Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
67
datasets available to search
ShareScore release 0.9.0
Dataset results
67 results for “movement modeling”
Hydrodynamic Model Output Used to Evaluate Chinook Salmon Movements and Distribution in the South Delta
This data release includes the output variables extracted from the UnTRIM Bay-Delta hydrodynamic model (hydrodynamic model) for use in evaluating the effects of hydrodynamics on the behavior of acoustically-tagged juvenile Chinook Salmon (Oncorhynchus tshawytscha) in the Sacramento-San Joaquin Delta. Work was funded by State Water Contractors (SWC) and completed by Anchor QEA; FlowWest, LLC; and University of Washington under a SWC 2023 Science Plan grant (study name Evaluation of the Influence of State Water Project and Central Valley Project on Chinook Salmon Movements and Distribution in the South Delta), contracted by SWC. Not all the hydrodynamic model output variables in the output provided with this memorandum were used in the final fish models used to analyze Chinook Salmon responses. Model output for additional variables and locations were included for completeness and to make these output files more broadly useful to researchers interested in other locations or variables in the Sacramento-San Joaquin Delta. Hydrodynamic model simulations were conducted for 2010, 2011, 2012, 2013, 2014, 2015, 2016, and 2017, with hydrodynamic model output variables provided at mostly the same locations for each period simulated. The years 2011 through 2016 were simulated previously for a prior project and model output provided through the Environmental Data Initiative (edi.1124.1). Files for these years were recreated from the prior simulations for this project to add an output location. Additional locations were added to the 2010 and 2017 simulations for the 2010 and 2017 hydrophone arrays, and thus 2010 and 2017 include additional model output, relative to 2011 through 2016. The model simulation for each year spanned the full period of Chinook Salmon detections in the telemetry data collected during that year.
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>
Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
<p><span><span>In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays. </span></span></p>
Individual-based simulation model of annual movement paths for the Darwin's frog (R code and data)
<p>Desprition of the R code</p> <p>I constructed an individual-based simulation model that describes the movement path of an individual<em> Rhinoderma darwinii</em> through 3-month displacement steps. This model was primarily developed to evaluate the age-specific movement behaviour of Darwin's frogs, however, I also used it to provide better estimates (i.e. alleviating for movement censoring) of age-specific annual displacements in the species. I developed several variations of this model through a combination of different random walk sub-models for juveniles and adults: uncorrelated non-stationary random walks (NRW), correlated non-stationary random walks (CRW), and stationary random walks (SRW). The NRW and CRW were modelled as a first-order Markovian process where the location of an individual <em>i</em> in time<em> t</em> depends on its spatial location in <em>t </em>- 1. The NRW is unbiased, i.e., there is no preferred direction in each movement step. In contrast, the CRW includes persistence in the directionality of movement, so there is a correlation between successive step orientations. Finally, the SRW assumes that individuals have an activity centre to which all their spatial locations are related.</p> <p>Related data are provided (y.txt, x.txt and age.txt)</p>
Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration
<p>Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration https://zenodo.org/records/14997527</p>
Raw data of: "Controlling Hand Movements Relying on Tactile Illusions: A Model Predictive Control Framework"
<p>in Fig4_a.txt: raw the data for the plot of Fig4_a (x and y of the first simulated trajectory from trajectory 1 to 50)</p> <p>in Fig4_b.txt: raw the data for the plot of Fig4_b </p> <p>in Fig4_c.txt raw the data for the plot of Fig4_b. Each column corresponds to the optimal angle of the plate for each of the 50 trajectories simulated in Fig4_a</p>
Figure 1. The Statechart for the Player movement and the Navigation System-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>The next section describes the Unified Modelling Language (UML) diagrams designed for the project, which are a state diagrams (also known as statecharts) for the Player movement, the Navigation system (Figure 1). In addition, we used a class diagram for the Player and Camera movement (Figure 2). When the avatar-based game starts, the state of the Player is Idle, i.e., Player_IDLE. When the user selects the building, it enables the navigation path towards the destination. If the user selects any arrow keys (Right, Left & Up) the state of the player will change to running (i.e., Player_Running). Also, the path will diminish along with the player movement; hence, the state of navigation path will change to Changing_Path.</p>
Figure 2. The class diagram for the Player and Camera movements-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>The next section describes the Unified Modelling Language (UML) diagrams designed for the project, which are a state diagrams (also known as statecharts) for the Player movement, the Navigation system (Figure 1). In addition, we used a class diagram for the Player and Camera movement (Figure 2). When the avatar-based game starts, the state of the Player is Idle, i.e., Player_IDLE. When the user selects the building, it enables the navigation path towards the destination. If the user selects any arrow keys (Right, Left & Up) the state of the player will change to running (i.e., Player_Running). Also, the path will diminish along with the player movement; hence, the state of navigation path will change to Changing_Path.</p>
Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
Open the record for dataset details and reuse information.
