FAIR Data Practices for Qualitative Research in Transdisciplinarity

FAIR Data Practices for Qualitative Research in Transdisciplinarity

Category

Explore

Institutions

ETH Zurich

Data type

Qualitative data, interview data

Field

Transdisciplinary

Researchers

Chapman, Mollie, B. Vienni-Baptista (ETHZ), E. Tilley (ETHZ)

Description

The FAIR principles specify best practices for sharing research data (Findable, Accessible, Interoperable, and Reusable). Yet these principles and the associated practices developed around them have originated in a quantitative data context, where most data are easily tabulated and impersonal. Qualitative data presents a suite of thorny challenges (from the practical to the ethical) to the researcher seeking to engage in open science. Important and long traditions in the qualitative social sciences prioritize confidentiality to protect research participants and create an atmosphere of trust. When that data comes from transdisciplinary projects, where the formats for engagement are even more creative, the practical challenges of managing and sharing diverse data formats, from audiovisual, to maps, to artistic outputs, to hard-to-capture experiences from workshops, are only multiplied. Td researchers open up the scientific process to not only input from societal actors but allow that process to even be guided by the needs of the stakeholders they work with. Considering that Td research is, at the heart, a practice of open science, creates a space of reflection, to consider broadly how the data collected should be managed, who owns them, and when, and in what form sharing data might be not only acceptable but even desirable for the project and participants. Our project—FAIRqual—works to map out these challenges and to develop tools for navigating them. While we are targeting our work towards qualitative Td-researchers, we believe that many of our insights about confidentiality, data management and ownership, will have valuable implications for many fields. Specifically, we held a workshop at a major international conference in the Td-field, and used the workshop to prototype novel data practices, inviting participants to co-author a data publication, something most had never heard of before, but were interested to try. In the second phase we plan a series of qualitative interviews with td-experts, a scientific paper, and a variety of outreach activities
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