TY - JOUR
T1 - Galaxy Training
T2 - A powerful framework for teaching!
AU - Hiltemann, Saskia
AU - Rasche, Helena
AU - Gladman, Simon
AU - Hotz, Hans Rudolf
AU - Larivière, Delphine
AU - Blankenberg, Daniel
AU - Jagtap, Pratik D.
AU - Wollmann, Thomas
AU - Bretaudeau, Anthony
AU - Goué, Nadia
AU - Griffin, Timothy J.
AU - Royaux, Coline
AU - Bras, Yvan Le
AU - Mehta, Subina
AU - Syme, Anna
AU - Coppens, Frederik
AU - Droesbeke, Bert
AU - Soranzo, Nicola
AU - Bacon, Wendi
AU - Psomopoulos, Fotis
AU - Gallardo-Alba, Cristóbal
AU - Davis, John
AU - Föll, Melanie Christine
AU - Fahrner, Matthias
AU - Doyle, Maria A.
AU - Serrano-Solano, Beatriz
AU - Fouilloux, Anne Claire
AU - van Heusden, Peter
AU - Maier, Wolfgang
AU - Clements, Dave
AU - Heyl, Florian
AU - Grüning, Björn
AU - Batut, Bérénice
N1 - Publisher Copyright:
© 2023 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
PY - 2023/1
Y1 - 2023/1
N2 - AThUe:rePilseaasnecoonngfiormintghaetxaplllhoesaiodninogflesvceilesnartiefircepdraetsaesnetetsdcboerirnegctglye:nerated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https:// training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.
AB - AThUe:rePilseaasnecoonngfiormintghaetxaplllhoesaiodninogflesvceilesnartiefircepdraetsaesnetetsdcboerirnegctglye:nerated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https:// training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.
UR - http://www.scopus.com/inward/record.url?scp=85145957474&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1010752
DO - 10.1371/journal.pcbi.1010752
M3 - Article
C2 - 36622853
AN - SCOPUS:85145957474
SN - 1553-734X
VL - 19
SP - e1010752
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 1
M1 - e1010752
ER -