The aim of our project is to train police officers’ on the procedure, through gamification technologies in a safe and controlled virtual environment. Essential tasks during the creation of LAW-GAME serious game are to virtualise and accurately recreate the real world. We will introduce an attractive approach to the development of core competencies required for performing intelligence analysis, through a series of AI-assisted procedures for crime analysis and prediction of illegal acts, within the LAW-GAME game realm. Building upon an in-depth analysis of police officers’ learning needs, we will develop an advanced learning experience, embedded into 3 comprehensive “gaming modes” dedicated to train police officers and measure their proficiency in: 1. conducting forensic examination, through a one-player or multi-player cooperative gaming scenario, played through the role of a forensics expert. Developed AI tools for evidence recognition and CSI and car accident analysis, will provide guidance to the trainee. 2. effective questioning, threatening, cajoling, persuasion, or negotiation. The trainee will be exposed to the challenges of the police interview tactics and trained to increase her emotional intelligence by interviewing a highly-realistic 3D digital character, advanced with conversational AI. 3. recognizing and mitigating potential terrorist attacks. The trainees will impersonate an intelligence analyst tasked with preventing an impending terrorist attack under a didactic and exciting “bad and good” multiplayer and AI-assisted game experience. The proposed learning experience focuses on the development of the key competences needed for successfully operating in diverse and distributed teams, as required by several cross-organisational and international cooperation situations. The learning methodology developed by the LAW-GAME consortium will be extensively validated by European end-users, in Greece, Lithuania, Romania, Moldavia and Estonia.

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.

FAME is a joint effort of world-class experts in data management, data technologies, the data economy, and digital finance to develop, deploy and launch to the global market a unique, trustworthy, energy-efficient, and secure federated data marketplace for Embedded Finance (EmFi). The FAME marketplace will alleviate the proclaimed limitations of centralized cloud marketplaces towards demonstrating the full potential of the data economy. In this direction, the project will enhance a state of the art data marketplace infrastructure (i.e., H2020 i3-Market marketplace) with novel functionalities in three complementary directions namely: • Secure, interoperable, and regulatory compliant data exchange across multiple federated cloud-based data providers in-line with emerging European initiatives like GAIA-X. • Decentralized, programmable, data assets trading and pricing leveraging blockchain tokenization techniques (including support for accruing data assets value in NFTs). • Integration of trusted and Energy Efficient (EE) analytics based on novel technologies such as Quantitative Explainable AI, Situation Aware Explainability (SAX), incremental EE analytics, and edge analytics. FAME will become operational in a federated cloud environment with multiple providers of EmFi data assets, including datasets, AI/ML models, and more. It will become interconnected with more than 12 data marketplaces that are operated by the project partners, as well as with other data infrastructures that will support the implementation of 7 pilots. Through this process, the catalog of the FAME marketplace will be populated with a critical mass of 1000+ data assets. Furthermore, FAME will establish a Learning Center (LC) for tech and non-tech users, as this is a key prerequisite for unlocking the potential of the data economy. FAME will build a vibrant community of EmFi stakeholders around the FAME platform, which will serve as a catalyst for the sustainability of the project’s results.

SERRANO’s overall ambition is to introduce a novel ecosystem of cloud-based technologies, spanning from specialized hardware resources up to software toolsets. This will enable application-specific service instantiation and optimal customizations based on the workloads to be processed, in a holistic manner, thus supporting highly demanding, dynamic and security-critical applications. SERRANO is not only tuned and fully aligned with current trends in the cloud computing sector towards the expansion of cloud infrastructures so as to efficiently integrate edge resources, but it also integrates transparently HPC resources ir to provide an infrastructure that goes beyond the scope of the “normal” cloud and realizes a true computing continuum. SERRANO introduces an abstraction layer that transforms the distributed edge, cloud and HPC resources into a single borderless infrastructure, while it also facilitates their automated and cognitive orchestration. It proposes the introduction and evolution of novel key concepts and approaches that aim to close existing technology gaps, towards the realization of advanced infrastructures, able to meet the stringent requirements of future applications and services. It will develop technologies and mechanisms related to security and privacy in distributed computing and storage infrastructures, hardware and software acceleration on cloud and edge, cognitive resource orchestration, dynamic data movement and task offloading between edge/cloud/HPC, transparent application deployment, energy-efficiency and real-time and zero-touch adaptability. Finally, to highlight the proposed ecosystem’s scientific and technological significance, SERRANO will demonstrate three high impact use cases related to (i) secure cloud and edge storage over a diversity of cloud resources, (ii) fintech by supporting latency-sensitive and safety-critical digital services in the financial sector and (iii) machine anomaly detection in manufacturing for Industry 4.0

In today’s changing climate is of urgent importance to understand the adverse impacts of climate change to the local and regional Arctic natural environments, infrastructures and industries. To this end, Earth Observation (EO) is the way forward, as it is extremely challenging to obtain long-term continuous ground observations. Recent advances in EO sensors, in cloud computing, geographical information systems (GIS) and in the field of socioeconomics provide unique opportunities to promote research and socioeconomic studies in the Arctic. Yet, despite their plethora, EO data are provided in a dispersed and unconnected way through several web platforms and in diverse formats, making their use difficult. EO-PERSIST proposes the development of a single cloud-based system that will allow in a unique way the availability of the collection, management and exploitation of the available EO data suitable to permafrost studies. The system leverages existing services, datasets and novel technologies to: a)create a continuously updated ecosystem with EO datasets suitable for permafrost studies, b)promote methodological advances in permafrost studies by exploiting the huge volume of EO datasets and c)provide indicators directly connected with socioeconomic effects to permafrost dynamics. Experimental analysis will also be carried with the system to showcase its use via five carefully selected and innovative Use Cases, that will serve as Key Performance Indicators of the system. EO-PERSIST brings together staff from academia and industry via a series of carefully-designed secondments, establishing a unique fertile collaborative research and innovation environment to promote pioneering research and socioeconomic studies implementation in the Arctic. A strong inter-sectoral experienced research team, of 5 academic and 6 industrial partners, coming from Greece(3), North Macedonia (1), Finland(1), Sweden(1), Cyprus (1), Poland(1), Romania(2) and Italy(1) constitute the project’s consortium.