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Community-Driven Data Analysis Training for Biology

Bérénice Batut 1 Saskia Hiltemann 2 Andrea Bagnacani 3 Dannon Baker 4 Vivek Bhardwaj 5 Clemens Blank 1 Anthony Bretaudeau 6, 7 Loraine Brillet-Guéguen 8 John K. Chilton 9 Dave Clements 4 Olivia Doppelt-Azeroual 10 Anika Erxleben 1 Mallory Ann Freeberg 11 Simon Gladman 12 Youri Hoogstrate 2 Hans-Rudolf Hotz 13 Torsten Houwaart 1 Pratik Jagtap 14 Delphine Larivière 9 Gildas Le Corguillé 8 Thomas Manke 15 Fabien Mareuil 10 Fidel Ramírez 15 Devon Ryan 15 Florian Christoph Sigloch 1 Nicola Soranzo 16 Joachim Wolff 1 Pavankumar Videm 1 Markus Wolfien 3 Aisanjiang Wubuli 17 Dilmurat Yusuf 1 James Taylor 4 Rolf Backofen 1, * Anton Nekrutenko 9, * Bjorn Grüning 1 
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
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Submitted on : Tuesday, September 8, 2020 - 10:31:07 AM
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Bérénice Batut, Saskia Hiltemann, Andrea Bagnacani, Dannon Baker, Vivek Bhardwaj, et al.. Community-Driven Data Analysis Training for Biology. 2020. ⟨pasteur-02613129⟩



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