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654 results for “deformance”
Real-time deformability cytometry reference data
<p>This dataset consists of four exemplary real-time fluorescence and deformability cytometry measurements. The HDF5-files can be opened with dclab [1] or Shape-Out [2].</p> <p><strong>calibration_beads.rtdc</strong><br> The calibartion beads (8 Peaks, PolyAN) consist of eight bead populations with different mixtures of fluorophores.</p> <p><br> <strong>CD34_HSPC.rtdc</strong><br> Hematopoietic stem and progenitor cells (HSPCs) were obtained using apheresis. The cells were tagged with a fluorescently labeled antibody that binds to the CD34 transmembrane protein. CD34-positive HSPCs are gated with `fl3_max > 90`. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> <strong>leukocytes.rtdc</strong><br> The leukocyte population (white blood cells) of this blood sample can be visualized by setting `aspect < 2` and `area_ratio < 1.05`. For more information, see e.g. [4].</p> <p><br> <strong>reticulocytes.rtdc</strong><br> Blood contains mostly red blood cells (RBCs) and about 1% reticulocytes (which develop into mature RBCs). Reticulocytes contain ribosomal RNA which was stained with Syto13 for this measurement. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> [1] <a href="https://github.com/ZellMechanik-Dresden/dclab">https://github.com/ZellMechanik-Dresden/dclab</a></p> <p>[2] <a href="https://github.com/ZellMechanik-Dresden/ShapeOut">https://github.com/ZellMechanik-Dresden/ShapeOut</a></p> <p>[3] Rosendahl et al., "Real-time fluorescence and deformability cytometry". Nature Methods, 15(5):355–358, 2018. doi:<a href="https://dx.doi.org/10.1038/nmeth.4639">10.1038/nmeth.4639</a>.</p> <p>[4] Toepfner et al., "Detection of human disease conditions by single-cell morpho-rheological phenotyping of whole blood". eLife, 7:e29213, 2017. doi:<a href="https://dx.doi.org/10.1101/145078">10.1101/145078</a>.</p> <p><br> SHA256 sums:<br> 08c2ef13eed903ef0f9e451727ab8484df09b5d3b39227dab726e0164dcbe244 calibration_beads.rtdc<br> 663b44a9db88d85996500045489e37a317cf115719223a531d617f8e3d450e79 CD34_HSPC.rtdc<br> 68bd538b42ffb990f1db52d5f3b21f37c9aff31208ab284f3910fd6872c40fdb leukocytes.rtdc<br> 5c323ea75bf7eeb2a28d922730772d50270dd872d6957e60d6062663f3628fb3 reticulocytes.rtdc</p>
Data for "Nano-scale characterisation of sheared β'' precipitates in a deformed Al-Mg-Si alloy"
<p>This dataset contains data used in the publication entitled "<strong>Nano-scale characterisation of sheared β'' precipitates in a deformed Al-Mg-Si alloy</strong>". This publication concerns how β'' precipitates are sheared by dislocations during deformation. The data contained in this repository are data acquired on various transmission electron microscopes of specimens of the aluminium alloy AA6060 in peak aged condition after uniaxial compression to 5%, 10%, and 20%, in addition to the undeformed reference alloy.</p> <p>There are five main types of data:</p> <ul> <li>Transmission electron microscopy (TEM) images</li> <li>High-resolution TEM images</li> <li>High angle annular dark field (HAADF) scanning TEM (STEM) images</li> <li>Scanning precession electron diffraction (SPED) data.</li> <li>Cross-sectional data of precipitates in undeformed and 20% compressed conditions.</li> </ul> <p>Data for the TEM, HRTEM, and STEM images are kept in zipped folders due to the large number of images (several hundreds for each compression condition). Folders are named following the format of "<alloy>_<compression>_<technique>", where technique refers to TEM, HRTEM, or STEM. Images are provided in both .hdf format and .jpg format (to aid in navigating the data). Please see <a href="https://www.hdfgroup.org/">HDF Group</a> for more information regarding the HDF file format, and <a href="https://www.hdfgroup.org/downloads/hdfview/">HDF View</a> for softaware to read and show HDF data. The Python package <a href="http://hyperspy.org/">HyperSpy</a>, is also useful for loading the HDF data for inspection, analysis, and presentation.</p> <p>For some STEM images, a stack of short-exposure STEM images acquired and analysed using the <a href="http://lewysjones.com/software/smart-align/"><em>SmartAlign</em></a> plugin to <a href="http://www.gatan.com/products/tem-analysis/gatan-microscopy-suite-software"><em>Gatan Digital Micrograph</em></a> is available. SmartAlign offers the possibility of rigidly and non-rigidly aligning the STEM images in the stack in order to reduce effect of specimen drift and scan noise during acquisition. The conventional STEM images are found in the zip archive labelled "STEM". When the filenames of the STEM images include "SAstack" and/or "SAimage", a STEM SmartAlign stack or the average through a non-rigidly aligned stack is available of the same field of view. In such cases, both the SmartAlign stack and the through-stack image is provided in the metadata in the .hdf file (note that not all stacks have been aligned, and in such cases no through-stack image is available). In addition, the SmartAlign stacks themselves are available in the subfolder "STEM\SmartAlign\" within each STEM folder. The through-stack images of the smart align stacks are also provided separately in the subfolder "STEM\SmartAlign\Aligned\". For the 20% compressed case, a lowloss electron energy loss spectroscopy (EELS) spectrum and thickness maps of the imaged areas are also provided, in the subfolder "STEM\EELS\".