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599 results for “health data”
Data from: mPRIME Study - Interaction of Insulin Resistance with Cognition, Lifestyle, and Mental Health
<p>The presented datasets were collected within the <em>m</em>PRIME study, a prospective, observational study of the H2020 project Prevention and Remediation of Insulin Multimorbidity in Europe (PRIME) (grant No. 847879). The study investigates the interaction of insulin resistance with cognition, lifestyle, and mental health by combining traditional methods with ambulatory assessment and sensor-based data collection. Recruitment took place between March 2021 and March 2023 at the University Hospital Frankfurt, Germany.</p> <p>The eligibility criteria for the study were as follows: Age above 18 years, no intake of antidiabetic medication, insulin or glucocorticoids, no existing type 1 diabetes mellitus or gestational diabetes, no diagnoses of bipolar I disorder, schizophrenia, organically caused mental disorders and substance dependence, no severe neurological disorders, no current pregnancy or breastfeeding, no non-correctable visual impairments, no participation in medication-related studies within the last 6 months, no use of weight-reducing medications or a diet within the last 3 months, sufficient proficiency in German to complete questionnaires and neuropsychological tests.</p> <p>All participants in the <em>m</em>PRIME study provided written informed consent. The study protocol and procedures were approved by the local ethics committee.</p> <p><strong>Study Design</strong></p> <p>Individuals completed a baseline assessment and a one-week ambulatory assessment. The baseline assessment included: socio-demographic information, blood samples, anthropometric measures, neuropsychological tests, and several questionnaires. In addition, individuals were introduced to smartphone-based ecological momentary assessment (EMA), food protocols, and the use of sensors (continuous glucose monitor, accelerometer). Food protocols and EMA were conducted on three consecutive days, including two weekdays and one weekend day (Thursday to Saturday or Sunday to Tuesday). Several times a day, individuals were prompted via their smartphone to complete a working memory task and answer questions about stress, affect, and food intake. The continuous glucose monitor and accelerometer were worn continuously for 1 week.</p> <p> </p>
Mohonk Preserve Forest Health Monitoring Data 2018-2021
In 2018, the Mohonk Preserve’s Daniel Smiley Research Center implemented a long-term research project aimed at inventorying forest vegetation and monitoring forest health. The protocol was adapted from the National Park Service’s Northeast Temperate Network (https://www.nps.gov/im/netn/forest-health.htm). This project monitors the composition and structure of the Mohonk Preserve forests, and collects data for assessing forest soil condition, impacts of white-tailed deer herbivory, and land cover. In 2018, 24 plots were established in four habitat types: Eastern hemlock forest (n = 6), white ash forest (n = 6), historic prescribed burn forest (n = 6), and randomly selected forest (n = 6). In 2021, an additional 14 plots were established in two historic Breeding Bird Survey research areas: Eastern hemlock forest (n = 8) and pitch pine forest (n = 6). All data collection occurred between the months of June through August. Plots are scheduled to be resampled every four years.
Data for the article "Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic"
<p>Dataset from a study of attitudes and perceptions of medicine and nursing students in Peru during the COVID-19 pandemic. Survey was applied from 2020-07-24 to 2021-04-16.</p> <p>This dataset is described in the article: </p> <p>Castagnetto, J.M., Hancco-Monrroy, D.E., Caballero-Apaza, L.M. <em>et al.</em> Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic. <em>Sci Data</em> <strong>12</strong>, 1259 (2025). <a href="https://doi.org/10.1038/s41597-025-05508-5">https://doi.org/10.1038/s41597-025-05508-5</a> (<a href="https://www.nature.com/articles/s41597-025-05508-5">https://www.nature.com/articles/s41597-025-05508-5</a>)</p>
Data for manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.
