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12,574 results for “health IT”
Symptoms in health care workers during the COVID-19 epidemic. A cross-sectional survey.
<p>data collected during the COVID-19 epidemics on workers of the Health Care Unit Roma4, Civitavecchia. Paper submitted.</p>
Data for The impact of information about tobacco-related reproductive vs. general health risks on South Indian women's tobacco use decisions
<p>Tobacco Intervention Study Mysore India March-April 2016</p> <p>Published version: <a href="https://doi.org/10.1017/ehs.2020.61">https://doi.org/10.1017/ehs.2020.61</a></p>
Brazilian Federal legislations and Health Professional Councils regulations about telemedicine, according to historical phases and public policy purposes from 1990 to 2018.
<p>The file contains one excel file with two datasheets, one with legislations from Brazilian Federal Government and other from Health Professional Councils, from 1990 to 2019. Each spreadsheet has the original database, the number ID of the normative, what institution the document is from, its publication date, the abstract (in Portuguese), its public URL, historical phases and the purpose of it.</p>
Data Visualization - Individual Project - G20 Countries - Military, Health Care and Educational Spendings
<p>This Project is part of the course work for Data visualization DATS 6401. In this project, I have created webpage to show data analysis on G20 countries - military, health care and educational spending from 2011 - 2017. Google Visualization API is used for all visualization graphs in the webpage.</p>
Disease and lesion maps - part of the Scientific opinion on the evaluation of public and animal health risks in case of a delayed post-mortem inspection in ungulates
<p>EFSA was requested to assess the impact on effectiveness of <em>post-mortem</em> inspection in terms of any change in the sensitivity of detection of animal diseases of domestic and wild ungulates listed according to Article 5 of Regulation (EC) No 2016/429 and septicaemia, pyaemia, toxaemia or viraemia, when carried out after up to 24 hours or up to 72 hours after slaughter in comparison to when it is carried out immediately after slaughter. In order to identify the main lesions associated with the target diseases, a so called “disease map” was built, with the information about the clinical forms of the diseases (acute, subacute, chronic, or latent forms) and clinical signs, as well as potential lesions that could be observed on the respective diseased animals.</p> <p>The following information is indicated for each disease/condition in the disease map:</p> <ol> <li>The susceptible animal species</li> <li>Whether there is any surveillance programme in place in the EU</li> <li>The signs associated with the disease that could be detected at <em>ante mortem </em>inspection;</li> <li>The lesions associated with the disease that could be detected at <em>post mortem</em> inspection</li> <li>The probability of detecting the disease during the PMI as normally carried out.</li> <li>Whether carcass swabbing and/or laboratory tests are normally carried out.</li> </ol> <p>From the disease map, the list of organs to be considered at <em>post mortem</em> inspection and the lesions, a “lesion map” was built by connecting animal species with organs, lesions and corresponding disease. This was done to facilitate the construction of a questionnaire where, for each organ and the five types of lesions, the respondents (meat inspectors) had to provide a numerical answer about how many carcasses out of 100 with the given lesion, will be still detected after 24- or 72-h of refrigerated storage.</p>
Dataset for English Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]
<p>Health-related advice directed to the public on twitterprovides insight into the use of social media duringa pandemic. This paper describes our data collection, sampling, and analysis of 44 million tweets in English in March 2020. We make reference to a parallel dataset and analysis of tweets in Arabic during thesame period. The contribution of this paper is a description of our dataset, our coding process to indicate tweets with health related advice, and our analysis and comparisons of the characteristics of the tweets with and without health-related advice. These contributions providethe basis for future research on semi-automated classifiers for health-related advice and efforts to reduce thespread of harmful health advice.</p>
AraHealth: A Dataset for Arabic Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]
<p>Health-related advice directed to the general public on Twitter provides insight into the use of social media during health emergencies. This paper describes our data collection, sampling, and analysis of 24 million tweets in Arabic in March and early April 2020. We make reference to a parallel dataset and analysis of tweets in English during the same period. The contribution of this paper is a description of our dataset, our coding process to indiciate tweets with health related advice, and our analysis and comparisons of the characteristics of the tweets with and without health-related advice. These contributions provide the basis for future research on semi-automated classifiers for health-related advice and efforts to reduce the spread of harmful health advice.</p>