</p> <p>The SPED data, acquired using the <em>ASTAR</em> system of <em><a href="https://www.nanomegas.com/">NanoMegas</a></em>, is provided as .hdf5 files in the root directory of the repository. They should be read using and <a href="https://github.com/pyxem/pyxem">pyXem</a>. The attached Jupyter Notebook "SPED_data_inspection.ipynb" can be used to access the SPED datasets. These datasets are 4D datasets, with two spatial and two reciprocal dimensions. They have been decomposed using the non-negative matrix factorization algorithm (NMF) used in HyperSpy. These decomposition results are included in the .hdf5 files. In addition, parameters used in the preprocessing of the datasets are attached in the metadata in these files. The metadata of these files are also provided separately as .txt files.</p> <p>Finally, measurements of the precipitate cross-sectional area and circularity is available as .csv files with the first column being the row index, the second the cross-sectional areas of precipitates measured in nanometers squared, the third column is the perimeters of the precipitates measured in nanometers, and column four is the <a href="https://imagej.nih.gov/ij/plugins/circularity.html">circularity</a> of the precipitates.</p>
Calibrated Earthquake Relocations from the TexNet Catalog (2017–2022) and Vertical Surface Deformation (2016–2022)
<p>This repository contains the relocated earthquake catalog for the Southern Delaware Basin, as presented in the manuscript titled "<em><strong>Insights into Spatiotemporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations from the TexNet Catalog (2017-2022)</strong>".</em></p> <h3>Citations:</h3> <p>Asiye Aziz Zanjani, Heather R. DeShon, Vamshi Karanam, Alexandros Savvaidis; <strong>Insights into Temporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations <em>from the TexNet Catalog (2017-2022</em>) (2025)</strong>. <em>Earth and Space Science, 12 (6), e2024EA004027. <a title="https://doi.org/10.1029/2024EA004027" href="https://doi.org/10.1029/2024EA004027"><strong>https://doi.org/10.1029/2024EA004027</strong></a></em></p> <p>The earthquake relocations were conducted using the Hypocentroidal Decomposition technique with the <strong>open-source MLOC code</strong>, achieving enhanced spatial resolution for over 5,000 events from the TexNet catalog. The relocated catalog includes critical hypocentral parameters—latitude, longitude, depth—as well as origin time, associated uncertainties, and magnitude for each event.</p> <p>This dataset is an essential resource for analyzing the spatiotemporal patterns of induced seismicity associated with anthropogenic activities, such as shallow fluid injection, in the Southern Delaware Basin following the operation of TexNet in 2017. It is suitable for use in seismic hazard assessments, modeling studies, and comparisons with other induced seismicity datasets. Additional data produced during this research includes vertical surface deformation measurements from 2016 through the end of 2022.</p> <p>The repository also contains data referenced in the manuscript’s “Data Availability Statement” and “Open Research” sections. </p> <p>List of files attached to this repository:</p> <ul> <li><strong>catalog.xls</strong>: Primary earthquake relocated catalog developed in this study</li> <li><strong>2016_2022_deformation.csv</strong>: Vertical displacement data (2016–2018) developed in this study</li> <li><strong>2019_2022_deformation.csv</strong>: Vertical displacement data (2016–2022) developed in this study</li> <li><strong>post-2017-injection.xlsx</strong>: Injection data from the Railroad Commission of Texas (<a href="https://www.rrc.texas.gov">source</a>)</li> <li><strong>Hydrofracking-post2017.xlsx</strong>: Hydrofracking well data from FracFocus (<a href="https://fracfocus.org">source</a>)</li> <li><strong>GrowClust-common.xls</strong>: 2-D GrowClust catalog for supplemental information (<a href="https://hirescatalog.texnet.beg.utexas.edu/">source</a>), https://doi.org/10.15781/76hj-ed46</li> <li><strong>TexNet-Catalog</strong>: Initial TexNet catalog's origin and phase data, https://doi.org/10.7914/SN/TX</li> </ul>
Data and code to perform the"Target deformation" workflow in R: virtual reconstruction of the Equus stenonis holotype skulll
<p>Data and code to perform the"Target deformation" workflow in R: virtual reconstruction of the Equus stenonis holotype skulll.</p> <p>TargetDeformation.R: R code with for the Target Deformation procedure.<br> IGF560.ply: 3D mesh of the holotype IGF560 in ply extension.<br> IGF560_set.txt: landmark set of the holotype IGF560.<br> Dm. 5/154.3/4.A4.5.ply: 3D mesh of Dm 5/154.3/4.A4.5 in .ply extension.<br> Dm_set.txt: landmark set of the Dm 5/154.3/4.A4.5 sample.<br> IGF11023: 3D mesh of IGF11023 in.ply extension.<br> IGF11023_set.txt: landmark set on the IGF11023 sample.<br> IGF560R: 3D mesh of IGF560R in.ply extension.<br> IGF560W: 3D mesh of IGF560W in.ply extension.<br> IGF560R-s: 3D mesh of IGF560R-s in.ply extension.<br> IGF560W-s: 3D mesh of IGF560W-s in.ply extension.<br> IGF560_IGF560R_IGF560W.html: file that contain WebGL code to reproduce the 3D meshes of IGF560, IGF560R and IGF560W in a browser.<br> IGF560Rs_IGF560Ws.html: file that contain WebGL code to reproduce the 3D meshes of IGF560R-S and IGF560W-S in a browser.<br> IGF560W Mesh area variation.html: file that contain WebGL code to reproduce two 3d meshes of IGF560W using localmeshDist() and meshdist() in a browser.<br> </p>
Experimental measurements of creep deformation of Tournemire shale loaded at specified pressure (10 MPa) and room temperature (26°C)
<p>Following the experimental protocol used in (Geng<em> et al.</em>, 2018), we performed the stepping creep experiments at a confining pressure of 10 MPa. We first loaded the samples under hydrostatic conditions up to 10 MPa at a pressure rate of 0.3 MPa/min. Hydrostatic conditions were maintained for ~18 h at 26 °C. Next, differential stress (axial stress minus confining pressure) was increased to a fixed initial stress (30 MPa) and maintained (creep status) for 24 h. The differential stress was repeatedly increased by 5 MPa and maintained for 24 h, until brittle failure. All the experiments were conducted using the triaxial apparatus installed at the Laboratoire de Géologie of ENS-Paris (France). There were few constraints on the natural saturation state of the samples because of their low permeability (10<sup>-19</sup> 10<sup>-21</sup> m<sup>2</sup>). To avoid exposition redundancy, an additional description of the technical performance of the triaxial apparatus can be referred to (Brantut<em> et al.</em>, 2011, Sarout & Guéguen, 2008).</p> <p>Compressive stresses and compactive strains are denoted as positive. Axial creep deformation was measured using three capacitive gap sensors that externally monitored the overall axial displacement of the piston during creep deformation. Volumetric strain during creep was estimated by adding the average of axial strains (axial displacement of the piston divided by the sample length) and two average radial strains measured by four radial strain gauges glued uniformly around the cylindrical rock surface. As the deformation rate generally stabilized during the last 8 h in most creep periods (Geng<em> et al.</em>, 2018), we estimated the average axial strain rate over the last 8 h of each step to characterize the creep strain rate under the corresponding axial loading stress. More technical details of the sample configuration and creep rates estimation can be found in (Geng<em> et al.</em>, 2018).</p>
A soft pneumatic actuator with integrated deformation sensing elements produced exclusively with extrusion based additive manufacturing
<p>In recent years, soft pneumatic actuators have come into the spotlight because of their simple control and the wide range of complex motions. To monitor the deformation of soft robotic systems, elastomer-based sensors are being used. However, the embedding of sensors into soft actuator modules by polymer casting is time consuming and difficult to upscale. In this study, it is shown how a pneumatic bending actuator with an integrated sensing element can be produced using an extrusion-based additive manufacturing method, e.g., fused deposition modeling (FDM). The advantage of FDM against direct printing or robocasting is the significantly higher resolution and the ability to print large objectives in a short amount of time. New, commercial launched, pellet-based FDM printers are able to 3D print thermoplastic elastomers of low shore hardness that are required for soft robotic applications, to avoid high pressure for activation. A soft pneumatic actuator with the in situ integrated piezoresistive sensor element was successfully printed using a commercial styrene-based thermoplastic elastomer (TPS) and a developed TPS/carbon black (CB) sensor composite. It has been demonstrated that the integrated sensing elements could monitor the deformation of the pneumatic soft robotic actuator. The findings of this study contribute to extending the applicability of additive manufacturing for integrated soft sensors in large soft robotic systems.</p>
Deformation composite of the RADARSAT Geophysical Processor System (RGPS) Lagrangian motion data
<p>Deformation composite constructed from the Lagrangian RADARSAT Geophysical Processor System (RGPS) Lagrangian motion data for January-February-March, 1997 to 2008. The nominal temporal and spatial scales for the composite data are T<sup>*</sup> = 3 days, and L<sup>*</sup> = 10 km. This data is analyzed and compared with model deformation statistics in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022).</p> <p>The original RGPS Lagrangian motion data set consists in lists of trajectories (time and positions records) for points that are tracked in sequential synthetic aperture radar (SAR) images. The trajectories are organized in different “streams”, corresponding to different initial satellite passes over which a set of tracked points were initialized. For all streams, the trajectories are initialized on a uniform 10 km x 10 km grid at the beginning of the winter in November. Each tracked point can therefore be assigned to <em>(i,j)</em> indices corresponding to its initialization location on the grid. As time increases and the position records are updated, the tracked points are no longer uniformly separated, but their assigned <em>(i,j)</em> indices do not change. The trajectory records are updated when the tracking algorithm detects the tracked points in a new SAR image. The update interval is therefore not always the same for all points, nor is it always on the same time/day within a given stream as the tracking algorithm may be unsuccessful for certain images/points. Moreover, the multiple streams can overlap spatially, such that more than one trajectory can be assigned to the same<em> (i,j)</em> indices. Computing strain rates directly from the original RGPS Lagrangian motion product therefore results in deformation estimates that can span a wide range of spatio-temporal scales, that are not temporally coherent across all streams, and that can also be spatially redundant. The goal of constructing a deformation composite from the original RGPS Lagrangian motion product is to generate a coherent set of non-overlapping Lagrangian deformation estimates at fixed time intervals and with a uniform spatial scale that can be used for statistical analysis.