Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
599
datasets available to search
ShareScore release 0.9.0
Dataset results
599 results for “health data”
Code + simulated + publically accessable data for "Evaluating health facility access using Bayesian spatial models and location analysis methods"
<p># README</p> <p>These files contain r data objects and R files that represent the key details of the paper, "Evaluating health facility access using Bayesian spatial models and location analysis methods".</p> <p>The following datasources are available for simulation of some of the ideas in the paper.</p> <p>- dat_grid_sim: simulated data of the grid and grid cells<br> - dat_ohca_cv_sim: simulated data containing the cross validated test/training sets of OHCA data<br> - dat_ohca_sim: simulated OHCA event data<br> - dat_aed_sim: simulated AED location data<br> - dat_bldg_sim: simulated building location data<br> - dat_municipality_sim: simulated municipality information<br> - table_1: Table 1 information containing key demographic data</p> <p>These data were produced using the code in 01-create-sim-data.R, and one of the statistical models is demonstrated in 02-demo-inla-model.R</p> <p>In terms of the paper itself, the functions and code used in the manuscript are located in:</p> <p>* 01_tidy.Rmd - analysis code used to tidy up the data</p> <p>* 02_fit_fixed_all_cv.Rmd - analysis code used to place AEDs</p> <p>* 02_model.Rmd - analysis code used to fit the model in INLA</p> <p>* 03_manuscript.Rmd - Full code and text used to create the paper</p> <p>* 04_supp_materials.Rmd - full code and text used to create the supplementary materials</p> <p>The following files are a part of an R package "swatial" that was developed along with the paper. These files are:</p> <p>* DESCRIPTION</p> <p>* NAMESPACE</p> <p>* LICENSE</p> <p>* LICENSE.md</p> <p>* decay.R</p> <p>* spherical-distance.R</p> <p>* test-figure-data-matches.R</p> <p>* test-table-data-matches.R</p> <p>* testthat.R</p> <p>* tidy-inla.R</p> <p>* tidy-posterior-coefs.R</p> <p>* tidy-predictions.R</p> <p>* utils-pipe.R</p> <p>* All files that end in .Rd are documentation files for the functions.</p> <p>## Regarding data sources</p> <p>Census information for Ticino was transcribed from the Annual Statistical Report of Canton Ticino from years 2010 to 2015. This data was taken from their publicly accessible annual reports - for example: (https://www3.ti.ch/DFE/DR/USTAT/allegati/volume/ast_2015.pdf). The raw data was extracted from these annual reports, and placed into the file: "swiss_census_popn_2010_2015.xlsx". These data are put into analysis ready format in the file “01_tidy.Rmd”</p> <p>Housing and other relevant geospatial data can be accessed via http://map.housing-stat.ch/ and https://data.geo.admin.ch/. The maps of buildings from the REA (Register of Buildings and Dwellings) can be found here: https://map.geo.admin.ch/?zoom=11&bgLayer=ch.swisstopo.pixelkarte-grau&lang=en&topic=ech&layers=ch.bfs.gebaeude_wohnungs_register,ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill,ch.bfs.volkszaehlung-gebaeudestatistik_gebaeude,ch.bfs.volkszaehlung-gebaeudestatistik_wohnungen,ch.swisstopo.swissbuildings3d_1.metadata,ch.swisstopo.swissbuildings3d_2.metadata&E=2717616.28&N=1096597.25&catalogNodes=687,696&layers_timestamp=,,2016,2016,,&layers_visibility=true,false,false,false,false,false&layers_opacity=1,1,1,1,1,0.75</p> <p>For further enquiries on this data, contact the Swiss federal Office of Statistics at the details listed here: https://www.bfs.admin.ch/bfs/en/home/services/contact.html</p> <p>The shapefiles of the Comuni can be accessed here: https://www4.ti.ch/dfe/de/ucr/documentazione/download-file/?noMobile=1</p> <p>Data from the people living in the Municipalities in Ticino can be downloaded here: https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=dati.home&tema=33&id2=61&id3=65&c1=01&c2=02&c3=02</p> <p>## Future work</p> <p>In the future, these functions from the paper may be generalised and put into their own package. If that happens, this repository will be updated with a link to updated functions.</p>
Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility testing and human health risks - Supplementary data
<p>This is a supplementary dataset to the paper:</p> <p>Hiller E., Faragó T., Kolesár M., Filová L., Mihaljevič M., Jurovič L., Demko R., Mchlica A., Štefánek J., Vítková M. (2024): Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility and human health risks. <em>Chemosphere</em> <strong>362</strong>, 142677. DOI: 10.1016/j.chemosphere.2024.142677</p> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks. The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
Data repository for manuscript "A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi"
<p>Dataset to accompany the publication <em>“A new approach to Health Benefits Package design: an application of the Thanzi La Onse model in Malawi”</em> by Margherita Molaro, Sakshi Mohan, Bingling She, Martin Chalkley, Tim Colbourn, Joseph H. Collins, Emilia Connolly, Matthew M. Graham, Eva Janoušková, Ines Li Lin, Gerald Manthalu, Emmanuel Mnjowe, Dominic Nkhoma, Pakwanja D. Twea, Andrew N. Phillips, Paul Revill, Asif U. Tamuri, Joseph Mfutso-Bengo, Tara Mangal, and Timothy B. Hallett.</p> <p>The Thanzi La Onse (TLO) model used to produce this data is open source and available for review and usage at<a href="https://github.com/UCL/TLOmodel"> https://github.com/UCL/TLOmodel</a>. In particular, the outputs analysed in this study can be reproduced from model tag "Molaro_et_al_2024_HBP_design" (accessible at https://github.com/UCL/TLOmodel/tags) using the scenario file src/scripts/healthsystem/impact_of_policy/scenario_impact_of_policy.py. All analysis scripts used to generate the plots in the manuscript are located in the same directory and have filenames beginning with "analysis_impact_of_policy_".</p> <p>This repository contains post-processed simulation outputs, which were generated using the script src/scripts/healthsystem/impact_of_policy/analysis_extract_data.py (available from the same tag). The data included have the following structure:</p> <p>"Draw": Represents a specific prioritisation-policy, identified by the acronyms listed in Table 1 of the publication.</p> <p>"Run": Represents a single simulation instance of a draw. Each draw was simulated 10 times, each with independent random sampling, resulting in 10 "runs" per draw.</p> <p>The data files included in this repository are:</p> <p><strong>DALYS_by_cause_with_time.csv</strong>: DALYs (as defined in the publication) incurred on a given year due to each of the causes of DALYs considered.</p> <p><strong>HSIs_requested_by_type_and_facility_level_with_time.csv</strong>: total number of requested HSIs on a given year, broken down by HSI type and the facility level at which they were requested.</p> <p><strong>HSIs_delivered_by_type_and_facility_level_with_time.csv</strong>:total number of HSIs delivered on a given year broken down by HSI type and the facility level at which they were delivered.</p> <p><strong>Population_with_time.csv</strong>:total population size on a given year. </p> <p> </p> <p> </p>
Study data for the journal article "Mental health of individuals at increased suicide risk after hospital discharge and initial findings on the usefulness of a suicide prevention project in Central Switzerland"
<p>Anonymized raw data of our cross-sectional survey study.</p> <p>id = study participant ID; se1-se10 = questions of the General Self-Efficacy Scale; sm1-sm5 = questions of the Self-Management Self-Test; hl1-hl12 = questions of the Health Literacy Questionnaire (Swiss version); prisms = question on the utilization of the PRISM-S technique; sp1-sp4 = questions on the utilization and perceived usefulness of the personal safety plan; app1-app7 = questions on the utilization and perceived usefulness of the SERO app; ensa = question on the participation in ensa courses; sex-income = sociodemographic questions</p>
Data from: Heterogeneous zonal impacts of climate change on a wide hyperendemic area of human and animal fascioliasis assessed within a One Health action for prevention and control
Open the record for dataset details and reuse information.
