This table represents consumers whose Medicaid/CHIP coverage was denied or terminated following renewal and 1) whose applications were processed by an SBM through an integrated Medicaid, CHIP, and Marketplace eligibility system or 2) whose applications/information was sent by a state Medicaid or CHIP agency to an SBM through an account transfer process. Consumers who submitted applications to an SBM that can be matched to a Medicaid/CHIP record are also included. See the \"Data Sources and Metrics Definition Overview\" at http://www.medicaid.gov for a full description of the differences between the SBM operating systems and resulting data metrics, measure definitions, and general data limitations. As of the September 2023 report, this table was updated to differentiate between SBMs with an integrated Medicaid, CHIP, and Marketplace eligibility system and those with an account transfer process to better represent the percentage of QHP selections in relation to applicable consumers received and processed by the relevant SBM. State-specific variations are: \n- Maine\u2019s data and Nevada\u2019s April and May 2023 data report all applications with Medicaid/CHIP denials or terminations, not only those part of the annual renewal process. \n- Connecticut, Massachusetts, and Washington also report applications with consumers determined ineligible for Medicaid/CHIP due to procedural reasons.\n- Minnesota and New York report on eligibility and enrollment for their Basic Health Programs (BHP). Effective April 1, 2024, New York transitioned its BHP to a program operated under a section 1332 waiver, which expands eligibility to individuals with incomes up to 250% of FPL. As of the March 2024 data, New York reports on consumers with expanded eligibility and enrollment under the section 1332 waiver program in the BHP data.\n- Idaho\u2019s April data on consumers eligible for a QHP with financial assistance do not depict a direct correlation to consumers with a QHP selection.\n- Virginia transitioned from using the HealthCare.gov platform in Plan Year 2023 to an SBM using its own eligibility and enrollment platform in Plan Year 2024. Virginia's data are reported in the HealthCare.gov and HeathCare.gov Transitions Marketplace Medicaid Unwinding Reports through the end of 2024 and is available in SBM reports as of the April 2024 report. Virginia's SBM data report all applications with Medicaid/CHIP denials or terminations, not only those part of the annual renewal process, and as a result are not directly comparable to their data in the HealthCare.gov data reports.\n- Only SBMs with an automatic plan assignment process have and report automatic QHP selections. These SBMs make automatic plan assignments into a QHP for a subset of individuals and provide a notification of options regarding active selection of an alternative plan and/or, if applicable, mak","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/be8ccf9c-20aa-45d8-8c41-5e7b4255f443","harvest_record_raw":"https://catalog.data.gov/harvest_record/be8ccf9c-20aa-45d8-8c41-5e7b4255f443/raw","has_download":true,"has_spatial":false,"identifier":"5670e72c-e44e-4282-ab67-4ebebaba3cbd","keyword":["marketplace","transitions in coverage"],"last_harvested_date":"2026-08-21T01:41:01.708846","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"},"popularity":2,"publisher":"Centers for Medicare & Medicaid Services","slug":"state-based-marketplace-sbm-medicaid-unwinding-report","spatial_centroid":null,"spatial_shape":null,"theme":["Unwinding"],"title":"State-based Marketplace (SBM) Medicaid Unwinding Report","type":"dataset"},{"_score":3.1736784,"_sort":[1787276393170,3.1736784,0,"8990f920-f831-496e-b9a0-a8cc91c3c03c"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"CDC-INFO","hasEmail":"mailto:cdcinfo@cdc.gov"},"description":"Percent positivity of 9 viral pathogens, by season and age group, 2017\u2013Present.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/kipu-qxy8/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/kipu-qxy8/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/kipu-qxy8/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/kipu-qxy8","isPartOf":"Yes","issued":"2024-01-30","keyword":["adenovirus","covid-19","enterovirus","evd-68","human coronaviruses","human metapneumovirus","influenza","medically attended illness","ncird-corvd","parainfluenza virus","pediatric","respiratory illness","rhinovirus","rsv","viral detections"],"landingPage":"https://www.cdc.gov/surveillance/nvsn/index.html","license":"https://www.usa.gov/government-works","modified":"2026-08-19","programCode":["009:026"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States","theme":["Public Health Surveillance"],"title":"Percent Positivity of Viral Detections Among Enrolled Children in the New Vaccine Surveillance Network (NVSN), Acute Respiratory Illnesses (ARI), 2017\u2013Present"},"description":"Percent positivity of 9 viral pathogens, by season and age group, 2017\u2013Present.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/b3458df4-9466-4d65-8616-f93c7406fa03","harvest_record_raw":"https://catalog.data.gov/harvest_record/b3458df4-9466-4d65-8616-f93c7406fa03/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/kipu-qxy8","keyword":["adenovirus","covid-19","enterovirus","evd-68","human coronaviruses","human metapneumovirus","influenza","medically attended illness","ncird-corvd","parainfluenza virus","pediatric","respiratory illness","rhinovirus","rsv","viral detections"],"last_harvested_date":"2026-08-21T01:39:53.170864","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"},"popularity":0,"publisher":"Centers for Disease Control and Prevention","slug":"percent-positivity-of-viral-detections-among-enrolled-children-in-the-new-vaccine-surveill","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":["Public Health Surveillance"],"title":"Percent Positivity of Viral Detections Among Enrolled Children in the New Vaccine Surveillance Network (NVSN), Acute Respiratory Illnesses (ARI), 2017\u2013Present","type":"dataset"},{"_score":8.219444,"_sort":[1787276342470,8.219444,19,"ab421edc-42a0-4f5d-bf7e-c420396e97a2"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"NNDSS Team","hasEmail":"mailto:NNDSSWeb@cdc.gov"},"description":"NNDSS - In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.\n\n\nNotes:\n\n\u2022 These are weekly cases of selected infectious national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables available at https://www.cdc.gov/nndss/infectious-disease/index.html. Cases reported by state health departments to CDC for weekly publication are subject to ongoing revision of information and delayed reporting. Therefore, numbers listed in later weeks may reflect changes made to these counts as additional information becomes available. Case counts in the tables are presented as published each week. See also Guide to Interpreting Provisional and Finalized NNDSS Data.\n\n\u2022 Notices, errata, and other notes are available in the Notice To Data Users page https://www.cdc.gov/nndss/infectious-disease/notice-to-data-users.html.\n\n\u2022 The list of national notifiable infectious diseases and conditions and their national surveillance case definitions are available at https://ndc.services.cdc.gov/. This list incorporates the Council of State and Territorial Epidemiologists (CSTE) position statements approved by CSTE for national surveillance.\n\nFootnotes:\n\n*Case counts for reporting years 2024 and 2025 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions. For further information on interpretation of these data, see https://www.cdc.gov/nndss/docs/Readers-Guide-WONDER-Tables-20210421-508.pdf.\n\u2020Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data).\n\u2022 Please refer to the Stacks publication for weekly updates to the footnote for influenza-associated pediatric mortality.\nU: Unavailable \u2014 The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data.\n-: No reported cases \u2014 The reporting jurisdiction did not submit any cases to CDC.\nN: Not reportable \u2014 The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction.\nNN: Not nationally notifiable \u2014 This condition was not designated as being nationally notifiable.\nNP: Nationally notifiable but not published.\nNC: Not calculated \u2014 There is insufficient data available to support the calculation of this statistic.\nCum: Cumulative year-to-date counts.\nMax: Maximum \u2014 Maximum case count during the previous 52 weeks.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/x9gk-5huc/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/x9gk-5huc/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/x9gk-5huc/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/x9gk-5huc/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/x9gk-5huc/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/x9gk-5huc/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/x9gk-5huc","issued":"2022-01-18","keyword":["2022","nndss","wonder"],"landingPage":"https://data.cdc.gov/d/x9gk-5huc","license":"http://opendefinition.org/licenses/odc-odbl/","modified":"2026-08-19","programCode":["009:020"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"theme":["NNDSS"],"title":"NNDSS Weekly Data"},"description":"NNDSS - In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.\n\n\nNotes:\n\n\u2022 These are weekly cases of selected infectious national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables available at https://www.cdc.gov/nndss/infectious-disease/index.html. Cases reported by state health departments to CDC for weekly publication are subject to ongoing revision of information and delayed reporting. Therefore, numbers listed in later weeks may reflect changes made to these counts as additional information becomes available. Case counts in the tables are presented as published each week. See also Guide to Interpreting Provisional and Finalized NNDSS Data.\n\n\u2022 Notices, errata, and other notes are available in the Notice To Data Users page https://www.cdc.gov/nndss/infectious-disease/notice-to-data-users.html.\n\n\u2022 The list of national notifiable infectious diseases and conditions and their national surveillance case definitions are available at https://ndc.services.cdc.gov/. This list incorporates the Council of State and Territorial Epidemiologists (CSTE) position statements approved by CSTE for national surveillance.\n\nFootnotes:\n\n*Case counts for reporting years 2024 and 2025 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions. For further information on interpretation of these data, see https://www.cdc.gov/nndss/docs/Readers-Guide-WONDER-Tables-20210421-508.pdf.\n\u2020Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data).\n\u2022 Please refer to the Stacks publication for weekly updates to the footnote for influenza-associated pediatric mortality.\nU: Unavailable \u2014 The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data.\n-: No reported cases \u2014 The reporting jurisdiction did not submit any cases to CDC.\nN: Not reportable \u2014 The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction.\nNN: Not nationally notifiable \u2014 This condition was not designated as being nationally notifiable.\nNP: Nationally notifiable but not published.\nNC: Not calculated \u2014 There is insufficient data available to support the calculation of this statistic.\nCum: Cumulative year-to-date counts.\nMax: Maximum \u2014 Maximum case count during the previous 52 weeks.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/c8a89d45-dfbe-4779-bd72-02a19b1bf97b","harvest_record_raw":"https://catalog.data.gov/harvest_record/c8a89d45-dfbe-4779-bd72-02a19b1bf97b/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cdc.gov/api/views/x9gk-5huc","keyword":["2022","nndss","wonder"],"last_harvested_date":"2026-08-21T01:39:02.470677","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"},"popularity":19,"publisher":"Centers for Disease Control and Prevention","slug":"nndss-weekly-data","spatial_centroid":null,"spatial_shape":null,"theme":["NNDSS"],"title":"NNDSS Weekly Data","type":"dataset"},{"_score":17.59198,"_sort":[1787276258705,17.59198,0,"c0920c3f-0339-4eac-9b66-6495cc508a41"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Office of the Assistant Secretary for Public Affairs","hasEmail":"mailto:media@acf.hhs.gov"},"description":"This data represents unaccompanied alien children who are taken into custody by Customs and Border Protection brought to a facility and processed for transfer to the Department of Health and Human Services (HHS) as required by law. HHS holds the child for testing and quarantine, and shelters the child until the child is placed with a sponsor here in the United States.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://healthdata.gov/api/views/ehpz-xc9n/columns.json","describedByType":"application/json","downloadURL":"https://healthdata.gov/api/v3/views/ehpz-xc9n/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://healthdata.gov/api/views/ehpz-xc9n/columns.xml","describedByType":"application/xml","downloadURL":"https://healthdata.gov/api/v3/views/ehpz-xc9n/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://healthdata.gov/api/v3/views/ehpz-xc9n/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://healthdata.gov/api/views/ehpz-xc9n","issued":"2024-03-01","keyword":["untagged"],"landingPage":"https://www.hhs.gov/programs/social-services/unaccompanied-children/index.html","license":"https://www.usa.gov/government-works","modified":"2026-08-20","programCode":["009:086"],"publisher":{"@type":"org:Organization","name":"HHS"},"temporal":"2021-03/P2M","theme":["National"],"title":"HHS Unaccompanied Alien Children Program"},"description":"This data represents unaccompanied alien children who are taken into custody by Customs and Border Protection brought to a facility and processed for transfer to the Department of Health and Human Services (HHS) as required by law. HHS holds the child for testing and quarantine, and shelters the child until the child is placed with a sponsor here in the United States.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/cdf8133b-36a9-4d24-8e40-bb0e7b608b7b","harvest_record_raw":"https://catalog.data.gov/harvest_record/cdf8133b-36a9-4d24-8e40-bb0e7b608b7b/raw","has_download":true,"has_spatial":false,"identifier":"https://healthdata.gov/api/views/ehpz-xc9n","keyword":["untagged"],"last_harvested_date":"2026-08-21T01:37:38.705238","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"},"popularity":0,"publisher":"HHS","slug":"hhs-unaccompanied-alien-children-program","spatial_centroid":null,"spatial_shape":null,"theme":["National"],"title":"HHS Unaccompanied Alien Children Program","type":"dataset"},{"_score":3.2045336,"_sort":[1787276165920,3.2045336,0,"5780eeef-b4cd-4325-a50c-fa729de72881"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:20"],"contactPoint":{"@type":"vcard:Contact","fn":"CDC-INFO","hasEmail":"mailto:cdcinfo@cdc.gov"},"description":"Percent positivity of 9 viral pathogens, and enrollment counts of children with ARI by week for the past 12 months (rolling x-axis).","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/r229-z6ma/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/r229-z6ma/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","downloadURL":"https://data.cdc.gov/api/v3/views/r229-z6ma/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"}],"identifier":"https://data.cdc.gov/api/views/r229-z6ma","isPartOf":"Yes","issued":"2024-02-28","keyword":["adenovirus","covid-19","enterovirus","evd-68","human coronaviruses","human metapneumovirus","influenza","medically attended illness","ncird-corvd","parainfluenza virus","pediatric","respiratory illness","rhinovirus","rsv","viral detections"],"landingPage":"https://www.cdc.gov/surveillance/nvsn/index.html","license":"https://www.usa.gov/government-works","modified":"2026-08-19","programCode":["009:026"],"publisher":{"@type":"org:Organization","name":"Centers for Disease Control and Prevention"},"spatial":"United States","theme":["Public Health Surveillance"],"title":"Pathogen Detections Among Enrolled Children in the New Vaccine Surveillance Network (NVSN), Acute Respiratory Illnesses (ARI), 12 Month Rolling Period"},"description":"Percent positivity of 9 viral pathogens, and enrollment counts of children with ARI by week for the past 12 months (rolling x-axis).","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/d563ee4f-6bed-4a89-aa3b-2a635e446843","harvest_record_raw":"https://catalog.data.gov/harvest_record/d563ee4f-6bed-4a89-aa3b-2a635e446843/raw","has_download":true,"has_spatial":true,"identifier":"https://data.cdc.gov/api/views/r229-z6ma","keyword":["adenovirus","covid-19","enterovirus","evd-68","human coronaviruses","human metapneumovirus","influenza","medically attended illness","ncird-corvd","parainfluenza virus","pediatric","respiratory illness","rhinovirus","rsv","viral detections"],"last_harvested_date":"2026-08-21T01:36:05.920840","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"},"popularity":0,"publisher":"Centers for Disease Control and Prevention","slug":"pathogen-detections-among-enrolled-children-in-the-new-vaccine-surveillance-network-nvsn-a","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":["Public Health Surveillance"],"title":"Pathogen Detections Among Enrolled Children in the New Vaccine Surveillance Network (NVSN), Acute Respiratory Illnesses (ARI), 12 Month Rolling Period","type":"dataset"},{"_score":10.346096,"_sort":[1787271497726,10.346096,0,"e78a95d6-fb89-42df-8816-db6f20f69fb2"],"dcat":{"accessLevel":"public","bureauCode":["010:12"],"contactPoint":{"@type":"vcard:Contact","fn":"C. 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The data available in this data release record the pre-implementation topography in the form of a digital surface model and vegetation using multispectral Near-Infrared data (for the Normalized Difference Vegetation Index) from multiple flyovers on the 25th of August 2025. These data will be paired with future post-implementation flyovers to look at topographic and vegetation changes resulting from BDA implementation. These UAS flyovers are being performed by Mountain Studies Institute, in cooperation with the USGS. \nIncluded herein is a (1) Digital Surface Model, (2) an orthophotograph, and (3) multispectral near-Infrared model, all at a 5 cm resolution. This dataset represents a control reach, or portion of the stream, that will have no BDA infrastructure and are labeled \u2018north.\u2019 They are also provided for a reach that will include BDA\u2019s and are labeled \u2018south.\u2019 Finally, an aerial flyover video is provided for the \u2018north\u2019 and \u2018south\u2019 sections of the Rio De Las Vacas.","distribution":[{"@type":"dcat:Distribution","accessURL":"https://doi.org/10.5066/P13FPYK4","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.6a3c353a1ba49b6b81f3b98e.xml","format":"XML","mediaType":"text/xml","title":"Original Metadata"}],"identifier":"http://datainventory.doi.gov/id/dataset/USGS_6a3c353a1ba49b6b81f3b98e","keyword":["Beaver Dam Analogs","Digital Surface Model","Jemez Mountains","New Mexico","Normalized Difference Vegetation Index","USGS:6a3c353a1ba49b6b81f3b98e","biota"],"modified":"2026-08-18T00:00:00Z","publisher":{"@type":"org:Organization","name":"U.S. Geological Survey"},"spatial":"-106.80155, 35.91837, -106.78621, 35.94366","theme":["geospatial"],"title":"Uncrewed Aerial System Digital Surface Model and Multispectral Footprints for the Rio De Las Vacas, New Mexico, USA, Summer 2025"},"description":"In this study we are deploying several sensor arrays within a small stream channel, the Rio De Las Vacas, in the Jemez mountains of Northern New Mexico in coordination with a local partner, who are implementing several low-tech process based restorative practices including Beaver Dam Analogs (BDAs). 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This data set includes Medicaid and CHIP enrollees in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands, ages 12 to 64 at the end of the calendar year, who were not dually eligible for Medicare and were continuously enrolled with comprehensive benefits for 12 months, with no more than one gap in enrollment exceeding 45 days. Enrollees who received services for both an MH condition and SUD in the year are counted toward both condition categories. Enrollees in Guam, American Samoa, the Northern Mariana Islands, and select states with TAF data quality issues are not included. Results shown for the race and ethnicity subpopulation topic exclude enrollees in the U.S. Virgin Islands. Results shown for the primary language subpopulation topic exclude select states with data quality issues with the primary language variable in TAF. Some rows in the data set have a value of \"DS,\" which indicates that data were suppressed according to the Centers for Medicare & Medicaid Services\u2019 Cell Suppression Policy for values between 1 and 10.\r\nThis data set is based on the brief: \"Medicaid and CHIP enrollees who received mental health or SUD services in 2020.\" Enrollees are assigned to an age group subpopulation using age as of December 31st of the calendar year. Enrollees are assigned to a sex or gender identity subpopulation using their latest reported sex in the calendar year. Enrollees are assigned to a race and ethnicity subpopulation using the state-reported race and ethnicity information in TAF when it is available and of good quality; if it is missing or unreliable, race and ethnicity is indirectly estimated using an enhanced version of Bayesian Improved Surname Geocoding (BISG) (Race and ethnicity of the national Medicaid and CHIP population in 2020). Enrollees are assigned to an urban or rural subpopulation based on the 2010 Rural-Urban Commuting Area (RUCA) code associated with their home or mailing address ZIP code in TAF (Rural Medicaid and CHIP enrollees in 2020). Enrollees are assigned to an eligibility category subpopulation using their latest reported eligibility group code, CHIP code, and age in the calendar year. Enrollees are assigned to a primary language subpopulation based on their reported ISO language code in TAF (English/missing, Spanish, and all other language codes) (Primary Language). Please refer to the full brief for additional context about the methodology and detailed findings. Future updates to this data set will include more recent data years as the TAF data become available.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://download.medicaid.gov/data/mh-and-sud-services-2020-2022-01162025.csv","mediaType":"text/csv"}],"identifier":"8062e2f4-4c0a-41c9-8217-979468a80986","issued":"2026-05-22","keyword":["behavioral health","chip","medicaid","mental health condition","service use","substance use disorder","t-msis analytic files"],"landingPage":"https://healthdata.gov/d/qwvy-gvkm","license":"https://www.usa.gov/publicdomain/label/1.0/","modified":"2025-01-17","programCode":["009:000"],"publisher":{"@type":"org:Organization","name":"Centers for Medicare & Medicaid Services"},"theme":["CMS"],"title":"Medicaid and CHIP enrollees who received mental health or SUD services"},"description":"This data set includes annual counts and percentages of Medicaid and Children\u2019s Health Insurance Program (CHIP) enrollees who received mental health (MH) or substance use disorder (SUD) services, overall and by six subpopulation topics: age group, sex or gender identity, race and ethnicity, urban or rural residence, eligibility category, and primary language.\r\nThese results were generated using Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Release 1 data and the Race/Ethnicity Imputation Companion File. This data set includes Medicaid and CHIP enrollees in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands, ages 12 to 64 at the end of the calendar year, who were not dually eligible for Medicare and were continuously enrolled with comprehensive benefits for 12 months, with no more than one gap in enrollment exceeding 45 days. Enrollees who received services for both an MH condition and SUD in the year are counted toward both condition categories. Enrollees in Guam, American Samoa, the Northern Mariana Islands, and select states with TAF data quality issues are not included. Results shown for the race and ethnicity subpopulation topic exclude enrollees in the U.S. Virgin Islands. Results shown for the primary language subpopulation topic exclude select states with data quality issues with the primary language variable in TAF. 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Enrollees are assigned to an urban or rural subpopulation based on the 2010 Rural-Urban Commuting Area (RUCA) code associated with their home or mailing address ZIP code in TAF (Rural Medicaid and CHIP enrollees in 2020). Enrollees are assigned to an eligibility category subpopulation using their latest reported eligibility group code, CHIP code, and age in the calendar year. Enrollees are assigned to a primary language subpopulation based on their reported ISO language code in TAF (English/missing, Spanish, and all other language codes) (Primary Language). Please refer to the full brief for additional context about the methodology and detailed findings. 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Data are not available if it did not meet BRFSS stability requirements. For more information on these requirements, as well as risk factors and calculated variables, see the Technical Documents and Survey Data for a specific year - http://www.cdc.gov/brfss/annual_data/annual_data.htm. Recommended citation: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System. 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Some states have serious data quality issues for one or more months, making the data unusable for calculating these measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable or of high concern based on DQ Atlas thresholds for the topics Enrollment in CMC, Enrollment in PCCM Programs, and Enrollment in BHO Plans. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. \n\nSome cells have a value of \u201cDS\u201d. 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Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. \r\n\r\nSome cells have a value of \u201cDS\u201d. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.","downloadURL":"https://download.medicaid.gov/data/ManagedCare-montly-102025.csv","mediaType":"text/csv","title":"Managed Care Information for Medicaid and CHIP Beneficiaries by Month"}],"identifier":"89baf100-259b-4763-b9e2-337972f988c4","issued":"2026-05-22","keyword":["chip","dq atlas","managed care","medicaid","t-msis analytic files"],"landingPage":"https://healthdata.gov/d/peth-adit","license":"http://www.usa.gov/publicdomain/label/1.0/","modified":"2025-10-20","programCode":["009:000"],"publisher":{"@type":"org:Organization","name":"Centers for Medicare & Medicaid Services"},"theme":["Enrollment"],"title":"Managed Care Information for Medicaid and CHIP Beneficiaries by Month"},"description":"This data set includes monthly enrollment counts of Medicaid and CHIP beneficiaries by managed care participation (comprehensive managed care, primary care case management, MLTSS, including PACE, behavioral health organizations, nonmedical prepaid health plans, medical-only prepaid health plans, and other).\n\nThese metrics are based on data in the T-MSIS Analytic Files (TAF). 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Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods.\n\nSome cells have a value of \u201cDS\u201d. 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Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Diagnosis Code - IP, Diagnosis Code - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of \u201cDQ\u201d indicate that data were suppressed due to unusable data. \r\n\r\nSome cells have a value of \u201cDS\u201d. 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Low birthweight live births, by race and Hispanic origin of mother, and state: United States, 2000 - 2002, 2003 - 2005, and 2006 - 2008","type":"dataset"},{"_score":61.43875,"_sort":[1787102178482,61.43875,1,"97d11db8-d29f-45f5-8c36-1a4a39f65812"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Medicaid.gov","hasEmail":"mailto:Medicaid.gov@cms.hhs.gov"},"description":"This data set includes monthly counts and rates (per 1,000 beneficiaries) of\u00a0health screenings provided to Medicaid and CHIP beneficiaries under the age of 19 (as of the first day of the month) by state. \n\nThese metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating screening services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Procedure Codes - OT Professional, Diagnosis Codes - OT, Claims Volume - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of \u201cDQ\u201d indicate that data were suppressed due to unusable data. \n\nSome cells have a value of \u201cDS\u201d. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.","distribution":[{"@type":"dcat:Distribution","description":"This data set includes monthly counts and rates (per 1,000 beneficiaries) of\u00a0health screenings provided to Medicaid and CHIP beneficiaries under the age of 19 (as of the first day of the month) by state. \r\n\r\nThese metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating screening services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Procedure Codes - OT Professional, Diagnosis Codes - OT, Claims Volume - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of \u201cDQ\u201d indicate that data were suppressed due to unusable data. \r\n\r\nSome cells have a value of \u201cDS\u201d. 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Some states have serious data quality issues for one or more months, making the data unusable for calculating screening services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Procedure Codes - OT Professional, Diagnosis Codes - OT, Claims Volume - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of \u201cDQ\u201d indicate that data were suppressed due to unusable data. \n\nSome cells have a value of \u201cDS\u201d. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.","distribution_titles":["Health Screenings Provided to Medicaid and CHIP Beneficiaries Under Age 19"],"harvest_record":"https://catalog.data.gov/harvest_record/26437116-2fc7-40ea-b5b1-62157b433eea","harvest_record_raw":"https://catalog.data.gov/harvest_record/26437116-2fc7-40ea-b5b1-62157b433eea/raw","has_download":true,"has_spatial":false,"identifier":"37b55242-7cd4-4818-b783-348adf39e50c","keyword":["chip","dq atlas","medicaid","screening","service use","t-msis analytic files"],"last_harvested_date":"2026-08-19T01:16:18.482326","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"},"popularity":1,"publisher":"Centers for Medicare & Medicaid Services","slug":"health-screenings-provided-to-medicaid-and-chip-beneficiaries-under-age-19","spatial_centroid":null,"spatial_shape":null,"theme":["Uncategorized"],"title":"Health Screenings Provided to Medicaid and CHIP Beneficiaries Under Age 19","type":"dataset"},{"_score":16.209972,"_sort":[1787102168229,16.209972,1,"108e710e-647e-45df-9e4a-e8bbe855e5a5"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["009:00"],"contactPoint":{"@type":"vcard:Contact","fn":"CDC INFO","hasEmail":"mailto:cdcinfo@cdc.gov"},"description":"Percentages are weighted to population characteristics. 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For each month from April 1, 2023, through June 30, 2024, states were required to submit to CMS (on a timely basis), and CMS was required to make public, certain monthly data, including the total number of beneficiaries who were enrolled in a separate CHIP program. Accordingly, this historic dataset contains separate CHIP enrollment by month and state between April 2023 and June 2024.
\r\nCMS will continue to publicly report separate CHIP enrollment by month and state (beyond the historic CAA/Unwinding period) in a new dataset, which is available at [link]. Please note that the methods used to count separate CHIP enrollees differ slightly between the two datasets; as a result, data users should exercise caution if comparing separate CHIP enrollment across the two datasets.
\r\nSources: T-MSIS Analytic Files (TAF) and state-submitted enrollment totals. The data notes indicate when a state\u2019s monthly total was a state-submitted value, rather than from T-MSIS.
