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

Extending Tree-Based Automated Machine Learning to Biomedical Image and Text Data Using Custom Feature Extractors

(2023)10.1145/3583133.3590584Source: DataRank Database

Extending Tree-Based Automated Machine Learning to Biomedical Image and Text Data Using Custom Feature Extractors is a research paper (2023). On theSindex it has a DataRank of 0. It has been cited 2 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

2 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 (4 grants)IDs (PubMed)

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.

Run a calibrated FAIR evaluation for this paper →

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

Authors (4)

Joseph D. Romano ORCID,Marylyn D. Ritchie ORCID,Jason H. Moore ORCID,Rachit Kumar ORCID

Live enrichment

PubMed:19162232

NIH (National Institutes of Health)

Grant: R01-LM010098

NIH (National Institutes of Health)

Grant: U01-AG066833

NIH (National Institutes of Health)

Grant: R00-LM013646

NIH (National Institutes of Health)

Grant: T32-GM007170