Data from: An open spatial capture–recapture model for estimating density, movement, and population dynamics from line-transect surveys
Open the record for dataset details and reuse information.
From pup to predator; generalized hidden Markov models reveal rapid development of movement strategies in a naïve long‐lived vertebrate
<p>Rapid development of a successful foraging strategy is critical for juvenile survival, especially for naïve animals that receive no parental guidance. However, this process is poorly understood for many species. Although observation of early-life movements is increasingly possible with miniaturisation of animalborne telemetry devices, analytical limitations remain. Here, we tracked 29 recently-weaned, grey seal <em>Halichoerus grypus</em> pups from colonies in two geographically distinct regions of the United Kingdom. We analysed at-sea movements of pups throughout their initial months of nutritional independence to investigate the ontogeny of behaviour-specific (foraging and travelling) movement patterns. Using generalized hidden Markov models (HMMs), we extended the conventional HMM framework to account for temporal changes in putative foraging and travelling movement characteristics, and investigate the effects of intrinsic (sex) and extrinsic (environment) factors on this process. Putative foraging behaviour became more tortuous with time, and travelling became faster and more directed, suggesting a reduction in search scale and an increase in travel efficiency as pups shifted from exploration to an adult-like repeatable foraging strategy. Sex differences in movement characteristics were evident from colony departure, but sex-specific activity budgets were only detected in one region. We show that sex-specific behavioural strategies emerge before sexual size dimorphism in grey seals, and suggest that this phenomenon may occur in other long-lived species. Our results also indicate that environmental variation may affect the emergence of sex-specific foraging behaviour, highlighting the need to consider interacting intrinsic and extrinsic factors in shaping movement strategies of long-lived vertebrates. Moreover, comparing the behavioural state estimations to those of a conventional HMM (no variation in statespecific movement parameters) revealed differences in the amount and location of foraging activity, with implications for spatial conservation management. Overlooking intrinsic and extrinsic variation in movement processes could distort our understanding of foraging ecology, population dynamics, and conservation requirements.</p>
Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space, and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi-state mark recapture methods using a hierarchical N-mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state-space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N-mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.
Data from: Climate-mediated hybrid zone movement revealed with genomics, museum collection and simulation modeling
Climate-mediated changes in hybridization will dramatically alter the genetic diversity, adaptive capacity and evolutionary trajectory of interbreeding species. Our ability to predict the consequences of such changes will be key to future conservation and management decisions. Here we tested through simulations how recent warming (over a 32-year period) is affecting the geographic extent of a climate-mediated developmental threshold implicated in maintaining a butterfly hybrid zone (Papilio glaucus and Papilio canadensis; Lepidoptera: Papilionidae). These simulations predict a 68 km shift of this hybrid zone. To empirically test this prediction, we assessed genetic and phenotypic changes using contemporary and museum collections and document a 40 km northward shift of this hybrid zone. Interactions between the two species appear relatively unchanged during hybrid zone movement. We found no change in the frequency of hybridization and regions of the genome that experience little to no introgression moved largely in concert with the shifting hybrid zone. Model predictions based on climate scenarios predict this hybrid zone will continue to move northward, but with substantial spatial heterogeneity in the velocity (55-144 km/1°C), shape, and contiguity of movement. Our findings suggest that the presence of non-climatic barriers (e.g., genetic incompatibilities) and/or non-linear responses to climatic gradients may preserve species boundaries as the species shift. Further, we show that variation in the "geography" of hybrid zone movement could result in evolutionary responses that differ for geographically distinct populations spanning hybrid zones and thus have implications for the conservation and management of genetic diversity.
