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12,574 results for “health IT”
SFB1451 - key mechanisms of motor control in health and disease
This is a collection of datasets created within the Collaborative Research Center 1451. The "superdataset" contains information about datasets stored in various locations. This information can be used to obtain programmatic, fine-grained data access to the level of individual files, depending on the level of detail provided by dataset authors, storage location, and access restrictions. The Collaborative Research Center 1451 (CRC1451; dt: Sonderforschungsbereich, SFB1451) brings together neuroscientists investigating genetic factors, cellular, and synaptic as well as systems/neural network processes underlying motor control in animals and humans, in both health and neuropsychiatric diseases. All investigators are committed to the CRC's multi-faceted, iterative, and integrative agenda with the long-term goal of identifying the essential mechanisms underlying normal and pathological motor control.
COMPREHENSIVE LIVESTOCK HEALTH PROGRAM: TARGETED TREATMENT AND HOLISTIC INTERVENTIONS FOR MAJOR PREVALENT DISEASES IN THE LIVESTOCK FARMING COMMUNITY OF DAYNILE DISTRICT, MOGADISHU, SOMALIA.
<p>The general objective of this project was to intervene with the most common livestock diseases in Dayniile district by carrying out a comprehensive campaign for treatment and control. The specific objectives consisted of a treatment campaign, improving infrastructure for establishing disinfectant foot dips and hand washing points, providing disinfectant tools, and finalising community engagement and education by doing training at the farm level.<br>The team visited different donors and added their contribution. After collecting sufficient funds from various sources, the team began the procurement of the necessary materials. This included purchasing veterinary drugs and supplies from local pharmacies and other essentials like stationery. The first activity was treatment campaigns, which were a central aspect of the project. Over 290 animals were treated for various diseases and conditions. The farm manager was informed of the diagnoses, and upon receiving their permission, the appropriate treatments were administered. The second intervention action was a vaccination campaign. The team vaccinated a total of 70 animals against clostridial bacteria, which is one of the most common camel diseases encountered in the area. The third intervention was the establishment of biosecurity facilities at select livestock farms. Among all the farms involved in the project, five were chosen for the provision of enhanced biosecurity measures. These measures included the installation of foot dips and teat dips. The fourth activity was educating livestock farmers on strategies for controlling and preventing livestock diseases. The training was held at Beder Camel Dairy Farm and attended by approximately 10 individuals, comprising 3 females and 7 males. The content of the training was three modules: the first was general farm biosecurity, the second was operational biosecurity, and the third was concern for vaccination. Recommendation: We recommend that each farm hire livestock health specialists to easily implement disease prevention steps and promptly solve each new case.<br> We recommend the livestock association, veterinary clinics, and other institutions working on livestock do routine campaigns that facilitate the determination of prevalent diseases and the treatment of those cases</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>
Bioinformatics for public health microbiologists: Module 1 dataset (South African Salmonella)
<p>Module 1 (WGS) dataset (paired-end Illumina reads of <em>Salmonella enterica </em>strains isolated from animals and animal products in South Africa)</p> <ul> <li> <p>module1_dataset_ZAsalmonella.tar.gz: raw Illumina paired-end reads</p> </li> <li> <p>trimmed_reads.tar.gz: trimmed Illumina paired-end reads (i.e., trimmed via fastp v0.23.4)</p> </li> <li> <p>contigs.tar.gz: assembled genomes (i.e., trimmed reads assembled into contigs using SKESA v2.5.1)</p> </li> <li> <p>prokka.tar.gz: whole-genome annotation results (i.e., produced via Prokka v1.14.6)</p> </li> <li> <p>enterobase_salmonella.tar.gz: publicly available assembled genomes (downloaded via Enterobase; https://enterobase.warwick.ac.uk/, accessed 1 June 2024)</p> </li> <li> <p>snippy_input.tsv: input file used for Snippy (https://github.com/tseemann/snippy)</p> </li> <li> <p><span>snippy_final.tar.gz: output files produced by Snippy (https://github.com/tseemann/snippy), Gubbins (https://github.com/nickjcroucher/gubbins), and SNP-sites (https://github.com/sanger-pathogens/snp-sites)</span></p> </li> </ul>
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>
Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action
<p>Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a ’care in the community’ framework.</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 2
<p>1996-1999, entomofauna inventory in cities and countrysides on 20 different habitats in The Netherlands using pitfalls and sweeping nets Jagers op Akkerhuis G, Dimmers W (2016). Alterra (NL) - Comparison entomofauna in cities en countrysides. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/guxqfv">https://doi.org/10.15468/guxqfv</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health 6
<p>This dataset contains the digitized treatments in Plazi based on the original journal article Viana, Jéssica Herzog, Ribeiro-Costa, Cibele Stramare (2013): Review of the largest species group of the New World seed beetle genus Sennius Bridwell (Coleoptera: Chrysomelidae), with host plant associations. Zootaxa 3736 (5): 501-535, DOI: <a href="http://dx.doi.org/10.11646/">http://dx.doi.org/10.11646/</a></p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 3
<p>1996-1999, entomofauna inventory in cities and countrysides on 20 different habitats in The Netherlands using pitfalls and sweeping nets Jagers op Akkerhuis G, Dimmers W (2016). Alterra (NL) - Comparison entomofauna in cities en countrysides. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/guxqfv">https://doi.org/10.15468/guxqfv</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health 7
<p>This dataset contains the digitized treatments in Plazi based on the original journal article Johnson, Robert A., Cover, Stefan P. (2015): A taxonomic revision of the seed-harvester ant genus Pogonomyrmex (Hymenoptera: Formicidae) on Hispaniola. Zootaxa 3972 (2): 231-249, DOI: <a href="http://dx.doi.org/10.11646/">http://dx.doi.org/10.11646/</a></p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment
<p>1996 till 2000, entomofauna inventory on clay digged off riversides and on reference site in The Netherlands using pyramidtraps Faber J, Dimmers W (2016). Alterra (NL) - Entomofauna inventory in riverside grasslands. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/hlhr1r">https://doi.org/10.15468/hlhr1r</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 7
<p>Spring 2010 and Summer 2011, microarthropod fauna inventory for foodweb analysis in five European countries using soil cores and tullgren extraction</p> <p>Bloem J, Dimmers W (2016). Alterra (NL) - Microarthropods inventory in European countries. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/zkqto2">https://doi.org/10.15468/zkqto2</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health 5
<p>This dataset contains the digitized treatments in Plazi based on the original journal article Michelsen, Verner (2012): Revision of the European Delia pruinosa species group (Diptera: Anthomyiidae) feeding as larvae in seed capsules of Silene L. (Caryophyllaceae). Zootaxa 3434: 31-48, DOI: 10.5281/zenodo.282060</p>
Agriculture - General: Animal Production and Health 4
<p>This dataset contains the digitized treatments in Plazi based on the original journal article Yus-Ramos, Rafael, Ventura, Daniel, Bensusan, Keith, Coello-García, Pedro, György, Zoltán, Stojanova, Anelia (2014): Alien seed beetles (Coleoptera: Chrysomelidae: Bruchinae) in Europe. Zootaxa 3826 (3): 401-448, DOI: <a href="http://dx.doi.org/10.11646/">http://dx.doi.org/10.11646/</a>...</p> <p>Stojanova A (2013). Alien seed beetles (Coleoptera: Chrysomelidae: Bruchinae) in Europe. Plazi.org taxonomic treatments database. Checklist dataset <a href="https://doi.org/10.11646/zoot">https://doi.org/10.11646/zoot</a>... accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health 8
<p>1992, invertebrate fauna inventory on peat swamps in The Netherlands using pyramidtraps Siepel H, Dimmers W (2016). Alterra (NL) - Entomofauna inventory in peat swamps. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/ibom6z">https://doi.org/10.15468/ibom6z</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 5
<p>2001 till 2002 and 2004, inventory of entomofauna in forestwalls banks, diches, road verges in The Netherlands using pitfalls and sweeping net</p> <p>Jagers op Akkerhuis G, Dimmers W (2016). Alterra (NL) - Comparison of entomofauna in four different habitats. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/mkoqqh">https://doi.org/10.15468/mkoqqh</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 6
