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Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model

Overview of attention for article published in Progress in Earth and Planetary Science, December 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#23 of 581)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

twitter
37 X users
facebook
2 Facebook pages

Citations

dimensions_citation
68 Dimensions

Readers on

mendeley
86 Mendeley
Title
Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model
Published in
Progress in Earth and Planetary Science, December 2018
DOI 10.1186/s40645-018-0245-y
Authors

Daisuke Matsuoka, Masuo Nakano, Daisuke Sugiyama, Seiichi Uchida

X Demographics

X Demographics

The data shown below were collected from the profiles of 37 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 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 86 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 20%
Student > Ph. D. Student 13 15%
Student > Master 10 12%
Other 4 5%
Student > Bachelor 3 3%
Other 14 16%
Unknown 25 29%
Readers by discipline Count As %
Earth and Planetary Sciences 19 22%
Engineering 13 15%
Computer Science 12 14%
Environmental Science 6 7%
Unspecified 3 3%
Other 8 9%
Unknown 25 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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 September 2021.
All research outputs
#1,500,040
of 24,993,752 outputs
Outputs from Progress in Earth and Planetary Science
#23
of 581 outputs
Outputs of similar age
#34,146
of 446,874 outputs
Outputs of similar age from Progress in Earth and Planetary Science
#3
of 29 outputs
Altmetric has tracked 24,993,752 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 581 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done particularly well, scoring higher than 96% 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 446,874 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.