Towards reusable qualitative data: lessons from the QualiFAIR project
Creators: name: Bochynska, Agata
name: Christiansen, Torgeir
name: Dikova, Maria
name: Klette, Kirsti
Publisher: name: Zenodo
Year: 2023
Subjects: Research Data Management, FAIR Data

description: Qualitative and context-sensitive data are, as the term(s) suggest, contextual, here-and-now specific and often person-identifying. This raises a number of problems for reuse and multiple uses of these data and creates barriers to transparency and reproducibility of qualitative research. Specifically, sharing and reuse of qualitative data is largely limited due to privacy and copyright regulations. Multiple uses of some qualitative data are also limited because of data’s context-sensitivity – the unique, authentic here-and-now character tied to the specific research situation. Additionally, while in the recent years quantitative data has been made increasingly FAIR (Findable, Accessible, Interoperable, Reusable) across disciplines, data management and archiving procedures for qualitative data are still in their infancy. Finally, researchers and students who work with qualitative research are still not oriented towards data sharing and reuse and not trained to practice open qualitative research. To tackle these issues, we have started a QualiFAIR project as a hub-node infrastructure at the University of Oslo in Norway. The project focuses on making qualitative and context-sensitive data more FAIR as well as raising awareness about both the need for sharing and reuse of qualitative data as well as its possible limitations. QualiFAIR is organized into five thematic areas: 1) Ethics and privacy, 2) Copyright, 3) Data management, 4) Infrastructure and 5) Metadata. Each area has responsible groups that drive the work in the hub. Working groups are assembled from academic, technical and administrative staff at the university, comprising of researchers, engineers, librarians and research administrators from a number of disciplines, including anthropology, political science, medicine, linguistics, psychology, music research, theology and education. In this way, QualiFAIR’s efforts are truly interdisciplinary, and project’s outputs are to serve qualitative research community across fields and levels of expertise. In this lightning talk, we briefly describe the project aims and structure and share main lessons learned in the process of helping to make qualitative data more FAIR that can be of use not only for qualitative researchers internationally, but also open science community more broadly. Presented lessons focus on five main areas: 1) Building a network of diverse actors involved with qualitative research across disciplines; 2) Developing skills in qualitative data sharing and reuse through seminars and workshops; 3) Creating routines, procedures and concrete instructions for making qualitative data more FAIR; 4) Involving researchers and their own projects as case studies for testing new solutions for qualitative data reuse, and 5) Working with stakeholders to move towards new policies and national solutions for qualitative data sharing and reuse. Based on the presented lessons from the project, we indicate future directions for the efforts focusing on making qualitative data more reusable.