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.

Identification of an NTPase motif in classical swine fever virus NS4B protein

Virology(2011)10.1016/j.virol.2010.12.028Source: DataRank Database

Identification of an NTPase motif in classical swine fever virus NS4B protein is a research paper published in Virology (2011). On theSindex it has a DataRank of 1.3. It has been cited 26 times, with 25 citing works in its 1-hop citation network.

N/A

1.3DataRank · unranked

1.3

26 citations · base score 3.3

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 38%Citation Network 62%

Base Score Contribution

0.494

From this paper's citation signal

Citation Network Contribution

0.790

From 24 citing papers with measurable signal

Learn more about DataRank methodology →

Top 2 citers driving the network score

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

DNA sequencing with chain-terminating inhibitors
Proceedings of the National Academy of Sciences197769,231 citationsDataRank 1.7
Characterization of NS3, NS5A and NS5B of classical swine fever virus through mutation and complementation analysis
Veterinary Microbiology201031 citationsDataRank 1.4

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 38% comes from its base citations and 62% from the citation network (24 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)

Boris K. Gavrilov,Lauren G. Holinka,Ignacio J. Fernandez-Sainz,N.G. Vepkhvadze,Kara Rogers