In the last decades, participatory qualitative and transdisciplinary (Td) research are on the rise. Td research allows for co-production of knowledge between societal and scientific actors in order to tackle sustainability challenges. This turn into more participatory and dialogical research settings has strong implications for qualitative methods, and therefore for the data produced. Data is no longer collected and/or analyzed solely by the researcher, but an active role from practitioners and other societal actors is expected. How to then deal with accessibility and management of this data and secure a fair and equitable access to it? In this way, transdisciplinary research implies a new frontier to qualitative methods and data as it poses new questions on who has the power to create and manage such data, who is entitled to access it and how.
Currently, many transdisciplinary researchers using qualitative methods face a quandary: Standard research ethics practices dictate that all human subjects’ data should be strictly confidential. Yet increasing demand from journals and even in cases from the project participants themselves, suggests that in many cases, the sharing of qualitative data could be valuable. While Td research involves many different methods and scientific disciplines, qualitative research often plays a central role—either to facilitate the Td process or as part of the scientific study itself. Sharing of data could allow important learning and insights to be more broadly shared and create the opportunity to compare across cases and over time. Beyond this, there may be cases where Td projects themselves would benefit from greater sharing of data, e.g., to build support for a new initiative or to increase the possibility of participation from diverse stakeholders.
The promise of confidentiality creates an atmosphere of trust, allowing the researcher to obtain an authentic and deep understanding of the research topics. Yet we are curious about the necessary ubiquity of the principle for all qualitative research. There are cases for semi-structured interviews where anonymisation of participants is possible, and the interview content itself does not contain sensitive information. Or there may be cases where participants want to be named and have their perspectives openly shared. In these cases, the research data can be made available for other researchers to use. This is a common practice in quantitative research where data is made available in a data repository such as Zenodo.
In the same fashion, transdisciplinary research might have greater societal impact and collaborative potential if FAIR (Findable, Accessible, Interoperable, and Resuable) principles are clearly and systematically integrated in processes in which qualitative methods are applied. FAIR principles are applied to both human-driven and machine-driven activities, however they emphasize machine-actionality to allow computational systems to find, access, interoperate, and use data with minimal human intervention. Confidentiality of the data itself does not imply that FAIR principles cannot be achieved, as metadata can still be published under FAIR principles. Some of the principles can be achieved with low effort, for example, principle F1: (Meta)data are assigned globally unique and persistent identifiers. This solely requires the generation of a Digital Object Identifier (DOI) by a trusted provider. Other principles require more effort, as community standards need to be identified or may not exist. For example, F2: Data are described with rich metadata, which in the context of qualitative research need to be be generous and extensive, including descriptive information about the context, quality and condition, or characteristics of the data. This principle focuses on the human-driven activity of discovering data and learning about how it was generated. In this workshop, we will begin a dialoge on the conditions under which qualitative data collected as part of Td processes can or should be shared following FAIR principles; consider best practices for doing so; and begin to build a community of practice for the sharing of data within the Td field.
Our workshop addresses all three conference themes, though with an emphasis on the first theme of enhancing the theoretical foundations of ITD. Within this our workshop considers the entanglement of knowledge and technology by examining tricky questions about sharing of qualitative data in online repositories and the risks and benefits for Td research. With the growing importance of large language models, qualitative data may become more accessible to re-use. In this context we seek to harness experiences and knowledge to develop guidelines and practices for navigating this new terrain. At the same time, we see our workshop and larger project as growing capacity for ITD both by providing guidance to an emerging challenge and by growing collaborative networks, connecting ITD and open science communities. We also see educational potential for our workshop, such as a longer-term goal to create digital tools to help train and guide ITD education on how to navigate the tricky questions of open science and data management.
Description of the workshop design:
90 minutes
We will base the workshop on an experiential component by working with case studies from the participants’ tricky and/or challenging experiences. For each case, we will show the problem and then decide on options to cope. A key outcome will be adapting the FAIR principles for qualitative data in Td research. An additional activity will be to consider ways to re-use such data. We will provide participants with information on qualitative datasets and identify what kinds of metadata (in other words, what key contextual information) would be needed to re-use the data. To achieve these aims, we will facilitate an interactive dialogue about the challenge and potential of sharing qualitative data from Td research. We will use the td-net toolbox (e.g. tell your story by means of an object; soft systems methodology) to share participants’ experiences and ideas, to identify the main challenges and to develop ways to address them.
We will use the cases and participants experiences as the basis to discuss questions such as:
1. Under what conditions does sharing of qualitative data generated during Td research provide benefits for a) research participants, b) the Td project itself, or c) the Td community?
2. What are the risks of sharing Td data openly? How can these be mitigated?
3. What kinds of concerns or benefits are specific to particular kinds of data? In other words, how is interview data different from workshop data?
The proposed agenda for the workshop will be: 15 minute to introduce open science and FAIR princples, providing some examples; 40 minutes activity to work through case studies of tricky experiences; 20 minutes activity to evaluate contextual (metadata) for re-use of qualtiative data; 15 minutes for knowledge harvesting and wrap-up. The results of this workshop will be shared with participants and feed into a larger project to develop guidelines for how and when to share and re-use qualitative data collected as part of Td research.
Key readings:
Alexander, Steven M., Kristal Jones, Nathan James Bennett, Amber Budden, Michael Cox, Mercamp x000E8 Crosas, Edward T. Game, et al. “Qualitative Data Sharing and Synthesis for Sustainability Science.” Nature Sustainability, November 13, 2019, 1–8. https://doi.org/10.1038/s41893-019-0434-8.
Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, et al. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3, no. 1 (March 15, 2016): 160018. https://doi.org/10.1038/sdata.2016.18.