<p>Dataset for the manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.</p> <p>The dataset contains all data needed to reproduce the results in the above cited manuscript. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the manuscript.</p> <p>The data was collected between April 2016 and March 2018 in Switzerland by the C-SURF study (<a href="http://www.c-surf.ch">www.c-surf.ch</a>). Participants were on average 25 years old when they answered the questionnaires. The final sample size used in the manuscript is 5516. Please note that the dataset contains 25 datasets created with multiple imputation, therefore there are no missing values in the dataset.</p> <p>The research protocol for this study was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07). Data collection was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493)</p>
Data for estimating spruce tree health using drone-based RGB and multispectral imagery
<p>The dataset contains multispectral and RGB orthomosaics (.tif), and photogrammetric point clouds (.laz) of four study areas (about 25 ha each), where bark beetle-related decline of Norway spruce has been observed in Helsinki, Finland. The filenames refer to Area 1 (Männikkötie), Area 2 (Maunulanmaja), Area 3 (Hakuninmaa), and Area 4 (Paloheinä), described in detail in Junttila et al. 2022. Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season, Remote Sensing 14(4), 909: <a href="https://doi.org/10.3390/rs14040909">https://doi.org/10.3390/rs14040909</a> </p> <p>The image data was acquired between 11th and 14th September 2020.</p> <p>RE = Red-Edge M multispectral data<br>RGB = RGB data (Phantom 4 Pro)<br>Altum = Altum multispectral data</p> <p>The ground sampling distances (GSD) were approximately 3 cm, 5 cm, and 8 cm for RGB, Altum, and RedEdge, respectively.</p> <p>The field reference data file contains 556 geolocated trees assessed in the field (between 11.9. and 17.9.2020), of which 203 were dead and 353 were alive. The data is in polygon format, representing the crown delineation done during the data processing. The file includes tree heights estimated from airborne laser scanning data, dbh (for a subset of trees), discoloration, defoliation, resin flow, bark structural damage, and canopy size estimates. More details are in the journal article mentioned above.</p> <p>Key for Field Reference:</p> <p>Z = tree height<br>dbh = diameter-at-breast-height (cm)<br>vari = Discoloration (score 0-5)<br>harsu = Defoliation (score 0-4)<br>pihka = Resin flows (score 0-2)<br>runko = Stem/bark structural damage (score 0-2)<br>latvus = Significantly decreased canopy size (score 0-1)</p>
Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment
<p><strong>Dataset name: </strong>asppl-dataset.csv</p> <p><strong>Version: </strong>1.0</p> <p><strong>Dataset period: </strong>06/07/2018- 05/25/2021</p> <p><strong>Dataset Characteristics: </strong>Multivalued</p> <p><strong>Number of Instances: </strong>4861</p> <p><strong>Number of Attributes: </strong>33</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education </p> <p><strong>Sources: </strong></p> <ul> <li> <p><strong>Primary</strong>: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];</p> </li> <li> <p><strong>Secondary: </strong></p> <ol> <li> <p>Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];</p> </li> <li> <p>National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].</p> </li> </ol> </li> </ul> <p><strong>Description: </strong>The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.</p> <p> </p>
Environmental data associated to particular health events example dataset
<p>The data represents and example output for environmental data (i.e. climate and pollution) linked with individual events through <strong>location</strong> and <strong>time</strong>. The linkage is the result of a semantic query that integrates environmental data <strong>within an area relevant to the event</strong> and selects a <strong>period of data before the event</strong>.</p> <p>The resulting event-environmental linked data contains:</p> <ul> <li>The data for analysis as a data table (.csv) and graph (.ttl)</li> <li>The metadata describing the linkage process and the data (.csv and .ttl)</li> <li>The interactive report to explore the (meta)data (.html)</li> </ul> <p>The graph files are ready to be shared and published as Findable, Accessible, Interoperable and Reusable (FAIR) data, including the necessary information to be reused by other researchers in different contexts.</p>
Data from “A Mixed Method Approach to Understanding the Public Health Impact of a School-Based Citizen Science Program to Reduce Arsenic in Private Well Water”
