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ShareScore release 0.9.0
Dataset results
76 results for “health state”
145/5000 Oral State and Knowledge, Attitudes and Practices of Pregnant Women on Their Oral Health and That of Their Unborn Child
ClinicalTrials.gov study NCT04269759. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Impact of Reactogenicity of the 2024-2025 COVID-19 Vaccines on Health Care Workers and First Responders in the United States
ClinicalTrials.gov study NCT06633835. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Impact Evaluation of Access to Free Low-Cost Health Insurance in Nigeria's Taraba State
ClinicalTrials.gov study NCT07313631. IPD Sharing: YES. Countries: 1. Publications: 0.
Development & Validation of Utilities for Health States Relevant to Cervical Cancer Patients
ClinicalTrials.gov study NCT00788216. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Heterogeneous Distribution of Health Literacy and Emotional State on Patients With Functional Dyspepsia.
ClinicalTrials.gov study NCT02737683. IPD Sharing: NO. Countries: 1. Publications: 0.
Penn State Hershey Sitting and Health Study
ClinicalTrials.gov study NCT03274635. IPD Sharing: NO. Countries: 1. Publications: 0.
State-wide Health Approach to Increase Reach and Effectiveness: Study 2
ClinicalTrials.gov study NCT01560130. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: The prevalence of MS in the United States: a population-based estimate using health claims data
Open the record for dataset details and reuse information.
Comorbidities and Indirect Health State Utility in Hip and Knee Osteoarthritis
ClinicalTrials.gov study NCT02879890. IPD Sharing: YES. Countries: 0. Publications: 0.
Expansion of the COPD At-risk Module in 4 States BRFSS Telephone Health Surveys
ClinicalTrials.gov study NCT02991391. IPD Sharing: YES. Countries: 0. Publications: 0.
Vehicle-Level Reasoning Systems: Integrating System-Wide data to Estimate Instantaneous Health State
One of the primary goals of Integrated Vehicle Health Management (IVHM) is to detect, diagnose, predict, and mitigate adverse events during the flight of an aircraft, regardless of the subsystem(s) from which the adverse event arises. To properly address this problem, it is critical to develop technologies that can integrate large, heterogeneous (meaning that they contain both continuous and discrete signals), asynchronous data streams from multiple subsystems in order to detect a potential adverse event, diagnose its cause, predict the effect of that event on the remaining useful life of the vehicle, and then take appropriate steps to mitigate the event if warranted. These data streams may have highly non-Gaussian distributions and can also contain discrete signals such as caution and warning messages which exhibit non-stationary and obey arbitrary noise models. At the aircraft level, a Vehicle-Level Reasoning System (VLRS) can be developed to provide aircraft with at least two significant capabilities: improvement of aircraft safety due to enhanced monitoring and reasoning about the aircraft’s health state, and also potential cost savings through Condition Based Maintenance (CBM). Along with the achieving the benefits of CBM, an important challenge facing aviation safety today is safeguarding against system- and component-level failures and malfunctions. Citation: A. N. Srivastava, D. Mylaraswamy, R. Mah, and E. Cooper, “Vehicle Level Reasoning Systems: Concept and Future Directions,” Society of Automotive Engineers Integrated Vehicle Health Management Book, Ian Jennions, Ed., 2011.
An Integrated Approach to Battery Health Monitoring using Bayesian Regression, Classification and State Estimation
The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation via Particle Filters (PF), proves to be a powerful tool to integrate the diagnosis and prognosis of battery health. Accurate estimates of the state-of-charge (SOC), the state-of-health (SOH) and state-of- life (SOL) for batteries provide a significant value addition to the management of any operation involving electrical systems. This is especially true for aerospace systems, where unanticipated battery performance may lead to catastrophic failures. Batteries, composed of multiple electro- chemical cells, are complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions and historical data, for which a Bayesian statistical approach is suitable. Accurate models of electro-chemical processes in the form of equivalent electric circuit parameters need to be combined with statistical models of state transitions, aging processes and measurement fidelity, need to be combined in a formal framework to make the approach viable. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for diagnosis as well as for model development. The PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL. Validation of this approach on experimental data from Li-ion batteries is presented.
MSL CHEMCAM STATE OF HEALTH EDR V1.0
The MSL ChemCam SOH EDR data set consists of all raw state of health data collected by the ChemCam instrument on the Mars Science Laboratory rover.
The human microglia response a resource to better understand microglia states in health and disease
GEO Series GSE249315. Homo sapiens. 398 samples. Type: Expression profiling by high throughput sequencing.
Characterizing the metabolomes of microglia, astrocytes, and neurons in the brain across states of health, aging, and disease
GEO Series GSE316928. Mus musculus. 35 samples. Type: Expression profiling by high throughput sequencing.
MSL CHEMCAM STATE OF HEALTH EDR V1.0
The MSL ChemCam SOH EDR data set consists of all raw state of health data collected by the ChemCam instrument on the Mars Science Laboratory rover.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.