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Early Detection of Nucleation Events From Solution in LC-TEM by Machine Learning

Overview of attention for article published in Frontiers in Chemistry, January 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

twitter
5 X users

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
4 Mendeley
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Title
Early Detection of Nucleation Events From Solution in LC-TEM by Machine Learning
Published in
Frontiers in Chemistry, January 2022
DOI 10.3389/fchem.2022.818230
Pubmed ID
Authors

Hiroyasu Katsuno, Yuki Kimura, Tomoya Yamazaki, Ichigaku Takigawa

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 25%
Professor > Associate Professor 1 25%
Unknown 2 50%
Readers by discipline Count As %
Earth and Planetary Sciences 1 25%
Materials Science 1 25%
Unknown 2 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 24 January 2022.
All research outputs
#14,167,477
of 24,213,825 outputs
Outputs from Frontiers in Chemistry
#836
of 6,382 outputs
Outputs of similar age
#217,321
of 506,751 outputs
Outputs of similar age from Frontiers in Chemistry
#41
of 385 outputs
Altmetric has tracked 24,213,825 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,382 research outputs from this source. They receive a mean Attention Score of 2.2. This one has done well, scoring higher than 86% 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 506,751 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 56% of its contemporaries.
We're also able to compare this research output to 385 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.