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.

The lysine methyltransferase SMYD3 interacts with hepatitis C virus NS5A and is a negative regulator of viral particle production

Virology(2014)10.1016/j.virol.2014.05.016Source: DataRank Database

The lysine methyltransferase SMYD3 interacts with hepatitis C virus NS5A and is a negative regulator of viral particle production is a research paper published in Virology (2014). On theSindex it has a DataRank of 1.1. It has been cited 20 times, with 17 citing works in its 1-hop citation network.

N/A

1.1DataRank · unranked

1.1

20 citations · base score 3.0

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.

Run a calibrated FAIR evaluation for this paper →

DataRank Breakdown

Base Score 40%Citation Network 60%

Base Score Contribution

0.457

From this paper's citation signal

Citation Network Contribution

0.679

From 15 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.

NIH Image to ImageJ: 25 years of image analysis
Nature Methods201263,587 citationsDataRank 1.7

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 40% comes from its base citations and 60% from the citation network (15 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

Authors (9)

Margarita Zayas,Alexey StukalovORCID,Andreas PichlmairORCID,Gualtiero Alvisi,André C. Müller