Kontext

The agri-food industry faces numerous challenges dealing with societal, public health, individual nutrition and environmental, food waste and overall food system sustainability challenges. Imbalances and disconnected food markets are generating undesirable trade-offs between the food supply, the consumption patterns, quality of nutrition and the environment. Interoperability and data sharing across agri-food supply networks is limited. Data can revolutionise the food industry and foster its contribution to inclusive and sustainable food systems. Data can be used to assist these stakeholders in making informed decisions on how to operate in a more sustainable and inclusive manner. In this way, they increase the efficiency of the food industry through the optimisation of relevant operations and the reduction of waste, promoting transparency and demonstrate their commitment to ethical and sustainable production. FoodDataQuest will develop ground-breaking data-driven solutions based on an integrated methodological framework that explores new types of private and public data sources, data from unconventional players and non-competitive data and leverages data sharing mechanisms in order to provide the EU food chain stakeholders with increased insights and enhance the transition towards sustainable healthy diets. The proposed framework will include guidelines and data collection strategies, to drive the food system transformation towards inclusive, sustainable, healthy diets within the boundaries of legal and policy frameworks. FoodDataQuest will co-create and test advanced data-driven solutions based on AI and ML algorithms, following a multi-actor approach that will serve as a lighthouse that positively impacts a fair, healthy and environmentally friendly food system. Last, FoodDataQuest will engage citizens into industry's data-driven innovations balancing between data openness and protection of private and sensitive data of multiple stakeholders.

Objectives

SET-UP a methodological framework consisting of innovative methods, state-of-the-art technologies, reliable processes widely accepted and open data standards, and interoperable systems that facilitate data sharing throughout the EU food chains and reduce the fragmentation and complexity of food systems.

ANALYSE the current landscape of data sharing in the entire food chain and examine the use of emerging digital technologies such as Data Analytics, Artificial Intelligence and Machine Learning towards the enhancement of food system sustainability.

DEMONSTRATE the effectiveness of the proposed methodological framework through the co-creation and validation of four (4) use cases with the active engagement of relevant stakeholders from the food chain that will pave the way for a sustainable, fair, healthy, and environmentally friendly food system.

INTRODUCE the project’s results based on digital and data technologies to relevant policy-making organizations and standardization initiatives to foster policy reforms related to inclusive and sustainable food systems and increase the global competitiveness of European data ecosystems.

ENSURE wide communication and scientific dissemination of the project’s outcomes by consolidating international and European links, raising awareness, engaging citizens, enhancing multi-stakeholder cooperation and information-sharing, and ensuring the technology transfer of the project’s results and their rapid uptake.

Activities

The project aims to establish an active, vibrant, and smart community for private data sharing and data-driven solutions in food systems. This community will emerge from the interplay of three core concepts:

1. Multi-actor use cases: Real-life cases will develop data-driven solutions to improve food systems and consumer choices using advanced analytics, forecasting, and AI. These use cases serve as practical demonstrations of how data can drive positive change.
2. Knowledge platform: The project will collect and provide access to cutting-edge knowledge on private data sharing in food systems. It will explore new data types, identify unconventional actors, and highlight the need for data collection, standards, and emerging technologies (e.g., AI, IoT, robotics).
3. Framework for private data sharing: A generic framework will be developed to enable non-competitive data sharing, with principles and good practices for using data to guide consumer choices. It will also cover business models, governance mechanisms, and alignment with existing and emerging legal and policy frameworks.

All activities aim to contribute to sustainable and healthy diets, a climate-neutral circular economy, and a fair, inclusive data economy.

The project will be implemented in three main phases:

1. Analysis and set-up (M1–M6)
This phase involves assessing the current state of data sharing in food systems, identifying trends, challenges, and opportunities, both broadly and within the use cases. Based on these insights and use case needs, the initial framework will be drafted. Use cases will be designed with clear objectives, KPIs, and detailed planning. Stakeholder analysis and early project meetings will lay the foundation for the smart community and dataspace.

2. Development and assessment (M7–M30)
In this phase, the project will develop in-depth knowledge by exploring new data types and engaging unconventional players. Use case communities will support this work. Framework components—such as data standards, techniques, and good practices—will be co-developed and tested through the use cases. Each use case will undergo impact assessments covering sustainability, circularity, fairness, and inclusiveness. Stakeholder consultations will continue building the community and enhancing the dataspace.

3. Consolidation and demonstration (M30–M42)
The final phase consolidates knowledge and the framework through scientific publications and policy recommendations. These outputs will demonstrate a clear vision for overcoming data availability biases. The final framework will serve as a practical guide for future data-driven solutions in food systems. The established data and knowledge space, along with technological components from current use cases, will be designed for reusability. The community will be further expanded through demonstrations, webinars, conferences, and other outreach activities.

By the project’s end, tangible data-driven solutions will have been delivered and a foundation laid for continued development through the established smart community, framework, and knowledge space.