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.

Proteolytic processing of a Murray Valley encephalitis virus non-structural polyprotein segment containing the viral proteinase: accumulation of a NS3-4A precursor which requires mature NS3 for efficient processing

Journal of General Virology(1992)10.1099/0022-1317-73-9-2305Source: DataRank Database

Proteolytic processing of a Murray Valley encephalitis virus non-structural polyprotein segment containing the viral proteinase: accumulation of a NS3-4A precursor which requires mature NS3 for efficient processing is a research paper published in Journal of General Virology (1992). On theSindex it has a DataRank of 1.8. It has been cited 30 times, with 19 citing works in its 1-hop citation network.

N/A

1.8DataRank · unranked

1.8

30 citations · base score 3.4

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 29%Citation Network 71%

Base Score Contribution

0.515

From this paper's citation signal

Citation Network Contribution

1.3

From 18 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.

Chaperone-Assisted Protein Folding Is Critical for Yellow Fever Virus NS3/4A Cleavage and Replication
Journal of Virology201629 citationsDataRank 1.2

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 29% comes from its base citations and 71% from the citation network (18 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