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200
datasets available to search
ShareScore release 0.9.0
Dataset results
200 results for “health research”
Early Childhood Caries and Health Professionals' Perception: a Qualitative Research Protocol to Assess Oral Health Stigma
ClinicalTrials.gov study NCT05284279. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Epidemiological Observatory on the Health Conditions of Inpatients Aged 65 or Older at the INRCA Research Hospitals
ClinicalTrials.gov study NCT01397682. IPD Sharing: Not stated. Countries: 1. Publications: 5.
COVID19 Severity Prediction and Health Services Research Evaluation
ClinicalTrials.gov study NCT04463706. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
4EVER - Efficacy, Safety, Health Economics, Translational Research of Postmenopausal Women With Estrogen Receptor Positive Locally Advanced or Metastatic Breast Cancer
ClinicalTrials.gov study NCT01626222. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Taiwan Real-world LDCT Screening Behavior and Outcome Research for High Risk Subjects Based on Health Promotion Administration
ClinicalTrials.gov study NCT05557487. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Cystic Fibrosis Reproductive and Sexual Health Collaborative: Building Online Research Partnerships
ClinicalTrials.gov study NCT04999865. IPD Sharing: NO. Countries: 1. Publications: 1.
Addressing Mental Health Disparities in Refugee Children: A Community-based Participatory Research (CBPR) Collaboration
ClinicalTrials.gov study NCT02562794. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: The rise of health biotechnology research in Latin America: a scientometric analysis of health biotechnology production and impact in Argentina, Brazil, Chile, Colombia, Cuba and Mexico.
Open the record for dataset details and reuse information.
The FAIR database: facilitating access to public health research literature
Open the record for dataset details and reuse information.
MiRoR1 - P1 - A scoping review describes methods used to identify, prioritize and display gaps in health research
<p>Data extraction, data set and protocol of a scoping review on describes methods used to identify, prioritize and display gaps in health research</p>
Supplementary material 2 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Landsat Products Used
Figure 3 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Figure 3 Mental health prescriptions vs winter-greenness at 250 m from home for pure-urban Data Zones beanplot (Kampstra 2008). Horizontal lines are individual values. Curves are density of data points at values. Thicker horizontal line is average for group.
Supplementary material 1 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
ESRI Shape file of SIMD DataZones
Figure 2 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Figure 2 Sampling of a single data point, postcode EH8 9AJ (orange dot) within Data Zone S01008671 (black outline). The 100, 250 and 500 metre buffer zones are shown in orange. Blue dots are other sampling points. Green shading represents greenness (NDVI).
Figure 4 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Figure 4 Mental health prescriptions vs seasonal difference at 250 m from home for pure-urban Data Zones beanplot (Kampstra 2008). Horizontal lines are individual values. Curves are density of data points at values. Thicker horizontal line is average for group.
Figure 1 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Figure 1 An area of south central Edinburgh illustrating distribution of sample points within Data Zones. Background is OpenStreetMap. Black outlines are DataZone boundaries. Blue dots are small user postcode locations that are used for sampling. Green shading represents summer-greenness (NDVI).
Supplementary material 4 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Results Data Tables
Supplementary material 3 from: Hyam R (2020) Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study. Research Ideas and Outcomes 6: e53542. https://doi.org/10.3897/rio.6.e53542
Small User Postcodes
Research Data Management in Health and Biomedical Citizen Science: Practices and Prospects
<p><b>Background:</b> Public engagement in health and biomedical research is being influenced by the paradigm of citizen science. However, conventional health and biomedical research relies on sophisticated research data management tools and methods. Considering these, what contribution can citizen science make in this field of research? How can it follow research protocols and produce reliable results?</p> <p><b>Objective:</b> The aim of this paper is to analyse research data management practices in existing biomedical citizen science studies, so as to provide insights for members of the public and of the research community considering this approach to research.</p> <p><b>Methods:</b> A scoping review was conducted on this topic to determine data management characteristics of health and bio medical citizen science research. From this review and related web searching, we chose five online platforms and a specific research project associated with each, to understand their research data management approaches and enablers.</p> <p><b>Results:</b> Health and biomedical citizen science platforms and projects are diverse in terms of types of work with data and data management activities that in themselves may have scientific merit. However, consistent approaches in the use of research data management models or practices seem lacking, or at least are not evident.</p> <p><b>Conclusions:</b> There is potential for important data collection and analysis activities to be opaque or irreproducible in health and biomedical citizen science initiatives without the implementation of a research data management model that is transparent and accessible to team members and to external audiences. This situation might be improved with participatory development of standards that can be applied to diverse projects and platforms, across the research data life cycle.<b> </b></p>
Data from: The effect of promoting current local research activities on large monitors on the population's interest in health-related research – a randomized controlled trial
OBJECTIVE: The objectives of this study were threefold: to estimate people's interest in health-related research, to understand to what extent people appreciate being actively informed about current local health-related research and to investigate whether their interest can be influenced by advertising local current health-related research using large TV monitors. DESIGN: Randomized controlled trial using a stepped wedge design. SETTING: The emergency department waiting room at two public hospitals in northern Queensland, Australia. PARTICIPANTS: Waiting patients and their accompanying friends and relatives in the emergency department waiting room not requiring immediate medical attention. INTERVENTIONS: A TV monitor advertising local current health-related research. MAIN OUTCOME MEASURES: Odds ratio for the effect of intervention on changing the interest in health-related research compared to a control group while adjusting for gender, age and socioeconomic standard. RESULTS: The intervention significantly increased the short-term interest in health-related research with an odds ratio of 1.3 (1.1-1.7, p=0.0063). We also noted that being female and being older was correlated to a higher interest in health-related research CONCLUSIONS: This study found that proactive information significantly increased the general populations' interest in health-related research. There are reasonable set up costs involved but the costs for maintaining the system were very low. Hence, it seems reasonable that research-active organizations should give much higher priority to this type of activity. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) with the registration number ACTRN12617001085369
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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.