Timeline / Data.gov — Health Datasets
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A new raw object was archived. Both versions are preserved. 2420 line(s) added, 2666 line(s) removed.
Evidence
| Source | Data.gov — Health Datasets |
|---|---|
| Agency | Data.gov |
| URL | https://api.gsa.gov/technology/datagov/v4/search?q=health&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY} |
| Observed by | Civic Memory, directly, on 2026-09-09T06:19:54+00:00 |
| Content type | application/json |
| Current object |
540c2dd41348bbc7000689df5f14f684504035d6235531688ea8af2c3ef4a9b6
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| Previous object |
4b16fb82a8656b0e1e63b8c753fc99fbc7caec0f2660d3acd11aff93fd8da7f4
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What changed derived
This diff is not evidence. It was produced by
civic-memory.diff_engine 1.1.0 at
2026-09-09T06:19:54+00:00 by normalizing the two archived objects above. The
objects are authoritative; this reading of them can be regenerated or deleted
without loss. 2420 line(s) added, 2666 line(s) removed.
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This data set supersedes all previous COSIMA datasets, like RO-C-COSIMA-3-V1.0, RO-C-COSIMA-3-V2.0, RO-C-COSIMA-3-V3.0, RO-CAL-COSIMA-2-V1.0 and RO-CAL-COSIMA-3-V3.0 Also, in the above previous versions, the values of the temperature in the HK data where given in Celsius although Kelvin was written in the description of the table in the FMT file", - "identifier": "urn:nasa:pds:context_pds3:data_set:data_set.ro-c-cosima-3-v4.0;urn:nasa:pds:context_pds3:data_set:data_set.ro-c-cosima-3-v4.0::1.0", - "keyword": [ - "__" - ], - "license": "https://www.usa.gov/government-works", - "modified": "2026-09-08", + "fn": "Miryam Strautkalns", + "hasEmail": "mailto:miryam.strautkalns@nasa.gov" + }, + "description": "This chapter discussed some of the algorithmic choices one encounters when designing an IVHM system. While it would be generally desirable to be able to pick a particular set of algorithms for a particular problem, the reality is a bit more complex. Depending on the budget, the performance requirements, the computational constraints, sensor availability, access to historical data, operational and environmental conditions, robustness to changing system configurations, algorithm maintenance needs, etc., no one algorithm will perform best in all situations. Indeed, it is necessary to evaluate these constraints during the algorithm design process and determine the best choice on a case-by-case analysis. The trade-offs between different choices are very real, and sometimes no solution can be found, which means that some of the constraints have to be relaxed. The simplest solution is generally preferred over a more complex one, but it is also important to consider that there is no free lunch. Finally, any health management solution also has to undergo verification and validation (V&V) and, in some cases, certification. Some of these issues are topics of other chapters in this book.", + "distribution": [ + { + "@type": "dcat:Distribution", + "description": "Pre_R-405_Chapter_7.pdf", + "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/Pre_R-405_Chapter_7.pdf", + "format": "PDF", + "mediaType": "application/pdf", + "title": "Pre_R-405_Chapter_7.pdf" + } + ], + "identifier": "DASHLINK_673", + "issued": "2013-03-20", + "keyword": [ + "ames", + "dashlink", + "nasa" + ], + "landingPage": "https://c3.nasa.gov/dashlink/resources/673/", + "modified": "2025-03-31", "programCode": [ - "026:000" + "026:029" ], "publisher": { "@type": "org:Organization", - "name": "Small Bodies" - }, - "theme": [ - "Planetary Science" - ], - "title": "ROSETTA-ORBITER 67P COSIMA 3 V4.0" - }, - "description": "The Rosetta COSIMA data contains the operational history of the 72 dust collecting substrates from the installation inside the instrument. This dataset contains data from 2002-05-29 up to 2015-10-20. 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This data set supersedes all previous COSIMA datasets, like RO-C-COSIMA-3-V1.0, RO-C-COSIMA-3-V2.0, RO-C-COSIMA-3-V3.0, RO-CAL-COSIMA-2-V1.0 and RO-CAL-COSIMA-3-V3.0 Also, in the above previous versions, the values of the temperature in the HK data where given in Celsius although Kelvin was written in the description of the table in the FMT file", - "distribution_titles": [], - "harvest_record": "https://catalog.data.gov/harvest_record/dd36c748-99da-40f3-9643-8142cfb9e66e", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/dd36c748-99da-40f3-9643-8142cfb9e66e/raw", - "has_download": false, + "name": "Dashlink" + }, + "title": "Algorithms and their Impact on Integrated Vehicle Health Management - Chapter 7" + }, + "description": "This chapter discussed some of the algorithmic choices one encounters when designing an IVHM system. While it would be generally desirable to be able to pick a particular set of algorithms for a particular problem, the reality is a bit more complex. Depending on the budget, the performance requirements, the computational constraints, sensor availability, access to historical data, operational and environmental conditions, robustness to changing system configurations, algorithm maintenance needs, etc., no one algorithm will perform best in all situations. Indeed, it is necessary to evaluate these constraints during the algorithm design process and determine the best choice on a case-by-case analysis. The trade-offs between different choices are very real, and sometimes no solution can be found, which means that some of the constraints have to be relaxed. The simplest solution is generally preferred over a more complex one, but it is also important to consider that there is no free lunch. Finally, any health management solution also has to undergo verification and validation (V&V) and, in some cases, certification. Some of these issues are topics of other chapters in this book.", + "distribution_titles": [ + "Pre_R-405_Chapter_7.pdf" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/36940522-844d-4a8b-9a05-0abbb8fef30c", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/36940522-844d-4a8b-9a05-0abbb8fef30c/raw", + "has_download": true, "has_spatial": false, - "identifier": "urn:nasa:pds:context_pds3:data_set:data_set.ro-c-cosima-3-v4.0;urn:nasa:pds:context_pds3:data_set:data_set.ro-c-cosima-3-v4.0::1.0", - "keyword": [ - "__" - ], - "last_harvested_date": "2026-09-09T00:18:35.812525", + "identifier": "DASHLINK_673", + "keyword": [ + "ames", + "dashlink", + "nasa" + ], + "last_harvested_date": "2026-09-09T01:16:00.503110", "organization": { "aliases": [ "" @@ -64,66 +79,133 @@ "slug": "nasa" }, "parent_identifier": null, - "popularity": 1, - "publisher": "Small Bodies", - "slug": "rosetta-orbiter-67p-cosima-3-v4-0-6781f", + "popularity": 3, + "publisher": "Dashlink", + "slug": "algorithms-and-their-impact-on-integrated-vehicle-health-management-chapter-7", "spatial_centroid": null, "spatial_shape": null, - "theme": [ - "Planetary Science" - ], - "title": "ROSETTA-ORBITER 67P COSIMA 3 V4.0", + "theme": [], + "title": "Algorithms and their Impact on Integrated Vehicle Health Management - Chapter 7", "type": "dataset" }, { - "_score": 9.601276, + "_score": 14.195237, "_sort": [ - 1788913041555, - 9.601276, - 1, - "a591c5a5-582d-4c19-aded-f6c40a573628" + 1788916548189, + 14.195237, + 2, + "1599dd9f-ee9d-47e7-b602-644d9a531cee" ], "dcat": { "@type": "dcat:Dataset", "accessLevel": "public", + "accrualPeriodicity": "irregular", "bureauCode": [ "026:00" ], "contactPoint": { "@type": "vcard:Contact", - "fn": "Planetary Data System", - "hasEmail": "mailto:pds-operator@jpl.nasa.gov" - }, - "description": "The Rosetta COSIMA data contains the operational history of the 72 dust collecting substrates from the installation inside the instrument. 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This was fixed in the version RO-C-COSIMA-3-V3.0.", - "identifier": "urn:nasa:pds:context_pds3:data_set:data_set.ro-c-cosima-3-v5.0;urn:nasa:pds:context_pds3:data_set:data_set.ro-c-cosima-3-v5.0::1.0", - "keyword": [ - "__" - ], - "license": "https://www.usa.gov/government-works", - "modified": "2026-09-08", + "fn": "Rajiv Shenoy", + "hasEmail": "mailto:rajiv.shenoy@gatech.edu" + }, + "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", + "description": "Coarse MixedElem Grid", + "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/coarse_mixed_nc.tar_2.gz", + "format": "GZ", + "mediaType": "application/x-gzip", + "title": "coarse_mixed_nc.tar.gz" + }, + { + "@type": "dcat:Distribution", + "description": "Coarse Tet Grid", + "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/coarse_tet_nc.tar.gz", + "format": "GZ", + "mediaType": "application/x-gzip", + "title": "coarse_tet_nc.tar.gz" + }, + { + "@type": "dcat:Distribution", + "description": "Fine MixedElem Grid", + "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/fine_mixed_nc.tar.gz", + "format": "GZ", + "mediaType": "application/x-gzip", + "title": "fine_mixed_nc.tar.gz" + }, + { + "@type": "dcat:Distribution", + "description": "Fine Tet Grid Part 1", + "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/fine_tet_nc.tar.gz_part1", + "format": "BIN", + "mediaType": "application/octet-stream", + "title": "fine_tet_nc.tar.gz_part1" + }, + { + "@type": "dcat:Distribution", + "description": "Fine Tet Grid Part 2", + "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/fine_tet_nc.tar.gz_part2", + "format": "BIN", + "mediaType": "application/octet-stream", + "title": "fine_tet_nc.tar.gz_part2" + }, + { + "@type": "dcat:Distribution", + "description": "Medium MixedElem Grid", + "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/medium_mixed_nc.tar.gz", + "format": "GZ", + "mediaType": "application/x-gzip", + "title": "medium_mixed_nc.tar.gz" + }, + { + "@type": "dcat:Distribution", + "description": "Medium Tet Grid", + "downloadURL": "https://c3.nasa.gov/dashlink/static/media/other/medium_tet_nc.tar.gz", + "format": "GZ", + "mediaType": "application/x-gzip", + "title": "medium_tet_nc.tar.gz" + } + ], + "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": "Small Bodies" - }, - "theme": [ - "Planetary Science" - ], - "title": "ROSETTA-ORBITER 67P COSIMA 3 V5.0" - }, - "description": "The Rosetta COSIMA data contains the operational history of the 72 dust collecting substrates from the installation inside the instrument. This dataset contains calibrated housekeeping and raw spectra with COSIMA onboard automatic mass calibration for the period from 2002-05-29 up to 2016-01-12. The operations are either expose, storage, spectra, peaks, scans, heating, imaging or grain lists. The data is grouped by the substrate and time. Up to the 2014-08 the aim of the data has been the instrument health and operational functionality, not statistically significant substrate background spectra. From 2014-08 onward the D0 substrate set was used to collect dust collected in the vicinity of 67P/CHURYUMOV GERASIMENKO 1 (1969 R1) and dust analysis with TOF-SIMS. From 2014-10-23 onward due to an instrument failure, the SIMS data became scientifically unusable. While other SIMS parameter sets were tested, substrates CF were exposed from mid 2015-12 and C7 from mid 2015-02. End of March 2015 SIMS became operational again. D1 and CD were exposed and SIMS was done with CD, CF and D1. In June TOF-SIMS was done with CF and D1. In July C7 was measured again, while CD was used for expose and TOF-SIMS up to October. D2 was exposed in September. This data set supersedes all previous COSIMA datasets, like RO-C-COSIMA-3-V1.0, RO-C-COSIMA-3-V2.0, RO-C-COSIMA-3-V3.0, RO-C-COSIMA-3-V4.0, RO-CAL-COSIMA-2-V1.0 and RO-CAL-COSIMA-3-V3.0 Also, in the above previous versions, the values of the temperature in the HK data where given in Celsius although Kelvin was written in the description of the table in the FMT file. 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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": [ + "coarse_mixed_nc.tar.gz", + "coarse_tet_nc.tar.gz", + "fine_mixed_nc.tar.gz", + "fine_tet_nc.tar.gz_part1", + "fine_tet_nc.tar.gz_part2", + "medium_mixed_nc.tar.gz", + "medium_tet_nc.tar.gz" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/5accc3d7-6552-43f7-b65b-fdfe1600d017", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/5accc3d7-6552-43f7-b65b-fdfe1600d017/raw", + "has_download": true, "has_spatial": false, - "identifier": "urn:nasa:pds:context_pds3:data_set:data_set.ro-c-cosima-3-v5.0;urn:nasa:pds:context_pds3:data_set:data_set.ro-c-cosima-3-v5.0::1.0", - "keyword": [ - "__" - ], - "last_harvested_date": "2026-09-09T00:17:21.555414", + "identifier": "DASHLINK_763", + "keyword": [ + "ames", + "dashlink", + "nasa" + ], + "last_harvested_date": "2026-09-09T01:15:48.189310", "organization": { "aliases": [ "" @@ -138,125 +220,79 @@ "slug": "nasa" }, "parent_identifier": null, - "popularity": 1, - "publisher": "Small Bodies", - "slug": "rosetta-orbiter-67p-cosima-3-v5-0-f3b88", + "popularity": 2, + "publisher": "Dashlink", + "slug": "towards-prognostics-for-electronics-components", "spatial_centroid": null, "spatial_shape": null, - "theme": [ - "Planetary Science" - ], - "title": "ROSETTA-ORBITER 67P COSIMA 3 V5.0", + "theme": [], + "title": "Towards Prognostics for Electronics Components", "type": "dataset" }, { - "_score": 3.4487114, + "_score": 11.032864, "_sort": [ - 1788912889443, - 3.4487114, + 1788916498429, + 11.032864, 2, - "f0a9e31a-f5fb-44c8-8a17-22a5228c3246" + "bab33ec7-dad1-40fa-9270-949a29c72e4d" ], "dcat": { "@type": "dcat:Dataset", "accessLevel": "public", + "accrualPeriodicity": "irregular", "bureauCode": [ "026:00" ], "contactPoint": { "@type": "vcard:Contact", - "fn": "NASA Space Physics Data Facility", - "hasEmail": "mailto:NASA-SPDF-Support@nasa.onmicrosoft.com" - }, - "description": "SPAN-E Level 2 Electron Full 3D Spectra Data\n--------------------------------------------\n\nFile Naming Format: psp_swp_spa_sf0_L2_16Ax8Dx32E_YYYYMMDD_v01.cdf\n\nThe SF0 products are the Full 3D Electron spectra from each individual SPAN-E instrument, SPAN-Ae and SPAN-B. Units are in differential energy flux, degrees, and eV. One spectrum comprises decreasing steps in Energy specified by the number in the filename, alternating sweeps in Theta/Deflection, also specified by the number in the filename, and a number of Phi/Anode directions, also specified by the number in the filename. The sample filename above includes 16 Anodes, 8 Deflections, and 32 Energies.\n\nThis data set covers all periods for which the instrument was turned on and taking data in the solar wind in \"Full Sweep\", normal cadence survey mode. This includes maneuvers affecting the spacecraft attitude and orientation. Measurements taken by SPAN-B during cruise phase periods when the spacecraft is pointed away from the sun are taken in sunlight.\n\nParker Solar Probe SWEAP Solar Probe Analyzer, SPAN, Electron Data Release Notes\n--------------------------------------------------------------------------------\n\nNovember 19, 2019 Initial Data Release\n--------------------------------------\n\nOverview of Measurements\n------------------------\n\nThe SWEAP team is pleased to release the data from Encounter 1 and Encounter 2. The files contain data from the time range October 31, 2018 - June 18, 2019.\n\nThe prime mission of Parker Solar Probe is to take data when within 0.25 AU of the Sun during its orbit. However, there has been some extended campaign measurements outside of this distance. The data are available for those days that are within 0.25 AU as well as those days when the instruments were operational outside of 0.25 AU.\n\nEach SWEAP data file includes a set of a particular type of measurements over a single observing day. Measurements are provided in Common Data Format (CDF), a self-documenting data framework for which convenient open source tools exist across most scientific computing platforms. Users are strongly encouraged to consult the global metadata in each file, and the metadata that are linked to each variable. The metadata includes comprehensive listings of relevant information, including units, coordinate systems, qualitative descriptions, measurement uncertainties, methodologies, links to further documentation, and so forth.\n\nSPAN-E Level 2 Version 01 Release Notes\n---------------------------------------\n\nThe SPAN-Ae and SPAN-B instruments together have fields of view covering >90% of the sky; major obstructions to the FOV include the spacecraft heat shield and other intrusions by spacecraft components. Each individual SPAN-E has FOV of ±60° in Theta and 240° in Phi. The rotation matrices to convert into the spacecraft frame can be found in the individual CDF files, or in the instrument paper.\n\nThis data set covers all periods for which the instrument was turned on and taking data in the solar wind in ion mode. This includes maneuvers affecting the spacecraft attitude and orientation. Measurements taken by SPAN-B when the spacecraft is pointed away from the sun are taken in sunlight.