TAF data were pulled as follows:
April 2023 enrollment - TAF as of August 2023
May 2023 enrollment - TAF as of August 2023
June 2023 enrollment - TAF as of September 2023
July 2023 enrollment - TAF as of October 2023
August 2023 enrollment - TAF as of November 2023
September 2023 enrollment - TAF as of December 2023
October 2023 enrollment - TAF as of January 2024
November 2023 enrollment - TAF as of February 2024
December 2023 enrollment - TAF as of March 2024
January 2024 enrollment - TAF as of April 2024
February 2024 enrollment - TAF as of May 2024
March 2024 enrollment - TAF as of June 2024
April 2024 enrollment \u2013 TAF as of July 2024
May 2024 enrollment \u2013 TAF as of August 2024
June 2024 enrollment \u2013 TAF as of September 2024
\t\r\nTAF are produced one month after the T-MSIS submission month. For example, TAF as of August 2023 is based on July T-MSIS submissions.
\r\nNotes: The separate CHIP enrollment in this report is not inclusive of enrollees covered by Medicaid expansion CHIP. Enrollment includes individuals enrolled in separate CHIP at any point during the month but excludes those enrolled in both Medicaid and separate CHIP during the month. See the Data Sources and Metrics Definitions Overview document for a full description of the data sources, metric definitions, and general data limitations.
Alaska, District of Columbia, Hawaii, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, South Carolina, Vermont, and Wyoming do not have separate CHIP Programs. Maryland has a separate CHIP program that began in July 2023; April 2023 - June 2023 data for Maryland represents retroactive coverage.
This document includes separate CHIP data submitted to CMS by states via T-MSIS or a separate collection form. These data include reporting metrics consistent with section 1902(tt)(1) of the Social Security Act.
CHIP: Children's Health Insurance Program
\t\r\nData notes: \r\n(a) State-submitted value; data not from T-MSIS
(b1) May 2023 enrollment pulled from TAF as of September 2023
(b2) Data was restated using TAF as of October 2023
(b3) Data was restated using TAF as of April 2024
(b4) Data was restated using TAF as of July 2024
(b5) Data was restated using TAF as of August 2024
(c) Enrollment counts include postpartum women with coverage funded via a Health Services Initiative
","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://download.medicaid.gov/data/chip-caa-reporting-metrics-long-table-november-2024-release.csv","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://download.medicaid.gov/data/chip-caa-reporting-metrics-long-table-november-2024-release.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"}],"identifier":"d30cfc7c-4b32-4df1-b2bf-e0a850befd77","issued":"2023-09-29","keyword":["applications","child enrollment","chip","eligibility determinations","enrollment","medicaid","program enrollment"],"landingPage":"https://healthdata.gov/d/33zq-xf3w","license":"https://www.usa.gov/publicdomain/label/1.0/","modified":"2024-12-27","programCode":["009:000"],"publisher":{"@type":"org:Organization","name":"Centers for Medicare & Medicaid Services"},"theme":["Enrollment","Unwinding"],"title":"Separate CHIP Enrollment by Month and State \u2013 Historic CAA/Unwinding Period"},"description":"This historic dataset with total enrollment in separate CHIP programs by month and state was created to fulfill reporting requirements under section 1902(tt)(1) of the Social Security Act, which was added by section 5131(b) of subtitle D of title V of division FF of the Consolidated Appropriations Act, 2023 (P.L. 117-328) (CAA, 2023). For each month from April 1, 2023, through June 30, 2024, states were required to submit to CMS (on a timely basis), and CMS was required to make public, certain monthly data, including the total number of beneficiaries who were enrolled in a separate CHIP program. Accordingly, this historic dataset contains separate CHIP enrollment by month and state between April 2023 and June 2024.
\r\nCMS will continue to publicly report separate CHIP enrollment by month and state (beyond the historic CAA/Unwinding period) in a new dataset, which is available at [link]. Please note that the methods used to count separate CHIP enrollees differ slightly between the two datasets; as a result, data users should exercise caution if comparing separate CHIP enrollment across the two datasets.
\r\nSources: T-MSIS Analytic Files (TAF) and state-submitted enrollment totals. The data notes indicate when a state\u2019s monthly total was a state-submitted value, rather than from T-MSIS.
TAF data were pulled as follows:
April 2023 enrollment - TAF as of August 2023
May 2023 enrollment - TAF as of August 2023
June 2023 enrollment - TAF as of September 2023
July 2023 enrollment - TAF as of October 2023
August 2023 enrollment - TAF as of November 2023
September 2023 enrollment - TAF as of December 2023
October 2023 enrollment - TAF as of January 2024
November 2023 enrollment - TAF as of February 2024
December 2023 enrollment - TAF as of March 2024
January 2024 enrollment - TAF as of April 2024
February 2024 enrollment - TAF as of May 2024
March 2024 enrollment - TAF as of June 2024
April 2024 enrollment \u2013 TAF as of July 2024
May 2024 enrollment \u2013 TAF as of August 2024
June 2024 enrollment \u2013 TAF as of September 2024
\t\r\nTAF are produced one month after the T-MSIS submission month. For example, TAF as of August 2023 is based on July T-MSIS submissions.
\r\nNotes: The separate CHIP enrollment in this report is not inclusive of enrollees covered by Medicaid expansion CHIP. Enrollment includes individuals enrolled in separate CHIP at any point during the month but excludes those enrolled in both Medicaid and separate CHIP during the month. See the Data Sources and Metrics Definitions Overview document for a full description of the data sources, metric definitions, and general data limitations.
Alaska, District of Columbia, Hawaii, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, South Carolina, Vermont, and Wyoming do not have separate CHIP Programs. Maryland has a separate CHIP program that began in July 2023; April 2023 - June 2023 data for Maryland represents retroactive coverage.
This document includes separate CHIP data submitted to CMS by states via T-MSIS or a separate collection form. These data include reporting metrics consistent with section 1902(tt)(1) of the Social Security Act.
CHIP: Children's Health Insurance Program
\t\r\nData notes: \r\n(a) State-submitted value; data not from T-MSIS
(b1) May 2023 enrollment pulled from TAF as of September 2023
(b2) Data was restated using TAF as of October 2023
(b3) Data was restated using TAF as of April 2024
(b4) Data was restated using TAF as of July 2024
(b5) Data was restated using TAF as of August 2024
(c) Enrollment counts include postpartum women with coverage funded via a Health Services Initiative
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The system will be integrated with a certified safety critical decision makers (such as vehicle health monitoring, collision avoidance, loss of control avoidance and restricts commands of higher level critical decision makers not certified to level A software. The project will integrate these systems onto a quad-rotor micro-UAV for inexpensive and quick flight testing of concepts and develop customized, low power hardware to house the control and decision making algorithms.</p><p>ASSUMPTIONS AND LIMITATIONS: The purpose of this CIF project is not to develop a full scale aircraft capable of these types of advancements, but only to develop a piloting system which make them possible. Initially, decisions associated with “where to fly” will be focused on and integrated into the algorithms. For this slice of the pie, the system will be required to navigate a potentially changing dense urban landscape. Routes will be planned based on time, distance, and potential risk. Additionally, terrain and obstacle avoidance algorithms will restrict these activities based on preloaded obstacle and terrain maps. Additionally, off nominal conditions such as loss of motor or other non-pre-programmed events will cause the aircraft to select landing or crashing locations based on population density maps, location of buildings, and other information. A hangar or small area will be turned into the urban city-center mockup with maps created of the mockup to facilitate flight test of concepts.</p><p><strong>Work to date: </strong>The hierarchical decision chain and framework, hardware, and embedded processing related to ground collision avoidance is in place for a sub-scale platform. Flight tests on a quad-rotor model helicopter demonstrated successful limitation of flight decisions when facing imminent ground collision. </p><p><strong>Looking ahead: </strong>The team is developing a full set of safety-critical functions for the sub-scale platforms and working to scale up to larger UAVs. </p><p><strong>Partners: </strong>University of California at Berkeley and Stanford University are developing algorithms, and the FAA is participating in the certification process. </p><p><strong>Benefits </strong></p><ul><li><strong>Increases safety: </strong>Integration of safety-critical functions improves outcomes in emergency situations. </li><li><strong>Certifiable: </strong>Removal of safety-critical functions from the autonomous control enables adaptable processes to be certified to a lower level. </li></ul><p><strong>Applications </strong></p><ul><li>UAVs and unmanned submersibles </li><li>Autonomous rail transport </li><li>Deep space exploration </li><li&g","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://techport.nasa.gov/xml-api/10855","format":"XML","mediaType":"application/xml"}],"identifier":"TECHPORT_10855","issued":"2011-10-01","keyword":["armstrong-flight-research-center","completed","evaa","project"],"landingPage":"http://techport.nasa.gov/view/10855","modified":"2025-03-31","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Space Technology Mission Directorate"},"references":["http://techport.nasa.gov/doc/home/TechPort_Advanced_Search.pdf","http://techport.nasa.gov/fetchFile?objectId=3447","http://techport.nasa.gov/fetchFile?objectId=3448","http://techport.nasa.gov/fetchFile?objectId=3456","http://techport.nasa.gov/fetchFile?objectId=6560","http://techport.nasa.gov/fetchFile?objectId=6561","http://techport.nasa.gov/fetchFile?objectId=6584","http://techport.nasa.gov/home"],"temporal":"2011-10-01T00:00:00Z/2012-10-01T00:00:00Z","title":"Expandable Variable-Autonomy Architecture Project"},"description":"<p>Effective multi-level autonomous piloting systems require integration with safety-critical functions. 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The system will be integrated with a certified safety critical decision makers (such as vehicle health monitoring, collision avoidance, loss of control avoidance and restricts commands of higher level critical decision makers not certified to level A software. The project will integrate these systems onto a quad-rotor micro-UAV for inexpensive and quick flight testing of concepts and develop customized, low power hardware to house the control and decision making algorithms.</p><p>ASSUMPTIONS AND LIMITATIONS: The purpose of this CIF project is not to develop a full scale aircraft capable of these types of advancements, but only to develop a piloting system which make them possible. Initially, decisions associated with “where to fly” will be focused on and integrated into the algorithms. For this slice of the pie, the system will be required to navigate a potentially changing dense urban landscape. Routes will be planned based on time, distance, and potential risk. 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RAJADAS**, AND CLYDE COELHO*\r\n\r\nAbstract. Fatigue damage and its prediction is one of the foremost concerns of structural integrity\r\nresearch community. The current research in structural health monitoring (SHM) is to\r\nprovide continuous (or on demand) information about the state of a structure. The SHM system\r\ncan be based on either active or passive sensor measurements. Though the current research on\r\nultrasonic wave propagation based active sensing approach has the potential to estimate very small\r\ndamage, it has severe drawbacks in terms of low sensing radius and external power requirements.\r\nTo alleviate these disadvantages passive sensing based SHM techniques can be used. Currently,\r\nfew efforts have been made towards, time-series fatigue damage state estimation over the entire\r\nfatigue life (stage-I, II & III). A majority of the available literature on passive sensing SHM techniques\r\ndemonstrates the clear trend in damage growth during the final failure regime (stage-III\r\nregime) or during when the damage is comparatively large enough. The present paper proposes a\r\npassive sensing technique that demonstrates a clear trend in damage growth almost over the entire\r\nstage-II and III damage growth regime. A strain gauge measurement based passive SHM frameworks\r\nthat can estimate the time-series fatigue damage state under random loading is proposed.\r\nFor this purpose, a Bayesian Gaussian process nonlinear dynamic model is developed to map the\r\nreference condition dynamic strain at a given instant of time. The predicted strains are compared\r\nwith the actual sensor measurements to estimate the corresponding error signals. The error signals\r\nestimated at two different locations are correlated to estimate the corresponding fatigue damage\r\nstate. The approach is demonstrated for an Al-2434 complex cruciform structure applied with\r\nbiaxial random loading.","distribution":[{"@type":"dcat:Distribution","description":"DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING","downloadURL":"https://c3.nasa.gov/dashlink/static/media/publication/Paper_21_.pdf","format":"PDF","mediaType":"application/pdf","title":"Paper 21 .pdf"},{"@type":"dcat:Distribution","description":"Presentation","downloadURL":"https://c3.nasa.gov/dashlink/static/media/publication/Paper21_presentation.pdf","format":"PDF","mediaType":"application/pdf","title":"Paper21_presentation.pdf"}],"identifier":"DASHLINK_243","issued":"2010-10-13","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/243/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING"},"description":"DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE\r\nESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING\r\n\r\nSUBHASISH MOHANTY*, ADITI CHATTOPADHYAY*, JOHN N. RAJADAS**, AND CLYDE COELHO*\r\n\r\nAbstract. Fatigue damage and its prediction is one of the foremost concerns of structural integrity\r\nresearch community. The current research in structural health monitoring (SHM) is to\r\nprovide continuous (or on demand) information about the state of a structure. The SHM system\r\ncan be based on either active or passive sensor measurements. Though the current research on\r\nultrasonic wave propagation based active sensing approach has the potential to estimate very small\r\ndamage, it has severe drawbacks in terms of low sensing radius and external power requirements.\r\nTo alleviate these disadvantages passive sensing based SHM techniques can be used. Currently,\r\nfew efforts have been made towards, time-series fatigue damage state estimation over the entire\r\nfatigue life (stage-I, II & III). A majority of the available literature on passive sensing SHM techniques\r\ndemonstrates the clear trend in damage growth during the final failure regime (stage-III\r\nregime) or during when the damage is comparatively large enough. The present paper proposes a\r\npassive sensing technique that demonstrates a clear trend in damage growth almost over the entire\r\nstage-II and III damage growth regime. A strain gauge measurement based passive SHM frameworks\r\nthat can estimate the time-series fatigue damage state under random loading is proposed.\r\nFor this purpose, a Bayesian Gaussian process nonlinear dynamic model is developed to map the\r\nreference condition dynamic strain at a given instant of time. The predicted strains are compared\r\nwith the actual sensor measurements to estimate the corresponding error signals. The error signals\r\nestimated at two different locations are correlated to estimate the corresponding fatigue damage\r\nstate. The approach is demonstrated for an Al-2434 complex cruciform structure applied with\r\nbiaxial random loading.","distribution_titles":["Paper 21 .pdf","Paper21_presentation.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/2da8b6ea-0edc-48fd-9ae5-749f961f7a9b","harvest_record_raw":"https://catalog.data.gov/harvest_record/2da8b6ea-0edc-48fd-9ae5-749f961f7a9b/raw","has_download":true,"has_spatial":false,"identifier":"DASHLINK_243","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-08-19T01:12:03.747333","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":1,"publisher":"Dashlink","slug":"dynamic-strain-mapping-and-real-time-damage-state-estimation-under-biaxial-random-fatigue-","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING","type":"dataset"},{"_score":52.811028,"_sort":[1787101918803,52.811028,9,"add20a54-e11c-4a66-a985-aa80a8268bc6"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"NSIDC Services","hasEmail":"mailto:nsidc@nsidc.org"},"description":"**Abstract**\r\n\r\nPrognostics solutions for mission critical systems require a comprehensive methodology for proactively detecting and isolating failures, recommending and guiding condition-based maintenance actions, and estimating in real time the remaining useful life of critical components and associated subsystems.\r\n\r\nA major challenge has been to extend the benefits of prognostics to include computer servers and other electronic components. The key enabler for prognostics capabilities is monitoring time series signals relating to the health of executing components and subsystems. Time series signals are processed in real time using pattern recognition for proactive anomaly detection and for remaining useful life estimation. Examples will be presented of the use of pattern recognition techniques for early detection of a number of mechanisms that are known to cause failures in electronic systems, including: environmental issues; software aging; degraded or failed sensors; degradation of hardware components; degradation of mechanical, electronic, and optical interconnects. Prognostics pattern classification is helping to substantially increase component reliability margins and system availability goals while reducing costly sources of \"no trouble found\"\r\n\r\nevents that have become a significant warranty-cost issue.\r\n\r\n \r\n\r\n**Bios**\r\n\r\nAleksey Urmanov is a research scientist at Sun Microsystems. He earned his doctoral degree in Nuclear Engineering at the University of Tennessee in 2002. Dr. Urmanov's research activities are centered around his interest in pattern recognition, statistical learning theory and ill-posed problems in engineering. His most recent activities at Sun focus on developing health monitoring and prognostics methods for EP-enabled computer servers. He is a founder and an Editor of the Journal of Pattern Recognition Research.\r\n\r\n \r\n\r\nAnton Bougaev holds a M.S. and a Ph.D. degrees in Nuclear Engineering from Purdue University. Before joining Sun Microsystems Inc. in 2007, he was a lecturer in Nuclear Engineering Department and a member of Applied Intelligent Systems Laboratory (AISL), of Purdue University, West Lafayette, USA. Dr. Bougaev is a founder and the Editor-in-Chief of the Journal of Pattern Recognition Research. His current focus is in reliability physics with emphasis on complex system analysis and the physics of failures which are based on the data driven pattern recognition techniques.","distribution":[{"@type":"dcat:Distribution","description":"Includes a user's guide, supplemental documents like ATBDs and academic papers, How Tos, FAQs, etc.","downloadURL":"https://doi.org/10.5067/ICESAT/GLAS/DATA101","format":"HTML","mediaType":"text/html","title":"View documentation related to this dataset"},{"@type":"dcat:Distribution","description":"NASA's newest search and order tool for subsetting, reprojecting, and reformatting data.","downloadURL":"https://search.earthdata.nasa.gov/search?q=GLAH01+V033","format":"HTML","mediaType":"text/html","title":"Download this dataset through Earthdata Search"},{"@type":"dcat:Distribution","description":"Provides access to data, documentation, tools, citation information, support, and other resources.","downloadURL":"https://doi.org/10.5067/ICESAT/GLAS/DATA101","format":"HTML","mediaType":"text/html","title":"This dataset's landing page"},{"@type":"dcat:Distribution","description":"Search results for publications that cite this dataset by its DOI.","downloadURL":"https://scholar.google.com/scholar?q=10.5067%2FICESAT%2FGLAS%2FDATA101","format":"HTML","mediaType":"text/html","title":"Google Scholar search results"}],"identifier":"DASHLINK_60","issued":"2010-09-10","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/60/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"Health Monitoring and Prognostics for Computer Servers"},"description":"**Abstract**\r\n\r\nPrognostics solutions for mission critical systems require a comprehensive methodology for proactively detecting and isolating failures, recommending and guiding condition-based maintenance actions, and estimating in real time the remaining useful life of critical components and associated subsystems.\r\n\r\nA major challenge has been to extend the benefits of prognostics to include computer servers and other electronic components. The key enabler for prognostics capabilities is monitoring time series signals relating to the health of executing components and subsystems. Time series signals are processed in real time using pattern recognition for proactive anomaly detection and for remaining useful life estimation. Examples will be presented of the use of pattern recognition techniques for early detection of a number of mechanisms that are known to cause failures in electronic systems, including: environmental issues; software aging; degraded or failed sensors; degradation of hardware components; degradation of mechanical, electronic, and optical interconnects. Prognostics pattern classification is helping to substantially increase component reliability margins and system availability goals while reducing costly sources of \"no trouble found\"\r\n\r\nevents that have become a significant warranty-cost issue.\r\n\r\n \r\n\r\n**Bios**\r\n\r\nAleksey Urmanov is a research scientist at Sun Microsystems. He earned his doctoral degree in Nuclear Engineering at the University of Tennessee in 2002. Dr. Urmanov's research activities are centered around his interest in pattern recognition, statistical learning theory and ill-posed problems in engineering. His most recent activities at Sun focus on developing health monitoring and prognostics methods for EP-enabled computer servers. He is a founder and an Editor of the Journal of Pattern Recognition Research.\r\n\r\n \r\n\r\nAnton Bougaev holds a M.S. and a Ph.D. degrees in Nuclear Engineering from Purdue University. Before joining Sun Microsystems Inc. in 2007, he was a lecturer in Nuclear Engineering Department and a member of Applied Intelligent Systems Laboratory (AISL), of Purdue University, West Lafayette, USA. Dr. Bougaev is a founder and the Editor-in-Chief of the Journal of Pattern Recognition Research. 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The technique is intended for condition monitoring in high reliability applications where the knowledge of impending failure is critical and the risks in terms of loss of functionality are too high to bear. Future state of the system has been estimated based on a second-order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage in the form of inelastic strain energy density. Performance of the prognostic health management algorithm during the vibration test has been quantified using performance evaluation metrics. The method- ology has been demonstrated on leadfree area-array electronic assemblies subjected to vibration. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including \u03b1 \u2212 \u03bb precision, \u03b2 accuracy, and relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness.","distribution":[{"@type":"dcat:Distribution","description":"2012_IEEE_TIE_shock.pdf","downloadURL":"https://c3.nasa.gov/dashlink/static/media/publication/2012_IEEE_TIE_shock.pdf","format":"PDF","mediaType":"application/pdf","title":"2012_IEEE_TIE_shock.pdf"}],"identifier":"DASHLINK_760","issued":"2013-06-19","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/760/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"Prognostics Health Management of Electronic Systems Under Mechanical Shock and Vibration Using Kalman Filter Models and Metrics"},"description":"Structural damage to ball grid array interconnects incurred during vibration testing has been monitored in the prefailure space using resistance spectroscopy-based state space vectors, rate of change of the state variable, and acceleration of the state variable. The technique is intended for condition monitoring in high reliability applications where the knowledge of impending failure is critical and the risks in terms of loss of functionality are too high to bear. Future state of the system has been estimated based on a second-order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage in the form of inelastic strain energy density. Performance of the prognostic health management algorithm during the vibration test has been quantified using performance evaluation metrics. The method- ology has been demonstrated on leadfree area-array electronic assemblies subjected to vibration. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including \u03b1 \u2212 \u03bb precision, \u03b2 accuracy, and relative accuracy have been used to assess the performance of the damage proxies. 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The Ares I-X Ground Diagnostic Prototype demonstrated anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage Thrust Vector Control and for the associated ground hydraulics while the vehicle was in the Vehicle Assembly Building at Kennedy Space Center (KSC) and while it was on the launch pad. The prototype combines three existing tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool from Qualtech Systems Inc. for fault isolation and diagnostics. The second tool, SHINE (Spacecraft Health Inference Engine), is a rule-based expert system that was developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification, and used the outputs of SHINE as inputs to TEAMS. The third tool, IMS (Inductive Monitoring System), is an anomaly detection tool that was developed at NASA Ames Research Center. The three tools were integrated and deployed to KSC, where they were interfaced with live data. This paper describes how the prototype performed during the period before the launch, including accuracy and computer resource usage. The paper concludes with some of the lessons that we learned from the experience of developing and deploying the prototype.","distribution":[{"@type":"dcat:Distribution","description":"Schwabacher et al Infotech 2010","downloadURL":"https://c3.nasa.gov/dashlink/static/media/publication/SchwabacherInfotech2010.pdf","format":"PDF","mediaType":"application/pdf","title":"SchwabacherInfotech2010.pdf"}],"identifier":"DASHLINK_194","issued":"2010-09-22","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/194/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"Ares I-X Ground Diagnostic Prototype"},"description":"The automation of pre-launch diagnostics for launch vehicles offers three potential benefits: improving safety, reducing cost, and reducing launch delays. The Ares I-X Ground Diagnostic Prototype demonstrated anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage Thrust Vector Control and for the associated ground hydraulics while the vehicle was in the Vehicle Assembly Building at Kennedy Space Center (KSC) and while it was on the launch pad. The prototype combines three existing tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool from Qualtech Systems Inc. for fault isolation and diagnostics. The second tool, SHINE (Spacecraft Health Inference Engine), is a rule-based expert system that was developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification, and used the outputs of SHINE as inputs to TEAMS. The third tool, IMS (Inductive Monitoring System), is an anomaly detection tool that was developed at NASA Ames Research Center. The three tools were integrated and deployed to KSC, where they were interfaced with live data. This paper describes how the prototype performed during the period before the launch, including accuracy and computer resource usage. 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We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.\r\n\r\n**Reference:**\r\n\r\nB. W. Ricks and O. J. Mengshoel, \"Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study.\" In Proc. of the First Annual Conference of the Prognostics and Health Management Society (PHM-09), San Diego, CA, September 27 \u2013 October 1, 2009.\r\n\r\n**BibTex Reference:**\r\n\r\n@inproceedings{ricks09methods,\r\n author = {Ricks, B. W. and Mengshoel, O. J.},\r\n title = {Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study},\r\n booktitle = {Proc. of the Annual Conference of the Prognostics and Health Management Society (PHM-09)},\r\n address = {San Diego, CA}, month = sep,\r\n year = {2009}\r\n}","distribution":[{"@type":"dcat:Distribution","description":"PHM-2009","downloadURL":"https://c3.nasa.gov/dashlink/static/media/publication/phmc_09_33.pdf","format":"PDF","mediaType":"application/pdf","title":"phmc_09_33.pdf"}],"identifier":"DASHLINK_101","issued":"2010-09-10","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/101/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"Methods for Probabilistic Fault Diagnosis: An EPS Case Study"},"description":"Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.\r\n\r\n**Reference:**\r\n\r\nB. W. Ricks and O. J. Mengshoel, \"Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study.\" In Proc. of the First Annual Conference of the Prognostics and Health Management Society (PHM-09), San Diego, CA, September 27 \u2013 October 1, 2009.\r\n\r\n**BibTex Reference:**\r\n\r\n@inproceedings{ricks09methods,\r\n author = {Ricks, B. W. and Mengshoel, O. J.},\r\n title = {Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study},\r\n booktitle = {Proc. of the Annual Conference of the Prognostics and Health Management Society (PHM-09)},\r\n address = {San Diego, CA}, month = sep,\r\n year = {2009}\r\n}","distribution_titles":["phmc_09_33.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/b4e57c1b-3360-4abd-91c7-b9bb08c59b7e","harvest_record_raw":"https://catalog.data.gov/harvest_record/b4e57c1b-3360-4abd-91c7-b9bb08c59b7e/raw","has_download":true,"has_spatial":false,"identifier":"DASHLINK_101","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-08-19T01:10:22.004554","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":2,"publisher":"Dashlink","slug":"methods-for-probabilistic-fault-diagnosis-an-eps-case-study","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Methods for Probabilistic Fault Diagnosis: An EPS Case Study","type":"dataset"},{"_score":53.676975,"_sort":[1787101807661,53.676975,8,"64ec5f4c-8c0c-4b8b-aa8d-2a9a71d89d84"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Kai Goebel","hasEmail":"mailto:kai.goebel@nasa.gov"},"description":"The chapter describes the application of prognostic techniques to the domain of structural health and\r\ndemonstrates the efficacy of the methods using fatigue data from a graphite-epoxy composite coupon. Prognostics denotes the in-situ assessment of the health of a component and the repeated estimation of remaining life, conditional on anticipated future usage. The methods shown here use a physics-based modeling approach whereby the behavior of the damaged components is encapsulated via mathematical equations that describe the characteristics of the components as it experiences increasing degrees of degradation. Mathematical rigorous techniques are used to extrapolate the remaining life to a failure threshold. Additionally, mathematical tools are used to calculate the uncertainty associated with making predictions. The information stemming from the predictions can be used in an operational context for go/no go decisions, quantify risk of ability to complete a (set of) mission or operation, and when to schedule maintenance.","distribution":[{"@type":"dcat:Distribution","description":"chapter","downloadURL":"https://c3.nasa.gov/dashlink/static/media/publication/2015_PrognosticsDesign.pdf","format":"PDF","mediaType":"application/pdf","title":"2015_PrognosticsDesign.pdf"}],"identifier":"DASHLINK_942","issued":"2016-01-14","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/942/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"Prognostics Design Solutions in Structural Health Monitoring Systems"},"description":"The chapter describes the application of prognostic techniques to the domain of structural health and\r\ndemonstrates the efficacy of the methods using fatigue data from a graphite-epoxy composite coupon. Prognostics denotes the in-situ assessment of the health of a component and the repeated estimation of remaining life, conditional on anticipated future usage. The methods shown here use a physics-based modeling approach whereby the behavior of the damaged components is encapsulated via mathematical equations that describe the characteristics of the components as it experiences increasing degrees of degradation. Mathematical rigorous techniques are used to extrapolate the remaining life to a failure threshold. Additionally, mathematical tools are used to calculate the uncertainty associated with making predictions. The information stemming from the predictions can be used in an operational context for go/no go decisions, quantify risk of ability to complete a (set of) mission or operation, and when to schedule maintenance.","distribution_titles":["2015_PrognosticsDesign.