Due to the increased incidence, prevalence and mortality of chronic diseases and multimorbidity, the need of palliative care (PC) resources is a challenge for health and social care systems. However, currently, access to PC remains inconsistent and, due to the ageing population, it is expected an increase in the rate of people requiring this kind of care over the next 25 years. The project Patient-centred pathways of early palliative care, supportive ecosystems and appraisal standard (INADVANCE) proposes a novel model of PC based on early integration and personalized pathways addressed specifically to older people with complex chronic conditions. Thus, the overall aim of INADVANCE is to improve the benefit of PC through the design of effective, replicable and cost-effective early PC interventions centred-on and oriented-by the patients. Interventions are defined for/orientated on patients, families, informal caregivers, and front-line care professionals. In order to achieve this main goal, INADVANCE will produce the following evidence-based outputs to assist care professionals, service managers and policy and decision-makers in their scalability and replicability: a) stratification tools to identify potential beneficiaries of early PC actions; b) optimized interventions co-designed by needs and preferences from patients and their relatives; c) eHealth tools addressed to empower palliative patients ecosystem; d) policy recommendations and clinical guidelines addressed to service providers and policy and decision makers; e) an appraisal standard and dashboard facilitating a critical and comprehensive comparison between actions and interventions derived from the project. The INADVANCE consortium brings together leading interdisciplinary academic, clinical and technological partners from EU organizations actively responding challenges from health and social care systems and policy-makers in the field.

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.

Older adults typically prefer living at their homes as long as possible. However, they often need to be institutionalized due to the age related problems. Homecare can benefit today from a range of existing technologies including smartphones, sensors, etc., however, their effectiveness is limited by the sense of fear and uncomfortableness that many older adults feel. Most current technologies, in fact, are often not designed for older adults, least of all for users with memory impairments. The derivative limitations become a major barrier, severely limiting use of technological assistance in a home environment. CAPTAIN proposes a “transparent” technology designed to turn the home of the older adult into a ubiquitous assistant specifically designed to compensate for their physical and memory impairments during their daily living. To do so, it leverages on a few state of the art technologies, as follows: “Projected augmented reality” to project, through use of micro-projectors, contextualised (directly on walls, floor, tables, etc.) information and instructions on top of the real environment. Real-time 3D sensing technologies to comprehend the “indoor space” (nature and position of objects and actions of the persons) and to allow transforming it into a ubiquitous tangible interface. 3D sensing, together with speech analysis, will allow device-free comprehension of the environment and of the behaviour of the adults (including its changes). Non-invasive physiological and emotional data analysis from facial micro-expressions and human body pose thanks to scalable, robust, and accurate deep learning and artificial intelligence. The coach will leverage on a motivational engine to promote correct nutrition, physical activity, cognitive and physical training, risk avoidance, and social participation. To achieve this CAPTAIN will foster a truly user-centered co-design philosophy -based on constant involvement of older adult in the design, development, and testing stages.

Researchers in the Health and Wellbeing domain need 1) convenient access to research infrastructures, 2) direct engagement of people in order to validate hypotheses, co-design solutions and services, evaluate their feasibility and effectiveness and participate in the translation and mobilization of outcomes and 3) experience from multidisciplinary domains (e.g. healthcare management, assistive technologies, data science) to deal with the complexity of health and wellbeing research. Over the last few years, Living Labs have emerged as resilient research and innovation infrastructures and have proved to be key to the integration of research and innovation processes in real life settings, focusing primarily on people and their engagement in research procedures. VITALISE opens up Living Lab Infrastructures as a means to facilitate and promote research activities in the Health and Wellbeing domain in Europe and beyond by enabling in-person Transnational Access to 17 Living Lab research infrastructures and by supporting remote digital access to datasets (Virtual Access) of rehabilitation, transitional care and everyday life activities through harmonized processes and common tools. VITALISE will design and develop ICT tools for shared access of similar devices and applications used across Living Labs, as well as for collecting, storing and sharing datasets. Lastly, VITALISE will invest in the development of Training methods towards the wider understanding and valorisation of Living Lab methodologies in the research community.

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