Timeline / Data.gov — Climate Datasets

changed Source changed

A new raw object was archived. Both versions are preserved. 5794 line(s) added, 4398 line(s) removed.

Evidence

What changed derived

This diff is not evidence. It was produced by civic-memory.diff_engine 1.1.0 at 2026-09-24T00:25:55+00:00 by normalizing the two archived objects above. The objects are authoritative; this reading of them can be regenerated or deleted without loss. 5794 line(s) added, 4398 line(s) removed.

--- previous
+++ current
@@ -1,11 +1,5793 @@
 {
- "after": "WzE3OTAxMjU1NDQ4NjUsMjAuNzc0MzAzLDEsImMyY2MyMjAzLWFiMzktNGVlMS05NjhlLTQwZmEyNmViNWEzYiJd",
+ "after": "WzE3OTAxMjU1ODM5NjIsMTAuOTU1MTg3LDcsIjY3YzRiNDEzLTkyYmUtNGQ4MC05NDYyLWI3ZjcwMGM2YTI4YSJd",
  "results": [
  {
- "_score": 14.454666,
+ "_score": 19.561703,
+ "_sort": [
+ 1790209546815,
+ 19.561703,
+ 1,
+ "36dabaad-0f1d-490c-9a8b-e706aee8e6b6"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609af892d34ea221ce370f0a.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609af892d34ea221ce370f0a",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609af892d34ea221ce370f0a",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "BCCM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/2c6792b2-f142-4a01-b703-fc22e7f60d1a",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/2c6792b2-f142-4a01-b703-fc22e7f60d1a/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609af892d34ea221ce370f0a",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609af892d34ea221ce370f0a",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:25:46.815010",
+ "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": 1,
+ "publisher": "U.S. Geological Survey",
+ "slug": "bccm-monthly-future-prescribed-burn-windows-for-the-southeast-united-states-2010-2099-rcp-",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "BCCM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5",
+ "type": "dataset"
+ },
+ {
+ "_score": 14.752387,
+ "_sort": [
+ 1790209494133,
+ 14.752387,
+ 0,
+ "c440db7e-3b19-436f-9cc6-9b14d78057e8"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Ali Mirchi",
+ "hasEmail": "mailto:amirchi@okstate.edu"
+ },
+ "description": "A fully calibrated and validated SWAT model was used to provide watershed simulation results including irrigation water availability, crop evapotranspiration, outflow, and other components of the water budget such as groundwater recharge under different climate conditions (Dry (access1_0 rcp85) and Wet (fio_esm rcp45); Samimi et al., 2022), and selected environmental water allocation scenarios. The results are presented in the annual time interface for each Hydrologic Response Unit (HRU) in the watershed. We later aggregated these data to determine the values of each related component across the watershed. The data are available as open source to the public.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.21429/933x-7w44",
+ "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.6582fc20d34ea2421d5aa6aa.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6582fc20d34ea2421d5aa6aa",
+ "keyword": [
+ "Irrigation water availability",
+ "Rio Bravo",
+ "Rio Grande",
+ "Soil and Water Analysis Tool (SWAT)",
+ "USGS:6582fc20d34ea2421d5aa6aa",
+ "Water budget components",
+ "groundwater",
+ "intelligenceMilitary",
+ "modeling",
+ "watershed management"
+ ],
+ "modified": "2026-09-17T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-107.9297, 31.6534, -105.8203, 34.9400",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Water Budget Components for a Sample Water Allocation Scenario for 2020 - 2036"
+ },
+ "description": "A fully calibrated and validated SWAT model was used to provide watershed simulation results including irrigation water availability, crop evapotranspiration, outflow, and other components of the water budget such as groundwater recharge under different climate conditions (Dry (access1_0 rcp85) and Wet (fio_esm rcp45); Samimi et al., 2022), and selected environmental water allocation scenarios. The results are presented in the annual time interface for each Hydrologic Response Unit (HRU) in the watershed. We later aggregated these data to determine the values of each related component across the watershed. The data are available as open source to the public.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/d2bb4dd1-9299-4b24-ac49-1d36ff043e4c",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/d2bb4dd1-9299-4b24-ac49-1d36ff043e4c/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6582fc20d34ea2421d5aa6aa",
+ "keyword": [
+ "Irrigation water availability",
+ "Rio Bravo",
+ "Rio Grande",
+ "Soil and Water Analysis Tool (SWAT)",
+ "USGS:6582fc20d34ea2421d5aa6aa",
+ "Water budget components",
+ "groundwater",
+ "intelligenceMilitary",
+ "modeling",
+ "watershed management"
+ ],
+ "last_harvested_date": "2026-09-24T00:24:54.133095",
+ "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": "water-budget-components-for-a-sample-water-allocation-scenario-for-2020-2036",
+ "spatial_centroid": {
+ "lat": 32.96804,
+ "lon": -107.08594000000001
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -107.9297,
+ 31.6534
+ ],
+ [
+ -107.9297,
+ 34.94
+ ],
+ [
+ -105.8203,
+ 34.94
+ ],
+ [
+ -105.8203,
+ 31.6534
+ ],
+ [
+ -107.9297,
+ 31.6534
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Water Budget Components for a Sample Water Allocation Scenario for 2020 - 2036",
+ "type": "dataset"
+ },
+ {
+ "_score": 43.854813,
+ "_sort": [
+ 1790209413857,
+ 43.854813,
+ 0,
+ "c5e3cdd3-073a-492f-b56d-481e8d618e01"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Diana Zamora-Reyes",
+ "hasEmail": "mailto:dzamora-reyes@usgs.gov"
+ },
+ "description": "This data release includes the revised downscaled climate inputs and hydrologic outputs from the Basin Characterization Model (BCM) version 8 (v8), described in Flint and others (2021a), in a 270 by 270-meter spatial resolution at a monthly time step from water years 1896 to 2025 for the Santa Ana River watershed. This data release includes five child items: 1. 30-year summaries, 2. Model Archive, 3. Monthly BCM hydrology variables, 4. Monthly climate variables, and 5. Water year summaries.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P14FPYYJ",
+ "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.69d42607b66b011e6f6e29e8.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_69d42607b66b011e6f6e29e8",
+ "keyword": [
+ "Atmospheric and Climatic Processes",
+ "California",
+ "Evaporation",
+ "Hydrology",
+ "Mathematical Modeling",
+ "Modeling",
+ "Orange County",
+ "Permeability",
+ "Precipitation (atmospheric)",
+ "Riverside County",
+ "San Bernardino County",
+ "Snow and Ice Cover",
+ "Soil Moisture",
+ "Streamflow",
+ "Surface Water (non-marine)",
+ "Transpiration",
+ "USGS:69d42607b66b011e6f6e29e8",
+ "United States",
+ "Water Budget",
+ "Water Cycle",
+ "Water Resources",
+ "Watershed Management",
+ "climatologyMeteorologyAtmosphere",
+ "environment",
+ "geoscientificInformation",
+ "inlandWaters"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-117.9500, 33.6200, -117.0100, 34.1500",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Santa Ana River Watershed, California 270-meter Basin Characterization Model - Monthly Historical Climate and Hydrology"
+ },
+ "description": "This data release includes the revised downscaled climate inputs and hydrologic outputs from the Basin Characterization Model (BCM) version 8 (v8), described in Flint and others (2021a), in a 270 by 270-meter spatial resolution at a monthly time step from water years 1896 to 2025 for the Santa Ana River watershed. This data release includes five child items: 1. 30-year summaries, 2. Model Archive, 3. Monthly BCM hydrology variables, 4. Monthly climate variables, and 5. Water year summaries.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/b4d5f30a-9c56-45fd-8b3d-4f787b00fa14",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/b4d5f30a-9c56-45fd-8b3d-4f787b00fa14/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_69d42607b66b011e6f6e29e8",
+ "keyword": [
+ "Atmospheric and Climatic Processes",
+ "California",
+ "Evaporation",
+ "Hydrology",
+ "Mathematical Modeling",
+ "Modeling",
+ "Orange County",
+ "Permeability",
+ "Precipitation (atmospheric)",
+ "Riverside County",
+ "San Bernardino County",
+ "Snow and Ice Cover",
+ "Soil Moisture",
+ "Streamflow",
+ "Surface Water (non-marine)",
+ "Transpiration",
+ "USGS:69d42607b66b011e6f6e29e8",
+ "United States",
+ "Water Budget",
+ "Water Cycle",
+ "Water Resources",
+ "Watershed Management",
+ "climatologyMeteorologyAtmosphere",
+ "environment",
+ "geoscientificInformation",
+ "inlandWaters"
+ ],
+ "last_harvested_date": "2026-09-24T00:23:33.857746",
+ "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": "santa-ana-river-watershed-california-270-meter-basin-characterization-model-monthly-histor",
+ "spatial_centroid": {
+ "lat": 33.832,
+ "lon": -117.574
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -117.95,
+ 33.62
+ ],
+ [
+ -117.95,
+ 34.15
+ ],
+ [
+ -117.01,
+ 34.15
+ ],
+ [
+ -117.01,
+ 33.62
+ ],
+ [
+ -117.95,
+ 33.62
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Santa Ana River Watershed, California 270-meter Basin Characterization Model - Monthly Historical Climate and Hydrology",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.417788,
+ "_sort": [
+ 1790209375467,
+ 19.417788,
+ 1,
+ "f47239d9-79c2-443f-8d26-c44ecc591221"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609c88e4d34ea221ce3ac900.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c88e4d34ea221ce3ac900",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c88e4d34ea221ce3ac900",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "MRI Historical Prescribed Burn Windows for the Southeast United States 1950-1999"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/90029160-aa4d-4b81-ab76-283768de6b41",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/90029160-aa4d-4b81-ab76-283768de6b41/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c88e4d34ea221ce3ac900",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c88e4d34ea221ce3ac900",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:22:55.467175",
+ "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": 1,
+ "publisher": "U.S. Geological Survey",
+ "slug": "mri-historical-prescribed-burn-windows-for-the-southeast-united-states-1950-1999",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "MRI Historical Prescribed Burn Windows for the Southeast United States 1950-1999",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.561703,
+ "_sort": [
+ 1790209288917,
+ 19.561703,
+ 0,
+ "2bbd181b-a6da-4dd8-932e-ed2a41806ecd"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.60872893d34eadd49d31b356.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_60872893d34eadd49d31b356",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:60872893d34eadd49d31b356",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Seasonal Future Prescribed Burn Windows for the Southeast United States - June-August 2010-2099 RCP 4.5"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/9ca88f3e-431d-4f2a-a0e3-515dbe9dfd5d",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/9ca88f3e-431d-4f2a-a0e3-515dbe9dfd5d/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_60872893d34eadd49d31b356",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:60872893d34eadd49d31b356",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:21:28.917280",
+ "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": "seasonal-future-prescribed-burn-windows-for-the-southeast-united-states-june-august-2010-2",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Seasonal Future Prescribed Burn Windows for the Southeast United States - June-August 2010-2099 RCP 4.5",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.536568,
+ "_sort": [
+ 1790209273707,
+ 19.536568,
+ 2,
+ "8e59a4fe-6c98-41ce-b4fd-c8862db3957c"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609c8369d34ea221ce3aaded.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c8369d34ea221ce3aaded",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c8369d34ea221ce3aaded",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "MRI Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/4811eb00-1767-4bdf-9d07-e88455209f1c",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/4811eb00-1767-4bdf-9d07-e88455209f1c/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c8369d34ea221ce3aaded",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c8369d34ea221ce3aaded",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:21:13.707160",
+ "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": "mri-monthly-future-prescribed-burn-windows-for-the-southeast-united-states-2010-2099-rcp-8",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "MRI Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5",
+ "type": "dataset"
+ },
+ {
+ "_score": 13.577579,
+ "_sort": [
+ 1790209253580,
+ 13.577579,
+ 0,
+ "f6afb860-eb9d-4d84-82b9-7e02606525e5"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "David Gutzler",
+ "hasEmail": "mailto:gutzler@unm.edu"
+ },
+ "description": "Data files used in the analysis of BCSD CMIP5 simulations as part of the broader analysis of the future of streamflow predictability for the Rio Grande headwaters. The data files were generated using the raw data from the Bureau of Reclamation publicly available datasets. The values contained in the files were generated to match the structure of the analysis that was performed on the historical data. All data from the ensemble of simulations forced by downscaled and bias-corrected GCMs were clipped to the domain of the Rio Grande Headwaters (HUC8 - 13010001). The following values are contained in files differentiated by RCP and climate variable of interest for the years 1951-2099. Variables below are listed and associated files names follow. XX in the file name can be replaced by one of four different RCPs (ie. 26,45,60,85)",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.21429/7q4d-k862",
+ "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.5f91dbf782ce720ee2d33c3d.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5f91dbf782ce720ee2d33c3d",
+ "keyword": [
+ "Precipitation",
+ "Streamflow",
+ "Temperature",
+ "USGS:5f91dbf782ce720ee2d33c3d",
+ "biota",
+ "ecological processes",
+ "freshwater ecosystems",
+ "mathematical modeling",
+ "precipitation (atmospheric)",
+ "river systems",
+ "state and transition modeling",
+ "streamflow",
+ "water circulation",
+ "water resources"
+ ],
+ "modified": "2026-09-18T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-108.7646, 34.0527, -103.2275, 38.6169",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Model Output for BCSD CMIP5 simulations for the Rio Grande from 1951-2099"
+ },
+ "description": "Data files used in the analysis of BCSD CMIP5 simulations as part of the broader analysis of the future of streamflow predictability for the Rio Grande headwaters. The data files were generated using the raw data from the Bureau of Reclamation publicly available datasets. The values contained in the files were generated to match the structure of the analysis that was performed on the historical data. All data from the ensemble of simulations forced by downscaled and bias-corrected GCMs were clipped to the domain of the Rio Grande Headwaters (HUC8 - 13010001). The following values are contained in files differentiated by RCP and climate variable of interest for the years 1951-2099. Variables below are listed and associated files names follow. XX in the file name can be replaced by one of four different RCPs (ie. 26,45,60,85)",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/8c894396-21d0-4681-82ea-816496823bfd",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/8c894396-21d0-4681-82ea-816496823bfd/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5f91dbf782ce720ee2d33c3d",
+ "keyword": [
+ "Precipitation",
+ "Streamflow",
+ "Temperature",
+ "USGS:5f91dbf782ce720ee2d33c3d",
+ "biota",
+ "ecological processes",
+ "freshwater ecosystems",
+ "mathematical modeling",
+ "precipitation (atmospheric)",
+ "river systems",
+ "state and transition modeling",
+ "streamflow",
+ "water circulation",
+ "water resources"
+ ],
+ "last_harvested_date": "2026-09-24T00:20:53.580000",
+ "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": "model-output-for-bcsd-cmip5-simulations-for-the-rio-grande-from-1951-2099",
+ "spatial_centroid": {
+ "lat": 35.87838000000001,
+ "lon": -106.54976000000002
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -108.7646,
+ 34.0527
+ ],
+ [
+ -108.7646,
+ 38.6169
+ ],
+ [
+ -103.2275,
+ 38.6169
+ ],
+ [
+ -103.2275,
+ 34.0527
+ ],
+ [
+ -108.7646,
+ 34.0527
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Model Output for BCSD CMIP5 simulations for the Rio Grande from 1951-2099",
+ "type": "dataset"
+ },
+ {
+ "_score": 10.014358,
+ "_sort": [
+ 1790209246244,
+ 10.014358,
+ 0,
+ "bfed7f71-3f0b-4fe6-ab34-52aab61bb742"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Climate Adaptation Science Centers",
+ "hasEmail": "mailto:casc-data@usgs.gov"
+ },
+ "description": "NOAA’s Greater Atlantic Region Marine Mammal Stranding Network Database was examined for its utility as a potential long-term time series for the evaluation of phenological patterns and shifts. Although records from stranding events represent sick or injured animals, these data have been found to be reasonably comparable to survey data and provide useful information on species’ distribution, abundance, and foraging ecology (Maldini et al., 2005; Staudinger et al., 2014; Johnston et al., 2012, 2015). In this study, we focused on stranding data of two marine mammal species, harbor seals (Phoca vitulina) and gray seals (Halichoerus grypus). It was anticipated these data would also be good indicators of the areas and habitats that seal populations use on a seasonal and annual basis in the region. Viable stranding data from Maine to North Carolina included 1,571 gray seals and 4,399 harbor seals from 2001 to 2015. This paper presents a summary of the spatial and temporal patterns of these data, and suggests their suitability as supplemental data to other Gulf of Maine marine species phenological studies, such as the North Atlantic Right Whale Consortium database modelling efforts.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.21429/cjsa-ac35",
+ "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.5ff47aaad34ea5387df02f23.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5ff47aaad34ea5387df02f23",
+ "keyword": [
+ "USGS:5ff47aaad34ea5387df02f23",
+ "biota",
+ "climate change",
+ "geospatial datasets",
+ "long-term ecological monitoring",
