In this paper we present DISC, a dataset of millimeter-wave channel impulse response measurements for integrated human activity sensing and communication. This is the first dataset collected with a software-defined radio testbed that transmits 60 GHz IEEE 802-11ay-compliant packets and estimates the channel response including reflections of the signal on the moving body parts of subjects moving in an indoor environment. The provided data contain the contribution of 7 subjects performing 4 different activities. Differently from available radar-based millimeter-wave sensing datasets, our measurements are collected using both uniform packet transmission times and sparse traffic patterns from real Wi-Fi deployments. Thanks to these unique characteristics, DISC serves as a multi-purpose benchmarking tool for machine learning-based human activity recognition, radio frequency gait analysis, and sparse sensing algorithms for next-generation integrated sensing and communication.