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.

Expression and Purification of Site-Specifically Lysine-Acetylated and Natively-Folded Proteins for Biophysical Investigations

Methods in Molecular Biology(2018)10.1007/978-1-4939-7574-7_11Source: DataRank Database

Expression and Purification of Site-Specifically Lysine-Acetylated and Natively-Folded Proteins for Biophysical Investigations is a research paper published in Methods in Molecular Biology (2018). On theSindex it has a DataRank of 0.462. It has been cited 13 times, with 2 citing works in its 1-hop citation network. Its calibrated FAIR score is 49/100.

N/A

0.462DataRank · unranked

0.462

13 citations · base score 2.6

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.

49FAIR score

F Findable

100

A Accessible

I Interoperable

R Reusable

Top 55% by FAIRdeterministic

Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →

DataRank Breakdown

Base Score 86%Citation Network 14%

Base Score Contribution

0.396

From this paper's citation signal

Citation Network Contribution

0.0662

From 1 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.

Enzymatic assembly of DNA molecules up to several hundred kilobases
Nature Methods200910,766 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 86% comes from its base citations and 14% from the citation network (1 citing paper 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 (1)

Michael LammersORCID