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Dataset’s chemical diversity limits the generalizability of machine learning predictions

Overview of attention for article published in Journal of Cheminformatics, November 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

twitter
19 X users

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
107 Mendeley
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Title
Dataset’s chemical diversity limits the generalizability of machine learning predictions
Published in
Journal of Cheminformatics, November 2019
DOI 10.1186/s13321-019-0391-2
Pubmed ID
Authors

Marta Glavatskikh, Jules Leguy, Gilles Hunault, Thomas Cauchy, Benoit Da Mota

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 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 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 107 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 25%
Researcher 16 15%
Student > Master 13 12%
Student > Bachelor 8 7%
Student > Doctoral Student 5 5%
Other 10 9%
Unknown 28 26%
Readers by discipline Count As %
Chemistry 30 28%
Engineering 9 8%
Computer Science 7 7%
Chemical Engineering 7 7%
Physics and Astronomy 5 5%
Other 18 17%
Unknown 31 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 03 June 2021.
All research outputs
#3,087,836
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#304
of 891 outputs
Outputs of similar age
#63,428
of 363,851 outputs
Outputs of similar age from Journal of Cheminformatics
#6
of 18 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has gotten more attention than average, scoring higher than 65% 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 363,851 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 18 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 72% of its contemporaries.