Insights into FAIR and Analyzable Data
Quality-controlled biomedical data, analyzed by AI/ML, holds the key to unlocking new treatments and cures.
It starts with the data.
FAIRLYZ Blog: Data Sharing in the Era of AI
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Part 2: Key Takeaways from the RDA Plenary 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…
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Part 1: Key Takeaways from the RDA Plenary on AI/ML and Interoperability
The 23rd RDA Plenary Meeting in San José, Costa Rica, brought together a global community of researchers to address the pressing issue of sustainable science. The plenary was divided into many sessions that ran simultaneously and were organized by RDA…
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Part 2: OntoGPT for AI-Driven Annotation of Biomedical Papers
In Part 1, we introduced a brief experiment using ChatGPT to extract specific metadata from an open-source scientific journal publication and map it to BioPortal classes. In Part 2, we will share the results of our tests with OntoGPT. To…
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Part 1: ChatGPT for AI-Driven Annotation of Biomedical Papers
Integrating and harmonizing biomedical datasets for larger studies necessitates annotating data from diverse sources using consistent terminology or ontology classes. This is crucial for machine learning and AI research, which rely on standardized datasets. Additionally, there is often a need…
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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…
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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…
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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…
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Part 2: Shared Metadata Practices in FAIRLYZ and NIAID Discovery Portal
In Part 1 of our series on the NIAID Data Ecosystem Discovery Portal, we discussed how the portal acts as a central hub for exploring Infectious and Immune-mediated Disease (IID) data. It accomplishes this by providing access to two key…
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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…
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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) –…
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