Timeline / Data.gov — Climate Datasets
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Evidence
| Source | Data.gov — Climate Datasets |
|---|---|
| Agency | Data.gov |
| URL | https://api.gsa.gov/technology/datagov/v4/search?q=climate&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY} |
| Observed by | Civic Memory, directly, on 2026-10-09T00:26:40+00:00 |
| Content type | application/json |
| Current object |
8b778c29765169966289a97600d56afb5c641e62eddadf72523613a01b3243a7
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| Previous object |
56b4dc4fe4e7d95c1b62267fe3aebea909876ed80c447f8df21984b9b2732828
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What changed derived
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civic-memory.diff_engine 1.1.0 at
2026-10-09T00:26:40+00:00 by normalizing the two archived objects above. The
objects are authoritative; this reading of them can be regenerated or deleted
without loss. 2621 line(s) added, 2215 line(s) removed.
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SMRF was developed to be used as an operational or research framework, where ease of use, efficiency, and ability to run in near real time are high priorities.</p>\n<p>Highlights</p>\n<ol>\n<li>Robust meteorological spatial forcing data development for physically based models</li>\n<li>The Python framework can be used for research or operational applications</li>\n<li>Parallel processing and multi-threading allow for large modeling domains at high resolution</li>\n<li>Real time and historical applications for water supply resourses</li>\n</ol>\n<p>Features\nSMRF was developed as a modular framework to enable new modules to be easily intigrated and utilized.</p>\n<ul>\n<li>Load data into SMRF from MySQL database, CSV files, or gridded climate models (i.e. WRF)</li>\n<li>Variables currently implemented: Air temperature; Vapor pressure; Precipitation mass, phase, density, and percent snow; Wind speed and direction; Solar radiation; Thermal radiation</li>\n<li>Output variables to NetCDF files</li>\n<li>Data queue for multithreaded application</li>\n<li>Computation tasks implemented in C\n<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: SMRF GitHub repository.</p> <p>File Name: Web Page, url: <a href=\"https://github.com/USDA-ARS-NWRC/smrf\" target=\"_blank\">https://github.com/USDA-ARS-NWRC/smrf</a> </p><p>SMRF was designed to increase the flexibility of taking measured weather data, or atmospheric models, and distributing the data across a watershed.</p></li></ul></li>\n</ul>", + "distribution_titles": [ + "https://github.com/USDA-ARS-NWRC/smrf" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/f4787767-b7e6-4d53-8ac7-c4402e716a8c", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/f4787767-b7e6-4d53-8ac7-c4402e716a8c/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10.5281/zenodo.898158", + "keyword": [ + "ARS", + "NP211", + "NetCDF", + "SMRF", + "Spatial Modeling for Resources Framework", + "data.gov", + "interpolation" + ], + "last_harvested_date": "2026-10-08T22:44:47.659129", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "352b4532-793d-4075-a03f-05b778a3c43a", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png", + "name": "Department of Agriculture", + "organization_type": "Federal Government", + "slug": "usda" + }, + "parent_identifier": null, + "popularity": 2, + "publisher": "Agricultural Research Service", + "slug": "spatial-modeling-for-resources-framework-smrf", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "Spatial Modeling for Resources Framework (SMRF)", + "type": "dataset" + }, + { + "_score": 13.742893, + "_sort": [ + 1791499472386, + 13.742893, + 5, + "e8da1bc3-4300-4baa-a346-fed37b98af31" + ], + "access_level": "public", + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Rotz, C. 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By testing and comparing different options with the model, those offering the greatest economic benefit with acceptable environmental impact can be found.</p>\n<p>Input information is supplied to the program through three parameter files. The farm parameter file contains data describing the farm such as crop areas, soil type, equipment and structures used, numbers of animals at various ages, harvest, tillage, and manure handling strategies, and prices for various farm inputs and outputs. The machinery file includes parameters for each machine available for use on a simulated farm.</p>\n<p>Simulation output is available in four files, which contain summary tables, report tables, optional tables, and parameter tables. The summary tables provide average performance, environmental impact, costs, and returns for the years simulated. These values consist of crop yields, feeds produced, feeds bought and sold, manure produced, nutrient losses to the environment, production costs, income from products sold, and the net return or profitability of the farm. Values are provided for the average and standard deviation of each over all simulated years. The report tables provide extensive output information including all the data given in the summary tables. In these tables, values are given for each simulated year of weather as well as the mean and variance over all simulated years. Optional tables are available for a closer inspection of how the components of the full simulation are functioning. These tables include very detailed data, often on a daily basis. Parameter tables summarize the input parameters specified for a given simulation. These tables provide a convenient method of documenting the parameter settings used for a simulation.</p>\n<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Projected Climate Data for IFSM.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00\" target=\"_blank\">https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00</a> </p><p>Downscaled climate data (1950 to 2100) are available for 78 locations across the United States formatted for use in IFSM. Each location includes 18 climate files created using 9 general circulation models (GCM) and 2 projected emission scenarios. Emission scenarios include Representative Concentration Pathways (RCP) 4.5 and 8.5 where RCP 4.5 represents a somewhat optimistic outlook for reducing greenhouse gas emissions and 8.5 represents continuing the current trend for emissions. </p></li></ul>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00", + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "mediaType": "text/html", + "title": "https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00" + } + ], + "identifier": "10113/AA7768", + "keyword": [ + "ARS", + "IFSM", + "Integrated Farm System Model", + "data.gov" + ], + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "modified": "2024-02-09", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "title": "Integrated Farm System Model (IFSM)" + }, + "description": "<p>The need for a research tool that integrates the many physical and biological processes on a farm has led to the development of the Integrated Farm System Model (IFSM). The model has been used to evaluate a wide variety of technologies and management strategies, and these analyses have been reported in the scientific and farm-trade literature. Systems research in dairy and beef production remains as the primary purpose of this tool, but the model also provides an effective teaching aid. With the model, students gain a better appreciation for the complexity of livestock forage systems. The learn how small changes affect many parts of the system, causing unanticipated results. They may also use the model to develop a more optimum food production system. When used in extension type teaching, producers can learn more about their farms and obtain information useful in strategic planning. By testing and comparing different options with the model, those offering the greatest economic benefit with acceptable environmental impact can be found.</p>\n<p>Input information is supplied to the program through three parameter files. The farm parameter file contains data describing the farm such as crop areas, soil type, equipment and structures used, numbers of animals at various ages, harvest, tillage, and manure handling strategies, and prices for various farm inputs and outputs. The machinery file includes parameters for each machine available for use on a simulated farm.</p>\n<p>Simulation output is available in four files, which contain summary tables, report tables, optional tables, and parameter tables. The summary tables provide average performance, environmental impact, costs, and returns for the years simulated. These values consist of crop yields, feeds produced, feeds bought and sold, manure produced, nutrient losses to the environment, production costs, income from products sold, and the net return or profitability of the farm. Values are provided for the average and standard deviation of each over all simulated years. The report tables provide extensive output information including all the data given in the summary tables. In these tables, values are given for each simulated year of weather as well as the mean and variance over all simulated years. Optional tables are available for a closer inspection of how the components of the full simulation are functioning. These tables include very detailed data, often on a daily basis. Parameter tables summarize the input parameters specified for a given simulation. These tables provide a convenient method of documenting the parameter settings used for a simulation.</p>\n<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Projected Climate Data for IFSM.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00\" target=\"_blank\">https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00</a> </p><p>Downscaled climate data (1950 to 2100) are available for 78 locations across the United States formatted for use in IFSM. Each location includes 18 climate files created using 9 general circulation models (GCM) and 2 projected emission scenarios. Emission scenarios include Representative Concentration Pathways (RCP) 4.5 and 8.5 where RCP 4.5 represents a somewhat optimistic outlook for reducing greenhouse gas emissions and 8.5 represents continuing the current trend for emissions. </p></li></ul>", + "distribution_titles": [ + "https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/dbfca6b3-53a8-4e29-94ee-abf8a5669f60", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/dbfca6b3-53a8-4e29-94ee-abf8a5669f60/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10113/AA7768", + "keyword": [ + "ARS", + "IFSM", + "Integrated Farm System Model", + "data.gov" + ], + "last_harvested_date": "2026-10-08T22:44:32.386348", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "352b4532-793d-4075-a03f-05b778a3c43a", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png", + "name": "Department of Agriculture", + "organization_type": "Federal Government", + "slug": "usda" + }, + "parent_identifier": null, + "popularity": 5, + "publisher": "Agricultural Research Service", + "slug": "integrated-farm-system-model-ifsm", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "Integrated Farm System Model (IFSM)", + "type": "dataset" + }, + { + "_score": 11.438145, + "_sort": [ + 1791499460537, + 11.438145, + 3, + "41c01bb6-e9a7-4a96-80d8-5bcabf4d2c97" + ], + "access_level": "public", + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Rotz, C. Alan", + "hasEmail": "mailto:al.rotz@ars.usda.gov" + }, + "description": "<p>The Dairy Gas Emissions Model (DairyGEM) uses process level simulation and process related emission factors to predict ammonia, hydrogen sulfide, VOC and greenhouse gas emissions along with the carbon, energy and water footprints of dairy production systems. A process-based simulation is used to predict ammonia, hydrogen sulfide, and VOC emissions as influenced by climate and farm management. Net carbon dioxide, methane, and nitrous oxide emissions are also estimated using process simulation or process related emission factors. Environmental footprints are determined that include both primary and secondary sources. Primary sources occur during the farm production process and secondary sources are those occurring during the production of resources used including machinery, fuel, electricity, fertilizer, pesticides, and plastic. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Dairy Gas Emissions Model - home.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/northeast-area/up-pa/pswmru/docs/dairy-gas-emissions-model/\">https://www.ars.usda.gov/northeast-area/up-pa/pswmru/docs/dairy-gas-emissions-model/</a> </p><p>Provides description of the DairyGEM model with links to reference manual, download instructions, and training video module.</p></li></ul><p></p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.ars.usda.gov/northeast-area/up-pa/pswmru/docs/dairy-gas-emissions-model/", + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "mediaType": "text/html", + "title": "https://www.ars.usda.gov/northeast-area/up-pa/pswmru/docs/dairy-gas-emissions-model/" + } + ], + "identifier": "10113/AA7203", + "keyword": [ + "ARS", + "DairyGEM", + "data.gov" + ], + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "modified": "2023-11-30", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "title": "Dairy Gas Emissions Model (DairyGEM)" + }, + "description": "<p>The Dairy Gas Emissions Model (DairyGEM) uses process level simulation and process related emission factors to predict ammonia, hydrogen sulfide, VOC and greenhouse gas emissions along with the carbon, energy and water footprints of dairy production systems. A process-based simulation is used to predict ammonia, hydrogen sulfide, and VOC emissions as influenced by climate and farm management. Net carbon dioxide, methane, and nitrous oxide emissions are also estimated using process simulation or process related emission factors. Environmental footprints are determined that include both primary and secondary sources. Primary sources occur during the farm production process and secondary sources are those occurring during the production of resources used including machinery, fuel, electricity, fertilizer, pesticides, and plastic. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Dairy Gas Emissions Model - home.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/northeast-area/up-pa/pswmru/docs/dairy-gas-emissions-model/\">https://www.ars.usda.gov/northeast-area/up-pa/pswmru/docs/dairy-gas-emissions-model/</a> </p><p>Provides description of the DairyGEM model with links to reference manual, download instructions, and training video module.</p></li></ul><p></p>", + "distribution_titles": [ + "https://www.ars.usda.gov/northeast-area/up-pa/pswmru/docs/dairy-gas-emissions-model/" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/f16d82c1-32d6-47da-8710-9396d1090846", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/f16d82c1-32d6-47da-8710-9396d1090846/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10113/AA7203", + "keyword": [ + "ARS", + "DairyGEM", + "data.gov" + ], + "last_harvested_date": "2026-10-08T22:44:20.537789", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "352b4532-793d-4075-a03f-05b778a3c43a", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png", + "name": "Department of Agriculture", + "organization_type": "Federal Government", + "slug": "usda" + }, + "parent_identifier": null, + "popularity": 3, + "publisher": "Agricultural Research Service", + "slug": "dairy-gas-emissions-model-dairygem", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "Dairy Gas Emissions Model (DairyGEM)", + "type": "dataset" + }, + { + "_score": 16.121572, + "_sort": [ + 1791499458326, + 16.121572, + 10, + "1535ec97-a3df-4233-aaf8-89797ef9e884" + ], + "access_level": "public", + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Frankenberger, Jim", + "hasEmail": "mailto:jim.frankenberger@ars.usda.gov" + }, + "description": "<p>Cligen is a stochastic weather generator which produces daily estimates of precipitation, temperature, dewpoint, wind, and solar radiation for a single geographic point, using monthly parameters (means, SD's, skewness, etc.) derived from the historic measurements. Unlike other climate generators, it produces individual storm parameter estimates, including time to peak, peak intensity, and storm duration, which are required to run the WEPP and the WEPS soil erosion models. Station parameter files to run Cligen for several thousand U. S. sites are available for download from this website: also data and software to build station files for international sites. With the exception of Tmin, Tmax, and Tdew temperatures (changed in January 2004), daily estimates for each parameter are generated independently of the others. With the current random number generator, subsequent runs on the same machine made with identical inputs will produce identical results.</p>\n<p>Users of daily simulation models should consider the impacts of Cligen's characteristics on their application. Individual parameter distributions may be expected to reproduce monthly historic distributions quite well. However, if the model in question is sensitive to the daily interactions of two or more of the parameters Cligen produces, Cligen may not be the most appropriate weather generator to use. This is because for a given day, it generates solar radiation, and maximum and minimum temperatures completely independently from precipitation. Experience and common sense tell us that these parameters are NOT independent. In practice this may not be a huge issue, since it is not uncommon for models to be sensitive to one weather parameter on a daily basis, and relatively insensitive to the others, as long as their monthly trends are preserved. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Cligen.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/\">https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/</a> </p><p>Overview, source code downloads, data files, and publications.</p></li></ul><p></p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/", + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "mediaType": "text/html", + "title": "https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/" + } + ], + "identifier": "10113/AA22467", + "keyword": [ + "Agricultural Research Service", + "Australia", + "Idaho", + "Indiana", + "Oklahoma", + "USDA Forest Service", + "United States", + "Water Erosion Prediction Project", + "autumn", + "cleaning", + "climate models", + "computer software", + "computers", + "death", + "dewpoint", + "engineering", + "ganders", + "genes", + "meteorological data", + "meteorology", + "models", + "people", + "prediction", + "quality control", + "rain intensity", + "runoff", + "simulation models", + "soil erosion", + "solar radiation", + "statistical analysis", + "stochastic processes", + "storms", + "streams", + "temperature", + "wind" + ], + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "modified": "2024-02-15", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "title": "Cligen" + }, + "description": "<p>Cligen is a stochastic weather generator which produces daily estimates of precipitation, temperature, dewpoint, wind, and solar radiation for a single geographic point, using monthly parameters (means, SD's, skewness, etc.) derived from the historic measurements. Unlike other climate generators, it produces individual storm parameter estimates, including time to peak, peak intensity, and storm duration, which are required to run the WEPP and the WEPS soil erosion models. Station parameter files to run Cligen for several thousand U. S. sites are available for download from this website: also data and software to build station files for international sites. With the exception of Tmin, Tmax, and Tdew temperatures (changed in January 2004), daily estimates for each parameter are generated independently of the others. With the current random number generator, subsequent runs on the same machine made with identical inputs will produce identical results.</p>\n<p>Users of daily simulation models should consider the impacts of Cligen's characteristics on their application. Individual parameter distributions may be expected to reproduce monthly historic distributions quite well. However, if the model in question is sensitive to the daily interactions of two or more of the parameters Cligen produces, Cligen may not be the most appropriate weather generator to use. This is because for a given day, it generates solar radiation, and maximum and minimum temperatures completely independently from precipitation. Experience and common sense tell us that these parameters are NOT independent. In practice this may not be a huge issue, since it is not uncommon for models to be sensitive to one weather parameter on a daily basis, and relatively insensitive to the others, as long as their monthly trends are preserved. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Cligen.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/\">https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/</a> </p><p>Overview, source code downloads, data files, and publications.</p></li></ul><p></p>", + "distribution_titles": [ + "https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/17034f76-a283-43ad-a1dd-27e0957d237d", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/17034f76-a283-43ad-a1dd-27e0957d237d/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10113/AA22467", + "keyword": [ + "Agricultural Research Service", + "Australia", + "Idaho", + "Indiana", + "Oklahoma", + "USDA Forest Service", + "United States", + "Water Erosion Prediction Project", + "autumn", + "cleaning", + "climate models", + "computer software", + "computers", + "death", + "dewpoint", + "engineering", + "ganders", + "genes", + "meteorological data", + "meteorology", + "models", + "people", + "prediction", + "quality control", + "rain intensity", + "runoff", + "simulation models", + "soil erosion", + "solar radiation", + "statistical analysis", + "stochastic processes", + "storms", + "streams", + "temperature", + "wind" + ], + "last_harvested_date": "2026-10-08T22:44:18.326680", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "352b4532-793d-4075-a03f-05b778a3c43a", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png", + "name": "Department of Agriculture", + "organization_type": "Federal Government", + "slug": "usda" + }, + "parent_identifier": null, + "popularity": 10, + "publisher": "Agricultural Research Service", + "slug": "cligen", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "Cligen", + "type": "dataset" + }, + { + "_score": 61.613953, + "_sort": [ + 1791499457534, + 61.613953, + 1, + "3bdc31ee-ec25-49bf-ab74-b2ce189b5811" + ], + "access_level": "public", + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Blackland Research and Extension Center", + "hasEmail": "mailto:epicapex@brc.tamus.edu" + }, + "description": "<p>Environmental Policy Integrated Climate (EPIC) model is a cropping systems model that was developed to estimate soil productivity as affected by erosion as part of the Soil and Water Resources Conservation Act analysis for 1980, which revealed a significant need for improving technology for evaluating the impacts of soil erosion on soil productivity. EPIC simulates approximately eighty crops with one crop growth model using unique parameter values for each crop. It can be configured for a wide range of crop rotations and other vegetative systems, tillage systems, and other management strategies. It predicts effects of management decisions on soil, water, nutrient and pesticide movements, and their combined impact on soil loss, water quality, and crop yields for areas with homogeneous soils and management. </p>\n<p>EPIC functions on a daily time step and can simulate hundreds of years. Since the initial development, EPIC has been continually improving through the additions of algorithms to simulate water quality, nitrogen and carbon cycling, climate change, and the effects of atmospheric carbon dioxide. The processes simulated include leaf interception of solar radiation; conversion to biomass; division of biomass into roots, above ground mass, and economic yield; root growth; water use; and nutrient uptake. It can be configured for a wide range of crop rotations and other vegetative systems, tillage systems, and other management practices. The model can also assess the cost of erosion for determining optimal management strategies. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Environmental Policy Integrated Climate (EPIC) Model.</p> <p>File Name: Web Page, url: <a href=\"https://epicapex.tamu.edu/epic/\">https://epicapex.tamu.edu/epic/</a> </p><p>Web site for the EPIC model: describes capabilities, examples of applications, and download links for executables, source code, and supporting tools.</p></li></ul><p></p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://epicapex.tamu.edu/epic/", + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "mediaType": "text/html", + "title": "https://epicapex.tamu.edu/epic/" + } + ], + "identifier": "10113/AA6643", + "keyword": [ + "ARS", + "EPIC", + "Environmental Policy Integrated Climate", + "NP211", + "NP212", + "NP215", + "data.gov" + ], + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "modified": "2024-02-09", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "title": "Environmental Policy Integrated Climate (EPIC) Model" + }, + "description": "<p>Environmental Policy Integrated Climate (EPIC) model is a cropping systems model that was developed to estimate soil productivity as affected by erosion as part of the Soil and Water Resources Conservation Act analysis for 1980, which revealed a significant need for improving technology for evaluating the impacts of soil erosion on soil productivity. 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It can be configured for a wide range of crop rotations and other vegetative systems, tillage systems, and other management practices. The model can also assess the cost of erosion for determining optimal management strategies. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Environmental Policy Integrated Climate (EPIC) Model.</p> <p>File Name: Web Page, url: <a href=\"https://epicapex.tamu.edu/epic/\">https://epicapex.tamu.edu/epic/</a> </p><p>Web site for the EPIC model: describes capabilities, examples of applications, and download links for executables, source code, and supporting tools.</p></li></ul><p></p>", + "distribution_titles": [ + "https://epicapex.tamu.edu/epic/" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/093f17b1-f73b-4743-bdd9-b57f3ecc1126", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/093f17b1-f73b-4743-bdd9-b57f3ecc1126/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10113/AA6643", + "keyword": [ + "ARS", + "EPIC", + "Environmental Policy Integrated Climate", + "NP211", + "NP212", + "NP215", + "data.gov" + ], + "last_harvested_date": "2026-10-08T22:44:17.534450", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "352b4532-793d-4075-a03f-05b778a3c43a", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png", + "name": "Department of Agriculture", + "organization_type": "Federal Government", + "slug": "usda" + }, + "parent_identifier": null, + "popularity": 1, + "publisher": "Agricultural Research Service", + "slug": "environmental-policy-integrated-climate-epic-model", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "Environmental Policy Integrated Climate (EPIC) Model", + "type": "dataset" + }, + { + "_score": 6.858905, + "_sort": [ + 1791499452472, + 6.858905, + 1, + "b333eb12-111e-4a2e-8d69-62c8ce12035f" + ], + "access_level": "public", + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Derner, Justin", + "hasEmail": "mailto:Justin.Derner@ars.usda.gov" + }, + "description": "<p><strong><em> Please note: This software is no longer being updated or maintained, and is out of date. </em></strong></p>\n<p>SPUR2 DOS ver. 2.2 is a general grassland ecosystem simulation model designed to determine beef cattle performance and production by simultaneously simulating production of up to 15 plant species on 36 heterogeneous grassland sites. SPUR2 simulates grassland hydrology, nitrogen cycling, and soil organic matter on grazed ecosystems as well as rangeland production under different climatic regimes, environmental conditions, and management alternatives. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: SPUR2 download page.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=241&modecode=30-12-30-25\">https://www.ars.usda.gov/research/software/download/?softwareid=241&modecode=30-12-30-25</a> </p></li></ul><p></p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.ars.usda.gov/research/software/download/?softwareid=241&modecode=30-12-30-25", + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "mediaType": "text/html", + "title": "https://www.ars.usda.gov/research/software/download/?softwareid=241&modecode=30-12-30-25" + } + ], + "identifier": "10113/AA22587", + "keyword": [ + "Agricultural Research Service", + "Great Plains region", + "Soil and Water Assessment Tool model", + "animals", + "beef cattle", + "biomass", + "climate", + "computer software", + "databases", + "ecosystems", + "environmental factors", + "equations", + "evapotranspiration", + "forage", + "geographic information systems", + "grasslands", + "grazing", + "models", + "nitrogen cycle", + "plant communities", + "prediction", + "rainfall simulation", + "rangelands", + "runoff", + "sediments", + "simulation models", + "soil", + "soil organic matter", + "soil water", + "storms", + "vegetation", + "watersheds" + ], + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "modified": "2024-02-13", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "title": "SPUR2" + }, + "description": "<p><strong><em> Please note: This software is no longer being updated or maintained, and is out of date. </em></strong></p>\n<p>SPUR2 DOS ver. 2.2 is a general grassland ecosystem simulation model designed to determine beef cattle performance and production by simultaneously simulating production of up to 15 plant species on 36 heterogeneous grassland sites. SPUR2 simulates grassland hydrology, nitrogen cycling, and soil organic matter on grazed ecosystems as well as rangeland production under different climatic regimes, environmental conditions, and management alternatives. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: SPUR2 download page.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=241&modecode=30-12-30-25\">https://www.ars.usda.gov/research/software/download/?softwareid=241&modecode=30-12-30-25</a> </p></li></ul><p></p>", + "distribution_titles": [ + "https://www.ars.usda.gov/research/software/download/?softwareid=241&modecode=30-12-30-25" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/ea0c6257-32ed-4fb9-a623-32b0be066fc6", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/ea0c6257-32ed-4fb9-a623-32b0be066fc6/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10113/AA22587", + "keyword": [ + "Agricultural Research Service", + "Great Plains region", + "Soil and Water Assessment Tool model", + "animals", + "beef cattle", + "biomass", + "climate", + "computer software", + "databases", + "ecosystems", + "environmental factors", + "equations", + "evapotranspiration", + "forage", + "geographic information systems", + "grasslands", + "grazing", + "models", + "nitrogen cycle", + "plant communities", + "prediction", + "rainfall simulation", + "rangelands", + "runoff", + "sediments", + "simulation models", + "soil", + "soil organic matter", + "soil water", + "storms", + "vegetation", + "watersheds" + ], + "last_harvested_date": "2026-10-08T22:44:12.472185", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "352b4532-793d-4075-a03f-05b778a3c43a", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png", + "name": "Department of Agriculture", + "organization_type": "Federal Government", + "slug": "usda" + }, + "parent_identifier": null, + "popularity": 1, + "publisher": "Agricultural Research Service", + "slug": "spur2", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "SPUR2", + "type": "dataset" + }, + { + "_score": 15.223294, + "_sort": [ + 1791499440160, + 15.223294, + 3, + "1fa21924-5532-4432-a45d-b453082170f1" + ], + "access_level": "public", + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Marks, Danny", + "hasEmail": "mailto:ars.danny@gmail.com" + }, + "description": "<p>iSnobal is a physically-based distributed snowmelt model. Snowmelt is the principal source for soil moisture, ground-water re-charge, and stream-flow in mountainous regions of the western US, Canada, and other similar regions of the world. Information on the timing, magnitude, and contributing area of melt under variable or changing climate conditions is required for successful water and resource management. A coupled energy and mass-balance model iSnobal is used to simulate the development and melting of the seasonal snowcover in several mountain basins in California, Idaho, and Utah. Simulations are done over basins varying from 1 to 2500 km2 , with simulation periods varying from a few days for the smallest basin, Emerald Lake watershed in California, to multiple snow seasons for the Park City area in Utah. The model is driven by topographically corrected estimates of radiation, temperature, humidity, wind, and precipitation. Simulation results in all basins closely match independently measured snow water equivalent, snow depth, or runoff during both the development and depletion of the snowcover. Spatially distributed estimates of snow deposition and melt allow us to better understand the interaction between topographic structure, climate, and moisture availability in mountain basins of the western US. Application of topographically distributed models such as this will lead to improved water resource and watershed management. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Image Processing Workbench (IPW).</p> <p>File Name: Web Page, url: <a href=\"https://gitlab.com/ars-snow/ipw\">https://gitlab.com/ars-snow/ipw</a> </p><p>GitHub repository where the model can be accessed and downloaded.