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114 results for “behavioral interactions”
Dataset associated with article "Robots mediating interactions between animals for interspecies collective behaviors"
<p>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), <em>Science Robotics</em>, <em>4</em>(28), doi: 10.1126/scirobotics.aau7897</p> <p>Contents: </p> <ul> <li>experimental data (logs from robotic systems, example videos)</li> <li>animal tracking analysis output</li> </ul> <p>See the readme and summary files contained within the archives for further details.</p>
Learning to embed lifetime social behavior from interaction dynamics - Data
<p><strong>Interaction matrices and metadata used in "Learning to embed lifetime social behavior from interaction dynamics"</strong></p> <p>The following files are included:</p> <ul> <li>interactions_bn16_sparse.npz and interactions_bn19_sparse.npz: These are the interaction affinity matrices for the BN16 and BN19 datasets as described in the publication. The data is stored as compressed sparse tensors with time on the first, and the individuals on the second and third dimensions. The data was stored using the <a href="http://sparse.pydata.org">pydata/sparse</a> library 0.9.1</li> <li> <p>alive_bn16.csv and alive_bn19.csv: These files contain the dates of emergence (also corresponding to the dates they were introduced into the colonies) and heuristically determined number days alive for all individuals in the interaction matrices. Death dates were determined using a bayesian changepoint model and the number of daily detections of each individual</p> </li> <li> <p>rhythmicity_bn16.csv and rhythmicity_bn19.csv: These files contain the circadian rhythmicity values used in the evaluation of the method. The circadian rhythmicity is the <span class="math-tex">\(R^2\)</span> value of a sine with a 24 hour period fitted to the individuals' movement velocities over a three day window</p> </li> <li> <p>indices_bn16.csv and indices_bn19.csv: These files contain the mapping between the original marker IDs used during the recording of the data (which has gaps, because not all markers were used) and the sequential indices used in the interaction matrices. These files can therefore be used to look up the original ID of an individual based on it's index in the interaction matrix and vice versa</p> </li> <li> <p>time_spent_on_substrates.csv: This data was used for the mapping from factors to the proportion of time spent on various cell substrates (Figure 5). The positions of the individuals were accumulated by minute, and the column "location_descriptor_count" contains the total number of minutes on the respective day that the individual was detected</p> </li> </ul> <p>See <a href="https://doi.org/10.1101/2020.05.06.076943">10.1101/2020.05.06.076943</a> for more details about the bayesian changepoint model, circadian rhythmicity calculation, and location mapping.</p>
Fig. 8 in Foraging behavior interactions between the invasive Nile Tilapia (Cichliformes: Cichlidae) and three large native predators
Fig. 8. Activity (inactive, swimming and avoidance) by the Nile Tilapia (mean ± SD) in the tanks with Pseudoplatystoma corruscans (white circles), Salminus brasiliensis (white squares) and Brycon orbignyanus (black triangles), for 0%, 50%, 100% and RD treatments. The three-way ANOVA for these data suggested interaction (P =0.029) among species, structural complexity and activity. The avoidance activity was not observed.
Behavioral data and analyses of competitive interactions between invasive and native ant species [from Cordonnier et al. 2021, Animals]
<p>This README accompanies the files "data_Cordonnier_Animals.txt" & "script_Cordonnier_Animals.txt"</p> <p> </p> <p>Associated publication : </p> <p>The native ant <em>Lasius niger</em> can limit the access to resources of the invasive Argentine ant</p> <p>M. Cordonnier, O. Blight, E. Angulo, and F. Courchamp</p> <p>Published in <em>Animals</em></p> <p> <br> ********************************** CONTENTS *****************************<br> 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. </p> <p> </p> <p>*******************************************************************************<br> Variable names and descriptions</p> <p> </p> <p>Status_Lh status of Linepithema humile (Colonizer or Resident) </p> <p>opp species of the opponent</p> <p>combirc combination of status and species interacting</p> <p>temp temperature during the test</p> <p>hygro hygrometry during the test</p> <p>categ interacting species combination</p> <p>n_deadtot_opp total number of dead opponent workers</p> <p>t_50dead_opp time when 50% of the opponent mortality load have been diagnosed</p> <p>t_interact time of the first interaction between L. humile and opponent workers</p> <p>t_maxfights time when the maximal number of simultaneous fights occurs</p> <p>ET_fights standard deviation of the numbers of fights over time</p> <p>mean_fights mean number of simultaneous fights during the contest</p> <p>n_deadtot_Lh total number of dead workers of L. humile</p> <p>t_50dead_Lh time when 50% of the L. humile