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

Practical considerations for variable screening in the super learner

arXiv (Cornell University)(2023)10.48550/arxiv.2311.03313Source: DataRank Database

Practical considerations for variable screening in the super learner is a research paper published in arXiv (Cornell University) (2023). On theSindex it has a DataRank of 0.

N/A

0DataRank · unranked

Scored on demand from live citation data

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 (6 grants)OA: greenTopics (27)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 (3)

King, Drew,Ying Huang ORCID,Brian D. Williamson ORCID

Live enrichment

PubMed:30945415 OpenAlex:https://openalex.org/W4388482170

National Institutes of Health

Grant: 5R37AI054165-12

Statistical Methods in HIV Vaccine Efficacy Trials

National Institutes of Health

Grant: 1S10OD028685-01

High-Performance Compute Cluster for Comprehensive Cancer and Infectious Diseases Research

National Institutes of Health

Grant: 5U24CA086368-15

Early Detection Research Network: Data Management and Coordinating Center

National Institutes of Health

Grant: 5R01CA277133-02

Accelerating biomarker development through novel statistical methods for analyzing phase III/IV studies

European Commission

Grant: 277133

Atypical Works in Pre-Industrial Europe. Pluriactivity, Mobility and Social Identities

National Institutes of Health

Grant: 1R01GM106177-01

Statistical Methods for Selection and Evaluation of Biomarkers

greenLicense: CC BY

repositoryhttps://arxiv.org/pdf/2311.03313

repositoryhttp://arxiv.org/abs/2311.03313

repositoryhttps://doi.org/10.48550/arxiv.2311.03313

https://doi.org/10.51387/25-nejsds82

https://dx.doi.org/10.48550/arxiv.2311.03313

Fields of Study

Machine Learning and Data ClassificationAdvanced Statistical Methods and ModelsNeural Networks and ApplicationsMachine Learning (stat.ML)Machine Learning (cs.LG)FOS: Computer and information sciences0101 mathematics01 natural sciences

Keywords

Lasso (programming language)Computer scienceDimension (graph theory)Set (abstract data type)Component (thermodynamics)Machine learningReduction (mathematics)Variable (mathematics)Dimensionality reductionFunction (biology)Artificial intelligenceStackingAlgorithmData miningMathematicsFOS: Computer and information sciencesMachine Learning (stat.ML)ArticleMachine Learning (cs.LG)

Sustainable Development Goals

SDG 0: No poverty