↓ Skip to main content

PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools

Overview of attention for article published in BMC Bioinformatics, January 2012
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
40 Dimensions

Readers on

mendeley
155 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools
Published in
BMC Bioinformatics, January 2012
DOI 10.1186/1471-2105-13-115
Pubmed ID
Authors

Sean O'Callaghan, David P De Souza, Andrew Isaac, Qiao Wang, Luke Hodkinson, Moshe Olshansky, Tim Erwin, Bill Appelbe, Dedreia L Tull, Ute Roessner, Antony Bacic, Malcolm J McConville, Vladimir A Likić

Abstract

Gas chromatography-mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 155 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Russia 2 1%
Australia 2 1%
United Kingdom 1 <1%
Brazil 1 <1%
Singapore 1 <1%
Denmark 1 <1%
Switzerland 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 142 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 28%
Researcher 41 26%
Student > Master 16 10%
Other 10 6%
Professor > Associate Professor 8 5%
Other 24 15%
Unknown 13 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 28%
Chemistry 25 16%
Computer Science 15 10%
Biochemistry, Genetics and Molecular Biology 15 10%
Engineering 13 8%
Other 27 17%
Unknown 16 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 25 August 2012.
All research outputs
#6,432,500
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#2,258
of 4,588 outputs
Outputs of similar age
#52,720
of 120,420 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 6 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,588 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 48th percentile – i.e., 48% 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 120,420 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.