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 2026-10-01T00:20:00+00:00 |
| Content type | application/json |
| Current object |
3d17cc7eadada48e087e9e56974a92cbe1bed5e408d9d5b8942ac2c1fe833eb2
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metadata
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| Previous object |
9777715ece32ed1b918bbbf3c1758dd2b809ff6f1c88f643f481adff0c85179d
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metadata
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What changed derived
This diff is not evidence. It was produced by
civic-memory.diff_engine 1.1.0 at
2026-10-01T00:20:00+00:00 by normalizing the two archived objects above. The
objects are authoritative; this reading of them can be regenerated or deleted
without loss. 1562 line(s) added, 1819 line(s) removed.
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The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573<div><br /></div><div><a href='https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Great+Basin+Montane' target='_blank' rel='nofollow ugc noopener noreferrer'>Metadata and Downloads</a><br /></div>", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_GreatBasinMountainRangesWatersheds_01/MapServer/1", + "format": "ArcGIS GeoServices REST API", + "mediaType": "application/json", + "title": "ArcGIS GeoService" + }, + { + "@type": "dcat:Distribution", + "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/0eebaa75bfe342d5a4f7437ddf0691bd/csv?layers=1", + "format": "CSV", + "mediaType": "text/csv", + "title": "CSV" + }, + { + "@type": "dcat:Distribution", + "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/0eebaa75bfe342d5a4f7437ddf0691bd/geojson?layers=1", + "format": "GeoJSON", + "mediaType": "application/vnd.geo+json", + "title": "GeoJSON" + }, + { + "@type": "dcat:Distribution", + "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/0eebaa75bfe342d5a4f7437ddf0691bd/kml?layers=1", + "format": "KML", + "mediaType": "application/vnd.google-earth.kml+xml", + "title": "KML" + }, + { + "@type": "dcat:Distribution", + "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/0eebaa75bfe342d5a4f7437ddf0691bd/shapefile?layers=1", + "format": "ZIP", + "mediaType": "application/zip", + "title": "Shapefile" + }, + { + "@type": "dcat:Distribution", + "accessURL": "https://data-usfs.hub.arcgis.com/datasets/usfs::great-basin-montane-watersheds-pour-points-feature-layer", + "format": "Web Page", + "mediaType": "text/html", + "title": "ArcGIS Hub Dataset" + }, + { + "@type": "dcat:Distribution", + "accessURL": "https://www.arcgis.com/sharing/rest/content/items/0eebaa75bfe342d5a4f7437ddf0691bd/info/metadata/metadata.xml?format=iso19139", + "conformsTo": "https://www.isotc211.org/2005/gmi", + "mediaType": "text/xml", + "title": "ISO-19139 metadata" + } + ], + "identifier": "https://www.arcgis.com/home/item.html?id=0eebaa75bfe342d5a4f7437ddf0691bd&sublayer=1", + "issued": "2022-11-03", + "keyword": [ + "Great Basin", + "Great Basin watershed characteristics", + "Great Basin watershed database", + "Open Data", + "climate", + "ecosystem resista", + "fire", + "geomorphology", + "geoscientificInformation", + "inlandWaters", + "meadows", + "mountain range delineation", + "riparian", + "species", + "watershed delineation" + ], + "landingPage": "https://data-usfs.hub.arcgis.com/datasets/usfs::great-basin-montane-watersheds-pour-points-feature-layer", + "license": "https://creativecommons.org/licenses/by/4.0/", + "modified": "2022-11-21", + "programCode": [ + "005:059" + ], + "progressCode": "onGoing", + "publisher": { + "name": "U.S. Forest Service", + "source": "U.S. Forest Service" + }, + "spatial": "-120.4120,37.6856,-111.5769,43.3975", + "theme": [ + "geospatial" + ], + "title": "Great Basin Montane Watersheds - Pour Points (Feature Layer)" + }, + "description": "Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573<div><br /></div><div><a href='https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Great+Basin+Montane' target='_blank' rel='nofollow ugc noopener noreferrer'>Metadata and Downloads</a><br /></div>", + "distribution_titles": [ + "ArcGIS GeoService", + "CSV", + "GeoJSON", + "KML", + "Shapefile", + "ArcGIS Hub Dataset", + "ISO-19139 metadata" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/51ff1905-ab85-427a-84a6-19b773ebe650", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/51ff1905-ab85-427a-84a6-19b773ebe650/raw", + "has_download": false, + "has_spatial": true, + "identifier": "https://www.arcgis.com/home/item.html?id=0eebaa75bfe342d5a4f7437ddf0691bd&sublayer=1", + "keyword": [ + "Great Basin", + "Great Basin watershed characteristics", + "Great Basin watershed database", + "Open Data", + "climate", + "ecosystem resista", + "fire", + "geomorphology", + "geoscientificInformation", + "inlandWaters", + "meadows", + "mountain range delineation", + "riparian", + "species", + "watershed delineation" + ], + "last_harvested_date": "2026-09-30T21:32:56.042921", + "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": "U.S. Forest Service", + "slug": "great-basin-montane-watersheds-pour-points-feature-layer", + "spatial_centroid": { + "lat": 39.97036, + "lon": -116.87796 + }, + "spatial_shape": { + "coordinates": [ + [ + [ + -120.412, + 37.6856 + ], + [ + -120.412, + 43.3975 + ], + [ + -111.5769, + 43.3975 + ], + [ + -111.5769, + 37.6856 + ], + [ + -120.412, + 37.6856 + ] + ] + ], + "type": "Polygon" + }, + "theme": [ + "geospatial" + ], + "title": "Great Basin Montane Watersheds - Pour Points (Feature Layer)", + "type": "dataset" + }, + { + "_score": 9.401405, + "_sort": [ + 1790803955484, + 9.401405, + 0, + "b6c5d6d6-15b9-4b5d-9be3-a9b7382ad8fc" + ], + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "bureauCode": [ + "005:18", + "005:20" + ], + "contactPoint": { + "fn": "Smart, Brian, C.", + "hasEmail": "mailto:brian.smart@ndsu.edu" + }, + "description": "<p dir=\"ltr\">Code accompanying the manuscript \"The stability of fatty acid composition in sunflower oil is dependent on environment and affected by structural variation\" (Ingold, Hulke et al.), submitted to Theoretical and Applied Genetics.</p><p dir=\"ltr\">The code implements a genome-wide association study of seed fatty acid composition and its stability in the sunflower (Helianthus annuus L.) association mapping (SAM) population of 287 varieties, grown in eight field trials across North America: Vancouver, British Columbia (2010); Moorhead, Minnesota (2015, early and late plantings 2016); Ames, Iowa (2010, 2013, 2014); and Athens, Georgia (2010). Palmitic, stearic, oleic, and linoleic acid were measured by gas chromatography.</p><p dir=\"ltr\">Multivariate GWAS was performed on four phenotype sets: mean fatty acid composition within each environment; the same omitting high oleic varieties; within-environment stability quantified by standard errors among replicate samples (alpha stability); and across-environment stability quantified by Eberhart and Russell's beta.</p><p dir=\"ltr\">Included are scripts for phenotype preparation, beta stability regression, CHELSA climate data extraction and correlation, genotype and kinship analysis (ADMIXTURE, VanRaden kinship, PCA, LD blocks), multivariate GWAS with GEMMA and univariate GWAS with vcf2gwas, post-GWAS candidate gene identification, and analysis of a chromosome 5 introgression associated with stability under hot, humid conditions.</p><p dir=\"ltr\">The code is archived at https://doi.org/10.5281/zenodo.22001495 and developed at https://github.com/BrianSmart/SunflowerFattyAcidStabilityGWAS. SNP genotypes are third-party and available from HelianthOME (http://www.helianthome.org/download/#genotype).</p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://geodata.nal.usda.gov/geonetwork/srv/api/records/e5eb6950-5358-45a0-b524-41b33ce2b954/formatters/xml", + "conformsTo": "https://www.isotc211.org/2005/gmd", + "format": "xml", + "mediaType": "text/xml", + "title": "Geodata ISO 19139 metadata" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://doi.org/10.5281/zenodo.22001495", + "mediaType": "text/html", + "title": "https://doi.org/10.5281/zenodo.22001495" + } + ], + "identifier": "10.5281/zenodo.22001495", + "keyword": [ + "GWAS", + "Helianthus annus L.", + "association mapping", + "fatty acid composition", + "genotype by environment (G×E) interaction", + "linoleic acid", + "oleic acid", + "seed oil quality", + "source code", + "structural variation", + "sunflower", + "trait stability" + ], + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "modified": "2026-08-28", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "spatial": "{\"type\": \"MultiPoint\", \"coordinates\": [[-123.1207, 49.2827], [-96.7678, 46.8738], [-93.6199, 42.0347], [-83.3576, 33.9519]]}", + "temporal": "2010-01-01/2016-12-31", + "theme": [ + "geospatial" + ], + "title": "Code for: The stability of fatty acid composition in sunflower oil is dependent on environment and affected by structural variation" + }, + "description": "<p dir=\"ltr\">Code accompanying the manuscript \"The stability of fatty acid composition in sunflower oil is dependent on environment and affected by structural variation\" (Ingold, Hulke et al.), submitted to Theoretical and Applied Genetics.</p><p dir=\"ltr\">The code implements a genome-wide association study of seed fatty acid composition and its stability in the sunflower (Helianthus annuus L.) association mapping (SAM) population of 287 varieties, grown in eight field trials across North America: Vancouver, British Columbia (2010); Moorhead, Minnesota (2015, early and late plantings 2016); Ames, Iowa (2010, 2013, 2014); and Athens, Georgia (2010). Palmitic, stearic, oleic, and linoleic acid were measured by gas chromatography.</p><p dir=\"ltr\">Multivariate GWAS was performed on four phenotype sets: mean fatty acid composition within each environment; the same omitting high oleic varieties; within-environment stability quantified by standard errors among replicate samples (alpha stability); and across-environment stability quantified by Eberhart and Russell's beta.</p><p dir=\"ltr\">Included are scripts for phenotype preparation, beta stability regression, CHELSA climate data extraction and correlation, genotype and kinship analysis (ADMIXTURE, VanRaden kinship, PCA, LD blocks), multivariate GWAS with GEMMA and univariate GWAS with vcf2gwas, post-GWAS candidate gene identification, and analysis of a chromosome 5 introgression associated with stability under hot, humid conditions.</p><p dir=\"ltr\">The code is archived at https://doi.org/10.5281/zenodo.22001495 and developed at https://github.com/BrianSmart/SunflowerFattyAcidStabilityGWAS. SNP genotypes are third-party and available from HelianthOME (http://www.helianthome.org/download/#genotype).</p>", + "distribution_titles": [ + "Geodata ISO 19139 metadata", + "https://doi.org/10.5281/zenodo.22001495" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/cf2c9546-b9bc-42b3-abdb-ec61759cf271", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/cf2c9546-b9bc-42b3-abdb-ec61759cf271/raw", + "has_download": true, + "has_spatial": true, + "identifier": "10.5281/zenodo.22001495", + "keyword": [ + "GWAS", + "Helianthus annus L.", + "association mapping", + "fatty acid composition", + "genotype by environment (G×E) interaction", + "linoleic acid", + "oleic acid", + "seed oil quality", + "source code", + "structural variation", + "sunflower", + "trait stability" + ], + "last_harvested_date": "2026-09-30T21:32:35.484474", + "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": 0, + "publisher": "Agricultural Research Service", + "slug": "code-for-the-stability-of-fatty-acid-composition-in-sunflower-oil-is-dependent-on-environm", + "spatial_centroid": { + "lat": 43.035775, + "lon": -99.2165 + }, + "spatial_shape": { + "coordinates": [ + [ + -123.1207, + 49.2827 + ], + [ + -96.7678, + 46.8738 + ], + [ + -93.6199, + 42.0347 + ], + [ + -83.3576, + 33.9519 + ] + ], + "type": "MultiPoint" + }, + "theme": [ + "geospatial" + ], + "title": "Code for: The stability of fatty acid composition in sunflower oil is dependent on environment and affected by structural variation", + "type": "dataset" + }, + { + "_score": 5.6510468, + "_sort": [ + 1790803954972, + 5.6510468, + 0, + "fb56cfff-57ad-4836-a5e6-65a0776b2e24" + ], + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accrualPeriodicity": "irregular", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Robles, Helix", + "hasEmail": "mailto:helix.roblez@gmail.com" + }, + "description": "<p dir=\"ltr\">This dataset contains spectral and soil chemistry data, along with accompanying R Markdown and R data files, from a study of whole orchard recycling (WOR) effects on soil organic matter (SOM) in sandy loam soils. The data and code attached here support the manuscript “Characterization of Particulate and Mineral-Associated Organic Matter in Wood Chip Amended Sandy Loam Soil: Composition and Distribution Across Space-Time” (Robles et al., 2026). </p><p dir=\"ltr\">Soil was collected in July and August 2023 from four experimental almond orchards that received wood chip amendments by whole orchard recycling (WOR) in 2008 (15 years old), 2017 (6 years old), 2019 (4 years old), and 2020 (3 years old); this serves as a space-for-time (SFT) substitution design, with four replicate blocks sampled for each Treatment × Field combination. The orchards are located at the University of California Kearney Agricultural Research and Extension Center (36.5966 °N, -119.5176 °W) in Parlier, California (USA), each having 4 replicated WOR and control blocks. WOR treatments received 33 – 85 metric tons of wood chips per acre. Chronologically, the 3-year-old orchard received 60 tons of wood per acre, the 4-year-old orchard received 61 tons of wood per acre, the 6-year-old orchard received 85 tons of wood per acre, and the 15-year-old orchard received 33 tons of wood per acre. Control treatments followed conventional management practices during orchard establishment. For the orchards recycled in 2017, 2019, and 2020, the control soils were unamended and burning served as the control treatment in the 2008 orchard. This yields a chronosequence of soil samples at 15-, 6-, 4-, and 3-years post WOR, as a space for time substitution design. The soil texture primarily consists of fine sandy loam, characterized as coarse-loamy, mixed nonacid thermic Typic Xerorthents of the Hanford series. The National Cooperative Soil Survey characterized the Hanford soil series to typically contain less than 1 % soil organic matter (SOM) content, decreasing with depth (Official Series Description - HANFORD Series, n.d.). The orchard experiences a Mediterranean climate, with dry, hot summers and cool, wet winters and precipitation levels generally falling below almond evapotranspiration requirements for a significant portion of the growing season. On average, the annual rainfall and temperature stand at 285 mm and 17°C, respectively. Thus, all crop production in the region is irrigated.