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There are several research infrastructures or other data services running in Europe that cover a multitude of marine-related sciences, providing specific datasets coming from observations collected with different methods. These infrastructures constitute a diverse world, each looking at a piece of the big picture, sometimes hindering collaboration and data sharing. Blue-Cloud aims to overcome fragmentation and build a bridge between thematic science clusters - such as marine, climate, food and agriculture sciences - and EOSC, creating a data federation and providing a common access to a so-called thematic EOSC for marine data. By connecting leading marine data management infrastructures with horizontal e-infrastructures, the project aims to maximise the exploitation of data resources available from different sources. The Blue-Cloud framework consists of two major technical components: (1) a Blue-Cloud Data Discovery and Access service, already presented in a previous EOSC in practice story, to serve federated discovery and access to blue data infrastructures, and (2) a Blue-Cloud Virtual Research Environment (VRE) to provide computing platforms and analytical services facilitating the collaboration between researchers, which is detailed hereafter. The Blue-Cloud VRE is powered by the D4Science Infrastructure. [M. Assante et al. (2019) Enacting open science by D4Science. Future Gener. Comput. Syst. 101: 555-563 10.1016/j.future.2019.05.063 ] The full list of EOSC in practice stories is available here

Momarsat 2022 cruise report: summary of dives and operations, and position of moorings and observation infrastructures and sampling locations

Despite the current high level of safety and the efforts to make passenger ships resilient to most fire and flooding scenarios, there are still gaps and challenges in the marine emergency response and ship evacuation processes. Those challenges arise from the fact that both processes are complex, multi-variable problems that rely on parameters involving not only people and technology but also procedural and managerial issues. SafePASS Project, funded under EU’s Horizon 2020 Research and Innovation Programme, is set to radically redefine the evacuation processes by introducing new equipment, expanding the capabilities of legacy systems on-board, proposing new Life-Saving Appliances and ship layouts, and challenging the current international regulations, hence reducing the uncertainty, and increasing the efficiency in all the stages of ship evacuation and abandonment process.

Microplastics are substrates for microbial activity and can influence biomass production. This has potentially important implications at the sea-surface microlayer, the marine boundary layer that controls gas exchange with the atmosphere and where biologically produced organic compounds can accumulate. In the present study, we used large scale mesocosms (filled with 3 m3 of seawater) to simulate future ocean scenarios. We explored microbial organic matter dynamics in the sea-surface microlayer in the presence and absence of microplastic contamination of the underlying water. Our study shows that microplastics increased both biomass production and enrichment of particulate carbohydrates and proteins in the sea-surface microlayer. Importantly, this resulted in a 3% reduction in the concentration of dissolved CO2 in the underlying water. This reduction suggests direct and indirect impacts of microplastic pollution on the marine uptake of CO2, by modifying the biogenic composition of the sea’s boundary layer with the atmosphere.

Abstract P2.11 | 2nd ISMRM Iberian Chapter Annual Meeting | 27-28 June 2022, Lisbon, Portugal

Abstract P1.09 | 2nd ISMRM Iberian Chapter Annual Meeting | 27-28 June 2022, Lisbon, Portugal

First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming essential to detect blow flow anomalies. However, the need for real-time acquisitions limits the achievable spatial resolution and coverage of the heart. To keep both within a reasonable range, FPP-CMR needs to be accelerated. A SElf-Supervised aCcelerated REconsTruction (SECRET) DL framework is presented to speed-up reconstruction of FPP-CMR images from undersampled (k,t)-space data. The physical reconstruction models are used to train deep neural networks without requiring fully sampled images. SECRET achieves good quality reconstructions at a variety of acceleration rates, with significant speed-ups compared to the state-of-the-art.

The documents in these folders represent part of the qualitative data collection documentation. Research has been performed in Flanders (Belgium) in 2016 and 2017. Involved stakehodlers were flemish sugar beet farmers, processors as well as other value chain members. Though, the main stakeholders involved were farmers. The raw data cannot be published. Anonymized interview transcripts and focus group transcripts exist. However, as indicated in the informed consent, farmers did not agree to the raw data being published. The codes that resulted from data analysis are in this folder. Interview questions differed slightly from farmer to farmer as follow up questions may have been posed if needed. First interviews were performed, then focus groups were conducted and finally a workshop was organized. The qualitative reserach followed the research strategy and plan determined by the SUFISA project. On the project webpage (https://www.sufisa.eu/) more information can be found.

This is the English version of the informed consent that has been used for staekholder interactions. Similar forms have been used for focus groups and workshops.