UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age
UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age is a dataset published in PLOS Medicine (2015). On theSindex it has a DataRank of 19.5, placing it in the top 5.5% of the data-sharing corpus. It has been cited 13,160 times, with 169 citing works in its 1-hop citation network. Its calibrated FAIR score is 72/100.
Abstract
Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
1.4
From this paper's citation signal
Citation Network Contribution
18.1
From 169 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Multimodal population brain imaging in the UK Biobank prospective epidemiological studyNature Neuroscience20162,267 citationsDataRank 10.1Top 21%
- Reference-based phasing using the Haplotype Reference Consortium panelNature Genetics20161,987 citationsDataRank 1.1
- The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex DiseaseCell20161,405 citationsDataRank 1.1
- Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank StudyPLOS ONE20171,248 citationsDataRank 1.1
- A structural variation reference for medical and population geneticsNature20201,157 citationsDataRank 9.9Top 21%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 7% comes from its base citations and 93% from the citation network (169 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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