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36 results for “Biomedical Data”
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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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: 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.
Data release for the Open Biomedical Network Benchmark
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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>
Data from: Development and application of a novel metric to assess effectiveness of biomedical data
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Research Data Management in Health and Biomedical Citizen Science: Practices and Prospects
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Data associated with Automated Biomedical Text Classification with Research Domain Criteria
<p>Each file contains a set of abstracts for a given RDoC construct. Each file is named after one RDoC category. In each file, each line represents one PubMed abstract that belong to the RDoC category. Each line has a PubMeD ID and an abstract text separated by a tab. The dataset was created on August 2018. </p> <p>Please cite: M. Anani and I. Kahanda, Automated Biomedical Text Classification with Research Domain Criteria, International Conference on Bioinformatics and Computational Biology, Las Vegas, NV, 2018.</p>
Data from: A cross-lingual similarity measure for detecting biomedical term translations
Bilingual dictionaries for technical terms such as biomedical terms are an important resource for machine translation systems as well as for humans who would like to understand a concept described in a foreign language. Often a biomedical term is first proposed in English and later it is manually translated to other languages. Despite the fact that there are large monolingual lexicons of biomedical terms, only a fraction of those term lexicons are translated to other languages. Manually compiling large-scale bilingual dictionaries for technical domains is a challenging task because it is difficult to find a sufficiently large number of bilingual experts. We propose a cross-lingual similarity measure for detecting most similar translation candidates for a biomedical term specified in one language (source) from another language (target). Specifically, a biomedical term in a language is represented using two types of features: (a) intrinsic features that consist of character n-grams extracted from the term under consideration, and (b) extrinsic features that consist of unigrams and bigrams extracted from the contextual windows surrounding the term under consideration. We propose a cross-lingual similarity measure using each of those feature types. First, to reduce the dimensionality of the feature space in each language, we propose prototype vector projection (PVP)—a non-negative lower-dimensional vector projection method. Second, we propose a method to learn a mapping between the feature spaces in the source and target language using partial least squares regression (PLSR). The proposed method requires only a small number of training instances to learn a cross-lingual similarity measure. The proposed PVP method outperforms popular dimensionality reduction methods such as the singular value decomposition (SVD) and non-negative matrix factorization (NMF) in a nearest neighbor prediction task. Moreover, our experimental results covering several language pairs such as English–French, English–Spanish, English–Greek, and English–Japanese show that the proposed method outperforms several other feature projection methods in biomedical term translation prediction tasks.
Data from: It's all in the timing: calibrating temporal penalties for biomedical data sharing
Objective: Biomedical science is driven by datasets that are being accumulated at an unprecedented rate, with ever-growing volume and richness. There are various initiatives to make these datasets more widely available to recipients who sign Data Use Certificate agreements, whereby penalties are levied for violations. A particularly popular penalty is the temporary revocation, often for several months, of the recipient's data usage rights. This policy is based on the assumption that the value of biomedical research data depreciates significantly over time; however, no studies have been performed to substantiate this belief. This study investigates whether this assumption holds true and the data science policy implications. Methods: This study tests the hypothesis that the value of data for scientific investigators, in terms of the impact of the publications based on the data, decreases over time. The hypothesis is tested formally through a mixed linear effects model using approximately 1200 publications between 2007 and 2013 that used datasets from the Database of Genotypes and Phenotypes, a data-sharing initiative of the National Institutes of Health. Results: The analysis shows that the impact factors for publications based on Database of Genotypes and Phenotypes datasets depreciate in a statistically significant manner. However, we further discover that the depreciation rate is slow, only ∼10% per year, on average. Conclusion: The enduring value of data for subsequent studies implies that revoking usage for short periods of time may not sufficiently deter those who would violate Data Use Certificate agreements and that alternative penalty mechanisms may need to be invoked.
Data from: A cross-lingual similarity measure for detecting biomedical term translations
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Data from: It’s all in the timing: calibrating temporal penalties for biomedical data sharing
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Inference and validation of predictive gene networks from biomedical literature and gene expression data
GEO Series GSE53091. Homo sapiens. 125 samples. Type: Expression profiling by array.
Modification of polyurethane materials with fluorine and siloxane groups for use as biomedical materials in 3D printing - experimental data
<p>Experimental data collected as part of the research project "Modification of polyurethane materials with fluorine and siloxane groups for use as biomedical materials in 3D printing" (grant no. 2022/06/X/ST4/00452) financed by the National Science Center (NCN) as part of the MINIATURA 6 program.</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.