mortality load have been diagnosed</p> <p>t_arena_opp time of the opponent entrance in the arena</p> <p>t_bait_opp time of opponent resources’ discovery</p> <p>t_maxarena_opp time when the max. number of opponent workers occurs in the arena</p> <p>mean_arena_opp mean number of opponent workers simultaneously present in the whole arena</p> <p>t_maxbait_opp time when the maximal number of opponent workers on the bait occurs</p> <p>mean_bait_opp mean number of opponent workers on the bait</p> <p>t_arena_Lh time of the entrance in the arena of L. humile</p> <p>t_maxarena_Lh time when the max. number of workers of L. humile occurs in the arena</p> <p>mean_arena_Lh mean number of L. humile workers simultaneously present in the whole arena</p> <p>n_totprey_Lh total number of preys brought by L. humile</p> <p>t_bait_Lh time of resources’ discovery by L. humile</p> <p>t_maxbait_Lh time when the maximal number of L. humile individuals on the bait occurs</p> <p>ETbait_Lh standard deviation of the numbers of L. humile workers on the bait over time</p> <p>mean_bait_Lh mean number of L. humile workers on the bait</p> <p>t_50prey_Lh time when 50% of the final prey load</p> <p> </p> <p>******************************** CONTACT *********************************<br> Please contact me at:</p> <p>Marion Cordonnier<br> e-mail: marion.cordonnier@hotmail.com</p> <p>*******************************************************************************</p> <p> </p>
A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction
<p>This file contains human-robot interaction data acquired during an experiment conducted at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). The campaign focuses on collecting non-identifying data, such as torso trajectories and the internal state of the system, from people in the proximity of a robot. The study spans three days in two different environments at the University Campus Est in Lugano, Switzerland.</p> <div> <div> <div> <div> <p>The campaign adheres to ethical guidelines and is approved by SUPSI's local ethics committee.</p> <p>Duration: Total of 5 hours and 7 minutes.</p> <p>Participants: 1777 individuals tracked.</p> <p><strong>Environments:</strong></p> <ul> <li>Entrance to the campus canteen (demographically diverse, including students and staff).</li> <li>Corridor between classrooms (mainly attended by students).</li> </ul> <p><strong>Data Types</strong>:</p> <ul> <li>Robot Sensor: Timestamps, user ID, 3D torso pose in Robot Sensor frame, interaction intention detector output.</li> <li>Environment Sensor: Timestamps, user ID, 3D poses of torso and hands in Environment Sensor frame, 2D torso positions in the sensor’s field of view.</li> <li>Robot State: Currently selected behavior, state (idle or performing an offering motion).</li> </ul> <p><strong>Key Events</strong>:</p> <ul> <li>Pick Motion: User's hand movement within 0.3 meters of the box.</li> <li>Robot Offer: Robot begins an offering motion.</li> <li>Successful Offer: Pick Motion within 6 seconds of a Robot Offer.</li> </ul> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Fig. 3 in The trade-off between the transmission of chemical cues and parasites: behavioral interactions between leaf-cutting ant workers of different age classes
Fig. 3. Mean ± s.e. frequencies that young and old ants were observed giving or receiving allogrooming during a 15 s observation period.
Fig. 2 in The trade-off between the transmission of chemical cues and parasites: behavioral interactions between leaf-cutting ant workers of different age classes
Fig. 2. Mean ± s.e. frequencies that young and old ants were: (a) observed selfgrooming and (b) observed engaged in mandible scraping with another ant, during a 15 s observation period.
Fig. 1 in The trade-off between the transmission of chemical cues and parasites: behavioral interactions between leaf-cutting ant workers of different age classes
Fig. 1. Overall activity levels. Mean ± s.e. frequencies that young and old ants were: (a) observed engaging in one of the focal behaviors and (b) observed engaged in antennation with another ant, during a 15 s observation period.
Fig. 1 in Behavioral repertoires and interactions between Apis mellifera (Hymenoptera: Apidae) and the native bee Lithurgus littoralis (Hymenoptera: Megachilidae) in flowers of Opuntia huajuapensis (Cactaceae) in the Tehuacán desert
Fig. 1. Behavior accumulation curves of bees in 150 flowers of Opuntia huajuapensis. A: Apis mellifera (1) and Lithurgus littoralis (2). B: L. littoralis females (3) and L. littoralis males (4). Dotted lines indicate the 95% confidence intervals.
Fig. 2 in Behavioral repertoires and interactions between Apis mellifera (Hymenoptera: Apidae) and the native bee Lithurgus littoralis (Hymenoptera: Megachilidae) in flowers of Opuntia huajuapensis (Cactaceae) in the Tehuacán desert
Fig. 2. Time spent (A) and mean feeding duration (B) in flowers of Opuntia huajuapensis by Apis mellifera females and Lithurgus littoralis females and males. No A. mellifera males were recorded at any time during the experiment. Vertical bars indicate 95% confidence intervals.
