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599 results for “health data”
Data from: Validation of the instrument of health literacy competencies for Chinese-speaking health professionals
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Data from: The effect of a transient immune activation on subjective health perception in two placebo controlled randomised experiments
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Data from: A large-scale field study examining effects of exposure to clothianidin seed-treated canola on honey bee colony health, development, and overwintering success
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West Africa Coastal Vulnerability Mapping: Demographic and Health Survey Data Sets
The West Africa Coastal Vulnerability Mapping: Demographic and Health Survey Data Sets present grids of maternal education levels and household wealth based on Demographic and Health Survey (DHS) cluster level data for ten West African countries. While the maternal education levels are comparable across countries, owing to different underlying indicators, the household wealth index is not. Education can directly influence risk perception, skills and knowledge and indirectly reduce poverty, improve health, and promote access to information and resources. When facing natural hazards or climate risks, educated individuals, households, and societies are assumed to be more empowered and more adaptive in their response to, preparation for, and recovery from disasters. Education is a key background indicator that helps contextualize a country's health and development situation. The household wealth index is a composite measure of a household's cumulative living standard. The wealth index is calculated using easy-to-collect data on a household's ownership of selected assets, such as televisions and bicycles, materials used for housing construction, and types of water access and sanitation facilities. Bayesian spatial interpolation methods were employed to create country level grids based on DHS cluster point data for each country. Data are from the following dates by country: Benin (2006), Cameroon (2011), Cote d'Ivoire (2012), Ghana (2008), Guinea (2012), Liberia (2011), Nigeria (2010), Sierra Leone (2008), and Togo (1998).
DISCOVER-AQ Colorado Deployment Colorado Department of Public Health and Environment Ground Site Data
DISCOVERAQ_Colorado_Ground_CDPHE_Data contains data collected by the Colorado Department of Public Health and Environment (CDPHE) at ground sites around the study area, including Chatfield Park, Denver-LaCasa, Fort Collins, NREL-Golden, Aurora-East, Boulder, Denver-CAMP, Denver-I25, Rocky Flats, Welch, and Weld Co. Tower as part of the Colorado (Denver) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Colorado deployment and data collection is complete.Understanding 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.DISCOVER-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).The 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.
Expression data of health individuals and newly diagnosed multiple myeloma
GEO Series GSE124489. Homo sapiens; synthetic construct. 24 samples. Type: Non-coding RNA profiling by array.
Data from: A problem of bias and response heterogeneity, in Standing With Giants: A Collection of Public Health Essays in Memoriam to Dr. Elizabeth M. Whelan
There is extensive literature on the question, "Does air quality have health effects?" For example, Google Scholar gives 199,000 hits for ("mortality" and "air pollution"). See Health Effects Institute (2010) and editorial by Brauer and Mancini (2014), for example. One paper that appeared in 1993 has over 6,000 citations. The vast majority of these papers find a positive association between air quality health effects (death). A few papers make the case that if potential bias is carefully taken into account then there is no association between air quality and deaths, e.g. Chay et al. (2003), Enstrom (2005), Janes et al. (2007), Greven et al. (2011), Cox et al. (2013). Clearly the weight of evidence is for a positive association, but for any particular type of claim, logically it takes only one true negative to negate all the positives associations with respect to causation for that claim. A real, causative claim should always be detected in a well-designed and properly run experiment. What are some of the factors that lead to these discordant literature results?
Data Evaluating Reporting Completeness in Oral Health Clinical Guidelines, from a meta-epidemiologic study
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A Method of Water Resources Accounting Based on Deep Clustering and Attention Mechanism under the Background of Integration of Public Health Data and Environmental Economy
<p>A dataset and code for paper</p>
Electronic Health Record-Integrated Patient-Generated Data
ClinicalTrials.gov study NCT06668870. IPD Sharing: YES. Countries: 0. Publications: 0.
Health Data Warehouse on Aortic Insufficiency
ClinicalTrials.gov study NCT06420895. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Utilising Hull Lung Health Study Data to Investigate the Performance of TidalSense Diagnostic Algorithms to Identify COPD and Pre-COPD Among Participants in the FRONTIER Programme.
ClinicalTrials.gov study NCT06788613. IPD Sharing: NO. Countries: 0. Publications: 0.
Data Donation Model for Inclusive Cardiovascular Prevention Using the TRAIN Health Platform
ClinicalTrials.gov study NCT07238036. IPD Sharing: Not stated. Countries: 0. Publications: 0.
ONCOlogy-targeted NLP-powered Federated Hyper-archItecture and Data Sharing Framework for Health Data Reusability
ClinicalTrials.gov study NCT05060835. IPD Sharing: NO. Countries: 0. Publications: 0.
Temporal expression data from 17 health human subjects before and after they were challenged with live influenza (H3N2/Wisconsin) viruses
GEO Series GSE30550. Homo sapiens. 268 samples. Type: Expression profiling by array.
Data from: A problem of bias and response heterogeneity, in Standing With Giants: A Collection of Public Health Essays in Memoriam to Dr. Elizabeth M. Whelan
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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.
Discovering System Health Anomalies using Data Mining Techniques
We discuss a statistical framework that underlies envelope detection schemes as well as dynamical models based on Hidden Markov Models (HMM) that can encompass both discrete and continuous sensor measurements for use in Integrated System Health Management (ISHM) applications. The HMM allows for the rapid assimilation, analysis, and discovery of system anomalies. We motivate our work with a discussion of an aviation problem where the identification of anomalous sequences is essential for safety reasons. The data in this application are discrete and continuous sensor measurements and can be dealt with seamlessly using the methods described here to discover anomalous flights. We specifically treat the problem of discovering anomalous features in the time series that may be hidden from the sensor suite and compare those methods to standard envelope detection methods on test data designed to accentuate the differences between the two methods. Identification of these hidden anomalies is crucial to building stable, reusable, and cost-efficient systems. We also discuss a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an ISHM system. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.
Data Mining in Systems Health Management
This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.
Rotor health monitoring combining spin tests and data-driven anomaly detection methods
Health monitoring is highly dependent on sensor systems that are capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system. Efforts are under way at NASA Glenn Research Center through support of the Intelligent Vehicle Health Management Project (IVHM) to develop and implement such sensor technology for a wide variety of applications. These efforts are focused on developing high temperature, wireless, low cost, and durable products. In an effort to address technical issues concerning health monitoring, this article considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip clearance and tip timing measurements in a rotating turbine engine-like-disk to detect the disk faults and assess its structural integrity. The experimental results composed at a range of rotational speeds from tests conducted at the NASA Glenn Research Center’s Rotordynamics Laboratory are evaluated and integrated into multiple data-driven anomaly detection techniques to identify faults and anomalies in the disk. In summary, this study presents a select evaluation of online health monitoring of a rotating disk using high caliber capacitive sensors and demonstrates the capability of the in-house spin system.
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.