CivicMemory

Timeline / Data.gov — Health Datasets

changed Source changed

A new raw object was archived. Both versions are preserved. 2709 line(s) added, 2638 line(s) removed.

Evidence

SourceData.gov — Health Datasets
AgencyData.gov
URLhttps://api.gsa.gov/technology/datagov/v4/search?q=health&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY}
Observed by Civic Memory, directly, on 2026-09-23T06:24:11+00:00
Content typeapplication/json
Current object 3785eb3632f669089ae2e10660d8779e635c779f8e63deb0c218418d3b0649e0 download raw metadata
Previous object c7e92d3610d765bd2efdedc9d273e89fd89c51311b87e0f7d5dcd695ffb64c22 download raw metadata

What changed derived

This diff is not evidence. It was produced by civic-memory.diff_engine 1.1.0 at 2026-09-23T06:24:11+00:00 by normalizing the two archived objects above. The objects are authoritative; this reading of them can be regenerated or deleted without loss. 2709 line(s) added, 2638 line(s) removed.

--- previous
+++ current
@@ -1,156 +1,151 @@
 {
- "after": "WzE3OTAxMjAyMzk2NDMsMTEuODg1ODcxLDMsIjIyNTI2MDUxLWZkZmQtNDQ1Ni04OGEzLTk4OWZkNGVmYjZiOCJd",
+ "after": "WzE3OTAxMjM4NDkwMDAsOS4xNjc2MzksMSwiZWNlYWExNzEtYmE3NS00MDE4LWIwNjQtMmY3MjdiN2NhYjdjIl0=",
  "results": [
  {
- "_score": 7.6398563,
+ "_score": 17.275215,
  "_sort": [
- 1790122592766,
- 7.6398563,
+ 1790127181329,
+ 17.275215,
  2,
- "5f064310-8ac9-442d-bb37-ba74013e97fe"
+ "d2a09131-82b5-486a-9713-027b64a7e630"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
  "accessLevel": "public",
+ "accrualPeriodicity": "irregular",
  "bureauCode": [
  "026:00"
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data contains in situ trace gas data collected onboard the P-3B aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. Measurements were obtained using a variety of instrumentation, including DACOM, TD-LIF, DFGAS, LICOR-6252, PTR-MS, TILDAS, and Chemiluminescence. This data product contains only data from the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.",
+ "fn": "JENNIFER HEEG",
+ "hasEmail": "mailto:jennifer.heeg@nasa.gov"
+ },
+ "description": "Prognostics technologies determine the health (or damage) state of a component or sub- system, and make end of life (EOL) and remaining useful life (RUL) predictions. Such infor- mation enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics method- ology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the ow of propellant, so failures may have a signi cant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle ltering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. We develop a prototype user interface for valve prognostics, and demonstrate the prognostics approach using historical pneumatic valve data from the Space Shuttle refueling system.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3880527363-LARC_CLOUD.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/citing-data",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/project/DISCOVER-AQ/pdocuments",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/soot/power-user/DISCOVERAQ/2014-CO",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880527363-LARC_CLOUD",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://discover-aq.larc.nasa.gov/media/#news",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://doi.org/10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data_1",
- "format": "HTML",
- "mediaType": "text/html"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/15/not-your-average-video-traffic-report/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_TraceabilityMatrixPage16.pdf",
+ "description": "Agenda_Sat afternoon",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/dataset/Slide2.JPG",
+ "format": "image/pjpeg",
+ "mediaType": "image/pjpeg",
+ "title": "Slide2.JPG"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Agenda_Sat morning",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/dataset/Slide1.JPG",
+ "format": "image/pjpeg",
+ "mediaType": "image/pjpeg",
+ "title": "Slide1.JPG"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Agenda_Sun morning",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/dataset/Slide3.JPG",
+ "format": "image/pjpeg",
+ "mediaType": "image/pjpeg",
+ "title": "Slide3.JPG"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Agenda_sun Afternoon",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/dataset/Slide4.JPG",
+ "format": "image/pjpeg",
+ "mediaType": "image/pjpeg",
+ "title": "Slide4.JPG"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Final Agenda, 1 page version",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/dataset/Agenda_final_1page.jpg",
+ "format": "image/pjpeg",
+ "mediaType": "image/pjpeg",
+ "title": "Agenda_final_1page.jpg"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Final Agenda, with Analysis Presentations included",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/dataset/Agenda_final_extended.pdf",
  "format": "PDF",
- "mediaType": "application/pdf"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_science.pdf",
+ "mediaType": "application/pdf",
+ "title": "Agenda_final_extended.pdf"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Final agenda, May 2, 2012 JH",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/dataset/Agenda_only.pptx",
+ "format": "application/vnd.openxmlformats-officedocument.presentationml.pre",
+ "mediaType": "application/vnd.openxmlformats-officedocument.presentationml.pre",
+ "title": "Agenda_only.pptx"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "HIRENASD analysts, May 2, 2012 JH",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/dataset/HIRENASD.talks_2.pdf",
  "format": "PDF",
- "mediaType": "application/pdf"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.nasa.gov/mission/discover-aq/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3880527363-LARC_CLOUD",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://www-air.larc.nasa.gov/missions/discover-aq/docs/Crawford_DISCOVER-AQ_Overview_05Oct2010.pdf",
+ "mediaType": "application/pdf",
+ "title": "HIRENASD.talks.pdf"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Summary of analysis entries, May 2, 2012 JH",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/dataset/Summary_of_Entries.pptx",
+ "format": "application/vnd.openxmlformats-officedocument.presentationml.pre",
+ "mediaType": "application/vnd.openxmlformats-officedocument.presentationml.pre",
+ "title": "Summary of Entries.pptx"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "workshop registration list, May 2, 2012 JH",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/dataset/AePW_Workshop_Attendees.pdf",
  "format": "PDF",
- "mediaType": "application/pdf"
+ "mediaType": "application/pdf",
+ "title": "AePW_Workshop_Attendees.pdf"
  }
  ],
- "identifier": "10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data_1",
- "keyword": [
- "earth-science-air-quality-atmosphere-volatile-organic-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-nitrogen-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "identifier": "DASHLINK_779",
+ "issued": "2013-06-19",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "landingPage": "https://c3.nasa.gov/dashlink/resources/779/",
+ "modified": "2025-03-31",
  "programCode": [
- "026:000"
+ "026:029"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "NASA/LARC/SD/ASDC"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -123.199, \"EastBoundingCoordinate\": 0, \"SouthBoundingCoordinate\": -5, \"NorthBoundingCoordinate\": 48}]], Maximum Altitude, 12 km",
- "temporal": "2014-07-06/2014-08-14",
- "theme": [
- "Earth Science"
- ],
- "title": "DISCOVER-AQ Colorado Deployment P-3B Aircraft In Situ Trace Gas Data"
- },
- "description": "DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data contains in situ trace gas data collected onboard the P-3B aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. Measurements were obtained using a variety of instrumentation, including DACOM, TD-LIF, DFGAS, LICOR-6252, PTR-MS, TILDAS, and Chemiluminescence. This data product contains only data from the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.",
+ "name": "Dashlink"
+ },
+ "title": "Prognostics for Ground Support Systems: Case Study on Pneumatic Valves"
+ },
+ "description": "Prognostics technologies determine the health (or damage) state of a component or sub- system, and make end of life (EOL) and remaining useful life (RUL) predictions. Such infor- mation enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics method- ology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the ow of propellant, so failures may have a signi cant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle ltering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. We develop a prototype user interface for valve prognostics, and demonstrate the prognostics approach using historical pneumatic valve data from the Space Shuttle refueling system.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/78470ebf-ac9b-4c43-9ce2-3f49e7f7e0db",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/78470ebf-ac9b-4c43-9ce2-3f49e7f7e0db/raw",
+ "Slide2.JPG",
+ "Slide1.JPG",
+ "Slide3.JPG",
+ "Slide4.JPG",
+ "Agenda_final_1page.jpg",
+ "Agenda_final_extended.pdf",
+ "Agenda_only.pptx",
+ "HIRENASD.talks.pdf",
+ "Summary of Entries.pptx",
+ "AePW_Workshop_Attendees.pdf"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/da2a35b5-ddbc-4da6-ae76-99c4c34ddb21",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/da2a35b5-ddbc-4da6-ae76-99c4c34ddb21/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Colorado_TraceGas_AircraftInSitu_P3B_Data_1",
- "keyword": [
- "earth-science-air-quality-atmosphere-volatile-organic-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-nitrogen-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species"
- ],
- "last_harvested_date": "2026-09-23T00:16:32.766476",
+ "has_spatial": false,
+ "identifier": "DASHLINK_779",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "last_harvested_date": "2026-09-23T01:33:01.329969",
  "organization": {
  "aliases": [
  ""
@@ -166,168 +161,78 @@
  },
  "parent_identifier": null,
  "popularity": 2,
- "publisher": "NASA/LARC/SD/ASDC",
- "slug": "discover-aq-colorado-deployment-p-3b-aircraft-in-situ-trace-gas-data",
+ "publisher": "Dashlink",
+ "slug": "prognostics-for-ground-support-systems-case-study-on-pneumatic-valves",
  "spatial_centroid": null,
  "spatial_shape": null,
- "theme": [
- "Earth Science"
- ],
- "title": "DISCOVER-AQ Colorado Deployment P-3B Aircraft In Situ Trace Gas Data",
+ "theme": [],
+ "title": "Prognostics for Ground Support Systems: Case Study on Pneumatic Valves",
  "type": "dataset"
  },
  {
- "_score": 8.1766205,
+ "_score": 14.084618,
  "_sort": [
- 1790122592396,
- 8.1766205,
+ 1790127165111,
+ 14.084618,
  2,
- "2c66400e-2b3b-483f-8f06-d18ae211f3d9"
+ "1599dd9f-ee9d-47e7-b602-644d9a531cee"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
  "accessLevel": "public",
+ "accrualPeriodicity": "irregular",
  "bureauCode": [
  "026:00"
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data contains remotely sensed data collected via the Cloud Absorption Radiometer (CAR) onboard NASA's P-3B aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.",
+ "fn": "Miryam Strautkalns",
+ "hasEmail": "mailto:miryam.strautkalns@nasa.gov"
+ },
+ "description": "Electronics components have an increasingly critical role in avionics systems and in the development of future aircraft systems. Prognostics of such components is becoming a very important research field as a result of the need to provide aircraft systems with system level health management information. This paper focuses on a prognostics application for electronics components within avionics systems, and in particular its application to an Isolated Gate Bipolar Transistor (IGBT). This application utilizes the remaining useful life prediction, accomplished by employing the particle filter framework, leveraging data from accelerated aging tests on IGBTs. These tests induced thermal-electrical overstresses by applying thermal cycling to the IGBT devices. In-situ state monitoring, including measurements of steady-state voltages and currents, electrical transients, and thermal transients are recorded and used as potential precursors of failure.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3880527708-LARC_CLOUD.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/citing-data",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/project/DISCOVER-AQ/pdocuments",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/soot/power-user/DISCOVERAQ/2014-CO",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://car.gsfc.nasa.gov/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880527708-LARC_CLOUD",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://discover-aq.larc.nasa.gov/media/#news",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://doi.org/10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data_1",
- "format": "HTML",
- "mediaType": "text/html"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/15/not-your-average-video-traffic-report/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_TraceabilityMatrixPage16.pdf",
+ "description": "2009_IEEEAerospace_Electronics.pdf",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/2009_IEEEAerospace_Electronics.pdf",
  "format": "PDF",
- "mediaType": "application/pdf"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_science.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.nasa.gov/mission/discover-aq/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3880527708-LARC_CLOUD",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://www-air.larc.nasa.gov/missions/discover-aq/docs/Crawford_DISCOVER-AQ_Overview_05Oct2010.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
+ "mediaType": "application/pdf",
+ "title": "2009_IEEEAerospace_Electronics.pdf"
  }
  ],
- "identifier": "10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data_1",
- "keyword": [
- "earth-science-atmospheric-radiation-atmosphere-reflectance",
- "earth-science-atmospheric-radiation-atmosphere-solar-irradiance",
- "earth-science-clouds-atmosphere-cloud-radiative-transfer"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "identifier": "DASHLINK_763",
+ "issued": "2013-06-19",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "landingPage": "https://c3.nasa.gov/dashlink/resources/763/",
+ "modified": "2025-03-31",
  "programCode": [
- "026:000"
+ "026:029"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "NASA/LARC/SD/ASDC"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 5, \"SouthBoundingCoordinate\": -13, \"NorthBoundingCoordinate\": 85}]], Maximum Altitude, 12 km",
- "temporal": "2014-07-06/2014-08-14",
- "theme": [
- "Earth Science"
- ],
- "title": "DISCOVER-AQ Colorado Deployment P-3B Aircraft Remotely Sensed Cloud Absorption Radiometer (CAR) Data"
- },
- "description": "DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data contains remotely sensed data collected via the Cloud Absorption Radiometer (CAR) onboard NASA's P-3B aircraft during the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.",
+ "name": "Dashlink"
+ },
+ "title": "Towards Prognostics for Electronics Components"
+ },
+ "description": "Electronics components have an increasingly critical role in avionics systems and in the development of future aircraft systems. Prognostics of such components is becoming a very important research field as a result of the need to provide aircraft systems with system level health management information. This paper focuses on a prognostics application for electronics components within avionics systems, and in particular its application to an Isolated Gate Bipolar Transistor (IGBT). This application utilizes the remaining useful life prediction, accomplished by employing the particle filter framework, leveraging data from accelerated aging tests on IGBTs. These tests induced thermal-electrical overstresses by applying thermal cycling to the IGBT devices. In-situ state monitoring, including measurements of steady-state voltages and currents, electrical transients, and thermal transients are recorded and used as potential precursors of failure.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/54a44d77-d0e0-41a7-b853-035b39c18ff2",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/54a44d77-d0e0-41a7-b853-035b39c18ff2/raw",
+ "2009_IEEEAerospace_Electronics.pdf"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/49374b83-058e-401f-a3d4-91f4f5a1d9d1",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/49374b83-058e-401f-a3d4-91f4f5a1d9d1/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Colorado_Radiation_AircraftRemoteSensing_CAR_Data_1",
- "keyword": [
- "earth-science-atmospheric-radiation-atmosphere-reflectance",
- "earth-science-atmospheric-radiation-atmosphere-solar-irradiance",
- "earth-science-clouds-atmosphere-cloud-radiative-transfer"
- ],
- "last_harvested_date": "2026-09-23T00:16:32.396348",
+ "has_spatial": false,
+ "identifier": "DASHLINK_763",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "last_harvested_date": "2026-09-23T01:32:45.111267",
  "organization": {
  "aliases": [
  ""
@@ -343,176 +248,74 @@
  },
  "parent_identifier": null,
  "popularity": 2,
- "publisher": "NASA/LARC/SD/ASDC",
- "slug": "discover-aq-colorado-deployment-p-3b-aircraft-remotely-sensed-cloud-absorption-radiometer-",
+ "publisher": "Dashlink",
+ "slug": "towards-prognostics-for-electronics-components",
  "spatial_centroid": null,
  "spatial_shape": null,
- "theme": [
- "Earth Science"
- ],
- "title": "DISCOVER-AQ Colorado Deployment P-3B Aircraft Remotely Sensed Cloud Absorption Radiometer (CAR) Data",
+ "theme": [],
+ "title": "Towards Prognostics for Electronics Components",
  "type": "dataset"
  },
  {
- "_score": 7.607725,
+ "_score": 21.330038,
  "_sort": [
- 1790122592027,
- 7.607725,
- 1,
- "8aeabd4f-2e88-4b98-a891-de33b8c33cc9"
+ 1790127157996,
+ 21.330038,
+ 0,
+ "0ae31ee6-9a12-4b92-8011-14fc29df5084"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
  "accessLevel": "public",
+ "accrualPeriodicity": "irregular",
  "bureauCode": [
  "026:00"
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data contains in situ aerosol data collected onboard NASA's P-3B aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. Instruments utilized to collect data found in this data product include the PSAP, APS, CPC, CCN, Nephelometer, LAS, PILS, , TOC, SMPS, SP2 and UHSAS. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.",
+ "fn": "Beth Beck",
+ "hasEmail": "mailto:beth.beck@nasa.gov"
+ },
+ "description": "FLTz flight simulator RecorderRun9 data are analyzed in order \r\nto compute systems health score using Granger causal connectovity \r\nanalysis. First G-causality is computed, and then causal density (cd) is \r\ncomputed in interval [0,1]. Numerical value of causal density for this data file is 0.4711, which corresponds to systems health score.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3880528862-LARC_CLOUD.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/citing-data",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/project/DISCOVER-AQ/pdocuments",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/soot/power-user/DISCOVERAQ/2013-TX",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880528862-LARC_CLOUD",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://discover-aq.larc.nasa.gov/media/#news",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://doi.org/10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data_1",
- "format": "HTML",
- "mediaType": "text/html"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/15/not-your-average-video-traffic-report/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_TraceabilityMatrixPage16.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_science.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.nasa.gov/mission/discover-aq/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3880528862-LARC_CLOUD",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://www-air.larc.nasa.gov/missions/discover-aq/docs/Crawford_DISCOVER-AQ_Overview_05Oct2010.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
+ "downloadURL": "http://nasa3d.arc.nasa.gov/shared_assets/models/pgt-3ds/PGT-3DS.zip",
+ "format": "image/x-3ds",
+ "mediaType": "image/x-3ds"
  }
  ],
- "identifier": "10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data_1",
- "keyword": [
- "earth-science-aerosols-atmosphere",
- "earth-science-aerosols-atmosphere-aerosol-backscatter",
- "earth-science-aerosols-atmosphere-aerosol-extinction",
- "earth-science-aerosols-atmosphere-aerosol-forward-scatter",