</p></li></ul><p></p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://gitlab.com/ars-snow/ipw", + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "mediaType": "text/html", + "title": "https://gitlab.com/ars-snow/ipw" + } + ], + "identifier": "10.5281/zenodo.1301290", + "keyword": [ + "ARS", + "NP211", + "data.gov" + ], + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "modified": "2024-02-09", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "title": "iSnobal" + }, + "description": "<p>iSnobal is a physically-based distributed snowmelt model. Snowmelt is the principal source for soil moisture, ground-water re-charge, and stream-flow in mountainous regions of the western US, Canada, and other similar regions of the world. Information on the timing, magnitude, and contributing area of melt under variable or changing climate conditions is required for successful water and resource management. A coupled energy and mass-balance model iSnobal is used to simulate the development and melting of the seasonal snowcover in several mountain basins in California, Idaho, and Utah. Simulations are done over basins varying from 1 to 2500 km2 , with simulation periods varying from a few days for the smallest basin, Emerald Lake watershed in California, to multiple snow seasons for the Park City area in Utah. The model is driven by topographically corrected estimates of radiation, temperature, humidity, wind, and precipitation. Simulation results in all basins closely match independently measured snow water equivalent, snow depth, or runoff during both the development and depletion of the snowcover. Spatially distributed estimates of snow deposition and melt allow us to better understand the interaction between topographic structure, climate, and moisture availability in mountain basins of the western US. Application of topographically distributed models such as this will lead to improved water resource and watershed management. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Image Processing Workbench (IPW).</p> <p>File Name: Web Page, url: <a href=\"https://gitlab.com/ars-snow/ipw\">https://gitlab.com/ars-snow/ipw</a> </p><p>GitHub repository where the model can be accessed and downloaded.</p></li></ul><p></p>", + "distribution_titles": [ + "https://gitlab.com/ars-snow/ipw" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/b1e969e4-5fe5-4e66-9b52-f5c6ad971da2", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/b1e969e4-5fe5-4e66-9b52-f5c6ad971da2/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10.5281/zenodo.1301290", + "keyword": [ + "ARS", + "NP211", + "data.gov" + ], + "last_harvested_date": "2026-10-08T22:44:00.160775", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "352b4532-793d-4075-a03f-05b778a3c43a", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png", + "name": "Department of Agriculture", + "organization_type": "Federal Government", + "slug": "usda" + }, + "parent_identifier": null, + "popularity": 3, + "publisher": "Agricultural Research Service", + "slug": "isnobal", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "iSnobal", + "type": "dataset" + }, + { + "_score": 8.905226, + "_sort": [ + 1791499438506, + 8.905226, + 4, + "d608d3bf-32f7-4eb4-a69d-a0d46f318a02" + ], + "access_level": "public", + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Bjorneberg, David", + "hasEmail": "mailto:DAVE.BJORNEBERG@USDA.GOV" + }, + "description": "<p>Effectively managing salt affected irrigated lands and judicially using irrigation water of marginal salinity quality requires understanding the interactions among many inputs. They include soil salinity, crop salt tolerances, soil physical properties, irrigation water quality, irrigation management, water table depth and quality, climatic factors and crop yield. Visualizing the simultaneous interactions among this many factors over a cropping season extends beyond the capacity of the human mind.</p>\n<p>An interactive computer program was developed to simulate the interactions among the above factors. It shows how changing one factor impacts the outcome of the other factors for a single growing season. The user selects a climate, a crop, and soil characteristics from menu lists, and then sets the water table depth and quality, irrigation (river or well) water quality and then develops an irrigation schedule. On execution, the relative yield reductions due to over irrigation, under irrigation, and salinity, water table rise or fall and surface runoff are shown numerically for the growing season. Soil water content, soil salinity, water table depth changes and rain and irrigation events during the season are also shown graphically.</p>\n<p>This is an educational tool designed to teach the concepts of salinity and irrigation management and IS NOT an irrigation scheduling program nor a management tool. Two versions have been developed, one using metric units, southern hemisphere growing seasons and Australian terminology, and a second using northern hemisphere growing seasons, with units and terminology currently used in US irrigated agriculture. An option in the US version also allows use of metric units.</p>\n<p>The SWAGMAN acronym and the SWAGMAN-Whatif program copyrights are owned by Commonwealth Scientific and Industrial Research Organization (CSIRO) of Australia. The SWAGMAN acronym is not to be used for other programs.</p>\n<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: SWAGMAN.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=207\" target=\"_blank\">https://www.ars.usda.gov/research/software/download/?softwareid=207</a> </p><p>download page</p></li></ul>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.ars.usda.gov/research/software/download/?softwareid=207", + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "mediaType": "text/html", + "title": "https://www.ars.usda.gov/research/software/download/?softwareid=207" + } + ], + "identifier": "10113/AA22541", + "keyword": [ + "Australia", + "United States", + "climatic factors", + "computer software", + "crop yield", + "educational materials", + "growing season", + "humans", + "irrigation scheduling", + "irrigation water", + "models", + "rain", + "rivers", + "runoff", + "salinity", + "soil physical properties", + "soil salinity", + "soil water", + "soil water content", + "terminology", + "water quality", + "water table" + ], + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "modified": "2023-11-30", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "title": "SWAGMAN-Whatif" + }, + "description": "<p>Effectively managing salt affected irrigated lands and judicially using irrigation water of marginal salinity quality requires understanding the interactions among many inputs. They include soil salinity, crop salt tolerances, soil physical properties, irrigation water quality, irrigation management, water table depth and quality, climatic factors and crop yield. Visualizing the simultaneous interactions among this many factors over a cropping season extends beyond the capacity of the human mind.</p>\n<p>An interactive computer program was developed to simulate the interactions among the above factors. It shows how changing one factor impacts the outcome of the other factors for a single growing season. The user selects a climate, a crop, and soil characteristics from menu lists, and then sets the water table depth and quality, irrigation (river or well) water quality and then develops an irrigation schedule. On execution, the relative yield reductions due to over irrigation, under irrigation, and salinity, water table rise or fall and surface runoff are shown numerically for the growing season. Soil water content, soil salinity, water table depth changes and rain and irrigation events during the season are also shown graphically.</p>\n<p>This is an educational tool designed to teach the concepts of salinity and irrigation management and IS NOT an irrigation scheduling program nor a management tool. Two versions have been developed, one using metric units, southern hemisphere growing seasons and Australian terminology, and a second using northern hemisphere growing seasons, with units and terminology currently used in US irrigated agriculture. An option in the US version also allows use of metric units.</p>\n<p>The SWAGMAN acronym and the SWAGMAN-Whatif program copyrights are owned by Commonwealth Scientific and Industrial Research Organization (CSIRO) of Australia. The SWAGMAN acronym is not to be used for other programs.</p>\n<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: SWAGMAN.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=207\" target=\"_blank\">https://www.ars.usda.gov/research/software/download/?softwareid=207</a> </p><p>download page</p></li></ul>", + "distribution_titles": [ + "https://www.ars.usda.gov/research/software/download/?softwareid=207" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/47eea2fc-f424-4283-9922-c1cd8b64a943", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/47eea2fc-f424-4283-9922-c1cd8b64a943/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10113/AA22541", + "keyword": [ + "Australia", + "United States", + "climatic factors", + "computer software", + "crop yield", + "educational materials", + "growing season", + "humans", + "irrigation scheduling", + "irrigation water", + "models", + "rain", + "rivers", + "runoff", + "salinity", + "soil physical properties", + "soil salinity", + "soil water", + "soil water content", + "terminology", + "water quality", + "water table" + ], + "last_harvested_date": "2026-10-08T22:43:58.506721", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "352b4532-793d-4075-a03f-05b778a3c43a", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png", + "name": "Department of Agriculture", + "organization_type": "Federal Government", + "slug": "usda" + }, + "parent_identifier": null, + "popularity": 4, + "publisher": "Agricultural Research Service", + "slug": "swagman-whatif", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "SWAGMAN-Whatif", + "type": "dataset" + }, + { + "_score": 17.503796, + "_sort": [ + 1791499428006, + 17.503796, + 1, + "4516b590-d5d0-486b-b68f-ab8152f0a3b7" + ], + "access_level": "public", + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Nearing, Mark", + "hasEmail": "mailto:mark.nearing@ars.usda.gov" + }, + "description": "<p>Global warming is expected to lead to a more vigorous hydrological cycle, including more total rainfall and more frequent high intensity rainfall events. Rainfall amounts and intensities increased on average in the United States during the 20th century and, according to climate change models, they are expected to continue to increase during the 21st century. These rainfall changes, along with expected changes in temperature, solar radiation, and atmospheric CO2 concentrations, will have significant impacts on soil erosion rates. The processes involved in the impact of climate change on soil erosion by water are complex, involving changes in rainfall amounts and intensities, number of days of precipitation, ratio of rain to snow, plant biomass production, plant residue decomposition rates, soil microbial activity, evapo-transpiration rates, and shifts in land use necessary to accommodate a new climatic regime. WEPPCAT is a web-based erosion simulation tool that allows for the assessment of changes in erosion rates as a consequence of user-defined climate change scenarios. This tool is based on the USDA-ARS Water Erosion Prediction Project (WEPP) erosion model. It has the capability of taking into account all of the erosion-affecting processes listed above.</p>\n<p>This applications has been packaged into a virtual machine. Please fill out the form below to download the application. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: WEPPCAT.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=WEPPCAT&modecode=20-22-10-00\">https://www.ars.usda.gov/research/software/download/?softwareid=WEPPCAT&modecode=20-22-10-00</a> </p><p>This applications has been packaged into a virtual machine. Please fill out the form below to download the application.</p></li></ul><p></p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.ars.usda.gov/research/software/download/?softwareid=WEPPCAT&modecode=20-22-10-00", + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "mediaType": "text/html", + "title": "https://www.ars.usda.gov/research/software/download/?softwareid=WEPPCAT&modecode=20-22-10-00" + } + ], + "identifier": "10113/AA22465", + "keyword": [ + "Agricultural Research Service", + "Internet", + "United States", + "Water Erosion Prediction Project", + "biomass production", + "carbon dioxide", + "computer software", + "evapotranspiration", + "global warming", + "hydrologic cycle", + "land use", + "microbial activity", + "models", + "phytomass", + "plant residues", + "rain", + "snow", + "soil", + "soil erosion", + "solar radiation", + "temperature" + ], + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "modified": "2023-11-30", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "title": "WEPPCAT" + }, + "description": "<p>Global warming is expected to lead to a more vigorous hydrological cycle, including more total rainfall and more frequent high intensity rainfall events. Rainfall amounts and intensities increased on average in the United States during the 20th century and, according to climate change models, they are expected to continue to increase during the 21st century. These rainfall changes, along with expected changes in temperature, solar radiation, and atmospheric CO2 concentrations, will have significant impacts on soil erosion rates. The processes involved in the impact of climate change on soil erosion by water are complex, involving changes in rainfall amounts and intensities, number of days of precipitation, ratio of rain to snow, plant biomass production, plant residue decomposition rates, soil microbial activity, evapo-transpiration rates, and shifts in land use necessary to accommodate a new climatic regime. WEPPCAT is a web-based erosion simulation tool that allows for the assessment of changes in erosion rates as a consequence of user-defined climate change scenarios. This tool is based on the USDA-ARS Water Erosion Prediction Project (WEPP) erosion model. It has the capability of taking into account all of the erosion-affecting processes listed above.</p>\n<p>This applications has been packaged into a virtual machine. Please fill out the form below to download the application. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: WEPPCAT.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=WEPPCAT&modecode=20-22-10-00\">https://www.ars.usda.gov/research/software/download/?softwareid=WEPPCAT&modecode=20-22-10-00</a> </p><p>This applications has been packaged into a virtual machine. Please fill out the form below to download the application.</p></li></ul><p></p>", + "distribution_titles": [ + "https://www.ars.usda.gov/research/software/download/?softwareid=WEPPCAT&modecode=20-22-10-00" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/c9ab5eeb-d5f4-4438-9ac9-8afa6d3cf946", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/c9ab5eeb-d5f4-4438-9ac9-8afa6d3cf946/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10113/AA22465", + "keyword": [ + "Agricultural Research Service", + "Internet", + "United States", + "Water Erosion Prediction Project", + "biomass production", + "carbon dioxide", + "computer software", + "evapotranspiration", + "global warming", + "hydrologic cycle", + "land use", + "microbial activity", + "models", + "phytomass", + "plant residues", + "rain", + "snow", + "soil", + "soil erosion", + "solar radiation", + "temperature" + ], + "last_harvested_date": "2026-10-08T22:43:48.006684", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "352b4532-793d-4075-a03f-05b778a3c43a", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png", + "name": "Department of Agriculture", + "organization_type": "Federal Government", + "slug": "usda" + }, + "parent_identifier": null, + "popularity": 1, + "publisher": "Agricultural Research Service", + "slug": "weppcat", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "WEPPCAT", + "type": "dataset" + }, + { + "_score": 56.783333, + "_sort": [ + 1791499427863, + 56.783333, + 3, + "3c9cecb5-5c85-41ed-bde1-dd886dec8c01" + ], + "access_level": "public", + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Mauget, Steve", + "hasEmail": "mailto:Steven.Mauget@ars.usda.gov" + }, + "description": "<p>West Texas Mesonet Agro-Climate Monitor shows the precipitation, temperature, weather forecast for mesonet stations in West Texas. Although dependent on rainfall and other climate factors to produce crops, West Texas crop consultants, extension agents, and agricultural producers have few tools that allow them to track the current growing season’s climate conditions and determine how current conditions compare with those of past years. The West Texas Mesonet Agro-Climate Monitor (ACM), a JavaScript web application based on daily data from Texas Tech University’s mesonet weather station network, was designed to meet this need. By displaying continuously updated information on variables such as soil temperature, cumulative growing degree days (GDD), cumulative precipitation, and first freeze dates, the ACM allows producers to monitor planting conditions, track crop development, and compare current conditions with those during the previous 10 yr’s growing seasons. In illustrating how mesonet data might be used as an operational climate data resource, the ACM might also serve as a conceptual model for other high resolution climate tools that estimate measures of current climate using continuously updated daily data sets. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: West Texas Mesonet Agro-Climate Monitor.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=484&modecode=30-96-05-00\">https://www.ars.usda.gov/research/software/download/?softwareid=484&modecode=30-96-05-00</a> </p><p>download page</p></li></ul><p></p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.ars.usda.gov/research/software/download/?softwareid=484&modecode=30-96-05-00", + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "mediaType": "text/html", + "title": "https://www.ars.usda.gov/research/software/download/?softwareid=484&modecode=30-96-05-00" + } + ], + "identifier": "10113/AA22624", + "keyword": [ + "Internet", + "Texas", + "climatic factors", + "computer software", + "consultants", + "crops", + "data collection", + "extension agents", + "growing season", + "heat sums", + "meteorological data", + "models", + "planting", + "rain", + "soil temperature", + "weather forecasting" + ], + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "modified": "2024-02-13", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "title": "West Texas Mesonet Agro-Climate Monitor" + }, + "description": "<p>West Texas Mesonet Agro-Climate Monitor shows the precipitation, temperature, weather forecast for mesonet stations in West Texas. Although dependent on rainfall and other climate factors to produce crops, West Texas crop consultants, extension agents, and agricultural producers have few tools that allow them to track the current growing season’s climate conditions and determine how current conditions compare with those of past years. The West Texas Mesonet Agro-Climate Monitor (ACM), a JavaScript web application based on daily data from Texas Tech University’s mesonet weather station network, was designed to meet this need. By displaying continuously updated information on variables such as soil temperature, cumulative growing degree days (GDD), cumulative precipitation, and first freeze dates, the ACM allows producers to monitor planting conditions, track crop development, and compare current conditions with those during the previous 10 yr’s growing seasons. In illustrating how mesonet data might be used as an operational climate data resource, the ACM might also serve as a conceptual model for other high resolution climate tools that estimate measures of current climate using continuously updated daily data sets. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: West Texas Mesonet Agro-Climate Monitor.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=484&modecode=30-96-05-00\">https://www.ars.usda.gov/research/software/download/?softwareid=484&modecode=30-96-05-00</a> </p><p>download page</p></li></ul><p></p>", + "distribution_titles": [ + "https://www.ars.usda.gov/research/software/download/?softwareid=484&modecode=30-96-05-00" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/c6d57606-c634-4de7-a8db-3aed58c094cd", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/c6d57606-c634-4de7-a8db-3aed58c094cd/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10113/AA22624", + "keyword": [ + "Internet", + "Texas", + "climatic factors", + "computer software", + "consultants", + "crops", + "data collection", + "extension agents", + "growing season", + "heat sums", + "meteorological data", + "models", + "planting", + "rain", + "soil temperature", + "weather forecasting" + ], + "last_harvested_date": "2026-10-08T22:43:47.863881", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "352b4532-793d-4075-a03f-05b778a3c43a", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png", + "name": "Department of Agriculture", + "organization_type": "Federal Government", + "slug": "usda" + }, + "parent_identifier": null, + "popularity": 3, + "publisher": "Agricultural Research Service", + "slug": "west-texas-mesonet-agro-climate-monitor", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "West Texas Mesonet Agro-Climate Monitor", + "type": "dataset" + }, + { + "_score": 9.249322, + "_sort": [ + 1791499423469, + 9.249322, + 8, + "c9bb6cf9-a45d-4d78-81c7-05f6d4a1fd96" + ], + "access_level": "public", + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Rango, Al", + "hasEmail": "mailto:al.rango@ars.usda.gov" + }, + "description": "<p>The Snowmelt-Runoff Model (WinSRM) is designed to simulate and forecast daily streamflow in mountain basins where snowmelt is a major runoff factor. The Snowmelt Runoff Model (SRM) is a simple degree-day model that requires remote sensing input in the form of basin or zonal snow cover extent. The model has been tested successfully on over 60 basins worldwide in the simulation and forecast modes. Model variables are derived from actual observations of temperature, precipitation, and snow covered area. Model parameters can either be derived from measurements or estimated by hydrological judgement taking into account the basin characteristics, physical laws, and theoretical or empirical relationships. To facilitate use of SRM, a microcomputer version of the program has been developed for IBM compatible personal computers. The program itself features user-oriented input and multiple self-help screens which allow the user to select the kind of data input employed and the output products desired. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Snowmelt Runoff Model for Windows (WinSRM).</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=7&modecode=80-42-05-10\">https://www.ars.usda.gov/research/software/download/?softwareid=7&modecode=80-42-05-10</a> </p><p>download page</p></li></ul><p></p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.ars.usda.gov/research/software/download/?softwareid=7&modecode=80-42-05-10", + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "mediaType": "text/html", + "title": "https://www.ars.usda.gov/research/software/download/?softwareid=7&modecode=80-42-05-10" + } + ], + "identifier": "10113/AA22716", + "keyword": [ + "Snowmelt Runoff Model", + "basins", + "climate", + "computer software", + "glaciers", + "heat sums", + "models", + "remote sensing", + "satellites", + "snow", + "snowmelt", + "snowpack", + "stream flow", + "temperature" + ], + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "modified": "2024-02-13", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "title": "Snowmelt Runoff Model for Windows (WinSRM)" + }, + "description": "<p>The Snowmelt-Runoff Model (WinSRM) is designed to simulate and forecast daily streamflow in mountain basins where snowmelt is a major runoff factor. The Snowmelt Runoff Model (SRM) is a simple degree-day model that requires remote sensing input in the form of basin or zonal snow cover extent. The model has been tested successfully on over 60 basins worldwide in the simulation and forecast modes. Model variables are derived from actual observations of temperature, precipitation, and snow covered area. Model parameters can either be derived from measurements or estimated by hydrological judgement taking into account the basin characteristics, physical laws, and theoretical or empirical relationships. To facilitate use of SRM, a microcomputer version of the program has been developed for IBM compatible personal computers. The program itself features user-oriented input and multiple self-help screens which allow the user to select the kind of data input employed and the output products desired. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Snowmelt Runoff Model for Windows (WinSRM).</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=7&modecode=80-42-05-10\">https://www.ars.usda.gov/research/software/download/?softwareid=7&modecode=80-42-05-10</a> </p><p>download page</p></li></ul><p></p>", + "distribution_titles": [ + "https://www.ars.usda.gov/research/software/download/?softwareid=7&modecode=80-42-05-10" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/310fe0f8-4b88-41db-bfba-a3a73c757c91", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/310fe0f8-4b88-41db-bfba-a3a73c757c91/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10113/AA22716", + "keyword": [ + "Snowmelt Runoff Model", + "basins", + "climate", + "computer software", + "glaciers", + "heat sums", + "models", + "remote sensing", + "satellites", + "snow", + "snowmelt", + "snowpack", + "stream flow", + "temperature" + ], + "last_harvested_date": "2026-10-08T22:43:43.469975", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "352b4532-793d-4075-a03f-05b778a3c43a", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png", + "name": "Department of Agriculture", + "organization_type": "Federal Government", + "slug": "usda" + }, + "parent_identifier": null, + "popularity": 8, + "publisher": "Agricultural Research Service", + "slug": "snowmelt-runoff-model-for-windows-winsrm", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "Snowmelt Runoff Model for Windows (WinSRM)", + "type": "dataset" + }, + { + "_score": 17.974842, + "_sort": [ + 1791499411847, + 17.974842, + 4, + "9d0d065a-e39e-47b8-9738-f24d10d831f9" + ], + "access_level": "public", + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Johnson, Jane", + "hasEmail": "mailto:Jane.m.johnson@usda.gov" + }, + "description": "<p>The purpose of this tool is to estimate daily maximum and minimum air temperatures for a yearly cycle at any location on the globe. Global TempSIM predicts the daily average air temperature based upon 30-yr (1961 • 1990) temperature records that were compiled and interpolated by Legates and Willmott (1990a and 1990b) with further improvements by Willmott and Matsuura (1995) (data located at <a href=\"http://climate.geog.udel.edu/~climate/\">http://climate.geog.udel.edu/~climate/</a>). </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Global TempSIM - Version 1.0.</p> <p>File Name: Web Page, url: <a href=\"https://www.ars.usda.gov/research/software/download/?softwareid=158&modecode=50-60-05-00\">https://www.ars.usda.gov/research/software/download/?softwareid=158&modecode=50-60-05-00</a> </p><p>download page</p></li></ul><p></p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.ars.usda.gov/research/software/download/?softwareid=158&modecode=50-60-05-00", + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "mediaType": "text/html", + "title": "https://www.ars.usda.gov/research/software/download/?softwareid=158&modecode=50-60-05-00" + } + ], + "identifier": "10113/AA22636", + "keyword": [ + "ARS", + "NP305", + "data.gov" + ], + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "modified": "2024-02-13", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "title": "Global TempSIM - Version 1.0" + }, + "description": "<p>The purpose of this tool is to estimate daily maximum and minimum air temperatures for a yearly cycle at any location on the globe. 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32.583588, + 32.580765, 7, "45da5901-9cf1-45be-95c2-78d11ddb5efe" ], @@ -136,10 +2693,10 @@ "type": "dataset" }, { - "_score": 32.644207, + "_score": 32.65436, "_sort": [ 1791435266611, - 32.644207, + 32.65436, 0, "76a59cf2-7564-45b1-bd99-62fc3a0ac451" ], @@ -276,10 +2833,10 @@ "type": "dataset" }, { - "_score": 21.855537, + "_score": 21.851154, "_sort": [ 1791435179403, - 21.855537, + 21.851154, 0, "90d4d761-77fc-4ee1-a191-43f0a9d716b9" ], @@ -419,10 +2976,10 @@ "type": "dataset" }, { - "_score": 46.497044, + "_score": 46.508774, "_sort": [ 1791435061102, - 46.497044, + 46.508774, 0, "bef095f6-7d75-4ce1-b26b-4431344f8698" ], @@ -562,10 +3119,10 @@ "type": "dataset" }, { - "_score": 26.746399, + "_score": 26.739506, "_sort": [ 1791434624294, - 26.746399, + 26.739506, 0, "205e4fa4-44fc-4ee6-98ad-cf9ac24ebe3d" ], @@ -711,10 +3268,10 @@ "type": "dataset" }, { - "_score": 22.270552, + "_score": 22.273394, "_sort": [ 1791434147414, - 22.270552, + 22.273394, 0, "51c8ca10-5d74-4f2a-bd4a-26e0aa7f9474" ], @@ -860,10 +3417,10 @@ "type": "dataset" }, { - 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9.273087, + 9.269302, 0, "e356e550-c63e-4ff9-a61a-8b00a6ee2c33" ], @@ -1713,10 +4270,10 @@ "type": "dataset" }, { - "_score": 9.281073, + "_score": 9.281995, "_sort": [ 1791412714712, - 9.281073, + 9.281995, 0, "cfeaa1eb-50a8-4ba4-9c34-3313f07a218e" ], @@ -1849,10 +4406,10 @@ "type": "dataset" }, { - "_score": 9.2470455, + "_score": 9.249322, "_sort": [ 1791412714519, - 9.2470455, + 9.249322, 0, "d187fbda-5e45-4c44-b19a-ec99f17daf6c" ], @@ -1985,10 +4542,10 @@ "type": "dataset" }, { - "_score": 9.273087, + "_score": 9.269302, "_sort": [ 1791412714260, - 9.273087, + 9.269302, 0, "b140f2de-52bf-4c28-8ade-d22e22f1caae" ], @@ -2121,10 +4678,10 @@ "type": "dataset" }, { - "_score": 9.2470455, + "_score": 9.249322, "_sort": [ 1791412693821, - 9.2470455, + 9.249322, 0, "6065ef08-a7db-4e07-99de-59845d2621c3" ], @@ -2257,10 +4814,10 @@ "type": "dataset" }, { - "_score": 9.273087, + "_score": 9.269302, "_sort": [ 1791412693636, - 9.273087, + 9.269302, 0, "69495bba-122a-4074-92dc-922d5be5e30c" ], @@ -2393,10 +4950,10 @@ "type": "dataset" }, { - 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9.281073, + 9.281995, 0, "55c5a20a-d3cc-49ec-b833-4ee5d9e4c79d" ], @@ -3209,10 +5766,10 @@ "type": "dataset" }, { - "_score": 9.281073, + "_score": 9.281995, "_sort": [ 1791412690774, - 9.281073, + 9.281995, 0, "5a016aff-df5a-4779-a2eb-6135632446a6" ], @@ -3345,10 +5902,10 @@ "type": "dataset" }, { - "_score": 9.309758, + "_score": 9.307538, "_sort": [ 1791412690588, - 9.309758, + 9.307538, 0, "d9e482c5-9de5-4696-a7d0-548eb4d40d3e" ], @@ -3481,10 +6038,10 @@ "type": "dataset" }, { - "_score": 9.23723, + "_score": 9.240406, "_sort": [ 1791412690401, - 9.23723, + 9.240406, 0, "17c9b55a-5f5f-4ca1-8b6c-68e156d464f1" ], @@ -3617,10 +6174,10 @@ "type": "dataset" }, { - "_score": 35.29431, + "_score": 35.272057, "_sort": [ 1791408974596, - 35.29431, + 35.272057, 3, "d796eb29-f37d-4d9d-88e6-bf2072b34dd1" ], @@ -3783,10 +6340,10 @@ "type": "dataset" }, { - "_score": 29.075132, + "_score": 29.068167, "_sort": [ 1791408964672, - 29.075132, + 29.068167, 3, "46cfaed2-82eb-426a-a34f-41af47dba1e9" ], @@ -3947,10 +6504,10 @@ "type": "dataset" }, { - 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The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. Steps for calculating the index can be found in in the \"An Assessment of San Francisco’s Vulnerability to Flooding & Extreme Storms\" located at <a href=\"https://www.sf.gov/sites/default/files/2023-05/FloodVulnerabilityReport_v5.pdf.pdf\">here</a>.\n\nData dictionary can be found in the attachments section of the metadata.", - "distribution_titles": [], - "harvest_record": "https://catalog.data.gov/harvest_record/7e036fdb-ad13-4b56-8056-67cb7446c9af", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/7e036fdb-ad13-4b56-8056-67cb7446c9af/raw", - "has_download": true, - "has_spatial": false, - "identifier": "https://data.sf.gov/api/views/cne3-h93g", - "keyword": [ - "@sfclimatehealth.org", - "climate change", - "community resiliency", - "dph", - "flood", - "health assessment", - "health impacts", - "public health", - "san francisco climate and health program", - "sea level rise", - "sfclimatehealth.org" - ], - "last_harvested_date": "2026-10-07T18:57:33.705316", - "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": 3, - "publisher": "data.sf.gov", - "slug": "san-francisco-flood-health-vulnerability", - "spatial_centroid": null, - "spatial_shape": null, - "theme": [ - "Health and Social Services" - ], - "title": "San Francisco Flood Health Vulnerability", - "type": "dataset" - }, - { - "_score": 9.905443, - "_sort": [ - 1791399372323, - 9.905443, - 1, - "3df876e7-53ee-4eab-805a-0d59702a1af2" - ], - "access_level": "public", - "dcat": { - "@type": "dcat:Dataset", - "accessLevel": "public", - "contactPoint": { - "@type": "vcard:Contact", - "fn": "NY Open Data", - "hasEmail": "mailto:no-reply@data.ny.gov" - }, - "description": "The Division of Building Standards and Codes (BSC) administers the mandatory statewide Uniform Fire Prevention and Building Code (Uniform Code) and State Energy Conservation Construction Code (Energy Code). 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The Uniform Code is designed to cover new construction, building rehabilitation, fire safety, and housing maintenance.\n\nThis dataset provides annually reported data on administration and enforcement of the State Energy Conservation Construction Code in each local government jurisdiction pursuant to 19 NYCRR 1203.", - "distribution": [ - { - "@type": "dcat:Distribution", - "describedBy": "https://data.ny.gov/api/views/vfqs-vuqv/columns.json", - "describedByType": "application/json", - "downloadURL": "https://data.ny.gov/api/v3/views/vfqs-vuqv/query.json?accessType=DOWNLOAD", - "mediaType": "application/json" - }, - { - "@type": "dcat:Distribution", - "describedBy": "https://data.ny.gov/api/views/vfqs-vuqv/columns.xml", - "describedByType": "application/xml", - "downloadURL": "https://data.ny.gov/api/v3/views/vfqs-vuqv/query.xml?accessType=DOWNLOAD", - "mediaType": "application/xml" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://data.ny.gov/api/v3/views/vfqs-vuqv/export.csv?accessType=DOWNLOAD", - "mediaType": "text/csv" - } - ], - "identifier": "https://data.ny.gov/api/views/vfqs-vuqv", - "issued": "2020-10-27", - "keyword": [ - "1203", - "bsi", - "building permits", - "ceo", - "climate", - "code", - "code enforcement", - "codebuilding", - "conservation", - "economy", - "efficiency", - "energy" - ], - "landingPage": "https://data.ny.gov/d/vfqs-vuqv", - "modified": "2026-10-01", - "publisher": { - "@type": "org:Organization", - "name": "data.ny.gov" - }, - "theme": [ - "Government & Finance" - ], - "title": "Energy Code Annual Report Submissions: Beginning 2007" - }, - "description": "The Division of Building Standards and Codes (BSC) administers the mandatory statewide Uniform Fire Prevention and Building Code (Uniform Code) and State Energy Conservation Construction Code (Energy Code). The Division provides technical assistance, administers variances, delivers educational courses, oversees the enforcement practices of local governments and serves as secretariat to the State Fire Prevention and Building Code Council. The Division program was created by Chapter 707 of the Laws of 1981. The New York Legislature enacted Article 18 of the Executive Law, directing the formulation of a Uniform Fire Prevention and Building Code (Uniform Code). The Uniform Code is designed to cover new construction, building rehabilitation, fire safety, and housing maintenance.