</p> <p>The RGPS Lagrangian deformation composite is constructed using the weighted-average pre-processing method described in Bouchat & Tremblay (2020) and Hutter et al. (2020) and summarized here. For each stream separately, we first define quadrilateral Lagrangian cells assigned to the <em>(i,j)</em> indices by combining records from the <em>(i,j),</em> <em>(i+1,j)</em>, <em>(i,j+1)</em>, and <em>(i+1, j+1)</em> available Lagrangian trajectories. For each <em>(i,j) </em>cell, we then compute the Lagrangian strain rates if, between any two update times, the cell's records have: (i) simultaneous (plus or minus 3 hours) start and end times for all fours corners, (ii) an average time interval for all corners that corresponds to the nominal temporal resolution of T<sup>*</sup>= 3 days, and (iii) an area at the start time that corresponds to the nominal spatial resolution of L<sup>*</sup>= 10 km. The strain rates, the cell area, and the start and end times used to compute the cell's strain rates are also assigned to the <em>(i,j) </em>indices. Then, to create the composite deformation estimates at the same fixed start and end dates for all cells, we average the strain rate and area records at each <em>(i,j)</em> indices in fixed 3-day periods starting on January 1st, using the overlapping time between their start/end date interval with the fixed 3-day periods as weight. For visualization purposes only, we also average the cells' corners' starting positions from all records overlapping with the fixed 3-day interval and use these averaged positions as approximate coordinates for the composite deformation cells. Finally, all streams are spatially combined into a single strain rate composite. In the case of spatial overlap between two or more streams, we keep the cells that have the longest time coverage and discard the other ones.</p> <p> </p> <p>There is one netCDF file per year. Data are organized in matrices where the <em>(i,j)</em> indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files and their structure. </p> <p> </p> <p><strong>1. Variables included</strong></p> <ul> <li><em>(x1,y1), (x1,y2), (x3,y3), (x4,y4)</em>: Average positions of the composite cells' corners. Used for visualization only (deformations should not be computed using these positions) - (meters);</li> <li><em>A</em>: Composite cells' area - (meters squared);</li> <li><em>dudx, dudy, dvdx, dvdy</em>: Composite cell's velocity derivatives (strain rates/deformation) - (1/seconds);</li> <li><em>d_dudx, d_dudy, d_dvdx, d_dvdy</em>: Trajectory error on the composite cells' velocity derivatives - (1/seconds);</li> <li><em>time</em>: Day of year.</li> </ul> <p><strong>*Note:</strong> The composite cells were removed if their average position was within 100 km from land. Before comparing the deformation statistics with sea-ice models, one should only keep cells available in both the model and the RGPS composite.</p> <p> </p> <p><strong>2. Variable structure</strong></p> <p>All variables (except <em>time</em>) are matrices with axes (<em>it, i, j </em>), where <em>it</em> is the time stamp/iteration and<em> i,j </em>are the cells identifiers. See below for how the cells are defined: </p> <p> |--------------------------------------------------------------><sub> <strong>j-axis</strong> </sub> <br> | <br> | <strong>(</strong><strong>x1_ij,y1_ij</strong><strong>)</strong> <strong>o</strong> --------------------<strong>o</strong> <strong>(</strong><strong>x2_ij,y2_ij</strong><strong>)</strong> <br> | | | <br> | | <strong>A_ij or dudx_ij</strong> | <strong> </strong><br> | | | <br> | <strong>(</strong><strong>x4_ij,y4_ij</strong><strong>) </strong><strong>o</strong> ------------------- <strong>o</strong> <strong>(</strong><strong>x3_ij,y3_ij</strong><strong>)</strong> <br> | <br> |<br> V<sub><strong>i-axis</strong></sub> </p> <p> </p> <p> </p> <p> </p> <p><strong>References:</strong><br> Bouchat, A., & Tremblay, B. (2020). Reassessing the Quality of Sea-Ice Deformation Estimates Derived From the RADARSAT Geophysical Processor System and Its Impact on the Spatiotemporal Scaling Statistics. Journal of Geophysical Research: Oceans, 125(8), https://doi.org/10.1029/2019JC015944</p> <p>Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, 2020.</p> <p>The original RGPS Lagrangian Motion data set can be accessed here: https://asf.alaska.edu/data-sets/derived-data-sets/seaice-measures/sea-ice-measures-data-products/</p>
Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I
<p>Model Lagrangian trajectories and deformation estimates for sea-ice models participating in the Sea Ice Rheology Experiment (SIREx) - Part I. Model Lagrangian trajectories are integrated offline, starting on January 1st with all available raw RGPS cells positions (interpolated to January 1st 00:00:00 UTC). The trajectories are advected at an hourly time step with the models daily velocity output until March 31st. The trajectories are then sampled at a 3-day interval to match the RGPS composite time stamps, and the velocity derivatives (deformation) are calculated using the line integral approximations on the cells' contour. All model trajectories and Lagrangian deformation data therefore have nominal temporal and spatial scales of 3-days and 10-km (same as the RGPS composite), regardless of the original resolution of the model output. The model Lagrangian deformation estimates form the basis quantity for the statistical and spatio-temporal scaling analysis presented in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the model trajectory integration and deformation calculation.</p> <p>There is one netCDF file per model, per year (1997 and/or 2008). Data are organized in matrices where the (i,j) indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files, their structure, and how to cite. </p> <p> </p> <p><strong>1. File naming convention</strong></p> <p>"< Model simulation label >" + _ + "deformation" + _ + "< year >" </p> <p> </p> <p><strong>2. Variables included</strong></p> <ul> <li><em>(x1,y1), (x1,y2), (x3,y3), (x4,y4)</em>: Position of the cells' corners (Lagrangian trajectories) - (meters);</li> <li><em>A</em>: Cells' area - (meters squared);</li> <li><em>dudx, dudy, dvdx, dvdy</em>: Cell's velocity derivatives (strain rates/deformation) - (1/seconds);</li> <li><em>d_dudx, d_dudy, d_dvdx, d_dvdy</em>: Trajectory error on cells' velocity derivatives - (1/seconds);</li> <li><em>time</em>: Day of year.</li> </ul> <p><strong>*Note:</strong> the model trajectories are terminated if they move within 100 km from land. Before computing deformation statistics to compare with RGPS composite data, one should mask both deformation sets to only keep cells available in both the model and RGPS data sets.</p> <p> </p> <p><strong>3. Variable structure</strong></p> <p>All variables (except <em>time</em>) are matrices with axes (<em>it, i, j </em>), where <em>it</em> is the time stamp/iteration and<em> i,j </em>are the cells identifiers. See below for how the cells are defined: </p> <p> |--------------------------------------------------------------><sub> <strong>j-axis</strong> </sub> <br> | <br> | <strong>(</strong><strong>x1_ij,y1_ij</strong><strong>)</strong> <strong>o</strong> -------------------<strong> o</strong> <strong>(</strong><strong>x2_ij,y2_ij</strong><strong>)</strong> <br> | | | <br> | | <strong>A_ij or dudx_ij</strong> | <strong> </strong><br> | | | <br> | <strong>(</strong><strong>x4_ij,y4_ij</strong><strong>) </strong><strong>o</strong> ------------------- <strong>o</strong> <strong>(</strong><strong>x3_ij,y3_ij</strong><strong>)</strong> <br> | <br> |<br> V<sub><strong>i-axis</strong></sub> </p> <p> </p> <p>Hence, coordinates are repeated between neighbouring cells, for example: (x2_ij,y2_ij) = (x1_ij+1,y1_ij+1) and (x4_ij,y4_ij) = (x1_i+1j,y1_i+1j)</p> <p> </p> <p><strong>4. Recommended citation usage</strong></p> <p>If <em>all</em> simulations included in the current archive are used in a future study, we ask to cite this archive and the SIREx paper (Bouchat et al., 2022). If only <em>selected </em>simulations are used, we ask to cite both this archive and the reference paper(s) applying to the selected simulation(s) (as stated indicated in Table 1 of the SIREx papers).</p>
Multi-channel seismic reflection profiles SALTFLU (Salt deformation and sub-salt fluid circulation in the Algero-Balearic abyssal plain) - Pre-Stack Kirchhoff Time & Depth Migration 2022
<p>This archive contains sections of reprocessed multi-channel seismic reflection profiles SALTFLU, acquired south of Ibiza (Spain) in 2012 with the OGS Explora (pre-stack Kirchhoff time and depth stacks, and migration velocities in SEG-Y format). It also contains the cruise report describing the survey acquisition in 2012. Connected articles describe the processing flow applied to this dataset and interpretations led by the first author. </p> <p>Field File Identification and Shot Numbers (FFID, SHOTNO) are linearly interpolated by matching the CMP numbers before and after migration. Bytes 73-76 and 77-80 are identical to bytes 181-184 and 185-188 and contain the CMP coordinates.</p> <p> </p> <p> </p> <p> </p>
Pyrene-Based Macrocrosslinkers with Supramolecular Mechanochromism for Elastic Deformation Sensing in Hydrogel Networks
<p>Primary data used in the manuscript (NMR, MS, fluorescence, GPC) sorted after Figure and associated panel in manuscipt and supporting information.</p>
Data and Models for "Probabilistic Imaging of Tsunamigenic Seafloor Deformation During the 2011 Tohoku-oki Earthquake"
<p><strong>Directory "waveform_data"</strong> includes 13 tsunami time series data from different instruments: TM1, TM2, KPG1, KPG2, GB801, GB802, GB803, GB804, GB806, GB807, D21401, D21413, and D21418. Each data file (*.dat) has two columns for (1) the time since earthquake initiation (min) and (2) ocean surface or seafloor displacement amplitude (m).</p> <p><strong>Directory “kin_models”</strong> includes the following:</p> <p>1. Seafloor Mesh Geometry</p> <ul> <li>The entire seafloor mesh consists of two separate parts (422 and 136 nodes each; 558 in total) due to the need to resolve potential discontinuity at the trench. The files “*Pt{1,2}-SM2.PointCoord.txt” consists of six columns for the ID, longitude (deg), latitude (deg), East (km), North (km) and depth (km) of the nodes in each triangular mesh. The E/N coordinates are calculated in UTM projection system, relative to an arbitrary reference point.</li> <li>The files “*.ClipPath.txt” includes the ID/lon/lat of mesh boundary nodes, which can be used for plotting.</li> <li>Visualization of the mesh parts are provided in PDF files.</li> <li>The file “*Total-SM2.PointCoord.txt” excludes boundary nodes and contains seafloor locations (504 nodes) that are directly used in tsunami arrival time calculations.</li> </ul> <p>2. Posterior Mean Models</p> <ul> <li>Ensemble-averaged models of seafloor displacements and uncertainty estimates, with no spatial averaging (“0R” in the file name) or with one-ring spatial averaging (“1R”). These models are shown in Figures 5 and 6 of <em>Jiang and Simons</em> (2016). The data files “posterior_mean_{0,1}R.txt” have three columns for (1) vertical seafloor displacement (m), (2) one-sigma standard deviation of displacement (m), and (3) corresponding resolution length (km). The model values (558 rows) correspond to nodes in files “*Pt{1,2}-SM2.PointCoord.txt” concatenated in sequential order.