In neuroscience, the ongoing technological revolution brings a continuous flow of new acquisition devices that produce exponentially growing volumes of data. In consequence, data management and data sharing have become prerequisites for efficient research. This is particularly true with multi-unit electrophysiological techniques which now allow recording from hundreds of neurons simultaneously. However, no standard data management solution is available for neurophysiologists, who still mostly rely on mancrafted research practices. The ShareElec project aims to help neurophysiologists climb several steps on the quality scale in research practices. For this, our consortium gathers one of the major actors in neuroinformatics research, INM6-Jülich, which has been continuously producing tools to improve management and analysis of neurophysiological data, with INT-Marseille, the neuroscience institute with the largest number of neurophysiologists studying non-human primates in France. The two partners have a long history of collaboration, that has yielded a stream of high quality publications for the last twenty years. Most recently, they have demonstrated together the crucial advantages offered by such data management tools in order to offer an open data set of unprecedented high quality to the neuroscience community, with a strong enrichment of metadata. With the ShareElec project, we will further develop these software tools in order to make them more generic, so that they can meet the diverse needs of the entire community of neurophysiologists, and ensure the interoperability with other data modalities used in neuroscience. We will distribute these tools in an open fashion, provide proofs of concepts of their genericity by releasing new open data sets and set up a training program for neurophysiologists. The ShareElec project should therefore help improving the overall level of research practices in neurophysiology.

Current methods for microplastic (MP) analysis can be divided into low-cost versus more advanced methods. ANDROMEDA recognizes that further development and validation is needed for both approaches. Low-cost methods are needed that can identify a broad range of MP polymers with acceptable accuracy. Advanced methods need further development in order to push the limit of detectability for smaller sizes of MP and nanoplastics (NP) and improve their ability to analyze MP types that are currently difficult to analyze by microspectroscopy. Moreover, to study plastic degradation mechanisms over a reasonable time frame, lab-based accelerated degradation approaches are required that mimic natural fragmentation and additive chemical leaching. Within ANDROMEDA, in situ MP detection, efficient sampling and cost-effective laboratory methods will be developed and optimized to analyze MP. Approaches will be based on hyperspectral imaging, chemical markers and fluorometric detection techniques. Advanced analysis techniques making use of µFTIR, Raman imaging and SEM-EDX (amongst others) will be applied to quantify and characterize MP and NP down to 1 µm, 0.2 µm or lower. Specific tasks will focus on challenging types of MP such as microfibers, tire wear particles (TWPs) and paint flakes. UV, hydrolytic and thermo-oxidative methods to study accelerated plastic degradation at the lab-scale will be developed and used to prepare partially degraded reference materials. Comprehensive degradation studies will be conducted to study in detail the mechanisms of UV and microbial degradation, as well as to investigate the influence of parameters such as temperature, pH and hyperbaric pressure, where attention will be paid to additive chemical leaching. Quality assurance will be a central theme in all aspects of the project. Partners specialized in dissemination, communication and data management will ensure strong stakeholder involvement and efficient outreach of the project results.

Trustworthy models for the dynamics of dense crowds are crucial for the prediction of pedestrian flows and the management of large crowds, but also from a fundamental perspective, to understand the roots that they share with active matter but also the pedestrian specifics. However, current models suffer from some severe deficiencies, especially at high density. In this context, MADRAS aims to develop innovative agent-based models to predict and understand dense crowd dynamics (from 2 to 8 ped/m2) and to apply these models in a large-scale case study. Two complementary modelling approaches will be pursued: (i) neural networks (NN) that will be trained on available data to predict pedestrian motion as a function of their local environment and trajectory. This data-based approach is bolstered by recent successes, which proved the potential of recurrent NN at low to intermediate density, but suitable descriptors for the agent's neighbourhood and the local geometry must be found to address dense crowds in complex geometries. (ii) a physics-based model coupling a decisional layer, where a desired velocity is selected according to an empirically validated collision-anticipation strategy, and a mechanical layer, which takes care of collisions and contacts. To push this approach to higher densities, integrating more realistic pedestrian shapes and better splitting the decision-making process from mechanical forces is necessary. These approaches will be confronted with novel validation methods, using data from controlled experiments. The models will then be exploited at larger scale to simulate the flows on crowded streets at a real mass gathering, the Festival of Lights in Lyon. To this end, empirical data will be collected by filming the streets from above and by immersing in the crowd participants wearing pressure-sensing jackets, to measure contacts. Emulating this real scenario will call for adequate data assimilation methods and efficient multi-agent simulations based on the two models. The latter will be combined in a single online platform, allowing one to visualise the predicted flows and compare them with the ground truth. Finally, the impact of different model ingredients and features (e.g., shape heterogeneities) on the large-scale flow predictions will be investigated by means of numerical simulations of the two models, using the Festival of Lights situation as reference scenario. To achieve this ambitious interdisciplinary project, four teams with different backgrounds (Computer Science, Statistical Physics, Applied Mathematics) will combine their strengths and research tools (simulation platform, continuous models, datasets, experimental analysis tools). Cooperation will be fostered by the common goal to reproduce a large-scale scenario and by extensive (3-month) research stays at another institute for the involved PhD students.