Data set for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study"
<p>Dataset has been created for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study" paper.</p> <p>We have created a smartphone based behaviour monitoring and recommendation system to aid patients recruited in a weight loss intervention programme.<br>The main interaction element for the patients is <strong>EghiFit</strong> application which was used in context of this dataset for data acquisition and secure transmission to our servers.</p> <p>The data consists of application usage per patient, steps achieved, nutritional information of meals logged, heart rate data, interaction data and more.<br>For more information about the dataset, please take a look at <strong>readme.md</strong> file and our paper.</p>
Underlying data for "Development of a framework of potential adverse effects of interventions to improve critical thinking about health choices: A mixed methods study."
Open the record for dataset details and reuse information.
Data set of study Examining the impact of cognitive control and discrimination on mental health outcomes in diverse Pakistani and Afghan communities
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>This dataset is part of a study examining the impact of cognitive control and discrimination on mental health outcomes among diverse Pakistani and Afghan communities. It includes demographic variables such as age, gender, and ethnicity, as well as psychological measures assessing cognitive control, discrimination experiences, and various mental health outcomes.</p> </div> </div> </div> </div> <div> <div> <div> <div> </div> <div><span>4o</span></div> <span></span></div> </div> </div> <div> </div> <div> <div> </div> </div> </div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> </div> <div> <div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
PSYCHE-D: predicting change in depression severity using person-generated health data (DATASET)
<p>This dataset is made available under <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)</a>. See LICENSE.pdf for details.</p> <p><strong>Dataset description</strong></p> <p>Parquet file, with:</p> <ul> <li>35694 rows</li> <li>154 columns</li> </ul> <p>The file is indexed on [<em>participant</em>]_[<em>month</em>], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.</p> <p>Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.</p> <p>File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (<a href="https://clinicaltrials.gov/ct2/show/NCT03421223">https://clinicaltrials.gov/ct2/show/NCT03421223</a>). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.</p> <p>The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.</p> <p>The data subset used in this work comprises the following:</p> <ul> <li>Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study</li> <li>Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities</li> <li>Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month</li> <li>Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity</li> </ul> <p>From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).</p> <p>The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.</p>
RNA-seq data and results from in-vitro experiments for "Genetic control of fetal placental genomics contributes to development of health and disease" (Bhattacharya et al 2021)
<p>This dataset includes data and results from RNA-seq data collected from in-vitro experiments presented in "Genetic control of fetal placental genomics contributes to development of health and disease" (Bhattacharya et al 2021). Please check the README.txt file for more details.</p>
Data for "Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing"
<p>This dataset includes the experiment and simulation data of a new structural health monitoring system using distributed fiber optic sensing (DFOS) and Isogeometric Analysis (IGA).</p> <p>The experiment setup was a 5mm thick PVC pipe with a fiber optic cable wrapped around the outer surface of the pipe. The PVC pipe was subjected to an applied deformation and the distributed strains along the optical fiber was measured with a Neubrescope (NBX7031) instrument using Rayleigh backscattering technology.</p> <p>The simulation was performed using the in-house code JWRIAN-IGA developed in Joining and Welding Research Institute, Osaka University. The simulated data includes deformation, stress and strain distributions of the pipe, and projected one-dimensional fiber strains. The visualization files are post-processed with ParaView software.</p>
Cross-sectional and prospective data on Framingham risk score, allostatic load, and ankle brachial index among Puerto Rican adults from the Boston Puerto Rican Health Study
