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First G-causality is computed, and then causal density (cd) is \r\ncomputed in interval [0,1].  Numerical value of causal density for this data file is 0.4711, which corresponds to systems health score.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://nasa3d.arc.nasa.gov/shared_assets/models/pgt-3ds/PGT-3DS.zip","format":"image/x-3ds","mediaType":"image/x-3ds"}],"identifier":"DASHLINK_625","issued":"2012-11-14","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/625/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"Indir Jaganjac"},"description":"FLTz flight simulator RecorderRun9 data are analyzed in order \r\nto compute systems health score using Granger causal connectovity \r\nanalysis. First G-causality is computed, and then causal density (cd) is \r\ncomputed in interval [0,1].  Numerical value of causal density for this data file is 0.4711, which corresponds to systems health score.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/02ffec45-87a6-43d3-b9f8-b8f76d9f905a","harvest_record_raw":"https://catalog.data.gov/harvest_record/02ffec45-87a6-43d3-b9f8-b8f76d9f905a/raw","has_download":true,"has_spatial":false,"identifier":"DASHLINK_625","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-09-23T01:32:37.996436","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":0,"publisher":"Dashlink","slug":"indir-jaganjac","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Indir Jaganjac","type":"dataset"},{"_score":10.871136,"_sort":[1790127113402,10.871136,2,"bab33ec7-dad1-40fa-9270-949a29c72e4d"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"EDWARD BALABAN","hasEmail":"mailto:edward.balaban@nasa.gov"},"description":"Prognostics is an emerging concept in condition based maintenance (CBM) of critical systems. Along with developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance.  Differences and similarities between these domains and health maintenance have been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a\r\nspecific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the \r\nleast, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.","distribution":[{"@type":"dcat:Distribution","description":"PHM_2008_Metrics.pdf","downloadURL":"https://c3.nasa.gov/dashlink/static/media/publication/PHM_2008_Metrics.pdf","format":"application/force-download","mediaType":"application/force-download","title":"PHM_2008_Metrics.pdf"}],"identifier":"DASHLINK_393","issued":"2011-06-07","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/393/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","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":["PHM_2008_Metrics.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/e35652b2-feeb-450e-a789-7819a336b87e","harvest_record_raw":"https://catalog.data.gov/harvest_record/e35652b2-feeb-450e-a789-7819a336b87e/raw","has_download":true,"has_spatial":false,"identifier":"DASHLINK_393","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-09-23T01:31:53.402911","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"Dashlink","slug":"metrics-for-evaluating-performance-of-prognostics-techniques","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Metrics for Evaluating Performance of Prognostics Techniques","type":"dataset"},{"_score":20.726263,"_sort":[1790127104708,20.726263,0,"d95acbf8-9537-4b51-a9e0-ae0c575adc99"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"MARK SCHWABACHER","hasEmail":"mailto:mark.a.schwabacher@nasa.gov"},"description":"Modern space propulsion and exploration system designs are becoming increasingly\r\nsophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by\r\nanalyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. Inductive Monitoring System uses a data mining technique called clustering to analyze archived system data and characterize\r\nnormal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or\r\nfor analysis of archived events. Ongoing and developing Inductive Monitoring System space operations applications include International Space Station flight control, spacecraft vehicle\r\nsystem health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the Inductive Monitoring\r\nSystem data-driven technique as related to several Integrated Systems Health Management elements. Thematically, the projects listed will be used as case studies. The maturation of Inductive Monitoring System via projects where it has been deployed or is currently being\r\nintegrated to aid in fault detection will be described. The paper will also explain how Inductive Monitoring System can be used to complement a suite of other Integrated System Health Management tools, providing initial fault detection support for diagnosis and recovery.","distribution":[{"@type":"dcat:Distribution","description":"IMS JACIC.pdf","downloadURL":"https://c3.nasa.gov/dashlink/static/media/publication/IMS_JACIC.pdf","format":"PDF","mediaType":"application/pdf","title":"IMS JACIC.pdf"}],"identifier":"DASHLINK_669","issued":"2013-02-01","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/669/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","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":["IMS JACIC.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/8d3c94da-c03c-4352-af69-cfe0dbdaec32","harvest_record_raw":"https://catalog.data.gov/harvest_record/8d3c94da-c03c-4352-af69-cfe0dbdaec32/raw","has_download":true,"has_spatial":false,"identifier":"DASHLINK_669","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-09-23T01:31:44.708190","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":0,"publisher":"Dashlink","slug":"general-purpose-data-driven-system-monitoring-for-space-operations","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"General Purpose Data-Driven System Monitoring for Space Operations","type":"dataset"},{"_score":17.691708,"_sort":[1790127088150,17.691708,2,"a35e6d84-5722-4711-bba9-b664f1486b01"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"NSIDC Services","hasEmail":"mailto:nsidc@nsidc.org"},"description":"Prognostics has received considerable attention recently as an emerging sub-discipline within SHM. Prognosis is here strictly defined as \u201cpredicting the time at which a component will no longer perform its intended function\u201d. Loss of function is often times the time at which a component fails. The predicted time to that point becomes then the remaining useful life (RUL). For prognostics to be effective, it must be performed well before deviations from normal performance propagate to a critical effect. This enables a failure preclusion or prevention function to repair or replace the offending components, or if the components cannot be repaired, to retire the system (or vehicle) before the critical failure occurs. Therefore, prognosis has the promise to provide critical information to system operators that will enable safer operation and more cost-efficient use. To that end, Department of Defense (DoD), NASA, and industry have been investigating this technology for use in their vehicle health management solutions. Dedicated prognostic algorithms (in conjunction with failure detection and fault isolation algorithms) must be developed that are capable of operating in an autonomous and real-time vehicle health management system software architecture that is possibly distributed in nature. This envisioned prognostic and health management system will be realized in a vehicle-level reasoner that must have visibility and insight into the results of local diagnostic and prognostic technologies implemented at the LRU and subsystem levels. Accomplishing this effectively requires an integrated suite of prognostic technologies that compute failure effect propagation through diverse subsystems and that can capture interactions that occur in these subsystems. In this chapter a generic set of selected prognostic algorithm approaches is presented and an overview of the required vehicle-level reasoning architecture needed to integrate the prognostic information across systems is provided.","distribution":[{"@type":"dcat:Distribution","description":"Direct download via HTTPS protocol.","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3286095947-NSIDC_CPRD","format":"HTML","mediaType":"text/html","title":"Download this dataset"},{"@type":"dcat:Distribution","description":"Direct download via HTTPS protocol.","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3286095947-NSIDC_CPRD","format":"HTML","mediaType":"text/html","title":"Download this dataset"},{"@type":"dcat:Distribution","description":"Direct download via HTTPS protocol.","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3286095947-NSIDC_CPRD","format":"HTML","mediaType":"text/html","title":"Download this dataset"},{"@type":"dcat:Distribution","description":"Includes a user's guide, supplemental documents like ATBDs and academic papers, How Tos, FAQs, etc.","downloadURL":"https://doi.org/10.5067/PP14EED9ZOE2","format":"HTML","mediaType":"text/html","title":"View documentation related to this dataset"},{"@type":"dcat:Distribution","description":"NASA's newest search and order tool for subsetting, reprojecting, and reformatting data.","downloadURL":"https://search.earthdata.nasa.gov/search?q=SV16M_V+V001","format":"HTML","mediaType":"text/html","title":"Download this dataset through Earthdata Search"},{"@type":"dcat:Distribution","description":"NASA's newest search and order tool for subsetting, reprojecting, and reformatting data.","downloadURL":"https://search.earthdata.nasa.gov/search?q=SV16M_V+V001","format":"HTML","mediaType":"text/html","title":"Download this dataset through Earthdata Search"},{"@type":"dcat:Distribution","description":"NASA's newest search and order tool for subsetting, reprojecting, and reformatting data.","downloadURL":"https://search.earthdata.nasa.gov/search?q=SV16M_V+V001","format":"HTML","mediaType":"text/html","title":"Download this dataset through Earthdata Search"},{"@type":"dcat:Distribution","description":"Provides access to data, documentation, tools, citation information, support, and other resources.","downloadURL":"https://doi.org/10.5067/PP14EED9ZOE2","format":"HTML","mediaType":"text/html","title":"This dataset's landing page"},{"@type":"dcat:Distribution","description":"Search results for publications that cite this dataset by its DOI.","downloadURL":"https://scholar.google.com/scholar?q=10.5067%2FPP14EED9ZOE2","format":"HTML","mediaType":"text/html","title":"Google Scholar search results"}],"identifier":"DASHLINK_940","issued":"2016-01-14","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/940/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"Prognostics"},"description":"Prognostics has received considerable attention recently as an emerging sub-discipline within SHM. Prognosis is here strictly defined as \u201cpredicting the time at which a component will no longer perform its intended function\u201d. Loss of function is often times the time at which a component fails. The predicted time to that point becomes then the remaining useful life (RUL). For prognostics to be effective, it must be performed well before deviations from normal performance propagate to a critical effect. This enables a failure preclusion or prevention function to repair or replace the offending components, or if the components cannot be repaired, to retire the system (or vehicle) before the critical failure occurs. Therefore, prognosis has the promise to provide critical information to system operators that will enable safer operation and more cost-efficient use. To that end, Department of Defense (DoD), NASA, and industry have been investigating this technology for use in their vehicle health management solutions. Dedicated prognostic algorithms (in conjunction with failure detection and fault isolation algorithms) must be developed that are capable of operating in an autonomous and real-time vehicle health management system software architecture that is possibly distributed in nature. This envisioned prognostic and health management system will be realized in a vehicle-level reasoner that must have visibility and insight into the results of local diagnostic and prognostic technologies implemented at the LRU and subsystem levels. Accomplishing this effectively requires an integrated suite of prognostic technologies that compute failure effect propagation through diverse subsystems and that can capture interactions that occur in these subsystems. In this chapter a generic set of selected prognostic algorithm approaches is presented and an overview of the required vehicle-level reasoning architecture needed to integrate the prognostic information across systems is provided.","distribution_titles":["Download this dataset","Download this dataset","Download this dataset","View documentation related to this dataset","Download this dataset through Earthdata Search","Download this dataset through Earthdata Search","Download this dataset through Earthdata Search","This dataset's landing page","Google Scholar search results"],"harvest_record":"https://catalog.data.gov/harvest_record/2cfb4bc1-72d1-4c46-994a-5d6cf843714f","harvest_record_raw":"https://catalog.data.gov/harvest_record/2cfb4bc1-72d1-4c46-994a-5d6cf843714f/raw","has_download":true,"has_spatial":false,"identifier":"DASHLINK_940","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-09-23T01:31:28.150967","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"Dashlink","slug":"prognostics","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Prognostics","type":"dataset"},{"_score":11.685389,"_sort":[1790126986420,11.685389,9,"09334128-0ded-4630-830c-c742ed56c217"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"MARK SCHWABACHER","hasEmail":"mailto:mark.a.schwabacher@nasa.gov"},"description":"Title: Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring.\r\n\r\nAbstract: This article describes the results of applying four unsupervised anomaly detection algorithms to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The article describes nine anomalies detected by the four algorithms. The four algorithms use four different definitions of anomalousness. Orca uses a nearest-neighbor approach, defining a point to be an anomaly if its nearest neighbors in the data space are far away from it. The Inductive Monitoring System clusters the training data, and then uses the distance to the nearest cluster as its measure of anomalousness. GritBot learns rules from the training data, and then classifies points as anomalous if they violate these rules. One-class support vector machines map the data into a high-dimensional space in which most of the normal points are on one side of a hyperplane, and then classify points on the other side of the hyperplane as anomalous. Because of these different definitions of anomalousness, different algorithms detect different anomalies. We therefore conclude that it is useful to use multiple