Community-driven data analysis training for biology
| Autor: | Bérénice Batut, Saskia Hiltemann, Andrea Bagnacani, Dannon Baker, Vivek Bhardwaj, Clemens Blank, Anthony Bretaudeau, Loraine Brillet-Guéguen, Martin Čech, John Chilton, Dave Clements, Olivia Doppelt-Azeroual, Anika Erxleben, Mallory Ann Freeberg, Simon Gladman, Youri Hoogstrate, Hans-Rudolf Hotz, Torsten Houwaart, Pratik Jagtap, Delphine Larivière, Gildas Le Corguillé, Thomas Manke, Fabien Mareuil, Fidel Ramírez, Devon Ryan, Florian Christoph Sigloch, Nicola Soranzo, Joachim Wolff, Pavankumar Videm, Markus Wolfien, Aisanjiang Wubuli, Dilmurat Yusuf, Galaxy Training Network, Rolf Backofen, James Taylor, Anton Nekrutenko, Björn Grüning |
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| Rok vydání: | 2017 |
| Předmět: |
0303 health sciences
business.industry 4. Education 0206 medical engineering 02 engineering and technology Space (commercial competition) Training (civil) Data science 03 medical and health sciences Software ComputingMilieux_COMPUTERSANDEDUCATION Key (cryptography) Feature (machine learning) Data analysis business Primary problem Curriculum 020602 bioinformatics 030304 developmental biology |
| DOI: | 10.1101/225680 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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