@article {9524,
	title = {A benchmark for computational analysis of animal behavior, using animal-borne tags},
	journal = {arXiv},
	year = {2023},
	month = {05/2023},
	abstract = {Animal-borne sensors ({\textquoteleft}bio-loggers{\textquoteright}) can record a suite of kinematic and environmental data, which can elucidate animal ecophysiology and improve conservation efforts. Machine learning techniques are useful for interpreting the large amounts of data recorded by bio-loggers, but there exists no standard for comparing the different machine learning techniques in this domain. To address this, we present the Bio-logger Ethogram Benchmark (BEBE), a collection of datasets with behavioral annotations, standardized modeling tasks, and evaluation metrics. BEBE is to date the largest, most taxonomically diverse, publicly available benchmark of this type, and includes 1654 hours of data collected from 149 individuals across nine taxa. We evaluate the performance of ten different machine learning methods on BEBE, and identify key challenges to be addressed in future work. Datasets, models, and evaluation code are made publicly available at https://github.com/earthspecies/ BEBE, to enable community use of BEBE as a point of comparison in methods development.},
	keywords = {Accelerometers, animal behavior, Bio-loggers, Clustering, Machine Learning, Time series},
	author = {Benjamin Hoffman and Maddie Cusimano and Vittorio Baglione and Daniela Canestrari and Chevallier, Damien and Dominic L. DeSantis and Lor{\`e}ne Jeantet and Monique A. Ladds and Takuya Maekawa and Vicente Mata-Silva and V{\'\i}ctor Moreno-Gonz{\'a}lez and Eva Trapote and Outi Vainio and Antti Vehkaoja and Ken Yoda and Katherine Zacarian and Ari Friedlaender and Christian Rutz}
}
