We are living amid a transformative period for neuroscience. Rapid progress in electrophysiology and imaging technology has allowed us to probe neural circuits at unprecedented scale and precision. Modern neural datasets routinely contain recordings from 100s to 1000s of neurons from the brains of animals engaged in complex tasks. In parallel, modern theoretical approaches have led to a paradigm shift, slowly transitioning neuroscience from data-driven to hypothesis-driven. However, these large-scale datasets have brought challenges in data processing and interpreting the processed data. This collection aims to bring together contributions from researchers at the forefront of developing cutting-edge methods to acquire, process, and understand these large-scale recordings.

1. This collection will exhibit a curated list of best practices for data acquisition and analysis for experimentalists engaged in similar research and best practices for theorists involved in similar research. This will ensure precision and reduce variability, for example, using best coding practices, standardized benchmarks, and automated processing pipelines.
2. We aim to analyze rich, large-scale datasets transparent for theorists and theory transparent for experimentalists across scientific fields. This is done hoping that the close interaction between methods/theoretical research and systems neuroscience is essential in an inherently multidisciplinary field. This transparency will add significant value for future research by theory better tailored to experimental constraints and experiments better tailored to improve theory.

Overall, we believe that the close exchange of experts across fields is a critical factor contributing to a more refined understanding of the neuronal mechanisms underlying complex behaviors and will drive progress toward treatments of tomorrow.