CSEA-DB: an omnibus for human complex trait and cell type associations is a dataset published in Nucleic Acids Research (2020). On theSindex it has a DataRank of 1.0, placing it in the top 20% of the data-sharing corpus. It has been cited 31 times, with 17 citing works in its 1-hop citation network.
Ranks in the top 20% for downstream scientific impact
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.520
From this paper's citation signal
Citation Network Contribution
0.498
From 15 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 17 citers.
National Institutes of Health
Grant: R01LM012806
Cancer Prevention and Research Institute of Texas
Grant: RP180734
Data Science and Informatics Core for Cancer Research
Grant: RP170668
Data Science and Informatics Core for Cancer Research
Grant: R03DE027711
National Institutes of Health
Grant: 1R01LM012806-01
Predicting Phenotype by Using Transcriptomic Alteration as Endophenotype
National Institutes of Health
Grant: 5R03DE027711-02
Deep learning methods to predict the function of genetic variants in orofacial clefts
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies
Additional file 1 of scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies
Additional file 2 of scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies
Additional file 2 of scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies