Galaxy Training: A powerful framework for teaching!

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.

Fig 1. Statistics about training events organized by the Galaxy community and visits of the GTN website over years.

(A) Number of events supported GTN materials, registered on the Galaxy Community Hub website [22], per year between 2018 and 2021. (B) Number of TIaaS events per year on Galaxy Europe (collected in May 2022). (C) Number of participants to TIaaS events per year on UseGalaxy.* (collected in May 2022). (D) Number of visits per year on the GTN website and per topics, initially tracked by Google Analytics and later with Plausible (collected in May 2022). The latest usage statistics are publicly available from https://plausible.galaxyproject.eu/training.galaxyproject.org.

Fig 2. Content of material available on the GTN and feedback from learners.

(A) Evolution of number of topics, tutorials, and contributors over the months between 2017 and 2022. (B) Number and type of tutorials per topics available on the GTN on April 2022. The latest statistics are publicly available from https://training.galaxyproject.org/stats. (C) Type of supporting materials for tutorials per topics available on the GTN on April 2022. (D) Score of the embedded feedback in the tutorials per topics. Three questions are asked in the form: “How much did you like this tutorial?” (from 1 (bad) to 5 (great)), “What did you like?”, “What could be improved?”. The latest feedback results are publicly available from https://training.galaxyproject.org/feedback.

Fig 3. Structure of a GTN tutorial and its features.

(A) Screenshots for parts of a GTN tutorial to show its structure. Title and authors are listed, followed by an overview box containing metadata about the tutorial (target audience, learning objectives, prerequisites, supporting materials, time estimate, etc). The tutorial content itself is a mix of theory (text) and so-called “hands-on” boxes describing practical steps to be performed in Galaxy. Question and answer boxes may be added at any point in the tutorial to allow students to self-evaluate as they progress. The end of a tutorial provides a box with take-home messages, references, and suggestions for further reading and follow-up tutorials. Additionally, a feedback form is embedded at the end of every tutorial. Access to FAQs, support channels, page view statistics, and translated versions of the materials are provided via the top menu of the webpage. (B) Features of a GTN tutorial grouped in 4 categories: rich metadata, structured content, strong (technical) support, and community oriented.

Fig 4. Typical process to create a new tutorial.

Authors usually start by identifying suitable input datasets and developing the workflow in Galaxy. Thus, a workflow can be automatically converted into a tutorial skeleton using the PTDK described in the next section. The tutorial is then tested, reviewed, and finally merged into the GTN.