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Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data

Overview of attention for article published in Frontiers in Neuroinformatics, September 2017
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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 (#31 of 828)
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

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44 X users
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1 Facebook page

Citations

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31 Dimensions

Readers on

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178 Mendeley
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Title
Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data
Published in
Frontiers in Neuroinformatics, September 2017
DOI 10.3389/fninf.2017.00060
Pubmed ID
Authors

Etienne Combrisson, Raphael Vallat, Jean-Baptiste Eichenlaub, Christian O'Reilly, Tarek Lajnef, Aymeric Guillot, Perrine M. Ruby, Karim Jerbi

Abstract

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.

X Demographics

X Demographics

The data shown below were collected from the profiles of 44 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 178 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 178 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 20%
Researcher 25 14%
Student > Master 18 10%
Student > Bachelor 14 8%
Student > Doctoral Student 11 6%
Other 30 17%
Unknown 44 25%
Readers by discipline Count As %
Neuroscience 39 22%
Engineering 24 13%
Computer Science 18 10%
Medicine and Dentistry 10 6%
Psychology 7 4%
Other 24 13%
Unknown 56 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 23 December 2017.
All research outputs
#1,384,470
of 25,382,250 outputs
Outputs from Frontiers in Neuroinformatics
#31
of 828 outputs
Outputs of similar age
#27,016
of 324,545 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#2
of 15 outputs
Altmetric has tracked 25,382,250 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 828 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. 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 324,545 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 91% of its contemporaries.
We're also able to compare this research output to 15 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.