A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy is a dataset published in Proceedings of the ACM on Management of Data (2023). On theSindex it has a DataRank of 0.533, placing it in the top 36.4% of the data-sharing corpus. It has been cited 13 times, with 9 citing works in its 1-hop citation network.
Ranks in the top 36% 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
0.396
From this paper's citation signal
Citation Network Contribution
0.137
From 4 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 9 citers.
NIH
Grant: R01LM014026
NSF
Grant: IIS-1910950, IIS-1909806, CNS-2125530, IIS-2128661
National Science Foundation
Grant: 2128661
III: Small: NeuroDB: A Neural Network Framework for Efficiently Answering Database Queries Approximately
National Science Foundation
Grant: 2125530
SCC-IRG JST: Hyperlocal Risk Monitoring and Pandemic Preparedness through Privacy-Enhanced Mobility and Social Interactions Analysis
National Science Foundation
Grant: 1910950
III: Small: Collaborative Research: PE4GQ - Practical Encryption for Geospatial Queries on Private Data
National Institutes of Health
Grant: 5R01LM014026-03
SCH: Wearables for Health and Disease Knowledge (W4H)
Fields of Study
Keywords