The Horizon Europe funded project, Data4Food2030 is making significant progress on the development of a groundbreaking monitoring system, which will play a key role in the project’s pursuit of data-driven innovation to improve our food systems. The goal is to monitor the data economy to support informed and targeted actions by policy makers and government agencies that address that many challenges around data sharing, faced by the food sector including, unfair data usage, ownership conflicts, imbalanced access to data and data security.
By recognising that the data economy and digitalisation are means and not the end, the project is able to work towards supporting a Data Economy for Food Systems that delivers positive impacts, while minimising negative repercussions. To achieve this goal, understanding the evolving needs of the project’s 9 integrated case studies in various sectors is a fundamental starting point. A robust monitoring system could benefit all related stakeholders. The monitoring system team, led by EV ILVO is gathering crucial information, gaining an understanding of the current situation around data exchange and usage in the food system, while also exploring the state-of-the-art in technology used for data spaces. The monitoring system will support the development of the data economy in food systems by providing detailed scenarios. These scenarios outline the potential impact on data services and decision support and establish benchmarking for comparison between data-sharing initiatives and analyse data economy indicators for future assessments.
The monitoring system and its accompanying dashboard will be the result of extensive collaboration between various activities to analyse and support the case studies within the Data4Food2030 project. To transform the idea into a feasible and efficient tool, multiple workshops and interactions with experts and stakeholders have been realised, aiming to ensure comprehensive coverage and inclusion of different types of stakeholders. The monitoring system is being designed to cater to different user groups, from policy makers, governmental actors, researchers, analysts, to new actors in the data economy for food systems.
The General View (Macro Level) offers a comprehensive overview of the Data Economy for Food Systems at EU level and per member state. It serves as a one-stop-shop for official statistics, research results, and aggregated data from cases and businesses. The dimensions covered include actors and network, data status, technical, skills, social and ethics, activities of the food system, legal status, IP/Patents & security, economy & business, environment, sustainability, data sovereignty, and fairness. Data Sharing in Practice (Micro level) provides insights into operational Data Sharing Initiatives (DSIs) and includes practical information, benchmarking, good practices, and potential links with other European projects. The dimensions at this level cover information on the scale of DSIs, and the features used in DSIs.
The monitoring system collects data from a variety of sources to provide a vast knowledge base for better insights. For the EU/Macro level, Eurostat is one of the main sources, and there are ongoing efforts to explore additional sources, research data, data from member states, and relevant EC studies (e.g., JRC). For the Data Sharing in Practice/Micro level, data from various initiatives (e.g., Horizon Europe projects) is collected through the Survey of AgriDataSpace project, and there are plans to incorporate log-in and registration mechanisms for data collection in the future.
The dashboard will offer a rich source of information, shedding light on the state-of-play, trends, impact, and related projects and studies within the Data Economy for Food Systems. By providing insights at the country level and the functioning of data sharing initiatives, valuable knowledge will be formed surrounding the system’s development, structure, function and how it can be improved from both institutional and business perspectives.
The Data4Food2030 project is continuing its efforts to enrich the monitoring system by diving deeper into research data, employing text mining techniques, and gathering input from the project’s 9 case studies. Additionally, the team is currently working on its design by transforming wireframes into mock-ups, and will begin integrating qualitative data, good practices, and other relevant documentation.
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