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49 results for “Animal behavior”

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zenodo44/100

Dataset associated with article "Robots mediating interactions between animals for interspecies collective behaviors"

This dataset contains results and analysis described in the study "Robots mediating interactions between animals for interspecies collective behaviors", Bonnet, F., Mills, R., Szopek, M., Schönwetter-Fuchs, S., Halloy, J., Bogdan, S., Correia, L., Mondada, F. and Schmickl, T. (2019), Science Robotics, 4(28), doi: 10.1126/scirobotics.aau7897 Contents:  <ul> <li>experimental data (logs from robotic systems, example videos)</li> <li>animal tracking analysis output</li> </ul> See the readme and summary files contained within the archives for further details.

opencc-by-4.0Mar 2019View details →

zenodo44/100

Bio-logger Ethogram Benchmark: A benchmark for computational analysis of animal behavior, using animal-borne tags

This repository contains the datasets and experiment results presented in our <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a>: <blockquote> 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. </blockquote> 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>.  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 <a href="https://github.com/earthspecies/BEBE-datasets/">https://github.com/earthspecies/BEBE-datasets/</a>. 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 <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (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 <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (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 <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> (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 <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> 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 <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> 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 <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> fig5d (bool): True if dataset+experiment was used in figure 5d of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> fig5e (bool): True if dataset+experiment was used in figure 5e of <a href="https://arxiv.org/abs/2305.10740">arxiv paper</a> 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

opencc-by-4.0Apr 2024View details →

zenodo40/100

Behavioral data and analyses of competitive interactions between invasive and native ant species [from Cordonnier et al. 2021, Animals]

This README accompanies the files "data_Cordonnier_Animals.txt" & "script_Cordonnier_Animals.txt"   Associated publication :  The native ant Lasius niger can limit the access to resources of the invasive Argentine ant M. Cordonnier, O. Blight, E. Angulo, and F. Courchamp Published in Animals   ********************************** CONTENTS ***************************** The data are in table form with TABs as variables field delimiters so they can be readily imported in any statistical package or spreadsheet program. Please, contact me if you need the file formatted otherwise.    ******************************************************************************* Variable names and descriptions   Status_Lh            status of Linepithema humile (Colonizer or Resident)        opp                      species of the opponent combirc                combination of status and species interacting temp                    temperature during the test hygro                   hygrometry during the test categ                    interacting species combination n_deadtot_opp       total number of dead opponent workers t_50dead_opp        time when 50% of the opponent mortality load have been diagnosed t_interact              time of the first interaction between L. humile and opponent workers t_maxfights           time when the maximal number of simultaneous fights occurs ET_fights              standard deviation of the numbers of fights over time mean_fights          mean number of simultaneous fights during the contest n_deadtot_Lh        total number of dead workers of L. humile t_50dead_Lh         time when 50% of the L. humile mortality load have been diagnosed t_arena_opp          time of the opponent entrance in the arena t_bait_opp             time of opponent resources’ discovery t_maxarena_opp    time when the max. number of opponent workers occurs in the arena mean_arena_opp   mean number of opponent workers simultaneously present in the whole arena t_maxbait_opp       time when the maximal number of opponent workers on the bait occurs mean_bait_opp      mean number of opponent workers on the bait t_arena_Lh            time of the entrance in the arena of L. humile t_maxarena_Lh     time when the max. number of workers of L. humile occurs in the arena mean_arena_Lh     mean number of L. humile workers simultaneously present in the whole arena n_totprey_Lh         total number of preys brought by L. humile t_bait_Lh               time of resources’ discovery by L. humile t_maxbait_Lh        time when the maximal number of L. humile individuals on the bait occurs ETbait_Lh             standard deviation of the numbers of L. humile workers on the bait over time mean_bait_Lh        mean number of L. humile workers on the bait t_50prey_Lh          time when 50% of the final prey load   ******************************** CONTACT ********************************* Please contact me at: Marion Cordonnier e-mail: marion.cordonnier@hotmail.com *******************************************************************************

opencc-by-4.0Dec 2020View details →

dryad40/100

Piecewise continuous sampling: a method for minimizing bias and sampling effort for estimated metrics of animal behavior

Capturing qualitative features of animal behavior requires recording occurrences of behavior over time. Continuous sampling is best for capturing brief behaviors, but can be very time consuming. Instantaneous sampling can reduce the amount of labor required, but can miss short-duration behaviors. We therefore synthesized these techniques by continuously sampling during randomly scattered time intervals; a technique we call piecewise continuous sampling. To optimize and test the efficacy of this technique, we collected a continuous behavioral dataset of harvester ant workers, and then we developed a protocol to estimate the amount of sampling time necessary to reconstruct the proportion of time animals spend in different behavioral states. This protocol finds the sample size needed for the variance of the sample to converge on the variation of the population. We then divided this estimated time into equal-duration intervals that were randomly distributed across the entire continuous dataset. Finally, we calculated both time-dependent and time-independent error from this sample. We found that 4 to 16 sampling intervals minimize both types of error simultaneously. This finding was robust to differences in underlying behavior and was validated with simulations, implying that this method could be used for many types of organisms.

opencc-zeroApr 2024View details →

ClinicalTrials.gov40/100

Integrating Animal-Assisted Therapy Into Trauma-Focused Cognitive-Behavioral Therapy for Maltreated Youth

ClinicalTrials.gov study NCT03135119. IPD Sharing: YES. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →

dryad40/100

Data from: Assessing the association between animal color and behavior: A meta-analysis of experimental studies

Open the record for dataset details and reuse information.

publicDec 2024View details →

dryad40/100

Piecewise continuous sampling: a method for minimizing bias and sampling effort for estimated metrics of animal behavior

Open the record for dataset details and reuse information.

publicApr 2024View details →

dryad36/100

HawkEar: A bird-borne visual and acoustic platform for eavesdropping the behavior of mobile animals

UAVs (drones) offer mobile platforms for ecological investigation, but can be impractical in some environments and the resulting noise can disturb wildlife. We developed a mobile alternative using a bird-borne platform to record the behavior of other animals in the field. This unit consists of a lightweight audio and video sensor that is carried by a trained Harris's hawk Parabuteo unicinctus. We tested the hypothesis that our bird-borne platform is a viable option for collecting behavioral data from mobile animals. We recorded acoustic and video data as the hawk flew through a dense group of Brazilian free-tailed bats Tadarida brasiliensis emerging from a cave, with a test case of investigating how echolocation calls changes depending on spatial position in the bat group. The HawkEar platform is an alternative for collecting behavioral data when a mobile platform that is less noisy and restrictive than traditional UAVs is needed. The design and software are open source and can be modified to accommodate additional sensor needs.

opencc-zeroApr 2024View details →

zenodo36/100

Dynamic cortical behavior of plant protoplasts reveals unexpected similarities between plant and animal cells

