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.

The overall objective of HOLiFOOD is to improve the integrated food safety risk analysis (RA) framework in Europe to i) meet future challenges arising from Green Deal policy driven transitions in particular in relation to climate driven changes, ii) contribute to the UN's Sustainable Development Goals and iii) support the realization of a truly secure and sustainable food production. HOLiFOOD will apply a system approach, which take the whole environment into account in which food is being produced, including economic, environmental and social aspects. Three supply chains will be considered (i.e. cereals [maize], legumes [lentils] and poultry [chicken]). Artificial Intelligence (AI) and big data technologies will be used in the development of early warning and emerging risks prediction systems for known and unknown food safety hazards. In addition, tools, methods and approaches will be developed for hazard detection and will be targeted and non-targeted and new holistic risk assessment methods will be develop in which food safety risk will be embedded in a comprehensive cost-benefit analysis of the food system including positive and negative health, environment and economic dimensions. An effective impact pathway will be developed and implemented through integration of the HOLiFOOD outputs into the legal framework associated with the food risk analysis process. The impact pathway will be supported by an electronic data and knowledge sharing platform aiming at the full digitalization of food (safety) systems and supporting transparency and impact for all stakeholders. In order to align with stakeholder priorities, preferences and user requirements, the HOLiFOOD innovations will be designed and tested in a multi actor approach (i.e. Living Lab) involving all stakeholders (e.g., authorities, food producers and citizens).

Our aim is to revolutionise the combat against food fraud and enhance transparency in our food supply chains. How? By establishing and mobilising The European Food Fraud Community of Practice (EFF-CoP), a sustainable Research and Innovation eco-system of food fraud researchers and end-users based on solid sociological principles. We have gathered a diverse group of stakeholders in our consortium, scientists, regulators, large and small-sized industry, the organic sector, existing networks with >4200 members, and more to lay the groundwork. They are catalysers to reach out rapidly to a large number of prospective EFF-CoP members. That is only the beginning. We will set out to understand the needs of the various stakeholder groups and the network structure to prioritise content and enhance information flows, and we set up the EFF-Hub as our virtual headquarters, an interactive collaborative space. From there, series of activities will be rolled out – sharing existing research and innovation resources and educational materials, developing new ones – Good Practice Recommendations, factsheets, and case study materials, and we will host engaging virtual and live events to facilitate interactive collaboration. These events involve food fraud festivals, gamification-based training courses, living labs, ‘authentic appetites’ podcast series, the EFF-CoP on Tour program, webinars, virtual cafes, a food fraud incident preparedness workshop, and dedicated events for the future generation. Our goal is to cultivate a vibrant community of over 5000 members at the end of the project, all dedicated to combating food fraud and learning from each other. To ensure our EFF-CoP stands the test of time, we are not only implementing the proven concept of ‘CoP design for evolution’ but also a comprehensive exploitation strategy and business plan. Together we will work towards a future with enhanced authenticity, traceability and transparency within our food supply chains.

EFRA will explore how extreme data mining, aggregation and analytics may address major scientific, economic and societal challenges associated with the safety and quality of the food that European consumers eat. EFRA’s goals are: i) develop and test solutions to discover and distil food risk data from heterogeneous and dispersed/scarce data sources with minimal delay and appropriate format; ii) design relevant human aspects & interactions with users to measure usefulness for human risk prevention actions in real-world use-cases iii) demonstrate how solutions enable the development of trustworthy, accurate, green and fair AI systems for food risk prevention iv) achieve groundbreaking advances in performance and effectiveness of food risk data discovery, collection, mining, filtering, and processing; v) integrate relevant technologies (big data, IoT, AI) to foster links to food data innovator communities vi) position its contributions into the overall ecosystem of public & private stakeholders that share data, technology and infrastructure to ensure the safety and quality of food in Europe. To achieve these goals, EFRA will design, test, and deploy tools and undertake appropriate initiatives to facilitate their uptake, elicit feedback, and engage stakeholders. The EFRA tools are: (i) EFRA Data Hub, offering intelligent crawlers and data annotation & linking modules to search, mine, process, annotate, and link dispersed, multilingual, heterogeneous, and deep/hidden food safety data sources (ii) EFRA Analytics Powerhouse: offering modules running over a green cloud HPC that distil useful insights & signals from the EFRA Data Hub to train privacy-preserving, explainable, green food risk prediction AI models (iii) EFRA Data & Analytics Marketplace: A front-facing user-friendly web app that allows interested users to discover, purchase/use, and contribute data, AI models, and analytics modules, creating an economy where data holders and data consumers engage and trade.

STELAR will design, develop, evaluate, and showcase an innovative Knowledge Lake Management System (KLMS) to support and facilitate a holistic approach for FAIR (Findable, Accessible, Interoperable, Reusable) and AI-ready (high-quality, reliably labeled) data. The STELAR KLMS will allow to (semi-)automatically turn a raw data lake into a knowledge lake. This is achieved by (1) enhancing the data lake with a knowledge layer, and (2) developing and integrating a set of data management tools and workflows. The knowledge layer will comprise: (a) a data catalog offering automatically enhanced metadata for the raw data assets in the lake, and (b) a knowledge graph that semantically describes and interlinks these data assets using suitable domain ontologies and vocabularies. The provided tools and workflows will offer novel functionalities for: (a) data discovery and quality management; (b) data linking and alignment; and (c) data annotation and synthetic data generation. The KLMS will combine both human-in-the-loop and automatic approaches, to leverage background knowledge of domain experts while minimizing their involvement. To reduce manual effort and time, it will increase the automation of finding and selecting relevant data sources, configuring, and tuning the involved data management tools, and designing, executing, and monitoring end-to-end data processing workflows adapted to different user needs. The KLMS will include specialized tools and functions for geospatial, temporal, and textual data. An organization, ranging from a data-intensive SME to the operator of a data marketplace, will be able to use the STELAR KLMS to increase the readiness of its data assets for use in AI applications and for being shared and exchanged within a common data space. The STELAR KLMS will be pilot tested in diverse, real-world use cases in the agrifood data space, one of the nine data spaces of strategic societal and economic importance identified in the European Strategy for Data.