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237 results for “Animal model”
Physiological parameters for three farm animal species (cattle, sheep, and swine) as the basis for the development of generic physiologically based kinetic models
<p><strong>IMPORTANT : PLEASE DISREGARD VERSION 1 OF THIS UPLOAD SINCE IT INCLUDES ERRONEOUS INFORMATION.</strong></p> <p>This excel file (DOI: 10.5281/zenodo.3433224) provides physiological parameters and their inter-individual variability (mean, coefficient of variation, sample size) for three farm animal species: cattle (<em>Bos taurus</em>), sheep (<em>Ovis aries</em>), and swine (<em>Sus scrofa domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020). This file is associated with R codes (DOI: 10.5281/zenodo.3432796) for generic PBK models, partition coefficient Quantitative Structure Activity Relationship (QSAR) models for each farm animal species and parameterisation of the model.</p> <p>The full data collection and implementation of the models using case studies are described in Lautz et al., 2020 (10.1016/j.toxlet.2019.10.008).</p>
VR-Together Pilot 3: 3D Character Models and Animation Data
<p>VR-Together Pilot 3 Character and Animation Dataset.</p> <p>This dataset contains the 3D characters and animations as used in <a href="https://vrtogether.eu/about-vr-together/pilots/pilot3/">Pilot 3 of the VR-Together project</a>. It contains the 4 characters of the associated experience and their post-processed motion capture animation data in the FBX format, as well as the texture data in the PNG format. </p> <p>The data contained in this dataset was prepared for the Unity game engine, but should be usable in other content creation systems without issue. </p> <p>VR-Together has been funded by the European Commission as part of the H2020 program, under the grant agreement 762111.</p>
Data from: Estimation in the multinomial reencounter model - Where do migrating animals go and how do they survive in their destination area?
<p><strong>Abstract</strong></p> <p>Spatial variation in survival has individual fitness consequences and influences population dynamics. Which space animals use during the annual cycle determines how they are affected by this spatial variability. Therefore, knowing spatial patterns of survival and space use is crucial to understand demography of migrating animals. Extracting information on survival and space use from observation data, in particular dead recovery data, requires explicitly identifying the observation process. We build a fully stochastic model for animals marked in populations of origin, which were found dead in spatially discrete destination areas. It acts on the population level and includes parameters for use of space, survival and recovery probability. The model is based on the division coefficient and the multinomial reencounter model. We use a likelihood-based approach, derive Restricted Maximum Likelihood-like estimates for all parameters and prove their existence and uniqueness. In a simulation study we demonstrate the performance of the model by using Bayesian estimators derived by the Markov chain Monte Carlo method. We obtain unbiased estimates for survival and recovery probability if the sample size is large enough. Moreover, we apply the model to real-world data of European robins <em>Erithacus rubecula</em> ringed at a stopover site. We obtain annual survival estimates for different spatially discrete non-breeding areas. Additionally, we can reproduce already known patterns of use of space for this species. We would like to thank the Greifswalder Oie Bird Observatory of the Verein Jordsand, Ahrensburg, and the Hiddensee Bird Ringing Centre, Güstrow, for providing the robin data.</p>
Simulated data from abmAnimalMovement: An R package for simulating animal movement using an agent-based model
<p>Contained here are the data simulated as part of the manuscript: "abmAnimalMovement: An R package for simulating animal movement using an agent-based model" that can be found at: https://github.com/BenMMarshall/abmAnimalMovement (and archived at: https://doi.org/10.5281/zenodo.6951937).</p> <p>- BADGER_locations.csv: A csv file that contains the realised locations of the example badger simulation, where each row is equal to a timestep. Columns include: timestep, the timestep as an integer; x, the x coordinate of the animal; y, the y coordinate of the animal; sl, the step length between locations used during the simulation; sl_rescale the rescale factor required to return step lengths back to the input scale; ta, turning angle between locations in degrees; behave, the behavioural mode the animal was in at a given timestep; chosen, the location chosen out of the number of options available; destination_x and destination_y the point the animal was attracted to at that time (note exploratory behaviour is not subject attraction).