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/cb0c1e9a-d24d-4abe-a49a-7112e1eaa01f","harvest_record_raw":"https://catalog.data.gov/harvest_record/cb0c1e9a-d24d-4abe-a49a-7112e1eaa01f/raw","has_download":true,"has_spatial":false,"identifier":"DASHLINK_942","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-08-19T01:10:07.661208","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":8,"publisher":"Dashlink","slug":"prognostics-design-solutions-in-structural-health-monitoring-systems","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Prognostics Design Solutions in Structural Health Monitoring Systems","type":"dataset"},{"_score":11.353861,"_sort":[1787101783894,11.353861,9,"3687bf55-6823-45de-8f75-bac3912fcb52"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Miryam Strautkalns","hasEmail":"mailto:miryam.strautkalns@nasa.gov"},"description":"Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reli- able than the system they are observing. This is in part due to the sensors\u2019 operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electromechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation.","distribution":[{"@type":"dcat:Distribution","description":"Access the data via HTTP.","downloadURL":"https://oco2.gesdisc.eosdis.nasa.gov/data/OCO2_DATA/OCO2_L1aIn_Pixel.11.2/","format":"HTML","mediaType":"text/html","title":"Download this dataset through a directory map"},{"@type":"dcat:Distribution","description":"Access the data via the OPeNDAP protocol.","downloadURL":"https://oco2.gesdisc.eosdis.nasa.gov/opendap/OCO2_L1aIn_Pixel.11.2/contents.html","format":"HTML","mediaType":"text/html","title":"Use OPeNDAP to access the dataset's data"},{"@type":"dcat:Distribution","description":"Access the dataset landing page from the GES DISC website.","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/OCO2_L1aIn_Pixel_11.2.html","format":"HTML","mediaType":"text/html","title":"This dataset's landing page"},{"@type":"dcat:Distribution","description":"Level 1A Software Interfase Specification.","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/SDOS_SIS_L1aIn.V7.pdf","format":"PDF","mediaType":"application/pdf","title":"View the primary investigator's documentation for this dataset"},{"@type":"dcat:Distribution","description":"OCO-2 Data Gaps","downloadURL":"https://disc.gsfc.nasa.gov/information/documents?title=OCO+Known+Data+Issues","format":"HTML","mediaType":"text/html","title":"View documentation related to this dataset"},{"@type":"dcat:Distribution","description":"OCO-2 Documentation","downloadURL":"https://disc.gsfc.nasa.gov/information/documents?title=OCO-2+Documents","format":"HTML","mediaType":"text/html","title":"View documentation related to this dataset"},{"@type":"dcat:Distribution","description":"OCO-2 Project Home Page","downloadURL":"https://ocov2.jpl.nasa.gov/","format":"HTML","mediaType":"text/html","title":"The dataset's project home page"},{"@type":"dcat:Distribution","description":"Publications from the Science Team","downloadURL":"https://ocov2.jpl.nasa.gov/science/publications/","format":"HTML","mediaType":"text/html","title":"View documentation related to this dataset"},{"@type":"dcat:Distribution","description":"README document.","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/README.OCO2.pdf","format":"PDF","mediaType":"application/pdf","title":"View this dataset's read me document"},{"@type":"dcat:Distribution","description":"Subset recipe using OPeNDAP","downloadURL":"https://disc.gsfc.nasa.gov/information/howto?title=How+to+download+a+spatial+and+variable+subset+of+Level+1B+data+using+OPeNDAP","format":"HTML","mediaType":"text/html","title":"View this dataset's how-to documentation"},{"@type":"dcat:Distribution","description":"This software (NASA NTR-49044) retrieves a set of atmospheric/surface/instrument parameters from a simultaneous fit to spectra from multiple absorption bands. The software uses an iterative, non-linear retrieval technique (optimal estimation). After the retrieval process has converged, the software performs an error analysis. The products of the software include all quantities needed to understand the information content of the measurement, its uncertainty, and its dependence on interfering atmospheric properties.\n\nJet Propulsion Laboratory, California Institute of Technology.\nCopyright 2016 California Institute of Technology.\nU.S. Government sponsorship acknowledged.","downloadURL":"https://github.com/nasa/RtRetrievalFramework","format":"HTML","mediaType":"text/html","title":"Downloadable software applications"},{"@type":"dcat:Distribution","description":"USER'S GUIDE","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/OCO2_V11_OCO3_V10_DUG.pdf","format":"PDF","mediaType":"application/pdf","title":"View this dataset's user's guide"},{"@type":"dcat:Distribution","description":"Use the Earthdata Search to find and retrieve data sets across multiple data centers.","downloadURL":"https://search.earthdata.nasa.gov/search?q=OCO2_L1aIn_Pixel","format":"HTML","mediaType":"text/html","title":"Download this dataset through Earthdata Search"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/OCO2_logo.jpg","format":"JPEG","mediaType":"image/jpeg","title":"Get a related visualization"}],"identifier":"DASHLINK_688","issued":"2013-04-10","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/688/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"A knowledge-based system approach for sensor fault modeling, detection and mitigation"},"description":"Sensors are vital components for control and advanced health management techniques. 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The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electromechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. 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All the protocols were approved at the Institute of Biomedical Problems \u2013 Russian Federation State Scientific Research Center in the frame of Protocon experiment. EBC samples were collected by commercial devices RTube one month before flight (background) at the Yu. A. Gagarin Research and Test Cosmonaut Training Center, immediately after landing of the landing modules in the field (R0), and on the seventh day after landing as a part of medical examination (R+7). Semi-quantitative label-free proteomic analysis of 13 EBC samples collected from 5 Russian cosmonauts before and after long-term (169\u2013199 days) spaceflights were performed and resulted in 164 different proteins. The highest number of proteins was detected in EBC after landing (R0). Pathways enrichment analysis via GO database large group of proteins that take part in keratinization processes (CASP14, DSG1, DSP, JUP, and etc). Nine proteins were KRT2, KRT9, KRT1, KRT10, KRT14, DCD, KRT6C, KRT6A, KRT5 were detected in all groups (background, R0, R+1). A two-sample Welch\u2019s t-test identified the significant changing of KRT2 and KRT9 levels after landing. Enrichment analysis via KEGG database revealed significant participation (2,07E-06) of detected proteins in pathogenic E. coli infection (ACTG1, TUBA1C, TUBA4A, TUBB, TUBB8, YWHAZ). Presumably, presents of this proteins can associated with changing of cosmonauts\u2019 microbial composition. Thus EBC can be used for noninvasive monitoring of health status and respiratory tract pathologies during the spaceflight. The obtained data are important for the development of medicine in extreme situations.\"']","distribution":[{"@type":"dcat:Distribution","description":"GeneLab Study Page","downloadURL":"https://genelab-data.ndc.nasa.gov/genelab/accession/GLDS-359","format":"HTML","mediaType":"text/html","title":"['\"Proteome profiling of the exhaled breath condensate after long-term spaceflights\"']"}],"identifier":"nasa_genelab_GLDS-359_wmcd-d5pu","issued":"2021-05-21","keyword":["sample-collection-extraction-mass-spectrometry-data-transformation","spaceflight-time"],"landingPage":"https://data.nasa.gov/dataset/proteome-profiling-of-the-exhaled-breath-condensate-after-long-term-spaceflights","license":"http://www.usa.gov/publicdomain/label/1.0/","modified":"2025-04-23","programCode":["026:005"],"publisher":{"@type":"org:Organization","name":"National Aeronautics and Space Administration"},"theme":["Earth Science"],"title":"['\"Proteome profiling of the exhaled breath condensate after long-term spaceflights\"']"},"description":"['\"The aim of the study was to analyze exhaled breath condensate (EBC) proteome changes due to the effects of spaceflight factors. 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This framework consists of Shared Socioeconomic Pathways (SSPs), Representative Concentration Pathways (RCPs), and Shared Policy Assumptions (SPAs), which together describe alternative visions of how society and climate may evolve over the coming decades, while providing a framework for combining these pathways in integrated studies. 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The database was generated from a Google Scholar search, followed by a manual examination of the results for papers that made substantial use of the SSPs. Each paper was then coded along a number of different dimensions, including categories of types of papers or analysis, number of subcategories for SSP Applications and SSP Extensions, particular Shared Socioeconomic Pathways (SSPs) used, particular Representative Concentration Pathways (RCPs) used, and particular SSP-RCP combinations used. Over the past ten years, the climate change research commUnity developed a scenario framework combining alternative futures of climate and society to facilitate integrated research and consistent assessment to inform policy. This framework consists of Shared Socioeconomic Pathways (SSPs), Representative Concentration Pathways (RCPs), and Shared Policy Assumptions (SPAs), which together describe alternative visions of how society and climate may evolve over the coming decades, while providing a framework for combining these pathways in integrated studies. The tracking of the use of this framework in the literature allows for assessment of how it is being used, whether it is achieving its original goals, and what improvements to the framework would benefit future research.","distribution_titles":["View documentation related to this dataset","Get a related visualization","Google Scholar search results"],"harvest_record":"https://catalog.data.gov/harvest_record/66f15443-ea45-4de6-b9f4-09f815dc4bb4","harvest_record_raw":"https://catalog.data.gov/harvest_record/66f15443-ea45-4de6-b9f4-09f815dc4bb4/raw","has_download":true,"has_spatial":true,"identifier":"C2022275525-SEDAC","keyword":["earth-science","economic-resources","human-dimensions","infrastructure","land-surface","land-use-land-cover","public-health","socioeconomics","sustainability"],"last_harvested_date":"2026-08-19T00:47:59.331054","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":2,"publisher":"SEDAC","slug":"shared-socioeconomic-pathways-ssps-literature-database-v1-2014-2019","spatial_centroid":null,"spatial_shape":null,"theme":["SSP","geospatial"],"title":"Shared Socioeconomic Pathways (SSPs) Literature Database, v1, 2014-2019","type":"dataset"},{"_score":13.78155,"_sort":[1787100466259,13.78155,5,"a04e25d0-8cdf-4960-9983-e4ef416604e8"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Christopher Teubert","hasEmail":"mailto:Christopher.A.Teubert@nasa.gov"},"description":"This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health.\n\nIn this dataset, four 18650 Li-ion batteries (Identified as RW25, RW26, RW27 and RW28) were continuously operated by repeatedly charging them to 4.2V and then discharging them to 3.2V using a randomized sequence of discharging currents between 0.5A and 5A. This type of discharging profile is referred to here as random walk (RW) discharging. A customized probability distribution is used in this experiment to select a new load setpoint every 1 minute during RW discharging operation. The custom probability distribution was designed to be skewed towards selecting higher currents. The ambient temperature at which the batteries are cycled was held at approximately 40C for these experiments.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.nasa.gov/docs/legacy/ames/4.RW_Skewed_High_40C_DataSet_2Post.zip","format":"ZIP","mediaType":"application/zip","title":"RW_Skewed_High_40C_DataSet_2Post.zip"}],"identifier":"https://data.nasa.gov/api/views/gah6-q2es","issued":"2022-10-20","keyword":["batteries","degradation","phm","prognostics"],"landingPage":"https://data.nasa.gov/dataset/randomized-battery-usage-4-40c-right-skewed-random-walk","license":"https://www.usa.gov/government-works","modified":"2025-12-09","programCode":["026:021"],"publisher":{"@type":"org:Organization","name":"PCoE"},"theme":["Raw Data"],"title":"Randomized Battery Usage 4: 40C Right-Skewed Random Walk"},"description":"This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health.\n\nIn this dataset, four 18650 Li-ion batteries (Identified as RW25, RW26, RW27 and RW28) were continuously operated by repeatedly charging them to 4.2V and then discharging them to 3.2V using a randomized sequence of discharging currents between 0.5A and 5A. This type of discharging profile is referred to here as random walk (RW) discharging. A customized probability distribution is used in this experiment to select a new load setpoint every 1 minute during RW discharging operation. The custom probability distribution was designed to be skewed towards selecting higher currents. The ambient temperature at which the batteries are cycled was held at approximately 40C for these experiments.","distribution_titles":["RW_Skewed_High_40C_DataSet_2Post.zip"],"harvest_record":"https://catalog.data.gov/harvest_record/2ae57b08-12b6-4f74-8e47-8167b2a8f1b4","harvest_record_raw":"https://catalog.data.gov/harvest_record/2ae57b08-12b6-4f74-8e47-8167b2a8f1b4/raw","has_download":true,"has_spatial":false,"identifier":"https://data.nasa.gov/api/views/gah6-q2es","keyword":["batteries","degradation","phm","prognostics"],"last_harvested_date":"2026-08-19T00:47:46.259633","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":5,"publisher":"PCoE","slug":"randomized-battery-usage-4-40c-right-skewed-random-walk","spatial_centroid":null,"spatial_shape":null,"theme":["Raw Data"],"title":"Randomized Battery Usage 4: 40C Right-Skewed Random Walk","type":"dataset"},{"_score":13.296598,"_sort":[1787100454737,13.296598,2,"f540ded7-c45d-4af2-b606-89069f27ff50"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Open Science Data Repository Help Desk","hasEmail":"mailto:arc-dl-osdr-help@mail.nasa.gov"},"description":"Translating fundamental biological discoveries from NASA Space Biology program into health risk from space flights has been an ongoing challenge. We propose to use NASA GeneLab database to gain new knowledge on potential systemic responses to space. Unbiased systems biology analysis of transcriptomic data from seven different rodent datasets reveals for the first time the existence of potential 'master regulators' coordinating a systemic response to microgravity and/or space radiation with TGF-\u03b21 being the most common regulator. We hypothesized the space environment leads to the release of biomolecules circulating inside the blood stream. Through datamining we identified 13 candidate microRNAs (miRNA) which are common in all studies and directly interact with TGF-\u03b21 that can be potential circulating factors impacting space biology. This study exemplifies the utility of the GeneLab data repository to aid in the process of performing novel hypothesis-based research.