Personalized prediction of early childhood asthma persistence: A machine learning approach is a dataset published in PLoS ONE (2021). On theSindex it has a DataRank of 2.4, placing it in the top 8.9% of the data-sharing corpus. It has been cited 61 times, with 61 citing works in its 1-hop citation network.
Ranks in the top 9% for downstream scientific impact
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
0.619
From this paper's citation signal
Citation Network Contribution
1.8
From 42 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 61 citers.
Pennsylvania Department of Health
Grant: SAP #4100072543
Foundation for the National Institutes of Health
Grant: K23HL136842
National Institutes of Health
Grant: 5K23HL136842-04
Incentive-based Mobile Health Adherence Intervention for High Risk Children with Asthma
FWCI
4.68
Citation Percentile
1.0%
Influential Citations
4
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals