RT Journal Article
SR Electronic
T1 Community-driven data analysis training for biology
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 225680
DO 10.1101/225680
A1 Bérénice Batut
A1 Saskia Hiltemann
A1 Andrea Bagnacani
A1 Dannon Baker
A1 Vivek Bhardwaj
A1 Clemens Blank
A1 Anthony Bretaudeau
A1 Loraine Brillet-Guéguen
A1 Martin Čech
A1 John Chilton
A1 Dave Clements
A1 Olivia Doppelt-Azeroual
A1 Anika Erxleben
A1 Mallory Ann Freeberg
A1 Simon Gladman
A1 Youri Hoogstrate
A1 Hans-Rudolf Hotz
A1 Torsten Houwaart
A1 Pratik Jagtap
A1 Delphine Larivière
A1 Gildas Le Corguillé
A1 Thomas Manke
A1 Fabien Mareuil
A1 Fidel Ramírez
A1 Devon Ryan
A1 Florian Christoph Sigloch
A1 Nicola Soranzo
A1 Joachim Wolff
A1 Pavankumar Videm
A1 Markus Wolfien
A1 Aisanjiang Wubuli
A1 Dilmurat Yusuf
A1 Galaxy Training Network
A1 Rolf Backofen
A1 Anton Nekrutenko
A1 Bjürn Gröning
YR 2017
UL http://biorxiv.org/content/early/2017/11/29/225680.abstract
AB The primary problem with the explosion of biomedical datasets is not the data itself, 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.