Standardization Vs. Preservation? Supporting Interoperability by Enhancing Thematic Metadata at Social Science Archives

The presentation "Standardization Vs. Preservation: Supporting Interoperability by Enhancing Thematic Metadata at Social Science Archives" addresses the challenges of data standardization and interoperability in social science research. Emphasizing the importance of effective metadata practices, the project ONTOLISST aims to study thematic ontologies with the purpose of improving data discoverability and sharing among diverse research infrastructures. The project, funded by the European Commission's Horizon Europe program, investigates the varied approaches to thematic metadata creation across research repositories containing social science survey data. By analyzing metadata structures and curation practices, the research seeks to identify and explain commonalities and discrepancies in metadata schemes that hinder interoperability. The study highlights the need for rich metadata documentation while navigating the complexities arising from competing standards and the diversity of data describing practices. Drawing on data documentation received from the repositories and extensive interviews with data management experts, the project presents two kinds of outcomes: research studies and technical innovation. The results of analysis feed into the development of a semi-automated thematic metadata-generating scheme based on a simplified thesaurus (LiSST). This tool aims to facilitate the integration and accessibility of social science data, fostering connectivity across disciplines and languages. Thus the anticipated outcome is a harmonized metadata structure that upholds the rich, nuanced meanings of original research while promoting discoverability and reuse. By focusing on the balance between standardization and preservation, ONTOLISST affirms that thoughtful approaches to thematic metadata can yield practical solutions to interoperability challenges, ultimately enhancing the usability and visibility of social science datasets in the global research landscape.