+ "monitoring networks"
+ ],
+ "modified": "2026-09-17T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-80.1563, 34.8859, -66.0938, 45.8288",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Data and Metadata for 'A regional analysis of long-term gray and harbor seal stranding events' and the seasonal migrants"
+ },
+ "description": "NOAA’s Greater Atlantic Region Marine Mammal Stranding Network Database was examined for its utility as a potential long-term time series for the evaluation of phenological patterns and shifts. Although records from stranding events represent sick or injured animals, these data have been found to be reasonably comparable to survey data and provide useful information on species’ distribution, abundance, and foraging ecology (Maldini et al., 2005; Staudinger et al., 2014; Johnston et al., 2012, 2015). In this study, we focused on stranding data of two marine mammal species, harbor seals (Phoca vitulina) and gray seals (Halichoerus grypus). It was anticipated these data would also be good indicators of the areas and habitats that seal populations use on a seasonal and annual basis in the region. Viable stranding data from Maine to North Carolina included 1,571 gray seals and 4,399 harbor seals from 2001 to 2015. This paper presents a summary of the spatial and temporal patterns of these data, and suggests their suitability as supplemental data to other Gulf of Maine marine species phenological studies, such as the North Atlantic Right Whale Consortium database modelling efforts.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/e290fe44-e9ce-469b-9939-84eadc4549c0",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/e290fe44-e9ce-469b-9939-84eadc4549c0/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5ff47aaad34ea5387df02f23",
+ "keyword": [
+ "USGS:5ff47aaad34ea5387df02f23",
+ "biota",
+ "climate change",
+ "geospatial datasets",
+ "long-term ecological monitoring",
+ "monitoring networks"
+ ],
+ "last_harvested_date": "2026-09-24T00:20:46.244240",
+ "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": "data-and-metadata-for-a-regional-analysis-of-long-term-gray-and-harbor-seal-stranding-even",
+ "spatial_centroid": {
+ "lat": 39.26306,
+ "lon": -74.5313
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -80.1563,
+ 34.8859
+ ],
+ [
+ -80.1563,
+ 45.8288
+ ],
+ [
+ -66.0938,
+ 45.8288
+ ],
+ [
+ -66.0938,
+ 34.8859
+ ],
+ [
+ -80.1563,
+ 34.8859
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Data and Metadata for 'A regional analysis of long-term gray and harbor seal stranding events' and the seasonal migrants",
+ "type": "dataset"
+ },
+ {
+ "_score": 18.769558,
+ "_sort": [
+ 1790209150067,
+ 18.769558,
+ 0,
+ "bef48902-a14b-4364-b593-1b9be8ade2e6"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "John Zak",
+ "hasEmail": "mailto:casc-data@usgs.gov"
+ },
+ "description": "This project facilitated the implementation of a multiyear project to understand how climate variability and management practices influence soil microbial and nutrient dynamics within a no-till cotton production system with stubble management. Three fields at the R.N. Hooper farm in Petersburgh, TX were used for this project and continue to be monitored with funds from Cotton Inc. The three fields are center-pivot irrigated to compensate for rainfall variability as needed and depending upon water availability. The three fields were planted into the following crops for 2017 : Field 1 – corn following cotton; Field 2 – cotton following corn, and Field 3 – Wheat/mixed summer cover following wheat. The sizes of the three no-till and fields are: Field 1: 78.48 m in diameter, Field 2: 971.29 m in diameter, and Field 3: 781.1 m in diameter. The conventional field was located a few miles from the no-till fields and was also center-pivot irrigated when needed and managed as a tilled cotton production system with a corn -cotton rotations for the past five years. The no-tilled fields have been rotated among, cotton, corn, winter wheat and summer cover (when possible) for the past five years. Within each system we set soil moisture and temperatures sensors at the surface and at 15 cm depth and monitored microbial and nutrient dynamics across the year. Fields were instrumented in March 2017 and were monitored continuously except for harvesting and planting periods. Soil samples were taken initially in May 2017 and then each month from six plots established across each field. The following parameters were evaluated at each location and within each field: % soil moisture, Microbial Biomass Carbon, extractable levels of NO3-N and NH4-N and % Soil Organic Matter.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P1UFY55J",
+ "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.5dee848ae4b02caea0f2fd74.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5dee848ae4b02caea0f2fd74",
+ "keyword": [
+ "Agroecosystems",
+ "Soil Health",
+ "Soil Microbial Dynamics",
+ "Soil Sustainability",
+ "Southern High Plains",
+ "USGS:5dee848ae4b02caea0f2fd74",
+ "biota",
+ "climate change",
+ "external research support"
+ ],
+ "modified": "2026-09-17T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-106.0400, 26.0765, -93.7793, 36.6332",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Initiating the Development of Regional Demonstration Fields for Implementing Soil Practices That Maximize Soil Health and Drought Resilience: Understanding Microbial-Temperature Dynamics"
+ },
+ "description": "This project facilitated the implementation of a multiyear project to understand how climate variability and management practices influence soil microbial and nutrient dynamics within a no-till cotton production system with stubble management. Three fields at the R.N. Hooper farm in Petersburgh, TX were used for this project and continue to be monitored with funds from Cotton Inc. The three fields are center-pivot irrigated to compensate for rainfall variability as needed and depending upon water availability. The three fields were planted into the following crops for 2017 : Field 1 – corn following cotton; Field 2 – cotton following corn, and Field 3 – Wheat/mixed summer cover following wheat. The sizes of the three no-till and fields are: Field 1: 78.48 m in diameter, Field 2: 971.29 m in diameter, and Field 3: 781.1 m in diameter. The conventional field was located a few miles from the no-till fields and was also center-pivot irrigated when needed and managed as a tilled cotton production system with a corn -cotton rotations for the past five years. The no-tilled fields have been rotated among, cotton, corn, winter wheat and summer cover (when possible) for the past five years. Within each system we set soil moisture and temperatures sensors at the surface and at 15 cm depth and monitored microbial and nutrient dynamics across the year. Fields were instrumented in March 2017 and were monitored continuously except for harvesting and planting periods. Soil samples were taken initially in May 2017 and then each month from six plots established across each field. The following parameters were evaluated at each location and within each field: % soil moisture, Microbial Biomass Carbon, extractable levels of NO3-N and NH4-N and % Soil Organic Matter.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/a4efe6e7-bf2e-403e-9381-306f73ce9279",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/a4efe6e7-bf2e-403e-9381-306f73ce9279/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5dee848ae4b02caea0f2fd74",
+ "keyword": [
+ "Agroecosystems",
+ "Soil Health",
+ "Soil Microbial Dynamics",
+ "Soil Sustainability",
+ "Southern High Plains",
+ "USGS:5dee848ae4b02caea0f2fd74",
+ "biota",
+ "climate change",
+ "external research support"
+ ],
+ "last_harvested_date": "2026-09-24T00:19:10.067940",
+ "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": "initiating-the-development-of-regional-demonstration-fields-for-implementing-soil-practice",
+ "spatial_centroid": {
+ "lat": 30.29918,
+ "lon": -101.13572
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -106.04,
+ 26.0765
+ ],
+ [
+ -106.04,
+ 36.6332
+ ],
+ [
+ -93.7793,
+ 36.6332
+ ],
+ [
+ -93.7793,
+ 26.0765
+ ],
+ [
+ -106.04,
+ 26.0765
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Initiating the Development of Regional Demonstration Fields for Implementing Soil Practices That Maximize Soil Health and Drought Resilience: Understanding Microbial-Temperature Dynamics",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.417788,
+ "_sort": [
+ 1790209115223,
+ 19.417788,
+ 0,
+ "4b3df90e-fae7-40d0-8cf3-74ef13aaa60d"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609c87fad34ea221ce3ac4db.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c87fad34ea221ce3ac4db",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c87fad34ea221ce3ac4db",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "IPSL-CM5A-MR Historical Prescribed Burn Windows for the Southeast United States 1950-1999"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/be3a054a-78bc-493f-9f8f-eb22bb1eaa62",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/be3a054a-78bc-493f-9f8f-eb22bb1eaa62/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c87fad34ea221ce3ac4db",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c87fad34ea221ce3ac4db",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:18:35.223159",
+ "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": "ipsl-cm5a-mr-historical-prescribed-burn-windows-for-the-southeast-united-states-1950-1999",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "IPSL-CM5A-MR Historical Prescribed Burn Windows for the Southeast United States 1950-1999",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.417788,
+ "_sort": [
+ 1790209086410,
+ 19.417788,
+ 0,
+ "8d407512-c416-4297-8038-568110aec056"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609afc96d34ea221ce3710d5.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609afc96d34ea221ce3710d5",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609afc96d34ea221ce3710d5",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "HADCC Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/e18551e0-572e-4999-9ad4-dd3d209d87ba",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/e18551e0-572e-4999-9ad4-dd3d209d87ba/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609afc96d34ea221ce3710d5",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609afc96d34ea221ce3710d5",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:18:06.410747",
+ "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": "hadcc-monthly-future-prescribed-burn-windows-for-the-southeast-united-states-2010-2099-rcp",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "HADCC Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.561703,
+ "_sort": [
+ 1790209070451,
+ 19.561703,
+ 3,
+ "9c212225-25e3-4b1b-846b-7a7a4dd8bf69"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609c8318d34ea221ce3aabce.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c8318d34ea221ce3aabce",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c8318d34ea221ce3aabce",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "MIROCESMCHM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/26d3a184-9004-497b-9094-8e49f6bcedee",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/26d3a184-9004-497b-9094-8e49f6bcedee/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c8318d34ea221ce3aabce",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c8318d34ea221ce3aabce",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:17:50.451068",
+ "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": "mirocesmchm-monthly-future-prescribed-burn-windows-for-the-southeast-united-states-2010-20",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "MIROCESMCHM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5",
+ "type": "dataset"
+ },
+ {
+ "_score": 26.446527,
+ "_sort": [
+ 1790209067477,
+ 26.446527,
+ 0,
+ "0e1a9ee6-8047-4f43-ad19-adc1461ae0ed"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "YinPhan Tsang",
+ "hasEmail": "mailto:tsangy@hawaii.edu"
+ },
+ "description": "This dataset contains information regarding where management should prioritize conservation efforts in the Hawaiian Island of Maui given current conditions and projected future conditions due to climate change. This dataset is an aggregation of many different datasets looking at the ecological and socio-cultural information inland and around the coast of the island.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.21429/gsn5-v968",
+ "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.64079fedd34e76f5f75e39cc.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_64079fedd34e76f5f75e39cc",
+ "keyword": [
+ "Hawaii",
+ "Maui",
+ "Pacific",
+ "USGS:64079fedd34e76f5f75e39cc",
+ "climate change",
+ "environment",
+ "external research support",
+ "geospatial datasets",
+ "natural resource management",
+ "watershed management"
+ ],
+ "modified": "2026-09-18T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-156.7071, 20.5655, -155.9688, 21.0406",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Land Prioritization for the Conservation of Aquatic Ecosystems of Maui in Hawaiʻi, Computed through Zonation"
+ },
+ "description": "This dataset contains information regarding where management should prioritize conservation efforts in the Hawaiian Island of Maui given current conditions and projected future conditions due to climate change. This dataset is an aggregation of many different datasets looking at the ecological and socio-cultural information inland and around the coast of the island.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/e4ca35c4-148d-4612-b309-88676056d106",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/e4ca35c4-148d-4612-b309-88676056d106/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_64079fedd34e76f5f75e39cc",
+ "keyword": [
+ "Hawaii",
+ "Maui",
+ "Pacific",
+ "USGS:64079fedd34e76f5f75e39cc",
+ "climate change",
+ "environment",
+ "external research support",
+ "geospatial datasets",
+ "natural resource management",
+ "watershed management"
+ ],
+ "last_harvested_date": "2026-09-24T00:17:47.477049",
+ "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": "land-prioritization-for-the-conservation-of-aquatic-ecosystems-of-maui-in-hawaii-computed-",
+ "spatial_centroid": {
+ "lat": 20.755540000000003,
+ "lon": -156.41178
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -156.7071,
+ 20.5655
+ ],
+ [
+ -156.7071,
+ 21.0406
+ ],
+ [
+ -155.9688,
+ 21.0406
+ ],
+ [
+ -155.9688,
+ 20.5655
+ ],
+ [
+ -156.7071,
+ 20.5655
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Land Prioritization for the Conservation of Aquatic Ecosystems of Maui in Hawaiʻi, Computed through Zonation",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.536568,
+ "_sort": [
+ 1790208911581,
+ 19.536568,
+ 1,
+ "cf16b0d0-ae10-428f-beb3-aea56a4c9841"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609c43d1d34ea221ce39d317.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c43d1d34ea221ce39d317",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c43d1d34ea221ce39d317",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "CSIRO Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/10b6700b-83d3-475b-a6f7-6f0fdd4313a0",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/10b6700b-83d3-475b-a6f7-6f0fdd4313a0/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c43d1d34ea221ce39d317",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c43d1d34ea221ce39d317",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:15:11.581975",
+ "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": 1,
+ "publisher": "U.S. Geological Survey",
+ "slug": "csiro-monthly-future-prescribed-burn-windows-for-the-southeast-united-states-2010-2099-rcp",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "CSIRO Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.619362,
+ "_sort": [
+ 1790208869358,
+ 19.619362,
+ 2,
+ "f096ea3e-0497-4bfa-9ac2-9b32808a919f"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609ea014d34ea221ce3f406d.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609ea014d34ea221ce3f406d",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609ea014d34ea221ce3f406d",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Seasonal Future Prescribed Burn Windows for the Southeast United States - September-November 2010-2099 RCP 8.5"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/1a3ebf2d-520f-483f-bc5f-f71fdeff38ba",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/1a3ebf2d-520f-483f-bc5f-f71fdeff38ba/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609ea014d34ea221ce3f406d",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609ea014d34ea221ce3f406d",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:14:29.358452",
+ "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": "seasonal-future-prescribed-burn-windows-for-the-southeast-united-states-september-november",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Seasonal Future Prescribed Burn Windows for the Southeast United States - September-November 2010-2099 RCP 8.5",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.623352,
+ "_sort": [
+ 1790208809519,
+ 19.623352,
+ 1,
+ "e735bd9e-6284-4826-9c2f-ed8716c28c7e"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609c86d6d34ea221ce3abff3.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c86d6d34ea221ce3abff3",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c86d6d34ea221ce3abff3",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "HADCC Historical Prescribed Burn Windows for the Southeast United States 1950-1999"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/f6fbd2c3-15d0-4eb3-8d9b-5b5decee1f0d",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/f6fbd2c3-15d0-4eb3-8d9b-5b5decee1f0d/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c86d6d34ea221ce3abff3",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c86d6d34ea221ce3abff3",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:13:29.519713",
+ "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": 1,
+ "publisher": "U.S. Geological Survey",
+ "slug": "hadcc-historical-prescribed-burn-windows-for-the-southeast-united-states-1950-1999",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "HADCC Historical Prescribed Burn Windows for the Southeast United States 1950-1999",
+ "type": "dataset"
+ },
+ {
+ "_score": 27.007807,
+ "_sort": [
+ 1790208540856,
+ 27.007807,
+ 2,
+ "6cae55a6-1ac9-453a-9715-a3c3302922ac"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Jordan S. Read",
+ "hasEmail": "mailto:jread@usgs.gov"
+ },
