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

Novel Approach Explains Spatio-Spectral Interactions in Raw Electroencephalogram Deep Learning Classifiers

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.02.26.530118Source: DataRank Database

Novel Approach Explains Spatio-Spectral Interactions in Raw Electroencephalogram Deep Learning Classifiers is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0. It has been cited 11 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access11 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 (5 grants)OA: greenTopics (20)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 (4)

Abhinav Sattiraju,Robyn L. Miller ORCID,Vince D. Calhoun ORCID,Charles A. Ellis ORCID

Live enrichment

PubMed:36909628 OpenAlex:https://openalex.org/W4322491158

National Institutes of Health

Grant: 1R01MH123610-01A1

Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivity

National Science Foundation

Grant: 2112455

CREST Center for Dynamic Multiscale and Multimodal Brain Mapping Over The Lifespan [D-MAP]

National Institutes of Health

Grant: 3R01MH118695-03S1

Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic models for characterizing and predicting psychosis and mood d

NIMH NIH HHS

Grant: R01 MH118695

NIMH NIH HHS

Grant: R01 MH123610

greenLicense: cc-by-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/02/27/2023.02.26.530118.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.02.26.530118

repositoryhttps://doi.org/10.1101/2023.02.26.530118

repositoryhttps://pubmed.ncbi.nlm.nih.gov/36909628

Fields of Study

EEG and Brain-Computer InterfacesFunctional Brain Connectivity StudiesNeural dynamics and brain function03 medical and health sciences0302 clinical medicine

Keywords

ElectroencephalographyArtificial intelligenceDeep learningComputer scienceContext (archaeology)Pattern recognition (psychology)Machine learningPsychologyNeuroscienceBiologyArticleExplainable AIMajor Depressive DisorderMulti-channel interactionsResting-state EEG

Sustainable Development Goals

SDG 0: Quality Education