</p><p dir=\"ltr\">Particulate organic matter (POM) and mineral-associated organic matter (MAOM) fractions were isolated from <2 mm soil and analyzed by diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Raw spectra (including bleached NaOCl-oxidized POM and MAOM samples) are stored in SFT_POM_MAOM_raw.rds and processed in Raw_spectra.rmd. Mineral contributions were removed by spectral subtraction (POM – PX and MAM – MX), and spectra were transformed using Kubelka–Munk (KM) functions in OMNIC; the resulting spectral subtraction data are stored in SFT_POM_MAOM_sub_KM.rds and SFT_POM_MAOM_sub_KM_ave.rds (replicate spectra are averaged) and processed in Subtracted_Spectra.rmd and Averaged_Spectra.rmd. Soil chemistry and index data (<2 mm soil, POM, MAOM) are in SFT_2023_Metadata.rds and Helix_Index123.rds and analyzed in SFT_Chem.rmd and SFT_stats.rmd. Variables include total carbon (TC), total nitrogen (TN), C:N ratios, SOM, water-holding capacity (WHC), pH, dissolved organic carbon (DOC), dissolved organic nitrogen (DON), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and CO2 respiration. These files allow reproduction of DRIFTS spectral analyses (raw, subtraction, and averaged spectra) and statistical analyses of soil chemistry and index values. The READ_ME file describes the main purpose of each data file/analysis, and defines acronyms used.</p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://geodata.nal.usda.gov/geonetwork/srv/api/records/81174c3b-9de9-4e8b-bb60-93c1867810b5/formatters/xml", + "format": "xml", + "mediaType": "text/xml", + "title": "Geodata ISO 19139 metadata" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292615", + "format": "txt", + "mediaType": "text/plain", + "title": "READ_ME.txt" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292618", + "format": "Rmd", + "mediaType": "text/plain", + "title": "SFT_Chem.Rmd" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292621", + "format": "Rmd", + "mediaType": "text/plain", + "title": "SFT_Stats.Rmd" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292624", + "format": "Rmd", + "mediaType": "text/plain", + "title": "Subtracted_Spectra.Rmd" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292627", + "format": "Rmd", + "mediaType": "text/plain", + "title": "Averaged_Spectra.Rmd" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292630", + "format": "Rmd", + "mediaType": "text/plain", + "title": "Raw_Spectra.Rmd" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292633", + "format": "csv", + "mediaType": "text/csv", + "title": "Helix_Index123_filt.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292636", + "format": "csv", + "mediaType": "text/csv", + "title": "WOR_SFT_2023_POM_MAOM_TC_TN_Subtraction.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292639", + "format": "csv", + "mediaType": "text/csv", + "title": "SFT_2023_Metadata.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292642", + "format": "rds", + "mediaType": "application/gzip", + "title": "Helix_Index123.rds" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292645", + "format": "rds", + "mediaType": "application/gzip", + "title": "SFT_2023_Metadata.rds" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292648", + "format": "rds", + "mediaType": "application/gzip", + "title": "POM_MAOM_TC_TN_Subtraction.rds" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292651", + "format": "rds", + "mediaType": "application/gzip", + "title": "SFT_POM_MAOM_sub_KM.rds" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292654", + "format": "rds", + "mediaType": "application/gzip", + "title": "SFT_POM_MAOM_raw.rds" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67292657", + "format": "rds", + "mediaType": "application/gzip", + "title": "SFT_POM_MAOM_sub_KM_ave.rds" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/67318229", + "format": "xlsx", + "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "title": "WOR_SFT_means_treatment_field.xlsx" + } + ], + "identifier": "10.15482/USDA.ADC/33135455.v1", + "keyword": [ + "DRIFTS Diffuse reflectance", + "Mid -infrared (MIR) spectroscopy", + "Particulate organic matter (POM)", + "Soil", + "Soil organic carbon pools", + "Whole orchard recycling", + "mineral associated organic matter", + "source code" + ], + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "modified": "2026-08-21", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "spatial": "{\"type\": \"Polygon\", \"coordinates\": [[[-119.52085947479276, 36.59204409985334], [-119.50248159287305, 36.59204409985334], [-119.50248159287305, 36.600049975939484], [-119.52085947479276, 36.600049975939484], [-119.52085947479276, 36.59204409985334]]]}", + "temporal": "2023-07-24/2023-08-23", + "title": "DRIFT Spectra of POM and MAOM in sandy loam soils amended by whole orchard recycling (WOR)" + }, + "description": "<p dir=\"ltr\">This dataset contains spectral and soil chemistry data, along with accompanying R Markdown and R data files, from a study of whole orchard recycling (WOR) effects on soil organic matter (SOM) in sandy loam soils. The data and code attached here support the manuscript “Characterization of Particulate and Mineral-Associated Organic Matter in Wood Chip Amended Sandy Loam Soil: Composition and Distribution Across Space-Time” (Robles et al., 2026). </p><p dir=\"ltr\">Soil was collected in July and August 2023 from four experimental almond orchards that received wood chip amendments by whole orchard recycling (WOR) in 2008 (15 years old), 2017 (6 years old), 2019 (4 years old), and 2020 (3 years old); this serves as a space-for-time (SFT) substitution design, with four replicate blocks sampled for each Treatment × Field combination. The orchards are located at the University of California Kearney Agricultural Research and Extension Center (36.5966 °N, -119.5176 °W) in Parlier, California (USA), each having 4 replicated WOR and control blocks. WOR treatments received 33 – 85 metric tons of wood chips per acre. Chronologically, the 3-year-old orchard received 60 tons of wood per acre, the 4-year-old orchard received 61 tons of wood per acre, the 6-year-old orchard received 85 tons of wood per acre, and the 15-year-old orchard received 33 tons of wood per acre. Control treatments followed conventional management practices during orchard establishment. For the orchards recycled in 2017, 2019, and 2020, the control soils were unamended and burning served as the control treatment in the 2008 orchard. This yields a chronosequence of soil samples at 15-, 6-, 4-, and 3-years post WOR, as a space for time substitution design. The soil texture primarily consists of fine sandy loam, characterized as coarse-loamy, mixed nonacid thermic Typic Xerorthents of the Hanford series. The National Cooperative Soil Survey characterized the Hanford soil series to typically contain less than 1 % soil organic matter (SOM) content, decreasing with depth (Official Series Description - HANFORD Series, n.d.). The orchard experiences a Mediterranean climate, with dry, hot summers and cool, wet winters and precipitation levels generally falling below almond evapotranspiration requirements for a significant portion of the growing season. On average, the annual rainfall and temperature stand at 285 mm and 17°C, respectively. Thus, all crop production in the region is irrigated.</p><p dir=\"ltr\">Particulate organic matter (POM) and mineral-associated organic matter (MAOM) fractions were isolated from <2 mm soil and analyzed by diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Raw spectra (including bleached NaOCl-oxidized POM and MAOM samples) are stored in SFT_POM_MAOM_raw.rds and processed in Raw_spectra.rmd. Mineral contributions were removed by spectral subtraction (POM – PX and MAM – MX), and spectra were transformed using Kubelka–Munk (KM) functions in OMNIC; the resulting spectral subtraction data are stored in SFT_POM_MAOM_sub_KM.rds and SFT_POM_MAOM_sub_KM_ave.rds (replicate spectra are averaged) and processed in Subtracted_Spectra.rmd and Averaged_Spectra.rmd. Soil chemistry and index data (<2 mm soil, POM, MAOM) are in SFT_2023_Metadata.rds and Helix_Index123.rds and analyzed in SFT_Chem.rmd and SFT_stats.rmd. Variables include total carbon (TC), total nitrogen (TN), C:N ratios, SOM, water-holding capacity (WHC), pH, dissolved organic carbon (DOC), dissolved organic nitrogen (DON), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and CO2 respiration. These files allow reproduction of DRIFTS spectral analyses (raw, subtraction, and averaged spectra) and statistical analyses of soil chemistry and index values. The READ_ME file describes the main purpose of each data file/analysis, and defines acronyms used.</p>", + "distribution_titles": [ + "Geodata ISO 19139 metadata", + "READ_ME.txt", + "SFT_Chem.Rmd", + "SFT_Stats.Rmd", + "Subtracted_Spectra.Rmd", + "Averaged_Spectra.Rmd", + "Raw_Spectra.Rmd", + "Helix_Index123_filt.csv", + "WOR_SFT_2023_POM_MAOM_TC_TN_Subtraction.csv", + "SFT_2023_Metadata.csv", + "Helix_Index123.rds", + "SFT_2023_Metadata.rds", + "POM_MAOM_TC_TN_Subtraction.rds", + "SFT_POM_MAOM_sub_KM.rds", + "SFT_POM_MAOM_raw.rds", + "SFT_POM_MAOM_sub_KM_ave.rds", + "WOR_SFT_means_treatment_field.xlsx" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/fdecdf16-3999-47ba-bcf0-86e9e257439e", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/fdecdf16-3999-47ba-bcf0-86e9e257439e/raw", + "has_download": true, + "has_spatial": true, + "identifier": "10.15482/USDA.ADC/33135455.v1", + "keyword": [ + "DRIFTS Diffuse reflectance", + "Mid -infrared (MIR) spectroscopy", + "Particulate organic matter (POM)", + "Soil", + "Soil organic carbon pools", + "Whole orchard recycling", + "mineral associated organic matter", + "source code" + ], + "last_harvested_date": "2026-09-30T21:32:34.972637", + "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": 0, + "publisher": "Agricultural Research Service", + "slug": "drift-spectra-of-pom-and-maom-in-sandy-loam-soils-amended-by-whole-orchard-recycling-wor", + "spatial_centroid": { + "lat": 36.5952464502878, + "lon": -119.51350832202488 + }, + "spatial_shape": { + "coordinates": [ + [ + [ + -119.52085947479276, + 36.59204409985334 + ], + [ + -119.50248159287305, + 36.59204409985334 + ], + [ + -119.50248159287305, + 36.600049975939484 + ], + [ + -119.52085947479276, + 36.600049975939484 + ], + [ + -119.52085947479276, + 36.59204409985334 + ] + ] + ], + "type": "Polygon" + }, + "theme": [], + "title": "DRIFT Spectra of POM and MAOM in sandy loam soils amended by whole orchard recycling (WOR)", + "type": "dataset" + }, + { + "_score": 8.670719, + "_sort": [ + 1790803952041, + 8.670719, + 1, + "8735f6d5-ca99-4844-bff2-b4d1f3804945" + ], + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accrualPeriodicity": "R/P1D", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Baffaut, Claire", + "hasEmail": "mailto:claire.baffaut@usda.gov" + }, + "description": "<p dir=\"ltr\">This dataset includes the monthly and daily data used for the analysis of historical and future trends in precipitation and temperature at five Long-Term Agroecosystem Research (LTAR) sites: Kellogg Biological Station (KBS) in Michigan, Upper Mississippi River Basin (UMRB) in Iowa, Central Mississippi River Basin (CMRB) in Missouri, Southern Plains (SP) in Oklahoma, and Lower Mississippi River Basin (LMRB) in Mississippi. Historical data include the longest available record of daily precipitation, minimum temperature, and maximum temperature at weather stations from KBS, UMRB, CMRB, and LMRB, and the monthly 1895-2020 data from the National Ocean and Atmospheric Administration for the climate divisions that represent the five LTAR sites. Future data include 2020-2100 monthly predictions for the five sites from 26 Earth System Models and two Shared Socio-economic Pathways (SSP): the middle of the road SSP245 (a continuation of current emission rates and geo-political conditions), and the fossil fueled development scenario SSP 585 (intensification of fossil fuel energy sources and corresponding emissions). In addition, the data includes the trends calculated from historical and future data, snippets of R code used to calculate these trends, and README files that detail the content of each file.</p><p dir=\"ltr\">Trends in records of 50 years or more showed that temperatures have changed from 1900-2020, more for minimum (0.1 - 0.3 ℃ decade<sup>-1</sup>) than maximum (-0.1 - 0.2 ℃<sup> </sup>decade<sup>-1</sup>), more for winter (-0.1 - 0.3 ℃<sup> </sup>decade<sup>-1</sup>) than summer (-0.1 - 0.1 ℃ decade<sup>-1</sup>), and more often in the north than in the south. Except in Mississippi, annual precipitation has increased at rates of 25 mm decade<sup>-1</sup> or greater over 1950-2020, but monthly trends were inconsistent. Projected trends suggest continued temperature increases, highlighting the need for research on management systems that are resilient to such increases.</p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://geodata.nal.usda.gov/geonetwork/srv/api/records/260ae977-85ee-4b78-88be-0d5b1f86a74c/formatters/xml", + "conformsTo": "https://www.isotc211.org/2005/gmd", + "format": "xml", + "mediaType": "text/xml", + "title": "Geodata ISO 19139 metadata" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/56608406", + "format": "txt", + "mediaType": "text/plain", + "title": "README.txt" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/56608409", + "format": "txt", + "mediaType": "text/plain", + "title": "README_LOCA2.txt" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/56608412", + "format": "xlsx", + "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "title": "Station Data.xlsx" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/56608418", + "format": "xlsx", + "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "title": "NOAA Data and trends.xlsx" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/56608421", + "format": "zip", + "mediaType": "application/zip", + "title": "LOCA2-LTAR.zip" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/56608451", + "format": "xlsx", + "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "title": "ESM_Data_trends_AllTrends.xlsx" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/56608478", + "format": "txt", + "mediaType": "text/plain", + "title": "Climate_Indicators.txt" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/56608481", + "format": "txt", + "mediaType": "text/plain", + "title": "Coeff_Var_Trender.txt" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/56608484", + "format": "txt", + "mediaType": "text/plain", + "title": "LOCA_MKTrend.txt" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/56608487", + "format": "txt", + "mediaType": "text/plain", + "title": "ViolinPlotter.txt" + } + ], + "identifier": "10.15482/USDA.ADC/29640977.v1", + "keyword": [ + "CMIP 6 dataset", + "LTAR", + "Trend Analysis Background Data", + "precipitation", + "source code", + "temperature" + ], + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "modified": "2026-08-25", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "spatial": "{\"type\": \"MultiPoint\", \"coordinates\": [[-85.4, 42.4], [-93.8, 42.3], [-92.1, 39.2], [-90.5, 33.45]]}", + "temporal": "1895-01-01/2100-01-01", + "theme": [ + "geospatial" + ], + "title": "Data from: Are historical trends in weather consistent with model predictions in the Central United States?" + }, + "description": "<p dir=\"ltr\">This dataset includes the monthly and daily data used for the analysis of historical and future trends in precipitation and temperature at five Long-Term Agroecosystem Research (LTAR) sites: Kellogg Biological Station (KBS) in Michigan, Upper Mississippi River Basin (UMRB) in Iowa, Central Mississippi River Basin (CMRB) in Missouri, Southern Plains (SP) in Oklahoma, and Lower Mississippi River Basin (LMRB) in Mississippi. Historical data include the longest available record of daily precipitation, minimum temperature, and maximum temperature at weather stations from KBS, UMRB, CMRB, and LMRB, and the monthly 1895-2020 data from the National Ocean and Atmospheric Administration for the climate divisions that represent the five LTAR sites. Future data include 2020-2100 monthly predictions for the five sites from 26 Earth System Models and two Shared Socio-economic Pathways (SSP): the middle of the road SSP245 (a continuation of current emission rates and geo-political conditions), and the fossil fueled development scenario SSP 585 (intensification of fossil fuel energy sources and corresponding emissions). In addition, the data includes the trends calculated from historical and future data, snippets of R code used to calculate these trends, and README files that detail the content of each file.</p><p dir=\"ltr\">Trends in records of 50 years or more showed that temperatures have changed from 1900-2020, more for minimum (0.1 - 0.3 ℃ decade<sup>-1</sup>) than maximum (-0.1 - 0.2 ℃<sup> </sup>decade<sup>-1</sup>), more for winter (-0.1 - 0.3 ℃<sup> </sup>decade<sup>-1</sup>) than summer (-0.1 - 0.1 ℃ decade<sup>-1</sup>), and more often in the north than in the south. Except in Mississippi, annual precipitation has increased at rates of 25 mm decade<sup>-1</sup> or greater over 1950-2020, but monthly trends were inconsistent. Projected trends suggest continued temperature increases, highlighting the need for research on management systems that are resilient to such increases.</p>", + "distribution_titles": [ + "Geodata ISO 19139 metadata", + "README.txt", + "README_LOCA2.txt", + "Station Data.xlsx", + "NOAA Data and trends.xlsx", + "LOCA2-LTAR.zip", + "ESM_Data_trends_AllTrends.xlsx", + "Climate_Indicators.txt", + "Coeff_Var_Trender.txt", + "LOCA_MKTrend.txt", + "ViolinPlotter.txt" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/73e60f2d-3810-4a41-960d-d3b6150db7c7", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/73e60f2d-3810-4a41-960d-d3b6150db7c7/raw", + "has_download": true, + "has_spatial": true, + "identifier": "10.15482/USDA.ADC/29640977.v1", + "keyword": [ + "CMIP 6 dataset", + "LTAR", + "Trend Analysis Background Data", + "precipitation", + "source code", + "temperature" + ], + "last_harvested_date": "2026-09-30T21:32:32.041052", + "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": "data-from-are-historical-trends-in-weather-consistent-with-model-predictions-in-the-centra", + "spatial_centroid": { + "lat": 39.3375, + "lon": -90.45 + }, + "spatial_shape": { + "coordinates": [ + [ + -85.4, + 42.4 + ], + [ + -93.8, + 42.3 + ], + [ + -92.1, + 39.2 + ], + [ + -90.5, + 33.45 + ] + ], + "type": "MultiPoint" + }, + "theme": [ + "geospatial" + ], + "title": "Data from: Are historical trends in weather consistent with model predictions in the Central United States?", + "type": "dataset" + }, + { + "_score": 7.71714, + "_sort": [ + 1790803895703, + 7.71714, + 4, + "1a85cb8e-bfcd-4ee3-b40f-ea15e087a213" + ], + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "accrualPeriodicity": "irregular", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Liebig, Mark A.", + "hasEmail": "mailto:mark.liebig@usda.gov" + }, + "description": "<p dir=\"ltr\">Retaining crop residue on the soil surface is important in semiarid cropping systems, where dry conditions and variable weather can render the soil susceptible to degradation and moisture loss. Conversely, removing crop residue can generate an additional income stream for agricultural producers, while broadening the spectrum of uses from harvestable commodities. Understanding potential tradeoffs of crop residue removal across a range of agroecosystems over the long-term is essential to adequately guide management decisions. A study was conducted to quantify crop and soil responses to continuous spring wheat treatments with and without straw removal, each managed under minimum and no-tillage. Treatments were replicated three times and deployed over a 24-yr period. Soil coverage by crop residue was measured annually using two 25 point transects spaced equally along a 7.6 m cable. Spring wheat aboveground biomass was measured prior to combine harvest using 0.33 m2 frames. Biomass samples were threshed to separate grain from straw. Soil samples were collected in 2018 with a hydraulic probe to a 152.4 cm depth in increments of 0-7.6, 7.6-15.2, 15.2-30.5, 30.5-61.0, 61.0-91.4, 91.4-121.9, and 121.9-152.4 cm. Separate samples for aggregate stability analysis were collected with a trowel from the 0-7.6 cm depth. Soil samples were evaluated for soil bulk density, water-stable aggregates (WSA), electrical conductivity, soil pH, nitrate-nitrogen, available phosphorus, sulfate-sulfur, exchangeable cations (Ca, Mg, K, Na), micronutrients (B, Cu, Fe, Mn, Zn), total soil nitrogen, total carbon, inorganic carbon, and particulate organic matter (POM) carbon and nitrogen. Particulate organic matter (POM) was estimated from material retained on a 0.053 mm sieve analyzed for carbon and nitrogen content by dry combustion. Analyses for POM and WSA were conducted for the 0-7.6 cm depth only. Data may be used to better understand crop and soil property responses to residue management and tillage practices under rainfed conditions within a semiarid continental climate. Applicable USDA soil types include Temvik, Wilton, Grassna, Linton, Mandan, and Williams.</p><p dir=\"ltr\">The SQM Data Dictionary describes element/value names, data type, etc. for each spreadsheet tab, and the Metadata files describe attributes and units for their respective data files.</p><p><br></p><p dir=\"ltr\"><br></p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673745", + "format": "xlsx", + "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "title": "SQM_Data Dictionary.xlsx" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673748", + "format": "xlsx", + "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "title": "SQM_AllDepthsSoil_2018.xlsx" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673751", + "format": "xlsx", + "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "title": "SQM_Crop_Aboveground Biomass.xlsx" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673754", + "format": "xlsx", + "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "title": "SQM_Soil Cover.xlsx" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673757", + "format": "xlsx", + "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "title": "SQM_WSA&POM.xlsx" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673760", + "format": "csv", + "mediaType": "text/csv", + "title": "SQM_AllDepthsSoil_2018_Data.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673763", + "format": "csv", + "mediaType": "text/csv", + "title": "SQM_AllDepthsSoil_2018_Metadata.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673766", + "format": "csv", + "mediaType": "text/csv", + "title": "SQM_Crop_Aboveground Biomass_Data.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673769", + "format": "csv", + "mediaType": "text/csv", + "title": "SQM_Crop_Aboveground Biomass_Metadata.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673772", + "format": "csv", + "mediaType": "text/csv", + "title": "SQM_Soil Cover_Data.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673775", + "format": "csv", + "mediaType": "text/csv", + "title": "SQM_Soil Cover_Metadata.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673778", + "format": "csv", + "mediaType": "text/csv", + "title": "SQM_WSA&POM_Data.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/66673781", + "format": "csv", + "mediaType": "text/csv", + "title": "SQM_WSA&POM_Metadata.csv" + } + ], + "identifier": "10.15482/USDA.ADC/32911835.v1", + "keyword": [ + "No-tillage", + "Residue management", + "Semiarid cropping systems", + "Soil cover", + "Spring wheat" + ], + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "modified": "2026-08-11", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "spatial": "{\"type\": \"Point\", \"coordinates\": [-100.95, 46.77099999999999]}", + "temporal": "1994-04-01/2018-04-01", + "title": "Data from: Crop and Soil Responses to 24 years of Wheat Residue Removal and Tillage" + }, + "description": "<p dir=\"ltr\">Retaining crop residue on the soil surface is important in semiarid cropping systems, where dry conditions and variable weather can render the soil susceptible to degradation and moisture loss. Conversely, removing crop residue can generate an additional income stream for agricultural producers, while broadening the spectrum of uses from harvestable commodities. Understanding potential tradeoffs of crop residue removal across a range of agroecosystems over the long-term is essential to adequately guide management decisions. A study was conducted to quantify crop and soil responses to continuous spring wheat treatments with and without straw removal, each managed under minimum and no-tillage. Treatments were replicated three times and deployed over a 24-yr period. Soil coverage by crop residue was measured annually using two 25 point transects spaced equally along a 7.6 m cable. Spring wheat aboveground biomass was measured prior to combine harvest using 0.33 m2 frames. Biomass samples were threshed to separate grain from straw. Soil samples were collected in 2018 with a hydraulic probe to a 152.4 cm depth in increments of 0-7.6, 7.6-15.2, 15.2-30.5, 30.5-61.0, 61.0-91.4, 91.4-121.9, and 121.9-152.4 cm. Separate samples for aggregate stability analysis were collected with a trowel from the 0-7.6 cm depth. Soil samples were evaluated for soil bulk density, water-stable aggregates (WSA), electrical conductivity, soil pH, nitrate-nitrogen, available phosphorus, sulfate-sulfur, exchangeable cations (Ca, Mg, K, Na), micronutrients (B, Cu, Fe, Mn, Zn), total soil nitrogen, total carbon, inorganic carbon, and particulate organic matter (POM) carbon and nitrogen. Particulate organic matter (POM) was estimated from material retained on a 0.053 mm sieve analyzed for carbon and nitrogen content by dry combustion. Analyses for POM and WSA were conducted for the 0-7.6 cm depth only. Data may be used to better understand crop and soil property responses to residue management and tillage practices under rainfed conditions within a semiarid continental climate. Applicable USDA soil types include Temvik, Wilton, Grassna, Linton, Mandan, and Williams.</p><p dir=\"ltr\">The SQM Data Dictionary describes element/value names, data type, etc. for each spreadsheet tab, and the Metadata files describe attributes and units for their respective data files.</p><p><br></p><p dir=\"ltr\"><br></p>", + "distribution_titles": [ + "SQM_Data Dictionary.xlsx", + "SQM_AllDepthsSoil_2018.xlsx", + "SQM_Crop_Aboveground Biomass.xlsx", + "SQM_Soil Cover.xlsx", + "SQM_WSA&POM.xlsx", + "SQM_AllDepthsSoil_2018_Data.csv", + "SQM_AllDepthsSoil_2018_Metadata.csv", + "SQM_Crop_Aboveground Biomass_Data.csv", + "SQM_Crop_Aboveground Biomass_Metadata.csv", + "SQM_Soil Cover_Data.csv", + "SQM_Soil Cover_Metadata.csv", + "SQM_WSA&POM_Data.csv", + "SQM_WSA&POM_Metadata.csv" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/84fcca8f-454c-4458-8141-4a9f51de279c", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/84fcca8f-454c-4458-8141-4a9f51de279c/raw", + "has_download": true, + "has_spatial": true, + "identifier": "10.15482/USDA.ADC/32911835.v1", + "keyword": [ + "No-tillage", + "Residue management", + "Semiarid cropping systems", + "Soil cover", + "Spring wheat" + ], + "last_harvested_date": "2026-09-30T21:31:35.703428", + "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": "data-from-crop-and-soil-responses-to-24-years-of-wheat-residue-removal-and-tillage", + "spatial_centroid": { + "lat": 46.77099999999999, + "lon": -100.95 + }, + "spatial_shape": { + "coordinates": [ + -100.95, + 46.77099999999999 + ], + "type": "Point" + }, + "theme": [], + "title": "Data from: Crop and Soil Responses to 24 years of Wheat Residue Removal and Tillage", + "type": "dataset" + }, + { + "_score": 4.16868, + "_sort": [ + 1790803845020, + 4.16868, + 3, + "64eb3d36-297a-42e0-8ada-993f0f668373" + ], + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Roeder, Karl, A.", + "hasEmail": "mailto:karl.roeder@usda.gov" + }, + "description": "<p dir=\"ltr\">Three .csv files. Two years of data collected on critical thermal maxima and minima (CTmax and CTmin) from 14 colonies in Oklahoma from 2017-2018. Additional data files represent thermal traits from 10 colonies in a short term (10 days) acclimation experiment and projections using environmental data for potential activity differences.</p><p dir=\"ltr\"><b><u>Abstract from paper:</u></b></p><p dir=\"ltr\">How do individuals tolerate both the hot and the cold climate of our planet? One possibility is that organisms have plastic traits like thermal tolerance that allow them to function in highly variable environments. In this study, we tested whether phenotypic plasticity of temperature tolerance (i.e. acclimatization in the field and acclimation in the lab) occurs in the red harvester ant, <i>Pogonomyrmex barbatus</i>, at two temporal scales. We first measured the upper and lower critical thermal limits (CT<sub>max</sub> and CT<sub>min</sub>) of ants monthly for two years while concurrently measuring environmental conditions. Both CT<sub>max</sub> and CT<sub>min</sub> co-varied with temperature in a predictable way; values increased in a positive, linear manner. We then experimentally tested whether CT<sub>max</sub> and CT<sub>min</sub> could shift within a shorter time period by exposing subcolonies of ants to cool (10°C), moderate (20°C), and hot (30°C) temperatures for 10 days. CT<sub>max</sub> increased only slightly at the hottest temperature treatment (+1.2°C), however CT<sub>min</sub> increased considerably under both moderate (+2.6°C) and hot treatments (+3.8°C). Combined, our results suggest that thermal tolerance of ants may be more plastic than originally hypothesized, potentially aiding an already thermophilic clade.</p><p dir=\"ltr\"><b><u>Methods from paper:</u></b></p><p dir=\"ltr\"><i>Study site and environmental temperature</i></p><p dir=\"ltr\">We sampled ant workers monthly during their annual active period in 2017 and 2018 (i.e. March-November) from 14 colonies in a 30-ha grazed prairie in the central Great Plains of Oklahoma (34.5478º N, -98.2311º W, 330 m elevation). Over two years, ground temperature was recorded every 10 minutes using HOBO U23 Pro v2 External Temperature Data loggers at three equidistant locations within the sampling area. Temperature values were then averaged per month.</p><p><br></p><p dir=\"ltr\"><i>Thermal tolerance across months</i></p><p dir=\"ltr\">During each sampling event (n = 18), we collected ~20 workers directly outside the nest of each colony and used 5 workers to measure critical thermal maximum (CT<sub>max</sub>) and 5 workers to measure critical thermal minimum (CT<sub>min</sub>). We did so using a heating/cooling assay to determine the temperature at which individuals lost muscle control. Thermal assays were conducted by placing individual ants into 1.5ml microcentrifuge tubes and plugging the tops with cotton to remove a potential thermal refuge in the cap. For CT<sub>max</sub>, tubes were placed randomly into a Thermal-Lok 2-position dry heat bath that was prewarmed to 36ºC. Every 10 minutes, individuals were checked to see if they had reached their critical thermal limit by rotating the tube to check for a righting response. The temperature was then increased by 2ºC, with the process repeated until all individuals had reached their critical thermal maximum. CT<sub>min</sub> was assayed in a similar manner, but we used a EchoThermTM IC20 chilling/heating dry bath that was precooled to 20ºC, following the methods above except with temperature lowered 2°C every 10 minutes. Additional ants from each colony were kept at ambient conditions as a control during each thermal assay, all of which survived. During each trial, we also confirmed the interior temperature of one unused vial using a thermocouple attached to an Extech MN35 Digital Mini MultiMeter. CT<sub>max</sub> and CT<sub>min</sub> values were averaged per colony for each month.</p><p><br></p><p dir=\"ltr\"><i>Thermal tolerance within a month</i></p><p dir=\"ltr\">In April of 2019, we collected ~200 workers from each of 10 separate colonies to assess if critical thermal limits could change within a single cohort of ants over a short period of time. We split each group of 200 workers into three sub-colonies containing 50 workers and placed these newly created sub-colonies into three environmental chambers set at 10ºC, 20ºC, and 30ºC with a 12:12 L:D cycle and 85% RH. The selected temperatures span the approximate range of average monthly temperatures at our study site during which ants were active. Each sub-colony was provided with water and 20% sucrose solution ad libitum in cotton plugged vials and a small petri dish with Plaster of Paris that was moistened every other day. Critical thermal limits (CT<sub>max</sub> and CT<sub>min</sub>) were assayed using five individuals from each colony immediately prior to the start of the experiment and for five individuals from each sub-colony after 10 days in the environmental chambers. CT<sub>max</sub> and CT<sub>min</sub> values were averaged per colony for each temperature treatment.</p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/52668251", + "format": "csv", + "mediaType": "text/csv", + "title": "File 2 Across Months Pogo Thermal.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/52668254", + "format": "csv", + "mediaType": "text/csv", + "title": "File 3 Within Month Pogo Thermal.csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://ndownloader.figshare.com/files/52668257", + "format": "csv", + "mediaType": "text/csv", + "title": "File 1 METADATA Pogo Thermal.csv" + } + ], + "identifier": "10.15482/USDA.ADC/28459058.v1", + "keyword": [ + "Critical thermal limits", + "Pogonomyrmex barbatus", + "temperature", + "traits" + ], + "license": "https://www.usa.gov/publicdomain/label/1.0/", + "modified": "2026-08-25", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "temporal": "2017-03-01/2018-11-30", + "title": "Data from: Temporal plasticity of thermal tolerance in ants" + }, + "description": "<p dir=\"ltr\">Three .csv files. Two years of data collected on critical thermal maxima and minima (CTmax and CTmin) from 14 colonies in Oklahoma from 2017-2018. Additional data files represent thermal traits from 10 colonies in a short term (10 days) acclimation experiment and projections using environmental data for potential activity differences.</p><p dir=\"ltr\"><b><u>Abstract from paper:</u></b></p><p dir=\"ltr\">How do individuals tolerate both the hot and the cold climate of our planet? One possibility is that organisms have plastic traits like thermal tolerance that allow them to function in highly variable environments. In this study, we tested whether phenotypic plasticity of temperature tolerance (i.e. acclimatization in the field and acclimation in the lab) occurs in the red harvester ant, <i>Pogonomyrmex barbatus</i>, at two temporal scales. We first measured the upper and lower critical thermal limits (CT<sub>max</sub> and CT<sub>min</sub>) of ants monthly for two years while concurrently measuring environmental conditions. Both CT<sub>max</sub> and CT<sub>min</sub> co-varied with temperature in a predictable way; values increased in a positive, linear manner. We then experimentally tested whether CT<sub>max</sub> and CT<sub>min</sub> could shift within a shorter time period by exposing subcolonies of ants to cool (10°C), moderate (20°C), and hot (30°C) temperatures for 10 days. CT<sub>max</sub> increased only slightly at the hottest temperature treatment (+1.2°C), however CT<sub>min</sub> increased considerably under both moderate (+2.6°C) and hot treatments (+3.8°C). Combined, our results suggest that thermal tolerance of ants may be more plastic than originally hypothesized, potentially aiding an already thermophilic clade.</p><p dir=\"ltr\"><b><u>Methods from paper:</u></b></p><p dir=\"ltr\"><i>Study site and environmental temperature</i></p><p dir=\"ltr\">We sampled ant workers monthly during their annual active period in 2017 and 2018 (i.e. March-November) from 14 colonies in a 30-ha grazed prairie in the central Great Plains of Oklahoma (34.5478º N, -98.2311º W, 330 m elevation). Over two years, ground temperature was recorded every 10 minutes using HOBO U23 Pro v2 External Temperature Data loggers at three equidistant locations within the sampling area. Temperature values were then averaged per month.</p><p><br></p><p dir=\"ltr\"><i>Thermal tolerance across months</i></p><p dir=\"ltr\">During each sampling event (n = 18), we collected ~20 workers directly outside the nest of each colony and used 5 workers to measure critical thermal maximum (CT<sub>max</sub>) and 5 workers to measure critical thermal minimum (CT<sub>min</sub>). We did so using a heating/cooling assay to determine the temperature at which individuals lost muscle control. Thermal assays were conducted by placing individual ants into 1.5ml microcentrifuge tubes and plugging the tops with cotton to remove a potential thermal refuge in the cap. For CT<sub>max</sub>, tubes were placed randomly into a Thermal-Lok 2-position dry heat bath that was prewarmed to 36ºC. Every 10 minutes, individuals were checked to see if they had reached their critical thermal limit by rotating the tube to check for a righting response. The temperature was then increased by 2ºC, with the process repeated until all individuals had reached their critical thermal maximum. CT<sub>min</sub> was assayed in a similar manner, but we used a EchoThermTM IC20 chilling/heating dry bath that was precooled to 20ºC, following the methods above except with temperature lowered 2°C every 10 minutes. Additional ants from each colony were kept at ambient conditions as a control during each thermal assay, all of which survived. During each trial, we also confirmed the interior temperature of one unused vial using a thermocouple attached to an Extech MN35 Digital Mini MultiMeter. CT<sub>max</sub> and CT<sub>min</sub> values were averaged per colony for each month.