Establishing accurate spatial correspondences across brains is a fundamental step in neuroscience research. Current methods use sophisticated algorithms to perform registrations between brains, providing a mapping between modalities, subjects, template spaces, and even different species. However, the accuracy of these mappings varies widely depending on factors such as the image quality, the registration algorithm, and its parameters. To quantify registration accuracy, a point-based set of 32 anatomical landmarks, termed anatomical fiducials (AFIDs), has recently been described and validated for human magnetic resonance imaging (MRI) datasets (Lau et al., 2019). The AFIDs-macaca project seeks to bridge the gap between human and animal models in neuroscience research by extending the AFIDs framework to a widely used non-human primate model, the macaques.