algorithms.","distribution":[{"@type":"dcat:Distribution","description":"Paper","downloadURL":"https://c3.nasa.gov/dashlink/static/media/publication/AIAA-42783-102.pdf","format":"PDF","mediaType":"application/pdf","title":"AIAA-42783-102.pdf"}],"identifier":"DASHLINK_171","issued":"2010-09-22","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/171/","modified":"2025-04-01","programCode":["026:029"],"publisher":{"@type":"org:Organization","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":["AIAA-42783-102.pdf"],"harvest_record":"https://catalog.data.gov/harvest_record/52d334c9-374e-46ab-88fb-89f3dc8ae7eb","harvest_record_raw":"https://catalog.data.gov/harvest_record/52d334c9-374e-46ab-88fb-89f3dc8ae7eb/raw","has_download":true,"has_spatial":false,"identifier":"DASHLINK_171","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-09-23T01:29:46.420097","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":9,"publisher":"Dashlink","slug":"unsupervised-anomaly-detection-for-liquid-fueled-rocket-prop","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Unsupervised Anomaly Detection for Liquid-Fueled Rocket Prop...","type":"dataset"},{"_score":16.58004,"_sort":[1790126976228,16.58004,4,"85f6a507-fd8b-4e46-8e9f-1ff7e57a00a7"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"undefined","hasEmail":"mailto:support-asdc@earthdata.nasa.gov"},"description":"This list of potential mission targets should not be interpreted as a complete list of viable NEAs for an actual human exploration mission. As the NEA orbits are updated, the viable mission targets and their mission parameters will change. 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After every fifty RW cycles a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/9636e81c-c0cc-4f29-a1db-f3ce1b0975cd","harvest_record_raw":"https://catalog.data.gov/harvest_record/9636e81c-c0cc-4f29-a1db-f3ce1b0975cd/raw","has_download":false,"has_spatial":false,"identifier":"https://data.nasa.gov/api/views/qghr-qkfw","keyword":["batteries","degradation","phm","prognostics"],"last_harvested_date":"2026-09-23T01:09:22.638085","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":11,"publisher":"PCoE","slug":"randomized-battery-usage-2-room-temperature-random-walk","spatial_centroid":null,"spatial_shape":null,"theme":["Raw Data"],"title":"Randomized Battery Usage 2: Room Temperature Random Walk","type":"dataset"},{"_score":13.306439,"_sort":[1790125750761,13.306439,3,"f540ded7-c45d-4af2-b606-89069f27ff50"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Christopher Teubert","hasEmail":"mailto:Christopher.A.Teubert@nasa.gov"},"description":"Translating fundamental biological discoveries from NASA Space Biology program into health risk from space flights has been an ongoing challenge. We propose to use NASA GeneLab database to gain new knowledge on potential systemic responses to space. Unbiased systems biology analysis of transcriptomic data from seven different rodent datasets reveals for the first time the existence of potential 'master regulators' coordinating a systemic response to microgravity and/or space radiation with TGF-\u03b21 being the most common regulator. We hypothesized the space environment leads to the release of biomolecules circulating inside the blood stream. Through datamining we identified 13 candidate microRNAs (miRNA) which are common in all studies and directly interact with TGF-\u03b21 that can be potential circulating factors impacting space biology. This study exemplifies the utility of the GeneLab data repository to aid in the process of performing novel hypothesis-based research.","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://data.nasa.gov/docs/legacy/ames/2.Battery_Uniform_Distribution_Discharge_Room_Temp_DataSet_2Post.zip","format":"ZIP","mediaType":"application/zip","title":"Battery_Uniform_Distribution_Discharge_Room_Temp_DataSet_2Post.zip"}],"identifier":"10.26030/jq04-0n51","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"Rodent Research-1 (RR1) NASA Validation Flight: Mouse liver transcriptomic, proteomic, epigenomic and histology data"},"description":"Translating fundamental biological discoveries from NASA Space Biology program into health risk from space flights has been an ongoing challenge. We propose to use NASA GeneLab database to gain new knowledge on potential systemic responses to space. Unbiased systems biology analysis of transcriptomic data from seven different rodent datasets reveals for the first time the existence of potential 'master regulators' coordinating a systemic response to microgravity and/or space radiation with TGF-\u03b21 being the most common regulator. We hypothesized the space environment leads to the release of biomolecules circulating inside the blood stream. Through datamining we identified 13 candidate microRNAs (miRNA) which are common in all studies and directly interact with TGF-\u03b21 that can be potential circulating factors impacting space biology. This study exemplifies the utility of the GeneLab data repository to aid in the process of performing novel hypothesis-based research.","distribution_titles":["Battery_Uniform_Distribution_Discharge_Room_Temp_DataSet_2Post.zip"],"harvest_record":"https://catalog.data.gov/harvest_record/5521e1ee-4a5f-4749-a3cd-587171d85e82","harvest_record_raw":"https://catalog.data.gov/harvest_record/5521e1ee-4a5f-4749-a3cd-587171d85e82/raw","has_download":true,"has_spatial":false,"identifier":"10.26030/jq04-0n51","keyword":["biological-and-physical-sciences","genelab","nasa"],"last_harvested_date":"2026-09-23T01:09:10.761540","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"Open Science Data Repository","slug":"rodent-research-1-rr1-nasa-validation-flight-mouse-liver-transcriptomic-proteomic-epigenom","spatial_centroid":null,"spatial_shape":null,"theme":["Biological and Physical Sciences"],"title":"Rodent Research-1 (RR1) NASA Validation Flight: Mouse liver transcriptomic, proteomic, epigenomic and histology data","type":"dataset"},{"_score":39.466995,"_sort":[1790125723585,39.466995,1,"0fe9d27b-9b0e-46dd-9499-ae5ea888f961"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Open Science Data Repository Help Desk","hasEmail":"mailto:arc-dl-osdr-help@mail.nasa.gov"},"description":"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 (\u223c47% in 1g vs. 58% in 0g and 72% in 0.4g animals, P equals 0.005). We conclude that in males testosterone deficiency at the initiation of unloading has little effect on the trajectory of muscle loss. In females, initial low estradiol status may result in greater musculoskeletal losses. This study derives results from Grip (Force Transducer).","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://purl.bioontology.org/ontology/NCBITAXON/10090","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"http://www.ebi.ac.uk/arrayexpress/experiments/E-GEOD-68875/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://lsda.jsc.nasa.gov/scripts/experiment/exper.aspx?exp_index=13524","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/studies/OSD-117","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.26030/z92y-7b97","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"Influence of gonadectomy on muscle health in micro- and partial-gravity environments in rats (Grip; force transducer)"},"description":"Gonadal hormones, such as testosterone and estradiol, modulate muscle size and strength in males and females. However, the influence of sex hormones on muscle strength in micro- and partial-gravity environments (e.g., the Moon or Mars) is not fully understood. The purpose of this study was to determine the influence of gonadectomy (castration/ovariectomy) on progression of muscle atrophy in both micro- and