Convolutional neural network-based common-path optical coherence tomography A-scan boundary-tracking training and validation using a parallel Monte Carlo synthetic dataset is a dataset published in Optics Express (2022). On theSindex it has a DataRank of 0.358, placing it in the top 49.5% of the data-sharing corpus. It has been cited 5 times, with 5 citing works in its 1-hop citation network.
Ranks in the top 50% 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
0.269
From this paper's citation signal
Citation Network Contribution
0.0895
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 5 citers.
National Institutes of Health
Grant: 1R01EY032127-01
Artificial intelligence Optical Coherence Tomography Guided Deep Anterior Lamellar Keratoplasty (AUTO-DALK)
NEI NIH HHS
Grant: R01 EY032127
National Eye Institute
FWCI
0.40
Citation Percentile
0.5%
Citation Trend
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