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.
9
datasets available to search
ShareScore release 0.9.0
Dataset results
9 results for “medical data sets”
Data set supplementing "Benchmarking triage capability of symptom checkers against that of medical laypersons: Survey study"
<p>This is the de-identified data set used to conduct the analyses in the study published as Original Research in the JMIR under the title "Benchmarking triage capability of symptom checkers against that of medical laypersons: Survey study" (https://doi.org/10.2196/24475)</p> <p>The data set contains the assessments of the urgency of symptoms to 45 fictitious clinical case vignettes by 91 US participants, and the participants' age, gender and level of education. Data for the symptom checker apps is needed to fully reproduce our study and can be found in the appendix of the paper "Evaluation of symptom checkers for self diagnosis and triage: audit study" by Semigran et al. (2015) (https://doi.org/10.1136/bmj.h3480).</p>
Data set for survey questionnaires to assess self-medication practices
<p>This is pubmed and web of science data sets for a review on self medication survey questionnaires. </p>
Data from: Implementation of a pediatric telemedicine and medication delivery service in a resource-limited setting: A pilot study for clinical safety and feasibility
<p>Objective: Determine the clinical safety and feasibility of implementing a telemedicine and medication delivery service (TMDS) to address gaps in nighttime healthcare access for children in low-resource settings.</p> <p>Results: A total of 391 cases were enrolled from September 9th, 2019 to January 19th, 2021; 89% (347) received a household visit. Most cases were triaged as mild or moderate (92%; 361). Among the severe cases, 83% (20) sought subsequent referred care. The most common complaint was a respiratory problem (63%; 246). At 10-days, 95% (329) of parents reported their child's condition as "improved" or "recovered". Ninety-nine percent (344) rated the TMDS as "good" or "great". The median phone consultation was 20 minutes, time to arrival at the household was 73 minutes and total workflow per case was 114 minutes.</p> <p>Conclusion: The TMDS was a feasible healthcare delivery model with high rates of improved clinical status at 10-days.</p>
Data from: Implementation of a pediatric telemedicine and medication delivery service in a resource-limited setting: A pilot study for clinical safety and feasibility
Open the record for dataset details and reuse information.
Medical Equipment Image Data set
<p>This data set aims to realise the medical equipment recognition to aid visual search through three deep learning models. The data set contains ten medical equipment classes: commodes, wheelchairs, walking frames, blood pressure monitors, breast pumps, thermometers, rippled mattresses, oximeters, crutches, and therapeutic ultrasound machines. We collected from online resources around 220 images for each medical equipment class. Each image class in the test set has around 40 images.</p> <p>Refer to the paper <a href="http://doi.org/10.3233/JIFS-212786">here</a> or in research gate (preprint).</p>
Data set supplementing "Determinants of Laypersons' Trust in Medical Decision Aids: Randomized Controlled Trial"
<p>This is the de-identified data set used to conduct the analyses in the preprint submitted to JMIR Human Factors under the title "Determinants of Laypersons’ Trust in Medical Decision Aids: Randomized Controlled Trial" (<a href="https://doi.org/10.2196/35219">https://doi.org/10.2196/35219</a>).</p> <p>This dataset contains 494 respondents' appraisals of a fictitious case vignette. They received support from a decision aid (that always disagreed with participants' first appraisal) showing a mock symptom checker logo, a decision aid framed as anthropomorphic or as an AI. Their second appraisal - taking into account the symptom checker advice - was collected again. </p> <p>Additionally, the data contains participants'</p> <ul> <li>age</li> <li>gender</li> <li>education</li> <li>medical training</li> <li>propensity to trust</li> <li>eHealth Literacy</li> <li>certainty in their appraisals</li> <li>trust in the decision aid</li> </ul>
Data from: Silver nanoparticles as a medical device in healthcare settings: a five-step approach for candidate screening of coating agents
Silver nanoparticle-based antimicrobials can promote a long lasting bactericidal effect without detrimental toxic side effects. However, there is not a clear and complete protocol to define and relate the properties of the particles (size, shape, surface charge, ionic content) with their specific activity. In this paper, we propose an effective multi-step approach for the identification of a 'purpose-specific active applicability window' to maximize the antimicrobial activity of medical devices containing silver nanoparticles (Ag NPs) (such as surface coaters), minimizing any consequent risk for human health (safety by design strategy). The antimicrobial activity and the cellular toxicity of four types of Ag NPs, differing in their coating composition and concentration have been quantified. Through the implementation of flow-field flow fractionation, Ag NPs have been characterized in terms of metal release, size and shape. The particles are fractionated in the process while being left unmodified, allowing for the identification of biological particle-specific contribution. Toxicity and inflammatory response in vitro have been assessed on human skin models, while antimicrobial activity has been monitored with both non-pathogenic and pathogenic Escherichia coli. The main benefit associated with such approach is the comprehensive assessment of the maximal effectiveness of candidate nanomaterials, while simultaneously indexing their properties against their safety.
Data from: Silver nanoparticles as a medical device in healthcare settings: a five-step approach for candidate screening of coating agents
Open the record for dataset details and reuse information.
Korean Colorectal Cancer Cohort RNA-seq data set from Asan Medical Center
<p>This study presents a comprehensive molecular analysis of colorectal cancer (CRC) in Korean patients using RNA-sequencing data and clinical information. Key findings include significant differences in gene expression between tumor and normal tissues and identification of dysregulated pathways related to tumor progression. CRC samples were stratified into molecular subtypes, confirming their similarity to established CRC classifications. Immunological analysis highlighted distinguishing features between tumor and normal tissues, and some patients were found to be responsive to immunotherapy, especially those with microsatellite stable (MSS) tumors, who demonstrated a better prognosis. These insights enhance the understanding of CRC in Korean patients and support the use of RNA-seq data to inform cancer treatment and improve patient care.</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.