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.

DETECTION OF NOVEL SPLICE FORMS IN HUMAN AND MOUSE USING CROSS-SPECIES APPROACH

Biocomputing 2004(2003)10.1142/9789812704856_0005Source: DataRank Database

DETECTION OF NOVEL SPLICE FORMS IN HUMAN AND MOUSE USING CROSS-SPECIES APPROACH is a research paper published in Biocomputing 2004 (2003). On theSindex it has a DataRank of 1.2. It has been cited 18 times, with 16 citing works in its 1-hop citation network.

N/A

1.2DataRank · unranked

1.2

18 citations · base score 2.9

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.

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DataRank Breakdown

Base Score 37%Citation Network 63%

Base Score Contribution

0.442

From this paper's citation signal

Citation Network Contribution

0.741

From 12 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.

Comparative Component Analysis of Exons with Different Splicing Frequencies
PLoS ONE20099 citationsDataRank 0.455

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 37% comes from its base citations and 63% from the citation network (12 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