Understanding the behavioral and neural dynamics of socially interacting animals is a goal of contemporary neuroscience. Many machine learning based techniques have emerged in recent years to make sense of complex video and neurophysiological data that result from these experiments. However, less focus has been placed on understanding how animals process acoustic information, including social vocalizations. A critical step to bridge this gap is determining the senders and receivers of acoustic information in social interactions. While sound source localization (SSL) is a classic problem in signal processing, existing approaches are limited in their ability to localize animal-generated sounds in standard laboratory environments. Advances in deep learning based algorithms for SSL are likely to help address these limitations, however there are currently no publicly available models, datasets, or benchmarks to systematically evaluate SSL algorithms in the domain of bioacoustics. Here, we present the VCL'24 Dataset: the first large-scale dataset for benchmarking SSL algorithms in rodents. We acquired synchronized video and multi-channel audio recordings of 770,547 sounds with annotated ground truth sources across 9 conditions. The dataset provides benchmarks which evaluate SSL performance on real data, simulated acoustic data, and a mixture of real and simulated data. We intend this benchmark to facilitate knowledge transfer between the neuroscience and acoustic machine learning communities, which have historically had limited overlap.