Computational neuroscience is focused on gaining an understanding of the principles of information processing in the brain. This involves the development of theories and models of brain function, as well as the design of analysis methods to probe for signatures of the corresponding dynamics in data from the brain. Models and experimental data cover various spatio-temporal scales, are represented by multiple modalities of observation, and address a diversity of neuroscientific concepts expressed on varying levels of abstraction. At the same time, neuroscientists have a diversified and constantly growing repertoire of methodologies to analyze their data, at the cost of higher complexity and computational expense. The team Data Science for Electro- and Optophysiology Behavioural Neuroscience addresses the challenges that arise from this situation in the context of data management and reproducible data analytics in neuroscience with concepts from physics and computer science. To this end, the team develops and maintains practical software solutions covering data acquisition, management, and analysis of electrophysiological data from experiment and simulation, and formalized approaches model validation. These solutions form the basis for neuroscience research conducted in the team that analyzes spatio-temporal brain activity observed across spatio-temporal scales and measurement modalities.