</p> <p>- BADGER_options.csv: A csv file that contains the options available to the example badger simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. Columns include: timestep, the timestep as an integer; oall_x, and oall_y show the x and y coordinates of all the options available to an animal at a timestep; oall_steplengths are the step lengths from the current location compared to all the options.</p> <p>- completelist.RDS: This RDS file contains a list object of length three, where the full simulation outputs from each three examples are stored. Each species slot contains the “locations” dataframe (see description of locations.csv), the "options" dataframe (see description of options.csv), and a nested list containing all the "inputs" used to generate the simulated results (split into subsections: inputs_basic that contains inputs linked to simulation duration and intensity, inputs_destination that contains inputs linked to destination and attraction aspects, inputs_movement that contains inputs linked to movement capacity and behavioural switching, inputs_cycle that contains inputs linked to activity cycling, inputs_layerSeed that contains the environmental matrices and seed). A fourth object is returned called "others" that captures all other outputs, mainly used internally for debugging and checking.</p> <p>- KINGCOBRA_locations.csv: A csv file that contains the realised locations of the example king cobra simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_options.csv file.<br>KINGCOBRA_options.csv: A csv file that contains the options available to the example king cobra simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_options.csv file.</p> <p>- VULTURE_locations.csv: A csv file that contains the realised locations of the example vulture simulation, where each row is equal to a timestep. The file structure follows the same as the BADGER_locations.csv file.<br>VULTURE_options.csv: A csv file that contains the options available to the example vulture simulation over the entire simulation duration, where each row is equal to an option repeated for each timestep. The file structure follows the same as the BADGER_locations.csv file.</p> <p>- eg_landscapedata_completelist.RDS: This RDS file contains the landscape matrices required for recreating the simulated outputs described in the manuscript named above. It is a list of three objects ("shelter", "forage", "movement"), each a numeric matrix of equal size, with values describing the quality of each landscape characteristic.</p> <p>- argument_table.csv: Descriptive table of the simulation inputs used in the walk-through manuscript.</p>
Computational analysis of cortical neuronal excitotoxicity in a large animal model of neonatal brain injury
<p>This is the dataset accompanying the manuscript:</p> <p><strong>"Computational Analysis of Cortical Neuronal Excitotoxicity in a Large Animal Model of Neonatal Brain Injury"</strong></p> <p>Panagiotis Kratimenos<sup>1,2,5 </sup>*, Abhya Vij<sup>5</sup>, Robinson Vidva<sup>6</sup>, Ioannis Koutroulis<sup>3,4,5</sup>, Maria Delivoria-Papadopoulos<sup>7</sup>**, Vittorio Gallo<sup>1,5</sup>, and Aaron Sathyanesan<sup>1,5</sup>*</p> <p><em><sup>1</sup></em><em>Center for Neuroscience Research, Children’s National Research Institute, Children’s National Hospital, Washington DC, USA</em></p> <p><em><sup>2</sup></em><em>Department of Pediatrics, Division of Neonatology, Children’s National Hospital, Washington DC, USA</em></p> <p><em><sup>3</sup></em><em>Department of Pediatrics, Division of Emergency Medicine, Children’s National Hospital, Washington, DC, USA</em></p> <p><em><sup>4</sup></em><em>Center for Genetic Medicine Research, Children’s National Research Institute and Department of Genomics and Precision Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, USA</em></p> <p><em><sup>5</sup></em><em>George Washington University School of Medicine and Health Sciences, Washington DC, USA</em></p> <p><em><sup>6</sup></em><em>Digirobi Solutions, Bengaluru, Karnataka, India</em></p> <p><em><sup>7</sup></em><em>Department of Pediatrics, Drexel University College of Medicine, Philadelphia, PA, USA</em></p> <p>*Corresponding Authors:</p> <p>Panagiotis Kratimenos, MD, PhD: <a href="mailto:panagiotis.kratimenos@childrensnational.org">panagiotis.kratimenos@childrensnational.org</a></p> <p>Aaron Sathyanesan, PhD: <a href="mailto:asathyanesan@childrensnational.org">asathyanesan@childrensnational.org</a></p> <p>111 Michigan Avenue, Washington, DC, 20010, USA</p>
Fig. 1 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 1. Spatial distribution of ignitions in 2010–2014 on studied area (dotted line is ATO zone's limits in 1.06– 30.09.2014).