The raw data presented in this folder corresponds to the publication # Dynamic cortical behavior of plant protoplasts reveals unexpected similarities between plant and animal cells Johanna E. M. Dickmann 1,2, Marjolaine Martin 1,§, Claire Lionnet 1,§, Zoe Nemec-Venza 1, Olivier Hamant 1,2 1 Laboratoire Reproduction et Développement des Plantes, ENS de Lyon, INRAE, CNRS, UCBL1   2 Correspondence: olivier.hamant@ens-lyon.fr, johanna.dickmann@ens-lyon.fr   § Equal contribution   ORCIDs: * Johanna Dickmann: 0000-0002-0861-4440 * Zoe Nemec-Venza: 0000-0002-2346-2596 * Olivier Hamant: 0000-0001-6906-6620 Submitted to bioRxiv in November 2024   This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 521501033 to J.D. and the European Research Council (ERC-2021-AdG-101019515 “Musix” to O.H.). ## Data organization The data are organized according to the figure panels in the publication. For large experiments, the folders may contain subfolders for each experimental repeat and/or condition. For explanations on the data and the methods, please refer to the publication. All data presented here are the original raw data output of the microscopes in the CZI format, a proprietary format developed by Zeiss, encapsulating both 4D image data and metadata, i.e. acquisition settings. This format is supported by open-source software such as Fiji and Open Microscopy Environment. Refer to the README files in the subfolders for information on which exact file was used to display in the figure. ## Explanation of the file names ### Arabidopsis experiments The filenames contain the following information, separated by underscores: * an experiment identifier (e.g.”PLA001”, “PRO077”) * the line of the imaged plant material (e.g. “pUBQ10-LTi6B-TdTomato”) * sometimes information on the ecotype of the line (e.g. “Col-0”) * the age of the plants (e.g. “7d” = 7 day old plants) * sometimes information on a stain added (e.g. “FM4-64_0_5ugPml” = FM4-64 dye at a final concentration of 5 ug/ml) * sometimes information on a treatment (e.g. “beforeFDA” = image taken before FDA was added; “FDA2.5ugPml” = after adding FDA at a final concentration of 2.5 ug/ml, sometimes with additional information on the time between adding the FDA and imaging, e.g. “25min”) * sometimes information on the centrifugation speed (e.g. “100g”) * sometimes information on the imaging support (e.g. “bucket” = NOA73 container, “wells” = NOA73 microwells, “coverslip”) * sometimes information on the imaging mode (“z-stack”, “t-series” = time series/timelapse, “6x” = zoom of 6 in Zen software) * the solution the sample was imaged in (“Solution A” or “A” = hyperosmotic buffer with D-mannitol, “AS” = hyperosmotic buffer with D-sorbitol) * sometimes information on experimental setup (“ON” = overnight timelapse imaging) * increasing numbers at the end of the file name indicate subsequent fields of view or positions imaged with the same settings * for Fig. 1A: information about the length of the plasmolysis (“50 min”) * for Fig. 4c,d: information on which solution the protoplasts are and have been imaged in: “A” = hyperosmotic buffer solution with 600 mM D-mannitol. “B” =  hyperosmotic buffer solution with 280 mM D-mannitol. Times indicate time between addition of new buffer and onset of imaging of the position list. * for Fig. 4e-f: the concentration of the hyperosmotic buffer solution is indicated. For the control, the number of additions of hyperosmotic buffer solution with 600 mM D-mannitol is indicated. ### Physcomitrium patens experiments The filenames contain the following information, separated by underscores: * an experiment identifier (e.g. "PyP001")   * “Physco_wt” referring to Physcomitrium patens wild type * the age of the moss tissue used for protoplasting (e.g. “6d” = 6 days) * information on the stain added (e.g. “ Fm4-64_2ugPml” = FM4-64 dye at a final concentration of 2 ug/ml) * information on the imaging support (e.g. “bucket” = NOA73 container, “coverslip”) ### Maize experiments The filenames contain the following information, separated by underscores: * the date on which the experiment was performed (yyyymmdd) * the plant species (“Maize”) * sometimes information in the solution used for digestion ("A+E" = hyperosmotic buffer solution with D-mannitol) ### Bead experiments The filenames contain the following information, separated by underscores: * an experiment identifier (e.g. “beads008”) * a description of the beads (“fluoresbrite1micron” = Fluoresbrite beads of a diameter of 1 um) * sometimes a short description of the protocol (e.g. “SolAwashed-2-3mLsolA” = NOA73 microwells were washed in hyperosmotic buffer with 600 mM D-mannitol 2x prior to imaging, beads were imaged in 3 ml hyperosmotic buffer solution with 600 mM D-mannitol.) * an information on the size of the field of view (e.g. “small FOV” = small field of view, i.e. one microwell with beads) * information at which approx. height of the microwell the image was taken (“TopOfWells” = close to the opening of the wells on the top) * sometimes information on the zoom of the Zen software (e.g. “7x”)

opencc-by-nc-4.0Nov 2024View details →

zenodo36/100

Medial prefrontal cortex and anteromedial thalamus interaction regulates motivation related behavior and dopaminergic neuron activity: Animal Behavior

The excel Source DATA file contains the data described in Figures 2c, 2d, 2f, and 3b and Supplementary Figure 3b and 3c. The fiber photometry data described in Supplementary Figure 9 are found in the CSV files. The CSV file names reflect animal IDs.

