FAIR Data Principles in Interdisciplinary Digitalization Research

The interdisciplinary field of digitalization research works with a diverse range of data types, from video and image files to ethnographic data, self-reports, or digital trace data, e.g., from social media. It is very common that different data types are combined within the same study or project. Managing these heterogeneous datasets requires the creation of shared data spaces based on common principles. The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, deeply rooted in an Open Science Framework, provide a framework for creating such data spaces, ensuring that data is not only sustainably organized and documented, but also usable for research(ers). This interactive workshop aims to develop and discuss a FAIR data framework tailored to the needs of interdisciplinary digitalization research. Participants will explore key questions, including:

•    What are the FAIR principles, and why do they benefit interdisciplinary digitalization research?
•    How can new data spaces be created considering the diversity of data types and their unique FAIRness requirements?
•    Which data types are most commonly used in interdisciplinary digitalization research, and how can they be managed sustainably?

The workshop will employ a co-creation approach to collaboratively identify an ideal-typical FAIR process applicable across different types of digitalization research. A guided groupwork session will facilitate the categorization and mapping of data types and their specific FAIR challenges. Participants are encouraged to bring datasets from their own research to discuss opportunities and obstacles in making their data FAIR. Beyond methodological insights, the workshop aims to foster networking and collaboration, helping researchers connect with potential collaborators and identify infrastructure solutions that can help them in achieving or strengthening FAIR data practices. The workshop is designed for a heterogeneous group of participants, and no prior knowledge of data management practices or FAIR data principles is required.  
By the end of the workshop, attendees should have learned about practical strategies for integrating FAIR principles into their research workflows, ensuring that the digitalization research they participate in and its outputs are more transparent, interoperable, and sustainable.