Collaborations
iMagine provides a portfolio of image datasets, high-performance image analysis tools empowered with Artificial Intelligence (AI), and Best Practice documents for scientific image analysis with focus on aquatic scientists.
AI4EOSC delivers the software used to built the iMagine platform, as well as technical support via the iMagine Competence Center.
AI4Life goal is to empower life science researchers to harness the full potential of Artificial Intelligence (AI) and Machine Learning (ML) methods for bioimage analysis.
AI4EOSC delivers a preview platform tailored for AI4Life, as well as connectors to provide interoperability of AI4Life models inside the AI4EOSC ecosystem
RAISE aspires to build an environment where research communities can share and process data with evidence-based authenticity of the data-analysis performed ensuring at the same time accreditation of their work.
AI4EOSC provides access to RAISE sample datasets within the platform.
EOSC Focus supports the co-programmed EOSC Partnership in delivering its mission of establishing Open Science as the “new normal”, aiming at achieving the ambitious goals set in the EOSC Co-Programmed Partnership Memorandum of Understanding.
AI4EOSC provides access to RAISE sample datasets within the platform.
EOSC-Future was an EU-funded H2020 project that adressed the existing gaps to deliver the EOSC during its early phases, focusing on technology and interoperability, resource availability, user engagement and user experience..
AI4EOSC delivered AI interoperability guidelines, and it integrated its services in the EOSC portal delivered by EOSC-Future
The DEEP project delivered a comprehensive platform for machine learning, deep learning and artificial intelligence in the European Open Science Cloud. Developing, training, sharing and deploying your model has never been easier.
AI4EOSC builds on top of the DEEP outcomes, enhancing the original platform to provide additional functionalities.
FlexiGrobots was an Innovation Action aiming to build a platform for flexible heterogeneous multi-robot systems for intelligent automation of precision agriculture operations, providing multiple benefits to farmers around the world.
EGI-ACE was a 30-month project coordinated by the EGI Foundation with a mission to empower researchers from all disciplines to collaborate in data- and compute-intensive research through free-at-point-of-use services.
AI4EOSC builds on top of the existing DEEP services delivered by the EGI-ACE project.
EOSC DIH is an international cooperation that supports companies in easily accessing the digital technologies and services offered by the EOSC.
AI4EOSC collaborates with the DIH providing early access for SMEs and innovators.
The project built on the expertise of leading research organizations, infrastructure providers, NRENs and user communities from Spain, Portugal, Germany, Poland, Czech Republic, Slovakia, Netherlands, United Kingdom and France, all already committed to the EOSC vision.
The CSIC AIHUB is a network of centres that brings together more than 400 researchers whose mission is to bring together CSIC research staff dedicated to AI to collaborate in research, training, transfer and communication activities.
The AI-on-Demand Platform (AIoD) is a community-driven channel designed to empower European research and innovation in Artificial Intelligence (AI), while ensuring the European seal of quality, trustworthiness and explainability.
Sria
Strategic Research
and Innovation Agenda
The overall purpose of the EOSC Strategic Research and Innovation Agenda (SRIA) is to define the general framework for future research, development and innovation activities in relation to the European Open Science Cloud.
This framework will be further developed in the context of the EOSC Partnership, drawing on inputs from EOSC Association member organisations and Task Forces.
The SRIA is of interest to all the individuals and organisations interested in or impacted by EOSC, both now and within the timeframe of Horizon Europe. This includes research-performing organisations, research funders, service providers, governmental organisations, companies/businesses and citizens.
[01] Metadata and ontologies
Metadata and ontologies are essential to realising Open Science, and thus are an important topic that needs to be addressed by EOSC. Metadata and ontologies have evolved organically over time, addressing the needs of individual communities and sub-communities.
Because of these community-specific drivers, to date an overarching, coordinated approach to metadata and ontologies for scholarly resources has for the most part been missing.Metadata and ontologies are essential to realising Open Science, and thus are an important topic that needs to be addressed by EOSC.
Metadata and ontologies have evolved organically over time, addressing the needs of individual communities and sub-communities. Because of these community-specific drivers, to date an overarching, coordinated approach to metadata and ontologies for scholarly resources has for the most part been missing.
[02] fair metrics
The FAIR principles are a recent concept so metrics are still under definition.
The principles were intentionally articulated broadly but this ambiguity leads to different interpretations and the risk that metrics do not fit different community practice.
The implementation of FAIR can only be achieved in an ecosystem. Research artefacts are made FAIR by the services in which they are created, discovered and reused. The FAIR principles therefore need to be applied to all components of the ecosystem, since FAIR data maturity depends on the capabilities and trustworthiness of services such as repositories and persistent identifier systems.
[03] Authentication and Authorisation
The purpose of authentication and authorisation infrastructure (AAI) in EOSC is to support the FAIR principles for data and services while enabling high-trust collaborations to be established and maintained with little or no friction to the end user.
As federated AAI provides trusted identity information and allows scalable management of roles and rights, it is a key concern for the security and trust of any collaboration. AAI for escience is developed not in a vacuum but in the context of a global marketplace of AAI products and services which typically focuses on the consumer-business relationship.
The goal of the EOSC AAI is to build a foundation for e-science AAI which will ensure longterm availability of the aspects of digital identity that are unique to scientific collaborations and which are often hard or even impossible to achieve using the tools and design patterns used to provide enterprise or consumer identity.
[04] User environments
Users are those individuals who access and benefit from the resources exposed through EOSC.
They may not be those agreeing or commissioning resources (the customers) but they are the ones interacting with them. In other words, EOSC users and providers include all actors in the scientific lifecycle, such as researchers, service providers, developers, funders, organisations, citizens, small and medium-sized enterprises (SMEs), etc.
[05] Resource Providers
EOSC is not a single monolithic organisation or resource provider but is rather a federation built out of many independent organisations and resource providers as in a system of systems approach.
As such, it ensures the independence and autonomy of resource providers. Resource providers are widely distributed across Europe, have the mandate to serve one or more research disciplines and have to comply with different national and European legislations.
[06] EOSC interoperability framework
Achieving a good level of interoperability within EOSC is essential to federate data and services and provide added value for EOSC users, across disciplines, countries and sectors.
In the context of the FAIR principles, interoperability is discussed in relation to the fact that ‘research data usually need to be integrated with other data’. Standards are critical to achieve this, at both the disciplinary and cross-domain level, and implementation must build on existing research culture and practices, as well as existing technologies such as the Semantic Web, linked data and knowledge graphs. Efforts should also focus on addressing gaps where standards do not yet exist, to avoid the risk of leaving certain research communities behind.
