Co-production in data centric projects
Data are both the basis of land systems analysis and a primary research output in the form of databases, visualizations, and other data products. Integrating data from diverse sources, formats and disciplines offers insights into multiscale and multifunctional land systems, but also introduces collaboration and knowledge production challenges: from designing data collection protocols to mobilizing data for policy. This presentation showcases co-production strategies within the Agrobiodiversity Information System (SIAgroBD) developed by the National Commission for the Knowledge and Use of Biodiversity (CONABIO) in Mexico, with FAO as the implementing agency and funding from GEF. SIAgroBD supports food security and conservation policies by gathering and providing access to information on native crops in traditional agriculture, and it serves a community of practice working on sustainable food systems. Co-production strategies within this data-centric project included capacity building, participatory tool design, and inclusive data governance. Training workshops on open data principles and FAIR standards empowered contributors to use the data they helped collect and increased their awareness of open data as a collaborative effort, thus extending their involvement beyond the data generation stage. The co-design of digital surveys and the participatory assessment of data collection tools through focus groups ensured the relevance of these tools to local needs. SIAgroBD also piloted the participatory development of data management plans that reflect the collective interests of data donors and protect sensitive information. These co-production strategies acknowledged the range of technical skills among stakeholders, increased the quality of data and ensured that the results were applicable in policy. As land systems research increasingly relies on data-driven methods, integrating diverse perspectives at all stages is essential to achieve more equitable outcomes. The lessons from our case study are relevant for other initiatives aiming to harness data for research and policy.
