101.01356000000001
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -104.063,
+ 42.4885
+ ],
+ [
+ -104.063,
+ 49.0
+ ],
+ [
+ -96.4394,
+ 49.0
+ ],
+ [
+ -96.4394,
+ 42.4885
+ ],
+ [
+ -104.063,
+ 42.4885
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Estimation of Climate-driven Acreage Change in Major Crops across North and South Dakota Counties during Historical and Future Periods (1981–2060)",
+ "type": "dataset"
+ },
+ {
+ "_score": 44.27888,
+ "_sort": [
+ 1791084252589,
+ 44.27888,
+ 0,
+ "add6a928-b11f-4069-9d82-948bff9ae654"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Bridget Thrasher",
+ "hasEmail": "mailto:bridget@climateanalyticsgroup.org"
+ },
+ "description": "This archive contains 234 projections of monthly BCSD CMIP5 projections of precipitation and monthly means of daily-average, daily maximum and daily minimum temperature over the contiguous United States. For more information visit http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/cmip5_bcsd",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6776f712d34ee88ba0b158de.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6776f712d34ee88ba0b158de",
+ "keyword": [
+ "USGS:6776f712d34ee88ba0b158de",
+ "air temperature",
+ "climate",
+ "climate change",
+ "climatologyMeteorologyAtmosphere",
+ "downscaled climate projections",
+ "geospatial datasets",
+ "precipitation",
+ "precipitation (atmospheric)"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-124.6800, 25.1800, -67.0500, 52.8100",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Bias Corrected Spatially Downscaled Monthly CMIP5 Climate Projections"
+ },
+ "description": "This archive contains 234 projections of monthly BCSD CMIP5 projections of precipitation and monthly means of daily-average, daily maximum and daily minimum temperature over the contiguous United States. For more information visit http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/d4b885c8-0358-4956-a6ac-62f3cf9998ea",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/d4b885c8-0358-4956-a6ac-62f3cf9998ea/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6776f712d34ee88ba0b158de",
+ "keyword": [
+ "USGS:6776f712d34ee88ba0b158de",
+ "air temperature",
+ "climate",
+ "climate change",
+ "climatologyMeteorologyAtmosphere",
+ "downscaled climate projections",
+ "geospatial datasets",
+ "precipitation",
+ "precipitation (atmospheric)"
+ ],
+ "last_harvested_date": "2026-10-04T03:24:12.589940",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "bias-corrected-spatially-downscaled-monthly-cmip5-climate-projections",
+ "spatial_centroid": {
+ "lat": 36.232,
+ "lon": -101.628
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -124.68,
+ 25.18
+ ],
+ [
+ -124.68,
+ 52.81
+ ],
+ [
+ -67.05,
+ 52.81
+ ],
+ [
+ -67.05,
+ 25.18
+ ],
+ [
+ -124.68,
+ 25.18
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Bias Corrected Spatially Downscaled Monthly CMIP5 Climate Projections",
+ "type": "dataset"
+ },
+ {
+ "_score": 24.131433,
+ "_sort": [
+ 1791083663462,
+ 24.131433,
+ 0,
+ "ec7dd01e-ef6a-4220-a89c-73f531d345a0"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Jonathan P. Price",
+ "hasEmail": "mailto:jpprice@hawaii.edu"
+ },
+ "description": "These layers depicts projected abundance of native plant species in the main Hawaiian Islands with high levels of uncertainty removed in post-processing. To estimate native and invasive species abundance in baseline climate conditions, a map was generated that considered abundance as percent cover and used high coefficient of variation values as a mask. The primary sources for post-processing the uncertainty masks are the Hawaiian Islands plant species abundance modeled means and standard deviation values. These maps cover the entire landscape (including urban and agricultural areas), and therefore they can be applied in a variety of ways. Maps can be utilized to evaluate baseline climate conditions for each species and to forecast changes to community structure through expected shifts in species abundance. Maps can be combined in order to predict community dominance, for example, by identifying the most abundant native species at a given location. This can be applied to: 1) spatial and temporal assessments of habitat quality and community structure comparisons; 2) defining specific ecological restoration objectives; and 3) identifying potential for key invasive species to threaten a site (even where they are presently not found). Future projected abundances can enhance conservation planning both by anticipating where native species may increase or decrease in abundance and by identifying areas where invasive species may extend their range. Combining this with assessments of relative response rates can aid managers in prioritizing native species to promote and invasive species to preemptively control at a given site.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P13LSDGG",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.55bbdbade4b033ef52100e2a.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_55bbdbade4b033ef52100e2a",
+ "keyword": [
+ "Hawaii",
+ "Hawaiian Islands",
+ "USGS:55bbdbade4b033ef52100e2a",
+ "climate change",
+ "dominance",
+ "external research support",
+ "geospatial datasets",
+ "invasive species",
+ "native species",
+ "plant communities",
+ "pre-SM502.8",
+ "species abundance models",
+ "terrestrial plant species",
+ "uncertainty",
+ "vegetation"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-159.8347, 18.7977, -154.5680, 22.3269",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "2015 Hawaiian Islands Plant Species Abundance Models"
+ },
+ "description": "These layers depicts projected abundance of native plant species in the main Hawaiian Islands with high levels of uncertainty removed in post-processing. To estimate native and invasive species abundance in baseline climate conditions, a map was generated that considered abundance as percent cover and used high coefficient of variation values as a mask. The primary sources for post-processing the uncertainty masks are the Hawaiian Islands plant species abundance modeled means and standard deviation values. These maps cover the entire landscape (including urban and agricultural areas), and therefore they can be applied in a variety of ways. Maps can be utilized to evaluate baseline climate conditions for each species and to forecast changes to community structure through expected shifts in species abundance. Maps can be combined in order to predict community dominance, for example, by identifying the most abundant native species at a given location. This can be applied to: 1) spatial and temporal assessments of habitat quality and community structure comparisons; 2) defining specific ecological restoration objectives; and 3) identifying potential for key invasive species to threaten a site (even where they are presently not found). Future projected abundances can enhance conservation planning both by anticipating where native species may increase or decrease in abundance and by identifying areas where invasive species may extend their range. Combining this with assessments of relative response rates can aid managers in prioritizing native species to promote and invasive species to preemptively control at a given site.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/b04b85c0-5d45-47d0-ad19-8ad439b5e41a",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/b04b85c0-5d45-47d0-ad19-8ad439b5e41a/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_55bbdbade4b033ef52100e2a",
+ "keyword": [
+ "Hawaii",
+ "Hawaiian Islands",
+ "USGS:55bbdbade4b033ef52100e2a",
+ "climate change",
+ "dominance",
+ "external research support",
+ "geospatial datasets",
+ "invasive species",
+ "native species",
+ "plant communities",
+ "pre-SM502.8",
+ "species abundance models",
+ "terrestrial plant species",
+ "uncertainty",
+ "vegetation"
+ ],
+ "last_harvested_date": "2026-10-04T03:14:23.462781",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "2015-hawaiian-islands-plant-species-abundance-models",
+ "spatial_centroid": {
+ "lat": 20.20938,
+ "lon": -157.72802000000001
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -159.8347,
+ 18.7977
+ ],
+ [
+ -159.8347,
+ 22.3269
+ ],
+ [
+ -154.568,
+ 22.3269
+ ],
+ [
+ -154.568,
+ 18.7977
+ ],
+ [
+ -159.8347,
+ 18.7977
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "2015 Hawaiian Islands Plant Species Abundance Models",
+ "type": "dataset"
+ },
+ {
+ "_score": 52.264835,
+ "_sort": [
+ 1791083601295,
+ 52.264835,
+ 0,
+ "21f9a921-d570-4ee4-8db1-8af313a733c9"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Climate Adaptation Science Centers",
+ "hasEmail": "mailto:casc-data@usgs.gov"
+ },
+ "description": "Historical (1981-2005) vs. Projected (2031-’55) Yields. Each year’s crop yields are calculated as an average of all counties in North and South Dakota. Hashed representations of projected yields are from RCP 4.5 emissions scenario from seven GCMs, namely CESM (Community Earth System Model), CNRM (Center National de Recherches Météorologiques (France)), GFDL (Geophysical Fluid Dynamics Laboratory), GISS (Goddard Institute of Space Studies), HADGEM (Hadley Global Environment Model), IPSL (Institut Pierre-Simon Laplace (France)) and MIROC (Model for Interdisciplinary Research on Climate). Median projection in a given year is calculated by taking the median yield value of the yield projections from each of seven climate model outputs in each county and then taking the average across counties. We restrict spring wheat and alfalfa yield forecasts to zero for years in which these are projected to be negative values.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P13V2U3X",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5c51df76e4b0708288fb10bc.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5c51df76e4b0708288fb10bc",
+ "keyword": [
+ "Dakota",
+ "North America",
+ "North Dakota",
+ "North Dakota and South Dakota",
+ "South Dakota",
+ "USGS:5c51df76e4b0708288fb10bc",
+ "agriculture",
+ "biota",
+ "climate change",
+ "effects of climate change",
+ "environment",
+ "external research support",
+ "farming",
+ "land use change",
+ "modeling"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-104.5898, 42.0982, -95.3613, 49.3251",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Evaluation of Historical vs. Modeled Future Crop Yields in North and South Dakota during 1981–2055 Using RCP4.5 Climate Projections"
+ },
+ "description": "Historical (1981-2005) vs. Projected (2031-’55) Yields. Each year’s crop yields are calculated as an average of all counties in North and South Dakota. Hashed representations of projected yields are from RCP 4.5 emissions scenario from seven GCMs, namely CESM (Community Earth System Model), CNRM (Center National de Recherches Météorologiques (France)), GFDL (Geophysical Fluid Dynamics Laboratory), GISS (Goddard Institute of Space Studies), HADGEM (Hadley Global Environment Model), IPSL (Institut Pierre-Simon Laplace (France)) and MIROC (Model for Interdisciplinary Research on Climate). Median projection in a given year is calculated by taking the median yield value of the yield projections from each of seven climate model outputs in each county and then taking the average across counties. We restrict spring wheat and alfalfa yield forecasts to zero for years in which these are projected to be negative values.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/32664f63-1ba6-471f-b65a-8cc60c18a8e6",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/32664f63-1ba6-471f-b65a-8cc60c18a8e6/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5c51df76e4b0708288fb10bc",
+ "keyword": [
+ "Dakota",
+ "North America",
+ "North Dakota",
+ "North Dakota and South Dakota",
+ "South Dakota",
+ "USGS:5c51df76e4b0708288fb10bc",
+ "agriculture",
+ "biota",
+ "climate change",
+ "effects of climate change",
+ "environment",
+ "external research support",
+ "farming",
+ "land use change",
+ "modeling"
+ ],
+ "last_harvested_date": "2026-10-04T03:13:21.295857",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "evaluation-of-historical-vs-modeled-future-crop-yields-in-north-and-south-dakota-during-19",
+ "spatial_centroid": {
+ "lat": 44.98896,
+ "lon": -100.8984
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -104.5898,
+ 42.0982
+ ],
+ [
+ -104.5898,
+ 49.3251
+ ],
+ [
+ -95.3613,
+ 49.3251
+ ],
+ [
+ -95.3613,
+ 42.0982
+ ],
+ [
+ -104.5898,
+ 42.0982
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Evaluation of Historical vs. Modeled Future Crop Yields in North and South Dakota during 1981–2055 Using RCP4.5 Climate Projections",
+ "type": "dataset"
+ },
+ {
+ "_score": 7.035857,
+ "_sort": [
+ 1791083486681,
+ 7.035857,
+ 0,
+ "a17b2604-b6b3-4ef7-a7ea-d40506bccbf3"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Thomas Giambelluca",
+ "hasEmail": "mailto:thomas@hawaii.edu"
+ },
+ "description": "The following files contain source data for use of the Community Land Model 4.0. Included are:\nBiometric data: 1) Growth increment data for Thurston and Olaa, based on dbh surveys done in four 10 m by 10 m plots at over a 12 year time period at Thurston and at six plots overs a 12 year period at Olaa, 2) Field measured leaf area index data. Measurements were made using LAI-2000 and LAI-2200 instruments, 3) Litterfall from Thurston and Olaa over a 17-mo period from June 2014 to Sep 2015. Data were sorted by species/litter type. Data are weights., 4) Field measured soil respiration data scaled to annual values and compared with tower-based measurements of ecosystem respiration.\nLeaf data: Leaf-scale gas exchange rates were used to compare the ecophysiological traits of the native species (Metrosideros polymorpha) and the invading species (Psidium cattleianum).\nMeteorological data: Meteorological data measured at Olaa and Thurston towers, including the following variables:\nVariable name\t\t\tUnits\t\tDescription\t\nTimeStamp\t\nDate and time\nRNET_SCR\t\t\tW m-2\t\tNet radiation\nK dn_SCR\t\t\tW m-2\t\tDownward shortwave radiation\nK up_SCR\t\t\tW m-2\t\tReflecrted shortwave radiation\nLdn_SCR\t\t\tW m-2\t\tDownward longwave radiation\nLup_SCR\t\t\tW m-2\t\tUpward (emitted) longwave radiation\nPAR_SCR\t\t\tµmol m-2 s-1\t\tPhotosynthetically active radiation\nT_HMP_SCR\t\t\tC\t\tAir temperature\nRH__HMP_SCR\t\t\t%\t\tRelative humidity\nH2O_hmp_SCR\t\t\tg cm-3\t\tSpecific Humidity\nPressure_SCR\t\t\tkPa\t\tAir pressure\nshf_avg1_SCR\t\t\tW m-2\t\tSoil heat flux at 8 cm depth (1)\nshf_avg2_SCR\t\t\tW m-2\t\tSoil heat flux at 8 cm depth (2)\nshf_avg3_SCR\t\t\tW m-2\t\tSoil heat flux at 8 cm depth (3)\nshf_avg4_SCR\t\t\tW m-2\t\tSoil heat flux at 8 cm depth (4)\nTsoil1_SCR\t \t\tC\t\tSoil temperature for upper 8 cm layer (1)\nTsoil2_SCR\t \t\tC\t\tSoil temperature for upper 8 cm layer (2)\nT_Soil_AVG_SCR\t\tC\t\tSoil temperature for upper 8 cm layer (average)\nT_soil_AVG_Diff_SCR\t \tC\t\tChange in soil temperature for upper 8 cm layer\nco2_mean_ scr agc + diags\t\tppm\t\tCarbon dioxide concentration\nco2_mean(1) scr agc + diags\t \tmg m-3\t\tScreened carbon dioxide concentration\nH2O_IRGA\t\t\tg m-3 \t\tSpecific humidity\nCO2_24.5m_LI-840\t\tppm\t\tCarbon dioxide concentratin\nWndspeed CSAT3_SCR\t\tm s-1\t\tWind speed\nWind_Direction_SCR\t\tdeg\t\tWind direction\nustar_SCR\t\t\tm s-2\t\tFriction velocity\nRF_SCR_calibrated_\t\tmm\t\tRainfall\nΔSCO2_SCR\t\t\tµmol m-2 s-1\t\tCarbon dioxide storage flux\nWindspeed_SCR\t\t\tm s-1\t\tScreened windspeed\nSoil Heat Flux_AVG_1_4_scr\t\tW m-2\nG_scr\t\t\tW m-2\nSM-1_0.04H_scr\t\t\t%_vol_wtr\t\tVol. soil moisture content at 4 cm depth\nSM2_0_0.3_scr\t\t\t%_vol_wtr\t\tVol. soil moisture content at 0-30 cm depth\nSM3_0.28_0.58_scr\t\t%_vol_wtr\t\tVol. soil moisture content at 28-58 cm depth\nSM4_183-213_scr\t\t%_vol_wtr\t\tVol. soil moisture content at 183-213 cm depth\nSM5_58_88_scr\t\t\t%_vol_wtr\t\tVol. soil moisture content at 58-88 cm depth\nSM6_88_119_scr\t\t%_vol_wtr\t\tVol. soil moisture content at 88-119 cm depth\nSM7_118-149__scr\t\t%_vol_wtr\t\tVol. soil moisture content at 118-149 cm depth\nPAR_AUX_total_scr\t\tµmol m-2 s-1\t\tPhotosynthetically active radiation\nPAR_dif_scr\t\t\tµmol m-2 s-1\t\tPhotosynthetically active radiation\nPAR_dir_scr\t\t\tµmol m-2 s-1\t\tPhotosynthetically active radiation\nVPD_scr\t\t\tkPa\t\tVapor pressure deficit\nT_hmp_bad\t\t\t1_yes_0_no\t\tAir temperature flag\nRH_hmp_bad\t\t\t1_yes_0_no\t\tRelative humidity flag",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P13V6FSF",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5bf491e2e4b045bfcae25e9b.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5bf491e2e4b045bfcae25e9b",
+ "keyword": [
+ "Ball-Berry's m",
+ "Gross primary production",
+ "USGS:5bf491e2e4b045bfcae25e9b",
+ "aboveground biomass",
+ "aboveground carbon density",
+ "alometric relationship",
+ "biometric measurements",
+ "biota",
+ "climate change",
+ "ecosystem carbon exchange",
+ "evapotranspiration",
+ "flux files",
+ "flux towers",
+ "growth increment",
+ "leaf area index",
+ "leaf-level measurement",
+ "litterfall",
+ "metrosideros polymorpha",
+ "modeling",
+ "photosynthesis",
+ "psidium cattleianum",
+ "soil respiration",
+ "stomatal conductance",
+ "transpiration",
+ "vcmax"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-155.23828, 19.41522, -155.21500, 19.47852",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Observed ecological inputs, 2004-2016, for running the Community Land Model 4.0 for Hawaii"
+ },
+ "description": "The following files contain source data for use of the Community Land Model 4.0. Included are:\nBiometric data: 1) Growth increment data for Thurston and Olaa, based on dbh surveys done in four 10 m by 10 m plots at over a 12 year time period at Thurston and at six plots overs a 12 year period at Olaa, 2) Field measured leaf area index data. Measurements were made using LAI-2000 and LAI-2200 instruments, 3) Litterfall from Thurston and Olaa over a 17-mo period from June 2014 to Sep 2015. Data were sorted by species/litter type. Data are weights., 4) Field measured soil respiration data scaled to annual values and compared with tower-based measurements of ecosystem respiration.\nLeaf data: Leaf-scale gas exchange rates were used to compare the ecophysiological traits of the native species (Metrosideros polymorpha) and the invading species (Psidium cattleianum).\nMeteorological data: Meteorological data measured at Olaa and Thurston towers, including the following variables:\nVariable name\t\t\tUnits\t\tDescription\t\nTimeStamp\t\nDate and time\nRNET_SCR\t\t\tW m-2\t\tNet radiation\nK dn_SCR\t\t\tW m-2\t\tDownward shortwave radiation\nK up_SCR\t\t\tW m-2\t\tReflecrted shortwave radiation\nLdn_SCR\t\t\tW m-2\t\tDownward longwave radiation\nLup_SCR\t\t\tW m-2\t\tUpward (emitted) longwave radiation\nPAR_SCR\t\t\tµmol m-2 s-1\t\tPhotosynthetically active radiation\nT_HMP_SCR\t\t\tC\t\tAir temperature\nRH__HMP_SCR\t\t\t%\t\tRelative humidity\nH2O_hmp_SCR\t\t\tg cm-3\t\tSpecific Humidity\nPressure_SCR\t\t\tkPa\t\tAir pressure\nshf_avg1_SCR\t\t\tW m-2\t\tSoil heat flux at 8 cm depth (1)\nshf_avg2_SCR\t\t\tW m-2\t\tSoil heat flux at 8 cm depth (2)\nshf_avg3_SCR\t\t\tW m-2\t\tSoil heat flux at 8 cm depth (3)\nshf_avg4_SCR\t\t\tW m-2\t\tSoil heat flux at 8 cm depth (4)\nTsoil1_SCR\t \t\tC\t\tSoil temperature for upper 8 cm layer (1)\nTsoil2_SCR\t \t\tC\t\tSoil temperature for upper 8 cm layer (2)\nT_Soil_AVG_SCR\t\tC\t\tSoil temperature for upper 8 cm layer (average)\nT_soil_AVG_Diff_SCR\t \tC\t\tChange in soil temperature for upper 8 cm layer\nco2_mean_ scr agc + diags\t\tppm\t\tCarbon dioxide concentration\nco2_mean(1) scr agc + diags\t \tmg m-3\t\tScreened carbon dioxide concentration\nH2O_IRGA\t\t\tg m-3 \t\tSpecific humidity\nCO2_24.5m_LI-840\t\tppm\t\tCarbon dioxide concentratin\nWndspeed CSAT3_SCR\t\tm s-1\t\tWind speed\nWind_Direction_SCR\t\tdeg\t\tWind direction\nustar_SCR\t\t\tm s-2\t\tFriction velocity\nRF_SCR_calibrated_\t\tmm\t\tRainfall\nΔSCO2_SCR\t\t\tµmol m-2 s-1\t\tCarbon dioxide storage flux\nWindspeed_SCR\t\t\tm s-1\t\tScreened windspeed\nSoil Heat Flux_AVG_1_4_scr\t\tW m-2\nG_scr\t\t\tW m-2\nSM-1_0.04H_scr\t\t\t%_vol_wtr\t\tVol. soil moisture content at 4 cm depth\nSM2_0_0.3_scr\t\t\t%_vol_wtr\t\tVol. soil moisture content at 0-30 cm depth\nSM3_0.28_0.58_scr\t\t%_vol_wtr\t\tVol. soil moisture content at 28-58 cm depth\nSM4_183-213_scr\t\t%_vol_wtr\t\tVol. soil moisture content at 183-213 cm depth\nSM5_58_88_scr\t\t\t%_vol_wtr\t\tVol. soil moisture content at 58-88 cm depth\nSM6_88_119_scr\t\t%_vol_wtr\t\tVol. soil moisture content at 88-119 cm depth\nSM7_118-149__scr\t\t%_vol_wtr\t\tVol. soil moisture content at 118-149 cm depth\nPAR_AUX_total_scr\t\tµmol m-2 s-1\t\tPhotosynthetically active radiation\nPAR_dif_scr\t\t\tµmol m-2 s-1\t\tPhotosynthetically active radiation\nPAR_dir_scr\t\t\tµmol m-2 s-1\t\tPhotosynthetically active radiation\nVPD_scr\t\t\tkPa\t\tVapor pressure deficit\nT_hmp_bad\t\t\t1_yes_0_no\t\tAir temperature flag\nRH_hmp_bad\t\t\t1_yes_0_no\t\tRelative humidity flag",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/1afc8fe9-8e78-49c7-bef1-4faf13cb3c0e",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/1afc8fe9-8e78-49c7-bef1-4faf13cb3c0e/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5bf491e2e4b045bfcae25e9b",
+ "keyword": [
+ "Ball-Berry's m",
+ "Gross primary production",
+ "USGS:5bf491e2e4b045bfcae25e9b",
+ "aboveground biomass",
+ "aboveground carbon density",
+ "alometric relationship",
+ "biometric measurements",
+ "biota",
+ "climate change",
+ "ecosystem carbon exchange",
+ "evapotranspiration",
+ "flux files",
+ "flux towers",
+ "growth increment",
+ "leaf area index",
+ "leaf-level measurement",
+ "litterfall",
+ "metrosideros polymorpha",
+ "modeling",
+ "photosynthesis",
+ "psidium cattleianum",
+ "soil respiration",
+ "stomatal conductance",
+ "transpiration",
+ "vcmax"
+ ],
+ "last_harvested_date": "2026-10-04T03:11:26.681210",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "observed-ecological-inputs-2004-2016-for-running-the-community-land-model-4-0-for-hawaii",
+ "spatial_centroid": {
+ "lat": 19.440540000000002,
+ "lon": -155.228968
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -155.23828,
+ 19.41522
+ ],
+ [
+ -155.23828,
+ 19.47852
+ ],
+ [
+ -155.215,
+ 19.47852
+ ],
+ [
+ -155.215,
+ 19.41522
+ ],
+ [
+ -155.23828,
+ 19.41522
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Observed ecological inputs, 2004-2016, for running the Community Land Model 4.0 for Hawaii",
+ "type": "dataset"
+ },
+ {
+ "_score": 27.797243,
+ "_sort": [
+ 1791083376636,
+ 27.797243,
+ 0,
+ "67cdbc7d-aa8c-4984-82f9-4ac3e969925e"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Mark Shafer",
+ "hasEmail": "mailto:mshafer@ou.edu"
+ },
+ "description": "This dataset was created to analyze the 20-year change in the number of days that were suitable for prescribed burns across the Southern Plains. This includes an analysis using one set of weather thresholds (base thresholds) across the region, an analysis with individual thresholds, and an analysis with climate change conditions.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.21429/C93D0X",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.59959353e4b0fe2b9fea7547.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_59959353e4b0fe2b9fea7547",
+ "keyword": [
+ "Climate change",
+ "Climatology",
+ "Controlled fires",
+ "USGS:59959353e4b0fe2b9fea7547",
+ "ecosystems",
+ "external research support",
+ "fires",
+ "geospatial datasets",
+ "modeling"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-103.0000, 26.0000, -93.0000, 43.0000",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Fire Risk Frequency Analysis- South Central Plains"
+ },
+ "description": "This dataset was created to analyze the 20-year change in the number of days that were suitable for prescribed burns across the Southern Plains. This includes an analysis using one set of weather thresholds (base thresholds) across the region, an analysis with individual thresholds, and an analysis with climate change conditions.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/dd114ade-1d0d-4c15-960e-b931fcd8a38a",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/dd114ade-1d0d-4c15-960e-b931fcd8a38a/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_59959353e4b0fe2b9fea7547",
+ "keyword": [
+ "Climate change",
+ "Climatology",
+ "Controlled fires",
+ "USGS:59959353e4b0fe2b9fea7547",
+ "ecosystems",
+ "external research support",
+ "fires",
+ "geospatial datasets",
+ "modeling"
+ ],
+ "last_harvested_date": "2026-10-04T03:09:36.636691",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "fire-risk-frequency-analysis-south-central-plains",
+ "spatial_centroid": {
+ "lat": 32.8,
+ "lon": -99.0
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -103.0,
+ 26.0
+ ],
+ [
+ -103.0,
+ 43.0
+ ],
+ [
+ -93.0,
+ 43.0
+ ],
+ [
+ -93.0,
+ 26.0
+ ],
+ [
+ -103.0,
+ 26.0
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Fire Risk Frequency Analysis- South Central Plains",
+ "type": "dataset"
+ },
+ {
+ "_score": 21.595955,
+ "_sort": [
+ 1791082451138,
+ 21.595955,
+ 0,
+ "51312957-3824-49b5-b0e4-06a1e2a58640"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Jeff Hicke",
+ "hasEmail": "mailto:casc-data@usgs.gov"
+ },
+ "description": "Estimates of weather suitability for the occurrence of mortality in whitebark pine from mountain pine beetles as determined from a logistic generalized additive model of the presence of mortality as functions of the number of trees killed last year, the percent whitebark pine in each cell, minimum winter temperature, average fall temperature, avverage April-Aug temperature, and cummulative current and previous year summer precipitation. Analysis done at a 1km grid cell resolution. Weather suitability index calculated by summing the weather terms in the model. Calculated for 2010 through 2099 based on numerous downscaled data under several emissions scenarios.\nGCMs include: BCC, CanESM, CCSM, CESM, CESM-BGC, CMCC, CNRM, Had-CC, Had-ES, and IPSL. RCPs vary from 2.6 to 8.5 depending on run. GCM/RCP combination is listed in the filename. Data are a list of points in comma separated text format. Point coordinates are the center of each 1km grid cell. GCMs are from the NASA NEX DCP30 data base",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P13EDGUV",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6abe8f161ba49b5905be45cb.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6abe8f161ba49b5905be45cb",
+ "keyword": [
+ "Dendroctonus ponderosae",
+ "Idaho",
+ "Montana",
+ "Mountain pine beetle",
+ "Oregon",
+ "Pinus albicaulis",
+ "USGS:6abe8f161ba49b5905be45cb",
+ "United States",
+ "Washington",
+ "Wyoming",
+ "atmospheric and climatic processes",
+ "environment",
+ "farming",
+ "modeling",
+ "whitebark pine"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-122.3101, 41.0131, -107.4902, 49.0031",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Weather Suitability for Mountain Pine Beetle Outbreaks in Whitebark Pine at 1‑km Resolution Under Multiple Downscaled GCM/RCP Climate Scenarios (2010–2099)"
+ },
+ "description": "Estimates of weather suitability for the occurrence of mortality in whitebark pine from mountain pine beetles as determined from a logistic generalized additive model of the presence of mortality as functions of the number of trees killed last year, the percent whitebark pine in each cell, minimum winter temperature, average fall temperature, avverage April-Aug temperature, and cummulative current and previous year summer precipitation. Analysis done at a 1km grid cell resolution. Weather suitability index calculated by summing the weather terms in the model. Calculated for 2010 through 2099 based on numerous downscaled data under several emissions scenarios.\nGCMs include: BCC, CanESM, CCSM, CESM, CESM-BGC, CMCC, CNRM, Had-CC, Had-ES, and IPSL. RCPs vary from 2.6 to 8.5 depending on run. GCM/RCP combination is listed in the filename. Data are a list of points in comma separated text format. Point coordinates are the center of each 1km grid cell. GCMs are from the NASA NEX DCP30 data base",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/fdf9f530-5bb0-48f6-8585-f9e3479678fc",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/fdf9f530-5bb0-48f6-8585-f9e3479678fc/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6abe8f161ba49b5905be45cb",
+ "keyword": [
+ "Dendroctonus ponderosae",
+ "Idaho",
+ "Montana",
+ "Mountain pine beetle",
+ "Oregon",
+ "Pinus albicaulis",
+ "USGS:6abe8f161ba49b5905be45cb",
+ "United States",
+ "Washington",
+ "Wyoming",
+ "atmospheric and climatic processes",
+ "environment",
+ "farming",
+ "modeling",
+ "whitebark pine"
+ ],
+ "last_harvested_date": "2026-10-04T02:54:11.138098",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "weather-suitability-for-mountain-pine-beetle-outbreaks-in-whitebark-pine-at-1km-resolution",
+ "spatial_centroid": {
+ "lat": 44.2091,
+ "lon": -116.38214
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -122.3101,
+ 41.0131
+ ],
+ [
+ -122.3101,
+ 49.0031
+ ],
+ [
+ -107.4902,
+ 49.0031
+ ],
+ [
+ -107.4902,
+ 41.0131
+ ],
+ [
+ -122.3101,
+ 41.0131
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Weather Suitability for Mountain Pine Beetle Outbreaks in Whitebark Pine at 1‑km Resolution Under Multiple Downscaled GCM/RCP Climate Scenarios (2010–2099)",
+ "type": "dataset"
+ },
+ {
+ "_score": 20.214893,
+ "_sort": [
+ 1791082032818,
+ 20.214893,
+ 0,
+ "aecf0319-a5c2-4334-a2ac-020f47c8c127"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "John Abatzoglou",
+ "hasEmail": "mailto:jabatzoglou@ucmerced.edu"
+ },
+ "description": "This archive contains daily downscaled meteorological and hydrological projections for the Columbia Basin in the United States at 1/16-deg resolution utilizing 9 different downscaling methods. The downscaled meteorological variables are maximum/minimum temperature(tasmax/tasmin), precipitation amount(pr), downward shortwave solar radiation(rsds), wind speed(was), and specific humidity(huss). The downscaling is based on the CCSM3e model from Phase 3 of the Coupled Model Inter-comparison Project (CMIP3) utlizing the historical 20C3M (1971-1999) and future SRESA2(2041-2070) scenarios. The downscaling methods include 3 statistical downscaling methods: Multivariate Adaptive Constructed Analogs (MACA) and monthly(and daily) Bias Correction Spatial Dissaggregation (mBCSD,dBCSD); 3 dynamical downscaling methods: interpolations of the outputs from North American Regional Climate Change Assessment Program(sdNARCCAP) regional downscaling utilizing 3 regional models: CRCM,MM5I and WRFG; and 3 hybrid downscaling methods: bias correction of interpolated outputs from NARCCAP regional downscaling using CRCM,MM5I and WRFG. All of the methods are used to downscale the entire suite of variables except for mBCSD (which is only used to downscale tmax/tmin/pr and these results are used with MTCLIM(Glassy et al., 1994) to generate rsds/was/huss). Each of the downscaling outputs are run through the Variable Infiltration Capacity (VIC) hydrologic model to generate the hydrological projections.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/maca-vic",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6776f7dad34ee88ba0b15905.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6776f7dad34ee88ba0b15905",
+ "keyword": [
+ "CMIP3",
+ "USGS:6776f7dad34ee88ba0b15905",
+ "climate change",
+ "climatologyMeteorologyAtmosphere",
+ "downward shortwave solar radiation",
+ "geospatial datasets",
+ "gridded hydrological data",
+ "gridded meteorlogical data",
+ "maximum temperature",
+ "minimum temperature",
+ "precipitation",
+ "specific humidity",
+ "wind speed"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-124.6000, 41.2100, -109.8000, 52.8400",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Columbia River Basin Daily MACA-VIC Results"
+ },
+ "description": "This archive contains daily downscaled meteorological and hydrological projections for the Columbia Basin in the United States at 1/16-deg resolution utilizing 9 different downscaling methods. The downscaled meteorological variables are maximum/minimum temperature(tasmax/tasmin), precipitation amount(pr), downward shortwave solar radiation(rsds), wind speed(was), and specific humidity(huss). The downscaling is based on the CCSM3e model from Phase 3 of the Coupled Model Inter-comparison Project (CMIP3) utlizing the historical 20C3M (1971-1999) and future SRESA2(2041-2070) scenarios. The downscaling methods include 3 statistical downscaling methods: Multivariate Adaptive Constructed Analogs (MACA) and monthly(and daily) Bias Correction Spatial Dissaggregation (mBCSD,dBCSD); 3 dynamical downscaling methods: interpolations of the outputs from North American Regional Climate Change Assessment Program(sdNARCCAP) regional downscaling utilizing 3 regional models: CRCM,MM5I and WRFG; and 3 hybrid downscaling methods: bias correction of interpolated outputs from NARCCAP regional downscaling using CRCM,MM5I and WRFG. All of the methods are used to downscale the entire suite of variables except for mBCSD (which is only used to downscale tmax/tmin/pr and these results are used with MTCLIM(Glassy et al., 1994) to generate rsds/was/huss). Each of the downscaling outputs are run through the Variable Infiltration Capacity (VIC) hydrologic model to generate the hydrological projections.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/65ca5483-92d6-4ce6-9508-07326c6c6232",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/65ca5483-92d6-4ce6-9508-07326c6c6232/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6776f7dad34ee88ba0b15905",
