SURF Research Day 2026

Beyond “Big” Data: Building Infrastructure for “Thick” Data
2026-05-19 , Mies Bouwman

Does your research rely on interviews, qualitative fieldwork, or creative methods — or do you build digital infrastructure for researchers who do? Either way, you are likely hitting the same gap from opposite sides.

While national and European data strategies race to create infrastructures to securely and efficiently manage "Big Data" (e.g., satellite imagery, sensors, IoT streams), a quieter crisis is unfolding for "Thick Data" (e.g., qualitative insights, narratives, observations, visuals), as it lacks appropriate infrastructure for it to thrive. Thick data is experiential, contextual, and meaning-rich, yet often unstructured, which makes it challenging for existing infrastructure to enable FAIR (Findable, Accessible, Interoperable, Reusable) use. Furthermore, thick data workflow is never neutral - it shapes whose experiences become data, who interprets them, and who gets to reuse them.

In this open discussion, you will explore an interactive dialogue on:
- Infrastructure gaps: What gaps do you face when collecting, storing, analysing, and sharing thick data, and where does the current infrastructure fall short?
- FAIR for thick data: How FAIR principles can be applied to thick data? You will debate how FAIR applies when your data is a sketch, an interview transcript - not a spreadsheet.
- AI tools: how emerging AI tools for transcription, thematic analysis, and pattern recognition are opening new possibilities for thick data research and what risks of bias or loss of nuance come with them.

Join us to take first steps toward a thick data infrastructure — one that treats human experience with the same rigor as numerical data.


What is the nature of your session?: Community With whom do you want to connect?:

Researchers from all backgrounds, Data Stewards, Research Infrastructure Strategiests, program managers, and developers

What is the key take away of your session?:

Clarity on where infrastructure fails thick data, a shared gap map, a richer understanding of FAIR for qualitative research, and first steps toward thick data infrastructure needs.

Abhigyan Singh is an Assistant Professor of Design Anthropology for Social Change at the faculty of Industrial Design Engineering at TU Delft and a Research Fellow at the Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute). His research investigates how energy and climate transitions unfold in everyday life at neighbourhood and community scales across the Global North and Global South. His work centres on fairness, inclusion, and justice; non-market value exchange (reciprocity, gifting, sharing); and he develops hybrid approaches that combine design, digital, computational, and traditional techniques for qualitative field research. His work has been exhibited at Dutch Design Week and recognized with the WWNA Apply Award.

Vanessa Monna is a Postdoctoral Researcher at TU Delft, Department of Human-Centred Design, Co-Designing Social Change section. She is involved in two EU-funded LIFE projects: Hands-On: Learning Processes to build Multi-stakeholder Energy and Climate Assemblies towards a Just and Clean Energy Transition and Irene: Catalysing Inclusive, Representative, Equitable Energy reNovation wavE. The projects regard energy transition and justice, enabling fair and inclusive pathways for systemic change. She is coordinating the Resilient Delta Initiative Tiny Lives Unseen, which explores how low-cost cameras can help people observe insect life in urban environments and how such technologies shape citizens’ engagement with biodiversity.
Before TU Delft, she was a Postdoctoral Fellow at Polimi DESIS Lab. Her PhD focused on Civic Design, and she was a Visiting PhD Scholar at the IIT Institute of Design in Chicago. Vanessa has over ten years of international teaching experience in Design.

Associate Professor in Social - Digital Innovations for Energy transitions and Climate change. Designing for just participation and feedback loops through data-driven narratives as boundary objects