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QMachine: commodity supercomputing in web browsers

Overview of attention for article published in BMC Bioinformatics, June 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
1 blog
twitter
17 tweeters
facebook
4 Facebook pages
wikipedia
1 Wikipedia page
googleplus
4 Google+ users

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
87 Mendeley
citeulike
2 CiteULike
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Title
QMachine: commodity supercomputing in web browsers
Published in
BMC Bioinformatics, June 2014
DOI 10.1186/1471-2105-15-176
Pubmed ID
Authors

Sean R Wilkinson, Jonas S Almeida

Abstract

Ongoing advancements in cloud computing provide novel opportunities in scientific computing, especially for distributed workflows. Modern web browsers can now be used as high-performance workstations for querying, processing, and visualizing genomics' "Big Data" from sources like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) without local software installation or configuration. The design of QMachine (QM) was driven by the opportunity to use this pervasive computing model in the context of the Web of Linked Data in Biomedicine.

Twitter Demographics

The data shown below were collected from the profiles of 17 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 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Brazil 2 2%
Sweden 1 1%
Norway 1 1%
United Kingdom 1 1%
Canada 1 1%
Germany 1 1%
Unknown 77 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 23%
Researcher 19 22%
Student > Master 13 15%
Student > Bachelor 12 14%
Professor > Associate Professor 7 8%
Other 11 13%
Unknown 5 6%
Readers by discipline Count As %
Computer Science 32 37%
Agricultural and Biological Sciences 25 29%
Biochemistry, Genetics and Molecular Biology 7 8%
Engineering 5 6%
Chemistry 3 3%
Other 7 8%
Unknown 8 9%

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 19 August 2016.
All research outputs
#779,898
of 15,949,522 outputs
Outputs from BMC Bioinformatics
#113
of 5,774 outputs
Outputs of similar age
#10,770
of 192,691 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 14 outputs
Altmetric has tracked 15,949,522 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,774 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 98% 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 192,691 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 94% of its contemporaries.
We're also able to compare this research output to 14 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 92% of its contemporaries.