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.

Genetic Relationship and Diversity Analysis of Faba Bean (Vicia Faba L. var. Minor) Genetic Resources Using Morphological and Microsatellite Molecular Markers

Plant Molecular Biology Reporter(2015)10.1007/s11105-015-0871-0Source: DataRank Database

Genetic Relationship and Diversity Analysis of Faba Bean (Vicia Faba L. var. Minor) Genetic Resources Using Morphological and Microsatellite Molecular Markers is a research paper published in Plant Molecular Biology Reporter (2015). On theSindex it has a DataRank of 1.8. It has been cited 34 times, with 32 citing works in its 1-hop citation network.

N/A

1.8DataRank · unranked

1.8

34 citations · base score 3.6

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 30%Citation Network 70%

Base Score Contribution

0.533

From this paper's citation signal

Citation Network Contribution

1.3

From 24 citing papers with measurable signal

Learn more about DataRank methodology →

Top 3 citers driving the network score

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

Mathematical model for studying genetic variation in terms of restriction endonucleases.
Proceedings of the National Academy of Sciences197910,579 citationsDataRank 1.4
Genetic Distance between Populations
The American Naturalist19729,828 citationsDataRank 1.4
Restriction fragment length polymorphisms as genetic markers in Vicia
Heredity199036 citationsDataRank 2.6

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 30% comes from its base citations and 70% 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