This use case deals with the conversion of data into FAIR digital objects using the ARC DataHub and the AI development platform Agri-Gaia.

In cooperation with:

Background

Objectives

A FAIR Annotated Image Dataset for Potato Viruses:
Provision and dissemination of a publicly usable, FAIR-annotated image dataset on potato viruses to enable insights through AI analysis.
Interoperability between Agri-Gaia and ARC DataHUB:
Establishment of seamless interoperability between the Agri-Gaia AI development platform and the ARC DataHUB for integrated model training and FAIR AI workflow documentation.
Integration and FAIR Data Management:
Progressive transformation of data into FAIR digital objects via the ARC DataHUB, supporting large datasets from diverse providers and connecting with other consortia (DataPLANT, Microbiota, Biodiversity).

Progress & next steps

UC Update: Agri-Gaia meets DataHUB: Connecting Technologies & Communities via a Potato Virus Dataset FAIRification

The newly launched AgDaFAIR use case presents an innovative bridge between the DataPLANT/DataHUB world and the Agri-Gaia platform to significantly strengthen interoperability and FAIR-compliant workflows in agricultural research. The focus is on the use of ARC as a FAIR digital object, which enables both the publication of structured data sets (e.g., drone images of PVY-infected potato plants) and their further processing in Agri-Gaia. Using the Agri-Gaia toolbox, researchers can then develop, train, and publish AI models – supported by Jupyter notebooks and modern dataspace technologies. The use case addresses a practical scenario in plant pathology and, for the first time, combines data collection, FAIR publication, and AI model development in a continuous process. This creates a transferable demonstration example of how FAIR Digital Objects and AI ecosystems can jointly generate added value for agricultural research.