- "earth-science-aerosols-atmosphere-aerosol-particle-properties",
- "earth-science-aerosols-atmosphere-chemical-composition",
- "earth-science-aerosols-atmosphere-cloud-condensation-nuclei",
- "earth-science-aerosols-atmosphere-nitrate-particles",
- "earth-science-aerosols-atmosphere-particulate-matter",
- "earth-science-aerosols-atmosphere-sulfate-particles"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "identifier": "DASHLINK_625",
+ "issued": "2012-11-14",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "landingPage": "https://c3.nasa.gov/dashlink/resources/625/",
+ "modified": "2025-03-31",
  "programCode": [
- "026:000"
+ "026:029"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "NASA/LARC/SD/ASDC"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -123.199, \"EastBoundingCoordinate\": -70, \"SouthBoundingCoordinate\": 23, \"NorthBoundingCoordinate\": 43}]], Maximum Altitude, 12 km",
- "temporal": "2013-08-26/2013-10-01",
- "theme": [
- "Earth Science"
- ],
- "title": "DISCOVER-AQ Texas Deployment P-3B Aircraft In Situ Aerosol Data"
- },
- "description": "DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data contains in situ aerosol data collected onboard NASA's P-3B aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. Instruments utilized to collect data found in this data product include the PSAP, APS, CPC, CCN, Nephelometer, LAS, PILS, , TOC, SMPS, SP2 and UHSAS. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.",
- "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/696c092a-9f64-46c3-9fe3-aa809adea3a4",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/696c092a-9f64-46c3-9fe3-aa809adea3a4/raw",
+ "name": "Dashlink"
+ },
+ "title": "Indir Jaganjac"
+ },
+ "description": "FLTz flight simulator RecorderRun9 data are analyzed in order \r\nto compute systems health score using Granger causal connectovity \r\nanalysis. First G-causality is computed, and then causal density (cd) is \r\ncomputed in interval [0,1]. Numerical value of causal density for this data file is 0.4711, which corresponds to systems health score.",
+ "distribution_titles": [],
+ "harvest_record": "https://catalog.data.gov/harvest_record/02ffec45-87a6-43d3-b9f8-b8f76d9f905a",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/02ffec45-87a6-43d3-b9f8-b8f76d9f905a/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Texas_Aerosol_AircraftInSitu_P3B_Data_1",
- "keyword": [
- "earth-science-aerosols-atmosphere",
- "earth-science-aerosols-atmosphere-aerosol-backscatter",
- "earth-science-aerosols-atmosphere-aerosol-extinction",
- "earth-science-aerosols-atmosphere-aerosol-forward-scatter",
- "earth-science-aerosols-atmosphere-aerosol-particle-properties",
- "earth-science-aerosols-atmosphere-chemical-composition",
- "earth-science-aerosols-atmosphere-cloud-condensation-nuclei",
- "earth-science-aerosols-atmosphere-nitrate-particles",
- "earth-science-aerosols-atmosphere-particulate-matter",
- "earth-science-aerosols-atmosphere-sulfate-particles"
- ],
- "last_harvested_date": "2026-09-23T00:16:32.027427",
+ "has_spatial": false,
+ "identifier": "DASHLINK_625",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "last_harvested_date": "2026-09-23T01:32:37.996436",
  "organization": {
  "aliases": [
  ""
@@ -527,169 +330,79 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 1,
- "publisher": "NASA/LARC/SD/ASDC",
- "slug": "discover-aq-texas-deployment-p-3b-aircraft-in-situ-aerosol-data",
+ "popularity": 0,
+ "publisher": "Dashlink",
+ "slug": "indir-jaganjac",
  "spatial_centroid": null,
  "spatial_shape": null,
- "theme": [
- "Earth Science"
- ],
- "title": "DISCOVER-AQ Texas Deployment P-3B Aircraft In Situ Aerosol Data",
+ "theme": [],
+ "title": "Indir Jaganjac",
  "type": "dataset"
  },
  {
- "_score": 7.607725,
+ "_score": 10.871136,
  "_sort": [
- 1790122591658,
- 7.607725,
- 1,
- "d3547668-bdc9-467c-94ec-0a50d2f3a9bd"
+ 1790127113402,
+ 10.871136,
+ 2,
+ "bab33ec7-dad1-40fa-9270-949a29c72e4d"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
  "accessLevel": "public",
+ "accrualPeriodicity": "irregular",
  "bureauCode": [
  "026:00"
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data contains in situ trace gas data collected onboard the P-3B aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. Measurements were obtained using a variety of instrumentation, including DACOM, TD-LIF, DFGAS, LICOR-6252, PTR-MS, Chemiluminescence, and Ultraviolet Pulsed Fluorescence (UVPF). This data product contains only data from the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.",
+ "fn": "EDWARD BALABAN",
+ "hasEmail": "mailto:edward.balaban@nasa.gov"
+ },
+ "description": "Prognostics is an emerging concept in condition based maintenance (CBM) of critical systems. Along with developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenance have been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a\r\nspecific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the \r\nleast, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3880528930-LARC_CLOUD.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/citing-data",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/project/DISCOVER-AQ/pdocuments",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/soot/power-user/DISCOVERAQ/2013-TX",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880528930-LARC_CLOUD",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://discover-aq.larc.nasa.gov/media/#news",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://doi.org/10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data_1",
- "format": "HTML",
- "mediaType": "text/html"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/15/not-your-average-video-traffic-report/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_TraceabilityMatrixPage16.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_science.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.nasa.gov/mission/discover-aq/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3880528930-LARC_CLOUD",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://www-air.larc.nasa.gov/missions/discover-aq/docs/Crawford_DISCOVER-AQ_Overview_05Oct2010.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
+ "description": "PHM_2008_Metrics.pdf",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/PHM_2008_Metrics.pdf",
+ "format": "application/force-download",
+ "mediaType": "application/force-download",
+ "title": "PHM_2008_Metrics.pdf"
  }
  ],
- "identifier": "10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data_1",
- "keyword": [
- "earth-science-air-quality-atmosphere-volatile-organic-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-nitrogen-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-sulfur-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "identifier": "DASHLINK_393",
+ "issued": "2011-06-07",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "landingPage": "https://c3.nasa.gov/dashlink/resources/393/",
+ "modified": "2025-03-31",
  "programCode": [
- "026:000"
+ "026:029"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "NASA/LARC/SD/ASDC"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -124, \"EastBoundingCoordinate\": -70, \"SouthBoundingCoordinate\": 23, \"NorthBoundingCoordinate\": 43}]], Maximum Altitude, 12 km",
- "temporal": "2013-08-26/2013-10-01",
- "theme": [
- "Earth Science"
- ],
- "title": "DISCOVER-AQ Texas Deployment P-3B Aircraft In Situ Trace Gas Data"
- },
- "description": "DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data contains in situ trace gas data collected onboard the P-3B aircraft during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. Measurements were obtained using a variety of instrumentation, including DACOM, TD-LIF, DFGAS, LICOR-6252, PTR-MS, Chemiluminescence, and Ultraviolet Pulsed Fluorescence (UVPF). This data product contains only data from the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.",
+ "name": "Dashlink"
+ },
+ "title": "Metrics for Evaluating Performance of Prognostics Techniques"
+ },
+ "description": "Prognostics is an emerging concept in condition based maintenance (CBM) of critical systems. Along with developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenance have been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a\r\nspecific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the \r\nleast, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/690abf17-565d-4361-8132-93289c4118e2",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/690abf17-565d-4361-8132-93289c4118e2/raw",
+ "PHM_2008_Metrics.pdf"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/e35652b2-feeb-450e-a789-7819a336b87e",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/e35652b2-feeb-450e-a789-7819a336b87e/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data_1",
- "keyword": [
- "earth-science-air-quality-atmosphere-volatile-organic-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-nitrogen-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-sulfur-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species"
- ],
- "last_harvested_date": "2026-09-23T00:16:31.658121",
+ "has_spatial": false,
+ "identifier": "DASHLINK_393",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "last_harvested_date": "2026-09-23T01:31:53.402911",
  "organization": {
  "aliases": [
  ""
@@ -704,177 +417,79 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 1,
- "publisher": "NASA/LARC/SD/ASDC",
- "slug": "discover-aq-texas-deployment-p-3b-aircraft-in-situ-trace-gas-data",
+ "popularity": 2,
+ "publisher": "Dashlink",
+ "slug": "metrics-for-evaluating-performance-of-prognostics-techniques",
  "spatial_centroid": null,
  "spatial_shape": null,
- "theme": [
- "Earth Science"
- ],
- "title": "DISCOVER-AQ Texas Deployment P-3B Aircraft In Situ Trace Gas Data",
+ "theme": [],
+ "title": "Metrics for Evaluating Performance of Prognostics Techniques",
  "type": "dataset"
  },
  {
- "_score": 7.6398563,
+ "_score": 20.726263,
  "_sort": [
- 1790122590912,
- 7.6398563,
- 1,
- "4aa9a773-132b-448c-aee4-00d46cb73012"
+ 1790127104708,
+ 20.726263,
+ 0,
+ "d95acbf8-9537-4b51-a9e0-ae0c575adc99"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
  "accessLevel": "public",
+ "accrualPeriodicity": "irregular",
  "bureauCode": [
  "026:00"
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "DISCOVERAQ_Texas_Ground_TCEQ_Data contains data collected by the Texas Commission on Environmental Quality (TCEQ) at various ground sites around the study area, including Aldine, Channelview, Clinton, Conroe Airport, Deer Park, Galveston, Harris County, LaPorte Airport, Manvel Croix, Seabrook Park, Smith Point, Texas Avenue, UH Coastal Center, UH Liberty, UH Sugarland, and West Houston as part of the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.",
+ "fn": "MARK SCHWABACHER",
+ "hasEmail": "mailto:mark.a.schwabacher@nasa.gov"
+ },
+ "description": "Modern space propulsion and exploration system designs are becoming increasingly\r\nsophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by\r\nanalyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. Inductive Monitoring System uses a data mining technique called clustering to analyze archived system data and characterize\r\nnormal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or\r\nfor analysis of archived events. Ongoing and developing Inductive Monitoring System space operations applications include International Space Station flight control, spacecraft vehicle\r\nsystem health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the Inductive Monitoring\r\nSystem data-driven technique as related to several Integrated Systems Health Management elements. Thematically, the projects listed will be used as case studies. The maturation of Inductive Monitoring System via projects where it has been deployed or is currently being\r\nintegrated to aid in fault detection will be described. The paper will also explain how Inductive Monitoring System can be used to complement a suite of other Integrated System Health Management tools, providing initial fault detection support for diagnosis and recovery.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3880529383-LARC_CLOUD.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/citing-data",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/project/DISCOVER-AQ/pdocuments",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://asdc.larc.nasa.gov/soot/power-user/DISCOVERAQ/2013-TX",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880529383-LARC_CLOUD",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://discover-aq.larc.nasa.gov/media/#news",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://doi.org/10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Texas_Ground_TCEQ_Data_1",
- "format": "HTML",
- "mediaType": "text/html"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/15/not-your-average-video-traffic-report/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_TraceabilityMatrixPage16.pdf",
+ "description": "IMS JACIC.pdf",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/IMS_JACIC.pdf",
  "format": "PDF",
- "mediaType": "application/pdf"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_science.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://science.nasa.gov/mission/discover-aq/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3880529383-LARC_CLOUD",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://www-air.larc.nasa.gov/missions/discover-aq/docs/Crawford_DISCOVER-AQ_Overview_05Oct2010.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://www.tceq.texas.gov/",
- "format": "BIN",
- "mediaType": "application/octet-stream"
+ "mediaType": "application/pdf",
+ "title": "IMS JACIC.pdf"
  }
  ],
- "identifier": "10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Texas_Ground_TCEQ_Data_1",
- "keyword": [
- "earth-science-aerosols-atmosphere-particulate-matter",
- "earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-nitrogen-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds",
- "earth-science-atmospheric-temperature-atmosphere-surface-temperature",
- "earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators",
- "earth-science-atmospheric-winds-atmosphere-surface-winds"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "identifier": "DASHLINK_669",
+ "issued": "2013-02-01",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "landingPage": "https://c3.nasa.gov/dashlink/resources/669/",
+ "modified": "2025-03-31",
  "programCode": [
- "026:000"
+ "026:029"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "NASA/LARC/SD/ASDC"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -110, \"EastBoundingCoordinate\": 96, \"SouthBoundingCoordinate\": 23, \"NorthBoundingCoordinate\": 43}]], Maximum Altitude, 70 m",
- "temporal": "2013-08-13/2013-10-02",
- "theme": [
- "Earth Science"
- ],
- "title": "DISCOVER-AQ Texas Deployment Texas Commission on Environmental Quality Ground Site Data"
- },
- "description": "DISCOVERAQ_Texas_Ground_TCEQ_Data contains data collected by the Texas Commission on Environmental Quality (TCEQ) at various ground sites around the study area, including Aldine, Channelview, Clinton, Conroe Airport, Deer Park, Galveston, Harris County, LaPorte Airport, Manvel Croix, Seabrook Park, Smith Point, Texas Avenue, UH Coastal Center, UH Liberty, UH Sugarland, and West Houston as part of the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.",
+ "name": "Dashlink"
+ },
+ "title": "General Purpose Data-Driven System Monitoring for Space Operations"
+ },
+ "description": "Modern space propulsion and exploration system designs are becoming increasingly\r\nsophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by\r\nanalyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. Inductive Monitoring System uses a data mining technique called clustering to analyze archived system data and characterize\r\nnormal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or\r\nfor analysis of archived events. Ongoing and developing Inductive Monitoring System space operations applications include International Space Station flight control, spacecraft vehicle\r\nsystem health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the Inductive Monitoring\r\nSystem data-driven technique as related to several Integrated Systems Health Management elements. Thematically, the projects listed will be used as case studies. The maturation of Inductive Monitoring System via projects where it has been deployed or is currently being\r\nintegrated to aid in fault detection will be described. The paper will also explain how Inductive Monitoring System can be used to complement a suite of other Integrated System Health Management tools, providing initial fault detection support for diagnosis and recovery.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/0aa5d46e-0c21-4d0b-8538-7a980c01239a",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/0aa5d46e-0c21-4d0b-8538-7a980c01239a/raw",
+ "IMS JACIC.pdf"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/8d3c94da-c03c-4352-af69-cfe0dbdaec32",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/8d3c94da-c03c-4352-af69-cfe0dbdaec32/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Texas_Ground_TCEQ_Data_1",
- "keyword": [
- "earth-science-aerosols-atmosphere-particulate-matter",
- "earth-science-atmospheric-chemistry-atmosphere-carbon-and-hydrocarbon-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-nitrogen-compounds",
- "earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds",
- "earth-science-atmospheric-temperature-atmosphere-surface-temperature",
- "earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators",
- "earth-science-atmospheric-winds-atmosphere-surface-winds"
- ],
- "last_harvested_date": "2026-09-23T00:16:30.912222",
+ "has_spatial": false,
+ "identifier": "DASHLINK_669",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "last_harvested_date": "2026-09-23T01:31:44.708190",
  "organization": {
  "aliases": [
  ""
@@ -889,95 +504,151 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 1,
- "publisher": "NASA/LARC/SD/ASDC",
- "slug": "discover-aq-texas-deployment-texas-commission-on-environmental-quality-ground-site-data",
+ "popularity": 0,
+ "publisher": "Dashlink",
+ "slug": "general-purpose-data-driven-system-monitoring-for-space-operations",
  "spatial_centroid": null,
  "spatial_shape": null,
- "theme": [
- "Earth Science"
- ],
- "title": "DISCOVER-AQ Texas Deployment Texas Commission on Environmental Quality Ground Site Data",
+ "theme": [],
+ "title": "General Purpose Data-Driven System Monitoring for Space Operations",
  "type": "dataset"
  },
  {
- "_score": 15.165865,
+ "_score": 17.691696,
  "_sort": [
- 1790122539340,
- 15.165865,
+ 1790127088150,
+ 17.691696,
  2,
- "c8aee688-1774-4591-ac57-51e10815e0a4"
+ "a35e6d84-5722-4711-bba9-b664f1486b01"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
  "accessLevel": "public",
+ "accrualPeriodicity": "irregular",
  "bureauCode": [
  "026:00"
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Ancillary Data component of the Indicators of Coastal Water Quality Collection includes a 5 arc-minute (approximately 9 x 9 km at the equator) sequence grid, grid cell centroids that relate to the grid cells in the tabular \"Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007\" data set, and a country buffer data set that is divided by exclusive economic zones (EEZ). The data are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "fn": "NSIDC Services",
+ "hasEmail": "mailto:nsidc@nsidc.org"
+ },
+ "description": "Prognostics has received considerable attention recently as an emerging sub-discipline within SHM. Prognosis is here strictly defined as “predicting the time at which a component will no longer perform its intended function”. Loss of function is often times the time at which a component fails. The predicted time to that point becomes then the remaining useful life (RUL). For prognostics to be effective, it must be performed well before deviations from normal performance propagate to a critical effect. This enables a failure preclusion or prevention function to repair or replace the offending components, or if the components cannot be repaired, to retire the system (or vehicle) before the critical failure occurs. Therefore, prognosis has the promise to provide critical information to system operators that will enable safer operation and more cost-efficient use. To that end, Department of Defense (DoD), NASA, and industry have been investigating this technology for use in their vehicle health management solutions. Dedicated prognostic algorithms (in conjunction with failure detection and fault isolation algorithms) must be developed that are capable of operating in an autonomous and real-time vehicle health management system software architecture that is possibly distributed in nature. This envisioned prognostic and health management system will be realized in a vehicle-level reasoner that must have visibility and insight into the results of local diagnostic and prognostic technologies implemented at the LRU and subsystem levels. Accomplishing this effectively requires an integrated suite of prognostic technologies that compute failure effect propagation through diverse subsystems and that can capture interactions that occur in these subsystems. In this chapter a generic set of selected prognostic algorithm approaches is presented and an overview of the required vehicle-level reasoning architecture needed to integrate the prognostic information across systems is provided.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550192741-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550192741-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://www.earthdata.nasa.gov/about/esdis",
- "format": "BIN",
- "mediaType": "application/octet-stream"
+ "description": "Direct download via HTTPS protocol.",
+ "downloadURL": "https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3286095947-NSIDC_CPRD",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Download this dataset"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Direct download via HTTPS protocol.",
+ "downloadURL": "https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3286095947-NSIDC_CPRD",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Download this dataset"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Direct download via HTTPS protocol.",
+ "downloadURL": "https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3286095947-NSIDC_CPRD",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Download this dataset"
+ },
+ {
+ "@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/PP14EED9ZOE2",
+ "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=SV16M_V+V001",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Download this dataset through Earthdata Search"
+ },
+ {
+ "@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=SV16M_V+V001",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Download this dataset through Earthdata Search"
+ },
+ {
+ "@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=SV16M_V+V001",
+ "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/PP14EED9ZOE2",
+ "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%2FPP14EED9ZOE2",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Google Scholar search results"
  }
  ],
- "identifier": "10.7927/H4J96490",
- "keyword": [
- "earth-science-ocean-chemistry-oceans-chlorophyll",
- "earth-science-public-health-human-dimensions-environmental-health-factors"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "identifier": "DASHLINK_940",
+ "issued": "2016-01-14",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "landingPage": "https://c3.nasa.gov/dashlink/resources/940/",
+ "modified": "2025-03-31",
  "programCode": [
- "026:000"
+ "026:029"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]",
- "temporal": "1998-01-01/2007-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Indicators of Coastal Water Quality: Ancillary Data"
- },
- "description": "The Ancillary Data component of the Indicators of Coastal Water Quality Collection includes a 5 arc-minute (approximately 9 x 9 km at the equator) sequence grid, grid cell centroids that relate to the grid cells in the tabular \"Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007\" data set, and a country buffer data set that is divided by exclusive economic zones (EEZ). The data are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "name": "Dashlink"
+ },
+ "title": "Prognostics"
+ },
+ "description": "Prognostics has received considerable attention recently as an emerging sub-discipline within SHM. Prognosis is here strictly defined as “predicting the time at which a component will no longer perform its intended function”. Loss of function is often times the time at which a component fails. The predicted time to that point becomes then the remaining useful life (RUL). For prognostics to be effective, it must be performed well before deviations from normal performance propagate to a critical effect. This enables a failure preclusion or prevention function to repair or replace the offending components, or if the components cannot be repaired, to retire the system (or vehicle) before the critical failure occurs. Therefore, prognosis has the promise to provide critical information to system operators that will enable safer operation and more cost-efficient use. To that end, Department of Defense (DoD), NASA, and industry have been investigating this technology for use in their vehicle health management solutions. Dedicated prognostic algorithms (in conjunction with failure detection and fault isolation algorithms) must be developed that are capable of operating in an autonomous and real-time vehicle health management system software architecture that is possibly distributed in nature. This envisioned prognostic and health management system will be realized in a vehicle-level reasoner that must have visibility and insight into the results of local diagnostic and prognostic technologies implemented at the LRU and subsystem levels. Accomplishing this effectively requires an integrated suite of prognostic technologies that compute failure effect propagation through diverse subsystems and that can capture interactions that occur in these subsystems. In this chapter a generic set of selected prognostic algorithm approaches is presented and an overview of the required vehicle-level reasoning architecture needed to integrate the prognostic information across systems is provided.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/e0d98c90-fef8-45db-a9be-b01afc4c95a3",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/e0d98c90-fef8-45db-a9be-b01afc4c95a3/raw",
+ "Download this dataset",
+ "Download this dataset",
+ "Download this dataset",
+ "View documentation related to this dataset",
+ "Download this dataset through Earthdata Search",
+ "Download this dataset through Earthdata Search",
+ "Download this dataset through Earthdata Search",
+ "This dataset's landing page",
+ "Google Scholar search results"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/2cfb4bc1-72d1-4c46-994a-5d6cf843714f",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/2cfb4bc1-72d1-4c46-994a-5d6cf843714f/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.7927/H4J96490",
- "keyword": [
- "earth-science-ocean-chemistry-oceans-chlorophyll",
- "earth-science-public-health-human-dimensions-environmental-health-factors"
- ],
- "last_harvested_date": "2026-09-23T00:15:39.340485",
+ "has_spatial": false,
+ "identifier": "DASHLINK_940",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "last_harvested_date": "2026-09-23T01:31:28.150967",
  "organization": {
  "aliases": [
  ""
@@ -993,94 +664,78 @@
  },
  "parent_identifier": null,
  "popularity": 2,
- "publisher": "ESDIS",
- "slug": "indicators-of-coastal-water-quality-ancillary-data-e208d",
+ "publisher": "Dashlink",
+ "slug": "prognostics",
  "spatial_centroid": null,
  "spatial_shape": null,
- "theme": [
- "Earth Science"
- ],
- "title": "Indicators of Coastal Water Quality: Ancillary Data",
+ "theme": [],
+ "title": "Prognostics",
  "type": "dataset"
  },
  {
- "_score": 14.915718,
+ "_score": 11.685389,
  "_sort": [
- 1790122537549,
- 14.915718,
- 3,
- "b371765a-bccd-4aa8-8e5b-dfb5c0f079b3"
+ 1790126986420,
+ 11.685389,
+ 9,
+ "09334128-0ded-4630-830c-c742ed56c217"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
  "accessLevel": "public",
+ "accrualPeriodicity": "irregular",
  "bureauCode": [
  "026:00"
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Change in Chlorophyll-a Concentrations 1998-2007 component of the Indicators of Coastal Water Quality Collection represents a tabular time series of the chlorophyll-a concentrations for each grid cell, derived from the \"Indicators of Coastal Water Quality: Annual Chlorophyll-a Concentration 1998-2007\" data set. Chlorophyll-a concentrations are derived from NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The grid cells are organized by country, and the percentage of change from 1998-2007 is calculated for each cell. Each time series was assessed with a linear regression, and cells with statistically significant trends in chlorophyll-a concentrations are identified. The rows of the table are linked to a sequence grid from the \"Indicators of Coastal Water Quality: Ancillary Data\" collection to facilitate the mapping of the trend values for selected countries and areas of interest. The data are processed and compiled by the Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "fn": "MARK SCHWABACHER",
+ "hasEmail": "mailto:mark.a.schwabacher@nasa.gov"
+ },
+ "description": "Title: Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring.\r\n\r\nAbstract: This article describes the results of applying four unsupervised anomaly detection algorithms to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The article describes nine anomalies detected by the four algorithms. The four algorithms use four different definitions of anomalousness. Orca uses a nearest-neighbor approach, defining a point to be an anomaly if its nearest neighbors in the data space are far away from it. The Inductive Monitoring System clusters the training data, and then uses the distance to the nearest cluster as its measure of anomalousness. GritBot learns rules from the training data, and then classifies points as anomalous if they violate these rules. One-class support vector machines map the data into a high-dimensional space in which most of the normal points are on one side of a hyperplane, and then classify points on the other side of the hyperplane as anomalous. Because of these different definitions of anomalousness, different algorithms detect different anomalies. We therefore conclude that it is useful to use multiple algorithms.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550192453-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550192453-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/icwq-change-in-chlorophyll-a-concentration-1998-2007/maps/services",
- "format": "BIN",
- "mediaType": "application/octet-stream"
+ "description": "Paper",
+ "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/AIAA-42783-102.pdf",
+ "format": "PDF",
+ "mediaType": "application/pdf",
+ "title": "AIAA-42783-102.pdf"
  }
  ],
- "identifier": "10.7927/H48W3B88",
- "keyword": [
- "earth-science-ocean-chemistry-oceans-chlorophyll",
- "earth-science-public-health-human-dimensions-environmental-health-factors"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "identifier": "DASHLINK_171",
+ "issued": "2010-09-22",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "landingPage": "https://c3.nasa.gov/dashlink/resources/171/",
+ "modified": "2025-04-01",
  "programCode": [
- "026:000"
+ "026:029"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]",
- "temporal": "1998-01-01/2007-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007"
- },
- "description": "The Change in Chlorophyll-a Concentrations 1998-2007 component of the Indicators of Coastal Water Quality Collection represents a tabular time series of the chlorophyll-a concentrations for each grid cell, derived from the \"Indicators of Coastal Water Quality: Annual Chlorophyll-a Concentration 1998-2007\" data set. Chlorophyll-a concentrations are derived from NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS). The grid cells are organized by country, and the percentage of change from 1998-2007 is calculated for each cell. Each time series was assessed with a linear regression, and cells with statistically significant trends in chlorophyll-a concentrations are identified. The rows of the table are linked to a sequence grid from the \"Indicators of Coastal Water Quality: Ancillary Data\" collection to facilitate the mapping of the trend values for selected countries and areas of interest. The data are processed and compiled by the Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "name": "Dashlink"
+ },
+ "title": "Unsupervised Anomaly Detection for Liquid-Fueled Rocket Prop..."
+ },
+ "description": "Title: Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring.\r\n\r\nAbstract: This article describes the results of applying four unsupervised anomaly detection algorithms to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The article describes nine anomalies detected by the four algorithms. The four algorithms use four different definitions of anomalousness. Orca uses a nearest-neighbor approach, defining a point to be an anomaly if its nearest neighbors in the data space are far away from it. The Inductive Monitoring System clusters the training data, and then uses the distance to the nearest cluster as its measure of anomalousness. GritBot learns rules from the training data, and then classifies points as anomalous if they violate these rules. One-class support vector machines map the data into a high-dimensional space in which most of the normal points are on one side of a hyperplane, and then classify points on the other side of the hyperplane as anomalous. Because of these different definitions of anomalousness, different algorithms detect different anomalies. We therefore conclude that it is useful to use multiple algorithms.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/001034aa-a84a-4ba1-a993-0b8d80d1a2da",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/001034aa-a84a-4ba1-a993-0b8d80d1a2da/raw",
+ "AIAA-42783-102.pdf"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/52d334c9-374e-46ab-88fb-89f3dc8ae7eb",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/52d334c9-374e-46ab-88fb-89f3dc8ae7eb/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.7927/H48W3B88",
- "keyword": [
- "earth-science-ocean-chemistry-oceans-chlorophyll",
- "earth-science-public-health-human-dimensions-environmental-health-factors"
- ],
- "last_harvested_date": "2026-09-23T00:15:37.549572",
+ "has_spatial": false,
+ "identifier": "DASHLINK_171",
+ "keyword": [
+ "ames",
+ "dashlink",
+ "nasa"
+ ],
+ "last_harvested_date": "2026-09-23T01:29:46.420097",
  "organization": {
  "aliases": [
  ""
@@ -1095,107 +750,159 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 3,
- "publisher": "ESDIS",
- "slug": "indicators-of-coastal-water-quality-change-in-chlorophyll-a-concentration-1998-2007-a55f7",
+ "popularity": 9,
+ "publisher": "Dashlink",
+ "slug": "unsupervised-anomaly-detection-for-liquid-fueled-rocket-prop",
  "spatial_centroid": null,
  "spatial_shape": null,
- "theme": [
- "Earth Science"
- ],
- "title": "Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007",
+ "theme": [],
+ "title": "Unsupervised Anomaly Detection for Liquid-Fueled Rocket Prop...",
  "type": "dataset"
  },
  {
- "_score": 16.792374,
+ "_score": 16.58004,
  "_sort": [
- 1790122534566,
- 16.792374,
- 1,
- "ee6ad4b7-5233-4056-8523-8a9786fb414e"
+ 1790126976228,
+ 16.58004,
+ 4,
+ "85f6a507-fd8b-4e46-8e9f-1ff7e57a00a7"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
  "accessLevel": "public",
+ "accrualPeriodicity": "irregular",
  "bureauCode": [
  "026:00"
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "Global Annual PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD) data set represents a series of annual average grids (2001-2010) of fine particulate matter (solid particles and liquid droplets) that were derived from MODIS and MISR AOD satellite data. Together the grids provide a continuous surface of concentrations in micrograms per cubic meter of particulate matter of 2.5 micrometers or smaller (PM2.5) for health and environmental research. The satellite AOD retrievals were converted to ground-level concentrations based on a conversion factor developed by researchers at Dalhousie University that accounts for spatial and temporal variations in aerosol properties and vertical structure as derived from a global 3-D chemical transport model (GEOS-Chem). The raster grids have a grid cell resolution of 30 arc-minutes (0.5 degree or approximately 50 sq. km at the equator) and cover the world from 70 degrees N to 60 degrees S latitude. The grids were produced by researchers at Battelle Memorial Institute in collaboration with the Center for International Earth Science Information Network/Columbia University under a NASA-ROSES project entitled \"Using Satellite Data to Develop Environmental Indicators: An Application of NASA Data Products to Support High Level Decisions for National and International Environmental Protection\". Exposure to fine particles is associated with premature death as well as increased morbidity from respiratory and cardiovascular disease, especially in the elderly, young children, and those already suffering from these illnesses. The World Health Organization guideline for PM2.5 average annual exposure is less than or equal to 10.0 micrograms per cubic meter, whereas the US Environmental Protection Agency (EPA) primary standard is less than or equal to 12.0 micrograms per cubic meter. The EPA primary standards are designed to protect public health with an adequate margin of safety.",
+ "fn": "undefined",
+ "hasEmail": "mailto:support-asdc@earthdata.nasa.gov"
+ },
+ "description": "This list of potential mission targets should not be interpreted as a complete list of viable NEAs for an actual human exploration mission. As the NEA orbits are updated, the viable mission targets and their mission parameters will change. To select an actual target and mission scenario, additional constraints must be applied including astronaut health and safety considerations, human space flight architecture elements, their performances and readiness, the physical nature of the target NEA and mission schedule constraints.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3540911807-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "http://beta.sedac.ciesin.columbia.edu/data/set/sdei-global-annual-avg-pm2-5-2001-2010/docs",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "http://beta.sedac.ciesin.columbia.edu/data/set/sdei-global-annual-avg-pm2-5-2001-2010/maps/services",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://doi.org/10.7927/H4H41PB4",
+ "description": "ASDC Data and Information for ATTREX",
+ "downloadURL": "https://asdc.larc.nasa.gov/project/ATTREX",
  "format": "HTML",
- "mediaType": "text/html"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3540911807-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
+ "mediaType": "text/html",
+ "title": "View documentation related to this dataset"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "ASDC Direct Data Download for ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1",
+ "downloadURL": "https://asdc.larc.nasa.gov/data/ATTREX/Aircraft_RemoteSensing_Temperature_Measurements_1/",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Download this dataset"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "DOI data set landing page for ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1",
+ "downloadURL": "https://doi.org/10.5067/ASDC_DAAC/ATTREX/0001",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "This dataset's landing page"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "ESPO Data Archive for ATTREX",
+ "downloadURL": "https://espoarchive.nasa.gov/archive/browse/attrex",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "The dataset's project home page"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "ESPO home page for ATTREX",
+ "downloadURL": "https://espo.nasa.gov/attrex/",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "The dataset's project home page"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Earthdata Search for ATTREX-Aircraft_RemoteSensing_Temperature_Measurements_1 (NASA Application to search, discover, visualize, refine, and access NASA Earth Observation data)",
+ "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C1536056465-LARC_ASDC",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Download this dataset through Earthdata Search"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "How to cite ASDC data",
+ "downloadURL": "https://asdc.larc.nasa.gov/citing-data",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "View this dataset's data citation policy"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Search results for publications that cite this dataset by its DOI.",
+ "downloadURL": "https://scholar.google.com/scholar?q=10.5067%2FASDC_DAAC%2FATTREX%2F0001",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Google Scholar search results"
  }
  ],
- "identifier": "10.7927/H4H41PB4",
- "keyword": [
- "earth-science-aerosols-atmosphere-particulate-matter",
- "earth-science-air-quality-atmosphere-particulates"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "identifier": "NASA-905",
+ "issued": "2015-01-01",
+ "keyword": [
+ "asteroid",
+ "comet",
+ "discovery",
+ "jpl",
+ "nea",
+ "near-earth-object",
+ "neo",
+ "orbit",
+ "small-body",
+ "solar-system"
+ ],
+ "landingPage": "http://neo.jpl.nasa.gov/cgi-bin/nhats",
+ "modified": "2025-04-01",
  "programCode": [
- "026:000"
+ "026:014"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90.0, \"WestBoundingCoordinate\": -180.0, \"EastBoundingCoordinate\": 180.0, \"SouthBoundingCoordinate\": -90.0}]]",
- "temporal": "2001-01-01/2010-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD)"
- },
- "description": "Global Annual PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD) data set represents a series of annual average grids (2001-2010) of fine particulate matter (solid particles and liquid droplets) that were derived from MODIS and MISR AOD satellite data. Together the grids provide a continuous surface of concentrations in micrograms per cubic meter of particulate matter of 2.5 micrometers or smaller (PM2.5) for health and environmental research. The satellite AOD retrievals were converted to ground-level concentrations based on a conversion factor developed by researchers at Dalhousie University that accounts for spatial and temporal variations in aerosol properties and vertical structure as derived from a global 3-D chemical transport model (GEOS-Chem). The raster grids have a grid cell resolution of 30 arc-minutes (0.5 degree or approximately 50 sq. km at the equator) and cover the world from 70 degrees N to 60 degrees S latitude. The grids were produced by researchers at Battelle Memorial Institute in collaboration with the Center for International Earth Science Information Network/Columbia University under a NASA-ROSES project entitled \"Using Satellite Data to Develop Environmental Indicators: An Application of NASA Data Products to Support High Level Decisions for National and International Environmental Protection\". Exposure to fine particles is associated with premature death as well as increased morbidity from respiratory and cardiovascular disease, especially in the elderly, young children, and those already suffering from these illnesses. The World Health Organization guideline for PM2.5 average annual exposure is less than or equal to 10.0 micrograms per cubic meter, whereas the US Environmental Protection Agency (EPA) primary standard is less than or equal to 12.0 micrograms per cubic meter. The EPA primary standards are designed to protect public health with an adequate margin of safety.",
+ "name": "National Aeronautics and Space Administration"
+ },
+ "theme": [
+ "Space Science"
+ ],
+ "title": "Near-Earth Object Human Space Flight Accessible Targets Study (NHATS)"
+ },
+ "description": "This list of potential mission targets should not be interpreted as a complete list of viable NEAs for an actual human exploration mission. As the NEA orbits are updated, the viable mission targets and their mission parameters will change. To select an actual target and mission scenario, additional constraints must be applied including astronaut health and safety considerations, human space flight architecture elements, their performances and readiness, the physical nature of the target NEA and mission schedule constraints.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/0d5963c1-185d-42cd-92a4-1fd929bfb6db",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/0d5963c1-185d-42cd-92a4-1fd929bfb6db/raw",
+ "View documentation related to this dataset",
+ "Download this dataset",
+ "This dataset's landing page",
+ "The dataset's project home page",
+ "The dataset's project home page",
+ "Download this dataset through Earthdata Search",
+ "View this dataset's data citation policy",
+ "Google Scholar search results"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/947b646f-d646-4c09-a9ae-191c43b91864",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/947b646f-d646-4c09-a9ae-191c43b91864/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.7927/H4H41PB4",
- "keyword": [
- "earth-science-aerosols-atmosphere-particulate-matter",
- "earth-science-air-quality-atmosphere-particulates"
- ],
- "last_harvested_date": "2026-09-23T00:15:34.566440",
+ "has_spatial": false,
+ "identifier": "NASA-905",
+ "keyword": [
+ "asteroid",
+ "comet",
+ "discovery",
+ "jpl",
+ "nea",
+ "near-earth-object",
+ "neo",
+ "orbit",
+ "small-body",
+ "solar-system"
+ ],
+ "last_harvested_date": "2026-09-23T01:29:36.228087",
  "organization": {
  "aliases": [
  ""
@@ -1210,111 +917,117 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 1,
- "publisher": "ESDIS",
- "slug": "global-annual-average-pm2-5-grids-from-modis-and-misr-aerosol-optical-depth-aod",
+ "popularity": 4,
+ "publisher": "National Aeronautics and Space Administration",
+ "slug": "near-earth-object-human-space-flight-accessible-targets-study-nhats",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
- "Earth Science"
- ],
- "title": "Global Annual Average PM2.5 Grids from MODIS and MISR Aerosol Optical Depth (AOD)",
+ "Space Science"
+ ],
+ "title": "Near-Earth Object Human Space Flight Accessible Targets Study (NHATS)",
  "type": "dataset"
  },
  {
- "_score": 10.662513,
+ "_score": 15.124415,
  "_sort": [
- 1790122531990,
- 10.662513,
- 3,
- "2d4edf1f-9338-4994-95c9-9c1aad995ab6"
+ 1790126956642,
+ 15.124415,
+ 1,
+ "f72f7357-7dd9-4ea7-ab7d-c06682fa908f"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
  "accessLevel": "public",
+ "accrualPeriodicity": "irregular",
  "bureauCode": [
  "026:00"
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "fn": "GeneLab Outreach",
+ "hasEmail": "mailto:genelab-outreach@lists.nasa.gov"
+ },
+ "description": "Anticipating the risk for infectious disease during space exploration and habitation is a critical factor to ensure safety health and performance of the crewmembers. As a ubiquitous environmental organism that is occasionally part of the human flora Pseudomonas aeruginosa could pose a health hazard for the immuno-compromised astronauts. In order to gain insights in the behavior of P. aeruginosa in spaceflight conditions two spaceflight-analogue culture systems i.e. the rotating wall vessel (RWV) and the random position machine (RPM) were used. Microarray analysis of P. aeruginosa PAO1 grown in the low shear modeled microgravity (LSMMG) environment of the RWV compared to the normal gravity control (NG) revealed a regulatory role for AlgU (RpoE). Specifically P. aeruginosa cultured in LSMMG exhibited increased alginate production and up-regulation of AlgU-controlled transcripts including those encoding stress-related proteins. This study also shows the involvement of Hfq in the LSMMG response consistent with its previously identified role in the Salmonella LSMMG- and spaceflight response. Furthermore cultivation in LSMMG increased heat and oxidative stress resistance and caused a decrease in the culture oxygen transfer rate. Interestingly the global transcriptional response of P. aeruginosa grown in the RPM was similar to that in NG. The possible role of differences in fluid mixing between the RWV and RPM is discussed with the overall collective data favoring the RWV as the optimal model to study the LSMMG-response of suspended cells. This study represents a first step towards the identification of specific virulence mechanisms of P. aeruginosa activated in response to spaceflight-analogue conditions and could direct future research regarding the risk assessment and prevention of Pseudomonas infections for the crew in flight and the general public.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550185878-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550185878-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/ndh-drought-hazard-frequency-distribution/maps/services",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt",
- "format": "TXT",
- "mediaType": "text/plain"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
+ "description": "GeneLab Study Page",
+ "downloadURL": "https://genelab-data.ndc.nasa.gov/genelab/accession/GLDS-53",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "['Spaceflight Modulates Gene Expression in Astronauts']"
  }
  ],
- "identifier": "10.7927/H4VX0DFT",
- "keyword": [
- "earth-science-climate-change-responses-human-dimensions-climate-adaptation",
- "earth-science-natural-hazards-human-dimensions-droughts",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-sustainability-human-dimensions"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "identifier": "nasa_genelab_GLDS-14_fg6b-h7es",
+ "issued": "2021-05-21",
+ "keyword": [
+ "bioassay_data_transformation",
+ "data-transformation",
+ "feature_extraction",
+ "genelab-microarray-data-processing-protocol",
+ "grow",
+ "hybridization",
+ "image_aquisition",
+ "labeling",
+ "microgravity-simulation",
+ "nucleic_acid_extraction",
+ "p-gse16970-1",
+ "p-gse16970-2",
+ "p-gse16970-3",
+ "p-gse16970-4",
+ "p-gse16970-5",
+ "p-gse16970-6",
+ "p-gse16970-7",
+ "p-gse16970-8",
+ "specified_biomaterial_action"
+ ],
+ "landingPage": "https://data.nasa.gov/dataset/response-of-pseudomonas-aeruginosa-pao1-to-low-shear-modeled-microgravity",
+ "license": "http://www.usa.gov/publicdomain/label/1.0/",
+ "modified": "2025-04-23",
  "programCode": [
- "026:000"
+ "026:005"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 85, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]",
- "temporal": "1980-01-01/2000-12-31",
+ "name": "National Aeronautics and Space Administration"
+ },
  "theme": [
  "Earth Science"
  ],
- "title": "Global Drought Hazard Frequency and Distribution"
- },
- "description": "The Global Drought Hazard Frequency and Distribution is a 2.5 minute grid based upon the International Research Institute for Climate Prediction's (IRI) Weighted Anomaly of Standardized Precipitation (WASP). Utilizing average monthly precipitation data from 1980 through 2000 at a resolution of 2.5 degrees, WASP assesses the precipitation deficit or surplus over a three month temporal window that is weighted by the magnitude of the seasonal cyclic variation in precipitation. The three months' averages are derived from the precipitation data and the median rainfall for the 21 year period is calculated for each grid cell. Grid cells where the three month running average of precipitation is less than 1 mm per day ae excluded. Drought events are identified when the magnitude of a monthly precipitation deficit is less than or equal to 50 percent of its longterm median value for three or more consecutive months. Grid cells are then divided into 10 classes having an approximately equal number of grid cells. Higher grid cell values denote higher frequencies of drought occurrences. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Columbia University International Research Institute for Climate Prediction (IRI), and Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "title": "Response of Pseudomonas aeruginosa PAO1 to low shear modeled microgravity"
+ },
+ "description": "Anticipating the risk for infectious disease during space exploration and habitation is a critical factor to ensure safety health and performance of the crewmembers. As a ubiquitous environmental organism that is occasionally part of the human flora Pseudomonas aeruginosa could pose a health hazard for the immuno-compromised astronauts. In order to gain insights in the behavior of P. aeruginosa in spaceflight conditions two spaceflight-analogue culture systems i.e. the rotating wall vessel (RWV) and the random position machine (RPM) were used. Microarray analysis of P. aeruginosa PAO1 grown in the low shear modeled microgravity (LSMMG) environment of the RWV compared to the normal gravity control (NG) revealed a regulatory role for AlgU (RpoE). Specifically P. aeruginosa cultured in LSMMG exhibited increased alginate production and up-regulation of AlgU-controlled transcripts including those encoding stress-related proteins. This study also shows the involvement of Hfq in the LSMMG response consistent with its previously identified role in the Salmonella LSMMG- and spaceflight response. Furthermore cultivation in LSMMG increased heat and oxidative stress resistance and caused a decrease in the culture oxygen transfer rate. Interestingly the global transcriptional response of P. aeruginosa grown in the RPM was similar to that in NG. The possible role of differences in fluid mixing between the RWV and RPM is discussed with the overall collective data favoring the RWV as the optimal model to study the LSMMG-response of suspended cells. This study represents a first step towards the identification of specific virulence mechanisms of P. aeruginosa activated in response to spaceflight-analogue conditions and could direct future research regarding the risk assessment and prevention of Pseudomonas infections for the crew in flight and the general public.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/637a9045-8127-499f-9e11-72c76c19cae9",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/637a9045-8127-499f-9e11-72c76c19cae9/raw",
+ "['Spaceflight Modulates Gene Expression in Astronauts']"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/c1aadd97-69ae-44bc-b6c4-0d24a73a9696",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/c1aadd97-69ae-44bc-b6c4-0d24a73a9696/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.7927/H4VX0DFT",
- "keyword": [
- "earth-science-climate-change-responses-human-dimensions-climate-adaptation",
- "earth-science-natural-hazards-human-dimensions-droughts",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-sustainability-human-dimensions"
- ],
- "last_harvested_date": "2026-09-23T00:15:31.990084",
+ "has_spatial": false,
+ "identifier": "nasa_genelab_GLDS-14_fg6b-h7es",
+ "keyword": [
+ "bioassay_data_transformation",
+ "data-transformation",
+ "feature_extraction",
+ "genelab-microarray-data-processing-protocol",
+ "grow",
+ "hybridization",
+ "image_aquisition",
+ "labeling",
+ "microgravity-simulation",
+ "nucleic_acid_extraction",
+ "p-gse16970-1",
+ "p-gse16970-2",
+ "p-gse16970-3",
+ "p-gse16970-4",
+ "p-gse16970-5",
+ "p-gse16970-6",
+ "p-gse16970-7",
+ "p-gse16970-8",
+ "specified_biomaterial_action"
+ ],
+ "last_harvested_date": "2026-09-23T01:29:16.642353",
  "organization": {
  "aliases": [
  ""
@@ -1329,24 +1042,24 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 3,
- "publisher": "ESDIS",
- "slug": "global-drought-hazard-frequency-and-distribution-4fad4",
+ "popularity": 1,
+ "publisher": "National Aeronautics and Space Administration",
+ "slug": "response-of-pseudomonas-aeruginosa-pao1-to-low-shear-modeled-microgravity-1bea8",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
  "Earth Science"
  ],
- "title": "Global Drought Hazard Frequency and Distribution",
+ "title": "Response of Pseudomonas aeruginosa PAO1 to low shear modeled microgravity",
  "type": "dataset"
  },
  {
- "_score": 10.058922,
+ "_score": 14.571165,
  "_sort": [
- 1790122531635,
- 10.058922,
- 4,
- "0e63b564-a902-478d-836e-d2b5c12e92ce"
+ 1790126931376,
+ 14.571165,
+ 23,
+ "ee184d4b-5040-4d8c-b447-e3682352386b"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -1356,86 +1069,60 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Global Drought Mortality Risks and Distribution is a 2.5 minute grid of global drought mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to drought hazard. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of drought hazard are obtained from the Global Drought Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of drought-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "fn": "Matteo Corbetta",
+ "hasEmail": "mailto:matteo.corbetta@nasa.gov"
+ },
+ "description": "Fatigue experiments were conducted on aluminum lap-joint specimens, and lamb wave signals were recorded for each specimen at several time points (i.e., defined as number of cycles in fatigue testing). Signals from piezo actuator-receiver sensor pairs were reported and it was observed that these signals were directly related to the crack lengths developed during fatigue testing. Optical measurements of surface crack lengths are also provided as the ground truth. The data set is split in training and validation to facilitate the application of data-driven methods. \n\nThis data set was generated at Arizona State University by Prof. Yongming Liu, Dr. Tishun Peng, and their collaborators. The data set was used for the Prognostics Health Management (PHM) Data Challenge for the 2019 Conference on Prognostics and Health Management. Other than the data set authors, the following individuals helped put together the 2019 PHM data challenge and make the data set publicly available: Matteo Corbetta and Portia Banerjee (KBR, Inc, NASA Ames), Kurt Doughty (Collins Aerospace), Kai Goebel (PARC), and Scott Clements (Lockheed Martin).\n\nData Set Citation: \nPeng T, He J, Xiang Y, Liu Y, Saxena A, Celaya J, Goebel K. Probabilistic fatigue damage prognosis of lap joint using Bayesian updating. Journal of Intelligent Material Systems and Structures. 2015 May;26(8):965-79.\n\nPublication Citation: \nHe J, Guan X, Peng T, Liu Y, Saxena A, Celaya J, Goebel K. A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves. Smart Materials and Structures. 2013 Sep 4;22(10):105007.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550188048-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550188048-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/ndh-drought-mortality-risks-distribution/maps/services",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt",
- "format": "TXT",
- "mediaType": "text/plain"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
+ "downloadURL": "https://data.nasa.gov/docs/legacy/PHMDC2019_Data.zip",
+ "format": "ZIP",
+ "mediaType": "application/zip",
+ "title": "PHMDC2019_Data.zip"
  }
  ],
- "identifier": "10.7927/H4R49NQV",
- "keyword": [
- "earth-science-climate-change-responses-human-dimensions-climate-adaptation",
- "earth-science-natural-hazards-human-dimensions-droughts",
- "earth-science-population-human-dimensions-mortality",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-sustainability-human-dimensions"
- ],
+ "identifier": "https://data.nasa.gov/api/views/awzu-cpt8",
+ "issued": "2023-03-31",
+ "keyword": [
+ "degradation",
+ "ivhm",
+ "phm",
+ "prognostics",
+ "structures"
+ ],
+ "landingPage": "https://data.nasa.gov/dataset/fatigue-crack-growth-in-aluminum-lap-joint",
  "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "modified": "2025-05-29",
  "programCode": [
- "026:000"
+ "026:001"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 85, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]",
- "temporal": "2000-01-01/2000-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Global Drought Mortality Risks and Distribution"
- },
- "description": "The Global Drought Mortality Risks and Distribution is a 2.5 minute grid of global drought mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to drought hazard. Mortality loss estimates per hazard event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of drought hazard are obtained from the Global Drought Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of drought-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "name": "PCoE"
+ },
+ "theme": [
+ "Raw Data"
+ ],
+ "title": "Fatigue Crack Growth in Aluminum Lap Joint"
+ },
+ "description": "Fatigue experiments were conducted on aluminum lap-joint specimens, and lamb wave signals were recorded for each specimen at several time points (i.e., defined as number of cycles in fatigue testing). Signals from piezo actuator-receiver sensor pairs were reported and it was observed that these signals were directly related to the crack lengths developed during fatigue testing. Optical measurements of surface crack lengths are also provided as the ground truth. The data set is split in training and validation to facilitate the application of data-driven methods. \n\nThis data set was generated at Arizona State University by Prof. Yongming Liu, Dr. Tishun Peng, and their collaborators. The data set was used for the Prognostics Health Management (PHM) Data Challenge for the 2019 Conference on Prognostics and Health Management. Other than the data set authors, the following individuals helped put together the 2019 PHM data challenge and make the data set publicly available: Matteo Corbetta and Portia Banerjee (KBR, Inc, NASA Ames), Kurt Doughty (Collins Aerospace), Kai Goebel (PARC), and Scott Clements (Lockheed Martin).\n\nData Set Citation: \nPeng T, He J, Xiang Y, Liu Y, Saxena A, Celaya J, Goebel K. Probabilistic fatigue damage prognosis of lap joint using Bayesian updating. Journal of Intelligent Material Systems and Structures. 2015 May;26(8):965-79.\n\nPublication Citation: \nHe J, Guan X, Peng T, Liu Y, Saxena A, Celaya J, Goebel K. A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves. Smart Materials and Structures. 2013 Sep 4;22(10):105007.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/36d92fa1-ae8c-438d-ba3b-075e7f6ba7d5",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/36d92fa1-ae8c-438d-ba3b-075e7f6ba7d5/raw",
