Water quality and quantity are under arising pressure from agricultural activities that may cause overexploitation of natural waters and pollutants runoffs (e.g., nutrients, pesticides). These stresses are also compounded by climate change effects. To address the complex challenges of agri-water management, the UNIVERSWATER consortium will adopt a ‘system of systems’ approach by developing and improving technologies designed to optimise water resources uses in a fully integrated way. A dedicated interdisciplinary and intersectoral consortium of 15 partners from six European countries will: a) develop innovative portable and in-situ sensors for a number of parameters and pollutants (salinity, nutrients, CEC, microbiological indicators) and b) couple them with earth observation imaging and advanced explainable and robust artificial intelligence techniques, as well as c) develop cost-effective, sustainable methods based on nature-based and technology-based solutions for water remediation at the point of need and d) promote the adoption of the developed methods through pricing incentive provision. These technologies will be integrated into decision support systems (DSSs) that will be tested at three case studies tackling on-farm treatment of dairy soiled water, mitigation of soil salination through water reuse, and optimisation of fertiliser/pesticide application for freshwater preservation. Going beyond, UNIVERSWATER will upscale these local DSSs into a common platform where a suite of DSS tools can be adapted to different situations after being tailored to the local factors, thereby developing a modular, extensible and holistic universal DSS.

RRI-LEADERS adopts a meso-level approach, and will explore the application and sustainability of the RRI paradigm within territorial innovation systems. In RRI-LEADERS a “territory” is understood as a confluence between three interconnected primary aspects: geographical location, socio-economic and cultural bonds, and administrative authority. The project will involve four distinct territories from different parts of Europe, representing different cultural and socio-economic backgrounds, different scope of territorial oversight, different institutional and decision-making infrastructures, different R&I landscapes, and different dynamics among territorial actors. As such, the four territories will represent a diverse range of opportunities and implications for RRI, which will enable the RRI-LEADERS consortium to carry out a thorough assessment of the RRI relevance to territorial governance and have the involved territories act as demonstrators for the potential of RRI on subnational level. The partners will engage in a multi-stage co-creation process (referred to as RRI-AIRR) that will mobilise territorial quadruple helix stakeholders towards the elaboration of future-oriented strategy and action plans - territorial outlooks for each of the participating territories. Territorial partners will have the leading role in this process, and will additionally work to ensure the broadest societal and governance-level endorsement. The Consortium will further use the accumulated knowledge to chart a detailed outlook for the future potential of RRI as a guiding framework in territorial governance of R&I, and will aim to provide an evolutionary perspective on RRI for the upcoming Horizon Europe programme.

The smart energy ecosystem constitutes the next technological leap of the conventional electrical grid, providing multiple benefits such as increased reliability, better service quality and efficient utilization of the existing infrastructures. However, despite the fact that it brings beneficial environmental, economic and social changes, it also generates significant security and privacy challenges, as it includes a combination of heterogeneous, co-existing smart and legacy technologies. Based on this reality, the SDN-microSENSE project intends to provide a set of secure, privacy-enabled and resilient to cyberattacks tools, thus ensuring the normal operation of EPES as well as the integrity and the confidentiality of communications. In particular, adopting an SDN-based technology, SDN-microSENSE will develop a three-layer security architecture, by deploying and implementing risk assessment processes, self-healing capabilities, large-scale distributed detection and prevention mechanisms, as well as an overlay privacy protection framework. Firstly, the risk assessment framework will identify the risk level of each component of EPES, identifying the possible threats and vulnerabilities. Accordingly, in the context of self-healing, islanding schemes and energy management processes will be deployed, isolating the critical parts of the network in the case of emergency. Furthermore, collaborative intrusion detection tools will be capable of detecting and preventing possible threats and anomalies timely. Finally, the overlay privacy protection framework will focus on the privacy issues, including homomorphic encryption and anonymity processes

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.

Artificial intelligence (AI) has lately proved to be a coin with two sides. On the one hand, it can be leveraged as a powerful defensive mechanism to improve system preparedness and response against cyber incidents and attacks, and on the other hand, it can be a formidable weapon attackers can use to damage, compromise or manipulate systems. AI4CYBER ambitions to provide an Ecosystem Framework of next-generation trustworthy cybersecurity services that leverage AI and Big Data technologies to support system developers and operators in effectively managing robustness, resilience, and dynamic response against advanced and AI-powered cyberattacks. The project will deliver a new breed of AI-driven software robustness and security testing services that significantly facilitates the testing experts work, through smarter flaw identification and code fixing automation. Moreover, the project will provide cybersecurity services for comprehension, detection and analysis of AI-powered attacks to prepare the critical systems to be resilient against them. Incident response support by AI4CYBER will offload security operators from complex and tedious tasks offering them mechanisms to optimize the orchestration of the most appropriate combination of security protections, and continuously learn from system status and defences’ efficiency. The AI4CYBER framework will ensure fundamental rights and values-based AI technology in its services, through the integration of demonstrable explainability, fairness and technology robustness (security) capabilities in the AI4CYBER components. The ecosystem will be validated in three scenarios: i) Detection and Mitigation of AI-powered Attacks against the Energy Sector, ii) Robustness and autonomous adaptation of Banking applications to face AI-powered attacks and iii) Resilient hospital services against advanced and AI-powered cyber-physical attacks.