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NEuronMOrphological analysis tool: open-source software for quantitative morphometrics

Overview of attention for article published in Frontiers in Neuroinformatics, January 2013
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4 X users

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Title
NEuronMOrphological analysis tool: open-source software for quantitative morphometrics
Published in
Frontiers in Neuroinformatics, January 2013
DOI 10.3389/fninf.2013.00002
Pubmed ID
Authors

Lucia Billeci, Chiara Magliaro, Giovanni Pioggia, Arti Ahluwalia

Abstract

Morphometric analysis of neurons and brain tissue is relevant to the study of neuron circuitry development during the first phases of brain growth or for probing the link between microstructural morphology and degenerative diseases. As neural imaging techniques become ever more sophisticated, so does the amount and complexity of data generated. The NEuronMOrphological analysis tool NEMO was purposely developed to handle and process large numbers of optical microscopy image files of neurons in culture or slices in order to automatically run batch routines, store data and apply multivariate classification and feature extraction using 3-way principal component analysis (PCA). Here we describe the software's main features, underlining the differences between NEMO and other commercial and non-commercial image processing tools, and show an example of how NEMO can be used to classify neurons from wild-type mice and from animal models of autism.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 3%
Italy 2 2%
Japan 2 2%
Belarus 1 1%
Spain 1 1%
United States 1 1%
Unknown 78 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 28%
Student > Ph. D. Student 24 27%
Student > Bachelor 9 10%
Student > Doctoral Student 5 6%
Student > Postgraduate 4 5%
Other 11 13%
Unknown 10 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 20%
Engineering 14 16%
Neuroscience 13 15%
Biochemistry, Genetics and Molecular Biology 7 8%
Computer Science 6 7%
Other 11 13%
Unknown 19 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 21 February 2014.
All research outputs
#15,042,498
of 24,321,976 outputs
Outputs from Frontiers in Neuroinformatics
#488
of 800 outputs
Outputs of similar age
#173,865
of 289,361 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#26
of 36 outputs
Altmetric has tracked 24,321,976 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 800 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 37th percentile – i.e., 37% 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 289,361 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.