Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models is a research paper published in SIMULATION (2020). On theSindex it has a DataRank of 0.425. It has been cited 16 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
0.425
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 Institute of Biomedical Imaging and Bioengineering
Grant: U01EB025825
u.s. department of energy
Grant: DE-AC02-06CH11357, DE-AC02-05CH11231
University of Chicago Computation Institute
Grant: 1S10OD018495-01
Biological Sciences Division of the University of Chicago and Argonne National Laboratory
Grant: DE-AC02-06CH11357
FWCI
1.40
Citation Percentile
0.8%
Citation Trend
Fields of Study
Keywords