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.

Sample Multiplexing with Cysteine-Selective Approaches: cysDML and cPILOT

Journal of the American Society for Mass Spectrometry(2015)10.1007/s13361-014-1059-9Source: DataRank Database

Sample Multiplexing with Cysteine-Selective Approaches: cysDML and cPILOT is a research paper published in Journal of the American Society for Mass Spectrometry (2015). On theSindex it has a DataRank of 0.950. It has been cited 33 times, with 17 citing works in its 1-hop citation network. Its calibrated FAIR score is 49/100.

N/A

0.950DataRank · unranked

0.950

33 citations · base score 3.5

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✓ full text read

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

DataRank Breakdown

Base Score 56%Citation Network 44%

Base Score Contribution

0.529

From this paper's citation signal

Citation Network Contribution

0.421

From 13 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.

Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics
Nature Protocols20091,400 citationsDataRank 14.3

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 56% comes from its base citations and 44% from the citation network (13 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