There is a need to develop an autonomous, reliable, cost effective technology to map vast terrains, in terms of mineral and raw material contents which will aid in reducing the cost of mineral exploration, currently performed by ROVs and dedicated SSVs and crew. Furthermore there is a need to identify, in an efficient and non-intrusive manner (minimum impact to the environment), the most rich mineral sites. This technology will aid the seabed mining industry, reduce the cost of exploration and especially the detailed identification of the raw materials contained in a mining sites and enable targeted mining only of the richest resources existing. The ROBUST proposal aims to tackle the aforementioned issue by developing sea bed in situ material identification through the fusion of two technologies, namely laser-based in-situ element-analyzing capability merged with underwater AUV (Autonomous Underwater Vehicle) technologies for sea bed 3D mapping. This will enable resource identification done by robotic control enabled by the synergy between AUV hovering and manipulator capabilities. The underwater robotic laser process is the Laser Induced Breakdown Spectroscopy (LIBS), used for identification of materials on the sea bed. The AUV Robotic vehicle will dive, identify the resources that are targeted for LIBS scanning through 3D real time mapping of the terrain (hydro-acoustically, laser scanners, photogrammetry) and position the LIBS in the required locations of mineral deposits on the ocean floor to autonomously perform qualitative and quantitative analyses.

Melanoma is one of the most aggressive cancers that can be discovered at an early stage, and it is responsible for 60% of lethal skin neoplasia. Its incidence has been increasing in white population and could become a public health challenge because of an increase in life expectancy of the elderly population. Total body skin examination, the primary screening mechanism for melanoma, checks each pigmented skin lesion individually in search of typical melanoma signs. This can be a very time consuming technique for patients with atypical mole syndrome or a large number of naevi. iToBoS aims at developing an AI diagnostic platform for early detection of melanoma. The platform includes a novel total body scanner and a Computer Aided Diagnostics (CAD) tool to integrate various data sources such as medical records, genomics data and in vivo imaging. This approach will lead to a highly patient-tailored, early diagnosis of melanoma. The project will develop and validate an AI cognitive assistant tool to empower healthcare practitioners, offering a risk assessment for every mole. Beyond integrating all available information about the patient to personalise the diagnostic, it will provide methods for visualising, explaining and interpreting AI models, thus overcoming the “black box” nature of current AI-enabled CAD systems, and providing dermatologists with valuable information for their clinical practice. The new total body scanner will be based on an existing prototype developed by 3 of the project partners, but powered with high-resolution cameras equipped with liquid lenses. These novel lenses, based on two immiscible fluids of different refractive index, will allow achieving unprecedented image quality of the whole body. The integration of such images with all available patient data using machine learning will lead to a new dermoscopic diagnostic tool providing prompt, reliable and highly personalised diagnostics for optimal judgement in clinical practice.

To unlock and boost new discoveries that will be crucial to face climate change, and regulate human actives, which will be key for both environmental conservation and socio-economical activities. We aim to make all the past, current, and future biological and oceanographic data available to everybody. Therefore, we will use relevant sleeping data, by designing new tools and methodologies to use and process relevant data already collected for different institutions, which may come from physical and chemical sensors, or video cameras. We will also harmonize the data, promoting tools to make them the standard among researchers and data-generator actors, developing protocols and best practices, like standardization tools as PUCK among marine sensors and monitoring platforms, and unifying libraries and resources (e.g., FanthomNet or Emodnet). At the same time, we will ensure a secured, sustained and reliable data flows by developing auto correction/validation methodologies and by publishing a set of tools and pipelines to ensure the trustfulness of data. Moreover, we will be using economies of scale and enhanced standardization to conduct several pilot sea-basin scale monitoring tests using two strategies: (1) using existing relevant sleeping data form online and partner repositories, and (2) using new data collected during field test demonstrations. Here, we will develop tools to better support assessment: studying and identifying key indicators and mechanisms to extract them from the data will generate the appropriate guidelines for policymaker, researchers, and socioeconomic sectors. Making those tools, methodologies, and implementations open source for the researchers and public in general will boost their utilization and improvement, even after the conclusion of the present project. With these demonstration examples, the international collaboration, and open source resources, we aim to make our proposal by fact the standard gold to follow in the following years

NEANIAS is an ambitious project that comprehensively addresses the ‘Prototyping New Innovative Services’ challenge set out in the recent ‘Roadmap for EOSC’ foreseen actions. NEANIAS will drive the co-design, delivery, and integration into EOSC of innovative thematic services, derived from state-of-the-art research assets and practices in three major sectors: underwater research, atmospheric research and space research. Each of these sectors engages a diverse set of research and business groups, practices, and technologies. Each thematic service will not only address its community-specific needs but will also enable the transition of the respective community to the EOSC concept and Open Science principles. In doing so, NEANIAS provides its communities with plentiful resource access, collaboration instruments, and interdisciplinary research mechanisms, which will amplify and broaden each community’s research and knowledge generation activities. From a technological perspective, NEANIAS will deliver a rich set of services that are designed to be flexible and extensible; they will be able to accommodate the needs of communities beyond their original definition and to adapt to neighboring cases, fostering reproducibility and re-usability. From a sustainability perspective, NEANIAS identifies promising, cutting-edge business cases across several user communities and lays out several concrete exploitation opportunities.