iPhotoCult aims to design, develop, demonstrate, and validate innovative and intelligent solutions that will allow for non-invasive and safe ways to inspect, document, diagnose, and monitor Cultural Heritage (CH) so as to ensure efficient adaptation of sustainable management plans. These solutions comprise (a) a unique suite of advanced diagnostic and monitoring tools along with methodologies for their effective and optimum use, combined with (b) an intelligent data processing, visualization and prediction software services platform, for the remote and/or on-site monitoring of CH buildings, monuments and artefacts impacted by a wide variety of environmental and anthropogenic threats. iPhotoCult contributes significantly to the preservation of CH by adding verifiable dimensions to preservation strategies. The iPhotoCult Solutions will enable and empower CH community to better assess CH in a cost-effective, efficient, reliable, user-friendly and safe manner. These solutions will revolutionize the capabilities of end-users to diagnose and continually monitor the condition of materials constituting CH including those used in their restoration. iPhotoCult interdisciplinary approach involving many different stakeholders and CH pilots across Europe will serve as use-cases for demonstration, testing and validation of the iPhotoCult Solutions delivered and applied in the form of demonstrators. The outcomes to a variety of CH pilots will provide crucial insights and enable the optimization of the tools and methodologies to help in the decision-making and serve for replication to Europe’s CH in many in real settings. Ultimately, iPhotoCult main goal is to contribute to the sustainable management of Europe's CH by leveraging advanced photonics technologies and intelligent software to contribute to the decision-making process for its preservation. iPhotoCult aims to develop a comprehensive, cost-effective, and scalable solution that can be deployed across Europe and beyond.

The mission of RAISE is to provide the infrastructure for a distributed crowdsourced data processing system, moving from open data to open access data for processing. RAISE will provide the mechanism for sending the algorithm to the dataset instead of sending the data to the algorithm. The real value of open data for the research community is not to access them but to process them as conveniently as possible in order to reduce time-to-result and increase productivity. RAISE aims at promoting a transparent way of sharing and processing data, enabling the research community to publish their work with evidence-based authenticity of the data-analysis performed, ensuring at the same time the accreditation of their work. RAISE will be grounded on the fundamental principle defined in the FAIR Guiding Principles for scientific data management and stewardship (Findability, Accessibility, Interoperability and Reusability). To do so, RAISE brings the processing algorithm (small size) to the dataset (large size) instead of downloading the dataset to the computer where the processing algorithm is. To increase the processing capacity of the dataset repositories, RAISE borrows the crowdsourcing concept where researchers can easily integrate in the existing workflows computers serving both their datasets and the processing capacity. RAISE will produce the following Outputs: 1. A trustworthy crowdsourced network of RAI Certified nodes offering data storing and processing resources 2. The RAI Cloud platform to orchestrate the data sharing, processing and finding. 3. The Research Analysis Identifier ? RAI , a unique identifier of any result along with the dataset information and the processing script, without disclosing any source code or raw data. 4. Dataset plagiarism identification and dataset proof-of-origin services, to maximise the level of trust of the RAISE system. 5. The RAI Synthetic Data Generator

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.

DIOPTRA aims to introduce a front-line screening tool that will consider risk factors and protein biomarkers for pinpointing individuals at a high risk for colorectal cancer (CRC) incidence. Tissue & blood samples will be examined towards a discriminative set of prognostic proteins that are detectable via standard bloodwork and can indicate a need for further evaluation (i.e. colonoscopy). Other data (e.g. medical, behavioural) will also be considered as potential risk factors. Artificial intelligence (AI) will be leveraged for assessing prognostic power, while personalised behavioural change will be promoted based on modifiable risk factors. Given the low citizen participation on CRC screening across EU, DIOPTRA seeks to broaden the evaluated population, boosting participation rates and bypassing age screening thresholds. This action is part of the Cancer Mission cluster of projects on ‘Prevention, including Screening’.

During the last decades, overweight & obesity rates have reached epidemic proportions across EU. Prevalence in childhood is particularly alarming, resulting in psychological impacts & increased risks for NCDs. However, although obesity-related research has provided interesting results, several issues arise due to the lack of meta-reviews & the nature of data collected. To address this, Bio-Streams aims on supporting the optimal use (and re-use) of health data (e.g. biological, demographic, epigenetic, etc.) to generate metadata/knowledge and provide new evidences, methodologies & tools for: a) creating and deploying the Bio-Streams Biobank that will act as a scientific platform for research in obesity and better diagnostic and therapeutic approaches;b) understanding the transition from metabolically healthy to unhealthy, preventing under age obesity; c) designing better strategies to educate & empower young citizens for weight self-management; d) coordinating authorities & policy makers to develop cross-sectoral solutions for health promotion & under age obesity prevention. Specifically, Bio-Streams delivers a dedicated under age obesity biobank providing real-world health data (including biospecimens, anthropometrics, behavioural and cost data) from retrospective and prospective sources, while taking into account efficient data harmonisation and standardisation principles, transforming data valorisation towards under age obesity prevention and future research. Moreover, the Bio-Streams framework for data handling, provides robust and transparent methodologies for operational procedures (data infrastructures), analysis and reporting (via meta-reviews and AI-based Apps/components, evaluated via 12 multi-site pilots (7 clinical and 5 schools)), citizen awareness and lifestyle alteration, delivering obesity prevention guidelines, knowledge generation while fostering considerable opportunities for regional and national health authorities/policymakers.