+ "keyword": [
+ "CMIP3",
+ "USGS:6776f7dad34ee88ba0b15905",
+ "climate change",
+ "climatologyMeteorologyAtmosphere",
+ "downward shortwave solar radiation",
+ "geospatial datasets",
+ "gridded hydrological data",
+ "gridded meteorlogical data",
+ "maximum temperature",
+ "minimum temperature",
+ "precipitation",
+ "specific humidity",
+ "wind speed"
+ ],
+ "last_harvested_date": "2026-10-04T02:47:12.818301",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "columbia-river-basin-daily-maca-vic-results",
+ "spatial_centroid": {
+ "lat": 45.862,
+ "lon": -118.67999999999999
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -124.6,
+ 41.21
+ ],
+ [
+ -124.6,
+ 52.84
+ ],
+ [
+ -109.8,
+ 52.84
+ ],
+ [
+ -109.8,
+ 41.21
+ ],
+ [
+ -124.6,
+ 41.21
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Columbia River Basin Daily MACA-VIC Results",
+ "type": "dataset"
+ },
+ {
+ "_score": 9.473773,
+ "_sort": [
+ 1791082023411,
+ 9.473773,
+ 3,
+ "894a7b61-1867-4060-80df-ffa87decb22d"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Paul C Hackley",
+ "hasEmail": "mailto:phackley@usgs.gov"
+ },
+ "description": "Large Igneous Provinces (LIPs) are associated with dramatic changes to the Earth system; their rapid release of carbon to the surface environment may initiate catastrophic climatic changes that cause mass extinction. This carbon release is often associated with volcanic emissions, yet metamorphic carbon production during LIP events may be substantial. Metamorphic carbon devolatilization occurs as dikes and sills interact with the sediment they intrude, potentially releasing high magnitudes of carbon. While this metamorphic flux has been modeled, relatively few studies use an observational approach. Here we provide results from petrographic maceral vitrinite reflectance analysis conducted on two boreholes in the Florida basement as part of a multi-pronged approach to understanding temperature changes and carbon devolatilization in sediment adjacent to basaltic intrusions of the Central Atlantic Magmatic Province (CAMP). We focus on sills in deep drill cores located in the panhandle and northeast part of Florida. The two bore holes, Well 1789 and 2012, were obtained from the deep drill core collection at the Florida Geologic Survey.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P1VUSVVS",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.697b56ecb66b0194ad8d9eaa.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_697b56ecb66b0194ad8d9eaa",
+ "keyword": [
+ "Central Atlantic Magmatic Province",
+ "Florida",
+ "USGS:697b56ecb66b0194ad8d9eaa",
+ "carbon",
+ "climate change",
+ "health",
+ "maceral",
+ "magmatism",
+ "organic matter",
+ "reflectance",
+ "thermal maturation"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-88.5059, 32.1384, -80.3101, 28.5170",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Maceral reflectance data for a release and recapture analysis of limited carbon mobilization in sediment adjacent to Central Atlantic Magmatic Province Sills, Florida, USA"
+ },
+ "description": "Large Igneous Provinces (LIPs) are associated with dramatic changes to the Earth system; their rapid release of carbon to the surface environment may initiate catastrophic climatic changes that cause mass extinction. This carbon release is often associated with volcanic emissions, yet metamorphic carbon production during LIP events may be substantial. Metamorphic carbon devolatilization occurs as dikes and sills interact with the sediment they intrude, potentially releasing high magnitudes of carbon. While this metamorphic flux has been modeled, relatively few studies use an observational approach. Here we provide results from petrographic maceral vitrinite reflectance analysis conducted on two boreholes in the Florida basement as part of a multi-pronged approach to understanding temperature changes and carbon devolatilization in sediment adjacent to basaltic intrusions of the Central Atlantic Magmatic Province (CAMP). We focus on sills in deep drill cores located in the panhandle and northeast part of Florida. The two bore holes, Well 1789 and 2012, were obtained from the deep drill core collection at the Florida Geologic Survey.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/f053ee91-f8a9-4b59-a348-7678f723d9f4",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/f053ee91-f8a9-4b59-a348-7678f723d9f4/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_697b56ecb66b0194ad8d9eaa",
+ "keyword": [
+ "Central Atlantic Magmatic Province",
+ "Florida",
+ "USGS:697b56ecb66b0194ad8d9eaa",
+ "carbon",
+ "climate change",
+ "health",
+ "maceral",
+ "magmatism",
+ "organic matter",
+ "reflectance",
+ "thermal maturation"
+ ],
+ "last_harvested_date": "2026-10-04T02:47:03.411865",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 3,
+ "publisher": "U.S. Geological Survey",
+ "slug": "maceral-reflectance-data-for-a-release-and-recapture-analysis-of-limited-carbon-mobilizati",
+ "spatial_centroid": {
+ "lat": 30.689839999999997,
+ "lon": -85.22758
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -88.5059,
+ 32.1384
+ ],
+ [
+ -88.5059,
+ 28.517
+ ],
+ [
+ -80.3101,
+ 28.517
+ ],
+ [
+ -80.3101,
+ 32.1384
+ ],
+ [
+ -88.5059,
+ 32.1384
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Maceral reflectance data for a release and recapture analysis of limited carbon mobilization in sediment adjacent to Central Atlantic Magmatic Province Sills, Florida, USA",
+ "type": "dataset"
+ },
+ {
+ "_score": 61.84197,
+ "_sort": [
+ 1791081519624,
+ 61.84197,
+ 2,
+ "a9c96e90-ef46-4ed1-bc3b-d67017706584"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Lorraine Flint",
+ "hasEmail": "mailto:lflint@usgs.gov"
+ },
+ "description": "The California Basin Characterization Model (CA-BCM 2014) dataset provides historical and projected climate and hydrologic surfaces for the region that encompasses the state of California and all the streams that flow into it (California hydrologic region ). The CA-BCM 2014 applies a monthly regional water-balance model to simulate hydrologic responses to climate at the spatial resolution of a 270-m grid. The model has been calibrated using a total of 159 relatively unimpaired watersheds for the California region. The historical data is based on 800m PRISM data spatially downscaled to 270 m using the gradient-inverse distance squared approach (GIDS), and the projected climate surfaces include five CMIP-3 (GFDL, PCM, MIROC3_2, CSIRO, GISS_AOM) and nine CMIP-5 (MIROC5, MIROC , GISS, MRI, MPI, CCSM4, IPSL, CNRM, FGOALS) General Circulation Models under a range of emission scenarios or representative concentration pathways (RCPs) for a total of 18 futures that have been statistically downscaled using BCSD to 800 m and further downscaled using GIDS to 270 m for model application. The BCM approach uses a regional water balance model based on this high resolution precipitation and temperature as well as elevation, geology, and soils to produce surfaces for the following variables: precipitation, air temperature, recharge, runoff, potential evapotranspiration (PET), actual evapotranspiration, and climatic water deficit, a parameter that is calculated as PET minus actual evapotranspiration. The following data are available in this archive: Raw, monthly model output for historical and future periods. Projected data is available for the following GCM and emission scenario or RCP combinations: GFDL-B1, GFDL-A2 PCM-B1, PCM-A2 MIROC3_2-A2 CSIRO-A1B GISS_AOM-A1B, MIROC5-RCP2.6, MIROC-RCP4.5, MIROC-RCP6.0, MIROC-RCP8.5 GISS-RCP2.6, MRI-RCP2.6, MPI- RCP4.5, CCSM4-RCP8.5, IPSL-RCP8.5, CNRM-RCP8.5, FGOALS-RCP8.5. Data variables: Actual evapotranspiration - water available between wilting point and field capacity, mm (aet); Climatic water deficit - Potential minus actual evapotranspiration, mm (cwd); Maximum monthly temperature, degrees C - (tmx); Minimum monthly temperature, degrees C - (tmn); Potential evapotranspiration - Water that could evaporate or transpire from plants if available, mm (pet); Recharge - Amount of water that penetrates below the root zone, mm (rch); Runoff - Amount of water that becomes stream flow, mm (run); Precipitation, mm - (ppt). Note that another archive, hosted by the California Climate Commons contains various climatological summaries of these data. That archive can be found at: http://climate.calcommons.org/",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/CA-BCM-2014",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6776bfbad34ee88ba0b15623.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6776bfbad34ee88ba0b15623",
+ "keyword": [
+ "CMIP3",
+ "CMIP5",
+ "Downscaled Climate and Hydrologic Response for California and the Great Basin",
+ "USGS:6776bfbad34ee88ba0b15623",
+ "actual evapotranspiration",
+ "climate change",
+ "climate projections",
+ "climatic water deficit",
+ "climatologyMeteorologyAtmosphere",
+ "dataset",
+ "geospatial datasets",
+ "gridded hydrological data",
+ "gridded meteorological data",
+ "hydrological projections",
+ "maximum temperature",
+ "minimum temperature",
+ "potential evapotranspiration",
+ "precipitation",
+ "recharge",
+ "runoff",
+ "service"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-125.4503, 31.1000, -112.4452, 44.3600",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "California Basin Characterization Model Downscaled Climate and Hydrology"
+ },
+ "description": "The California Basin Characterization Model (CA-BCM 2014) dataset provides historical and projected climate and hydrologic surfaces for the region that encompasses the state of California and all the streams that flow into it (California hydrologic region ). The CA-BCM 2014 applies a monthly regional water-balance model to simulate hydrologic responses to climate at the spatial resolution of a 270-m grid. The model has been calibrated using a total of 159 relatively unimpaired watersheds for the California region. The historical data is based on 800m PRISM data spatially downscaled to 270 m using the gradient-inverse distance squared approach (GIDS), and the projected climate surfaces include five CMIP-3 (GFDL, PCM, MIROC3_2, CSIRO, GISS_AOM) and nine CMIP-5 (MIROC5, MIROC , GISS, MRI, MPI, CCSM4, IPSL, CNRM, FGOALS) General Circulation Models under a range of emission scenarios or representative concentration pathways (RCPs) for a total of 18 futures that have been statistically downscaled using BCSD to 800 m and further downscaled using GIDS to 270 m for model application. The BCM approach uses a regional water balance model based on this high resolution precipitation and temperature as well as elevation, geology, and soils to produce surfaces for the following variables: precipitation, air temperature, recharge, runoff, potential evapotranspiration (PET), actual evapotranspiration, and climatic water deficit, a parameter that is calculated as PET minus actual evapotranspiration. The following data are available in this archive: Raw, monthly model output for historical and future periods. Projected data is available for the following GCM and emission scenario or RCP combinations: GFDL-B1, GFDL-A2 PCM-B1, PCM-A2 MIROC3_2-A2 CSIRO-A1B GISS_AOM-A1B, MIROC5-RCP2.6, MIROC-RCP4.5, MIROC-RCP6.0, MIROC-RCP8.5 GISS-RCP2.6, MRI-RCP2.6, MPI- RCP4.5, CCSM4-RCP8.5, IPSL-RCP8.5, CNRM-RCP8.5, FGOALS-RCP8.5. Data variables: Actual evapotranspiration - water available between wilting point and field capacity, mm (aet); Climatic water deficit - Potential minus actual evapotranspiration, mm (cwd); Maximum monthly temperature, degrees C - (tmx); Minimum monthly temperature, degrees C - (tmn); Potential evapotranspiration - Water that could evaporate or transpire from plants if available, mm (pet); Recharge - Amount of water that penetrates below the root zone, mm (rch); Runoff - Amount of water that becomes stream flow, mm (run); Precipitation, mm - (ppt). Note that another archive, hosted by the California Climate Commons contains various climatological summaries of these data. That archive can be found at: http://climate.calcommons.org/",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/b3b1078d-3221-43e4-970a-9b04832011a0",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/b3b1078d-3221-43e4-970a-9b04832011a0/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6776bfbad34ee88ba0b15623",
+ "keyword": [
+ "CMIP3",
+ "CMIP5",
+ "Downscaled Climate and Hydrologic Response for California and the Great Basin",
+ "USGS:6776bfbad34ee88ba0b15623",
+ "actual evapotranspiration",
+ "climate change",
+ "climate projections",
+ "climatic water deficit",
+ "climatologyMeteorologyAtmosphere",
+ "dataset",
+ "geospatial datasets",
+ "gridded hydrological data",
+ "gridded meteorological data",
+ "hydrological projections",
+ "maximum temperature",
+ "minimum temperature",
+ "potential evapotranspiration",
+ "precipitation",
+ "recharge",
+ "runoff",
+ "service"
+ ],
+ "last_harvested_date": "2026-10-04T02:38:39.624755",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 2,
+ "publisher": "U.S. Geological Survey",
+ "slug": "california-basin-characterization-model-downscaled-climate-and-hydrology",
+ "spatial_centroid": {
+ "lat": 36.404,
+ "lon": -120.24826
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -125.4503,
+ 31.1
+ ],
+ [
+ -125.4503,
+ 44.36
+ ],
+ [
+ -112.4452,
+ 44.36
+ ],
+ [
+ -112.4452,
+ 31.1
+ ],
+ [
+ -125.4503,
+ 31.1
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "California Basin Characterization Model Downscaled Climate and Hydrology",
+ "type": "dataset"
+ },
+ {
+ "_score": 61.317802,
+ "_sort": [
+ 1791081437353,
+ 61.317802,
+ 0,
+ "d12477e1-e725-4571-a196-41187bb792d3"
+ ],
+ "access_level": "public",
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Jared Oyler",
+ "hasEmail": "mailto:jaredwo@gmail.com"
+ },
+ "description": "This dataset is a shapefile that contains the grid outlines and identifiers for the tiles produced by the TopoWx (\"Topographical Weather/Climate\") temperature dataset as applied to the USGS North Central Climate Center Domain and the surrounding area of Montana. The TopoWx dataset contains gridded daily temperature and is an interpolated spatio-temporal dataset in the same vein as the well-known PRISM (http://www.prism.oregonstate.edu) and Daymet products (http://daymet.ornl.gov). Daily Tmin and Tmax are provided at a 30-arcsec resolution (~800m) from 1948-2012 along with the latest 30-year monthly normals (1981-2010). The goals of the TopoWx project \nwere to produce a dataset that: (1) incorporates key landscape-scale physiographic and biophysical factors that influence spatial spatial patterns of temperature;(2) provides estimates of uncertainty; (3) is appropriate for analyzing trends; and (4) is open to the research community for further analysis and improvements.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P13KEY3J",
+ "description": "Landing page for access to the data",
+ "format": "XML",
+ "mediaType": "application/http",
+ "title": "Digital Data"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "The metadata original format",
+ "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.52ebe44de4b0a0815e249ed5.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_52ebe44de4b0a0815e249ed5",
+ "keyword": [
+ "Colorado",
+ "Kansas",
+ "Montana",
+ "Nebraska",
+ "North Dakota",
+ "Numerical Terradynamic Simulation Group",
+ "South Dakota",
+ "TopoWx",
+ "USGS:52ebe44de4b0a0815e249ed5",
+ "Wyoming",
+ "climate",
+ "climate change",
+ "climatologyMeteorologyAtmosphere",
+ "geospatial datasets",
+ "temperature"
+ ],
+ "modified": "2026-10-01T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-114.9609, 34.5970, -95.2734, 48.6910",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "TopoWx (\"Topographical Weather/Climate\") temperature dataset tile grid"
+ },
+ "description": "This dataset is a shapefile that contains the grid outlines and identifiers for the tiles produced by the TopoWx (\"Topographical Weather/Climate\") temperature dataset as applied to the USGS North Central Climate Center Domain and the surrounding area of Montana. The TopoWx dataset contains gridded daily temperature and is an interpolated spatio-temporal dataset in the same vein as the well-known PRISM (http://www.prism.oregonstate.edu) and Daymet products (http://daymet.ornl.gov). Daily Tmin and Tmax are provided at a 30-arcsec resolution (~800m) from 1948-2012 along with the latest 30-year monthly normals (1981-2010). The goals of the TopoWx project \nwere to produce a dataset that: (1) incorporates key landscape-scale physiographic and biophysical factors that influence spatial spatial patterns of temperature;(2) provides estimates of uncertainty; (3) is appropriate for analyzing trends; and (4) is open to the research community for further analysis and improvements.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/e5663c63-d5ec-4c96-80f6-943a0c517272",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/e5663c63-d5ec-4c96-80f6-943a0c517272/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_52ebe44de4b0a0815e249ed5",
+ "keyword": [
+ "Colorado",
+ "Kansas",
+ "Montana",
+ "Nebraska",
+ "North Dakota",
+ "Numerical Terradynamic Simulation Group",
+ "South Dakota",
+ "TopoWx",
+ "USGS:52ebe44de4b0a0815e249ed5",
+ "Wyoming",
+ "climate",
+ "climate change",
+ "climatologyMeteorologyAtmosphere",
+ "geospatial datasets",
+ "temperature"
+ ],
+ "last_harvested_date": "2026-10-04T02:37:17.353378",
+ "organization": {
+ "aliases": [
+ "dept"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "name": "Department of the Interior",
+ "organization_type": "Federal Government",
+ "slug": "doi"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "U.S. Geological Survey",
+ "slug": "topowx-topographical-weather-climate-temperature-dataset-tile-grid",
+ "spatial_centroid": {
+ "lat": 40.2346,
+ "lon": -107.0859
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -114.9609,
+ 34.597
+ ],
+ [
+ -114.9609,
+ 48.691
+ ],
+ [
+ -95.2734,
+ 48.691
+ ],
+ [
+ -95.2734,
+ 34.597
+ ],
+ [
+ -114.9609,
+ 34.597
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "TopoWx (\"Topographical Weather/Climate\") temperature dataset tile grid",
+ "type": "dataset"
+ },
+ {
+ "_score": 65.39316,
  "_sort": [
  1790997589369,
- 65.40135,
+ 65.39316,
  0,
  "0ec32046-8e1e-47b8-8f2a-41ed237e23ec"
  ],
@@ -139,10 +2179,10 @@
  "type": "dataset"
  },
  {
- "_score": 26.284483,
+ "_score": 26.281757,
  "_sort": [
  1790997516946,
- 26.284483,
+ 26.281757,
  0,
  "a57b3635-7a64-4d3a-b08e-dafc5d850479"
  ],
@@ -266,10 +2306,10 @@
  "type": "dataset"
  },
  {
- "_score": 11.560799,
+ "_score": 11.558195,
  "_sort": [
  1790997283036,
- 11.560799,
+ 11.558195,
  0,
  "09b243f9-1391-46aa-96ee-e169030b0ce1"
  ],
@@ -409,10 +2449,10 @@
  "type": "dataset"
  },
  {
- "_score": 25.36899,
+ "_score": 25.363293,
  "_sort": [
  1790996947551,
- 25.36899,
+ 25.363293,
  0,
  "abaa1e48-ee68-4f2d-88de-bf948b7c0b2e"
  ],
@@ -536,10 +2576,10 @@
  "type": "dataset"
  },
  {
- "_score": 34.708965,
+ "_score": 34.68492,
  "_sort": [
  1790996843117,
- 34.708965,
+ 34.68492,
  0,
  "9866d0ee-8c2e-4521-b2b0-f3a39e658a9f"
  ],
@@ -685,10 +2725,10 @@
  "type": "dataset"
  },
  {
- "_score": 45.83752,
+ "_score": 45.83458,
  "_sort": [
  1790996404112,
- 45.83752,
+ 45.83458,
  0,
  "97e398c3-597a-4839-a863-748894a16840"
  ],
@@ -826,10 +2866,10 @@
  "type": "dataset"
  },
  {
- "_score": 53.53079,
+ "_score": 53.497246,
  "_sort": [
  1790995941844,
- 53.53079,
+ 53.497246,
  0,
  "31237e59-e151-420c-a2f4-20fce29bdcdd"
  ],
@@ -963,10 +3003,10 @@
  "type": "dataset"
  },
  {
- "_score": 57.815773,
+ "_score": 57.799904,
  "_sort": [
  1790995829240,
- 57.815773,
+ 57.799904,
  0,
  "9ab2175a-3463-4100-bf52-7c30e432b3ca"
  ],
@@ -1096,10 +3136,10 @@
  "type": "dataset"
  },
  {
- "_score": 53.413002,
+ "_score": 53.379524,
  "_sort": [
  1790995227568,
- 53.413002,
+ 53.379524,
  0,
  "5b3afc97-e72b-4d6f-87f0-b49e5e787d9c"
  ],
@@ -1229,10 +3269,10 @@
  "type": "dataset"
  },
  {
- "_score": 43.501827,
+ "_score": 43.47205,
  "_sort": [
  1790994685425,
- 43.501827,
+ 43.47205,
  0,
  "8a49c0ea-4e90-4edc-a035-1333713b756a"
  ],
@@ -1364,10 +3404,10 @@
  "type": "dataset"
  },
  {
- "_score": 26.236746,
+ "_score": 26.23043,
  "_sort": [
  1790993767124,
- 26.236746,
+ 26.23043,
  0,
  "1cc09137-3464-4127-b163-41d132669638"
  ],
@@ -1491,10 +3531,10 @@
  "type": "dataset"
  },
  {
- "_score": 8.501463,
+ "_score": 8.500846,
  "_sort": [
  1790993686303,
- 8.501463,
+ 8.500846,
  0,
  "010f5ae4-8158-40a5-8996-297ecfe8d6ba"
  ],
@@ -1648,10 +3688,10 @@
  "type": "dataset"
  },
  {
- "_score": 9.488384,
+ "_score": 9.485195,
  "_sort": [
  1790993673630,
- 9.488384,
+ 9.485195,
  0,
  "5e38c7a8-5eb1-4375-acea-73d532c6c946"
  ],
@@ -1789,10 +3829,10 @@
  "type": "dataset"
  },
  {
- "_score": 17.129112,
+ "_score": 17.124939,
  "_sort": [
  1790993548242,
- 17.129112,
+ 17.124939,
  0,
  "63a9f5ae-d6f5-4d77-9e25-41df831a5675"
  ],
@@ -1916,10 +3956,10 @@
  "type": "dataset"
  },
  {
- "_score": 26.236746,
+ "_score": 26.23043,
  "_sort": [
  1790993276463,
- 26.236746,
+ 26.23043,
  4,
  "04b55e90-4681-4d37-9a83-d758492af953"
  ],
@@ -2043,10 +4083,10 @@
  "type": "dataset"
  },
  {
- "_score": 26.236746,
+ "_score": 26.23043,
  "_sort": [
  1790993248506,
- 26.236746,
+ 26.23043,
  0,
  "3456b064-6b0d-491e-bad4-2f8994767fdc"
  ],
@@ -2170,10 +4210,10 @@
  "type": "dataset"
  },
  {
- "_score": 53.53079,
+ "_score": 53.497246,
  "_sort": [
  1790993156180,
- 53.53079,
+ 53.497246,
  0,
  "aefa47c0-7e72-4fd5-bda7-bfd3acb87608"
  ],
@@ -2307,10 +4347,10 @@
  "type": "dataset"
  },
  {
- "_score": 11.883791,
+ "_score": 11.883209,
  "_sort": [
  1790970829122,
- 11.883791,
+ 11.883209,
  0,
  "c4596ca7-61a6-404a-a530-e293fe233b54"
  ],
@@ -2635,10 +4675,10 @@
  "type": "dataset"
  },
  {
- "_score": 11.056835,
+ "_score": 11.056446,
  "_sort": [
  1790970801664,
- 11.056835,
+ 11.056446,
  0,
  "431d0531-fb26-40f5-8640-4d2888ec8b37"
  ],
@@ -2971,10 +5011,10 @@
  "type": "dataset"
  },
  {
- "_score": 10.417113,
+ "_score": 10.415338,
  "_sort": [
  1790970781691,
- 10.417113,
+ 10.415338,
  0,
  "a90682d4-8e79-4a04-a87c-03fc83666753"
  ],
@@ -3071,10 +5111,10 @@
  "type": "dataset"
  },
  {
- "_score": 10.454376,
+ "_score": 10.456804,
  "_sort": [
  1790970781542,
- 10.454376,
+ 10.456804,
  0,
  "faa594b6-b93d-4d2f-94f1-8cb05fa430ba"
  ],
@@ -3190,10 +5230,10 @@
  "type": "dataset"
  },
  {
- "_score": 8.4481125,
+ "_score": 8.445471,
  "_sort": [
  1790970752211,
- 8.4481125,
+ 8.445471,
  0,
  "7a70facc-94a5-4465-b4d8-155fb411a2b7"
  ],
@@ -3391,10 +5431,10 @@
  "type": "dataset"
  },
  {
- "_score": 10.080471,
+ "_score": 10.08246,
  "_sort": [
  1790970750591,
- 10.080471,
+ 10.08246,
  0,
  "9e431de4-2d83-4ba6-88e4-2ea9e6cfc9b4"
  ],
@@ -3645,10 +5685,10 @@
  "type": "dataset"
  },
  {
- "_score": 8.566196,
+ "_score": 8.564371,
  "_sort": [
  1790970749680,
- 8.566196,
+ 8.564371,
  0,
  "414a8c3e-c27f-4a8c-9cd1-010b2e65e37f"
  ],
@@ -3821,10 +5861,10 @@
  "type": "dataset"
  },
  {
- "_score": 10.0236845,
+ "_score": 10.022522,
  "_sort": [
  1790970744498,
- 10.0236845,
+ 10.022522,
  0,
  "3ebd1513-85f7-4874-a800-b899ae669c02"
  ],
@@ -4019,10 +6059,10 @@
  "type": "dataset"
  },
  {
- "_score": 23.760763,
+ "_score": 23.750454,
  "_sort": [
  1790970739058,
- 23.760763,
+ 23.750454,
  0,
  "beb51b99-06ac-4cc9-baac-73f6918b8280"
  ],
@@ -4210,10 +6250,10 @@
  "type": "dataset"
  },
  {
- "_score": 8.840584,
+ "_score": 8.838681,
  "_sort": [
  1790970737738,
- 8.840584,
+ 8.838681,
  0,
  "d0136588-8ff8-4555-9e41-182fd6daeb79"
  ],
@@ -4232,1738 +6272,4 @@
  "description": "Database and annotated bibliography of contaminants in tissues from albatross (Family Diomedeidae) species. Database is saved as current Microsoft Access 365 .accdb file and a backward compatible to Access 2000 .mdb file and a Microsoft Excel .xlsx file which has the database output information, introduction with description of headers, and some derived data. A free viewer for Excel is at: https://www.microsoft.com/en-us/p/xlsx-viewer-free/9nblggh6hbf4?activetab=pivot:overviewtab. These data supersede https://doi.org/10.18434/m31933.",
  "distribution": [
  {
- "accessURL": "https://doi.org/10.18434/mds2-2304",
- "title": "DOI Access for Albatross Species Chemical Database and Annotated Bibliography"
- },
- {
- "description": "Chemical data and and annotated bibliography of published literature of albatross species (Family Diomedeidae) exported to Microsoft Excel 365. Also contains spreadsheet with introduction, header definitions, and disclaimer and derived data spreadsheets. A free viewer for Excel is available at: https://www.microsoft.com/en-us/p/xlsx-viewer-free/9nblggh6hbf4?activetab=pivot:overviewtab",
- "downloadURL": "https://data.nist.gov/od/ds/mds2-2304/Albatross%20Species%20Chemical%20Database%20and%20Annotated%20Bibliograpy%20results.xlsx",
- "format": "Microsoft Excel",
- "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
- "title": "Albatross Species Chemical Database and Annotated Bibliography (ASCDAB) Results"
- },
- {
- "description": "Chemical data and and annotated bibliography of published literature of albatross species (Family Diomedeidae) in Microsoft Access 2000",
- "downloadURL": "https://data.nist.gov/od/ds/mds2-2304/Albatross%20Species%20Chemical%20Database%20and%20Annotated%20Bibliography.mdb",
- "format": "Microsoft Access 2000",
- "mediaType": "application/msaccess",
- "title": "Albatross Species Chemical Database and Annotated Bibliography (ASCDAB)"
- },
- {
- "description": "Chemical data and and annotated bibliography of published literature of albatross species (Family Diomedeidae) in Microsoft Access 365",
- "downloadURL": "https://data.nist.gov/od/ds/mds2-2304/Albatross%20Species%20Chemical%20Database%20and%20Annotated%20Bibliography.accdb",
- "format": "Microsoft Access 365",
- "mediaType": "application/msaccess",
- "title": "Albatross Species Chemical Database and Annotated Bibliography (ASCDAB)"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/mds2-2304/Albatross%20Species%20Chemical%20Database%20and%20Annotated%20Bibliography.accdb.sha256",
- "mediaType": "text/plain",
- "title": "SHA256 File for Albatross Species Chemical Database and Annotated Bibliography (ASCDAB)"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/mds2-2304/Albatross%20Species%20Chemical%20Database%20and%20Annotated%20Bibliography.mdb.sha256",
- "mediaType": "text/plain",
- "title": "SHA256 File for Albatross Species Chemical Database and Annotated Bibliography (ASCDAB)"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/mds2-2304/Albatross%20Species%20Chemical%20Database%20and%20Annotated%20Bibliograpy%20results.xlsx.sha256",
- "mediaType": "text/plain",
- "title": "SHA256 File for Albatross Species Chemical Database and Annotated Bibliography (ASCDAB) Results"
- }
- ],
- "identifier": "ark:/88434/mds2-2304",
- "issued": "2020-10-26",
- "keyword": [
- "Environment and Climate",
- "PAHs",
- "PBDEs",
- "PCBs",
- "PCDDs",
- "PCDF",
- "chemistry",
- "heavy metals",
- "inorganic",
- "mercury",
- "organic",
- "perflourinated acids",
- "phenols",
- "seabird",
- "stable isotopes",
- "tissues",
- "toxicology pesticides",
- "trace elements"
- ],
- "landingPage": "https://data.nist.gov/od/id/mds2-2304",
- "language": [
- "en"
- ],
- "license": "https://www.nist.gov/open/license",
- "modified": "2020-09-25 00:00:00",
- "programCode": [
- "006:052"
- ],
- "publisher": {
- "@type": "org:Organization",
- "name": "National Institute of Standards and Technology"
- },
- "references": [
- "https://doi.org/10.18434/m31933"
- ],
- "theme": [
- "Chemistry:Analytical chemistry",
- "Environment:Environmental health",
- "Environment:Marine science"
- ],
- "title": "Albatross Species Chemical Database and Annotated Bibliography"
- },
- "description": "Database and annotated bibliography of contaminants in tissues from albatross (Family Diomedeidae) species. Database is saved as current Microsoft Access 365 .accdb file and a backward compatible to Access 2000 .mdb file and a Microsoft Excel .xlsx file which has the database output information, introduction with description of headers, and some derived data. A free viewer for Excel is at: https://www.microsoft.com/en-us/p/xlsx-viewer-free/9nblggh6hbf4?activetab=pivot:overviewtab. These data supersede https://doi.org/10.18434/m31933.",
- "distribution_titles": [
- "DOI Access for Albatross Species Chemical Database and Annotated Bibliography",
- "Albatross Species Chemical Database and Annotated Bibliography (ASCDAB) Results",
- "Albatross Species Chemical Database and Annotated Bibliography (ASCDAB)",
- "Albatross Species Chemical Database and Annotated Bibliography (ASCDAB)",
- "SHA256 File for Albatross Species Chemical Database and Annotated Bibliography (ASCDAB)",
- "SHA256 File for Albatross Species Chemical Database and Annotated Bibliography (ASCDAB)",
- "SHA256 File for Albatross Species Chemical Database and Annotated Bibliography (ASCDAB) Results"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/bc17654a-b5fe-496a-a94a-ae833752a9eb",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/bc17654a-b5fe-496a-a94a-ae833752a9eb/raw",
- "has_download": true,
- "has_spatial": false,
- "identifier": "ark:/88434/mds2-2304",
- "keyword": [
- "Environment and Climate",
- "PAHs",
- "PBDEs",
- "PCBs",
- "PCDDs",
- "PCDF",
- "chemistry",
- "heavy metals",
- "inorganic",
- "mercury",
- "organic",
- "perflourinated acids",
- "phenols",
- "seabird",
- "stable isotopes",
- "tissues",
- "toxicology pesticides",
- "trace elements"
- ],
- "last_harvested_date": "2026-10-02T19:52:17.738174",
- "organization": {
- "aliases": [
- "dept",
- "doc"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "16980d1c-5e8f-4188-b962-42446f2d3f63",
- "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png",
- "name": "Department of Commerce",
- "organization_type": "Federal Government",
- "slug": "commerce"
- },
- "parent_identifier": null,
- "popularity": 0,
- "publisher": "National Institute of Standards and Technology",
- "slug": "albatross-species-chemical-database-and-annotated-bibliography-f0fb6",
- "spatial_centroid": null,
- "spatial_shape": null,
- "theme": [
- "Chemistry:Analytical chemistry",
- "Environment:Environmental health",
- "Environment:Marine science"
- ],
- "title": "Albatross Species Chemical Database and Annotated Bibliography",
- "type": "dataset"
- },
- {
- "_score": 9.566887,
- "_sort": [
- 1790970734173,
- 9.566887,
- 0,
- "695a8671-eec5-4d32-8865-6274079d0e7f"
- ],
- "access_level": "public",
- "dcat": {
- "@type": "dcat:Dataset",
- "accessLevel": "public",
- "bureauCode": [
- "006:55"
- ],
- "contactPoint": {
- "fn": "Abneesh Srivastava",
- "hasEmail": "mailto:abneesh.srivastava@nist.gov"
- },
- "description": "Data for mass concentration analysis and spectral analysis for the paper titled, \"Comparison of primary laser spectroscopy and mass spectrometry methods for measuring mass concentration of gaseous elemental mercury\"",
- "distribution": [
- {
- "accessURL": "https://doi.org/10.18434/M32280",
- "title": "DOI Access for Comparison of primary laser spectroscopy and mass spectrometry methods for measuring mass concentration of gaseous elemental mercury"
- },
- {
- "description": "Figure Data 2020_NIST_Data_Hg-LAS-MS-Comparison.xlsx",
- "downloadURL": "https://data.nist.gov/od/ds/mds2-2280/Figure%20Data%202020_NIST_Data_Hg-LAS-MS-Comparison.xlsx",
- "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
- "title": "Figure Data 2020_NIST_Data_Hg-LAS-MS-Comparison.xlsx"
- },
- {
- "description": "SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx",
- "downloadURL": "https://data.nist.gov/od/ds/mds2-2280/SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx",
- "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
- "title": "SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/mds2-2280/Figure%20Data%202020_NIST_Data_Hg-LAS-MS-Comparison.xlsx.sha256",
- "mediaType": "text/plain",
- "title": "SHA256 File for Figure Data 2020_NIST_Data_Hg-LAS-MS-Comparison.xlsx"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/mds2-2280/SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx.sha256",
- "mediaType": "text/plain",
- "title": "SHA256 File for SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx"
- }
- ],
- "identifier": "ark:/88434/mds2-2280",
- "issued": "2021-02-11",
- "keyword": [
- "Environment and Climate",
- "ID-CV-ICP-MS",
- "Laser Absorption Spectroscopy",
- "Mercury",
- "SI traceability",
- "Standard Generator",
- "primary measurement method"
- ],
- "landingPage": "https://data.nist.gov/od/id/mds2-2280",
- "language": [
- "en"
- ],
- "license": "https://www.nist.gov/open/license",
- "modified": "2020-07-30 00:00:00",
- "programCode": [
- "006:045"
- ],
- "publisher": {
- "@type": "org:Organization",
- "name": "National Institute of Standards and Technology"
- },
- "references": [
- "https://pubs.acs.org/doi/abs/10.1021/acs.analchem.0c04002"
- ],
- "theme": [
- "Chemistry:Analytical chemistry",