Objectives We have approached the problem of low well water testing rates in Maine and New Hampshire communities by developing the All About Arsenic (AAA) project, which engages secondary school teachers and students as citizen scientists in collecting well water samples for analysis of arsenic and other toxic metals and supports their outreach efforts to their communities. Methods We assessed this project’s public health impact by analyzing student data relative to existing well water quality datasets in both states. In addition, we surveyed private well owners who contributed well water samples to the project to determine the actions taken to mitigate arsenic in well water. Data The data presented here are used in the analyses performed for the publication: "A Mixed Method Approach to Understanding the Public Health Impact of a School-Based Citizen Science Program to Reduce Arsenic in Private Well Water.” Additional data may be available at: The Anecdata Project Page: https://anecdata.org/projects/view/299 The project website: https://www.allaboutarsenic.org/
Data relating to Clyne et al. Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study
<p>Data relating to the study reported in the paper "Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study".</p>
Source Data and ambient ozone dataset generated in "Substantially underestimated global health risks of current ozone pollution"
<p>Existing assessments might have underappreciated ozone-related health impacts worldwide. Here our study assesses current global ozone pollution using the high-resolution (0.05°) estimation from a geo-ensemble learning model, with key focuses on population exposure and all-cause mortality burden. Our model demonstrates strong performance, achieving a mean bias of less than -1.5 parts per billion against in-situ measurements. We estimate that 66.2% of the global population is exposed to excess ozone for short term (> 30 days per year), and 94.2% suffers from long-term exposure. Furthermore, severe ozone exposure levels are observed in Cropland areas, particularly over Asia. Importantly, the all-cause ozone-attributable deaths significantly surpass previous recognition from specific diseases worldwide. Notably, mid-latitude Asia (30°N) and the western United States show high mortality burden, contributing substantially to global ozone-attributable deaths. Our study highlights current significant global ozone-related health risks and may benefit the ozone-exposed population in the future.</p>
EJPSOIL ARTEMIS on-farm monitoring of soil health and ecosystems services (meta)data
<p>This database includes the data and metadata from the initial on-farm monitoring od soil health and soil related ecosystem services of the EJPSOIL ARTEMIS project. </p>
Code and measurement data - State of charge and state of health diagnosis of batteries with voltage-controlled models
<p><strong>This dataset contains the research data (code and measurement data) of the journal article: <a href="https://doi.org/10.1016/j.jpowsour.2022.231828">J. A. Braun, R. Behmann, D. Schmider, W. G. Bessler, "State of charge and state of health diagnosis of batteries with voltage-controlled models", Journal of Power Sources 544 (2022), 231828</a>.</strong></p> <p> </p> <p><strong>Abstract:</strong><br> The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.</p> <p> </p> <p><strong>Intellectual property information:</strong><br> The Matlab codes and the research data provided here are under <strong><a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a></strong> license. Please note that the algorithms themselves are subject to industrial property rights, including, but not necessarily limited to, German patent <strong><a href="https://patents.google.com/patent/DE102019127828B4/en">DE102019127828B4</a></strong> and international patent application <strong><a href="https://patents.google.com/patent/WO2021073690A2/en">WO2021073690A2</a></strong>. Any use of the codes and algorithms presented here is subject to these property rights.</p> <p> </p> <p><strong>Overview of files:</strong><br> <strong>SOC_SOH_simple_model.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "simple" equivalent circuit model. The script also reproduces the figures shown in the manuscript.</p> <p><strong>SOC_SOH_simple_extended.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "extended" equivalent circuit model. The script also creates figures of additional data not shown in the manuscript.</p> <p><strong>Experimental_data_fresh_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a fresh lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>Experimental_data_aged_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a pre-aged lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>OCV_vs_SOC_curve.csv:</strong> Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC). 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</p> <p><strong>readme.txt:</strong> Overview of files with a short description.</p>