Fig. 5 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 5. Distribution of two snake species, H. caspius and E. dione, in Ukrainian East (ATO zone is indicated by dotted line, burnt area marked inside zone).
Fig. 3 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 3. Spatial local distribution of ignitions in 2010–2014 in the outskirts of Slavyanoserbsk, Luhansk Region.
Data from: A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography
<p>1. The management of sustainable harvest of animal populations is of great ecological and conservation importance. Development of formal quantitative tools to estimate and mitigate the impacts of harvest on animal populations has positively impacted conservation efforts.</p> <p>2. The vast majority of existing harvest models, however, do not simultaneously estimate ecological and harvest impacts on demographic parameters and population trends. Given that the impacts of ecological drivers are often equal to or greater than the effects of harvest, and can covary with harvest, this disconnect has the potential to lead to flawed inference.</p> <p>3. In this study, we used Bayesian hierarchical models and a 43-year capture-mark-recovery dataset from 404,241 female mallards (Anas platyrhynchos) released in the North American midcontinent to estimate mallard demographic parameters. Further, we model the dynamics of waterfowl hunters and habitat, and the direct and indirect effects of anthropogenic and ecological processes on mallard demographic parameters.</p> <p>4. We demonstrate that density-dependence, habitat conditions, and harvest can simultaneously impact demographic parameters of female mallards, and discuss implications for existing and future harvest management models.</p> <p>5. Our results demonstrate the importance of controlling for multicollinearity among demographic drivers in harvest management models, and provide evidence for multiple mechanisms that lead to partial compensation of mallard harvest. We provide a novel model structure to assess these relationships that may allow for improved inference and prediction in future iterations of harvest management models across taxa.</p>
From optimality to prestige: Investigating human-animal interactions at Late Mesolithic Hoge Vaart-A27 (Almere, the Netherlands) using a Prey Choice Model
<h2><strong>This dataset is from my Research Master's Thesis from Groningen University. </strong></h2> <p><strong>The dataset includes;</strong></p> <ul> <li>The CSV data necessary to recreate all models and graphs from the thesis</li> <li>The R.Script with all codes</li> </ul> <p><strong>Used packages and programming language: </strong></p> <ul> <li>Kassambara, A. (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots (R package version 0.6.0) [R; Rstudio]. R Foundation for Statistical Computing.<a href="https://doi.org/%3Chttps://CRAN.R-project.org/package=ggpubr%3E."> <https://CRAN.R-project.org/package=ggpubr>.</a></li> <li>Mei, W., Yu, G., & Greenwell, B. M. (2022). ggtrendline: Add Trendline and Confidence Interval to ‘ggplot’ (R package version 1.0.3) [R; Rstudio]. R Foundation for Statistical Computing.<a href="https://cran.r-project.org/package=ggtrendline"> https://CRAN.R-project.org/package=ggtrendline</a></li> <li>Pedersen, T. L. (2024). patchwork: The Composer of Plots (R package version 1.2.0) [R; RStudio]. R Foundation for Statistical Computing.<a href="https://cran.r-project.org/package=patchwork"> https://CRAN.R-project.org/package=patchwork</a></li> <li>R Core Team. (2022). R: A Language and Environment for Statistical Computing (R version 4.2.1) [R; RStudio]. R Foundation for Statistical Computing.<a href="https://www.r-project.org/"> https://www.R-project.org/</a></li> <li>Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC.</li> <li>Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.<a href="https://ggplot2.tidyverse.org"> https://ggplot2.tidyverse.org</a></li> <li>Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686.<a href="https://doi.org/10.21105/joss.01686"> https://doi.org/10.21105/joss.01686</a></li> </ul> <p><strong><span>Abstract</span></strong></p> <p><em><span>The research of the faunal assemblage from Hoge Vaart-A27 (Almere, the Netherlands) provides a new perspective on investigating the connections between foraging strategies and socio-cultural dynamics of the Late Mesolithic and Early Swifterbant communities in Northwest Europe. The significance of this topic lies in its potential to help elucidate the broader socio-cultural aspects of foraging and human-animal interactions during this period. Despite extensive research, there remains a gap in comprehending how prestige influenced prey selection alongside optimal foraging strategies. The study aims to address this gap by employing zooarchaeological methods combined with prey