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
H2M survey data on commercialisation training needs of Health Researchers
<p>Health-2-Market was a 3-year long Coordination and Support Action, funded by the European Union’s Seventh Framework Programme for research, technological development and demonstration (Grant Agreement No 305532). H2M aimed at providing training and individual support to Health / Life Sciences researchers in the process of translating their research results into successful new business ideas.</p> <p>With a view to properly adapting the training offer of the project to the needs of Health / Life Sciences researchers in terms of entrepreneurship and business skill development a Training Needs Analysis (TNA) was conducted. In this context, H2M launched an online survey targeted at Health / Life Sciences researchers who have been involved in EU health projects. In particular, the objectives of the survey were:</p> <ul> <li>To formulate a descriptive understanding of various aspects of commercialisation and training needs of the main target group of the project;</li> <li>To divide this target group into homogeneous sub-groups (clusters) along a number of key characteristics such as demographics, commercialisation attitudes and needs;</li> <li>To understand preferences and importance of different aspects and needs through the analysis of: <ul> <li>Knowledge areas that can influence commercialisation behaviour;</li> <li>Training modalities that have an effect on the intention to participate and /or on the perception of the usefulness of a commercialisation training;</li> <li>Variations identified over different sub-groups.</li> </ul> </li> </ul> <p>The survey was dispatched to a database composed of 7,991 unique contacts of participants in previous health projects, accessed through the Directorate General for Health and Food Safety of the European Commission. The initial aim of at least 50 complete responses was overwhelmingly surpassed: 637 respondents completed the survey in full.</p> <p>The “H2M survey data on commercialisation training needs of Health Researchers” dataset contains the raw, anonymised data that were collected from these respondents, along with the questionnaire items that were utilised.</p>
Internal and Predictive Validity of the French Health of the Nation Outcome Scales Dataset
<p>This dataset is related to Golay P, Basterrechea L, Conus P, Bonsack C (2016). Internal and Predictive Validity of the French Health of the Nation Outcome Scales: Need for Future Directions. PLoS ONE 11(8): e0160360. doi:10.1371/journal.pone.0160360.</p> <p>http://journals.plos.org/plosone/article/related?id=10.1371%2Fjournal.pone.0160360</p> <p><br /> It includes 19 variables and 2722 cases.</p>
Semantic Enrichment of the Laboratory Data Dictionary of the Study of Health in Pomerania (SHIP-START-4) with LOINC; Detailed Mapping Results
<p>Unlike West Germany, high morbidity and mortality have been observed in East Germany over the last century. The regional population-based Study of Health in Pomerania (SHIP) therefore investigates the long-term progression of sub-clinical findings, their determinants and prognostic values, to acquire knowledge that facilitates early diagnosis and thus helps prevent the progression of disease. The SHIP covers various areas of patient health. Each SHIP data set is accompanied by a data dictionary (DD) which provides descriptions of variables and definitions.</p> <p>This work shows the detailed mapping results of the semantic enrichment of the SHIP-START-4 medical laboratory data dictionary with LOINC codes. This work also provides detailed descriptions of the concepts applied in the semnatic enrichment. The results of this work serve as a critical step towards improving its interoperability and hence FAIRness for the SHIP laboratory-related measurements. </p>
Research Data Management in Selected Health Research Institutions in Uganda
<p>This data set was collected from Researchers in three purposively selected health reseach Institutions in Uganda. The purpose of the study was to explore compliance to FAIR data princiles and Open science initiative given the increasing dependence on donor funding and need to fulfill the requirement for good research practices. </p>
EFSA Opinion Update of risks for animal health related to the presence of ochratoxin A (OTA) in feed: Annexes on Occurrence data in feed submitted to EFSA
<p>Annexes to EFSA's Update Opinion on the risks for animal health related to the presence of OTA in feed. Annex B includes the occurrence data in feed extracted from EFSA Data Warehouse for the period from 2012 to 2021. Annex C contains the occurrence data expressed in dry matter following analysis and cleansing of the dataset as detailed in EFSA's Opinion. Annex D lists the samples of 'Compound feed' and other feed materials except forage expressed in whole weight. The number of samples across some of the feed categories differ among the two annex C and D because in few cases the moisture content was not reported (and no assumption on the moisture could be done), precluding the conversion of the analytical results to either whole weight or dry matter. </p>
