This paper presents a dedicated dataset for the measurement and prediction of VHF signal propagation path error, aiming to mitigate their adverse effects on the ranging and positioning accuracy of terrestrial navigation systems. The Automatic Identification System (AIS), as a critical maritime collision-avoidance technology, enables terrestrial-based positioning using coastal AIS stations, offering significant advantages in terms of deployment and maintenance costs. However, propagation path error remains one of the primary sources of positioning inaccuracies, and no specialized datasets have yet been developed to support its systematic measurement and prediction. To address this limitation, a comprehensive data acquisition and processing framework for AIS-related VHF-band propagation path error is proposed. Based on this framework, a multidimensional dataset is constructed, incorporating temperature, relative humidity, air pressure, instantaneous wind speed, salinity, and measured propagation path error. The measured propagation path error data are collected using a self-developed additional secondary phase correction system. Hydrometeorological parameters obtained from authoritative sources at the same time and location are integrated with the measured data to form experimental samples with rich feature representations. Data cleaning and preprocessing procedures are further applied to improve dataset quality. The final dataset comprises 1,296,000 samples and is suitable for training and evaluating machine learning and deep learning models for VHF signal propagation path error prediction, thereby supporting enhanced positioning accuracy and the improved reliability of maritime navigation systems.