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

Using machine learning to predict COVID-19 infection and severity risk among 4,510 aged adults: a UK Biobank cohort study

medRxiv(2020)10.1101/2020.06.09.20127092Source: DataRank Database

Using machine learning to predict COVID-19 infection and severity risk among 4,510 aged adults: a UK Biobank cohort study is a dataset published in medRxiv (2020). On theSindex it has a DataRank of 0.835, placing it in the top 24.1% of the data-sharing corpus. It has been cited 9 times, with 8 citing works in its 1-hop citation network.

Top 24%percentile

0.835DataRank

Ranks in the top 24% 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 (2 grants)OA: greenTopics (26)IDs (PubMed, OpenAlex)SDGs (2)

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 41%Citation Network 59%

Base Score Contribution

0.345

From this paper's citation signal

Citation Network Contribution

0.489

From 7 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.

High Prevalence of Obesity in Severe Acute Respiratory Syndrome Coronavirus‐2 (SARS‐CoV‐2) Requiring Invasive Mechanical Ventilation
Obesity20202,176 citations21 referencesContributes 0.366
COVID and nutrition: A machine learning perspective
Informatics in Medicine Unlocked20224 citations30 referencesContributes 0.054
Does reactivation of cytomegalovirus contribute to severe COVID-19 disease?
Immunity & Ageing202152 citations76 referencesContributes 0.052
Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning
Interdisciplinary Sciences Computational Life Sciences202258 citations98 referencesContributes 0.042
Machine Learning at the Service of Survival Analysis: Predictions Using Time-to-Event Decomposition and Classification Applied to a Decrease of Blood Antibodies against COVID-19
Mathematics20237 citations61 referencesContributes 0.034

Authors (11)

Sara A. Willette,Qian Wang ORCID,Colleen Pappas ORCID,Brandon S. Klinedinst ORCID,Scott T. Le

Live enrichment

PubMed:32577673 OpenAlex:https://openalex.org/W3034492248

NIA NIH HHS

Grant: K99 AG047282

NIA NIH HHS

Grant: R00 AG047282

greenLicense: cc-by-nc-nd

repositoryhttps://www.medrxiv.org/content/medrxiv/early/2021/01/05/2020.06.09.20127092.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2020.06.09.20127092

repositoryhttps://doi.org/10.1101/2020.06.09.20127092

repositoryhttps://pubmed.ncbi.nlm.nih.gov/32577673

Fields of Study

COVID-19 Clinical Research StudiesImmune responses and vaccinationsSARS-CoV-2 and COVID-19 Research

Keywords

Receiver operating characteristicMedicineCoronavirus disease 2019 (COVID-19)SerologyCohortFramingham Risk ScoreBiobankInternal medicineImmunologyInfectious disease (medical specialty)DiseaseAntibodyBioinformaticsBiologyCOVID-19SARS-CoV-2antibodiescohort studyepidemiologyhost responselinear discriminant analysismachine learningnon-parametric

Sustainable Development Goals

SDG 0: Reduced inequalitiesSDG 0: Peace, Justice and strong institutions