Turn your data into interactive, user-friendly apps with the R package Shiny. This Data Snack demonstrates how Shiny makes research more transparent and reproducible, while also building practical skills in data science and software use. See why it’s a powerful tool not just for analysis, but also for teaching, collaboration, and long-term research training.

Discover how real research data can enrich university teaching. This Data Snack explores how integrating data into the classroom strengthens students’ data literacy, supports research-based learning, and offers new opportunities for teachers to connect teaching with their own research. Join us for insights, examples, and discussion on the benefits, challenges, and strategies of teaching with data.

Discover how real research data can enrich university teaching. This Data Snack explores how integrating data into the classroom strengthens students’ data literacy, supports research-based learning, and offers new opportunities for teachers to connect teaching with their own research. Join us for insights, examples, and discussion on the benefits, challenges, and strategies of teaching with data.

This Data Snack explores how current approaches often overlook diverse cognitive needs – and shows how more inclusive practices can make RDM training clearer, more effective, and accessible to everyone. Join to learn practical strategies, common pitfalls, and ways to build a research culture where all minds can engage meaningfully with data.

Ready to share your code? This Data Snack walks you through the essential steps for preparing your software for publication – from what to include in a repository to choosing the right license. Learn how to make your code sustainable, reusable, and citable – so others can build on your work. Perfect for beginners, with useful takeaways even for experienced developers.

AI is changing qualitative research – but not just through ChatGPT. This Data Snack introduces practical, open-source tools that can support time-intensive tasks like transcription and analysis, while also raising critical questions about data protection, bias, and methodological transparency. Join to discover what’s possible, what to watch out for, and how AI can (and can’t) assist in qualitative research.