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 2026-09-29T00:23:17+00:00 |
| Content type | application/json |
| Current object |
b50f5a429a88dfcdfbd5e05741553826d1b02c74728370e9c6b5b1b6db9d9cb1
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| Previous object |
02e3b9841d14db8f62704391d865ab2afeb2aaa2f3fee584e55a819bd1db09a1
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What changed derived
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civic-memory.diff_engine 1.1.0 at
2026-09-29T00:23:17+00:00 by normalizing the two archived objects above. The
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without loss. 2113 line(s) added, 2994 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/f25247b1-7e75-4ec1-8847-13b03c1bdeb6", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/f25247b1-7e75-4ec1-8847-13b03c1bdeb6/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": "2026-09-28T21:08:59.591724", + "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": 9.325315, + "_sort": [ + 1790629716692, + 9.325315, + 0, + "2322d71d-7b41-4281-9b1d-3ae822eb8b46" + ], + "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/c45507ed-fa75-4302-b0cb-8f4223aa6e38", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/c45507ed-fa75-4302-b0cb-8f4223aa6e38/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": "2026-09-28T21:08:36.692344", + "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": 5.6522274, + "_sort": [ + 1790629716054, + 5.6522274, + 0, + "b4e7bd9a-2bfd-4d64-b342-41764a79011b" + ], + "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/dc5a8641-21e4-4864-84f9-649d3e217e35", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/dc5a8641-21e4-4864-84f9-649d3e217e35/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": "2026-09-28T21:08:36.054148", + "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": 8.577326, + "_sort": [ + 1790629713360, + 8.577326, + 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/e1515725-4b2b-4262-ace6-759f3ddcefad", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/e1515725-4b2b-4262-ace6-759f3ddcefad/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": "2026-09-28T21:08:33.360834", + "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": 7.61611, + "_sort": [ + 1790629650867, + 7.61611, + 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/1d70e129-e6b3-4726-b9b1-bd295b73befe", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/1d70e129-e6b3-4726-b9b1-bd295b73befe/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": "2026-09-28T21:07:30.867016", + "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": 4.1188526, + "_sort": [ + 1790629596117, + 4.1188526, + 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. 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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. 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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/a53eef07-2219-4adb-9d57-ee99d5899e03", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/a53eef07-2219-4adb-9d57-ee99d5899e03/raw", + "has_download": true, + "has_spatial": false, + "identifier": "10779/USDA.ADC.31151371.v1", + "keyword": [ + "EPIC crop model", + "Python", + "Spatial Modeling Approach" + ], + "last_harvested_date": "2026-09-28T21:06:33.294352", + "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": 15.007774, + "_sort": [ + 1790627147421, + 15.007774, + 0, + "2a97fdb4-c319-49c1-8ff4-bbbd67a56189" + ], + "dcat": { + "@type": "Dataset", + "accessRights": "public", + "contactPoint": [ + { + "@type": "Kind", + "fn": "Office of Data Science Strategy (ODSS)", + "hasEmail": "mailto:ODSSInfo@mail.nih.gov" + } + ], + "description": "The National Neighborhood Data Archive (NaNDA) is a publicly available data archive containing contextual measures for locations across the United States. NaNDA offers theoretically derived, spatially referenced, nationwide measures of the physical and social environment. Each NaNDA dataset represents a set of measures on a single topic of interest. 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NaNDA offers theoretically derived, spatially referenced, nationwide measures of the physical and social environment. Each NaNDA dataset represents a set of measures on a single topic of interest. Examples of topics to be found in NaNDA (now or in the future) include socioeconomic disadvantage and affluence, walkability, crime, land use, recreational centers, libraries, fast food, climate, healthcare, housing, public transit, and more.", + "distribution_titles": [], + "harvest_record": "https://catalog.data.gov/harvest_record/000057b6-c82b-45af-8e9d-a341ad068e21", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/000057b6-c82b-45af-8e9d-a341ad068e21/raw", + "has_download": false, + "has_spatial": false, + "identifier": "https://www.icpsr.umich.edu/web/ICPSR/series/1920", + "keyword": [ + "Behavioral and social sciences" + ], + "last_harvested_date": "2026-09-28T20:25:47.421539", + "organization": { + "aliases": [ + "US", + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "2c2fc21f-21d0-4450-af01-cf8c69b44156", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png", + "name": "U.S. Department of Health & Human Services", + "organization_type": "Federal Government", + "slug": "hhs" + }, + "parent_identifier": null, + "popularity": 0, + "publisher": "National Institute of Nursing Research (NINR)", + "slug": "national-neighborhood-data-archive-nanda", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [ + "human" + ], + "title": "National Neighborhood Data Archive (NaNDA)", + "type": "dataset" + }, + { + "_score": 11.004747, + "_sort": [ + 1790626214864, + 11.004747, + 0, + "4bea04d4-95b3-4714-a356-3f9d4e7f8e01" + ], + "dcat": { + "@type": "Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "009:20" + ], + "contactPoint": [ + { + "@type": "Kind", + "fn": "Craig Kassinger", + "hasEmail": "mailto:nephtrackingsupport@cdc.gov" + } + ], + "description": "This dataset provides data at the county level for the contiguous United States. 