Community-Driven Data Analysis Training for Biology

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.

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Additional Info

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Author Batut, Bérénice
Last Updated November 20, 2019, 16:51 (UTC)
Created August 1, 2019, 10:29 (UTC)
Article Host Type repository
Article Is Open Access true
Article License Type
Article Version Type acceptedVersion
Citation Report https://scite.ai/reports/10.1016/j.cels.2018.05.012
DOI 10.1016/j.cels.2018.05.012
Date Last Updated 2019-08-01T10:28:28.144729
Evidence oa repository (via pmcid lookup)
Funder code(s) National Institutes of Health (U41 HG006620, R01 AI134384-01); National Science Foundation (1661497); Collaborative Research Centre 992 Medical Epigenetics (SFB 992/1 2012); German Federal Ministry of Education and Research (031 A538A RBC)
Journal Is Open Access false
Open Access Status green
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Publisher URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6296361