Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets is a dataset published in EBioMedicine (2024). On theSindex it has a DataRank of 0.639, placing it in the top 31% of the data-sharing corpus. It has been cited 27 times, with 12 citing works in its 1-hop citation network.
Ranks in the top 31% 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
0.500
From this paper's citation signal
Citation Network Contribution
0.139
From 8 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 12 citers.
National Institutes of Health
Grant: U01AG052564
Deutsche Forschungsgemeinschaft
Grant: PROFILNRW-2020-107-A
Deutsche Forschungsgemeinschaft
Grant: 431549029 \u2013 SFB 1451
Deutsche Forschungsgemeinschaft
Grant: GE 2835/4-1
Deutsche Forschungsgemeinschaft
Grant: GE 2835/2\u20131
National Institutes of Health
Grant: 3U54MH091657-03S1
Mapping the Human Connectome: Structure, Function, and Heritability
Deutsche Forschungsgemeinschaft
Grant: unidentified
unidentified
National Institutes of Health
Grant: 3U01AG052564-02S2
Mapping the Human Connectome During Typical Aging
NIMH NIH HHS
Grant: U54 MH091657
FWCI
9.88
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets