Data cooperatives starter kit: A governance artifact with fuzzy-logic stewardship evaluation

Abstract

Citizen data from participatory sensing, community monitoring, and civic technology initiatives are increasingly relevant for public decision-making and value creation across contexts. However, initiatives that seek to share such data with institutions face a persistent governance challenge: enabling data reuse while ensuring privacy protection, collective control, and accountability. Existing data sharing paradigms, such as Open Government Data (OGD) and emerging federated data spaces, primarily address institutional data exchange and provide limited operational guidance for citizen-led data stewardship, while recent policy frameworks recognize this gap but remain implementation-light. This paper proposes a Data Cooperatives Starter Kit (DC SK), a governance-first artifact designed to support citizen data initiatives in establishing accountable and interoperable data sharing structures, whether founded from scratch or retrofitted onto existing initiatives. Developed using a Design Science Research approach, the starter kit organizes governance into five interdependent packs, covering bylaws, dynamic consent mechanisms, Data Use Agreements, assurance instruments, and quality control, which enable controlled data access while preserving community oversight. To support governance evaluation, we introduce a fuzzy multi-criteria stewardship framework that assesses six dimensions, including data protection, accountability, and interoperability. Using a weakest-link aggregation logic based on a weighted geometric mean, the framework highlights governance vulnerabilities that traditional additive scoring methods may obscure. A worked example and sensitivity analysis show that a critical shortfall in one dimension cannot be masked by strong performance elsewhere. The approach is illustrated through governance scenarios inspired by citizen data initiatives in Switzerland and Greece – two contrasting European governance contexts. The contribution is twofold: for practice – an auditable governance infrastructure that lowers the barriers to trustworthy data sharing; for research – an implementation layer that operationalizes abstract governance principles into a transparent, living accountability mechanism. Together, structured cooperative governance and fuzzy evaluation can strengthen trustworthy citizen data ecosystems and support responsible data reuse in public-sector contexts.