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Teaching the Fundamentals of Biological Data Integration Using Classroom Games

Overview of attention for article published in PLoS Computational Biology, December 2012
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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 (97th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
twitter
45 X users
facebook
4 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
165 Mendeley
citeulike
8 CiteULike
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Title
Teaching the Fundamentals of Biological Data Integration Using Classroom Games
Published in
PLoS Computational Biology, December 2012
DOI 10.1371/journal.pcbi.1002789
Pubmed ID
Authors

Maria Victoria Schneider, Rafael C. Jimenez

Abstract

This article aims to introduce the nature of data integration to life scientists. Generally, the subject of data integration is not discussed outside the field of computational science and is not covered in any detail, or even neglected, when teaching/training trainees. End users (hereby defined as wet-lab trainees, clinicians, lab researchers) will mostly interact with bioinformatics resources and tools through web interfaces that mask the user from the data integration processes. However, the lack of formal training or acquaintance with even simple database concepts and terminology often results in a real obstacle to the full comprehension of the resources and tools the end users wish to access. Understanding how data integration works is fundamental to empowering trainees to see the limitations as well as the possibilities when exploring, retrieving, and analysing biological data from databases. Here we introduce a game-based learning activity for training/teaching the topic of data integration that trainers/educators can adopt and adapt for their classroom. In particular we provide an example using DAS (Distributed Annotation Systems) as a method for data integration.

X Demographics

X Demographics

The data shown below were collected from the profiles of 45 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 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 5%
Germany 3 2%
Sweden 2 1%
Spain 2 1%
France 1 <1%
United Kingdom 1 <1%
Ukraine 1 <1%
Canada 1 <1%
Kenya 1 <1%
Other 4 2%
Unknown 141 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 19%
Student > Ph. D. Student 24 15%
Student > Master 18 11%
Professor > Associate Professor 17 10%
Professor 17 10%
Other 44 27%
Unknown 14 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 55 33%
Biochemistry, Genetics and Molecular Biology 24 15%
Computer Science 20 12%
Medicine and Dentistry 11 7%
Immunology and Microbiology 6 4%
Other 30 18%
Unknown 19 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 42. 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 21 November 2014.
All research outputs
#985,617
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#763
of 8,960 outputs
Outputs of similar age
#7,761
of 288,792 outputs
Outputs of similar age from PLoS Computational Biology
#8
of 121 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done particularly well, scoring higher than 91% 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 288,792 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 97% of its contemporaries.
We're also able to compare this research output to 121 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 93% of its contemporaries.