Data bases for Measuring impacts of oral health promotion interventions on health inequities: the example of New Caledonia
<p>Extract from the New Caledonian (NC) epidemiological survey database for identifying the determinants and risk factors explaining the presence of untreated dental caries and to compare the prevalence and severity of dental caries between 2012 and 2019, in order to identify potential changes that occurred in NC.</p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</p>
Data for "Meat and dairy substitutes – better for health and the environment? Impacts on nutrition and sustainability; consumer perspectives; ethical and legal considerations"
<p>Combined data reposatory for the output of the project "Meat and dairy substitutes – better for health and the environment? Impacts on nutrition and sustainability; consumer perspectives; ethical and legal considerations"</p> <p><em>Kombiniertes Datenarchiv für die Ergebnisse des Projekts "Fleisch- und Milchersatzprodukte – besser für Gesundheit und Umwelt? Auswirkungen auf Ernährung und Nachhaltigkeit, die Sicht der Konsumentinnen und Konsumenten sowie ethische und rechtliche Überlegungen"</em></p> <p>The project was funded by the Foundation for Technology Assessment (TA-Swiss) "<a href="https://ror.org/02shtak05">ror.org/02shtak05</a>".</p> <p><em>Das Projekt wurde von der Stiftung für Technologiefolgen-Abschätzung (TA-Swiss) finanziert "<a href="https://ror.org/02shtak05">ror.org/02shtak05</a>". </em></p>
Respondents' perspectives on the impact of digital data-based health services on disaster risk management in Indonesia.
<p>This data contains respondents' perspectives on the impact of digital data-based health services on disaster risk management. Digital health services are the implementation of digital, information, and communication technologies in the context of health services. Digital health services include: mHealth, Health Information Technology, Wearable Devices, Telehealth and Telemedicine, and Personalized Medicine. </p> <p>Data was collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381) would be advisable.</p>
Data archive for "Modified rice bran arabinoxylan as a nutraceutical in health and disease — A scoping review with bibliometric analysis"
<p>v1.0.0 Release with the publication of the paper on PLoS One</p> <p>Ooi, S. L., Micalos, P. S., & Pak, S. C. (2023). Modified rice bran arabinoxylan as a nutraceutical in health and disease—A scoping review with bibliometric analysis. PLOS ONE, 18(8), e0290314. https://doi.org/10.1371/journal.pone.0290314</p> <p><strong>Full Changelog</strong>: https://github.com/sooi10/RBACScoping/commits/NetworkAnalysis</p>
Qualitative Data on Effects of Early and Prolonged Parent-Child Separation: Understanding Mental Health of Separated-Reunited Chinese American Children
<div> <div> <div> <div>Early and prolonged parent-child separation due to parental migration or immigration may result in attachment disruption that can threaten the long-term mental health and functioning of affected children, and these risks can persist following reunification and through adulthood. Although sending infants back to the home country for rearing is often practiced among <em>Chinese</em> immigrants, especially low-income families, research has been sparse in understanding the long-term impact of early and prolonged parent-child separation and reunification on disparities in mental health and functioning among separated-reunited children and the mechanism through which such relationships may operate.</div> <div>Funded by National Institute on Minority Health and Health Disparities (NIMH), we collected semi-structured interview data from 24 parent-child dyads who have experienced separation. The data included interview scrpits with primary coding to understand the mental health impacts, risk/protective factors, and service needs among separated-reunited <em>Chinese</em> American children.</div> </div> </div> </div> <div> </div>
Data for One Health and Veterinary Systems in Africa
<p>One Health Database Africa<br> The “One Health and Veterinary Systems in Africa: Taking stock of current coverage, needs, and opportunities to meet present and changing threats and optimize collaboration” is reviewing current capacity and programmatic status, gaps, and operations in each country and by sub-regions of Africa.</p> <p>This datasets combines multiple sources to produce a single One Health database containing selected indicators. The database encompasses 54 countries across the continent of Africa (according the UN).</p>
ScienceDex guides
Understand access before you commit
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.