partial-gravity environments in male and female rats. Male and female Fischer rats (n equals 120) underwent castration/ovariectomy (CAST/OVX) or sham surgery (SHAM) at 11 weeks of age. After 2 weeks of recovery, rats were exposed to hindlimb unloading (0g), partial weight bearing at 40% of normal loading (0.4g, Martian gravity), or normal loading (1.0g) for 28 days. In males, CAST did not exacerbate body weight loss or other metrics of musculoskeletal health. In females, OVX animals tended to have greater body weight loss and greater gastrocnemius loss. Within 7 days of exposure to either microgravity or partial gravity, females had detectable changes to estrous cycle, with greater time spent in low-estradiol phases diestrus and metestrus (\u223c47% in 1g vs. 58% in 0g and 72% in 0.4g animals, P equals 0.005). We conclude that in males testosterone deficiency at the initiation of unloading has little effect on the trajectory of muscle loss. In females, initial low estradiol status may result in greater musculoskeletal losses. This study derives results from Grip (Force Transducer).","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/cf76c72a-72a9-4a0f-94dd-1da32fedd3e7","harvest_record_raw":"https://catalog.data.gov/harvest_record/cf76c72a-72a9-4a0f-94dd-1da32fedd3e7/raw","has_download":true,"has_spatial":false,"identifier":"10.26030/z92y-7b97","keyword":["biological-and-physical-sciences","genelab","nasa"],"last_harvested_date":"2026-09-23T01:08:43.585256","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"Open Science Data Repository","slug":"influence-of-gonadectomy-on-muscle-health-in-micro-and-partial-gravity-environments-in-rat-73163","spatial_centroid":null,"spatial_shape":null,"theme":["Biological and Physical Sciences"],"title":"Influence of gonadectomy on muscle health in micro- and partial-gravity environments in rats (Grip; force transducer)","type":"dataset"},{"_score":8.824263,"_sort":[1790125721657,8.824263,3,"ac201880-f37d-42c0-a2b8-4fd598ba4410"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Open Science Data Repository Help Desk","hasEmail":"mailto:arc-dl-osdr-help@mail.nasa.gov"},"description":"The health risks associated with spaceflight-induced ocular structural and functional damage has become a recent concern for NASA. The goal of the present study was to characterize the effects of spaceflight and reentry to 1 g on the structure and integrity of the retina and blood-retinal barrier (BRB) in the eye. To investigate possible mechanisms, changes in protein expression profiles were examined in mouse ocular tissue after spaceflight. Ten week old male C57BL/6 mice were launched to the International Space Station (ISS) on Space-X 12 at the Kennedy Space Center (KSC) on August, 2017. After a 35-day mission, mice were returned to Earth alive. Within 38 +/\u2212 4 hours of splashdown, mice were euthanized and ocular tissues were collected for analysis. Ground control (GC) and vivarium control mice were maintained on Earth in flight hardware or normal vivarium cages respectively. Repeated intraocular pressure (IOP) measurements were performed before the flight launch and re-measured before the mice were euthanized after splashdown. IOP was significantly lower in post-flight measurements compared to that of pre-flight (14.4\u201319.3 mmHg vs 16.3\u201320.3 mmHg) (p less than 0.05) for the left eye. Flight group had significant apoptosis in the retina and retinal vascular endothelial cells compared to control groups (p less than 0.05). Immunohistochemical analysis of the retina revealed that an increased expression of aquaporin-4 (AQP-4) in the flight mice compared to controls gave strong indication of disturbance of BRB integrity. There were also a significant increase in the expression of platelet endothelial cell adhesion molecule-1 (PECAM-1) and a decrease in the expression of the BRB-related tight junction protein, Zonula occludens-1 (ZO-1). Proteomic analysis showed that many key proteins and pathways responsible for cell death, cell cycle, immune response, mitochondrial function and metabolic stress were significantly altered in the flight mice compared to ground control animals. These data indicate a complex cellular response that may alter retina structure and BRB integrity following long-term spaceflight.  This dataset derives results from Molecular Cellular Imaging (Microscopy) assay.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://purl.bioontology.org/ontology/NCBITAXON/10116","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/experiments/OS-891","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://osdr.nasa.gov/bio/repo/data/studies/OSD-652","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.26030/d09k-4e68","keyword":["biological-and-physical-sciences","genelab","nasa"],"license":"https://www.usa.gov/government-works","modified":"2026-08-10","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Open Science Data Repository"},"theme":["Biological and Physical Sciences"],"title":"Characterization of mouse ocular responses (Microscopy) to a 35-day (RR-9) spaceflight mission: Evidence of blood-retinal barrier disruption and ocular adaptations"},"description":"The health risks associated with spaceflight-induced ocular structural and functional damage has become a recent concern for NASA. The goal of the present study was to characterize the effects of spaceflight and reentry to 1 g on the structure and integrity of the retina and blood-retinal barrier (BRB) in the eye. To investigate possible mechanisms, changes in protein expression profiles were examined in mouse ocular tissue after spaceflight. Ten week old male C57BL/6 mice were launched to the International Space Station (ISS) on Space-X 12 at the Kennedy Space Center (KSC) on August, 2017. After a 35-day mission, mice were returned to Earth alive. Within 38 +/\u2212 4 hours of splashdown, mice were euthanized and ocular tissues were collected for analysis. Ground control (GC) and vivarium control mice were maintained on Earth in flight hardware or normal vivarium cages respectively. Repeated intraocular pressure (IOP) measurements were performed before the flight launch and re-measured before the mice were euthanized after splashdown. IOP was significantly lower in post-flight measurements compared to that of pre-flight (14.4\u201319.3 mmHg vs 16.3\u201320.3 mmHg) (p less than 0.05) for the left eye. Flight group had significant apoptosis in the retina and retinal vascular endothelial cells compared to control groups (p less than 0.05). Immunohistochemical analysis of the retina revealed that an increased expression of aquaporin-4 (AQP-4) in the flight mice compared to controls gave strong indication of disturbance of BRB integrity. There were also a significant increase in the expression of platelet endothelial cell adhesion molecule-1 (PECAM-1) and a decrease in the expression of the BRB-related tight junction protein, Zonula occludens-1 (ZO-1). Proteomic analysis showed that many key proteins and pathways responsible for cell death, cell cycle, immune response, mitochondrial function and metabolic stress were significantly altered in the flight mice compared to ground control animals. These data indicate a complex cellular response that may alter retina structure and BRB integrity following long-term spaceflight.  This dataset derives results from Molecular Cellular Imaging (Microscopy) assay.