Timeline / Data.gov — Health Datasets
changed Source changed
A new raw object was archived. Both versions are preserved. 4258 line(s) added, 4254 line(s) removed.
Evidence
| Source | Data.gov — Health Datasets |
|---|---|
| Agency | Data.gov |
| URL | https://api.gsa.gov/technology/datagov/v4/search?q=health&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY} |
| Observed by | Civic Memory, directly, on 2026-10-01T00:20:07+00:00 |
| Content type | application/json |
| Current object |
9879f4053b22623e86841e95632da9ff447ac5f1f2004fd5a7783ad8e588d730
download raw
metadata
|
| Previous object |
2108ce64c2c2ae61a75786cef4f0b8f6ce6c2261f3eb475e05e3a1425388b295
download raw
metadata
|
What changed derived
This diff is not evidence. It was produced by
civic-memory.diff_engine 1.1.0 at
2026-10-01T00:20:07+00:00 by normalizing the two archived objects above. The
objects are authoritative; this reading of them can be regenerated or deleted
without loss. 4258 line(s) added, 4254 line(s) removed.
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The Community Development Block Grant (CDBG) is a federal block grant distributed (via formula) to states and local governments. Recipients use the grant funds to carry out housing, economic development, and public improvement efforts that serve low, and moderate-income communities. Such activities may fall within Asset Acquisition, Economic Development, Housing, Public Improvements, and Public Services. The Community Development Block Grant (CDBG) is a federal block grant distributed (via formula) to states and local governments. Recipients use the grant funds to carry out housing, economic development, and public improvement efforts that serve low, and moderate-income communities. This dataset provides point location, and relevant information of CDBG activities that have taken place since 1996, and which HUD classifies using one of the following categories: Asset Acquisition - activity related to acquisition, including disposition, clearance and demolition, and clean-up of contaminated Sites/brownfields. Economic Development - activity related to economic development, including commercial or industrial rehab, commercial or industrial land acquisition, commercial or industrial construction, commercial or industrial infrastructure development, direct assistance to businesses, and micro-enterprise assistance. Housing - activity related to housing, including multifamily rehab, housing services, code enforcement, operation and repair of foreclosed property and public housing modernization. Public Improvements - activity related to public improvements, including senior centers, youth centers, parks, street improvements, water/sewer improvements, child care centers, fire stations, health centers, non-residential historic preservation, etc. Public Services - activity related to public services, including senior services, legal services, youth services, employment training, health services, homebuyer counseling, food banks, etc. Other - activity related to urban renewal completion, non-profit organization capacity building, and assistance to institutions of higher education. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. Note that these data only include latitude and longitude coordinates and associated attributes for those addresses that can be geocoded to an interpolated point along a street segment, or to a ZIP+4 centroid location. While not all records are able to be geocoded and mapped, we are continuously working to improve the address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. To learn more about the CDBG Program visit: https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_CDBG Program Activity Date of Coverage: Up to 11/2023 Last Updated: 11/2023", + "@type": "vcard:Contact", + "fn": "HHS Office of the Chief Data Officer", + "hasEmail": "mailto:CDO@hhs.gov" + }, + "description": "HISTORICAL ARCHIVE (Updated through June 2026): This page contains previous versions of the HHS Data Inventory preserved for historical reference. As of September 2026, HHS modernized the inventory to support routine, dataset-level updates using the DCAT-US v3.0 metadata standard. Because the inventory methodology has evolved, record counts here are not directly comparable to current releases.\n\nTo access active data assets, view the primary machine-readable feed at <a href=\"https://tech.hhs.gov/data.json\">tech.hhs.gov/data.json</a> or search the web interface at <a href=\"https://catalog.data.gov/organization/hhs\">Catalog.Data.gov</a>.\n\nThe U.S. Department of Health and Human Services (HHS) publishes the HHS Data Inventory—a comprehensive metadata catalog of public and non-public data assets across the Department—in accordance with the Foundations for Evidence-Based Policymaking Act of 2018 (Evidence Act, Pub. L. 115-435). This inventory enhances information discovery, data usability, and government transparency by providing public visibility into data assets from all HHS Divisions.\n\n<b>Versions:</b>\n<ul>\n<li><b>118,698 metadata assets</b> (Version 2.5</a> CSV, published 06/22/26)</li>\n<li>117,809 metadata assets</b> (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2026-06-22T16-30-14.csv\">Version 2.4</a> CSV, published 05/26/26)</li>\n<li>118,407 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2026-03-27T13-00-14.csv\">Version 2.3</a> CSV, published 04/23/26)</li>\n<li>118,403 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2026-03-27T13-00-14.csv\">Version 2.2</a> CSV, published 03/27/26)</li>\n<li>113,242 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2026-02-27T11-53-28.csv\">Version 2.1</a> CSV, published 02/27/26)</li>\n<li>112,628 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2025-12-19T16-20-11.csv\">Version 2.0</a> CSV, published 12/18/25)</li>\n<li>18,641 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2025-09-22T17-52-03.csv\">Version 1.1</a> CSV, published 9/22/25)</li>\n<li>8,431 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2025-07-30T16-18-24.csv\">Version 1.0</a> CSV, published 07/30/25)</li>\n</ul>\nPublic use of this metadata and open data assets helps identify gaps, errors, and opportunities for improvement. Your feedback is essential to enhancing metadata quality and the underlying data that matters most to you. HHS is committed to continuous improvement in information quality.