The use of acoustic sensors in marine mammal monitoring depends on our ability to discern between different species’ vocal repertoires. For dolphin echolocation clicks there is heavy overlap within the bioacoustic niche, which presents challenges in signal processing. Furthermore, because echolocation is a highly directional sign al, it is imperative to identify a robust categorizing characteristic that is consistent across different methods, animal position, and behavior. A pilot study between May-August 2021 placed 4 autonomous acoustic recorders in the Skagerrak and North Seas, was reviewed for white-beaked dolphin (WBD) events in PAMGuard and R (n=23). Here, we present the raw data processed for the pilot study, which includes the PAMGuard databases and binary files. In addition, we include some of the output generated from the R code library https://github.com/etgriffiths/peaky-echos, which was used to identify times of possible WBD activity (WBDevent csv files), identify strong peaks above the signal to noise ratio in both WBD and noise events (FPstrongWBD rds files), and which WBD clicks were used in either the model training or testing dataset (WBD classifierBuild rda file).