According to the European Research Data Landscape – Final report, a survey involving almost 9,898 responders, highlighted some of the main barriers to management and sharing of research data: time, effort, storage, skills required, and the lack of recognition and data protection. RAISE Suite will develop a system specifically designed to remove barriers to data sharing, replacing technological achievements that do not influence researchers’ attitude towards sharing data. To do so, RAISE Suite will develop the solutions required to automate the process from data collection to dataset generation, guided by a FAIR-by-design principle to remove barriers such as perceived effort, time, as well as skills required for data sharing. At the same time, EOSC-RAISE will be integrated into RAISE Suite, for a platform which supports simple dataset sharing and exploitation, mitigating the sense of lack of recognition and data protection among researchers. Furthermore, RAISE Suite will implement a DMP-guided data collection and management policy. In particular, RAISE Suite will not only adopt a Machine Actionable Data Management Plan (ma-DMP), but further extend it to support designated actions, τurning the persistent identifier DMP-ID into the main reference point for the whole data lifecycle, following research activities, making the connections with underlying algorithms and data, and updating the DMP accordingly from collection, depositing and storing, to discovery, management, processing, reusing and exploitation. RAISE Suite capitalises on the results of a previously funded EC initiative. To this end, RAISE Suite will leverage work done by the EOSC-RAISE project, incorporating its technical platform that moves from open data to data open for processing, introducing the technology required to cover the data lifecycle from the data collection to the dataset generation.

The overall objective of Crop-MATCHING is to set up a holistic approach to identify information, underutilized knowledge and best sustainable practices from small-scale farms, mix and match them into crop combinations (such as crop rotation, strip cropping, or vertical cropping) and generate new impartial and tailored knowledge specific to small farmers'/foresters' requirements. Crop-MATCHING by proposing successful and easy-to-use small-scale farming practices, aims to provide an ‘automotive’ solution to the problems smallholder farmers/foresters face in EU agriculture and at the same time, line up with the European Green Deal, Rural Vision, Climate policy and Farm to Fork strategy. What is of great concern is the fact that the number of small farms follows a sharp decrease. In this context small farms produce most of the healthy and biodiverse food that we eat every day, they provide local jobs and sustain rural activities, and they secure the resilience of our food system. Crop-MATCHING considering the important role of smallholder farmers/foresters and their need to have sustainably fair economic returns to continue farming activities and remain in the rural areas proposes the wide use of successful and easy-to-use farming practices. Through, the exploitation of various networks it is aimed to identify, diffuse, and convince farmers/foresters to adopt these successful practices. Through the proposed procedure, initially, the underutilised knowledge that appears in the literature (or identified in previous EU projects) will be mapped (more than 100 practices); almost all European territory and several pedoclimatic zones will be covered, as small and subsistence farms don't present the same structure and need everywhere. Conceptualized actions to communicate ready-to-use practices, through Crop-MATCHING, including digital cloud solutions, info packages, fact sheets, practice groups, open-access databases, e-learning, infographics, videos, website and social media.

ICAERUS proposes an “application-oriented” approach, through the selection of five (5) specific drone applications, to explore the multi-purpose application potential of drones in agricultural production, forestry and rural communities. The selected drone applications represent the most important sectoral and societal drone usage purposes in Europe and cover multiple applications that are interconnected within the complex rural European landscape. The ICAERUS vision is to explore opportunities and provide a more complete and interconnected account of the potential and impact of drones as multi-purpose vehicles in EU agriculture, forestry and rural areas. The aim is to showcase and support, through application, the effective, efficient and safe deployment of drones as well as, identify the risks and added values associated with their use. “Taking off” from the current state-of-the-art in the drone ecosystem, ICAERUS will “rise up” by advancing existing software technology, platform components and knowledge in regard to drones, to exploit the potential of drones and strengthen capacities to reduce their risks, achieve better informed decision-making, enhance sustainability performance and competitiveness in agriculture, forestry and rural areas. This will be showcased in two directions: fundamental applications representing an “eye-in-the-sky”, using the drone as a positioning system for optical observation and recording, and a “hand-in-the-sky” applications, for spraying and goods delivery. ICAERUS plans to scale-up through research, technology optimisation, demonstration and education about drones to create an efficient, trusted and safe enabling environment for the EU drone services market to achieve the EU’s decarbonisation, digitalisation and resilience ambitions. ICAERUS consists of a balanced, multi-actor, cross sectoral and well-experienced consortium, including research organisations, SME technology providers, associations and non-profit organisations.

In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of healthcare data. The linkage of cross-border health data from various sources, including genomics, and analysis via innovative approaches based on artificial intelligence (AI) will enable a better understanding of risk factors, causes, and the development of optimal treatment in different disease areas. Nevertheless, the reuse of patient data is often limited to datasets available at a single medical centre. The main reasons why health data is not shared across institutional borders rely on ethical, legal, and privacy aspects and rules. Therefore, in order to (1) enable health data sharing across national borders, (2) fully comply with present GDPR privacy guidelines / regulations and (3) innovate by pushing research beyond the state of the art, BETTER proposes a robust decentralised privacy-preserving infrastructure which will empower researchers, innovators and healthcare professionals to exploit the full potential of larger sets of multi-source health data via tailored made AI tools useful to compare, integrate, and analyse in a secure, cost-effective fashion; with the very final aim of supporting the improvement of citizen’s health outcomes. In detail, this interdisciplinary project proposes the co-creation of 3 clinical use cases involving 7 medical centres located in the EU and beyond, where sensitive patient data, including genomics, are made available and analysed in a GDPR-compliant mechanism via a Distributed Analytics (DA) paradigm called the Personal Health Train (PHT). The main principle of the PHT is that the analytical task is brought to the data provider (medical centre) and the data instances remain in their original location. In this project, two mature implementations of the PHT (PADME and Vantage6) already validated in real-world scenarios will be fused together to build the BETTER platform.

European manufacturing SMEs represent a major pillar of the EU economy but, even though some of these SMEs are world’s champion in their own business area, they are still threatened by the lack of radical technical innovation as well as successive crises of their supply chains. The MARS project aims to remedy to both issues by enabling SMEs to access advanced European breakthrough innovations in the field of AI-driven digital manufacturing processes and enter into process chains that are geographically distributed. Specifically, by gathering diverse expertise coming from complementary European partners, MARS will develop Industry4.0 emerging technologies including digital twins of products, processes and machines, bio-intelligent production devices with local intelligence and high sensing coverage, central intelligence with fleet learning approaches, data-driven manufacturing process models from different sources, blockchain technology for data hashing, traceability and securitization, multi-agent based manufacturing planning, multi-criteria intelligent optimization of processes and resources especially addressing environmental footprint. As a result, the impact of the project will lie into introducing radical flexibility in all different aspects of manufacturing processes, in particular by redefining the process route, raw material, resources, technology, throughput, manufacturing site, delivery date in no time, while keeping up with product’s requirements, proven product quality and sustainability of both processes and products. By demonstrating its results on two case studies exhibiting advanced manufacturing processes (incl. homogeneous and heterogenous data), MARS will show how SMEs can decrease time delivery under difficult economical boundary conditions, while targeting ambitious energy-saving environmental objectives.