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://purl.bioontology.org/ontology/NCBITAXON/10116","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://lsda.jsc.nasa.gov/Experiment/exper/30","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/missions/STS-40","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/studies/OSD-422","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.26030/jq04-0n51","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"Rodent Research-1 (RR1) NASA Validation Flight: Mouse liver transcriptomic, proteomic, epigenomic and histology data"},"description":"Translating fundamental biological discoveries from NASA Space Biology program into health risk from space flights has been an ongoing challenge. We propose to use NASA GeneLab database to gain new knowledge on potential systemic responses to space. Unbiased systems biology analysis of transcriptomic data from seven different rodent datasets reveals for the first time the existence of potential 'master regulators' coordinating a systemic response to microgravity and/or space radiation with TGF-\u03b21 being the most common regulator. We hypothesized the space environment leads to the release of biomolecules circulating inside the blood stream. Through datamining we identified 13 candidate microRNAs (miRNA) which are common in all studies and directly interact with TGF-\u03b21 that can be potential circulating factors impacting space biology. This study exemplifies the utility of the GeneLab data repository to aid in the process of performing novel hypothesis-based research.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/01123c39-0983-4dd0-a053-e5c88fce877e","harvest_record_raw":"https://catalog.data.gov/harvest_record/01123c39-0983-4dd0-a053-e5c88fce877e/raw","has_download":true,"has_spatial":false,"identifier":"10.26030/jq04-0n51","keyword":["biological-and-physical-sciences","genelab","nasa"],"last_harvested_date":"2026-08-19T00:47:34.737955","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":2,"publisher":"Open Science Data Repository","slug":"rodent-research-1-rr1-nasa-validation-flight-mouse-liver-transcriptomic-proteomic-epigenom","spatial_centroid":null,"spatial_shape":null,"theme":["Biological and Physical Sciences"],"title":"Rodent Research-1 (RR1) NASA Validation Flight: Mouse liver transcriptomic, proteomic, epigenomic and histology data","type":"dataset"},{"_score":12.012108,"_sort":[1787100449047,12.012108,0,"261ba34d-dc83-4eb1-9ebc-277800ba8518"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Open Science Data Repository Help Desk","hasEmail":"mailto:arc-dl-osdr-help@mail.nasa.gov"},"description":"Fresh, nutritious, palatable produce for crew consumption on long-duration spaceflight missions may provide health-promoting, bioavailable nutrients and enhance the dietary experience. VEG-04A and VEG-04B explored growing leafy greens on the International Space Station using the Veggie Vegetable Production System. Two flight tests with ground controls were conducted in 2019 growing mizuna mustard, where Veggie chambers were set to different red-to-blue-to-green light formulations. Light quality affects plant growth, nutrition, microbiology, and organoleptic characteristics on Earth, and we examined how these vary in microgravity and under different harvest scenarios. Astronauts harvested and weighed mizuna and completed organoleptic evaluations. Flight samples were returned to Earth for nutritional quality and microbial food safety analyses. Yield and chemistry differed between ground and flight samples and light treatments, and bacterial and fungal counts were lower in ground than in flight samples. This research helps increase our understanding of the requirements for growing high-quality crops in spaceflight. This study derives results from the Phenolic Content, Elemental Analysis, and ORAC assays using leaf samples.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://purl.bioontology.org/ontology/NCBITAXON/10090","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/missions/SpaceX-16","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/studies/OSD-920","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.26030/k1f5-1p32","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"Pick-and-eat space crop production flight testing on the International Space Station (Elemental Analysis, ORAC, Phenolic Content)"},"description":"Fresh, nutritious, palatable produce for crew consumption on long-duration spaceflight missions may provide health-promoting, bioavailable nutrients and enhance the dietary experience. VEG-04A and VEG-04B explored growing leafy greens on the International Space Station using the Veggie Vegetable Production System. Two flight tests with ground controls were conducted in 2019 growing mizuna mustard, where Veggie chambers were set to different red-to-blue-to-green light formulations. Light quality affects plant growth, nutrition, microbiology, and organoleptic characteristics on Earth, and we examined how these vary in microgravity and under different harvest scenarios. Astronauts harvested and weighed mizuna and completed organoleptic evaluations. Flight samples were returned to Earth for nutritional quality and microbial food safety analyses. Yield and chemistry differed between ground and flight samples and light treatments, and bacterial and fungal counts were lower in ground than in flight samples. This research helps increase our understanding of the requirements for growing high-quality crops in spaceflight. This study derives results from the Phenolic Content, Elemental Analysis, and ORAC assays using leaf samples.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/99d8d0cc-478d-46fd-922c-19661537c3b2","harvest_record_raw":"https://catalog.data.gov/harvest_record/99d8d0cc-478d-46fd-922c-19661537c3b2/raw","has_download":true,"has_spatial":false,"identifier":"10.26030/k1f5-1p32","keyword":["biological-and-physical-sciences","genelab","nasa"],"last_harvested_date":"2026-08-19T00:47:29.047575","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":0,"publisher":"Open Science Data Repository","slug":"pick-and-eat-space-crop-production-flight-testing-on-the-international-space-station-eleme","spatial_centroid":null,"spatial_shape":null,"theme":["Biological and Physical Sciences"],"title":"Pick-and-eat space crop production flight testing on the International Space Station (Elemental Analysis, ORAC, Phenolic Content)","type":"dataset"},{"_score":8.78635,"_sort":[1787100436222,8.78635,3,"f953350b-415f-472f-b7d4-91de7d312cdb"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Open Science Data Repository Help Desk","hasEmail":"mailto:arc-dl-osdr-help@mail.nasa.gov"},"description":"Adverse effects of spaceflight on musculoskeletal health increase the risk of bone injury and impairment of fracture healing. Its yet elusive molecular comprehension warrants immediate attention, since space travel is becoming more frequent. Here we examined the effects of spaceflight on bone fracture healing using a 2\u202fmm femoral segmental bone defect (SBD) model. Forty, 9-week-old, male C57BL/6J mice were randomized into 4 groups: 1) Sham surgery on Ground (G-Sham); 2) Sham surgery housed in Spaceflight (FLT-Sham); 3) SBD surgery on Ground (G-Surgery); and 4) SBD surgery housed in Spaceflight (FLT-Surgery). Surgery procedures occurred 4\u202fdays prior to launch; post-launch, the spaceflight mice were house in the rodent habitats on the International Space Station (ISS) for approximately 4\u202fweeks before euthanasia. Mice remaining on the Earth were subjected to identical housing and experimental conditions. The right femur from half of the spaceflight and ground groups was investigated by micro-computed tomography (\u00b5CT). In the remaining mice, the callus regions from surgery groups and corresponding femoral segments in sham mice were probed by global transcriptomic and metabolomic assays. \u00b5CT confirmed escalated bone loss in FLT-Sham compared to G-Sham mice. Comparing to their respective on-ground counterparts, the morbidity gene-network signal was inhibited in sham spaceflight mice but activated in the spaceflight callus. \u00b5CT analyses of spaceflight callus revealed increased trabecular spacing and decreased trabecular connectivity. Activated apoptotic signals in spaceflight callus were synchronized with inhibited cell migration signals that potentially hindered the wound site to recruit growth factors. A major pro-apoptotic and anti-migration gene network, namely the RANK-NF\u03baB axis, emerged as the central node in spaceflight callus. Concluding, spaceflight suppressed a unique biomolecular mechanism in callus tissue to facilitate a failed regeneration, which merits a customized intervention strategy. Source name abbreviation key: surgically operated on using the sham saline treatment (Sh); non-surgical control (NS); Ground Control group (G); Flight Group (F); Whole Body frozen as sample on ISS (W).","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://purl.bioontology.org/ontology/NCBITAXON/10090","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://lsda.jsc.nasa.gov/Experiment/exper/13932","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/missions/SpaceX-10","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/studies/OSD-396","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161618","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.26030/ce4f-xx71","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"Gene-metabolite networks associated with impediment of bone fracture repair in spaceflight"},"description":"Adverse effects of spaceflight on musculoskeletal health increase the risk of bone injury and impairment of fracture healing. Its yet elusive molecular comprehension warrants immediate attention, since space travel is becoming more frequent. Here we examined the effects of spaceflight on bone fracture healing using a 2\u202fmm femoral segmental bone defect (SBD) model. Forty, 9-week-old, male C57BL/6J mice were randomized into 4 groups: 1) Sham surgery on Ground (G-Sham); 2) Sham surgery housed in Spaceflight (FLT-Sham); 3) SBD surgery on Ground (G-Surgery); and 4) SBD surgery housed in Spaceflight (FLT-Surgery). Surgery procedures occurred 4\u202fdays prior to launch; post-launch, the spaceflight mice were house in the rodent habitats on the International Space Station (ISS) for approximately 4\u202fweeks before euthanasia. Mice remaining on the Earth were subjected to identical housing and experimental conditions. The right femur from half of the spaceflight and ground groups was investigated by micro-computed tomography (\u00b5CT). In the remaining mice, the callus regions from surgery groups and corresponding femoral segments in sham mice were probed by global transcriptomic and metabolomic assays. \u00b5CT confirmed escalated bone loss in FLT-Sham compared to G-Sham mice. Comparing to their respective on-ground counterparts, the morbidity gene-network signal was inhibited in sham spaceflight mice but activated in the spaceflight callus. \u00b5CT analyses of spaceflight callus revealed increased trabecular spacing and decreased trabecular connectivity. Activated apoptotic signals in spaceflight callus were synchronized with inhibited cell migration signals that potentially hindered the wound site to recruit growth factors. A major pro-apoptotic and anti-migration gene network, namely the RANK-NF\u03baB axis, emerged as the central node in spaceflight callus. Concluding, spaceflight suppressed a unique biomolecular mechanism in callus tissue to facilitate a failed regeneration, which merits a customized intervention strategy. Source name abbreviation key: surgically operated on using the sham saline treatment (Sh); non-surgical control (NS); Ground Control group (G); Flight Group (F); Whole Body frozen as sample on ISS (W).","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/1e381848-c877-4964-a0e4-5773e415186c","harvest_record_raw":"https://catalog.data.gov/harvest_record/1e381848-c877-4964-a0e4-5773e415186c/raw","has_download":true,"has_spatial":false,"identifier":"10.26030/ce4f-xx71","keyword":["biological-and-physical-sciences","genelab","nasa"],"last_harvested_date":"2026-08-19T00:47:16.222204","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":3,"publisher":"Open Science Data Repository","slug":"gene-metabolite-networks-associated-with-impediment-of-bone-fracture-repair-in-spaceflight","spatial_centroid":null,"spatial_shape":null,"theme":["Biological and Physical Sciences"],"title":"Gene-metabolite networks associated with impediment of bone fracture repair in spaceflight","type":"dataset"},{"_score":10.826578,"_sort":[1787100428828,10.826578,2,"286751a9-adbc-46a9-a9f1-4fc961f33cb2"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Open Science Data Repository Help Desk","hasEmail":"mailto:arc-dl-osdr-help@mail.nasa.gov"},"description":"Interplanetary space travel poses many hazards to the human body. To protect astronaut health and performance on critical missions, there is first a need to understand the effects of deep space hazards, including ionizing radiation, confinement, and altered gravity. Previous studies of rodents exposed to a single such stressor document significant deficits, but our study is the first to investigate possible cumulative and synergistic impacts of simultaneous ionizing radiation, confinement, and altered gravity on behavior and cognition. Our cohort was divided between 6\u2010month\u2010old female and male mice in group, social isolation, or hindlimb unloading housing, exposed to 0 or 50 cGy of 5 ion simplified simulated galactic cosmic radiation (GCRsim). We report interactions and independent effects of GCRsim exposure and housing conditions on behavioral and cognitive performance. Exposure to GCRsim drove changes in immune cell populations in peripheral blood collected early after irradiation, while housing conditions drove changes in blood collected at a later point. Female mice were largely resilient to deficits observed in male mice. Finally, we used principal component analysis to represent total deficits as principal component scores, which were predicted by general linear models using GCR exposure, housing condition, and early blood biomarkers. This dataset derives results from the flow cytometry assay using blood samples from same source animals used for behavioral studies in OSD-618.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://purl.bioontology.org/ontology/NCBITAXON/10090","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/studies/OSD-640","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.26030/8k7j-dn29","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"Combined space stressors induce independent behavioral deficits predicted by early peripheral blood monocytes (flow cytometry)"},"description":"Interplanetary space travel poses many hazards to the human body. To protect astronaut health and performance on critical missions, there is first a need to understand the effects of deep space hazards, including ionizing radiation, confinement, and altered gravity. Previous studies of rodents exposed to a single such stressor document significant deficits, but our study is the first to investigate possible cumulative and synergistic impacts of simultaneous ionizing radiation, confinement, and altered gravity on behavior and cognition. Our cohort was divided between 6\u2010month\u2010old female and male mice in group, social isolation, or hindlimb unloading housing, exposed to 0 or 50 cGy of 5 ion simplified simulated galactic cosmic radiation (GCRsim). We report interactions and independent effects of GCRsim exposure and housing conditions on behavioral and cognitive performance. Exposure to GCRsim drove changes in immune cell populations in peripheral blood collected early after irradiation, while housing conditions drove changes in blood collected at a later point. Female mice were largely resilient to deficits observed in male mice. Finally, we used principal component analysis to represent total deficits as principal component scores, which were predicted by general linear models using GCR exposure, housing condition, and early blood biomarkers. This dataset derives results from the flow cytometry assay using blood samples from same source animals used for behavioral studies in OSD-618.