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National Environmental Public Health Tracking Network. Web. 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National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking. \r\n\r\nProblems or Questions? \r\nEmail trackingsupport@cdc.gov.", + "distribution_titles": [], + "harvest_record": "https://catalog.data.gov/harvest_record/f17db3f3-a0a7-49e3-9588-a27b0f9c82e2", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/f17db3f3-a0a7-49e3-9588-a27b0f9c82e2/raw", + "has_download": true, + "has_spatial": false, + "identifier": "https://data.cdc.gov/api/views/xbk2-5i4e", + "keyword": [ + "drought", + "environmental health" + ], + "last_harvested_date": "2026-09-28T20:10:14.864873", + "organization": { + "aliases": [ + "US", + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "2c2fc21f-21d0-4450-af01-cf8c69b44156", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png", + "name": "U.S. Department of Health & Human Services", + "organization_type": "Federal Government", + "slug": "hhs" + }, + "parent_identifier": null, + "popularity": 0, + "publisher": "ynz9", + "slug": "standardized-precipitation-index-1895-2016-66000", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "Standardized Precipitation Index, 1895-2016", + "type": "dataset" + }, + { + "_score": 10.69936, + "_sort": [ + 1790626177254, + 10.69936, + 0, + "9ba67666-f4e2-413a-98be-194135780418" + ], + "dcat": { + "@type": "Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "009:20" + ], + "contactPoint": [ + { + "@type": "Kind", + "fn": "Craig Kassinger", + "hasEmail": "mailto:nephtrackingsupport@cdc.gov" + } + ], + "description": "This dataset provides data at the county level for the contiguous United States. 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Learn more about drought on the Tracking Network's website: https://ephtracking.cdc.gov/showDroughtLanding.\r\n\r\nBy using these data, you signify your agreement to comply with the following requirements: \r\n1.\tUse the data for statistical reporting and analysis only. \r\n2.\tDo not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. \r\n3.\tDo not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. \r\n4.\tDo not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. \r\n5.\tAcknowledge, in all reports or presentations based on these data, the original source of the data and CDC. \r\n6.\tSuggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking. \r\n\r\nProblems or Questions? \r\nEmail trackingsupport@cdc.gov.", + "distribution": [ + { + "@type": "Distribution", + "accessURL": "https://data.cdc.gov/d/spsk-9jj6", + "describedBy": "{\"@type\": \"Standard\", \"accessURL\": \"https://data.cdc.gov/api/views/spsk-9jj6/columns.json\", \"title\": \"Columns for this asset\"}", + "downloadURL": "https://data.cdc.gov/api/v3/views/spsk-9jj6/query.json?accessType=DOWNLOAD", + "mediaType": "application/json", + "modified": "2026-09-09" + }, + { + "@type": "Distribution", + "accessURL": "https://data.cdc.gov/d/spsk-9jj6", + "describedBy": "{\"@type\": \"Standard\", \"accessURL\": \"https://data.cdc.gov/api/views/spsk-9jj6/columns.xml\", \"title\": \"Columns for this asset\"}", + "downloadURL": "https://data.cdc.gov/api/v3/views/spsk-9jj6/query.xml?accessType=DOWNLOAD", + "mediaType": "application/xml", + "modified": "2026-09-09" + }, + { + "@type": "Distribution", + "accessURL": "https://data.cdc.gov/d/spsk-9jj6", + "downloadURL": "https://data.cdc.gov/api/v3/views/spsk-9jj6/export.csv?accessType=DOWNLOAD", + "mediaType": "text/csv", + "modified": "2026-09-09" + } + ], + "identifier": "https://data.cdc.gov/api/views/spsk-9jj6", + "inventoried": "2026-09-27", + "keyword": [ + "drought", + "environmental health" + ], + "landingPage": { + "@type": "Document", + "accessURL": "https://data.cdc.gov/d/spsk-9jj6", + "description": "United States Drought Monitor, 2000-2016 - Dataset Home", + "issued": "2018-08-08", + "publisher": "[{\"@id\": \"https://data.cdc.gov/user/985s-3b5v\", \"@type\": \"Organization\", \"name\": \"ynz9\"}]", + "title": "United States Drought Monitor, 2000-2016 - Dataset Home" + }, + "modified": "2026-09-09", + "programCode": [ + "009:032" + ], + "publisher": { + "@id": "https://data.cdc.gov/user/985s-3b5v", + "@type": "Organization", + "name": "ynz9" + }, + "title": "United States Drought Monitor, 2000-2016" + }, + "description": "This dataset provides data at the county level for the contiguous United States. 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Learn more about drought on the Tracking Network's website: https://ephtracking.cdc.gov/showDroughtLanding.\r\n\r\nBy using these data, you signify your agreement to comply with the following requirements: \r\n1.\tUse the data for statistical reporting and analysis only. \r\n2.