</p><p><br></p><p dir=\"ltr\"><i>Thermal tolerance within a month</i></p><p dir=\"ltr\">In April of 2019, we collected ~200 workers from each of 10 separate colonies to assess if critical thermal limits could change within a single cohort of ants over a short period of time. We split each group of 200 workers into three sub-colonies containing 50 workers and placed these newly created sub-colonies into three environmental chambers set at 10ºC, 20ºC, and 30ºC with a 12:12 L:D cycle and 85% RH. The selected temperatures span the approximate range of average monthly temperatures at our study site during which ants were active. Each sub-colony was provided with water and 20% sucrose solution ad libitum in cotton plugged vials and a small petri dish with Plaster of Paris that was moistened every other day. Critical thermal limits (CT<sub>max</sub> and CT<sub>min</sub>) were assayed using five individuals from each colony immediately prior to the start of the experiment and for five individuals from each sub-colony after 10 days in the environmental chambers. CT<sub>max</sub> and CT<sub>min</sub> values were averaged per colony for each temperature treatment.</p>", + "distribution_titles": [ + "File 2 Across Months Pogo Thermal.csv", + "File 3 Within Month Pogo Thermal.csv", + "File 1 METADATA Pogo Thermal.csv" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/baca902d-dd40-4535-bead-faa5a68dce8f", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/baca902d-dd40-4535-bead-faa5a68dce8f/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10.15482/USDA.ADC/28459058.v1", + "keyword": [ + "Critical thermal limits", + "Pogonomyrmex barbatus", + "temperature", + "traits" + ], + "last_harvested_date": "2026-09-30T21:30:45.020435", + "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": "data-from-temporal-plasticity-of-thermal-tolerance-in-ants", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "Data from: Temporal plasticity of thermal tolerance in ants", + "type": "dataset" + }, + { + "_score": 14.382005, + "_sort": [ + 1790803842515, + 14.382005, + 1, + "a592d3cf-ad2e-4a98-a2bf-e2f7fdd71671" + ], + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "bureauCode": [ + "005:18" + ], + "contactPoint": { + "fn": "Bandaru, Varaprasad", + "hasEmail": "mailto:prasad.bandaru@usda.gov" + }, + "description": "<p dir=\"ltr\">The Environmental Policy Integrated Climate (EPIC) model is a comprehensive, field-scale agroecosystem model widely used for both diagnostic and prognostic analyses in agriculture. However, its application at regional scales is limited due to its original design to simulate a limited number of fields. Custom Python or R scripts have attempted to scale EPIC, but they are often inefficient, non-standardized, and not publicly available. To address these issues, a comprehensive Python package, named as GeoEPIC, that streamlines spatial EPIC implementation. GeoEPIC automates input generation from spatial datasets, model calibration, simulation execution, and output post-processing.</p>", + "distribution": [ + { + "@type": "dcat:Distribution", + "downloadURL": "https://smarsgroup.github.io/geo_epic_win/reference/api/core/", + "mediaType": "text/html", + "title": "https://smarsgroup.github.io/geo_epic_win/reference/api/core/" + } + ], + "identifier": "10779/USDA.ADC.31151371.v1", + "keyword": [ + "EPIC crop model", + "Python", + "Spatial Modeling Approach" + ], + "license": "https://opensource.org/license/BSD-3-Clause", + "modified": "2026-08-25", + "programCode": [ + "005:040" + ], + "publisher": { + "@type": "org:Organization", + "name": "Agricultural Research Service" + }, + "temporal": "2024-06-01/2025-11-30", + "title": "GeoEPIC Python Package" + }, + "description": "<p dir=\"ltr\">The Environmental Policy Integrated Climate (EPIC) model is a comprehensive, field-scale agroecosystem model widely used for both diagnostic and prognostic analyses in agriculture. However, its application at regional scales is limited due to its original design to simulate a limited number of fields. Custom Python or R scripts have attempted to scale EPIC, but they are often inefficient, non-standardized, and not publicly available. To address these issues, a comprehensive Python package, named as GeoEPIC, that streamlines spatial EPIC implementation. GeoEPIC automates input generation from spatial datasets, model calibration, simulation execution, and output post-processing.</p>", + "distribution_titles": [ + "https://smarsgroup.github.io/geo_epic_win/reference/api/core/" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/8efe138f-32f1-43cf-ad94-fc669543fae2", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/8efe138f-32f1-43cf-ad94-fc669543fae2/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10779/USDA.ADC.31151371.v1", + "keyword": [ + "EPIC crop model", + "Python", + "Spatial Modeling Approach" + ], + "last_harvested_date": "2026-09-30T21:30:42.515787", + "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": "geoepic-python-package", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "GeoEPIC Python Package", + "type": "dataset" + }, + { + "_score": 7.664015, + "_sort": [ + 1790794424534, + 7.664015, + 4, + "374f22e6-71ff-41c8-ad59-226d7c041e6e" + ], + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "contactPoint": { + "@type": "vcard:Contact", + "fn": "OpenData", + "hasEmail": "mailto:no-reply@data.sf.gov" + }, + "description": "<strong>A. SUMMARY</strong>\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the <u><a href=\"https://www.sfenvironment.org/files/events/2021_climate_action_plan.pdf\">2021 Climate Action Plan</a></u> and is included in the <u><a href=\"https://codelibrary.amlegal.com/codes/san_francisco/latest/sf_environment/0-0-0-577\">Environment Code Chapter 7</a></u> (Municipal Green Building Requirements). \n\n<strong>B. HOW THE DATASET IS CREATED</strong>\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City’s <u><a href=\"https://data.sfgov.org/City-Infrastructure/City-Facilities/nc68-ngbr/about_datax\">Facility Systems of Record</a></u> and the <u><a href=\"https://sfpuc.org/about-us/reports/municipal-buildings-energy-benchmarking\">2020 municipal benchmarking report</a></u> to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\n<strong>C. 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Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573<div><br /></div><div><a href='https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Great+Basin+Montane' target='_blank' rel='nofollow ugc noopener noreferrer'>Metadata and Downloads</a><br /></div>", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_GreatBasinMountainRangesWatersheds_01/MapServer/1", - "format": "ArcGIS GeoServices REST API", - "mediaType": "application/json", - "title": "ArcGIS GeoService" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/0eebaa75bfe342d5a4f7437ddf0691bd/csv?layers=1", - "format": "CSV", - "mediaType": "text/csv", - "title": "CSV" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/0eebaa75bfe342d5a4f7437ddf0691bd/geojson?layers=1", - "format": "GeoJSON", - "mediaType": "application/vnd.geo+json", - "title": "GeoJSON" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/0eebaa75bfe342d5a4f7437ddf0691bd/kml?layers=1", - "format": "KML", - "mediaType": "application/vnd.google-earth.kml+xml", - "title": "KML" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://data-usfs.hub.arcgis.com/api/download/v1/items/0eebaa75bfe342d5a4f7437ddf0691bd/shapefile?layers=1", - "format": "ZIP", - "mediaType": "application/zip", - "title": "Shapefile" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://data-usfs.hub.arcgis.com/datasets/0eebaa75bfe342d5a4f7437ddf0691bd_1", - "format": "Web Page", - "mediaType": "text/html", - "title": "ArcGIS Hub Dataset" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://www.arcgis.com/sharing/rest/content/items/0eebaa75bfe342d5a4f7437ddf0691bd/info/metadata/metadata.xml?format=iso19139", - "conformsTo": "https://www.isotc211.org/2005/gmi", - "mediaType": "text/xml", - "title": "ISO-19139 metadata" - } - ], - "identifier": "https://www.arcgis.com/home/item.html?id=0eebaa75bfe342d5a4f7437ddf0691bd&sublayer=1", - "issued": "2022-11-03", - "keyword": [ - "Great Basin", - "Great Basin watershed characteristics", - "Great Basin watershed database", - "Open Data", - "climate", - "ecosystem resista", - "fire", - "geomorphology", - "geoscientificInformation", - "inlandWaters", - "meadows", - "mountain range delineation", - "riparian", - "species", - "watershed delineation" - ], - "landingPage": "https://data-usfs.hub.arcgis.com/datasets/0eebaa75bfe342d5a4f7437ddf0691bd_1", - "license": "https://creativecommons.org/licenses/by/4.0/", - "modified": "2022-11-21", - "programCode": [ - "005:059" - ], - "progressCode": "onGoing", - "publisher": { - "name": "U.S. Forest Service", - "source": "U.S. Forest Service" - }, - "spatial": "-120.4120,37.6856,-111.5769,43.3975", - "theme": [ - "geospatial" - ], - "title": "Great Basin Montane Watersheds - Pour Points (Feature Layer)" - }, - "description": "Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573<div><br /></div><div><a href='https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Great+Basin+Montane' target='_blank' rel='nofollow ugc noopener noreferrer'>Metadata and Downloads</a><br /></div>", - "distribution_titles": [ - "ArcGIS GeoService", - "CSV", - "GeoJSON", - "KML", - "Shapefile", - "ArcGIS Hub Dataset", - "ISO-19139 metadata" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/9f779d0c-71e4-4bc1-842f-76caf2a34f6a", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/9f779d0c-71e4-4bc1-842f-76caf2a34f6a/raw", - "has_download": false, - "has_spatial": true, - "identifier": "https://www.arcgis.com/home/item.html?id=0eebaa75bfe342d5a4f7437ddf0691bd&sublayer=1", - "keyword": [ - "Great Basin", - "Great Basin watershed characteristics", - "Great Basin watershed database", - "Open Data", - "climate", - "ecosystem resista", - "fire", - "geomorphology", - "geoscientificInformation", - "inlandWaters", - "meadows", - "mountain range delineation", - "riparian", - "species", - "watershed delineation" - ], - "last_harvested_date": "2026-09-29T21:31:16.316546", - "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": "U.S. Forest Service", - "slug": "great-basin-montane-watersheds-pour-points-feature-layer", - "spatial_centroid": { - "lat": 39.97036, - "lon": -116.87796 - }, - "spatial_shape": { - "coordinates": [ - [ - [ - -120.412, - 37.6856 - ], - [ - -120.412, - 43.3975 - ], - [ - -111.5769, - 43.3975 - ], - [ - -111.5769, - 37.6856 - ], - [ - -120.412, - 37.6856 - ] - ] - ], - "type": "Polygon" - }, - "theme": [ - "geospatial" - ], - "title": "Great Basin Montane Watersheds - Pour Points (Feature Layer)", - "type": "dataset" - }, - { - "_score": 7.6334896, - "_sort": [ - 1790717452418, - 7.6334896, - 4, - "1a85cb8e-bfcd-4ee3-b40f-ea15e087a213" - ], - "dcat": { - "@type": "dcat:Dataset", - "accessLevel": "public", - "accrualPeriodicity": "irregular", - "bureauCode": [ - "005:18" - ], - "contactPoint": { - "fn": "Liebig, Mark A.", - "hasEmail": "mailto:mark.liebig@usda.gov" - }, - "description": "<p dir=\"ltr\">Retaining crop residue on the soil surface is important in semiarid cropping systems, where dry conditions and variable weather can render the soil susceptible to degradation and moisture loss. Conversely, removing crop residue can generate an additional income stream for agricultural producers, while broadening the spectrum of uses from harvestable commodities. Understanding potential tradeoffs of crop residue removal across a range of agroecosystems over the long-term is essential to adequately guide management decisions. A study was conducted to quantify crop and soil responses to continuous spring wheat treatments with and without straw removal, each managed under minimum and no-tillage. Treatments were replicated three times and deployed over a 24-yr period. Soil coverage by crop residue was measured annually using two 25 point transects spaced equally along a 7.6 m cable. Spring wheat aboveground biomass was measured prior to combine harvest using 0.33 m2 frames. Biomass samples were threshed to separate grain from straw. Soil samples were collected in 2018 with a hydraulic probe to a 152.4 cm depth in increments of 0-7.6, 7.6-15.2, 15.2-30.5, 30.5-61.0, 61.0-91.4, 91.4-121.9, and 121.9-152.4 cm. Separate samples for aggregate stability analysis were collected with a trowel from the 0-7.6 cm depth. Soil samples were evaluated for soil bulk density, water-stable aggregates (WSA), electrical conductivity, soil pH, nitrate-nitrogen, available phosphorus, sulfate-sulfur, exchangeable cations (Ca, Mg, K, Na), micronutrients (B, Cu, Fe, Mn, Zn), total soil nitrogen, total carbon, inorganic carbon, and particulate organic matter (POM) carbon and nitrogen. Particulate organic matter (POM) was estimated from material retained on a 0.053 mm sieve analyzed for carbon and nitrogen content by dry combustion. Analyses for POM and WSA were conducted for the 0-7.6 cm depth only. Data may be used to better understand crop and soil property responses to residue management and tillage practices under rainfed conditions within a semiarid continental climate. Applicable USDA soil types include Temvik, Wilton, Grassna, Linton, Mandan, and Williams.