Literature DB >> 29953864

Community-Driven Data Analysis Training for Biology.

Bérénice Batut1, Saskia Hiltemann2, Andrea Bagnacani3, Dannon Baker4, Vivek Bhardwaj5, Clemens Blank1, Anthony Bretaudeau6, Loraine Brillet-Guéguen7, Martin Čech8, John Chilton8, Dave Clements4, Olivia Doppelt-Azeroual9, Anika Erxleben1, Mallory Ann Freeberg10, Simon Gladman11, Youri Hoogstrate2, Hans-Rudolf Hotz12, Torsten Houwaart1, Pratik Jagtap13, Delphine Larivière8, Gildas Le Corguillé14, Thomas Manke15, Fabien Mareuil9, Fidel Ramírez15, Devon Ryan15, Florian Christoph Sigloch1, Nicola Soranzo16, Joachim Wolff1, Pavankumar Videm1, Markus Wolfien3, Aisanjiang Wubuli17, Dilmurat Yusuf1, James Taylor4, Rolf Backofen18, Anton Nekrutenko19, Björn Grüning20.   

Abstract

The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org.
Copyright © 2018. Published by Elsevier Inc.

Entities:  

Keywords:  data analysis; genomics; next-generation sequencing; proteomics; training

Mesh:

Year:  2018        PMID: 29953864      PMCID: PMC6296361          DOI: 10.1016/j.cels.2018.05.012

Source DB:  PubMed          Journal:  Cell Syst        ISSN: 2405-4712            Impact factor:   10.304


  10 in total

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Journal:  Appl Environ Microbiol       Date:  2009-10-02       Impact factor: 4.792

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Authors:  Jason J Williams; Tracy K Teal
Journal:  Ann N Y Acad Sci       Date:  2016-09-07       Impact factor: 5.691

3.  Best practices in bioinformatics training for life scientists.

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Journal:  F1000Res       Date:  2014-02-19

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Journal:  Bioinformatics       Date:  2013-03-11       Impact factor: 6.937

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Journal:  Sci Data       Date:  2016-03-15       Impact factor: 6.444

  10 in total
  43 in total

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Authors: 
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