\n\nPlease send suggestions to <a href=\"mailto:cdo@hhs.gov\">cdo@hhs.gov</a>.", "distribution": [ { - "@type": "dcat:DataService", - "accessURL": "https://services.arcgis.com/VTyQ9soqVukalItT/arcgis/rest/services/CDBG_PROGRAM_ACTIVITY/FeatureServer/0", - "downloadURL": "https://services.arcgis.com/VTyQ9soqVukalItT/arcgis/rest/services/CDBG_PROGRAM_ACTIVITY/FeatureServer/0", - "format": "GeoServices REST", - "mediaType": "GeoServices REST", - "title": "ArcGIS REST service" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/csv?layers=0", - "downloadURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/csv?layers=0", - "format": "CSV", - "mediaType": "ftype/CSV", - "title": "CSV" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/excel?layers=0", - "downloadURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/excel?layers=0", - "format": "Excel", - "mediaType": "ftype/XLSX", - "title": "XLSX" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/featureCollection?layers=0", - "downloadURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/featureCollection?layers=0", - "format": "Feature Collection", - "mediaType": "ftype/TXT", - "title": "TXT" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/filegdb?layers=0", - "downloadURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/filegdb?layers=0", - "format": "File Geodatabase", - "mediaType": "ftype/ZIP", - "title": "ZIP" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/geoPackage?layers=0", - "downloadURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/geoPackage?layers=0", - "format": "GeoPackage", - "mediaType": "ftype/GPKG", - "title": "GPKG" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/geojson?layers=0", - "downloadURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/geojson?layers=0", - "format": "GeoJSON", - "mediaType": "ftype/GEOJSON", - "title": "GeoJSON" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/kml?layers=0", - "downloadURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/kml?layers=0", - "format": "KML", - "mediaType": "ftype/KML", - "title": "KML" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/shapefile?layers=0", - "downloadURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/shapefile?layers=0", - "format": "Shapefile", - "mediaType": "ftype/ZIP", - "title": "ZIP" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/sqlite?layers=0", - "downloadURL": "https://hudgis-hud.opendata.arcgis.com/api/download/v1/items/9ad2b3d92d0647e9afe10fe7c77d09ad/sqlite?layers=0", - "format": "SQLite Geodatabase", - "mediaType": "ftype/GDB", - "title": "GDB" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://hudgis-hud.opendata.arcgis.com/datasets/HUD::community-development-block-grant-activity", - "downloadURL": "https://hudgis-hud.opendata.arcgis.com/datasets/HUD::community-development-block-grant-activity", - "format": "Web Page", - "mediaType": "ftype/HTML", - "title": "ArcGIS Hub Dataset" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://www.arcgis.com/home/item.html?id=9ad2b3d92d0647e9afe10fe7c77d09ad&sublayer=0", - "downloadURL": "https://www.arcgis.com/home/item.html?id=9ad2b3d92d0647e9afe10fe7c77d09ad&sublayer=0", - "format": "A data layer is used to access and display data in a map or scene. Layers support ready-to-use properties and settings of data services.", - "mediaType": "ftype/HTML", - "title": "ArcGIS Data Layer" + "@type": "dcat:Distribution", + "describedBy": "https://healthdata.gov/api/views/kaw8-4tez/columns.json", + "describedByType": "application/json", + "downloadURL": "https://healthdata.gov/api/v3/views/kaw8-4tez/query.json?accessType=DOWNLOAD", + "mediaType": "application/json" + }, + { + "@type": "dcat:Distribution", + "describedBy": "https://healthdata.gov/api/views/kaw8-4tez/columns.xml", + "describedByType": "application/xml", + "downloadURL": "https://healthdata.gov/api/v3/views/kaw8-4tez/query.xml?accessType=DOWNLOAD", + "mediaType": "application/xml" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://healthdata.gov/api/v3/views/kaw8-4tez/export.csv?accessType=DOWNLOAD", + "mediaType": "text/csv" } ], - "identifier": "https://www.arcgis.com/home/item.html?id=9ad2b3d92d0647e9afe10fe7c77d09ad", - "issued": "2024-12-04", - "keyword": [ - "Boundaries", - "CDBG", - "CPD", - "Community Development Block Grant", - "Community Development Block Grant Activity", - "Community Development Programs", - "Community Planning and Development", - "Economy", - "HUD", - "HUD.Official.Content", - "Location", - "Society", - "U.S Department of Housing and Urban Development" - ], - "landingPage": { - "@id": "https://hudgis-hud.opendata.arcgis.com/datasets/HUD::community-development-block-grant-activity", - "@type": "Document", - "title": "Community Development Block Grant Activity landing page" - }, - "modified": "2025-06-02T15:51:09.327Z", + "identifier": "https://healthdata.gov/api/views/kaw8-4tez", + "issued": "2025-11-25", + "keyword": [ + "accountability", + "catalog", + "data", + "data distribution", + "discoverability", + "enterprise", + "evidence-based-guidelines", + "inventory" + ], + "landingPage": "https://healthdata.gov/d/kaw8-4tez", + "license": "https://www.usa.gov/government-works", + "modified": "2026-09-30", "programCode": [ - "025:000" + "009:110" ], "publisher": { "@type": "org:Organization", - "name": "Office of Policy Development and Research, GIS Helpdesk and Open Data Division", - "subOrganizationOf": [ - { + "name": "HHS Office of the Chief Data Officer" + }, + "theme": [ + "HHS" + ], + "title": "HHS Data Inventory Archive (Through June 2026)" + }, + "description": "HISTORICAL ARCHIVE (Updated through June 2026): This page contains previous versions of the HHS Data Inventory preserved for historical reference. As of September 2026, HHS modernized the inventory to support routine, dataset-level updates using the DCAT-US v3.0 metadata standard. Because the inventory methodology has evolved, record counts here are not directly comparable to current releases.\n\nTo access active data assets, view the primary machine-readable feed at <a href=\"https://tech.hhs.gov/data.json\">tech.hhs.gov/data.json</a> or search the web interface at <a href=\"https://catalog.data.gov/organization/hhs\">Catalog.Data.gov</a>.\n\nThe U.S. Department of Health and Human Services (HHS) publishes the HHS Data Inventory—a comprehensive metadata catalog of public and non-public data assets across the Department—in accordance with the Foundations for Evidence-Based Policymaking Act of 2018 (Evidence Act, Pub. L. 115-435). This inventory enhances information discovery, data usability, and government transparency by providing public visibility into data assets from all HHS Divisions.\n\n<b>Versions:</b>\n<ul>\n<li><b>118,698 metadata assets</b> (Version 2.5</a> CSV, published 06/22/26)</li>\n<li>117,809 metadata assets</b> (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2026-06-22T16-30-14.csv\">Version 2.4</a> CSV, published 05/26/26)</li>\n<li>118,407 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2026-03-27T13-00-14.csv\">Version 2.3</a> CSV, published 04/23/26)</li>\n<li>118,403 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2026-03-27T13-00-14.csv\">Version 2.2</a> CSV, published 03/27/26)</li>\n<li>113,242 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2026-02-27T11-53-28.csv\">Version 2.1</a> CSV, published 02/27/26)</li>\n<li>112,628 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2025-12-19T16-20-11.csv\">Version 2.0</a> CSV, published 12/18/25)</li>\n<li>18,641 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2025-09-22T17-52-03.csv\">Version 1.1</a> CSV, published 9/22/25)</li>\n<li>8,431 metadata assets (<a href=\"https://us-dhhs-aa.s3.us-east-2.amazonaws.com/kaw8-4tez_2025-07-30T16-18-24.csv\">Version 1.0</a> CSV, published 07/30/25)</li>\n</ul>\nPublic use of this metadata and open data assets helps identify gaps, errors, and opportunities for improvement. Your feedback is essential to enhancing metadata quality and the underlying data that matters most to you. HHS is committed to continuous improvement in information quality.