Data from: Correcting a bias in the computation of behavioral time budgets that are based on supervised learning

Yehezkel Resheff, Hanna Bensch, Markus Zottl & Shay Rotics
Supervised learning of behavioral modes from body-acceleration data has become a widely used research tool in Behavioral Ecology over the past decade. One of the primary usages of this tool is to estimate behavioral time budgets from the distribution of behaviors as predicted by the model. These serve as the key parameters to test predictions about the variation in animal behavior. In this paper we show that the widespread computation of behavioral time budgets is...
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