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.
36
datasets available to search
ShareScore release 0.9.0
Dataset results
36 results for “movement prediction”
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>
Fast prediction in marmoset reach-to-grasp movements for dynamic prey - Reach Data and Supplemental Video
<p>Supplemental video and data corresponding to Shaw, L., Wang, K.H., Mitchell, J. (2023) Fast prediction in marmoset reach-to-grasp movements for dynamic prey.</p> <p>1. Video Files</p> <p>MarmoReach 1 is an illustrative example.</p> <p>MarmoReach 2 illustrates the reaching trial shown in Figure 3D.</p> <p>MarmoReach 3-5 are example reach to grasps from grasp clusters found in Figure 2. </p> <p>2. Data</p> <p>marmo_reach_model.mat is a Matlab struct.</p> <p>2D position data of hand and cricket used for analyses related to Figure 3 and Figure 4. </p> <p>x.hand,y.hand = position data of the central hand marker for each trial.</p> <p>x.cricket,y.cricket = position data of the cricket marker for each trial.</p> <p>x.cricketexfull,y.cricketexfull = position data of the cricket marker preceding reach onset for delay analyses. </p> <p>To reconstruct cricket position from beginning to end with the inclusion of the exfull data (prior to reach to reach end) for the second reach, for example, [model.x.cricketexfull{2}' model.x.cricket{2}'].</p> <p> </p>
Data from: The impact of task context on predicting finger movements in a brain-machine interface
<p>A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor function will be their robustness to changes in a task. With functional electrical stimulation (FES) for example, the patient's own hand will be used to produce a wide range of forces in otherwise similar movements. To investigate the impact of task changes on BMI performance, we trained two rhesus macaques to control a virtual hand with their physical hand while we added springs to each finger group (index or middle-ring-small) or altered their wrist posture. Using simultaneously recorded intracortical neural activity, finger positions, and electromyography, we found that predicting finger kinematics and finger-related muscle activations across contexts led to significant increases in prediction error, especially for muscle activations. However, with respect to online BMI control of the virtual hand, changing either training task context or the hand's physical context during online control had little effect on online performance. We explain this dichotomy by showing that the structure of neural population activity remained similar in new contexts, which could allow for fast adjustment online. Additionally, we found that neural activity shifted trajectories proportional to the required muscle activation in new contexts, possibly explaining biased kinematic predictions and suggesting a feature that could help predict different magnitude muscle activations while producing similar kinematics.</p>
Data from: The impact of task context on predicting finger movements in a brain-machine interface
Open the record for dataset details and reuse information.
Asymmetric retinal direction tuning predicts optokinetic eye movements across stimulus conditions
Open the record for dataset details and reuse information.
LRP predicts smooth pursuit eye movement onset during the ocular tracking of self-generated movements
<p>Dataset relative to the following publication:</p> <p>Chen, J., Valsecchi, M. & Gegenfurtner, K.R. (2016). LRP predicts smooth pursuit eye movement onset during the ocular tracking of self-generated movements. <em>Journal of Neurophysiology, </em>in press</p> <p>Each folder contains the data relative to one experiment and the script that was used to generate them. Please refer to "Description on data format.txt" for the usage of the data.</p> <p>Additional information can be deducted from the experimental scripts.</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>
Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks
<p>This dataset contains the following:</p> <ul> <li><strong>data</strong>: <ul> <li><em>preprocessed</em>: hourly European weather radar data (here: <em>radar</em>) and aggregated simulation outputs (here: <em>abm</em>), combined with ERA5 reanalysis data and Voronoi tessellation details</li> <li><em>shapes: </em>geographical shapes used for plotting</li> </ul> </li> <li><strong>results</strong><em>:</em> trained models and corresponding results for both simulated data (here: <em>abm</em>) and weather radar data (here: <em>radar</em>).</li> <li><strong>figures</strong><em>: </em>final figures presented in our paper "Learning to predict spatio-temporal movement dynamics from weather radar networks" to summarize the results</li> </ul> <p>The corresponding code used to train and evaluate models is archived here: <a href="https://doi.org/10.5281/zenodo.6921595">10.5281/zenodo.6921595</a>.</p>
Predictive perception of self-generated movements: Commonalities and differences in the neural processing of tool and hand actions
<p>Dataset relative to the following publication:</p> <p>Pazen, M., Uhlmann, L., van Kemenade, B.M., Steinsträter, O., Straube, B., Kircher, T. Predictive perception of self-generated movements: Commonalities and differences in the neural processing of tool and hand actions. <em>NeuroImage</em>. DOI: <a href="https://doi.org/10.1016/j.neuroimage.2019.116309">10.1016/j.neuroimage.2019.116309</a></p> <p>Details are specified in the "readme.docx" file.</p>
Data from: Differential effects of environmental predictability on ungulate movement behavior in disparate ecosystems
Open the record for dataset details and reuse information.
Data from: Predicting functional responses in agro-ecosystems from animal movement data to improve management of invasive pests
Open the record for dataset details and reuse information.
Data from: Corridors or risk? movement along, and use of, linear features vary predictably among large mammal predator and prey species
<p>1. Space-use behaviour reflects trade-offs in meeting ecological needs and can have consequences for individual survival and population demographics. The mechanisms underlying space-use can be understood by simultaneously evaluating habitat selection and movement patterns, and fine-resolution locational data are increasing our ability to do so. 2. We use high-resolution location data and an integrated step-selection analysis to evaluate caribou, moose, bear, and wolf habitat selection and movement behavior in response to anthropogenic habitat modification, though caribou data were limited. Space-use response to anthropogenic linear features (LFs) by predators and prey are hypothesized to increase predator hunting efficiency and are thus believed to be a leading factor in woodland caribou declines in western Canada. 3. We found that all species moved faster while on LFs. Wolves and bears were also attracted towards LFs, whereas prey species avoided them. Predators and prey responded less strongly and consistently to natural features such as streams, rivers and lakeshores. These findings are consistent with the hypothesis that LFs facilitate predator movement and increase hunting efficiency, while prey perceive such features as risky. 4. Understanding the behavioural mechanisms underlying space-use patterns is important in understanding how future land-use may impact predator-prey interactions. Explicitly linking behaviour to fitness and demography will be important to fully understand the implications of management strategies.</p>
Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps - data
<p>Data from the article<strong><em> Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps</em></strong> published in Journal of Vision.</p> <p>code: https://zenodo.org/record/5999215<br> data: https://zenodo.org/record/5999046</p> <p><br> Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> February 2022</p>
Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement
<p>Volition - the sense of control or agency over one's voluntary actions - is widely recognized as the basis of both human subjective experience and natural behavior in non-human animals. To date, several human studies have found peaks in neural activity preceding voluntary actions, e.g. the readiness potential (RP), and some have shown upcoming actions could be decoded even before awareness. While these findings may pose a challenge to traditional accounts of human volition, some have proposed that random processes underlie and explain pre-movement neural activity. Here we seek to address part of this controversy by evaluating whether pre-movement neural activity in mice contains structure beyond that present in random neural activity. Implementing a self-initiated water-rewarded lever pull paradigm in mice while recording widefield [Ca++] neural activity we find that cortical activity changes in variance seconds prior to movement and that upcoming lever pulls or spontaneous body movements could be predicted between 1 second to more than 10 seconds prior to movement, similar to but even earlier than in human studies. We show that mice, like humans, are biased towards initiation of voluntary actions during specific phases of neural activity oscillations but that the pre-movement neural code in mice changes over time and is widely distributed as behavior prediction improved when using all vs single cortical areas. These findings support the presence of structured multi-second neural dynamics preceding voluntary action beyond that expected from random processes. Our results also suggest that neural mechanisms underlying self-initiated voluntary action could be preserved between mice and humans.</p>
Personality and predictability in farmed calves using movement and space-use behaviours quantified by Ultra-wideband sensors
