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20 results for “ethogram”
Bio-logger Ethogram Benchmark: A benchmark for computational analysis of animal behavior, using animal-borne tags
<p>This repository contains the datasets and experiment results presented in our <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a>:</p> <blockquote> <p>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.</p> </blockquote> <p>Standardized code to implement, train, and evaluate models can be found at <a href="https://github.com/earthspecies/BEBE/">https://github.com/earthspecies/BEBE/</a>. </p> <p>Please note the licenses in each dataset folder.</p> <p><strong>Zip folders beginning with "formatted":</strong> These are the datasets we used to run the experiments reported in the benchmark paper. </p> <p><strong>Zip folders beginning with "raw": </strong>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 <a href="https://github.com/earthspecies/BEBE-datasets/">https://github.com/earthspecies/BEBE-datasets/</a>.</p> <p><strong>Zip folders beginning with "experiments": </strong>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.</p> <p><em>Results used in Fig. 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (deep neural networks vs. classical models)</em><br>{dataset}_ harnet_nogyr<br>{dataset}_CRNN<br>{dataset}_CNN<br>{dataset}_dt<br>{dataset}_rf<br>{dataset}_svm<br>{dataset}_wavelet_dt<br>{dataset}_wavelet_rf<br>{dataset}_wavelet_svm</p> <p><em>Results used in Fig. 5D of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (full data setting)<br></em>If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs):<br>{dataset}_harnet_nogyr<br>{dataset}_harnet_random_nogyr<br>{dataset}_harnet_unfrozen_nogyr<br>{dataset}_RNN_nogyr<br>{dataset}_CRNN_nogyr<br>{dataset}_rf_nogyr<br><br>Otherwise:<br>{dataset}_harnet_nogyr<br>{dataset}_harnet_unfrozen_nogyr<br>{dataset}_harnet_random_nogyr<br>{dataset}_RNN_nogyr<br>{dataset}_CRNN<br>{dataset}_rf</p> <p><em>Results used in Fig. 5E of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (reduced data setting)<br></em>If dataset contains gyroscope (HAR, jeantet_turtles, vehkaoja_dogs):<br>{dataset}_harnet_low_data_nogyr<br>{dataset}_harnet_random_low_data_nogyr<br>{dataset}_harnet_unfrozen_low_data_nogyr<br>{dataset}_RNN_low_data_nogyr<br>{dataset}_wavelet_RNN_low_data_nogyr<br>{dataset}_CRNN_low_data_nogyr<br>{dataset}_rf_low_data_nogyr</p> <p>Otherwise:<br>{dataset}_harnet_low_data_nogyr<br>{dataset}_harnet_random_low_data_nogyr<br>{dataset}_harnet_unfrozen_low_data_nogyr<br>{dataset}_RNN_low_data_nogyr<br>{dataset}_wavelet_RNN_low_data_nogyr<br>{dataset}_CRNN_low_data<br>{dataset}_rf_low_data<br><br></p> <p><strong>CSV files</strong>: we also include summaries of the experimental results in experiments_summary.csv, experiments_by_fold_individual.csv, experiments_by_fold_behavior.csv. </p> <p><em>experiments_summary.csv - results averaged over individuals and behavior classes<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>f1_mean (float): mean of macro-averaged F1 score, averaged over individuals in test folds<br>f1_std (float): standard deviation of macro-averaged F1 score, computed over individuals in test folds<br>prec_mean, prec_std (float): analogous for precision<br>rec_mean, rec_std (float): analogous for recall<em><br><br>experiments_by_fold_individual.csv - results per individual in the test folds<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fold (int): test fold index<br>individual (int): individuals are numbered zero-indexed, starting from fold 1<br>f1 (float): macro-averaged f1 score for this individual<br>precision (float): macro-averaged precision for this individual<br>recall (float): macro-averaged recall for this individual<em><br></em></p> <p><em>experiments_by_fold_behavior.csv - results per behavior class, for each test fold<br></em>dataset (str): name of dataset<br>experiment (str): name of model with experiment setting <br>fig4 (bool): True if dataset+experiment was used in figure 4 of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a><br>fold (int): test fold index<br>behavior_class (str): name of behavior class<br>f1 (float): f1 score for this behavior, averaged over individuals in the test fold<br>precision (float): precision for this behavior, averaged over individuals in the test fold<br>recall (float): recall for this behavior, averaged over individuals in the test fold<br>train_ground_truth_label_counts (int): number of timepoints labeled with this behavior class, in the training set<em><br></em></p>
Behavioral Ethogram for Corvus macrorhynchos
