🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge
Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

Studying the Elusive Environment in Large Scale

JAMA(2014)10.1001/jama.2014.4129Source: DataRank Database

Studying the Elusive Environment in Large Scale is a research paper published in JAMA (2014). On theSindex it has a DataRank of 3.0. It has been cited 131 times, with 74 citing works in its 1-hop citation network.

N/A
3.0DataRank · unranked
3.0
Open Access131 citations · base score 4.9
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

It is possible that more than 50% of complex disease risk is attributed to differences in an individual’s environment.1 Air pollution, smoking, and diet are documented environmental factors affecting health, yet these factors are but a fraction of the “exposome,” the totality of the exposure load occurring throughout a person’s lifetime.1 Investigating one or a handful of exposures at a time has led to a highly fragmented literature of epidemiologic associations. Much of that literature is not reproducible, and selective reporting may be a major reason for the lack of reproducibility. A new model is required to discover environmental exposures associated with disease while mitigating possibilities of selective reporting.

Data sources & pipeline
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

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.

      DataRank Breakdown

      Base Score 24%Citation Network 76%

      Base Score Contribution

      0.732

      From this paper's citation signal

      Citation Network Contribution

      2.3

      From 59 citing papers with measurable signal

      Learn more about DataRank methodology →

      Top 5 citers driving the network score

      Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.

      Why this DataRank?

      DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 24% comes from its base citations and 76% from the citation network (59 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.

      Read the full methodology →

      Click a node to highlight its connections. Use scroll to zoom. Drag to pan.

      Node colors:CenterData PaperData + Open AccessNon-dataSelected & links| Node size = percentile rank

      Related Papers (10)

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      N/A
      0.897DataRank · unranked
      Science(2014)
      co-cited
      10.1126/science.aaa2709
      Nature Reviews Neuroscience(2013)
      co-cited
      10.1038/nrn3475
      BMJ(2013)
      co-cited
      10.1136/bmj.f6698