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599 results for “health data”
Data Sharing Practices in the MRC Circadian Mental Health Network.
<p>This dataset supports the research conducted within the MRC Circadian Mental Health Network which assesses data sharing practices among Principal Investigators' publications in 2023. This work aims to identify trends, challenges, and inform future recommendations and policies based on the findings. The dataset includes various files that detail the methodology, data collected, and analyses performed.</p> <p> </p> <p><strong>Repository Contents:</strong></p> <ol> <li> <p><strong>Methods and Analysis Report - Data Sharing Practices in the MRC CMHN.pdf</strong></p> <ul> <li>This report provides the methodologies used for selecting and assessing research papers within the network, along with detailed results, tables, and discussions from the evaluation.</li> </ul> </li> <li> <p><strong>CMHN_All_Data.xlsx</strong></p> <ul> <li>An Excel workbook containing: <ul> <li><strong>Sheet 1</strong>: All data and variables collected and analysed for this project.</li> <li><strong>Sheet 2</strong>: A README file that explains each variable and its values.<br><br></li> </ul> </li> </ul> </li> <li> <p><strong>CMHN DataType Scoring.xlsx</strong></p> <ul> <li>An Excel workbook detailing: <ul> <li><strong>Sheet 1</strong>: All datatypes, both code and datasets, evaluated in this study.</li> <li><strong>Sheet 2</strong>: A README explaining the variables evaluated and their specific values.<br><br></li> </ul> </li> </ul> </li> <li> <p><strong>CMHN Data Extraction Survey.pdf</strong></p> <ul> <li>A copy of the Microsoft Form used to systematically evaluate data-sharing practices from selected publications, describing the structured data extraction process used.<br><br></li> </ul> </li> <li> <p><strong>CMHN DataType Scoring Survey.pdf</strong></p> <ul> <li>A Microsoft Form used to assess the types of data (code and datasets) shared.<br><br></li> </ul> </li> <li> <p><strong>Data_CSV_Code.csv</strong></p> <ul> <li>This file is the original, uncleaned dataset directly extracted from the initial response data of the Microsoft Form used in the project. It served as the primary dataset for all subsequent data analysis and code execution within the study.<br><br></li> </ul> </li> <li> <p><strong>CMHN Code.Rmd</strong></p> <ul> <li>An R Markdown file containing the code used for data analysis; predominantly descriptive statistics due to the limited number of papers with shared data.</li> </ul> </li> </ol> <p><strong><br>Recommended Use:</strong> For comparative purposes or further analysis, researchers are encouraged to utilise the cleaned datasets available in "CMHN_All_Data.xlsx" and "CMHN DataType Scoring.xlsx."<br><br><strong>Contact:</strong> For further inquiries, please email us at <a href="mailto:bio_rdm@ed.ac.uk" target="_blank" rel="noopener">bio_rdm@ed.ac.uk</a>.</p>
Data and Charting information Animus Prime Health Research
<p>Data and Charting 3 information. More HealthCare Veteran Data.For utilization for Education and Research purposes for Animus Prime Research.</p>
Figure 5 in First data on water mite (Acari, Hydrachnidia) assemblages of Point Rosa Marsh, Harrison Township, Michigan, USA, and their use as environmental bioindicators of aquatic health
Figure 5 Frequency of water mite genera collected from habitats surrounding Point Rosa Marsh including Lake St. Clair. Comparable samples were collected on ten collection dates during 2017, 2018 and 2019. Graphs are arranged (left to right, and then by row) in the order of the overall frequency of each genus. Each bar graph shows the number of taxa collected on the six collection dates with bars color-coded to assist in comparing graphs on various dates. Dark blue (B) [Oct. 18 2017], red (A) [Oct. 20 2017 (1)], light green (B) [Oct. 20 2017], dark green (D&C) [Oct. 27 2017], black (A) [Aug. 7 2018], orange (A) [Aug. 21 2018], grey (D&C) [Aug. 31 2018], yellow (A) [Sept. 16 2019], light
Figure 4 in First data on water mite (Acari, Hydrachnidia) assemblages of Point Rosa Marsh, Harrison Township, Michigan, USA, and their use as environmental bioindicators of aquatic health
Figure 4 Frequency of water mite genera collected in Point Rosa Marsh. Comparable samples were collected on nine collection dates during 2017, 2018 and 2019. Graphs are arranged (left to right, and then by row) in the order of the overall frequency of each genus. Each bar graph shows the number of taxa collected on the six collection dates with bars color-coded to assist in comparing graphs on various dates. Dark blue (1&2) [Oct. 18 2017], red (4) [Oct. 19 2017], light green (3) [Oct. 18 2017 (2)], no data (1&2) [Aug. 7 2018], black (1&2) [Aug. 21 2018],
Data for Effects of household composition on infant feeding and mother-infant health in northern Kenya
<p>This file (Ariaal_household&foodsecurity.v3) contains the data utilized for a journal article manuscript, "Effects of household composition on infant feeding and mother-infant health in northern Kenya" by Vankayalapati, Wamwere-Njoroge, and Fujita, under review for publication as of May 2023. The data are found under the Data tab, and the description of variables and coding information are found under the Code & Info tab). This is version 3 file (variable description edited for clarity). The above-mentioned article describes the context of the original survey among Ariaal mothers of northern Kenya in 2006. </p> <p>Please contact Masako Fujita (ORCID 0000-0001-9173-6678, E-mail masakof@msu.edu) for questions regarding the data. </p> <p><strong>Condition for data use</strong> </p> <p>Please cite the DOI of this data file (10.5281/zenodo.7899883) and the article (recommended citation style below) <em>and </em>acknowledge the grant support from institutions listed below. </p> <p><em>Data and article:</em> </p> <ul> <li>Fujita M. 2023. Data for Effects of household composition on infant feeding and mother-infant health in northern Kenya. Version 3. DOI: 10.5281/zenodo.7899883 </li> <li>Vankayalapati A, Wamwere-Njoroge G, M Fujita. [Year]. Effects of household composition on infant feeding and mother-infant health in northern Kenya. [Journal name. Article DOI]. </li> </ul> <p><em>Grant support:</em> </p> <ul> <li> National Science Foundation (BCS-0622358, BCS-1638167) </li> <li> Wenner-Gren Foundation (Gr. 7460, Gr. 9278) </li> <li> Provost Undergraduate Research Initiative Grant, Michigan State University </li> </ul> <p> </p> <p> </p>
Data for: Consequences of microsporidian prior exposure for virus infection outcomes and bumble bee host health
<p>Host-parasite interactions do not occur in a vacuum but in connected multi-parasite networks. Resulting co-exposures and coinfections during an individual host's lifetime can affect host health and infectious disease ecology, including disease outbreaks. However, many host-parasite studies examine pairwise interactions, meaning we still lack a general understanding of the influence of co-exposures and coinfections. Using the bumble bee <em>Bombus</em> <em>impatiens</em>, we study the effects of larval exposure to a microsporidian, <em>Nosema</em> <em>bombi</em>, implicated in bumble bee declines, and adult exposure to Israeli Acute Paralysis Virus (IAPV), an emerging infectious disease from honey bee parasite spillover. We hypothesize that infection outcomes will be modified by co-exposure or coinfection depending on relevant temporal interactions, due to changes in host immune allocation or condition. <em>Nosema</em> <em>bombi</em> is a potentially severe, larval-infecting parasite, and we predict that prior exposure will result in decreased host resistance to adult IAPV infection. We predict a double exposure will also reduce host tolerance, as measured by host survival. Although our larval <em>Nosema</em> exposure mostly did not result in viable infections, it reduced resistance to adult IAPV infection. Exposure to <em>Nosema</em> also negatively affected survival, potentially due to a cost of immunity in resisting the exposure. There was also a significant negative effect of IAPV exposure on survivorship, but in contrast to resistance, prior <em>Nosema</em> exposure did not alter this survival outcome. These results again demonstrate that infection outcomes within multi-parasite host networks can be non-independent, even when exposure to one parasite does not result in a substantial infection. </p>
Dataset: Preliminary analysis of open data pertaining to the services available through the Health Insurance Institute of Slovenia and provided by family medicine
