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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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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.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 164 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 140 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 16 10%
Other 44 27%
Unknown 14 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 54 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%