+ "description": "Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum observed lake depth, land-cover based estimates of surrounding canopy height and observed water temperature profiles (used here for validation). This unique dataset offers landscape-level insight into the future impact of climate change on lakes. This data set contains the following parameters: ice_duration_days, ice_on_date, ice_off_date, winter_dur_0-4, coef_var_30-60, coef_var_0-30, stratification_onset_yday, stratification_duration, sthermo_depth_mean, peak_temp, gdd_wtr_0c, gdd_wtr_5c, gdd_wtr_10c, bottom_temp_at_strat, schmidt_daily_annual_sum, mean_surf_jas, max_surf_jas, mean_bot_jas, max_bot_jas, mean_surf_jan, max_surf_jan, mean_bot_jan, max_bot_jan, mean_surf_feb, max_surf_feb, mean_bot_feb, max_bot_feb, mean_surf_mar, max_surf_mar, mean_bot_mar, max_bot_mar, mean_surf_apr, max_surf_apr, mean_bot_apr, max_bot_apr, mean_surf_may, max_surf_may, mean_bot_may, max_bot_may, mean_surf_jun, max_surf_jun, mean_bot_jun, max_bot_jun, mean_surf_jul, max_surf_jul, mean_bot_jul, max_bot_jul, mean_surf_aug, max_surf_aug, mean_bot_aug, max_bot_aug, mean_surf_sep, max_surf_sep, mean_bot_sep, max_bot_sep, mean_surf_oct, max_surf_oct, mean_bot_oct, max_bot_oct, mean_surf_nov, max_surf_nov, mean_bot_nov, max_bot_nov, mean_surf_dec, max_surf_dec, mean_bot_dec, max_bot_dec, which are defined below.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/F7DV1H10",
+ "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.57d9e887e4b090824ffb1098.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_57d9e887e4b090824ffb1098",
+ "keyword": [
+ "IA",
+ "IL",
+ "IN",
+ "Illinois",
+ "Indiana",
+ "Iowa",
+ "MI",
+ "MN",
+ "Michigan",
+ "Minnesota",
+ "SD",
+ "South Dakota",
+ "US",
+ "USGS:57d9e887e4b090824ffb1098",
+ "United States",
+ "WI",
+ "Wisconsin",
+ "biota",
+ "climate change",
+ "environment",
+ "inlandWaters",
+ "modeling",
+ "reservoirs",
+ "temperate lakes",
+ "thermal profiles",
+ "water"
+ ],
+ "modified": "2026-09-17T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-96.8589114267623, 48.7289513243629, -83.0573307815185, 41.7553570685206",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Thermal metrics: A large-scale database of modeled contemporary and future water temperature data for 10,774 Michigan, Minnesota and Wisconsin Lakes"
+ },
+ "description": "Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum observed lake depth, land-cover based estimates of surrounding canopy height and observed water temperature profiles (used here for validation). This unique dataset offers landscape-level insight into the future impact of climate change on lakes. This data set contains the following parameters: ice_duration_days, ice_on_date, ice_off_date, winter_dur_0-4, coef_var_30-60, coef_var_0-30, stratification_onset_yday, stratification_duration, sthermo_depth_mean, peak_temp, gdd_wtr_0c, gdd_wtr_5c, gdd_wtr_10c, bottom_temp_at_strat, schmidt_daily_annual_sum, mean_surf_jas, max_surf_jas, mean_bot_jas, max_bot_jas, mean_surf_jan, max_surf_jan, mean_bot_jan, max_bot_jan, mean_surf_feb, max_surf_feb, mean_bot_feb, max_bot_feb, mean_surf_mar, max_surf_mar, mean_bot_mar, max_bot_mar, mean_surf_apr, max_surf_apr, mean_bot_apr, max_bot_apr, mean_surf_may, max_surf_may, mean_bot_may, max_bot_may, mean_surf_jun, max_surf_jun, mean_bot_jun, max_bot_jun, mean_surf_jul, max_surf_jul, mean_bot_jul, max_bot_jul, mean_surf_aug, max_surf_aug, mean_bot_aug, max_bot_aug, mean_surf_sep, max_surf_sep, mean_bot_sep, max_bot_sep, mean_surf_oct, max_surf_oct, mean_bot_oct, max_bot_oct, mean_surf_nov, max_surf_nov, mean_bot_nov, max_bot_nov, mean_surf_dec, max_surf_dec, mean_bot_dec, max_bot_dec, which are defined below.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/02b3a9d3-7b46-4655-8355-28c542440478",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/02b3a9d3-7b46-4655-8355-28c542440478/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_57d9e887e4b090824ffb1098",
+ "keyword": [
+ "IA",
+ "IL",
+ "IN",
+ "Illinois",
+ "Indiana",
+ "Iowa",
+ "MI",
+ "MN",
+ "Michigan",
+ "Minnesota",
+ "SD",
+ "South Dakota",
+ "US",
+ "USGS:57d9e887e4b090824ffb1098",
+ "United States",
+ "WI",
+ "Wisconsin",
+ "biota",
+ "climate change",
+ "environment",
+ "inlandWaters",
+ "modeling",
+ "reservoirs",
+ "temperate lakes",
+ "thermal profiles",
+ "water"
+ ],
+ "last_harvested_date": "2026-09-24T00:09:00.856238",
+ "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": "thermal-metrics-a-large-scale-database-of-modeled-contemporary-and-future-water-temperatur",
+ "spatial_centroid": {
+ "lat": 45.939513622025984,
+ "lon": -91.33827916866478
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -96.8589114267623,
+ 48.7289513243629
+ ],
+ [
+ -96.8589114267623,
+ 41.7553570685206
+ ],
+ [
+ -83.0573307815185,
+ 41.7553570685206
+ ],
+ [
+ -83.0573307815185,
+ 48.7289513243629
+ ],
+ [
+ -96.8589114267623,
+ 48.7289513243629
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Thermal metrics: A large-scale database of modeled contemporary and future water temperature data for 10,774 Michigan, Minnesota and Wisconsin Lakes",
+ "type": "dataset"
+ },
+ {
+ "_score": 26.120543,
+ "_sort": [
+ 1790208460959,
+ 26.120543,
+ 0,
+ "96927bf3-6683-41e9-8387-c2d2539d80c4"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Climate Adaptation Science Centers",
+ "hasEmail": "mailto:casc-data@usgs.gov"
+ },
+ "description": "Researchers developed a custom model that integrates gSSURGO soil property data with condensed climate data from PRISM (e.g., drought index) to predict fraction of available water for a given soil. The model was trained with in situ measured soil moisture data (point measurements) and expanded to spatial extent with gSSURGO maps and PRISM data. The code for the model was developed using a combination of statistical and GIS languages (R, Matlab, ArcGIS, etc.).",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P149PKWK",
+ "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.617ac7fbd34ea58c3c6ffe8f.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_617ac7fbd34ea58c3c6ffe8f",
+ "keyword": [
+ "USGS:617ac7fbd34ea58c3c6ffe8f",
+ "biota",
+ "climate change",
+ "external research support",
+ "soil",
+ "soil moisture"
+ ],
+ "modified": "2026-09-17T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-114.6094, 28.3044, -91.4063, 39.9097",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Custom Soil Moisture Model Code"
+ },
+ "description": "Researchers developed a custom model that integrates gSSURGO soil property data with condensed climate data from PRISM (e.g., drought index) to predict fraction of available water for a given soil. The model was trained with in situ measured soil moisture data (point measurements) and expanded to spatial extent with gSSURGO maps and PRISM data. The code for the model was developed using a combination of statistical and GIS languages (R, Matlab, ArcGIS, etc.).",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/d66cffc9-65b7-40e1-bb70-1674148d06ad",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/d66cffc9-65b7-40e1-bb70-1674148d06ad/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_617ac7fbd34ea58c3c6ffe8f",
+ "keyword": [
+ "USGS:617ac7fbd34ea58c3c6ffe8f",
+ "biota",
+ "climate change",
+ "external research support",
+ "soil",
+ "soil moisture"
+ ],
+ "last_harvested_date": "2026-09-24T00:07:40.959279",
+ "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": "custom-soil-moisture-model-code",
+ "spatial_centroid": {
+ "lat": 32.94652,
+ "lon": -105.32816
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -114.6094,
+ 28.3044
+ ],
+ [
+ -114.6094,
+ 39.9097
+ ],
+ [
+ -91.4063,
+ 39.9097
+ ],
+ [
+ -91.4063,
+ 28.3044
+ ],
+ [
+ -114.6094,
+ 28.3044
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Custom Soil Moisture Model Code",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.619362,
+ "_sort": [
+ 1790208391906,
+ 19.619362,
+ 1,
+ "61d1bb80-611b-4e55-b8ac-67336e245010"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609af708d34ea221ce370e4d.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609af708d34ea221ce370e4d",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609af708d34ea221ce370e4d",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Seasonal Future Prescribed Burn Windows for the Southeast United States - December-February 2010-2099 RCP 4.5"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/76ddd121-4f13-4b29-9e1f-dbea235daaad",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/76ddd121-4f13-4b29-9e1f-dbea235daaad/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609af708d34ea221ce370e4d",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609af708d34ea221ce370e4d",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:06:31.906399",
+ "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": 1,
+ "publisher": "U.S. Geological Survey",
+ "slug": "seasonal-future-prescribed-burn-windows-for-the-southeast-united-states-december-february-",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Seasonal Future Prescribed Burn Windows for the Southeast United States - December-February 2010-2099 RCP 4.5",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.623352,
+ "_sort": [
+ 1790208368259,
+ 19.623352,
+ 1,
+ "111449b3-e46c-46cd-98be-6817296c3320"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609aff36d34ea221ce371206.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609aff36d34ea221ce371206",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609aff36d34ea221ce371206",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "MIROC Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/8ca4ca8d-1cdc-441f-94cc-81c0d92f7755",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/8ca4ca8d-1cdc-441f-94cc-81c0d92f7755/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609aff36d34ea221ce371206",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609aff36d34ea221ce371206",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-24T00:06:08.259265",
+ "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": 1,
+ "publisher": "U.S. Geological Survey",
+ "slug": "miroc-monthly-future-prescribed-burn-windows-for-the-southeast-united-states-2010-2099-rcp",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "MIROC Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 4.5",
+ "type": "dataset"
+ },
+ {
+ "_score": 47.79354,
+ "_sort": [
+ 1790208342798,
+ 47.79354,
+ 2,
+ "402468fa-0df4-412e-9eca-b7f2a0189236"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "April Taylor",
+ "hasEmail": "mailto:casc-data@usgs.gov"
+ },
+ "description": "THE CHICKASAW NATION HOSTED THIS RESEARCH SYMPOSIUM ON MARCH 22-24, 2022, HELD AT THE CHICKASAW RETREAT AND CONFERENCE CENTER IN SULPHUR, OK.\nAfter the Climate Workshop for The Chickasaw Nation in 2019, the Tribal managers discussed the need to include culture in conservation efforts and decided to seek a grant for culturally significant plants. This event was organized and led by the Chickasaw Nation and was funded by the U.S. Geological Survey through the South Central Climate Adaptation Science Center.\nThere were 106 attendees with 75 Native representatives from 21 different tribes. There were 11 student attendees. Attendees came from all across the United States including Maine, New York, Michigan, Montana, California, Alabama, and North Carolina. One of the speakers even presented via livestream from Hawaii.\nThe event brought tribes and researchers together on equal levels to learn about what they are doing related to plants and climate change and discuss collaboration opportunities. This dataset contains evaluation responses from participants who attended the research symposium. The purpose of the evaluation responses is to determine what was successful about the symposium and what could be improved upon.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.21429/491y-sk69",
+ "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.63b463a8d34e92aad3ca9d1e.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_63b463a8d34e92aad3ca9d1e",
+ "keyword": [
+ "Conservation",
+ "Culturally significant plants",
+ "Indigenous Peoples",
+ "Louisiana",
+ "New Mexico",
+ "Oklahoma",
+ "Plants",
+ "Texas",
+ "Tribes and Tribal Organizations",
+ "USGS:63b463a8d34e92aad3ca9d1e",
+ "biota",
+ "environment",
+ "social sciences",
+ "society"
+ ],
+ "modified": "2026-09-17T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-109.1602, 25.6019, -89.8242, 37.0902",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Evaluation Responses for Research Symposium: Culturally Significant Plants and Climate Change"
+ },
+ "description": "THE CHICKASAW NATION HOSTED THIS RESEARCH SYMPOSIUM ON MARCH 22-24, 2022, HELD AT THE CHICKASAW RETREAT AND CONFERENCE CENTER IN SULPHUR, OK.\nAfter the Climate Workshop for The Chickasaw Nation in 2019, the Tribal managers discussed the need to include culture in conservation efforts and decided to seek a grant for culturally significant plants. This event was organized and led by the Chickasaw Nation and was funded by the U.S. Geological Survey through the South Central Climate Adaptation Science Center.\nThere were 106 attendees with 75 Native representatives from 21 different tribes. There were 11 student attendees. Attendees came from all across the United States including Maine, New York, Michigan, Montana, California, Alabama, and North Carolina. One of the speakers even presented via livestream from Hawaii.\nThe event brought tribes and researchers together on equal levels to learn about what they are doing related to plants and climate change and discuss collaboration opportunities. This dataset contains evaluation responses from participants who attended the research symposium. The purpose of the evaluation responses is to determine what was successful about the symposium and what could be improved upon.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/5e7e9a9c-f06d-4079-a799-2f320022703f",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/5e7e9a9c-f06d-4079-a799-2f320022703f/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_63b463a8d34e92aad3ca9d1e",
+ "keyword": [
+ "Conservation",
+ "Culturally significant plants",
+ "Indigenous Peoples",
+ "Louisiana",
+ "New Mexico",
+ "Oklahoma",
+ "Plants",
+ "Texas",
+ "Tribes and Tribal Organizations",
+ "USGS:63b463a8d34e92aad3ca9d1e",
+ "biota",
+ "environment",
+ "social sciences",
+ "society"
+ ],
+ "last_harvested_date": "2026-09-24T00:05:42.798554",
+ "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": "evaluation-responses-for-research-symposium-culturally-significant-plants-and-climate-chan",
+ "spatial_centroid": {
+ "lat": 30.197220000000005,
+ "lon": -101.42580000000001
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -109.1602,
+ 25.6019
+ ],
+ [
+ -109.1602,
+ 37.0902
+ ],
+ [
+ -89.8242,
+ 37.0902
+ ],
+ [
+ -89.8242,
+ 25.6019
+ ],
+ [
+ -109.1602,
+ 25.6019
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Evaluation Responses for Research Symposium: Culturally Significant Plants and Climate Change",
+ "type": "dataset"
+ },
+ {
+ "_score": 14.503485,
+ "_sort": [
+ 1790208333917,
+ 14.503485,
+ 0,
+ "a652883d-5b3b-44c0-8745-995def73ed04"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Steven M Quiring",
+ "hasEmail": "mailto:casc-data@usgs.gov"
+ },
+ "description": "This dataset contains standardized in-situ soil moisture measurements from four sparse soil moisture monitoring networks in the South Central United States. This includes measurements in Oklahoma, Texas, Arkansas and Louisiana. The raw soil moisture measurements from a total of 220 stations over the four states were obtained from the Oklahoma Mesonet, West Texas Mesonet, USDA Soil Climate Analysis Network and NOAA Climate Reference Network. The measurements have been screened using the Quality Assurance and Quality Control (QAQC) process that is described in Quiring et al. (2016). The measurements were standardized to common depths using the methods described in Zhang et al. (2017) and they are provided at a daily time-scale at four depths (5 cm, 20 cm, 50 cm and 75 cm) from 2008 to 2017. The soil moisture data are available in two forms: volumetric water content (VWC) (cm3cm-3) and soil moisture percentiles (Ford and Quiring, 2014). \nReferences:\nFord, T.W. and S.M. Quiring. 2014. Comparison and application of multiple methods for temporal interpolation of daily soil moisture. International Journal of Climatology 34: 2604-2621.\nQuiring, S.M., T.W. Ford, J.K. Wang, A. Khong, E. Harris, T. Lindgren, et al. 2016. The North American Soil Moisture Database: Development and Applications. Bulletin of the American Meteorological Society 97: 1441-1459. doi:10.1175/bams-d-13-00263.1.\nZhang, N., Quiring, S. M., Ochsner, T. and T. W. Ford. 2017. Comparison of Three Methods for Vertical Extrapolation of Soil Moisture in Oklahoma. Vadose Zone, 16(10): doi:10.2136/vzj2017.04.0085",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P14XZZZQ",
+ "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.5c5b4e7ae4b070828902b5e0.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5c5b4e7ae4b070828902b5e0",
+ "keyword": [
+ "Arkansas",
+ "Louisiana",
+ "Oklahoma",
+ "Texas",
+ "USGS:5c5b4e7ae4b070828902b5e0",
+ "biota",
+ "external research support",
+ "modeling",
+ "soil moisture",
+ "soil mositure"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-107.1387, 25.0856, -88.7695, 37.4400",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Standardized Soil Moisture Measurements from Sparse Networks in South Central United States from 2008 to 2017"
+ },
+ "description": "This dataset contains standardized in-situ soil moisture measurements from four sparse soil moisture monitoring networks in the South Central United States. This includes measurements in Oklahoma, Texas, Arkansas and Louisiana. The raw soil moisture measurements from a total of 220 stations over the four states were obtained from the Oklahoma Mesonet, West Texas Mesonet, USDA Soil Climate Analysis Network and NOAA Climate Reference Network. The measurements have been screened using the Quality Assurance and Quality Control (QAQC) process that is described in Quiring et al. (2016). The measurements were standardized to common depths using the methods described in Zhang et al. (2017) and they are provided at a daily time-scale at four depths (5 cm, 20 cm, 50 cm and 75 cm) from 2008 to 2017. The soil moisture data are available in two forms: volumetric water content (VWC) (cm3cm-3) and soil moisture percentiles (Ford and Quiring, 2014). \nReferences:\nFord, T.W. and S.M. Quiring. 2014. Comparison and application of multiple methods for temporal interpolation of daily soil moisture. International Journal of Climatology 34: 2604-2621.\nQuiring, S.M., T.W. Ford, J.K. Wang, A. Khong, E. Harris, T. Lindgren, et al. 2016. The North American Soil Moisture Database: Development and Applications. Bulletin of the American Meteorological Society 97: 1441-1459. doi:10.1175/bams-d-13-00263.1.\nZhang, N., Quiring, S. M., Ochsner, T. and T. W. Ford. 2017. Comparison of Three Methods for Vertical Extrapolation of Soil Moisture in Oklahoma. Vadose Zone, 16(10): doi:10.2136/vzj2017.04.0085",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/0c1aa536-4acc-4a05-99d9-e685368d1655",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/0c1aa536-4acc-4a05-99d9-e685368d1655/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5c5b4e7ae4b070828902b5e0",