- "Environment:Air / water / soil quality",
- "Environment:Environmental health",
- "Metrology",
- "Physics:Spectroscopy"
- ],
- "title": "Comparison of primary laser spectroscopy and mass spectrometry methods for measuring mass concentration of gaseous elemental mercury"
- },
- "description": "Data for mass concentration analysis and spectral analysis for the paper titled, \"Comparison of primary laser spectroscopy and mass spectrometry methods for measuring mass concentration of gaseous elemental mercury\"",
- "distribution_titles": [
- "DOI Access for Comparison of primary laser spectroscopy and mass spectrometry methods for measuring mass concentration of gaseous elemental mercury",
- "Figure Data 2020_NIST_Data_Hg-LAS-MS-Comparison.xlsx",
- "SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx",
- "SHA256 File for Figure Data 2020_NIST_Data_Hg-LAS-MS-Comparison.xlsx",
- "SHA256 File for SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/cc6ddd34-f4b9-4106-99ee-3142158af4d3",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/cc6ddd34-f4b9-4106-99ee-3142158af4d3/raw",
- "has_download": true,
- "has_spatial": false,
- "identifier": "ark:/88434/mds2-2280",
- "keyword": [
- "Environment and Climate",
- "ID-CV-ICP-MS",
- "Laser Absorption Spectroscopy",
- "Mercury",
- "SI traceability",
- "Standard Generator",
- "primary measurement method"
- ],
- "last_harvested_date": "2026-10-02T19:52:14.173163",
- "organization": {
- "aliases": [
- "dept",
- "doc"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "16980d1c-5e8f-4188-b962-42446f2d3f63",
- "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png",
- "name": "Department of Commerce",
- "organization_type": "Federal Government",
- "slug": "commerce"
- },
- "parent_identifier": null,
- "popularity": 0,
- "publisher": "National Institute of Standards and Technology",
- "slug": "comparison-of-primary-laser-spectroscopy-and-mass-spectrometry-methods-for-measuring-mass--3af78",
- "spatial_centroid": null,
- "spatial_shape": null,
- "theme": [
- "Chemistry:Analytical chemistry",
- "Environment:Air / water / soil quality",
- "Environment:Environmental health",
- "Metrology",
- "Physics:Spectroscopy"
- ],
- "title": "Comparison of primary laser spectroscopy and mass spectrometry methods for measuring mass concentration of gaseous elemental mercury",
- "type": "dataset"
- },
- {
- "_score": 18.876417,
- "_sort": [
- 1790970732466,
- 18.876417,
- 0,
- "9d0516c8-9b40-44f8-8d8c-979fcc5adbe2"
- ],
- "access_level": "public",
- "dcat": {
- "@type": "dcat:Dataset",
- "accessLevel": "public",
- "bureauCode": [
- "006:55"
- ],
- "contactPoint": {
- "fn": "Joseph M. Conny",
- "hasEmail": "mailto:joseph.conny@nist.gov"
- },
- "description": "The project that produced these data involved the analysis and modeling of atmospheric Asian dust particles and background marine air particles collected in Hawaii, USA at the Mauna Loa Observatory (MLO) of the National Oceanic and Atmospheric Administration. Individual heterogeneous dust particles were analyzed with focused ion-beam scanning electron microscopy and energy-dispersive X-ray spectroscopy. Three-dimensional spatial and composition models of the particles from focused ion-beam tomography were used in the calculation of optical properties by the discrete dipole approximation method with incident light at 589 nm. Optical properties included the phase function, extent of linear polarization, scattering, backscattering, and extinction cross sections, and the backscatter fraction. In addition, the project involved optical property calculations for particles as geometric shapes with volumes equivalent to those of the MLO particles. Shapes modeled for each particle included spheres, spheroids, ellipsoids, cubes, square prisms, rectangular prisms, tetrahedra, and triangular pyramids. The representation of particles as geometric shapes is an important component of algorithms used in remote sensing (satellite-based and ground-based) for determining how atmospheric dust affects climate. \n\n\nThe dataset consists of Microsoft Excel and Word files, text files, and bitmap image files. The Asian dust particles in the dataset are labeled: CaMg 1D, CaMg 2N, CaMg 3D, CaMg 4N1, Ca-rich 1D, Ca-rich 2N, Ca-rich 3D, Ca-rich 4N1, Ca-rich 4N2. The background marine air particles are labeled: Ca-S 1D, Ca-S 2N, Ca-S 3D, Ca-S 4N.\n\n\nData are contained in seven folders arranged by the following topics:\n\n\n1 -- Particle compositions by FIB-SEM-EDX and volumes of material phases within particles (folder: 1_Particle_Compositions_Volumes);\n2 -- Spatial and optical parameters for optical modeling of particles and geometric shapes (folder: 2_Particles_Shapes_Spatial_Optical_Parameters (and subfolders));\n3 -- Complex refractive indices for particles and shapes based on Maxwell Garnett average dielectric function (folder: 3_Complex_RIs_Maxwell_Garnett (and subfolders));\n4 -- Results from discrete dipole approximation modeling software DDSCAT ver. 7.3 (folder: 4_DDSCAT_Scattering_Output (and subfolders));\n5 -- Mueller scattering matrix elements (folder: 5_Matrix_Elements (and subfolders));\n6 -- Root-mean-square calculations for phase function and degree of linear polarization (folder: 6_PhaseFunction_LinearPolarization_RMS);\n7 -- Calculations for the backscatter fraction (folder: 7_Backscatter_Fraction)",
- "distribution": [
- {
- "accessURL": "https://doi.org/10.18434/M32263",
- "title": "DOI Access for Optical Modeling of Single Asian Dust and Marine Air Particles: A Comparison with Geometric Particle Shapes for Remote Sensing"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/ark:/88434/mds2-2263/Dataset_Catalog.pdf",
- "mediaType": "application/pdf"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/ark:/88434/mds2-2263/Dataset_Catalog.pdf.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/ark:/88434/mds2-2263/NISTdataset_ModelingSingleAsianDustParticlesAndGeometricShapes.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/ark:/88434/mds2-2263/NISTdataset_ModelingSingleAsianDustParticlesAndGeometricShapes.zip.sha256",
- "mediaType": "text/plain"
- }
- ],
- "identifier": "ark:/88434/mds2-2263",
- "issued": "2020-07-20",
- "keyword": [
- "Asian dust",
- "EDX",
- "FIB-SEM",
- "atmospheric aerosol",
- "climate change",
- "discrete dipole approximation method",
- "energy-dispersive X-ray spectroscopy",
- "environment and climate",
- "focused ion-beam scanning electron microscopy",
- "focused ion-beam tomography",
- "light absorption",
- "light scattering",
- "optical property modeling",
- "radiative forcing"
- ],
- "landingPage": "https://data.nist.gov/od/id/mds2-2263",
- "language": [
- "en"
- ],
- "license": "https://www.nist.gov/open/license",
- "modified": "2020-05-11 00:00:00",
- "programCode": [
- "006:045"
- ],
- "publisher": {
- "@type": "org:Organization",
- "name": "National Institute of Standards and Technology"
- },
- "references": [
- "https://doi.org/10.1016/j.jqsrt.2020.107197"
- ],
- "theme": [
- "Environment:Air / water / soil quality",
- "Environment:Environmental health",
- "Physics:Optical physics",
- "Physics:Spectroscopy"
- ],
- "title": "Optical Modeling of Single Asian Dust and Marine Air Particles: A Comparison with Geometric Particle Shapes for Remote Sensing"
- },
- "description": "The project that produced these data involved the analysis and modeling of atmospheric Asian dust particles and background marine air particles collected in Hawaii, USA at the Mauna Loa Observatory (MLO) of the National Oceanic and Atmospheric Administration. Individual heterogeneous dust particles were analyzed with focused ion-beam scanning electron microscopy and energy-dispersive X-ray spectroscopy. Three-dimensional spatial and composition models of the particles from focused ion-beam tomography were used in the calculation of optical properties by the discrete dipole approximation method with incident light at 589 nm. Optical properties included the phase function, extent of linear polarization, scattering, backscattering, and extinction cross sections, and the backscatter fraction. In addition, the project involved optical property calculations for particles as geometric shapes with volumes equivalent to those of the MLO particles. Shapes modeled for each particle included spheres, spheroids, ellipsoids, cubes, square prisms, rectangular prisms, tetrahedra, and triangular pyramids. The representation of particles as geometric shapes is an important component of algorithms used in remote sensing (satellite-based and ground-based) for determining how atmospheric dust affects climate. \n\n\nThe dataset consists of Microsoft Excel and Word files, text files, and bitmap image files. The Asian dust particles in the dataset are labeled: CaMg 1D, CaMg 2N, CaMg 3D, CaMg 4N1, Ca-rich 1D, Ca-rich 2N, Ca-rich 3D, Ca-rich 4N1, Ca-rich 4N2. The background marine air particles are labeled: Ca-S 1D, Ca-S 2N, Ca-S 3D, Ca-S 4N.\n\n\nData are contained in seven folders arranged by the following topics:\n\n\n1 -- Particle compositions by FIB-SEM-EDX and volumes of material phases within particles (folder: 1_Particle_Compositions_Volumes);\n2 -- Spatial and optical parameters for optical modeling of particles and geometric shapes (folder: 2_Particles_Shapes_Spatial_Optical_Parameters (and subfolders));\n3 -- Complex refractive indices for particles and shapes based on Maxwell Garnett average dielectric function (folder: 3_Complex_RIs_Maxwell_Garnett (and subfolders));\n4 -- Results from discrete dipole approximation modeling software DDSCAT ver. 7.3 (folder: 4_DDSCAT_Scattering_Output (and subfolders));\n5 -- Mueller scattering matrix elements (folder: 5_Matrix_Elements (and subfolders));\n6 -- Root-mean-square calculations for phase function and degree of linear polarization (folder: 6_PhaseFunction_LinearPolarization_RMS);\n7 -- Calculations for the backscatter fraction (folder: 7_Backscatter_Fraction)",
- "distribution_titles": [
- "DOI Access for Optical Modeling of Single Asian Dust and Marine Air Particles: A Comparison with Geometric Particle Shapes for Remote Sensing"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/bac90358-e92c-43ad-9bdc-d93aadaef094",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/bac90358-e92c-43ad-9bdc-d93aadaef094/raw",
- "has_download": true,
- "has_spatial": false,
- "identifier": "ark:/88434/mds2-2263",
- "keyword": [
- "Asian dust",
- "EDX",
- "FIB-SEM",
- "atmospheric aerosol",
- "climate change",
- "discrete dipole approximation method",
- "energy-dispersive X-ray spectroscopy",
- "environment and climate",
- "focused ion-beam scanning electron microscopy",
- "focused ion-beam tomography",
- "light absorption",
- "light scattering",
- "optical property modeling",
- "radiative forcing"
- ],
- "last_harvested_date": "2026-10-02T19:52:12.466687",
- "organization": {
- "aliases": [
- "dept",
- "doc"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "16980d1c-5e8f-4188-b962-42446f2d3f63",
- "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png",
- "name": "Department of Commerce",
- "organization_type": "Federal Government",
- "slug": "commerce"
- },
- "parent_identifier": null,
- "popularity": 0,
- "publisher": "National Institute of Standards and Technology",
- "slug": "optical-modeling-of-single-asian-dust-and-marine-air-particles-a-comparison-with-geometric-e7ba1",
- "spatial_centroid": null,
- "spatial_shape": null,
- "theme": [
- "Environment:Air / water / soil quality",
- "Environment:Environmental health",
- "Physics:Optical physics",
- "Physics:Spectroscopy"
- ],
- "title": "Optical Modeling of Single Asian Dust and Marine Air Particles: A Comparison with Geometric Particle Shapes for Remote Sensing",
- "type": "dataset"
- },
- {
- "_score": 9.407541,
- "_sort": [
- 1790970706436,
- 9.407541,
- 0,
- "05b8c221-acd6-4adc-9572-5502e8f4511a"
- ],
- "access_level": "public",
- "dcat": {
- "@type": "dcat:Dataset",
- "accessLevel": "public",
- "bureauCode": [
- "006:55"
- ],
- "contactPoint": {
- "fn": "David Duewer",
- "hasEmail": "mailto:david.duewer@nist.gov"
- },
- "description": "The Micronutrients Measurement Quality Assurance Program (MMQAP) was coordinated by the Chemical Sciences Division and supported measurement technology for selected fat- and water-soluble vitamins and carotenoids in human serum. This program was initiated in 1984 by the National Cancer Institute Division of Cancer Prevention and Control to ensure the long-term reliability of the measurements made while studying the possible cancer chemoprevention roles of these compounds. This program provided participants with measurement comparability assessment through use of interlaboratory comparison studies, Standard Reference Materials and control materials, and methods development and validation. The MMQAP concluded in 2017, and parts of the MMQAP community will now be served through the NIST Health Assessment Measurements Quality Assurance Program (HAMQAP).",
- "distribution": [
- {
- "accessURL": "https://doi.org/10.18434/T4/1503374",
- "description": "DOI Access to NIST Micronutrients Measurement Quality Assurance Program Winter and Summer 2017 Comparability Studies",
- "format": "text/html",
- "title": "DOI Access to NIST Micronutrients Measurement Quality Assurance Program Winter and Summer 2017 Comparability Studies"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/7B0767A00BDB25A5E0532457068151011987/FSV_RR81.xlsx",
- "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/7B0767A00BDB25A5E0532457068151011987/FSV_RR81.xlsx.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/7B0767A00BDB25A5E0532457068151011987/FSV_RR82.xlsx",
- "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
- },
- {
- "downloadURL": "https://data.nist.gov/od/ds/7B0767A00BDB25A5E0532457068151011987/FSV_RR82.xlsx.sha256",
- "mediaType": "text/plain"
- }
- ],
- "identifier": "7B0767A00BDB25A5E0532457068151011987",
- "keyword": [
- "Biosciences and Health",
- "Carotenoids",
- "Energy",
- "Environment and Climate",
- "Fat-Soluble Vitamins",
- "Food and Nutrition",
- "Human Serum",
- "Interlaboratory Study",
- "Vitamin C",
- "interlaboratory comparisons"
- ],
- "landingPage": "https://data.nist.gov/od/id/7B0767A00BDB25A5E0532457068151011987",
- "language": [
- "en"
- ],
- "license": "https://www.nist.gov/open/license",
- "modified": "2018-01-01",
- "programCode": [
- "006:045"
- ],
- "publisher": {
- "@type": "org:Organization",
- "name": "National Institute of Standards and Technology"
- },
- "references": [
- "https://doi.org/10.6028/NIST.IR.7880-48"
- ],
- "theme": [
- "Chemistry:Analytical chemistry",
- "Standards:Conformity assessment",
- "Standards:Reference materials"
- ],
- "title": "NIST Micronutrients Measurement Quality Assurance Program Winter and Summer 2017 Comparability Studies"
- },
- "description": "The Micronutrients Measurement Quality Assurance Program (MMQAP) was coordinated by the Chemical Sciences Division and supported measurement technology for selected fat- and water-soluble vitamins and carotenoids in human serum. This program was initiated in 1984 by the National Cancer Institute Division of Cancer Prevention and Control to ensure the long-term reliability of the measurements made while studying the possible cancer chemoprevention roles of these compounds. This program provided participants with measurement comparability assessment through use of interlaboratory comparison studies, Standard Reference Materials and control materials, and methods development and validation. The MMQAP concluded in 2017, and parts of the MMQAP community will now be served through the NIST Health Assessment Measurements Quality Assurance Program (HAMQAP).",
- "distribution_titles": [
- "DOI Access to NIST Micronutrients Measurement Quality Assurance Program Winter and Summer 2017 Comparability Studies"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/54a01471-7071-44ca-8518-b0395ada3ef0",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/54a01471-7071-44ca-8518-b0395ada3ef0/raw",
- "has_download": true,
- "has_spatial": false,
- "identifier": "7B0767A00BDB25A5E0532457068151011987",
- "keyword": [
- "Biosciences and Health",
- "Carotenoids",
- "Energy",
- "Environment and Climate",
- "Fat-Soluble Vitamins",
- "Food and Nutrition",
- "Human Serum",
- "Interlaboratory Study",
- "Vitamin C",
- "interlaboratory comparisons"
- ],
- "last_harvested_date": "2026-10-02T19:51:46.436324",
- "organization": {
- "aliases": [
- "dept",
- "doc"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "16980d1c-5e8f-4188-b962-42446f2d3f63",
- "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png",
- "name": "Department of Commerce",
- "organization_type": "Federal Government",
- "slug": "commerce"
- },
- "parent_identifier": null,
- "popularity": 0,
- "publisher": "National Institute of Standards and Technology",
- "slug": "nist-micronutrients-measurement-quality-assurance-program-winter-and-summer-2017-comparabi-14dab",
- "spatial_centroid": null,
- "spatial_shape": null,
- "theme": [
- "Chemistry:Analytical chemistry",
- "Standards:Conformity assessment",
- "Standards:Reference materials"
- ],
- "title": "NIST Micronutrients Measurement Quality Assurance Program Winter and Summer 2017 Comparability Studies",
- "type": "dataset"
- },
- {
- "_score": 10.454376,
- "_sort": [
- 1790970691760,
- 10.454376,
- 0,
- "a484d945-7196-4d93-a639-7e21bbc60746"
- ],
- "access_level": "public",
- "dcat": {
- "@type": "dcat:Dataset",
- "accessLevel": "public",
- "accrualPeriodicity": "irregular",
- "bureauCode": [
- "006:55"
- ],
- "contactPoint": {
- "fn": "Keana C. K. Scott",
- "hasEmail": "mailto:keana.scott@nist.gov"
- },
- "description": "This folder contains image data sets from 14 separate serial sectioning sessions. The entire data folder consists of 1379 8 bit tif images and is 47.3 GB in size. Serial sectioning was performed using FEI Helios 660 NanoLab focused ion beam scanning electron microscope (FIB SEM) and Auto Slice and View G3 software. The sample was a heavy metal stained and resin embedded Caenorhabditis elegans (C. elegans) that were exposed to 60 nm Au nanoparticles. Detailed descriptions of the worm preparation and resin block processing are described in Johnson, M.E. et al. (ACS Nano, 2016). Although the images were collected over 14 different sessions, they represent a contiguous section of a worm.",
- "distribution": [
- {
- "accessURL": "https://doi.org/10.18434/M3C09F"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_C.elegansFIBSEMDataManifest.docx",
- "mediaType": "application/msword"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_C.elegansFIBSEMDataManifest.docx.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set1-106.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set1-106.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set10-100.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set10-100.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set11-50.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set11-50.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set12-79.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set12-79.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set13-32.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set13-32.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set14-120.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set14-120.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set2-120.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set2-120.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set3-120.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set3-120.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set4-120.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set4-120.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set5-120.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set5-120.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set6-98.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set6-98.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set7-86.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set7-86.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set8-120.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set8-120.zip.sha256",
- "mediaType": "text/plain"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set9-108.zip",
- "mediaType": "application/zip"
- },
- {
- "downloadURL": "https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set9-108.zip.sha256",
- "mediaType": "text/plain"
- }
- ],
- "identifier": "4E3AF57724AEF2DAE0531A57068162B11834",
- "keyword": [
- "Advanced Materials",
- "Biosciences and Health",
- "Environment and Climate",
- "Manufacturing",
- "Nanotechnology",
- "Visualization Research"
- ],
- "landingPage": "https://data.nist.gov/od/id/4E3AF57724AEF2DAE0531A57068162B11834",
- "language": [
- "en"
- ],
- "license": "https://www.nist.gov/open/license",
- "modified": "2015-09-20 00:00:00",
- "programCode": [
- "006:045"
- ],
- "publisher": {
- "@type": "org:Organization",
- "name": "National Institute of Standards and Technology"
- },
- "references": [
- "http://pubs.acs.org/doi/abs/10.1021/acsnano.6b06582"
- ],
- "theme": [
- "Bioscience",
- "Environment",
- "Health",
- "Manufacturing",
- "Materials",
- "Nanotechnology"
- ],
- "title": "FIB SEM image data set of Caenorhabditis elegans exposed to 60 nm Au nanoparticles"
- },
- "description": "This folder contains image data sets from 14 separate serial sectioning sessions. The entire data folder consists of 1379 8 bit tif images and is 47.3 GB in size. Serial sectioning was performed using FEI Helios 660 NanoLab focused ion beam scanning electron microscope (FIB SEM) and Auto Slice and View G3 software. The sample was a heavy metal stained and resin embedded Caenorhabditis elegans (C. elegans) that were exposed to 60 nm Au nanoparticles. Detailed descriptions of the worm preparation and resin block processing are described in Johnson, M.E. et al. (ACS Nano, 2016). Although the images were collected over 14 different sessions, they represent a contiguous section of a worm.",
- "distribution_titles": [],
- "harvest_record": "https://catalog.data.gov/harvest_record/7666c56b-d2cb-4700-a428-64504e65ca71",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/7666c56b-d2cb-4700-a428-64504e65ca71/raw",
- "has_download": true,
- "has_spatial": false,
- "identifier": "4E3AF57724AEF2DAE0531A57068162B11834",
- "keyword": [
- "Advanced Materials",
- "Biosciences and Health",
- "Environment and Climate",
- "Manufacturing",
- "Nanotechnology",
- "Visualization Research"
- ],
- "last_harvested_date": "2026-10-02T19:51:31.760181",
- "organization": {
- "aliases": [
- "dept",
- "doc"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "16980d1c-5e8f-4188-b962-42446f2d3f63",
- "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png",
- "name": "Department of Commerce",
- "organization_type": "Federal Government",
- "slug": "commerce"
- },
- "parent_identifier": null,
- "popularity": 0,
- "publisher": "National Institute of Standards and Technology",
- "slug": "fib-sem-image-data-set-of-caenorhabditis-elegans-exposed-to-60-nm-au-nanoparticles-01fbb",
- "spatial_centroid": null,
- "spatial_shape": null,
- "theme": [
- "Bioscience",
- "Environment",
- "Health",
- "Manufacturing",
- "Materials",
- "Nanotechnology"
- ],
- "title": "FIB SEM image data set of Caenorhabditis elegans exposed to 60 nm Au nanoparticles",
- "type": "dataset"
- },
- {
- "_score": 9.566887,
- "_sort": [
- 1790970684229,
- 9.566887,
- 0,
- "5a8fbcb0-2a3c-4f83-ac91-1cda58da8263"
- ],
- "access_level": "public",
- "dcat": {
- "@type": "dcat:Dataset",
- "accessLevel": "public",
- "bureauCode": [
- "006:55"
- ],
- "contactPoint": {
- "fn": "Daniel Siderius",
- "hasEmail": "mailto:daniel.siderius@nist.gov"
- },
- "description": "The NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials is a free, web-based catalog of adsorbent materials and measured adsorption properties of numerous materials obtained from article entries from the scientific literature. Search fields for the database include adsorbent material, adsorbate gas, experimental conditions (pressure, temperature), and bibliographic information (author, title, journal), and results from queries are provided as a list of articles matching the search parameters. The database also contains adsorption isotherms digitized from the cataloged articles, which can be compared visually online in the web application or exported for offline analysis.",
- "distribution": [
- {
- "accessURL": "https://adsorption.nist.gov/isodb/index.php#apis",
- "description": "A list of APIs that can be used to directly access the resource.",
- "title": "APIs to access NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials"
- },
- {
- "accessURL": "https://dx.doi.org/10.18434/T43882",
- "mediaType": "text/html",
- "title": "DOI Access to NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials"
- }
- ],
- "identifier": "FF429BC1787D8B3EE0431A570681E858236",
- "keyword": [
- "Advanced Materials",
- "Energy",
- "Environment and Climate",
- "Manufacturing",
- "adsorbate",
- "adsorbent",
- "adsorption",
- "isotherm",
- "metal organic Framework",
- "porous Material",
- "surface science"
- ],
- "landingPage": "https://data.nist.gov/od/id/FF429BC1787D8B3EE0431A570681E858236",
- "language": [
- "en"
- ],
- "license": "https://www.nist.gov/open/license",
- "modified": "2020-09-17 00:00:00",
- "programCode": [
- "006:052"
- ],
- "publisher": {
- "@type": "org:Organization",
- "name": "National Institute of Standards and Technology"
- },
- "references": [
- "https://adsorption.nist.gov/isodb/index.php#user-guide"
- ],
- "theme": [
- "Materials"
- ],
- "title": "NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials"
- },
- "description": "The NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials is a free, web-based catalog of adsorbent materials and measured adsorption properties of numerous materials obtained from article entries from the scientific literature. Search fields for the database include adsorbent material, adsorbate gas, experimental conditions (pressure, temperature), and bibliographic information (author, title, journal), and results from queries are provided as a list of articles matching the search parameters. The database also contains adsorption isotherms digitized from the cataloged articles, which can be compared visually online in the web application or exported for offline analysis.",
- "distribution_titles": [
- "APIs to access NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials",
- "DOI Access to NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/29702a30-0521-4f0f-9143-6f1a415eaf97",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/29702a30-0521-4f0f-9143-6f1a415eaf97/raw",
- "has_download": false,
- "has_spatial": false,
- "identifier": "FF429BC1787D8B3EE0431A570681E858236",
- "keyword": [
- "Advanced Materials",
- "Energy",
- "Environment and Climate",
- "Manufacturing",
- "adsorbate",
- "adsorbent",
- "adsorption",
- "isotherm",
- "metal organic Framework",
- "porous Material",
- "surface science"
- ],
- "last_harvested_date": "2026-10-02T19:51:24.229497",
- "organization": {
- "aliases": [
- "dept",
- "doc"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "16980d1c-5e8f-4188-b962-42446f2d3f63",
- "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/commerce.png",
- "name": "Department of Commerce",
- "organization_type": "Federal Government",
- "slug": "commerce"
- },
- "parent_identifier": null,
- "popularity": 0,
- "publisher": "National Institute of Standards and Technology",
- "slug": "nist-arpa-e-database-of-novel-and-emerging-adsorbent-materials-f1f90",
- "spatial_centroid": null,
- "spatial_shape": null,
- "theme": [
- "Materials"
- ],
- "title": "NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials",
- "type": "dataset"
- },
- {
- "_score": 51.019375,
- "_sort": [
- 1790910200939,
- 51.019375,
- 3,
- "ce072083-3d60-446e-ae55-4434e132e84c"
- ],
- "access_level": "public",
- "dcat": {
- "accessLevel": "public",
- "bureauCode": [
- "010:12"
- ],
- "contactPoint": {
- "@type": "vcard:Contact",
- "fn": "Michelle A Stern",
- "hasEmail": "mailto:mstern@usgs.gov"
- },
- "description": "This data release contains monthly 270-meter resolution Basin Characterization Model (BCMv8) climate and hydrologic variables for Localized Constructed Analog (LOCA; Pierce et al., 2014)-downscaled Global Climate Models (GCMs) for Representative Concentration Pathway (RCP) 4.5 (medium-low emissions) and 8.5 (high emissions) for hydrologic California. The 20 future climate scenarios consist of ten GCMs with RCP 4.5 and 8.5 each: ACCESS 1.0, CanESM2, CCSM4, CESM1-BGC, CMCC-CMS, CNRM-CM5, GFDL-CM3, HadGEM2-CC, HadGEM2-ES, and MIROC5. The LOCA climate scenarios span water years 1950 to 2099 with greenhouse-gas forcings beginning in 2006. The LOCA downscaling method has been shown to produce better estimates of extreme events and reduces the common downscaling problem of too many low-precipitation days (Pierce et al., 2014). Ten GCMs were selected from the full ensemble of models from the fifth Coupled Model Intercomparison Project from the World Climate Research Programme (CMIP5) based on GCM historical performance to address specific needs for California water-resource planning (California Department of Water Resources Climate Change Technical Advisory Group, 2015). The 10 GCMs with RCP 4.5 and 8.5 each were statistically downscaled using the LOCA method (Pierce et al., 2014) from 2-degree (approximately 222-kilometer; km) quadrangles to 6-km resolution. Next, the scenarios were spatially downscaled from 6 km to 270 meters (Flint and Flint, 2012) and run through the BCMv8 using the same model parameters and input files as the historical BCM model (BCMv8; Flint et al., 2021).\nDownscaled gridded climate variables include precipitation (ppt), minimum temperature (tmn), maximum temperature (tmx), and potential evapotranspiration (pet). Gridded hydrologic variables include actual evapotranspiration (aet), climatic water deficit (cwd), snowpack (pck), recharge (rch), runoff (run), and soil storage (str). The units for temperature variables are degrees Celsius, and all other variables are in millimeters per month. Monthly variables from water years 1951 to 2099 are summarized into water year files (for example, water year 1951 includes October 1950 - September 1951) and 30-year average summaries from 1951 to 2099. Raster grids are in the NAD83 California Teale Albers, (meters) projection in an open format ascii text file (*.asc). \nThis data release includes a child item for each GCM. Each GCM child item contains two RCP (4.5 & 8.5) child items. Each RCP child item contains 4 child items:\n1. 30-year summaries (Water year files averaged for selected 30-year periods, zipped by variable)\n2. Monthly BCM hydrology variables (monthly BCM hydrology variables zipped by decade)\n3. Monthly climate variables (monthly climate variables zipped by decade)\n4. Water year summaries (monthly files summed (aet, cwd, pck, rch, run, str, pet, and ppt) or averaged (tmn and tmx) by water year, zipped by variable)\nReferences cited:\nCalifornia Department of Water Resources Climate Change Technical Advisory Group, 2015, Perspectives and guidance for climate change analysis: Sacramento, Calif., California Department of Water Resources Technical Information Record, 142 p.\nFlint, L.E., Flint, A.L., and Stern, M.A., 2021, The Basin Characterization Model - A monthly regional water balance software package (BCMv8) data release and model archive for hydrologic California (ver. 3.0, June 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9PT36UI.\nFlint, L.E., and Flint, A.L., 2012, Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis: Ecological Processes, v. 1, no. 2, 15 p., https://doi.org/10.1186/2192-1709-1-2.\nPierce, D.W., Cayan, D.R. and Thrasher, B.L., 2014. Statistical downscaling using localized constructed analogs (LOCA). Journal of hydrometeorology, 15(6), pp.2558-2585.",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "accessURL": "https://doi.org/10.5066/P9K23J25",
- "description": "Landing page for access to the data",
- "format": "XML",
- "mediaType": "application/http",
- "title": "Digital Data"
- },
- {
- "@type": "dcat:Distribution",
- "description": "The metadata original format",
- "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.61e069f3d34e8911d9fe9dba.xml",
- "format": "XML",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- }
- ],
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_61e069f3d34e8911d9fe9dba",
- "keyword": [
- "California",
- "USGS:61e069f3d34e8911d9fe9dba",
- "United States",
- "atmospheric and climatic processes",
- "climate change",
- "climatologyMeteorologyAtmosphere",
- "environment",
- "evaporation",
- "geoscientificInformation",
- "geospatial datasets",
- "hydrology",
- "inlandWaters",
- "mathematical modeling",
- "permeability",
- "precipitation (atmospheric)",
- "snow and ice cover",
- "soil moisture",
- "streamflow",
- "surface water (non-marine)",
- "transpiration",
- "water budget",
- "water cycle",
- "water resources",
- "watershed management"
- ],
- "modified": "2026-09-29T00:00:00Z",
- "publisher": {
- "@type": "org:Organization",
- "name": "U.S. Geological Survey"
- },
- "spatial": "-124.9805, 32.5300, -114.1200, 43.4210",
- "theme": [
- "geospatial"
- ],