+ "PHMDC2019_Data.zip"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/3435d076-bf17-4410-bb7b-8760e7b9f729",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/3435d076-bf17-4410-bb7b-8760e7b9f729/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.7927/H4R49NQV",
- "keyword": [
- "earth-science-climate-change-responses-human-dimensions-climate-adaptation",
- "earth-science-natural-hazards-human-dimensions-droughts",
- "earth-science-population-human-dimensions-mortality",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-sustainability-human-dimensions"
- ],
- "last_harvested_date": "2026-09-23T00:15:31.635426",
+ "has_spatial": false,
+ "identifier": "https://data.nasa.gov/api/views/awzu-cpt8",
+ "keyword": [
+ "degradation",
+ "ivhm",
+ "phm",
+ "prognostics",
+ "structures"
+ ],
+ "last_harvested_date": "2026-09-23T01:28:51.376077",
  "organization": {
  "aliases": [
  ""
@@ -1450,24 +1137,24 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 4,
- "publisher": "ESDIS",
- "slug": "global-drought-mortality-risks-and-distribution-dc4bc",
+ "popularity": 23,
+ "publisher": "PCoE",
+ "slug": "fatigue-crack-growth-in-aluminum-lap-joint",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
- "Earth Science"
- ],
- "title": "Global Drought Mortality Risks and Distribution",
+ "Raw Data"
+ ],
+ "title": "Fatigue Crack Growth in Aluminum Lap Joint",
  "type": "dataset"
  },
  {
- "_score": 10.509521,
+ "_score": 14.084618,
  "_sort": [
- 1790122525593,
- 10.509521,
- 5,
- "d25fd9d6-8bc5-427c-90d2-963b6107ed88"
+ 1790126930078,
+ 14.084618,
+ 18,
+ "0901663a-4ec1-451f-841a-932e7979a242"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -1477,84 +1164,56 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "fn": "Christopher Teubert",
+ "hasEmail": "mailto:Christopher.A.Teubert@nasa.gov"
+ },
+ "description": "This dataset describes the degradation of an aircraft engine. The dataset was used for the prognostics challenge competition at the International Conference on Prognostics and Health Management (PHM08). The challenge is still open for the researchers to develop and compare their efforts against the winners of the challenge in 2008.\n\nData sets consist of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different aircraft engine – i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data are contaminated with sensor noise.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550186069-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550186069-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution/maps/services",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt",
- "format": "TXT",
- "mediaType": "text/plain"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
+ "downloadURL": "https://data.nasa.gov/docs/legacy/Challenge_Data.zip",
+ "format": "ZIP",
+ "mediaType": "application/zip",
+ "title": "Challenge_Data.zip"
  }
  ],
- "identifier": "10.7927/H4668B3D",
- "keyword": [
- "earth-science-climate-change-responses-human-dimensions-climate-adaptation",
- "earth-science-natural-hazards-human-dimensions-floods",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-sustainability-human-dimensions"
- ],
+ "identifier": "https://data.nasa.gov/api/views/nk8v-ckry",
+ "issued": "2023-02-16",
+ "keyword": [
+ "degradation",
+ "phm",
+ "prognostics"
+ ],
+ "landingPage": "https://data.nasa.gov/dataset/phm-2008-challenge",
  "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "modified": "2025-05-29",
  "programCode": [
- "026:000"
+ "026:001"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 85, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]",
- "temporal": "1985-01-01/2003-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Global Flood Hazard Frequency and Distribution"
- },
- "description": "The Global Flood Hazard Frequency and Distribution is a 2.5 minute grid derived from a global listing of extreme flood events between 1985 and 2003 (poor or missing data in the early/mid 1990s) compiled by Dartmouth Flood Observatory and georeferenced to the nearest degree. The resultant flood frequency grid was then classified into 10 classes of approximately equal number of grid cells. The greater the grid cell value in the final data set, the higher the relative frequency of flood occurrence. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR) and Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "name": "PCoE"
+ },
+ "theme": [
+ "Raw Data"
+ ],
+ "title": "PHM 2008 Challenge"
+ },
+ "description": "This dataset describes the degradation of an aircraft engine. The dataset was used for the prognostics challenge competition at the International Conference on Prognostics and Health Management (PHM08). The challenge is still open for the researchers to develop and compare their efforts against the winners of the challenge in 2008.\n\nData sets consist of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different aircraft engine – i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data are contaminated with sensor noise.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/44fc8ec6-b715-4016-aef0-8c6de862877d",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/44fc8ec6-b715-4016-aef0-8c6de862877d/raw",
+ "Challenge_Data.zip"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/653b41e3-43ed-40b9-b985-2234d57f6bca",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/653b41e3-43ed-40b9-b985-2234d57f6bca/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.7927/H4668B3D",
- "keyword": [
- "earth-science-climate-change-responses-human-dimensions-climate-adaptation",
- "earth-science-natural-hazards-human-dimensions-floods",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-sustainability-human-dimensions"
- ],
- "last_harvested_date": "2026-09-23T00:15:25.593280",
+ "has_spatial": false,
+ "identifier": "https://data.nasa.gov/api/views/nk8v-ckry",
+ "keyword": [
+ "degradation",
+ "phm",
+ "prognostics"
+ ],
+ "last_harvested_date": "2026-09-23T01:28:50.078181",
  "organization": {
  "aliases": [
  ""
@@ -1569,24 +1228,24 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 5,
- "publisher": "ESDIS",
- "slug": "global-flood-hazard-frequency-and-distribution-a536d",
+ "popularity": 18,
+ "publisher": "PCoE",
+ "slug": "phm-2008-challenge",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
- "Earth Science"
- ],
- "title": "Global Flood Hazard Frequency and Distribution",
+ "Raw Data"
+ ],
+ "title": "PHM 2008 Challenge",
  "type": "dataset"
  },
  {
- "_score": 10.058922,
+ "_score": 13.128073,
  "_sort": [
- 1790122524146,
- 10.058922,
- 2,
- "660c8dad-ef3f-421b-b723-864540370450"
+ 1790126928999,
+ 13.128073,
+ 41,
+ "06028cd4-09b6-4460-849f-44b9225a3afa"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -1596,86 +1255,55 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Global Flood Mortality Risks and Distribution is a 2.5 minute grid of global flood mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provided a baseline population per grid cell from which to estimate potential mortality risks due to flood hazard. Mortality loss estimates per flood event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of flood hazard are obtained from the Global Flood Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and the procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing hazard with an approximately equal number of grid cells per class, producing a relative estimate of flood-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550185779-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550185779-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/ndh-flood-mortality-risks-distribution/maps/services",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt",
- "format": "TXT",
- "mediaType": "text/plain"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
- }
- ],
- "identifier": "10.7927/H42F7KCP",
- "keyword": [
- "earth-science-climate-change-responses-human-dimensions-climate-adaptation",
- "earth-science-natural-hazards-human-dimensions-floods",
- "earth-science-population-human-dimensions-mortality",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-sustainability-human-dimensions"
- ],
+ "fn": "Matteo Corbetta",
+ "hasEmail": "mailto:matteo.corbetta@nasa.gov"
+ },
+ "description": "***An accelerated Life Testing Dataset for Lithium-Ion Batteries with Constant and Variable Loading Conditions***\n\nWe present an accelerated Li-ion battery life cycle dataset focused on a large range of load levels and the characterization of the life cycle of a battery pack composed of two 18650 battery cells. The life cycle study is conducted with a total of 26 battery packs that are grouped by constant and random loading conditions, loading levels and number of load level changes. Furthermore, we conducted load cycling on second-life batteries, where surviving cells from previously aged battery packs were assembled to second- life packs.\n\nThe dataset was generated from a custom-made testbed to cycle battery packs designed and developed by Kajetan Fricke, Renato Nascimento, and Prof. Felipe Viana, from the Probabilistic Mechanics Laboratory at the University of Central Florida (UCF).",
+ "identifier": "https://data.nasa.gov/api/views/xg3n-ngei",
+ "issued": "2023-12-18",
+ "keyword": [
+ "batteries",
+ "capacity",
+ "current",
+ "degradation",
+ "health-management",
+ "prognostics",
+ "temperature",
+ "voltage"
+ ],
+ "landingPage": "https://data.nasa.gov/dataset/randomized-and-recommissioned-battery-dataset",
  "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "modified": "2025-05-29",
  "programCode": [
- "026:000"
+ "026:001"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 85, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]",
- "temporal": "2000-01-01/2000-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Global Flood Mortality Risks and Distribution"
- },
- "description": "The Global Flood Mortality Risks and Distribution is a 2.5 minute grid of global flood mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provided a baseline population per grid cell from which to estimate potential mortality risks due to flood hazard. Mortality loss estimates per flood event are calculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of flood hazard are obtained from the Global Flood Hazard Frequency and Distribution data set. In order to more accurately reflect the confidence associated with the data and the procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing hazard with an approximately equal number of grid cells per class, producing a relative estimate of flood-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
- "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/78d553fb-1d93-4810-bc4c-ad6774b55bcd",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/78d553fb-1d93-4810-bc4c-ad6774b55bcd/raw",
- "has_download": true,
- "has_spatial": true,
- "identifier": "10.7927/H42F7KCP",
- "keyword": [
- "earth-science-climate-change-responses-human-dimensions-climate-adaptation",
- "earth-science-natural-hazards-human-dimensions-floods",
- "earth-science-population-human-dimensions-mortality",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-sustainability-human-dimensions"
- ],
- "last_harvested_date": "2026-09-23T00:15:24.146421",
+ "name": "PCoE"
+ },
+ "theme": [
+ "Raw Data"
+ ],
+ "title": "Randomized and Recommissioned Battery Dataset"
+ },
+ "description": "***An accelerated Life Testing Dataset for Lithium-Ion Batteries with Constant and Variable Loading Conditions***\n\nWe present an accelerated Li-ion battery life cycle dataset focused on a large range of load levels and the characterization of the life cycle of a battery pack composed of two 18650 battery cells. The life cycle study is conducted with a total of 26 battery packs that are grouped by constant and random loading conditions, loading levels and number of load level changes. Furthermore, we conducted load cycling on second-life batteries, where surviving cells from previously aged battery packs were assembled to second- life packs.\n\nThe dataset was generated from a custom-made testbed to cycle battery packs designed and developed by Kajetan Fricke, Renato Nascimento, and Prof. Felipe Viana, from the Probabilistic Mechanics Laboratory at the University of Central Florida (UCF).",
+ "distribution_titles": [],
+ "harvest_record": "https://catalog.data.gov/harvest_record/01580e33-6ba0-437e-be4f-e506c8c79dd3",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/01580e33-6ba0-437e-be4f-e506c8c79dd3/raw",
+ "has_download": false,
+ "has_spatial": false,
+ "identifier": "https://data.nasa.gov/api/views/xg3n-ngei",
+ "keyword": [
+ "batteries",
+ "capacity",
+ "current",
+ "degradation",
+ "health-management",
+ "prognostics",
+ "temperature",
+ "voltage"
+ ],
+ "last_harvested_date": "2026-09-23T01:28:48.999577",
  "organization": {
  "aliases": [
  ""
@@ -1690,24 +1318,24 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 2,
- "publisher": "ESDIS",
- "slug": "global-flood-mortality-risks-and-distribution-7065e",
+ "popularity": 41,
+ "publisher": "PCoE",
+ "slug": "randomized-and-recommissioned-battery-dataset",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
- "Earth Science"
- ],
- "title": "Global Flood Mortality Risks and Distribution",
+ "Raw Data"
+ ],
+ "title": "Randomized and Recommissioned Battery Dataset",
  "type": "dataset"
  },
  {
- "_score": 10.107268,
+ "_score": 65.89876,
  "_sort": [
- 1790122522057,
- 10.107268,
- 3,
- "f73a9a54-1e44-405c-b3b5-f2143f27bc22"
+ 1790126791482,
+ 65.89876,
+ 2,
+ "d1478f07-6cd8-4429-a651-d6ed8b76638c"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -1717,74 +1345,98 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Millennium Ecosystem Assessment: MA Ecosystems provides data and information on the extent and classification of ecosystems circa 2000, including coastal, cultivated, forest and woodlands, inland water bodies, islands, marine, mountains (elevation), polar, and urban. The data set also includes socioeconomic reporting Units and the location of regional MA projects. The data were used in a number of different ways in the assessment, contributing to an understanding of how humans have altered ecosystems, how changes in ecosystem services have affected human well-being, and how ecosystem changes may affect people in future decades.",
+ "fn": "undefined",
+ "hasEmail": "mailto:metadata@ciesin.columbia.edu"
+ },
+ "description": "The Natural Resource Protection and Child Health Indicators, 2022 Release, is produced in support of the U.S. Millennium Challenge Corporation (MCC) as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 220 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 195 countries derived from the average of three proximity-to-target scores for access to at least basic water and sanitation together with child mortality rates. The 2022 release includes a consistent time series of NRPI scores for 2010 to 2022 and CHI scores for 2010 to 2020.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550194184-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550194184-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://www.earthdata.nasa.gov/about/esdis",
- "format": "BIN",
- "mediaType": "application/octet-stream"
+ "description": "Data Download Page",
+ "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/nrmi-natural-resource-protection-child-health-indicators-2022/data-download",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Download this dataset"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Sample browse graphic of the data set.",
+ "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/maps/nrmi/nrmi-natural-resource-protection-child-health-indicators-2022/sedac-logo.jpg",
+ "format": "JPEG",
+ "mediaType": "image/jpeg",
+ "title": "Get a related visualization"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Search results for publications that cite this dataset by its DOI.",
+ "downloadURL": "https://scholar.google.com/scholar?q=10.7927%2F70tj-g487",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Google Scholar search results"
  }
  ],
- "identifier": "10.7927/H4KW5CZ6",
- "keyword": [
- "earth-science-climate-change-responses-human-dimensions-climate-adaptation",
- "earth-science-ecosystems-biosphere",
- "earth-science-environmental-impacts-human-dimensions-conservation",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-sustainability-human-dimensions"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "identifier": "C2601447797-SEDAC",
+ "issued": "2022-12-31",
+ "keyword": [
+ "earth-science",
+ "environmental-impacts",
+ "human-dimensions",
+ "public-health",
+ "sustainability"
+ ],
+ "language": [
+ "en-US"
+ ],
+ "modified": "2025-07-17",
  "programCode": [
- "026:000"
+ "026:001"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 90, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]",
- "temporal": "2000-01-01/2000-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Millennium Ecosystem Assessment: MA Ecosystems"
- },
- "description": "The Millennium Ecosystem Assessment: MA Ecosystems provides data and information on the extent and classification of ecosystems circa 2000, including coastal, cultivated, forest and woodlands, inland water bodies, islands, marine, mountains (elevation), polar, and urban. The data set also includes socioeconomic reporting Units and the location of regional MA projects. The data were used in a number of different ways in the assessment, contributing to an understanding of how humans have altered ecosystems, how changes in ecosystem services have affected human well-being, and how ecosystem changes may affect people in future decades.",
+ "name": "SEDAC"
+ },
+ "references": [
+ "https://doi.org/10.7927/5bbs-e174",
+ "https://doi.org/10.7927/6t8a-es66",
+ "https://doi.org/10.7927/7ppx-6m60",
+ "https://doi.org/10.7927/80dp-h987",
+ "https://doi.org/10.7927/H41Z4299",
+ "https://doi.org/10.7927/H45Q4T1N",
+ "https://doi.org/10.7927/H46M34RP",
+ "https://doi.org/10.7927/H48913TX",
+ "https://doi.org/10.7927/H49G5JRZ",
+ "https://doi.org/10.7927/H4G73BM2",
+ "https://doi.org/10.7927/H4NZ85MP",
+ "https://doi.org/10.7927/H4SQ8XGT",
+ "https://doi.org/10.7927/r6mv-sv82"
+ ],
+ "spatial": "-180.0 -55.0 180.0 90.0",
+ "temporal": "2010-01-01T00:00:00Z/2022-12-31T00:00:00Z",
+ "theme": [
+ "NRMI",
+ "geospatial"
+ ],
+ "title": "Natural Resource Protection and Child Health Indicators, 2022 Release"
+ },
+ "description": "The Natural Resource Protection and Child Health Indicators, 2022 Release, is produced in support of the U.S. Millennium Challenge Corporation (MCC) as selection criteria for funding eligibility. The Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 220 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 195 countries derived from the average of three proximity-to-target scores for access to at least basic water and sanitation together with child mortality rates. The 2022 release includes a consistent time series of NRPI scores for 2010 to 2022 and CHI scores for 2010 to 2020.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/84851450-9436-417b-9211-c3537f8fb6f4",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/84851450-9436-417b-9211-c3537f8fb6f4/raw",
+ "Download this dataset",
+ "Get a related visualization",
+ "Google Scholar search results"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/88aee62e-82a4-4e4a-a0af-95593e106737",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/88aee62e-82a4-4e4a-a0af-95593e106737/raw",
  "has_download": true,
  "has_spatial": true,
- "identifier": "10.7927/H4KW5CZ6",
- "keyword": [
- "earth-science-climate-change-responses-human-dimensions-climate-adaptation",
- "earth-science-ecosystems-biosphere",
- "earth-science-environmental-impacts-human-dimensions-conservation",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-sustainability-human-dimensions"
- ],
- "last_harvested_date": "2026-09-23T00:15:22.057416",
+ "identifier": "C2601447797-SEDAC",
+ "keyword": [
+ "earth-science",
+ "environmental-impacts",
+ "human-dimensions",
+ "public-health",
+ "sustainability"
+ ],
+ "last_harvested_date": "2026-09-23T01:26:31.482183",
  "organization": {
  "aliases": [
  ""
@@ -1799,24 +1451,25 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 3,
- "publisher": "ESDIS",
- "slug": "millennium-ecosystem-assessment-ma-ecosystems-1a004",
+ "popularity": 2,
+ "publisher": "SEDAC",
+ "slug": "natural-resource-protection-and-child-health-indicators-2022-release",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
- "Earth Science"
- ],
- "title": "Millennium Ecosystem Assessment: MA Ecosystems",
+ "NRMI",
+ "geospatial"
+ ],
+ "title": "Natural Resource Protection and Child Health Indicators, 2022 Release",
  "type": "dataset"
  },
  {
- "_score": 10.193435,
+ "_score": 64.94738,
  "_sort": [
- 1790122517659,
- 10.193435,
- 0,
- "4b238897-5878-4e89-ae71-b4671af13a14"
+ 1790126459343,
+ 64.94738,
+ 3,
+ "dc4fb067-0ecc-414a-8436-001ad1ea4dab"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -1826,72 +1479,64 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a \"change consistent with warming\" or a \"change not consistent with warming\", based on information from the underlying studies.",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550191570-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550191570-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/ipcc-ar4-observed-climate-impacts/maps/services",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- }
- ],
- "identifier": "10.7927/H4542KJV",
- "keyword": [
- "earth-science-atmospheric-ocean-indicators-climate-indicators-sea-level-rise",
- "earth-science-environmental-impacts-human-dimensions",
- "earth-science-environmental-impacts-human-dimensions-water-resources",
- "earth-science-public-health-human-dimensions-food-security"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "fn": "undefined",
+ "hasEmail": "mailto:metadata@ciesin.columbia.edu"
+ },
+ "description": "The Natural Resource Protection and Child Health Indicators, 2013 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 221 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 188 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2013 release includes a consistent time series of NRPIs and CHIs for 2006 to 2013.",
+ "identifier": "C1000000420-SEDAC",
+ "issued": "2013-12-31",
+ "keyword": [
+ "earth-science",
+ "environmental-impacts",
+ "human-dimensions",
+ "public-health",
+ "sustainability"
+ ],
+ "language": [
+ "en-US"
+ ],
+ "modified": "2025-07-17",
  "programCode": [
- "026:000"
+ "026:001"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -90}]]",
- "temporal": "1970-01-01/2014-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "IPCC Fourth Assessment Report (AR4) Observed Climate Change Impacts Database"
- },
- "description": "The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a \"change consistent with warming\" or a \"change not consistent with warming\", based on information from the underlying studies.",
- "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/90b751ce-cd96-486a-a98c-43163b2737d1",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/90b751ce-cd96-486a-a98c-43163b2737d1/raw",
- "has_download": true,
+ "name": "SEDAC"
+ },
+ "references": [
+ "https://doi.org/10.7927/6t8a-es66",
+ "https://doi.org/10.7927/H41Z4299",
+ "https://doi.org/10.7927/H45Q4T1N",
+ "https://doi.org/10.7927/H46M34RP",
+ "https://doi.org/10.7927/H48913TX",
+ "https://doi.org/10.7927/H49G5JRZ",
+ "https://doi.org/10.7927/H4G73BM2",
+ "https://doi.org/10.7927/H4SQ8XGT",
+ "https://doi.org/10.7927/r6mv-sv82"
+ ],
+ "spatial": "-180.0 -55.0 180.0 90.0",
+ "temporal": "2006-01-01T00:00:00Z/2012-12-31T00:00:00Z",
+ "theme": [
+ "NRMI",
+ "geospatial"
+ ],
+ "title": "Natural Resource Protection and Child Health Indicators, 2013 Release"
+ },
+ "description": "The Natural Resource Protection and Child Health Indicators, 2013 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 221 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 188 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2013 release includes a consistent time series of NRPIs and CHIs for 2006 to 2013.",
+ "distribution_titles": [],
+ "harvest_record": "https://catalog.data.gov/harvest_record/8582ddce-ed6d-4782-8379-3ae0000c423b",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/8582ddce-ed6d-4782-8379-3ae0000c423b/raw",
+ "has_download": false,
  "has_spatial": true,
- "identifier": "10.7927/H4542KJV",
- "keyword": [
- "earth-science-atmospheric-ocean-indicators-climate-indicators-sea-level-rise",
- "earth-science-environmental-impacts-human-dimensions",
- "earth-science-environmental-impacts-human-dimensions-water-resources",
- "earth-science-public-health-human-dimensions-food-security"
- ],
- "last_harvested_date": "2026-09-23T00:15:17.659256",
+ "identifier": "C1000000420-SEDAC",
+ "keyword": [
+ "earth-science",
+ "environmental-impacts",
+ "human-dimensions",
+ "public-health",
+ "sustainability"
+ ],
+ "last_harvested_date": "2026-09-23T01:20:59.343081",
  "organization": {
  "aliases": [
  ""
@@ -1906,24 +1551,25 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 0,
- "publisher": "ESDIS",
- "slug": "ipcc-fourth-assessment-report-ar4-observed-climate-change-impacts-database-2572a",
+ "popularity": 3,
+ "publisher": "SEDAC",
+ "slug": "natural-resource-protection-and-child-health-indicators-2013-release",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
- "Earth Science"
- ],
- "title": "IPCC Fourth Assessment Report (AR4) Observed Climate Change Impacts Database",
+ "NRMI",
+ "geospatial"
+ ],
+ "title": "Natural Resource Protection and Child Health Indicators, 2013 Release",
  "type": "dataset"
  },
  {
- "_score": 14.625553,
+ "_score": 13.306016,
  "_sort": [
- 1790122516956,
- 14.625553,
- 1,
- "258e226e-4e5c-436a-83b6-60d464a33969"
+ 1790126164298,
+ 13.306016,
+ 6,
+ "b286214f-f877-43d6-9ad7-851e165d0e25"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -1933,68 +1579,56 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set includes predictions of PM2.5 concentration in grid cells at a resolution of 1-km for the years 2000-2016. A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, and others. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions. In version 1.10, the completeness of daily PM2.5 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily PM2.5 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily and annual PM2.5 predictions allow public health researchers to respectively estimate the short- and long-term effects of PM2.5 exposures on human health, supporting the U.S. Environmental Protection Agency (EPA) for the revision of the National Ambient Air Quality Standards for 24-hour average and annual average concentrations of PM2.5. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3540930144-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3540930144-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/aqdh-pm2-5-concentrations-contiguous-us-1-km-v1-10-2000-2016-readme.txt",
- "format": "TXT",
- "mediaType": "text/plain"
- }
- ],
- "identifier": "10.7927/g2n9-ca10",
- "keyword": [
- "earth-science-aerosols-atmosphere-particulate-matter",
- "earth-science-air-quality-atmosphere-particulates"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "fn": "Thomas Morgan",
+ "hasEmail": "mailto:thomas.h.morgan@nasa.gov"
+ },
+ "description": "The Population Exposure Estimates in Proximity to Nuclear Power Plants, Locations data set combines information from a global data set developed by Declan Butler of Nature News and the Power Reactor Information System (PRIS), an up-to-date database of nuclear reactors maintained by the International Atomic Energy Agency (IAEA). The locations of nuclear reactors around the world are represented as point features associated with reactor specification and performance history attributes as of March 2012.",
+ "identifier": "C1000000480-SEDAC",
+ "issued": "2015-01-21",
+ "keyword": [
+ "earth-science",
+ "environmental-impacts",
+ "human-dimensions",
+ "population",
+ "public-health"
+ ],
+ "language": [
+ "en-US"
+ ],
+ "modified": "2025-07-17",
  "programCode": [
- "026:000"
+ "026:001"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 72, \"EastBoundingCoordinate\": -65, \"SouthBoundingCoordinate\": 17}]]",
- "temporal": "2000-01-01/2016-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016)"
- },
- "description": "The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set includes predictions of PM2.5 concentration in grid cells at a resolution of 1-km for the years 2000-2016. A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, and others. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions. In version 1.10, the completeness of daily PM2.5 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily PM2.5 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily and annual PM2.5 predictions allow public health researchers to respectively estimate the short- and long-term effects of PM2.5 exposures on human health, supporting the U.S. Environmental Protection Agency (EPA) for the revision of the National Ambient Air Quality Standards for 24-hour average and annual average concentrations of PM2.5. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.",
- "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/e2201b8d-4dbb-4f8e-a6ba-076085001fbf",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/e2201b8d-4dbb-4f8e-a6ba-076085001fbf/raw",
- "has_download": true,
+ "name": "SEDAC"
+ },
+ "references": [
+ "https://doi.org/10.7927/H41834D6"
+ ],
+ "spatial": "-124.21 -34.0 166.45 68.05",
+ "temporal": "1956-01-01T00:00:00Z/2012-12-31T00:00:00Z",
+ "theme": [
+ "ENERGY",
+ "geospatial"
+ ],
+ "title": "Population Exposure Estimates in Proximity to Nuclear Power Plants, Locations"
+ },
+ "description": "The Population Exposure Estimates in Proximity to Nuclear Power Plants, Locations data set combines information from a global data set developed by Declan Butler of Nature News and the Power Reactor Information System (PRIS), an up-to-date database of nuclear reactors maintained by the International Atomic Energy Agency (IAEA). The locations of nuclear reactors around the world are represented as point features associated with reactor specification and performance history attributes as of March 2012.",
+ "distribution_titles": [],
+ "harvest_record": "https://catalog.data.gov/harvest_record/b8141c16-21ea-4361-b4d0-96e3f273e773",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/b8141c16-21ea-4361-b4d0-96e3f273e773/raw",
+ "has_download": false,
  "has_spatial": true,
- "identifier": "10.7927/g2n9-ca10",
- "keyword": [
- "earth-science-aerosols-atmosphere-particulate-matter",
- "earth-science-air-quality-atmosphere-particulates"
- ],
- "last_harvested_date": "2026-09-23T00:15:16.956755",
+ "identifier": "C1000000480-SEDAC",
+ "keyword": [
+ "earth-science",
+ "environmental-impacts",
+ "human-dimensions",
+ "population",
+ "public-health"
+ ],
+ "last_harvested_date": "2026-09-23T01:16:04.298513",
  "organization": {
  "aliases": [
  ""
@@ -2009,95 +1643,74 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 1,
- "publisher": "ESDIS",
- "slug": "daily-and-annual-pm2-5-concentrations-for-the-contiguous-united-states-1-km-grid-2000-2016-2ad7b",
+ "popularity": 6,
+ "publisher": "SEDAC",
+ "slug": "population-exposure-estimates-in-proximity-to-nuclear-power-plants-locations",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
- "Earth Science"
- ],
- "title": "Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016)",
+ "ENERGY",
+ "geospatial"
+ ],
+ "title": "Population Exposure Estimates in Proximity to Nuclear Power Plants, Locations",
  "type": "dataset"
  },
  {
- "_score": 13.244623,
+ "_score": 12.0058365,
  "_sort": [
- 1790122516253,
- 13.244623,
- 1,
- "a5e06d71-d51e-4f60-a98c-de08558de68d"
+ 1790126146532,
+ 12.0058365,
+ 2,
+ "5997466c-ff78-4b27-9a98-0f763a67ba97"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
  "accessLevel": "public",
+ "accrualPeriodicity": "irregular",
  "bureauCode": [
  "026:00"
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Natural Resource Management Index (NRMI), 2011 Release is a composite index for 174 countries derived from the average of four proximity-to-target indicators for eco-region protection (weighted average percentage of biomes under protected status), access to improved sanitation, access to improved water and child mortality. The 2011 release of the NRMI includes a consistent time series of NRMIs for 2006 to 2011. In addition, the 2011 release includes two new indicators that will eventually supplant the NRMI: a Natural Resource Protection Indicator (NRPI) that is solely composed of the eco-region protection indicator, and a Child Health Indicator (CHI), which is an unweighted average of the proximtiy-to-target scores for access to water, access to sanitation, and child mortality. The data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Yale Center for Environmental Law and Policy (YCELP), Yale University.",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3540930854-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "http://sedac.ciesin.columbia.edu/data/set/nrmi-natural-resource-management-index-2011/docs",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3540930854-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- }
- ],
- "identifier": "10.7927/H45Q4T1N",
- "keyword": [
- "earth-science-environmental-impacts-human-dimensions-conservation",
- "earth-science-sustainability-human-dimensions-environmental-sustainability"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "fn": "Thomas Morgan",
+ "hasEmail": "mailto:thomas.h.morgan@nasa.gov"
+ },
+ "description": "This volume contains Experiment Data acquired by GIADA during 'Mars swing-by' phase. More in detail it refers to the data provided during the following in-flight tests: 'Active Payload Checkout n. 4' (PC4) held on 24/25-11-2006 and 04-12-2006; 'Passive Payload Checkout n. 5' (PC5) held on 20/21-05-2007. It also contains documentation which describes the GIADA experiment. The data reported in this data set have been converted from ADC counts to engineering values. The quality of the Housekeeping and Calibration data is good. Scientific data are due to noise, as no grain event is expected during this mission phase. These data must be only considered to evaluate GIADA behaviour and not as real scientific data. Data reported by GDS and IS are due to noise as no dust event is expected during this mission phase. MBS frequency changes, once normalised for frequency vs. temperature dependence, if present, are due to deposition of contaminants existing in the S/C environment. Housekeeping and Calibration data from all GIADA sub-systems are useful to evaluate instrument health and behaviour when compared with similar data acquired during other mission phases.",
+ "identifier": "urn:nasa:pds:context_pds3:data_set:data_set.ro-x-gia-2-mars-marsswingby-v1.0_pjdt-s5sa",
+ "issued": "2018-06-26",
+ "keyword": [
+ "international-rosetta-mission",
+ "unknown"
+ ],
+ "landingPage": "https://pds.nasa.gov/ds-view/pds/viewDataset.jsp?dsid=RO-X-GIA-2-MARS-MARSSWINGBY-V1.0",
+ "modified": "2025-07-17",
  "programCode": [
- "026:000"
+ "026:005"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -55}]]",
- "temporal": "2006-01-01/2010-12-31",
+ "name": "National Aeronautics and Space Administration"
+ },
+ "references": [
+ "https://pds.nasa.gov"
+ ],
  "theme": [
  "Earth Science"
  ],
- "title": "Natural Resource Management Index (NRMI), 2011 Release"
- },
- "description": "The Natural Resource Management Index (NRMI), 2011 Release is a composite index for 174 countries derived from the average of four proximity-to-target indicators for eco-region protection (weighted average percentage of biomes under protected status), access to improved sanitation, access to improved water and child mortality. The 2011 release of the NRMI includes a consistent time series of NRMIs for 2006 to 2011. In addition, the 2011 release includes two new indicators that will eventually supplant the NRMI: a Natural Resource Protection Indicator (NRPI) that is solely composed of the eco-region protection indicator, and a Child Health Indicator (CHI), which is an unweighted average of the proximtiy-to-target scores for access to water, access to sanitation, and child mortality. The data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Yale Center for Environmental Law and Policy (YCELP), Yale University.",
- "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/18912372-22f0-4ccb-ab8b-17cc561dfadf",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/18912372-22f0-4ccb-ab8b-17cc561dfadf/raw",
- "has_download": true,
- "has_spatial": true,
- "identifier": "10.7927/H45Q4T1N",
- "keyword": [
- "earth-science-environmental-impacts-human-dimensions-conservation",
- "earth-science-sustainability-human-dimensions-environmental-sustainability"
- ],
- "last_harvested_date": "2026-09-23T00:15:16.253820",
+ "title": "ROSETTA-ORBITER CHECK GIADA 2 MARS MARSSWINGBY V1.0"
+ },
+ "description": "This volume contains Experiment Data acquired by GIADA during 'Mars swing-by' phase. More in detail it refers to the data provided during the following in-flight tests: 'Active Payload Checkout n. 4' (PC4) held on 24/25-11-2006 and 04-12-2006; 'Passive Payload Checkout n. 5' (PC5) held on 20/21-05-2007. It also contains documentation which describes the GIADA experiment. The data reported in this data set have been converted from ADC counts to engineering values. The quality of the Housekeeping and Calibration data is good. Scientific data are due to noise, as no grain event is expected during this mission phase. These data must be only considered to evaluate GIADA behaviour and not as real scientific data. Data reported by GDS and IS are due to noise as no dust event is expected during this mission phase. MBS frequency changes, once normalised for frequency vs. temperature dependence, if present, are due to deposition of contaminants existing in the S/C environment. Housekeeping and Calibration data from all GIADA sub-systems are useful to evaluate instrument health and behaviour when compared with similar data acquired during other mission phases.",
+ "distribution_titles": [],
+ "harvest_record": "https://catalog.data.gov/harvest_record/445d1d2f-479d-4650-9532-54bed793ad97",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/445d1d2f-479d-4650-9532-54bed793ad97/raw",
+ "has_download": false,
+ "has_spatial": false,
+ "identifier": "urn:nasa:pds:context_pds3:data_set:data_set.ro-x-gia-2-mars-marsswingby-v1.0_pjdt-s5sa",
+ "keyword": [
+ "international-rosetta-mission",
+ "unknown"
+ ],
+ "last_harvested_date": "2026-09-23T01:15:46.532081",
  "organization": {
  "aliases": [
  ""
@@ -2112,24 +1725,24 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 1,
- "publisher": "ESDIS",
- "slug": "natural-resource-management-index-nrmi-2011-release-3af45",
+ "popularity": 2,
+ "publisher": "National Aeronautics and Space Administration",
+ "slug": "rosetta-orbiter-check-giada-2-mars-marsswingby-v1-0-13c31",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
  "Earth Science"
  ],
- "title": "Natural Resource Management Index (NRMI), 2011 Release",
+ "title": "ROSETTA-ORBITER CHECK GIADA 2 MARS MARSSWINGBY V1.0",
  "type": "dataset"
  },
  {
- "_score": 14.137062,
+ "_score": 64.71156,
  "_sort": [
- 1790122514962,
- 14.137062,
- 1,
- "b20c1614-018f-4c7f-9015-10683986d6f4"
+ 1790126101603,
+ 64.71156,
+ 4,
+ "453e4be9-9206-44cb-92c9-3110bd93c487"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -2139,66 +1752,64 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set contains daily predictions of Nitrogen Dioxide (NO2) concentrations at a high resolution (1-km grid cells) for the years 2000 to 2016. An ensemble modeling framework was used to assess NO2 levels with high accuracy, which combined estimates from three machine learning models (neural network, random forest, and gradient boosting), with a generalized additive model. Predictor variables included NO2 column concentrations from satellites, land-use variables, meteorological variables, predictions from two chemical transport models, GEOS-Chem and the U.S. Environmental Protection Agency (EPA) CommUnity Multiscale Air Quality Modeling System (CMAQ), along with other ancillary variables. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensemble produced a cross-validated R-squared value of 0.79 overall, a spatial R-squared value of 0.84, and a temporal R-squared value of 0.73. In version 1.10, the completeness of daily NO2 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily NO2 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily and annual NO2 predictions allow public health researchers to respectively estimate the short- and long-term effects of NO2 exposures on human health, supporting the U.S. EPA for the revision of the National Ambient Air Quality Standards for daily average and annual average concentrations of NO2. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3540929605-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3540929605-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/aqdh-no2-concentrations-contiguous-us-1-km-v1-10-2000-2016-readme.txt",
