Data from: Automated peak detection method for behavioral event identification: detecting Balaenoptera musculus and Grampus griseus feeding attempts

The desire of animal behaviorists for more flexible methods of conducting
inter-study and inter-specific comparisons and meta-analysis of various
animal behaviors compelled us to design an automated, animal behavior peak
detection method that is potentially generalizable to a wide variety of
data types, animals, and behaviors. We detected the times of feeding
attempts by 12 Risso’s dolphins (Grampus griseus) and 36 blue whales
(Balaenoptera musculus) using the norm-jerk (rate of change of
acceleration) time series. The automated peak detection algorithm
identified median true-positive rates of 0.881 for blue whale lunges and
0.410 for Risso’s dolphin prey capture attempts, with median
false-positive rates of 0.096 and 0.007 and median miss rates of 0.113 and
0.314, respectively. Our study demonstrates that our peak detection method
is efficient at automatically detecting animal behaviors from multisensor
tag data with high accuracy for behaviors that are appropriately
characterized by the data time series.