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Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning

Overview of attention for article published in Nature Communications, November 2020
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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 (80th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
21 tweeters

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
27 Mendeley
Title
Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning
Published in
Nature Communications, November 2020
DOI 10.1038/s41467-020-19673-1
Pubmed ID
Authors

Jieming Li, Leyou Zhang, Alexander Johnson-Buck, Nils G. Walter

Twitter Demographics

The data shown below were collected from the profiles of 21 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 15%
Researcher 3 11%
Student > Postgraduate 3 11%
Student > Doctoral Student 2 7%
Professor 1 4%
Other 2 7%
Unknown 12 44%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 30%
Neuroscience 2 7%
Physics and Astronomy 1 4%
Computer Science 1 4%
Agricultural and Biological Sciences 1 4%
Other 2 7%
Unknown 12 44%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 December 2020.
All research outputs
#2,411,677
of 18,478,499 outputs
Outputs from Nature Communications
#21,522
of 36,659 outputs
Outputs of similar age
#81,506
of 425,975 outputs
Outputs of similar age from Nature Communications
#3,918
of 6,074 outputs
Altmetric has tracked 18,478,499 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 36,659 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 53.1. This one is in the 41st percentile – i.e., 41% 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 425,975 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 80% of its contemporaries.
We're also able to compare this research output to 6,074 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.