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/0a16a662-7f4c-4653-8228-65cecd5a8ec4","harvest_record_raw":"https://catalog.data.gov/harvest_record/0a16a662-7f4c-4653-8228-65cecd5a8ec4/raw","has_download":true,"has_spatial":false,"identifier":"10.26030/d09k-4e68","keyword":["biological-and-physical-sciences","genelab","nasa"],"last_harvested_date":"2026-09-23T01:08:41.657799","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":3,"publisher":"Open Science Data Repository","slug":"characterization-of-mouse-ocular-responses-microscopy-to-a-35-day-rr-9-spaceflight-mission","spatial_centroid":null,"spatial_shape":null,"theme":["Biological and Physical Sciences"],"title":"Characterization of mouse ocular responses (Microscopy) to a 35-day (RR-9) spaceflight mission: Evidence of blood-retinal barrier disruption and ocular adaptations","type":"dataset"},{"_score":8.132816,"_sort":[1790125705983,8.132816,1,"bc6ab910-b4c7-41fc-99b0-83d34ae36717"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"DISCOVERAQ_Texas_Ground_ManvelCroix_Data contains data collected at the Manvel Croix ground site during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.\r\n\r\nDISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.","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/C3880528712-LARC_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/project/DISCOVER-AQ/pdocuments","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/soot/power-user/DISCOVERAQ/2013-TX","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880528712-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://discover-aq.larc.nasa.gov/media/#news","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Texas_Ground_ManvelCroix_Data_1","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://earthobservatory.nasa.gov/blogs/earthmatters/2011/07/15/not-your-average-video-traffic-report/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_TraceabilityMatrixPage16.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://science.larc.nasa.gov/wp-content/uploads/sites/147/2022/09/DISCOVER-AQ_science.pdf","format":"PDF","mediaType":"application/pdf"},{"@type":"dcat:Distribution","downloadURL":"https://science.nasa.gov/mission/discover-aq/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3880528712-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://www-air.larc.nasa.gov/missions/discover-aq/docs/Crawford_DISCOVER-AQ_Overview_05Oct2010.pdf","format":"PDF","mediaType":"application/pdf"}],"identifier":"10.5067/ASDC/SUBORBITAL/DISCOVERAQ_Texas_Ground_ManvelCroix_Data_1","keyword":["earth-science-atmospheric-chemistry-atmosphere-nitrogen-compounds"],"license":"https://www.usa.gov/government-works","modified":"2026-09-15","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -95.4, \"EastBoundingCoordinate\": -95.39, \"SouthBoundingCoordinate\": 29.52, \"NorthBoundingCoordinate\": 29.53}]], Maximum Altitude, 70 m","temporal":"2013-08-30/2013-09-28","theme":["Earth Science"],"title":"DISCOVER-AQ Texas Deployment Manvel Croix Ground Site Data"},"description":"DISCOVERAQ_Texas_Ground_ManvelCroix_Data contains data collected at the Manvel Croix ground site during the Texas (Houston) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Texas deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. 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These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).\r\n\r\nThe first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. 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A fixed location instrument named TMTOL has been contributing ozone profiles to the Network for the Detection of Atmospheric Composition Change (NDACC), and the Tropospheric Ozone Lidar Network (TOLNet) since 2000. Five mobile instruments (SMOL-1, SMOL-2, SMOL-3, SMOL-4 and SMOL-5) started contributing ozone profiles to TOLNet in 2023, 2024 and 2025, depending on the instrument. Data collection for this product from all lidar instruments is ongoing.\n\nIn the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of seven Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. The remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.\n\nTOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. The third objective of TOLNET is to perform basic scientific research into the processes create and destroy the ubiquitously observed ozone laminae and other ozone features in the troposphere. To help fulfill these objectives, lidars that are a part of TOLNet have been deployed to support nearly ten campaigns thus far. This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry \u00c9xperiment (FRAPP\u00c9), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water\u2013Land Environmental Transition Study (OWLETS).","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/C3880797717-LARC_CLOUD.iso19115","format":"ISO","mediaType":"text/xml","title":"Original Metadata"},{"@type":"dcat:Distribution","downloadURL":"https://amt.copernicus.org/articles/18/405/2025/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/citing-data","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/outreach-material/introduction-to-tolnet-storymap","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/outreach-material/tolnet-stratospheric-intrusion-storymap","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://asdc.larc.nasa.gov/wagdocuments/473/TOLNet_Lidars_and_Corresponding_Campaigns.docx","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cmr.earthdata.nasa.gov/virtual-directory/collections/C3880797717-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1175/JTECH-D-10-05043.1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1175/JTECH-D-10-05044.1","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.1364/AO.41.007550","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5067/Lidar/Ozone/TOLNet/NASA-JPL","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5194/amt-10-3865-2017","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5194/amt-6-801-2013","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://doi.org/10.5194/amt-7-3529-2014","format":"HTML","mediaType":"text/html"},{"@type":"dcat:Distribution","downloadURL":"https://dx.doi.org/10.1364/AO.52.003557","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://search.earthdata.nasa.gov/search/granules?p=C3880797717-LARC_CLOUD","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"10.5067/Lidar/Ozone/TOLNet/NASA-JPL","keyword":["earth-science-air-quality-atmosphere-tropospheric-ozone","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species"],"license":"https://www.usa.gov/government-works","modified":"2026-09-15","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"NASA/LARC/SD/ASDC"},"spatial":"[\"CARTESIAN\", [{\"WestBoundingCoordinate\": -118.2, \"EastBoundingCoordinate\": 4.93, \"SouthBoundingCoordinate\": 29.71, \"NorthBoundingCoordinate\": 51.98}]]","temporal":"2000-01-04/2026-09-07","theme":["Earth Science"],"title":"TOLNet NASA Jet Propulsion Laboratory Data"},"description":"TOLNet_JPL_Data are lidar data collected by several ozone Differential Absorption Lidar instruments developed at the NASA Jet Propulsion Laboratory Table Mountain Facility (JPL-TMF). A fixed location instrument named TMTOL has been contributing ozone profiles to the Network for the Detection of Atmospheric Composition Change (NDACC), and the Tropospheric Ozone Lidar Network (TOLNet) since 2000. Five mobile instruments (SMOL-1, SMOL-2, SMOL-3, SMOL-4 and SMOL-5) started contributing ozone profiles to TOLNet in 2023, 2024 and 2025, depending on the instrument. Data collection for this product from all lidar instruments is ongoing.