\n\nThe data quality flags for the SPAN data can be found in the CDF files as: QUALITY_FLAG (0=good, 1=bad)\n\nGeneral Remarks for Version 01 Data\n-----------------------------------\n\nUsers interested in field-aligned electrons should take care regarding potential blockages from the heat shield when B is near radial, especially in SPAN-Ae. Artificial reductions in strahl width can result.\n\nDue to the relatively high electron temperature in the inner heliosphere, many secondary electrons are generated from spacecraft and instrument surfaces. As a result, electron measurements in this release below 30 eV are not advised for scientific analysis.\n\nThe fields of view in SPAN-Ae and SPAN-B have many intrusions by the spacecraft, and erroneous pixels discovered in analysis, in particular near the edges of the FOV, should be viewed with skepticism. Details on FOV intrusion are found in the instrument paper, forthcoming, or by contacting the SPAN-E instrument scientist.\n\nThe instrument mechanical attentuators are engaged during the eight days around perihelia 1 and perihelia 2, which results in a factor of about 10 reduction of the total electron flux into the instrument. During these eight days, halo electron measurements are artificially enhanced in the L2 products as a result of the reduced instrument geometric factor and subsequent ground corrections.\n\nA general note for Encounter 1 and Encounter 2 data: a miscalculation in the deflection tables loaded to both SPAN-Ae and SPAN-B resulted in over-deflection of the outermost Theta angles during these encounters. As such, pixels at large Thetas should be ignored. This error was corrected by a table upload prior to Encounter 3.\n\nLastly, when viewing time gaps in the SPAN-E measurements, be advised that the first data point produced by the instrument after a power-on is the maximum value permitted by internal instrument counters. Therefore, the first data point after powerup is erroneous and should be discarded, as indicated by quality flags.\n\nSPAN-E Encounter 1 Remarks\n--------------------------\n\nSPAN-E operated nominally for the majority of the first encounter. Exceptions to this include: a few instances of corrupted, higher-energy sweep tables, and an instrument commanding error for the two hours surrounding perihelion 1. These and other instrument diagnostic tests are indicated with the QUALITY_FLAG variable in the CDFs.\n\nThe mechanical attentuator was engaged for the 8 days around perihelion 1: as a result the microchannel plate, MCP, noise due to thermal effects and cosmic rays are artificially enhanced and are particularly obvious at higher energies. Exercise caution with this data release if looking for halo electrons when the mechanical attenuator is engaged.\n\nSPAN-E Cruise Phase Remarks\n---------------------------\n\nThe cruise mode rates of SPAN-E are greatly reduced compared to the encounter mode rates. When the PSP spacecraft is in a communications slew, the SPAN-B instrument occasionally reaches its maximum allowable operating temperature and is powered off by SWEM.\n\nTiming for the SF1 products in cruise phase is not corrected in v01, and thus it is not advised to use the data at this time for scientific analysis. The typical return of SF0 products is one spectrum out of every 32 survey spectra is returned every 15 minutes or so. One out of every four 27.75 s SF1 spectra is produced every 111 s.\n\nSPAN-E Encounter 2 Remarks\n--------------------------\n\nSPAN-E operated nominally for the majority of the second encounter. Exceptions include instrument diagnostic and health checks and a few instances of corrupted high-energy sweep tables. These tests and corrupted table loads are indicated with the QUALITY_FLAG parameter.\n\nThe mechanical attentuator was engaged for the 8 days around perihelion 2: as a result the MCP noise due to thermal effects and cosmic rays are artificially enhanced and are particularly obvious at higher energies. Exercise caution in this data release if looking for halo electrons when the mechanical attenuator is engaged.\n\nParker Solar Probe SWEAP Rules of the Road\n------------------------------------------\n\nAs part of the development of collaboration with the broader Heliophysics community, the mission has drafted a \"Rules of the Road\" to govern how PSP instrument data are to be used.\n\n* 1) Users should consult with the PI to discuss the appropriate use of instrument data or model results and to ensure that the users are accessing the most recently available versions of the data and of the analysis routines. Instrument team Science Operations Centers, SOCs, and/or Virtual Observatories, VOs, should facilitate this process serving as the contact point between PI and users in most cases.\n\n* 2) Users should heed the caveats of investigators to the interpretations and limitations of data or model results. Investigators supplying data or models may insist that such caveats be published. Data and model version numbers should also be specified.\n\n* 3) Browse products, Quicklook, and Planning data are not intended for science analysis or publication and should not be used for those purposes without consent of the PI.\n\n* 4) Users should acknowledge the sources of data used in all publications, presentations, and reports: \"We acknowledge the NASA Parker Solar Probe Mission and the SWEAP team led by J. Kasper for use of data.\".\n\n* 5) Users are encouraged to provide the PI a copy of each manuscript that uses the PI data prior to submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI and any other providers of data.", + "fn": "Miryam Strautkalns", + "hasEmail": "mailto:miryam.strautkalns@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", - "downloadURL": "ftps://spdf.gsfc.nasa.gov/pub/data/psp/sweap/spe/l2/spa_sf0_16ax8dx32e/", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://cdaweb.gsfc.nasa.gov/cgi-bin/eval2.cgi?dataset=PSP_SWP_SPA_SF0_L2_16AX8DX32E&index=sp_phys", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://cdaweb.gsfc.nasa.gov/hapi", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://doi.org/10.1007/s11214-015-0206-3", - "format": "HTML", - "mediaType": "text/html" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://helio.data.nasa.gov/dataset/ParkerSolarProbe_SWEAP_SPAN-A_Level2_ElectronsFull3D_PT14S", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://hpde.io/NASA/NumericalData/ParkerSolarProbe/SWEAP/SPAN-A/Level2/ElectronsFull3D/PT14S", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://spdf.gsfc.nasa.gov/pub/data/psp/sweap/spe/l2/spa_sf0_16ax8dx32e/", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://sweap.cfa.harvard.edu", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://sweap.cfa.harvard.edu/Data.html", - "format": "HTML", - "mediaType": "text/html" + "description": "Chapter_OVG V5.1.pdf", + "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/Chapter_OVG_V5.1.pdf", + "format": "PDF", + "mediaType": "application/pdf", + "title": "Chapter_OVG V5.1.pdf" } ], - "identifier": "https://doi.org/10.48322/7j5c-zg65", - "keyword": [ - "instrumentstatus", - "thermalplasma" - ], - "landingPage": "https://doi.org/10.48322/7j5c-zg65", - "license": "https://www.usa.gov/government-works", - "modified": "2026-09-08", + "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": "SPDF" - }, - "theme": [ - "Heliophysics" - ], - "title": "PSP Solar Wind Electrons Alphas and Protons (SWEAP) SPAN-A Full 3D Electron Spectra, Level 2 (L2), 14 s Data" - }, - "description": "SPAN-E Level 2 Electron Full 3D Spectra Data\n--------------------------------------------\n\nFile Naming Format: psp_swp_spa_sf0_L2_16Ax8Dx32E_YYYYMMDD_v01.cdf\n\nThe SF0 products are the Full 3D Electron spectra from each individual SPAN-E instrument, SPAN-Ae and SPAN-B. Units are in differential energy flux, degrees, and eV. One spectrum comprises decreasing steps in Energy specified by the number in the filename, alternating sweeps in Theta/Deflection, also specified by the number in the filename, and a number of Phi/Anode directions, also specified by the number in the filename. The sample filename above includes 16 Anodes, 8 Deflections, and 32 Energies.\n\nThis data set covers all periods for which the instrument was turned on and taking data in the solar wind in \"Full Sweep\", normal cadence survey mode. This includes maneuvers affecting the spacecraft attitude and orientation. Measurements taken by SPAN-B during cruise phase periods when the spacecraft is pointed away from the sun are taken in sunlight.\n\nParker Solar Probe SWEAP Solar Probe Analyzer, SPAN, Electron Data Release Notes\n--------------------------------------------------------------------------------\n\nNovember 19, 2019 Initial Data Release\n--------------------------------------\n\nOverview of Measurements\n------------------------\n\nThe SWEAP team is pleased to release the data from Encounter 1 and Encounter 2. The files contain data from the time range October 31, 2018 - June 18, 2019.