Prolificacy Assessment of Spermatozoan via State-of-the-Art Deep Learning Frameworks is a research paper published in IEEE Access (2022). On theSindex it has a DataRank of 0.538. It has been cited 35 times.
Scored on demand from live citation data
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.
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Base Score Contribution
0.538
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 →U.S. NIH
Grant: R01GM134020
U.S. NIH
Grant: P41GM103712
NSF
Grant: DBI-1949629
NSF
Grant: IIS-2007595
Mark Foundation for Cancer Research
Grant: 19-044-ASP
King Abdullah University of Science and Technology (KAUST) Office of Research Administration
Grant: URF/1/4352-01-01
King Abdullah University of Science and Technology (KAUST) Office of Research Administration
Grant: REI/1/0018-01-01
King Abdullah University of Science and Technology (KAUST) Office of Research Administration
Grant: REI/1/4216-01-01
King Abdullah University of Science and Technology (KAUST) Office of Research Administration
Grant: REI/1/4437-01-01
King Abdullah University of Science and Technology (KAUST) Office of Research Administration
Grant: REI/1/4473-01-01
King Abdullah University of Science and Technology (KAUST) Office of Research Administration
Grant: URF/1/4379-01-01
National Science Foundation
Grant: 2007595
III: Small: Improving automation and speed of macromolecule recognition and localization in cryo-electron tomography using unsupervised deep learning
National Institutes of Health
Grant: 1R01GM134020-01A1
Novel machine learning approaches for improving structural discrimination in cryo-electron tomography
National Science Foundation
Grant: 1949629
IIBR Informatics: Reducing the training data annotation cost for learning-based macromolecule identification in cellular electron cryo-tomography
National Institutes of Health
Grant: 5P41GM103712-08
High Performance Computing for Multiscale Modeling of Biological Systems
FWCI
5.92
Citation Percentile
1.0%
Citation Trend
Fields of Study
Keywords