\n\nThis dataset provides annually reported data on administration and enforcement of the State Energy Conservation Construction Code in each local government jurisdiction pursuant to 19 NYCRR 1203.", - "distribution_titles": [], - "harvest_record": "https://catalog.data.gov/harvest_record/036884ba-950a-474b-9ec4-7037ae2c6ebf", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/036884ba-950a-474b-9ec4-7037ae2c6ebf/raw", - "has_download": true, - "has_spatial": false, - "identifier": "https://data.ny.gov/api/views/vfqs-vuqv", - "keyword": [ - "1203", - "bsi", - "building permits", - "ceo", - "climate", - "code", - "code enforcement", - "codebuilding", - "conservation", - "economy", - "efficiency", - "energy" - ], - "last_harvested_date": "2026-10-07T18:56:12.323279", - "organization": { - "aliases": [ - "ny" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "dde3fc99-e074-41cf-a843-14aa9c777889", - "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/state_NY.png", - "name": "State of New York", - "organization_type": "State Government", - "slug": "new-york" - }, - "parent_identifier": null, - "popularity": 1, - "publisher": "data.ny.gov", - "slug": "energy-code-annual-report-submissions-beginning-2007", - "spatial_centroid": null, - "spatial_shape": null, - "theme": [ - "Government & Finance" - ], - "title": "Energy Code Annual Report Submissions: Beginning 2007", - "type": "dataset" - }, - { - "_score": 11.489391, - "_sort": [ - 1791399364280, - 11.489391, - 4, - "a5fbbe1f-4479-45ca-9837-2c8bee90dead" - ], - "access_level": "public", - "dcat": { - "@type": "dcat:Dataset", - "accessLevel": "public", - "contactPoint": { - "@type": "vcard:Contact", - "fn": "NY Open Data", - "hasEmail": "mailto:no-reply@data.ny.gov" - }, - "description": "This dataset identifies areas throughout the State that meet the interim criteria identified for a disadvantaged community as defined by New York State. It contains the 4,145 Census block groups that make up the current interim Disadvantaged Communities (DAC) approach. NYSERDA currently hosts a map on its website showing the interim DAC areas with an address look-up function that allows users to enter an address to determine if the address falls inside or outside an interim DAC: https://www.nyserda.ny.gov/ny/disadvantaged-communities\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.", - "distribution": [ - { - "@type": "dcat:Distribution", - "describedBy": "https://data.ny.gov/api/views/t6wd-tdrv/columns.json", - "describedByType": "application/json", - "downloadURL": "https://data.ny.gov/api/v3/views/t6wd-tdrv/query.json?accessType=DOWNLOAD", - "mediaType": "application/json" - }, - { - "@type": "dcat:Distribution", - "describedBy": "https://data.ny.gov/api/views/t6wd-tdrv/columns.xml", - "describedByType": "application/xml", - "downloadURL": "https://data.ny.gov/api/v3/views/t6wd-tdrv/query.xml?accessType=DOWNLOAD", - "mediaType": "application/xml" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://data.ny.gov/api/v3/views/t6wd-tdrv/export.csv?accessType=DOWNLOAD", - "mediaType": "text/csv" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://data.ny.gov/api/v3/views/t6wd-tdrv/export.kml?accessType=DOWNLOAD", - "mediaType": "application/vnd.google-earth.kml+xml" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://data.ny.gov/api/v3/views/t6wd-tdrv/export.kmz?accessType=DOWNLOAD", - "mediaType": "application/vnd.google-earth.kmz" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://data.ny.gov/api/v3/views/t6wd-tdrv/query.geojson?accessType=DOWNLOAD", - "mediaType": "application/geo+json" - } - ], - "identifier": "https://data.ny.gov/api/views/t6wd-tdrv", - "issued": "2021-01-22", - "keyword": [ - "cjwg", - "clcpa", - "climate justice working group", - "climate leadership and community protection act", - "dac", - "disadvantaged communities", - "ej", - "environmental justice", - "lmi", - "low to moderate income" - ], - "landingPage": "https://data.ny.gov/d/t6wd-tdrv", - "modified": "2026-10-01", - "publisher": { - "@type": "org:Organization", - "name": "data.ny.gov" - }, - "theme": [ - "Energy & Environment" - ], - "title": "Interim Disadvantaged Communities (DAC): 2020" - }, - "description": "This dataset identifies areas throughout the State that meet the interim criteria identified for a disadvantaged community as defined by New York State. It contains the 4,145 Census block groups that make up the current interim Disadvantaged Communities (DAC) approach. NYSERDA currently hosts a map on its website showing the interim DAC areas with an address look-up function that allows users to enter an address to determine if the address falls inside or outside an interim DAC: https://www.nyserda.ny.gov/ny/disadvantaged-communities\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.", - "distribution_titles": [], - "harvest_record": "https://catalog.data.gov/harvest_record/4ad7471f-f0b3-4770-97e3-ff3f175493b8", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/4ad7471f-f0b3-4770-97e3-ff3f175493b8/raw", - "has_download": true, - "has_spatial": false, - "identifier": "https://data.ny.gov/api/views/t6wd-tdrv", - "keyword": [ - "cjwg", - "clcpa", - "climate justice working group", - "climate leadership and community protection act", - "dac", - "disadvantaged communities", - "ej", - "environmental justice", - "lmi", - "low to moderate income" - ], - "last_harvested_date": "2026-10-07T18:56:04.280761", - "organization": { - "aliases": [ - "ny" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "dde3fc99-e074-41cf-a843-14aa9c777889", - "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/state_NY.png", - "name": "State of New York", - "organization_type": "State Government", - "slug": "new-york" - }, - "parent_identifier": null, - "popularity": 4, - "publisher": "data.ny.gov", - "slug": "interim-disadvantaged-communities-dac-2020", - "spatial_centroid": null, - "spatial_shape": null, - "theme": [ - "Energy & Environment" - ], - "title": "Interim Disadvantaged Communities (DAC): 2020", - "type": "dataset" - }, - { - "_score": 79.27327, - "_sort": [ - 1791399351638, - 79.27327, - 32, - "1707633b-78a4-449c-91f3-b74b0cf3c0cb" - ], - "access_level": "public", - "dcat": { - "@type": "dcat:Dataset", - "accessLevel": "public", - "contactPoint": { - "@type": "vcard:Contact", - "fn": "NY Open Data", - "hasEmail": "mailto:no-reply@data.ny.gov" - }, - "description": "The preferred citation when using this dataset is: Stevens, A., & Lamie, C., Eds. (2024). New York State Climate Impacts Assessment: Understanding and preparing for our changing climate. The New York State Climate Impacts Assessment is an investigation into how climate change will affect New York State’s communities, ecosystems, and economy. The data and information presented will help New Yorkers plan and prepare for the impacts of climate change. The assessment also strives to show how addressing climate change provides opportunities to enhance equity and reduce the vulnerability of those most at risk.\n\nAs part of the assessment, Columbia University developed climate change projections for temperature and precipitation, extreme events, degree days, and sea level rise, downscaled to 12 regions of New York State. This dataset includes those projections of future climate conditions in New York State, for the 2030s through 2100.\n\nFor more information on these projections or to read the full NYS Climate Impacts Assessment, visit the assessment website at https://nysclimateimpacts.org/. \n\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. 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To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.", - "distribution_titles": [], - "harvest_record": "https://catalog.data.gov/harvest_record/df03b3bb-c536-4281-b98b-2cc30f433357", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/df03b3bb-c536-4281-b98b-2cc30f433357/raw", - "has_download": true, - "has_spatial": false, - "identifier": "https://data.ny.gov/api/views/necg-zeuh", - "keyword": [ - "climate change", - "climate change assessment", - "climate impacts assessment", - "climate projections", - "degree days", - "extreme events", - "extreme heat", - "extreme precipitation", - "precipitation", - "sea level", - "sea level rise", - "temperature" - ], - "last_harvested_date": "2026-10-07T18:55:51.638890", - "organization": { - "aliases": [ - "ny" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "dde3fc99-e074-41cf-a843-14aa9c777889", - "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/state_NY.png", - "name": "State of New York", - "organization_type": "State Government", - "slug": "new-york" - }, - "parent_identifier": null, - "popularity": 32, - "publisher": "data.ny.gov", - "slug": "nys-climate-impacts-assessment-climate-change-projections", - "spatial_centroid": null, - "spatial_shape": null, - "theme": [ - "Energy & Environment" - ], - "title": "NYS Climate Impacts Assessment: Climate Change Projections", - "type": "dataset" - }, - { - "_score": 11.716303, - "_sort": [ - 1791399338812, - 11.716303, - 7, - "a10e5023-e04a-489e-bb3e-1a02043b2958" - ], - "access_level": "public", - "dcat": { - "@type": "dcat:Dataset", - "accessLevel": "public", - "contactPoint": { - "@type": "vcard:Contact", - "fn": "NY Open Data", - "hasEmail": "mailto:no-reply@data.ny.gov" - }, - "description": "Energy storage is critical to New York’s clean energy future. 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Please see https://www.nyserda.ny.gov/All-Programs/Energy-Storage-Program.\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. 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As renewable power sources like wind and solar provide a larger portion of New York’s electricity, storage will allow clean energy to be available when and where it is most needed. The 2019 Climate Act set a statewide goal of 3,000 MW of Energy Storage by 2030, further increased to 6,000 MW of Energy Storage by 2030 by Governor Kathy Hochul. \n\nThis dataset tracks progress towards these statewide goals by compiling data on installed energy storage projects. Projects that received funding support from NYSERDA, as well as unincentivized projects, are included in this dataset\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help develop energy storage projects. Please see https://www.nyserda.ny.gov/All-Programs/Energy-Storage-Program.\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. 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PLEASE READ DISCLAIMER BEFORE USING DATA. This dataset backcasts estimated modeled savings for a subset of 2007-2012 completed projects in the Home Performance with ENERGY STAR® Program against normalized savings calculated by an open source energy efficiency meter available at https://www.openee.io/. Open source code uses utility-grade metered consumption to weather-normalize the pre- and post-consumption data using standard methods with no discretionary independent variables. The open source energy efficiency meter allows private companies, utilities, and regulators to calculate energy savings from energy efficiency retrofits with increased confidence and replicability of results. This dataset is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector, and to help inform stakeholders interested in pay for performance programs, where providers are paid for realizing measurable weather-normalized results. To download the open source code, please visit the website at https://github.com/openeemeter/eemeter/releases\n\nD I S C L A I M E R: \nNormalized Savings using open source OEE meter. Several data elements, including, Evaluated Annual Elecric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), and Post-retrofit Usage Gas (MMBtu) are direct outputs from the open source OEE meter.