\n\nThe prime mission of Parker Solar Probe is to take data when within 0.25 AU of the Sun during its orbit. However, there has been some extended campaign measurements outside of this distance. The data are available for those days that are within 0.25 AU as well as those days when the instruments were operational outside of 0.25 AU.\n\nEach SWEAP data file includes a set of a particular type of measurements over a single observing day. Measurements are provided in Common Data Format (CDF), a self-documenting data framework for which convenient open source tools exist across most scientific computing platforms. Users are strongly encouraged to consult the global metadata in each file, and the metadata that are linked to each variable. The metadata includes comprehensive listings of relevant information, including units, coordinate systems, qualitative descriptions, measurement uncertainties, methodologies, links to further documentation, and so forth.\n\nSPAN-E Level 2 Version 01 Release Notes\n---------------------------------------\n\nThe SPAN-Ae and SPAN-B instruments together have fields of view covering >90% of the sky; major obstructions to the FOV include the spacecraft heat shield and other intrusions by spacecraft components. Each individual SPAN-E has FOV of ±60° in Theta and 240° in Phi. The rotation matrices to convert into the spacecraft frame can be found in the individual CDF files, or in the instrument paper.\n\nThis data set covers all periods for which the instrument was turned on and taking data in the solar wind in ion mode. This includes maneuvers affecting the spacecraft attitude and orientation. Measurements taken by SPAN-B when the spacecraft is pointed away from the sun are taken in sunlight.\n\nThe data quality flags for the SPAN data can be found in the CDF files as: QUALITY_FLAG (0=good, 1=bad)\n\nGeneral Remarks for Version 01 Data\n-----------------------------------\n\nUsers interested in field-aligned electrons should take care regarding potential blockages from the heat shield when B is near radial, especially in SPAN-Ae. Artificial reductions in strahl width can result.\n\nDue to the relatively high electron temperature in the inner heliosphere, many secondary electrons are generated from spacecraft and instrument surfaces. As a result, electron measurements in this release below 30 eV are not advised for scientific analysis.\n\nThe fields of view in SPAN-Ae and SPAN-B have many intrusions by the spacecraft, and erroneous pixels discovered in analysis, in particular near the edges of the FOV, should be viewed with skepticism. Details on FOV intrusion are found in the instrument paper, forthcoming, or by contacting the SPAN-E instrument scientist.\n\nThe instrument mechanical attentuators are engaged during the eight days around perihelia 1 and perihelia 2, which results in a factor of about 10 reduction of the total electron flux into the instrument. During these eight days, halo electron measurements are artificially enhanced in the L2 products as a result of the reduced instrument geometric factor and subsequent ground corrections.\n\nA general note for Encounter 1 and Encounter 2 data: a miscalculation in the deflection tables loaded to both SPAN-Ae and SPAN-B resulted in over-deflection of the outermost Theta angles during these encounters. As such, pixels at large Thetas should be ignored. This error was corrected by a table upload prior to Encounter 3.\n\nLastly, when viewing time gaps in the SPAN-E measurements, be advised that the first data point produced by the instrument after a power-on is the maximum value permitted by internal instrument counters. Therefore, the first data point after powerup is erroneous and should be discarded, as indicated by quality flags.\n\nSPAN-E Encounter 1 Remarks\n--------------------------\n\nSPAN-E operated nominally for the majority of the first encounter. Exceptions to this include: a few instances of corrupted, higher-energy sweep tables, and an instrument commanding error for the two hours surrounding perihelion 1. These and other instrument diagnostic tests are indicated with the QUALITY_FLAG variable in the CDFs.\n\nThe mechanical attentuator was engaged for the 8 days around perihelion 1: as a result the microchannel plate, MCP, noise due to thermal effects and cosmic rays are artificially enhanced and are particularly obvious at higher energies. Exercise caution with this data release if looking for halo electrons when the mechanical attenuator is engaged.\n\nSPAN-E Cruise Phase Remarks\n---------------------------\n\nThe cruise mode rates of SPAN-E are greatly reduced compared to the encounter mode rates. When the PSP spacecraft is in a communications slew, the SPAN-B instrument occasionally reaches its maximum allowable operating temperature and is powered off by SWEM.\n\nTiming for the SF1 products in cruise phase is not corrected in v01, and thus it is not advised to use the data at this time for scientific analysis. The typical return of SF0 products is one spectrum out of every 32 survey spectra is returned every 15 minutes or so. One out of every four 27.75 s SF1 spectra is produced every 111 s.\n\nSPAN-E Encounter 2 Remarks\n--------------------------\n\nSPAN-E operated nominally for the majority of the second encounter. Exceptions include instrument diagnostic and health checks and a few instances of corrupted high-energy sweep tables. These tests and corrupted table loads are indicated with the QUALITY_FLAG parameter.\n\nThe mechanical attentuator was engaged for the 8 days around perihelion 2: as a result the MCP noise due to thermal effects and cosmic rays are artificially enhanced and are particularly obvious at higher energies. Exercise caution in this data release if looking for halo electrons when the mechanical attenuator is engaged.\n\nParker Solar Probe SWEAP Rules of the Road\n------------------------------------------\n\nAs part of the development of collaboration with the broader Heliophysics community, the mission has drafted a \"Rules of the Road\" to govern how PSP instrument data are to be used.\n\n* 1) Users should consult with the PI to discuss the appropriate use of instrument data or model results and to ensure that the users are accessing the most recently available versions of the data and of the analysis routines. Instrument team Science Operations Centers, SOCs, and/or Virtual Observatories, VOs, should facilitate this process serving as the contact point between PI and users in most cases.\n\n* 2) Users should heed the caveats of investigators to the interpretations and limitations of data or model results. Investigators supplying data or models may insist that such caveats be published. Data and model version numbers should also be specified.\n\n* 3) Browse products, Quicklook, and Planning data are not intended for science analysis or publication and should not be used for those purposes without consent of the PI.\n\n* 4) Users should acknowledge the sources of data used in all publications, presentations, and reports: \"We acknowledge the NASA Parker Solar Probe Mission and the SWEAP team led by J. Kasper for use of data.\".