\tDo not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. \r\n3.\tDo not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. \r\n4.\tDo not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. \r\n5.\tAcknowledge, in all reports or presentations based on these data, the original source of the data and CDC. \r\n6.\tSuggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking. \r\n\r\nProblems or Questions? \r\nEmail trackingsupport@cdc.gov.", + "distribution_titles": [], + "harvest_record": "https://catalog.data.gov/harvest_record/a504ee2e-25c4-4947-88a8-02e46ac76487", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/a504ee2e-25c4-4947-88a8-02e46ac76487/raw", + "has_download": true, + "has_spatial": false, + "identifier": "https://data.cdc.gov/api/views/spsk-9jj6", + "keyword": [ + "drought", + "environmental health" + ], + "last_harvested_date": "2026-09-28T20:09:37.254555", + "organization": { + "aliases": [ + "US", + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "2c2fc21f-21d0-4450-af01-cf8c69b44156", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png", + "name": "U.S. Department of Health & Human Services", + "organization_type": "Federal Government", + "slug": "hhs" + }, + "parent_identifier": null, + "popularity": 0, + "publisher": "ynz9", + "slug": "united-states-drought-monitor-2000-2016-940a8", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "United States Drought Monitor, 2000-2016", + "type": "dataset" + }, + { + "_score": 11.010408, + "_sort": [ + 1790626069263, + 11.010408, + 0, + "aa65ddf0-6c74-47ab-9a12-3945c9347a5d" + ], + "dcat": { + "@type": "Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "009:20" + ], + "contactPoint": [ + { + "@type": "Kind", + "fn": "Craig Kassinger", + "hasEmail": "mailto:nephtrackingsupport@cdc.gov" + } + ], + "description": "This dataset provides data at the county level for the contiguous United States. It includes monthly Palmer Drought Severity Index (PDSI) data from 1895-2016 provided by the Cooperative Institute for Climate and Satellites - North Carolina. Please refer to the metadata attachment for more information.\r\n\r\nLearn more about drought on the Tracking Network's website: https://ephtracking.cdc.gov/showDroughtLanding.\r\n\r\nBy using these data, you signify your agreement to comply with the following requirements: \r\n1.\tUse the data for statistical reporting and analysis only. \r\n2.\tDo not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. \r\n3.\tDo not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. \r\n4.\tDo not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. \r\n5.\tAcknowledge, in all reports or presentations based on these data, the original source of the data and CDC. \r\n6.\tSuggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking. \r\n\r\nProblems or Questions? \r\nEmail trackingsupport@cdc.gov.", + "distribution": [ + { + "@type": "Distribution", + "accessURL": "https://data.cdc.gov/d/en5r-5ds4", + "describedBy": "{\"@type\": \"Standard\", \"accessURL\": \"https://data.cdc.gov/api/views/en5r-5ds4/columns.json\", \"title\": \"Columns for this asset\"}", + "downloadURL": "https://data.cdc.gov/api/v3/views/en5r-5ds4/query.json?accessType=DOWNLOAD", + "mediaType": "application/json", + "modified": "2026-09-10" + }, + { + "@type": "Distribution", + "accessURL": "https://data.cdc.gov/d/en5r-5ds4", + "describedBy": "{\"@type\": \"Standard\", \"accessURL\": \"https://data.cdc.gov/api/views/en5r-5ds4/columns.xml\", \"title\": \"Columns for this asset\"}", + "downloadURL": "https://data.cdc.gov/api/v3/views/en5r-5ds4/query.xml?accessType=DOWNLOAD", + "mediaType": "application/xml", + "modified": "2026-09-10" + }, + { + "@type": "Distribution", + "accessURL": "https://data.cdc.gov/d/en5r-5ds4", + "downloadURL": "https://data.cdc.gov/api/v3/views/en5r-5ds4/export.csv?accessType=DOWNLOAD", + "mediaType": "text/csv", + "modified": "2026-09-10" + } + ], + "identifier": "https://data.cdc.gov/api/views/en5r-5ds4", + "inventoried": "2026-09-27", + "keyword": [ + "drought", + "environmental health" + ], + "landingPage": { + "@type": "Document", + "accessURL": "https://data.cdc.gov/d/en5r-5ds4", + "description": "Palmer Drought Severity Index, 1895-2016 - Dataset Home", + "issued": "2018-07-26", + "publisher": "[{\"@id\": \"https://data.cdc.gov/user/985s-3b5v\", \"@type\": \"Organization\", \"name\": \"ynz9\"}]", + "title": "Palmer Drought Severity Index, 1895-2016 - Dataset Home" + }, + "modified": "2026-09-10", + "programCode": [ + "009:032" + ], + "publisher": { + "@id": "https://data.cdc.gov/user/985s-3b5v", + "@type": "Organization", + "name": "ynz9" + }, + "title": "Palmer Drought Severity Index, 1895-2016" + }, + "description": "This dataset provides data at the county level for the contiguous United States. It includes monthly Palmer Drought Severity Index (PDSI) data from 1895-2016 provided by the Cooperative Institute for Climate and Satellites - North Carolina. Please refer to the metadata attachment for more information.\r\n\r\nLearn more about drought on the Tracking Network's website: https://ephtracking.cdc.gov/showDroughtLanding.\r\n\r\nBy using these data, you signify your agreement to comply with the following requirements: \r\n1.\tUse the data for statistical reporting and analysis only. \r\n2.\tDo not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. \r\n3.\tDo not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. \r\n4.\tDo not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. \r\n5.\tAcknowledge, in all reports or presentations based on these data, the original source of the data and CDC. \r\n6.\tSuggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. Accessed: insert date. www.cdc.gov/ephtracking. \r\n\r\nProblems or Questions? \r\nEmail trackingsupport@cdc.gov.", + "distribution_titles": [], + "harvest_record": "https://catalog.data.gov/harvest_record/422d8f4f-cfe8-40e7-846e-a70d38be5ee4", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/422d8f4f-cfe8-40e7-846e-a70d38be5ee4/raw", + "has_download": true, + "has_spatial": false, + "identifier": "https://data.cdc.gov/api/views/en5r-5ds4", + "keyword": [ + "drought", + "environmental health" + ], + "last_harvested_date": "2026-09-28T20:07:49.263891", + "organization": { + "aliases": [ + "US", + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "2c2fc21f-21d0-4450-af01-cf8c69b44156", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png", + "name": "U.S. Department of Health & Human Services", + "organization_type": "Federal Government", + "slug": "hhs" + }, + "parent_identifier": null, + "popularity": 0, + "publisher": "ynz9", + "slug": "palmer-drought-severity-index-1895-2016-73abb", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [], + "title": "Palmer Drought Severity Index, 1895-2016", + "type": "dataset" + }, + { + "_score": 10.6095495, + "_sort": [ + 1790625991368, + 10.6095495, + 0, + "195b09f9-9b1c-4cb2-a6b9-ee0ddbfa35ca" + ], + "dcat": { + "@type": "Dataset", + "accessLevel": "public", + "accessRights": "public", + "bureauCode": [ + "009:20" + ], + "contactPoint": [ + { + "@type": "Kind", + "fn": "Craig Kassinger", + "hasEmail": "mailto:nephtrackingsupport@cdc.gov" + } + ], + "description": "This dataset provides data at the county level for the contiguous United States. It includes monthly Standardized Precipitation Evapotranspiration Index (SPEI) data from 1895-2016 provided by the Cooperative Institute for Climate and Satellites - North Carolina. Please refer to the metadata attachment for more information.\r\n\r\nThese data are used by the CDC's National Environmental Public Health Tracking Network to generate drought measures. Learn more about drought on the Tracking Network's website: https://ephtracking.cdc.gov/showDroughtLanding.\r\n\r\nBy using these data, you signify your agreement to comply with the following requirements: \r\n1.\tUse the data for statistical reporting and analysis only. \r\n2.\tDo not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. \r\n3.\tDo not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. \r\n4.\tDo not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. \r\n5.\tAcknowledge, in all reports or presentations based on these data, the original source of the data and CDC. \r\n6.\tSuggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. 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Learn more about drought on the Tracking Network's website: https://ephtracking.cdc.gov/showDroughtLanding.\r\n\r\nBy using these data, you signify your agreement to comply with the following requirements: \r\n1.\tUse the data for statistical reporting and analysis only. \r\n2.\tDo not attempt to learn the identity of any person included in the data and do not combine these data with other data for the purpose of matching records to identify individuals. \r\n3.\tDo not disclose of or make use of the identity of any person or establishment discovered inadvertently and report the discovery to: trackingsupport@cdc.gov. \r\n4.\tDo not imply or state, either in written or oral form, that interpretations based on the data are those of the original data sources and CDC unless the data user and data source are formally collaborating. \r\n5.\tAcknowledge, in all reports or presentations based on these data, the original source of the data and CDC. \r\n6.\tSuggested citation: Centers for Disease Control and Prevention. National Environmental Public Health Tracking Network. Web. 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Thousands of other children and families, however, participated in the Transition Demonstration Program, since supports and educational enhancements were offered to all children and families in the classrooms. The datasets are organized into four broad categories: Family Units -- There are six family unit files. A \"family unit\" record consists of information about a child or family as the result of source data taken from family interviews, records of child test scores on a child instrument, school archival records, or teacher questionnaires (Part B). If data were available from any combination of these source documents, a family unit record was generated. A broad range of variables are included under this heading. Variables range from simple demographics to standardized scores of social skill ratings as well as neighborhood factor scores and child outcome scores in reading and mathematics. These files are associated with each year of the child's schooling (kindergarten through third grade). School Unit -- There are five school unit files, organized around the year of data collection. A \"school unit\" record consists of information about a school as the result of source data captured from family interviews, a classroom teacher, or the school principal. The structure of this data file is different from others in that rather than being