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Crowdsourcing the nodulation gene network discovery environment

Overview of attention for article published in BMC Bioinformatics, May 2016
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

twitter
9 tweeters
googleplus
1 Google+ user

Citations

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4 Dimensions

Readers on

mendeley
16 Mendeley
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Title
Crowdsourcing the nodulation gene network discovery environment
Published in
BMC Bioinformatics, May 2016
DOI 10.1186/s12859-016-1089-3
Pubmed ID
Authors

Yupeng Li, Scott A. Jackson

Abstract

The Legumes (Fabaceae) are an economically and ecologically important group of plant species with the conspicuous capacity for symbiotic nitrogen fixation in root nodules, specialized plant organs containing symbiotic microbes. With the aim of understanding the underlying molecular mechanisms leading to nodulation, many efforts are underway to identify nodulation-related genes and determine how these genes interact with each other. In order to accurately and efficiently reconstruct nodulation gene network, a crowdsourcing platform, CrowdNodNet, was created. The platform implements the jQuery and vis.js JavaScript libraries, so that users are able to interactively visualize and edit the gene network, and easily access the information about the network, e.g. gene lists, gene interactions and gene functional annotations. In addition, all the gene information is written on MediaWiki pages, enabling users to edit and contribute to the network curation. Utilizing the continuously updated, collaboratively written, and community-reviewed Wikipedia model, the platform could, in a short time, become a comprehensive knowledge base of nodulation-related pathways. The platform could also be used for other biological processes, and thus has great potential for integrating and advancing our understanding of the functional genomics and systems biology of any process for any species. The platform is available at http://crowd.bioops.info/ , and the source code can be openly accessed at https://github.com/bioops/crowdnodnet under MIT License.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 19%
Student > Ph. D. Student 3 19%
Researcher 3 19%
Student > Master 2 13%
Student > Doctoral Student 1 6%
Other 3 19%
Unknown 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 25%
Biochemistry, Genetics and Molecular Biology 3 19%
Computer Science 2 13%
Environmental Science 1 6%
Unspecified 1 6%
Other 3 19%
Unknown 2 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 08 June 2016.
All research outputs
#4,617,928
of 17,800,904 outputs
Outputs from BMC Bioinformatics
#1,835
of 6,267 outputs
Outputs of similar age
#73,987
of 274,179 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 22 outputs
Altmetric has tracked 17,800,904 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 6,267 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 70% 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 274,179 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 72% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.