- "title": "Future Climate and Hydrology from Twenty Localized Constructed Analog (LOCA) Scenarios and the Basin Characterization Model (BCMv8) (ver. 1.1, November 2024)"
- },
- "description": "This data release contains monthly 270-meter resolution Basin Characterization Model (BCMv8) climate and hydrologic variables for Localized Constructed Analog (LOCA; Pierce et al., 2014)-downscaled Global Climate Models (GCMs) for Representative Concentration Pathway (RCP) 4.5 (medium-low emissions) and 8.5 (high emissions) for hydrologic California. The 20 future climate scenarios consist of ten GCMs with RCP 4.5 and 8.5 each: ACCESS 1.0, CanESM2, CCSM4, CESM1-BGC, CMCC-CMS, CNRM-CM5, GFDL-CM3, HadGEM2-CC, HadGEM2-ES, and MIROC5. The LOCA climate scenarios span water years 1950 to 2099 with greenhouse-gas forcings beginning in 2006. The LOCA downscaling method has been shown to produce better estimates of extreme events and reduces the common downscaling problem of too many low-precipitation days (Pierce et al., 2014). Ten GCMs were selected from the full ensemble of models from the fifth Coupled Model Intercomparison Project from the World Climate Research Programme (CMIP5) based on GCM historical performance to address specific needs for California water-resource planning (California Department of Water Resources Climate Change Technical Advisory Group, 2015). The 10 GCMs with RCP 4.5 and 8.5 each were statistically downscaled using the LOCA method (Pierce et al., 2014) from 2-degree (approximately 222-kilometer; km) quadrangles to 6-km resolution. Next, the scenarios were spatially downscaled from 6 km to 270 meters (Flint and Flint, 2012) and run through the BCMv8 using the same model parameters and input files as the historical BCM model (BCMv8; Flint et al., 2021).\nDownscaled gridded climate variables include precipitation (ppt), minimum temperature (tmn), maximum temperature (tmx), and potential evapotranspiration (pet). Gridded hydrologic variables include actual evapotranspiration (aet), climatic water deficit (cwd), snowpack (pck), recharge (rch), runoff (run), and soil storage (str). The units for temperature variables are degrees Celsius, and all other variables are in millimeters per month. Monthly variables from water years 1951 to 2099 are summarized into water year files (for example, water year 1951 includes October 1950 - September 1951) and 30-year average summaries from 1951 to 2099. Raster grids are in the NAD83 California Teale Albers, (meters) projection in an open format ascii text file (*.asc). \nThis data release includes a child item for each GCM. Each GCM child item contains two RCP (4.5 & 8.5) child items. Each RCP child item contains 4 child items:\n1. 30-year summaries (Water year files averaged for selected 30-year periods, zipped by variable)\n2. Monthly BCM hydrology variables (monthly BCM hydrology variables zipped by decade)\n3. Monthly climate variables (monthly climate variables zipped by decade)\n4. Water year summaries (monthly files summed (aet, cwd, pck, rch, run, str, pet, and ppt) or averaged (tmn and tmx) by water year, zipped by variable)\nReferences cited:\nCalifornia Department of Water Resources Climate Change Technical Advisory Group, 2015, Perspectives and guidance for climate change analysis: Sacramento, Calif., California Department of Water Resources Technical Information Record, 142 p.\nFlint, L.E., Flint, A.L., and Stern, M.A., 2021, The Basin Characterization Model - A monthly regional water balance software package (BCMv8) data release and model archive for hydrologic California (ver. 3.0, June 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9PT36UI.\nFlint, L.E., and Flint, A.L., 2012, Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis: Ecological Processes, v. 1, no. 2, 15 p., https://doi.org/10.1186/2192-1709-1-2.\nPierce, D.W., Cayan, D.R. and Thrasher, B.L., 2014. Statistical downscaling using localized constructed analogs (LOCA). Journal of hydrometeorology, 15(6), pp.2558-2585.",
- "distribution_titles": [
- "Digital Data",
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/623fc74d-3f20-4e29-9880-4b2f99351f63",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/623fc74d-3f20-4e29-9880-4b2f99351f63/raw",
- "has_download": true,
- "has_spatial": true,
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_61e069f3d34e8911d9fe9dba",
- "keyword": [
- "California",
- "USGS:61e069f3d34e8911d9fe9dba",
- "United States",
- "atmospheric and climatic processes",
- "climate change",
- "climatologyMeteorologyAtmosphere",
- "environment",
- "evaporation",
- "geoscientificInformation",
- "geospatial datasets",
- "hydrology",
- "inlandWaters",
- "mathematical modeling",
- "permeability",
- "precipitation (atmospheric)",
- "snow and ice cover",
- "soil moisture",
- "streamflow",
- "surface water (non-marine)",
- "transpiration",
- "water budget",
- "water cycle",
- "water resources",
- "watershed management"
- ],
- "last_harvested_date": "2026-10-02T03:03:20.939683",
- "organization": {
- "aliases": [
- "dept"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
- "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
- "name": "Department of the Interior",
- "organization_type": "Federal Government",
- "slug": "doi"
- },
- "parent_identifier": null,
- "popularity": 3,
- "publisher": "U.S. Geological Survey",
- "slug": "future-climate-and-hydrology-from-twenty-localized-constructed-analog-loca-scenarios--2024",
- "spatial_centroid": {
- "lat": 36.8864,
- "lon": -120.6363
- },
- "spatial_shape": {
- "coordinates": [
- [
- [
- -124.9805,
- 32.53
- ],
- [
- -124.9805,
- 43.421
- ],
- [
- -114.12,
- 43.421
- ],
- [
- -114.12,
- 32.53
- ],
- [
- -124.9805,
- 32.53
- ]
- ]
- ],
- "type": "Polygon"
- },
- "theme": [
- "geospatial"
- ],
- "title": "Future Climate and Hydrology from Twenty Localized Constructed Analog (LOCA) Scenarios and the Basin Characterization Model (BCMv8) (ver. 1.1, November 2024)",
- "type": "dataset"
- },
- {
- "_score": 51.188046,
- "_sort": [
- 1790909747668,
- 51.188046,
- 0,
- "e70d2755-b24d-4849-af73-9e8d8dab856a"
- ],
- "access_level": "public",
- "dcat": {
- "accessLevel": "public",
- "bureauCode": [
- "010:12"
- ],
- "contactPoint": {
- "@type": "vcard:Contact",
- "fn": "Lisa Gaines",
- "hasEmail": "mailto:lisa.gaines@oregonstate.edu"
- },
- "description": "Management actions may have a higher probability of being successful if they are informed by available scientific knowledge and findings; a systematic review process provides a mechanism to scientifically assess management questions. By evaluating specific actions on scientific knowledge and findings, we may be able to increase management effectiveness and efficiency. The goal of the Available Science Assessment Project (ASAP) is to synthesize and evaluate the body of scientific knowledge on specific, on-the-ground CAAs to determine the conditions, timeframes, and geographic areas where particular CAAs may be most effective for resource managers. We have derived a methodology that utilizes interviews, a systematic review process, and extensive engagement with natural resource managers and scientists throughout the Northwest Climate Science Center (NW CSC) region. For a test case, we will evaluate the science behind specific fire management actions in national forests in the region.",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "accessURL": "https://doi.org/10.5066/P13M7JPQ",
- "description": "Landing page for access to the data",
- "format": "XML",
- "mediaType": "application/http",
- "title": "Digital Data"
- },
- {
- "@type": "dcat:Distribution",
- "description": "The metadata original format",
- "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5818c1dfe4b0bb36a4c8806e.xml",
- "format": "XML",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- }
- ],
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5818c1dfe4b0bb36a4c8806e",
- "keyword": [
- "Idaho",
- "Oregon",
- "USGS:5818c1dfe4b0bb36a4c8806e",
- "Washington",
- "adaptive management",
- "climate change",
- "datasets",
- "decision support methods",
- "environment",
- "external research support",
- "fire",
- "fire management",
- "fires",
- "management methods",
- "natural resource management"
- ],
- "modified": "2026-09-29T00:00:00Z",
- "publisher": {
- "@type": "org:Organization",
- "name": "U.S. Geological Survey"
- },
- "spatial": "-124.6289, 41.8368, -111.0059, 48.9225",
- "theme": [
- "geospatial"
- ],
- "title": "Catalog of Fire-Related Climate Adaptation Actions: Phase 3 Literature Review"
- },
- "description": "Management actions may have a higher probability of being successful if they are informed by available scientific knowledge and findings; a systematic review process provides a mechanism to scientifically assess management questions. By evaluating specific actions on scientific knowledge and findings, we may be able to increase management effectiveness and efficiency. The goal of the Available Science Assessment Project (ASAP) is to synthesize and evaluate the body of scientific knowledge on specific, on-the-ground CAAs to determine the conditions, timeframes, and geographic areas where particular CAAs may be most effective for resource managers. We have derived a methodology that utilizes interviews, a systematic review process, and extensive engagement with natural resource managers and scientists throughout the Northwest Climate Science Center (NW CSC) region. For a test case, we will evaluate the science behind specific fire management actions in national forests in the region.",
- "distribution_titles": [
- "Digital Data",
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/d45168a7-3e0c-4ab2-9b97-dc001f1f6782",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/d45168a7-3e0c-4ab2-9b97-dc001f1f6782/raw",
- "has_download": true,
- "has_spatial": true,
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5818c1dfe4b0bb36a4c8806e",
- "keyword": [
- "Idaho",
- "Oregon",
- "USGS:5818c1dfe4b0bb36a4c8806e",
- "Washington",
- "adaptive management",
- "climate change",
- "datasets",
- "decision support methods",
- "environment",
- "external research support",
- "fire",
- "fire management",
- "fires",
- "management methods",
- "natural resource management"
- ],
- "last_harvested_date": "2026-10-02T02:55:47.668877",
- "organization": {
- "aliases": [
- "dept"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
- "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
- "name": "Department of the Interior",
- "organization_type": "Federal Government",
- "slug": "doi"
- },
- "parent_identifier": null,
- "popularity": 0,
- "publisher": "U.S. Geological Survey",
- "slug": "catalog-of-fire-related-climate-adaptation-actions-phase-3-literature-review",
- "spatial_centroid": {
- "lat": 44.671079999999996,
- "lon": -119.1797
- },
- "spatial_shape": {
- "coordinates": [
- [
- [
- -124.6289,
- 41.8368
- ],
- [
- -124.6289,
- 48.9225
- ],
- [
- -111.0059,
- 48.9225
- ],
- [
- -111.0059,
- 41.8368
- ],
- [
- -124.6289,
- 41.8368
- ]
- ]
- ],
- "type": "Polygon"
- },
- "theme": [
- "geospatial"
- ],
- "title": "Catalog of Fire-Related Climate Adaptation Actions: Phase 3 Literature Review",
- "type": "dataset"
- },
- {
- "_score": 16.023226,
- "_sort": [
- 1790909373549,
- 16.023226,
- 0,
- "94ef42de-1fd9-45e9-97b7-8a188aa8c2b1"
- ],
- "access_level": "public",
- "dcat": {
- "accessLevel": "public",
- "bureauCode": [
- "010:12"
- ],
- "contactPoint": {
- "@type": "vcard:Contact",
- "fn": "Matthew Maldonado",
- "hasEmail": "mailto:steven.chipps@sdstate.edu"
- },
- "description": "Dataset contains angler effort information for waterbodies in North Dakota and South Dakota collected through creel surveys between 1991 - 2019. The dataset also contains waterbody surface area estimates for each sampled waterbody that were gathered through remote sensing. Finally, the dataset contains waterbody surface area estimates for all public, managed waterbodies within the Devils Lake Basin, North Dakota, USA between 2004 - 2021, along with the climate period that the Devils Lake BAsin was undergoing in a given year.",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "accessURL": "https://doi.org/10.5066/P13PVWKY",
- "description": "Landing page for access to the data",
- "format": "XML",
- "mediaType": "application/http",
- "title": "Digital Data"
- },
- {
- "@type": "dcat:Distribution",
- "description": "The metadata original format",
- "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6a43c4ad1ba49b79e8251b28.xml",
- "format": "XML",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- }
- ],
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6a43c4ad1ba49b79e8251b28",
- "keyword": [
- "Angler Behavior",
- "Ecology",
- "Human Dimensions",
- "USGS:6a43c4ad1ba49b79e8251b28",
- "climatologyMeteorologyAtmosphere"
- ],
- "modified": "2026-09-29T00:00:00Z",
- "publisher": {
- "@type": "org:Organization",
- "name": "U.S. Geological Survey"
- },
- "spatial": "-103.9746, 42.4883, -96.5500, 49.0000",
- "theme": [
- "geospatial"
- ],
- "title": "Remotely sensed waterbody surface area estimates and angler effort estimates in North Dakota and South Dakota between 1991 - 2021"
- },
- "description": "Dataset contains angler effort information for waterbodies in North Dakota and South Dakota collected through creel surveys between 1991 - 2019. The dataset also contains waterbody surface area estimates for each sampled waterbody that were gathered through remote sensing. Finally, the dataset contains waterbody surface area estimates for all public, managed waterbodies within the Devils Lake Basin, North Dakota, USA between 2004 - 2021, along with the climate period that the Devils Lake BAsin was undergoing in a given year.",
- "distribution_titles": [
- "Digital Data",
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/0b051c7f-f738-44ea-a762-7839e4093746",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/0b051c7f-f738-44ea-a762-7839e4093746/raw",
- "has_download": true,
- "has_spatial": true,
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6a43c4ad1ba49b79e8251b28",
- "keyword": [
- "Angler Behavior",
- "Ecology",
- "Human Dimensions",
- "USGS:6a43c4ad1ba49b79e8251b28",
- "climatologyMeteorologyAtmosphere"
- ],
- "last_harvested_date": "2026-10-02T02:49:33.549289",
- "organization": {
- "aliases": [
- "dept"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
- "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
- "name": "Department of the Interior",
- "organization_type": "Federal Government",
- "slug": "doi"
- },
- "parent_identifier": null,
- "popularity": 0,
- "publisher": "U.S. Geological Survey",
- "slug": "remotely-sensed-waterbody-surface-area-estimates-and-angler-effort-estimates-in--1991-2021",
- "spatial_centroid": {
- "lat": 45.09298,
- "lon": -101.00476
- },
- "spatial_shape": {
- "coordinates": [
- [
- [
- -103.9746,
- 42.4883
- ],
- [
- -103.9746,
- 49.0
- ],
- [
- -96.55,
- 49.0
- ],
- [
- -96.55,
- 42.4883
- ],
- [
- -103.9746,
- 42.4883
- ]
- ]
- ],
- "type": "Polygon"
- },
- "theme": [
- "geospatial"
- ],
- "title": "Remotely sensed waterbody surface area estimates and angler effort estimates in North Dakota and South Dakota between 1991 - 2021",
- "type": "dataset"
- },
- {
- "_score": 37.315247,
- "_sort": [
- 1790908919811,
- 37.315247,
- 0,
- "56cf6a70-58bf-4eef-9b3c-00e681bcdd1a"
- ],
- "access_level": "public",
- "dcat": {
- "accessLevel": "public",
- "bureauCode": [
- "010:12"
- ],
- "contactPoint": {
- "@type": "vcard:Contact",
- "fn": "Guillaume Mauger",
- "hasEmail": "mailto:gmauger@uw.edu"
- },
- "description": "This zipped file contains the model code, configuration files, and run scripts used to run the VIC model. All were developed by Mingliang liu at Washington State University (WSU).",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "accessURL": "https://doi.org/10.5066/P13ICZXY",
- "description": "Landing page for access to the data",
- "format": "XML",
- "mediaType": "application/http",
- "title": "Digital Data"
- },
- {
- "@type": "dcat:Distribution",
- "description": "The metadata original format",
- "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.624f2b4fd34e21f82769b6ed.xml",
- "format": "XML",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- }
- ],
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_624f2b4fd34e21f82769b6ed",
- "keyword": [
- "USGS:624f2b4fd34e21f82769b6ed",
- "adaptation",
- "climate change",
- "climatologyMeteorologyAtmosphere",
- "culvert",
- "inlandWaters",
- "modeling",
- "streamflow",
- "structure"
- ],
- "modified": "2026-09-29T00:00:00Z",
- "publisher": {
- "@type": "org:Organization",
- "name": "U.S. Geological Survey"
- },
- "spatial": "-124.78125, 45.53125, -116.90625, 49.03125",
