Submission date: 
21 June 2022

Archaeology is one of the leading proponents of Open Data in the arts and humanities, and already exhibits broad interest in FAIR, but the diversity of data types and methods used by archaeologists means adoption of FAIR will pose significant challenges, further necessitating urgent collaboration around best practice. A recurring theme is the amount of time and effort it takes to do the kinds of work that allows data to be made FAIR, by both the data creators and the repository. Archaeology is an exemplar of why FAIR will be more difficult to implement for Social Sciences and Humanities.

This deliverable attempted to create an assessment workflow to address FAIR data quality, using the ADS as a case study. The qualitative assessments resulted in recommendations for improvement that are fed back to those who advise the data creators (data managers) and at a policy level. This deliverable also includes an automated assessment of the FAIRness of ADS using the F-UJI tool developed by the FAIRsFAIR Project. It was found to be an incredibly useful way to both see where ADS data is not FAIR in the ways it was expected, and for the explicit way in which it specifies the form the tool expects. Just as important is that F-UJI finds the ways where FAIRness can be improved at a technical level and fed back to technical staff.

This deliverable also sought to further contextualise the archaeology case study by synthesising recent, proximal work undertaken in collaboration with ADS that is highly relevant, such as the comprehensive international survey of repository practices undertaken by Geser (2021) for the ARIADNEplus project, and the work of SEADDA Working Group 1: Stewardship of Archaeological Data, and its survey on Digital Archiving in Archaeology: The State of the Art (Richards et al. 2021). Taken together these elements constitute a comprehensive report on opening access to research data in the archaeology domain regarding implementation of the FAIR Principles.

Publication type: 
Deliverable