\n\nPlease send suggestions to <a href=\"mailto:cdo@hhs.gov\">cdo@hhs.gov</a>.", + "distribution_titles": [], + "harvest_record": "https://catalog.data.gov/harvest_record/67681697-1812-4b60-815d-b3fe9e1a1f17", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/67681697-1812-4b60-815d-b3fe9e1a1f17/raw", + "has_download": true, + "has_spatial": false, + "identifier": "https://healthdata.gov/api/views/kaw8-4tez", + "keyword": [ + "accountability", + "catalog", + "data", + "data distribution", + "discoverability", + "enterprise", + "evidence-based-guidelines", + "inventory" + ], + "last_harvested_date": "2026-09-30T22:07:45.457928", + "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": 19, + "publisher": "HHS Office of the Chief Data Officer", + "slug": "hhs-data-inventory", + "spatial_centroid": null, + "spatial_shape": null, + "theme": [ + "HHS" + ], + "title": "HHS Data Inventory Archive (Through June 2026)", + "type": "dataset" + }, + { + "_score": 3.8775964, + "_sort": [ + 1790806061331, + 3.8775964, + 28, + "91285c37-3e59-42d1-8b57-4d75a2ec2fb6" + ], + "dcat": { + "@type": "dcat:Dataset", + "accessLevel": "public", + "bureauCode": [ + "009:25" + ], + "contactPoint": { + "@type": "vcard:Contact", + "fn": "National Library of Medicine", + "hasEmail": "mailto:custserv@nlm.nih.gov" + }, + "description": "The Learning Resources Database is a catalog of interactive tutorials, videos, online classes, finding aids, and other instructional resources on National Library of Medicine (NLM) products and services. 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No warranty, expressed or implied is made with regard to the accuracy of the spatial accuracy, and no liability is assumed by the U.S. Government in general, the dataset creators or the U.S. Department of Housing and Urban Development specifically, as to the spatial or attribute accuracy of the data." - ], - "spatial": "{\"@type\": \"Location\", \"bbox\": {\"coordinates\": [[[-179.147339, 71.390482], [179.778467, 71.390482], [179.778467, 17.881327], [-179.147339, 17.881327], [-179.147339, 71.390482]]], \"type\": \"Polygon\"}, \"prefLabel\": \"United States and Minor Outlying Islands\"}", - "title": "Deteriorated Paint Index by Tract" - }, - "description": "The Deteriorated Paint Index (DPI) data predicts areas at-risk of containing several pre-1980 households with large areas of deteriorated paint, a significant and common predictor of lead dust, at the Tract level. Funding for remediation and abatement is limited. To adequately target households eligible for home remediation and associated intervention efforts, local healthy homes and environmental health program administrators must identify neighborhoods that are the most “at risk” of residential lead exposure where deteriorated paint is the primary source. To address this need, the DPI uses household-level data to predict a household’s risk of deteriorated paint. The Deteriorated Paint Index (DPI) data predicts areas at-risk of containing several pre-1980 households with large areas of deteriorated paint, a significant and common predictor of lead dust. Funding for remediation and abatement is limited. To adequately target households eligible for home remediation and associated intervention efforts, local healthy homes and environmental health program administrators must identify neighborhoods that are the most “at risk” of residential lead exposure where deteriorated paint is the primary source. To address this need, the DPI uses household-level data to predict a household’s risk of deteriorated paint. Predicted risk scores were calculated using microdata from the 2011 American Housing Survey (AHS) and the 2009-2013 American Community Survey (ACS) to develop a predicted risk measure. This metric estimates the predicted percentage of occupied housing units with large areas of deteriorated paint for three geographic levels: state, county, and tract. The primary methodological goal of the analysis was to post-fit ACS households with beta parameters from an AHS model that predicted the presence of a large area of deteriorated paint. Prior research shows this methodology can be used for small area estimation. Analyses were conducted using SAS Version 9.1.4 (SAS Institute Inc., Cary, NC). Household-level ACS and AHS microdata were used. Analyses were conducted in a Census-approved partner institution Federal Statistical Research Data Center. Exposure to residential lead dust will continue to be a public health problem until housing with deteriorated lead paint is remediated. Public health practitioners interested in strategically allocating healthy homes funding should consult this dataset and overlay predicted rates of deteriorated paint with important and unique local data to develop comprehensive targeting strategies. To learn more about the Deteriorated Paint Index and associated datasets visit: Deteriorated Paint Index Interactive Web Map Deteriorated Paint Index Interactive Map User's Guide Mapping Efforts to Identify Populations at Higher Risk of Lead Exposure: HUD Perspective For questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. 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Potential health hazards exposure is a linear combination of standardized EPA estimates of air quality carcinogenic, respiratory and neurological hazards with indexing census tracts. ENVIRONMENTAL HEALTH HAZARD INDEX Summary\n\nThe environmental health hazard exposure index\nsummarizes potential exposure to harmful toxins at a neighborhood level.\nPotential health hazards exposure is a linear combination of standardized EPA\nestimates of air quality carcinogenic (c), respiratory (r) and\nneurological (n) hazards with i indexing census tracts.\n\nWhere means and standard\nerrors are estimated over\nthe national distribution.\n\nInterpretation Values are inverted and then percentile ranked\nnationally. Values range from 0 to 100. The higher the index value, the less\nexposure to toxins harmful to human health. Therefore, the higher the value,\nthe better the environmental quality of a neighborhood, where a neighborhood is\na census block-group.\n\nData Source: National Air Toxics Assessment (NATA) data, 2014.\nRelated AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 13.\n\nReferences: https://www.epa.gov/ttn/atw/natamain/\n\nTo learn more about the Environmental Health Hazard Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020", + "fn": "Morrison, William R.", + "hasEmail": "mailto:william.morrison@usda.gov" + }, + "description": "<p dir=\"ltr\">[NOTE: 2026-07-31: Data files added, see README file for descriptions]</p><p dir=\"ltr\"><br></p><p dir=\"ltr\"><i>Trapping in 2023 with a linear set of dosages of </i>(E)<i>-8-dodecenyl acetate</i></p><p dir=\"ltr\">Field trapping was done according to the methodology in Ruiz et al. 2022. The fields were located in North-Central Kansas at the Land Institute near Salina, KS. No pesticides were applied to these fields during the experiment in 2023. Starting the first week of June, six transects were set out, two in each <i>Silphium integrifolium </i>field. Each transect contained seven 30.4 cm x 30.4 cm sticky card traps (Alpha Scents, Canby, OR, USA) affixed to the top of a 1.27 cm diameter, three foot in length PVC pole that was hammered into the ground until sturdy. The cards were affixed using a 271 cm long sticky card ring holder (Olson Products Inc., Medina, OH, USA) that was bent to a 90° angle and placed inside the PVC pipe. Two large binder clips were also used to anchor the sticky card to its card holder.