Category: Data Sharing
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Part 2: Key Takeaways from RDA 23 on Omics Data Curation Workflows
The first part of a two-part blog series highlighted key takeaways from the 23rd RDA Plenary Meeting in San José, Costa Rica, presenting the AI/ML and interoperability topics that are relevant to FAIRLYZ. This second part will present and discuss current efforts to develop practical omics data curation workflows that include data annotation, curation, and…
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The FAIRLYZ Survey Report: Optimizing Data Sharing for Scientific Progress
Biomedical data sharing, especially crucial for AI’s reliance on large, high-quality datasets, fuels scientific progress by enabling new research and collaborations around data reuse, and the development of new tools trained on the data. To gain insights into current data sharing practices and challenges, the FAIRLYZ project surveyed NIAID-funded researchers. An analysis of the survey…
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The Practice of Licensing Biomedical data
There are several reasons why a researcher might choose to license their data using permissive licenses like MIT, Apache 2.0, or LGPL including increased accessibility and collaboration. Benefits Benefits for the scientific community: Benefits for the Researcher: Alignment with Funding Agencies: Downsides to Consider: Overall, researchers often choose permissive licenses to promote collaboration, transparency, and…
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Submitting Data to NIH Repositories
For scientific researchers nearing publication, a crucial step involves uploading data to a public repository. However, navigating the diverse landscape of data repositories, each with unique data types and submission processes, can still be a tedious task even as it has improved over the years. dbGaP, GEO, and SRA Below are links to submission guides…
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Part 1: Data Providers at the NIAID Data Ecosystem Discovery Portal
The NIAID Data Ecosystem Discovery Portal allows exploring Infectious and Immune-mediated Disease (IID) data across many repositories through Resource Catalogs (collections of scientific information or research outputs) and Dataset Repositories (collections of data of a particular experimental type) that are currently included in the NIAID Data Ecosystem. The NIAID Data Ecosystem Discovery Portal supports exploration…
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Earlier Detection, Better Outcomes with FAIR Data
Improving Early Disease Detection: FAIR data is being used to develop AI-powered tools that can analyze medical scans and detect diseases like Alzheimer’s or diabetic retinopathy at earlier stages, leading to better treatment outcomes. National Institutes of Health (NIH) – Big Data to Knowledge (BD2K): This NIH initiative focuses on using big data for biomedical…
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Beyond the Hype: Why FAIR Data Matters for Real-World Medical AI Applications
The field of medical AI is buzzing with potential. From diagnosing diseases with superhuman accuracy to designing personalized treatment plans, AI promises to revolutionize healthcare. However, amidst the excitement, there’s a crucial element often overlooked: FAIR data. FAIR stands for Findable, Accessible, Interoperable, and Reusable. While it may sound technical, FAIR data principles are the…