+ "keyword": [
+ "Arkansas",
+ "Louisiana",
+ "Oklahoma",
+ "Texas",
+ "USGS:5c5b4e7ae4b070828902b5e0",
+ "biota",
+ "external research support",
+ "modeling",
+ "soil moisture",
+ "soil mositure"
+ ],
+ "last_harvested_date": "2026-09-24T00:05:33.917489",
+ "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": "standardized-soil-moisture-measurements-from-sparse-networks-in-south-central-united--2017",
+ "spatial_centroid": {
+ "lat": 30.027359999999998,
+ "lon": -99.79102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -107.1387,
+ 25.0856
+ ],
+ [
+ -107.1387,
+ 37.44
+ ],
+ [
+ -88.7695,
+ 37.44
+ ],
+ [
+ -88.7695,
+ 25.0856
+ ],
+ [
+ -107.1387,
+ 25.0856
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Standardized Soil Moisture Measurements from Sparse Networks in South Central United States from 2008 to 2017",
+ "type": "dataset"
+ },
+ {
+ "_score": 11.344564,
+ "_sort": [
+ 1790208228875,
+ 11.344564,
+ 0,
+ "5705969a-2bcd-4912-86fd-61bb1f1c3ce9"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Thomas Giambelluca",
+ "hasEmail": "mailto:thomas@hawaii.edu"
+ },
+ "description": "The outputs of two versions of the Single-Layer Wet Canopy Water Balance model. Parameters include cloud water interception, evaporation of rainwater or fog water from wet canopy (interception evaporation), and canopy water storage.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.21429/hs9c-jj02",
+ "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.626321a9d34e85fa62bbda49.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_626321a9d34e85fa62bbda49",
+ "keyword": [
+ "Canopy Water Balance",
+ "Climate Model",
+ "Climate Variables",
+ "Soil Data",
+ "USGS:626321a9d34e85fa62bbda49",
+ "Wind Speed",
+ "evaporation",
+ "external research support",
+ "farming",
+ "modeling",
+ "water resources",
+ "wind"
+ ],
+ "modified": "2026-09-15T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-160.3500, 18.8000, -154.6500, 22.6000",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Canopy Water Balance Data for 5 sites in Hawai'i from 2016-2019"
+ },
+ "description": "The outputs of two versions of the Single-Layer Wet Canopy Water Balance model. Parameters include cloud water interception, evaporation of rainwater or fog water from wet canopy (interception evaporation), and canopy water storage.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/e664ef08-fb35-4b3d-8c1f-31af1ef6c6b0",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/e664ef08-fb35-4b3d-8c1f-31af1ef6c6b0/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_626321a9d34e85fa62bbda49",
+ "keyword": [
+ "Canopy Water Balance",
+ "Climate Model",
+ "Climate Variables",
+ "Soil Data",
+ "USGS:626321a9d34e85fa62bbda49",
+ "Wind Speed",
+ "evaporation",
+ "external research support",
+ "farming",
+ "modeling",
+ "water resources",
+ "wind"
+ ],
+ "last_harvested_date": "2026-09-24T00:03:48.875855",
+ "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": "canopy-water-balance-data-for-5-sites-in-hawaii-from-2016-2019",
+ "spatial_centroid": {
+ "lat": 20.32,
+ "lon": -158.07
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -160.35,
+ 18.8
+ ],
+ [
+ -160.35,
+ 22.6
+ ],
+ [
+ -154.65,
+ 22.6
+ ],
+ [
+ -154.65,
+ 18.8
+ ],
+ [
+ -160.35,
+ 18.8
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Canopy Water Balance Data for 5 sites in Hawai'i from 2016-2019",
+ "type": "dataset"
+ },
+ {
+ "_score": 9.781436,
+ "_sort": [
+ 1790208130191,
+ 9.781436,
+ 0,
+ "9ed4307b-8682-46bf-b442-d65ec1cb83b7"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "John Zak",
+ "hasEmail": "mailto:casc-data@usgs.gov"
+ },
+ "description": "Precipitation amounts, and frequencies are major regulators of soil heat-load profiles as the interval between rainfall events allows for increased heat storage during cloudless days. The extreme drought of 2011 and the subsequent Flash Drought that occurred in summer, 2012, developed in part due to soil temperature dynamics across the landscape of the Southern High Plains. For both agroecosystems and natural landscapes within the South-Central Region, soil temperature has a major role in determining the success of annual cropping systems, the ability of perennial plants to either establish or maintain themselves and the ability of the soil bacteria and fungi to carry out important decomposition and nutrient cycling, which are crucial for ecosystem services. Not only does soil temperature dynamics influence plant growth and contribute to drought development, soil temperatures levels and the amount of variability in soil temperatures across a 24 hr period can also have a role in creating and promoting conditions that increase the capabilities of dried vegetation to maintain a fire. This project utilized the soil temperature data collected by the West Texas Mesonet (WTM) system for the period 2000 to 2014 to evaluate landscape level soil temperature dynamics across the Southern High Plains. For our analyses, we used thirty-three WTM locations that had continuous data from 2000 to 2014. We used several analytical approaches to look at how seasonal and yearly weather patterns influences soil temperatures and temperature variability across the landscape. From the Mesonet data we were able to generate three types of graphic representations of the soil temperature dynamics: 1) soil temperature profiles for a wet year (2004), an average year (2006) and a dry year (2011), three-dimensional representation soil temperature profiles from 12:00 AM through 11:59PM for the period 2000-2014 and daily temperature profiles for the period 2000-2014. Based upon these analyses two major determinants of soil temperature dynamics across the region were observed: 1) rainfall amounts and frequency was a major determinant of soil temperature patterns and amount of daily temperature variability observed, and 2) the responses of soils to precipitation/temperature interactions and subsequent seasonal patterns is highly dependent on soil type (amount of sand, silt and clay). Based upon these analyses how the landscape enters and subsequently responds to drought and contributes to drought is very location specific and is dependent upon soil type and vegetation cover. Moreover, how soils store heat and differ in their seasonal temperature dynamics at various locations on the SHP in response to yearly differences in weather contribute to the high variability in fire weather outcomes.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P14TT22G",
+ "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.5d600e6fe4b01d82ce9853f9.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5d600e6fe4b01d82ce9853f9",
+ "keyword": [
+ "Daily Temperature Range",
+ "Soils",
+ "USGS:5d600e6fe4b01d82ce9853f9",
+ "biota",
+ "climate change",
+ "external research support",
+ "soil temperature variability"
+ ],
+ "modified": "2026-09-17T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-106.6456, 25.8374, -93.5081, 37.0023",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Examining Soil and Drought Dynamics to Improve Fire Forecasting in the southern Great Plains 2002-2011"
+ },
+ "description": "Precipitation amounts, and frequencies are major regulators of soil heat-load profiles as the interval between rainfall events allows for increased heat storage during cloudless days. The extreme drought of 2011 and the subsequent Flash Drought that occurred in summer, 2012, developed in part due to soil temperature dynamics across the landscape of the Southern High Plains. For both agroecosystems and natural landscapes within the South-Central Region, soil temperature has a major role in determining the success of annual cropping systems, the ability of perennial plants to either establish or maintain themselves and the ability of the soil bacteria and fungi to carry out important decomposition and nutrient cycling, which are crucial for ecosystem services. Not only does soil temperature dynamics influence plant growth and contribute to drought development, soil temperatures levels and the amount of variability in soil temperatures across a 24 hr period can also have a role in creating and promoting conditions that increase the capabilities of dried vegetation to maintain a fire. This project utilized the soil temperature data collected by the West Texas Mesonet (WTM) system for the period 2000 to 2014 to evaluate landscape level soil temperature dynamics across the Southern High Plains. For our analyses, we used thirty-three WTM locations that had continuous data from 2000 to 2014. We used several analytical approaches to look at how seasonal and yearly weather patterns influences soil temperatures and temperature variability across the landscape. From the Mesonet data we were able to generate three types of graphic representations of the soil temperature dynamics: 1) soil temperature profiles for a wet year (2004), an average year (2006) and a dry year (2011), three-dimensional representation soil temperature profiles from 12:00 AM through 11:59PM for the period 2000-2014 and daily temperature profiles for the period 2000-2014. Based upon these analyses two major determinants of soil temperature dynamics across the region were observed: 1) rainfall amounts and frequency was a major determinant of soil temperature patterns and amount of daily temperature variability observed, and 2) the responses of soils to precipitation/temperature interactions and subsequent seasonal patterns is highly dependent on soil type (amount of sand, silt and clay). Based upon these analyses how the landscape enters and subsequently responds to drought and contributes to drought is very location specific and is dependent upon soil type and vegetation cover. Moreover, how soils store heat and differ in their seasonal temperature dynamics at various locations on the SHP in response to yearly differences in weather contribute to the high variability in fire weather outcomes.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/40c161b8-2910-4ed3-84e2-36ebdf44820b",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/40c161b8-2910-4ed3-84e2-36ebdf44820b/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5d600e6fe4b01d82ce9853f9",
+ "keyword": [
+ "Daily Temperature Range",
+ "Soils",
+ "USGS:5d600e6fe4b01d82ce9853f9",
+ "biota",
+ "climate change",
+ "external research support",
+ "soil temperature variability"
+ ],
+ "last_harvested_date": "2026-09-24T00:02:10.191235",
+ "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": "examining-soil-and-drought-dynamics-to-improve-fire-forecasting-in-the-southern--2002-2011",
+ "spatial_centroid": {
+ "lat": 30.303359999999998,
+ "lon": -101.39059999999999
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -106.6456,
+ 25.8374
+ ],
+ [
+ -106.6456,
+ 37.0023
+ ],
+ [
+ -93.5081,
+ 37.0023
+ ],
+ [
+ -93.5081,
+ 25.8374
+ ],
+ [
+ -106.6456,
+ 25.8374
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Examining Soil and Drought Dynamics to Improve Fire Forecasting in the southern Great Plains 2002-2011",
+ "type": "dataset"
+ },
+ {
+ "_score": 27.188398,
+ "_sort": [
+ 1790208076124,
+ 27.188398,
+ 0,
+ "744d4e36-680f-4846-9a31-9c49aa398b44"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Kevin Brinck",
+ "hasEmail": "mailto:kbrinck@usgs.gov"
+ },
+ "description": "This dataset is a list of variables (in columns) corresponding to nodes in a categorical network model. Geographic variables vary according to the specific climate downscaling model used to project plant species range into the future. Continuous variables were discretized into two to five categories as required by the model, usually based on quantiles of distribution.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P139JZRE",
+ "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.57f41118e4b0bc0bec033f35.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_57f41118e4b0bc0bec033f35",
+ "keyword": [
+ "USGS:57f41118e4b0bc0bec033f35",
+ "biota",
+ "climate change",
+ "elevation",
+ "location",
+ "modeling",
+ "network model"
+ ],
+ "modified": "2026-09-18T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Merged traits used to fit the Hawaiian native plant vulnerability model"
+ },
+ "description": "This dataset is a list of variables (in columns) corresponding to nodes in a categorical network model. Geographic variables vary according to the specific climate downscaling model used to project plant species range into the future. Continuous variables were discretized into two to five categories as required by the model, usually based on quantiles of distribution.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/2785d515-4884-4dd0-ac64-d2aace94f349",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/2785d515-4884-4dd0-ac64-d2aace94f349/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_57f41118e4b0bc0bec033f35",
+ "keyword": [
+ "USGS:57f41118e4b0bc0bec033f35",
+ "biota",
+ "climate change",
+ "elevation",
+ "location",
+ "modeling",
+ "network model"
+ ],
+ "last_harvested_date": "2026-09-24T00:01:16.124423",
+ "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": "merged-traits-used-to-fit-the-hawaiian-native-plant-vulnerability-model",
+ "spatial_centroid": null,
+ "spatial_shape": null,
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Merged traits used to fit the Hawaiian native plant vulnerability model",
+ "type": "dataset"
+ },
+ {
+ "_score": 10.385309,
+ "_sort": [
+ 1790207933964,
+ 10.385309,
+ 0,
+ "6fd9ad26-17af-4599-97ff-3bf4e48c4739"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Thomas Giambelluca",
+ "hasEmail": "mailto:thomas@hawaii.edu"
+ },
+ "description": "Data of a calibrated fog gauge at each of the five stations. Parameters include total fog water collection, wind-driven rain collection, fog-only water collection, cloud water flux, and cloud liquid water content.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.21429/as5e-6e02",
+ "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.62632128d34e85fa62bbda42.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_62632128d34e85fa62bbda42",
+ "keyword": [
+ "Climate Variables",
+ "Fog Gauge",
+ "USGS:62632128d34e85fa62bbda42",
+ "external research support",
+ "farming"
+ ],
+ "modified": "2026-09-15T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-160.3500, 18.8000, -154.6500, 22.6000",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Calibrated Fog Gauge Data for 5 sites in Hawai'i from 2016-2019"
+ },
+ "description": "Data of a calibrated fog gauge at each of the five stations. Parameters include total fog water collection, wind-driven rain collection, fog-only water collection, cloud water flux, and cloud liquid water content.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/6d7a9ae9-380b-409e-a4f5-79f61ffde47f",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/6d7a9ae9-380b-409e-a4f5-79f61ffde47f/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_62632128d34e85fa62bbda42",
+ "keyword": [
+ "Climate Variables",
+ "Fog Gauge",
+ "USGS:62632128d34e85fa62bbda42",
+ "external research support",
+ "farming"
+ ],
+ "last_harvested_date": "2026-09-23T23:58:53.964030",
+ "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": "calibrated-fog-gauge-data-for-5-sites-in-hawaii-from-2016-2019",
+ "spatial_centroid": {
+ "lat": 20.32,
+ "lon": -158.07
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -160.35,
+ 18.8
+ ],
+ [
+ -160.35,
+ 22.6
+ ],
+ [
+ -154.65,
+ 22.6
+ ],
+ [
+ -154.65,
+ 18.8
+ ],
+ [
+ -160.35,
+ 18.8
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Calibrated Fog Gauge Data for 5 sites in Hawai'i from 2016-2019",
+ "type": "dataset"
+ },
+ {
+ "_score": 56.93843,
+ "_sort": [
+ 1790207845394,
+ 56.93843,
+ 1,
+ "8edc93d4-0a63-46aa-af92-0f539eb69ec0"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adrienne M. Wootten",
+ "hasEmail": "mailto:amwootte@ou.edu"
+ },
+ "description": "Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005) and future projections (2006-2100) are provided.\nApplied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P13NMKWU",
+ "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.66ce1f32d34e98e8a924407f.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_66ce1f32d34e98e8a924407f",
+ "keyword": [
+ "Edwards Aquifer",
+ "San Antonio",
+ "Texas",
+ "USGS:66ce1f32d34e98e8a924407f",
+ "atmospheric and climatic processes",
+ "climate change",
+ "climatologyMeteorologyAtmosphere",
+ "downscaling",
+ "geospatial datasets"
+ ],
+ "modified": "2026-09-17T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-104.8975, 27.0983, -94.1309, 34.3797",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Downscaled Climate Projections for the Edwards Aquifer Region (EAR) using CMIP5 for the years 2006 – 2100"
+ },
+ "description": "Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005) and future projections (2006-2100) are provided.\nApplied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/512a9ed8-f85e-4650-a2f4-7cd024683855",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/512a9ed8-f85e-4650-a2f4-7cd024683855/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_66ce1f32d34e98e8a924407f",
+ "keyword": [
+ "Edwards Aquifer",
+ "San Antonio",
+ "Texas",
+ "USGS:66ce1f32d34e98e8a924407f",
+ "atmospheric and climatic processes",
+ "climate change",
+ "climatologyMeteorologyAtmosphere",
+ "downscaling",
+ "geospatial datasets"
+ ],
+ "last_harvested_date": "2026-09-23T23:57:25.394768",
+ "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": 1,
+ "publisher": "U.S. Geological Survey",
+ "slug": "downscaled-climate-projections-for-the-edwards-aquifer-region-ear-using-cmip5-fo-2006-2100",
+ "spatial_centroid": {
+ "lat": 30.010859999999997,
+ "lon": -100.59085999999999
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -104.8975,
+ 27.0983
+ ],
+ [
+ -104.8975,
+ 34.3797
+ ],
+ [
+ -94.1309,
+ 34.3797
+ ],
+ [
+ -94.1309,
+ 27.0983
+ ],
+ [
+ -104.8975,
+ 27.0983
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Downscaled Climate Projections for the Edwards Aquifer Region (EAR) using CMIP5 for the years 2006 – 2100",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.623352,
