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

Characterizing phenotypic abnormalities associated with high-risk individuals developing lung cancer using electronic health records from the All of Us researcher workbench

Journal of the American Medical Informatics Association(2021)10.1093/jamia/ocab174Source: DataRank Database

Characterizing phenotypic abnormalities associated with high-risk individuals developing lung cancer using electronic health records from the All of Us researcher workbench is a dataset published in Journal of the American Medical Informatics Association (2021). On theSindex it has a DataRank of 0.543, placing it in the top 35.8% of the data-sharing corpus. It has been cited 9 times, with 8 citing works in its 1-hop citation network.

Top 36%percentile

0.543DataRank

Ranks in the top 36% for downstream scientific impact

Data paper Open Access9 citations · 8 citing works in its network

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 (42 grants)Impact (FWCI 0.78)OA: greenTopics (32)IDs (PubMed, PMC, 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 (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.

DataRank Breakdown

Base Score 64%Citation Network 36%

Base Score Contribution

0.345

From this paper's citation signal

Citation Network Contribution

0.197

From 6 citing papers with measurable signal

Learn more about DataRank methodology →

Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 8 citers.

Machine Learning Based Predictive Models for Lung Cancer Detection and Classification
20241 citations10 referencesContributes 0.069
Optimizing lung cancer prediction: leveraging Kernel PCA with dendritic neural models
Computer Methods in Biomechanics & Biomedical Engineering20246 citations48 referencesContributes 0.041
A scoping review of OMOP CDM adoption for cancer research using real world data
npj Digital Medicine202515 citations75 referencesContributes 0.037
An efficient interpretable stacking ensemble model for lung cancer prognosis
Computational Biology and Chemistry202410 citations66 referencesContributes 0.036
Adoption of the OMOP CDM for Cancer Research using Real-world Data: Current Status and Opportunities
medRxiv20246 citations78 referencesContributes 0.025

Authors (7)

Nansu Zong ORCID,Chen Wang ORCID,David E. Midthun ORCID,Yuan Luo ORCID,Ping Yang ORCID

Live enrichment

PubMed:34505903 PMC:PMC8510381 OpenAlex:https://openalex.org/W3198874920

Community Partners

Grant: 1 OT2 OD025277

Office of the Director: Regional Medical Centers

Grant: 1 OT2 OD026555

Biobank

Grant: 1 U24 OD023121

Federally Qualified Health Centers

Grant: 263201600085U

Communications and Engagement

Grant: 3 OT2 OD023206

NIH HHS

Grant: OT2 OD023206

NIH HHS

Grant: OT2 OD025315

NIH HHS

Grant: OT2 OD025337

NIH HHS

Grant: OT2 OD026555

NCI NIH HHS

Grant: P30 CA015083

NIH HHS

Grant: U2C OD023196

Office of the Director: Regional Medical Centers

Grant: 1 OT2 OD026554

NIH HHS

Grant: 1 U24 OD023163

NIH HHS

Grant: OT2 OD026550

NIH HHS

Grant: OT2 OD026557

NIH HHS

Grant: U24 OD023176

Office of the Director: Regional Medical Centers

Grant: 1 OT2 OD026551

Office of the Director: Regional Medical Centers

Grant: IAA #: AOD 16037

NIH HHS

Grant: OT2 OD025276

NIH HHS

Grant: OT2 OD026551

Office of the Director: Regional Medical Centers

Grant: 1 OT2 OD 026552

Office of the Director: Regional Medical Centers

Grant: 1 OT2 OD026550

Office of the Director: Regional Medical Centers

Grant: 1 OT2 OD026553

NIH HHS

Grant: 3 OT2 OD023205

NIH HHS

Grant: 5 U2C OD023196

NIH HHS

Grant: OT2 OD026553

NCATS NIH HHS

Grant: UL1 TR001422

Community Partners

Grant: 1 OT2 OD025276

Community Partners

Grant: 1 OT2 OD025337

Office of the Director: Regional Medical Centers

Grant: 1 OT2 OD026548

NIH HHS

Grant: 1 OT2 OD026549

NIH HHS

Grant: OT2 OD026554

NIH HHS

Grant: OT2 OD026556

NIH HHS

Grant: U24 OD023163

Office of the Director: Regional Medical Centers

Grant: 1 OT2 OD026556

Community Partners

Grant: 3 OT2 OD025315

NIH HHS

Grant: OT2 OD026552

Office of the Director: Regional Medical Centers

Grant: 1 OT2 OD026557

NIH HHS

Grant: HHSN 263201600085U

NIH HHS

Grant: OT2 OD026548

NIH HHS

Grant: U24 OD023121

National Institutes of Health

FWCI

0.78

Citation Percentile

0.7%

Citation Trend

2022

2024

2025

2026

green

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/8510381

journalhttps://doi.org/10.1093/jamia/ocab174

repositoryhttps://pubmed.ncbi.nlm.nih.gov/34505903

Fields of Study

Lung Cancer Treatments and MutationsHealth, Environment, Cognitive AgingGenetic Associations and Epidemiology

MeSH Terms

Population HealthHumansLung NeoplasmsPhenotypeEarly Detection of CancerGenome-Wide Association StudyElectronic Health Records

Keywords

MedicineLung cancerPopulationCohortPhenomeOdds ratioBonferroni correctionLogistic regressionCohort studyRisk assessmentGerontologyInternal medicineEnvironmental healthBiologyGeneticsPhenotypeComputer scienceElectronic Health Records (Ehrs)Lung Cancer ScreeningCommon Data ModelAll Of Us Research ProgramPhenome Wide Association Study (Phewas)

Sustainable Development Goals

SDG 0: Good health and well-being