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MSiReader: An Open-Source Interface to View and Analyze High Resolving Power MS Imaging Files on Matlab Platform

Overview of attention for article published in Journal of the American Society for Mass Spectrometry, March 2013
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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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

patent
4 patents
wikipedia
3 Wikipedia pages

Citations

dimensions_citation
340 Dimensions

Readers on

mendeley
162 Mendeley
Title
MSiReader: An Open-Source Interface to View and Analyze High Resolving Power MS Imaging Files on Matlab Platform
Published in
Journal of the American Society for Mass Spectrometry, March 2013
DOI 10.1007/s13361-013-0607-z
Pubmed ID
Authors

Guillaume Robichaud, Kenneth P. Garrard, Jeremy A. Barry, David C. Muddiman

Abstract

During the past decade, the field of mass spectrometry imaging (MSI) has greatly evolved, to a point where it has now been fully integrated by most vendors as an optional or dedicated platform that can be purchased with their instruments. However, the technology is not mature and multiple research groups in both academia and industry are still very actively studying the fundamentals of imaging techniques, adapting the technology to new ionization sources, and developing new applications. As a result, there important varieties of data file formats used to store mass spectrometry imaging data and, concurrent to the development of MSi, collaborative efforts have been undertaken to introduce common imaging data file formats. However, few free software packages to read and analyze files of these different formats are readily available. We introduce here MSiReader, a free open source application to read and analyze high resolution MSI data from the most common MSi data formats. The application is built on the Matlab platform (Mathworks, Natick, MA, USA) and includes a large selection of data analysis tools and features. People who are unfamiliar with the Matlab language will have little difficult navigating the user-friendly interface, and users with Matlab programming experience can adapt and customize MSiReader for their own needs.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 162 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
United Kingdom 1 <1%
Denmark 1 <1%
Canada 1 <1%
Unknown 157 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 50 31%
Researcher 27 17%
Student > Master 16 10%
Student > Bachelor 15 9%
Student > Doctoral Student 7 4%
Other 21 13%
Unknown 26 16%
Readers by discipline Count As %
Chemistry 59 36%
Biochemistry, Genetics and Molecular Biology 19 12%
Agricultural and Biological Sciences 15 9%
Computer Science 9 6%
Engineering 8 5%
Other 22 14%
Unknown 30 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 22 March 2021.
All research outputs
#2,866,361
of 25,394,764 outputs
Outputs from Journal of the American Society for Mass Spectrometry
#131
of 3,836 outputs
Outputs of similar age
#23,367
of 210,323 outputs
Outputs of similar age from Journal of the American Society for Mass Spectrometry
#1
of 19 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,836 research outputs from this source. They receive a mean Attention Score of 3.8. 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 210,323 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 88% of its contemporaries.
We're also able to compare this research output to 19 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 94% of its contemporaries.