+ "_sort": [
+ 1790207774650,
+ 19.623352,
+ 0,
+ "7db83b03-ba5b-4b1e-9ce7-ff5a68e7c575"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.609c446ed34ea221ce39d363.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c446ed34ea221ce39d363",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c446ed34ea221ce39d363",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "GFDLM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/7ca4cde9-1f1f-4862-85f6-7b9ec49839d0",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/7ca4cde9-1f1f-4862-85f6-7b9ec49839d0/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_609c446ed34ea221ce39d363",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:609c446ed34ea221ce39d363",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-23T23:56:14.650991",
+ "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": "gfdlm-monthly-future-prescribed-burn-windows-for-the-southeast-united-states-2010-2099-rcp",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "GFDLM Monthly Future Prescribed Burn Windows for the Southeast United States 2010-2099 RCP 8.5",
+ "type": "dataset"
+ },
+ {
+ "_score": 9.45854,
+ "_sort": [
+ 1790207763768,
+ 9.45854,
+ 0,
+ "f2d314eb-7563-4f8b-98d0-ad6e004980af"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Kristen A. Baum",
+ "hasEmail": "mailto:kristen.baum@okstate.edu"
+ },
+ "description": "We used land cover projections for 2011 and 2050 of two scenarios derived from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES). Scenario A1B emphasizes economic growth with a global orientation and scenario B2 focuses on environmental sustainability with a regional view. Our study area included counties within the southern Great Plains ecoregion in Oklahoma, Texas, and New Mexico. We calculated changes in landscape connectivity (dECA) between 2011 and 2050 for different species groups and landscape scenarios. We also calculated changes in habitat suitability (dA). We assessed the degree to which changes in landscape connectivity were influenced by changes in grassland cover (i.e., the effect of fragmentation per se) by comparing dECA and dA. We also estimated the resistance of the landscape matrix to species movement based on the degree of naturalness of the land-cover type, with more natural land-cover types being less resistant to movement. We calculated the differences between the median value of landscape resistance between 2050 and 2011 for each county to evaluate changes in matrix permeability (i.e., resistance to species movement) for scenario A1B and B2. This data set includes the difference between changes in connectivity (dECA) and changes in grassland cover (dA) for species with a median dispersal distance (MDD) of 1, 5, 10 and 25 km for scenario A1B and scenario B2. For both scenarios results are for counties with net grassland area loss (dA < 0) and counties with net grassland area gain (dA > 0). For scenario A1B, 99% of the counties exhibited net grassland area loss, and for scenario B2, 81% of the counties exhibited net grassland area loss.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P134QLFC",
+ "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.5a1dad01e4b09fc93dd7bfb4.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5a1dad01e4b09fc93dd7bfb4",
+ "keyword": [
+ "USGS:5a1dad01e4b09fc93dd7bfb4",
+ "dispersal (organisms)",
+ "external research support",
+ "geospatial datasets",
+ "habitat fragmentation",
+ "modeling"
+ ],
+ "modified": "2026-09-17T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-106.841476109, 25.423224141, -93.46097938, 37.317147073",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Future changes in landscape connectivity for grassland species in the southern Great Plains based on a scenario of future land-use change that emphasizes economic growth with a global orientation"
+ },
+ "description": "We used land cover projections for 2011 and 2050 of two scenarios derived from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES). Scenario A1B emphasizes economic growth with a global orientation and scenario B2 focuses on environmental sustainability with a regional view. Our study area included counties within the southern Great Plains ecoregion in Oklahoma, Texas, and New Mexico. We calculated changes in landscape connectivity (dECA) between 2011 and 2050 for different species groups and landscape scenarios. We also calculated changes in habitat suitability (dA). We assessed the degree to which changes in landscape connectivity were influenced by changes in grassland cover (i.e., the effect of fragmentation per se) by comparing dECA and dA. We also estimated the resistance of the landscape matrix to species movement based on the degree of naturalness of the land-cover type, with more natural land-cover types being less resistant to movement. We calculated the differences between the median value of landscape resistance between 2050 and 2011 for each county to evaluate changes in matrix permeability (i.e., resistance to species movement) for scenario A1B and B2. This data set includes the difference between changes in connectivity (dECA) and changes in grassland cover (dA) for species with a median dispersal distance (MDD) of 1, 5, 10 and 25 km for scenario A1B and scenario B2. For both scenarios results are for counties with net grassland area loss (dA < 0) and counties with net grassland area gain (dA > 0). For scenario A1B, 99% of the counties exhibited net grassland area loss, and for scenario B2, 81% of the counties exhibited net grassland area loss.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/22a2894f-976f-4230-8af3-cdd2cc781e31",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/22a2894f-976f-4230-8af3-cdd2cc781e31/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_5a1dad01e4b09fc93dd7bfb4",
+ "keyword": [
+ "USGS:5a1dad01e4b09fc93dd7bfb4",
+ "dispersal (organisms)",
+ "external research support",
+ "geospatial datasets",
+ "habitat fragmentation",
+ "modeling"
+ ],
+ "last_harvested_date": "2026-09-23T23:56:03.768139",
+ "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": "future-changes-in-landscape-connectivity-for-grassland-species-in-the-southern-great-plain",
+ "spatial_centroid": {
+ "lat": 30.180793313800002,
+ "lon": -101.4892774174
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -106.841476109,
+ 25.423224141
+ ],
+ [
+ -106.841476109,
+ 37.317147073
+ ],
+ [
+ -93.46097938,
+ 37.317147073
+ ],
+ [
+ -93.46097938,
+ 25.423224141
+ ],
+ [
+ -106.841476109,
+ 25.423224141
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Future changes in landscape connectivity for grassland species in the southern Great Plains based on a scenario of future land-use change that emphasizes economic growth with a global orientation",
+ "type": "dataset"
+ },
+ {
+ "_score": 59.84746,
+ "_sort": [
+ 1790207753716,
+ 59.84746,
+ 0,
+ "cc239bf7-b30b-4086-af3e-0d3cc44c83f9"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Michael D Samuel",
+ "hasEmail": "mailto:msamuel@usgs.gov"
+ },
+ "description": "This data set describes the predicted daily climate (temperature and rainfall) for low, mid, and high-elevations on Mona Loa, Island of Hawaii from 2098-2100. Climate predictions are based on 3 alternative climate scenarios (RCP 4.5, A1B, and RCP 8.5) - see Liao et al. 2015 for more details and climate references. The predicted daily risk of susceptible Hawaiian honeycreepers are based on the daily climate data, mosquito abundance and other factors. Also see Samuel et al. 2011 The dynamics, transmission, and population impacts of avian malaria in native Hawaiian birds: a modeling approach. Ecological Applications 21:2960-2973 for description of the epidemiological model used for avian malaria risk predictions.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P1CCJ2QF",
+ "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.57ec5fbce4b090825010b908.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_57ec5fbce4b090825010b908",
+ "keyword": [
+ "Culex mosquitoes",
+ "Downscaled climate models",
+ "Hawaii",
+ "Hawaiian honeycreepers",
+ "Mona Loa",
+ "USGS:57ec5fbce4b090825010b908",
+ "avian malaria",
+ "biota",
+ "birds",
+ "climate change",
+ "disease vectors",
+ "ecology",
+ "epidemiological model",
+ "modeling",
+ "pre-SM502.8",
+ "precipitation (atmospheric)"
+ ],
+ "modified": "2026-09-18T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-162.0000, 18.0000, -152.0000, 23.0000",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Predicted climate and avian malaria risk to Hawaiian honeycreepers on the Island of Hawaii from 2098-2100"
+ },
+ "description": "This data set describes the predicted daily climate (temperature and rainfall) for low, mid, and high-elevations on Mona Loa, Island of Hawaii from 2098-2100. Climate predictions are based on 3 alternative climate scenarios (RCP 4.5, A1B, and RCP 8.5) - see Liao et al. 2015 for more details and climate references. The predicted daily risk of susceptible Hawaiian honeycreepers are based on the daily climate data, mosquito abundance and other factors. Also see Samuel et al. 2011 The dynamics, transmission, and population impacts of avian malaria in native Hawaiian birds: a modeling approach. Ecological Applications 21:2960-2973 for description of the epidemiological model used for avian malaria risk predictions.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/f8965386-dd62-40d4-ae51-ae3c81bc19f0",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/f8965386-dd62-40d4-ae51-ae3c81bc19f0/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_57ec5fbce4b090825010b908",
+ "keyword": [
+ "Culex mosquitoes",
+ "Downscaled climate models",
+ "Hawaii",
+ "Hawaiian honeycreepers",
+ "Mona Loa",
+ "USGS:57ec5fbce4b090825010b908",
+ "avian malaria",
+ "biota",
+ "birds",
+ "climate change",
+ "disease vectors",
+ "ecology",
+ "epidemiological model",
+ "modeling",
+ "pre-SM502.8",
+ "precipitation (atmospheric)"
+ ],
+ "last_harvested_date": "2026-09-23T23:55:53.716493",
+ "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": "predicted-climate-and-avian-malaria-risk-to-hawaiian-honeycreepers-on-the-island-2098-2100",
+ "spatial_centroid": {
+ "lat": 20.0,
+ "lon": -158.0
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -162.0,
+ 18.0
+ ],
+ [
+ -162.0,
+ 23.0
+ ],
+ [
+ -152.0,
+ 23.0
+ ],
+ [
+ -152.0,
+ 18.0
+ ],
+ [
+ -162.0,
+ 18.0
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Predicted climate and avian malaria risk to Hawaiian honeycreepers on the Island of Hawaii from 2098-2100",
+ "type": "dataset"
+ },
+ {
+ "_score": 61.26716,
+ "_sort": [
+ 1790207747093,
+ 61.26716,
+ 0,
+ "c681c4a5-00e3-4cae-aa42-020274742b83"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Frank R. Thompson",
+ "hasEmail": "mailto:casc-data@usgs.gov"
+ },
+ "description": "Forests in the Eastern United States are in the early- and mid-successional stages recovering from historical land use. Succession, harvest, and climate are potentially important factors affecting forest composition and structure in the region. The goal of this project was to predict the distribution and abundance of dominant tree species across portions of the Eastern U.S. under alternative climate scenarios from present to the end of the century. We used the forest landscape change LANDIS PRO and hybrid empirical-physiological ecosystem model LINKAGES to model changes in forest biomass and species abundances and distribution in the North Atlantic region of the U.S. while accounting for climate change, succession, and harvest. Three climate scenarios were considered, defined by a general circulation model and emission scenario: PCM B1, CGCM A2, and GFDL A1FI. We then compared results from three alternative modeling approaches; LANDIS PRO, LINKAGES, and TreeAtlas for the Central Hardwood, Central Appalachian, Mid-Atlantic, and New England regions to determine agreement among models and establish a stronger inference for projected changes through model averaging. Tree Atlas is a statistically derived enhanced niche model in contrast to the process driven ecosystem and landscape models LINKAGES and LANDIS PRO. Researchers on this project actively worked with the Gulf Coast Plains and Ozarks LCC and the Northern Institute of Applied Climate Change (NIACS). NIACS is a partnership between the U.S. Forest Service, state agencies, and NGOs with a focus on climate change adaptation.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P13RW6HT",
+ "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.58110f4de4b0f497e798387c.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_58110f4de4b0f497e798387c",
+ "keyword": [
+ "USGS:58110f4de4b0f497e798387c",
+ "biota",
+ "climate change",
+ "environment",
+ "geospatial datasets",
+ "pre-SM502.8"
+ ],
+ "modified": "2026-09-17T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-80.8640, 35.5060, -66.0161, 48.9913",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Changes in Forested Landscapes of the Northeastern U.S. Under Alternative Climate Scenarios"
+ },
+ "description": "Forests in the Eastern United States are in the early- and mid-successional stages recovering from historical land use. Succession, harvest, and climate are potentially important factors affecting forest composition and structure in the region. The goal of this project was to predict the distribution and abundance of dominant tree species across portions of the Eastern U.S. under alternative climate scenarios from present to the end of the century. We used the forest landscape change LANDIS PRO and hybrid empirical-physiological ecosystem model LINKAGES to model changes in forest biomass and species abundances and distribution in the North Atlantic region of the U.S. while accounting for climate change, succession, and harvest. Three climate scenarios were considered, defined by a general circulation model and emission scenario: PCM B1, CGCM A2, and GFDL A1FI. We then compared results from three alternative modeling approaches; LANDIS PRO, LINKAGES, and TreeAtlas for the Central Hardwood, Central Appalachian, Mid-Atlantic, and New England regions to determine agreement among models and establish a stronger inference for projected changes through model averaging. Tree Atlas is a statistically derived enhanced niche model in contrast to the process driven ecosystem and landscape models LINKAGES and LANDIS PRO. Researchers on this project actively worked with the Gulf Coast Plains and Ozarks LCC and the Northern Institute of Applied Climate Change (NIACS). NIACS is a partnership between the U.S. Forest Service, state agencies, and NGOs with a focus on climate change adaptation.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/9aec520a-40a5-4cb7-ac00-3a2f016f361c",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/9aec520a-40a5-4cb7-ac00-3a2f016f361c/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_58110f4de4b0f497e798387c",
+ "keyword": [
+ "USGS:58110f4de4b0f497e798387c",
+ "biota",
+ "climate change",
+ "environment",
+ "geospatial datasets",
+ "pre-SM502.8"
+ ],
+ "last_harvested_date": "2026-09-23T23:55:47.093814",
+ "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": "changes-in-forested-landscapes-of-the-northeastern-u-s-under-alternative-climate-scenarios",
+ "spatial_centroid": {
+ "lat": 40.90012,
+ "lon": -74.92483999999999
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -80.864,
+ 35.506
+ ],
+ [
+ -80.864,
+ 48.9913
+ ],
+ [
+ -66.0161,
+ 48.9913
+ ],
+ [
+ -66.0161,
+ 35.506
+ ],
+ [
+ -80.864,
+ 35.506
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Changes in Forested Landscapes of the Northeastern U.S. Under Alternative Climate Scenarios",
+ "type": "dataset"
+ },
+ {
+ "_score": 19.417788,
+ "_sort": [
+ 1790207633063,
+ 19.417788,
+ 1,
+ "3dff84d4-ecf5-4e5d-a0ac-d09490e15c36"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Adam J Terando",
+ "hasEmail": "mailto:aterando@usgs.gov"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P95BV7GE",
+ "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.606775e8d34edc0435c09d67.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_606775e8d34edc0435c09d67",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:606775e8d34edc0435c09d67",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "modified": "2026-09-16T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-102.1475, 25.0631, -73.6063, 43.1045",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Observed Prescribed Burn Windows for the Southeast United States 1980-2017"
+ },
+ "description": "Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria are met. This dataset contains results from a study examining the potential impacts of projected climatic change on prescribed burning in the southeastern United States. A set of burn window criteria (suitable weather conditions within which burning may occur based on maximum daily temperature, daily average relative humidity, and daily average wind speed), were applied to projections from an ensemble of Global Climate Models (GCM) under two greenhouse gas emission scenarios, as well as past observations for comparison. Data are provided as decadal output for observed conditions, and for individual GCM results for the historical climate scenario and the two future climate scenarios are provided. In addition, summary statistics (e.g., multi-model mean, and for selected quantiles) are provided for the GCM ensemble as a whole by decade.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/00261426-a6d4-4300-ae67-d0d0439639c4",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/00261426-a6d4-4300-ae67-d0d0439639c4/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_606775e8d34edc0435c09d67",
+ "keyword": [
+ "Alabama",
+ "Arkansas",
+ "Florida",
+ "Georgia",
+ "Kentucky",
+ "Louisiana",
+ "Mississippi",
+ "Missouri",
+ "North Carolina",
+ "Oklahoma",
+ "South Carolina",
+ "Southeast United states",
+ "Tennessee",
+ "Texas",
+ "USGS:606775e8d34edc0435c09d67",
+ "Virginia",
+ "West Virginia",
+ "farming",
+ "fires",
+ "geoscientificInformation",
+ "managed fire regimes",
+ "statistical downscaling",
+ "wildfires"
+ ],
+ "last_harvested_date": "2026-09-23T23:53:53.063568",
+ "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": 1,
+ "publisher": "U.S. Geological Survey",
+ "slug": "observed-prescribed-burn-windows-for-the-southeast-united-states-1980-2017",
+ "spatial_centroid": {
+ "lat": 32.27966,
+ "lon": -90.73102
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -102.1475,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 43.1045
+ ],
+ [
+ -73.6063,
+ 25.0631
+ ],
+ [
+ -102.1475,
+ 25.0631
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Observed Prescribed Burn Windows for the Southeast United States 1980-2017",
+ "type": "dataset"
+ },
+ {
+ "_score": 43.563095,
+ "_sort": [
+ 1790207515975,
+ 43.563095,
+ 0,
+ "f96f882a-00f9-4688-94a3-59d6de888c98"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Thomas Giambelluca",
+ "hasEmail": "mailto:thomas@hawaii.edu"
+ },
+ "description": "Measurements of fog, wind, fog interception, soil moisture, and fog effects on plant water use and plant survival were collected to test a model to estimate CWI as a function of fog-water movement and vegetation characteristics.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.21429/ezae-7t56",
+ "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.62632243d34e85fa62bbda4d.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_62632243d34e85fa62bbda4d",
+ "keyword": [
+ "Canopy Water Balance",
+ "Climate Model",
+ "Climate Variables",
+ "Soil Data",
+ "USGS:62632243d34e85fa62bbda4d",
+ "Wind Speed",
+ "biota"
+ ],
+ "modified": "2026-09-17T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-160.3500, 18.8000, -154.6500, 22.6000",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Climate Data for 5 sites in Hawai'i from 2016-2019"
+ },
+ "description": "Measurements of fog, wind, fog interception, soil moisture, and fog effects on plant water use and plant survival were collected to test a model to estimate CWI as a function of fog-water movement and vegetation characteristics.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/5c4ffe80-45ac-47e7-8560-589fbb58faea",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/5c4ffe80-45ac-47e7-8560-589fbb58faea/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_62632243d34e85fa62bbda4d",
+ "keyword": [
+ "Canopy Water Balance",
+ "Climate Model",
+ "Climate Variables",
+ "Soil Data",
+ "USGS:62632243d34e85fa62bbda4d",
+ "Wind Speed",
+ "biota"
+ ],
+ "last_harvested_date": "2026-09-23T23:51:55.975844",
+ "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": "climate-data-for-5-sites-in-hawaii-from-2016-2019",
+ "spatial_centroid": {
+ "lat": 20.32,
+ "lon": -158.07
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -160.35,
+ 18.8
+ ],
+ [
+ -160.35,
+ 22.6
+ ],
+ [
+ -154.65,
+ 22.6
+ ],
+ [
+ -154.65,
+ 18.8
+ ],
+ [
+ -160.35,
+ 18.8
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Climate Data for 5 sites in Hawai'i from 2016-2019",
+ "type": "dataset"
+ },
+ {
+ "_score": 8.361776,
+ "_sort": [
+ 1790207435827,
+ 8.361776,
+ 0,
+ "d4c9be48-d775-4844-b9f8-c222a8803f99"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:12"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "Noelani Puniwai",
+ "hasEmail": "mailto:npuniwai@hawaii.edu"
+ },
+ "description": "The purpose of this study was to record nearshore current patterns as witnessed and experienced by ocean users living in the greater Hilo area of Hawai'i Island, Hawai'i. This study relied on ocean-expert knowledge gained from place-based first-hand work and lifestyle experience. The ocean-experts were categorized as fisher, paddler, sailor, surfers, or others. The interviews were collected during 2014-2015, with prototype interviews collected in 2009. During the interview the ocean-expert was asked to select the appropriate map scale for their area of expertise (1:5000, 1:20,000, 1:40,000, or 1:100,000) and to describe and draw the nearshore currents. Sixteen of the interviewees visually documented on hard copy paper maps their understanding of the near shore currents. There are 30 combinations of location and map scale with four combinations being selected by multiple participants.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "accessURL": "https://doi.org/10.5066/P14PERSV",
+ "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.57f2f202e4b0bc0bec00b0a4.xml",
+ "format": "XML",
+ "mediaType": "text/xml",
+ "title": "Original Metadata"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_57f2f202e4b0bc0bec00b0a4",
+ "keyword": [
+ "2014, 2015",
+ "Coastal",
+ "Communities",
+ "Culture",
+ "Currents",
+ "Hawai'i",
+ "Hawai`i",
+ "Hawaii",
+ "Local Ecological Knowledge.",
+ "Place-based Knowledge",
+ "Resiliency",
+ "Surf",
+ "USGS:57f2f202e4b0bc0bec00b0a4",
+ "climate change",
+ "environment",
+ "geospatial datasets",
+ "location",
+ "oceans",
+ "pre-SM502.8"
+ ],
+ "modified": "2026-09-18T00:00:00Z",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "U.S. Geological Survey"
+ },
+ "spatial": "-155.9817, 18.8763, -154.8166, 20.3234",
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Qualitatively Observed Nearshore Ocean Currents, 2014-15, Hilo, Hawai'i"
+ },
+ "description": "The purpose of this study was to record nearshore current patterns as witnessed and experienced by ocean users living in the greater Hilo area of Hawai'i Island, Hawai'i. This study relied on ocean-expert knowledge gained from place-based first-hand work and lifestyle experience. The ocean-experts were categorized as fisher, paddler, sailor, surfers, or others. The interviews were collected during 2014-2015, with prototype interviews collected in 2009. During the interview the ocean-expert was asked to select the appropriate map scale for their area of expertise (1:5000, 1:20,000, 1:40,000, or 1:100,000) and to describe and draw the nearshore currents. Sixteen of the interviewees visually documented on hard copy paper maps their understanding of the near shore currents. There are 30 combinations of location and map scale with four combinations being selected by multiple participants.",
+ "distribution_titles": [
+ "Digital Data",
+ "Original Metadata"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/ec6c0734-3d67-4723-ab75-056f2c4d50a5",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/ec6c0734-3d67-4723-ab75-056f2c4d50a5/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/USGS_57f2f202e4b0bc0bec00b0a4",
+ "keyword": [
+ "2014, 2015",
+ "Coastal",
+ "Communities",
+ "Culture",
+ "Currents",
+ "Hawai'i",
+ "Hawai`i",
+ "Hawaii",
+ "Local Ecological Knowledge.",
+ "Place-based Knowledge",
+ "Resiliency",
+ "Surf",
+ "USGS:57f2f202e4b0bc0bec00b0a4",
+ "climate change",
+ "environment",
+ "geospatial datasets",
+ "location",
+ "oceans",
+ "pre-SM502.8"
+ ],
+ "last_harvested_date": "2026-09-23T23:50:35.827231",
+ "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": "qualitatively-observed-nearshore-ocean-currents-2014-15-hilo-hawaii",
+ "spatial_centroid": {
+ "lat": 19.45514,
+ "lon": -155.51565999999997
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -155.9817,
+ 18.8763
+ ],
+ [
+ -155.9817,
+ 20.3234
+ ],
+ [
+ -154.8166,
+ 20.3234
+ ],
+ [
+ -154.8166,
+ 18.8763
+ ],
+ [
+ -155.9817,
+ 18.8763
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "geospatial"
+ ],
+ "title": "Qualitatively Observed Nearshore Ocean Currents, 2014-15, Hilo, Hawai'i",
+ "type": "dataset"
+ },
+ {
+ "_score": 70.662025,
+ "_sort": [
+ 1790196607342,
+ 70.662025,
+ 0,
+ "8c3b4927-301b-434e-a9df-2571d0dafc81"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:24"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "NPS IRMA Help",
+ "hasEmail": "mailto:NRSS_DataStore@nps.gov"
+ },
+ "description": "Weather and climate data deliverables from the Central Alaska Inventory and Monitoring Network for 2026. Files may include: protocol, standard operating procedures, site maps, site visit worksheets, datalogger programs, photos, raw data, corrected data, operations report, sensor calibration certificates, and/or periodic reports.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "description": "Annual operations/site visit maintenance report for 2026 field season.",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/762303?Reference=2319298",
+ "format": "pdf",
+ "mediaType": "application/pdf",
+ "title": "WC_H_2026_CAKN Annual Operations Report.pdf"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "CAKN 2026 datalogger program code",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/761264?Reference=2319298",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_D_2026_CAKN_Datalogger Program Code.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Calibration certificates for sensors calibrated in 2026",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/762307?Reference=2319298",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_I_2026_CAKN Calibration Certificates.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Corrected and cumulative climate data through 2026 site visits.",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/761499?Reference=2319298",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_G_2026_CAKN_Corrected Data.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Map and text file of climate station locations in 2026.",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/761242?Reference=2319298",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_B_2026 CAKN Weather and Climate Site Maps.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Photos from the site visits in 2026",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/763048?Reference=2319298",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_E_2026_CAKN Site Photo Sets.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Raw data downloads from CAKN climate stations in 2026.",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/762302?Reference=2319298",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_F_2026_CAKN Raw Data Downloads.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Read me file. Helpful for navigating files above.",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/762304?Reference=2319298",
+ "format": "txt",
+ "mediaType": "text/plain",
+ "title": "CAKN_README.txt"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Site visit worksheets from field visits in 2026",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/761261?Reference=2319298",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_C_2026_CAKN Site Visit Worksheets.zip"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/NPS_DataStore_2319298",
+ "issued": "2026-01-01T00:00:00Z",
+ "keyword": [
+ "Ecological Framework: Air and Climate | Weather and Climate | Weather and Climate",
+ "alaska",
+ "climate",
+ "weather"
+ ],
+ "landingPage": "https://irma.nps.gov/DataStore/Reference/Profile/2319298",
+ "modified": "2026-01-01T00:00:00Z",
+ "programCode": [
+ "010:118",
+ "010:119"
+ ],
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "National Park Service"
+ },
+ "spatial": "-154.776428,59.3119049,-138.641663,70.05848",
+ "temporal": "2025-01-01/2026-01-01",
+ "theme": [
+ "Generic Dataset"
+ ],
+ "title": "2026 CAKN Weather and Climate Data Deliverables"
+ },
+ "description": "Weather and climate data deliverables from the Central Alaska Inventory and Monitoring Network for 2026. Files may include: protocol, standard operating procedures, site maps, site visit worksheets, datalogger programs, photos, raw data, corrected data, operations report, sensor calibration certificates, and/or periodic reports.",
+ "distribution_titles": [
+ "WC_H_2026_CAKN Annual Operations Report.pdf",
+ "WC_D_2026_CAKN_Datalogger Program Code.zip",
+ "WC_I_2026_CAKN Calibration Certificates.zip",
+ "WC_G_2026_CAKN_Corrected Data.zip",
+ "WC_B_2026 CAKN Weather and Climate Site Maps.zip",
+ "WC_E_2026_CAKN Site Photo Sets.zip",
+ "WC_F_2026_CAKN Raw Data Downloads.zip",
+ "CAKN_README.txt",
+ "WC_C_2026_CAKN Site Visit Worksheets.zip"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/5da6bc7a-bf5f-4463-8bf5-ae93b191c2a5",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/5da6bc7a-bf5f-4463-8bf5-ae93b191c2a5/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/NPS_DataStore_2319298",
+ "keyword": [
+ "Ecological Framework: Air and Climate | Weather and Climate | Weather and Climate",
+ "alaska",
+ "climate",
+ "weather"
+ ],
+ "last_harvested_date": "2026-09-23T20:50:07.342844",
+ "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": "National Park Service",
+ "slug": "2026-cakn-weather-and-climate-data-deliverables",
+ "spatial_centroid": {
+ "lat": 63.61053494,
+ "lon": -148.322522
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -154.776428,
+ 59.3119049
+ ],
+ [
+ -154.776428,
+ 70.05848
+ ],
+ [
+ -138.641663,
+ 70.05848
+ ],
+ [
+ -138.641663,
+ 59.3119049
+ ],
+ [
+ -154.776428,
+ 59.3119049
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "Generic Dataset"
+ ],
+ "title": "2026 CAKN Weather and Climate Data Deliverables",
+ "type": "dataset"
+ },
+ {
+ "_score": 54.520676,
+ "_sort": [
+ 1790196391882,
+ 54.520676,
+ 0,
+ "d27d05f0-c63b-4762-a6d2-9724dd9fb614"
+ ],
+ "dcat": {
+ "accessLevel": "public",
+ "bureauCode": [
+ "010:24"
+ ],
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "NPS IRMA Help",
+ "hasEmail": "mailto:NRSS_DataStore@nps.gov"
+ },
+ "description": "Data deliverables from Arctic Network for 2026. Files may include protocol, standard operating procedures, site maps, site visit worksheets, datalogger programs, photos, raw data, corrected data, operations report, sensor calibration certificates, and/or periodic reports.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "description": "ARCN 2026 datalogger program code",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/761265?Reference=2319031",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_D_2026_ARCN Datalogger Program Code.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Annual operations/site visit maintenance report for 2026 field season.",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/762294?Reference=2319031",
+ "format": "pdf",
+ "mediaType": "application/pdf",
+ "title": "WC_H_2026_ARCN Annual Operations Report.pdf"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Calibration certificates for sensors calibrated in 2026",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/762308?Reference=2319031",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_I_2026_ARCN Calibration Certificates.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Corrected and cumulative climate data through 2026 site visits.",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/761500?Reference=2319031",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_G_2026_ARCN_Corrected Data.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Map and text file of climate station locations in 2026",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/761244?Reference=2319031",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_B_2026 ARCN Climate Station Locations.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Photos from the site visits in 2026",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/762571?Reference=2319031",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_E_2026_ARCN Site Photo Sets.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Raw data downloads from ARCN climate stations in 2026.",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/762305?Reference=2319031",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_F_2026_ARCN Raw Data Downloads.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Site visit worksheets from field visits in 2026",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/761263?Reference=2319031",
+ "format": "zip",
+ "mediaType": "application/zip",
+ "title": "WC_C_2026_ARCN Site Visit Worksheets.zip"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/762306?Reference=2319031",
+ "format": "txt",
+ "mediaType": "text/plain",
+ "title": "ARCN_README.txt"
+ }
+ ],
+ "identifier": "http://datainventory.doi.gov/id/dataset/NPS_DataStore_2319031",
+ "issued": "2026-01-01T00:00:00Z",
+ "keyword": [
+ "Ecological Framework: Air and Climate | Weather and Climate | Weather and Climate",
+ "climate",
+ "weather"
+ ],
+ "landingPage": "https://irma.nps.gov/DataStore/Reference/Profile/2319031",
+ "modified": "2026-01-01T00:00:00Z",
+ "programCode": [
+ "010:118",
+ "010:119"
+ ],
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "National Park Service"
+ },
+ "spatial": "-174,58.5,-145.4,71.3",
+ "temporal": "2025-01-01/2026-01-01",
+ "theme": [
+ "Generic Dataset"
+ ],
+ "title": "2026 ARCN Weather and Climate Data Deliverables"
+ },
+ "description": "Data deliverables from Arctic Network for 2026. Files may include protocol, standard operating procedures, site maps, site visit worksheets, datalogger programs, photos, raw data, corrected data, operations report, sensor calibration certificates, and/or periodic reports.",
+ "distribution_titles": [
+ "WC_D_2026_ARCN Datalogger Program Code.zip",
+ "WC_H_2026_ARCN Annual Operations Report.pdf",
+ "WC_I_2026_ARCN Calibration Certificates.zip",
+ "WC_G_2026_ARCN_Corrected Data.zip",
+ "WC_B_2026 ARCN Climate Station Locations.zip",
+ "WC_E_2026_ARCN Site Photo Sets.zip",
+ "WC_F_2026_ARCN Raw Data Downloads.zip",
+ "WC_C_2026_ARCN Site Visit Worksheets.zip",
+ "ARCN_README.txt"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/cd81f062-5206-4b23-bc69-14190cb18e6f",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/cd81f062-5206-4b23-bc69-14190cb18e6f/raw",
+ "has_download": true,
+ "has_spatial": true,
+ "identifier": "http://datainventory.doi.gov/id/dataset/NPS_DataStore_2319031",
+ "keyword": [
+ "Ecological Framework: Air and Climate | Weather and Climate | Weather and Climate",
+ "climate",
+ "weather"
+ ],
+ "last_harvested_date": "2026-09-23T20:46:31.882293",
+ "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": "National Park Service",
+ "slug": "2026-arcn-weather-and-climate-data-deliverables",
+ "spatial_centroid": {
+ "lat": 63.620000000000005,
+ "lon": -162.56
+ },
+ "spatial_shape": {
+ "coordinates": [
+ [
+ [
+ -174,
+ 58.5
+ ],
+ [
+ -174,
+ 71.3
+ ],
+ [
+ -145.4,
+ 71.3
+ ],
+ [
+ -145.4,
+ 58.5
+ ],
+ [
+ -174,
+ 58.5
+ ]
+ ]
+ ],
+ "type": "Polygon"
+ },
+ "theme": [
+ "Generic Dataset"
+ ],
+ "title": "2026 ARCN Weather and Climate Data Deliverables",
+ "type": "dataset"
+ },
+ {
+ "_score": 10.879398,
+ "_sort": [
+ 1790190443885,
+ 10.879398,
+ 10,
+ "a78712a4-13be-40f3-8a4a-bf015771ab7b"
+ ],
+ "dcat": {
+ "@type": "dcat:Dataset",
+ "accessLevel": "public",
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "svc dmesb",
+ "hasEmail": "mailto:no-reply@data.montgomerycountymd.gov"
+ },
+ "description": "The monthly Greenhouse Gas (GHG) emission data represents Montgomery County Facilities and Fleet by month beginning July 2019. \nFacilities: The Facilities GHG data represents physical structures used by County residents and County staff who provide services for County residents. Examples include recreation, libraries, theater and arts, health and human services, liquor retail, courthouses, general services, maintenance facilities, correctional facilities, police stations, fire stations, volunteer fire stations, garages, parking lots, bus shelters and park & ride locations. Facilities use the following fuel sources: grid electricity, natural gas, propane and diesel fuel. \nFacilities GHG data DOES NOT include Montgomery County Public Schools, Montgomery College and Montgomery Parks Maryland-National Capital Park and Planning Commission (M-NCPPC). \nFleet: The Fleet GHG data represents Montgomery County vehicles used by County staff who provide services for County residents. Examples include mass transit buses, snowplows, liquor trucks, light duty trucks, police cars, fire engines and fire service equipment, etc. Each County vehicle use different fuel sources (i.e. diesel, mobil diesel, compressed natural gas, unleaded and E-85). \nFleet GHG data DOES NOT include Montgomery County Public School buses, Montgomery College and Montgomery Parks Maryland-National Capital Park and Planning Commission (M-NCPPC) vehicles. \nGHG Calculation Method: Facilities and Fleet fuel sources are converted into one common unit of energy- 1 Million British thermal units (MMBtu) which are then used with emissions factors and 100-year global warming potential (GWP) to calculate GHG emissions into one common unit of measure- Metric Tons of CO2 Equivalent (MTCO2e). \nFor more information go to: \n•\tHow to Calculate GHG emissions at https://www.youtube.com/watch?v=zq5wTjvLqnY&t=186s\n•\tEmissions & Generation Resource Integrated Database (eGRID) at https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid\n•\tEmission Factors for GHG Inventories at https://www.epa.gov/sites/production/files/2018-03/documents/emission-factors_mar_2018_0.pdf\nUpdate Frequency : Monthly",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "describedBy": "https://data.montgomerycountymd.gov/api/views/stmn-fdnc/columns.json",
+ "describedByType": "application/json",
+ "downloadURL": "https://data.montgomerycountymd.gov/api/v3/views/stmn-fdnc/query.json?accessType=DOWNLOAD",
+ "mediaType": "application/json"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "describedBy": "https://data.montgomerycountymd.gov/api/views/stmn-fdnc/columns.xml",
+ "describedByType": "application/xml",
+ "downloadURL": "https://data.montgomerycountymd.gov/api/v3/views/stmn-fdnc/query.xml?accessType=DOWNLOAD",
+ "mediaType": "application/xml"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://data.montgomerycountymd.gov/api/v3/views/stmn-fdnc/export.csv?accessType=DOWNLOAD",
+ "mediaType": "text/csv"
+ }
+ ],
+ "identifier": "https://data.montgomerycountymd.gov/api/views/stmn-fdnc",
+ "issued": "2020-05-05",
+ "keyword": [
+ "carbondioxide",
+ "climate",
+ "emissions",
+ "estimated",
+ "gas",
+ "greenhouse"
+ ],
+ "landingPage": "https://data.montgomerycountymd.gov/d/stmn-fdnc",
+ "modified": "2026-09-17",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "data.montgomerycountymd.gov"
+ },
+ "theme": [
+ "Environment"
+ ],
+ "title": "Greenhouse Gas Emissions"
+ },
+ "description": "The monthly Greenhouse Gas (GHG) emission data represents Montgomery County Facilities and Fleet by month beginning July 2019. \nFacilities: The Facilities GHG data represents physical structures used by County residents and County staff who provide services for County residents. Examples include recreation, libraries, theater and arts, health and human services, liquor retail, courthouses, general services, maintenance facilities, correctional facilities, police stations, fire stations, volunteer fire stations, garages, parking lots, bus shelters and park & ride locations. Facilities use the following fuel sources: grid electricity, natural gas, propane and diesel fuel. \nFacilities GHG data DOES NOT include Montgomery County Public Schools, Montgomery College and Montgomery Parks Maryland-National Capital Park and Planning Commission (M-NCPPC). \nFleet: The Fleet GHG data represents Montgomery County vehicles used by County staff who provide services for County residents. Examples include mass transit buses, snowplows, liquor trucks, light duty trucks, police cars, fire engines and fire service equipment, etc. Each County vehicle use different fuel sources (i.e. diesel, mobil diesel, compressed natural gas, unleaded and E-85). \nFleet GHG data DOES NOT include Montgomery County Public School buses, Montgomery College and Montgomery Parks Maryland-National Capital Park and Planning Commission (M-NCPPC) vehicles. \nGHG Calculation Method: Facilities and Fleet fuel sources are converted into one common unit of energy- 1 Million British thermal units (MMBtu) which are then used with emissions factors and 100-year global warming potential (GWP) to calculate GHG emissions into one common unit of measure- Metric Tons of CO2 Equivalent (MTCO2e). \nFor more information go to: \n•\tHow to Calculate GHG emissions at https://www.youtube.com/watch?v=zq5wTjvLqnY&t=186s\n•\tEmissions & Generation Resource Integrated Database (eGRID) at https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid\n•\tEmission Factors for GHG Inventories at https://www.epa.gov/sites/production/files/2018-03/documents/emission-factors_mar_2018_0.pdf\nUpdate Frequency : Monthly",
+ "distribution_titles": [],
+ "harvest_record": "https://catalog.data.gov/harvest_record/b5c55691-825a-4e3a-b598-a344464916c9",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/b5c55691-825a-4e3a-b598-a344464916c9/raw",
+ "has_download": true,
+ "has_spatial": false,
+ "identifier": "https://data.montgomerycountymd.gov/api/views/stmn-fdnc",
+ "keyword": [
+ "carbondioxide",
+ "climate",
+ "emissions",
+ "estimated",
+ "gas",
+ "greenhouse"
+ ],
+ "last_harvested_date": "2026-09-23T19:07:23.885870",
+ "organization": {
+ "aliases": null,
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "23fadd55-de9c-44e6-8cea-b328f5eccf22",
+ "logo": "",
+ "name": "Montgomery County of Maryland",
+ "organization_type": "County Government",
+ "slug": "montgomery-county-md"
+ },
+ "parent_identifier": null,
+ "popularity": 10,
+ "publisher": "data.montgomerycountymd.gov",
+ "slug": "greenhouse-gas-emissions",
+ "spatial_centroid": null,
+ "spatial_shape": null,
+ "theme": [
+ "Environment"
+ ],
+ "title": "Greenhouse Gas Emissions",
+ "type": "dataset"
+ },
+ {
+ "_score": 7.7947025,
+ "_sort": [
+ 1790189390882,
+ 7.7947025,
+ 4,
+ "374f22e6-71ff-41c8-ad59-226d7c041e6e"
+ ],
+ "dcat": {
+ "@type": "dcat:Dataset",
+ "accessLevel": "public",
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "OpenData",
+ "hasEmail": "mailto:no-reply@data.sf.gov"
+ },
+ "description": "<strong>A. SUMMARY</strong>\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the <u><a href=\"https://www.sfenvironment.org/files/events/2021_climate_action_plan.pdf\">2021 Climate Action Plan</a></u> and is included in the <u><a href=\"https://codelibrary.amlegal.com/codes/san_francisco/latest/sf_environment/0-0-0-577\">Environment Code Chapter 7</a></u> (Municipal Green Building Requirements). \n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City’s <u><a href=\"https://data.sfgov.org/City-Infrastructure/City-Facilities/nc68-ngbr/about_datax\">Facility Systems of Record</a></u> and the <u><a href=\"https://sfpuc.org/about-us/reports/municipal-buildings-energy-benchmarking\">2020 municipal benchmarking report</a></u> to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\n<strong>C. UPDATE PROCESS</strong>\nThe inventory will be regularly updated by department representatives via the inventory PowerApp. When a gas-powered equipment item is retired or replaced, departments are asked to mark it as no longer in use and provide information on any electric replacement equipment, if applicable. While departments have the flexibility to update the inventory at any time, they are encouraged to do so at 6 month intervals at the minimum. \n\nUpdated inventory data will be automatically reflected in this dataset. \n\n<strong>D. HOW TO USE THIS DATASET</strong>\nIt is important to note that this dataset does not include facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities, in accordance with Environment Code Chapter 7 exemptions.",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "describedBy": "https://data.sf.gov/api/views/vc6r-v7av/columns.json",
+ "describedByType": "application/json",
+ "downloadURL": "https://data.sf.gov/api/v3/views/vc6r-v7av/query.json?accessType=DOWNLOAD",
+ "mediaType": "application/json"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "describedBy": "https://data.sf.gov/api/views/vc6r-v7av/columns.xml",
+ "describedByType": "application/xml",
+ "downloadURL": "https://data.sf.gov/api/v3/views/vc6r-v7av/query.xml?accessType=DOWNLOAD",
+ "mediaType": "application/xml"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://data.sf.gov/api/v3/views/vc6r-v7av/export.csv?accessType=DOWNLOAD",
+ "mediaType": "text/csv"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://data.sf.gov/api/v3/views/vc6r-v7av/export.kml?accessType=DOWNLOAD",
+ "mediaType": "application/vnd.google-earth.kml+xml"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://data.sf.gov/api/v3/views/vc6r-v7av/export.kmz?accessType=DOWNLOAD",
+ "mediaType": "application/vnd.google-earth.kmz"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://data.sf.gov/api/v3/views/vc6r-v7av/query.geojson?accessType=DOWNLOAD",
+ "mediaType": "application/geo+json"
+ }
+ ],
+ "identifier": "https://data.sf.gov/api/views/vc6r-v7av",
+ "issued": "2024-03-28",
+ "keyword": [
+ "environment",
+ "environmental health",
+ "greenhouse gas emissions",
+ "natural gas"
+ ],
+ "landingPage": "https://data.sf.gov/d/vc6r-v7av",
+ "modified": "2026-09-18",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "data.sf.gov"
+ },
+ "theme": [
+ "Energy and Environment"
+ ],
+ "title": "San Francisco Municipal Natural Gas Equipment Inventory"
+ },
+ "description": "<strong>A. SUMMARY</strong>\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the <u><a href=\"https://www.sfenvironment.org/files/events/2021_climate_action_plan.pdf\">2021 Climate Action Plan</a></u> and is included in the <u><a href=\"https://codelibrary.amlegal.com/codes/san_francisco/latest/sf_environment/0-0-0-577\">Environment Code Chapter 7</a></u> (Municipal Green Building Requirements). \n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City’s <u><a href=\"https://data.sfgov.org/City-Infrastructure/City-Facilities/nc68-ngbr/about_datax\">Facility Systems of Record</a></u> and the <u><a href=\"https://sfpuc.org/about-us/reports/municipal-buildings-energy-benchmarking\">2020 municipal benchmarking report</a></u> to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\n<strong>C. UPDATE PROCESS</strong>\nThe inventory will be regularly updated by department representatives via the inventory PowerApp. When a gas-powered equipment item is retired or replaced, departments are asked to mark it as no longer in use and provide information on any electric replacement equipment, if applicable. While departments have the flexibility to update the inventory at any time, they are encouraged to do so at 6 month intervals at the minimum. \n\nUpdated inventory data will be automatically reflected in this dataset. \n\n<strong>D. HOW TO USE THIS DATASET</strong>\nIt is important to note that this dataset does not include facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities, in accordance with Environment Code Chapter 7 exemptions.",
+ "distribution_titles": [],
+ "harvest_record": "https://catalog.data.gov/harvest_record/faa8b2e4-f158-4313-8e39-926fdc59d7e3",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/faa8b2e4-f158-4313-8e39-926fdc59d7e3/raw",
+ "has_download": true,
+ "has_spatial": false,
+ "identifier": "https://data.sf.gov/api/views/vc6r-v7av",
+ "keyword": [
+ "environment",
+ "environmental health",
+ "greenhouse gas emissions",
+ "natural gas"
+ ],
+ "last_harvested_date": "2026-09-23T18:49:50.882638",
+ "organization": {
+ "aliases": [
+ "sf",
+ "california"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png",
+ "name": "City of San Francisco",
+ "organization_type": "City Government",
+ "slug": "san-francisco-ca"
+ },
+ "parent_identifier": null,
+ "popularity": 4,
+ "publisher": "data.sf.gov",
+ "slug": "san-francisco-municipal-natural-gas-equipment-inventory",
+ "spatial_centroid": null,
+ "spatial_shape": null,
+ "theme": [
+ "Energy and Environment"
+ ],
+ "title": "San Francisco Municipal Natural Gas Equipment Inventory",
+ "type": "dataset"
+ },
+ {
+ "_score": 9.976896,
+ "_sort": [
+ 1790189385022,
+ 9.976896,
+ 0,
+ "f38ec6b1-0e62-4fbc-a758-65f0796265bd"
+ ],
+ "dcat": {
+ "@type": "dcat:Dataset",
+ "accessLevel": "public",
+ "contactPoint": {
+ "@type": "vcard:Contact",
+ "fn": "OpenData",
+ "hasEmail": "mailto:no-reply@data.sf.gov"
+ },
+ "description": "<strong>A. SUMMARY</strong>\nSan Francisco International Airport (SFO) keeps track of aircraft noise levels in communities around the airport 24/7. Measured aircraft noise events are used in calculations to determine Aircraft Community Noise Equivalent Level (CNEL). This noise metric is used to assess and regulate aircraft noise exposure in residential communities surrounding the airport. The annual Aircraft CNEL helps validate the 65‐decibel noise impact contour, an output of computer noise modeling.\n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThis dataset consists of measured aircraft noise events at each of SFO’s 29 community noise monitoring sites. Also provided as part of this dataset is ANEEM Aircraft CNEL. This aircraft climate is derived using ANEEM algorithms that can measure quieter aircraft noise levels below that of conventional threshold correlation methodology resulting in improved noise to aircraft correlations.\n\n<strong>C. UPDATE PROCESS</strong>\nData is available starting in March 2017. Aircraft climates derived using ANEEM algorithms are available starting January 2023. This dataset will be updated on a monthly basis.\n\n<strong>D. HOW TO USE THIS DATASET</strong>\nThis information is used to produce the monthly Aircraft Noise Levels section on page 1 of the Airport Director’s Report. These reports are presented at the SFO Airport Community Roundtable Meetings and available online at https://www.flysfo.com/about/community-noise/noise-office/reports/airport-directors-report\n\nPlease contact the Noise Abatement Office at NoiseAbatementOffice@flysfo.com for any questions regarding this data.\n\nDate created: June 27, 2023",
+ "distribution": [
+ {
+ "@type": "dcat:Distribution",
+ "describedBy": "https://data.sf.gov/api/views/qxw2-ncq3/columns.json",
+ "describedByType": "application/json",
+ "downloadURL": "https://data.sf.gov/api/v3/views/qxw2-ncq3/query.json?accessType=DOWNLOAD",
+ "mediaType": "application/json"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "describedBy": "https://data.sf.gov/api/views/qxw2-ncq3/columns.xml",
+ "describedByType": "application/xml",
+ "downloadURL": "https://data.sf.gov/api/v3/views/qxw2-ncq3/query.xml?accessType=DOWNLOAD",
+ "mediaType": "application/xml"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://data.sf.gov/api/v3/views/qxw2-ncq3/export.csv?accessType=DOWNLOAD",
+ "mediaType": "text/csv"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://data.sf.gov/api/v3/views/qxw2-ncq3/export.kml?accessType=DOWNLOAD",
+ "mediaType": "application/vnd.google-earth.kml+xml"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://data.sf.gov/api/v3/views/qxw2-ncq3/export.kmz?accessType=DOWNLOAD",
+ "mediaType": "application/vnd.google-earth.kmz"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://data.sf.gov/api/v3/views/qxw2-ncq3/query.geojson?accessType=DOWNLOAD",
+ "mediaType": "application/geo+json"
+ }
+ ],
+ "identifier": "https://data.sf.gov/api/views/qxw2-ncq3",
+ "issued": "2023-11-20",
+ "keyword": [
+ "untagged"
+ ],
+ "landingPage": "https://data.sf.gov/d/qxw2-ncq3",
+ "license": "http://opendatacommons.org/licenses/pddl/1.0/",
+ "modified": "2026-09-22",
+ "publisher": {
+ "@type": "org:Organization",
+ "name": "data.sf.gov"
+ },
+ "theme": [
+ "Transportation"
+ ],
+ "title": "Aircraft Noise Climates"
+ },
+ "description": "<strong>A. SUMMARY</strong>\nSan Francisco International Airport (SFO) keeps track of aircraft noise levels in communities around the airport 24/7. Measured aircraft noise events are used in calculations to determine Aircraft Community Noise Equivalent Level (CNEL). This noise metric is used to assess and regulate aircraft noise exposure in residential communities surrounding the airport. The annual Aircraft CNEL helps validate the 65‐decibel noise impact contour, an output of computer noise modeling.\n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThis dataset consists of measured aircraft noise events at each of SFO’s 29 community noise monitoring sites. Also provided as part of this dataset is ANEEM Aircraft CNEL. This aircraft climate is derived using ANEEM algorithms that can measure quieter aircraft noise levels below that of conventional threshold correlation methodology resulting in improved noise to aircraft correlations.\n\n<strong>C. UPDATE PROCESS</strong>\nData is available starting in March 2017. Aircraft climates derived using ANEEM algorithms are available starting January 2023. This dataset will be updated on a monthly basis.\n\n<strong>D. HOW TO USE THIS DATASET</strong>\nThis information is used to produce the monthly Aircraft Noise Levels section on page 1 of the Airport Director’s Report. These reports are presented at the SFO Airport Community Roundtable Meetings and available online at https://www.flysfo.com/about/community-noise/noise-office/reports/airport-directors-report\n\nPlease contact the Noise Abatement Office at NoiseAbatementOffice@flysfo.com for any questions regarding this data.\n\nDate created: June 27, 2023",
+ "distribution_titles": [],
+ "harvest_record": "https://catalog.data.gov/harvest_record/ec3bc8e0-3aeb-4545-a41b-7d0bd00a3af8",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/ec3bc8e0-3aeb-4545-a41b-7d0bd00a3af8/raw",
+ "has_download": true,
+ "has_spatial": false,
+ "identifier": "https://data.sf.gov/api/views/qxw2-ncq3",
+ "keyword": [
+ "untagged"
+ ],
+ "last_harvested_date": "2026-09-23T18:49:45.022520",
+ "organization": {
+ "aliases": [
+ "sf",
+ "california"
+ ],
+ "code_repo_exempt": false,
+ "code_repo_url": null,
+ "description": null,
+ "id": "7f3c6c7a-ef5f-4ee0-87ce-a0300ba767d5",
+ "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_san_francisco_ca.png",
+ "name": "City of San Francisco",
+ "organization_type": "City Government",
+ "slug": "san-francisco-ca"
+ },
+ "parent_identifier": null,
+ "popularity": 0,
+ "publisher": "data.sf.gov",
+ "slug": "aircraft-noise-climates",
+ "spatial_centroid": null,
+ "spatial_shape": null,
+ "theme": [
+ "Transportation"
+ ],
+ "title": "Aircraft Noise Climates",
+ "type": "dataset"
+ },
+ {
+ "_score": 14.456228,
  "_sort": [
  1790127215031,
- 14.454666,
+ 14.456228,
  0,
  "1cadc744-8510-4ea1-b7e7-f4031921f8b6"
  ],
@@ -145,10 +5927,10 @@
  "type": "dataset"
  },
  {
- "_score": 25.960983,
+ "_score": 25.995068,
  "_sort": [
  1790127199443,
- 25.960983,
+ 25.995068,
  2,
  "43ae5a7d-875c-430a-904a-61df894dcf5a"
  ],
@@ -286,10 +6068,10 @@
  "type": "dataset"
  },
  {
- "_score": 10.535811,
+ "_score": 10.541817,
  "_sort": [
  1790127170126,
- 10.535811,
+ 10.541817,
  1,
  "21ec6506-eb58-4d45-93e6-5168f6380549"
  ],
@@ -431,10 +6213,10 @@
  "type": "dataset"
  },
  {
- "_score": 8.45816,
+ "_score": 8.446569,
  "_sort": [
  1790127052842,
- 8.45816,
+ 8.446569,
  1,
  "283777eb-31ff-4bd9-834e-6bcb0368ab5e"
  ],
@@ -586,10 +6368,10 @@
  "type": "dataset"
  },
  {
- "_score": 19.271717,
+ "_score": 19.27983,
  "_sort": [
  1790127015453,
- 19.271717,
+ 19.27983,
  0,
  "95bda1fd-02e6-4ed6-aed8-373bef493cb9"
  ],
@@ -693,4390 +6475,4 @@
  ],
  "title": "ATTREX-Aircraft_navigational_and_meteorological_Measurements"
  },
- "description": "ATTREX-Aircraft_navigational_meteorological_Measurements are in-situ navigational and meteorological measurements collected onboard the Global Hawk Unihabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of in-situ meteorological and navigational properties collected by the Meteorological Measurement System (MMS) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete.\nEven though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedback. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively.",
- "distribution_titles": [
- "View documentation related to this dataset",
- "Download this dataset",
- "This dataset's landing page",
- "The dataset's project home page",
- "Download this dataset through Earthdata Search",
- "View this dataset's data citation policy",
- "Google Scholar search results"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/02d38e11-16d1-40dd-af1a-c41fcfdd24a6",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/02d38e11-16d1-40dd-af1a-c41fcfdd24a6/raw",
- "has_download": true,
- "has_spatial": true,
- "identifier": "C1536056463-LARC_ASDC",
- "keyword": [
- "altitude",
- "atmosphere",
- "atmospheric-pressure",
- "atmospheric-temperature",
- "atmospheric-winds",
- "earth-science"
- ],
- "last_harvested_date": "2026-09-23T01:30:15.453777",
- "organization": {
- "aliases": [
- ""
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "f4ca4614-8901-409b-8553-2e994ad10023",
- "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png",
- "name": "National Aeronautics and Space Administration",
- "organization_type": "Federal Government",
- "slug": "nasa"
- },
- "parent_identifier": null,
- "popularity": 0,
- "publisher": "NASA/LARC/SD/ASDC",
- "slug": "attrex-aircraft_navigational_and_meteorological_measurements",
- "spatial_centroid": null,
- "spatial_shape": null,
- "theme": [
- "ATTREX",
- "geospatial"
- ],
- "title": "ATTREX-Aircraft_navigational_and_meteorological_Measurements",
- "type": "dataset"
- },
- {
- "_score": 8.449841,
- "_sort": [
- 1790127007530,
- 8.449841,
- 2,
- "0f8e2faa-e973-41ca-8e58-b51ae15981bf"
- ],
- "dcat": {
- "@type": "dcat:Dataset",
- "accessLevel": "public",
- "bureauCode": [
- "026:00"
- ],
- "contactPoint": {
- "@type": "vcard:Contact",
- "fn": "GREG STENSAAS",
- "hasEmail": "mailto:stensaas@usgs.gov"
- },
- "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nInstrumented Sites:\nLa Crau, France is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.  In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "description": "Collection-specific granule Open Search Descriptor Document",
- "downloadURL": "https://cmr.earthdata.nasa.gov/opensearch/granules/descriptor_document.xml?collectionConceptId=C1220566843-USGS_LTA",
- "format": "application/opensearchdescription+xml",
- "mediaType": "application/opensearchdescription+xml",
- "title": "Retrieve the OpenSearch Get Capabilities document"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Test Sites.",
- "downloadURL": "http://calvalportal.ceos.org/web/guest/home",
- "format": "HTML",
- "mediaType": "text/html",
- "title": "The dataset's project home page"
- }
- ],
- "identifier": "C1220566843-USGS_LTA",
- "issued": "1972-08-16",
- "keyword": [
- "earth-science",
- "land-surface",
- "land-use-land-cover",
- "national-geospatial-data-asset",
- "ngda",
- "sensor-characteristics",
- "spectral-engineering",
- "surface-radiative-properties",
- "surface-thermal-properties"
- ],
- "landingPage": "https://cmr.earthdata.nasa.gov:443/search/concepts/C1220566843-USGS_LTA.html",
- "language": [
- "en-US"
- ],
- "modified": "2025-04-01",
- "programCode": [
- "026:001"
- ],
- "publisher": {
- "@type": "org:Organization",
- "name": "DOI/USGS/EROS"
- },
- "spatial": "1.92 41.86 6.49 45.63",
- "temporal": "1972-08-16T00:00:00Z/2023-03-01T00:00:00Z",
- "theme": [
- "CWIC",
- "geospatial"
- ],
- "title": "CEOS Cal Val Test Site - La Crau, France - Instrumented Site"
- },
- "description": "On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes:\n\nBackground:\n\nReference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities.\nRequirement:\n\nInitiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner.\n\nInstrumented Sites:\nLa Crau, France is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner.  In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner.",
- "distribution_titles": [
- "Retrieve the OpenSearch Get Capabilities document",
- "The dataset's project home page"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/246abd23-5529-4bb3-8a54-4b49a9fb60f0",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/246abd23-5529-4bb3-8a54-4b49a9fb60f0/raw",
- "has_download": true,
- "has_spatial": true,
- "identifier": "C1220566843-USGS_LTA",
- "keyword": [
- "earth-science",
- "land-surface",
- "land-use-land-cover",
- "national-geospatial-data-asset",
- "ngda",
- "sensor-characteristics",
- "spectral-engineering",
- "surface-radiative-properties",
- "surface-thermal-properties"
- ],
- "last_harvested_date": "2026-09-23T01:30:07.530173",
- "organization": {
- "aliases": [
- ""
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "f4ca4614-8901-409b-8553-2e994ad10023",
- "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png",
- "name": "National Aeronautics and Space Administration",
- "organization_type": "Federal Government",
- "slug": "nasa"
- },
- "parent_identifier": null,
- "popularity": 2,
- "publisher": "DOI/USGS/EROS",
- "slug": "ceos-cal-val-test-site-la-crau-france-instrumented-site",
- "spatial_centroid": null,
- "spatial_shape": null,
- "theme": [
- "CWIC",
- "geospatial"
- ],
- "title": "CEOS Cal Val Test Site - La Crau, France - Instrumented Site",
- "type": "dataset"
- },
- {
- "_score": 17.861115,
- "_sort": [
- 1790126996078,
- 17.861115,
- 2,
- "351eaf24-b6dc-4db1-ac2c-45e72902897e"
- ],
- "dcat": {
- "@type": "dcat:Dataset",
- "accessLevel": "public",
- "accrualPeriodicity": "irregular",
- "bureauCode": [
- "026:00"
- ],
- "contactPoint": {
- "@type": "vcard:Contact",
- "fn": "Brian Kumanchik",
- "hasEmail": "mailto:brian.kumanchik@jpl.nasa.gov"
- },
- "description": "The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite provides new insight into the role that clouds and atmospheric aerosols (airborne particles) play in regulating Earth's weather, climate, and air quality.",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "downloadURL": "http://nasa3d.arc.nasa.gov/shared_assets/models/calipso/Calipso-v016-trip.fbx.zip",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- }
- ],
- "identifier": "NASA-323",
- "issued": "2018-06-25",
- "keyword": [
- "3d-model",
- "a-train",
- "calipso",
- "eoss",
- "eyes-on-the-earth-3d",
- "eyes-on-the-solar-system",
- "satellite",
- "spacecraft"
- ],
- "landingPage": "http://nasa3d.arc.nasa.gov/detail/calipso",
- "modified": "2025-04-01",
- "programCode": [
- "026:007"
- ],
- "publisher": {
- "@type": "org:Organization",
- "name": "National Aeronautics and Space Administration"
- },
- "references": [
- "http://www-calipso.larc.nasa.gov/"
- ],
- "theme": [
- "Space Science"
- ],
- "title": "NASA 3D Models: CALIPSO"
- },
- "description": "The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite provides new insight into the role that clouds and atmospheric aerosols (airborne particles) play in regulating Earth's weather, climate, and air quality.",
- "distribution_titles": [],
- "harvest_record": "https://catalog.data.gov/harvest_record/c97438d6-8b0d-4c52-8c7c-38f64444a166",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/c97438d6-8b0d-4c52-8c7c-38f64444a166/raw",
- "has_download": true,
- "has_spatial": false,
- "identifier": "NASA-323",
- "keyword": [
- "3d-model",
- "a-train",
- "calipso",
- "eoss",
- "eyes-on-the-earth-3d",
- "eyes-on-the-solar-system",
- "satellite",
- "spacecraft"
- ],
- "last_harvested_date": "2026-09-23T01:29:56.078004",
- "organization": {
- "aliases": [
- ""
- ],
- "code_repo_exempt": false,
- "code_repo_url": null,
- "description": null,
- "id": "f4ca4614-8901-409b-8553-2e994ad10023",
- "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png",
- "name": "National Aeronautics and Space Administration",
- "organization_type": "Federal Government",
- "slug": "nasa"
- },
- "parent_identifier": null,
- "popularity": 2,
- "publisher": "National Aeronautics and Space Administration",
- "slug": "nasa-3d-models-calipso",
- "spatial_centroid": null,
- "spatial_shape": null,
- "theme": [
- "Space Science"
- ],
- "title": "NASA 3D Models: CALIPSO",
- "type": "dataset"
- },
- {
- "_score": 19.090155,
- "_sort": [
- 1790126980016,
- 19.090155,
- 0,
- "f08c69e7-4085-4896-80ee-40027183f9a0"
- ],
- "dcat": {
- "@type": "dcat:Dataset",
- "accessLevel": "public",
- "bureauCode": [
- "026:00"
- ],
- "contactPoint": {
- "@type": "vcard:Contact",
- "fn": "undefined",
- "hasEmail": "mailto:support-asdc@earthdata.nasa.gov"
- },
- "description": "ATTREX-Aircraft_RemoteSensing_Temperature_Measurements are remotely sensed temperature profiles collected onboard the Global Hawk Unihabited Aerial System (UAS) during the Airborne Tropical TRopopause EXperiment (ATTREX) campaign. This collection consists of remotely sensed temperature profiles collected by the Microwave Temperature Profiler (MTP) during the 2011 and 2013 deployments over California, and 2014 deployment over Guam. Data collection is complete.\nEven though it is typically found in low concentrations, stratospheric water vapor has large impacts on the Earth’s climate and energy budget. Studies have suggested that even relatively small changes in stratospheric humidity may have significant climate impacts and future changes in stratospheric humidity and ozone concentration in response to a changing climate are significant climate feedback. Tropospheric water vapor climate feedback is typically well represented in global models. However, predictions of future changes in stratospheric humidity are highly uncertain due to gaps in our understanding of physical processes occurring in the region of the atmosphere that controls the composition of the stratosphere, the Tropical Tropopause Layer (TTL, ~13-18 km). The ability to predict future changes in stratospheric ozone are also limited due to uncertainties in the chemical composition of the TTL. In order to address these uncertainties, the Airborne Tropical Tropopause Experiment (ATTREX) was completed. Instruments during ATTREX provided measurements to trace the movement of reactive halogen-containing compounds and other important chemical species, the size and shape of cirrus cloud particles, water vapor, and winds in three dimensions through the TTL. Bromine-containing gases were measured to improve understanding of stratospheric ozone. ATTREX consisted of four NASA Global Hawk Uninhabited Aerial System (UAS) campaigns deployed from NASA’s Armstrong Flight Research Center (formally Dryden Flight Research Center). Campaigns were deployed over Edwards, CA, Guam, Hawaii, and Darwin, Australia in Boreal summer, winter, fall, and summer, respectively.",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "description": "Friday: Andrew Ptak, On-the-fly and Grid Analysis of Astronomical Images",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/4.30_AISRP-2009-ptak.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "4.30_AISRP-2009-ptak.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Friday: Brian Williams, Demonstration of Safe Human / Robot Coordination on the Athlete Lunar Rover",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/Wiliams_NASA_AISR_Talk_Oct_09.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "Wiliams_NASA_AISR_Talk_Oct_09.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Friday: Edward Belbruno, Controlling Sensitive Trajectories and Transfer Between Planetary Systems",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/Belbruno_AISR2009Conf.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "Belbruno_AISR2009Conf.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Friday: Gabor Toth Adding New Physics and Numerics into the BATSRUS MHD Code",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/talk.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "talk.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Friday: Jeffrey Gardner, Astronomy in the Cloud",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/JGardner-AISR09.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "JGardner-AISR09.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Friday: Jeffrey Jewell, Parallel-Processing Astrophysical Image-Analysis Tools",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/jewell_aisr_2009_fv.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "jewell_aisr_2009_fv.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Friday: Kenneith Mighell, Parallel-Processing Astrophysical Image-Analysis Tools",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/mighell2009OCT16.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "mighell2009OCT16.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Friday: Mario Juric, GPU‐based Tools for Computational Astrophysics: N‐Body Integrators for Planetary Systems",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/AISRP_-_Final.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "AISRP_-_Final.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Friday: Michael Burl, Directed Exploration of Complex Systems",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/20091016_AISR_BURL.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "20091016_AISR_BURL.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Friday: Robin Morris, How Well Do You Know That?",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/Morris_aisrp_pi_2009.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "Morris_aisrp_pi_2009.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Friday: Ron Li, Integration of Orbital, Descent and Ground Imagery for Topographic Capability Analysis in Mars Landed Missions",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/AISRP_PI_Meeting_Li_2009_pub.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "AISRP_PI_Meeting_Li_2009_pub.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Thrusday: Stephen Fong, An Integrated Systems for Creating and Evaluating Biological Models",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/08.30_SSF_10_15_09.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "08.30_SSF_10_15_09.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Thursday: Hillol Kargupta, Distributed and Peer-to-Peer Data Mining for Scalable Analysis of Data from Virtual Observatories",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/Kargupta-Slides.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "Kargupta-Slides.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Thursday: Joel Allred, Solar Magnetogram Synthesis: A Vital Data Analysis Component of A Space Weather Prediction Infrastructure",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/aisrp09.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "aisrp09.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Thursday: Ken Lang, NASA’S COSMOS",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/LANG-AISR_2009.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "LANG-AISR_2009.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Thursday: Martin Lo, Science Discovery with MTool on Massive High Dimensional Data",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/AISR09InvstgWrkshpLo.Final.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "AISR09InvstgWrkshpLo.Final.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Thursday: Thomas Quinn, Visualizing Terascale Datasets with Impostors",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/Quinnaisr09.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "Quinnaisr09.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Thursday: Zachary Moratto, Automated Digital Terrain Model Generation from HiRISE & LROC Imagery",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/AISRP_moratto.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "AISRP_moratto.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Wednesday: Andrew Connolly, Streaming the Sky: Data Interfaces with Gadgets",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/connolly.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "connolly.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Wednesday: Ashit Talukder, GLYDER: Global Cyclone Detection and Tracking from Remote Satellite Data",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/Talukder-AISR_PIMeeting_Oct2009-7.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "Talukder-AISR_PIMeeting_Oct2009-7.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Wednesday: Chad Schafer, Stochastic Models for Nonstandard, High-Dimensional Data",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/4_AISR_Schafer.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "4_AISR_Schafer.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Wednesday: Istvan Szapudi, Algorithms for Higher Order Spatial Statistics",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/4.15Szapudi-AISR.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "4.15Szapudi-AISR.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Wednesday: James Tilton, Object-Based Image Analysis Utilizing Image Segmentation Hierarchies",
- "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/3.45_Tilton_AISR_CIDU_2009.pdf",
- "format": "PDF",
- "mediaType": "application/pdf",
- "title": "3.45_Tilton_AISR_CIDU_2009.pdf"
- },
- {
- "@type": "dcat:Distribution",
- "description": "Wedn