Many provenance databases do not meet FAIR standards.This paper emphasises the need for FAIR provenance data and proposes a method to create structured and FAIR data that can be achieved by non-experts. It also critically discusses why FAIR provenance data may not always be better.

Advanced computational methods in digital humanities have increased demand for text-mining files, including third-party licensed datasets, which present data sharing challenges. This presentation explores navigating these challenges through a case study of a librarian assisting a PhD candidate in sharing licensed research data from various vendors.

The FAIR principles (Wilkinson et al. 2016) are important in sustainable research data management. Applying FAIR assessment tools in a real-world, domain-specific context, we find the overall FAIRness score and ranking roughly comparable between tools, while the individual categories (Findability, Accessibility, Interoperability, Reusablility) vary due to different test collections.

We suggest combining curation with research-driven approaches: (1) digitization and indexing of archival material as well as (2) collection and analysis of underlying meanings and perspectives of the actors through ethnographic methods. This is showcased regarding everyday culture surrounding community function halls with restaurants, central establishments for rural communities.