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/8f8851b2-3913-4630-8762-26f8f306310f","harvest_record_raw":"https://catalog.data.gov/harvest_record/8f8851b2-3913-4630-8762-26f8f306310f/raw","has_download":true,"has_spatial":false,"identifier":"10.26030/8k7j-dn29","keyword":["biological-and-physical-sciences","genelab","nasa"],"last_harvested_date":"2026-08-19T00:47:08.828600","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":2,"publisher":"Open Science Data Repository","slug":"combined-space-stressors-induce-independent-behavioral-deficits-predicted-by-early-periphe-803bd","spatial_centroid":null,"spatial_shape":null,"theme":["Biological and Physical Sciences"],"title":"Combined space stressors induce independent behavioral deficits predicted by early peripheral blood monocytes (flow cytometry)","type":"dataset"},{"_score":8.219444,"_sort":[1787100425620,8.219444,3,"69b87a1a-7a9d-48d8-8533-dc545c92e4f5"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Open Science Data Repository Help Desk","hasEmail":"mailto:arc-dl-osdr-help@mail.nasa.gov"},"description":"The environmental microbiome study was designed to decipher microbial diversity of the International Space Station surfaces in terms of spatial and temporal distributions using 16S and ITS iTag Illumina sequencing. We hypothesized that the microbial population of environmental surfaces changes in time due to astronauts' activity and might be location specific. The environmental samples were collected with the polyester wipes from eight different locations in the ISS during two consecutive sampling sessions (three months apart). The specific objective was to unveil the viable microbial diversity of each location during two separate sessions in terms of abundance and richness of the communities. The International Space Station (ISS) as a closed built environment has its own environmental microbiome which is shaped by microgravity, radiation, and limited human presence. The microbial diversity associated with ISS environmental surfaces was investigated during this study. Polyester wipes and contact slides were used for sampling of eight various surface locations on the ISS at different time periods. The samples were retrieved and analyzed immediately upon the return to the Earth (via Soyuz TMA-14M or Dragon capsule from SpaceX). After surface sample collection, contact slides containing nutrient media for the growth of bacteria and fungi were incubated at 25C. The polyester wipes were processed to measure microbial burden (R2A, Blood Agar, and Potato Dextrose Agar) and recover cultivable bacteria as well as fungi. Subsequently, viable microbial burden was assessed using Adenosine Triphosphate (ATP) assay, and quantitative polymerase chain reaction (PCR) methods after propidium monoazide (PMA) treatment. The 16S-tag and metagenome analyses were used to elucidate viable microbial diversity. The cultivable bacterial population yield from the polyester wipes was very high (5 to 7-logs) when compared with the contact slides (102 to 103 CFU/m2). The PMA-qPCR analysis showed considerable variation of viable bacterial population (105 to 109 16S rDNA gene copies/m2) among locations sampled. Unlike contact slides, polyester wipes cover much larger sample surface (~1 m2) and produce much more reliable results of the microbial diversity of the ISS covering both cultivable and non-cultivable species. The cultivable, total, and viable microbial diversity was determined utilizing state-of-the art molecular techniques. The implementation of the PMA assay before DNA extraction allowed distinguishing viable microorganisms, which is crucial for determining their role to the crew health, the ISS maintenance and the general knowledge of the closed environmentally controlled built systems.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://purl.bioontology.org/ontology/NCBITAXON/13613","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://nlsp.nasa.gov/view/lsdapub/lsda_experiment/c4df82ca-6b22-5b77-9515-722c0ac999c3","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/missions/SpaceX-5","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/missions/SpaceX-6","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/studies/OSD-65","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.26030/k2s7-ke78","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"Microbial Observatory (ISS-MO): Microbial diversity"},"description":"The environmental microbiome study was designed to decipher microbial diversity of the International Space Station surfaces in terms of spatial and temporal distributions using 16S and ITS iTag Illumina sequencing. We hypothesized that the microbial population of environmental surfaces changes in time due to astronauts' activity and might be location specific. The environmental samples were collected with the polyester wipes from eight different locations in the ISS during two consecutive sampling sessions (three months apart). The specific objective was to unveil the viable microbial diversity of each location during two separate sessions in terms of abundance and richness of the communities. The International Space Station (ISS) as a closed built environment has its own environmental microbiome which is shaped by microgravity, radiation, and limited human presence. The microbial diversity associated with ISS environmental surfaces was investigated during this study. Polyester wipes and contact slides were used for sampling of eight various surface locations on the ISS at different time periods. The samples were retrieved and analyzed immediately upon the return to the Earth (via Soyuz TMA-14M or Dragon capsule from SpaceX). After surface sample collection, contact slides containing nutrient media for the growth of bacteria and fungi were incubated at 25C. The polyester wipes were processed to measure microbial burden (R2A, Blood Agar, and Potato Dextrose Agar) and recover cultivable bacteria as well as fungi. Subsequently, viable microbial burden was assessed using Adenosine Triphosphate (ATP) assay, and quantitative polymerase chain reaction (PCR) methods after propidium monoazide (PMA) treatment. The 16S-tag and metagenome analyses were used to elucidate viable microbial diversity. The cultivable bacterial population yield from the polyester wipes was very high (5 to 7-logs) when compared with the contact slides (102 to 103 CFU/m2). The PMA-qPCR analysis showed considerable variation of viable bacterial population (105 to 109 16S rDNA gene copies/m2) among locations sampled. Unlike contact slides, polyester wipes cover much larger sample surface (~1 m2) and produce much more reliable results of the microbial diversity of the ISS covering both cultivable and non-cultivable species. The cultivable, total, and viable microbial diversity was determined utilizing state-of-the art molecular techniques. The implementation of the PMA assay before DNA extraction allowed distinguishing viable microorganisms, which is crucial for determining their role to the crew health, the ISS maintenance and the general knowledge of the closed environmentally controlled built systems.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/4a576877-57f7-4f86-87be-e8b2bf8fdbe3","harvest_record_raw":"https://catalog.data.gov/harvest_record/4a576877-57f7-4f86-87be-e8b2bf8fdbe3/raw","has_download":true,"has_spatial":false,"identifier":"10.26030/k2s7-ke78","keyword":["biological-and-physical-sciences","genelab","nasa"],"last_harvested_date":"2026-08-19T00:47:05.620223","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"popularity":3,"publisher":"Open Science Data Repository","slug":"microbial-observatory-iss-mo-microbial-diversity","spatial_centroid":null,"spatial_shape":null,"theme":["Biological and Physical Sciences"],"title":"Microbial Observatory (ISS-MO): Microbial diversity","type":"dataset"},{"_score":5.067415,"_sort":[1787100395060,5.067415,37,"bff8753a-f2a5-433b-a589-2e1fc9acadf4"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"WakeCountyGovernment","hasEmail":"mailto:gisapps@wake.gov"},"description":"The Wake County health department inspects food service facilities\nthroughout Wake County. The department permits and inspects these facilities,\nand responds to citizen complaints. In the event of disease outbreak, the\ndepartment investigates to determine the source of the infection, and prevent\nfurther illness.\n\nThis dataset captures the restaurants that are\ninspected.\u00a0 The data set is geocoded\nbased on address with approximately 85% of the locations having a valid\ngeo-location.\u00a0
\n\nYou can find out additional information about our restaurant\ninspections on our website:\u00a0 Food Safety and Sanitation
\n\nThis table captures all Wake County sanitation\ninspections from September 20, 2012 to Present.
\n\n
This table is part of a set of data that combined will give\nyou a picture of all restaurant inspections.\u00a0\nThose three tables are:
\n\n1.\u00a0\u00a0\u00a0\u00a0 Restaurants:\nThis table captures all active facilities\nwhere Wake County performs sanitations inspections.\u00a0 Facilities that are closed are removed from\nall three files in this dataset.\u00a0 Per NC\nState regulations, facilities that have a change in ownership are considered\nclosed and the restaurant re-opens under a new permit, even if there is not a change\nin the name of the restaurant.
\n\n2.\u00a0\u00a0\u00a0\u00a0 Food Inspections:\nThis table captures all Wake County\nperforms sanitations inspections at active restaurants since September 20, 2012
\n\n3.\u00a0\u00a0\u00a0\u00a0 Food Inspection Violations:\nThis table captures all violations\nidentified during specific Wake County sanitations inspections at active\nrestaurants since September 20, 2012.\u00a0 It\nreports the results in code violations and according to CDC Risk Factors.\u00a0 You can find additional information\nabout the CDC Risk Factors on the FDA website: Retail Risk Factor\nStudy
\n\n\u00a0
\n\nThe tables can be connected through the HSISID\nfield.\u00a0
\n\n\n\nThe frequency of facility inspections fall under\nthe following rules:
\n\nInspected once per year:
\n\nRisk Category I applies to food service\nestablishments that prepare only non-potentially hazardous foods.
Inspected twice per year:
\n\nRisk Category II applies to food service\nestablishments that cook and cool no more than two potentially hazardous foods.\nPotentially hazardous raw ingredients shall be received in a ready-to-cook\nform.
Inspected three times per year
\n\nRisk Category III applies to food service\nestablishments that cook and cool no more than three potentially hazardous\nfoods.
Inspected four times per year
\n\nRisk Category IV applies to\nfood service establishments that cook and cool an unlimited number of\npotentially hazardous foods. This category also includes those facilities using\nspecialized processes or serving a highly susceptible population.
\n\n\n \n | \n \u00a0 \n \n \n Field \n | \n \n Description \n | \n
\n \n | \n HSISID \n | \n \n State code identifying the\n restaurant (also the primary key to identify the restaurant) \n | \n
\n \n | \n Score \n | \n \n Final score for this inspection \n | \n
\n \n | \n Date \n | \n \n Date of inspection \n | \n
\n \n | \n Description \n | \n \n General comments not that may or\n may not be tied to a inspection question \n | \n
\n \n | \n Type \n | \n \n Type of inspection: Inspection, Re-inspection,\n Visit \n | \n
\n \n \n | \n PermitID \n | \n \n Deprecated. No longer provided. \n | \n
\n
","distribution":[{"@type":"dcat:Distribution","accessURL":"https://data-wake.opendata.arcgis.com/api/download/v1/items/ebe3ae7f76954fad81411612d7c4fb17/csv?layers=1","format":"CSV","mediaType":"text/csv","title":"CSV"},{"@type":"dcat:Distribution","accessURL":"https://data-wake.opendata.arcgis.com/api/download/v1/items/ebe3ae7f76954fad81411612d7c4fb17/geojson?layers=1","format":"GeoJSON","mediaType":"application/vnd.geo+json","title":"GeoJSON"},{"@type":"dcat:Distribution","accessURL":"https://data-wake.opendata.arcgis.com/api/download/v1/items/ebe3ae7f76954fad81411612d7c4fb17/kml?layers=1","format":"KML","mediaType":"application/vnd.google-earth.kml+xml","title":"KML"},{"@type":"dcat:Distribution","accessURL":"https://data-wake.opendata.arcgis.com/api/download/v1/items/ebe3ae7f76954fad81411612d7c4fb17/shapefile?layers=1","format":"ZIP","mediaType":"application/zip","title":"Shapefile"},{"@type":"dcat:Distribution","accessURL":"https://data-wake.opendata.arcgis.com/datasets/Wake::food-inspections","format":"Web Page","mediaType":"text/html","title":"ArcGIS Hub Dataset"},{"@type":"dcat:Distribution","accessURL":"https://maps.wake.gov/arcgis/rest/services/Inspections/RestaurantInspectionsOpenData/MapServer/1","format":"ArcGIS GeoServices REST API","mediaType":"application/json","title":"ArcGIS GeoService"}],"identifier":"https://www.arcgis.com/home/item.html?id=ebe3ae7f76954fad81411612d7c4fb17&sublayer=1","issued":"2016-08-12T18:31:22.000Z","keyword":["Food Safety","Inspections","NC","North Carolina","Permit","Restaurants","Wake","Wake County"],"landingPage":"https://data-wake.opendata.arcgis.com/datasets/Wake::food-inspections","license":"https://creativecommons.org/licenses/by/4.0","modified":"2026-08-18T04:30:16.000Z","publisher":{"name":"Wake County"},"spatial":"-78.9746,35.5378,-78.2667,36.0504","theme":["geospatial"],"title":"Food Inspections"},"description":"The Wake County health department inspects food service facilities\nthroughout Wake County. The department permits and inspects these facilities,\nand responds to citizen complaints. In the event of disease outbreak, the\ndepartment investigates to determine the source of the infection, and prevent\nfurther illness.\n\nThis dataset captures the restaurants that are\ninspected.\u00a0 The data set is geocoded\nbased on address with approximately 85% of the locations having a valid\ngeo-location.\u00a0
\n\nYou can find out additional information about our restaurant\ninspections on our website:\u00a0 Food Safety and Sanitation
\n\nThis table captures all Wake County sanitation\ninspections from September 20, 2012 to Present.
\n\n
This table is part of a set of data that combined will give\nyou a picture of all restaurant inspections.\u00a0\nThose three tables are:
\n\n1.\u00a0\u00a0\u00a0\u00a0 Restaurants:\nThis table captures all active facilities\nwhere Wake County performs sanitations inspections.\u00a0 Facilities that are closed are removed from\nall three files in this dataset.\u00a0 Per NC\nState regulations, facilities that have a change in ownership are considered\nclosed and the restaurant re-opens under a new permit, even if there is not a change\nin the name of the restaurant.
\n\n2.\u00a0\u00a0\u00a0\u00a0 Food Inspections:\nThis table captures all Wake County\nperforms sanitations inspections at active restaurants since September 20, 2012
\n\n3.\u00a0\u00a0\u00a0\u00a0 Food Inspection Violations:\nThis table captures all violations\nidentified during specific Wake County sanitations inspections at active\nrestaurants since September 20, 2012.\u00a0 It\nreports the results in code violations and according to CDC Risk Factors.\u00a0 You can find additional information\nabout the CDC Risk Factors on the FDA website: Retail Risk Factor\nStudy
\n\n\u00a0
\n\nThe tables can be connected through the HSISID\nfield.\u00a0
\n\n\n\nThe frequency of facility inspections fall under\nthe following rules:
\n\nInspected once per year:
\n\nRisk Category I applies to food service\nestablishments that prepare only non-potentially hazardous foods.
Inspected twice per year:
\n\nRisk Category II applies to food service\nestablishments that cook and cool no more than two potentially hazardous foods.\nPotentially hazardous raw ingredients shall be received in a ready-to-cook\nform.