\n\nIn the troposphere, ozone is considered a pollutant and is important to understand due to its harmful effects on human health and vegetation. Tropospheric ozone is also significant for its impact on climate as a greenhouse gas. Operating since 2011, TOLNet is an interagency collaboration between NASA, NOAA, and the EPA designed to perform studies of air quality and atmospheric modeling as well as validation and interpretation of satellite observations. TOLNet is currently comprised of seven Differential Absorption Lidars (DIAL). Each of the lidars are unique, and some have had a long history of ozone observations prior to joining the network. Five lidars are mobile systems that can be deployed at remote locations to support field campaigns. This includes the Langley Mobile Ozone Lidar (LMOL) at NASA Langley Research Center (LaRC), the Tropospheric Ozone (TROPOZ) lidar at the Goddard Space Flight Center (GSFC), the Tunable Optical Profile for Aerosol and oZone (TOPAZ) lidar at the NOAA Chemical Sciences Laboratory (CSL) in Boulder, Colorado, the Autonomous Mobile Ozone LIDAR instrument for Tropospheric Experiments (AMOLITE) lidar at Environment and Climate Change Canada (ECCC) in Toronto, Canada, and the Rocket-city O3 Quality Evaluation in the Troposphere (RO3QET) lidar at the University of Alabama in Huntsville, Alabama. The remaining lidars, the Table Mountain Facility (TMF) tropospheric ozone lidar system located at the NASA Jet Propulsion Laboratory (JPL), and City College of New York (CCNY) New York Tropospheric Ozone Lidar System (NYTOLS) are fixed systems.\n\nTOLNet seeks to address three science objectives. The primary objective of the network is to provide high spatio-temporal measurements of ozone from near the surface to the top of the troposphere. Detailed observations of ozone structure allow science teams and the modeling community to better understand ozone in the lower-atmosphere and to assess the accuracy and vertical resolution with which geosynchronous instruments could retrieve the observed laminar ozone structures. Another objective of TOLNet is to identify an ozone lidar instrument design that would be suitable to address the needs of NASA, NOAA, and EPA air quality scientists who express a desire for these ozone profiles. 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This includes campaigns such as the Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) mission, the Korea United States Air Quality Study (KORUS-AQ), the Tracking Aerosol Convection ExpeRiment \u2013 Air Quality (TRACER-AQ) campaign, the Front Range Air Pollution and Photochemistry \u00c9xperiment (FRAPP\u00c9), the Long Island Sound Tropospheric Ozone Study (LISTOS), and the Ozone Water\u2013Land Environmental Transition Study (OWLETS).","distribution_titles":["Original Metadata"],"harvest_record":"https://catalog.data.gov/harvest_record/2d561162-8f35-47cf-b75b-aef3f08de501","harvest_record_raw":"https://catalog.data.gov/harvest_record/2d561162-8f35-47cf-b75b-aef3f08de501/raw","has_download":true,"has_spatial":true,"identifier":"10.5067/Lidar/Ozone/TOLNet/NASA-JPL","keyword":["earth-science-air-quality-atmosphere-tropospheric-ozone","earth-science-atmospheric-chemistry-atmosphere-oxygen-compounds","earth-science-atmospheric-chemistry-atmosphere-trace-gases-trace-species"],"last_harvested_date":"2026-09-23T01:06:11.342306","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"NASA/LARC/SD/ASDC","slug":"tolnet-nasa-jet-propulsion-laboratory-data","spatial_centroid":null,"spatial_shape":null,"theme":["Earth Science"],"title":"TOLNet NASA Jet Propulsion Laboratory Data","type":"dataset"},{"_score":7.6328945,"_sort":[1790125557359,7.6328945,7,"484b6aa2-0bff-4aab-bb18-90043bc6af18"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Earthdata Forum","hasEmail":"mailto:earthdata-support@nasa.gov"},"description":"This CYGNSS Level 1 (L1) science data record dataset contains the version 3.2 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.1: https://doi.org/10.5067/CYGNS-L1X31. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. \n\nThe correction for coarse quantization effects that was implemented in v3.1 for the signal portion of the DDM has been updated to include a correction to the noise floor portion of the DDM. This update is found to improve the sensitivity to soil moisture over land and to have a minimal effect on the sensitivity to wind speed over ocean. An update is made to the correction for the temperature dependence of the receiver electronics. This update reduces slow variations in calibration bias associated with a ~60 day oscillation in the mean temperature of the satellites. L1 variables over land and ocean are now combined in common netcdf data files, with additional details added regarding the specular point calculation over land. Nadir (science) antenna pattern and NBRCS rescaling has been updated to improve the inter-satellite consistency of the L1 calibration.\n\nThe CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space\u2010based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35\u00b0 from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. 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This data product contains data for only the Denver deployment and data collection is complete.\r\n\r\nUnderstanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. 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This N2O gas cell acts as a filter for the effects of N2O present in the atmosphere. The global distribution of N2O is well known, so the N2O signal can be used to detect the presence of clouds in the field of view and to correct the simultaneous CO measurement for systematic errors in the data.SRL-1 Mission GoalsThe MAPS SRL-1 mission took place during Northern Hemisphere Spring when global biomass burning does not typically occur. Some burning may occur for the purpose of clearing the damaged and felled trees in the forests of North America after the rather severe winter. The goals of the MAPS SRL-1 mission are to provide a validated, near-global atlas of the distribution of tropospheric Carbon Monoxide during the mission, and to assess the health status of the MAPS instrument as the mission progresses. SL1 SummaryHigh concentrations of carbon monoxide over the Northern Hemisphere can be seen in measurements made by the Measurement of Air Pollution from Space(MAPS) instrument. 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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-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/fa38f35b-4dcc-4004-921b-c94f1fe2f971","harvest_record_raw":"https://catalog.data.gov/harvest_record/fa38f35b-4dcc-4004-921b-c94f1fe2f971/raw","has_download":false,"has_spatial":false,"identifier":"urn:nasa:pds:context_pds3:data_set:data_set.ro-c-cosima-3-v3.0;urn:nasa:pds:context_pds3:data_set:data_set.ro-c-cosima-3-v3.0::1.0","keyword":["__"],"last_harvested_date":"2026-09-23T00:50:23.012140","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"Small Bodies","slug":"rosetta-orbiter-67p-cosima-3-v3-0-f22ee","spatial_centroid":null,"spatial_shape":null,"theme":["Planetary Science"],"title":"ROSETTA-ORBITER 67P COSIMA 3 V3.0","type":"dataset"},{"_score":10.388544,"_sort":[1790124328571,10.388544,1,"57705fcf-2436-497c-8b87-0ef6bab98b43"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Planetary Data System","hasEmail":"mailto:pds-operator@jpl.nasa.gov"},"description":"Payload Checkout 8 (PC8) was an active checkout where a target independent opportunity to perform interactive operations and request spacecraft