GREEN.DAT.AI aims to channel the potential of AI towards the goals of the European Green Deal, by developing novel Energy-Efficient Large-Scale Data Analytics Services, ready-to-use in industrial AI-based systems, while reducing the environmental impact of data management processes. GREEN.DAT.AI will demonstrate the efficiencies of the new analytics services in four industries (Smart Energy, Smart Agriculture/Agri-food, Smart Mobility, Smart Banking) and six different application scenarios, leveraging the use of European Data Spaces. The ambition is to exploit mature (TRL5 or higher) solutions already developed in recent H2020 projects and deliver an efficient, massively distributed, open-source, green, AI/FL - ready platform, and a validated go-to-market TRL7/8 Toolbox for AI-ready Data Spaces. The services will cover AI-enabled data enrichment, Incentive mechanisms for Data Sharing, Synthetic Data Generation, Large-scale learning at the Edge/Fog, Federated & Auto ML at the edge/fog, Explainable AI/Feature Learning with Privacy Preservation, Federated & Automatic Transfer Learning, Adaptive FL for Digital Twin Applications, Automated IoT event-based change detection/ forecasting. The GREEN.DAT.AI Consortium consists of a multidisciplinary group of 17 partners from 10 different countries (and one associated party), well balanced in terms of expertise. The vast majority of partners already have key roles in a number of projects funded under the Big Data PPP (ICT-16-2017) topic, namely BigDataStack, CLASS, Track & Know, and I-BiDaaS and are serving as active members of the BDVA/DAIRO Association, FIWARE, AIOTI, and ETSI. In addition, partners come from a variety of sectors, such as banking, mobility, energy, and agriculture, constituting a representative workforce of their respective domains, which will contribute to industry adoption and stimulate uptake in other sectors as well.

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

Mobility in the urban and maritime domains hugely impacts the global economy, generating data at high rates from an increasing number of moving objects. Management of the complete lifecycle of such data implies that trustworthy and privacy-preserving infrastructures need to be put in place, so that reliable and secure data operations can be provided. Meanwhile, the mobility data exploitation has still a wide potential due to the emerging applications and the environmental footprint caused by mobility (e.g., carbon emissions, energy consumption). Motivated by these pressing needs, MobiSpaces delivers an end-to-end mobility-aware and mobility-optimized data governance platform with key differentiating factor that the outcomes of mobility analytics will be utilized to optimize the complete data path, in terms of efficient, reliable, secure, fair and trustworthy data processing. MobiSpaces promises the extraction of actionable insights from ubiquitous mobile sensor data and IoT devices in a decentralized way, offering intelligent transportation services, enforcing privacy constraints at the expected point of action. XAI techniques will be applied at the level of data management and machine learning, supporting the creation of comprehensive and interpretable prediction models, while all the research outcomes will be validated through five use cases: 1) intelligent public transportation services in urban environments, 2) intelligent infrastructure traffic sensing for smart cities, 3) vessel tracking for non-cooperative vessels, 4) decentralized processing on-board of vessels, and 5) enhanced nautical maps via crowdsourced bathymetry vessels data. These actions will be continuously adapted and monitored for their environmental sustainability, whereas MobiSpaces will create a widely accepted standard of data processing and analytics, alongside a data-rich ecosystem providing trustworthy and actionable data that is vital for enabling the growth of the EU digital economy.

The Message in a Bottle (MiB) project is a system for providing specialized traceability information at the bottle level. This is an application, which will give a new "form" to the wines produced by the Lyraraki winery - GEA SA, providing the possibility of interaction of the final consumer with the bottle. At the same time, the MiB project will contribute to the increase of fidelity to the product (wine), providing augmented reality functions to the consumer, but also a wealth of information that will be available for each bottle separately. The aim of the project is to use traceability data, as well as geological, historical, meteorological and other data, in order to produce a personalized bottle story, both for the ordinary consumer and for the specialized oenologist and wine expert. The story of the bottle tells how a specific wine was produced, at the bottle level, connecting the bottle (and its contents) with its oenological, cultural, geographical, historical and ecological context. Apart from telling the wine story, the system suggests ways of consuming it, so that its special characteristics are highlighted. In this way the winery will be able to highlight all the quality characteristics of the wines it produces, adding value to its products, but also to come close to its customers by making them part of his story.

In an increasingly instrumented and inter-connected digital world, citizens generate vast amounts of data, much of it being valuable and a significant part of it being personal. However, controlling who can collect it, limiting what they can do with it, and determining how best to protect it, remain deeply undecided issues. CAP-A will deploy a socio-technical solution based on collective awareness and informed consent, where by data collection and use by digital products are driven by the expectations and needs of the consumers themselves, through a collaborative participatory process and the configuration of collective privacy norms.

The VRE4EIC project will create a Europe-wide interoperable Virtual Research Environment to empower multidisciplinary research communities and accelerate innovation and collaboration. It will addresses key data and software challenges in supporting multidisciplinary data driven sciences, including: 1) understanding complex user requirements across domains by closely involving committed user communities; 2) improving the quality of VRE user experience by providing user centered, secure, privacy compliant, sustainable environments for accessing data, composing workflows and tracking data publications; 3) increasing VRE usage in multidisciplinary research domains by abstracting and reusing building blocks and workflows from existing VRE initiatives; 4) improving the interoperability of heterogeneous discovery, contextual and detailed metadata across all layers of the VRE; and 5) promoting the exploitation of VRE4EIC solutions to different research communities and commercially.

