🏆 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.

Comprehensive genomic characterization of squamous cell lung cancers

Nature(2012)10.1038/nature11404Source: DataRank Database

Comprehensive genomic characterization of squamous cell lung cancers is a dataset published in Nature (2012). On theSindex it has a DataRank of 18.7, placing it in the top 7% of the data-sharing corpus. It has been cited 4,005 times, with 200 citing works in its 1-hop citation network. Its calibrated FAIR score is 51/100.

Top 7%percentile
18.7DataRank
18.7Top 7%
Dataset Open Access4005 citations · base score 8.3
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

Lung squamous cell carcinoma is a common type of lung cancer, causing approximately 400,000 deaths per year worldwide. Genomic alterations in squamous cell lung cancers have not been comprehensively characterized, and no molecularly targeted agents have been specifically developed for its treatment. As part of The Cancer Genome Atlas, here we profile 178 lung squamous cell carcinomas to provide a comprehensive landscape of genomic and epigenomic alterations. We show that the tumour type is characterized by complex genomic alterations, with a mean of 360 exonic mutations, 165 genomic rearrangements, and 323 segments of copy number alteration per tumour. We find statistically recurrent mutations in 11 genes, including mutation of TP53 in nearly all specimens. Previously unreported loss-of-function mutations are seen in the HLA-A class I major histocompatibility gene. Significantly altered pathways included NFE2L2 and KEAP1 in 34%, squamous differentiation genes in 44%, phosphatidylinositol-3-OH kinase pathway genes in 47%, and CDKN2A and RB1 in 72% of tumours. We identified a potential therapeutic target in most tumours, offering new avenues of investigation for the treatment of squamous cell lung cancers.

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

FAIR Checklist

Context only (not used in score)
Findable (2/2)
  • Has DOI
  • Indexed in repositories
Accessible (1/2)
  • Open Access
Interoperable (2/2)
  • DataCite relations
  • Linked datasets
Reusable (1/3)
  • Dataset classification

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

51FAIR score
F Findable
65
A Accessible
55
I Interoperable
50
R Reusable
33
Top 21% by FAIRLLM-assessed✓ full text read

Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →

DataRank Breakdown

Base Score 7%Citation Network 93%

Base Score Contribution

1.2

From this paper's citation signal

Citation Network Contribution

17.5

From 200 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 7% comes from its base citations and 93% from the citation network (200 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

Authors (331)

Gad GetzORCID, Stephen E. Schumacher, Petar Stojanov,Chang‐Jiun WuORCID,Hailei ZhangORCID