Artificial Intelligence for the European Open Science Cloud
The AI4EOSC (Artificial Intelligence for the European Open Science Cloud) delivers an enhanced set of advanced services for the development of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) models and applications in the European Open Science Cloud (EOSC). These services are bundled together into a comprehensive platform providing advanced features such as distributed, federated and split learning; novel provenance metadata for AI/ML/DL models; event-driven data processing services or provisioning of AI/ML/DL services based on serverless computing. The project builds on top of the DEEP-Hybrid-DataCloud outcomes and the EOSC compute platform and services in order to provide this specialized compute platform. Moreover, AI4EOSC offers customization components in order to provide tailor made deployments of the platform, adapting to the evolving user needs.
The main outcomes of the AI4EOSC project will be a measurable increase of the number of advanced, high level, customizable services available through the EOSC portal, serving as a catalyst for researchers, facilitating the collaboration, easing access to high-end pan-European resources and reducing the time to results; paired with concrete contributions to the EOSC exploitation perspective, creating a new channel to support the build-up of the EOSC Artificial Intelligence and Machine Learning community of practice.
Secure Interactive Environments for SensiTive data Analytics
The SIESTA project aims to provide a set of tools, services, and methodologies for the effective sharing of sensitive data in the EOSC, following a cloud-based model and approach. SIESTA will provide user-friendly tools with the aim of fostering the uptake of sensitive data sharing and processing in the EOSC. The project will deliver trusted cloud-based environments for the management and sharing of sensitive data that are built in a reproducible way, together with a set of services and tools to ease the secure sharing of sensitive data in the EOSC through state-of-the-art anonymization techniques. The overall objective is to enhance the EOSC Exchange services by delivering a set of cloud-based trusted environments for the analysis of sensitive data in the EOSC demonstrating the feasibility of the FAIR principles over them.
The Copernicus Data Store (CDS) Modernization will provide state-of-the-art technologies for the current CDS operated nby ECMWF, resulting in a more robust infrastructure and services.
A Digital Twin for GEOphysical Extremes
Merging research on geosciences and supercomputing to analyze and forecast tsunamis, earthquakes and volcanic eruptions.
Coordinated by GEO3BCN – CSIC, DT-GEO comprises 26 European organisations across research and technology.
Each partner will collaborate closely to ensure an integrated approach, whilst bringing its special focus to the project.
Cancer Image Europe is pioneering a pan-European federated infrastructure for cancer images, fuelling AI innovations. Our mission is to build a pan-European digital federated infrastructure of cancer-related images, which will be used for the development of AI tools toward Precision Medicine. We hope that this infrastructure will provide the means to develop AI tools that will be able to enhance the (cancer) diagnosis procedure, treatment and the identification of the need for predictive medicine benefiting patients across Europe.
Cancer Image Europe provides a robust, trustworthy platform for researchers, clinicians, and innovators to access diverse cancer images, enabling the benchmarking, testing, and piloting of AI-driven technologies. By connecting high-quality cancer image data and AI experts, Cancer Image Europe facilitates collaboration and accelerates the development of cutting-edge solutions for cancer diagnosis and treatment.
FACE - FAir Computational Epidemiology
The FACE project envisions a huge opportunity in providing an open modeling and simulation platform where scientists can exploit a wide variety of datasets, with different access and privacy policies, as a way to provide rapid responses to epidemics outbreaks through research infrastructure services underpinning and supporting epidemics research.
Greener Future Digital Research Infrastructures
GreenDIGIT brings together 4 major distributed Digital Infrastructures at different lifecycle stages, EGI, SLICES, SoBigData, EBRAINS, to tackle the challenge of environmental impact reduction with the ambition to provide solutions that are reusable across the whole spectrum of digital services on the ESFRI landscape, and play a role model. GreenDIGIT will capture good practices and existing solutions and will develop new technologies and solutions for all aspects of the digital continuum: from service provisioning to monitoring, job scheduling, resources allocation, architecture, workload and Open Science practices, task execution, storage, and use of green energy. GreenDIGIT will deliver these solutions as building blocks, with a reference architecture and guidelines for RIs to lower their environmental footprint. It will include the extension of a workload manager, Virtual Machine Manager, AI/ML training framework, and IoT/5G/network management solutions from 4 participating RIs with new brokering logic to optimize task execution toward low-energy use. User-side tools and Virtual Research Environments will also be expanded with energy usage reporting and reproducibility capabilities to motivate users to apply low-energy practices. The new solutions will be validated through reference scientific use cases from diverse disciplines and will be promoted to providers and users through an active dissemination and training programme, in order to prepare the next generation of Digital RIs with a low environmental footprint.
Imaging data and services for aquatic science
iMagine provides a portfolio of free at the point of use image datasets, high-performance image analysis tools empowered with Artificial Intelligence (AI), and Best Practice documents for scientific image analysis. These services and materials enable better and more efficient processing and analysis of imaging data in marine and freshwater research, accelerating our scientific insights about processes and measures relevant for healthy oceans, seas, coastal and inland waters.
By building on the computing platform of the European Open Science Cloud (EOSC) the project delivers a generic framework for AI model development, training, and deployment, which can be adopted by researchers for refining their AI-based applications for water pollution mitigation, biodiversity and ecosystem studies, climate change analysis and beach monitoring, but also for developing and optimising other AI-based applications in this field.
The iMagine compute layer consists of providers from the pan-European EGI federation infrastructure, collectively offering over 132,000 GPU-hours, 6,000,000 CPU-hours and 1500 TB-month for image hosting and processing. The iMagine AI framework offers neural networks, parallel post-processing of very large data, and analysis of massive online data streams in distributed environments. 13 RIs will share over 9 million images and 8 AI-powered applications through the framework. Having representatives so many RIs and IT experts, developing a portfolio of eye-catching image processing services together will also give rise to Best Practices. The synergies between aquatic use cases will lead to common solutions in data management, quality control, performance, integration, provenance, and FAIRness, contributing to harmonisation across RIs and providing input for the iMagine Best Practice guidelines. The project results will be integrated into and will bring important contributions from RIs and e-infrastructures to EOSC and AI4EU.
The overarching objective of I4C is to improve the quality, accessibility and usability of near-term climate information and services at local to regional scales to strengthen and support end-user adaptation planning and action. I4C will commit to Open Science through development of open access tools and exploitation of data/model outputs via relevant platforms thereby ensuring improved accessibility and usability of climate knowledge in the context of the EOSC.
Co-designing and prototyping an interdisciplinary Digital Twin Engine.
interTwin is an EU-funded project with the goal to co-design and implement the prototype of an interdisciplinary Digital Twin Engine – an open source platform based on open standards that offers the capability to integrate with application-specific Digital Twins.
Its functional specifications and implementation are based on a co-designed interoperability framework and conceptual model of a DT for research – the DTE blueprint architecture.
TÍTULO DEL PROYECTO: Un enfoque integral para entender la relación entre el consumo de vino, la dieta y la modulación del microbioma en la enfermedad de Alzheimer (WineGut_BrainUP project) (PID2019-108851RB)
PROMOTOR: CONSEJO SUPERIOR DE INVESTIGACIONES CIENTÍFICAS (CSIC)
CENTRO DE ADSCRIPCIÓN: Instituto de Investigación en Ciencias de la Alimentación (CIAL, Madrid) e Instituto de Ciencias de la Vid y el Vino (ICVV, La Rioja)