<p>BACKGROUND: The Health Insurance Institute of Slovenia (ZZZS) began publishing service-related data in May 2023, following a directive from the Ministry of Health (MoH). The ZZZS website provides easily accessible information about the services provided by individual doctors, including their names. The user is provided relevant information about the doctor's employer, including whether it is a public or private institution. The data provided is useful for studying the public system's operations and identifying any errors or anomalies. </p> <p>METHODS: The data for services provided in May 2023 was downloaded and analysed. The published data were cross-referenced using the provider's RIZDDZ number with the daily updated data on ambulatory workload from June 9, 2023, published by ZZZS. The data mentioned earlier were found to be inaccurate and were improved using alerts from the zdravniki.sledilnik.org portal. Therefore, they currently provide an accurate representation of the current situation. The total number of services provided by each provider in a given month was determined by adding up the individual services and then assigning them to the corresponding provider. </p> <p>RESULTS: A pivot table was created to identify 307 unique operators, with 15 operators not appearing in both lists. There are 66 public providers, which make up about 72% of the contractual programme in the public system. There are 241 private providers, accounting for about 28% of the contractual programme. In May 2023, public providers accounted for 69% (n=646,236) of services in the family medicine system, while private providers contributed 31% (n=291,660). The total number of services provided by public and private providers was 937,896. Three linear correlations were analysed. The initial analysis of the entire sample yielded a high R-squared value of .998 (adjusted R-squared value of .996) and a significant level below 0.001. The second analysis of the data from private providers showed a high R Squared value of .904 (Adjusted R Squared = .886), indicating a strong correlation between the variables. Furthermore, the significance level was < 0.001, providing additional support for the statistical significance of the results. The third analysis used data from public providers and showed a strong level of explanatory power, with a R Squared value of 1.000 (Adjusted R Squared = 1.000). Furthermore, the statistical significance of the findings was established with a p-value < 0.001. </p> <p>CONCLUSION: Our analysis shows a strong linear correlation between contract size of the program signed and number services rendered by family medicine providers. A stronger linear correlation is observed among providers in the public system compared to those in the private system. Our study found that private providers generally offer more services than public providers. However, it is important to acknowledge that the evaluation framework for assessing services may have inherent flaws when examining the data. Prescribing a prescription and resuscitating a patient are both assigned a rating of one service. It is crucial to closely monitor trends and identify comparable databases for pairing at the secondary and tertiary levels.</p>
Data from: Impacts of weathered microplastic ingestion on gastrointestinal microbial communities and health endpoints in fathead minnows (Pimephales promelas)
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Data for: Consequences of microsporidian prior exposure for virus infection outcomes and bumble bee host health
Open the record for dataset details and reuse information.
Data from: Health trade-offs of boiling drinking water with solid fuels: A modeling study
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Data from: Impacts of proactive health management on cattle and horse diets and dung biodiversity in Danish rewilding areas
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Data from: A village doctor-led mobile health intervention for cardiovascular risk reduction in rural China: cluster randomised controlled trial
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Data from: Tool use increases mechanical foraging success and tooth health in southern sea otters (Enhydra lutris nereis)
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Prevalence data complementing the European Union One Health 2018 Zoonoses Report
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agentsin foodstuffs. Relevant EU legislation is:Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Food and waterborne outbreaks data complementing the European Union One Health 2018 Zoonoses Report
<p>Food and waterborne outbreaks data reported under the framework of Directive 2003/99/EC and in accordance with the update of the technical specifications for harmonised reporting of FBOs through the EU reporting system in accordance with Directive 2003/99/EC. This dataset includes the number of outbreaks, as well as the number of human cases, hospitalisations and deaths, per causative agent. In addition, other information can include data on causative agents, food vehicles, and the factors in food preparation and handling that contributed to the food-borne outbreaks. Reporting countries can also provide information on the nature of the evidence supporting the suspicion of the food vehicle. This evidence can be epidemiological, microbiological, descriptive environmental, or based on product tracing investigations. </p>
Sample based prevalence data complementing the European Union One Health 2018 Zoonoses Report
<p>This dataset contains monitoring sample based data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agentsin foodstuffs. Relevant EU legislation is:Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014. </p>
Alzheimer's Disease versus Bipolar Disorder versus Health Control MRI data and processed results
<p><strong>README</strong></p> <p>The data is structured as follows:</p> <p>Clinical_data folder contains the .csv that can be read by spreadsheet software, as well as from Python, Matlab or R. There are separate files for each biomarker. The file "clinical_data_id_age_gender.csv" contains the numerical random key of the patient for anonymity, diagnostic key, age and gender for each entry in the other files. The file "clinical_data_corrected.csv" can be ignored.</p> <p>Diagnostic keywords: "crl" == healthy control, "tb" == bipolar disorder, "ea" == Alheimer's disease</p> <p>Imaging data is nifti encoded. The name of the file starts with the diagnostic key followed by the numerical random key and some nemotechnic for the contents. For instance: "crl_132_diff_dti_FA_FA_to_target.nii.gz" is the spatially normalized FA data of healthy control 132. Imaging data can be read with FSL, SPM, and any other nifti reading soft.</p> <p>Imaging folders contain the following data<br> DWI_origin - > the original diffusion weighted MRI data and their corresponding b-vector values</p> <p>FA - > the FA coefficients computed using FSL</p> <p>FA_to_target - > the FA volumes registered to MNI template using FSL tools</p> <p>T1_preprocessed - > the T1-weighted volumes at 1mm resolution registered to the MNI template using FSL no-linear registration tools</p> <p>T1_VBM_SPM_1mm - > the results of applying SPM implementation of voxel based morphometry (VBM) on the T1-weighted data, including results of the correlation between biomarkers and the detected clusters . Results can be checked using SPM (https://www.fil.ion.ucl.ac.uk/spm/)</p> <p>TBSS_results -> contains track based spatial statistics (TBSS) results obtained with FSL software (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS)</p> <p> </p> <p><strong>Publications using this dataset</strong></p> <p>M. Graña, M. Termenon, A. Savio, A. Gonzalez-Pinto, J. Echeveste, J. M. Pérez, A. Besga, Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson’s correlation, Neuroscience letters,Volume 502, Issue 3, 20 September 2011, Pages 225-229</p> <p>A. Besga, M. Termenon, M. Graña, J. Echeveste, J. M. Perez, A. Gonzalez-Pinto "Discovering Alzheimer's disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features, <strong>Neuroscience Letters</strong>, Volume 520, Issue 1, 27 June 2012, Pages 71–76.</p> <p>M. Termenon, M. Graña, A. Besga, J. Echeveste, A. Gonzalez-Pinto, Lattice Independent Component Analysis feature selection on Diffusion Weighted Imaging for Alzheimer’s Disease Classification, Neurocomputing (2013) Volume 114, 19 August 2013, Pages 132–141</p> <p>Ariadna Besga, Itxaso González-Ortega, Enrique Echeburúa, Alexandre Savio, Borja Ayerdi, Darya Chyzhyk, Jose LM Madrigal, Juan C. Leza, Manuel Graña, Ana González-Pinto, "Discrimination between Alzheimer’s Disease and Late Onset Bipolar Disorder using multivariate analysis" Frontiers in Aging Neuroscience, 7:231</p> <p>Ariadna Besga-Basterra, Darya Chyzhyk, Itxaso González-Ortega, Alexandre Savio, Borja Ayerdi, Jon Echeveste, Manuel Graña, Ana González-Pinto, Eigenanatomy on fractional anisotropy imaging provides white matter anatomical features discriminating between Alzheimer’s Disease and Late Onset Bipolar Disorder, Current Alzheimer Research, 13(5): 557 - 565 (2016)</p> <p>Ariadna Besga, Darya Chyzhyk, Itxaso Gonzalez Ortega, Jon Echeveste, Marina Grana-Lecuona, Manuel Grana, Ana González-Pinto, White Matter Tract Integrity in Alzheimer’s Disease versus Late Onset Bipolar Disorder and its Correlation with Systemic Inflammation and Oxidative Stress Biomarkers, Frontiers in Aging Neuroscience, 9:179 (2017)</p>
Data from: Association between night-shift work, sleep quality, and health-related quality of life : a cross-sectional study among manufacturing workers in a middle-income setting
