Overirrigation and excessive use of synthetic fertilizers and pesticides increase yield cost, contaminate the aquifer and destroy the biodiversity, while suboptimal livestock production increases GhG emissions, contributing to the global warming. The solution on the triangle a) global food needs, b) competitiveness/farmers’ fair income and c) sustainable farming/protection of the environment lies in knowledge. Modern farms create a huge amount of data based on IoT sensors and drones, while vast EO data become available via Copernicus Hubs. AgriDataSpace aims to establish itself as the “Game Changer” in Smart Farming and agri-environmental monitoring, and strengthen the smart-farming capacities, competitiveness and fair income by introducing an innovative, intelligent and multi-technology, fully distributed platform of platforms. To achieve technological maturity and massive acceptance, AgriDataSpace adopts and adapts a multidimensional approach that combines state of the art big data and data-spaces’ technologies (BDVA/IDSA/GAIA-X) with agricultural knowledge, new business models and agri-environment policies, leverages on existing platforms and edge computing, and introduces novel concepts, methods, tools, pilots and engagement campaigns to go beyond today’s state of the art, perform breakthrough research and create sustainable innovation in upscaling (real-time) sensor data, already evident within the project lifetime. AgriDataSpace will be validated via 24 Use cases in 23 pilots in 9 countries, representing more than 181,000ha with 25 types of crops that span from southwest to northeast Europe, outdoor and greenhouse crops, organic and non-organic production, and more than 2,000 animals of 5 types. More than 4,200 farmers will provide insights and more than 89,000 will be directly informed. More than 1,600 sensors will be utilized and more than 4,500 additional sensors will be installed to measure (real-time) data, including more than 2,500 RFID tags.

TITAN will enrich the EOSC Interoperability Framework (IF) with a software platform solution for confidential data collaboration and secure and privacy-preserving data processing. The platform will enable access to sensitive data sets from public entities and government agencies and will be compatible by design with the EOSC IF on the technical, semantic, organisational and legal layers. To promote community adoption of TITAN’s open-source software artefacts, the solution will be practically demonstrated in several vertical cross-border scenarios - notably in the public administration and healthcare sector

PolicyCLOUD delivers a unique integrated environment addressing the full lifecycle of policy management: modelling, monitoring, enforcing, simulation, analysis and compliance. The environment will utilize the capabilities offered by the European Cloud Initiative, with an emphasis on data analysis to facilitate evidence-based policy making. PolicyCLOUD introduces a pioneering approach for the development of policies “collections” to exploit collective knowledge towards policy co-creation and cross-sector optimization. The co-creation of multi-modal policies will follow an innovative modelling process for structural representation of schema-based policies. PolicyCLOUD environment seamlessly integrates a methodology for utilizing cloud environments and data in policy making structures considering sociological, cultural, political, legal and economic properties. PolicyCLOUD environment will integrate a set of reusable models and tools, ranging from social dynamics and behaviour analysis, to situational knowledge acquisition, opinion mining and sentiment analysis, complemented with tools for data aggregation, linking and cleaning. Citizen participation will be ensured exploiting techniques for incentives management and a Living Lab approach put forward by PolicyCLOUD. A toolkit which allows the specification of transferable and re-usable analytics tasks in a declarative way, their integration in the policies management path, as well as an adaptive visualization environment, will realize PolicyCLOUD’s vision of openness and extensibility. Targeting high impact, PolicyCLOUD delivers a data marketplace enabling the creation of an entire ecosystem of stakeholders contributing, producing, processing and using policy-related data assets. The reusable models and tools and this marketplace provide the ground for the proposed dual-business plan, which has been compiled by the consortium to ensure the long-term sustainability and take-up of the PolicyCLOUD results.

ABRACADABRA is based on the prior assumption that non-energy-related benefits play a key role in the deep renovation of existing buildings. In particular, ABRA actions will focus on the creation of a substantial increase of the real estate value of the existing buildings through a significant energy and architectural transformation. The central goals of the proposal consist of an important reduction of the pay back time of the interventions, a strengthening of the key investors’ confidence, increasing quality and attractiveness of the existing buildings’ stock and, finally, reaching a concrete market acceleration towards the Nearly Zero Energy Buildings target. The actual investment gap in the deep renovation sector is due to the fact that high investments are required up-front and they are generally characterised by an excessively high degree of risk and long payback times. It is therefore necessary to develop harmonized, concerted and innovative actions to unlock the needed public and private funds, fill the energy efficiency investment gap and ultimately contribute to re-launch the construction market and create new jobs. Therefore, ABRA aims at demonstrating to the key stakeholders and financial investors the attractiveness of a new renovation strategy based on AdoRe, intended as one (or a set of) Assistant Building unit(s) - like aside or façade addictions, rooftop extensions or even an entire new building construction - that adopt the existing buildings (the Assisted Buildings). The creation of these new Assistant Buildings’ Additions integrated with Renewable Energy Sources aims at reducing the initial investment allocated for the deep renovation of the existing building creating an up-grading synergy between old and new. The ABRA strategy results in the implementation of a punctual densification policy that has been proven capable of fostering the investments in deep renovation of the existing built environment throughout Europe

Availability of large volumes of user data combined with tailored statistical analysis present a unique opportunity for organizations across the spectrum to adapt and finetune their services according to individual needs. Having shown remarkable results in analyzing user data, machine learning models attracted global adulation and are applied in a plethora of applications including medical diagnostics, pattern recognition, and threat intelligence. However, such service improvements and personalization based on user data analysis come at the heavy cost of privacy loss. Furthermore, practice showed that systems that use such models incorporate proxies that are often inexact, biased and often unfair. In HARPOCRATES, we focus on setting the foundations of digitally blind evaluation systems that will, by design, eliminate proxies such as geography, gender, race, and others and eventually have a tangible impact on building fairer, democratic and unbiased societies. To do so, we plan to design several practical cryptographic schemes (Functional Encryption and Hybrid Homomorphic Encryption) for analyzing data in a privacy-preserving way. Besides processing statistical data in a privacy-preserving way, we also aim to enable a richer, more balanced and comprehensive approach where data analytics and cryptography go hand in hand with a shift towards increased privacy. In HARPOCRATES we will first show how to effectively combine cryptography with the principles of differential privacy to secure and privatise databases. Next, we will build privacy-preserving machine learning models able to classify encrypted data by performing high accuracy predictions directly on ciphertexts across federated data spaces. Finally, to demonstrate how these solutions respond to users’ needs, we will implement two real-world cross-border data sharing scenarios related to health data analysis for sleep medicine and threat intelligence for local authorities.