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Statistical Uncertainty Analysis for Small-Sample, High Log-Variance Data: Cautions for Bootstrapping and Bayesian Bootstrapping.

Overview of attention for article published in Journal of Chemical Theory and Computation, April 2019
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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10 X users

Citations

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26 Dimensions

Readers on

mendeley
48 Mendeley
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Title
Statistical Uncertainty Analysis for Small-Sample, High Log-Variance Data: Cautions for Bootstrapping and Bayesian Bootstrapping.
Published in
Journal of Chemical Theory and Computation, April 2019
DOI 10.1021/acs.jctc.9b00015
Pubmed ID
Authors

Barmak Mostofian, Daniel M Zuckerman

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 23%
Researcher 10 21%
Student > Doctoral Student 5 10%
Student > Bachelor 3 6%
Professor 3 6%
Other 5 10%
Unknown 11 23%
Readers by discipline Count As %
Physics and Astronomy 8 17%
Biochemistry, Genetics and Molecular Biology 7 15%
Chemistry 7 15%
Engineering 4 8%
Agricultural and Biological Sciences 3 6%
Other 7 15%
Unknown 12 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 September 2019.
All research outputs
#6,331,669
of 25,186,033 outputs
Outputs from Journal of Chemical Theory and Computation
#1,752
of 7,319 outputs
Outputs of similar age
#108,191
of 357,857 outputs
Outputs of similar age from Journal of Chemical Theory and Computation
#40
of 134 outputs
Altmetric has tracked 25,186,033 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,319 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 357,857 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.