merged on a common key, the records are actually stacked one upon the other in groups. The first part of the file consists of family data, the middle portion consists of teacher data, and the final portion consists of principal data. A key variable to the construction of this dataset is the REC_SRC (record source) variable. It informs the user as to the source of the data in the record. The abbreviations are \"fi,\" \"ta,\" and \"qp\" for family interview, teacher questionnaire (Part A), and questionnaire for principals, respectively. The data viewed as the centerpiece of these datasets are the school climate survey variables and their associated factor scores. Classroom Unit -- There are five classroom unit files organized around the year of data collection. The data recorded focus on the classroom and are from the following sources: classroom composition, assessment profile, a developmentally appropriate practice template, and a teacher questionnaire (part a). Some of the data available address the social skills the teacher views as important to his or her particular classroom. Variables addressing diversity of both gender and ethnicity within a single classroom are included when available. Exit Interviews -- There are five Exit Interviews. Exit information was collected from the following groups: experimental and control families, family service specialists, school principals, and classroom teachers. These exit interviews were conducted upon exit from the third grade, and have been combined for both cohorts. Community Characteristics Data -- The community characteristics dataset is a hierarchical file having four distinct levels of data. The type of information available in this file may include data that describe the site, county, school district, or study school. Users of these data are strongly encouraged to consult the accompanying documentation before attempting to use these files.", + "distribution": [ + { + "@type": "Distribution", + "accessURL": "https://www.childandfamilydataarchive.org/cfda/archives/cfda/studies/4712" + } + ], + "identifier": "acf-opre-0087", + "inventoried": "2026-09-27", + "keyword": [ + "early childhood services", + "education", + "family support services", + "public health" + ], + "landingPage": { + "@type": "Document", + "accessURL": "https://www.childandfamilydataarchive.org/cfda/pages/cfda/data.html", + "title": "National Head Start/Public School Early Childhood Transition Demonstration Study, 1991-1999 - Landing Page" + }, + "publisher": { + "@type": "Organization", + "name": "Office of Planning, Research & Evaluation (OPRE)", + "subOrganizationOf": [ + { + "@type": "Organization", + "name": "Administration for Children and Families (ACF)" + } + ] + }, + "spatial": "[{\"@type\": \"Location\", \"prefLabel\": \"united states\"}]", + "theme": [ + { + "@type": "Concept", + "prefLabel": "human services" + } + ], + "title": "National Head Start/Public School Early Childhood Transition Demonstration Study, 1991-1999" + }, + "description": "In 1990, the United States Congress authorized a major program designed to enhance the early public school transitions of former Head Start children and their families. Former Head Start children, like many other children living in poverty, were at risk for poor school achievement. This new program was launched to test the value of extending comprehensive, Head Start-like supports \"upward\" through the first four years of elementary school. This project, administered by the Head Start Bureau of the Administration on Children, Youth, and Families, funded 31 local Transition Demonstration Programs in 30 states and the Navajo Nation from the 1991-1992 school year through the 1997-1998 school year and involved more than 450 public schools. The National Transition Demonstration Study was conducted to provide information about the implementation of this program and its impact on children, families, schools, and communities. More than 7,500 former Head Start children and families were enrolled in the National Study. Thousands of other children and families, however, participated in the Transition Demonstration Program, since supports and educational enhancements were offered to all children and families in the classrooms. The datasets are organized into four broad categories: Family Units -- There are six family unit files. A \"family unit\" record consists of information about a child or family as the result of source data taken from family interviews, records of child test scores on a child instrument, school archival records, or teacher questionnaires (Part B). If data were available from any combination of these source documents, a family unit record was generated. A broad range of variables are included under this heading. Variables range from simple demographics to standardized scores of social skill ratings as well as neighborhood factor scores and child outcome scores in reading and mathematics. These files are associated with each year of the child's schooling (kindergarten through third grade). School Unit -- There are five school unit files, organized around the year of data collection. A \"school unit\" record consists of information about a school as the result of source data captured from family interviews, a classroom teacher, or the school principal. The structure of this data file is different from others in that rather than being merged on a common key, the records are