 Hi, everyone. My name is Hannah Chap-Twin and I'm a PhD student at the Uni of Southern Denmark. Today I'd like to present our computational learning module for improving skills in R and reproducibility. First, a little background. There is an increasing amount of biological data created every year, which comes with an increased need for the knowledge and skills to manage, process and analyse that data. However, training and modern computational skills has not kept up, particularly in the field of biomedical research. With the open science movement also on the rise, methods and analytic processes are expected to be more reproducible, though researchers may have limited training in reproducibility practices. So we've developed an open educational resource designed to improve skills in R and reproducibility, and we'd like to introduce you to R-Cubed. Here I'm showing a screenshot of the index page for this online learning module. On the left-hand side there's a menu where you can jump to the different sub-modules and lectures on Alpha. And you can visit R-Cubed at r-cubed.rostools.org. It features five sub-modules focused on R and reproducibility, as well as three standalone lectures relating to collaboration and open science. The module is designed for in-person settings and has been delivered as a workshop three times previously. The module is based on the needs of biomedical researchers, but it can be used by instructors and learners in other fields. Instructors of the workshop can make use of it directly as a teaching reference, and others interested in teaching can modify the content to build their own lessons. Learners attending the workshop can use it before, during and after to support their learning, and other learners can use it independently for self-teaching. This resource is different from others in a few ways. We place greater emphasis on reproducibility in general workflow. Secondly, there's greater cohesion between sub-modules where later sessions build on skills taught earlier on. And finally, instructors are provided with comprehensive instructions on how to deliver this material effectively, and in that way it targets both learners and instructors. Here I'm showing a screenshot of the Instructions for Instructors page, which describes how participants can be grouped and then helpers assigned to each group. The first sub-module introduces management of R projects, including RStudio and using packages. The second sub-module introduces version control with Git, including how to synchronize Git with GitHub and RStudio. The third sub-module covers data management and wrangling, including some basic functions for data transformation. The fourth sub-module introduces R Markdown as an analytically reproducible document. And the fifth sub-module covers data visualization and plotting. The module includes four teaching methods. Participatory live coding is where participants join with instructors to code along. They actively engage with the material and learn how to troubleshoot. Independent reading involves participants reading through small sections of text to introduce an idea before they work with them. Brief exercises allow participants the opportunity to practice what they've learned. And finally, the group assignment involves participants working in groups of four to five collaboratively to apply their newly learned skills. We've delivered this three times previously as a three to four day workshop where instructors are recent grads or postdoc researchers who have recently learned R themselves, which makes them more relatable to the participants. Participants are PhD and postdoc researchers who are total beginners in R. We've found that a ratio of one instructor to four to six participants works best. Participants complete pre and post workshop surveys and we use their feedback to improve the material. Survey feedback shows that while version control with Git was a bit difficult for some, most participants found the module easy to follow with a good balance between teaching methods and most saw noticeable skill improvements following the workshop. So I'd like to finish by encouraging you to visit, use and share this resource. We'd love for more people to know about it.