{"after":"WzE3OTEzMjg0MDU3MjQsNzEuODUzODMsMzYsImRjNWFiYzZkLWUxMWQtNGJkYy04NDc5LWJhZTQzYWQwYWY3MSJd","results":[{"_score":15.964565,"_sort":[1791335028960,15.964565,2,"d2a09131-82b5-486a-9713-027b64a7e630"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Miryam Strautkalns","hasEmail":"mailto:miryam.strautkalns@nasa.gov"},"description":"Prognostics technologies determine the health (or damage) state of a component or sub- system, and make end of life (EOL) and remaining useful life (RUL) predictions. Such infor- mation enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics method- ology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the ow of propellant, so failures may have a signi cant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle ltering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. We develop a prototype user interface for valve prognostics, and demonstrate the prognostics approach using historical pneumatic valve data from the Space Shuttle refueling system.","distribution":[{"@type":"dcat:Distribution","description":"2011_AIAA_Valves.pdf","downloadURL":"https://c3.nasa.gov/dashlink/static/media/publication/2011_AIAA_Valves.pdf","format":"PDF","mediaType":"application/pdf","title":"2011_AIAA_Valves.pdf"}],"identifier":"DASHLINK_779","issued":"2013-06-19","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/779/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"Prognostics for Ground Support Systems: Case Study on Pneumatic Valves"},"description":"Prognostics technologies determine the health (or damage) state of a component or sub- system, and make end of life (EOL) and remaining useful life (RUL) predictions. Such infor- mation enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics method- ology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the ow of propellant, so failures may have a signi cant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle ltering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. 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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","description":"FLTz FOQA G-causality Run9.zip","downloadURL":"https://c3.nasa.gov/dashlink/static/media/dataset/FLTz_FOQA_G-causality_Run9.zip","format":"ZIP","mediaType":"application/zip","title":"FLTz FOQA G-causality Run9.zip"}],"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. 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The technique is intended for condition monitoring in high reliability applications where the knowledge of impending failure is critical and the risks in terms of loss of functionality are too high to bear. Future state of the system has been estimated based on a second-order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage in the form of inelastic strain energy density. Performance of the prognostic health management algorithm during the vibration test has been quantified using performance evaluation metrics. The method- ology has been demonstrated on leadfree area-array electronic assemblies subjected to vibration. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including \u03b1 \u2212 \u03bb precision, \u03b2 accuracy, and relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://curator.jsc.nasa.gov/lunar/catalogs/other/A16_4_10mm.pdf","format":"PDF","mediaType":"application/pdf"}],"identifier":"DASHLINK_760","issued":"2013-06-19","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/760/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"Prognostics Health Management of Electronic Systems Under Mechanical Shock and Vibration Using Kalman Filter Models and Metrics"},"description":"Structural damage to ball grid array interconnects incurred during vibration testing has been monitored in the prefailure space using resistance spectroscopy-based state space vectors, rate of change of the state variable, and acceleration of the state variable. 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Prognostic metrics including \u03b1 \u2212 \u03bb precision, \u03b2 accuracy, and relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/84d2c31b-f859-4166-9701-56919e7f66ca","harvest_record_raw":"https://catalog.data.gov/harvest_record/84d2c31b-f859-4166-9701-56919e7f66ca/raw","has_download":true,"has_spatial":false,"identifier":"DASHLINK_760","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-10-07T01:02:46.628214","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":"Dashlink","slug":"prognostics-health-management-of-electronic-systems-under-mechanical-shock-and-vibration-u","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Prognostics Health Management of Electronic Systems Under Mechanical Shock and Vibration Using Kalman Filter Models and Metrics","type":"dataset"},{"_score":15.6816025,"_sort":[1791334933955,15.6816025,1,"31f584dc-749e-43ab-8e07-c1a266d71ef7"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Jose Celaya Galvan","hasEmail":"mailto:jose.r.celayagalvan@nasa.gov"},"description":"A remaining useful life prediction