Preliminary biohybrid experiments with the Behavioral Observation & Biohybrid Interaction framework (featuring the LureBot)
<p>This is the dataset that corresponds to preliminary biohybrid interaction experiments conducted with the Behavioral Observation & Biohybrid Interaction (BOBI) framework, featuring the LureBot.</p> <p> </p> <p>The directory contains:</p> <ol> <li>Trajectories of fish and/or robot agents for different scenarios (<filename>.dat).</li> <li>A robot index file (<filename>_ridx.dat) that corresponds to each trajectory file, that contains the robot's ID (starting from 0) in the experiment, or a negative ID in the case of fish-only experiments.</li> </ol> <p> </p> <p>A brief explanation of the directory's contents:</p> <ul> <li>[Open-loop dynamics] <strong>Circular</strong> trajectories <ul> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure performing a circular trajectory while a single H. rhodostomus is in the tank.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure performing a circular trajectory while a single H. rhodostomus is in the tank.</li> </ul> </li> </ul> <p> </p> <ul> <li>[Open-loop dynamics] <strong>Eightfold rose</strong> trajectories <ul> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure performing an eightfold rose trajectory while a single H. rhodostomus is in the tank.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure performing an eightfold rose trajectory while a single H. rhodostomus is in the tank.</li> </ul> </li> </ul> <p> </p> <ul> <li>[Closed-loop dynamics] <strong>Biomimetic Interaction Model</strong> trajectories <ul> <li><strong>single_agent</strong>: <ul> <li><strong>Fish</strong>: 1-hour-long trajectories of a single H. rhodostomus interacting with the wall alone.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of a single robot following a biomimetic model.</li> </ul> </li> <li><strong>pair_agents</strong>: <ul> <li><strong>Fish-only</strong>: 1-hour-long trajectories of a pair of H. rhodostomus interacting with the wall and with each other.</li> <li><strong>Disc-shaped</strong>: 1-hour-long trajectories of the LureBot with a disc-shaped lure biomimetically interacting with a single H. rhodostomus.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure biomimetically interacting with a single H. rhodostomus.</li> </ul> </li> <li><strong>group5_agents</strong>: <ul> <li><strong>Fish-only</strong>: 1-hour-long trajectories of 5 H. rhodostomus interacting with the wall and with each other.</li> <li><strong>Biomimetic</strong>: 1-hour-long trajectories of the LureBot with a biomimetic lure biomimetically interacting with 4 H. rhodostomus.</li> </ul> </li> </ul> </li> </ul>
Evolution in interacting species alters predator life history traits, behavior and morphology in experimental microbial communities
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Data from: Advancing the understanding of spearfisher-fish behavioral interactions and its management implications
Open the record for dataset details and reuse information.
Data from: Behavioral hypervolumes of predator groups and predator-predator interactions shape prey survival rates and selection on prey behavior
Predator-prey interactions often vary on the basis of the traits of the individual predators and prey involved. Here we examine whether the multidimensional behavioral diversity of predator groups shapes prey mortality rates and selection on prey behavior. We ran individual sea stars (Pisaster ochraceus) through three behavioral assays to characterize individuals' behavioral phenotype along three axes. We then created groups that varied in the volume of behavioral space that they occupied. We further manipulated the ability of predators to interact with one another physically via the addition of barriers. Prey snails (Chlorostome funebralis) were also run through an assay to evaluate their predator avoidance behavior before their use in mesocosm experiments. We then subjected pools of prey to predator groups and recorded the number of prey consumed and their behavioral phenotypes. We found that predator-predator interactions changed survival selection on prey traits: when predators were prevented from interacting, more fearful snails had higher survival rates, whereas prey fearfulness had no effect on survival when predators were free to interact. We also found that groups of predators that occupied a larger volume in behavioral trait space consumed 35% more prey snails than homogeneous predator groups. Finally, we found that behavioral hypervolumes were better predictors of prey survival rates than single behavioral traits or other multivariate statistics (i.e., principal component analysis). Taken together, predator-predator interactions and multidimensional behavioral diversity determine prey survival rates and selection on prey traits in this system.
