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195
datasets available to search
ShareScore release 0.9.0
Dataset results
195 results for “biomedical”
Biomedical Shirt-based ECG Monitoring
ClinicalTrials.gov study NCT03068169. IPD Sharing: YES. Countries: 1. Publications: 1.
Criteria Associated With Patient Willingness to Participate in Biomedical Research
ClinicalTrials.gov study NCT03098303. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Qualitative study of physicians' varied uses of biomedical research in the USA
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Data from: How can we get close to zero?: the potential contribution of biomedical prevention and the investment framework towards an effective response to HIV
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Data from: A reference set of curated biomedical data and metadata from clinical case reports
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Impact of Programming Workshops on Biomedical Reproducibility (IPWBR)
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CT DICOM studies from: In vivo measurements of lung volumes in ringed seals: insights from biomedical imaging
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"MiRoR13-P2 Journal editors' perspectives on the roles and tasks of peer reviewers in biomedical journals: a qualitative study"
<p>This dataset is related to the publication "Journal editors' perspectives on roles and tasks of peer reviewers in biomedical journals: a qualitative study"</p>
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>
Reproducible results for: pmparser and PMDB: resources for large-scale, open studies of the biomedical literature
<p>The zip file contains all code and results. See the included readme.</p>
Data from: Development and application of a novel metric to assess effectiveness of biomedical data
Objective: Design a metric to assess the comparative effectiveness of biomedical data elements within a study that incorporates their statistical relatedness to a given outcome variable as well as a measurement of the quality of their underlying data. Materials and methods: The cohort consisted of 874 patients with adenocarcinoma of the lung, each with 47 clinical data elements. The p value for each element was calculated using the Cox proportional hazard univariable regression model with overall survival as the endpoint. An attribute or A-score was calculated by quantification of an element's four quality attributes; Completeness, Comprehensiveness, Consistency and Overall-cost. An effectiveness or E-score was obtained by calculating the conditional probabilities of the p-value and A-score within the given data set with their product equaling the effectiveness score (E-score). Results: The E-score metric provided information about the utility of an element beyond an outcome-related p value ranking. E-scores for elements age-at-diagnosis, gender and tobacco-use showed utility above what their respective p values alone would indicate due to their relative ease of acquisition, that is, higher A-scores. Conversely, elements surgery-site, histologic-type and pathological-TNM stage were down-ranked in comparison to their p values based on lower A-scores caused by significantly higher acquisition costs. Conclusions: A novel metric termed E-score was developed which incorporates standard statistics with data quality metrics and was tested on elements from a large lung cohort. Results show that an element's underlying data quality is an important consideration in addition to p value correlation to outcome when determining the element's clinical or research utility in a study.
Figure 1 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 1 Conceptual model of a Knowledge Object (KO) containing a payload, machine-actionable service and deployment specifications, metadata and a unique persistent identifier. We are exploring aligning our conceptual model with emerging best practices for FAIR Digital Objects. Derived from Wittenburg et al's Digital Objects as Drivers towards Convergence in Data Infrastructures (Wittenburg et al. 2019).
Figure 3 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 3 This figure illustrates the dual nature of Knowledge Objects: knowledge-as-resource and knowledge-as-service. A KO can be curated and maintained in a repository, pass metadata to a knowledge graph or deployed into applications. Different to other digital objects, the methods to deploy the KO to applications via custom or generic runtimes called by microservices are built into the KO.
Figure 2 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 2 (L) Sample KO as viewed from the KGrid Library, from which the KO can be implemented in a hosted runtime environment or downloaded. (R) Sample output results from deploying the KO.
Supplementary Videos for dissertation: Tunable magnetic microparticles and hydrogel biomaterials for biomedical imaging and tissue engineering applications
<p>This work contains supplementary videos of cell migration in hydrogels for the chapter 4 of the dissertation.</p>
Figure 3. L in Biomedical role of L-carnitine in several organ systems, cellular tissues, and COVID-19
Figure 3. L-Carnitine biosynthesis takes place in six steps, interspersed with four important enzymes (green box) (Furusawa et al., 2008).
Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences Derived from Biomedical Literature in the PubChemRDF
<p>This is the dataset used for the publication of "Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences Derived from Biomedical Literature in the PubChemRDF". </p>
Data release for the Open Biomedical Network Benchmark
<p> </p> <div> </div>
Harvard Biomedical Research Data Lifecycle
<p>The Biomedical Data Lifecycle is a representation of stages in your research regarding data collection, use, and storage. At the core is how to "Store & Manage" the data for your project. How data is managed is integral to each stage in the diagram. Though the process is generally linear from "Plan & Design" to "Publish & Reuse," you may find yourself jumping around this lifecycle throughout your project. For example, "Data Management Plans" are created at the planning stage but will be used throughout the research process in subsequent stages. The plan dictates how you will handle the data collected and created, and how you will share and disseminate that data, all while considering data documentation, safety, and reuse. </p> <p>This lifecycle diagram was created by the <a href="https://datamanagement.hms.harvard.edu/">Harvard Longwood Medical Area Research Data Management Working Group</a>.</p> <p><strong>RDM-lifecycle-v5.png</strong>: This diagram depicts the core stages of the research lifecycle. The center of the wheel has a grey circle labeled "Store & Manage." The second layer is cut into six segments labeled "Plan & Design" in dark blue, "Collect & Create" in light green, "Analyze & Collaborate" in dark green, "Evaluate & Archive" in gold, "Share & Disseminate" in red, and "Publish & Reuse" in orange. Guiding arrows are at the edge of each segment, showing the process to be continuous like a wheel. In this version, all segment labels are in title case, and the "Evaluate & Archive" segment has been updated to gold to increase accessibility.</p> <p><strong>RDM-lifecycle-2tier-v5.png</strong>: This diagram includes an outer layer that represents the processes and concepts integral to each stage. The third layer expands the colors of each of the six sections and includes sub-elements of activities or resources that are involved in each of the six segments. In this version, all section labels are in title case, and the "Evaluate & Archive" segment has been updated to gold to increase accessibility.</p> <p><strong>RDM-lifecycle-2tier-v5-template.ai</strong>: Adobe Illustrator template can be used to customize the outer second tier text and the number of segments 2, 3, or 4, to meet the needs of different schools or data groups. Complete instructions for using the template can be found in the <a href="https://zenodo.org/record/8075934">zip file included in version 2</a>. These materials are licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
Stability and Control of RT-VCM employed for Moving Mirror Control in FTIR Spectroscopy within the Realms of Biomedical Applications using FOPID Controller
<p>Table. 2 Variation and Selection of non-integer order with lamda 0.2</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.