FAIRification of mass spectrometry workflows using FDOs
Project
https://w3id.org/spaces/knowledgepixels/incubator/project4 ^
Date
About
Mass spectrometry research confronts challenges endemic to data-intensive science: vendor-specific proprietary formats from multiple Mass Spectrometry instruments, complex multi-consortium governance, and the near-impossibility of tracking provenance across thousands of heterogeneous digital objects spanning clinical samples, instrument logs, processing workflows, and publications. Traditional approaches that retrofit FAIR principles onto existing repositories fail at scale and perpetuate the research integrity and reproducibility crises. This project addresses this systemic failure by implementing nanopublication-based FAIR Digital Objects (FDOs) as minimal, but precise metadata tags at the source, combining automated and human curated machine-actionable assertions that establish unambiguous provenance chains before data enters any workflow. This source-level FAIRification demonstrates that trusted AI in biomedicine requires fundamentally reliable data infrastructure where agentic AI can automatically assess the quality, context, and trustworthiness of every datum from its moment of creation.
Resources
(none)
Roles:
Pinned