<p>Individuals within a population often show consistent between individual differences in their average behavioral expression (personality), and consistent differences in their within-individual variability of behavior around the mean (predictability). Where correlations between different personality traits and/or the predictability of traits exist, these represent behavioral or predictability syndromes. In wild populations, behavioral syndromes have consequences for individual's survival and reproduction and affect the structure and functioning of groups and populations. The consequences of behavioral syndromes for farm animals is less well explored, partly due to the challenges in quantifying behavior of many individuals across time and context in a farm setting. Here, we use Ultra-Wideband location sensors to provide precise measures of movement and space use for 60 calves over 40-48 days. We find that individuals show consistent within and between individual variation in movement and space use and show correlations in personality and predictability, indicating the existence of "exploratory" and "active" personality types in farmed calves. We consider the consequences of such individual variability for cattle behavior and welfare and how such data may be used to inform management decision in farm animals.</p>
Nest shape influences colony organization in ants: spatial distribution and connectedness of colony members differs from that predicted by random movement and is affected by nest space
<p><strong>Overview</strong></p> <p>Data used for the manuscript: Nest shape influences colony organization in ants: spatial distribution and connectedness of colony members differs from that predicted by random movement and is affected by available space</p> <p><strong>Purpose of the study</strong></p> <p>Investigating how nest shape influences how <em>Temnothorax rugatulus</em> colonies spatially organize in their nests. This includes physical location of colony members and their distances from the entrance, mobile colony member distance to the brood center, worker distance to the physical center of the nest, and comparing worker distributions with those predicted by a random walk model.</p> <p><strong>Structure of the data</strong></p> <p>EMPIRICAL DATA</p> <p>WORKERS: FullDataCoordWorkers.csv, FullDataCoordWorkersRD2.csv</p> <p>Raw experimental data with worker x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>ColorID: The unique color marking assigned to an individual worker's head, thorax, abdomen1, abdomen2 (i.e., Yellow, White, Green, Green = Y,W,G,G)</li> <li>Density: The density treatment (High / Low)</li> </ul> <p>BROOD / QUEENS: FullDataCoordBrood.csv, FullDataCoordBroodRD2.csv; FullDataCoordQueen.csv, FullDataCoordQueenRD2.csv</p> <p>Raw experimental data with brood (OR) queen x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>Density: The density treatment (High / Low)</li> </ul> <p>ALATES: FullDataCoordAlate.csv</p> <p>Raw experimental data with alate (winged reproductive individuals) x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>SexID: The unique sex assignment and number given to an individual alate: Sex, SexNumber, TotalNumber (i.e., the first male alate observation that came after three queen alates making it the fourth total observation = M,1,4)</li> </ul> <p>NETLOGO SIMULATIONS: ArchitectureMoveModelFull.csv</p> <p>Raw netlogo simulation data with agent x and y positions in nests</p> <ul> <li>RunNumber: The simulation number - 1 to 4000 - there are 1000 simulations for each combination of nest shape and size</li> <li>NestSize: The size of the nest area that agents were allowed to move throughout (Small / Large)</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>TimeStep: The duration of each simulation (should be 50000)</li> <li>xcor: a list of every agent x coordinate position at the end of the simulation</li> <li>ycor: a list of every agent y coordinate position at the end of the simulation</li> </ul> <p>REFERENCE DATA </p> <p>NEST BINS: Empirical</p> <p>BinsNullFull.csv</p> <p>Null data sheet with eight bins for tube and circle nests in every colony</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> </ul> <p>BinCoordFull.csv</p> <p>Reference binning coordinates to group empirical coordinates into nest sections</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>CoordID: The unique coordinate identifier within each colony and nest combination</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> </ul> <p>NEST BINS: Netlogo Simulations</p> <p>BinsNullNetlogo.csv</p> <p>Null data sheet with eight bins for tube and circle nests in each simulation treatment</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>Bin: Nest section identifier (1-8)</li> </ul> <p>BinCoordNetlogo.csv</p> <p>Reference binning coordinates to group Netlogo simulation coordinates into nest sections</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>ScaledX: X-axis coordinate</li> <li>ScaledY: Y-axis coordinate</li> <li>CoordID: The unique coordinate identifier within each colony and nest combination</li> </ul> <p>CORNERS: Empirical</p> <p>CornerFull.csv</p> <p>Whether a nest section has a corner or not</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> <li>Corner: Presence of a corner (Y / N)</li> </ul> <p>CORNERS: Empirical</p> <p>CornerFullSim.csv</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> <li>Corner: Presence of a corner (Y / N)</li> </ul> <p>REFERENCE DATA </p> <p>DISTANCES IN THE NEST: Empirical</p> <p>DistBinsFull.csv</p> <p>Reference coordinates for the entrance of nest sections (Bin) front-to-back and shortest distance to the entrance from each nest section entrance</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Distance: Reference shortest distance from a nest section to the entrance</li> <li>Bin: Nest section identifier (1-8)</li> <li>BinX: X-axis reference coodinate for a nest section entrance</li> <li>BinY: Y-axis reference coodinate for a nest section entrance</li> <li>Xmax: Max X-axis coordinate possible within the nest</li> <li>Ymax: Max Y-axis coordinate possible within the nest</li> <li>MaxDist: Max possible shortest distance from the nest entrance</li> <li>TubeRatio: Ratio of shortest distance to the nest entrance in circle nest / tube nest</li> </ul> <p>DISTANCES IN THE NEST: Netlogo Simulations</p> <p>DistBinsFullNetlogo.csv</p> <p>Reference coordinates for the entrance of nest sections (Bin) front-to-back and shortest distance to the entrance from each nest section entrance</p> <ul> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Distance: Reference shortest distance from a nest section to the entrance</li> <li>Bin: Nest section identifier (1-8)</li> <li>BinX: X-axis reference coodinate for a nest section entrance</li> <li>BinY: Y-axis reference coodinate for a nest section entrance</li> <li>Xmax: Max X-axis coordinate possible within the nest</li> <li>Ymax: Max Y-axis coordinate possible within the nest</li> <li>MaxDist: Max possible shortest distance from the nest entrance</li> <li>TubeRatio: Ratio of shortest distance to the nest entrance in circle nest / tube nest</li> </ul> <p>REFERENCE DATA </p> <p>WORKER SITE FIDELITY (SPATIAL FIDELITY & OCCURRENCE ZONE SIZES), ALSO RELATING SIZES TO DISTANCES IN THE NEST</p> <p>ColorRefFull.csv</p> <p>Reference of all possible unique color identifiers paint marked workers</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Head: Head color mark</li> <li>Thorax: Thorax color mark</li> <li>Abd1: Left side abdomen mark</li> <li>Abd2: Right side abdomen mark</li> </ul> <p>NestAreaFull.csv</p> <p>Reference for colony size (number of workers in the colony) and nest area</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Number.ants: Number of workers in the colony after painting</li> <li>Diameter: The diameter of the circle nest</li> <li>Area: The area of the nest</li> </ul> <p>ScalingCircleSFZ.csv</p> <p>Reference to scale the radius of circle nests to make coordinates representing fidelity zone bins</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Scaling: The scaling factor that is applied to the radius of each circle nest</li> </ul>
Pathogen-induced alterations in fine-scale movement behaviour predict impaired reproductive success
<p><strong>This R-Project contains the following code files (in folder R):</strong></p> <p><em>01_movement_data_cleaning: Code to clean the raw ATLAS tracking data</em></p> <p><em>02_nestbox_data_cleaning: Code to clean nest box control sheets</em></p> <p><em>03_pathogen_data_cleaning</em>: <em>Code to clean the laboratory results for blood parasites PCRs</em></p> <p><em>04_ctmm_models</em>: Code to prepare data, applying continuous time movement models (incl. model selection) on movement data. Moreover, track reconstruction is performed</p> <p><em>05_Neural_Network: Code to perform Neural Networks for classification of behavioural states</em></p> <p><em>06_behavioural classification_analyses: Code to analyse Neural Network output</em></p> <p><em>07_HSF: Code for Habitat Selection Functions (HSF)</em></p> <p><em>08_ISSF: : Code for integrated Step Selection Functions (iSSF)</em></p> <p><em>09_breeding_success_HSF: Multivariate model to perform on HSF estimates</em></p> <p><em>10_breeding_success_ISSF: Multivariate model to perform on iSSF estimates</em></p> <p><strong>This repository contains the following data files (in folder data-raw)</strong> :</p> <p><em>Nestboxes_2021: Nest box monitoring sheets 2021</em></p> <p><em><em>Nestboxes_2022: Nest box monitoring sheets 2022</em></em></p> <p><em><em>Nestboxes_2023: Nest box monitoring sheets 2023</em></em></p> <p><em>Starlings_ring: Starling ringing data</em></p> <p><em>pathogen_data</em>: Blood parasite infection data </p> <p> </p>
Predicting readers' prototypical eye-movement behavior using MASC, a model of Attention in the Superior Colliculus: Stimulus materials, model code, data, and statistical analyses.
