Proposals are expected to contribute to the creation of a thematic network in the area of food fraud, including all of the following activities:

• Development of a community of practice to foster knowledge exchange between end-users and research and innovation ecosystems who will work together mapping existing food fraud practices. Traditional and local food products should be taken into consideration in this community of practice;
• Compilation of a comprehensive description of the state of new knowledge, practices, procedures, systems and technologies tackling food fraud (including not only technologies for detection but also preventative approaches). Proposals should build on existing and new available knowledge and trends, data and models (including big data tools and/or artificial intelligence applied to food fraud). Proposals should focus on the cost/benefit aspects of the practices and innovations collected and build on existing and new available knowledge, data and models enabling the practical implementation of solutions;
• Creation of tailor-made communication materials summarizing, sharing and presenting, in a language easily understandable for end-users, existing best practices and innovations that are close to implementation into practice, but not sufficiently known by end-users;
• Identification and mapping of possible relations and synergies with other networks, projects, initiatives and policy and funding instruments at regional, national and European level, that could help disseminate and exploit knowledge and results, showing the added value of these inter-connections. Dissemination via public events, publication of case studies, dissemination papers and reports, and the creation of an on-line collaborative space that remain active in the long-term including the availability of materials for training and education;
• Cross-articulation with the other data spaces, and notably with the European Open Science Cloud (EOSC) should be foreseen, exploiting synergies and complementarities of the different approaches. Efforts should be made to ensure that the data produced in the context of this topic is FAIR (Findable, Accessible, Interoperable and Re-usable).