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478 results for “3D data”
Nanoresolution real-time 3D orbital tracking for studying mitochondrial trafficking in vertebrate axons in vivo - Data Set 1
<p>24.01.2014 fish 5 was analyzed in main figures 1,2 and supplementary figure 4</p> <p>21.03.2014 fish 1 was analyzed in main figures 2,3 and supplementary figure 4</p>
Dynamic 3D Tomographic X-ray Data of Ladybug
<p>This is the documentation of the 3D dynamic tomographic X-ray (CT) data of a ladybug.<br> It can be freely used for scientific purposes with appropriate references to the data and to this document in http://arxiv.org/.</p>
Data Tables and figures for "Quantification of 3D thermal anomalies from surface observations of an orogenic geothermal system (Grimsel Pass, Swiss Alps)"
<p>Tables and figures containing the data used in the manuscript called</p> <p><strong>"Quantification of 3D thermal anomalies from surface observations of an orogenic geothermal system (Grimsel Pass, Swiss Alps)", </strong></p> <p>which was submitted to JGR:Solid Earth</p>
DIC Images and Data of In-Plane Cyclic Testing of a 3D-Woven Layer-to-Layer Angle Interlock Composite
<p>Data behind the publications:</p> <ul> <li>C. Oddy, M. Song, C. Stewart, B. El Said, M. Ekh, S. Hallett and Martin Fagerström: On and Off-Axis Cyclic Behaviour of 3D-Woven Composites: Experimental Testing and Macroscale Modelling. Submitted for international publication.</li> </ul> <p>For each test sample orientation and excel file is provided. This includes the machinedata giving the time, machine displacement and force. On another sheet, informationfrom the DIC analysis is given, including the time and force signal organised accordingto the image frame number. The DIC images are also provided.</p> <p>The testing was carried out using a serial camera DIC system. However, as part of this publication, the images have only been provided from one of the camera systems. They have also been exported at a lower image quality than the original analysis. The authors' can recommend the use of open source DIC software, for example DICe (https://github.com/dicengine/dice), to carry out any type of further image processing. Processing the stereo image as a 2D analysis at a lower image quality may lead to some deviations in the extracted strain or displacement fields. The authors however have compared the results and have not seen any notable inconsistencies. We hope that these images can be useful to you in your own research endeavours! </p> <p> </p>
Data corresponding to paper: Improving 3D deep learning segmentation with biophysically motivated cell synthesis
<div> <h1>Improving 3D deep learning segmentation with biophysically motivated cell synthesis</h1> <a href="https://github.com/bruchr/cell_synthesis#improving-3d-deep-learning-segmentation-with-biophysically-motivated-cell-synthesis"></a></div> <h3><strong>Roman Bruch, Mario Vitacolonna, Elina Nürnberg, Simeon Sauer, Rüdiger Rudolf and Markus Reischl</strong></h3>
Data for '3D Radio Frequency Mapping and Polarization Observations Show Lightning is Ignited by Cosmic-ray Shower' by Shao et al., 2024
<p>Data for manuscript "<a name="_Hlk177653706"></a><strong><span>3D Radio Frequency Mapping and Polarization Observations Show Lightning is Ignited by Cosmic-ray Shower" <br></span></strong></p>
Data of 3D PPMLR-MHD model Simulation for manuscript "Formation and Evolution of Nightside Transpolar arc and Its Relationship with Energetic Plasma in the Magnetotail Lobe"
<p><span>Data of 3D PPMLR-MHD model Simulation for manuscript "Formation and Evolution of Nightside Transpolar arc and Its Relationship with Energetic Plasma in the Magnetotail Lobe"</span></p> <p><span>These data come from a fully run of a 3D MHD Simulation model that is named PPMLR-MHD model (detailed descriptions below).</span></p> <p><span>There are 2 types of data files:</span></p> <p><span>1) X15dXXXX.mat is saved simulation parameters. </span></p> <p><span>2) Xing15XXXX_heatflux.mat is saved heat flux from simulation parameters. </span></p> <p><span>XXXX is the number of files, and files with the same serial number correspond to the same time.</span></p> <p><span> </span></p> <p><span>The first type files of data include the following parameters:</span></p> <p><span>time, x, y, z, logrho, Vx, Vy, Vz, Bx, By, Bz, Pr, Jx, Jy, Jz</span></p> <p><span>Where, time is simulation time, which need to plus the start time to transfer them to universal time: time+16:00.</span></p> <p><span> (x,y,z) are the three components of the position of simulation point in GSM coordinates;</span></p> <p><span> logrho is the plasma density at the simulation point;</span></p> <p><span> (Vx, Vy,Vz) are the three components of plasma velocity at the simulation point in GSM coordinates;</span></p> <p><span> (Bx, By,Bz) are the three components of magnetic field at the simulation point in GSM coordinates;</span></p> <p><span> Pr is the plasma dynamic presure at the simulation point;</span></p> <p><span> (Jx, Jy,Jz) are the three components of plasma electric current at the simulation point in GSM coordinates;</span></p> <p><span> </span></p> <p><span>The second type file of data includes the simulated heat flux along the magnetic field lines at the simulation point in GSM coordinates. </span></p> <p><span>PPMLR-MHD model</span></p> <p><span>The PPMLR-MHD model is on the basis of an extension of the piecewise parabolic method (1) with a Lagrangian remap to magnetohydrodynamics (MHD) (2, 3). It is a three-dimensional MHD model, designed specially for the solar wind–magnetosphere–ionosphere system (4-6). The model possesses a high resolution in capturing MHD shocks and discontinuities and a low numerical dissipation in examining possible instabilities inherent in the system (4).