- "theme": [
- "geospatial"
- ],
- "title": "VIC model, configuration files, and run scripts for Washington State Culverts and Climate Change project"
- },
- "description": "This zipped file contains the model code, configuration files, and run scripts used to run the VIC model. All were developed by Mingliang liu at Washington State University (WSU).",
- "distribution_titles": [
- "Digital Data",
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/93965a6d-2059-4c35-99f7-f33944df5629",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/93965a6d-2059-4c35-99f7-f33944df5629/raw",
- "has_download": true,
- "has_spatial": true,
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_624f2b4fd34e21f82769b6ed",
- "keyword": [
- "USGS:624f2b4fd34e21f82769b6ed",
- "adaptation",
- "climate change",
- "climatologyMeteorologyAtmosphere",
- "culvert",
- "inlandWaters",
- "modeling",
- "streamflow",
- "structure"
- ],
- "last_harvested_date": "2026-10-02T02:41:59.811568",
- "organization": {
- "aliases": [
- "dept"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
- "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
- "name": "Department of the Interior",
- "organization_type": "Federal Government",
- "slug": "doi"
- },
- "parent_identifier": null,
- "popularity": 0,
- "publisher": "U.S. Geological Survey",
- "slug": "vic-model-configuration-files-and-run-scripts-for-washington-state-culverts-and-climate-ch",
- "spatial_centroid": {
- "lat": 46.93125,
- "lon": -121.63125
- },
- "spatial_shape": {
- "coordinates": [
- [
- [
- -124.78125,
- 45.53125
- ],
- [
- -124.78125,
- 49.03125
- ],
- [
- -116.90625,
- 49.03125
- ],
- [
- -116.90625,
- 45.53125
- ],
- [
- -124.78125,
- 45.53125
- ]
- ]
- ],
- "type": "Polygon"
- },
- "theme": [
- "geospatial"
- ],
- "title": "VIC model, configuration files, and run scripts for Washington State Culverts and Climate Change project",
- "type": "dataset"
- },
- {
- "_score": 7.1910114,
- "_sort": [
- 1790908364882,
- 7.1910114,
- 0,
- "ebedf159-c2bb-429c-8686-a4d3b5abafde"
- ],
- "access_level": "public",
- "dcat": {
- "accessLevel": "public",
- "bureauCode": [
- "010:12"
- ],
- "contactPoint": {
- "@type": "vcard:Contact",
- "fn": "Robert C Klinger",
- "hasEmail": "mailto:casc-data@usgs.gov"
- },
- "description": "Density estimates of four mammal species in the upper subalpine and alpine zones of the Sierra Nevada range, 2008 - 2012. The estimates were derived from variable distance data collected 3-4 per year along each of 21 transects (10 km in length). The transects were randomly selected from a pool of 53 potential routes. Nine transects were sampled in 2008, 12 were sampled in 2009, 19 were sampled in 2010, 21 were sampled in 2011, and 17 were sampled in 2012. All counts were done in July and August each year. Replicate samples within a given year were done within 2-8 days of each other. All counts were done by single observers. \nThe spreadsheet has six worksheets, including three with density estimates for each species at different scales, one worksheet with definitions of the fields, one worksheet with the species names, and a worksheet that defines the scale and units of the estimates in the five worksheets for density",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "accessURL": "https://doi.org/10.5066/P1QZMNOE",
- "description": "Landing page for access to the data",
- "format": "XML",
- "mediaType": "application/http",
- "title": "Digital Data"
- },
- {
- "@type": "dcat:Distribution",
- "description": "The metadata original format",
- "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.54494827e4b0f888a81bb4e1.xml",
- "format": "XML",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- }
- ],
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_54494827e4b0f888a81bb4e1",
- "keyword": [
- "Abundance",
- "Alpine",
- "Belding's ground squirrel",
- "Distance sampling",
- "Golden-mantled ground squirrel",
- "Habitat",
- "Humboldt-Toiyabe National Forest",
- "Inyo National Forest",
- "Line transect counts",
- "Marmot",
- "Occupancy",
- "Pika",
- "Sequoia & Kings Canyon National Parks",
- "Sierra National Forest",
- "Sierra Nevada",
- "Stanislaus National Forest",
- "USGS:54494827e4b0f888a81bb4e1",
- "Yosemite National Park",
- "biota",
- "climate"
- ],
- "modified": "2026-09-29T00:00:00Z",
- "publisher": {
- "@type": "org:Organization",
- "name": "U.S. Geological Survey"
- },
- "spatial": "-120.3332, 35.9689, -117.7694, 38.7574",
- "theme": [
- "geospatial"
- ],
- "title": "AllSpecies0812SNVTranDensity"
- },
- "description": "Density estimates of four mammal species in the upper subalpine and alpine zones of the Sierra Nevada range, 2008 - 2012. The estimates were derived from variable distance data collected 3-4 per year along each of 21 transects (10 km in length). The transects were randomly selected from a pool of 53 potential routes. Nine transects were sampled in 2008, 12 were sampled in 2009, 19 were sampled in 2010, 21 were sampled in 2011, and 17 were sampled in 2012. All counts were done in July and August each year. Replicate samples within a given year were done within 2-8 days of each other. All counts were done by single observers. \nThe spreadsheet has six worksheets, including three with density estimates for each species at different scales, one worksheet with definitions of the fields, one worksheet with the species names, and a worksheet that defines the scale and units of the estimates in the five worksheets for density",
- "distribution_titles": [
- "Digital Data",
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/56947243-4500-40d2-bc1d-0514cfc1bc52",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/56947243-4500-40d2-bc1d-0514cfc1bc52/raw",
- "has_download": true,
- "has_spatial": true,
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_54494827e4b0f888a81bb4e1",
- "keyword": [
- "Abundance",
- "Alpine",
- "Belding's ground squirrel",
- "Distance sampling",
- "Golden-mantled ground squirrel",
- "Habitat",
- "Humboldt-Toiyabe National Forest",
- "Inyo National Forest",
- "Line transect counts",
- "Marmot",
- "Occupancy",
- "Pika",
- "Sequoia & Kings Canyon National Parks",
- "Sierra National Forest",
- "Sierra Nevada",
- "Stanislaus National Forest",
- "USGS:54494827e4b0f888a81bb4e1",
- "Yosemite National Park",
- "biota",
- "climate"
- ],
- "last_harvested_date": "2026-10-02T02:32:44.882282",
- "organization": {
- "aliases": [
- "dept"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
- "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
- "name": "Department of the Interior",
- "organization_type": "Federal Government",
- "slug": "doi"
- },
- "parent_identifier": null,
- "popularity": 0,
- "publisher": "U.S. Geological Survey",
- "slug": "allspecies0812snvtrandensity",
- "spatial_centroid": {
- "lat": 37.0843,
- "lon": -119.30768
- },
- "spatial_shape": {
- "coordinates": [
- [
- [
- -120.3332,
- 35.9689
- ],
- [
- -120.3332,
- 38.7574
- ],
- [
- -117.7694,
- 38.7574
- ],
- [
- -117.7694,
- 35.9689
- ],
- [
- -120.3332,
- 35.9689
- ]
- ]
- ],
- "type": "Polygon"
- },
- "theme": [
- "geospatial"
- ],
- "title": "AllSpecies0812SNVTranDensity",
- "type": "dataset"
- },
- {
- "_score": 6.5198708,
- "_sort": [
- 1790907919298,
- 6.5198708,
- 0,
- "56770119-4e30-41a4-b0fe-7b43dc027357"
- ],
- "access_level": "public",
- "dcat": {
- "accessLevel": "public",
- "bureauCode": [
- "010:12"
- ],
- "contactPoint": {
- "@type": "vcard:Contact",
- "fn": "Richard D. Inman",
- "hasEmail": "mailto:rdinman@usgs.gov"
- },
- "description": "This dataset contains raster layers of environmental characteristics used to predict habitat suitability for multiple species of perennial cactus and herbs in the arid southwest. These data were developed from Sentinel-2 imagery, a digital elevation model, and one obtained from the Google Earth Engine data catalog. These datasets can be used to model habitat suitability of species with distributions that are influenced by terrain and soil characteristics. These layers are grouped into 2 categories:\nsentinel.zip: this file includes each of the raster layers listed below, along with sentinel.xml\nbgsi_10m.tif: Sentinel Bare Ground Index (bgsi): bare soil index to identify barren ground (Nguyen et al. 2021).\nclay_10m.tif: Clay (clay): identify soils rich in Al-OH, such as those clay and sulphate minerals produced from hydrothermal fluids and associated with porphyry copper deposits and vegetation (Imbroane et al. 2007).\nFe2O3_10m.tif: Iron Oxide simple ratio (Fe203): Highlights surface soils that are rich in ferric iron oxide (limonite), for the hydrothermal alteration or the oxidation of Fe-Mg silicates (Imbroane et al., 2007).\nFerrous_10m.tif: Ferrous minerals simple ratio (ferrous): Highlights surface soils that are rich in ferrous iron (Imbroane et al., 2007).\ngyp_10m.tif: Gypsum simple ratio (gypsum): Highlights surface soils that are rich in gypsum (Radwin & Bowen 2021).\nri_10m.tif: Rock index (ri): Highlights surface soils that are more likely to contain minerals (Imbroane et al. 2007).\nterrain.zip: this file includes each of the raster layers listed below, along with terrain.xml\nmtpi_10m.tif: Multi-Scale Topographic Position Index (mtpi): mTPI distinguishes ridge from valley forms. It is calculated using elevation data for each location subtracted by the mean elevation within a neighborhood. (Theobald et al. 2015)\ntwi_10m.tif: Terrain wetness index (twi): Describes how terrain controls hydrology and soil moisture and identifies potential zones of water accumulation, runoff generation, and soil saturation based entirely on a digital elevation model (Moore et al. 1991)\nchili_10m.tif: Continuous Heat-Insolation Load Index (chili): surrogate for effects of insolation and topographic shading on evapotranspiration represented by calculating insolation at early afternoon, sun altitude equivalent to equinox (Theobald et al. 2015).\nAll layers were produced as geotiff raster files. \nReferences:\nImbroane, M. A., Melenti, C. and Gorgan, D. \"Mineral Explorations by Landsat Image Ratios,\" Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007), Timisoara, Romania, 2007, pp. 335-340, doi: 10.1109/SYNASC.2007.52\nMoore, I.D., R.B. Grayson, and A.R. Ladson, 1991. Digital Terrain Modeling: A Review of Hydrological, Geomorphological, and Biological Applications. Hydrological Processes 5:3-30. https://doi.org/10.1002/hyp.3360050103\nNguyen, C.T., Chidthaisong A., Diem, P.K., Huo, L. 2021. A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8. 10, 231. Page 3. URL: https://www.mdpi.com/2073-445X/10/3/231/pdf, https://doi.org/10.3390/land10030231\nRadwin, M. and Bowen, B. B. 2021. \"Mapping mineralogy in evaporite basins through time using multispectral Landsat Data: Examples from the Bonneville Basin, Utah, USA.\" Earth Surface Processes and Landforms, 46(6), 1111-1126. https://doi.org/10.1002/esp.5089.\nTheobald, D. M., Harrison-Atlas, D., Monahan, W. B., and Albano, C. M. 2015. Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PloS one, 10(12), e0143619. https://doi.org/10.1371/journal.pone.0143619",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "accessURL": "https://doi.org/10.5066/P1KDBQS6",
- "description": "Landing page for access to the data",
- "format": "XML",
- "mediaType": "application/http",
- "title": "Digital Data"
- },
- {
- "@type": "dcat:Distribution",
- "description": "The metadata original format",
- "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.6aa0718c1ba49bc75115293d.xml",
- "format": "XML",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- }
- ],
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6aa0718c1ba49bc75115293d",
- "keyword": [
- "Species Distribution Modeling",
- "USGS:6aa0718c1ba49bc75115293d",
- "biogeography",
- "biota",
- "digital elevation models",
- "environmental predictors",
- "habitat suitability",
- "maps and atlases",
- "remote sensing",
- "spatial analysis"
- ],
- "modified": "2026-09-29T00:00:00Z",
- "publisher": {
- "@type": "org:Organization",
- "name": "U.S. Geological Survey"
- },
- "spatial": "-111.9727, 28.3157, -100.3755, 39.1661",
- "theme": [
- "geospatial"
- ],
- "title": "Fine-scale Habitat Environmental Predictor Layers for rare Cacti and Herbaceous Plants in the Arid Southwest"
- },
- "description": "This dataset contains raster layers of environmental characteristics used to predict habitat suitability for multiple species of perennial cactus and herbs in the arid southwest. These data were developed from Sentinel-2 imagery, a digital elevation model, and one obtained from the Google Earth Engine data catalog. These datasets can be used to model habitat suitability of species with distributions that are influenced by terrain and soil characteristics. These layers are grouped into 2 categories:\nsentinel.zip: this file includes each of the raster layers listed below, along with sentinel.xml\nbgsi_10m.tif: Sentinel Bare Ground Index (bgsi): bare soil index to identify barren ground (Nguyen et al. 2021).\nclay_10m.tif: Clay (clay): identify soils rich in Al-OH, such as those clay and sulphate minerals produced from hydrothermal fluids and associated with porphyry copper deposits and vegetation (Imbroane et al. 2007).\nFe2O3_10m.tif: Iron Oxide simple ratio (Fe203): Highlights surface soils that are rich in ferric iron oxide (limonite), for the hydrothermal alteration or the oxidation of Fe-Mg silicates (Imbroane et al., 2007).\nFerrous_10m.tif: Ferrous minerals simple ratio (ferrous): Highlights surface soils that are rich in ferrous iron (Imbroane et al., 2007).\ngyp_10m.tif: Gypsum simple ratio (gypsum): Highlights surface soils that are rich in gypsum (Radwin & Bowen 2021).\nri_10m.tif: Rock index (ri): Highlights surface soils that are more likely to contain minerals (Imbroane et al. 2007).\nterrain.zip: this file includes each of the raster layers listed below, along with terrain.xml\nmtpi_10m.tif: Multi-Scale Topographic Position Index (mtpi): mTPI distinguishes ridge from valley forms. It is calculated using elevation data for each location subtracted by the mean elevation within a neighborhood. (Theobald et al. 2015)\ntwi_10m.tif: Terrain wetness index (twi): Describes how terrain controls hydrology and soil moisture and identifies potential zones of water accumulation, runoff generation, and soil saturation based entirely on a digital elevation model (Moore et al. 1991)\nchili_10m.tif: Continuous Heat-Insolation Load Index (chili): surrogate for effects of insolation and topographic shading on evapotranspiration represented by calculating insolation at early afternoon, sun altitude equivalent to equinox (Theobald et al. 2015).\nAll layers were produced as geotiff raster files. \nReferences:\nImbroane, M. A., Melenti, C. and Gorgan, D. \"Mineral Explorations by Landsat Image Ratios,\" Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007), Timisoara, Romania, 2007, pp. 335-340, doi: 10.1109/SYNASC.2007.52\nMoore, I.D., R.B. Grayson, and A.R. Ladson, 1991. Digital Terrain Modeling: A Review of Hydrological, Geomorphological, and Biological Applications. Hydrological Processes 5:3-30. https://doi.org/10.1002/hyp.3360050103\nNguyen, C.T., Chidthaisong A., Diem, P.K., Huo, L. 2021. A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8. 10, 231. Page 3. URL: https://www.mdpi.com/2073-445X/10/3/231/pdf, https://doi.org/10.3390/land10030231\nRadwin, M. and Bowen, B. B. 2021. \"Mapping mineralogy in evaporite basins through time using multispectral Landsat Data: Examples from the Bonneville Basin, Utah, USA.\" Earth Surface Processes and Landforms, 46(6), 1111-1126. https://doi.org/10.1002/esp.5089.\nTheobald, D. M., Harrison-Atlas, D., Monahan, W. B., and Albano, C. M. 2015. Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PloS one, 10(12), e0143619. https://doi.org/10.1371/journal.pone.0143619",
- "distribution_titles": [
- "Digital Data",
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/a7e857f9-b15e-4c22-bb41-aac8a58b04a3",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/a7e857f9-b15e-4c22-bb41-aac8a58b04a3/raw",
- "has_download": true,
- "has_spatial": true,
- "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6aa0718c1ba49bc75115293d",
- "keyword": [
- "Species Distribution Modeling",
- "USGS:6aa0718c1ba49bc75115293d",
- "biogeography",
- "biota",
- "digital elevation models",
- "environmental predictors",
- "habitat suitability",
- "maps and atlases",
- "remote sensing",
- "spatial analysis"
- ],
- "last_harvested_date": "2026-10-02T02:25:19.298374",
- "organization": {
- "aliases": [
- "dept"
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "143529f7-2eef-4a07-b227-93ac9e84fad8",
- "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png",
+ "acc