<p>The goal of the present research was to determine the role of rudimentary visuo-motor pathways, from the retina and the primary visual cortex to the superior colliculus (SC), in the guidance of human eye movement during reading. To this end, we used MASC, our model of Attention in the Superior Colliculus (Adeli et al., Journal of Neuroscience 2017), a model that relies on well-established saccade-programming principles in the SC. MASC predicts sequences of fixations over an input image by spatially integrating incoming signals in the space of the SC.</p> <p>Here, MASC computed the distribution of luminance contrast over sentences' images (visual-saliency map), after blurring it proportional to retinal eccentricity (retina transformation). It then projected the visual-saliency map into SC space, using a logarithmic afferent-mapping function (magnification factor). Input signals were averaged over retinotopically organized populations of neurons (point images) of constant size, first in the visual map and then in a spatially-registered motor map. The most active population was identified through a winner-take-all process. After jitter applied to the winning population, the next fixation location was determined using inverse efferent mapping. This sequence of events was then repeated to predict following fixation locations, but inserting after each saccade an inhibitory spatial tag (Inhibition of Saccade Return; ISR -referred to as IOR in the uploaded files). All MASC's parameters, but one, were biologically determined, using electrophysiological data in macaque; the ISR window was the one fit parameter.</p> <p>MASC was tested by comparing its predicted sequences of fixations over sentences from the French-Sentence Corpus (FSC) to the eye-movement behavior of 40 French-native speakers reading the same sentences for comprehension (Albrengues et al., Plos One 2019). Then, MASC was dissected to determine the crucial processing steps enabling prediction of human behavior (10 comparison models -see the general README file). Finally, to address crucial issues in the reading literature, i.e., the role of inter-word spacing and character print size in eye-movement guidance, MASC was additionally tested in four additional display conditions: the same sentences from the FSC, but with blank spaces between words being either filled or removed, or with the screen width angle being multiplied by 2 or 4, such that characters were larger in angular size (0.5° and 1°) than in the original experiment (0.25°). MASC's predicted effects of inter-word spacing and print size were compared to previously published data.</p> <p>All material relevant to the project is reported here, including the FSC materials (bitmap and information text files), the Matlab code for our MASC model, raw simulation data for MASC and all our comparison models, as well as MASC's simulations in the different display conditions, the scripts we developed in R to transform raw simulation data into data matrices for statistical analyses of (word-based) eye-movement behavior, the resulting data matrices for all models as well as the data matrix for FSC readers, the R-scripts for statistical comparison of oculomotor behavior between data sets and conditions, literature-review tables of previously published data (for comparison with MASC's predictions), and the R-scripts generating the figures summarizing our results.</p> <p>Further information can be found in the general README file as well as in the README files attached to each folder. The authors' respective contributions to the project, the licence attached to the included materials and their condition of use are listed in the general README file.</p> <p>A manuscript reporting and discussing these modeling data is in preparation (Vitu, F., Adeli, H. & Zelinsky, G. J.); A reference will be provided here when the manuscript appears in a journal.</p> <p>Other references to be cited:</p> <p>- For the model code: Adeli, H., Vitu, F., & Zelinsky, G. J. (2017). A model of the superior colliculus predicts fixation locations during scene viewing and visual search. Journal of Neuroscience, 37(6), 1453-1467. http://www.jneurosci.org/content/37/6/1453</p> <p>- For FSC materials and data: Albrengues, C., Lavigne, F., Aguilar, C., Castet, E., & Vitu, F. (2019). Linguistic processes do not beat visuo-motor constraints, but they modulate where the eyes move regardless of word boundaries: Evidence against top-down word-based eye-movement control during reading. PLoS ONE 14(7): e0219666. https://doi.org/10.1371/journal.pone.0219666<br> </p>
Data from: Does movement behaviour predict population densities? a test with 25 butterfly species
Open the record for dataset details and reuse information.
Personality and predictability in farmed calves using movement and space-use behaviours quantified by Ultra-wideband sensors
Open the record for dataset details and reuse information.
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.