Inspected three times per year
\n\nRisk Category III applies to food service\nestablishments that cook and cool no more than three potentially hazardous\nfoods.
Inspected four times per year
\n\nRisk Category IV applies to\nfood service establishments that cook and cool an unlimited number of\npotentially hazardous foods. This category also includes those facilities using\nspecialized processes or serving a highly susceptible population.
\n\n\n \n | \n \u00a0 \n \n \n Field \n | \n \n Description \n | \n
\n \n | \n HSISID \n | \n \n State code identifying the\n restaurant (also the primary key to identify the restaurant) \n | \n
\n \n | \n Score \n | \n \n Final score for this inspection \n | \n
\n \n | \n Date \n | \n \n Date of inspection \n | \n
\n \n | \n Description \n | \n \n General comments not that may or\n may not be tied to a inspection question \n | \n
\n \n | \n Type \n | \n \n Type of inspection: Inspection, Re-inspection,\n Visit \n | \n
\n \n \n | \n PermitID \n | \n \n Deprecated. No longer provided. \n | \n
\n
","distribution_titles":["CSV","GeoJSON","KML","Shapefile","ArcGIS Hub Dataset","ArcGIS GeoService"],"harvest_record":"https://catalog.data.gov/harvest_record/36447183-ef91-4e44-b19e-1e52b31565a6","harvest_record_raw":"https://catalog.data.gov/harvest_record/36447183-ef91-4e44-b19e-1e52b31565a6/raw","has_download":false,"has_spatial":true,"identifier":"https://www.arcgis.com/home/item.html?id=ebe3ae7f76954fad81411612d7c4fb17&sublayer=1","keyword":["Food Safety","Inspections","NC","North Carolina","Permit","Restaurants","Wake","Wake County"],"last_harvested_date":"2026-08-19T00:46:35.060478","organization":{"aliases":["north carolina"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"6f5c454f-8dd6-44dd-8d70-89f04303d273","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/county_wake_nc.png","name":"Wake County","organization_type":"County Government","slug":"wake-county-nc"},"popularity":37,"publisher":"Wake County","slug":"food-inspections-f168d","spatial_centroid":{"lat":35.74284,"lon":-78.69144},"spatial_shape":{"coordinates":[[[-78.9746,35.5378],[-78.9746,36.0504],[-78.2667,36.0504],[-78.2667,35.5378],[-78.9746,35.5378]]],"type":"Polygon"},"theme":["geospatial"],"title":"Food Inspections","type":"dataset"},{"_score":7.6855316,"_sort":[1787100394848,7.6855316,37,"41c3b6dd-8660-44ed-9ca4-75ee2854c77f"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"WakeCountyGovernment","hasEmail":"mailto:gisapps@wake.gov"},"description":"The Wake County health department inspects food service facilities throughout Wake County. The department permits and inspects these facilities, and responds to citizen complaints. In the event of disease outbreak, the department investigates to determine the source of the infection, and prevent further illness.
This dataset captures the restaurants that are inspected. The data set is geocoded based on address with approximately 85% of the locations having a valid geo-location.
You can find out additional information about our restaurant inspections on our website: Food Safety and Sanitation
This table captures all Wake County sanitation inspections from September 20, 2012 to Present.
This table is part of a set of data that combined will give you a picture of all restaurant inspections. Those three tables are:
1. Restaurants: This table captures all active facilities where Wake County performs sanitations inspections. Facilities that are closed are removed from all three files in this dataset. Per NC State regulations, facilities that have a change in ownership are considered closed and the restaurant re-opens under a new permit, even if there is not a change in the name of the restaurant.
2. Food Inspections: This table captures all Wake County performs sanitations inspections at active restaurants since September 20, 2012
3. Food Inspection Violations: This table captures all violations identified during specific Wake County sanitations inspections at active restaurants since September 20, 2012. It reports the results in code violations and according to CDC Risk Factors. You can find additional information about the CDC Risk Factors on the FDA website: Retail Risk Factor Study
The tables can be connected through the HSISID field.
The frequency of facility inspections fall under the following rules:
Inspected once per year:
Risk Category I applies to food service establishments that prepare only non-potentially hazardous foods.
Inspected twice per year:
Risk Category II applies to food service establishments that cook and cool no more than two potentially hazardous foods. Potentially hazardous raw ingredients shall be received in a ready-to-cook form.
Inspected three times per year
Risk Category III applies to food service establishments that cook and cool no more than three potentially hazardous foods.
Inspected four times per year
Risk Category IV applies to food service establishments that cook and cool an unlimited number of potentially hazardous foods. This category also includes those facilities using specialized processes or serving a highly susceptible population.
Field Descriptions are available here.
","distribution":[{"@type":"dcat:Distribution","accessURL":"https://data-wake.opendata.arcgis.com/api/download/v1/items/9b04d0c39abd4e049cbd4656a0a04ba3/csv?layers=2","format":"CSV","mediaType":"text/csv","title":"CSV"},{"@type":"dcat:Distribution","accessURL":"https://data-wake.opendata.arcgis.com/api/download/v1/items/9b04d0c39abd4e049cbd4656a0a04ba3/geojson?layers=2","format":"GeoJSON","mediaType":"application/vnd.geo+json","title":"GeoJSON"},{"@type":"dcat:Distribution","accessURL":"https://data-wake.opendata.arcgis.com/api/download/v1/items/9b04d0c39abd4e049cbd4656a0a04ba3/kml?layers=2","format":"KML","mediaType":"application/vnd.google-earth.kml+xml","title":"KML"},{"@type":"dcat:Distribution","accessURL":"https://data-wake.opendata.arcgis.com/api/download/v1/items/9b04d0c39abd4e049cbd4656a0a04ba3/shapefile?layers=2","format":"ZIP","mediaType":"application/zip","title":"Shapefile"},{"@type":"dcat:Distribution","accessURL":"https://data-wake.opendata.arcgis.com/datasets/Wake::food-inspection-violations","format":"Web Page","mediaType":"text/html","title":"ArcGIS Hub Dataset"},{"@type":"dcat:Distribution","accessURL":"https://maps.wake.gov/arcgis/rest/services/Inspections/RestaurantInspectionsOpenData/MapServer/2","format":"ArcGIS GeoServices REST API","mediaType":"application/json","title":"ArcGIS GeoService"}],"identifier":"https://www.arcgis.com/home/item.html?id=9b04d0c39abd4e049cbd4656a0a04ba3&sublayer=2","issued":"2016-08-12T18:35:12.000Z","keyword":["Food Safety","Inspections","NC","North Carolina","Permit","Restaurants","Wake","Wake County"],"landingPage":"https://data-wake.opendata.arcgis.com/datasets/Wake::food-inspection-violations","license":"https://creativecommons.org/licenses/by/4.0","modified":"2026-08-18T04:30:18.000Z","publisher":{"name":"Wake County"},"spatial":"-78.9746,35.5378,-78.2667,36.0504","theme":["geospatial"],"title":"Food Inspection Violations"},"description":"The Wake County health department inspects food service facilities throughout Wake County. The department permits and inspects these facilities, and responds to citizen complaints. In the event of disease outbreak, the department investigates to determine the source of the infection, and prevent further illness.
This dataset captures the restaurants that are inspected. The data set is geocoded based on address with approximately 85% of the locations having a valid geo-location.
You can find out additional information about our restaurant inspections on our website: Food Safety and Sanitation
This table captures all Wake County sanitation inspections from September 20, 2012 to Present.
This table is part of a set of data that combined will give you a picture of all restaurant inspections. Those three tables are:
1. Restaurants: This table captures all active facilities where Wake County performs sanitations inspections. Facilities that are closed are removed from all three files in this dataset. Per NC State regulations, facilities that have a change in ownership are considered closed and the restaurant re-opens under a new permit, even if there is not a change in the name of the restaurant.
2. Food Inspections: This table captures all Wake County performs sanitations inspections at active restaurants since September 20, 2012
3. Food Inspection Violations: This table captures all violations identified during specific Wake County sanitations inspections at active restaurants since September 20, 2012. It reports the results in code violations and according to CDC Risk Factors. You can find additional information about the CDC Risk Factors on the FDA website: Retail Risk Factor Study
The tables can be connected through the HSISID field.
The frequency of facility inspections fall under the following rules:
Inspected once per year:
Risk Category I applies to food service establishments that prepare only non-potentially hazardous foods.
Inspected twice per year:
Risk Category II applies to food service establishments that cook and cool no more than two potentially hazardous foods. Potentially hazardous raw ingredients shall be received in a ready-to-cook form.
Inspected three times per year
Risk Category III applies to food service establishments that cook and cool no more than three potentially hazardous foods.
Inspected four times per year
Risk Category IV applies to food service establishments that cook and cool an unlimited number of potentially hazardous foods. This category also includes those facilities using specialized processes or serving a highly susceptible population.
Field Descriptions are available here.
","distribution_titles":["CSV","GeoJSON","KML","Shapefile","ArcGIS Hub Dataset","ArcGIS GeoService"],"harvest_record":"https://catalog.data.gov/harvest_record/fed00afc-63ee-46e3-817b-2ca24fafdf9c","harvest_record_raw":"https://catalog.data.gov/harvest_record/fed00afc-63ee-46e3-817b-2ca24fafdf9c/raw","has_download":false,"has_spatial":true,"identifier":"https://www.arcgis.com/home/item.html?id=9b04d0c39abd4e049cbd4656a0a04ba3&sublayer=2","keyword":["Food Safety","Inspections","NC","North Carolina","Permit","Restaurants","Wake","Wake County"],"last_harvested_date":"2026-08-19T00:46:34.848404","organization":{"aliases":["north carolina"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"6f5c454f-8dd6-44dd-8d70-89f04303d273","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/county_wake_nc.png","name":"Wake County","organization_type":"County Government","slug":"wake-county-nc"},"popularity":37,"publisher":"Wake County","slug":"food-inspection-violations","spatial_centroid":{"lat":35.74284,"lon":-78.69144},"spatial_shape":{"coordinates":[[[-78.9746,35.5378],[-78.9746,36.0504],[-78.2667,36.0504],[-78.2667,35.5378],[-78.9746,35.5378]]],"type":"Polygon"},"theme":["geospatial"],"title":"Food Inspection Violations","type":"dataset"},{"_score":7.2478466,"_sort":[1787100394184,7.2478466,15,"447a7af9-67f8-4991-be0e-e19701f5da73"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"WakeCountyGovernment","hasEmail":"mailto:gisapps@wake.gov"},"description":"The Wake County health department inspects food service facilities throughout Wake County. The department permits and inspects these facilities, and responds to citizen complaints. In the event of disease outbreak, the department investigates to determine the source of the infection, and prevent further illness.
This dataset captures the restaurants that are inspected. The data set is geocoded based on address with approximately 85% of the locations having a valid geo-location.
You can find out additional information about our restaurant inspections on our website: Food Safety and Sanitation
This table captures all Wake County sanitation inspections from September 20, 2012 to Present.
This table is part of a set of data that combined will give you a picture of all restaurant inspections. Those three tables are:
1. Restaurants: This table captures all active facilities where Wake County performs sanitations inspections. Facilities that are closed are removed from all three files in this dataset. Per NC State regulations, facilities that have a change in ownership are considered closed and the restaurant re-opens under a new permit, even if there is not a change in the name of the restaurant.
2. Food Inspections: This table captures all Wake County performs sanitations inspections at active restaurants since September 20, 2012
3. Food Inspection Violations: This table captures all violations identified during specific Wake County sanitations inspections at active restaurants since September 20, 2012. It reports the results in code violations and according to CDC Risk Factors. You can find additional information about the CDC Risk Factors on the FDA website: Retail Risk Factor Study
The tables can be connected through the HSISID field.
T The frequency of facility inspections fall under the following rules:
Inspected once per year:
Risk Category I applies to food service establishments that prepare only non-potentially hazardous foods.
Inspected twice per year:
Risk Category II applies to food service establishments that cook and cool no more than two potentially hazardous foods. Potentially hazardous raw ingredients shall be received in a ready-to-cook form.
Inspected three times per year
Risk Category III applies to food service establishments that cook and cool no more than three potentially hazardous foods.
Inspected four times per year
Risk Category IV applies to food service establishments that cook and cool an unlimited number of potentially hazardous foods. This category also includes those facilities using specialized processes or serving a highly susceptible population.
Field Descriptions are available here.
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This dataset captures the restaurants that are inspected. The data set is geocoded based on address with approximately 85% of the locations having a valid geo-location.
You can find out additional information about our restaurant inspections on our website: Food Safety and Sanitation
This table captures all Wake County sanitation inspections from September 20, 2012 to Present.
This table is part of a set of data that combined will give you a picture of all restaurant inspections. Those three tables are:
1. Restaurants: This table captures all active facilities where Wake County performs sanitations inspections. Facilities that are closed are removed from all three files in this dataset. Per NC State regulations, facilities that have a change in ownership are considered closed and the restaurant re-opens under a new permit, even if there is not a change in the name of the restaurant.
2. Food Inspections: This table captures all Wake County performs sanitations inspections at active restaurants since September 20, 2012
3. Food Inspection Violations: This table captures all violations identified during specific Wake County sanitations inspections at active restaurants since September 20, 2012. It reports the results in code violations and according to CDC Risk Factors. You can find additional information about the CDC Risk Factors on the FDA website: Retail Risk Factor Study
The tables can be connected through the HSISID field.
T The frequency of facility inspections fall under the following rules:
Inspected once per year:
Risk Category I applies to food service establishments that prepare only non-potentially hazardous foods.
Inspected twice per year:
Risk Category II applies to food service establishments that cook and cool no more than two potentially hazardous foods. Potentially hazardous raw ingredients shall be received in a ready-to-cook form.
Inspected three times per year
Risk Category III applies to food service establishments that cook and cool no more than three potentially hazardous foods.
Inspected four times per year
Risk Category IV applies to food service establishments that cook and cool an unlimited number of potentially hazardous foods. This category also includes those facilities using specialized processes or serving a highly susceptible population.
Field Descriptions are available here.
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