AgData — EU Partnership on Agriculture of Data

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UC 30 — Culture2FAIR

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Key Information
Title
Upscale research data literacy and management know-how and technology adaptation under ´real-life` conditions tailored to community needs
Acronym
Culture2FAIR
Coordinator
Leibniz Institute for Agricultural Engineering and Bioeconomy (Germany)Research Institute
Duration
24 months
Budget
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Maturity
Committed
Source
In Proposal
Use Case Summit Pitch
Federated Data Space, Interoperability & Governance
Sectors
Arable cropOther
Data Types
Machine sensor dataEarth observation dataTest and experimental facilities (TEF research) data
SRIA Activities

3.1.2 Data integration and data quality

  • 3.1.2.2 Link databases & computing capacities
  • 3.1.2.5 Standardized metadata scheme & ontologies
  • 3.1.2.6 Boost data re-usability through quality control

3.2.2 Farm modelling systems

  • 3.2.2.1 Take stock of existing modelling approaches
  • 3.2.2.2 Novel forecasting & prediction methodologies
  • 3.2.2.3 Whole-farm & landscape environmental impact

4.2 Data governance, standards and security (R&I activities)

  • 4.2.4.1 Stock-take existing data ecosystems

4.3 Uptake & innovation management (R&I activities)

  • 4.3.1.5 Capacity building & data literacy
Themes
Other / data-quality / infrastructure
Partners (1)
  • Leibniz Institute for Agricultural Engineering and BioeconomyP1
    Coordinator

    GermanyResearch Institute

#OrganisationCountryType
P1Leibniz Institute for Agricultural Engineering and BioeconomyCoordinatorGermanyResearch Institute

Introduction This Use Case builds on FAIRagro, the consortium for agrosystem sciences within the German National Research Data Infrastructure (NFDI). FAIRagro brings together more than 30 partners from agricultural research institutions, universities, and infrastructure providers to build a FAIR research data management landscape for the agrosystems research community in Germany. Culture2FAIR focuses on the cultural change dimension. It strengthens research data literacy, FAIR RDM know-how and the adoption of data technologies under real-life research conditions tailored to community needs. Methodologies Culture2FAIR understands FAIR RDM as a socio-technical endeavour and builds on Nosek’s strategy for cultural change. Infrastructures, services and support make FAIR practices possible and easier to apply; incentives, institutional anchoring and policy conditions help make them rewarding and required. Culture2FAIR focuses on the normative level: through participatory and co- creative approaches, it aims to embed FAIR RDM as a shared and everyday practice in agricultural research. By Brian Nosek ´Strategy for Culture Change` (2019) Outputs Culture2FAIR will provide concepts, blueprints and best practices to drive cultural change, including: Cultural • Change Toolkit: modular measures, templates and engagement formats for institutions and communities to lower barriers and support behavioural change towards FAIR practices. • Roadmap for Cross-Sector Data Reuse: guidance for scaling reuse of agricultural research data across research, public and private sectors, including standards, governance and incentive aspects. • Policy Brief “Knowledge Grows When Data Is Shared”: recommendations for strengthening open and FAIR agricultural research data across institutions, domains and national borders. Impact Culture2FAIR helps bridge the gap between engagement, guidance and implementation by translating shared FAIR values into practical, community-owned RDM practices. Building on trust, participation and active responsibility, the Use Case strengthens the conditions under which FAIR RDM can become part of everyday research routines rather than remaining an external requirement. In this way, Culture2FAIR supports a shift from isolated tools and support offers towards sustainable FAIR data practices that are embedded in institutions, communities and agricultural research workflows. What is needed Cultural change strategies and impact assessment: experiences with strategies that drive cultural change, including KPIs to monitor and evaluate their impact. Cross-sector data reuse requirements: lessons learned from national and international cross-sector data sharing projects and initiatives. What can be offered • FAIR RDM and cultural change expertise: stakeholder engagement, capacity building and participatory approaches to translate community needs into practice. • Reusable and saleable FAIR implementation concepts: toolkit elements, blueprints and benchmarks for service-community integration and institutional embedding. Sennhenn, A., Anderson, J.M., Boße, S., Hoffmann, C., Kirchgessner, O., König, M., Leroy, B., Lindstädt, B., Mazón, E.R., Sahwan, W., Schmidt, M., Singson, L.S., Svoboda, N., Vedder, L., Stahl, U., 2026. The community engagement and empowerment cycle: FAIRagro’s framework to foster cultural change towards FAIR RDM practices in agrosystem science and beyond. J. Integr. Bioinform. https://www.degruyterbrill.com/document/doi/10.1515/jib-2025-0049/html

Source: Annual AgData Use Case Summit 2026 booklet.

Models & Macro data
Statistics Registers data
Other
  • 4.3.1.6 Training in advanced digital skills (DEP)
  • 4.3.1.11 Promote open science
  • 4.3.1.12 Communication, brokerage & events