A roadmap for the Human Developmental Cell Atlas
A roadmap for the Human Developmental Cell Atlas is a dataset published in Nature (2021). On theSindex it has a DataRank of 3.8, placing it in the top 30.6% of the data-sharing corpus. It has been cited 189 times, with 148 citing works in its 1-hop citation network. Its calibrated FAIR score is 25/100.
Abstract
The Human Developmental Cell Atlas (HDCA) initiative, which is part of the Human Cell Atlas, aims to create a comprehensive reference map of cells during development. This will be critical to understanding normal organogenesis, the effect of mutations, environmental factors and infectious agents on human development, congenital and childhood disorders, and the cellular basis of ageing, cancer and regenerative medicine. Here we outline the HDCA initiative and the challenges of mapping and modelling human development using state-of-the-art technologies to create a reference atlas across gestation. Similar to the Human Genome Project, the HDCA will integrate the output from a growing community of scientists who are mapping human development into a unified atlas. We describe the early milestones that have been achieved and the use of human stem-cell-derived cultures, organoids and animal models to inform the HDCA, especially for prenatal tissues that are hard to acquire. Finally, we provide a roadmap towards a complete atlas of human development.
›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
0.779
From this paper's citation signal
Citation Network Contribution
3.0
From 107 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.
- The FAIR Guiding Principles for scientific data management and stewardshipScientific Data201617,221 citationsDataRank 1.5
- Visualization and analysis of gene expression in tissue sections by spatial transcriptomicsScience20163,731 citationsDataRank 1.2
- Comprehensive single-cell transcriptional profiling of a multicellular organismScience20171,554 citationsDataRank 14.0Top 14%
- Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcodingScience20181,513 citationsDataRank 1.1
- Cell diversity and network dynamics in photosensitive human brain organoidsNature20171,334 citationsDataRank 12.0
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 20% comes from its base citations and 80% from the citation network (107 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.
Click a node to highlight its connections. Use scroll to zoom. Drag to pan.