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Please note that all times are shown in the time zone of the conference. The current conference time is: 14th June 2025, 06:24:03pm WEST
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.
When you cannot begin as you mean to go on: The challenge open data when using third-party licensed text mining datasets
Marcela Isuster1, Alisa Rod2
1McGill Library, Canada; 2McGill Library, Canada
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.
How equal are tests of FAIRness? - A comparative evaluation from a domain-specific perspective
Steffen Pielström, Kerstin Jung, Patrick Helling
University of Würzburg, Germany
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.
Building Digital Archives with Curation-Research-Driven Approaches
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.