This repository contains the datasets and experiment results presented in our arxiv paper: 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, "A benchmark for computational analysis of animal behavior, using animal-borne tags," 2023. 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. Note that dt, rf, and svm refer to the feature set from Nathan et al., 2012. Results used in Fig. 4 of arxiv paper (deep neural networks vs. classical models) {dataset}_ harnet_nogyr {dataset}_CRNN {dataset}_CNN {dataset}_dt {dataset}_rf {dataset}_svm {dataset}_wavelet_dt {dataset}_wavelet_rf {dataset}_wavelet_svm Results used in Fig. 5D of arxiv paper (full data setting) If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs): {dataset}_harnet_nogyr {dataset}_harnet_random_nogyr {dataset}_harnet_unfrozen_nogyr {dataset}_RNN_nogyr {dataset}_CRNN_nogyr {dataset}_rf_nogyr Otherwise: {dataset}_harnet_nogyr {dataset}_harnet_unfrozen_nogyr {dataset}_harnet_random_nogyr {dataset}_RNN_nogyr {dataset}_CRNN {dataset}_rf Results used in Fig. 5E of arxiv paper (reduced data setting) If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs): {dataset}_harnet_low_data_nogyr {dataset}_harnet_random_low_data_nogyr {dataset}_harnet_unfrozen_low_data_nogyr {dataset}_RNN_low_data_nogyr {dataset}_wavelet_RNN_low_data_nogyr {dataset}_CRNN_low_data_nogyr {dataset}_rf_low_data_nogyr Otherwise: {dataset}_harnet_low_data_nogyr {dataset}_harnet_random_low_data_nogyr {dataset}_harnet_unfrozen_low_data_nogyr {dataset}_RNN_low_data_nogyr {dataset}_wavelet_RNN_low_data_nogyr {dataset}_CRNN_low_data {dataset}_rf_low_data CSV files: we also include summaries of the experimental results in experiments_summary.csv, experiments_by_fold_individual.csv, experiments_by_fold_behavior.csv. experiments_summary.csv - results averaged over individuals and behavior classes dataset (str): name of dataset experiment (str): name of model with experiment setting fig4 (bool): True if dataset+experiment was used in figure 4 of arxiv paper fig5d (bool): True if dataset+experiment was used in figure 5d of arxiv paper fig5e (bool): True if dataset+experiment was used in figure 5e of arxiv paper f1_mean (float): mean of macro-averaged F1 score, averaged over individuals in test folds f1_std (float): standard deviation of macro-averaged F1 score, computed over individuals in test folds prec_mean, prec_std (float): analogous for precision rec_mean, rec_std (float): analogous for recall experiments_by_fold_individual.csv - results per individual in the test folds dataset (str): name of dataset experiment (str): name of model with experiment setting fig4 (bool): True if dataset+experiment was used in figure 4 of arxiv paper fig5d (bool): True if dataset+experiment was used in figure 5d of arxiv paper fig5e (bool): True if dataset+experiment was used in figure 5e of arxiv paper fold (int): test fold index individual (int): individuals are numbered zero-indexed, starting from fold 1 f1 (float): macro-averaged f1 score for this individual precision (float): macro-averaged precision for this individual recall (float): macro-averaged recall for this individual experiments_by_fold_behavior.csv - results per behavior class, for each test fold dataset (str): name of dataset experiment (str): name of model with experiment setting fig4 (bool): True if dataset+experiment was used in figure 4 of arxiv paper fig5d (bool): True if dataset+experiment was used in figure 5d of arxiv paper fig5e (bool): True if dataset+experiment was used in figure 5e of arxiv paper fold (int): test fold index behavior_class (str): name of behavior class f1 (float): f1 score for this behavior, averaged over individuals in the test fold precision (float): precision for this behavior, averaged over individuals in the test fold recall (float): recall for this behavior, averaged over individuals in the test fold train_ground_truth_label_counts (int): number of timepoints labeled with this behavior class, in the training set

2024