Contractions, movement and projections in the model sponge Tethya wilhelma
<p>Video based on a photo series of the model sponge Tethya wilhelma. It shows contractions, movement and projections in this sponge.</p>
Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Location Data
<p>This dataset is meant to be used with "Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma - Results and Data". It provides spatial output data for 4 different setups (spheroid, traditional, 3d gravity, and traditional floating) of an agent-based model of <i>in vitro </i>tuberculosis infection models. </p>
Combining bioenergetics and movement models to improve understanding of the population consequences of disturbance
<p>We developed dynamic bioenergetics models to investigate how behavioural responses to anthropogenic disturbance events might affect the population dynamics of three marine mammal species (harbour porpoise, grey seal and harbour seal) with contrasting life-history traits (capital vs income breeders) and movement behaviour (resident vs nomadic). We used these models to analyse how individual vital rates were affected by differences in the probability of disturbance and the duration of any behavioural response, while taking account of uncertainty in the model parameters and heterogeneity in behaviour. The outputs of individual movement models and telemetry data were then used to determine how the probability of exposure might vary among species, individuals, and geographical locations. We then demonstrate how these estimated probabilities of exposure can be translated into probabilities of disturbance. For illustrative purposes, we modelled the potential effects of a temporary decrease in energy assimilation associated with a series of disturbance events that might realistically occur during the construction of an offshore windfarm. Offspring starvation mortality was the vital rate that was most affected by these disturbance events. Monitoring of rates should be considered as standard practice so that populations' responses can be detected as early as possible. Predicted effects on individual vital rates depended on the species' movement behaviour and the likely density of animals where the modelled construction activity was assumed to take place. The magnitude of these effects also depended critically on the assumed duration of the reduction in energy assimilation. No direct estimates of this variable are currently available, but we suggest some ways in which it could be estimated. The described approach could be extended to other species and activities, given sufficient information to parameterise the component models. However, we emphasise the need to account for among-individual heterogeneities and uncertainties in the values of the many model parameters.</p>
Targeting fin whale conservation in the North-Western Mediterranean Sea: Insights on movements and behaviour from biologging and habitat modelling
<p>Biologging and habitat modelling are key tools supporting the development of conservation measures and mitigating the effects of anthropogenic pressures on marine species. Here, we analysed satellite telemetry data and foraging habitat preferences in relation to chlorophyll-a productivity fronts to understand the movements and behaviour of endangered Mediterranean fin whales (<em>Balaenoptera physalus)</em> during their spring-summer feeding aggregation in the North-Western Mediterranean Sea. Eleven individuals were equipped with Argos satellite transmitters across three years, with transmissions averaging 23.5 ± 11.3 days. Hidden Markov Models were used to identify foraging behaviour, revealing how individuals showed consistency in their use of seasonal core feeding grounds; this was supported by the distribution of potential foraging habitat. Importantly, tracked whales spent most of their time in areas with no explicit protected status within the study region. This highlights the need for enhanced time- and place-based conservation actions to mitigate the effects of anthropogenic impacts for this species, notably ship strike risk and noise disturbance in an area of exceptionally high maritime traffic levels. These findings strengthen the need to further assess critical habitats and Important Marine Mammal Areas that are crucial for focussed conservation, management, and mitigation efforts.</p>
Movements during sleep reveal the developmental emergence of a cerebellar-dependent internal model in motor thalamus
<p>With our eyes closed, we can track a limb's moment-to-moment location in space. If this capacity relied on sensory feedback from the limb, we would always be a step behind because sensory feedback takes time: For the execution of rapid and precise movements, such lags are not tolerable. Nervous systems solve this problem by computing representations—or internal models—that mimic movements as they are happening, with the associated neural activity occurring after the motor command but before the sensory feedback. Research in adults indicates that the cerebellum is necessary to compute internal models. What is not known, however, is when—and under what conditions—this computational capacity develops. Here, taking advantage of the unique kinematic features of the discrete, spontaneous limb twitches that characterize active sleep, we captured the developmental emergence of a cerebellar-dependent internal model. Using rats at postnatal days (P) 12, P16, and P20, we compared neural activity in the ventral posterior (VP) and ventral lateral (VL) thalamic nuclei, both of which receive somatosensory input but only the latter of which receives cerebellar input. At all ages, twitch-related activity in VP lagged behind the movement, consistent with sensory processing; similar activity was observed in VL through P16. At P20, however, VL activity no longer lagged behind movement, but instead precisely mimicked the movement itself; this activity depended on cerebellar input. In addition to demonstrating the emergence of internal models of movement, these findings implicate twitches in their development and calibration through, at least, the preweanling period.</p>
Data from: Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model
<p>Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and thus omit linkages between individual-level and population-level processes. We describe an integrated spatial capture-recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km<sup>2</sup> survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture-recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from < 0.75 bears/625 km<sup>2</sup> grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53–69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multi-year integrated population model using capture-recapture and telemetry data (2008–2016; Regehr et al. 2018). Overall, the SCR-movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR-movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.</p>
Integrated animal movement and spatial capture-recapture models: simulation, implementation, and inference
<p>Over the last decade, spatial capture-recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies -- individual animals observed at specific locations and times -- can provide a rich source of information about how these processes relate to demographic rates. We developed SCR models that integrate complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using Bayesian analysis methods. We evaluated their performance through a simulation study, where we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T=25 to T=15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but movement models developed here could be extended to open SCR models. The movement process models could also be extended to accommodate additional "building blocks'' of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.</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.