opencc-by-3.0-usDec 2021View details →

dryad36/100

Data from: An inexpensive and open-source method to study large terrestrial animal diet and behavior using time-lapse video and GPS

1. The behavior of free-ranging animals is difficult to study, especially on the large spatial and temporal scales relevant to long-lived large species. Animal-borne video and environmental data collection systems (AVEDs) record behavior and other data in real time as animals conduct daily activities. However, few studies have combined systematically collected, long term AVED foraging data with environmental and movement data to test hypotheses on animal foraging. Additionally, AVEDs are often either prohibitively expensive, or require extensive fabrication and programming knowledge. 2. The video and coordinate animal-mounted system (VACAMS) is an animal-mounted data collection system based on a modified GoPro® action camera platform that records short, first "person" perspective videos of animal behavior on an automated time-lapse schedule. As most videos are georeferenced, researchers can return to the locations of specific behaviors and collect accurate, fine-grained data on non-woody vegetation and other habitat characteristics that may influence animal behavior. Moreover, VACAMS are inexpensive and easy to use. 3. This study describes VACAMS preliminary data on cattle foraging and a hypothesis exploring free-ranging cattle browsing habits throughout the rainy season in the tropical dry forest of Sonora, Mexico. I generated a database of vegetation types consumed by cows each month (Annual, Woody, and Leaf litter) and compared actual vegetation type frequencies to a priori assumptions based on seasonal patterns of forage availability. During the monsoons, when palatable vegetation was abundant, frequencies of annual and woody perennial vegetation in cattle diets did not differ from month to month. When the rains ceased and palatable vegetation became scarce, cows switched to leaf litter, dead annual vegetation, twigs, and dried leguminous fruits. 4. Open source software and commercially available hardware make VACAMS financially attainable for many researchers, land managers, students, and other user groups. VACAMS could be used on a range of domestic and semi-domestic free-ranging animals, particularly in dense forests where conventional observations are impossible. With improvements to GPS battery life and durability, the weakest points of the system, VACAMS could also potentially apply to studies of other large terrestrial animals.

opencc-zeroDec 2018View details →

dryad36/100

Fijian sea krait behavior relates to fine‐scale environmental heterogeneity in old‐growth coastal forest: The importance of integrated land–sea management for protecting amphibious animals

Here the data for "Fijian sea krait behaviour relates to fine-scale environmental heterogeneity in old growth forest: the importance of integrated land-sea management for protecting amphibious animals" by Lowe, C., Keppel, G., Waqa, K., Peters, S., Fisher, R.N., Scanlon, A., Osborne-Naikatini, T, and Thomas-Moko, N is provided. This article investigates the habitat of Yellow Lipped Sea Kraits, Laticauda colubrina, in the terrestrial realm on Leluvia Island, a small, topographically flat atoll in Fiji with coastal forest. The investigation uses concurrent microclimate measurements and behaviour surveys, as well as vegetation surveys, and the data collected for these analyses are provided here. Microclimates were significantly related to canopy cover, leaf litter depth, and distance from the high-water mark (HWM). Sea kraits were almost exclusively observed in coastal forest within 30 m of the HWM. Sloughing of skins only occurred within crevices of mature or dying trees. Resting L. colubrina were significantly more likely to occur at locations with higher mean diurnal temperatures, lower leaf litter depths, and shorter distances from the HWM. On Leleuvia, behaviour of L. colubrina therefore relates to environmental heterogeneity created by old-growth coastal forests, particularly canopy cover and crevices in mature and dead tree trunks. The importance of healthy coastal habitats, both terrestrial and marine, for L. colubrina suggests it could be a good flagship species for advocating integrated land-sea management. Furthermore, our study highlights the importance of coastal forests and topographically flat atolls for biodiversity conservation. Effective conservation management of amphibious species that utilise land- and seascapes is therefore likely to require a holistic approach that incorporates connectivity among ecosystems and environmental heterogeneity at all relevant scales.