</p><p dir=\"ltr\">The sticky traps in each transect were spaced 10 meters apart around the perimeter of the field. Within each transect, traps were baited with a linear increase in concentrations in 2023, including either a control (50 µl of acetone), a low concentration (50 µl of a solution made by mixing 5.75 µl of (<i>E</i>)-8-dodecenyl acetate in 5 ml of acetone), or a doubled concentration (11.5 µl of (<i>E</i>)-8-dodecenyl acetate diluted in 5 ml of acetone) of (<i>E</i>)-8-dodecenyl acetate (Alfa Chemistry, Ronkonkoma, NY, USA). All lures were added to a 3-ml LDPE dropping bottle (Wheaton, DWK Life Sciences, Millville, NJ, USA). The clear sticky card traps were collected and replaced biweekly until the first <i>E. giganteana </i>adult was caught, then traps were changed weekly. The lures and control bottles were replaced once every two weeks (with lure emissions confirmed out to 14 d in Ruiz et al. 2022) and their position in the field rotated at each change. Each lure was in each position twice over the course of the season.</p><p dir=\"ltr\">When collected, the sticky cards were held in a 7.6 L (=2 gal) labeled Ziploc<sup>©</sup> bag transported back to USDA-ARS. All collected sticky traps were placed in a freezer for approximately 24 h. The total number of <i>E. giganteana</i> per trap and their distance from the lure in millimeters was recorded. In addition, the number of nontarget lepidoptera was recorded on each trap. Individual <i>E. giganteana </i>and non-target lepidoptera were only counted if more than half of the specimen was remaining on the sticky trap at the time of counting to ensure positive identification.</p><p dir=\"ltr\"><i>Trapping in 2024 with an exponential set of concentrations of </i>(E)<i>-8-dodecenyl acetate</i></p><p dir=\"ltr\">Field trapping in 2024 was conducted similarly to that in 2023 with the following modifications. Three different fields located at the Land Institute were used (<a href=\"\" target=\"_blank\">Table 1). </a><a href=\"#_msocom_1\" target=\"_blank\">[HS1]</a> Pesticides were applied once to one of the fields and adjacent to one of the others. Three transects were deployed in each of the three fields. Each transect contained four traps for a total of 36 traps. The traps were assembled similarly to those used in 2023, but a hand-made sticky card was used instead of a manufactured one to improve captures. These sticky cards were made of a laminated 21.6 × 27.9 cm (=8.5 by 11 in) piece of white cardstock paper (Astrobright, Neenah, WI, USA) coated on both sides with TAD<sup>Ⓡ</sup> all-weather adhesive (Trécé Adhesives Division, Adair, OK, USA). The sticky sides were covered with wax paper for ease of travel. Additionally, the sticky cards had a chicken wire cage placed over them in the field to try to prevent the capture of birds and other nontargets on the traps. Traps in 2024 were baited with an exponential set of concentrations of (<i>E</i>)-8-dodecenyl acetate. In each transect, there was a solvent only control (50 µl of acetone), a low concentration equivalent to the 2023 treatment (50 µl of a solution made of 5.75 µl of (<i>E</i>)-8-dodecenyl acetate diluted in 5 ml of acetone), a medium concentration (50 µl of a solution made of 78.5 µl of (<i>E</i>)-8-dodecenyl acetate diluted in 5 ml of acetone), and a high concentration (50 µl of a solution made of 580.4 µl of (<i>E</i>)-8-dodecenyl acetate diluted in 5 ml of acetone). The traps were replaced weekly, and the lures were replaced biweekly, as well as rotated positions in the transect. Each lure was in each position twice over the course of the season.</p><p dir=\"ltr\"><i>Eucosma giganteana and Silphium integrifolium collections from the field</i></p><p dir=\"ltr\"><i>Eucosma giganteana</i> cannot yet be reared successfully in the laboratory, thus we sourced all specimens from the field. Adult <i>E. giganteana </i>individuals were carefully captured by hand in one of the fields planted to <i>Silphium integrifolium</i> at the Land Institute (38.769622, -97.598576) between 22:00 and 24:00 five times a week from June to August 2024. Moths were immediately sexed and individually placed in small deli cups with appropriate labels. They were brought back to the USDA-ARS Center for Grain and Animal Health (39.1955486, -96.5987334) for the experiments described below. Once in the lab but prior to use in experiments, moths were kept in a quiet environment at approximately 23 ± 0.1℃ and 16:8 L:D photoperiod. Importantly, no lures were used to capture insects to avoid biasing the results of the experiments below. <i>Silphium integrifolium</i> flower heads were cut 1 cm below the flower and brought back on a weekly basis during the same timeframe and stored at 4°C until needed for experiments. Flower heads were never more than 4 days old prior to use.</p><p><br></p><p dir=\"ltr\"><i>Headspace Characterization</i></p><p dir=\"ltr\">Headspace was collected from the following treatments: 10 <i>E. giganteana</i> male moths only, 10 <i>E. giganteana</i> female moths only, an even mix of male and female moths (5:5), flower cuttings of <i>S. integrifolium</i>, and a blank control. For the <i>E. giganteana</i> treatments, only alive, healthy adult moths that were collected within five days were used. For the <i>S. integrifolium </i>collections, approximately 25 grams of flower heads cut the same week as collections were used.</p><p dir=\"ltr\">For each treatment, <i>E. giganteana</i> or <i>S. integrifolium</i> were placed in a clean 100-mL beaker. To prevent moth escapees, a metal mesh top was constructed and affixed to the opening of the beaker. The beaker was then placed in one of eight 500-mL glass headspace collection containers with a PTFE septum and lid. A Pora-Pak Q volatile collection trap (VCT) was inserted in the output end. The VCT consisted of an angled drip-tip collection point borosilicate glass tube with a mesh (Stainless Steel #316 screen), packed with 20 mg of PoraPak-Q™ chemical absorbent held in place with a borosilicate glass wool plug, and followed by a PTFE Teflon™ compression seal. A PTFE tube spanned from the flow meter (CADS-4CPP, Clean Air Delivery System, Sigma Scientific, LLC, Micanopy, FL, USA) to the input end of the headspace container at a flow rate of 1 L/min. Prior to that, the air was scrubbed with an activated carbon filter and was pumped in using the central air pump for the center. Samples ran for 24 h. Each volatile collection trap was collected and eluted with 150 µl of dichloromethane in a fume hood into a 2-mL GC vial containing a 250 µl glass insert with polymer feet. The solvent was gently pushed through the volatile collection trap with N<sub>2</sub> gas. At the end of collecting all the samples, 1 µl of an internal standard, tetradecane (190.5 ng), was added to each of the samples. The samples were then all capped with a magnetic screw top lid and secured with PTFE tape before being placed in a freezer at -20 ℃ until they could be run. All headspace samples were collected within 5 weeks. After each replication, the headspace collection containers were all washed with methanol and then hexane. VCTs were rinsed in triplicate with dichloromethane. A total of at least n = 5 replicates were tested for each treatment.</p><p><br></p><p dir=\"ltr\"><i>Gas Chromatography Coupled with Mass Spectrometry</i></p><p dir=\"ltr\">All headspace collection sample extracts were run on an Agilent 7890B gas chromatograph (GC) equipped with an Agilent Durabond HP-5 column (30 m length, 0.250 mm diameter and 0.25 μm film thickness) with He as the carrier gas at a constant 1.2 mL/min flow and 40 cm/s velocity. The GC was coupled with a single-quadrupole Agilent 5997B mass spectrometer (MS). The compounds were separated by auto-injecting 1 μl of each sample under splitless into the GC–MS at room temperature (approximately 23 °C). The flow rate was 18 ml/min. The GC program consisted of 40 °C for 1 min followed by 10 °C/min increases to 300 °C and then held for 26.5 min. After a solvent delay of 3 min, mass ranges between 50 and 550 atomic mass units were scanned. Compounds were tentatively identified by comparison of spectral data with those from the NIST 14 library and by GC retention index. The samples were normalized according to the following formula: (Pk<sub>sam</sub> – Pk<sub>min</sub>)/(Pk<sub>max</sub> – Pk<sub>min</sub>), where Pk<sub>sam</sub> is the peak area from the sample, Pk<sub>min </sub>is the global minimum peak area, and Pk<sub>max</sub> is the global max peak area.