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The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again

Overview of attention for article published in BMC Bioinformatics, January 2014
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  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

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5 X users

Citations

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21 Dimensions

Readers on

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60 Mendeley
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3 CiteULike
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Title
The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-s1-s11
Pubmed ID
Authors

Alejandra González-Beltrán, Steffen Neumann, Eamonn Maguire, Susanna-Assunta Sansone, Philippe Rocca-Serra

Abstract

The ISA-Tab format and software suite have been developed to break the silo effect induced by technology-specific formats for a variety of data types and to better support experimental metadata tracking. Experimentalists seldom use a single technique to monitor biological signals. Providing a multi-purpose, pragmatic and accessible format that abstracts away common constructs for describing Investigations, Studies and Assays, ISA is increasingly popular. To attract further interest towards the format and extend support to ensure reproducible research and reusable data, we present the Risa package, which delivers a central component to support the ISA format by enabling effortless integration with R, the popular, open source data crunching environment.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 5%
Brazil 2 3%
Germany 1 2%
Hong Kong 1 2%
Sweden 1 2%
Canada 1 2%
Unknown 51 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 25%
Student > Ph. D. Student 12 20%
Student > Bachelor 9 15%
Other 5 8%
Professor > Associate Professor 4 7%
Other 7 12%
Unknown 8 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 32%
Computer Science 12 20%
Engineering 5 8%
Biochemistry, Genetics and Molecular Biology 3 5%
Mathematics 2 3%
Other 9 15%
Unknown 10 17%
Attention Score in Context

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 12 February 2014.
All research outputs
#13,512,822
of 24,143,470 outputs
Outputs from BMC Bioinformatics
#3,707
of 7,506 outputs
Outputs of similar age
#161,517
of 314,249 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 100 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,506 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 50% 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 314,249 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 100 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 55% of its contemporaries.