pointing was given to all Rosetta payload t All Rosetta payload took part in this scenario.The Active Payl Checkout 8 ran for 2 consecutive days (05-06 July2008) plus 26 consecutive days starting on the 9th July 2008 until the 1st A 2008. This is approximately twice the allocated time of the ac PC6 scenario that preceded it. PC8 consists of two pha similar to the previous Passive Payload Checkouts the 2nd phas an active test; GD02 is a Non nominal operational configuratio test (Only Impact Sensor operational and cover closed), in GD0 we have successfully tested a non-standard configuration, in was a test to investigate interference from other instruments. Redundant I/Fs in sequence and executing similar procedures fo two cases. GD02, GD03 and GD_INT were executed only on Main I/ ADC counts to engineering values. The quality of the Housekeeping and Calibration data is good. Scientific data are due to noise, as no grain event is expected during this mission phase. These data must be only considered to evaluate GIADA behaviour and not as real scientific data. Data reported by GDS and IS are due to noise as no dust event is expected during this mission phase. MBS frequency changes, once normalized for frequency vs. temperature dependence, if present, are due to deposition of contaminants existing in the S/C environment. Housekeeping and Calibration data from all GIADA sub-systems are useful to evaluate instrument health and behaviour when compared with similar data acquired during other mission phases.","identifier":"urn:nasa:pds:context_pds3:data_set:data_set.ro-x-gia-2-cr4a-cruise4a-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.ro-x-gia-2-cr4a-cruise4a-v1.0::1.0","keyword":["__"],"license":"https://www.usa.gov/government-works","modified":"2026-09-21","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Small Bodies"},"theme":["Planetary Science"],"title":"ROSETTA-ORBITER CHECK GIADA 2 CR4A CRUISE4A V1.0"},"description":"Payload Checkout 8 (PC8) was an active checkout where a target independent opportunity to perform interactive operations and request spacecraft pointing was given to all Rosetta payload t All Rosetta payload took part in this scenario.The Active Payl Checkout 8 ran for 2 consecutive days (05-06 July2008) plus 26 consecutive days starting on the 9th July 2008 until the 1st A 2008. This is approximately twice the allocated time of the ac PC6 scenario that preceded it. PC8 consists of two pha similar to the previous Passive Payload Checkouts the 2nd phas an active test; GD02 is a Non nominal operational configuratio test (Only Impact Sensor operational and cover closed), in GD0 we have successfully tested a non-standard configuration, in was a test to investigate interference from other instruments. Redundant I/Fs in sequence and executing similar procedures fo two cases. GD02, GD03 and GD_INT were executed only on Main I/ ADC counts to engineering values. The quality of the Housekeeping and Calibration data is good. Scientific data are due to noise, as no grain event is expected during this mission phase. These data must be only considered to evaluate GIADA behaviour and not as real scientific data. Data reported by GDS and IS are due to noise as no dust event is expected during this mission phase. MBS frequency changes, once normalized for frequency vs. temperature dependence, if present, are due to deposition of contaminants existing in the S/C environment. Housekeeping and Calibration data from all GIADA sub-systems are useful to evaluate instrument health and behaviour when compared with similar data acquired during other mission phases.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/13df3dc2-e93a-4549-9bb6-3d3160fbfe91","harvest_record_raw":"https://catalog.data.gov/harvest_record/13df3dc2-e93a-4549-9bb6-3d3160fbfe91/raw","has_download":false,"has_spatial":false,"identifier":"urn:nasa:pds:context_pds3:data_set:data_set.ro-x-gia-2-cr4a-cruise4a-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.ro-x-gia-2-cr4a-cruise4a-v1.0::1.0","keyword":["__"],"last_harvested_date":"2026-09-23T00:45:28.571562","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"Small Bodies","slug":"rosetta-orbiter-check-giada-2-cr4a-cruise4a-v1-0-0c35f","spatial_centroid":null,"spatial_shape":null,"theme":["Planetary Science"],"title":"ROSETTA-ORBITER CHECK GIADA 2 CR4A CRUISE4A V1.0","type":"dataset"},{"_score":3.4155464,"_sort":[1790124172000,3.4155464,2,"67e6ffeb-8035-4ff9-b6bc-9e7fbf2b2775"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","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_spb_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 \u00b160\u00b0 in Theta and 240\u00b0 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":[{"@type":"dcat:Distribution","downloadURL":"ftps://spdf.gsfc.nasa.gov/pub/data/psp/sweap/spe/l2/spb_sf0_16ax8dx32e/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cdaweb.gsfc.nasa.gov/cgi-bin/eval2.cgi?dataset=PSP_SWP_SPB_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-B_Level2_Electrons3D_PT14S","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://hpde.io/NASA/NumericalData/ParkerSolarProbe/SWEAP/SPAN-B/Level2/Electrons3D/PT14S","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://spdf.gsfc.nasa.gov/pub/data/psp/sweap/spe/l2/spb_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"}],"identifier":"https://doi.org/10.48322/f1vx-0f86","keyword":["instrumentstatus","thermalplasma"],"landingPage":"https://doi.org/10.48322/f1vx-0f86","license":"https://www.usa.gov/government-works","modified":"2026-09-21","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"SPDF"},"theme":["Heliophysics"],"title":"PSP Solar Wind Electrons Alphas and Protons (SWEAP) SPAN-B 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_spb_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 \u00b160\u00b0 in Theta and 240\u00b0 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/c554b9bf-7d15-424d-af93-630813541917","harvest_record_raw":"https://catalog.data.gov/harvest_record/c554b9bf-7d15-424d-af93-630813541917/raw","has_download":true,"has_spatial":false,"identifier":"https://doi.org/10.48322/f1vx-0f86","keyword":["instrumentstatus","thermalplasma"],"last_harvested_date":"2026-09-23T00:42:52.000441","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"SPDF","slug":"psp-solar-wind-electrons-alphas-and-protons-sweap-span-b-full-3d-electron-spectra-level-2-","spatial_centroid":null,"spatial_shape":null,"theme":["Heliophysics"],"title":"PSP Solar Wind Electrons Alphas and Protons (SWEAP) SPAN-B Full 3D Electron Spectra, Level 2 (L2), 14 s Data","type":"dataset"},{"_score":3.6267014,"_sort":[1790124168177,3.6267014,1,"0371a43c-b561-4dd1-b742-98f04649d586"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","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_spb_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 \u00b160\u00b0 in Theta and 240\u00b0 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":[{"@type":"dcat:Distribution","downloadURL":"ftps://spdf.gsfc.nasa.gov/pub/data/psp/sweap/spe/l2/spb_sf1_32e/","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://cdaweb.gsfc.nasa.gov/cgi-bin/eval2.cgi?dataset=PSP_SWP_SPB_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-B_Level2_ElectronsFullSpectra_PT1.74S","format":"BIN","mediaType":"application/octet-stream"},{"@type":"dcat:Distribution","downloadURL":"https://hpde.io/NASA/NumericalData/ParkerSolarProbe/SWEAP/SPAN-B/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/spb_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"}],"identifier":"https://doi.org/10.48322/db2p-rk78","keyword":["instrumentstatus","thermalplasma"],"landingPage":"https://doi.org/10.48322/db2p-rk78","license":"https://www.usa.gov/government-works","modified":"2026-09-21","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"SPDF"},"theme":["Heliophysics"],"title":"PSP Solar Wind Electrons Alphas and Protons (SWEAP) SPAN-B 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_spb_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 \u00b160\u00b0 in Theta and 240\u00b0 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. 