anndata: Access and store annotated data matrices
anndata: Access and store annotated data matrices is a dataset published in Journal of Open Source Software (2024). On theSindex it has a DataRank of 2.9, placing it in the top 32.5% of the data-sharing corpus. It has been cited 182 times, with 171 citing works in its 1-hop citation network. Its calibrated FAIR score is 49/100.
›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
0.748
From this paper's citation signal
Citation Network Contribution
2.1
From 92 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.
- PyTorch: An Imperative Style, High-Performance Deep Learning LibraryarXiv (Cornell University)201916,186 citationsDataRank 1.5
- Integrated analysis of multimodal single-cell dataCell202115,542 citationsDataRank 1.4
- Generalizing RNA velocity to transient cell states through dynamical modelingNature Biotechnology20203,016 citationsDataRank 1.2
- Tidy DataJournal of Statistical Software2014885 citationsDataRank 1.0
- Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscapeGenome Biology2021187 citationsDataRank 4.4Top 29%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 26% comes from its base citations and 74% from the citation network (92 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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