\n\n* 5) Users are encouraged to provide the PI a copy of each manuscript that uses the PI data prior to submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI and any other providers of data.", - "distribution_titles": [], - "harvest_record": "https://catalog.data.gov/harvest_record/f432746a-bb1d-46d2-913b-59b1e332e25f", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/f432746a-bb1d-46d2-913b-59b1e332e25f/raw", + "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": [ + "Chapter_OVG V5.1.pdf" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/1d065650-6878-4bec-8d5b-b9de3bbb331e", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/1d065650-6878-4bec-8d5b-b9de3bbb331e/raw", "has_download": true, "has_spatial": false, - "identifier": "https://doi.org/10.48322/7j5c-zg65", - "keyword": [ - "instrumentstatus", - "thermalplasma" - ], - "last_harvested_date": "2026-09-09T00:14:49.443815", + "identifier": "DASHLINK_393", + "keyword": [ + "ames", + "dashlink", + "nasa" + ], + "last_harvested_date": "2026-09-09T01:14:58.429981", "organization": { "aliases": [ "" @@ -272,124 +308,78 @@ }, "parent_identifier": null, "popularity": 2, - "publisher": "SPDF", - "slug": "psp-solar-wind-electrons-alphas-and-protons-sweap-span-a-full-3d-electron-spectra-level-2-", + "publisher": "Dashlink", + "slug": "metrics-for-evaluating-performance-of-prognostics-techniques", "spatial_centroid": null, "spatial_shape": null, - "theme": [ - "Heliophysics" - ], - "title": "PSP Solar Wind Electrons Alphas and Protons (SWEAP) SPAN-A Full 3D Electron Spectra, Level 2 (L2), 14 s Data", + "theme": [], + "title": "Metrics for Evaluating Performance of Prognostics Techniques", "type": "dataset" }, { - "_score": 3.6842098, + "_score": 21.022121, "_sort": [ - 1788912887601, - 3.6842098, - 1, - "e07f598f-c4f8-42f7-9298-2a61ac608591" + 1788916489892, + 21.022121, + 0, + "d95acbf8-9537-4b51-a9e0-ae0c575adc99" ], "dcat": { "@type": "dcat:Dataset", "accessLevel": "public", + "accrualPeriodicity": "irregular", "bureauCode": [ "026:00" ], "contactPoint": { "@type": "vcard:Contact", - "fn": "NASA Space Physics Data Facility", - "hasEmail": "mailto:NASA-SPDF-Support@nasa.onmicrosoft.com" - }, - "description": "SPAN-E Level 2 ELectron Energy Spectra Data\n-------------------------------------------\n\nFile Naming Format: psp_swp_spa_sf1_L2_32E_YYYYMMDD_v01.cdf\n\nThe SF1 product is an energy spectrum produced on the spacecraft by summing over the Theta and Phi directions. The units are differential energy flux and eV. The sample filename above includes 32 Energies.\n\nThe larger Theta angles (deflection angles) are artificially enhanced in the \"sf1\" energy spectra data products due to the method of spectra production on the SPAN-E instrument (straight summing). Thus, SF1 energy spectra are not recommended for rigid statistical analysis.\n\nParker Solar Probe SWEAP Solar Probe Analyzer, SPAN, Electron Data Release Notes\n--------------------------------------------------------------------------------\n\nNovember 19, 2019 Initial Data Release\n--------------------------------------\n\nOverview of Measurements\n------------------------\n\nThe SWEAP team is pleased to release the data from Encounter 1 and Encounter 2. The files contain data from the time range October 31, 2018 - June 18, 2019.\n\nThe prime mission of Parker Solar Probe is to take data when within 0.25 AU of the Sun during its orbit. However, there has been some extended campaign measurements outside of this distance. The data are available for those days that are within 0.25 AU as well as those days when the instruments were operational outside of 0.25 AU.\n\nEach SWEAP data file includes a set of a particular type of measurements over a single observing day. Measurements are provided in Common Data Format (CDF), a self-documenting data framework for which convenient open source tools exist across most scientific computing platforms. Users are strongly encouraged to consult the global metadata in each file, and the metadata that are linked to each variable. The metadata includes comprehensive listings of relevant information, including units, coordinate systems, qualitative descriptions, measurement uncertainties, methodologies, links to further documentation, and so forth.\n\nSPAN-E Level 2 Version 01 Release Notes\n---------------------------------------\n\nThe SPAN-Ae and SPAN-B instruments together have fields of view covering >90% of the sky; major obstructions to the FOV include the spacecraft heat shield and other intrusions by spacecraft components. Each individual SPAN-E has FOV of ±60° in Theta and 240° in Phi. The rotation matrices to convert into the spacecraft frame can be found in the individual CDF files, or in the instrument paper.\n\nThis data set covers all periods for which the instrument was turned on and taking data in the solar wind in ion mode. This includes maneuvers affecting the spacecraft attitude and orientation. Measurements taken by SPAN-B when the spacecraft is pointed away from the sun are taken in sunlight.\n\nThe data quality flags for the SPAN data can be found in the CDF files as: QUALITY_FLAG (0=good, 1=bad)\n\nGeneral Remarks for Version 01 Data\n-----------------------------------\n\nUsers interested in field-aligned electrons should take care regarding potential blockages from the heat shield when B is near radial, especially in SPAN-Ae. Artificial reductions in strahl width can result.\n\nDue to the relatively high electron temperature in the inner heliosphere, many secondary electrons are generated from spacecraft and instrument surfaces. As a result, electron measurements in this release below 30 eV are not advised for scientific analysis.\n\nThe fields of view in SPAN-Ae and SPAN-B have many intrusions by the spacecraft, and erroneous pixels discovered in analysis, in particular near the edges of the FOV, should be viewed with skepticism. Details on FOV intrusion are found in the instrument paper, forthcoming, or by contacting the SPAN-E instrument scientist.\n\nThe instrument mechanical attentuators are engaged during the eight days around perihelia 1 and perihelia 2, which results in a factor of about 10 reduction of the total electron flux into the instrument. During these eight days, halo electron measurements are artificially enhanced in the L2 products as a result of the reduced instrument geometric factor and subsequent ground corrections.\n\nA general note for Encounter 1 and Encounter 2 data: a miscalculation in the deflection tables loaded to both SPAN-Ae and SPAN-B resulted in over-deflection of the outermost Theta angles during these encounters. As such, pixels at large Thetas should be ignored. This error was corrected by a table upload prior to Encounter 3.\n\nLastly, when viewing time gaps in the SPAN-E measurements, be advised that the first data point produced by the instrument after a power-on is the maximum value permitted by internal instrument counters. Therefore, the first data point after powerup is erroneous and should be discarded, as indicated by quality flags.\n\nSPAN-E Encounter 1 Remarks\n--------------------------\n\nSPAN-E operated nominally for the majority of the first encounter. Exceptions to this include: a few instances of corrupted, higher-energy sweep tables, and an instrument commanding error for the two hours surrounding perihelion 1. These and other instrument diagnostic tests are indicated with the QUALITY_FLAG variable in the CDFs.\n\nThe mechanical attentuator was engaged for the 8 days around perihelion 1: as a result the microchannel plate, MCP, noise due to thermal effects and cosmic rays are artificially enhanced and are particularly obvious at higher energies. Exercise caution with this data release if looking for halo electrons when the mechanical attenuator is engaged.\n\nSPAN-E Cruise Phase Remarks\n---------------------------\n\nThe cruise mode rates of SPAN-E are greatly reduced compared to the encounter mode rates. When the PSP spacecraft is in a communications slew, the SPAN-B instrument occasionally reaches its maximum allowable operating temperature and is powered off by SWEM.\n\nTiming for the SF1 products in cruise phase is not corrected in v01, and thus it is not advised to use the data at this time for scientific analysis. The typical return of SF0 products is one spectrum out of every 32 survey spectra is returned every 15 minutes or so. One out of every four 27.75 s SF1 spectra is produced every 111 s.\n\nSPAN-E Encounter 2 Remarks\n--------------------------\n\nSPAN-E operated nominally for the majority of the second encounter. Exceptions include instrument diagnostic and health checks and a few instances of corrupted high-energy sweep tables. These tests and corrupted table loads are indicated with the QUALITY_FLAG parameter.\n\nThe mechanical attentuator was engaged for the 8 days around perihelion 2: as a result the MCP noise due to thermal effects and cosmic rays are artificially enhanced and are particularly obvious at higher energies. Exercise caution in this data release if looking for halo electrons when the mechanical attenuator is engaged.\n\nParker Solar Probe SWEAP Rules of the Road\n------------------------------------------\n\nAs part of the development of collaboration with the broader Heliophysics community, the mission has drafted a \"Rules of the Road\" to govern how PSP instrument data are to be used.\n\n* 1) Users should consult with the PI to discuss the appropriate use of instrument data or model results and to ensure that the users are accessing the most recently available versions of the data and of the analysis routines. Instrument team Science Operations Centers, SOCs, and/or Virtual Observatories, VOs, should facilitate this process serving as the contact point between PI and users in most cases.