Innovation and creativity are predictors of success in a knowledge-based. Yet the fuzziness and unpredictability of the creative workflow remains an obstacle for effective ICT support. Tools that require users to formalize and structure their ideas and working processes to a degree at odds with creative practice are frequently rejected. The IdeaGarden project therefore starts from an understanding of creative problem solving as a complex and situated knowledge practice rather than as a set of well-defined methods and techniques. The project will develop a creative learning environment, capitalizing on the notion of visual information mash-ups as catalysts for creative working and learning. Adopting a practice-oriented approach will further the understanding of creativity in different settings and open up new perspectives for ICT support. This perspective will also give rise to new methods for seeding and cultivating creative knowledge practices in workplace and educational settings. To leverage the capabilities of current ICT systems, new interactions techniques will be devised that enable users to stay in control and collaboratively navigate the creative process, handling multiple types of resources. In addition, taking benefit of the linked Data paradigm will provide new possibilities for creative search, the construction of knowledge as well as the reflection of the collaborative process.

Currently, much of the data we use, e.g. demographic records, clinical statistics, personal and enterprise data as well as many scientific measurements cannot be reproduced. However, there is overwhelming evidence that we should keep such data where it is technically and economically feasible to do so. Until now this problem has been approached by keeping this information in fixed data sets and using extensions to the standard methods of disseminating and archiving traditional (paper) artifacts. Given the complexity, the interlinking and the dynamic nature of current data, especially linked Open Data, radically new methods are needed. DIACHRON tackles this problem with a fundamental assumption: that the processes of publishing and preservation data are one and the same. Data are archived at the point of creation and archiving and dissemination are synonymous. DIACHRON takes on the challenges of evolution, archiving, provenance, annotation, citation, and data quality in the context of linked Open Data and modern database systems. DIACHRON intends to automate the collection of metadata, provenance and all forms of contextual information so that data are accessible and usable at the point of creation and remain so indefinitely. The results of DIACHRON are evaluated in three large-scale use cases: open governmental data life-cycles, large enterprise data intranets and scientific data ecosystems in the life-sciences.

The creation of 3D models for ancient monuments is a not an easy task, due to the difficulty of creating a dimensionally accurate 3D model and a 3D virtual representation of the monument. In the last 5 years Terrestrial Laser Scanners (TLS) have been introduced in order to enhance the development of 3D models of monuments and the promotion of cultural heritage. The process of scanning, data processing and documentation of cultural heritage monuments, includes several stages in order to create a complete 3D model. Furthermore, even if TLS has clear advantages against conventional 3D model techniques, as far as accuracy, speed, reliability and quality of 3D data obtained is concerned, it has only a small infiltration to the market. This is due to the complexity of the overall 3D scan process and the lack of standardization of the different requested products. The Project 3DSYSTEK aims to cover these needs by developing a novel system for 3D Documentation, Promotion and Exploitation of Cultural Heritage Monuments via complete 3D data acquisition, 3D modeling and Metadata recording. It will combine: 3D terrestrial laser scanning procedures,3D modeling procedures, Recording of Metadata in all phases (3D data acquisition, 3D data processing, production of final products), Recording of annotations that can be used in all phases. The production of automated product results, including all integrated Metadata, according to the user needs.

The 3D-COFORM project will advance the state-of-the-art in 3D-digitsation and make 3D-documentation an everyday practical choice for digital documentation campaigns in the cultural heritage sector. The project addresses all aspects of 3D-capture, 3D-processing, the semantics of shape, material properties, metadata and provenance, integration with other sources (textual and other media); search, research and dissemination to the public and professional alike. A strong technical research program is complemented by research into practical business aspects: business models for exploitation of 3D assets, workflow planning and execution for mass digitisation, socio-economic impact assessment; and above all the creation of a Virtual Centre of Competence in 3D digitization.

The ICS-FORTH Ambient Intelligence (AmI) Programme is a long-term horizontal interdisciplinary RTD Programme aiming to develop and apply pioneering human-centric AmI technologies and Smart Environments.The Programme constitutes a platform for cooperative research towards developing and studying AmI-related technologies and assessing their impact upon the individual, as well as upon the society as a whole, but also as a unique showcase for demonstrating the potential, added-value and benefits of AmI technologies in different aspects of everyday life and activities. The AmI Programme builds on the scientific know-how and technical expertise of ICS-FORTH in a number of contributing critical domains such as Human-Computer Interaction, Universal Access, Artificial Intelligence, Semantic-based Knowledge Systems, Robotics, Computational Vision, Networks and Telecommunications, Information Security, Distributed Systems, Computer Architectures, Microelectronics, Sensors and Biomedical Informatics.

The Vikelaia Municipal Library of Heraklion, with funding from Greece and the European Community, implemented a digital library of historical documents within the framework of the national Information Society programme. The purpose of the project was to develop a digital library of historical documents that would support the electronic documentation , the thematic classification of documents and the creation of a website for information and easy, controlled access to the archival material. The digital library except from traditional finding aid capabilities, supports the semantic indexing of documents and their multifaceted classification based on concepts. The archival material concerns the Turkish Archive of Heraklion , the Archives of Dimogerontia and the archives of newspapers and magazines of the Vikelaia Municipal Library. The digitized material included 100,000 A3 pages of the Turkish Archive of Heraklion, 100,000 A3 pages of the Archives of Dimogerontia and 300,000 A2 pages of the newspapers and magazines archive