Short and long term impacts of Covid-19 on Older childreN's healTh-Related behAviours, learning and wellbeing STudy (CONTRAST) dataset
<p>The CONTRAST study explored how the Covid-19 (lockdown) restrictions affected lives of older children in the UK, particularly how they have influenced learning, eating, physical and other activities and wellbeing.</p>
Tables, figures, and country data complementing the European Union One Health Zoonoses 2022 Report
<p>European Food Safety Authority; European Centre for Disease Prevention and Control</p><p>All summary tables and figures produced for the European Union One Health 2022 Zoonoses Report are provided as archives containing Excel files for tables, and as PDF or PNG files for figures.</p><p><strong>All country data connected to this Report are published SEPARATELY on Knowledge Junction - see related identifiers. This is because DATA OWNERSHIP for country data stays with the organisation(s) of the country submitting the data - for further reference see doi:10.2903/sp.efsa.2019.EN-1544.</strong></p><p>Supplementary datasets submitted are given in the related identifier section, however for clarity we give here the information on what they refer to:</p><p><strong>10.5281/zenodo.10255165 </strong>Foodborne outbreaks</p><p><strong>10.5281/zenodo.10256864 </strong>Disease status</p><p><strong>10.5281/zenodo.10255061 </strong>Animal Population</p><p><strong>10.5281/zenodo.10246432 </strong>Prevalence</p><p><i><strong>Sample-based data submitted by specific countries</strong></i></p><p><strong>10.5281/zenodo.10257184 </strong>Finland</p><p><strong>10.5281/zenodo.10257162 </strong>Croatia</p><p><strong>10.5281/zenodo.10257105 </strong>Norway</p><p><strong>10.5281/zenodo.10257034 </strong>Luxembourg</p><p><strong>10.5281/zenodo.10257142 </strong>United Kingdom (Northern Ireland)</p><p><strong>10.5281/zenodo.10257210 </strong>Ireland</p><p><strong>10.5281/zenodo.10257388 </strong>Sweden</p><p>Journal article: 10.2903/j.efsa. 2023.8442 </p><p>Citation</p><p>EFSA (European Food Safety Authority) & ECDC (European Centre for Disease Prevention and Control). (2023). Tables, figures, and country data complementing the European Union One Health Zoonoses 2022 Report [Dataset]. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.10057302&data=05%7C01%7C%7Cd53ce1fd0eeb4b027cb608dbf6528f55%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C638374606688640910%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=46n1ois6rCgqRamDnk4%2B42K2XGwwg1T1nvG5ezYqqyg%3D&reserved=0">https://doi.org/10.5281/zenodo.10057302</a></p>
From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability
<p>Code: https://github.com/srijitseal/BioMorph_Space<br> <br> Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict <em>in vitro</em> and <em>in vivo</em> drug effects. However, Cell Painting features are based on image statistics, and are, therefore, often not readily biologically interpretable. In this study, we introduce an approach that maps specific Cell Painting features into the BioMorph space using readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. In summary, BioMorph space offers a more biologically relevant way to interpret cell morphological features from the Cell Painting assays and to generate hypotheses for experimental validation.</p> <p> </p> <p>The following datasets are released:<br> </p> <p>Cell_Health_median_357_profiles_70_labels.csv :<br> The Cell Heath dataset for CRISPR perturbations. Contains median consensus signatures for the 357 consensus profiles (119 CRISPR perturbations × 3 cell lines) Ref: Way et al.</p> <p>Cell_Painitng_CRISPR_Perturbations_357_profiles_827_features_scaled.csv:<br> The Cell Painting dataset for CRISPR perturbations. Contains 827 morphology features (and metadata annotation) for 357 consensus profiles (119 CRISPR perturbations × 3 cell lines). Ref: Way et al.</p> <p>Cell_Painting_data_658_compounds_827_Features_scaled.csv<br> The Cell Painting dataset for compound perturbations. Contains 658 structurally unique compounds with 827 Cell Painting features. Ref: Bray et al</p> <p>Endpoints_9_Mitotox_biological_activities_658_compounds.csv<br> The biological assay activity labels for compound perturbations. Contains 658 structurally unique compounds with 9 biological activity consensus hit calls. Ref: ToxCast/MoleculeNet</p> <p>BioMoprh_pvalue_658_compunds_398_BioMorph_terms.csv:<br> The dataset of standardised BioMorph term p-values. Contains 398 BioMorph terms for the 658 compounds in the biological activity dataset. <br> <br> References: <br> Way et al. Predicting cell health phenotypes using image-based morphology profiling. Mol Biol Cell. 2021;32(9):995-1005.<br> Bray et al. A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. Gigascience. 2017;6(12):1-5. <br> MoleculeNet: Wu et al. MoleculeNet: A benchmark for molecular machine learning. Chem Sci. 2018;9(2):513-530. <br> ToxCast: Exploring ToxCast Data | US EPA https://www.epa.gov/chemical-research/exploring-toxcast-data (accessed Jul 9, 2023).</p>
