Atrial fibrillation (AF) and stroke are major health care problems in Europe. They are most often the clinical expression of atrial cardiomyopathy, which is under-recognised due to the lack of specific diagnostic tools. Multidisciplinary research and stratified approaches are urgently needed to prevent, diagnose, and treat AF and stroke and preempt the AF-related threat to healthy ageing in Europe. MAESTRIA is a European consortium of 18 clinicians, scientists and Pharma industrials who are at the forefront of research and medical care of AF and stroke patients. It will create multi-parametric digital tools based on a new generation of biomarkers that integrate artificial intelligence (AI) processing and big data from cutting edge imaging, electrocardiography and omics technologies. It will develop novel biomarkers, diagnostic tools and personalized therapies for atrial cardiomyopathy. Digital Twin technologies, a rich data integrator combining biophysics and AI will be used to generate virtual twins of the human atria using patient-specific data. Unique experimental large-animal models, ongoing patient cohorts and a prospective MAESTRIA cohort of patients will provide rigorous validation for new biomarkers and newly developed tools. A dedicated core lab will collect and homogenize clinical data. MAESTRIA will be organized as a user-centered platform, easily accessible via clinical parameters routinely used in European hospitals. A Scientific Advisory Board comprising potential clinician users will help MAESTRIA meet clinical and market needs. Dissemination and visibility of the MAESTRIA consortium mission will benefit from participation of the German Competence Network on Atrial Fibrillation (AFNET), and support from the European Society of Cardiology, clinicians, scientists, and other professional societies. MAESTRIA will be ready to tackle the major challenges of data integration and personalized medicine focused on atrial cardiomyopathy, AF and stroke.

EUHubs4Data will set up a European federation of Big Data Digital Innovation Hubs (DIHs), with the ambition of becoming a reference instrument for data-driven cross-border experimentation and innovation, and support the growth of European SMEs and start-ups in a global Data Economy. Based on the concept “European catalogue, local offer”, EUHubs4Data will establish a Europe-wide, sustainable ecosystem drawing upon local expertise and achievements of European initiatives and national/regional Big Data DIHs, with the three-fold objective of: (1) creating a European catalogue of data sources and federated data-driven services and solutions; (2) making this offer accessible at the regional level so that European SMEs, start-ups and web entrepreneurs have access to the most valuable assets and expertise on the continent; (3) fostering cross-border and cross-sector data-driven experimentation facilitated through data sharing, and data & service interoperability. To achieve these objectives, the project relies on: • a strong initial ecosystem of 12 Big Data DIHs from the 4 EU poles, linked to European Data Incubators and SME networks; • a multi-dimensional approach (governance, operations, technical, ethical and legal aspects, interoperability, skills) for the expansion of the federation (growing up to 30 DIHs); • a strong offer of services and access to data sources, based on an initial set of solutions for data and service interoperability, and including a coherent and diverse training programme; • the capacity to attract, support and engage SMEs, start-ups and web entrepreneurs (40 cross-border experiments, and 60-80 companies directly involved in the data-driven innovation programme); • a community and ongoing collaborations with 60 relevant European Initiatives; EUHubs4Data initially covers 12 EU regions in 9 countries, and plans to expand to more than 20 regions and 14 countries during the project, establishing a long-lasting sustainable ecosystem

In 2019, SMEs accounted for 99.8% of all enterprises in the EU-28 non-financial business sector and accounted for the majority of the increase in value added (60%) – EU Annual Report on SMEs, (November 2019). In the last years, the EC acknowledged that a number of areas require immediate action at EU level to ensure that: (1) SMEs will increase Europe’s competitiveness in the AI landscape leaving no SME behind (2) New technologies and AI-based product, processes and services are based on European ethical values (3) SMEs get fast, trusted and secure links to data assets, AI methods and tools that can be used expediently and build on digital sovereignty. DIHs (Digital Innovation Hubs) are a fundamental European instrument that has emerged over the last decade to address these limitations and encourage the uptake of AI across the economy. We simply need to establish the most needed link between the DIH engine and the AI4EU service platform power to ignite a virtuous cross-sectorial European economy of intelligence for SMEs at scale; precisely the SME-friendly AI vision that DIH4AI aims at fulfilling. The DIH4AI project aims at building a network of AI-on-demand innovation and collaboration platforms, supporting joint development and provision of ecosystem-business-technology-transformation services through a sustainable network of regional DIHs specialized in AI and targeting local SMEs and local tech governmental agencies. The DIH4AI regional platforms are by design interoperable with the pan-EU AI4EU platform thanks to an interoperability framework operating at Portal, Data and Cloud levels, allowing SME-DIH-EU virtuous bi-directional collaborations at the level of shared AI resources, AI-oriented standard data models and ontologies, AI ready FAIR datasets, AI-driven user interaction and services (SAAS) and AI-compatible advanced computation facilities (PAAS and IAAS).

Orchestrating an interoperable sovereign federated Multi-vector Energy data space built on open standards and ready for GAia-X The aim of OMEGA-X is to implement a data space (based on European common standards), including federated infrastructure, data marketplace and service marketplace, involving data sharing between different stakeholders and demonstrating its value for real and concrete Energy use cases and needs, while guaranteeing scalability and interoperability with other data space initiatives, not just for energy but also cross-sector. The proposed concept and architecture heavily rely on the approaches adopted by IDSA, GAIA-X, FIWARE, BDVA/DAIRO and SGAM as major EU references regarding data spaces. It will pursue the GAIA-X label, which ensures highest standards on protection, security, transparency, openess and trust, avoids vendor lock-in and restricted to EU countries. • Federated infrastructure for data ingestion. There are a lot of independent platforms for data ingestion/storage, open and private. The goal is to define the minimum interoperability and federation requirements needed for these platforms to adhere to the Energy Data Space and be able to share data in a trusted and secure way. • Data Space Marketplaces. This is the common ground where data, which is already harmonized semantically, is indexed, and referenced, maintaining always the required standards of identity, trust and sovereignty. Using the data space as baseline, a marketplace is implemented for stakeholders to share, use and monetize data and services. Data/service providers will be able to advertise their data/services, and data/service users will be able to discover multiple data sets and services. • Advanced Energy Use Case demonstration. Using all aforementioned layers underneath, 4 use cases families (Renewables, LEC, Electromobility and Flexibiilty) will showcased o prove the value of having a common data space for a particular problem identified by energy stakeholders.

Considering Artificial Intelligence (AI) capabilities and potential risks, and taking into account its limitations, AI4CCAM will develop an open environment for integrating trustworthy-by-design AI models of vulnerable road user behaviour anticipation in urban traffic conditions, and accounting for improved road safety and user acceptance. Leveraging the Trustworthy AI guidelines for general intelligent software systems and the ethics recommendations for connected automated vehicles, AI4CCAM will support AI-based scenarios management in which pedestrian/cyclist behaviour anticipation models will integrate visual gaze estimation and where explainable ego car trajectory prediction models are simulated with ethical dilemmas and multiplied with generative adversarial networks and metamorphic testing techniques. The AI4CCAM open environment will include an interoperable digital framework for managing and generating AI-based urban-traffic scenarios in which trustworthy-by-design AI models can be tested and an online participatory space to foster acceptance of AI in automated driving, determine AI risks and identify biases in datasets and cyber-threats. Simulation scenarios of road users interacting with automated vehicles will be developed and evaluated in three complementary use cases covering the whole sense-plan-act paradigm and user acceptance. As such, the project will advance knowledge in building trustworthy-by-design AI-based solutions for CCAM applications.