Una.Resin, WP2, T2.2: Thematic mapping of Research Infrastructures and Resources in the field of Cultural Heritage

The dataset is related to the Horizon 2020 Una.Resin project Work Package 2 mapping exercise (Task 2.2) on Research Infrastructures and Resources (RIRs). This task, carried out from July to October 2021, intended to map RIRs with the aim of providing a landscape analysis of the RIRs available within the Una Europa universities in the field of Cultural Heritage, for analyzing specific barriers and enablers to sharing RIRs across the alliance and for exploring opportunities for collaboration. Building on the Focus Area of Cultural Heritage and linking to the Focus Areas of European Studies and Data Science & AI, the scope of the thematic questionnaire was defined under the thematic challenge “Exploring cultural heritage to strengthen inclusion and participative communities”. A Concept Note, that was developed in collaboration with the Self-Steering Committee Cultural Heritage, defined the main orientations of the questionnaire, thus providing a clear thematic framework for the identification of the RIRs.
Each institution was invited to identify the most important and promising RIRs and provide a maximum of 30 questionnaires. The total number of the RIRs mapped in the field of Cultural Heritage was 113. The target audience consisted of researchers and experts working in the field of the thematic challenge on Cultural Heritage, as defined in the Concept Note, with affinity to RIRs. The members of the Research Infrastructure Cluster acted as institutional contacts of the eight Una Europa universities participating in the Una.Resin project University of Edinburgh (UEDIN), University of Helsinki (UH), University of Bologna (UNIBO), Complutense University of Madrid (UCM), Freie Universität Berlin (FUB), Katholieke Universiteit Leuven (KU Leuven), Jagiellonian University (JU), Université Paris 1 Panthéon-Sorbonne (UP1). They were invited to identify the RIRs and consult the target audience with a methodology that could guarantee the quality of the collected data.