actually stacked one upon the other in groups. The first part of the file consists of family data, the middle portion consists of teacher data, and the final portion consists of principal data. A key variable to the construction of this dataset is the REC_SRC (record source) variable. It informs the user as to the source of the data in the record. The abbreviations are \"fi,\" \"ta,\" and \"qp\" for family interview, teacher questionnaire (Part A), and questionnaire for principals, respectively. The data viewed as the centerpiece of these datasets are the school climate survey variables and their associated factor scores. Classroom Unit -- There are five classroom unit files organized around the year of data collection. The data recorded focus on the classroom and are from the following sources: classroom composition, assessment profile, a developmentally appropriate practice template, and a teacher questionnaire (part a). Some of the data available address the social skills the teacher views as important to his or her particular classroom. Variables addressing diversity of both gender and ethnicity within a single classroom are included when available. Exit Interviews -- There are five Exit Interviews. Exit information was collected from the following groups: experimental and control families, family service specialists, school principals, and classroom teachers. These exit interviews were conducted upon exit from the third grade, and have been combined for both cohorts. Community Characteristics Data -- The community characteristics dataset is a hierarchical file having four distinct levels of data. The type of information available in this file may include data that describe the site, county, school district, or study school. Users of these data are strongly encouraged to consult the accompanying documentation before attempting to use these files.", + "distribution_titles": [], + "harvest_record": "https://catalog.data.gov/harvest_record/8e43b650-29db-40ce-a16f-7e5b8d2d0460", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/8e43b650-29db-40ce-a16f-7e5b8d2d0460/raw", + "has_download": false, + "has_spatial": true, + "identifier": "acf-opre-0087", + "keyword": [ + "early childhood services", + "education", + "family support services", + "public health" + ], + "last_harvested_date": "2026-09-28T20:03:19.376882", + "organization": { + "aliases": [ + "US", + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "2c2fc21f-21d0-4450-af01-cf8c69b44156", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png", + "name": "U.S. Department of Health & Human Services", + "organization_type": "Federal Government", + "slug": "hhs" + }, + "parent_identifier": null, + "popularity": 0, + "publisher": "Office of Planning, Research & Evaluation (OPRE)", + "slug": "national-head-start-public-school-early-childhood-transition-demonstration-study-1991-1999-1bf47", + "spatial_centroid": { + "lat": 34.4819914, + "lon": -101.6218762 + }, + "spatial_shape": { + "coordinates": [ + [ + [ + [ + -124.733253, + 24.544245 + ], + [ + -124.733253, + 49.388611 + ], + [ + -66.954811, + 49.388611 + ], + [ + -66.954811, + 24.544245 + ], + [ + -124.733253, + 24.544245 + ] + ] + ] + ], + "type": "MultiPolygon" + }, + "theme": [ + "human services" + ], + "title": "National Head Start/Public School Early Childhood Transition Demonstration Study, 1991-1999", + "type": "dataset" + }, + { + "_score": 23.982765, "_sort": [ 1790558805887, - 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The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought; habitat loss; coastal protection) to biogeochemical processes (e.g., carbon storage; water quality). In this research, our focus was on the impact of mangrove forest migration on coastal wetland soil processes and the consequent implications for coastal wetland responses to sea level rise, ecosystem resilience, and carbon storage. Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. We quantified relationships between plant community composition and structure, soil and porewater physicochemical properties, hydroperiod, and climatic conditions. The suite of measurements that we collected provide initial insights into how different geographic areas of an ecotone, with different environmental conditions, may be impacted by mangrove forest expansion and development, and how these changes may alter the supply of specific ecosystem goods and services. This file includes the site-level elevation data.\nThis work was conducted via a collaborative effort between scientists at the U.S. Geological Survey National Wetland Research Center and the Department of Biology of the University of Louisiana at Lafayette.", - "distribution_titles": [ - "Digital Data", - "Original Metadata" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/cc23b262-c377-48aa-aa35-9763db0eed9b", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/cc23b262-c377-48aa-aa35-9763db0eed9b/raw", - "has_download": true, - "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_545cfdb9e4b0ba8303f713e7", - "keyword": [ - "USGS:545cfdb9e4b0ba8303f713e7", - "climate change", - "coastal", - "elevation", - "range shift", - "wetlands" - ], - "last_harvested_date": "2026-09-27T01:33:50.505839", - "organization": { - "aliases": [ - "dept" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", - "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", - "name": "Department of the Interior", - "organization_type": "Federal Government", - "slug": "doi" - }, - "parent_identifier": null, - "popularity": 0, - "publisher": "U.S. Geological Survey", - "slug": "site-level-elevation-collection-data", - "spatial_centroid": null, - "spatial_shape": null, - "theme": [ - "geospatial" - ], - "title": "Site Level Elevation Collection Data", - "type": "dataset" - }, - { - "_score": 8.163963, - "_sort": [ - 1790472756481, - 8.163963, - 0, - "d2a41847-8a2e-4d32-b875-f8b738c3d850" - ], - "dcat": { - "accessLevel": "public", - "bureauCode": [ - "010:12" - ], - "contactPoint": { - "@type": "vcard:Contact", - "fn": "Jacob A Fleck", - "hasEmail": "mailto:jafleck@usgs.gov" - }, - "description": "Little is known about mercury concentrations at bay margins, and how climate change, including changes in temperature and precipitation, will affect mercury exposure and methylmercury (MeHg) production. In addition, the San Francisco South Bay Salt Ponds Restoration Program (SBSPRP) needs more efficient and effective mercury monitoring approaches at larger spatial scales to understand changes in mercury at a regional level. The goal of this new project, a collaboration between the USGS Western Geographic Science Center, Water Mission Area, and the California Water Science Center (CAWSC), is to develop the capacity to map total mercury (THg) and MeHg in South San Francisco Bay (SFB) through the satellite remote sensing (Sentinel-2) of two known mercury indicators: total suspended solids (TSS) and colored dissolved organic matter (CDOM). In addition to quantifying the accuracy of this approach, this research aims in South SFB using the Sentinel-2 satellite in order to 1) improve understanding of region-wide mercury spatial and temporal trends associated with weather events and wetland restoration management activities, and 2) improve mercury monitoring efficiencies and capabilities moving forward. One main project objective is to assess the capacity to detect anomalies in remotely sensed TSS, CDOM, and mercury species associated with recent weather events or restoration activities through time series analysis. \nThe optical measurements reported here were collected to aid in the characterization of water sources and mixtures and establish proxies (surrogates) for mercury and methylmercury concentrations and to provide ground-truthing for remotely sensed models of dissolved organic carbon (DOC) concentrations. Data are compiled into five tables: 1) full fluorescence spectra in vectorized format (LSB_Hg_RS_EEMs_vectors.csv), 2) full absorbance spectra for 1 centimeter (cm) path measurements (LSB_Hg_RS_1cm_ABS_scans.csv), 3) full absorbance spectra for 10 cm path measurements (SSFB_Hg_RS_10cm_ABS_scans.csv), 4) absorption coefficients derived from the 10cm path measurements (SSFB_Hg_RS_10cm_ag_scans.csv), and 5) summary file of commonly extracted optical indicators and calculated wavelength-array values derived from the optical data that correspond to arrays measured by field-based sensors for use in statistical analyses and model development (LSB_Hg_RS_OMRL_Sample_Summary.csv).", - "distribution": [ - { - "@type": "dcat:Distribution", - "accessURL": "https://doi.org/10.5066/P13JOXXA", - "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.6a1f4a72b66b018da518f7ee.xml", - "format": "XML", - "mediaType": "text/xml", - "title": "Original Metadata" - } - ], - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6a1f4a72b66b018da518f7ee", - "keyword": [ - "Absorbance", - "Aqualog", - "Dissolved Organic Matter", - "Fluorescence", - "Hydrology", - "USGS:6a1f4a72b66b018da518f7ee", - "Water Quality", - "environment", - "geoscientificInformation" - ], - "modified": "2026-09-22T00:00:00Z", - "publisher": { - "@type": "org:Organization", - "name": "U.S. Geological Survey" - }, - "spatial": "-122.4200, 37.4000, -121.9000, 37.6400", - "theme": [ - "geospatial" - ], - "title": "Optical measurements for surface water samples collected within South San Francisco Bay in support of mercury modeling" - }, - "description": "Little is known about mercury concentrations at bay margins, and how climate change, including changes in temperature and precipitation, will affect mercury exposure and methylmercury (MeHg) production. In addition, the San Francisco South Bay Salt Ponds Restoration Program (SBSPRP) needs more efficient and effective mercury monitoring approaches at larger spatial scales to understand changes in mercury at a regional level. The goal of this new project, a collaboration between the USGS Western Geographic Science Center, Water Mission Area, and the California Water Science Center (CAWSC), is to develop the capacity to map total mercury (THg) and MeHg in South San Francisco Bay (SFB) through the satellite remote sensing (Sentinel-2) of two known mercury indicators: total suspended solids (TSS) and colored dissolved organic matter (CDOM). In addition to quantifying the accuracy of this approach, this research aims in South SFB using the Sentinel-2 satellite in order to 1) improve understanding of region-wide mercury spatial and temporal trends associated with weather events and wetland restoration management activities, and 2) improve mercury monitoring efficiencies and capabilities moving forward. One main project objective is to assess the capacity to detect anomalies in remotely sensed TSS, CDOM, and mercury species associated with recent weather events or restoration activities