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@ARTICLE{Hiltemann:186694,
      author       = {S. Hiltemann and H. Rasche and S. Gladman and H.-R. Hotz
                      and D. Larivière and D. Blankenberg and P. D. Jagtap and T.
                      Wollmann and A. Bretaudeau and N. Goué and T. J. Griffin
                      and C. Royaux and Y. Le Bras and S. Mehta and A. Syme and F.
                      Coppens and B. Droesbeke and N. Soranzo and W. Bacon and F.
                      Psomopoulos and C. Gallardo-Alba and J. Davis and M. C.
                      Föll$^*$ and M. Fahrner$^*$ and M. A. Doyle and B.
                      Serrano-Solano and A. C. Fouilloux and P. van Heusden and W.
                      Maier and D. Clements and F. Heyl and B. Grüning and B.
                      Batut},
      collaboration = {G. T. Network},
      title        = {{G}alaxy {T}raining: {A} powerful framework for teaching!},
      journal      = {PLoS Computational Biology},
      volume       = {19},
      number       = {1},
      issn         = {1553-734X},
      address      = {San Francisco, Calif.},
      publisher    = {Public Library of Science},
      reportid     = {DKFZ-2023-00063},
      pages        = {e1010752 -},
      year         = {2023},
      abstract     = {There is an ongoing explosion of scientific datasets being
                      generated, 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.},
      cin          = {FR01},
      ddc          = {610},
      cid          = {I:(DE-He78)FR01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36622853},
      doi          = {10.1371/journal.pcbi.1010752},
      url          = {https://inrepo02.dkfz.de/record/186694},
}