</li> <li>Tsunami arrival times (in sec) are calculated from the posterior mean values of propagation speeds, with zero sec at the earthquake epicenter. The coordinates (508 nodes) are included in geometry file “*Total-SM2.PointCoord.txt.”</li> </ul> <p><strong>Directory “kin_ensemble”</strong> includes the entire posterior model ensemble (98304 samples) in HDF5 format. Using a Linux command <em>h5dump</em> will show the following information about the contained datasets, with their names and dimensions. The main datasets are: (1) Covariance (1008×1008); (2) Data Log-likelihood (98304×1); (3) Posterior Log-likelihood (98304×1); and (4) Sample Set (98304×1008). Each model has 1008 parameters (504 for displacement and 504 for propagation speeds). The source coordinates (504 nodes) are included in geometry file "*Total-SM2-Parameter.PointCoord.txt."</p> <p><strong>Note:</strong> three different geometries files above are used for (1) posterior mean displacements (558 nodes), (2) arrival time calculation (508 nodes), and (3) source inversion models (504 nodes). </p>
Supporting material of deliverable D2.1 (StretchBio_D.2.1_Report on relation deformation-force for the nanopillars)
<p>Nanopillars - Analytical vs numerical bending - Spreadsheet</p> <p>Nanopillars - Deformation_pillars_on_SiO2substrate_figure10 at deliverable D2.1</p> <p>Nanopillars - vonMises_stress_pillars_on_SiO2substrate_figure14 at deliverable D2.1</p>
Three-Dimensional Characterization of Deformation-induced Damage in Dual Phase Steel using Deep Learning
<p>High performance sheet metals with a multi-phase microstructure suffer from deformation induced damage formation during forming in the constituent phases but importantly also where these intersect. To capture damage in terms of the physical processes in three dimensions (3D) and its stochastic nature during deformation, two challenges remain to be tackled: First, bridging high resolution analysis towards large scales to consider statistical data and, second, characterising in 3D with a resolution appropriate for sub-micron sized voids at a large scale. Here, we present how this can be achieved using panoramic scanning electron microscopy (SEM), metallographic serial sectioning, and deep-learning assisted automatic image analysis. This brings together the 3D evolution of active damage mechanisms with volumetric and environmental information for thousands of individual damage sites. We also assess potential surface preparation artefacts in 2D analyses. Overall, we find that for the material considered here, a dual phase (DP800) steel, martensite cracking is the dominant but not sole origin of deformation induced damage and that for a quantitative comparison of damage density, metallographic preparation can induce additional surface damage density far exceeding what is commonly induced between uniaxial straining steps.</p> <p>https://doi.org/10.1016/j.matdes.2023.112108</p>
Indentation Fracture and Deformation Data, v. 2
<p>This document (2024) updates and supersedes a prior compilation and analysis (2020) of indentation fracture and deformation data.</p>
A Global Plate Model Including Lithospheric Deformation Along Major Rifts and Orogens Since the Triassic
<p>Global deep‐time plate motion models have traditionally followed a classical rigid plate approach, even though plate deformation is known to be significant. Here we present a global Mesozoic–Cenozoic deforming plate motion model that captures the progressive extension of all continental margins since the initiation of rifting within Pangea at ~240 Ma. The model also includes major failed continental rifts and compressional deformation along collision zones. The outlines and timing of regional deformation episodes are reconstructed from a wealth of published regional tectonic models and associated geological and geophysical data. We reconstruct absolute plate motions in a mantle reference frame with a joint global inversion using hot spot tracks for the last 80 million years and minimizing global trench migration velocities and net lithospheric rotation. In our optimized model, net rotation is consistently below 0.2°/Myr, and trench migration scatter is substantially reduced. Distributed plate deformation reaches a Mesozoic peak of 30 × 106 km2 in the Late Jurassic (~160–155 Ma), driven by a vast network of rift systems. After a mid‐Cretaceous drop in deformation, it reaches a high of 48 x 106 km2 in the Late Eocene (~35 Ma), driven by the progressive growth of plate collisions and the formation of new rift systems. About a third of the continental crustal area has been deformed since 240 Ma, partitioned roughly into 65% extension and 35% compression. This community plate model provides a framework for building detailed regional deforming plate networks and form a constraint for models of basin evolution and the plate‐mantle system.</p> <p> </p> <p>The agegrids associated with this model can be accessed at: <a href="https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Muller_etal_2019_Tectonics/" target="_blank" rel="noopener">https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Muller_etal_2019_Tectonics/</a></p>
Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from subpixel correlation of Copernicus Sentinel-2 optical images
<p>Surface deformation of the Mw 6.4 and Mw 7.1 Ridgecrest earthquakes measured from subpixel correlation of Copernicus Sentinel-2 optical images </p>
Data for: Drying-Induced Deformation in Concrete: Insights from a 5-Year Study
<p>The experimental dataset from Havlasek, Smilauer, and Nezerka's paper "Drying-Induced Deformation in Concrete: Insights from a 5-Year Study" is available in five archives. These archives contain the data organized according to the specified structure and accompanying Gnuplot source files for easier data visualization.</p> <p>Full experimental description is given in the paper and in the preceding publication entitled "Shrinkage-induced deformations and creep of structural concrete: 1-year measurements and numerical prediction accessible from https://www.sciencedirect.com/science/article/pii/S000888462100051X</p> <p>All Gnulot input files (*.gnu) generate a corresponding *.pdf file with the same base name. These PDFs are included, but they're not listed.<br>Usage: $ gnuplot < plot_name_of_gnuplot_file.gnu</p> <p>The full experimental dataset is reduced to 100 time points using resampling. Initially, geometric progression is applied, but once the time step reaches 30 days, it remains constant.</p> <p>*** FILES STRUCTURE ***</p> <p>|--- ambient_conditions<br>| | # history of ambient humidity and temperature<br>| |<br>| |--- plot_large_beams_ambient.gnu<br>| | # time "0" corresponds to the onset of drying of the beams, i.e. concrete age 34.063 days<br>| |<br>| |--- data<br>| | # time "0" corresponds to concrete set<br>| |--- humidity_function.dat<br>| |--- temperature_function.dat <br>|<br>|--- creep_shrinkage_prisms<br>| | # development of creep and shrinkage of beams 100x100x400 mm exposed to drying at the concrete age of 28 days; concrete specimens are drying from two lateral surfaces only<br>| |<br>| |--- axial_shrinkage.gnu<br>| | # total shrinkage<br>| |--- eccentric_creep.gnu<br>| | # total creep in axial direction and bending <br>| |<br>| |--- data<br>| |--- Jtot_ecc_axial_toi.csv<br>| |--- Jtot_ecc_bending_toi.csv<br>| |--- shrinkage_toi.csv<br>|<br>|--- curvature_beams<br>| # curvature of concrete beams with various sizes and sealing configurations, time "0" = installation time = onset of drying = concrete age of 34.063 days<br>| | <br>| |--- beam_1.gnu<br>| | # 100 Top: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, drying from the top surface<br>| |--- beam_2.gnu<br>| | # 100 Sealed: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, sealed surface<br>| |--- beam_3.gnu<br>| | # 100 Bottom: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, drying from the bottom surface<br>| |--- beam_4.gnu<br>| | # 100 Both: breadth B = 0.10 m, height H = 0.10 m, span = 2.50 m, length = 2.70 m, drying from the top and bottom surfaces<br>| |--- beam_5.gnu<br>| | # 50 Top: breadth B = 0.10 m, height H = 0.05 m, span = 1.75 m, length = 1.95 m, drying from the top surface<br>| |--- beam_6.gnu<br>| | # 200 Top: breadth B = 0.10 m, height H = 0.20 m, span = 3.00 m, length = 3.20 m, drying from the top surface, loading with external weights 2 x 30 kg placed 0.6 m from the support<br>| |--- beam_7.gnu<br>| | # 150 Top: breadth B = 0.10 m, height H = 0.15 m, span = 3.00 m, length = 3.20 m, drying from the top surface<br>| |--- beams_100_ABC.gnu<br>| | # individual responses of the beams with height 100 and different sealing configurations<br>| |--- beams_100_mean.gnu<br>| | # mean responses of the beams with height 100 and different sealing configurations<br>| |--- beams_sizes_ABC.gnu<br>| | # individual responses of the beams with different height and drying from the top surface<br>| |--- beams_sizes_mean.gnu<br>| | # mean responses of the beams with different height and drying from the top surface<br>| |<br>| |--- data<br>| |--- automatic<br>| | # measurement with post-mounted linear potentiometer installed at midspan of each specimen<br>| | |--- 1_mean_curvature_toi.csv<br>| | | # columns: time, mean curvature and standard deviation of curvature; the values are calculated from specimens A, B and C<br>| | |--- 1A_curvature_toi.csv<br>| | | # columns: time and curvature<br>| | |--- 1B_curvature_toi.csv<br>| | |--- 1C_curvature_toi.csv<br>| | |--- 2_mean_curvature_toi.csv<br>| | |--- 2A_curvature_toi.csv<br>| | |--- 2B_curvature_toi.csv<br>| | |--- 2C_curvature_toi.csv<br>| | |--- 3_mean_curvature_toi.csv<br>| | |--- 3A_curvature_toi.csv<br>| | |--- 3B_curvature_toi.csv<br>| | |--- 3C_curvature_toi.csv<br>| | |--- 4_mean_curvature_toi.csv<br>| | |--- 4A_curvature_toi.csv<br>| | |--- 4B_curvature_toi.csv<br>| | |--- 4C_curvature_toi.csv<br>| | |--- 5_mean_curvature_toi.csv<br>| | |--- 5A_curvature_toi.csv<br>| | |--- 5B_curvature_toi.csv<br>| | |--- 5C_curvature_toi.csv<br>| | |--- 6_mean_curvature_toi.csv<br>| | |--- 6A_curvature_toi.csv<br>| | |--- 6B_curvature_toi.csv<br>| | |--- 6C_curvature_toi.csv<br>| | |--- 7_mean_curvature_toi.csv<br>| | |--- 7A_curvature_toi.csv<br>| | |--- 7B_curvature_toi.csv<br>| | |--- 7C_curvature_toi.csv<br>| |--- dial_gauge<br>| | # measurement using a set of 5 digital indicators with a fixed position from the left support, only the front specimens (A) is measured using this technique<br>| | # columns: time, curvature and standard devidation of curvature. Weights corresponding to theoretical normalized deflections are applied.<br>| | |--- 1a_weighted_toi.csv<br>| | |--- 2a_weighted_toi.csv<br>| | |--- 3a_weighted_toi.csv<br>| | |--- 4a_weighted_toi.csv<br>| | |--- 5a_weighted_toi.csv<br>| | |--- 6a_weighted_toi.csv<br>| | |--- 7a_weighted_toi.csv<br>| |--- DIC<br>| | # measurement using digital image correlation, locations of the reference plates are above the support and at quarter-spans<br>| |--- 1_stat_toi.csv<br>| | # columns: time, mean curvature and standard deviation of curvature. The values are calculated from specimens A, B and C. The weights are given by the standard deviation of curvature of the individual specimens<br>| |--- 1a_stat_toi.csv<br>| | # columns: time, mean curvature and standard deviation of curvature.