EBRAINS

Advances in neuroscience depend on diverse perspectives and inter- and transdisciplinary collaboration. However, persistent barriers to equitable representation of underrepresented groups at advanced stages of their careers pose a significant challenge. This systemic problem not only reduces the talent pool, but also has high costs for individuals, research and society. Addressing these challenges requires a concerted effort by different stakeholders, such as universities and research institutions, as well as funding bodies. To promote a more inclusive, equitable and diverse scientific landsca

…the digital research infrastructure, EBRAINS, offers a suite of integrative services to share, discover, and process neuroscience data with state-of-the-art computing systems. EBRAINS promotes open science together with responsible research and innovation practices to ensure a holistic understanding of neuroscience. At its core, the EBRAINS Data & Knowledge Service offers a centralized data management system with standardized metadata and a

Over the past decade we have demonstrated that the fusion of subject-specific structural information of the human brain with mathematical dynamic models allows building biologically realistic brain network models, which have a predictive value, beyond the explanatory power of each approach independently. The network nodes hold neural population models, which are derived using mean field techniques from statistical physics expressing ensemble activity via collective variables. Our hybrid approach fuses data-driven with forward-modeling-based techniques and has been successfully applied to expla

Many of the collaborations we encounter in our scientific careers are centered on a common idea that can be associated with certain resources, such as a dataset, an algorithm, or a model. All partners in a collaboration need to develop a common understanding of these resources, and need to be able to access them in a simple and unambiguous manner in order to avoid incorrect conclusions especially in highly cross-disciplinary contexts. While digital computers have entered to assist scientific workflows in experiment and simulation for many decades, the high degree of heterogeneity in the field