Linked Open Usable Data for cultural heritage: perspectives on community practices and semantic interoperability

Digital technologies have fundamentally transformed the ways in which Cultural Heritage (CH) collections are accessed and engaged with. Linked Open Usable Data (LOUD) specifications, including the International Image Interoperability Framework (IIIF) Presentation API 3.0, Linked Art, and the Web Annotation Data Model (WADM), have emerged as web standards to facilitate the description and dissemination of these valuable resources. Despite the widespread adoption of IIIF, the implementation of LOUD specifications, especially in combination, remains challenging. This is particularly true in the development and assessment of infrastructures, or sites of assemblage, that support these standards.
The research is guided by two perspectives, namely community practices and semantic interoperability. The former aims to assess how organisations, individuals, and apparatuses engage with and contribute to the consensus-making processes surrounding LOUD. By examining community practices, the social fabrics of the LOUD ecosystem can be better understood. The latter perspective explores how data can be made meaningful to machines in a standardised and interoperable approach that promotes the exchange of well-formed information. This work is grounded as part of the SNSF-funded research project Participatory Knowledge Practices in Analogue and Digital Image Archives (PIA) (2021-2025), which aims to develop a citizen science platform around three photographic collections from the Cultural Anthropology Switzerland (CAS) archives. The theoretical framework is firmly rooted in Actor-Network Theory (ANT), as one of the aims of the thesis is to describe the collaborative structures of the LOUD ecosystem and to highlight the importance of non-human actors.
Beyond the implementation of LOUD standards within the PIA research project, the empirical research includes an analysis of the social fabrics of the IIIF and Linked Art communities, as well as an investigation of LUX, Yale Collections Discovery platform. The research aims to identify the socio-technical requirements for developing specifications consistent with LOUD design principles. It also seeks to understand how the implementation of LOUD standards within PIA contributes to recognising their potential benefits and limitations in terms of facilitating data reuse and wider participation. The thesis further examines the implementation strategies, challenges, and outcomes of Yale University’s large-scale deployment of LOUD standards, focusing on how ensuring the consistency of Linked Art and IIIF resources within the LUX platform contributes to the CH domain.
The core methodology of this thesis is an actor- and practice-centred inquiry, focusing on the detailed examination of specific cosmologies within LOUD-driven communities, PIA, and LUX. This approach aims to unravel the intricate web of cultural processes and constellations through a micro-perspective, rich in empirical evidence.
Key empirical findings indicate that LOUD improves the discoverability and integration of data in CH, which requires community-driven consensus on model interoperability. Significant challenges include engaging marginalised groups, sustaining long-term engagement, and balancing technological and social considerations. The strategic use of technology and the capture of digital materiality are critical, yet LOUD presents challenges in terms of resource investment, data consistency, and the wider implementation of complex patterns.
LOUD should clearly lead efforts to improve the accessibility and usability of CH data. The community-driven methodology of IIIF and Linked Art inherently promotes collaboration and transparency, making these standards critical tools in the ongoing evolution of data management. Even for projects and institutions that do not adopt these specifications, LOUD's socio-technical practices provide crucial insights into effective digital stewardship and community engagement strategies.