<p>Objectives: Night-shift work may adversely affect health. This study aimed to determine the impact of night-shift work on health-related quality of life (HRQoL), and assess whether sleep quality was a mediating factor.</p> <p>Design: Cross-sectional study.</p> <p>Setting: 11 manufacturing factories in Malaysia.</p> <p>Participants: 177 night-shift workers aged 40 to 65 years old were compared with 317 non-night-shift work.</p> <p>Primary and secondary outcomes: Participants completed a self-administered questionnaire on socio-demography and lifestyle factors, short Form-12v2 Health Survey (SF-12), and the Pittsburgh Sleep Quality Index (PSQI). Baron and Kenny's method, Sobel test and multiple mediation model with bootstrapping were used to determine whether PSQI score or its components mediated the association between night-shift work and HRQoL.</p> <p>Results: Night-shift work was associated with sleep impairment and HRQoL. Night-shift workers had significantly lower mean scores in all the eight SF-12 domains (p<0.001). Compared to non-night shift workers, night-shift workers were significantly more likely to report poorer sleep quality, longer sleep latency, shorter sleep duration, sleep disturbances, and daytime dysfunction (p<0.001). Mediation analyses showed that PSQI global score mediated the association between night-shift work and HRQoL. "Subjective sleep quality" (indirect effect=-0.24, standard error [SE]=0.14, bias corrected 95%Confidence Interval [BC 95%CI]: -0.58 to -0.01) and "sleep disturbances" (indirect effect=-0.79, SE=0.22, BC 95%CI: -1.30 to -0.42) were mediators for the association between night-shift work and physical wellbeing, whereas "sleep latency" (indirect effect=-0.51, SE=0.21, BC 95%CI: -1.02 to -0.16) and "daytime dysfunction" (indirect effect=-1.11, SE=0.32, BC 95%CI: -1.86 to -0.58) were mediators with respect to mental wellbeing.</p> <p>Conclusion: Sleep quality partially explains the association between night-shift work and poorer HRQoL. Organisations should treat the sleep quality of night-shift workers as a top priority area for action in order to improve their employees' overall wellbeing.</p>
Data from: City sicker? a meta-analysis of wildlife health and urbanization
Urban development can alter resource availability, land use, and community composition, in turn influencing wildlife health. Generalizable relationships between wildlife health and urbanization have yet to be quantified, and could vary across health metrics and animal taxonomy. We present a phylogenetic meta-analysis of 516 records spanning 81 wildlife species from 106 studies comparing the toxicant loads, parasitism, body condition, or stress of urban and non-urban wildlife populations in 30 countries. We find a significantly negative relationship between urbanization and wildlife health, driven by higher toxicant loads and greater parasitism by parasites transmitted through close contact. Invertebrates and amphibians were particularly affected, with higher toxicant loads and physiological stress in urban populations as compared to their non-urban counterparts. We also found strong geographic and taxonomic bias in research effort, highlighting future research needs. Our results suggest urban wildlife experience several health risks with potential threats to conservation.
Mental Health-related subreddits data
<p>We gathered all posts, comments and metadata created during 2017 from the four mental health related Reddit communities with the largest number of publications, namely Depression, SuicideWatch, Anxiety e Bipolar. Unprocessed data is publicly available at http://files.pushshift.io/reddit. After extracting the zipped file, there will be three files for each subreddit <subreddit>:</p> <ul> <li><subreddit>_post2data.pkl: a python pickle file containing a dict indexed by post id, where the value corresponds to the data associated with the post (comments excluded)</li> <li><subreddit>_post2comments.pkl: a python pickle file containing a dict indexed by post id, where the value corresponds to the list of comments present in the thread associated with the post</li> <li><subreddit>_comment2data.pkl: a python pickle file containing a dict indexed by comment id, where the value corresponds to the data associated with the comment</li> </ul> <p>If you use this dataset, please cite<br> Silveira, Bárbara, Fabricio Murai, and Ana Paula Couto da Silva. "Predicting User Emotional Tone in Mental Disorder Online Communities." arXiv preprint arXiv:2005.07473 (2020).</p>
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.