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OMPC: an Open-Source MATLAB®-to-Python Compiler

Overview of attention for article published in Frontiers in Neuroinformatics, February 2009
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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7 X users
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

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8 Dimensions

Readers on

mendeley
80 Mendeley
citeulike
2 CiteULike
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Title
OMPC: an Open-Source MATLAB®-to-Python Compiler
Published in
Frontiers in Neuroinformatics, February 2009
DOI 10.3389/neuro.11.005.2009
Pubmed ID
Authors

Peter Jurica, Cees van Leeuwen

Abstract

Free access to scientific information facilitates scientific progress. Open-access scientific journals are a first step in this direction; a further step is to make auxiliary and supplementary materials that accompany scientific publications, such as methodological procedures and data-analysis tools, open and accessible to the scientific community. To this purpose it is instrumental to establish a software base, which will grow toward a comprehensive free and open-source language of technical and scientific computing. Endeavors in this direction are met with an important obstacle. MATLAB((R)), the predominant computation tool in many fields of research, is a closed-source commercial product. To facilitate the transition to an open computation platform, we propose Open-source MATLAB((R))-to-Python Compiler (OMPC), a platform that uses syntax adaptation and emulation to allow transparent import of existing MATLAB((R)) functions into Python programs. The imported MATLAB((R)) modules will run independently of MATLAB((R)), relying on Python's numerical and scientific libraries. Python offers a stable and mature open source platform that, in many respects, surpasses commonly used, expensive commercial closed source packages. The proposed software will therefore facilitate the transparent transition towards a free and general open-source lingua franca for scientific computation, while enabling access to the existing methods and algorithms of technical computing already available in MATLAB((R)). OMPC is available at http://ompc.juricap.com.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 8%
Japan 2 3%
Switzerland 1 1%
Italy 1 1%
Cuba 1 1%
Sweden 1 1%
Netherlands 1 1%
United Kingdom 1 1%
Norway 1 1%
Other 2 3%
Unknown 63 79%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 35%
Student > Ph. D. Student 17 21%
Student > Master 7 9%
Professor > Associate Professor 6 8%
Professor 4 5%
Other 11 14%
Unknown 7 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 21%
Engineering 12 15%
Psychology 8 10%
Computer Science 7 9%
Earth and Planetary Sciences 4 5%
Other 24 30%
Unknown 8 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 29 September 2020.
All research outputs
#3,407,350
of 25,371,288 outputs
Outputs from Frontiers in Neuroinformatics
#163
of 833 outputs
Outputs of similar age
#16,644
of 188,866 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#1
of 9 outputs
Altmetric has tracked 25,371,288 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 833 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has done well, scoring higher than 80% 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 188,866 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 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them