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236 results for “Behavior Modeling”
Source code for dynamic models and simulations of mate sampling behavior
<p>Theory predicts that the strength of sexual selection (i.e., how well a trait predicts mating or fertilization success) should increase with population density, yet empirical support remains mixed. We explore how this discrepancy might reflect a disconnect between current theory and our understanding of the strategies individuals use to choose mates. We demonstrate that the density-dependence of sexual selection predicted by previous theory arises from the assumption that individuals automatically sample more potential mates at higher densities. We provide an updated theoretical framework for the density-dependence of sexual selection by (1) developing models that clarify the mechanisms through which density-dependent mate sampling strategies might be favored by selection and (2) using simulations to determine how sexual selection changes with population density when individuals use those strategies. We find that sexual selection may increase strongly with density if sampling strategies change adaptively in response to density-dependent sampling costs, whereas within-individual plasticity in sampling over time (e.g., due to adaptation to increasing sampling costs as the breeding season progresses) produces weaker density-dependent sexual selection. Our findings suggest that density-dependence of sexual selection depends on the ecological context in which mate sampling has evolved.</p>
Climate trends and behavior of a model Amazonian terrestrial insectivore, Black-faced Antthrush, indicate adjustment to hot and dry conditions
<p>Rainforest loss threatens terrestrial insectivorous birds throughout the world's tropics. Recent evidence suggests these birds are declining in undisturbed Amazonian rainforest, possibly due to climate change. Here, we first asked whether Amazonian terrestrial insectivorous birds were exposed to increasingly extreme ambient conditions using 38 years of climate data. We found long-term trends in temperature and precipitation at our study site, especially in the dry season, which was ~1.3 °C hotter and 21% drier in 2019 than in 1981. Second, to test whether birds actively avoided hot and dry conditions, we used field sensors to identify periodic intervals of ambient extremes and prospective microclimate refugia within undisturbed rainforest from 2017–2019. Simultaneously, we examined how tagged Black-faced Antthrushes (Formicarius analis) used this space. We collected >1.3 million field measurements quantifying ambient conditions in the forest understory, including along elevation gradients. For 11 birds, we obtained GPS data to test whether birds adjusted their cover usage using variation in GPS fix success (<em>n</em> = 2,724) as a proxy and elevation using successful locations (<em>n</em> = 640) across seasonal and daily cycles. For four additional birds, we collected >180,000 light and temperature readings to assess exposure. Field measurements in the modern landscape revealed that temperature was higher in the dry season and highest on plateaus. Thus, low-lying areas were relatively buffered, providing microclimate refugia during hot afternoons in the dry season. At those times, birds apparently entered cover and shifted downslope. Because climate change intensifies the hot, dry conditions that antthrushes seemingly avoid, our results are consistent with the hypothesis that climate change decreases habitat quality for this species. If other terrestrial insectivores are similarly sensitive, climate-induced changes to otherwise intact rainforest may be related to their recent declines.</p>
Multi-omic brain and behavioral correlates of cell-free fetal DNA methylation in macaque maternal obesity models (NMR datasets, maternal plasma and infant brain)
<p>Maternal obesity during pregnancy is associated with neurodevelopmental disorder (NDD) risk. We utilized integrative multi-omics to examine maternal obesity effects on offspring neurodevelopment in rhesus macaques by comparison to lean controls and two interventions. Differentially methylated regions (DMRs) from longitudinal maternal blood-derived cell-free fetal DNA (cffDNA) significantly overlapped with DMRs from infant brain. The DMRs were enriched for neurodevelopmental functions, methylation-sensitive developmental transcription factor motifs, and human NDD DMRs identified from brain and placenta. Brain and cffDNA methylation levels from a large region overlapping mir-663 correlated with maternal obesity, metabolic and immune markers, and infant behavior. A DUX4 hippocampal co-methylation network correlated with maternal obesity, infant behavior, infant hippocampal lipidomic and metabolomic profiles, and maternal blood measurements of DUX4 cffDNA methylation, cytokines, and metabolites. Ultimately, maternal obesity altered infant brain and behavior, and these differences were detectable in pregnancy through integrative analyses of cffDNA methylation with immune and metabolic factors. </p>
Artifact for the Scalability Study of the STTT Paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking"
<p>Scripts and infrastructure for the scalability study on DSMC published in the STTT paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking".</p>
A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging: geometry and simulation data
<p>This dataset contains the original µCT scan data, the scripts and intermediate results for the generation of the geometrical fiber model, as well as the structural simulation files and their experimental validation data described in the paper <a href="https://journals.sagepub.com/doi/10.1177/00405175221137009">"A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging"</a>, published in Textile Research Journal.</p>
