Welcome to the FAIRqual project website repository. This website documents our work exploring how to make FAIR data practices an integral part of qualitative data management in transdisciplinary research.
Open research data (ORD) practices are becoming more widespread and are increasingly required by funders, publishers, and institutions. While there is already momentum towards ORD for quantitative data, qualitative data presents unique challenges. Qualitative data is more difficult to process and make openly available, and ethical norms require confidentiality of research subjects - with interview transcripts, workshops, or other types of qualitative data sometimes difficult or impossible to anonymize.
However, sharing qualitative data can be valuable. In transdisciplinary (Td) research, where new forms of engagement between science and society are central to co-producing problem framings and project outcomes, sharing this data could allow for improved learning between Td processes and increased engagement between science and society.
The FAIRqual project approaches this challenge from multiple angles:
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Exploration: We explore different facets of applying FAIR principles to qualitative data in Td research through workshops and expert interviews with Td researchers, information scientists, data authority experts, and ethicists.
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Technical Development: We develop workflows and prototypes to facilitate the technical aspects of sharing qualitative data.
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Knowledge Sharing: We create guidelines and case studies demonstrating how to integrate FAIR principles into qualitative data management in Td research.
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Community Building: We build a community of practice around FAIR qualitative data in Td research through networking and educational events such as workshops and webinars.
The FAIR Principles (Findable, Accessible, Interoperable, and Reusable) promote transparent description of dataset content, location, and metadata. Importantly, FAIR does not require that data be made available to everyone. This flexibility is crucial for qualitative data, allowing researchers to decide how much of a dataset is publicly available and to control who can access it and for what purpose.
The FAIRqual project is funded by the Open Research Data Program of the ETH Board and carried out by researchers from Tdlab and the Global Health Engineering group at ETH Zurich.
This website includes:
- Blog: Updates on project activities, conference participation, and reflections
- Events: Information about workshops, webinars, and presentations
- Slides: Presentations from conferences and talks
- Proposal: The full project proposal with detailed information about our work
We welcome engagement from researchers, practitioners, and anyone interested in FAIR data practices for qualitative research.
- Visit our website: fairqual.github.io/website
- Email us: mollie.chapman@usys.ethz.ch
- Follow our work: GitHub
If you notice any issues with the website or have suggestions, please open an issue on this repository.
The site is built with Quarto and served by GitHub Pages from the gh-pages branch. After changes are merged into main, publish from main:
quarto publish gh-pagesThe former address fairqual.org is a redirect on Netlify that sends every path to the GitHub Pages site.
The content of this website (text, images, slides, and documents by the FAIRqual team) is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). See LICENSE.md. Material by third parties keeps its own licence.