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Dataset results
18 results for “motion prediction”
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Figures
<p>This entry contains the sources for the figures included in the body of the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes, as well as those found in the supplementary information.</p> <p>It accompanies the dataset found at the DOI: 10.5281/zenodo.1477004</p> <p> </p> <p> </p>
Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state (Data Underlying Figures)
<p>Data underlying figures in A. Bain 'Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state' RSC Environmental Science: Nano, 2022. </p> <p>CA = citric acid<br> NaCl = sodium chloride<br> AS = ammonium sulfate</p> <p>rho = difference in density (g/cm^3)<br> Rh = % relative humidity<br> radius is in micrometers<br> Pe0 are the calculated dimensionless Peclet numbers<br> </p>
The Influence of Active and Passive Motion Experience on Infants' Visual Prediction Ability
<p>Data set of the article: The Influence of Active and Passive Motion Experience on Infants’ Visual Prediction Ability</p>
Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory: Dataset
<p>This dataset collects the unprocessed (= outputs from calculations) and processed (= plots, average values for ionization energies) results discussed in the paper titled "Predictive simulations of ionization energies of solvated halide ions with relativistic embedded Equation of Motion Coupled-Cluster Theory", by Yassine Bouchafra, Avijit Shee, Florent Réal, Valérie Vallet and André Severo Pereira Gomes.</p> <p>In each archive file there is a README explaining how to use the bundled scripts to process the data.</p>
Predicting the distribution of serotonergic axons: A supercomputing simulation of reflected fractional Brownian motion in a 3D-mouse brain model
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Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks
<p>String-like motions (SLMs) cooperative, "snake"-like movements of particles—are crucial for dynamics in diverse glass formers. Despite their ubiquity, questions persist: do SLMs prefer specific paths? If so, can we predict these paths? Here, in Al-Sm glasses, our iso-configurational ensemble simulations reveal that SLMs indeed follow certain paths. By designing a graph neural network (GNN) to featurize the environment around directional paths, we achieve a high-fidelity prediction of likely SLM pathways solely based on the static structure. GNN gauges a structural measure to assess each path's propensity to engage in SLMs, akin to a "softness" metric, but for paths rather than for atoms. Our GNN interpretation reveals the critical role of the bottleneck zone along paths in steering SLMs. By monitoring "path-softness", we elucidate SLM-favored paths transit from fragmented to interconnected upon glass transition. Our findings reveal that, beyond atoms or clusters, glasses have another dimension of structural heterogeneity: "paths".</p>
data for "Horizontal connectivity in V1 : Prediction of coherence in contour and motion integration'
<p>Data associated with the article : "Horizontal connectivity in V1 : Prediction of coherence in contour and motion integration", Plos One</p>
Code and data repository for the role of topography, geotechnical layering, and attenuation on ground motion prediction
<p>Data and scripts to reproduce research on the influence of topography, geotechnical layer, and attenuation on ground motion prediction in Salton Trough.</p>
Dataset - seismic data from central-western Italy used in the paper on rapid prediction of ground motion using a Convolutional Neural Network
<p>The dataset published here is the central-western Italy dataset used in the paper "<em>Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data"</em> (<a href="https://arxiv.org/abs/2105.05075">https://arxiv.org/abs/2105.05075</a>). The code for the paper is available at <a href="https://github.com/djozinovi/TLpredIM">https://github.com/djozinovi/TLpredIM</a>. The abstract of the paper:</p> <blockquote> <p>In a recent study (Jozinović et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs from the standard procedure adopted by earthquake early warning systems (EEWSs) that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation waveforms for the 2016 earthquake sequence in central Italy for 915 events (CI dataset). The CI dataset has a large number of spatially concentrated earthquakes and a dense station network. In this work, we applied the CNN model to an area around area near Pisa, Italy. In our initial application of the technique, we used a dataset consisting of 266 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller dataset performed worse compared to the results presented in the original study by Jozinović et al. (2020). To counter the lack of data, we adopted transfer learning (TL) using two approaches: first, by using a pre-trained model built on the CI dataset and, next, by using a pre-trained model built on a different (seismological) problem that has a larger dataset available for training. We show that the use of TL improves the results in terms of outliers, bias, and variability of the residuals between predicted and true IMs values. We also demonstrate that adding knowledge of station positions as an additional layer in the neural network improves the results. The possible use for EEW is demonstrated by the times for the warnings that would be received at the station PII.</p> </blockquote>
Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks
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Dataset for Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality
<p>Distributed Virtual Reality (DVR) systems enable geographically dispersed users to interact in a shared virtual environment. The realism of the interaction is crucial to increase the feeling of co-presence. Latency, produced either by hard- or software components of DVR applications, impedes reaching high realism levels of the DVR experience. For example, the time delay between the user's motion and the corresponding display rendering of the DVR system might lead to adverse effects such as a reduced sense of presence or motion sickness. One way of minimizing the latency is to predict user's motion and thus compensate for the inherent latency in the system. In order to address this problem, we propose a neural network 3D pose tracking and prediction system with latency guarantees for end-to-end avatar reconstruction. We evaluate and compare our system against multiple traditional methods and provide a thorough analysis on real-world human motion data. Datasets used in the paper experiments. Datasets used in paper experiments.</p>
Neural network for predicting Peierls barrier spectrum and its influence of dislocation motion
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Data from: An ultrasound image-based dynamic fusion modeling method for predicting the quantitative impact of in vivo liver motion on intraoperative HIFU therapies: investigations in a porcine model
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Data from Schmitt et al. 2018: Preattentive and Predictive Processing of Visual Motion
<p><strong>Dataset associated with the following publication:</strong></p> <p>Schmitt C, Klingenhoefer S, Bremmer F. Preattentive and Predictive Processing of Visual Motion. <em>Sci Rep</em>. 2018;8(1):12399. Published 2018 Aug 17. doi:10.1038/s41598-018-30832-9</p> <p><strong>Description of dataset:</strong></p> <p>The dataset contains preprocessed EEG data recorded from the 15 electrodes (Cz, Fz, FCz, FC1, FC2, F3, F4, FC5, FC6, P3, P4, P7, P8, PO3, PO4) used for analysis in our paper. Data containing eye movements, blinks or a movement response of the participants were removed. As a reference the average signal of the mastoid electrodes TP9 and TP10 was used.</p> <p>In a first preporcessing step data were low pass filtered with a cut-off frequency of 70 Hz and additionally a Notch filter at 50 Hz was applied. In a second step data were aligned to the (re)appearance of the moving target next to the central occluder and cut into 850 ms long epochs ranging from 200 ms before this (re)appearance to 650 ms after this time point. The average signal from a 50 ms long time window starting 50 ms before the (re)appearance was used for a baseline correction of each epoch before epochs were averaged separately for subjects and conditions as a last preprocessing step. </p> <p>Each uploaded file contains data of all 8 participants and all 15 electrodes separately for the four different conditions: 1) target movement to the right in complete trajectory trails (data_movedirR_complete_trajectory.mat); 2) target movement to the left in complete trajectory trails (data_movedirL_complete_trajectory.mat); 3) target movement to the right in half trajectory trails (data_movedirR_half_trajectory.mat); 4) target movement to the left in half trajectory trails (data_movedirL_half_trajectory.mat).</p> <p>Each file contains 30 matrices for the half trajectory conditions and 60 matrices for the complete trajectory conditions. Each matrix presents the data recorded at one electrode, for one type of trial (standard "AllS", deviant "AllD" or half trajectory "AllH") and one attention condition (attention to the fixation target, central "1" and attention to the moving target, peripheral "2"). Example: "P3_AllD_1"</p> <p>Each matrix consists of 8 lines representing the 8 participants. Data for the relevant condition was averaged for each participant and is presented in a separate line. The matrices consist of 850 columns representing the length of the presented recording time of 850 ms. Data from 200 ms before to 650 ms after the (re)appearance of the moving target is presented. The uploaded matrices contain values in µV.</p>
Use of Lumbar Motion Monitor to Predict Response to Radiofrequency Ablation After Medial Branch Diagnostic Blocks
ClinicalTrials.gov study NCT03894319. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Value of Diaphragmatic Motion Measurement at Ultrasonography to Predict Poor Prognosis in Emergency Department Patients With Acute Respiratory Failure
ClinicalTrials.gov study NCT04119505. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Prediction and Prevention of Disease by Using Big Data in Motion Analysis
ClinicalTrials.gov study NCT04867148. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Predictability of Distalization and Derotation of the Carriere Motion Appliance.
ClinicalTrials.gov study NCT05094973. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.