DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals is a research paper published in PLoS ONE (2023). On theSindex it has a DataRank of 0. It has been cited 2 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph β 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 β never from an abstract alone.
National Institute of General Medical Sciences (NIGMS), National Institutes of Health
Grant: GM132600
Division of Integrative Organismal Systems (IOS), National Science Foundation
Grant: 2015907
COLLABORATIVE RESEARCH: Multimodal Signaling in Rhinoceros Beetles
NIGMS NIH HHS
Grant: R01 GM132600
National Institutes of Health
Grant: 7R01GM132600-05
Machine learning approaches for improved accuracy and speed in sequence annotation
FWCI
0.42
Citation Percentile
0.6%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals