We present the CleanVibe dataset, a structural vibration dataset induced by handwashing from 3 people. Proper hand washing is critical for the prevention of healthcare-associated infections (HCAIs) (those that occur in or around healthcare settings) and for reducing disease transmission rates. During the COVID-19 outbreak, hand washing was identified as one of the best practices for reducing transmission. Existing approaches for detection and monitoring of hand washing in healthcare settings include direct observation as well as sensing-based techniques such as vision, radio frequency (RF), acoustics, and wearables. However, each of these prior approaches is limited in many real-world applications due to deployment restrictions such as line of sight/perceived privacy concerns (vision), sparse/insufficient monitoring (direct observation), sensitivity to ambient noise (acoustics), and requiring users to wear and/or carry a device (RF, wearables). To overcome the limitations of these prior works, we introduce the CleanVibe dataset, which leverages structural vibrations to monitor hand-washing activity. The primary insight behind this approach is that the various phases of hand washing (i.e., walking to the hand washing station, turning on the water, using soap, and rinsing hands in the water), all generate excitations in the sink structure and/or surrounding floor structure. By measuring vibrations of the sink structure, we can accurately detect whether each activity has occurred, and monitor its duration to ensure proper compliance with hand washing guidelines. This dataset enables the development of non-intrusive monitoring of hand washing in a variety of settings without the need for persons to wear or carry a device.