The NCBI Taxonomy database is a dataset published in Nucleic Acids Research (2011). On theSindex it has a DataRank of 25.5, placing it in the top 1.5% of the data-sharing corpus. It has been cited 1,541 times, with 199 citing works in its 1-hop citation network. Its calibrated FAIR score is 84/100.
The NCBI Taxonomy database (http://www.ncbi.nlm.nih.gov/taxonomy) is the standard nomenclature and classification repository for the International Nucleotide Sequence Database Collaboration (INSDC), comprising the GenBank, ENA (EMBL) and DDBJ databases. It includes organism names and taxonomic lineages for each of the sequences represented in the INSDC's nucleotide and protein sequence databases. The taxonomy database is manually curated by a small group of scientists at the NCBI who use the current taxonomic literature to maintain a phylogenetic taxonomy for the source organisms represented in the sequence databases. The taxonomy database is a central organizing hub for many of the resources at the NCBI, and provides a means for clustering elements within other domains of NCBI web site, for internal linking between domains of the Entrez system and for linking out to taxon-specific external resources on the web. Our primary purpose is to index the domain of sequences as conveniently as possible for our user community.
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
1.1
From this paper's citation signal
Citation Network Contribution
24.4
From 199 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 4% comes from its base citations and 96% from the citation network (199 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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