</p><p dir=\"ltr\">The SQM Data Dictionary describes element/value names, data type, etc. for each spreadsheet tab, and the Metadata files describe attributes and units for their respective data files.</p><p><br></p><p dir=\"ltr\"><br></p>", - "distribution": [ - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673745", - "format": "xlsx", - "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", - "title": "SQM_Data Dictionary.xlsx" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673748", - "format": "xlsx", - "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", - "title": "SQM_AllDepthsSoil_2018.xlsx" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673751", - "format": "xlsx", - "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", - "title": "SQM_Crop_Aboveground Biomass.xlsx" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673754", - "format": "xlsx", - "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", - "title": "SQM_Soil Cover.xlsx" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673757", - "format": "xlsx", - "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", - "title": "SQM_WSA&POM.xlsx" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673760", - "format": "csv", - "mediaType": "text/csv", - "title": "SQM_AllDepthsSoil_2018_Data.csv" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673763", - "format": "csv", - "mediaType": "text/csv", - "title": "SQM_AllDepthsSoil_2018_Metadata.csv" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673766", - "format": "csv", - "mediaType": "text/csv", - "title": "SQM_Crop_Aboveground Biomass_Data.csv" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673769", - "format": "csv", - "mediaType": "text/csv", - "title": "SQM_Crop_Aboveground Biomass_Metadata.csv" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673772", - "format": "csv", - "mediaType": "text/csv", - "title": "SQM_Soil Cover_Data.csv" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673775", - "format": "csv", - "mediaType": "text/csv", - "title": "SQM_Soil Cover_Metadata.csv" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673778", - "format": "csv", - "mediaType": "text/csv", - "title": "SQM_WSA&POM_Data.csv" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/66673781", - "format": "csv", - "mediaType": "text/csv", - "title": "SQM_WSA&POM_Metadata.csv" - } - ], - "identifier": "10.15482/USDA.ADC/32911835.v1", - "keyword": [ - "No-tillage", - "Residue management", - "Semiarid cropping systems", - "Soil cover", - "Spring wheat" - ], - "license": "https://creativecommons.org/publicdomain/zero/1.0/", - "modified": "2026-07-24", - "programCode": [ - "005:040" - ], - "publisher": { - "@type": "org:Organization", - "name": "Agricultural Research Service" - }, - "spatial": "{\"type\": \"Point\", \"coordinates\": [-100.95, 46.77099999999999]}", - "temporal": "1994-04-01/2018-04-01", - "title": "Data from: Crop and Soil Responses to 24 years of Wheat Residue Removal and Tillage" - }, - "description": "<p dir=\"ltr\">Retaining crop residue on the soil surface is important in semiarid cropping systems, where dry conditions and variable weather can render the soil susceptible to degradation and moisture loss. Conversely, removing crop residue can generate an additional income stream for agricultural producers, while broadening the spectrum of uses from harvestable commodities. Understanding potential tradeoffs of crop residue removal across a range of agroecosystems over the long-term is essential to adequately guide management decisions. A study was conducted to quantify crop and soil responses to continuous spring wheat treatments with and without straw removal, each managed under minimum and no-tillage. Treatments were replicated three times and deployed over a 24-yr period. Soil coverage by crop residue was measured annually using two 25 point transects spaced equally along a 7.6 m cable. Spring wheat aboveground biomass was measured prior to combine harvest using 0.33 m2 frames. Biomass samples were threshed to separate grain from straw. Soil samples were collected in 2018 with a hydraulic probe to a 152.4 cm depth in increments of 0-7.6, 7.6-15.2, 15.2-30.5, 30.5-61.0, 61.0-91.4, 91.4-121.9, and 121.9-152.4 cm. Separate samples for aggregate stability analysis were collected with a trowel from the 0-7.6 cm depth. Soil samples were evaluated for soil bulk density, water-stable aggregates (WSA), electrical conductivity, soil pH, nitrate-nitrogen, available phosphorus, sulfate-sulfur, exchangeable cations (Ca, Mg, K, Na), micronutrients (B, Cu, Fe, Mn, Zn), total soil nitrogen, total carbon, inorganic carbon, and particulate organic matter (POM) carbon and nitrogen. Particulate organic matter (POM) was estimated from material retained on a 0.053 mm sieve analyzed for carbon and nitrogen content by dry combustion. Analyses for POM and WSA were conducted for the 0-7.6 cm depth only. Data may be used to better understand crop and soil property responses to residue management and tillage practices under rainfed conditions within a semiarid continental climate. Applicable USDA soil types include Temvik, Wilton, Grassna, Linton, Mandan, and Williams.</p><p dir=\"ltr\">The SQM Data Dictionary describes element/value names, data type, etc. for each spreadsheet tab, and the Metadata files describe attributes and units for their respective data files.</p><p><br></p><p dir=\"ltr\"><br></p>", - "distribution_titles": [ - "SQM_Data Dictionary.xlsx", - "SQM_AllDepthsSoil_2018.xlsx", - "SQM_Crop_Aboveground Biomass.xlsx", - "SQM_Soil Cover.xlsx", - "SQM_WSA&POM.xlsx", - "SQM_AllDepthsSoil_2018_Data.csv", - "SQM_AllDepthsSoil_2018_Metadata.csv", - "SQM_Crop_Aboveground Biomass_Data.csv", - "SQM_Crop_Aboveground Biomass_Metadata.csv", - "SQM_Soil Cover_Data.csv", - "SQM_Soil Cover_Metadata.csv", - "SQM_WSA&POM_Data.csv", - "SQM_WSA&POM_Metadata.csv" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/b353826f-7144-495a-b581-1fe539e78a67", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/b353826f-7144-495a-b581-1fe539e78a67/raw", - "has_download": true, - "has_spatial": true, - "identifier": "10.15482/USDA.ADC/32911835.v1", - "keyword": [ - "No-tillage", - "Residue management", - "Semiarid cropping systems", - "Soil cover", - "Spring wheat" - ], - "last_harvested_date": "2026-09-29T21:30:52.418875", - "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": "data-from-crop-and-soil-responses-to-24-years-of-wheat-residue-removal-and-tillage", - "spatial_centroid": { - "lat": 46.77099999999999, - "lon": -100.95 - }, - "spatial_shape": { - "coordinates": [ - -100.95, - 46.77099999999999 - ], - "type": "Point" - }, - "theme": [], - "title": "Data from: Crop and Soil Responses to 24 years of Wheat Residue Removal and Tillage", - "type": "dataset" - }, - { - "_score": 8.5716915, - "_sort": [ - 1790717450222, - 8.5716915, - 1, - "8735f6d5-ca99-4844-bff2-b4d1f3804945" - ], - "dcat": { - "@type": "dcat:Dataset", - "accessLevel": "public", - "accrualPeriodicity": "R/P1D", - "bureauCode": [ - "005:18" - ], - "contactPoint": { - "fn": "Baffaut, Claire", - "hasEmail": "mailto:claire.baffaut@usda.gov" - }, - "description": "<p dir=\"ltr\">This dataset includes the monthly and daily data used for the analysis of historical and future trends in precipitation and temperature at five Long-Term Agroecosystem Research (LTAR) sites: Kellogg Biological Station (KBS) in Michigan, Upper Mississippi River Basin (UMRB) in Iowa, Central Mississippi River Basin (CMRB) in Missouri, Southern Plains (SP) in Oklahoma, and Lower Mississippi River Basin (LMRB) in Mississippi. Historical data include the longest available record of daily precipitation, minimum temperature, and maximum temperature at weather stations from KBS, UMRB, CMRB, and LMRB, and the monthly 1895-2020 data from the National Ocean and Atmospheric Administration for the climate divisions that represent the five LTAR sites. Future data include 2020-2100 monthly predictions for the five sites from 26 Earth System Models and two Shared Socio-economic Pathways (SSP): the middle of the road SSP245 (a continuation of current emission rates and geo-political conditions), and the fossil fueled development scenario SSP 585 (intensification of fossil fuel energy sources and corresponding emissions). In addition, the data includes the trends calculated from historical and future data, snippets of R code used to calculate these trends, and README files that detail the content of each file.</p><p dir=\"ltr\">Trends in records of 50 years or more showed that temperatures have changed from 1900-2020, more for minimum (0.1 - 0.3 ℃ decade<sup>-1</sup>) than maximum (-0.1 - 0.2 ℃<sup> </sup>decade<sup>-1</sup>), more for winter (-0.1 - 0.3 ℃<sup> </sup>decade<sup>-1</sup>) than summer (-0.1 - 0.1 ℃ decade<sup>-1</sup>), and more often in the north than in the south. Except in Mississippi, annual precipitation has increased at rates of 25 mm decade<sup>-1</sup> or greater over 1950-2020, but monthly trends were inconsistent. Projected trends suggest continued temperature increases, highlighting the need for research on management systems that are resilient to such increases.</p>", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://geodata.nal.usda.gov/geonetwork/srv/api/records/260ae977-85ee-4b78-88be-0d5b1f86a74c/formatters/xml", - "conformsTo": "https://www.isotc211.org/2005/gmd", - "format": "xml", - "mediaType": "text/xml", - "title": "Geodata ISO 19139 metadata" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/56608406", - "format": "txt", - "mediaType": "text/plain", - "title": "README.txt" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/56608409", - "format": "txt", - "mediaType": "text/plain", - "title": "README_LOCA2.txt" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/56608412", - "format": "xlsx", - "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", - "title": "Station Data.xlsx" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/56608418", - "format": "xlsx", - "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", - "title": "NOAA Data and trends.xlsx" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/56608421", - "format": "zip", - "mediaType": "application/zip", - "title": "LOCA2-LTAR.zip" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/56608451", - "format": "xlsx", - "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", - "title": "ESM_Data_trends_AllTrends.xlsx" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/56608478", - "format": "txt", - "mediaType": "text/plain", - "title": "Climate_Indicators.txt" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/56608481", - "format": "txt", - "mediaType": "text/plain", - "title": "Coeff_Var_Trender.txt" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/56608484", - "format": "txt", - "mediaType": "text/plain", - "title": "LOCA_MKTrend.txt" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/56608487", - "format": "txt", - "mediaType": "text/plain", - "title": "ViolinPlotter.txt" - } - ], - "identifier": "10.15482/USDA.ADC/29640977.v1", - "keyword": [ - "CMIP 6 dataset", - "LTAR", - "Trend Analysis Background Data", - "precipitation", - "source code", - "temperature" - ], - "license": "https://creativecommons.org/publicdomain/zero/1.0/", - "modified": "2026-07-27", - "programCode": [ - "005:040" - ], - "publisher": { - "@type": "org:Organization", - "name": "Agricultural Research Service" - }, - "spatial": "{\"type\": \"MultiPoint\", \"coordinates\": [[-85.4, 42.4], [-93.8, 42.3], [-92.1, 39.2], [-90.5, 33.45]]}", - "temporal": "1895-01-01/2100-01-01", - "theme": [ - "geospatial" - ], - "title": "Data from: Are historical trends in weather consistent with model predictions in the Central United States?" - }, - "description": "<p dir=\"ltr\">This dataset includes the monthly and daily data used for the analysis of historical and future trends in precipitation and temperature at five Long-Term Agroecosystem Research (LTAR) sites: Kellogg Biological Station (KBS) in Michigan, Upper Mississippi River Basin (UMRB) in Iowa, Central Mississippi River Basin (CMRB) in Missouri, Southern Plains (SP) in Oklahoma, and Lower Mississippi River Basin (LMRB) in Mississippi. Historical data include the longest available record of daily precipitation, minimum temperature, and maximum temperature at weather stations from KBS, UMRB, CMRB, and LMRB, and the monthly 1895-2020 data from the National Ocean and Atmospheric Administration for the climate divisions that represent the five LTAR sites. Future data include 2020-2100 monthly predictions for the five sites from 26 Earth System Models and two Shared Socio-economic Pathways (SSP): the middle of the road SSP245 (a continuation of current emission rates and geo-political conditions), and the fossil fueled development scenario SSP 585 (intensification of fossil fuel energy sources and corresponding emissions). In addition, the data includes the trends calculated from historical and future data, snippets of R code used to calculate these trends, and README files that detail the content of each file.</p><p dir=\"ltr\">Trends in records of 50 years or more showed that temperatures have changed from 1900-2020, more for minimum (0.1 - 0.3 ℃ decade<sup>-1</sup>) than maximum (-0.1 - 0.2 ℃<sup> </sup>decade<sup>-1</sup>), more for winter (-0.1 - 0.3 ℃<sup> </sup>decade<sup>-1</sup>) than summer (-0.1 - 0.1 ℃ decade<sup>-1</sup>), and more often in the north than in the south. Except in Mississippi, annual precipitation has increased at rates of 25 mm decade<sup>-1</sup> or greater over 1950-2020, but monthly trends were inconsistent. Projected trends suggest continued temperature increases, highlighting the need for research on management systems that are resilient to such increases.</p>", - "distribution_titles": [ - "Geodata ISO 19139 metadata", - "README.txt", - "README_LOCA2.txt", - "Station Data.xlsx", - "NOAA Data and trends.xlsx", - "LOCA2-LTAR.zip", - "ESM_Data_trends_AllTrends.xlsx", - "Climate_Indicators.txt", - "Coeff_Var_Trender.txt", - "LOCA_MKTrend.txt", - "ViolinPlotter.txt" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/2815fb5f-3ff8-4f47-9868-052c07bb8b43", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/2815fb5f-3ff8-4f47-9868-052c07bb8b43/raw", - "has_download": true, - "has_spatial": true, - "identifier": "10.15482/USDA.ADC/29640977.v1", - "keyword": [ - "CMIP 6 dataset", - "LTAR", - "Trend Analysis Background Data", - "precipitation", - "source code", - "temperature" - ], - "last_harvested_date": "2026-09-29T21:30:50.222042", - "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": "data-from-are-historical-trends-in-weather-consistent-with-model-predictions-in-the-centra", - "spatial_centroid": { - "lat": 39.3375, - "lon": -90.45 - }, - "spatial_shape": { - "coordinates": [ - [ - -85.4, - 42.4 - ], - [ - -93.8, - 42.3 - ], - [ - -92.1, - 39.2 - ], - [ - -90.5, - 33.45 - ] - ], - "type": "MultiPoint" - }, - "theme": [ - "geospatial" - ], - "title": "Data from: Are historical trends in weather consistent with model predictions in the Central United States?", - "type": "dataset" - }, - { - "_score": 4.109354, - "_sort": [ - 1790717354228, - 4.109354, - 3, - "64eb3d36-297a-42e0-8ada-993f0f668373" - ], - "dcat": { - "@type": "dcat:Dataset", - "accessLevel": "public", - "bureauCode": [ - "005:18" - ], - "contactPoint": { - "fn": "Roeder, Karl, A.", - "hasEmail": "mailto:karl.roeder@usda.gov" - }, - "description": "<p dir=\"ltr\">Three .csv files. Two years of data collected on critical thermal maxima and minima (CTmax and CTmin) from 14 colonies in Oklahoma from 2017-2018. Additional data files represent thermal traits from 10 colonies in a short term (10 days) acclimation experiment and projections using environmental data for potential activity differences.</p><p dir=\"ltr\"><b><u>Abstract from paper:</u></b></p><p dir=\"ltr\">How do individuals tolerate both the hot and the cold climate of our planet? One possibility is that organisms have plastic traits like thermal tolerance that allow them to function in highly variable environments. In this study, we tested whether phenotypic plasticity of temperature tolerance (i.e. acclimatization in the field and acclimation in the lab) occurs in the red harvester ant, <i>Pogonomyrmex barbatus</i>, at two temporal scales. We first measured the upper and lower critical thermal limits (CT<sub>max</sub> and CT<sub>min</sub>) of ants monthly for two years while concurrently measuring environmental conditions. Both CT<sub>max</sub> and CT<sub>min</sub> co-varied with temperature in a predictable way; values increased in a positive, linear manner. We then experimentally tested whether CT<sub>max</sub> and CT<sub>min</sub> could shift within a shorter time period by exposing subcolonies of ants to cool (10°C), moderate (20°C), and hot (30°C) temperatures for 10 days. CT<sub>max</sub> increased only slightly at the hottest temperature treatment (+1.2°C), however CT<sub>min</sub> increased considerably under both moderate (+2.6°C) and hot treatments (+3.8°C). Combined, our results suggest that thermal tolerance of ants may be more plastic than originally hypothesized, potentially aiding an already thermophilic clade.</p><p dir=\"ltr\"><b><u>Methods from paper:</u></b></p><p dir=\"ltr\"><i>Study site and environmental temperature</i></p><p dir=\"ltr\">We sampled ant workers monthly during their annual active period in 2017 and 2018 (i.e. March-November) from 14 colonies in a 30-ha grazed prairie in the central Great Plains of Oklahoma (34.5478º N, -98.2311º W, 330 m elevation). Over two years, ground temperature was recorded every 10 minutes using HOBO U23 Pro v2 External Temperature Data loggers at three equidistant locations within the sampling area. Temperature values were then averaged per month.</p><p><br></p><p dir=\"ltr\"><i>Thermal tolerance across months</i></p><p dir=\"ltr\">During each sampling event (n = 18), we collected ~20 workers directly outside the nest of each colony and used 5 workers to measure critical thermal maximum (CT<sub>max</sub>) and 5 workers to measure critical thermal minimum (CT<sub>min</sub>). We did so using a heating/cooling assay to determine the temperature at which individuals lost muscle control. Thermal assays were conducted by placing individual ants into 1.5ml microcentrifuge tubes and plugging the tops with cotton to remove a potential thermal refuge in the cap. For CT<sub>max</sub>, tubes were placed randomly into a Thermal-Lok 2-position dry heat bath that was prewarmed to 36ºC. Every 10 minutes, individuals were checked to see if they had reached their critical thermal limit by rotating the tube to check for a righting response. The temperature was then increased by 2ºC, with the process repeated until all individuals had reached their critical thermal maximum. CT<sub>min</sub> was assayed in a similar manner, but we used a EchoThermTM IC20 chilling/heating dry bath that was precooled to 20ºC, following the methods above except with temperature lowered 2°C every 10 minutes. Additional ants from each colony were kept at ambient conditions as a control during each thermal assay, all of which survived. During each trial, we also confirmed the interior temperature of one unused vial using a thermocouple attached to an Extech MN35 Digital Mini MultiMeter. CT<sub>max</sub> and CT<sub>min</sub> values were averaged per colony for each month.</p><p><br></p><p dir=\"ltr\"><i>Thermal tolerance within a month</i></p><p dir=\"ltr\">In April of 2019, we collected ~200 workers from each of 10 separate colonies to assess if critical thermal limits could change within a single cohort of ants over a short period of time. We split each group of 200 workers into three sub-colonies containing 50 workers and placed these newly created sub-colonies into three environmental chambers set at 10ºC, 20ºC, and 30ºC with a 12:12 L:D cycle and 85% RH. The selected temperatures span the approximate range of average monthly temperatures at our study site during which ants were active. Each sub-colony was provided with water and 20% sucrose solution ad libitum in cotton plugged vials and a small petri dish with Plaster of Paris that was moistened every other day. Critical thermal limits (CT<sub>max</sub> and CT<sub>min</sub>) were assayed using five individuals from each colony immediately prior to the start of the experiment and for five individuals from each sub-colony after 10 days in the environmental chambers. CT<sub>max</sub> and CT<sub>min</sub> values were averaged per colony for each temperature treatment.</p>", - "distribution": [ - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/52668251", - "format": "csv", - "mediaType": "text/csv", - "title": "File 2 Across Months Pogo Thermal.csv" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/52668254", - "format": "csv", - "mediaType": "text/csv", - "title": "File 3 Within Month Pogo Thermal.csv" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://ndownloader.figshare.com/files/52668257", - "format": "csv", - "mediaType": "text/csv", - "title": "File 1 METADATA Pogo Thermal.csv" - } - ], - "identifier": "10.15482/USDA.ADC/28459058.v1", - "keyword": [ - "Critical thermal limits", - "Pogonomyrmex barbatus", - "temperature", - "traits" - ], - "license": "https://www.usa.gov/publicdomain/label/1.0/", - "modified": "2025-12-19", - "programCode": [ - "005:040" - ], - "publisher": { - "@type": "org:Organization", - "name": "Agricultural Research Service" - }, - "temporal": "2017-03-01/2018-11-30", - "title": "Data from: Temporal plasticity of thermal tolerance in ants" - }, - "description": "<p dir=\"ltr\">Three .csv files. Two years of data collected on critical thermal maxima and minima (CTmax and CTmin) from 14 colonies in Oklahoma from 2017-2018. Additional data files represent thermal traits from 10 colonies in a short term (10 days) acclimation experiment and projections using environmental data for potential activity differences.</p><p dir=\"ltr\"><b><u>Abstract from paper:</u></b></p><p dir=\"ltr\">How do individuals tolerate both the hot and the cold climate of our planet? One possibility is that organisms have plastic traits like thermal tolerance that allow them to function in highly variable environments. In this study, we tested whether phenotypic plasticity of temperature tolerance (i.e. acclimatization in the field and acclimation in the lab) occurs in the red harvester ant, <i>Pogonomyrmex barbatus</i>, at two temporal scales. We first measured the upper and lower critical thermal limits (CT<sub>max</sub> and CT<sub>min</sub>) of ants monthly for two years while concurrently measuring environmental conditions. Both CT<sub>max</sub> and CT<sub>min</sub> co-varied with temperature in a predictable way; values increased in a positive, linear manner. We then experimentally tested whether CT<sub>max</sub> and CT<sub>min</sub> could shift within a shorter time period by exposing subcolonies of ants to cool (10°C), moderate (20°C), and hot (30°C) temperatures for 10 days. CT<sub>max</sub> increased only slightly at the hottest temperature treatment (+1.2°C), however CT<sub>min</sub> increased considerably under both moderate (+2.6°C) and hot treatments (+3.8°C). Combined, our results suggest that thermal tolerance of ants may be more plastic than originally hypothesized, potentially aiding an already thermophilic clade.</p><p dir=\"ltr\"><b><u>Methods from paper:</u></b></p><p dir=\"ltr\"><i>Study site and environmental temperature</i></p><p dir=\"ltr\">We sampled ant workers monthly during their annual active period in 2017 and 2018 (i.e. March-November) from 14 colonies in a 30-ha grazed prairie in the central Great Plains of Oklahoma (34.5478º N, -98.2311º W, 330 m elevation). Over two years, ground temperature was recorded every 10 minutes using HOBO U23 Pro v2 External Temperature Data loggers at three equidistant locations within the sampling area. Temperature values were then averaged per month.</p><p><br></p><p dir=\"ltr\"><i>Thermal tolerance across months</i></p><p dir=\"ltr\">During each sampling event (n = 18), we collected ~20 workers directly outside the nest of each colony and used 5 workers to measure critical thermal maximum (CT<sub>max</sub>) and 5 workers to measure critical thermal minimum (CT<sub>min</sub>). We did so using a heating/cooling assay to determine the temperature at which individuals lost muscle control. Thermal assays were conducted by placing individual ants into 1.5ml microcentrifuge tubes and plugging the tops with cotton to remove a potential thermal refuge in the cap. For CT<sub>max</sub>, tubes were placed randomly into a Thermal-Lok 2-position dry heat bath that was prewarmed to 36ºC. Every 10 minutes, individuals were checked to see if they had reached their critical thermal limit by rotating the tube to check for a righting response. The temperature was then increased by 2ºC, with the process repeated until all individuals had reached their critical thermal maximum. CT<sub>min</sub> was assayed in a similar manner, but we used a EchoThermTM IC20 chilling/heating dry bath that was precooled to 20ºC, following the methods above except with temperature lowered 2°C every 10 minutes. Additional ants from each colony were kept at ambient conditions as a control during each thermal assay, all of which survived. During each trial, we also confirmed the interior temperature of one unused vial using a thermocouple attached to an Extech MN35 Digital Mini MultiMeter. CT<sub>max</sub> and CT<sub>min</sub> values were averaged per colony for each month.