The GTN is a collection of free, FAIR, open-source, reusable e-Learning materials for life sciences and beyond

Our mission: provide a platform for accessible, interactive training materials for everyone.

Our Pillars

Free & Open
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Automatically FAIR
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Community Driven
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Accessible
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Easy to Contribute
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Classroom or Self-study
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A global view

Our materials have grown significantly over the years. We have a global audience, with users from over 200 countries. Our materials are used by thousands of learners every month. See more detailed statistics on our dedicated page.

33
Topics
441
Tutorials
20
Learning Paths
471
FAQs
437
Contributors
9.5
Years
102
News Posts
200
Videos (135.0h)

Findable, Accessible, Interoperable, Reusable

A screenshot of a radar plot visibly from fair-checker's site showing a perfect score.
FAIR Checker reports a 100% FAIRness score for GTN materials, better than some competing e-learning material infrastructures.

The GTN infrastructure has been developed in accordance with the FAIR (Findable, Accessible, Interoperable, Reusable) principles for training materials (Garcia et al. 2020). Following these principles enables trainers and trainees to find, reuse, adapt, and improve the available tutorials.

By contributing to the GTN, you can be certain that materials are 100% FAIR and compliant with the 10 Simple Rules for FAIR training materials. Every learning resource contributed to the GTN automatically receives:

  • Automatic Persistent URLs
  • Automatic TeSS registration
  • Automatic BioSchemas compliant LD+JSON metadata
  • Automatic monthly archival copies on the GTN archive
  • Page view statistics via Plausible

We have an open and welcoming collaborative peer-review and curation process as users contribute materials to the GTN.

The GTN is integrated with the ELIXIR Training e-Support System (TeSS), which is a platform for discovering and sharing learning materials and events. Every tutorial, slide deck, event, and learning pathway in the GTN is automatically registered with TeSS, making it easy for users to find and access the materials.

Accessibility

The GTN strives to meet WCAG 2.0 AA compliance, and WCAG 2.0 AAA where possible. We regularly test our materials for their screen-reader accessibility and have implemented a range of features to ensure the website is accessible to everyone, regardless of disability.

e-Learning Materials

Materials in the GTN are designed to be interactive, engaging, and accessible. Many are additionally designed to reproduce a published analysis from literature, helping document and showcase reproducibility.

Tutorials are designed around rich metadata, every tutorial including extensive metadata that help learners and educators decide if the materials are useful to them:

  • Audience
  • Difficulty Level
  • SMART Learning Objectives
  • Key Points
  • Pre-requisite materials
  • License (always open source!)
  • Duration (for teaching)
  • and more

Every tutorial is highly structured with both expository information, clearly demarcated hands-on exercises, and then formative assessments to help learners check their understanding.

GTN e-Learning materials are complemented with excellent technical support such as input datasets available on Zenodo, as well as workflows which match the tutorial’s steps. Some tutorials additionally feature recordings from previous times this material was taught. Galaxy tutorials also indicate which public servers can be used for the tutorial, identifying servers which support every tool used in a given learning material.

Screenshot of a GTN learning material showing metadata and authors for a tutorial
GTN e-Learning materials feature robust metadata and a clear, easy to follow structure.

Proven Impact

Please read further about our community driven approach and the infrastructure we have built to support both educators and learners in Batut et al. 2018 and Hiltemann et al. 2023.

Community

Our community has been key to the GTN’s success, providing materials, feedback, and support to learners. We have a growing number of contributors, who have helped to create our diverse set of materials.

Easy Contributions

We aim to make contribution easy: all contributions to the GTN can be done in a web browser, without the installation of any additional software to decrease burdens of contributors. Additionally we take many steps to make it easier for contributors to contribute. Based on their feedback we support adding events, news, FAQs, and more via Google forms.

Stay in Touch

You can follow us on Mastodon or Bluesky, or get in touch with us via Matrix Chat.

Thanks to our 440 GTN contributors!

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Brought to you by 50 contributing organisations and 20 supporting grants!

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We are always looking for new contributors, so if you have a tutorial you would like to share, please get in touch!

How You Can Help

Join the GTN today, sharing your learning and expertise! Get Involved

History

The GTN started in July of 2015 as a collection of just a handful of Galaxy-specific training materials. A few slides, and a few hands-on tutorials were available covering topics like RNA-Seq, ChIP-Seq, and other NGS topics.

The GTN is a community-driven project, with contributions from people around the world.

Over time, it has grown to include materials for a wide range of topics, in bioinformatics and beyond! We now cover a range of scientific topics and methodologies.

screenshot of the gtn on the left and galaxy on the right, both are very old looking
The GTN started out in July 2015, and has changed significantly since then

Free & Open Source

Both the GTN Framework, and all materials in the GTN are openly licensed and free to use. The GTN code and libraries is available under MIT, and most tutorials are available under a Creative Commons license CC-BY-4.0

References

  1. Batut, B., S. Hiltemann, A. Bagnacani, D. Baker, V. Bhardwaj et al., 2018 Community-Driven Data Analysis Training for Biology. Cell Systems 6: 752–758.e1. 10.1016/j.cels.2018.05.012
  2. Garcia, L., B. Batut, M. L. Burke, M. Kuzak, F. Psomopoulos et al., 2020 Ten simple rules for making training materials FAIR (S. Markel, Ed.). PLOS Computational Biology 16: e1007854. 10.1371/journal.pcbi.1007854
  3. Hiltemann, S., H. Rasche, S. Gladman, H.-R. Hotz, D. Larivière et al., 2023 Galaxy Training: A powerful framework for teaching! PLOS Computational Biology 19: e1010752. 10.1371/journal.pcbi.1010752