Belonging in Data Science: Designing Inclusive Research Infrastructures
Open Science isn’t just about access — it’s about belonging. This world café workshop explores how to foster inclusive participation in data science, where technical barriers and cultural norms often exclude women and newcomers. Through real-life use cases and rotating group discussions, we will co-design strategies to make research infrastructures more welcoming, equitable, and effective. Bring your experiences, ideas, and curiosity, and help shape a more inclusive data future.
For science to be truly open, people need access not only to resources but also to the confidence to use them — especially in data science, where technical tools are key to sharing data and code under FAIR principles. However, the tech space remains male-dominated, and many women and newcomers feel out of place. This lack of belonging can prevent people from engaging fully.
As staff at the Data Science Centre, we regularly encounter situations where inclusivity is a barrier. We believe research infrastructures like ours can drive change by actively welcoming diverse participation and designing thoughtful interventions. We can share our experience to that end with use cases and we’re eager to learn from the audience: what have you already tried, and which ideas do you consider useful?
We propose a world café format, where we divide participants into small groups and assign them to tables, each representing one of the following use cases:
- A Q&A chat about a computational topic
- A 2 days course on git
- A scientific workflow management system
- A meeting with mixed scientific knowledge
- A newsletter promoting standardisation of practices
The groups work on their use cases following discussion prompts and tasks. Groups will switch tables three times and pick up the work of other groups. This format ensures a collaborative atmosphere and nudges participants to discover new topics.
Together, we aim to identify strategies and best practices for fostering inclusiveness in the Data Sciences.
