The digital and circular economy (CE) transition of bio-based industries is a critical objective for Europe’s climate ambitions and its economic competitiveness. Given the urgency of these demands, Europe’s bio-based industries need to leapfrog over past digital technologies and implementations, pioneering innovative solutions for creating bio-based products and services that are circular, as well as environmentally and socially sustainable. To realise these ambitions, the bi0SpaCE project will deliver a suite of technologies, services, guidance frameworks, and standards, combined into the open-access bi0S platform, for rapid deployment and scaling of (CE) solutions and services across bio-based industries and their value chains. bi0SpaCE will advance the creation and implementation of Industry 4.0 enhanced Digital Product Passports (DPPs), linked to an International Dataspace (IDS) compliant CE dataspace, enabling the creation of dynamic and decentralised DPPs for secure and trustworthy sharing of CE and sustainability performance data of bio-based products across the value chain, as well as providing transparency of green and CE claims to consumers. bi0SpaCE brings together a consortium consisting of 1 HEI, 4 RTOs, 1 SMEs, 1 industrial assoc., 2 large industries, and 1 startup, across 5 EU and 2 associated countries, over a 36-month project period. The knowledge and technologies created in bi0SpaCE will be demonstrated and validated across 4 complementary bio-based sectors: (i) paperboard production, (ii) eco-industrial parks with bio-based energy and products producers and consumers, (iii) plant-based products and cosmetics, and (iv) bio-derived industrial chemicals.

Multi-stage manufacturing, which is typical in important industrial sectors such as automotive, house hold appliance and semiconductor manufacturing just to name few, is inherently complex. The main idea of GO0D MAN project is to integrate and combine process and quality control for a multi –stage manufacturing production into a distributed system architecture built on agent-based Cyber-Physical Systems (CPS) and smart inspection tools designed to support Zero-Defect Manufacturing (ZDM) strategies. Data analytics tools provide a mean for knowledge build-up, system control and ZDM management. Real time and early identification of deviations and trends, performed at local level, allow to prevent the generation of defects at single stage and their propagation to down-stream processes, enabling the global system to be predictive (early detection of process faults) and proactive (self-adaptation to different conditions). The GO0D MAN project is based on the results of previous successful EU projects and integrates them to realize and deploy a Zero Defect Manufacturing framework for multi-stage production lines, in collaboration with industry partners, a system integrator, two technology providers and three end users. The use cases are representative of key European industrial sectors and have different types of multi-stage production systems: the first use case concerns highly automated serial mass production of automotive components, the second use case is about batch production of high precision mechanical components for automotive electro valves, the third use case produces professional customized products such as ovens for restaurants. Successful completion of this project will provide a replicable system architecture for ZDM. The results will be broadly applicable in a variety of industries to improve the overall quality and productivity of production systems.

The Da.Re. project (Da.Re. stands for “Data science pathways to Re-imagine education”) is an initiative proposed and managed by 11 different partners (Loccioni AEA s.r.l., Università degli Studi di Camerino, Nissatec, Maisis - Information Systems Lda., The Open University, Abelium d.o.o., raziskave in razvoj, Univerza na Primorskem Università del Litorale, Instituto Politécnico de Bragança, Vision Scientific Ltd., e-consulenza, Associazione degli Industriali) based in 5 European countries (Italy, Slovenia, Portugal, Serbia and the UK).The project was created for responding to a very important issue raised by different kind of organizations in the world: data scientist will represent one of the most required professional figures in the next years, while the shortage of this kind of professionals is already an issue quantified in hundreds of thousands of uncovered job positions.More, the distance between Higher Education Institutions (HEI) and the business sector is still very big and the so called “knowledge triangle” should be helped in its crucial connective function.It is also well known in Europe that data increasingly drives all social and economic activity and innovation, and that strength in data science is fundamental to economic success and social wellbeing. Over the last decade it has become clear that data has huge latent value with companies such as Facebook, Amazon, and Google finding ways to monetize that latency to gain spectacular revenues. This has been highly disruptive to societies worldwide creating new kinds of social and economic interactions with both positive and negative effects. Through initiatives such as the General Data Protection Regulations (GDPR) Europe is a world leader in mitigating the negative effects of the big data revolution and establishing ethical principles for the use of personal information. However, Europe lags behind the USA in the emergence of companies and organisations able to manage big data and extract value from it.In this framework the Da.Re. project was intended to obtain a fundamental target, the creation of a full training curriculum of European Qualifications Framework (EQF) level 7 organized to be:-Cross sectoral-International-Multi-targeted-AccessibleIn order to do so, the Consortium created 3 different phases intended to 1.analyse the EU State of the Art in the field of Data Science, comparing the training offer with the demand of the European organizations for such a kind of professional, in order to find gaps among these 2 elements (Intellectual Output 1);2.work creating modules to be experimented on real case studies for training a new generation of Data Scientists (Intellectual Output 2);3.apply the knowledge and experience gained during the 2 previous phases to better shape the final curriculum and make propositions to HEIs and Ministries of Education in EU (Intellectual Output 3).Thanks to the effort put on this action by the different partners, Da.Re. raised an international interest that went even beyond the European borders, reaching more than 120 countries. The initiative collected the direct interest of stakeholders in different ways, leveraging on social networks, the project website, newsletters, participatory and multiplier events, conferences and seminars. In total, the project directly involved more than 1.000 stakeholders thanks to these different initiatives.The pilot course created with the project obtained more than 200 applications (while the available material online already reached more than 600 downloads), and the modules created, together with the final curriculum produced will constitute a heritage for the future work to be made in this crucial and horizontal sector represented by Data Science.

PrEstoCloud project will make substantial research contributions in the cloud computing and real-time data intensive applications domains, in order to provide a dynamic, distributed, self-adaptive and proactively configurable architecture for processing Big Data streams. In particular, PrEstoCloud aims to combine real-time Big Data, mobile processing and cloud computing research in a unique way that entails proactiveness of cloud resources use and extension of the fog computing paradigm to the extreme edge of the network. The envisioned PrEstoCloud solution is driven by the microservices paradigm and has been structured across five different conceptual layers: i) Meta-management; ii) Control; iii) Cloud infrastructure; iv) Cloud/Edge communication and v) Devices, layers. This innovative solution will address the challenge of cloud-based self-adaptive real-time Big Data processing, including mobile stream processing and will be demonstrated and assessed in several challenging, complementary and commercially-promising pilots. There will be three PrEstoCloud pilots from the logistics, mobile journalism and security surveillance, application domains. The objective is to validate the PrEstoCloud solution, prove that it is domain agnostic and demonstrate its added-value for attracting early adopters, thus initialising the exploitation process early on.