This is the official dataset of Recurrent All-Pairs Field Transforms for Particle Image Velocimetry Data (RAFT-PIV) published in Nature Machine Intelligence. In this work, we propose a deep neural network-based approach for learning displacement fields in an end-to-end manner, focusing on the specific case of Particle Image Velocimetry (PIV). PIV is a key approach in experimental fluid dynamics and of fundamental importance in diverse applications, including automotive, aerospace, and biomedical engineering. In contrast to standard PIV methods, our RAFT-PIV approach is general, largely automated, and provides much higher spatial resolution. This dataset is given as binary TFRECORD format.