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.
21
datasets available to search
ShareScore release 0.9.0
Dataset results
21 results for “Movement network”
Data for: Brain control of bimanual movement enabled by recurrent neural networks
<p>Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. In this study, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders.</p> <p>This dataset represents all neural activity recorded during these experiments. This includes the neural activity corresponding to unimanual and bimanual hand movements during (1) instructed delay experiments and (2) real-time BCI control of two cursors. </p> <p>Code associated with the data can be found here: <a href="https://github.com/d-r-deo/bimanualBCI" target="_blank" rel="noopener">https://github.com/d-r-deo/bimanualBCI</a></p>
Hierarchically embedded scales of movement shape the social networks of vampire bats
<p>Social structure can emerge from <em>hierarchically embedded scales of movement</em>, where movement at one scale is constrained within a larger scale (e.g., among branches, trees, forests). In most studies of animal social networks, some scales of movement are unobserved, and the relative importance of the observed scales of movement is unclear. Here, we asked: how does individual variation in movement, at multiple nested spatial scales, influence each individual's social connectedness? Using existing data from common vampire bats (<em>Desmodus rotundus</em>), we created an agent-based model of how three nested scales of movement—among roosts, clusters, and grooming partners—each influence a bat's grooming network centrality. In each of 10 simulations, virtual bats lacking social and spatial preferences moved at each scale at empirically-derived rates that were either fixed or individually variable and either independent or correlated across scales. We found the number of partners groomed per bat was driven more by within-roost movements than by roost switching, highlighting that co-roosting networks do not fully capture bat social structure. Simulations revealed how individual variation in movement at nested spatial scales can cause false discovery and misidentification of preferred social relationships. Our model provides several insights into how nonsocial factors shape social networks.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 8. Flowchart for movements of creature
<p>Window time has a fixed length between and and is an appropriate time interval [5]. So in each time window the total number of spikes in each three neurons is compared with other three neurons and artificial creature moves toward direction that the respective neurons fired maximum number of spikes. These fixed time<br> windows consist of 600 time-steps. Each time step is 0.5 ms. Flowchart in Figure 8 shows details.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 7. Effect of different type of movement on the image in the vision of artificial creature.
<p>Figure 7a, Figure 7b, Figure 7c and Figure 7d, shows effect of different type of movement on the image in the vision of artificial creature if food be on vision boundaries. As mentioned each part of the image equal 7.5 degree.<br> Therefore 15 degree left or right rotation locomotion equivalent two parts shift toward left or right.<br> For motion to forward direction, size of the image has been reduplicated so that each part has been become to the two similar parts. Then half of new image in right side and left side has been deleted in order to create new close image in vision. Accordingly, if food be on vision boundaries, the number of black parts of the image for the food object in ultimate location is 2, by one movement to forward direction the number of these parts become to 4, by one movement to forward direction the number of these parts become to 8 and so on. After four movements to forward all part of the image is black and the creature is succeed find the food object.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 15. Movements a typical successful creature to find food
<p>Figure 15 (a, b, c, d, e, f, g, h) illustrates movements of a typical successful creature for finding one food object.</p> <p>We believe that this study can be a step forward in understanding the morphology of artificial creatures. Also this paper suggests more complex artificial life examination by adding different part to these networks similar to different segments of the brain such as: vision, locomotion, hippocampus and communication in a future work. Because of the different axonal conduction delay between every two neurons in the neural network of artificial creatures in this paper, our next study is to enhance the artificial lives by STDP learning.</p>
gazeNet: End-to-end eye-movement event detection with deep neural networks
<p>This repository contains synthetic eye-movement dataset used to train deep learning based eye-movement event detection algorithm described in Zemblys, R., Niehorster, D.C. & Holmqvist, K. (2018). gazeNet: End-to-end eye-movement event detection with deep neural networks. Behavior research methods, pp 1–25. <a href="https://doi.org/10.3758/s13428-018-1133-5">https://doi.org/10.3758/s13428-018-1133-5</a></p> <p>Code used to train a model can be found on github <a href="https://github.com/r-zemblys/gazeNet">here</a>. Code to generate synthetic eye-movement data can be downloaded from <a href="https://github.com/r-zemblys/gazeGenNet">here</a>.</p>
Plant pests influence the movement of plant-fixed carbon and fungal-acquired nutrients through arbuscular mycorrhizal networks
Open the record for dataset details and reuse information.
