International collaboration as a common approach to improve the FAIRness of nonnumeric/qualitative research data

The future of sharing and reusing qualitative research materials will be shaped by technological innovations, ethical considerations, privacy and data security issues, collaborative efforts, and an increased focus on improving the rigor and applicability of findings derived from existing non-numeric/qualitative datasets. Due to the sensitivity and comprehensiveness of the data, the integration of (AI-based) technology in data curation, data sharing and data analysis workflows needs to be carefully addressed, as well as issues of interdisciplinary approaches. Furthermore, researchers from different disciplines will be interested in using qualitative data, and vice versa, qualitative researchers will be interested in using data from other methodological backgrounds that lend themselves to the application of qualitative analysis. In the face of these developments and challenges, collaboration between data infrastructures with expertise in the curation and provision of qualitative data is key. Networks such as QualidataNet, a community-centered federated network of RDCs, seek to share and discuss common solutions for harmonization and standardization where possible. Its focus is to provide a single point of access to qualitative datasets from different data providers and to promote the sharing and reuse of qualitative data in line with the FAIR Data Principles. QualidataNet is part of the Consortium for Social, Behavioral, Educational and Economic Sciences (KonsortSWD) at the National Research Data Infrastructure (NFDI) in Germany. It coordinates the international cooperation within the DDI via the DDI-CDI Subgroup "Non-Numeric, Non-Code Datums", which aims at a standardized and comprehensive metadata description of data objects to enable cross domain integration. We will present the current work, future steps and plans of this international effort: In addition, we would like to continue exploring opportunities for international collaboration and exchange, and invite for participation with the goal of shaping FAIR's ways of processing, archiving, and reusing qualitative data objects.