<p><strong>Background</strong><br> Puerto Ricans have higher odds of peripheral artery disease (PAD) compared with Mexican Americans. Limited studies have examined relationships between clinical risk assessment scores with PAD assessments.</p> <p><strong>Methods</strong><br> Using 2004-2015 data from the Boston Puerto Rican Health Study (BPRHS) (n = 370-583), cross-sectional, 5-y change and patterns of change in Framingham Risk Score (FRS) and allostatic load (AL) with ankle brachial index (ABI) at 5-y follow-up was assessed among Puerto Rican adults (45-75 y). Analysis were conducted in 2020. FRS and AL were calculated at baseline, 2-y and 5-y follow-up. Multivariable linear regression models examined cross-sectional and 5-y changes in FRS and AL with ABI at 5-y. Latent growth mixture modeling identified trajectories of FRS and AL over 5-y, and multivariable linear regression models were used to test associations between trajectory groups at 5-y.</p> <p><strong>Results</strong><br> Greater FRS at 5-y and increases in FRS from baseline were associated with lower ABI at 5-y (β = -0.149, p = 0.010; β = -0.171, p = 0.038, respectively). AL was not associated with ABI in cross-sectional or change analyses. Participants in low-ascending (vs. no change) FRS trajectory, and participants in moderate-ascending (vs. low-ascending) AL trajectory, had lower 5-y ABI (β = -0.025, p = 0.044; β = -0.016, p = 0.023, respectively).</p> <p><strong>Conclusions</strong><br> FRS was a better overall predictor of ABI, compared with AL. FRS may be a clinically feasible measure of PAD risk in Puerto Ricans, an understudied population. Additional research examining relationships between FRS and AL and development of PAD is warranted.</p>
Data set - Stress, Mental Health and Sociocultural Adjustment in Third Culture Kids: The Mediating Roles of Resilience and Family Functioning
<p>this data set contains data derived from a cross-sectional study which explores the contributions of proximal and contextual factors in the adjustment process of a sample of internationally mobile children and adolescents having relocated to Switzerland. </p> <p>scales include child perceived stress (PSS-C; White, 2014), acculturative stress (ASIC; Suarez-Morales et al., 2007), resilience (CYRM-12; Liebenberg et al., 2013), mental health difficulties SDQ (R. Goodman, 1997), socio cultural adjustmen (SCAS-Child; Ward & Kennedy, 1999) and family functioning (McMaster Family Assessment Device (Epstein et al., 1983)). </p> <p>child age, arrival in Switzerland and cemographic information on country of origin are included</p>
Survey data of the health literacy on COVID-19 and COVID-19 vaccination in Indonesia
<p><span><strong>Introduction</strong>: </span><span>Health literacy on COVID-19 and COVID-19 vaccination is valuable during the pandemic. The objective of this study was to determine the levels of health literacy about the COVID-19 vaccine and vaccination (Vaccine and Vaccination literacy—VL) in the Indonesian adult general population, assessing the perceptions of the respondents/interviewees about current adult immunization and beliefs about vaccination in general, and analyzing correlations of these variables with the VL levels.</span><span> </span></p> <p><span><strong>Methods</strong>: </span><span>A rapid survey was administered via the web. Data were analyzed using descriptive and inferential stats; the internal consistency of the VL scales was assessed through Cronbach's alpha coefficient, and a Principal Component Analysis (PCA) was conducted to investigate how the questions of the functional and interactive-critical VL scales were related to one another and whether the underlying components (factors) and each question's load on the components could be identified as anticipated. An alpha level lesser than 0.05 was considered significant.</span></p> <p><span><strong>Results</strong>: </span><span>Answers to functional- and interactive/ critical- VL questions showed good/ acceptable internal consistency (Cronbach's alpha = 0.817 and 0.699, respectively), lowest values observed were 0.806 for functional scale and 0.640 for the interactive-critical scale. PCA showed two components accounting for 52.45% of the total variability. Approximately 60% of respondents were females (n=686). Almost all respondents used the internet to seek information regarding COVID-19 and COVID-19 vaccination. Many used at least one social media actively with 74.4% of respondents sometimes believing the validity of this information.</span></p> <p><span><strong>Conclusions:</strong> </span><span>High scores were observed in both functional- and interactive/ critical-VL, and were quite balanced between genders in the prior VL and higher in females for the latter; these were also closely related to the educational level and age group. It is crucial to increase public health literacy on managing the pandemic.</span></p>
Prevalence data complementing the European Union One Health 2021 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 agents in 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>
Animal population data complementing the European Union One Health 2021 Zoonoses Report
<p>This dataset includes animal population aggregated data under the framework of Directive 2003/99/EC.</p>
Food and waterborne outbreaks data complementing the European Union One Health 2021 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 2021 Zoonoses Report - Croatia
<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 agents in foodstuffs. Relevant EU legislation: 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>
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - the United Kingdom (Northern Ireland)
<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 agents in foodstuffs. Relevant EU legislation: 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>
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - Finland
<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 agents in foodstuffs. Relevant EU legislation: 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>
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.