Making Qualitative Data Open Access: Guidance document for making qualitative data FAIR - Findable, Accessible, Interoperable, and Reusable - using the GENNOVATE case study

Muchiri, C., Lopez, D.E. and Kruseman, G. 2024. Making Qualitative Data Open Access: Guidance document for making qualitative data FAIR - Findable, Accessible, Interoperable, and Reusable - using the GENNOVATE case study. CGIAR GENDER Impact Platform Report. Nairobi, Kenya: ILRI.

The effective management and sharing of qualitative data are critical to advancing research, fostering collaboration, and driving impact, especially in the context of addressing complex gender and social dynamics in agrifood systems in Low- and Middle-Income Countries (LMICs). This document outlines guidelines for enabling qualitative data to adhere to the principles of FAIR—Findable, Accessible, Interoperable, and Reusable—while respecting ethical considerations and the sensitivity of such data. The CGIAR Open Access Policy (2013) and the Open and FAIR Data Assets Policy (2021) emphasize that research and development outputs, including data, are international public goods. These policies underscore the importance of disseminating and utilizing data to benefit agrifood system actors in LMICs. Open access or FAIR data enhances the speed, efficiency, efficacy and interdisciplinarity of research, while fostering novel insights and contributing to global knowledge. The need for FAIR qualitative gender research data, that provides deeper insights into lived experiences, gender roles, social norms, power relations, and inequalities, is especially urgent. As highlighted in the FAO Status of Women in Agrifood Systems (2023), closing gender gaps and tackling structural inequalities require high-quality, data disaggregated by gender and intersectional axes of differentiation. Yet, significant gaps persist in the availability of both quantitative and qualitative data. By following the principles and recommendations laid out in these guidelines, researchers can ensure their qualitative data is not only accessible and reusable but also instrumental in driving equitable and sustainable development outcomes in agricultural research for development (AR4D).