<p>A behavioral ethogram developed for wild <em>Corvus macrorhynchos levaillantii</em><em> </em>(Eastern Jungle Crow) in Northeast Thailand. Audio and/or video examples of the behaviors can be found in the accompanying Examples.zip.</p> <p>Category = The broad catagory the behavior falls within.<br>Behavior = The name of the behavior.<br>Adapted.From = The source from which the behavior definition was drawn from or based upon.<br>Description = A textual description of the behavior.<br>Example1 = The file path to an audio or visual example of the behavior. Example file may contain multiple behaviors in one file. Behaviors without an Example1 were not observed in the wild.<br>Example2 = A file path to a second example if available. NAs fill those without examples.<br>Example3 = A file path to a second example if available. NAs fill those without examples.</p> <p>*1 body length = bill tip to tail tip of bird in question</p>
Evaluating Qualitative Behavioural Assessment and ethogram techniques for captive black rhinoceros (Diceros bicornis)
<p>Data set + R Code for: Evaluating Qualitative Behavioural Assessment and ethogram techniques <br>for captive black rhinoceros (<em>Diceros bicornis</em>)</p>
Data from: Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring
<p><span>Our dataset, Nest Monitoring of the Kagu, consists of around ten days (253 hours) of continuous monitoring sampled at 25 frames per second. Our proposed dataset aims to facilitate computer vision research that relates to event detection and localization. We fully annotated the entire dataset (23M frames) with spatial localization labels in the form of a tight bounding box. Additionally, we provide temporal event segmentation labels of five unique bird activities: Feeding, Pushing leaves, Throwing leaves, Walk-In, and Walk-Out. The feeding event represents the period of time when the birds feed the chick. The nest-building events (pushing/throwing leaves) occur when the birds work on the nest during incubation. Pushing leaves is a nest-building behavior during which the birds form a crater by pushing leaves with their legs toward the edges of the nest while sitting on the nest. Throwing leaves is another nest-building behavior during which the birds throw leaves with the bill towards the nest while being, most of the time, outside the nest. Walk-in and walkout events represent the transitioning events from an empty nest to incubation or brooding, and vice versa. We also provide five additional labels that are based on time-of-day and lighting conditions: Day, Night, Sunrise, Sunset, and Shadows. In our manuscript, we provide a baseline approach that detects events and spatially localizes the bird in each frame using an attention mechanism. Our approach does not require any labels and uses a predictive deep learning architecture that is inspired by cognitive psychology studies, specifically, Event Segmentation Theory (EST). We split the dataset such that the first two days are used for validation, and performance evaluation is done on the last eight days.</span></p>
Data from: Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring
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Figure 6 in Ethogram and time-activity budget of the collared peccary (Pecari tajacu, Tayassuidae): implications for husbandry and welfare
Figure 6. Behaviours exhibited by captive-born collared peccaries grouped into the territorial and exploratory categories. a) Dorsal gland mark; b) Rub head on ground/object; c) Nose ground; d) Nose air; e) Tasting; f) Root.
Figure 4 in Ethogram and time-activity budget of the collared peccary (Pecari tajacu, Tayassuidae): implications for husbandry and welfare
Figure 4. Behaviours exhibited by captive-born collared peccaries grouped into the social affiliative category. a) Head toss; b) Mutual rub neck; c) Mutual rub; d) Weak bite; e) Roll; f) Play in mud.
Figure 3 in Ethogram and time-activity budget of the collared peccary (Pecari tajacu, Tayassuidae): implications for husbandry and welfare
Figure 3. Behaviours exhibited by the captive-born collared peccaries grouped into reproductive category. a) Chin on back; b) Chin on head; c) Nurse; d) Mount.
Figure 1 in Ethogram and time-activity budget of the collared peccary (Pecari tajacu, Tayassuidae): implications for husbandry and welfare
Figure 1. Behaviours exhibited by the captive-born collared peccaries grouped into the locomotion and inactivity categories. a) Sequence from left to right: inactive, walking, trotting and running; b) Yawning; c) Stretching, and d) Sleeping.
Figure 2 in Ethogram and time-activity budget of the collared peccary (Pecari tajacu, Tayassuidae): implications for husbandry and welfare
Figure 2. Behaviours exhibited by the captive-born collared peccaries grouped into the social aggressive category. a) Stand over; b) Agonistic squabble; c) Tooth clack; d) Kneel; e) Lift with snout.
Figure 7 in Ethogram and time-activity budget of the collared peccary (Pecari tajacu, Tayassuidae): implications for husbandry and welfare
Figure 7. Behaviours exhibited by captive-born collared peccaries grouped into the exploratory, antipredator and maintenance categories. a) Flehmen; b) Erect dorsal bristles; c) Escaping; d) Scratching; e) Grooming.
Figure 5 in Ethogram and time-activity budget of the collared peccary (Pecari tajacu, Tayassuidae): implications for husbandry and welfare
Figure 5. Behaviours exhibited by captive-born collared peccaries grouped into the social affiliative category. a) Nose; b) Nose back; c) Nose rump; d) Nose dorsal gland; e) Nose nose; f) Nose belly.
Additional data - Ethogram of the domestic cat
<p>With the manuscript "Ethogram of the Domestic Cat" we have summarised and illustrated our six years of surveys in a broad overview of domestic cat behaviours to pave the way for easy, quick and effective use. Our aim is to contribute to the targeted research and observation of cat welfare and its variations.<br> These additional data give a deeper insight into the sample of animals observed and into the literature mainly used to develop the label of the behaviour units of our ethogram.</p>
Figure 2. Ethogram depicting a in Expulsion and consumption of male ejaculates by promiscous female Euxesta eluta and Euxesta mazorca (Diptera: Ulidiidae)
Figure 2. Ethogram depicting a typical mating sequence of Euxesta mazorca. The ethogram was constructed after 1-hour observations of 11 sexually mature pairs. The ethogram was reconstructed based on records of partial and complete records of mating sequences.
Figure 1. Ethogram depicting a in Expulsion and consumption of male ejaculates by promiscous female Euxesta eluta and Euxesta mazorca (Diptera: Ulidiidae)
Figure 1. Ethogram depicting a typical mating sequence of Euxesta eluta. The ethogram was constructed after 1-hour observations of 50 sexually mature pairs. The ethogram was reconstructed based on records of partial and complete records of mating sequences.
FIG. 2 in An Ethogram Describing the Nesting Behavior of Green Sea Turtles (Chelonia mŋdas)
FIG. 2.—Schematic representation of the relationships between different nesting stages of Green Sea Turtles (Chelonia mŋdas). Solid lines indicate progression leading to a successful nesting attempt. Dashed lines represent variations that might result in abandoned nesting attempts prior to oviposition. Action patterns within each nesting stage are designated as follows: SQC ¼ simultaneous quadrupedal crawl; RFFSW ¼ rear flipper flick sweep; RFSW ¼ rear flipper sweep; FFSW ¼ front flipper sweep; RFFSC ¼ rear flipper flick scoop; RFK ¼ rear flipper knead.
FIG. 1 in An Ethogram Describing the Nesting Behavior of Green Sea Turtles (Chelonia mŋdas)
FIG. 1.—Illustrations of movements performed by nesting Green Sea Turtles (Chelonia mŋdas): (A) simultaneous rear and front flippers, (B) paired front flippers, (C) alternating rear flippers, (D) single left front flipper, (E) single right front flipper, (F) single left rear flipper, and (G) single right rear flipper. Diagrams modified from Eckert et al. (1999).
Videos used in creating ethograms for Callistoctopus furvus
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FIG. 4 in An Ethogram Describing the Nesting Behavior of Green Sea Turtles (Chelonia mŋdas)
FIG. 4.—The rate of rear flipper movement as a function of the completion of two behavioral stages for video-recorded Green Sea Turtles (Chelonia mŋdas) nesting on beaches in Brevard and Indian River counties, Florida, USA. (A) The digging stage (n ¼ 6); (B) the covering stage (n ¼ 8). See Results for detailed descriptions of behaviors involved in each stage; trend lines were used for visualization purposes only.
FIG. 3 in An Ethogram Describing the Nesting Behavior of Green Sea Turtles (Chelonia mŋdas)
FIG. 3.—The rate of flipper movement as a function of the completion of two behavioral stages for video-recorded Green Sea Turtles (Chelonia mŋdas) nesting on beaches in Brevard and Indian River counties, Florida, USA. (A) The body pitting stage (n ¼ 4); (B) the camouflaging stage (n ¼ 8). See Results for detailed descriptions of behaviors involved in each stage; trend lines were used for visualization purposes only.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.