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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Has Generative AI Killed the PID Star? – PIDs Part 2
The role of Persistent Identifiers (PIDs) – those enduring signposts in the digital landscape – is facing a fascinating challenge from the rise of Generative AI. At a recent meeting of the Research Data Alliance (RDA) PID Interest Group, experts…
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The PID Paradox – PIDs Part 1
The digital age has ushered in an unprecedented era for scientific discovery, promising accelerated progress through open data sharing. Yet, beneath the surface of progressive policies and sophisticated technological solutions lies a potent psychological barrier: the fear of being “scooped.”…
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Federated Data Platforms vs. Data Visiting Technologies
What technologies and methods enable the access and analysis of sensitive data using AI/ML, while preserving data privacy and avoiding centralization? That’s the challenge federated data and data visitation technologies aim to solve. But are they the same thing? Not…
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Dataset Digital Twin in Biomedical Research
Imagine a holographic view of a patient—a Biomedical Digital Twin—reflecting their unique physiology, lifestyle, and medical history. This isn’t science fiction; it’s a rapidly evolving field transforming healthcare and research. This article explores various types of digital twins and their…
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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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