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.

Bistability and Nonmonotonic Induction of the lac Operon in the Natural Lactose Uptake System

Biophysical Journal(2017)10.1016/j.bpj.2017.03.038Source: DataRank Database

Bistability and Nonmonotonic Induction of the lac Operon in the Natural Lactose Uptake System is a research paper published in Biophysical Journal (2017). On theSindex it has a DataRank of 0.542. It has been cited 10 times, with 10 citing works in its 1-hop citation network.

N/A

0.542DataRank · unranked

0.542

10 citations · base score 2.4

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.

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DataRank Breakdown

Base Score 66%Citation Network 34%

Base Score Contribution

0.360

From this paper's citation signal

Citation Network Contribution

0.182

From 6 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.

The three operators of the lac operon cooperate in repression.
The EMBO Journal1990492 citationsDataRank 17.5
The lac and gal Operons Today
Regulation of Gene Expression in Escherichia coli199617 citationsDataRank 1.2
Switching off: The phenotypic transition to the uninduced state of the lactose uptake pathway
Biophysical Journal20220 citationsDataRank 0

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 66% comes from its base citations and 34% from the citation network (6 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