Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

DAX-1 Blocks Steroid Production at Multiple Levels1

10.1210/en.139.10.4237Source: DataRank Database

DAX-1 Blocks Steroid Production at Multiple Levels1 is a research paper. On theSindex it has a DataRank of 3.5. It has been cited 68 times, with 63 citing works in its 1-hop citation network. Its calibrated FAIR score is 43/100.

N/A

3.5DataRank · unranked

3.5

68 citations · base score 4.2

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

43FAIR score

F Findable

100

A Accessible

I Interoperable

R Reusable

Top 79% by FAIRdeterministic

Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →

DataRank Breakdown

Base Score 18%Citation Network 82%

Base Score Contribution

0.635

From this paper's citation signal

Citation Network Contribution

2.8

From 60 citing papers with measurable signal

Learn more about DataRank methodology →

Top 1 citer driving the network score

Ranked by citation count — the same ordering the engine uses when summing log1p(C_q) over citers.

StAR Protein and the Regulation of Steroid Hormone Biosynthesis
Annual Review of Physiology2001903 citationsDataRank 12.5

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 18% comes from its base citations and 82% from the citation network (60 citing papers contributed measurable signal).

Base score B(p): log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
Network N(p): Σ over citers of log1p(C_q) ÷ max(outdegree_q, 1). Being cited by a highly-cited paper with few references counts most.
Damping factor d = 0.85: DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
Self-citations excluded: Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

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.

Read the full methodology →

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Node colors:CenterData PaperData + Open AccessNon-dataSelected & links| Node size = percentile rank