The ocean is key in the global C cycle, taking up ca. 25% of the CO2 we emit, slowing climate change and giving us more time to mitigate and adapt to climate change. The Ocean C Value Chain (VC) of observations, data QC & analysis delivers key information around this to decision makers such as the Conference of the Parties. The RIs play a pivotal role in the VC via their ability to operate at scale & pool resources to ensure common data standards and operational practices. The urgency of the climate crisis drives us to put this VC on a much more robust footing with the World Meteorological Organisation leading the planning of a Global Greenhouse Gas Watch (G3W) covering all components of the Earth System. Unfortunately the VC currently delivers estimates of Ocean C uptake much larger than those from models, leading to a damaged ability to manage climate change. However further work suggests that observations at a much higher density in the Southern Ocean (SO) would substantially resolve this issue. Our ability to deliver these via ships is limited by the small number that enter the SO and we therefore need many more observations from research vessels, citizen science platforms, autonomous robotic floats & surface platforms. This step change requires substantial technological innovation and complex data synthesis. TRICUSO will address these needs by a) improving the sensing technologies on floats and small uncrewed surface vessels, b) supporting citizen science on yachts and potentially cruise and expedition vessels, c) integrating biological observations into the work flow, d) improving data flows to scientists, e) evaluating the density of observations needed & f) proposing fit for purpose governance structures that allow the RIs to operate within the G3W. These actions will enable us to have a much firmer grip of how and why Ocean Carbon uptake varies and thus a much firmer evidence base on which to make decisions around managing climate change impacts.

The ambition of AtlantECO is to develop and apply a novel, unifying framework for providing knowledge-based resources to design policies, support decisions making and engage with citizens to encourage responsible behaviour to manage the Atlantic system and protect its Ecosystem Services (ES) provision. The aim of AtlantECO is to determine the structure and function of Atlantic microbiome in the context of ocean circulation and presence of pollutants, e.g., plastics, to assess its role in driving the dynamics of Atlantic ecosystems at basin and regional scales; its potential of being used as a sensor of ecosystem state and the mechanisms by which it drives the provision of 5ES. This is key to improve our predictions on future provision of ES in the basin and to favour the establishment of a sustainable Blue Growth strategy for an All-Atlantic community. To realise this vision, AtlantECO has four objectives which are to 1) Assess dynamics of Atlantic marine ecosystems, their ES provision and the interplay of both with socio-economic activities; 2 increase knowledge and data on microbiomes, plastics, the plastisphere and carbon fluxes that support ecosystems at basin scale using best practices and integrative sampling strategies, novel genomics, imaging and biogeochemical methods, bioinformatics and modelling approaches; 3) Assess and predict the cumulative impacts of multiple stressors on ecosystem status and dynamics and ES provision, identifying their drivers and role on tipping points, assessing their changes in recovery of ecosystem structures, functions and services, and developing eco-socio-economic models to predict future trajectories and 4) Deploy a systemic strategy to build capacity and transfer knowledge for a seamless engagement between science, industry, policy, and society. To achieve these objectives AtlantECO brings together experts and pioneers from Europe, South America and South Africa with the relevant resources, knowledge and experience.

The ocean and its biodiversity are essential to life on this planet. Comprehensive data on biodiversity, and related human and environmental pressures are crucial to understand its current state and how this may change. Protecting and restoring biodiversity is one of three objectives of the Horizon Europe Mission to restore our oceans and waters by 2030, enabling the EU to reach its Green Deal and Biodiversity 2030 targets. Identified as one of the Mission "enablers", the EU will build on “a digital knowledge system” to include a Digital Twin of the Ocean (DTO) allowing simulation of ‘what if’ scenarios, advancing ocean knowledge, informing evidence-based policy and offering a range of societal applications. To effectively replicate the ocean’s ecology, the DTO requires sustained flows of data on biodiversity and associated pressures. Despite myriad actors collecting biodiversity data, and the development of novel cost-effective monitoring technologies, much of these data are inaccessible or unusable for a variety of reasons, hampering the development of the DTO biological component and limiting its efficacy. DTO-BioFlow will activate access to ("sleeping") marine biodiversity data and enable the sustainable integration of existing and new Artificial Intelligence processed and automated data flows from various sources to EMODnet and into the EDITO infrastructure serving the EU DTO. Combining sustained data flows, models and new algorithms, DTO-BioFlow will develop and integrate the biological component of the DTO, including new digital tools and services. Policy-relevant use cases, will demonstrate the benefit for marine ecosystems of continuous data streams flowing through EMODnet and usable by the EU DTO infrastructures and ultimate end-users. Mobilising the marine biodiversity community towards increasing the availability of biodiversity monitoring data into 2030, DTO-BioFlow and its outputs will support the Mission’s actions to protect and restore biodiversity.

EOSC Future responds to INFRAEOSC-03-2020 call in order to integrate, consolidate, and connect e-infrastructures, research communities, and initiatives in Open Science to further develop the EOSC Portal, EOSC-Core and EOSC-Exchange of the European Open Science Cloud (EOSC). EOSC Future is structured around six thematic pillars that strategically group the work package tasks and activities and coherently present the key objectives and activities of the project to stakeholders: Pillar 1 on Policy & Strategy will coordinate and support a strategic vision for the future EOSC; Pillar 2 on Connection & Integration will connect and integrate EOSC infrastructures, data, and services; Pillar 3 on Excellent Science & Interdisciplinarity will extend the EOSC value Chain with scientific use cases; Pillar 4 on Growth & Innovation will grow and innovate EOSC with value-added services; Pillar 5 on Skills & Training will train users and providers of the EOSC ecosystem; and Pillar 6 on Engagement & Communications will engage the wider EOSC Community at a global level. EOSC Future will unlock the potential of European research via a vision of Open Science for Society by (1) bringing all major stakeholders in the EOSC ecosystem together under one project umbrella to break the disciplinary and community silos and consolidate key EOSC project outputs, (2) developing scientific use cases in collaboration with the thematic communities showcasing the benefits and societal value of EOSC for doing excellent and interdisciplinary research, (3) engaging the wider EOSC community and increasing the visibility of EOSC through communications campaigns, marketing strategies, and physical and online engagement events, and (4) including the EOSC community in developing the EOSC Portal (including the long tail of science, public and private sectors, and international partners) via co-creation open calls.

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.