The Single-cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors is a dataset (2024). On theSindex it has a DataRank of 0.424, placing it in the top 49.8% of the data-sharing corpus. It has been cited 11 times, with 11 citing works in its 1-hop citation network. Its calibrated FAIR score is 35/100.
The Single-cell Pediatric Cancer Atlas (ScPCA) Portal ( https://scpca.alexslemonade.org/ ) is a data resource for uniformly processed single-cell and single-nuclei RNA sequencing (RNA-seq) data and de-identified metadata from pediatric tumor samples. Originally comprised of data from 10 projects funded by Alex’s Lemonade Stand Foundation (ALSF), the Portal currently contains summarized gene expression data for over 700 samples across 55 cancer types from ALSF-funded and community-contributed datasets. Downloads include gene expression data as SinglecellExperiment or AnnData objects containing raw and normalized counts, PCA and UMAP coordinates, and automated cell type annotations, along with summary reports. Some samples have additional data from bulk RNA-seq, spatial transcriptomics, and/or feature barcoding (e.g., CITE-seq and cell hashing) included in the download. All data on the Portal were uniformly processed using scpca-nf , an efficient and open-source Nextflow workflow that uses alevin-fry to quantify gene expression. Comprehensive documentation, including descriptions of file contents and a guide to getting started, is available at https://scpca.readthedocs.io .
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 →
Base Score Contribution
0.360
From this paper's citation signal
Citation Network Contribution
0.0641
From 5 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 85% comes from its base citations and 15% from the citation network (5 citing papers contributed measurable signal).
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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