methodology for electrolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated aging test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation progression data from accelerated aging, provides an avenue for validation of applications of the Kalman filter based prognostics methods typically used for remaining useful life predictions in other applications.","distribution":[{"@type":"dcat:Distribution","description":"Access the data via HTTPS.","downloadURL":"https://acdisc.gesdisc.eosdis.nasa.gov/data/UARS_Correlative_Level4/UARZCUKM/","format":"HTML","mediaType":"text/html","title":"Download this dataset through a directory map"},{"@type":"dcat:Distribution","description":"Access the dataset landing page from the GES DISC website.","downloadURL":"https://disc.gsfc.nasa.gov/datacollection/UARZCUKM_001.html","format":"HTML","mediaType":"text/html","title":"This dataset's landing page"},{"@type":"dcat:Distribution","description":"README Document","downloadURL":"https://acdisc.gesdisc.eosdis.nasa.gov/data/UARS_Correlative_Level4/UARZCUKM/doc/README.UARCorr.doc","format":"DOC","mediaType":"application/msword","title":"View this dataset's read me document"},{"@type":"dcat:Distribution","description":"The UARS Project Homepage.","downloadURL":"https://uars.gsfc.nasa.gov/","format":"HTML","mediaType":"text/html","title":"The dataset's project home page"},{"@type":"dcat:Distribution","description":"Use the Earthdata Search to find and retrieve data sets across multiple data centers.","downloadURL":"https://search.earthdata.nasa.gov/search?q=UARZCUKM","format":"HTML","mediaType":"text/html","title":"Download this dataset through Earthdata Search"},{"@type":"dcat:Distribution","downloadURL":"https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/UARZCUKM_001.png","format":"PNG","mediaType":"image/png","title":"Get a related visualization"}],"identifier":"DASHLINK_856","issued":"2013-12-12","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/856/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"A Model-Based Prognostics Methodology For Electrolytic Capacitors Based On Electrical Overstress Accelerated Aging"},"description":"A remaining useful life prediction methodology for electrolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated aging test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation progression data from accelerated aging, provides an avenue for validation of applications of the Kalman filter based prognostics methods typically used for remaining useful life predictions in other applications.","distribution_titles":["Download this dataset through a directory map","This dataset's landing page","View this dataset's read me document","The dataset's project home page","Download this dataset through Earthdata Search","Get a related visualization"],"harvest_record":"https://catalog.data.gov/harvest_record/153123fc-2888-4331-bc9e-32a744203c54","harvest_record_raw":"https://catalog.data.gov/harvest_record/153123fc-2888-4331-bc9e-32a744203c54/raw","has_download":true,"has_spatial":false,"identifier":"DASHLINK_856","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-10-07T01:02:13.955422","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":"Dashlink","slug":"a-model-based-prognostics-methodology-for-electrolytic-capacitors-based-on-electrical-over-fbaca","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"A Model-Based Prognostics Methodology For Electrolytic Capacitors Based On Electrical Overstress Accelerated Aging","type":"dataset"},{"_score":10.07247,"_sort":[1791334929679,10.07247,1,"284dd584-765a-4c8b-ab9c-2b77ea99f501"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Beth Beck","hasEmail":"mailto:beth.beck@nasa.gov"},"description":"Prognostics is an emerging concept in condition basedmaintenance(CBM)ofcriticalsystems.Alongwith 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 maintenancehave 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 specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.","distribution":[{"@type":"dcat:Distribution","downloadURL":"http://nasa3d.arc.nasa.gov/shared_assets/models/agena-c/agena-c.zip","format":"BIN","mediaType":"application/octet-stream"}],"identifier":"DASHLINK_739","issued":"2013-05-13","keyword":["ames","dashlink","nasa"],"landingPage":"https://c3.nasa.gov/dashlink/resources/739/","modified":"2025-03-31","programCode":["026:029"],"publisher":{"@type":"org:Organization","name":"Dashlink"},"title":"A Survey of Metrics for Performance Evaluation of Prognostics"},"description":"Prognostics is an emerging concept in condition basedmaintenance(CBM)ofcriticalsystems.Alongwith 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 maintenancehave 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 specific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the least, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/5cc880dc-c860-406e-a39b-c3d25e75928c","harvest_record_raw":"https://catalog.data.gov/harvest_record/5cc880dc-c860-406e-a39b-c3d25e75928c/raw","has_download":true,"has_spatial":false,"identifier":"DASHLINK_739","keyword":["ames","dashlink","nasa"],"last_harvested_date":"2026-10-07T01:02:09.679890","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":"Dashlink","slug":"a-survey-of-metrics-for-performance-evaluation-of-prognostics","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"A Survey of Metrics for Performance Evaluation of Prognostics","type":"dataset"},{"_score":16.38534,"_sort":[1791334926801,16.38534,7,"a35e6d84-5722-4711-bba9-b664f1486b01"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["026:00"],"contactPoint":{"@type":"vcard:Contact","fn":"Kai Goebel","hasEmail":"mailto:kai.goebel@nasa.gov"},"description":"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":"chapter","downloadURL":"https://c3.nasa.gov/dashlink/static/media/publication/c17_2.pdf","format":"PDF","mediaType":"application/pdf","title":"c17.pdf"}],"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. 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Current state-of-the-art PHM systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to loss of functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become unsuitable for successful deployment, and efficient distributed architectures are required. A distributed architecture though, is not effective unless there is an algorithmic framework to take advantage of its unique abilities. The health management paradigm envisaged here incorporates a heterogeneous set of system components monitored by a varied suite of sensors and a particle filtering (PF) framework that has the power and the flexibility to adapt to the different diagnostic and prognostic needs. Both the diagnostic and prognostic tasks are formulated as a particle filtering problem in order to explicitly represent and manage uncertainties; however, typically the complexity of the prognostic routine is higher than the computational power of one computational element (CE). Individual CEs run diagnostic routines until the system variable being monitored crosses beyond a nominal threshold, upon which it coordinates with other networked CEs to run the prognostic routine in a distributed fashion. 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In each File, following a detailed Description of the Format, the Data consist of a Listing of Counts accumulated in the H10-H27 Spin-Averaged Counting Rates during the Intervals between successive Readouts, organized into Columns as described below. The first four Columns give the Start Time of the Counting Rate Accumulation Period as Fractional Year (to 12 Decimal Places) and as Year since 1900, Day of Year, and Milliseconds of Day at the Start of the Accumulation, followed by one Column giving the Duration of the Accumulation Period in Milliseconds (it is the same for all Rates except H13, which is read out twice in each Accumulation Period, and is thus represented as H13a and H13b, see below and embedded Documentation in each Daily File), and 19 Columns giving the Counts accumulated in that Period for each Rate. All Fields except for the Fractional Year are in Integer Format. The Start Time for H13a is the Time given as the Start Time for the Line, but (to sufficient accuracy) its Accumulation Period is half the Period given under \"Coverage\". Again, to sufficient accuracy, the Start Time for H13b is the Time given plus half the Accumulation Period, and the Accumulation Period for H13b is half the Period given under \"Coverage\". Each Line contains Data for one single Readout of the Rates H10-H12, H13a, H13b, and H14-H27. All Readouts where at least one of the Rates has a Non-fill Value are included. Where Fill does occur it is indicated by -1. Since the Accumulation Period for each Readout is forced to include an Integral Number of Spacecraft Spins in order to produce pure Spin-Averaged Measurements, the Lengths of the Accumulation Periods vary in a Cyclic Manner as the Period of the Telemetry Cycle beats with the Spacecraft Spin Period. At the most common Science Telemetry Rate, 2048 bps, each Rate in this File is Readout on average once every 128 s, except for H13 which is Readout on average twice in every 128 s. At 2048 bps, the Cycle of Accumulation Periods is 132, 132, 120, 132, 132,, 120, 132, etc., seconds. 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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. 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The Health Department does not endorse datasets using these data that may be created by others. The dataset contains the self-inspections of drinking water tanks that have been reported to the DOHMH through the NYC Drinking Water Tank Inspection Reporting Site [https://www.nyc.gov/site/doh/business/permits-and-licenses/drinking-water-tank-inspection-reporting.page] by a certified water tank inspector on behalf of the building owner. Submissions of 0 Drinking Water Tanks are not displayed in this dataset.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.cityofnewyork.us/api/views/gjm4-k24g/columns.json","describedByType":"application/json","downloadURL":"https://data.cityofnewyork.us/api/v3/views/gjm4-k24g/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.cityofnewyork.us/api/views/gjm4-k24g/columns.xml","describedByType":"application/xml","downloadURL":"https://data.cityofnewyork.us/api/v3/views/gjm4-k24g/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cityofnewyork.us/api/v3/views/gjm4-k24g/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.cityofnewyork.us/api/views/gjm4-k24g","issued":"2018-10-23","keyword":["drinking water","inspection","watertanks"],"landingPage":"https://data.cityofnewyork.us/d/gjm4-k24g","modified":"2026-10-06","publisher":{"@type":"org:Organization","name":"data.cityofnewyork.us"},"theme":["Health"],"title":"Self-Reported Drinking Water Tank Inspection Results"},"description":"This is an official DOHMH dataset. The Health Department does not endorse datasets using these data that may be created by others. The dataset contains the self-inspections of drinking water tanks that have been reported to the DOHMH through the NYC Drinking Water Tank Inspection Reporting Site [https://www.nyc.gov/site/doh/business/permits-and-licenses/drinking-water-tank-inspection-reporting.page] by a certified water tank inspector on behalf of the building owner. Submissions of 0 Drinking Water Tanks are not displayed in this dataset.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/0be4dfc2-4f06-4494-b767-e965382a71c5","harvest_record_raw":"https://catalog.data.gov/harvest_record/0be4dfc2-4f06-4494-b767-e965382a71c5/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cityofnewyork.us/api/views/gjm4-k24g","keyword":["drinking water","inspection","watertanks"],"last_harvested_date":"2026-10-06T23:18:55.554761","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"1149ee63-2fff-494e-82e5-9aace9d3b3bf","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_new_york_ny.png","name":"City of New York","organization_type":"City Government","slug":"nyc-ny"},"parent_identifier":null,"popularity":1,"publisher":"data.cityofnewyork.us","slug":"self-reported-drinking-water-tank-inspection-results","spatial_centroid":null,"spatial_shape":null,"theme":["Health"],"title":"Self-Reported Drinking Water Tank Inspection Results","type":"dataset"},{"_score":5.4193163,"_sort":[1791328687916,5.4193163,4,"094a50a4-11b4-4ad0-96d9-11188d0b749f"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"NYC OpenData","hasEmail":"mailto:no-reply@data.cityofnewyork.us"},"description":"Indoor Environmental Complaints \r\n\r\nThis data is largely received via complaints to 311; Each record represents a single complaint; This data can be used to help determine if the DOHMH has received an environmental complaint from a particular address; The listing of a particular address does not indicate that a condition or violation was found, only that a complaint was made to DOHMH","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.cityofnewyork.us/api/views/9jgj-bmct/columns.json","describedByType":"application/json","downloadURL":"https://data.cityofnewyork.us/api/v3/views/9jgj-bmct/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.cityofnewyork.us/api/views/9jgj-bmct/columns.xml","describedByType":"application/xml","downloadURL":"https://data.cityofnewyork.us/api/v3/views/9jgj-bmct/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cityofnewyork.us/api/v3/views/9jgj-bmct/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.cityofnewyork.us/api/views/9jgj-bmct","issued":"2018-10-09","keyword":["untagged"],"landingPage":"https://data.cityofnewyork.us/d/9jgj-bmct","modified":"2026-10-06","publisher":{"@type":"org:Organization","name":"data.cityofnewyork.us"},"theme":["Health"],"title":"DOHMH Indoor Environmental Complaints"},"description":"Indoor Environmental Complaints \r\n\r\nThis data is largely received via complaints to 311; Each record represents a single complaint; This data can be used to help determine if the DOHMH has received an environmental complaint from a particular address; The listing of a particular address does not indicate that a condition or violation was found, only that a complaint was made to DOHMH","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/89c41b63-ce57-47cc-8222-53ce28212fcb","harvest_record_raw":"https://catalog.data.gov/harvest_record/89c41b63-ce57-47cc-8222-53ce28212fcb/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cityofnewyork.us/api/views/9jgj-bmct","keyword":["untagged"],"last_harvested_date":"2026-10-06T23:18:07.916032","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"1149ee63-2fff-494e-82e5-9aace9d3b3bf","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_new_york_ny.png","name":"City of New York","organization_type":"City Government","slug":"nyc-ny"},"parent_identifier":null,"popularity":4,"publisher":"data.cityofnewyork.us","slug":"dohmh-indoor-environmental-complaints","spatial_centroid":null,"spatial_shape":null,"theme":["Health"],"title":"DOHMH Indoor Environmental Complaints","type":"dataset"},{"_score":14.949815,"_sort":[1791328680375,14.949815,16,"ae6187c0-bf83-47ae-978b-c707399b709c"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"NYC OpenData","hasEmail":"mailto:no-reply@data.cityofnewyork.us"},"description":"The Fire Incident Dispatch Data file contains data that is generated by the Starfire Computer Aided Dispatch System. The data spans from the time the incident is created in the system to the time the incident is closed in the system.  