</p><p><br></p><p dir=\"ltr\"><i>Thermal tolerance within a month</i></p><p dir=\"ltr\">In April of 2019, we collected ~200 workers from each of 10 separate colonies to assess if critical thermal limits could change within a single cohort of ants over a short period of time. We split each group of 200 workers into three sub-colonies containing 50 workers and placed these newly created sub-colonies into three environmental chambers set at 10ºC, 20ºC, and 30ºC with a 12:12 L:D cycle and 85% RH. The selected temperatures span the approximate range of average monthly temperatures at our study site during which ants were active. Each sub-colony was provided with water and 20% sucrose solution ad libitum in cotton plugged vials and a small petri dish with Plaster of Paris that was moistened every other day. Critical thermal limits (CT<sub>max</sub> and CT<sub>min</sub>) were assayed using five individuals from each colony immediately prior to the start of the experiment and for five individuals from each sub-colony after 10 days in the environmental chambers. CT<sub>max</sub> and CT<sub>min</sub> values were averaged per colony for each temperature treatment.</p>", - "distribution_titles": [ - "File 2 Across Months Pogo Thermal.csv", - "File 3 Within Month Pogo Thermal.csv", - "File 1 METADATA Pogo Thermal.csv" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/c9de25a1-6dbb-4130-8884-edf13d785946", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/c9de25a1-6dbb-4130-8884-edf13d785946/raw", - "has_download": true, - "has_spatial": false, - "identifier": "10.15482/USDA.ADC/28459058.v1", - "keyword": [ - "Critical thermal limits", - "Pogonomyrmex barbatus", - "temperature", - "traits" - ], - "last_harvested_date": "2026-09-29T21:29:14.228362", - "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": "data-from-temporal-plasticity-of-thermal-tolerance-in-ants", - "spatial_centroid": null, - "spatial_shape": null, - "theme": [], - "title": "Data from: Temporal plasticity of thermal tolerance in ants", - "type": "dataset" - }, - { - "_score": 14.256802, - "_sort": [ - 1790717341856, - 14.256802, - 1, - "a592d3cf-ad2e-4a98-a2bf-e2f7fdd71671" - ], - "dcat": { - "@type": "dcat:Dataset", - "accessLevel": "public", - "bureauCode": [ - "005:18" - ], - "contactPoint": { - "fn": "Bandaru, Varaprasad", - "hasEmail": "mailto:prasad.bandaru@usda.gov" - }, - "description": "<p dir=\"ltr\">The Environmental Policy Integrated Climate (EPIC) model is a comprehensive, field-scale agroecosystem model widely used for both diagnostic and prognostic analyses in agriculture. 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GeoEPIC automates input generation from spatial datasets, model calibration, simulation execution, and output post-processing.</p>", - "distribution_titles": [ - "https://smarsgroup.github.io/geo_epic_win/reference/api/core/" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/6447e1b6-a153-4284-af30-90b2f33b3567", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/6447e1b6-a153-4284-af30-90b2f33b3567/raw", - "has_download": true, - "has_spatial": false, - "identifier": "10779/USDA.ADC.31151371.v1", - "keyword": [ - "EPIC crop model", - "Python", - "Spatial Modeling Approach" - ], - "last_harvested_date": "2026-09-29T21:29:01.856568", - "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": "geoepic-python-package", - "spatial_centroid": null, - "spatial_shape": null, - "theme": [], - "title": "GeoEPIC Python Package", - "type": "dataset" - }, - { - "_score": 15.701256, + "_score": 15.82171, "_sort": [ 1790716967608, - 15.701256, + 15.82171, 0, "b2ec759a-d36c-4a8a-8737-6ad81de3c29f" ], @@ -2298,10 +3039,10 @@ "type": "dataset" }, { - "_score": 15.011588, + "_score": 15.132757, "_sort": [ 1790627147421, - 15.011588, + 15.132757, 0, "2a97fdb4-c319-49c1-8ff4-bbbd67a56189" ], @@ -2389,10 +3130,10 @@ "type": "dataset" }, { - "_score": 11.00169, + "_score": 11.115782, "_sort": [ 1790626214864, - 11.00169, + 11.115782, 0, "4bea04d4-95b3-4714-a356-3f9d4e7f8e01" ], @@ -2498,10 +3239,10 @@ "type": "dataset" }, { - "_score": 10.691224, + "_score": 10.804387, "_sort": [ 1790626177254, - 10.691224, + 10.804387, 0, "9ba67666-f4e2-413a-98be-194135780418" ], @@ -2607,10 +3348,10 @@ "type": "dataset" }, { - "_score": 10.998594, + "_score": 11.106911, "_sort": [ 1790626069263, - 10.998594, + 11.106911, 0, "aa65ddf0-6c74-47ab-9a12-3945c9347a5d" ], @@ -2716,10 +3457,10 @@ "type": "dataset" }, { - "_score": 10.609986, + "_score": 10.718442, "_sort": [ 1790625991368, - 10.609986, + 10.718442, 0, "195b09f9-9b1c-4cb2-a6b9-ee0ddbfa35ca" ], @@ -2825,10 +3566,10 @@ "type": "dataset" }, { - "_score": 6.690813, + "_score": 6.774248, "_sort": [ 1790625807855, - 6.690813, + 6.774248, 0, "b5d2a808-de12-45de-bcee-2696e0e118de" ], @@ -2949,10 +3690,10 @@ "type": "dataset" }, { - "_score": 6.679386, + "_score": 6.765745, "_sort": [ 1790625807543, - 6.679386, + 6.765745, 0, "5867b442-5645-45c0-a6bd-21d467fafb64" ], @@ -3073,10 +3814,10 @@ "type": "dataset" }, { - "_score": 5.624161, + "_score": 5.7003593, "_sort": [ 1790625799376, - 5.624161, + 5.7003593, 0, "a594f1ae-14e3-4ffc-8e27-789ab01b5f70" ], @@ -3201,10 +3942,10 @@ "type": "dataset" }, { - "_score": 23.991798, + "_score": 24.167027, "_sort": [ 1790558805887, - 23.991798, + 24.167027, 0, "212db81d-5389-43b8-8ca1-7e2620d9efa0" ], @@ -3361,10 +4102,10 @@ "type": "dataset" }, { - "_score": 24.532293, + "_score": 24.703028, "_sort": [ 1790558576514, - 24.532293, + 24.703028, 0, "f4f44ad4-d0f5-40f0-afee-7954aae64e28" ], @@ -3521,10 +4262,10 @@ "type": "dataset" }, { - "_score": 23.702938, + "_score": 23.866062, "_sort": [ 1790558194744, - 23.702938, + 23.866062, 0, "79ae3bf4-8b57-4695-acd8-fb902a2a14ce" ], @@ -3681,10 +4422,10 @@ "type": "dataset" }, { - "_score": 24.765182, + "_score": 24.950487, "_sort": [ 1790558150225, - 24.765182, + 24.950487, 0, "66bdb0c0-0a01-4ec7-b687-d237ceb6c06b" ], @@ -3831,10 +4572,10 @@ "type": "dataset" }, { - "_score": 29.216888, + "_score": 29.455347, "_sort": [ 1790558055217, - 29.216888, + 29.455347, 4, "423fc091-fe9a-4198-ba15-a156134fd8fe" ], @@ -3961,10 +4702,10 @@ "type": "dataset" }, { - "_score": 8.604595, + "_score": 8.648157, "_sort": [ 1790557674977, - 8.604595, + 8.648157, 0, "23a3b81c-25e7-43d4-8a7f-deaefef6723b" ], @@ -3978,1002 +4719,4 @@ "fn": "Curtis M. Belyea", "hasEmail": "mailto:cbelyea@ncsu.edu" }, - "description": "This dataset represents the extent of urbanization (for the year indicated) predicted by the model SLEUTH, developed by Dr. Keith C. Clarke, at the University of California, Santa Barbara, Department of Geography and modified by David I. Donato of the United States Geological Survey (USGS) Eastern Geographic Science Center (EGSC). Further model modification and implementation was performed at the Biodiversity and Spatial Information Center at North Carolina State University. Purpose: Urban growth probability extents throughout the 21st century for the Southeast Regional Assessment Project, which encompasses the states of Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee and Virginia and parts of the states of Arkansas, Illinois, Indiana, Louisiana, Maryland, Missouri, Ohio and West Virginia. Credit: Southeast Regional Assessment Project; Biodiversity and Spatial Information Center, North Carolina State University, Raleigh, North Carolina 27695, Curtis M. Belyea. Use Limitation: This data set is not intended for site-specific analyses. Interpretations derived from its use are suited for regional and planning purposes only. These data are not intended to be used at scales larger than 1:100,000. Acknowledgment of Biodiversity and Spatial Analysis Center at North Carolina State University is appreciated.", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://api.water.usgs.gov/gdp/pygeoapi/stac/stac-collection/serap/serap_urb", - "description": "Landing page for access to the data", - "format": "XML", - "mediaType": "application/http", - "title": "Digital Data" - }, - { - "@type": "dcat:Distribution", - "description": "The metadata original format", - "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.67780a7ed34edab7af6e2877.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_67780a7ed34edab7af6e2877", - "keyword": [ - "Alabama", - "Florida", - "Georgia", - "North Carolina", - "SLEUTH", - "South Carolina", - "Southeast US", - "USGS:67780a7ed34edab7af6e2877", - "Virginia", - "climate change", - "climatologyMeteorologyAtmosphere", - "development", - "geospatial datasets", - "land use change", - "project Gigalopolis", - "society", - "structure", - "transportation", - "urbanization" - ], - "modified": "2026-09-25T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-85.4825, 28.2716, -74.9808, 38.1625", - "theme": [ - "geospatial" - ], - "title": "Urban Growth Projection for Southeast Regional Assessment Project" - }, - "description": "This dataset represents the extent of urbanization (for the year indicated) predicted by the model SLEUTH, developed by Dr. Keith C. Clarke, at the University of California, Santa Barbara, Department of Geography and modified by David I. Donato of the United States Geological Survey (USGS) Eastern Geographic Science Center (EGSC). Further model modification and implementation was performed at the Biodiversity and Spatial Information Center at North Carolina State University. Purpose: Urban growth probability extents throughout the 21st century for the Southeast Regional Assessment Project, which encompasses the states of Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee and Virginia and parts of the states of Arkansas, Illinois, Indiana, Louisiana, Maryland, Missouri, Ohio and West Virginia. Credit: Southeast Regional Assessment Project; Biodiversity and Spatial Information Center, North Carolina State University, Raleigh, North Carolina 27695, Curtis M. Belyea. Use Limitation: This data set is not intended for site-specific analyses. Interpretations derived from its use are suited for regional and planning purposes only. These data are not intended to be used at scales larger than 1:100,000. 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This is a subset of the OBIS-USA dataset where Bivalvia records were queried on December 2, 2014. After initial queries, the remaining data were further queried to retain only samples within 1000 kilometers of the U.S. shoreline. Spatial queries were then used to remove samples overlaying land masses. Data are provided in a geodatabase format, as well as a comma seperated values format. \nOBIS-USA provides aggregated, interoperable biogeographic data collected primarily from U.S. waters and oceanic regions--the Arctic, the Atlantic and Pacific oceans, the Caribbean Sea, Gulf of Mexico and the Great Lakes. It provides access to datasets from state and federal agencies as well as educational and research institutions. OBIS-USA handles both specimen-based data and survey results. Survey data come from recovered archives and current research activities. The datasets document where and when species were observed or collected, bringing together marine biogeographic data that are spatially, taxonomically, and temporally comprehensive. The public OBIS-USA site (http://www.usgs.gov/obis-usa) provides actual data contents as well as summary data about what is contained in each dataset to assist users in evaluating suitability for use. Current functionality allows the user to locate, view, and aggregate the datasets and FGDC compliant metadata as well as to view and search the taxonomic, geographic, and temporal extent. To promote data interoperability, the data are available in accordance with the marine-focused implementation of the Darwin Core data standard. In addition to basic download functions (tab-delimited), OBIS-USA offers web services for query flexibility and a wide range of output formats, such as kml, NetCDF, MATLAB, json, and graph or map output, to enable diverse types of scientific and geospatial data use and analysis platforms and products. OBIS-USA's two web services (ERDDAP and GeoServer) enable integration of OBIS-USA biogeographic data with other data types, such as seafloor geology, physical oceanography, water chemistry, and climate data. The NOAA Environmental Research Division Data Access Program(ERRDDAP) enables users to query scientific data by flexible parameters and obtain output in many formats. Access can be found at http://www1.usgs.gov/erddap/tabledap/AllMBG.html. OBIS-USA uses the tabledap component of ERDDAP to access Darwin-Core-type ta + "description": "This dataset represents the extent of urbanization (for the year indicated) predicted by the model SLEUTH, developed by Dr. Keith C. Clarke