\n\n* 2) Users should heed the caveats of investigators to the interpretations and limitations of data or model results. Investigators supplying data or models may insist that such caveats be published. Data and model version numbers should also be specified.\n\n* 3) Browse products, Quicklook, and Planning data are not intended for science analysis or publication and should not be used for those purposes without consent of the PI.\n\n* 4) Users should acknowledge the sources of data used in all publications, presentations, and reports: \"We acknowledge the NASA Parker Solar Probe Mission and the SWEAP team led by J. Kasper for use of data.\".\n\n* 5) Users are encouraged to provide the PI a copy of each manuscript that uses the PI data prior to submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI and any other providers of data.", + "fn": "EDWARD BALABAN", + "hasEmail": "mailto:edward.balaban@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", - "downloadURL": "ftps://spdf.gsfc.nasa.gov/pub/data/psp/sweap/spe/l2/spa_sf1_32e/", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://cdaweb.gsfc.nasa.gov/cgi-bin/eval2.cgi?dataset=PSP_SWP_SPA_SF1_L2_32E&index=sp_phys", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://cdaweb.gsfc.nasa.gov/hapi", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://doi.org/10.1007/s11214-015-0206-3", - "format": "HTML", - "mediaType": "text/html" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://helio.data.nasa.gov/dataset/ParkerSolarProbe_SWEAP_SPAN-A_Level2_ElectronsFullSpectra_PT1.74S", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://hpde.io/NASA/NumericalData/ParkerSolarProbe/SWEAP/SPAN-A/Level2/ElectronsFullSpectra/PT1.74S", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://spdf.gsfc.nasa.gov/pub/data/psp/sweap/spe/l2/spa_sf1_32e/", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://sweap.cfa.harvard.edu", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://sweap.cfa.harvard.edu/Data.html", - "format": "HTML", - "mediaType": "text/html" + "description": "IEEE_Aero_2004_PITEX.pdf", + "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/IEEE_Aero_2004_PITEX.pdf", + "format": "PDF", + "mediaType": "application/pdf", + "title": "IEEE_Aero_2004_PITEX.pdf" } ], - "identifier": "https://doi.org/10.48322/9pz0-sj43", - "keyword": [ - "instrumentstatus", - "thermalplasma" - ], - "landingPage": "https://doi.org/10.48322/9pz0-sj43", - "license": "https://www.usa.gov/government-works", - "modified": "2026-09-08", + "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": "SPDF" - }, - "theme": [ - "Heliophysics" - ], - "title": "PSP Solar Wind Electrons Alphas and Protons (SWEAP) SPAN-A Electron Energy Spectra, Level 2 (L2), 1.74 s Data" - }, - "description": "SPAN-E Level 2 ELectron Energy Spectra Data\n-------------------------------------------\n\nFile Naming Format: psp_swp_spa_sf1_L2_32E_YYYYMMDD_v01.cdf\n\nThe SF1 product is an energy spectrum produced on the spacecraft by summing over the Theta and Phi directions. The units are differential energy flux and eV. The sample filename above includes 32 Energies.\n\nThe larger Theta angles (deflection angles) are artificially enhanced in the \"sf1\" energy spectra data products due to the method of spectra production on the SPAN-E instrument (straight summing). Thus, SF1 energy spectra are not recommended for rigid statistical analysis.\n\nParker Solar Probe SWEAP Solar Probe Analyzer, SPAN, Electron Data Release Notes\n--------------------------------------------------------------------------------\n\nNovember 19, 2019 Initial Data Release\n--------------------------------------\n\nOverview of Measurements\n------------------------\n\nThe SWEAP team is pleased to release the data from Encounter 1 and Encounter 2. The files contain data from the time range October 31, 2018 - June 18, 2019.\n\nThe prime mission of Parker Solar Probe is to take data when within 0.25 AU of the Sun during its orbit. However, there has been some extended campaign measurements outside of this distance. The data are available for those days that are within 0.25 AU as well as those days when the instruments were operational outside of 0.25 AU.\n\nEach SWEAP data file includes a set of a particular type of measurements over a single observing day. Measurements are provided in Common Data Format (CDF), a self-documenting data framework for which convenient open source tools exist across most scientific computing platforms. Users are strongly encouraged to consult the global metadata in each file, and the metadata that are linked to each variable. The metadata includes comprehensive listings of relevant information, including units, coordinate systems, qualitative descriptions, measurement uncertainties, methodologies, links to further documentation, and so forth.\n\nSPAN-E Level 2 Version 01 Release Notes\n---------------------------------------\n\nThe SPAN-Ae and SPAN-B instruments together have fields of view covering >90% of the sky; major obstructions to the FOV include the spacecraft heat shield and other intrusions by spacecraft components. Each individual SPAN-E has FOV of ±60° in Theta and 240° in Phi. The rotation matrices to convert into the spacecraft frame can be found in the individual CDF files, or in the instrument paper.\n\nThis data set covers all periods for which the instrument was turned on and taking data in the solar wind in ion mode. This includes maneuvers affecting the spacecraft attitude and orientation. Measurements taken by SPAN-B when the spacecraft is pointed away from the sun are taken in sunlight.\n\nThe data quality flags for the SPAN data can be found in the CDF files as: QUALITY_FLAG (0=good, 1=bad)\n\nGeneral Remarks for Version 01 Data\n-----------------------------------\n\nUsers interested in field-aligned electrons should take care regarding potential blockages from the heat shield when B is near radial, especially in SPAN-Ae. Artificial reductions in strahl width can result.\n\nDue to the relatively high electron temperature in the inner heliosphere, many secondary electrons are generated from spacecraft and instrument surfaces. As a result, electron measurements in this release below 30 eV are not advised for scientific analysis.\n\nThe fields of view in SPAN-Ae and SPAN-B have many intrusions by the spacecraft, and erroneous pixels discovered in analysis, in particular near the edges of the FOV, should be viewed with skepticism. Details on FOV intrusion are found in the instrument paper, forthcoming, or by contacting the SPAN-E instrument scientist.\n\nThe instrument mechanical attentuators are engaged during the eight days around perihelia 1 and perihelia 2, which results in a factor of about 10 reduction of the total electron flux into the instrument. During these eight days, halo electron measurements are artificially enhanced in the L2 products as a result of the reduced instrument geometric factor and subsequent ground corrections.\n\nA general note for Encounter 1 and Encounter 2 data: a miscalculation in the deflection tables loaded to both SPAN-Ae and SPAN-B resulted in over-deflection of the outermost Theta angles during these encounters. As such, pixels at large Thetas should be ignored. This error was corrected by a table upload prior to Encounter 3.\n\nLastly, when viewing time gaps in the SPAN-E measurements, be advised that the first data point produced by the instrument after a power-on is the maximum value permitted by internal instrument counters. Therefore, the first data point after powerup is erroneous and should be discarded, as indicated by quality flags.\n\nSPAN-E Encounter 1 Remarks\n--------------------------\n\nSPAN-E operated nominally for the majority of the first encounter. Exceptions to this include: a few instances of corrupted, higher-energy sweep tables, and an instrument commanding error for the two hours surrounding perihelion 1. These and other instrument diagnostic tests are indicated with the QUALITY_FLAG variable in the CDFs.\n\nThe mechanical attentuator was engaged for the 8 days around perihelion 1: as a result the microchannel plate, MCP, noise due to thermal effects and cosmic rays are artificially enhanced and are particularly obvious at higher energies. Exercise caution with this data release if looking for halo electrons when the mechanical attenuator is engaged.\n\nSPAN-E Cruise Phase Remarks\n---------------------------\n\nThe cruise mode rates of SPAN-E are greatly reduced compared to the encounter mode rates. When the PSP spacecraft is in a communications slew, the SPAN-B instrument occasionally reaches its maximum allowable operating temperature and is powered off by SWEM.\n\nTiming for the SF1 products in cruise phase is not corrected in v01, and thus it is not advised to use the data at this time for scientific analysis. The typical return of SF0 products is one spectrum out of every 32 survey spectra is returned every 15 minutes or so. One out of every four 27.75 s SF1 spectra is produced every 111 s.\n\nSPAN-E Encounter 2 Remarks\n--------------------------\n\nSPAN-E operated nominally for the majority of the second encounter. Exceptions include instrument diagnostic and health checks and a few instances of corrupted high-energy sweep tables. These tests and corrupted table loads are indicated with the QUALITY_FLAG parameter.\n\nThe mechanical attentuator was engaged for the 8 days around perihelion 2: as a result the MCP noise due to thermal effects and cosmic rays are artificially enhanced and are particularly obvious at higher energies. Exercise caution in this data release if looking for halo electrons when the mechanical attenuator is engaged.\n\nParker Solar Probe SWEAP Rules of the Road\n------------------------------------------\n\nAs part of the development of collaboration with the broader Heliophysics community, the mission has drafted a \"Rules of the Road\" to govern how PSP instrument data are to be used.\n\n* 1) Users should consult with the PI to discuss the appropriate use of instrument data or model results and to ensure that the users are accessing the most recently available versions of the data and of the analysis routines. Instrument team Science Operations Centers, SOCs, and/or Virtual Observatories, VOs, should facilitate this process serving as the contact point between PI and users in most cases.\n\n* 2) Users should heed the caveats of investigators to the interpretations and limitations of data or model results. Investigators supplying data or models may insist that such caveats be published. Data and model version numbers should also be specified.\n\n* 3) Browse products, Quicklook, and Planning data are not intended for science analysis or publication and should not be used for those purposes without consent of the PI.\n\n* 4) Users should acknowledge the sources of data used in all publications, presentations, and reports: \"We acknowledge the NASA Parker Solar Probe Mission and the SWEAP team led by J. Kasper for use of data.\".\n\n* 5) Users are encouraged to provide the PI a copy of each manuscript that uses the PI data prior to submission of that manuscript for consideration of publication. On publication, the citation should be transmitted to the PI and any other providers of data.", - "distribution_titles": [], - "harvest_record": "https://catalog.data.gov/harvest_record/50e7701c-76b7-4644-94f1-775c79eba8f5", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/50e7701c-76b7-4644-94f1-775c79eba8f5/raw", + "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": [ + "IEEE_Aero_2004_PITEX.pdf" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/80774f0d-6feb-4677-8f02-748278c09412", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/80774f0d-6feb-4677-8f02-748278c09412/raw", "has_download": true, "has_spatial": false, - "identifier": "https://doi.org/10.48322/9pz0-sj43", - "keyword": [ - "instrumentstatus", - "thermalplasma" - ], - "last_harvested_date": "2026-09-09T00:14:47.601875", + "identifier": "DASHLINK_669", + "keyword": [ + "ames", + "dashlink", + "nasa" + ], + "last_harvested_date": "2026-09-09T01:14:49.892441", "organization": { "aliases": [ "" @@ -404,117 +394,115 @@ "slug": "nasa" }, "parent_identifier": null, - "popularity": 1, - "publisher": "SPDF", - "slug": "psp-solar-wind-electrons-alphas-and-protons-sweap-span-a-electron-energy-spectra-level-2-l", + "popularity": 0, + "publisher": "Dashlink", + "slug": "general-purpose-data-driven-system-monitoring-for-space-operations", "spatial_centroid": null, "spatial_shape": null, - "theme": [ - "Heliophysics" - ], - "title": "PSP Solar Wind Electrons Alphas and Protons (SWEAP) SPAN-A Electron Energy Spectra, Level 2 (L2), 1.74 s Data", + "theme": [], + "title": "General Purpose Data-Driven System Monitoring for Space Operations", "type": "dataset" }, { - "_score": 19.876, + "_score": 11.858278, "_sort": [ - 1788912800569, - 19.876, - 1, - "69cf11d5-5a7a-41fe-bb32-6c1f8560ef55" + 1788916373812, + 11.858278, + 9, + "09334128-0ded-4630-830c-c742ed56c217" ], "dcat": { "@type": "dcat:Dataset", "accessLevel": "public", + "accrualPeriodicity": "irregular", "bureauCode": [ "026:00" ], "contactPoint": { "@type": "vcard:Contact", - "fn": "NASA Space Physics Data Facility", - "hasEmail": "mailto:NASA-SPDF-Support@nasa.onmicrosoft.com" - }, - "description": "STEREO-A, In-situ Measurements of Particles and CME Transients, IMPACT, Instrument, State of Health and Housekeeping, Level 1, Data", + "fn": "undefined", + "hasEmail": "mailto:sdps@oceancolor.gsfc.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", - "downloadURL": "ftps://spdf.gsfc.nasa.gov/pub/data/stereo/ahead/l1/impact/hkp/", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "http://sprg.ssl.berkeley.edu/impact/", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://cdaweb.gsfc.nasa.gov/cgi-bin/eval2.cgi?dataset=STA_L1_IMPACT_HKP&index=sp_phys", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://cdaweb.gsfc.nasa.gov/hapi", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://helio.data.nasa.gov/dataset/STEREO-A_IMPACT_Housekeeping_L1_PT2S", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://hpde.io/NASA/NumericalData/STEREO-A/IMPACT/Housekeeping/L1/PT2S", - "format": "BIN", - "mediaType": "application/octet-stream" - }, - { - "@type": "dcat:Distribution", - "downloadURL": "https://spdf.gsfc.nasa.gov/pub/data/stereo/ahead/l1/impact/hkp/", - "format": "BIN", - "mediaType": "application/octet-stream" + "description": "NASA Ocean Color Web - Algorithm Description Documentation", + "downloadURL": "https://oceancolor.gsfc.nasa.gov/resources/atbd/", + "format": "HTML", + "mediaType": "text/html", + "title": "View this dataset's algorithm theoretical basis document" + }, + { + "@type": "dcat:Distribution", + "description": "NASA Ocean Color Web - Data Citation Guidelines", + "downloadURL": "https://oceancolor.gsfc.nasa.gov/resources/how-to-cite/", + "format": "HTML", + "mediaType": "text/html", + "title": "View information related to this dataset" + }, + { + "@type": "dcat:Distribution", + "description": "NASA Ocean Color Web - Data Distribution Site", + "downloadURL": "https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Mapped/Terra-MODIS/", + "format": "HTML", + "mediaType": "text/html", + "title": "Download this dataset through a directory map" + }, + { + "@type": "dcat:Distribution", + "description": "NASA Ocean Color Web - Processing History", + "downloadURL": "https://oceancolor.gsfc.nasa.gov/data/reprocessing/", + "format": "HTML", + "mediaType": "text/html", + "title": "View this dataset's processing history" + }, + { + "@type": "dcat:Distribution", + "description": "OB.DAAC OPeNDAP Site for Terra MODIS Standard Mapped Image (SMI) Product", + "downloadURL": "https://oceandata.sci.gsfc.nasa.gov/opendap/MODIST/L3SMI/", + "format": "HTML", + "mediaType": "text/html", + "title": "Use OPeNDAP to access the dataset's data" } ], - "identifier": "https://doi.org/10.48322/ze43-sh27", - "keyword": [ - "energeticparticles", - "instrumentstatus", - "magneticfield", - "thermalplasma" - ], - "landingPage": "https://doi.org/10.48322/ze43-sh27", - "license": "https://www.usa.gov/government-works", - "modified": "2026-09-08", + "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": "CDAWeb" - }, - "theme": [ - "Heliophysics" - ], - "title": "STEREO-A In-Situ Measurements of Particles and CME Transients (IMPACT) Housekeeping" - }, - "description": "STEREO-A, In-situ Measurements of Particles and CME Transients, IMPACT, Instrument, State of Health and Housekeeping, Level 1, Data", - "distribution_titles": [], - "harvest_record": "https://catalog.data.gov/harvest_record/80fedd00-f206-4c22-9ac8-ddb5a6b77744", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/80fedd00-f206-4c22-9ac8-ddb5a6b77744/raw", + "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": [ + "View this dataset's algorithm theoretical basis document", + "View information related to this dataset", + "Download this dataset through a directory map", + "View this dataset's processing history", + "Use OPeNDAP to access the dataset's data" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/7127de80-9709-446a-91aa-a95ef77e5b8b", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/7127de80-9709-446a-91aa-a95ef77e5b8b/raw", "has_download": true, "has_spatial": false, - "identifier": "https://doi.org/10.48322/ze43-sh27", - "keyword": [ - "energeticparticles", - "instrumentstatus", - "magneticfield", - "thermalplasma" - ], - "last_harvested_date": "2026-09-09T00:13:20.569892", + "identifier": "DASHLINK_171", + "keyword": [ + "ames", + "dashlink", + "nasa" + ], + "last_harvested_date": "2026-09-09T01:12:53.812592", "organization": { "aliases": [ "" @@ -529,117 +517,115 @@ "slug": "nasa" }, "parent_identifier": null, - "popularity": 1, - "publisher": "CDAWeb", - "slug": "stereo-a-in-situ-measurements-of-particles-and-cme-transients-impact-housekeeping", + "popularity": 9, + "publisher": "Dashlink", + "slug": "unsupervised-anomaly-detection-for-liquid-fueled-rocket-prop", "spatial_centroid": null, "spatial_shape": null, - "theme": [ - "Heliophysics" - ], - "title": "STEREO-A In-Situ Measurements of Particles and CME Transients (IMPACT) Housekeeping", + "theme": [], + "title": "Unsupervised Anomaly Detection for Liquid-Fueled Rocket Prop...", "type": "dataset" }, { - "_score": 19.782455, + "_score": 15.43871, "_sort": [ - 1788912785956, - 19.782455, + 1788916345957, + 15.43871, 1, - "7996374c-8133-47b8-b40a-ae56ab628208" + "f72f7357-7dd9-4ea7-ab7d-c06682fa908f" ], "dcat": { "@type": "dcat:Dataset", "accessLevel": "public", + "accrualPeriodicity": "irregular", "bureauCode": [ "026:00" ], "contactPoint": { "@type": "vcard:Contact", - "fn": "NASA Space Physics Data Facility", - "hasEmail": "mailto:NASA-SPDF-Support@nasa.onmicrosoft.com" - }, - "description": "STEREO-B, In-situ Measurements of Particles and CME Transients, IMPACT, Instrument, State of Health and Housekeeping, Level 1, Data", + "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. 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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": "SEDAC" + }, + "references": [ + "https://doi.org/10.7927/eavc-4k45", + "https://doi.org/10.7927/fcre-m572", + "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 11.2.1: Urban Access to Public Transport, 2023 Release" + }, + "description": "The SDG Indicator 11.2.1: Urban Access to Public Transport, 2023 Release, part of the SDGI collection, measures the proportion of the population in a city that has convenient access to public transport. UN SDG 11 is \"make cities and human settlements inclusive, safe, resilient and sustainable\". 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The data set is available at both national and level 2 subnational resolutions.", + "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\", [{\"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. 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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.", + "fn": "Open Science Data Repository Help Desk", + "hasEmail": "mailto:arc-dl-osdr-help@mail.nasa.gov" + }, + "description": "Spaceflights significantly impacts astronaut health, causing muscle atrophy, bone loss, cardiovascular deconditioning and immune system dysregulation, especially during long missions. To explore molecular changes, we generated a single-cell RNA sequencing dataset, of 216 samples from 28 mouse organs and tissues under spaceflight and control conditions. Since spaceflight effects closely resemble aspects of aging, we included two age groups (3 and 8 months) to assess age-related influences. Our data revealed that spaceflight drives age-independent systemic changes, primarily affecting tissue remodeling and alterations in cytoskeleton, membrane and extracellular matrix (ECM), particularly in endothelial cells. In contrast, aging has a weaker impact, mainly altering immune regulation in an age-dependent manner. These findings suggest that cytoskeletal and ECM changes may contribute to immune Dysregulation.", "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", + "downloadURL": "http://purl.bioontology.org/ontology/NCBITAXON/10090", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://osdr.nasa.gov/bio/repo/data/missions/SpaceX-16", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://osdr.nasa.gov/bio/repo/data/studies/OSD-918", + "format": "BIN", + "mediaType": "application/octet-stream" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi", "format": "BIN", "mediaType": "application/octet-stream" } ], - "identifier": "10.7927/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" + "identifier": "10.26030/necm-6v21", + "keyword": [ + "biological-and-physical-sciences", + "genelab", + "nasa" ], "license": "https://www.usa.gov/government-works", - "modified": "2026-09-08", + "modified": "2026-08-10", "programCode": [ "026:000" ], "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. 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To explore molecular changes, we generated a single-cell RNA sequencing dataset, of 216 samples from 28 mouse organs and tissues under spaceflight and control conditions. Since spaceflight effects closely resemble aspects of aging, we included two age groups (3 and 8 months) to assess age-related influences. Our data revealed that spaceflight drives age-independent systemic changes, primarily affecting tissue remodeling and alterations in cytoskeleton, membrane and extracellular matrix (ECM), particularly in endothelial cells. In contrast, aging has a weaker impact, mainly altering immune regulation in an age-dependent manner. These findings suggest that cytoskeletal and ECM changes may contribute to immune Dysregulation.", + "distribution_titles": [], + "harvest_record": "https://catalog.data.gov/harvest_record/c823b4e5-31d1-4e68-9923-4b097a8e66d0", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/c823b4e5-31d1-4e68-9923-4b097a8e66d0/raw", "has_download": true, - "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": "2026-09-09T00:05:51.966252", + "has_spatial": false, + "identifier": "10.26030/necm-6v21", + "keyword": [ + "biological-and-physical-sciences", + "genelab", + "nasa" + ], + "last_harvested_date": "2026-09-09T00:52:51.033028", "organization": { "aliases": [ "" @@ -1672,23 +1582,23 @@ }, "parent_identifier": null, "popularity": 0, - "publisher": "ESDIS", - "slug": "ipcc-fourth-assessment-report-ar4-observed-climate-change-impacts-database-2572a", + "publisher": "Open Science Data Repository", + "slug": "spaceflight-induces-systemic-effects-on-extracellular-matrix-and-immune-system-in-differen-c0f35", "spatial_centroid": null, "spatial_shape": null, "theme": [ - "Earth Science" - ], - "title": "IPCC Fourth Assessment Report (AR4) Observed Climate Change Impacts Database", + "Biological and Physical Sciences" + ], + "title": "Spaceflight induces systemic effects on extracellular matrix and immune system in different age-stages - Blood data", "type": "dataset" }, { - "_score": 14.73392, + "_score": 40.416367, "_sort": [ - 1788912351278, - 14.73392, - 1, - "258e226e-4e5c-436a-83b6-60d464a33969" + 1788915151312, + 40.416367, + 0, + "a2bf562e-3e62-49ed-af91-c6fc6edeb06a" ], "dcat": { "@type": "dcat:Dataset", @@ -1698,74 +1608,63 @@ ], "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.", + "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. 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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/0330f79c-2bde-4ed7-8e8d-8b3c179ccfe2", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/0330f79c-2bde-4ed7-8e8d-8b3c179ccfe2/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 (Estrous Cycle Monitoring)" + }, + "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. 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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;urn:nasa:pds:context_pds3:data_set:data_set.ro-x-gia-2-mars-marsswingby-v1.0::1.0", + "keyword": [ + "__" ], "license": "https://www.usa.gov/government-works", - "modified": "2026-09-08", + "modified": "2026-09-07", "programCode": [ "026:000" ], "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. 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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).", + "title": "MASTER: Western Diversity Time Series Campaign, WDTS, California, USA, Spring 2021" + }, + "description": "This dataset includes Level 1B (L1B) and Level 2 (L2) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Western Diversity Time Series (WDTS, formerly HyspIRI) airborne campaign during nine flights aboard a NASA ER-2 aircraft over selected areas of California and Nevada, U.S., from 2021-02-09 to 2021-04-02. The WDTS campaign will observe California's ecosystems and provide critical information on natural disasters such as volcanoes, wildfires, and drought. 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A p