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.

Defining and benchmarking open problems in single-cell analysis

Nature Biotechnology(2025)10.1038/s41587-025-02694-wSource: DataRank Database

Defining and benchmarking open problems in single-cell analysis is a research paper published in Nature Biotechnology (2025). On theSindex it has a DataRank of 0.937. It has been cited 29 times, with 27 citing works in its 1-hop citation network.

N/A

0.937DataRank · unranked

0.937

Open Access29 citations · base score 3.4

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 (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

DataRank Breakdown

Base Score 54%Citation Network 46%

Base Score Contribution

0.510

From this paper's citation signal

Citation Network Contribution

0.427

From 13 citing papers with measurable signal

Learn more about DataRank methodology →

Top 5 citers driving the network score

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

Benchmarking atlas-level data integration in single-cell genomics
Nature Methods20211,376 citationsDataRank 10.3Top 21%
Best practices for single-cell analysis across modalities
Nature Reviews Genetics20231,013 citationsDataRank 1.0
Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
Nature Biotechnology2020526 citationsDataRank 0.940
Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics
202412 citationsDataRank 0.532Top 48%
FADVI: disentangled representation learning for robust integration of single-cell and spatial omics data
20251 citationsDataRank 0.104

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 54% comes from its base citations and 46% 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