choice modelling to investigate prey selection complexities in Flevoland’s transitioning cultural and physical landscape. The methods include analyses of species abundance and detailed examination of red deer, horse, aurochs and wild boar. The key findings reveal that while (optimal) foraging strategies were practised at Hoge Vaart-A27, they were significantly influenced by motivations such as prestige. The results contribute significantly to research by offering a glimpse into how Northwest European foragers could have perceived and interacted with big game beyond subsistence. </span></em></p> <p><strong><span>Keywords</span></strong><span> </span><span>human-animal relationships, prestige, profitability, prey choice, Swifterbant Culture, optimal foraging theory, costly signalling theory</span></p>
Figure 9 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 9. Simulation results for the lift forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).
Figure 8 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 8. Simulation results for the drag forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).
Figure 4. CFD simulation results for Models A in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 4. CFD simulation results for Models A (panels A and C) and B (panels B and D) in steady 5.6 m/s flow. The blue, yellow, and green regions are areas of reduced flow speed that generate forces on the tags. The upper panels show the flow speed over a horizontal cross-section at the tag midline. The lower panels show flow speed over a vertical cross-section at the centerline of the tag. The improved flow around Model B is evident in the smaller magnitude of blue coloration in the wake behind the tag.
Figure 2 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 2. An illustration of the Model A tag in the computational domain used for the simulations of all tag designs. The fluid flow is from left to right and representative orientations of the tag to the flow are shown at the bottom of the figure.
Figure 7 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 7. Simulation results for the lift forces acting on the Model A and B tags (top panel) in variable flow (0.25–10 m/s). Measured results in variable flow speed (1–5.6 m/s) are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).
Figure 10 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 10. Experimental load testing of a Model A tag attached at three test sites on a common dolphin cadaver using four silicone suction cups. Applied force vs. calculated total cup attachment force at the four silicone suction cups are shown for lift (bottom panel) and drag (top panel) loading. The average pressure difference for the four cups is also shown on the right hand axis, where 1 atmosphere is approximately 100 kPa. Forces were applied via a line attached to the tag by pulling either perpendicular to the body (lift) or parallel (drag). The curves end where the cup attachment failed or the cups began to slide. The average of two trials at each site is shown in each panel.
Figure 6 in Drag of suction cup tags on swimming animals: Modeling and measurement
Figure 6. Simulation results for the drag forces acting on the Model A and B tags (top panel) in variable flow (0.25–10 m/s) with fixed orientation (0º). Measured results in variable flow speed (1–5.6 m/s) are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).
A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance
<p>Simulated data associated with the paper "A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance".</p> <p>The main dataset is contained within the mermaids.csv, with columns explained within the associated README file. Epigenetic and social network information are contained within the other two datasets</p>
Dihydrosphingolipids are associated with steatosis and increased fibrosis damage in human and animal models of non-alcoholic fatty liver disease
<p>Data sets used for the article entitled: "Accumulation of dihydrosphingolipids and neutral lipids is related to steatosis and fibrosis damage in human and animal models of non-alcoholic fatty liver disease"</p> <p>- Patient data: DATA Patients JLR.xlsx</p> <p>- Mouse data: DATA Mice.xlsx</p>
Counting animals in aerial images with a density map estimation model
<p>Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual post-processing has been used extensively, however, volumes of such data are increasing, necessitating some level of automation, either for complete counting or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as <em>Pygoscelis</em> penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations, and demonstrably improve monitoring efforts from aerial imagery. </p>
ScienceDex guides
Understand access before you commit
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.