General RDM and FAIR Principles in Agroecology

KNOWLEDGE HUB
 

Why data management matters in agroecology 

Effective Research Data Management (RDM) is crucial in agro-ecological research to ensure that data are organized, documented, preserved, and shared in ways that maximize their value and reusability. Given the complexity and heterogeneity of agroecological data—including field observations, genomic data, remote sensing, and socio-economic datasets—proper data management practices are essential for enabling interdisciplinary collaboration and long-term accessibility.
In line with the principles of Open Science, RDM practices in agro-ecology are guided by the FAIR principles: Findable, Accessible, Interoperable, and Reusable. These principles ensure that data are properly described and easily discoverable, accessible under clear usage conditions, integrated with other datasets, and reusable for future research. By adopting FAIR principles, agro-ecological research can enhance transparency, reproducibility, and innovation.

• Findable: Ensure datasets are properly indexed and searchable through the use of standardized metadata and persistent identifiers (e.g., DOIs).
• Accessible: Store data in open-access repositories with clear access policies and licenses, supporting transparent data sharing.
• Interoperable: Use standardized formats and controlled vocabularies (e.g., AgroPortal ontologies) to enhance data integration and compatibility across platforms.
• Reusable: Provide detailed metadata documentation, including provenance and context, to support data interpretation and reuse in future research.