</p><p><br></p><p dir=\"ltr\"><i>Electroantennography of E. giganteana</i></p><p dir=\"ltr\">All<i> </i>electroantennogram (EAG) recordings of <i>E. giganteana</i> were taken from 19:00 to 23:00 which corresponded to the peak activity period of <i>E. giganteana</i> based on prior literature (Ruiz et al. 2022). Prior to recordings, the machine and software were powered on and given 30 min to warm up. 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"identifier": "https://www.arcgis.com/home/item.html?id=c7e2c62560bd4a999f0e0b2f4cee2494", - "issued": "2023-07-05", - "keyword": [ - "Boundaries", - "Economic Development", - "Economy", - "Environmental Health Hazard Index", - "Fair Housing", - "Governmental Units", - "HUD", - "Location", - "Location Affordability", - "Society", - "Tract", - "US Department of Housing and Urban Development", - "and Administrative and Statistical Boundaries", - "hud.official.content" - ], - "landingPage": { - "@id": "https://hudgis-hud.opendata.arcgis.com/datasets/HUD::environmental-health-hazard-index", - "@type": "Document", - "title": "Environmental Health Hazard Index landing page" - }, - "modified": "2023-07-21T19:08:56.471Z", + "identifier": "10.15482/USDA.ADC/28055111.v2", + "keyword": [ + "ars", + "attract-and-kill", + "behavior", + "behavioral ecology", + "behaviorally-based management", + "cgahr", + "cup plant", + "eag", + "eucosma giganteana", + "flight mill", + "giant eucosma moth", + "insect behavior", + "insect flight", + "integrated pest management", + "kansas", + "lepidoptera", + "manhattan, ks", + "mating disruption", + "monitoring", + "pest", + "physiology", + "prairie", + "semiochemicals", + "silphium", + "silphium integrifolium", + "the land institute", + "tortricidae", + "trapping", + "usda" + ], + "license": "https://creativecommons.org/publicdomain/zero/1.0/", + "modified": "2026-07-31", "programCode": [ - "025:000" + "005:040" ], "publisher": { "@type": "org:Organization", - "name": "Office of Policy Development and Research, GIS Helpdesk and Open Data Division", - "subOrganizationOf": [ - { - "@type": "org:Organization", - "name": "Department of Housing and Urban Development, Office of Policy Development and Research" - } - ] - }, - "rights": [ - "HUD and the dataset and metadata authors assume no responsibility for the use or misuse of the dataset. No warranty, expressed or implied is made with regard to the accuracy of the spatial accuracy, and no liability is assumed by the U.S. Government in general, the dataset creators or the U.S. Department of Housing and Urban Development specifically, as to the spatial or attribute accuracy of the data." - ], - "spatial": "{\"@type\": \"Location\", \"bbox\": {\"coordinates\": [[[-179.147339, 71.390482], [179.778467, 71.390482], [179.778467, 17.881327], [-179.147339, 17.881327], [-179.147339, 71.390482]]], \"type\": \"Polygon\"}, \"prefLabel\": \"United States and Minor Outlying Islands\"}", - "title": "Environmental Health Hazard Index" - }, - "description": "The Environmental Health Hazard Exposure Index summarizes potential exposure to harmful toxins at a neighborhood level. Potential health hazards exposure is a linear combination of standardized EPA estimates of air quality carcinogenic, respiratory and neurological hazards with indexing census tracts. ENVIRONMENTAL HEALTH HAZARD INDEX Summary\n\nThe environmental health hazard exposure index\nsummarizes potential exposure to harmful toxins at a neighborhood level.\nPotential health hazards exposure is a linear combination of standardized EPA\nestimates of air quality carcinogenic (c), respiratory (r) and\nneurological (n) hazards with i indexing census tracts.\n\nWhere means and standard\nerrors are estimated over\nthe national distribution.\n\nInterpretation Values are inverted and then percentile ranked\nnationally. Values range from 0 to 100. The higher the index value, the less\nexposure to toxins harmful to human health. Therefore, the higher the value,\nthe better the environmental quality of a neighborhood, where a neighborhood is\na census block-group.\n\nData Source: National Air Toxics Assessment (NATA) data, 2014.\nRelated AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 13.\n\nReferences: https://www.epa.gov/ttn/atw/natamain/\n\nTo learn more about the Environmental Health Hazard Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020", + "name": "Agricultural Research Service" + }, + "temporal": "2023-05-01/2024-10-01", + "title": "Data from: Behavioral and physiological response of <i>Eucosma giganteana </i>to semiochemicals from conspecifics and <i>Silphium integrifolium</i>" + }, + "description": "<p dir=\"ltr\">[NOTE: 2026-07-31: Data files added, see README file for descriptions]</p><p dir=\"ltr\"><br></p><p dir=\"ltr\"><i>Trapping in 2023 with a linear set of dosages of </i>(E)<i>-8-dodecenyl acetate</i></p><p dir=\"ltr\">Field trapping was done according to the methodology in Ruiz et al. 2022. The fields were located in North-Central Kansas at the Land Institute near Salina, KS. No pesticides were applied to these fields during the experiment in 2023. Starting the first week of June, six transects were set out, two in each <i>Silphium integrifolium </i>field. Each transect contained seven 30.4 cm x 30.4 cm sticky card traps (Alpha Scents, Canby, OR, USA) affixed to the top of a 1.27 cm diameter, three foot in length PVC pole that was hammered into the ground until sturdy. The cards were affixed using a 271 cm long sticky card ring holder (Olson Products Inc., Medina, OH, USA) that was bent to a 90° angle and placed inside the PVC pipe. Two large binder clips were also used to anchor the sticky card to its card holder.</p><p dir=\"ltr\">The sticky traps in each transect were spaced 10 meters apart around the perimeter of the field. Within each transect, traps were baited with a linear increase in concentrations in 2023, including either a control (50 µl of acetone), a low concentration (50 µl of a solution made by mixing 5.75 µl of (<i>E</i>)-8-dodecenyl acetate in 5 ml of acetone), or a doubled concentration (11.5 µl of (<i>E</i>)-8-dodecenyl acetate diluted in 5 ml of acetone) of (<i>E</i>)-8-dodecenyl acetate (Alfa Chemistry, Ronkonkoma, NY, USA). All lures were added to a 3-ml LDPE dropping bottle (Wheaton, DWK Life Sciences, Millville, NJ, USA). The clear sticky card traps were collected and replaced biweekly until the first <i>E. giganteana </i>adult was caught, then traps were changed weekly. The lures and control bottles were replaced once every two weeks (with lure emissions confirmed out to 14 d in Ruiz et al. 2022) and their position in the field rotated at each change. Each lure was in each position twice over the course of the season.