The discovAIR project: a roadmap towards the Human Lung Cell Atlas
The discovAIR project: a roadmap towards the Human Lung Cell Atlas is a dataset published in European Respiratory Journal (2022). On theSindex it has a DataRank of 0.786, placing it in the top 44.1% of the data-sharing corpus. It has been cited 26 times, with 16 citing works in its 1-hop citation network. Its calibrated FAIR score is 38/100.
Abstract
The Human Cell Atlas (HCA) consortium aims to establish an atlas of all organs in the healthy human body at single-cell resolution to increase our understanding of basic biological processes that govern development, physiology and anatomy, and to accelerate diagnosis and treatment of disease. The Lung Biological Network of the HCA aims to generate the Human Lung Cell Atlas as a reference for the cellular repertoire, molecular cell states and phenotypes, and cell-cell interactions that characterise normal lung homeostasis in healthy lung tissue. Such a reference atlas of the healthy human lung will facilitate mapping the changes in the cellular landscape in disease. The discovAIR project is one of six pilot actions for the HCA funded by the European Commission in the context of the H2020 framework programme. discovAIR aims to establish the first draft of an integrated Human Lung Cell Atlas, combining single-cell transcriptional and epigenetic profiling with spatially resolving techniques on matched tissue samples, as well as including a number of chronic and infectious diseases of the lung. The integrated Human Lung Cell Atlas will be available as a resource for the wider respiratory community, including basic and translational scientists, clinical medicine, and the private sector, as well as for patients with lung disease and the interested lay public. We anticipate that the Human Lung Cell Atlas will be the founding stone for a more detailed understanding of the pathogenesis of lung diseases, guiding the design of novel diagnostics and preventive or curative interventions.
›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
0.494
From this paper's citation signal
Citation Network Contribution
0.292
From 15 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.
- Comprehensive Integration of Single-Cell DataCell201916,515 citationsDataRank 1.5
- Benchmarking atlas-level data integration in single-cell genomicsNature Methods20211,376 citationsDataRank 10.3Top 21%
- A cellular census of human lungs identifies novel cell states in health and in asthmaNature Medicine2019956 citationsDataRank 1.0
- Deep learning and alignment of spatially resolved single-cell transcriptomes with TangramNature Methods2021856 citationsDataRank 1.0
- scGen predicts single-cell perturbation responsesNature Methods2019666 citationsDataRank 0.975
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 63% comes from its base citations and 37% from the citation network (15 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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