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Dataset results
124 results for “behavior prediction”
Dataset from Maith, O., Baladron, J., Einhäuser, W., & Hamker, F. H. (2023). Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model. Submitted to iScience.
<p>This dataset contains all analyzed data from the study "Maith, O., Baladron, J., Einhäuser, W., & Hamker, F. H. (2023). Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model. Submitted to iScience.". It includes the behavioral data of 20 human participants (folder "psychExp") and of simulations of a neuro-computational basal ganglia model (folder "simulations") of the study.</p> <p>To replicate the results of the study, the dataset can be analyzed using the code provided separately under the following identifier: https://doi.org/10.5281/zenodo.6555886. The dataset is organized in the directory structure required for this purpose.</p> <p>For the human participants, only preprocessed eye-tracking and general behavioral data (.mat files) and the final analyzed behavioral data (output files) generated with the script "get_vps_outputs.m" (folder psychExp/..../3_srcAna/) are available. For more information about preprocessing steps as well as raw data of the eye-tracking experiment, please contact us by email (click <a href="https://www.tu-chemnitz.de/urz/mail/adrx.html?1-d29sZmdhbmcuZWluaGFldXNlci10cmV5ZXJAcGh5c2lrLg==">here</a>).</p>
A dataset for Customer Churn Prediction for Video Websites Incorporating Behavioral Sequence Features
<p>In order to study the issue of network customer churn, the iQiyi customer dataset was collected. Behavioral sequence features were extracted from it to build a deep learning model and experiments were conducted.Here, we provide the corresponding raw dataset, including all the data we used.</p>
Condition dependence of (un)predictability in escape behavior of a grasshopper species
<p>(Un)predictability has only recently been recognized as an important dimension of animal behavior. Currently, we neither know if (un)predictability encompasses one or multiple traits nor how (un)predictability is dependent on individual conditions. Knowledge about condition dependence, in particular, could inform us about whether predictability or unpredictability is costly in a specific context. Here, we study the condition dependence of (un)predictability in the escape behavior of the steppe grasshopper <em>Chorthippus dorsatus</em>. Predator–prey interactions represent a behavioral context in which we expect unpredictability to be particularly beneficial. By exposing grasshoppers to an immune challenge, we explore if individuals in poor condition become more or less predictable. We quantified three aspects of escape behavior (flight initiation distance, jump distance, and jump angle) in a standardized setup and analyzed the data using a multivariate double-hierarchical generalized linear model. The immune challenge did not affect (un)predictability in flight initiation distance and jump angle, but decreased unpredictability in jump distances, suggesting that unpredictability can be costly. Variance decomposition shows that 3–7% of the total phenotypic variance was explained by individual differences in (un)predictability. Covariation between traits was found both among averages and among unpredictabilities for one of the three trait pairs. The latter might suggest an (un)predictability syndrome, but the lack of (un)predictability correlation in the third trait suggests modularity. Our results indicated condition dependence of (un)predictability in grasshopper escape behavior in one of the traits, and illustrate the value of mean and residual variance decomposition for analyzing animal behavior.</p>
Predictive neural computations in cerebellar circuits contribute to motor planning and faster behavioral responses in larval zebrafish
<p>This dataset contains raw and processed data along with jupyter notebooks to generate figures in Narayanan et al., 2023. Instructions for navigating through the dataset and for running the analysis codes are in README.pdf.</p>
Predictive Analytics and Behavioral Nudges to Improve Palliative Care in Advanced Cancer
ClinicalTrials.gov study NCT05590962. IPD Sharing: NO. Countries: 1. Publications: 1.
Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs - Effectiveness Study
ClinicalTrials.gov study NCT06834763. IPD Sharing: NO. Countries: 1. Publications: 2.
Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs - Efficacy Study
ClinicalTrials.gov study NCT06815562. IPD Sharing: NO. Countries: 1. Publications: 2.
Habitat structural complexity predicts cognitive performance and behavior in western mosquitofish
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Condition dependence of (un)predictability in escape behavior of a grasshopper species
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Beyond sex and aggression: Testosterone rapidly matches behavioral responses to social context and tries to predict the future
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Harvest and natural predation shape selection for behavioral predictability in male wild turkeys
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Data from: Differential effects of environmental predictability on ungulate movement behavior in disparate ecosystems
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Data for: Telomere length predicts timing and intensity of migratory behavior in a nomadic songbird
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Data and Code for: Food distribution, but not market forces, predict behavioral social tolerance in rhesus macaques
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The predator activity landscape predicts the anti‐predator behavior and distribution of prey in a tundra community
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With or without you: Gut microbiota does not predict aggregation behavior in European earwig females
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Data for: Ecology and behavior predict an evolutionary trade-off between song complexity and elaborate plumages in antwrens (Aves, Thamnophilidae)
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Predicting disease in transition dairy cattle based on behaviors measured before calving
<p>Data set and code for the article "Predicting disease in transition dairy cattle based on behaviors measured before calving"</p>
Data for: Early-life behavior predicts first-year survival in a long-distance avian migrant
<p>Early-life conditions have critical, long-lasting effects on the fate of individuals, yet early-life activity has rarely been linked to subsequent survival of animals in the wild. Using high-resolution GPS and body-acceleration data of 93 juvenile white storks (<i>Ciconia ciconia</i>), we examined the links between behavior during both pre-fledging and post-fledging (fledging-to-migration) periods and subsequent first year survival. Juvenile daily activity (based on overall dynamic body acceleration) showed repeatable between-individual variation, the juveniles' pre and post-fledging activity levels were correlated, and both were positively associated with subsequent survival. Daily activity increased gradually throughout the post-fledging period, and the relationship between post-fledging activity and survival was stronger in individuals who increased their daily activity level faster (an interaction effect). We suggest that high activity profiles signified individuals with increased pre-migratory experience, higher individual quality and perhaps more proactive personality, which could underlie their superior survival rates. The duration of individuals' fledging-to-migration periods had a hump-shaped relationship with survival: higher survival was associated with intermediate rather than short or long durations. Short durations reflect lower pre-migratory experience, whereas very long ones were associated with slower increases in daily activity level which possibly reflects slow behavioral development. In accordance with previous studies, heavier nestlings and those that hatched and migrated earlier had increased survival. Using extensive tracking data, our study exposed new links between early-life attributes and survival, suggesting that early activity profiles in migrating birds can explain variation in first-year survival.</p>
Data from: The rate of transient beta frequency events predicts behavior across tasks and species
Beta oscillations (15-29Hz) are among the most prominent signatures of brain activity. Beta power is predictive of healthy and abnormal behaviors, including perception, attention and motor action. In non-averaged signals, beta can emerge as transient high-power 'events'. As such, functionally relevant differences in averaged power across time and trials can reflect changes in event number, power, duration, and / or frequency span. We show that functionally relevant differences in averaged beta power in primary somatosensory neocortex reflect a difference in the number of high-power beta events per trial, i.e. event rate. Further, beta events occurring close to the stimulus were more likely to impair perception. These results are consistent across detection and attention tasks in human magnetoencephalography, and in local field potentials from mice performing a detection task. These results imply that an increased propensity of beta events predicts the failure to effectively transmit information through specific neocortical representations.
ScienceDex guides
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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)
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.