- "format": "TXT",
- "mediaType": "text/plain"
- }
- ],
- "identifier": "10.7927/rz28-p167",
- "keyword": [
- "earth-science-atmospheric-chemistry-atmosphere-nitrogen-compounds"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "fn": "undefined",
+ "hasEmail": "mailto:metadata@ciesin.columbia.edu"
+ },
+ "description": "The Natural Resource Protection and Child Health Indicators, 2014 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 221 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 188 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2014 release includes a consistent time series of NRPIs and CHIs for 2006 to 2014.",
+ "identifier": "C1000000640-SEDAC",
+ "issued": "2014-12-31",
+ "keyword": [
+ "earth-science",
+ "environmental-impacts",
+ "human-dimensions",
+ "public-health",
+ "sustainability"
+ ],
+ "language": [
+ "en-US"
+ ],
+ "modified": "2025-07-17",
  "programCode": [
- "026:000"
+ "026:001"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 72, \"EastBoundingCoordinate\": -65, \"SouthBoundingCoordinate\": 17}]]",
- "temporal": "2000-01-01/2016-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016)"
- },
- "description": "The Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set contains daily predictions of Nitrogen Dioxide (NO2) concentrations at a high resolution (1-km grid cells) for the years 2000 to 2016. An ensemble modeling framework was used to assess NO2 levels with high accuracy, which combined estimates from three machine learning models (neural network, random forest, and gradient boosting), with a generalized additive model. Predictor variables included NO2 column concentrations from satellites, land-use variables, meteorological variables, predictions from two chemical transport models, GEOS-Chem and the U.S. Environmental Protection Agency (EPA) CommUnity Multiscale Air Quality Modeling System (CMAQ), along with other ancillary variables. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensemble produced a cross-validated R-squared value of 0.79 overall, a spatial R-squared value of 0.84, and a temporal R-squared value of 0.73. In version 1.10, the completeness of daily NO2 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily NO2 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily and annual NO2 predictions allow public health researchers to respectively estimate the short- and long-term effects of NO2 exposures on human health, supporting the U.S. EPA for the revision of the National Ambient Air Quality Standards for daily average and annual average concentrations of NO2. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.",
- "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/48de165b-3b09-4beb-961c-8b49a02a5e20",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/48de165b-3b09-4beb-961c-8b49a02a5e20/raw",
- "has_download": true,
+ "name": "SEDAC"
+ },
+ "references": [
+ "https://doi.org/10.7927/6t8a-es66",
+ "https://doi.org/10.7927/H41Z4299",
+ "https://doi.org/10.7927/H45Q4T1N",
+ "https://doi.org/10.7927/H48913TX",
+ "https://doi.org/10.7927/H49G5JRZ",
+ "https://doi.org/10.7927/H4G73BM2",
+ "https://doi.org/10.7927/H4NZ85MP",
+ "https://doi.org/10.7927/H4SQ8XGT",
+ "https://doi.org/10.7927/r6mv-sv82"
+ ],
+ "spatial": "-180.0 -55.0 180.0 90.0",
+ "temporal": "2006-01-01T00:00:00Z/2013-12-31T00:00:00Z",
+ "theme": [
+ "NRMI",
+ "geospatial"
+ ],
+ "title": "Natural Resource Protection and Child Health Indicators, 2014 Release"
+ },
+ "description": "The Natural Resource Protection and Child Health Indicators, 2014 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 221 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 188 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2014 release includes a consistent time series of NRPIs and CHIs for 2006 to 2014.",
+ "distribution_titles": [],
+ "harvest_record": "https://catalog.data.gov/harvest_record/4369e4be-1f22-4274-bf9c-081384668fa2",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/4369e4be-1f22-4274-bf9c-081384668fa2/raw",
+ "has_download": false,
  "has_spatial": true,
- "identifier": "10.7927/rz28-p167",
- "keyword": [
- "earth-science-atmospheric-chemistry-atmosphere-nitrogen-compounds"
- ],
- "last_harvested_date": "2026-09-23T00:15:14.962898",
+ "identifier": "C1000000640-SEDAC",
+ "keyword": [
+ "earth-science",
+ "environmental-impacts",
+ "human-dimensions",
+ "public-health",
+ "sustainability"
+ ],
+ "last_harvested_date": "2026-09-23T01:15:01.603962",
  "organization": {
  "aliases": [
  ""
@@ -2213,24 +1824,25 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 1,
- "publisher": "ESDIS",
- "slug": "daily-and-annual-no2-concentrations-for-the-contiguous-united-states-1-km-grids--2000-2016-20fd2",
+ "popularity": 4,
+ "publisher": "SEDAC",
+ "slug": "natural-resource-protection-and-child-health-indicators-2014-release",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
- "Earth Science"
- ],
- "title": "Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016)",
+ "NRMI",
+ "geospatial"
+ ],
+ "title": "Natural Resource Protection and Child Health Indicators, 2014 Release",
  "type": "dataset"
  },
  {
- "_score": 63.01371,
+ "_score": 12.255362,
  "_sort": [
- 1790122514241,
- 63.01371,
- 3,
- "22d6e4b6-fb46-4e96-b9f3-63f9a67cb714"
+ 1790125821385,
+ 12.255362,
+ 13,
+ "4509c29b-ca53-4283-9706-89ec0a659b6d"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -2240,70 +1852,103 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Natural Resource Protection and Child Health Indicators, 2012 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 235 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 175 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2012 release includes a consistent time series of NRPIs and CHIs for 2006 to 2012.",
+ "fn": "undefined",
+ "hasEmail": "mailto:metadata@ciesin.columbia.edu"
+ },
+ "description": "The SDG Indicator 9.1.1: The Rural Access Index (RAI), 2023 Release data set, part of the SDGI collection, measures the proportion of the rural population who live within 2 kilometers of an all-season road for a given statistical area. UN SDG 9 is \"build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation\". Addressing inadequate access to roads, especially in rural areas, is critical to achieving SDG 9. According to the UN Sustainable Transport, Sustainable Development 2021 Interagency Report, sustainable transportation helps to eliminate poverty, promote food security, improve access to key health services, increase trade competitiveness, and bolster human rights. As one measure of progress towards SDG 9, the UN has established SDG indicator 9.1.1. The indicator was computed as the proportion of WorldPop gridded population within 2 kilometers to an OpenStreetMap (OSM) all-season road. The SDG indicator 9.1.1 data set provides estimates for the proportion of the rural population with access to all-season roads for 209 countries and 45,073 subnational Units. The data set is available at both national and level 2 subnational resolutions.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3540911987-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3540911987-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://www.earthdata.nasa.gov/about/esdis",
- "format": "BIN",
- "mediaType": "application/octet-stream"
+ "description": "Data Download Page",
+ "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/sdgi-9-1-1-rai-2023/data-download",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Download this dataset"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Data Set Overview Page",
+ "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/sdgi-9-1-1-rai-2023",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "View documentation related to this dataset"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Maps Download Page",
+ "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/sdgi-9-1-1-rai-2023/maps",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Get a related map visualization"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Sample browse graphic of the data set.",
+ "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/maps/sdgi/sdgi-9-1-1-rai-2023/sdgi-9-1-1-rai-2023-national-thumbnail.jpg",
+ "format": "JPEG",
+ "mediaType": "image/jpeg",
+ "title": "Get a related visualization"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "Search results for publications that cite this dataset by its DOI.",
+ "downloadURL": "https://scholar.google.com/scholar?q=10.7927%2Ffcre-m572",
+ "format": "HTML",
+ "mediaType": "text/html",
+ "title": "Google Scholar search results"
  }
  ],
- "identifier": "10.7927/H41Z4299",
- "keyword": [
- "earth-science-environmental-impacts-human-dimensions-conservation",
- "earth-science-public-health-human-dimensions",
- "earth-science-sustainability-human-dimensions-environmental-sustainability"
- ],
- "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "identifier": "C2762297641-SEDAC",
+ "issued": "2023-07-31",
+ "keyword": [
+ "earth-science",
+ "human-dimensions",
+ "infrastructure"
+ ],
+ "language": [
+ "en-US"
+ ],
+ "modified": "2025-07-17",
  "programCode": [
- "026:000"
+ "026:001"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"NorthBoundingCoordinate\": 90, \"WestBoundingCoordinate\": -180, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -55}]]",
- "temporal": "2006-01-01/2011-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Natural Resource Protection and Child Health Indicators, 2012 Release"
- },
- "description": "The Natural Resource Protection and Child Health Indicators, 2012 Release, are produced in support of the U.S. Millennium Challenge Corporation as selection criteria for funding eligibility. These indicators are successors to the Natural Resource Management Index (NRMI), which was produced from 2006 to 2011 and was based on the same underlying data. Like the NRMI, the Natural Resource Protection Indicator (NRPI) and Child Health Indicator (CHI) are based on proximity-to-target scores ranging from 0 to 100 (at target). The NRPI covers 235 countries and is calculated based on the weighted average percentage of biomes under protected status. The CHI is a composite index for 175 countries derived from the average of three proximity-to-target scores for access to improved sanitation, access to improved water, and child mortality. The 2012 release includes a consistent time series of NRPIs and CHIs for 2006 to 2012.",
+ "name": "SEDAC"
+ },
+ "references": [
+ "https://doi.org/10.7927/1a5z-3h71",
+ "https://doi.org/10.7927/eavc-4k45",
+ "https://doi.org/10.7927/gxnr-sx57",
+ "https://doi.org/10.7927/zc4h-hh18"
+ ],
+ "spatial": "-180.0 -90.0 180.0 90.0",
+ "temporal": "2015-01-01T00:00:00Z/2022-12-31T00:00:00Z",
+ "theme": [
+ "SDGI",
+ "geospatial"
+ ],
+ "title": "SDG Indicator 9.1.1: Rural Access Index (RAI), 2023 Release"
+ },
+ "description": "The SDG Indicator 9.1.1: The Rural Access Index (RAI), 2023 Release data set, part of the SDGI collection, measures the proportion of the rural population who live within 2 kilometers of an all-season road for a given statistical area. UN SDG 9 is \"build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation\". Addressing inadequate access to roads, especially in rural areas, is critical to achieving SDG 9. According to the UN Sustainable Transport, Sustainable Development 2021 Interagency Report, sustainable transportation helps to eliminate poverty, promote food security, improve access to key health services, increase trade competitiveness, and bolster human rights. As one measure of progress towards SDG 9, the UN has established SDG indicator 9.1.1. The indicator was computed as the proportion of WorldPop gridded population within 2 kilometers to an OpenStreetMap (OSM) all-season road. The SDG indicator 9.1.1 data set provides estimates for the proportion of the rural population with access to all-season roads for 209 countries and 45,073 subnational Units. The data set is available at both national and level 2 subnational resolutions.",
  "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/7cce0495-4a8e-463e-a810-58515a096c3c",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/7cce0495-4a8e-463e-a810-58515a096c3c/raw",
+ "Download this dataset",
+ "View documentation related to this dataset",
+ "Get a related map visualization",
+ "Get a related visualization",
+ "Google Scholar search results"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/b9c59930-ace8-46a4-8c8f-6a70e81e366a",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/b9c59930-ace8-46a4-8c8f-6a70e81e366a/raw",
  "has_download": true,
  "has_spatial": true,
- "identifier": "10.7927/H41Z4299",
- "keyword": [
- "earth-science-environmental-impacts-human-dimensions-conservation",
- "earth-science-public-health-human-dimensions",
- "earth-science-sustainability-human-dimensions-environmental-sustainability"
- ],
- "last_harvested_date": "2026-09-23T00:15:14.241753",
+ "identifier": "C2762297641-SEDAC",
+ "keyword": [
+ "earth-science",
+ "human-dimensions",
+ "infrastructure"
+ ],
+ "last_harvested_date": "2026-09-23T01:10:21.385445",
  "organization": {
  "aliases": [
  ""
@@ -2318,24 +1963,25 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 3,
- "publisher": "ESDIS",
- "slug": "natural-resource-protection-and-child-health-indicators-2012-release-5911f",
+ "popularity": 13,
+ "publisher": "SEDAC",
+ "slug": "sdg-indicator-9-1-1-rural-access-index-rai-2023-release",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
- "Earth Science"
- ],
- "title": "Natural Resource Protection and Child Health Indicators, 2012 Release",
+ "SDGI",
+ "geospatial"
+ ],
+ "title": "SDG Indicator 9.1.1: Rural Access Index (RAI), 2023 Release",
  "type": "dataset"
  },
  {
- "_score": 10.862338,
+ "_score": 18.849297,
  "_sort": [
- 1790122513504,
- 10.862338,
- 19,
- "4a50bea6-697b-41c7-8a68-a79daedf8ae8"
+ 1790125762638,
+ 18.849297,
+ 11,
+ "244dfb62-35fd-4c25-88ac-fa395122c1ed"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -2345,84 +1991,47 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Global Landslide Hazard Distribution is a 2.5 minute grid of global landslide and snow avalanche hazards based upon work of the Norwegian Geotechnical Institute (NGI). The hazards mapping of NGI incorporates a range of data including slope, soil, soil moisture conditions, precipitation, seismicity, and temperature. Shuttle Radar Topography Mission (SRTM) elevation data at 30 seconds resolution are also incorporated. Hazards values less than or equal to 4 are considered negligible and only values 5 through 9 are utilized in further analyses. To ensure compatibility with other data sets, value 1 is added to each of the values to provide a hazard ranking ranging 6 through 10 in increasing hazard. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Norwegian Geotechnical Institute (NGI), and Columbia University Center for International Earth Science and Information Network (CIESIN).",
- "distribution": [
- {
- "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550189895-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550189895-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/ndh-landslide-hazard-distribution/maps/services",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt",
- "format": "TXT",
- "mediaType": "text/plain"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
- }
- ],
- "identifier": "10.7927/H4P848VZ",
- "keyword": [
- "earth-science-natural-hazards-human-dimensions-landslides",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-snow-ice-terrestrial-hydrosphere-avalanche",
- "earth-science-sustainability-human-dimensions"
- ],
+ "fn": "Thomas Morgan",
+ "hasEmail": "mailto:thomas.h.morgan@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 RW3, RW4, RW5 and RW6) 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 4A. This type of discharging profile is referred to here as random walk (RW) discharging. After every fifty RW cycles a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.",
+ "identifier": "https://data.nasa.gov/api/views/qghr-qkfw",
+ "issued": "2022-10-20",
+ "keyword": [
+ "batteries",
+ "degradation",
+ "phm",
+ "prognostics"
+ ],
+ "landingPage": "https://data.nasa.gov/dataset/randomized-battery-usage-2-room-temperature-random-walk",
  "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "modified": "2025-12-09",
  "programCode": [
- "026:000"
+ "026:021"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 85, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]",
- "temporal": "2000-01-01/2000-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Global Landslide Hazard Distribution"
- },
- "description": "The Global Landslide Hazard Distribution is a 2.5 minute grid of global landslide and snow avalanche hazards based upon work of the Norwegian Geotechnical Institute (NGI). The hazards mapping of NGI incorporates a range of data including slope, soil, soil moisture conditions, precipitation, seismicity, and temperature. Shuttle Radar Topography Mission (SRTM) elevation data at 30 seconds resolution are also incorporated. Hazards values less than or equal to 4 are considered negligible and only values 5 through 9 are utilized in further analyses. To ensure compatibility with other data sets, value 1 is added to each of the values to provide a hazard ranking ranging 6 through 10 in increasing hazard. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), Norwegian Geotechnical Institute (NGI), and Columbia University Center for International Earth Science and Information Network (CIESIN).",
- "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/c37d930e-cdf0-4d65-8c8e-9c98cfc5205c",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/c37d930e-cdf0-4d65-8c8e-9c98cfc5205c/raw",
- "has_download": true,
- "has_spatial": true,
- "identifier": "10.7927/H4P848VZ",
- "keyword": [
- "earth-science-natural-hazards-human-dimensions-landslides",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-snow-ice-terrestrial-hydrosphere-avalanche",
- "earth-science-sustainability-human-dimensions"
- ],
- "last_harvested_date": "2026-09-23T00:15:13.504674",
+ "name": "PCoE"
+ },
+ "theme": [
+ "Raw Data"
+ ],
+ "title": "Randomized Battery Usage 2: Room Temperature 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 RW3, RW4, RW5 and RW6) 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 4A. This type of discharging profile is referred to here as random walk (RW) discharging. After every fifty RW cycles a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.",
+ "distribution_titles": [],
+ "harvest_record": "https://catalog.data.gov/harvest_record/9636e81c-c0cc-4f29-a1db-f3ce1b0975cd",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/9636e81c-c0cc-4f29-a1db-f3ce1b0975cd/raw",
+ "has_download": false,
+ "has_spatial": false,
+ "identifier": "https://data.nasa.gov/api/views/qghr-qkfw",
+ "keyword": [
+ "batteries",
+ "degradation",
+ "phm",
+ "prognostics"
+ ],
+ "last_harvested_date": "2026-09-23T01:09:22.638085",
  "organization": {
  "aliases": [
  ""
@@ -2437,24 +2046,24 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 19,
- "publisher": "ESDIS",
- "slug": "global-landslide-hazard-distribution-8c393",
+ "popularity": 11,
+ "publisher": "PCoE",
+ "slug": "randomized-battery-usage-2-room-temperature-random-walk",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
- "Earth Science"
- ],
- "title": "Global Landslide Hazard Distribution",
+ "Raw Data"
+ ],
+ "title": "Randomized Battery Usage 2: Room Temperature Random Walk",
  "type": "dataset"
  },
  {
- "_score": 10.1571665,
+ "_score": 13.306441,
  "_sort": [
- 1790122512813,
- 10.1571665,
- 1,
- "8c3b815d-f25b-4bc0-9b50-e1659f18c53b"
+ 1790125750761,
+ 13.306441,
+ 3,
+ "f540ded7-c45d-4af2-b606-89069f27ff50"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -2464,86 +2073,54 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Global Landslide Proportional Economic Loss Risk Deciles is a 2.5 minute grid of landslide hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "fn": "Christopher Teubert",
+ "hasEmail": "mailto:Christopher.A.Teubert@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-β1 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-β1 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",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550191624-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550191624-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/ndh-landslide-proportional-economic-loss-risk-deciles/maps/services",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt",
- "format": "TXT",
- "mediaType": "text/plain"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
+ "downloadURL": "https://data.nasa.gov/docs/legacy/ames/2.Battery_Uniform_Distribution_Discharge_Room_Temp_DataSet_2Post.zip",
+ "format": "ZIP",
+ "mediaType": "application/zip",
+ "title": "Battery_Uniform_Distribution_Discharge_Room_Temp_DataSet_2Post.zip"
  }
  ],
- "identifier": "10.7927/H4DR2SDX",
- "keyword": [
- "earth-science-natural-hazards-human-dimensions-landslides",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-snow-ice-terrestrial-hydrosphere-avalanche",
- "earth-science-socioeconomics-human-dimensions",
- "earth-science-sustainability-human-dimensions"
+ "identifier": "10.26030/jq04-0n51",
+ "keyword": [
+ "biological-and-physical-sciences",
+ "genelab",
+ "nasa"
  ],
  "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "modified": "2026-08-10",
  "programCode": [
  "026:000"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 86, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]",
- "temporal": "2000-01-01/2000-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Global Landslide Proportional Economic Loss Risk Deciles"
- },
- "description": "The Global Landslide Proportional Economic Loss Risk Deciles is a 2.5 minute grid of landslide hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This dataset is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "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-β1 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-β1 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": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/cfbc73c2-f781-441d-ba59-8bfa375a8acd",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/cfbc73c2-f781-441d-ba59-8bfa375a8acd/raw",
+ "Battery_Uniform_Distribution_Discharge_Room_Temp_DataSet_2Post.zip"
+ ],
+ "harvest_record": "https://catalog.data.gov/harvest_record/5521e1ee-4a5f-4749-a3cd-587171d85e82",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/5521e1ee-4a5f-4749-a3cd-587171d85e82/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.7927/H4DR2SDX",
- "keyword": [
- "earth-science-natural-hazards-human-dimensions-landslides",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-snow-ice-terrestrial-hydrosphere-avalanche",
- "earth-science-socioeconomics-human-dimensions",
- "earth-science-sustainability-human-dimensions"
- ],
- "last_harvested_date": "2026-09-23T00:15:12.813891",
+ "has_spatial": false,
+ "identifier": "10.26030/jq04-0n51",
+ "keyword": [
+ "biological-and-physical-sciences",
+ "genelab",
+ "nasa"
+ ],
+ "last_harvested_date": "2026-09-23T01:09:10.761540",
  "organization": {
  "aliases": [
  ""
@@ -2558,24 +2135,24 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 1,
- "publisher": "ESDIS",
- "slug": "global-landslide-proportional-economic-loss-risk-deciles-189de",
+ "popularity": 3,
+ "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": [
- "Earth Science"
- ],
- "title": "Global Landslide Proportional Economic Loss Risk Deciles",
+ "Biological and Physical Sciences"
+ ],
+ "title": "Rodent Research-1 (RR1) NASA Validation Flight: Mouse liver transcriptomic, proteomic, epigenomic and histology data",
  "type": "dataset"
  },
  {
- "_score": 10.09636,
+ "_score": 39.467014,
  "_sort": [
- 1790122512112,
- 10.09636,
- 2,
- "233f7456-b0eb-4716-bced-ca8354e2d960"
+ 1790125723585,
+ 39.467014,
+ 1,
+ "0fe9d27b-9b0e-46dd-9499-ae5ea888f961"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -2585,86 +2162,69 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Global Landslide Mortality Risks and Distribution is a 2.5 minute grid of global landslide mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to landslide hazard. Mortality loss estimates per hazard event are caculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of landslide hazard are obtained from the Global Landslide Hazard Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of landslide-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "fn": "Open Science Data Repository Help Desk",
+ "hasEmail": "mailto:arc-dl-osdr-help@mail.nasa.gov"
+ },
+ "description": "Gonadal hormones, such as testosterone and estradiol, modulate muscle size and strength in males and females. However, the influence of sex hormones on muscle strength in micro- and partial-gravity environments (e.g., the Moon or Mars) is not fully understood. The purpose of this study was to determine the influence of gonadectomy (castration/ovariectomy) on progression of muscle atrophy in both micro- and partial-gravity environments in male and female rats. Male and female Fischer rats (n equals 120) underwent castration/ovariectomy (CAST/OVX) or sham surgery (SHAM) at 11 weeks of age. After 2 weeks of recovery, rats were exposed to hindlimb unloading (0g), partial weight bearing at 40% of normal loading (0.4g, Martian gravity), or normal loading (1.0g) for 28 days. In males, CAST did not exacerbate body weight loss or other metrics of musculoskeletal health. In females, OVX animals tended to have greater body weight loss and greater gastrocnemius loss. Within 7 days of exposure to either microgravity or partial gravity, females had detectable changes to estrous cycle, with greater time spent in low-estradiol phases diestrus and metestrus (∼47% in 1g vs. 58% in 0g and 72% in 0.4g animals, P equals 0.005). We conclude that in males testosterone deficiency at the initiation of unloading has little effect on the trajectory of muscle loss. In females, initial low estradiol status may result in greater musculoskeletal losses. This study derives results from Grip (Force Transducer).",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550191066-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550191066-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/ndh-landslide-mortality-risks-distribution/maps/services",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt",
- "format": "TXT",
- "mediaType": "text/plain"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
+ "downloadURL": "http://purl.bioontology.org/ontology/NCBITAXON/10090",
+ "format": "BIN",
+ "mediaType": "application/octet-stream"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "http://www.ebi.ac.uk/arrayexpress/experiments/E-GEOD-68875/",
+ "format": "BIN",
+ "mediaType": "application/octet-stream"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://lsda.jsc.nasa.gov/scripts/experiment/exper.aspx?exp_index=13524",
+ "format": "BIN",
+ "mediaType": "application/octet-stream"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://osdr.nasa.gov/bio/repo/data/studies/OSD-117",
+ "format": "BIN",
+ "mediaType": "application/octet-stream"
  }
  ],
- "identifier": "10.7927/H4JH3J4N",
- "keyword": [
- "earth-science-natural-hazards-human-dimensions-landslides",
- "earth-science-population-human-dimensions-mortality",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-snow-ice-terrestrial-hydrosphere-avalanche",
- "earth-science-sustainability-human-dimensions"
+ "identifier": "10.26030/z92y-7b97",
+ "keyword": [
+ "biological-and-physical-sciences",
+ "genelab",
+ "nasa"
  ],
  "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "modified": "2026-08-10",
  "programCode": [
  "026:000"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 85, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]",
- "temporal": "2000-01-01/2000-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Global Landslide Mortality Risks and Distribution"
- },
- "description": "The Global Landslide Mortality Risks and Distribution is a 2.5 minute grid of global landslide mortality risks. Gridded Population of the World, Version 3 (GPWv3) data provide a baseline estimation of population per grid cell from which to estimate potential mortality risks due to landslide hazard. Mortality loss estimates per hazard event are caculated using regional, hazard-specific mortality records of the Emergency Events Database (EM-DAT) that span the 20 years between 1981 and 2000. Data regarding the frequency and distribution of landslide hazard are obtained from the Global Landslide Hazard Distribution data set. In order to more accurately reflect the confidence associated with the data and procedures, the potential mortality estimate range is classified into deciles, 10 classes of increasing risk with an approximately equal number of grid cells per class, producing a relative estimate of landslide-based mortality risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
- "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/e1c83de1-3793-43d9-9c6c-bf9cadbff18a",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/e1c83de1-3793-43d9-9c6c-bf9cadbff18a/raw",
+ "name": "Open Science Data Repository"
+ },
+ "theme": [
+ "Biological and Physical Sciences"
+ ],
+ "title": "Influence of gonadectomy on muscle health in micro- and partial-gravity environments in rats (Grip; force transducer)"
+ },
+ "description": "Gonadal hormones, such as testosterone and estradiol, modulate muscle size and strength in males and females. However, the influence of sex hormones on muscle strength in micro- and partial-gravity environments (e.g., the Moon or Mars) is not fully understood. The purpose of this study was to determine the influence of gonadectomy (castration/ovariectomy) on progression of muscle atrophy in both micro- and partial-gravity environments in male and female rats. Male and female Fischer rats (n equals 120) underwent castration/ovariectomy (CAST/OVX) or sham surgery (SHAM) at 11 weeks of age. After 2 weeks of recovery, rats were exposed to hindlimb unloading (0g), partial weight bearing at 40% of normal loading (0.4g, Martian gravity), or normal loading (1.0g) for 28 days. In males, CAST did not exacerbate body weight loss or other metrics of musculoskeletal health. In females, OVX animals tended to have greater body weight loss and greater gastrocnemius loss. Within 7 days of exposure to either microgravity or partial gravity, females had detectable changes to estrous cycle, with greater time spent in low-estradiol phases diestrus and metestrus (∼47% in 1g vs. 58% in 0g and 72% in 0.4g animals, P equals 0.005). We conclude that in males testosterone deficiency at the initiation of unloading has little effect on the trajectory of muscle loss. In females, initial low estradiol status may result in greater musculoskeletal losses. This study derives results from Grip (Force Transducer).",
+ "distribution_titles": [],
+ "harvest_record": "https://catalog.data.gov/harvest_record/cf76c72a-72a9-4a0f-94dd-1da32fedd3e7",
+ "harvest_record_raw": "https://catalog.data.gov/harvest_record/cf76c72a-72a9-4a0f-94dd-1da32fedd3e7/raw",
  "has_download": true,
- "has_spatial": true,
- "identifier": "10.7927/H4JH3J4N",
- "keyword": [
- "earth-science-natural-hazards-human-dimensions-landslides",
- "earth-science-population-human-dimensions-mortality",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-snow-ice-terrestrial-hydrosphere-avalanche",
- "earth-science-sustainability-human-dimensions"
- ],
- "last_harvested_date": "2026-09-23T00:15:12.112335",
+ "has_spatial": false,
+ "identifier": "10.26030/z92y-7b97",
+ "keyword": [
+ "biological-and-physical-sciences",
+ "genelab",
+ "nasa"
+ ],
+ "last_harvested_date": "2026-09-23T01:08:43.585256",
  "organization": {
  "aliases": [
  ""
@@ -2679,24 +2239,24 @@
  "slug": "nasa"
  },
  "parent_identifier": null,
- "popularity": 2,
- "publisher": "ESDIS",
- "slug": "global-landslide-mortality-risks-and-distribution-1b7ad",
+ "popularity": 1,
+ "publisher": "Open Science Data Repository",
+ "slug": "influence-of-gonadectomy-on-muscle-health-in-micro-and-partial-gravity-environments-in-rat-73163",
  "spatial_centroid": null,
  "spatial_shape": null,
  "theme": [
- "Earth Science"
- ],
- "title": "Global Landslide Mortality Risks and Distribution",
+ "Biological and Physical Sciences"
+ ],
+ "title": "Influence of gonadectomy on muscle health in micro- and partial-gravity environments in rats (Grip; force transducer)",
  "type": "dataset"
  },
  {
- "_score": 10.204917,
+ "_score": 8.82427,
  "_sort": [
- 1790122510687,
- 10.204917,
- 1,
- "a5d9e289-d681-45a1-9568-a7498194ec4d"
+ 1790125721657,
+ 8.82427,
+ 3,
+ "ac201880-f37d-42c0-a2b8-4fd598ba4410"
  ],
  "dcat": {
  "@type": "dcat:Dataset",
@@ -2706,86 +2266,63 @@
  ],
  "contactPoint": {
  "@type": "vcard:Contact",
- "fn": "Earthdata Forum",
- "hasEmail": "mailto:earthdata-support@nasa.gov"
- },
- "description": "The Global Landslide Total Economic Loss Risk Deciles is a 2.5 minute grid of global landslide total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational Unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by landslide hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
+ "fn": "Open Science Data Repository Help Desk",
+ "hasEmail": "mailto:arc-dl-osdr-help@mail.nasa.gov"
+ },
+ "description": "The health risks associated with spaceflight-induced ocular structural and functional damage has become a recent concern for NASA. The goal of the present study was to characterize the effects of spaceflight and reentry to 1 g on the structure and integrity of the retina and blood-retinal barrier (BRB) in the eye. To investigate possible mechanisms, changes in protein expression profiles were examined in mouse ocular tissue after spaceflight. Ten week old male C57BL/6 mice were launched to the International Space Station (ISS) on Space-X 12 at the Kennedy Space Center (KSC) on August, 2017. After a 35-day mission, mice were returned to Earth alive. Within 38 +/− 4 hours of splashdown, mice were euthanized and ocular tissues were collected for analysis. Ground control (GC) and vivarium control mice were maintained on Earth in flight hardware or normal vivarium cages respectively. Repeated intraocular pressure (IOP) measurements were performed before the flight launch and re-measured before the mice were euthanized after splashdown. IOP was significantly lower in post-flight measurements compared to that of pre-flight (14.4–19.3 mmHg vs 16.3–20.3 mmHg) (p less than 0.05) for the left eye. Flight group had significant apoptosis in the retina and retinal vascular endothelial cells compared to control groups (p less than 0.05). Immunohistochemical analysis of the retina revealed that an increased expression of aquaporin-4 (AQP-4) in the flight mice compared to controls gave strong indication of disturbance of BRB integrity. There were also a significant increase in the expression of platelet endothelial cell adhesion molecule-1 (PECAM-1) and a decrease in the expression of the BRB-related tight junction protein, Zonula occludens-1 (ZO-1). Proteomic analysis showed that many key proteins and pathways responsible for cell death, cell cycle, immune response, mitochondrial function and metabolic stress were significantly altered in the flight mice compared to ground control animals. These data indicate a complex cellular response that may alter retina structure and BRB integrity following long-term spaceflight. This dataset derives results from Molecular Cellular Imaging (Microscopy) assay.",
  "distribution": [
  {
  "@type": "dcat:Distribution",
- "conformsTo": "http://www.isotc211.org/2005/gmi",
- "description": "The metadata's original source.",
- "downloadURL": "https://cmr.earthdata.nasa.gov/search/concepts/C3550189781-ESDIS.iso19115",
- "format": "ISO",
- "mediaType": "text/xml",
- "title": "Original Metadata"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://search.earthdata.nasa.gov/search/granules?p=C3550189781-ESDIS",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/data/set/ndh-landslide-total-economic-loss-risk-deciles/maps/services",
- "format": "BIN",
- "mediaType": "application/octet-stream"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-citation2.txt",
- "format": "TXT",
- "mediaType": "text/plain"
- },
- {
- "@type": "dcat:Distribution",
- "downloadURL": "https://sedac.ciesin.columbia.edu/downloads/docs-repo/ndh-synthesis-report-general-documentation.pdf",
- "format": "PDF",
- "mediaType": "application/pdf"
+ "downloadURL": "http://purl.bioontology.org/ontology/NCBITAXON/10116",
+ "format": "BIN",
+ "mediaType": "application/octet-stream"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://osdr.nasa.gov/bio/repo/data/experiments/OS-891",
+ "format": "BIN",
+ "mediaType": "application/octet-stream"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "downloadURL": "https://osdr.nasa.gov/bio/repo/data/studies/OSD-652",
+ "format": "BIN",
+ "mediaType": "application/octet-stream"
  }
  ],
- "identifier": "10.7927/H49021PW",
- "keyword": [
- "earth-science-natural-hazards-human-dimensions-landslides",
- "earth-science-public-health-human-dimensions-food-security",
- "earth-science-snow-ice-terrestrial-hydrosphere-avalanche",
- "earth-science-socioeconomics-human-dimensions",
- "earth-science-sustainability-human-dimensions"
+ "identifier": "10.26030/d09k-4e68",
+ "keyword": [
+ "biological-and-physical-sciences",
+ "genelab",
+ "nasa"
  ],
  "license": "https://www.usa.gov/government-works",
- "modified": "2026-09-22",
+ "modified": "2026-08-10",
  "programCode": [
  "026:000"
  ],
  "publisher": {
  "@type": "org:Organization",
- "name": "ESDIS"
- },
- "spatial": "[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -180, \"NorthBoundingCoordinate\": 86, \"EastBoundingCoordinate\": 180, \"SouthBoundingCoordinate\": -58}]]",
- "temporal": "2000-01-01/2000-12-31",
- "theme": [
- "Earth Science"
- ],
- "title": "Global Landslide Total Economic Loss Risk Deciles"
- },
- "description": "The Global Landslide Total Economic Loss Risk Deciles is a 2.5 minute grid of global landslide total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational Unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by landslide hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).",
- "distribution_titles": [
- "Original Metadata"
- ],
- "harvest_record": "https://catalog.data.gov/harvest_record/df238da8-8009-4060-b919-9bcdf5acfee0",
- "harvest_record_raw": "https://catalog.data.gov/harvest_record/df238da8-8009-4060-b919-9bcdf5acfee0/raw",
+ "name": "Open Science Data Repository"
+ },
+ "theme": [
+ "Biological and Physical Sciences"
+ ],
+ "title": "Characterization of mouse ocular responses (Microscopy) to a 35-day (RR-9) spaceflight mission: Evidence of blood-retinal barrier disruption and ocular adaptations"
+ },
+ "description": "The health risks associated with spaceflight-induced ocular structural and functional damage has become a recent concern for NASA. The goal of the present study was to characterize the effects of spaceflight and reentry to 1 g on the structure and integrity of the retina and blood-retinal barrier (BRB) in the eye. To investigate possible mechanisms, changes in protein expression profiles were examined in mouse ocular tissue after spaceflight. Ten week old male C57BL/6 mice were launched to the International Space Station (ISS) on Space-X 12 at the Kennedy Space Center (KSC) on August, 2017. After a 35-day mission, mice were returned to Earth alive. Within 38 +/− 4 hours of splashdown, mice were euthanized and ocular tissues were collected for analysis. Ground control (GC) and vivarium control mice were maintained on Earth in flight hardware or normal vivarium cages respectively. Repeated intraocular pressure (IOP) measurements were performed before the flight launch and re-measured before the mice were euthanized after splashdown. IOP was significantly lower in post-flight measurements compared to that of pre-flight (14.4–19.3 mmHg vs 16.3–20.3 mmHg) (p less than 0.05) for the left eye. Flight group had significant apoptosis in the retina and retinal vascular endothelial cells compared to control groups (p less than 0.05). Immunohistochemical analysis of the retina revealed that an increased expression of aquaporin-4 (AQP-4) in the flight mice compared to controls gave strong indication of disturbance of BRB integrity. There were also a significant increase in the expression of platelet endothelial cell adhesion molecule-1 (PECAM-1) and a decrease in the expression of the BRB-related tight junction protein, Zonula occl