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. We developed an infrastructure that facilitates data analysis training in life sciences. It is an interactive learning platform tuned for current types of data and research problems. Importantly, it provides a means for community-wide content creation and maintenance and, finally, enables trainers and trainees to use the tutorials in a variety of situations, such as those where reliable Internet access is unavailable.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 752-758.e1 |
| Journal | Cell Systems |
| Volume | 6 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 27 2018 |
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All Science Journal Classification (ASJC) codes
- Pathology and Forensic Medicine
- Histology
- Cell Biology
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Community-Driven Data Analysis Training for Biology. / Galaxy Training Network.
In: Cell Systems, Vol. 6, No. 6, 27.06.2018, p. 752-758.e1.Research output: Contribution to journal › Article
TY - JOUR
T1 - Community-Driven Data Analysis Training for Biology
AU - Galaxy Training Network
AU - Batut, Bérénice
AU - Hiltemann, Saskia
AU - Bagnacani, Andrea
AU - Baker, Dannon
AU - Bhardwaj, Vivek
AU - Blank, Clemens
AU - Bretaudeau, Anthony
AU - Brillet-Guéguen, Loraine
AU - Čech, Martin
AU - Chilton, John
AU - Clements, Dave
AU - Doppelt-Azeroual, Olivia
AU - Erxleben, Anika
AU - Freeberg, Mallory Ann
AU - Gladman, Simon
AU - Hoogstrate, Youri
AU - Hotz, Hans Rudolf
AU - Houwaart, Torsten
AU - Jagtap, Pratik
AU - Larivière, Delphine
AU - Le Corguillé, Gildas
AU - Manke, Thomas
AU - Mareuil, Fabien
AU - Ramírez, Fidel
AU - Ryan, Devon
AU - Sigloch, Florian Christoph
AU - Soranzo, Nicola
AU - Wolff, Joachim
AU - Videm, Pavankumar
AU - Wolfien, Markus
AU - Wubuli, Aisanjiang
AU - Yusuf, Dilmurat
AU - Taylor, James
AU - Backofen, Rolf
AU - Nekrutenko, Anton
AU - Grüning, Björn
PY - 2018/6/27
Y1 - 2018/6/27
N2 - 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. We developed an infrastructure that facilitates data analysis training in life sciences. It is an interactive learning platform tuned for current types of data and research problems. Importantly, it provides a means for community-wide content creation and maintenance and, finally, enables trainers and trainees to use the tutorials in a variety of situations, such as those where reliable Internet access is unavailable.
AB - 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. We developed an infrastructure that facilitates data analysis training in life sciences. It is an interactive learning platform tuned for current types of data and research problems. Importantly, it provides a means for community-wide content creation and maintenance and, finally, enables trainers and trainees to use the tutorials in a variety of situations, such as those where reliable Internet access is unavailable.
UR - http://www.scopus.com/inward/record.url?scp=85048348317&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048348317&partnerID=8YFLogxK
U2 - 10.1016/j.cels.2018.05.012
DO - 10.1016/j.cels.2018.05.012
M3 - Article
C2 - 29953864
AN - SCOPUS:85048348317
VL - 6
SP - 752-758.e1
JO - Cell Systems
JF - Cell Systems
SN - 2405-4712
IS - 6
ER -