How can universities safeguard quality in times of war and rapid technological disruption? This roundtable brings together academic leaders and policy experts from Ukraine and Ghent University to explore strategies for maintaining excellence amid crisis and the rise of Generative AI.

The programme features unique insights on institutional resilience, pedagogical transformation in the AI era, and innovative Quality Assurance approaches addressing both war-driven disruption in Ukraine and AI-driven challenges in Belgium.

The event concludes with a forward-looking panel discussion on future-proofing higher education, ethics, risks, and new opportunities for collaborattion.

The scientific method is historically linked to the possibility that other researchers can replicate and verify its results. As scientific analysis becomes more complex and interdisciplinary, ensuring reproducibility becomes more challenging specially in fields that combine different expertise.

To promote transparency, consistency and robustness in science, journals, funders, and institutions are encouraging the use of tools and practices that enhance reproducibility. Lifelong learning helps professionals to keep up with the fast-paced scientific developments and to foster creativity and innovation.

By learning about version control, containers, pipelines and data reproducibility, scientists of all levels can improve the reproducibility of their research, as well as the impact and reliability of their findings and methods. Moreover, using the methods introduced here they can collaborate and experiment in ways that allow reproducibility and creativity to coexist and thrive.

This course provides a practical introduction to R, a powerful and flexible environment for statistical computing and data visualization. Participants learn how to work with R’s interface, use the command line, and understand key data structures.

The course covers essential data management tasks such as importing and exporting data, creating variables, subsetting, merging, and reshaping datasets. You will explore data using common statistical and graphical techniques, including histograms, boxplots, scatterplots, correlations, and summary statistics.

By the end, participants will be able to perform basic analyses, manage data efficiently, and automate routine procedures, gaining a solid foundation for further work in data science and statistics.

This course introduces participants to key challenges in survey research, focusing on representation and measurement errors. Using real‑world examples, we explore questions about sampling, question wording, respondent behaviour, and data quality.

Day one covers representation errors, including probability sampling, complex sample designs, unequal selection probabilities, and the impact of nonresponse on estimates.

Day two focuses on measurement errors such as recall problems, social desirability, validity, and reliability, and examines how these issues affect comparisons across groups or languages.

Throughout the 2.5‑day programme, participants work on a small survey analysis project, presented on the final day. 

The course offers a clear overview of the main issues in survey design and analysis, supported by practical exercises and pointers to further reading and software tools.