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Editorial: Advances in machine learning methods facilitating collaborative image-based decision making for neuroscience

Overview of attention for article published in Frontiers in Computational Neuroscience, September 2023
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1 X user

Readers on

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2 Mendeley
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Title
Editorial: Advances in machine learning methods facilitating collaborative image-based decision making for neuroscience
Published in
Frontiers in Computational Neuroscience, September 2023
DOI 10.3389/fncom.2023.1267489
Pubmed ID
Authors

Chengjia Wang, Heye Zhang, Georgios Papanastasiou, Guang Yang

Timeline

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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.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Student > Doctoral Student 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Agricultural and Biological Sciences 1 50%
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 01 October 2023.
All research outputs
#21,982,990
of 24,527,858 outputs
Outputs from Frontiers in Computational Neuroscience
#1,216
of 1,420 outputs
Outputs of similar age
#187,103
of 229,611 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#5
of 13 outputs
Altmetric has tracked 24,527,858 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 1,420 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. 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 229,611 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 13 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.