through time series analysis. \nThe optical measurements reported here were collected to aid in the characterization of water sources and mixtures and establish proxies (surrogates) for mercury and methylmercury concentrations and to provide ground-truthing for remotely sensed models of dissolved organic carbon (DOC) concentrations. Data are compiled into five tables: 1) full fluorescence spectra in vectorized format (LSB_Hg_RS_EEMs_vectors.csv), 2) full absorbance spectra for 1 centimeter (cm) path measurements (LSB_Hg_RS_1cm_ABS_scans.csv), 3) full absorbance spectra for 10 cm path measurements (SSFB_Hg_RS_10cm_ABS_scans.csv), 4) absorption coefficients derived from the 10cm path measurements (SSFB_Hg_RS_10cm_ag_scans.csv), and 5) summary file of commonly extracted optical indicators and calculated wavelength-array values derived from the optical data that correspond to arrays measured by field-based sensors for use in statistical analyses and model development (LSB_Hg_RS_OMRL_Sample_Summary.csv).", - "distribution_titles": [ - "Digital Data", - "Original Metadata" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/15ca54a1-0bba-449c-add3-c77050e14527", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/15ca54a1-0bba-449c-add3-c77050e14527/raw", - "has_download": true, - "has_spatial": true, - "identifier": "http://datainventory.doi.gov/id/dataset/USGS_6a1f4a72b66b018da518f7ee", - "keyword": [ - "Absorbance", - "Aqualog", - "Dissolved Organic Matter", - "Fluorescence", - "Hydrology", - "USGS:6a1f4a72b66b018da518f7ee", - "Water Quality", - "environment", - "geoscientificInformation" - ], - "last_harvested_date": "2026-09-27T01:32:36.481698", - "organization": { - "aliases": [ - "dept" - ], - "code_repo_exempt": false, - "code_repo_url": null, - "description": null, - "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", - "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", - "name": "Department of the Interior", - "organization_type": "Federal Government", - "slug": "doi" - }, - "parent_identifier": null, - "popularity": 0, - "publisher": "U.S. Geological Survey", - "slug": "optical-measurements-for-surface-water-samples-collected-within-south-san-francisco-bay-in", - "spatial_centroid": { - "lat": 37.495999999999995, - "lon": -122.21200000000002 - }, - "spatial_shape": { - "coordinates": [ - [ - [ - -122.42, - 37.4 - ], - [ - -122.42, - 37.64 - ], - [ - -121.9, - 37.64 - ], - [ - -121.9, - 37.4 - ], - [ - -122.42, - 37.4 - ] - ] - ], - "type": "Polygon" - }, - "theme": [ - "geospatial" - ], - "title": "Optical measurements for surface water samples collected within South San Francisco Bay in support of mercury modeling", - "type": "dataset" - }, - { - "_score": 11.82296, - "_sort": [ - 1790472742953, - 11.82296, - 0, - "2de4c217-bd1c-4956-a485-727d64cb6f41" - ], - "dcat": { - "accessLevel": "public", - "bureauCode": [ - "010:12" - ], - "contactPoint": { - "@type": "vcard:Contact", - "fn": "Michael Osland", - "hasEmail": "mailto:mosland@usgs.gov" - }, - "description": "Winter climate change has the potential to have a large impact on coastal wetlands in the southeastern U.S. Warmer winter temperatures and reductions in the intensity of freeze events would likely lead to mangrove forest range expansion and salt marsh displacement in parts of the U.S. Gulf of Mexico and Atlantic coast. 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The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought; habitat loss; coastal protection) to biogeochemical processes (e.g., carbon storage; water quality). In this research, our focus was on the impact of mangrove forest migration on coastal wetland soil processes and the consequent implications for coastal wetland responses to sea level rise, ecosystem resilience, and carbon storage. 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Dam removal practitioners also indicated that they most often rely on climate change information garnered from conversations with colleagues, rather than from scientific research products. These results suggest that the co-production of relevant, salient research questions and readily accessible and interpretable research products (e.g., technical summaries, open access articles) may encourage practitioners to incorporate climate change science more consistently and efficiently into dam removal decisions. 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Our study specifically addressed the following questions: (1) How do ecological processes and ecosystem properties differ between salt marshes and mangrove forests; (2) As mangrove forests develop, how do their ecosystem properties change and how do these properties compare to salt marshes; (3) How do plant-soil interactions across mangrove forest structural gradients differ among three distinct locations that span the northern Gulf of Mexico; and (4) What are the implications of mangrove forest encroachment and development into salt marsh in terms of soil development, carbon and nitrogen storage, and soil strength? To address these questions, we utilized the salt marshes and natural mangrove forest structural gradients present at three distinct locations in the northern Gulf of Mexico: Cedar Key (Florida), Port Fourchon (Louisiana), and Port Aransas (Texas). Each of these locations represents a distinct combination of climate-driven abiotic conditions. 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