</p><p dir=\"ltr\">When collected, the sticky cards were held in a 7.6 L (=2 gal) labeled Ziploc<sup>©</sup> bag transported back to USDA-ARS. All collected sticky traps were placed in a freezer for approximately 24 h. The total number of <i>E. giganteana</i> per trap and their distance from the lure in millimeters was recorded. In addition, the number of nontarget lepidoptera was recorded on each trap. Individual <i>E. giganteana </i>and non-target lepidoptera were only counted if more than half of the specimen was remaining on the sticky trap at the time of counting to ensure positive identification.</p><p dir=\"ltr\"><i>Trapping in 2024 with an exponential set of concentrations of </i>(E)<i>-8-dodecenyl acetate</i></p><p dir=\"ltr\">Field trapping in 2024 was conducted similarly to that in 2023 with the following modifications. Three different fields located at the Land Institute were used (<a href=\"\" target=\"_blank\">Table 1). </a><a href=\"#_msocom_1\" target=\"_blank\">[HS1]</a> Pesticides were applied once to one of the fields and adjacent to one of the others. Three transects were deployed in each of the three fields. Each transect contained four traps for a total of 36 traps. The traps were assembled similarly to those used in 2023, but a hand-made sticky card was used instead of a manufactured one to improve captures. These sticky cards were made of a laminated 21.6 × 27.9 cm (=8.5 by 11 in) piece of white cardstock paper (Astrobright, Neenah, WI, USA) coated on both sides with TAD<sup>Ⓡ</sup> all-weather adhesive (Trécé Adhesives Division, Adair, OK, USA). The sticky sides were covered with wax paper for ease of travel. Additionally, the sticky cards had a chicken wire cage placed over them in the field to try to prevent the capture of birds and other nontargets on the traps. Traps in 2024 were baited with an exponential set of concentrations of (<i>E</i>)-8-dodecenyl acetate. In each transect, there was a solvent only control (50 µl of acetone), a low concentration equivalent to the 2023 treatment (50 µl of a solution made of 5.75 µl of (<i>E</i>)-8-dodecenyl acetate diluted in 5 ml of acetone), a medium concentration (50 µl of a solution made of 78.5 µl of (<i>E</i>)-8-dodecenyl acetate diluted in 5 ml of acetone), and a high concentration (50 µl of a solution made of 580.4 µl of (<i>E</i>)-8-dodecenyl acetate diluted in 5 ml of acetone). The traps were replaced weekly, and the lures were replaced biweekly, as well as rotated positions in the transect. Each lure was in each position twice over the course of the season.</p><p dir=\"ltr\"><i>Eucosma giganteana and Silphium integrifolium collections from the field</i></p><p dir=\"ltr\"><i>Eucosma giganteana</i> cannot yet be reared successfully in the laboratory, thus we sourced all specimens from the field. Adult <i>E. giganteana </i>individuals were carefully captured by hand in one of the fields planted to <i>Silphium integrifolium</i> at the Land Institute (38.769622, -97.598576) between 22:00 and 24:00 five times a week from June to August 2024. Moths were immediately sexed and individually placed in small deli cups with appropriate labels. They were brought back to the USDA-ARS Center for Grain and Animal Health (39.1955486, -96.5987334) for the experiments described below. Once in the lab but prior to use in experiments, moths were kept in a quiet environment at approximately 23 ± 0.1℃ and 16:8 L:D photoperiod. Importantly, no lures were used to capture insects to avoid biasing the results of the experiments below. <i>Silphium integrifolium</i> flower heads were cut 1 cm below the flower and brought back on a weekly basis during the same timeframe and stored at 4°C until needed for experiments. Flower heads were never more than 4 days old prior to use.</p><p><br></p><p dir=\"ltr\"><i>Headspace Characterization</i></p><p dir=\"ltr\">Headspace was collected from the following treatments: 10 <i>E. giganteana</i> male moths only, 10 <i>E. giganteana</i> female moths only, an even mix of male and female moths (5:5), flower cuttings of <i>S. integrifolium</i>, and a blank control. For the <i>E. giganteana</i> treatments, only alive, healthy adult moths that were collected within five days were used. For the <i>S. integrifolium </i>collections, approximately 25 grams of flower heads cut the same week as collections were used.</p><p dir=\"ltr\">For each treatment, <i>E. giganteana</i> or <i>S. integrifolium</i> were placed in a clean 100-mL beaker. To prevent moth escapees, a metal mesh top was constructed and affixed to the opening of the beaker. The beaker was then placed in one of eight 500-mL glass headspace collection containers with a PTFE septum and lid. A Pora-Pak Q volatile collection trap (VCT) was inserted in the output end. The VCT consisted of an angled drip-tip collection point borosilicate glass tube with a mesh (Stainless Steel #316 screen), packed with 20 mg of PoraPak-Q™ chemical absorbent held in place with a borosilicate glass wool plug, and followed by a PTFE Teflon™ compression seal. A PTFE tube spanned from the flow meter (CADS-4CPP, Clean Air Delivery System, Sigma Scientific, LLC, Micanopy, FL, USA) to the input end of the headspace container at a flow rate of 1 L/min. Prior to that, the air was scrubbed with an activated carbon filter and was pumped in using the central air pump for the center. Samples ran for 24 h. Each volatile collection trap was collected and eluted with 150 µl of dichloromethane in a fume hood into a 2-mL GC vial containing a 250 µl glass insert with polymer feet. The solvent was gently pushed through the volatile collection trap with N<sub>2</sub> gas. At the end of collecting all the samples, 1 µl of an internal standard, tetradecane (190.5 ng), was added to each of the samples. The samples were then all capped with a magnetic screw top lid and secured with PTFE tape before being placed in a freezer at -20 ℃ until they could be run. All headspace samples were collected within 5 weeks. After each replication, the headspace collection containers were all washed with methanol and then hexane. VCTs were rinsed in triplicate with dichloromethane. A total of at least n = 5 replicates were tested for each treatment.</p><p><br></p><p dir=\"ltr\"><i>Gas Chromatography Coupled with Mass Spectrometry</i></p><p dir=\"ltr\">All headspace collection sample extracts were run on an Agilent 7890B gas chromatograph (GC) equipped with an Agilent Durabond HP-5 column (30 m length, 0.250 mm diameter and 0.25 μm film thickness) with He as the carrier gas at a constant 1.2 mL/min flow and 40 cm/s velocity. The GC was coupled with a single-quadrupole Agilent 5997B mass spectrometer (MS). The compounds were separated by auto-injecting 1 μl of each sample under splitless into the GC–MS at room temperature (approximately 23 °C). The flow rate was 18 ml/min. The GC program consisted of 40 °C for 1 min followed by 10 °C/min increases to 300 °C and then held for 26.5 min. After a solvent delay of 3 min, mass ranges between 50 and 550 atomic mass units were scanned. Compounds were tentatively identified by comparison of spectral data with those from the NIST 14 library and by GC retention index. The samples were normalized according to the following formula: (Pk<sub>sam</sub> – Pk<sub>min</sub>)/(Pk<sub>max</sub> – Pk<sub>min</sub>), where Pk<sub>sam</sub> is the peak area from the sample, Pk<sub>min </sub>is the global minimum peak area, and Pk<sub>max</sub> is the global max peak area.