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654 results for “deformable”
Dataset for the paper "The magnetopause deformation indicated by fast cold ion motion"
<p>This dataset is used for the paper "The magnetopause deformation indicated by fast cold ion motion" and contains pubilicly available data of MMS dayside magnetopause crossing events with fast cold ion motion from 2015 to 2021, which includes crossing time and position, solar wind parameters (dynamic pressure, IMF, solar wind speed) and data source, the normals from the model (Shue et al., 1997 and Liu et al., 2015) and observations (Timing and MVA methods), the normal speed of magnetopause (VMP) from Timing method, the component speeds of cold ion along normal and tangential directions, the deflection angle between the observed magnetopause normal and the prediction, and the magnetopause deformation amplitude calculated by integrating cold ion speed (Positive or negative values indicate whether the magnetopause is moving outward or inward) of all 30 crossings in geocentric solar magnetospheric (GSM) coordinates.</p>
Data from: facial growth and development trajectories based on 3D images: geometric morphometrics with a deformation perspective
<p>Developmental changes of facial shape are commonly investigated through geometric morphometrics. A limitation with this approach is the inability to investigate patterns of morphological changes at local scale. This could be addressed through quantifying the deformation required to deform one shape to another. This study aimed to investigate changes in mean, rate, and variance of facial shape at local scale using geometric morphometrics through deformation perspective. 2112 Europeans 3 to 40 years-old from the 3D Facial Norms project were included. Shape and rate trajectories from partial least-squares regressions revealed that the developmentally protrusive nasal bridge was due to local expansion in surrounding tissues as opposed to shape changes in nasal bridge per-ser. Local expansion of the supraorbital region, in particular the medial part in males, resulted in the sloping forehead and deep-situated eyes with development. Facial shape variation increased non-linearly with age (p < 0.05), with features having larger rate of change becoming more developmentally diversified. In summary, our deformation perspective facilitates unravelling morphogenetic processes underlying shape changes. Our extended analytical scope inspires novel measures worthy of consideration while establishing facial growth charts. The analytical framework in this study is broadly applicable for analysis of shape changes in general.</p>
Data for the research article "High-Temperature Deformation of Enstatite-Olivine Aggregates" published in JGR Solid Earth
<p>The data available in this repository is the original data presented in the research article: Bystricky, M., Lawlis, J., Mackwell, S., & Heidelbach, F. (2024). High-temperature deformation of enstatite-olivine aggregates, Journal of Geophysical Research: Solid Earth, 129, e2023JB027699. https://doi.org/10.1029/2023JB027699.</p> <p>Version v1: data at time of original submission (2023)</p> <p>Version v2: data at time of publication (2024)</p>
Craters of Habit: Patterns of Deformation in the Western Galápagos
<p>Datasets used in Reddin et al., Craters of Habit: Patterns of Deformation in the Western Galápagos.</p> <p>LiCSBAS output files [in .h5 format] used in this study are provided, given by 128Dcum_filt.h5 for descending, and 106Acum_filt.h5 for ascending. These files contain displacement maps for both Isabela and Fernandina, and are used to produce time series, and conduct source modelling. </p> <p>.txt files contain time series information for their corresponding volcano, with track direction included [e.g. Darwin_Asc.txt].</p> <p>Text files not beginning with Alcedo or Darwin contain time series of the 2020 eruption of Fernandina, with the location of the time series point, and corresponding track direction included in the title [e.g. NEFlank_ts_A.txt].</p>
Mapping Deformation Processes using InSAR PS+DS Timeseries Estimation in Northern California, U.S.
Open the record for dataset details and reuse information.
The upper crustal deformation field of Greece inferred from GPS data and its correlation with earthquake occurrence
<p>These are Tables S1 and S2 which accompany the Article "The upper crustal deformation field of Greece inferred from GPS data and its correlation with earthquake occurrence" by Konstantinos Chousianitis, Sotirios Sboras, Vasiliki Mouslopoulou, Gerasimos Chouliaras, and Dionissios T. Hristopulos.</p>
Data set for "Omphacite breakdown: nucleation and deformation of clinopyroxene-plagioclase symplectites"
<p>EBSD raw data used in the publication:</p> <p>Zertani, S., Morales, L.F.G., Menegon, L. (2024). Omphacite breakdown: nucleation and deformation of clinopyroxene-plagioclase symplectites. Contributions to Mineralogy and Petrology, 179:xx. https://doi.org/10.1007/s00410-024-02125-0</p>
Mechanical Metamaterial: Square Array of Circular Holes Under Deformation
<p>This repository contains data for a research project involving graph neural networks (GNNs) applied to mechanical metamaterials and their deformations. </p> <p>The mechanical metamaterials of interest consist of a flexible rubber-like material with a square grid of almost-circular holes in it, of various diameters. The holes are not quite circular, because they were made slightly elliptic to avoid the bifurcation point.</p> <p>This data was used to obtain the results in the paper 'Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials'.</p> <p>The data includes PyTorch Geometric Graph objects, raw data from MATLAB simulations, and files describing the finite element mesh. The Jupyter notebook 'data_create_graphs.ipynb' in the GitHub repository can be used to turn the raw data and mesh information into the graph objects.</p> <p>GitHub repository with the code: <a title="https://github.com/FHendriks11/SimEGNN" href="https://github.com/FHendriks11/SimEGNN">https://github.com/FHendriks11/SimEGNN</a></p> <p>Link to the corresponding paper <em>Similarity Equivariant Graph Neural Networks for Homogenization of Metamaterials</em>: <a href="https://www.sciencedirect.com/science/article/pii/S0045782525001392">https://www.sciencedirect.com/science/article/pii/S0045782525001392</a> (also on ArXiv: <a title="https://arxiv.org/abs/2404.17365" href="https://arxiv.org/abs/2404.17365">https://arxiv.org/abs/2404.17365</a>)</p> <p>The .pkl files are pickle files and can be opened in Python using the standard pickle library. The mesh files, which have the extension .mat, are MatLab files and can be opened either in MatLab or in Python using scipy.io.loadmat from the scipy library.</p>
Dimensions, stability and deformability of DOPC-cholesterol Giant Unilamellar Vesicles formed by droplet transfer – Extended Data
<p>This dataset contains the Underlying Data to the paper “ Dimensions, stability and deformability of DOPC-cholesterol Giant Unilamellar Vesicles formed by droplet transfer”.</p> <ul> <li> “deformation_size” folder containing scatter plots of σ with respect to GUVs rest radii <ul> <li>sd_deform_scatter_H1</li> <li>sd_deform_scatter_H2</li> <li>sd_deform_scatter_H3</li> </ul> </li> <li>“magnetic_device_support” folder containing the .stl files for 3D-printing the magnets-support of the magnetic device <ul> <li>magnetic_device_support_part1</li> <li>magnetic_device_support_part2</li> </ul> </li> <li>“size_distribution_magnetic” folder containing size distribution histograms comparing 100:0 DOPC:cholesterol and 60:40 DOPC:cholesterol samples, under the application of magnetic fields <ul> <li>sd_magnetic_size_dist_allfields</li> <li>sd_magnetic_size_dist_H1</li> <li>sd_magnetic_size_dist_H2</li> <li>sd_magnetic_size_dist_H3</li> </ul> </li> <li>“size_distribution_T0vsON” folder containing size distribution histograms comparing pristine samples (t<sub>0</sub>) and samples after overnight storage (ON), for different DOPC:cholesterol ratios <ul> <li>sd_size_dist_60_40</li> <li>sd_size_dist_71_29</li> <li>sd_size_dist_85_15</li> <li>sd_size_dist_100_0</li> </ul> </li> </ul>
InSAR coseismic deformation for the 22 January 2024, Mw 7.0, Wushi (northwestern China) earthquake
<p>This dataset includes coseismic InSAR deformation for the 2024 Mw 7.0 Wushi (northwestern China) earthquake, slip models as well as Coulomb stress change distributions.</p>
Datasets corresponding to publication: Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes
<p>This repository contains all dataset that correspond to the publication "Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes". Furthermore, Python scripts are provided which allow to reproduce all analyses shown in the manuscript. Execution of the scripts requires a Python environment with packages as stated in the Methods section of the manuscript, or by using PyBox 0.1.0. PyBox is a readily installed Python environment containing all packages at the required version. PyBox is publicly available on GitHub: <a href="https://github.com/maikherbig/PyBox">https://github.com/maikherbig/PyBox</a>.</p>
Present-day surface deformation of Sicily: Insights from Sentinel-1 data processed by a PS-InSAR approach
