Machine learning and deep learning are transforming research by enabling data-driven discoveries and predictive modeling. For many researchers, these techniques can unlock new insights from complex datasets, but getting started can feel overwhelming. 

Are you a research staff member working with bioinformatics data? This hands-on training is designed to help you build scalable, reproducible data analysis pipelines using Nextflow. As data volumes grow and analyses become more complex, mastering workflow management tools is essential for ensuring reproducibility and efficiency. In this course, you’ll learn how to develop and run pipelines on your local machine, HPC clusters, and cloud platforms. Starting from the basics, we’ll guide you through building a simple pipeline and progressively enhance it using Nextflow’s modern DSL2 syntax.

The container workshop will give an introduction to Docker & Apptainer, which are great tools for achieving portability and reproducibility in your analysis. You will learn how to use containers and how to build a container from scratch, share it with others, and re-use and modify existing containers. After an extensive explanation of Docker containers, the Apptainer application (formerly Singularity) and its use in HPC will also be highlighted. 

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In this half-day introduction, you will have the opportunity to know a bit more about the structure of the HPC (tiers), what resources you have available, and most importantly, how to use and navigate the HPC. 

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This course is designed for researchers and support staff who are new to version control and want to adopt best practices for managing code and collaborating on scientific projects. As research becomes increasingly computational, using free tools like Git and GitHub is essential for tracking changes, ensuring reproducibility, and working efficiently in teams. In this hands-on session, you will learn how to set up Git, manage your project history, and collaborate using GitHub. 

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Join us to brainstorm the challenges and solutions of data analysis reproducibility. 

You will have the chance to brainstorm, discuss, and use your creativity to solve real-life projects with a group of people. No matter your background or level of expertise, this is a great opportunity to improve critical thinking, problem-solving, and to exchange with experts.

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Effective research data management (RDM) is more than just a compliance requirement - it's a practical tool that simplifies your workflow, strengthens your research, and enhances the impact of your publications. This training is designed to help researchers embed RDM practices into their projects from the very beginning, always keeping the publication process in focus. 

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