Intrapartum biometry is of vital significance in monitoring labor progress. However, the realization of AI-based end-to-end intrapartum biometry and labor progress assessment requires intrapartum ultrasound video datasets with multi - category annotations, and currently, there is no such public dataset available. To bridge this gap, we have publicly released, for the first time, a multi-center, multi-device, and multi-category labeled intrapartum ultrasound dataset. This dataset comprises 774 videos / 68,106 images, along with corresponding standard plane classification labels, multi-class segmentation labels of pubic symphysis and fetal head, and two ultrasound parameter labels that characterize labor progress. This dataset can facilitate research on multi-task learning methods and the development of end-to-end automated approaches, especially in the automation of obstetric processes and auxiliary decision - making.