SSP2017 - Experiment Data - Towards Extracting Realistic User Behavior Models
<p>This package contains the monitoring data, the ideal behavior models, computed clustering results for behavior models based on the monitoring data, and the interpretation of the analysis results. We processed these data with the following two tooling sources:</p> <p>Experiment setup: https://doi.org/10.5281/zenodo.883069</p> <p>Analysis software: https://doi.org/10.5281/zenodo.883061</p>
Experimental data for Understanding frictional behavior in fascia tissues through tribological modeling and material substitution
<p>Dataset (experimental data) for publication titled: Understanding frictional behavior in fascia tissues through tribological modeling and material substitution. The article deals with the development of a tribological model of fascia. As in the paper, the dataset is divided into the three phenomena studied: Effect of geometry, Models, Hyaluronic acid - model E. </p>
Additional raw video and pose estimation data of top view mouse behavior recordings (marble burying test, light-dark box, fear conditioning box) of acute and chronic stress models
<p>This repository contains raw data for 296 different behavioral recordings of mice (marble burying test, light-dark box, fear conditioning box). These include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.</p>
Data for "Unsupervised learning of sequence-specific aggregation behavior for a model copolymer"
<p>These are the data associated with the paper, "Unsupervised learning of sequence-specific aggregation behavior for a model copolymer" (DOI 10.1039/D1SM01012C). Each of the directories contains subdirectories with `GSD` files dumped from HOOMD. Each subdirectory roughly corresponds to one or two of the figures in the paper.</p>
UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
<p>This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density. </p> <p>Below are the description of each data file:</p> <ul> <li><strong>high_density_both_lane_raw_AI_speed.csv</strong> <ul> <li><strong>Description: </strong>extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li><strong>Data Fields:</strong> <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>
Drugs prescribed in Phelan-McDermid Syndrome differentially impact sensory behaviors in shank3 zebrafish models
<p>Altered sensory processing is a pervasive symptom in individuals with Autism Spectrum Disorders (ASD). Especially in cases of profound autism, individuals are often medicated to manage behaviors like aggression and/or self-harm and/or epilepsy, and it remains unclear how these medications might impact perception/sensory processing. To test this, we screened three medications, risperidone, Lithium Chloride (LiCl), and carbamazepine (CBZ), prescribed to individuals with Phelan-McDermid Syndrome (PMS) and one drug, 2-Methyl-6-(phenylethynyl) pyridine (MPEP) tested in rodent models of PMS, for their effects on a sensory-induced behavior in two zebrafish PMS models with frameshift mutations in the N- or C- termini. PMS is caused by deletions of the terminal end of chromosome 22 or point mutations in <em>Shank3</em>. People with PMS can present with an array of symptoms including ASD, epilepsy, gastrointestinal distress, and reduced responses to sensory stimuli. Our zebrafish mutant shank3ab models likewise show reduced responses in a Visual Motor Response (VMR) assay, in which increased locomotion is triggered by light-to-dark transitions. To test how pharmacological treatments affect the VMR, we exposed larvae to selected drugs for twenty-four hours and then quantified their locomotion during four ten-minute cycles of lights on-to-off stimuli. We found that risperidone normalized the VMR in <em>shank3</em> models, LiCl and CBZ had no effect on the VMR in any of the three genotypes. MPEP reduced the VMR in WT to levels seen in <em>shank3</em> models but caused no changes in either <em>shank3</em> model. Our work shows that the effects of drugs on sensory processing is varied in a way that can be highly genotype- and drug-dependent.</p>
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>
Raw video and pose estimation data of top view open field mouse behavior recordings of acute and chronic stress models
<p>This repository contains raw data for 411 different open field recordings of mice. these include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.</p>
Modeling Multi-level Dyadic Behavior to Transform the Science and Practice of Psychotherapy Process and Outcome.
ClinicalTrials.gov study NCT03594773. IPD Sharing: NO. Countries: 1. Publications: 2.
Multi-omic brain and behavioral correlates of cell-free fetal DNA methylation in macaque maternal obesity models (GC-FID dataset infant brains)
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A hierarchical N-mixture model to estimate behavioral variation and a case study of Neotropical birds
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Behavioral data for: A preclinical model of THC edibles that produces high-dose cannabimimetic responses
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Source code for dynamic models and simulations of mate sampling behavior
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Quantitative variables related to color, territory, behavior, and morphology for male lesser prairie-chickens used in discrete choice models in mate choice study
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Climate trends and behavior of a model Amazonian terrestrial insectivore, Black-faced Antthrush, indicate adjustment to hot and dry conditions
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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.