Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides is a research paper published in Nature Communications (2024). On theSindex it has a DataRank of 0.633. It has been cited 67 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
0.633
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 →RCUK | Engineering and Physical Sciences Research Council (EPSRC)
Grant: EP/R018634/1
Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics
NCI NIH HHS
Grant: P30 CA016087
Biotechnology and Biological Sciences Research Council
Grant: BB/V016067/1
Investigating Host and Viral Factors for Improved Design of Future Live Attenuated Vaccines for IBV
National Institutes of Health
Grant: 5U01CA214195-04
The EDRN Mesothelioma Biomarker Discovery Laboratory
National Institutes of Health
Grant: 5P30CA016087-32
Experimental Pathology
FWCI
15.86
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals