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

Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks

bioRxiv (Cold Spring Harbor Laboratory)(2020)10.1101/2020.06.29.159673Source: DataRank Database

Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2020). 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 (2 grants)OA: greenTopics (25)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)

Michael S. Barker ORCID,Ryan N. Gutenkunst ORCID,Paul D. Blischak ORCID

Live enrichment

PubMed:23304386 OpenAlex:https://openalex.org/W3037189768

National Institutes of Health

Grant: 1R01GM127348-01A1

Joint inferences of natural selection between sites and populations

National Science Foundation

Grant: 1811784

Improving Plant Breeding in Polyploid Crops Using Models for Demography and Selection

greenLicense: cc-by

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2020/06/29/2020.06.29.159673.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2020.06.29.159673

repositoryhttps://doi.org/10.1101/2020.06.29.159673

https://doi.org/10.1111/1755-0998.13355

Fields of Study

Genetic diversity and population structureGenomics and Phylogenetic StudiesChromosomal and Genetic Variations0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

Coalescent theoryArtificial intelligenceConvolutional neural networkComputer scienceInferencePopulationBiologyPattern recognition (psychology)Machine learningGeneticsPhylogenetic treeGeneGenetic SpeciationAnimalsHybridization, GeneticNeural Networks, ComputerButterfliesChromosomesPhylogeny

Sustainable Development Goals

SDG 0: Life in Land