Agriculture is being managed more tightly than ever before and is generating more data than ever before, but the potential of a data economy in agriculture remains unexplored. The reasons for this are varied, and include technical interoperability, business relationships between stakeholders, and social acceptability issues around data ownership and market transparency. Individual stakeholders make use of the data they generate at their own particular stage in the agri-food supply chain. However, the sharing of this data with others along the chain and its collective analysis needs more development and demonstration if more efficiencies are to be introduced and further value added to the agri-data economy. While some sharing is taking place on an ad-hoc basis, each new set of potential data sharers must start from scratch and work through the same issues common to all such arrangements. Equally, the lack of data sharing precedents in agriculture inhibits data owners from taking a more exploratory view of the world. Several dimensions must be considered in policy-making if a fully functioning data economy in the agriculture domain is to emerge. Such a multi-disciplinary approach is at the core of the DIVINE consortium, which encompasses technical (agriculture and ICT), markets, and social sciences expertise. It will build an agri-data ecosystem that incorporates existing common agri data spaces while deploying industry-led pilots built on data sharing arrangements, to demonstrate the cost-benefit and added value in sharing agri data. DIVINE will assess its ecosystem at the level of policy impacts, the uptake of digital technologies, and economic and environmental performance. DIVINE will promote its ecosystem and its assessments to technology providers, policy-makers, farm representatives, and various other agri-data stakeholders. It will take the first real concrete steps towards mature data markets in European and global agriculture.

The global food system, responsible for up to 37% of GHG emissions, requires urgent transformation due to challenges from urbanisation and unsustainable diets. Additionally, climate change and biodiversity loss exacerbate the vulnerability of European food systems, as seen in recent climate-related disasters like wildfires and droughts, compounded by disruptions such as the COVID-19 pandemic. Despite food abundance in Europe, food insecurity threatens millions of European citizens, necessitating a comprehensive approach encompassing knowledge, technologies, behaviours, and policies that promote healthier and more sustainable food systems. Given citizen science (CS) as a potent tool for achieving these goals, SPOON takes the innovative approach to food insecurity by employing CS to empower citizens in creating a more inclusive and sustainable food environment. SPOON’s four main aims are to: deepen scientific knowledge about food environments; increase capacity of policymakers in data-driven decision-making; foster cross-sector collaboration; increase agency of citizens to change their food consumption behaviour and local food environments; and foster more confidence in citizens in sharing personal food data. SPOON bridges the intention-action gap towards healthier and more sustainable diets by placing citizens at the forefront of transforming the food system through CS integration. SPOON's conceptual framework centres around six CS Labs in Europe, coordinated by local partners and utilising a multi-actor approach. Citizens engage as both researchers and subjects, testing and validating innovative digital tools to collect, analyse and interpret data on their food consumption behaviors and local food environments to then co-design and run small-scale behaviour change interventions together with other stakeholders. SPOON prioritises GDPR compliance and FAIR principles in its data management.

The AfriConEu project envisions to essentially strengthen and reinforce the digital innovation ecosystems in Africa by targeting existing Digital Innovation Hubs (DIHs) and supporting them through capacity building and networking activities. African DIHs are playing a central role in the development of digital entrepreneurship and by raising their capacities to tackle the challenges they face they will be more effective in driving digital innovation forward. To achieve its mission, the AfriConEu project will connect DIHs from Nigeria, Uganda and Tanzania with DIHs from Europe with the aim to (i) facilitate knowledge and experience sharing, (ii) drive the development of mutually beneficial partnerships and (iii) support the creation of collective projects for boosting digital economy, empowering youth and fostering innovation and growth. To realize its mission the project will develop, test and validate the “AfriConEu Networking Academy” an innovative mechanism for connecting and sharing best practices, experiences and resources between DIHs in Africa and between DIHs in Africa and EU, in a comprehensive, replicable and self-sustaining way. Through two flagships programmes, the AfriConEu Networking Academy will empower and enable African DIHs to best serve their local industry, boost their start-up ecosystem and empower the youth population with the necessary skills to thrive in a digitalized world.

The digital transformation of food systems has entered a twilight zone: data-driven innovations have proven to be promising, but it is still unclear how to upscale adoption and have broader acceptance. The Data4Food2030 project aims to improve the data economy for food systems (DE4FS) by expanding its definition, mapping its development, performance and impact to create new insights and opportunities. This contributes to a more competitive and sustainable food system in the EU and supports implementation and adaptation of relevant policies such as a Digital Single Market, Green Deal and the Common Agricultural Policy. Data4Food2030 is a 4-year project that aims to 1) enlarge the knowledge base and insight into the DE4FS, 2) develop a system that monitors and evaluates the development, performance and impact of the DE4FS on relevant EU policies 3) identify drivers and barriers and turn these into opportunities, recommendations and solutions, 4) test solutions and evaluate recommendations in case studies and through stakeholder dialogues and 5) provide future scenarios and a roadmap and sustain the monitoring system to support policy development and accelerate the desired future state of the DE4FS. Data4Food2030’s approach is targeted at an improved future state of the DE4FS from which clear design principles, recommendations and solutions are derived for improving and adapting policies and practices at public and private level. As an essential part of the project, stakeholders are deeply engaged to provide input to various DE4FS concepts and evaluate several project outcomes to increase the impact of the project. Nine case studies provide real-life examples of the DE4FS at micro- and meso-economic level, deploying data and technologies, which are used for mapping and improvement to promote data-enabled business models. In this way, Data4Food2030 creates credible pathways to navigate properly through the twilight zone towards a fair, inclusive and innovative DE4FS.

GREEN.DAT.AI aims to channel the potential of AI towards the goals of the European Green Deal, by developing novel Energy-Efficient Large-Scale Data Analytics Services, ready-to-use in industrial AI-based systems, while reducing the environmental impact of data management processes. GREEN.DAT.AI will demonstrate the efficiencies of the new analytics services in four industries (Smart Energy, Smart Agriculture/Agri-food, Smart Mobility, Smart Banking) and six different application scenarios, leveraging the use of European Data Spaces. The ambition is to exploit mature (TRL5 or higher) solutions already developed in recent H2020 projects and deliver an efficient, massively distributed, open-source, green, AI/FL - ready platform, and a validated go-to-market TRL7/8 Toolbox for AI-ready Data Spaces. The services will cover AI-enabled data enrichment, Incentive mechanisms for Data Sharing, Synthetic Data Generation, Large-scale learning at the Edge/Fog, Federated & Auto ML at the edge/fog, Explainable AI/Feature Learning with Privacy Preservation, Federated & Automatic Transfer Learning, Adaptive FL for Digital Twin Applications, Automated IoT event-based change detection/forecasting. The GREEN.DAT.AI Consortium consists of a multidisciplinary group of 17 partners from 10 different countries (and one associated party), well balanced in terms of expertise. The vast majority of partners already have key roles in a number of projects funded under the Big Data PPP (ICT-16-2017) topic, namely BigDataStack, CLASS, Track & Know, and I-BiDaaS and are serving as active members of the BDVA/DAIRO Association, FIWARE, AIOTI, and ETSI. In addition, partners come from a variety of sectors, such as banking, mobility, energy, and agriculture, constituting a representative workforce of their respective domains, which will contribute to industry adoption and stimulate uptake in other sectors as well.