Periodic Reporting for period 1 - FoodDataQuest (On a Quest for Data-Driven Innovations to fuel Sustainable Food Systems)
Reporting period: 2024-02-01 to 2025-10-31
Europe’s food systems generate vast amounts of data across the entire value chain, yet much of this data remains fragmented or underused due to technical, legal, and trust-related barriers. At the same time, artificial intelligence (AI) and machine learning (ML) offer strong potential to improve sustainability, transparency, efficiency, and healthy dietary choices. FoodDataQuest addresses this challenge by exploring how conventional and unconventional datasets can be responsibly integrated and shared across food value chains, while respecting privacy, ethics, and regulatory requirements.
The project’s overall objective is to develop and validate a trusted framework for AI-enabled data integration in food systems. It combines technical design with social, economic, legal, and ethical analysis to ensure data sharing is technically feasible, socially acceptable, economically viable, and policy-aligned. Social sciences and humanities play a central role by analysing stakeholder needs, trust perceptions, governance models, ethical issues, and behavioural aspects, which are embedded into AI solution design.
FoodDataQuest operationalises this approach through four complementary use cases: (1) a DNA-based, AI-driven system for food sustainability and traceability; (2) data-driven optimisation of food services in hospitals and elderly care homes; (3) sustainability insights for a healthy and climate-neutral food chain; and (4) AI-enabled communication tools to enhance consumer awareness of sustainable choices. These use cases test the framework in real-world contexts and generate actionable insights.
In terms of impact, FoodDataQuest contributes to a key EU priority: integrating AI and data spaces across food value chains. The project delivers a reusable framework and blueprint, while the use cases demonstrate how AI-driven data integration can reduce fraud risks, improve sustainability performance, support healthier diets, and empower stakeholders. Through this combined approach, FoodDataQuest supports Europe’s transition toward more sustainable, transparent, and data-enabled food systems and informs policy, standardisation, and future innovation initiatives.
The project’s overall objective is to develop and validate a trusted framework for AI-enabled data integration in food systems. It combines technical design with social, economic, legal, and ethical analysis to ensure data sharing is technically feasible, socially acceptable, economically viable, and policy-aligned. Social sciences and humanities play a central role by analysing stakeholder needs, trust perceptions, governance models, ethical issues, and behavioural aspects, which are embedded into AI solution design.
FoodDataQuest operationalises this approach through four complementary use cases: (1) a DNA-based, AI-driven system for food sustainability and traceability; (2) data-driven optimisation of food services in hospitals and elderly care homes; (3) sustainability insights for a healthy and climate-neutral food chain; and (4) AI-enabled communication tools to enhance consumer awareness of sustainable choices. These use cases test the framework in real-world contexts and generate actionable insights.
In terms of impact, FoodDataQuest contributes to a key EU priority: integrating AI and data spaces across food value chains. The project delivers a reusable framework and blueprint, while the use cases demonstrate how AI-driven data integration can reduce fraud risks, improve sustainability performance, support healthier diets, and empower stakeholders. Through this combined approach, FoodDataQuest supports Europe’s transition toward more sustainable, transparent, and data-enabled food systems and informs policy, standardisation, and future innovation initiatives.
By Month 21 (M21), FoodDataQuest has completed substantial technical and scientific work to design and validate AI-enabled data integration and sharing across food value chains, progressing from analysis to framework, architecture, and use-case development.
The project analysed the agri-food data-sharing landscape through literature reviews, expert interviews, and large-scale consumer surveys, generating evidence on key drivers, barriers, incentives, and trust dynamics related to private and unconventional data sharing. These insights informed the technical design.
An integrated framework for secure and trusted data sharing was developed and applied across all four use cases, and operationalised through a Reference Architecture and Data Model aligned with major European Data Space initiatives (e.g. GAIA-X, IDSA, DSSC), embedding privacy-by-design, data sovereignty, and interoperability principles.
In parallel, the four use cases advanced to structured technical design, covering traceability and fraud prevention, healthcare food services, sustainability decision support, and AI-enabled consumer communication. Conventional and unconventional datasets were integrated, with cross-cutting challenges such as data quality, interoperability, and ethical compliance addressed throughout.
Overall, by M21 the project has delivered key foundational results—a validated framework, an EU-aligned reference architecture, empirical insights into data-sharing practices, and four advancing use cases—preparing the project for piloting and validation in the second half of implementation.
The project analysed the agri-food data-sharing landscape through literature reviews, expert interviews, and large-scale consumer surveys, generating evidence on key drivers, barriers, incentives, and trust dynamics related to private and unconventional data sharing. These insights informed the technical design.
An integrated framework for secure and trusted data sharing was developed and applied across all four use cases, and operationalised through a Reference Architecture and Data Model aligned with major European Data Space initiatives (e.g. GAIA-X, IDSA, DSSC), embedding privacy-by-design, data sovereignty, and interoperability principles.
In parallel, the four use cases advanced to structured technical design, covering traceability and fraud prevention, healthcare food services, sustainability decision support, and AI-enabled consumer communication. Conventional and unconventional datasets were integrated, with cross-cutting challenges such as data quality, interoperability, and ethical compliance addressed throughout.
Overall, by M21 the project has delivered key foundational results—a validated framework, an EU-aligned reference architecture, empirical insights into data-sharing practices, and four advancing use cases—preparing the project for piloting and validation in the second half of implementation.
FoodDataQuest advances the state of the art by addressing how AI and machine learning can be integrated with heterogeneous, private, and unconventional data across food value chains in a trustworthy and interoperable way. Instead of a single technical solution, the project delivers a comprehensive framework combining technical architecture, governance, policy analysis, consumer insights, and real-world validation.
A key innovation is the integrated framework that jointly addresses food and data value chains, linking value creation, governance, ethics, interoperability, AI readiness, and business models. This is operationalised through a reference architecture aligned with major European Data Space initiatives (GAIA-X, IDSA, DSSC), enabling reuse and scalability.
The project further advances the field through four complementary use cases, delivering exploitable results such as AI-enabled traceability, data-driven optimisation of food services, sustainability decision support, and personalised consumer communication. These use cases integrate private and unconventional datasets and embed legal and ethical considerations directly into design.
Finally, large-scale interdisciplinary consumer research provides EU-level evidence on trust, transparency, and societal acceptance of AI and data sharing. Together, these results demonstrate how AI-driven data integration can be responsibly embedded in food systems at a level of integration not previously achieved.
A key innovation is the integrated framework that jointly addresses food and data value chains, linking value creation, governance, ethics, interoperability, AI readiness, and business models. This is operationalised through a reference architecture aligned with major European Data Space initiatives (GAIA-X, IDSA, DSSC), enabling reuse and scalability.
The project further advances the field through four complementary use cases, delivering exploitable results such as AI-enabled traceability, data-driven optimisation of food services, sustainability decision support, and personalised consumer communication. These use cases integrate private and unconventional datasets and embed legal and ethical considerations directly into design.
Finally, large-scale interdisciplinary consumer research provides EU-level evidence on trust, transparency, and societal acceptance of AI and data sharing. Together, these results demonstrate how AI-driven data integration can be responsibly embedded in food systems at a level of integration not previously achieved.