It covers information about the incident as it relates to the assignment of resources and the Fire Department\u2019s response to the emergency. To protect personal identifying information in accordance with the Health Insurance Portability and Accountability Act (HIPAA), specific locations of incidents are not included and have been aggregated to a higher level of detail.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.cityofnewyork.us/api/views/8m42-w767/columns.json","describedByType":"application/json","downloadURL":"https://data.cityofnewyork.us/api/v3/views/8m42-w767/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.cityofnewyork.us/api/views/8m42-w767/columns.xml","describedByType":"application/xml","downloadURL":"https://data.cityofnewyork.us/api/v3/views/8m42-w767/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cityofnewyork.us/api/v3/views/8m42-w767/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.cityofnewyork.us/api/views/8m42-w767","issued":"2023-11-24","keyword":["2018od4a-report","dispatch","fdny","fire"],"landingPage":"https://data.cityofnewyork.us/d/8m42-w767","modified":"2026-10-05","publisher":{"@type":"org:Organization","name":"data.cityofnewyork.us"},"theme":["Public Safety"],"title":"Fire Incident Dispatch Data"},"description":"The Fire Incident Dispatch Data file contains data that is generated by the Starfire Computer Aided Dispatch System. The data spans from the time the incident is created in the system to the time the incident is closed in the system.  It covers information about the incident as it relates to the assignment of resources and the Fire Department\u2019s response to the emergency. To protect personal identifying information in accordance with the Health Insurance Portability and Accountability Act (HIPAA), specific locations of incidents are not included and have been aggregated to a higher level of detail.","distribution_titles":[],"harvest_record":"https://catalog.data.gov/harvest_record/77957232-c963-4abe-bee1-cdb0f583f3df","harvest_record_raw":"https://catalog.data.gov/harvest_record/77957232-c963-4abe-bee1-cdb0f583f3df/raw","has_download":true,"has_spatial":false,"identifier":"https://data.cityofnewyork.us/api/views/8m42-w767","keyword":["2018od4a-report","dispatch","fdny","fire"],"last_harvested_date":"2026-10-06T23:18:00.375624","organization":{"aliases":[""],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"1149ee63-2fff-494e-82e5-9aace9d3b3bf","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/city_new_york_ny.png","name":"City of New York","organization_type":"City Government","slug":"nyc-ny"},"parent_identifier":null,"popularity":16,"publisher":"data.cityofnewyork.us","slug":"fire-incident-dispatch-data","spatial_centroid":null,"spatial_shape":null,"theme":["Public Safety"],"title":"Fire Incident Dispatch Data","type":"dataset"},{"_score":16.199497,"_sort":[1791328670131,16.199497,5,"7559a255-0228-43a1-b712-0d331f1c20f7"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"NYC OpenData","hasEmail":"mailto:no-reply@data.cityofnewyork.us"},"description":"Daily inmates in custody with attributes (custody level, mental health designation, race, gender, age, leagal status, sealed status, security risk group membership, top charge,  and infraction flag). This data set excludes Sealed Cases. Resulting summaries may differ slightly from other published statistics.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.cityofnewyork.us/api/views/7479-ugqb/columns.json","describedByType":"application/json","downloadURL":"https://data.cityofnewyork.us/api/v3/views/7479-ugqb/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.cityofnewyork.us/api/views/7479-ugqb/columns.xml","describedByType":"application/xml","downloadURL":"https://data.cityofnewyork.us/api/v3/views/7479-ugqb/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.cityofnewyork.us/api/v3/views/7479-ugqb/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"}],"identifier":"https://data.cityofnewyork.us/api/views/7479-ugqb","issued":"2016-03-23","keyword":["untagged"],"landingPage":"https://data.cityofnewyork.us/d/7479-ugqb","modified":"2026-10-06","publisher":{"@type":"org:Organization","name":"data.cityofnewyork.us"},"theme":["Public Safety"],"title":"Daily Inmates In Custody"},"description":"Daily inmates in custody with attributes (custody level, mental health designation, race, gender, age, leagal status, sealed status, security risk group membership, top charge,  and infraction flag). 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