\n\nHome Performance with ENERGY STAR® Estimated Savings. Several data elements, including, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and Estimated First Year Energy Savings represent contractor-reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the Home Performance with ENERGY STAR impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf. \n\nThis dataset includes the following data points for a subset of projects completed in 2007-2012: Contractor ID, Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units, Year Home Built, Total Project Cost, Contractor Incentive, Total Incentives, Amount Financed through Program, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, Estimated First Year Energy Savings, Evaluated Annual Electric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), Post-retrofit Usage Gas (MMBtu), Central Hudson, Consolidated Edison, LIPA, National Grid, National Fuel Gas, New York State Electric and Gas, Orange and Rockland, Rochester Gas and Electric.\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.", - "distribution": [ - { - "@type": "dcat:Distribution", - "describedBy": "https://data.ny.gov/api/views/5vqm-4rpf/columns.json", - "describedByType": "application/json", - "downloadURL": "https://data.ny.gov/api/v3/views/5vqm-4rpf/query.json?accessType=DOWNLOAD", - "mediaType": "application/json" - }, - { - "@type": "dcat:Distribution", - "describedBy": "https://data.ny.gov/api/views/5vqm-4rpf/columns.xml", - "describedByType": "application/xml", - "downloadURL": "https://data.ny.gov/api/v3/views/5vqm-4rpf/query.xml?accessType=DOWNLOAD", - "mediaType": "application/xml" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://data.ny.gov/api/v3/views/5vqm-4rpf/export.csv?accessType=DOWNLOAD", - "mediaType": "text/csv" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://data.ny.gov/api/v3/views/5vqm-4rpf/export.kml?accessType=DOWNLOAD", - "mediaType": "application/vnd.google-earth.kml+xml" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://data.ny.gov/api/v3/views/5vqm-4rpf/export.kmz?accessType=DOWNLOAD", - "mediaType": "application/vnd.google-earth.kmz" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://data.ny.gov/api/v3/views/5vqm-4rpf/query.geojson?accessType=DOWNLOAD", - "mediaType": "application/geo+json" - } - ], - "identifier": "https://data.ny.gov/api/views/5vqm-4rpf", - "issued": "2019-02-12", - "keyword": [ - "baseline", - "consumption", - "energy efficiency", - "energy savings", - "evaluated", - "home performance with energy star", - "hpwes", - "kwh", - "meter", - "mmbtu", - "p4p", - "pay for performance", - "residential", - "savings measurement", - "usage", - "weather normalized" - ], - "landingPage": "https://data.ny.gov/d/5vqm-4rpf", - "modified": "2026-10-01", - "publisher": { - "@type": "org:Organization", - "name": "data.ny.gov" - }, - "theme": [ - "Energy & Environment" - ], - "title": "Residential Existing Homes (One to Four Units) Energy Efficiency Meter Evaluated Project Data: 2007 – 2012" - }, - "description": "IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. This dataset backcasts estimated modeled savings for a subset of 2007-2012 completed projects in the Home Performance with ENERGY STAR® Program against normalized savings calculated by an open source energy efficiency meter available at https://www.openee.io/. Open source code uses utility-grade metered consumption to weather-normalize the pre- and post-consumption data using standard methods with no discretionary independent variables. The open source energy efficiency meter allows private companies, utilities, and regulators to calculate energy savings from energy efficiency retrofits with increased confidence and replicability of results. This dataset is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector, and to help inform stakeholders interested in pay for performance programs, where providers are paid for realizing measurable weather-normalized results. To download the open source code, please visit the website at https://github.com/openeemeter/eemeter/releases\n\nD I S C L A I M E R: \nNormalized Savings using open source OEE meter. Several data elements, including, Evaluated Annual Elecric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), and Post-retrofit Usage Gas (MMBtu) are direct outputs from the open source OEE meter.\n\nHome Performance with ENERGY STAR® Estimated Savings. Several data elements, including, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and Estimated First Year Energy Savings represent contractor-reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the Home Performance with ENERGY STAR impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf. \n\nThis dataset includes the following data points for a subset of projects completed in 2007-2012: Contractor ID, Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units, Year Home Built, Total Project Cost, Contractor Incentive, Total Incentives, Amount Financed through Program, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, Estimated First Year Energy Savings, Evaluated Annual Electric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), Post-retrofit Usage Gas (MMBtu), Central Hudson, Consolidated Edison, LIPA, National Grid, National Fuel Gas, New York State Electric and Gas, Orange and Rockland, Rochester Gas and Electric.\n\nThe New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.", - "distribution_titles": [], - "harvest_record": "https://catalog.data.gov/harvest_record/7bc3299b-a486-450a-bb54-26da1f25b3e3", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/7bc3299b-a486-450a-bb54-26da1f25b3e3/raw", - "has_download": true, - "has_spatial": false, - "identifier": "https://data.ny.gov/api/views/5vqm-4rpf", - "keyword": [ - "baseline", - "consumption", - "energy efficiency", - "energy savings", - "evaluated", - "home performance with energy star", - "hpwes", - "kwh", - "meter", - "mmbtu", - "p4p", - "pay for performance", - "residential", - "savings measurement", - "usage", - "weather normalized" - ], - "last_harvested_date": "2026-10-07T18:55:03.686747", - "organization": { - "aliases": [ - "ny" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "dde3fc99-e074-41cf-a843-14aa9c777889", - "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/state_NY.png", - "name": "State of New York", - "organization_type": "State Government", - "slug": "new-york" - }, - "parent_identifier": null, - "popularity": 1, - "publisher": "data.ny.gov", - "slug": "residential-existing-homes-one-to-four-units-energy-efficiency-meter-evaluated-p-2007-2012", - "spatial_centroid": null, - "spatial_shape": null, - "theme": [ - "Energy & Environment" - ], - "title": "Residential Existing Homes (One to Four Units) Energy Efficiency Meter Evaluated Project Data: 2007 – 2012", - "type": "dataset" - }, - { - "_score": 8.552238, - "_sort": [ - 1791335038027, - 8.552238, - 1, - "e3db3d13-7407-4fe1-8ba0-18aaf07daf7f" - ], - "access_level": "public", - "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\nPseudo-Invariant Calibration Sites (PICS):\nMauritania 1 is one of six CEOS reference Pseudo-Invariant Calibration Sites (PICS) that are CEOS Reference Test Sites. Besides the nominally good site characteristics (temporal stability, uniformity, homogeneity, etc.), these six PICS were selected by also taking into account their heritage and the large number of datasets from multiple instruments that already existed in the EO archives and the long history of characterization performed over these sites. The PICS have high reflectance and are usually made up of sand dunes with climatologically low aerosol loading and practically no vegetation. 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Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- bias_sst — Sea Surface Temperature bias (°C)\n- flags_sst — Product-specific flags, Sea Surface Temperature (integer bitmask)\n- l2_flags — Level-2 Processing Flags (integer bitmask; see bit definitions)\n- qual_sst — Quality levels for Sea Surface Temperature (integer quality flag)\n- sst — Sea Surface Temperature (°C)\n- sstref — Sea Surface Temperature reference (°C)\n- stdv_sst — Sea Surface Temperature standard deviation (°C)", - "distribution": [ - { - "@type": "dcat:Distribution", - "conformsTo": "http://www.isotc211.org/2005/gmi", - "description": "The metadata's original source.", - "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3166805774-OB_DAAC.iso19115", - "format": "ISO", - "mediaType": "text/xml", - "title": "Original Metadata" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://nasa.github.io/oceandata-notebooks/", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://oceancolor.gsfc.nasa.gov/data/reprocessing/", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://oceancolor.gsfc.nasa.gov/files/atbd/legacy/atbd-obdaac-long-wave-sea-surface-temperature.pdf", - "format": "PDF", - "mediaType": "application/pdf" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://oceancolor.gsfc.nasa.gov/files/obdaac-ancillary-data-sources.pdf", - "format": "PDF", - "mediaType": "application/pdf" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-2/NOAA20-VIIRS/", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://search.earthdata.nasa.gov/search?q=%2522VIIRSJ1_L2_SST%2522", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://www.earthdata.nasa.gov/data/instruments/VIIRS", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://www.earthdata.nasa.gov/data/platforms/space-based-platforms/NOAA-20", - "format": "BIN", - "mediaType": "application/octet-stream" - } - ], - "identifier": "/SDE/CMR_API/|C3166805774-OB_DAAC", - "keyword": [ - "earth-science-ocean-temperature-oceans-sea-surface-temperature" - ], - "license": "https://www.usa.gov/government-works", - "modified": "2026-09-28", - "programCode": [ - "026:000" - ], - "publisher": { - "@type": "org:Organization", - "name": "NASA/GSFC/SED/ESD/GCDC/OB.DAAC;OBPG" - }, - "spatial": "[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"SouthBoundingCoordinate\": -90, \"EastBoundingCoordinate\": 180, \"WestBoundingCoordinate\": -180}]]", - "temporal": "2017-11-29/2026-09-21", - "theme": [ - "Earth Science" - ], - "title": "NOAA-20 VIIRS Level-2 Regional 11µm Daytime Sea Surface Temperature (SST) Data, version 2024.0" - }, - "description": "SST provides global sea surface temperature derived primarily from thermal infrared observations during daytime. 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Note that IR-based SST is sensitive to clouds, aerosols, and diurnal warming; for minimum diurnal effects, pair with NSST (nighttime SST) and refer to per-file flags before analysis.\n\n Geophysical variables in this suite include:\n- bias_sst — Sea Surface Temperature bias (°C)\n- flags_sst — Product-specific flags, Sea Surface Temperature (integer bitmask)\n- l2_flags — Level-2 Processing Flags (integer bitmask; see bit definitions)\n- qual_sst — Quality levels for Sea Surface Temperature (integer quality flag)\n- sst — Sea Surface Temperature (°C)\n- sstref — Sea Surface Temperature reference (°C)\n- stdv_sst — Sea Surface Temperature standard deviation (°C)", - "distribution_titles": [ - "Original Metadata" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/1965e5a3-e5d6-4454-9726-224244ae051b", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/1965e5a3-e5d6-4454-9726-224244ae051b/raw", - "has_download": true, - "has_spatial": true, - "identifier": "/SDE/CMR_API/|C3166805774-OB_DAAC", - "k + "H