A large-scale dataset of functional mouse ganglion cell layer responses.

We present the ALL-GCL dataset, a large-scale resource of functional two-photon Ca2+-imaging recordings with rich metadata information from more than 80,000 cells in the ganglion cell layer (GCL) of the ex vivo mouse retina. Collected over nine years across more than 155 experimental sessions, the dataset provides recordings of light-evoked responses to various stimuli, ranging from a shared set of core stimuli to natural movies. To enable cell-type-specific analyses, cells are probabilistically assigned to 46 previously characterised functional groups, including retinal ganglion cells and displaced amacrine cells. Further, we assessed the influence of experimental and biological factors on the functional responses. Classifier-based analyses identified measurable signatures associated with acquisition conditions and recording sessions, providing a quantitative characterisation of dataset structure and potential sources of batch effects. The ALL-GCL dataset offers a comprehensive and standardised reference for studying retinal computation at scale. It supports population-level analyses, computational modelling, and the development of machine learning approaches for biological time-series data. Future releases will expand the dataset with additional mouse lines and light stimuli, creating a growing resource for the vision science community.