Euro-BioImaging ERIC is the ESFRI landmark to imaging excellence. It has completely changed the premises of European researchers by providing them with an open gateway to cutting-edge biological and biomedical imaging technologies, including access to expertise, data services, and training - all of which are essential ingredients for scientific breakthroughs and innovations to occur. Euro-BioImaging was established as an ERIC in December 2019 and since has grown quickly to currently 35 National Nodes representing 173 individual imaging facilities. 16 European countries and EMBL have committed to jointly operate this pan-European research infrastructure. The EVOLVE project, presented in this proposal, aims to significantly strengthen the young ERIC to reach its next level as a pan-European organization, and to live up to the needs and expectations expressed by its continuously expanding communities of users, partners, ERIC members, industry and other relevant stakeholders. Building on the EVOLVE project during the next 3,5 years, Euro-BioImaging will significantly strengthen its cost-efficient administration and set-up the next generation of its user access web portal. Furthermore it will foster the family of Euro-BioImaging Nodes and Hub at the personal, institutional, national and European level. The ERIC will implement a sustainable strategy for external relations with policy makers and funders, as well as boost its outreach and communication activities with new user communities for widening participation. An increased service offer, in particular for training (users and Nodes’ staff) and FAIR image data services will underpin excellence-driven user access and Open Science. Finally, EVOLVE will empower Euro-BioImaging facilities across Europe to underpin the Green and Digital Transition. With successful project conclusion Euro-BioImaging ERIC will have demonstrated the impact of this globally unique research infrastructure for European life sciences and beyond

Digital image data is a crucial part of biological research, fueling an exponentially growing body of research output. A surge in volume and diversity of bioimage data, owing to the fast developing imaging technologies, has brought to light many challenges including in sharing open image data. Resource owners, research infrastructures as well as grass-roots communities are actively tackling these challenges by developing file formats, ontologies, metadata models, to ensure that shared data is in line with the FAIR principles. However, many of these solutions remain regional and a global coordination effort is necessary to enable seamless image data sharing. This project will lay the foundation for a Global Image Data Ecosystem (GIDE) that connects different biological and biomedical image data resources across the globe to allow metadata and data sharing. GIDE will bring European research infrastructure and image data resource owners together with their counterparts from Australia and Japan to develop the basis of image data sharing. These interactions can then be used as a blueprint to engage with the wider global bioimage data community while strengthening the position of European infrastructures in the landscape. To enable partner resources and research infrastructures to communicate in one language, coordinated technical development towards use of harmonised imaging ontologies and metadata models will be carried out. Proof of concept implementations using harmonised image data and metadata will showcase the project developments and encourage adoption of these recommendations globally. These developments will also lay a crucial and strong foundation to build a future federated image data ecosystem on. Together, global efforts towards GIDE will provide a necessary framework for bioimage data to grow into a democratised resource enabling innovative research in Europe and across the globe.

Sustainable agriculture strategies have become central to European and global orientations as the demand of food increases with the rising world population. Current technologies qualitatively drive the application of Nitrogen (N)-fertilizers to stressed field areas but cannot precisely modulate the needed amount, resulting in dangerous N dispersion, ecological and economic losses. RE-IMAGINE-CROPS envisions applying basic plant science into crop management by quantitative “in-field” measurements of biophysical processes occurring at tissue and cellular level. The consortium will implement the first real-time in-field portable multimodal multiscale Positron Emission Tomography (PET)-Multiphoton Endoscope (ME) technology. PET measures the metabolic processes in the crops at the scale of hundreds of micrometers, while guiding the ME system to visualize functional mechanisms at a cellular level. The aim is to tailor the amount of fertilizer to be used by enabling measurement of functional processes reflecting the local and systemic crop signal pathways triggered by N-fertilizer shortly after application. RE-IMAGINE-CROPS involves real-time PET reconstruction, hydrodynamic modelling, optical fiber transmission optics, and features identification in multiple scales. It enables a new multimodal, mobile technology for the first time, with unprecedented spatial resolution (0.6 mm;1um) and real-time functionality at a rate of (2;10) Hz. These are enabled by a multimodal PET-ME tracer based on the Lifeact-Venus protein labeled with 89Zr. RE-IMAGINE-CROPS brings together eight institutions with leading scientists and cross-disciplinary expertise. Among them, EURO-BIOIMAGING ERIC and BFEDU, a large agrifood enterprise, will boost the impact and dissemination of this novel technology in to the European scientific and industrial landscape. On this basis, RE-IMAGINE-CROPS has the potential to support and shape the essential priorities in the EU’s future sustainable crop management.

Machine learning (ML) has enabled and accelerated frontier research in the life sciences, but democratised access to such methods is, unfortunately, not a given. Access to necessary hardware and software, knowledge and training, is limited, while methods are typically insufficiently documented and hard to find. Furthermore, even though modern AI-based methods typically generalize well to unseen data, no standard exists to enable sharing and fine-tuning of pretrained models between different analysis tools. Existing user-facing platforms operate entirely independently from each other, often failing to comply with FAIR data and Open Science standards. The field of AI and ML is developing at a staggering pace, making it impossible for the non-specialist to stay up to date. To enable the life science communities to benefit from AI/ML-powered image analysis methods, AI4LIFE will build bridges, providing urgently needed services on the common European research infrastructures. We will build an open, accessible, community-driven repository of FAIR pre-trained AI models and develop services to deliver these models to life scientists, including those without substantial computational expertise. Our direct support and ample training activities will prepare life scientists for responsible use of AI methods, while contributor services and open standards will drive community contributions of new models and interoperability between analysis tools. Open calls and public challenges will provide state-of-the-art solutions to yet unsolved image analysis problems in the life sciences. Our consortium brings together AI/ML researchers, developers of popular open source image analysis tools, providers of European-scale storage and compute services and European life sciences Research Infrastructures -- all united behind the common goal to enable life scientists to fully benefit from the untapped but potentially tremendous power of AI-based analysis methods.

The eRImote project is the first to consider solutions for digital and remote service provision across RI domains and to look for transferable practices and new developments that will improve accessibility and resilience of RI infrastructures. While existing processes will be collected, eRImote will also explore new solutions using defined use cases to develop and test their implementation in RI scenarios. This will take us beyond the state-of-the-art for concrete solutions. The eRImote consortium is relatively small with eight beneficiary participants representing four main ESFRI RI Roadmap domains. However, the consortium extends much more broadly through the existing contacts and networking partners of each of the project participants, increasing the reach out to hundreds of individuals, other European and global RIs, scientific networks, RI users, industry partners and policy makers. eRImote is intended for 30 months to enable timely interventions. The project is outlined relatively straightforward by four main activities. eRImote will create an online information platform with a publicly available data store on best practises and tools based on a landscape analysis, also with needs and impact. This will be translated into strategies on transition and use cases (Green Paper). All this is based on broad outreach and extensive dissemination beyond the consortium. The eRImote consortium will identify strategies and solutions to enable transition to remote and digital access to RI services that will help to enhance and make more accessible the service capacities of RIs while reducing the need for physical access to RI sites, bringing benefits to the green economy, reducing the footprint of RIs and increasing their inclusiveness as a result. In addition, eRImote will contribute to increased efficiency of remote access service provision at RIs, enhance mutual knowledge and trust, and develop joint strategies across the RI domains on transition to