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We outline a principled approach to data FAIRification rooted in the notions of experimental design, and whose main intent is to clarify the semantics of data matrices. Using two related… Click to show full abstract
We outline a principled approach to data FAIRification rooted in the notions of experimental design, and whose main intent is to clarify the semantics of data matrices. Using two related metabolomics datasets associated to journal articles, we perform retrospective data and metadata curation and re-annotation, using community, open, interoperability standards. The results are semantically-anchored data matrices, deposited in public archives, which are readable by software agents for data-level queries, and which can support the reproducibility and reuse of the data underpinning the publications.
Journal Title: Scientific Data
Year Published: 2019
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