Hierarchically embedded scales of movement shape the social networks of vampire bats
Open the record for dataset details and reuse information.
Data for: Brain control of bimanual movement enabled by recurrent neural networks
Open the record for dataset details and reuse information.
Decoding Movement Goals from the Fronto-Parietal Reach Network
<p>Here we provide fMRI data used for MVPA in the following project (for details see data description file):</p> <p>Gertz H, Lingnau A and Fiehler K (2017). Decoding Movement Goals from the Fronto-Parietal Reach Network. <em>Front. Hum. Neurosci.</em> <strong>11</strong>:84. doi: 10.3389/fnhum.2017.00084</p> <p>During reach planning, fronto-parietal brain areas need to transform sensory information into a motor code. It is debated whether these areas maintain a sensory representation of the visual cue or a motor representation of the upcoming movement goal. Here, we present results from a delayed pro-/anti-reach task which allowed for dissociating the position of the visual cue from the reach goal. In this task, the visual cue was combined with a context rule (pro vs. anti) to infer the movement goal. Different levels of movement goal specification during the delay were obtained by presenting the context rule either before the delay together with the visual cue (specified movement goal) or after the delay (underspecified movement goal). By applying fMRI multivoxel pattern analysis (MVPA) we demonstrate movement goal encoding in the left dorsal premotor cortex (PMd) and bilateral superior parietal lobule (SPL) when the reach goal is specified. This suggests that fronto-parietal reach regions maintain a prospective motor code during reach planning. When the reach goal is underspecified, only area PMd but not SPL represents the visual cue position indicating an incomplete state of sensorimotor integration. Moreover, this result suggests a potential role of PMd in movement goal selection.</p>
The Global Navigation Satellite System (GNSS) data came from the Crustal Movement Observation Network of China
<p>The dataset reports the estimated vertical Total Electron Content (TEC) from 52 GPS receivers came from the Crustal Movement Observation Network of China on 4 March 2014. Every receiver's data is saved in a TXT file, whose time resolution is thirty seconds.</p>
Data from: Network analysis of sea turtle movements and connectivity: a tool for conservation prioritization
<p><strong>Aim</strong>: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analysing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts.</p> <p><strong>Location</strong>: Global.</p> <p><strong>Methods</strong>: We collated telemetry data from 1,235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections.</p> <p><strong>Results</strong>: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links.</p> <p><strong>Main conclusions</strong>: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness, but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed towards areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.</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>
Analysis of temporal patterns in animal movement networks
Open the record for dataset details and reuse information.
Data from: Network analysis of sea turtle movements and connectivity: a tool for conservation prioritization
Open the record for dataset details and reuse information.
Supplementary material 1 from: Fernandes N, Ferreira EM, Pita R, Mira A, Santos SM (2022) The effect of habitat reduction by roads on space use and movement patterns of an endangered species, the Cabrera vole Microtus cabrerae. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 177-196. https://doi.org/10.3897/natureconservation.47.71864
The effect of habitat encroachment by roads on space use and movement patterns of an endangered vole
Brain Network Activation in Patients With Movement Disorders
ClinicalTrials.gov study NCT03269201. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Movement-Related Brain Networks Involved in Hand Dystonia
ClinicalTrials.gov study NCT00137384. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Parkinsonism-Related Oscillations in the Cortico-Basal Ganglia-Thalamic Network During Movement: Beyond the Frequency Range
ClinicalTrials.gov study NCT06438419. IPD Sharing: NO. Countries: 1. Publications: 0.
Gait Mate: Examining Neural Networks Engaged During Lower Extremity Movement in the MRI
ClinicalTrials.gov study NCT03604367. IPD Sharing: YES. Countries: 1. Publications: 0.
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.