Presentation Attack Detection (PAD) is essential for ensuring the security of face recognition (FR) systems, particularly in the context of mobile authentication in various sectors, such as online banking and government services. However, current PAD methods are often sensitive to the data domain, partly due to the limitations of training PAD datasets. In this paper, we introduce the SO-TERIA dataset, which provides captures of bona-fide and diverse Presentation Attacks (PAs) recorded using smart-phones. The dataset was collected responsibly from 70 consenting individuals, as opposed to web scraping. It includes face videos, motion data, and depth information (when available) as well as a novel projector-based replay attack. To demonstrate the utility of the SOTERIA dataset, we evaluate the vulnerability of a SOTA FR model (IRes-Net100) to the PAs in the dataset. We also analyze the PAD capabilities of a SOTA PAD model (DeepPixBis) through cross-dataset experiments as well as on real attacks observed in an industrial application. Our findings show the effectiveness and versatility of the SOTERIA dataset in advancing PAD research, in particular toward generalization.