<p>The directory DATASET.zip provides PS-InSAR data used in Henriquet et al., (2022). The data set contains for each Sentinel-1 track (44, 117, 22, 124) the mean PS velocities along the LOS, before (ps_mean_v.xy.v-dos) and after (ps_mean_v-dos_adjusted2GPS.xy) their adjustment to the 3D-GNSS velocity field, as well as the disparities of the PS velocities (ps_mean_disp.xy). The data set also includes the East- and Up-component of the reconstructed mean PS velocity field (East.grd and Up.grd) used in the Figures 7 to 12 in the paper.</p>
Simulation dataset of "Deformation of electron distributions due to Landau trapping by the whistler-mode wave"
<p>The file "Figure1a_data.dat" stores the data which is used to plot Figure1a. The file "Figure1cd_data.dat" stores the data which are used to plot Figure1c and Figure1c. And the other files are similar. </p> <p>The name and array of each parameter corresponding to that in Figures are stored in the files.</p>
Seagrass deformation affects fluid instability and tracer exchange in canopy flow
<p>Data and code used for the preparation of the manuscript "Seagrass deformation affects fluid instability and tracer exchange in canopy flow" (Vieira, Allshouse & Mahadevan 2022).</p> <p><em>Data and Code Repository Organization</em></p> <ul> <li><strong>data/ </strong>: contains the data presented in the manuscript (in .cdf and .mat format);</li> <li><strong>code/ </strong>: contains the code used for the numerical simulations (PSOM) and in processing the data and generating figures (MATLAB)</li> </ul> <p><em>Manuscript Abstract:</em></p> <p>Monami is the synchronous waving of a submerged seagrass bed in response to unidirectional fluid flow. Here we develop a multiphase model for the dynamical instabilities and flow-driven collective motions of buoyant, deformable seagrass. We show that the impedance to flow due to the seagrass results in an unstable velocity shear layer at the canopy interface, leading to a periodic array of vortices that propagate downstream. Each passing vortex locally weakens the along-stream velocity at the canopy top, reducing the drag and allowing the deformed grass to straighten up just beneath it. This causes the grass to oscillate periodically. Crucially, the maximal grass deflection is out of phase with the vortices. A phase diagram for the onset of instability shows its dependence on the fluid Reynolds number and an effective buoyancy parameter. Less buoyant grass is more easily deformed by the flow and forms a weaker shear layer, with smaller vortices and less material exchange across the canopy top. While higher Reynolds number leads to stronger vortices and larger waving amplitudes of the seagrass, waving is maximized at intermediate grass buoyancy. All together, our theory and computations correct some misconceptions in interpretation of the mechanism and provide a robust explanation consistent with a number of experimental observations.</p>
Deformation of post-spinel under the lower mantle conditions
<p>Data used in</p> <p> </p> <p><strong>Deformation of post-spinel under the lower mantle conditions</strong></p> <p>F. Xu<sup>1, 2</sup>, D. Yamazaki<sup>1</sup>, S. A. Hunt<sup>3, 2</sup>, N. Tsujino<sup>1</sup>, Y. Higo<sup>4</sup>, Y. Tange<sup>4</sup>, K. Ohara<sup>4</sup>, D. P. Dobson<sup>2</sup></p> <p><sup>1</sup> Institute for Planetary Materials, Okayama University, Misasa, 682-0193 Tottori, Japan</p> <p><sup>2</sup> Department of Earth Sciences, University College London, Gower Street, London WC1E 6BT, United Kingdom</p> <p><sup>3</sup> Department of Materials, University of Manchester, Sackville Street Building, Manchester M1 3BB, United Kingdom</p> <p><sup>4</sup> Japan Synchrotron Radiation Research Institute, SPring-8, Sayo, Hyogo 679-5198, Japan</p>
DLOFTBs - Deformable Linear Objects Tracking with B-splines - dataset
<p>A dataset associated with the paper "DLOFTBs - Fast Tracking of Deformable Linear Objects with B-splines".</p> <p>Contains 2D images and ROS bags with 3D videos of deformable linear objects manipulated by humans and robots.</p>
Dynamic deformation calculation of articular cartilage and cells using resonance-driven laser scanning microscopy - Deformable Registration Validation Dataset
<p>This dataset includes the supporting input files, scripts, and output files for validation tests of lsmgridtrack v0.3 applied to resonance scanned images. </p>
Lithospheric deformation due to the 2015 M7.2 Sarez (Pamir) earthquake constrained by 5 years of space geodetic observations
<p>This zip file contains observations of postseismic surface deformation due to the 2015 Mw 7.2 Sarez earthquake derived from Sentinel-1 and ALOS-2 SAR data and GNSS positions over a time period of five years after the mainshock.</p>
A high pressure, high temperature gas medium apparatus to measure acoustic velocities during deformation of rock: supporting information
<p>Processed mechanical data and raw transmitted waveforms.</p>
InSAR data for "Transcrustal compressible fluid flow explains the Altiplano-Puna deformation anomaly"
<p>InSAR data presented in the paper "Transcrustal compressible fluid flow explains the Altiplano-Puna deformation anomaly". See file "README" for detailed descriptions of each item.</p>
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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.