opencc-zeroFeb 2023View details →

dryad36/100

Fijian sea krait behavior relates to fine‐scale environmental heterogeneity in old‐growth coastal forest: The importance of integrated land–sea management for protecting amphibious animals

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publicFeb 2023View details →

dryad36/100

Data from: An inexpensive and open-source method to study large terrestrial animal diet and behavior using time-lapse video and GPS

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publicJan 2019View details →

dryad36/100

HawkEar: A bird-borne visual and acoustic platform for eavesdropping the behavior of mobile animals

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publicApr 2024View details →

dryad36/100

Data from: Animal personalities influence male mating behavior and reproductive fitness

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publicNov 2024View details →

zenodo32/100

SpaceAnimal: Pose estimation and tracking dataset for multi-animal behavior analysis on the China Space Station

Pose estimation and tracking dataset for multi-animal behavior analysis on the China Space Station. Scientific Data, 2025

opencc-by-4.0Jul 2024View details →

dryad32/100

Selfee: Self-supervised features extraction of animal behaviors

Fast and accurately characterizing animal behaviors is crucial for neuroscience research. Deep learning models are efficiently used in laboratories for behavior analysis. However, it has not been achieved to use an end-to-end unsupervised neural network to extract comprehensive and discriminative features directly from social behavior video frames for annotation and analysis purposes. Here, we report a self-supervised feature extraction (Selfee) convolutional neural network with multiple downstream applications to process video frames of animal behavior in an end-to-end way. Visualization and classification of the extracted features (Meta-representations) validate that Selfee processes animal behaviors in a way similar to human perception. We demonstrate that Meta-representations can be efficiently used to detect anomalous behaviors that are indiscernible to human observation and hint in-depth analysis. Furthermore, time-series analyses of Meta-representations reveal the temporal dynamics of animal behaviors. In conclusion, we present a self-supervised learning approach to extract comprehensive and discriminative features directly from raw video recordings of animal behaviors and demonstrate its potential usage for various downstream applications.

opencc-zeroJan 2022View details →

dryad32/100

Caution is warranted when using animal space-use and movement to infer behavioral states

Background: Identifying the behavioral state for wild animals that can't be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals. Methods: Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events. Results: For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual's UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events. Conclusions: Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.

opencc-zeroDec 2020View details →

dryad32/100

The effects of exploratory behavior on physical activity in a common animal model of human disease, zebrafish (Danio rerio)

Zebrafish (Danio rerio) are widely accepted as a multidisciplinary vertebrate model for neurobehavioral and clinical studies, and more recently have become established as a model for exercise physiology and behavior. Individual differences in activity level (e.g., exploration) have been characterized in zebrafish, however, how different levels of exploration correspond to differences in motivation to engage in swimming behavior has not yet been explored. We screened individual zebrafish in two tests of exploration: the open field and novel tank diving tests. The fish were then exposed to a tank in which they could choose to enter a compartment with a flow of water (as a means of testing voluntary motivation to exercise). After a 2-day habituation period, behavioral observations were conducted. We used correlative analyses to investigate the robustness of the different exploration tests. Due to the complexity of dependent behavioral variables, we used machine learning to determine the personality variables that were best at predicting swimming behavior. Our results show that contrary to our predictions, the correlation between novel tank diving test variables and open field test variables was relatively weak. Novel tank diving variables were more correlated with themselves than open field variables were to each other. Males exhibited stronger relationships between behavioral variables than did females. In terms of swimming behavior, fish that spent more time in the swimming zone spent more time actively swimming, however, swimming behavior was inconsistent across the time of the study. All relationships between swimming variables and exploration tests were relatively weak, though novel tank diving test variables had stronger correlations. Machine learning showed that three novel tank diving variables (entries top/bottom, movement rate, average top entry duration) and one open field variable (proportion of time spent frozen) were the best predictors of swimming behavior, demonstrating that the novel tank diving test is a powerful tool to investigate exploration. Increased knowledge about how individual differences in exploration may play a role in swimming behavior in zebrafish is fundamental to their utility as a model of exercise physiology and behavior.

opencc-zeroAug 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record