Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson’s disease is a research paper published in Journal of NeuroEngineering and Rehabilitation (2021). On theSindex it has a DataRank of 0.550. It has been cited 38 times.
Scored on demand from live citation data
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
0.550
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Institutes of Health
Grant: P50 NS098573
CTSI
Grant: 26431
National Institutes of Health
Grant: T32MH115886-01
NIMH NIH HHS
Grant: T32 MH115886
National Institutes of Health
Grant: 5T32MH115886-05
Using Computation to Achieve Breakthroughs in Neuroscience
National Institutes of Health
Grant: 5P50NS098573-04
Circuit-based deep brain stimulation for Parkinson's disease
Parkinson Study Group
Parkinson’s Disease Foundation
MnDRIVE Brain Conditions Fellowship
FWCI
2.29
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Automated calibration of somatosensory stimulation using reinforcement learning
Additional file 1 of Automated calibration of somatosensory stimulation using reinforcement learning