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The dataset includes state-of-health data, suchas temperature and voltage readings, needed for the analysis of the countingdata. The EDR is an intermediate data product derived from the raw datarecords using reversible operations. All higher order data products arederived from the EDR. An automated pipeline is used to process the EDR fromthe raw data records.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/f956ec98-bf91-4b68-9f6f-8b063ec769b3","harvest_record_raw":"https://catalog.data.gov/harvest_record/f956ec98-bf91-4b68-9f6f-8b063ec769b3/raw","has_download":false,"has_spatial":false,"identifier":"urn:nasa:pds:context_pds3:data_set:data_set.dawn-m-grand-2-edr-mars-counts-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.dawn-m-grand-2-edr-mars-counts-v1.0::3.0","keyword":["__"],"last_harvested_date":"2026-09-23T00:42:19.509700","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":1,"publisher":"Small Bodies","slug":"dawn-grand-raw-edr-mars-flyby-counts-v1-0-46930","spatial_centroid":null,"spatial_shape":null,"theme":["Planetary Science"],"title":"DAWN GRAND RAW (EDR) MARS FLYBY COUNTS V1.0","type":"dataset"},{"_score":11.995376,"_sort":[1790124100007,11.995376,2,"8a224164-6c9d-4069-a7ca-3eedfe18c7d3"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Planetary Data System","hasEmail":"mailto:pds-operator@jpl.nasa.gov"},"description":"This volume contains Experiment Data acquired by GIADA during 'Commissioning 2' phase. More in detail it refers to the data provided during the following in-flight scenarios: 'Interference 1' held on 20/21/22-09-2004; 'Pointing 1' held on 23-09-2004; 'Pointing 2' held on 30-09-2004; 'Interference 2' held on 12/13/14-10-2004. It also contains documentation which describes the GIADA experiment. The data reported in this data set have been converted from ADC counts to engineering values. The quality of the Housekeeping and Calibration data is good. Scientific data are due to noise, as no grain event is expected during this mission phase. These data must be only considered to evaluate GIADA behaviour and not as real scientific data. Data reported by GDS and IS are due to noise as no dust event is expected during this mission phase. MBS frequency changes, once normalised for frequency vs. temperature dependence, if present, are due to deposition of contaminants existing in the S/C environment. Housekeeping and Calibration data from all GIADA sub-systems are useful to evaluate instrument health and behaviour when compared with similar data acquired during other mission phases.","identifier":"urn:nasa:pds:context_pds3:data_set:data_set.ro-x-gia-2-cvp2-commissioning2-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.ro-x-gia-2-cvp2-commissioning2-v1.0::1.0","keyword":["__"],"license":"https://www.usa.gov/government-works","modified":"2026-09-21","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Small Bodies"},"theme":["Planetary Science"],"title":"ROSETTA-ORBITER CHECK GIADA 2 CVP2 COMMISSIONING2 V1.0"},"description":"This volume contains Experiment Data acquired by GIADA during 'Commissioning 2' phase. More in detail it refers to the data provided during the following in-flight scenarios: 'Interference 1' held on 20/21/22-09-2004; 'Pointing 1' held on 23-09-2004; 'Pointing 2' held on 30-09-2004; 'Interference 2' held on 12/13/14-10-2004. It also contains documentation which describes the GIADA experiment. The data reported in this data set have been converted from ADC counts to engineering values. The quality of the Housekeeping and Calibration data is good. Scientific data are due to noise, as no grain event is expected during this mission phase. These data must be only considered to evaluate GIADA behaviour and not as real scientific data. Data reported by GDS and IS are due to noise as no dust event is expected during this mission phase. MBS frequency changes, once normalised for frequency vs. temperature dependence, if present, are due to deposition of contaminants existing in the S/C environment. Housekeeping and Calibration data from all GIADA sub-systems are useful to evaluate instrument health and behaviour when compared with similar data acquired during other mission phases.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/d7601677-aff5-4e17-9f73-99242c2614d2","harvest_record_raw":"https://catalog.data.gov/harvest_record/d7601677-aff5-4e17-9f73-99242c2614d2/raw","has_download":false,"has_spatial":false,"identifier":"urn:nasa:pds:context_pds3:data_set:data_set.ro-x-gia-2-cvp2-commissioning2-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.ro-x-gia-2-cvp2-commissioning2-v1.0::1.0","keyword":["__"],"last_harvested_date":"2026-09-23T00:41:40.007727","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"f4ca4614-8901-409b-8553-2e994ad10023","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png","name":"National Aeronautics and Space Administration","organization_type":"Federal Government","slug":"nasa"},"parent_identifier":null,"popularity":2,"publisher":"Small Bodies","slug":"rosetta-orbiter-check-giada-2-cvp2-commissioning2-v1-0-12690","spatial_centroid":null,"spatial_shape":null,"theme":["Planetary Science"],"title":"ROSETTA-ORBITER CHECK GIADA 2 CVP2 COMMISSIONING2 V1.0","type":"dataset"},{"_score":16.109661,"_sort":[1790124089389,16.109661,1,"2d2d4f6e-f788-4e85-a0b0-b90abd800b2f"],"dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Planetary Data System","hasEmail":"mailto:pds-operator@jpl.nasa.gov"},"description":"The GRaND EDR are a time-ordered collection of gamma rayand neutron counting data and histograms acquired by GRaND during allphases of the Dawn mission. The dataset includes state-of-health data,such as temperature and voltage readings, needed for the analysis of thecounting data. The EDR is an intermediate data product derived from theraw data records using reversible operations. All higher order dataproducts are derived from the EDR. An automated pipeline is used toprocess the EDR from the raw data records.","identifier":"urn:nasa:pds:context_pds3:data_set:data_set.dawn-a-grand-2-edr-ceres-counts-v1.0;urn:nasa:pds:context_pds3:data_set:data_set.dawn-a-grand-2-edr-ceres-counts-v1.0::6.0","keyword":["__"],"license":"https://www.usa.gov/government-works","modified":"2026-09-21","programCode":["026:000"],"publisher":{"@type":"org:Organization","name":"Small Bodies"},"theme":["Planetary Science"],"title":"DAWN GRAND RAW (EDR) CERES COUNTS V1.0"},"description":"The GRaND EDR are a time-ordered collection of gamma rayand neutron counting data and histograms acquired by GRaND during allphases of the Dawn mission. The dataset includes state-of-health data,such as temperature and voltage readings, needed for the analysis of thecounting data. The EDR is an intermediate data product derived from theraw data records using reversible operations. All higher order dataproducts are derived from the EDR. 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