District level health and well-being data
<p>Self-reported health and well-being data collected in the Living Labs district and in a control district of the Front Runner Cities of proGIreg project (Dortmund: Huckarde and Mengede districts; Turin: Mirafiori Sud and Barriera di Milano districts; Zagreb: Sesvete and Špansko-Jug districts), before and after the project NBS implementations (2019 and 2022). More details about methodology are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.</p> <p>More details about results are reported in: Baldacchini, C., Calfapietra, C. (2023): Living Labs impact at the district level, Deliverable No.4.8, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, pp 93.</p>
Mining the health disparities and minority health bibliome: A computational scoping review and gap analysis of 200,000+ articles
<p>Without comprehensive examination of available literature on health disparities and minority health (HDMH), the field is left vulnerable to disproportionately focus on specific populations or conditions, curtailing our ability to fully advance health equity. Using scalable open-source methods, we conducted a computational scoping review of more than 200,000 articles to investigate major populations, conditions, and themes in the literature as well as notable gaps. We also compared trends in studied conditions to their relative prevalence in the general population using insurance claims (42 million Americans). HDMH publications represent 1% of articles in MEDLINE. Most studies are observational in nature, though randomized trial reporting has increased five-fold in the last twenty years. Half of all HDMH articles concentrate on only three disease groups (cancer, mental health, endocrine/metabolic disorders), while hearing, vision, and skin-related conditions are among the least well-represented despite substantial prevalence. To support further investigation, we also present HDMH Monitor, an interactive dashboard and repository generated from the HDMH bibliome.</p>
Dataset for the paper "Mental Health and Burnout during Medical School: Longitudinal Evolution and Covariates" published in PLOS ONE (2024)
<p><strong>Full reference of the paper:</strong></p> <p>Carrard V, Berney S, Bourquin C, Ranjbar S, Castelao E, Schlegel K, et al. (2024) Mental health and burnout during medical school: Longitudinal evolution and covariates. PLoS ONE 19(4): e0295100. <a href="https://doi.org/10.1371/journal.pone.0295100">https://doi.org/10.1371/journal.pone.0295100</a></p>
Title: Health-Related Quality of Life and DNA Methylation-Based Aging Biomarkers among Survivors of Childhood Cancer
<p><strong><span>Abstract</span></strong></p> <p><strong><span>Background: </span></strong><span>Childhood cancer survivors are at high risk for morbidity and mortality and poor patient-reported outcomes, typically health-related-quality-of-life (HRQOL). However, </span><span>associations between DNA methylation (DNAm)-based aging biomarkers and HRQOL have not been evaluated.</span></p> <p><strong><span>Methods: </span></strong><span>DNAm was generated with Infinium EPIC BeadChip on blood-derived DNA (median[range] for age at blood draw=34.5[18.5-66.6] years) and HRQOL was assessed with age at survey (32.3[18.4-64.5] years) from 2,206 survivors in the St. Jude Lifetime Cohort. DNAm-based aging biomarkers, including epigenetic age using multiple clocks (e.g., <span>GrimAge</span>) and others (e.g., DNAmB2M: beta-2-microglobulin; DNAmADM: adrenomedullin), were derived from the DNAm Age Calculator (https://dnamage.genetics.ucla.edu). HRQOL was assessed using the Medical Outcomes Study 36-Item Short-Form Health Survey to capture eight domains, and physical and mental component summaries (PCS and MCS). General linear models evaluated associations between HRQOL and epigenetic age acceleration (EAA, e.g., EAA_GrimAge) or other age-adjusted DNAm-based biomarkers (e.g., ageadj_DNAmB2M) after adjusting for age at blood draw, sex, cancer treatments, and DNAm-based surrogate for smoking pack-years. All P values were 2-sided.</span></p> <p><strong><span>Results: </span></strong><span>Worse HRQOL was associated with greater EAA_GrimAge (</span><span>PCS: β[95%CI]=-0.18[-0.251,-0.11] years, P=1.85×10<sup>-5</sup>; and four individual HRQOL domains), followed by ageadj_DNAmB2M (PCS: -0.08[-0.124,-0.037], P=0.003; and three individual HRQOL domains), and ageadj_DNAmADM (PCS: -0.082[-0.125,-0.039], P=0.002; and two HRQOL domains). EAA_Hannum (Hannum clock) was not associated with any HRQOL.</span></p> <p><strong>Conclustion: </strong>Overall and domain-specific measures of HRQOL are associated with DNAm measures of biological aging. Future longitudinal studies should test biological aging as a potential mechanism.</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.