<p>2007, 2011 and 2012, microarthropod fauna inventory in a nature restauration experiment (re-introduction) on a calcareous grassland and three reverence sites in the province of Limburg using pF-cores</p> <p>Smits N, Dimmers W (2016). Alterra (NL) - Microarthropods inventory in calcareous grasslands. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/28jocn">https://doi.org/10.15468/28jocn</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 4
<p>1993 till 2001, entomofauna inventory in cattle grazed versus non-grazed dune grassland using pitfalls</p> <p>van Wingerden W, Dimmers W (2016). Alterra (NL) - Entomofauna inventory in cattle grazed dune grassland. Version 2.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/zp5oif">https://doi.org/10.15468/zp5oif</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 8
<p>2000-2003, microarthropod fauna inventory of arable land and grassland on sandy soil using pF-cores. Points of interest are biodiversity, nutrients and disease suppression</p> <p>Faber J, Dimmers W (2016). Alterra (NL) - Microarthropods inventory in grassland and arable land. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/fwvi7m">https://doi.org/10.15468/fwvi7m</a> accessed via GBIF.org</p>
MHMisinfo - Video-based Mental Health Misinformation Dataset
<p>MHMisinfo-Gold and MHMisinfo-Large datasets, as described in the paper "Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms" (forthcoming at ICWSM 2025). Videos and comments for each dataset are seperately stored in different .csv files</p> <p><strong>Dataset schema, videos</strong></p> <table> <tbody> <tr> <th><strong>Column Name</strong></th> <th><strong>Description</strong></th> </tr> <tr> <td><strong>video_id</strong></td> <td>ID of the Video, as assigned by their respective platforms</td> </tr> <tr> <td><strong>video_title</strong></td> <td>The title of the video</td> </tr> <tr> <td><strong>video_description</strong></td> <td>The description of the video, given by the video creators</td> </tr> <tr> <td><strong>audio_transcript</strong></td> <td>Text transcription of the video's audio track, as generated by Whisper speech-to-text model</td> </tr> <tr> <td><strong>video_view_count</strong></td> <td>View count of the video, at the time of data collection</td> </tr> <tr> <td><strong>video_like_count</strong></td> <td>Like count of the video, at the time of data collection</td> </tr> <tr> <td><strong>video_comment_count</strong></td> <td>Comment count of the video, at the time of data collection</td> </tr> <tr> <td><strong>label_ioi</strong></td> <td>"Information of Interventions" label of video, annotated by experts. 1 = High-quality information on interventions, -1 = Low-quality information on interventions</td> </tr> <tr> <td><strong>label_ebt</strong></td> <td>"Evidence-based Treatment" label of video, annotated by experts. 1 = Encourages evidence-based treatment, -1 = Discourages evidence-based treatment</td> </tr> <tr> <td><strong>label_aoc</strong></td> <td>"Alignment of Consensus" label of video, annotated by experts, 1 = High Alignment with Consensus, -1 = Low Alignment with Consensus</td> </tr> <tr> <td><strong>label</strong></td> <td>Overall mental health misinformation label of the video. 0 = non-MHMisinfo videos, and -1 = MHMisinfo videos</td> </tr> <tr> <td><strong>platform</strong></td> <td>Platform of the video</td> </tr> </tbody> </table> <p><strong>Dataset schema, comments</strong></p> <table> <tbody> <tr> <th><strong>Column Name</strong></th> <th><strong>Description</strong></th> </tr> <tr> <td><strong>text</strong></td> <td>The raw text of the comment</td> </tr> <tr> <td> <p><strong>commenter_channel_display_name</strong></p> </td> <td>The display name of the user who posted the comment.</td> </tr> <tr> <td> <p><strong>comment_publish_date</strong></p> </td> <td>The time when the comment was orignally published, .</td> </tr> <tr> <td> <p><strong>video_id</strong></p> </td> <td>ID of the Video associated by the platform, as assigned by their respective platforms</td> </tr> <tr> <td> <p><strong>platform</strong></p> </td> <td>Platform of the video associated with the comment</td> </tr> <tr> <td> <p><strong>label</strong></p> </td> <td>Overall mental health misinformation label of the video associated with the comment. 0 = non-MHMisinfo videos, and -1 = MHMisinfo videos</td> </tr> </tbody> </table> <p> </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.