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

Additional file 6 of Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

Figshare(2020)10.6084/m9.figshare.12285518Source: DataRank Database

Additional file 6 of Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations is a dataset published in Figshare (2020). On theSindex it has a DataRank of 0, placing it in the top 100% of the data-sharing corpus.

Top 100%percentile

0DataRank

Ranks in the top 100% for downstream scientific impact

Data paper Open Access0 citations

Free full text

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 (15)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (1/3)

Dataset classification

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 (5)

Michael Zietz ORCID,Vincent Rubinetti ORCID,Daniel S. Himmelstein ORCID,Casey S. Greene ORCID,Gregory P. Way ORCID

Live enrichment

PubMed:19910364 OpenAlex:https://openalex.org/W4394311701

National Institutes of Health

Grant: 5R01CA237170-06

Characterization of high-grade serous ovarian cancer subtypes via single-cell profiling

National Institutes of Health

Grant: 5R01HG010067-05

Network-based algorithms for target identification and drug repositioning from genetic associations

National Institutes of Health

Grant: 3T32HG000046-15S1

Training Grant in Computational Genomics

greenLicense: cc-by

repositoryhttps://doi.org/10.6084/m9.figshare.12285518

https://dx.doi.org/10.6084/m9.figshare.12285518.v1

https://dx.doi.org/10.6084/m9.figshare.12285518

Fields of Study

Gene expression and cancer classificationSingle-cell and spatial transcriptomicsBioinformatics and Genomic Networks0206 medical engineering02 engineering and technology

Keywords

Space (punctuation)Expression (computer science)Computer scienceGeneComputational biologyTheoretical computer scienceBiologyGeneticsProgramming languageOperating system