Experiment design driven FAIRification of omics data matrices, an exemplar is a dataset published in Scientific Data (2019). On theSindex it has a DataRank of 2.9, placing it in the top 32.2% of the data-sharing corpus. It has been cited 21 times, with 19 citing works in its 1-hop citation network. Its calibrated FAIR score is 70/100.
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.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.464
From this paper's citation signal
Citation Network Contribution
2.5
From 17 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 16% comes from its base citations and 84% from the citation network (17 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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