Behavioral interactions between bacterivorous nematodes and predatory bacteria in a synthetic community
Theory and empirical studies in metazoans predict that apex predators should shape the behavior and ecology of mesopredators and prey at lower trophic levels. Despite the ecological importance of microbial communities, few studies of predatory microbes examine such behavioral res-ponses and the multiplicity of trophic interactions. Here, we sought to assemble a three-level microbial food chain and to test for behavioral interactions between the predatory nematode Caenorhabditis elegans and the predatory social bacterium Myxococcus xanthus when cultured together with two basal prey bacteria that both predators can eat—Escherichia coli and Flavobacterium johnsoniae. We found that >90% of C. elegans worms failed to interact with M. xanthus even when it was the only potential prey species available, whereas most worms were attracted to pure patches of E. coli and F. johnsoniae. In addition, M. xanthus altered nematode predatory behavior on basal prey, repelling C. elegans from two-species patches that would be attractive without M. xanthus, an effect similar to that of C. elegans pathogens. The nematode also influenced the behavior of the bacterial predator: M. xanthus increased its predatory swarming rate in response to C. elegans in a manner dependent both on basal-prey identity and on worm density. Our results suggest that M. xanthus is an unattractive prey for some soil nematodes and is actively avoided when other prey are available. Most broadly, we found that nematode and bacterial predators mutually influence one another's predatory behavior, with likely consequences for coevolution within complex microbial food webs.
Data from: Thermal sensitivity and the role of behavior in driving an intertidal predator-prey interaction
Environmental stress models (ESM) provide a useful framework to study the direct and indirect ecological drivers of community diversity and resilience. ESMs make predictions about the relative importance of structuring processes (e.g., predation) based on the relative stress suffered by consumers and prey. Their practical application, i.e., determining the conditions under which consumers and prey performance is more negatively affected, has been limited because the roles of behavior and physiology are not usually considered. We examined the role of thermal sensitivity and behavior on the thermal performance of the rocky intertidal predator Pisaster ochraceus and its main prey Mytilus californianus. We propose a novel framework that merges thermal performance curves (TPC) with observations of microhabitat use to provide a realistic perspective of the relative physiological conditions of predator and prey. First, by deriving aquatic and aerial TPCs for both species and from two sites, we found differences in parameter values that in some cases correspond to the individuals' origins. Second, we calculated realized thermal performance in the field by combining TPCs with body temperatures recorded with biomimetic sensors. Notably, thermal performance of Pisaster was higher than that for Mytilus (i.e., prey-stress model), contrary to previous expectations based on caging experiments. Third, these estimates of thermal performance corresponded loosely with a measured indicator of overall physiological condition (body mass index, BMI) and a marker for extreme thermal stress (heat-shock proteins 70 kDa), suggesting that environmental drivers other than temperature, such as food supply, must be considered. We found no evidence that Pisaster movement significantly influences thermal performance under typical conditions, suggesting instead that its preference for sheltered microhabitats provides a mechanism for avoiding exposure to extreme environmental conditions. Through the application of TPCs and ESMs, this study provides a unique perspective on the importance of physiology and behavior in driving the sensitivity of species interactions to environmental change. Crucially, this framework allowed clarifying that this system behaves as a prey- instead of consumer-stress model, which may also apply to many other ectotherm species interactions.
Medial prefrontal cortex and anteromedial thalamus interaction regulates motivation related behavior and dopaminergic neuron activity: Animal Behavior
<p>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. </p>
Interaction-Based Behavioral Analysis in Twitter Social Network
<p>Literature studies usually use data sets consisting of data collected from many different metrics and user counts collected over different time periods. The data set used in this article was formed using completely up-to-date data obtained as a result of metrics measured in terms of scope and efficiency, sufficient and effective user counts, and filtering processes. To classify users correctly and make the classification performance high—in addition to parameters used in the literature such as tweets, account age, follower rank, average retweets and average likes—other parameters such as diameter, density, reciprocity, centralization and modularity were used. These metrics are the parameters that focus on a different area to reveal many aspects in which social network users interact. The data used to create the data set was collected from Twitter. The metric data forming the data set was extracted using Twitter Rest API V1.1 supporting search/tweet endpoints by means of the SocialBlade and Netlytic platforms.</p>
BMF CP 101: The influence of engaging children with nature interactions to pro-environmental behaviors under different conditions
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>How is engaging children with nature interactions associated with their pro-environmental behaviors?</span></li> <li><span>Is the association between engaging children with nature interactions and their pro-environmental behaviors conditional on the children’s sex?</span></li> <li><span>Is the association between engaging children with nature interactions and their pro-environmental behaviors conditional on the children’s grades?</span></li> <li><span>Is the association between engaging children with nature interactions and their pro-environmental behaviors conditional on the children’s family income?</span></li> <li><span>Is the association between engaging children with nature interactions and their pro-environmental behaviors conditional on the frequency of environmental discussion with parents?</span></li> </ul>
Dataset Questionnaire Driving Repeat Purchases and E-WOM: How Price, Reputation, Hedonic Appeal, and Social Interaction Shape Consumer Behavior in Indonesia's E-Commerce Smartphone Market
<p>The following dataset is a dataset from a study that investigated price advantage, reputation, hedonic effort, and social interaction influence customer satisfaction, which in turn impacts repurchase intention and e-WOM (electronic word-of-mouth).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
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.
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.