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Published May 19, 2023 | Version 1.0.0
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Bio-logger Ethogram Benchmark: A benchmark for computational analysis of animal behavior, using animal-borne tags

  • 1. Earth Species Project
  • 2. Universidad de León
  • 3. Centre national de la recherche scientifique Borea
  • 4. Georgia College & State University
  • 5. African Institute for Mathematical Sciences, Stellenbosch University
  • 6. Department of Conservation, New Zealand
  • 7. Osaka University
  • 8. University of Texas, El Paso
  • 9. University of Helsinki
  • 10. Tampere University
  • 11. Nagoya University
  • 12. University of California, Santa Cruz
  • 13. University of St. Andrews

Description

This repository contains the datasets presented in our forthcoming work:

B. Hoffman, M. Cusimano, V. Baglione, D. Canestrari, D. Chevallier, D. DeSantis, L. Jeantet, M. Ladds, T. Maekawa, V. Mata-Silva, V. Moreno-González, A. Pagano, E. Trapote, O. Vainio, A. Vehkaoja, K. Yoda, K. Zacarian, A. Friedlaender, and C. Rutz, "A benchmark for computational analysis of animal behavior, using animal-borne tags," 2023.

It also contains the experiment results which are reported in the paper. Standardized code to implement, train, and evaluate models can be found at https://github.com/earthspecies/BEBE/

Please note the licenses in each dataset folder.

Zip folders beginning with "formatted": These are the datasets we used to run the experiments reported in the benchmark paper. 

Zip folders beginning with "raw": These are the unprocessed datasets used in BEBE. Code to process these raw datasets into the formatted ones used by BEBE can be found at https://github.com/earthspecies/BEBE-datasets/.

Zip folders beginning with "experiments": Results of the cross-validation experiments  reported in the paper, as well as hyperparameter optimization. Confusion matrices for all experiments can also be found here.

 

Files

experiments_baglione_crows.zip

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Additional details

References

  • DeSantis DL, Mata-Silva V, Johnson JD and Wagler AE (2020) Integrative Framework for Long-Term Activity Monitoring of Small and Secretive Animals: Validation With a Cryptic Pitviper. Front. Ecol. Evol. 8:169. doi: 10.3389/fevo.2020.00169
  • Jorge-L. Reyes-Ortiz, Luca Oneto, Albert Sam‡, Xavier Parra, Davide Anguita. Transition-Aware Human Activity Recognition Using Smartphones. Neurocomputing. Springer 2015.
  • Jeantet L et al. 2020. Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology. R. Soc. Open Sci. 7: 200139. http://dx.doi.org/10.1098/rsos.200139
  • Ladds et al (2017). Super machine learning: Improving accuracy and reducing variance of behaviour classification from accelerometry
  • Korpela, J., Suzuki, H., Matsumoto, S., Mizutani, Y., Samejima, M., Maekawa, T., Nakai, J., & Yoda, K. (2020). Machine learning enables improved runtime and precision for bio-loggers on seabirds. Communications Biology, 3.
  • Antti Vehkaoja, Sanni Somppi, Heini Törnqvist, Anna Valldeoriola Cardó, Pekka Kumpulainen, Heli Väätäjä, Päivi Majaranta, Veikko Surakka, Miiamaaria V. Kujala, and Outi Vainio, Description of movement sensor dataset for dog behavior classification, Data in Brief 40 (2022), 107822.
  • Pagano, A. M., 2018, Metabolic Rate, Body Composition, Foraging Success, Behavior, and GPS Locations of Female Polar Bears (Ursus maritimus), Beaufort Sea, Spring, 2014-2016 and Resting Energetics of an Adult Female Polar Bear: U.S. Geological Survey data release, https://doi.org/10.5066/F7XW4H0P.
  • Friedlaender, A.S., Tyson, R.B., Stimpert, A.K., Read, A.J., & Nowacek, D.P. (2013). Extreme diel variation in the feeding behavior of humpback whales along the western Antarctic Peninsula during autumn. Marine Ecology Progress Series, 494, 281-289.