</p><p><br></p><p dir=\"ltr\"><i>Electroantennography of E. giganteana</i></p><p dir=\"ltr\">All<i> </i>electroantennogram (EAG) recordings of <i>E. giganteana</i> were taken from 19:00 to 23:00 which corresponded to the peak activity period of <i>E. giganteana</i> based on prior literature (Ruiz et al. 2022). Prior to recordings, the machine and software were powered on and given 30 min to warm up. 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DEFINING EXTREME TEMPERATURE EVENTS\n\nFor the purposes of this data, a\ndaytime extreme heat event is defined as daily maximum temperature (tmax) that\nmeets or exceeds the 90th percentile daily tmax for June, July, and\nAugust (JJA) during the reference period 1961-1990 and lasting for at least 3\nconsecutive days. A lower bound is set to 90 degrees Fahrenheit (F) to define\nthe minimum temperature qualifying as a daytime heat event. Likewise, a night time extreme\nheat event is defined as daily minimum temperature (tmin) that meets or exceeds\nthe 90th percentile daily tmin for JJA during the reference period\n1961-1990 and lasting for at least 3 consecutive nights. A lower bound is set\nto 75 F to define the minimum temperature qualifying as a night time heat\nevent.\nA daytime extreme cold event is\ndefined as daily maximum temperature (tmax) that is at least 10 F less than the\nmedian daily climatological January tmax over the reference period 1961-1990\nand lasting for at least 3 consecutive days. An upper bound is set at 32 F to\ndefine the maximum temperature qualifying as a daytime cold event, and a lower\nbound is set to -10 F, where any 3 or more consecutives days colder than this\nlimit is considered a cold event. A night time extreme cold event\nis defined as daily minimum temperature (tmin) that is at least 10 F less than\nthe median daily climatological January tmin over the reference period\n1961-1990 and lasting for at least 3 consecutive days. An upper bound is set at\n32 F to define the maximum temperature qualifying as a night time cold event,\nand a lower bound is set to -10 F, where any 3 or more consecutives nights\ncolder than this limit is considered a cold event. CREATING EXTREME TEMPERATURE\nSEVERITY INDEXES\n\nThe average annual event\nfrequency (events/yr), average event intensity compared to a seasonally\nrepresentative temperature (F), and the average event duration (days) are\ncomputed using the Berkeley Earth temperature observations as well as the above\ndefinitions for extreme heat and cold events. Results of those calculations are classified according to a quartile distribution of all values relative to attribute, and each cell receives a score according to its quartile class: 0 points for a cell value less than the 25th percentile, 1 point if between the 25th and 50th percentile, 2 points if between the 50th and 75th percentile, 3 points if greater than the 75th percentile. The index value represents the aggregation of quartile points awarded for each attribute of a particular cell. SUGGESTED USE OF DATA\nFields ending with the suffix, “_INDX” provide spatially relevant severity indices for min/max cold snaps and heat waves. As described previously, the value for each index represents the summation of attributes scores determined by a quartile distribution of all values for each facet of analysis. Index scores for these fields range from 0 to 9 providing for a relatively smooth surface map illustrating spatial variability.\nIn contrast, fields ending with the suffix, “_IND” are binary attributes that indicate areas where the index values for both night-time (tmin) and day-time (tmax) is >= 5 relative to each event type. Given the boolean nature of data in these fields they are best used to quickly identify areas of extreme temperature to answer policy related questions, and not necessarily for illustration or spatial analysis. For questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov . Data Dictionary: DD_Temperature Severity Index Date of Coverage: 1913 - 2013", + "fn": "Anderson, Christopher, L.", + "hasEmail": "mailto:christopher.anderson2@usda.gov" + }, + "description": "<p dir=\"ltr\">Live-attenuated vaccines are an effective pre-harvest intervention to reduce <i>Salmonella</i> colonization in food animals, but vaccine strains can persist through production and be detected on food products. Accurate identification of vaccine strains is important to ensure poultry processing facilities are not penalized by regulatory agencies for using live-attenuated vaccines. Here, in a proof-of-concept study, we evaluated whole-cell matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry as a rapid, cost-effective alternative to whole genome sequencing for distinguishing native <i>Salmonella</i> <i>enterica</i> serovar Typhimurium strains from AviPro Megan Vac 1, a live-attenuated vaccine strain of <i>Salmonella</i> Typhimurium commonly used in poultry production. Despite their close phylogenetic relationship, MALDI-TOF spectral profiles of Megan Vac 1 isolates were distinct from those of native <i>Salmonella</i> Typhimurium strains. Temporal drift was identified as the main source of spectral variation in Megan Vac 1 isolates, exceeding the effects of sample preparation method. Random forest classifiers trained on a subset of peaks demonstrated a maximum balanced accuracy of 97.4% on a test set of spectra collected at later time points. A rule-based decision tree based on intensities from a single peak at 6046 m/z retained high predictive performance, achieving 97.0% balanced accuracy, 97.8% sensitivity, and 96.2% specificity. These findings indicate that MADLI-TOF mass spectrometry is a promising alternative to whole genome sequencing for identifying vaccine strains. 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No warranty, expressed or implied is made with regard to the accuracy of the spatial accuracy, and no liability is assumed by the U.S. Government in general, the dataset creators or the U.S. Department of Housing and Urban Development specifically, as to the spatial or attribute accuracy of the data." - ], - "spatial": "{\"@type\": \"Location\", \"bbox\": {\"coordinates\": [[[-179.147339, 71.390482], [179.778467, 71.390482], [179.778467, -14.548699], [-179.147339, -14.548699], [-179.147339, 71.390482]]], \"type\": \"Polygon\"}, \"prefLabel\": \"United States and Minor Outlying Islands\"}", - "title": "Temperature Severity Indicators" - }, - "description": "The Temperature Severity Indicator data distills observational information of prolonged temperature events in the contiguous United States to inform housing and community development policy, planning, and decision making. The indicators, conveyed as a grid of 1-degree latitude by 1-degree longitude cells, are created from observational data (Berkeley Earth Lab gridded daily maximum and minimum temperature ) and consider the frequency, intensity, and duration of extreme heat and extreme cold weather events that occurred in the US between 1913 and 2012. The Temperature Severity Indicator data identifies areas subject to extreme heat and cold events in the contiguous United States in an effort to inform temperature-related housing and planning research. The indicators, conveyed as a grid of 1-degree latitude by 1-degree longitude cells, are created from observational data (Berkeley Earth Lab gridded daily maximum and minimum temperature ) and consider the frequency, intensity, and duration of extreme heat and extreme cold weather events that occurred in the US between 1913 and 2012. DEFINING EXTREME TEMPERATURE EVENTS\n\nFor the purposes of this data, a\ndaytime extreme heat event is defined as daily maximum temperature (tmax) that\nmeets or exceeds the 90th percentile daily tmax for June, July, and\nAugust (JJA) during the reference period 1961-1990 and lasting for at least 3\nconsecutive days. A lower bound is set to 90 degrees Fahrenheit (F) to define\nthe minimum temperature qualifying as a daytime heat event. Likewise, a night time extreme\nheat event is defined as daily minimum temperature (tmin) that meets or exceeds\nthe 90th percentile daily tmin for JJA during the reference period\n1961-1990 and lasting for at least 3 consecutive nights. A lower bound is set\nto 75 F to define the minimum temperature qualifying as a night time heat\nevent.