Introduction
The FoodDataQuest (FDQ) project aims to enhance the efficiency, transparency, and sustainability of food systems by developing data-driven innovations. However, the agricultural sector often struggles with barriers to data sharing, particularly due to privacy, data sovereignty, and interoperability issues.
To address these, FDQ focuses on collaborative, multistakeholder approaches to leverage data across the whole food value chain — from production and processing to distribution and consumption — while honoring legal, ethical, and trust requirements.
The main goals of FDQ include:
- Designing a framework for trustworthy and scalable data sharing in the agri-food sector.
- Determining data provenance and collection mechanisms to maximize interoperability across stakeholders.
- Defining guiding principles for trustworthy data sharing to enable collaboration while preserving data control and protecting sensitive information.
This framework will be implemented and tested in 4 use cases, which collectively reflect a range of real-world scenarios in the food chain (olive oil, hospital food service, farm-to-fork, and consumer awareness).
The framework will serve as a basis for developing data-sharing strategies, guidelines, and tools that can foster collaboration, boost transparency, reduce waste, and empower consumers to make more sustainable choices — all while honoring stakeholders’ preferences and legal requirements.
Furthermore, this task will connect the policy, legal, and technical dimensions of data sharing and lay the foundation for subsequent tasks in the project.
Methodology
This section introduces the methodology that was used to develop the Framework and the Toolset. To start with, a literature review was carried out on data sharing in agri-food, and some reference models that have been used in this context were summarized. However, the main focus in this chapter is on the definition of the framework and the development of key components. After the framework and tool were set up, the data was collected from the use cases (UCs) by applying the T2.1 tool. Task 2.1 reviews and assesses the data collected in the agri-food system value chain.
Because the word “framework” is used in different contexts and with different meanings, we first define what a framework means in this project. Next, we introduce the method of developing the framework.
Last but not least, a glossary is provided to clarify terminology and avoid potential confusion arising from different backgrounds and disciplines. This glossary aims to guide constructive discussions among stakeholders, not only about the framework but also about the project as a whole.