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A lightweight, flow-based toolkit for parallel and distributed bioinformatics pipelines

Overview of attention for article published in BMC Bioinformatics, February 2011
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

patent
1 patent
reddit
1 Redditor

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
98 Mendeley
citeulike
13 CiteULike
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Title
A lightweight, flow-based toolkit for parallel and distributed bioinformatics pipelines
Published in
BMC Bioinformatics, February 2011
DOI 10.1186/1471-2105-12-61
Pubmed ID
Authors

Marcin Cieślik, Cameron Mura

Abstract

Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 9%
Netherlands 2 2%
France 2 2%
Brazil 2 2%
Japan 2 2%
Italy 1 1%
Sweden 1 1%
United Kingdom 1 1%
Canada 1 1%
Other 6 6%
Unknown 71 72%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 33%
Student > Ph. D. Student 16 16%
Student > Master 9 9%
Student > Bachelor 8 8%
Professor > Associate Professor 8 8%
Other 16 16%
Unknown 9 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 42%
Computer Science 26 27%
Biochemistry, Genetics and Molecular Biology 8 8%
Engineering 6 6%
Medicine and Dentistry 3 3%
Other 5 5%
Unknown 9 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 15 November 2017.
All research outputs
#7,186,266
of 22,715,151 outputs
Outputs from BMC Bioinformatics
#2,857
of 7,260 outputs
Outputs of similar age
#37,458
of 106,496 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 36 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,260 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 58% 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 106,496 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 63% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.