<br>| |--- 1b_stat_toi.csv<br>| |--- 1c_stat_toi.csv<br>| |--- 2_stat_toi.csv<br>| |--- 2a_stat_toi.csv<br>| |--- 2b_stat_toi.csv<br>| |--- 2c_stat_toi.csv<br>| |--- 3_stat_toi.csv<br>| |--- 3a_stat_toi.csv<br>| |--- 3b_stat_toi.csv<br>| |--- 3c_stat_toi.csv<br>| |--- 4_stat_toi.csv<br>| |--- 4a_stat_toi.csv<br>| |--- 4b_stat_toi.csv<br>| |--- 4c_stat_toi.csv<br>| |--- 5_stat_toi.csv<br>| |--- 5a_stat_toi.csv<br>| |--- 5b_stat_toi.csv<br>| |--- 5c_stat_toi.csv<br>| |--- 6_stat_toi.csv<br>| |--- 6a_stat_toi.csv<br>| |--- 6b_stat_toi.csv<br>| |--- 6c_stat_toi.csv<br>| |--- 7_stat_toi.csv<br>| |--- 7a_stat_toi.csv<br>| |--- 7b_stat_toi.csv<br>| |--- 7c_stat_toi.csv<br>|<br>|--- drying_cylinders<br>| # data of moisture loss measured on cylinders drying from the top and bottom surface and with a sealed circumference, onset of drying at concrete age 28 days<br>| |<br>| |--- drying_cylinders_average.gnu<br>| |<br>| |--- data<br>| | # columns: duration of drying, average moisture loss [kg/m^3], standard deviation of moisture loss<br>| |--- dwdV_25_toi.csv<br>| |--- dwdV_50_toi.csv<br>| |--- dwdV_100_toi.csv<br>| |--- dwdV_150_toi.csv<br>| |--- dwdV_200_toi.csv<br>|<br>|---isotherm<br>| # data for sorption isotherm expressed as a dependence of moisture content [kg/m^3] on relative humidity [-]<br>| |<br>| |--- isotherm.gnu<br>| | experimental data and a least-squares fit with a VanGenuchten expression<br>| |<br>| |--- data<br>| | # columns: relative humidity, average moisture content and standard deviation of moisture content<br>| |--- isotherm_beam.dat<br>| | # data measured on specimens cut from a spare concrete beam<br>| |--- isotherm_cube.dat<br>| | # data measured on specimens cut from a standard concrete cube </p>
High-resolution sea ice drift and deformation example data derived from Sentinel-1 in the Arctic Ocean during MOSAiC
<p>This data set contains two high-resolution sea ice drift and deformation fields from 30/31 December 2019 and 20/21 June 2021. They were acquired in the Transpolar Drift along the drift track of the research campaign "Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). Drift fields were calculated from Sentinel-1, HH polarization SAR images acquired in enhanced wide mode. These had a pixel resolution of 50 m in Polar Stereographic North projection (latitude of true scale: 70 N, center longitude: 45 W). We used an ice tracking algorithm introduced by Thomas et al. (2008, 2011) and modified by Hollands and Dierking (2011) to derive drift from sequential pairs. The time step between two sequential images is approximately one day. The resulting drift data set was defined on a regular grid with a spatial resolution of 700 m. Outliers in the velocity data were reduced by a 3x3 point running median filter covering an area of 2.1x2.1 km. For the deformation estimates, we calculated deformation using a linear approximation based on Green's Theorem that relates the double integral over a plane to the line integral along a simple curve surrounding the plane. We discretized the curve applying the trapezoid method that linearly interpolates velocity between the vertices of the grid cells. This work contains modified Copernicus Sentinel data (2020)</p> <p>Related publications:</p> <p><strong>von Albedyll, L., Haas, C., and Dierking, W.</strong>: Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations, The Cryosphere, 15, 2167–2186, <a href="https://doi.org/10.5194/tc-15-2167-2021">https://doi.org/10.5194/tc-15-2167-2021</a>, 2021.</p> <p><strong>Hollands, Thomas; Dierking, Wolfgang (2011):</strong> Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results. <em>Annals of Glaciology</em>, <strong>52(57)</strong>, 311-317, <a href="https://doi.org/10.3189/172756411795931462">https://doi.org/10.3189/172756411795931462</a></p> <p><strong>Thomas, Mani; Geiger, Cathleen A; Kambhamettu, Chandra (2008):</strong> High resolution (400 m) motion characterization of sea ice using ERS-1 SAR imagery. <em>Cold Regions Science and Technology</em>, <strong>52(2)</strong>, 207-223, <a href="https://doi.org/10.1016/j.coldregions.2007.06.006">https://doi.org/10.1016/j.coldregions.2007.06.006</a></p> <p><strong>Thomas, Mani; Kambhamettu, Chandra; Geiger, Cathleen A (2011):</strong> Motion Tracking of Discontinuous Sea Ice. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <strong>49(12)</strong>, 5064-5079, <a href="https://doi.org/10.1109/TGRS.2011.2158005">https://doi.org/10.1109/TGRS.2011.2158005</a></p>
Dataset for article " Deformation of polycrystalline MgO up to 8.3 GPa and 1270 K: microstructures, dominant slip-systems, and transition to grain boundary sliding"
<p>This archive countains the dataset used to produce the paper "Deformation of polycrystalline MgO up to 8.3 GPa and 1270 K: microstructures, dominant slip-systems, and transition to grain boundary sliding" in press by Frontiers in Earth Science.</p> <p>(c) Estelle Ledoux, Université de Lille, France, 2020</p>
New insights into crustal deformation of the Indonesia-Australia-New Guinea collision zone from a broad-scale kinematic model: Supplementary Model Files
<p>Supplementary kinematic model input for the JGR: Solid Earth publication "New insights into crustal deformation of the Indonesia-Australia-New Guinea collision zone from a broad-scale kinematic model".</p>
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.