FasTag: Automatic text classification of unstructured medical narratives is a research paper published in PLoS ONE (2020). On theSindex it has a DataRank of 0.578. It has been cited 46 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.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.578
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 Human Genome Research Institute
Grant: R01HG010140
U.S. National Library of Medicine
Grant: 4T15LM007033-33
Biomedical Informatics Training at Stanford
National Human Genome Research Institute
Grant: 5U01 HG009080
U.S. National Library of Medicine
Grant: T15 LM 007033
NHGRI NIH HHS
Grant: U01 HG009080
National Institutes of Health
Grant: 3U01HG009080-05S1
Optimizing imputation for diverse populations in a distributed framework
National Institutes of Health
Grant: 5R01HG010140-03
SOFTWARE FOR LARGE-SCALE INFERENCE OF THE GENETICS OF LIFESTYLE MEASURES, BIOMARKERS, AND COMMON AND RARE DISEASES
FWCI
2.02
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals