Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

How many markers are needed to robustly determine a cell’s type?

bioRxiv (Cold Spring Harbor Laboratory)(2021)10.1101/2021.04.16.439807Source: DataRank Database

How many markers are needed to robustly determine a cell’s type? is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2021). On theSindex it has a DataRank of 0. It has been cited 6 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access6 citations

Download PDF

Cite:

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)OA: greenTopics (28)IDs (PubMed, OpenAlex)SDGs (1)

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 →

We only score data papers we can read in full — never from an abstract alone.

Authors (2)

Jesse Gillis ORCID,Stephan Fischer ORCID

Live enrichment

PubMed:26207759 OpenAlex:https://openalex.org/W3177392103

National Institutes of Health

Grant: 5U19MH114821-05

A Comprehensive Center for Mouse Brain Cell Atlas

National Institutes of Health

Grant: 5R01LM012736-02

Heuristics to evaluate biomedical and genomic knowledge bases for validity

National Institutes of Health

Grant: 5R01MH113005-02

Revealing the transcriptomic basis of neuronal identity through functional meta-analysis

greenLicense: cc-by

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2021/06/30/2021.04.16.439807.full.pdf

GREENhttps://www.biorxiv.org/content/biorxiv/early/2021/06/30/2021.04.16.439807.full.pdf

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2021/06/30/2021.04.16.439807.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2021.04.16.439807

repositoryhttps://doi.org/10.1101/2021.04.16.439807

Fields of Study

Single-cell and spatial transcriptomicsCell Image Analysis TechniquesGene Regulatory Network AnalysisBiology0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

DeconvolutionAnnotationCell typeComputational biologyComputer scienceGeneNoise (video)Selection (genetic algorithm)BiologyCellArtificial intelligenceGeneticsImage (mathematics)AlgorithmCell biologyComplex system biologyScienceQCorrectionTranscriptomicsArticle

Sustainable Development Goals

SDG 3: 3. Good health