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Augmenting Basin-Hopping With Techniques From Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobility

Overview of attention for article published in Frontiers in Chemistry, August 2019
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1 X user

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29 Mendeley
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Title
Augmenting Basin-Hopping With Techniques From Unsupervised Machine Learning: Applications in Spectroscopy and Ion Mobility
Published in
Frontiers in Chemistry, August 2019
DOI 10.3389/fchem.2019.00519
Pubmed ID
Authors

Ce Zhou, Christian Ieritano, William Scott Hopkins

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 28%
Student > Bachelor 5 17%
Researcher 4 14%
Student > Master 2 7%
Lecturer 1 3%
Other 1 3%
Unknown 8 28%
Readers by discipline Count As %
Chemistry 10 34%
Biochemistry, Genetics and Molecular Biology 2 7%
Physics and Astronomy 2 7%
Materials Science 2 7%
Mathematics 1 3%
Other 4 14%
Unknown 8 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 07 August 2019.
All research outputs
#20,576,667
of 23,153,849 outputs
Outputs from Frontiers in Chemistry
#2,959
of 6,064 outputs
Outputs of similar age
#293,381
of 345,119 outputs
Outputs of similar age from Frontiers in Chemistry
#85
of 150 outputs
Altmetric has tracked 23,153,849 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,064 research outputs from this source. They receive a mean Attention Score of 2.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 345,119 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 150 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.