</span></p> <p><span>The model uses a Cartesian coordinate system with the Earth’s center at the origin and X, Y, and Z axes pointing towards the Sun, the dawn-dusk direction, and the north, respectively. The size of the numerical box extends from 25 RE to –100 RE along the Sun-Earth line and from –50 RE to 50 RE in Y and Z directions, with 240×240×240 grid points and a minimum grid spacing of 0.2 RE. An inner boundary of radius 3 RE is set for the magnetosphere to avoid the complexities associated with the plasmasphere and large MHD characteristic velocity from the strong magnetic field (6). An electrostatic ionosphere shell with height-integrated conductance is imbedded, allowing an electrostatic coupling process introduced between the ionosphere and the magnetospheric inner boundary. The Earth’s magnetic field is approximated by a dipole field with a dipole moment of 8.06×1022 A/m in magnitude. The model is run to solve the whole system by inputting the real interplanetary conditions for the current event.</span></p>
Data for Manuscript "3D Optofluidic Control Using Reconfigurable Thermal Barriers"
<p>Included in this compressed Data file are all trajectories required to recreated the experimental part of all figure plots:</p> <p>Fig. 2A,C,E</p> <p>Fig. 3A,C,E,G</p> <p>Fig. 4A-E</p> <p>Fig 5B,D</p> <p>The data is in the form of trajectories of the tracked particles over time. X, Y, Z coordinate data are given in addition to the time trace T.</p> <p>All other necessary meta data are included in the files. </p>
3D HDF5 data from numerical relativity simulations
<p>3D HDF5 data from numerical relativity simulations. Test data for visualization purposes.</p>
CT data and 3D models associated with: Palaeoneurology of the Early Cretaceous iguanodont Proa valdearinnoensis and its bearing on the parallel developments of cognitive abilities in theropod and ornithopod dinosaurs
<p><i>Proa valdearinnoensis </i>is a relatively large-headed and stocky iguanodontian dinosaur from the latest Early Cretaceous of Spain. Its braincase is known from three specimens. Similar to that of other dinosaurs, it shows a mosaic ossification pattern in which most of the bones seem to have fused together indistinguishably while a few bones (frontoparietal, basioccipital) might have remained loosely attached. The endocasts of the three specimens are described based on CT data and digital reconstructions. They show unmistakable morphological similarities with the endocast of closely related taxa, such as <i>Sirindhorna khoratensis </i>(which is close in age but from Thailand). This supports a high conservatism of the endocranial cavity. The issue of volumetric correspondence between endocranial cavity and brain in dinosaurs is analysed. Although a brain-to-endocranial cavity (BEC) index of 0.50 has been traditionally used, we employ instead 0.73. This is indeed the mid-value between the situation in adults of <i>Alligator mississippiensis</i> and <i>Gallus gallus</i>, which are members of the extant bracketing taxa of dinosaurs (Crocodilia and Aves). We thence gauge the level of encephalisation of <i>Proa valdearinnoensis</i> by the calculation of the Encephalisation Quotient (EQ), which remains valuable as a metric for assessing the degree of cognitive function in extinct taxa, especially those with fully ossified braincases like dinosaurs and other archosaurs. The EQ obtained for <i>Proa valdearinnoensis</i> (3.611) suggests that this species was significantly more encephalised than most if not all extant non-avian, non-mammalian amniotes. Our work adds to the growing body of data concerning theoretical cognitive capabilities in dinosaurs and supports the idea that increasing encephalisations were fostered not only once in theropods but also in parallel in the shorter-lived lineage of ornithopods. <i>Proa valdearinnoensis</i> was ill-equipped to respond to theropod dinosaurs and possibly lived in groups as a strategy to mitigate the risk of being predated upon. We hypothesize that group-living and protracted caring of juveniles in this and possibly many other iguanodontian ornithopods favoured a degree of encephalisation that was outstanding by reptile standards.</p>
Mental comparison of 3D objects is based on 2D optical flow computations: human data
<p>Human behavioural data for manuscript:</p> <p><strong>Mental comparison of 3D objects is based on 2D optical flow computations.</strong></p>
FIGURE 6. 3D in Perennial shrubs in Egypt: current status and updated red data list
FIGURE 6. 3D scatter plot showing the ordination of the recorded species along habitat, geographical, and abundance gradients.
Preliminary DOI/Repository of ALPINE3D and SNOWPACK data of the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>There are 2 zip folders in this repository.</p> <p>"a3d_jgr.zip" contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.<br> The setup contains both input and output data as well as the model configuration as used in the submitted manuscript <br> "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 3 main folders:</p> <ul> <li>base_setup_files</li> <li>a3d_jgr_alpha1</li> <li> a3d_jgr_alpha3</li> </ul> <p>The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects.</p> <p>To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p> <p>_________________________________________________________________________________________________________<br> <br> "SNOWPACK_JGR.zip" contains both input and output data for SNOWPACK as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 2 main folders: </p> <ul> <li>SNOWPACK_JGR_ALPHA1</li> <li>SNOWPACK_JGR_ALPHA3</li> </ul> <p>In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p>
3D ED / MicroED data of BI-3812
<p>The sample was obtained from Boehringer Ingelheim within a frame of opnme project <a href="https://www.opnme.com">https://www.opnme.com</a></p> <p>Electron diffraction tilt series were recorded with GLACIOS TEM (Thermo Fisher) at liquid nitrogen temperature. Measurements were performed using the EPU-D (Thermo Fisher) module in continuous rotation mode with 200 kV electrons (wavelength of 0.0251 Å). Tilt series were collected within ±60° goniometer tilt range with 1° frame interval and rotation speed of 1° per second. The data were collected using an electron dose rate of 6.016 e/nm<sup>2</sup>s. The electron beam size used for diffraction data collection was 1.2 µm. The pixel size within diffraction patterns is 0.001075 Å<sup>-1</sup>.</p>
Open data for "3D integration enables ultra-low-noise isolator-free lasers in Si photonics"
<p>Open data for "3D integration enables ultra-low-noise isolator-free lasers in Si photonics"</p>
Magnetotelluric data in Subei area, northern Tibet and the 3D isotropic/anisotropic models
<p>This dataset contains four folders. They are ‘aniinv’, ‘isoinv’, ‘sensitivity_test’, ‘syn_mod_test’, respectively. In the ‘aniinv’ folder, there are three sub-folders include ‘azimu_ani_inv’, ‘gener_ani_inv’, ‘verti_ani_inv’, indicating the inversion results for azimuthal, general, and vertical anisotropic inversions, respectively. The ‘isoinv’ folder contains results for isotropic inversion. The ‘sensitivity_test’ folders contains modeified models and their responses for sensitivity tests of anomalies. The ‘syn_mod_test’ folder contains a synthetic model constructed according the final model, the responses of this model, and the recovering for this model. In each folder, there is a ‘readme.txt’ file describing the details of individual files.</p>
Data and code for: Order-of-Magnitude SNR Improvement for High-Field EPR Spectrometers via 3D-Printed Quasioptical Sample Holders
<p>*.py and data files for publication titled "Order-of-Magnitude SNR Improvement for High-Field EPR Spectrometers via 3D-Printed Quasioptical Sample Holders"</p>
Supplemental 3D Model Data - Hapalosiphonacean cyanobacteria (Nostocales) thrived amid emerging embryophytes in a 407-million-year-old landscape
<p>Three-dimensional reconstruction models of cyanobacteria from a 407 million year old fossil from the Lower Devonian Rhynie chert, UK. Thin sections SU.PB. 2023.0.1.2.8 from the Palaeobotany Collection in the Pôle Collections scientifiques et patrimoniales. Bibliothèque de Sorbonne Université, Paris (France).</p> <p>Imaris files (.IMS) can be viewed using the Imaris Viewer software, freely available in both Windows and Mac versions from https://imaris.oxinst.com/imaris-viewer. </p>
Data for: Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images
<p>Replication data for: Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images</p> <p> </p> <p> </p>
Overtrust 3D dataset - hilbert order data
<p>Overtrust 3D dataset hilber order data. 0-199 frequency, rank matrix, U and V bases.</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.