\nA daytime extreme cold event is\ndefined as daily maximum temperature (tmax) that is at least 10 F less than the\nmedian daily climatological January tmax over the reference period 1961-1990\nand lasting for at least 3 consecutive days. An upper bound is set at 32 F to\ndefine the maximum temperature qualifying as a daytime cold event, and a lower\nbound is set to -10 F, where any 3 or more consecutives days colder than this\nlimit is considered a cold event. A night time extreme cold event\nis defined as daily minimum temperature (tmin) that is at least 10 F less than\nthe median daily climatological January tmin over the reference period\n1961-1990 and lasting for at least 3 consecutive days. An upper bound is set at\n32 F to define the maximum temperature qualifying as a night time cold event,\nand a lower bound is set to -10 F, where any 3 or more consecutives nights\ncolder than this limit is considered a cold event. CREATING EXTREME TEMPERATURE\nSEVERITY INDEXES\n\nThe average annual event\nfrequency (events/yr), average event intensity compared to a seasonally\nrepresentative temperature (F), and the average event duration (days) are\ncomputed using the Berkeley Earth temperature observations as well as the above\ndefinitions for extreme heat and cold events. Results of those calculations are classified according to a quartile distribution of all values relative to attribute, and each cell receives a score according to its quartile class: 0 points for a cell value less than the 25th percentile, 1 point if between the 25th and 50th percentile, 2 points if between the 50th and 75th percentile, 3 points if greater than the 75th percentile. The index value represents the aggregation of quartile points awarded for each attribute of a particular cell. SUGGESTED USE OF DATA\nFields ending with the suffix, “_INDX” provide spatially relevant severity indices for min/max cold snaps and heat waves. As described previously, the value for each index represents the summation of attributes scores determined by a quartile distribution of all values for each facet of analysis. Index scores for these fields range from 0 to 9 providing for a relatively smooth surface map illustrating spatial variability.\nIn contrast, fields ending with the suffix, “_IND” are binary attributes that indicate areas where the index values for both night-time (tmin) and day-time (tmax) is >= 5 relative to each event type. Given the boolean nature of data in these fields they are best used to quickly identify areas of extreme temperature to answer policy related questions, and not necessarily for illustration or spatial analysis. For questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov . Data Dictionary: DD_Temperature Severity Index Date of Coverage: 1913 - 2013", + "name": "Agricultural Research Service" + }, + "temporal": "2025-11-20/2026-02-10", + "title": "Data from: Distinguishing a live-attenuated Salmonella Typhimurium vaccine strain from native Salmonella Typhimurium strains using whole-cell MALDI-TOF mass spectrometry" + }, + "description": "<p dir=\"ltr\">Live-attenuated vaccines are an effective pre-harvest intervention to reduce <i>Salmonella</i> colonization in food animals, but vaccine strains can persist through production and be detected on food products. Accurate identification of vaccine strains is important to ensure poultry processing facilities are not penalized by regulatory agencies for using live-attenuated vaccines. Here, in a proof-of-concept study, we evaluated whole-cell matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry as a rapid, cost-effective alternative to whole genome sequencing for distinguishing native <i>Salmonella</i> <i>enterica</i> serovar Typhimurium strains from AviPro Megan Vac 1, a live-attenuated vaccine strain of <i>Salmonella</i> Typhimurium commonly used in poultry production. Despite their close phylogenetic relationship, MALDI-TOF spectral profiles of Megan Vac 1 isolates were distinct from those of native <i>Salmonella</i> Typhimurium strains. Temporal drift was identified as the main source of spectral variation in Megan Vac 1 isolates, exceeding the effects of sample preparation method. Random forest classifiers trained on a subset of peaks demonstrated a maximum balanced accuracy of 97.4% on a test set of spectra collected at later time points. A rule-based decision tree based on intensities from a single peak at 6046 m/z retained high predictive performance, achieving 97.0% balanced accuracy, 97.8% sensitivity, and 96.2% specificity. These findings indicate that MADLI-TOF mass spectrometry is a promising alternative to whole genome sequencing for identifying vaccine strains. 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Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. Note that these data only include latitude and longitude coordinates and associated attributes for those addresses that can be geocoded to an interpolated point along a street segment, or to a ZIP+4 centroid location. While not all records are able to be geocoded and mapped, we are continuously working to improve the address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. To learn more about the CDBG program visit: https://www.hud.gov/hudprograms/home-program. Data Dictionary: DD_HOME Program Activity Date of Coverage: 06/2026 Last Updated: Quarterly", + "029:15" + ], + "contactPoint": [ + { + "@type": "Kind", + "fn": "National Center for PTSD", + "hasEmail": "mailto:ncptsd@va.gov" + } + ], + "description": "The Study Interventions dataset includes information about each of the specific treatment arms that were studied in all RCTs. Each study arm was coded to indicate the type of intervention or comparison condition. This dataset includes the study-level Study Class as well as individual variables for each category of treatment, coded as Yes or No for each arm. Study arm treatment category variables are as follows: Pharmacotherapy (as well as a subclass such as antidepressant, antianxiety, etc.); Psychotherapy (as well as a subclass to identify trauma-focused or non-trauma-focused therapy); Complementary and Integrative Health (CIH; as well as a subclass such as relaxation or meditation); Nonpharmacologic Biological; Nonpharmacologic Cognitive; Collaborative Care; Other Treatments; Control. \n\nThe Study Intervention dataset also includes information on the format of the treatment (individual, group, couples, mixed); treatment delivery method (in person, by phone, by video, technology alone, technology assisted, written or mixed); dose or amount of treatment and, treatment completion and adherence. Use this dataset to learn about treatment studies of a particular type.\n\nEach record is an arm of the study, labeled as A, B, C, or D. 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No warranty, expressed or implied is made with reg