Doppler ultrasound measurements have been recorded since the 1970s across the world and provide a valuable data resource for learning, analysis, and potential training of deep learning algorithms to recognise and grade venous gas emboli (VGE) allowing assessment of decompression sickness (DCS) risk. We collected a ‘big database’ of Doppler recordings and associated metadata. Audio tapes with recorded Doppler data were converted to digital files, then cut into individual recordings and matched with their metadata, including subject and pressure profile information. The audio signals and their Doppler grades were then processed further for suitability to train an algorithm to identify VGE. A total of 10,099 Doppler ultrasound recordings were compiled. Divers (n=≤311; 170 identified, ≤141 unidentified) were male, with a median age of 31.5 years (n=170). The maximum depth of the dives included ranged from 80 ft (24 m) to 300 ft (91.4 m). The timing of the Doppler measurements ranged from two minutes post-dive to 594 min post dive, with a median time of 52 min. Breathing gases included air, nitrox, and heliox. DCS was noted in only 12 individuals. The dataset centred around lower VGE loads (Spencer Grades 0, I, and II). This database represents a landmark in DCS investigation as the audio dataset and metadata collected have been released under a public domain license for further use. The large number of data points has also allowed the development of a deep learning algorithm that can grade bubble loads without a human operator.