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227 results for “4D”
Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Example Data)
<p><strong>Overview </strong></p> <p>This repository contains 2 example 4D-STEM datasets format from the paper <a href="https://arxiv.org/abs/2202.12611">"Phase Object Reconstruction for 4D-STEM using Deep Learning"</a>. The data was written to hdf5 for compatibility with the python programming language. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly.</p> <p> </p> <p><strong>Parameters</strong></p> <p>The twisted bilayer graphene dataset is simulated. The smaller file is an experimental SrTiO<sub>3</sub> dataset.</p> <table> <thead> <tr> <th scope="row"> </th> <th scope="col">Graphene</th> <th scope="col">STO</th> </tr> </thead> <tbody> <tr> <th scope="row">E0</th> <td>200kV</td> <td>300kV</td> </tr> <tr> <th scope="row">Apeture</th> <td>25 mrad</td> <td>20 mrad</td> </tr> <tr> <th scope="row">Detector Size</th> <td>2.5 Å<sup>-1</sup></td> <td>1.6671 Å<sup>-1</sup></td> </tr> <tr> <th scope="row">Dimensions</th> <td>101x101x128x128</td> <td>60x60x64x64</td> </tr> <tr> <th scope="row">Step Size</th> <td>0.2</td> <td>0.1818</td> </tr> </tbody> </table> <p><br> </p>
A high-resolution 4D geospatial laser scan dataset of the beach at Mariakerke Bad, Belgium
<p>This dataset contains a high resolution (in both time and space) laser scan data set of a 1-year measurement campaign in 2017 and 2018 in the seaside resort of Mariakerke Bad in Belgium. The measurements consist of 8417 hourly laserscans of a 400 meter stretch of beach. The measurement campained was performed to study variations in shoreward sand transport at urbanized beaches. </p> <p>Laserscan data is stored in local coordinates. Time dependent corrections per laserscan epoch are provided next to a global transformation matrix to transform the local coordinates to the Belgium Lambert 2008 coordinate system.</p> <p>This data is provided as is and is licensed under the Creative Commons Attribution 4.0 International (CC-BY-4.0). See the provided PDF on more information about the CC-BY-4.0.</p> <p>Version 1 contained an error in the global transformation matrix. Version 2 corrects this.</p>
X-ray radiography 4D particle tracking of heavy spheres suspended in a turbulent jet
<p>This database report 3d trajectories of heavy spheres suspended in a turbulent upward jet. A cylindrical tank is filled with water and the jet nozzle is placed on its axis on the bottom wall, and a constant flowrate (Q) of water is fed through the nozzle. Conditions at 1700 and 2200 mL/min are considered, and the number of spheres is varied between 1 and 12 (Nsphere). The spheres are glass and are detected using X-ray radiography at 60Hz. The 4d kinematics are obtained with this setup using radioSphere (E. Ando et<br> al., Measurement Science and Technology, 32(9), 095405, 2021). Each condition has a series of files named based on the number of spheres in the tank Nsphere and the flowrate Q, with each sphere of index isphere having its own file. Each file is 3 columns of doubles representing the 3d coordinates x, y, and z of the sphere, in mm, where z is the axis of the cylinder and the points up, against gravity.</p> <p>Results from this database are published here: https://doi.org/10.1016/j.ijmultiphaseflow.2023.104406<br> O. Stamati, B. Marks, E. Ando, S. Roux, N. Machicoane, X-ray radiography 4D particle tracking of heavy spheres suspended in a turbulent jet, <em>International Journal of Multiphase Flow</em> 162, 104406, 2023.</p>
Simulated calibration dataset for 4D scanning transmission electron microscopy
<p>4D-STEM data frequently requires a number of calibrations in order to make accurate measurement: for instance, in various cases, it can be essential to measure and correct for diffraction shifts, account for ellipticity in the diffraction patterns, or determine the rotational offset between the real and diffraction planes.</p> <p>We've prepared a simulated 4D-STEM dataset which includes diffraction shifting, elliptical distortion, and an r-space/k-space rotational offset. Two HDF5 files each include the simulated data for two different electron probes: a standard probe, using a circular probe-forming aperture, and a 'bullseye' probe, using a patterned aperture. Each HDF5 file contains the following data objects:</p> <p>(a) the 'experimental' 4D-STEM scan of a strained single-crystal gold nanoparticle (size: (100,84,250,250) )</p> <p>(b) a 4D-STEM scan of a calibration sample of polycrystalline gold (size: (100,84,250,250) )</p> <p>(c) a stack of diffraction images of the electron probe over vacuum (size: (250,250,20) )</p> <p>(d) a single image of the electron probe over the sample and far from focus, such that the CBED forms a shadow image (size: (512,512) )</p>
4D cone beam computed tomography phantom data set
<p>This data set accompanies the following Medical Physics publication: <a href="https://doi.org/10.1002/mp.14441"><i>Madesta, F., Sentker, T., Gauer, T., & Werner, R. (2020). Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction. Medical Physics, 47(11), 5619-5631</i></a><i>.</i></p><p>It comprises 6 time-resolved (4D) cone-beam computed tomography scans with the following scan configurations:</p><ul><li>4D CBCT Scanner: Varian TrueBeam (the detailed scan geometry and further details can be found in Scan.xml included in each scan)</li><li>Phantom: <a href="https://www.cirsinc.com/products/radiation-therapy/dynamic-thorax-motion-phantom/">Dynamic Thorax Phantom: Model 008A</a></li><li>The following motion patterns are included:<ol><li>SI amplitude of insert: ±10mm, pattern: sin, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 7.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 7.5s</li></ol></li></ul>
Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Training Data)
<p><strong>Overview </strong></p> <p>This repository contains 742,688 samples of simulated Convergent Beam Electron Diffraction patterns (CBEDs); the training data for the paper <a href="https://arxiv.org/abs/2202.12611">"Phase Object Reconstruction for 4D-STEM using Deep Learning"</a>. The folder contains multiple hdf5 datasets. Each dataset has a corresponding Excel-sheet containing detailed information and simulation parameters for every datapoint, as well as a summary-report containing the parameter distributions, hdf5-infos and random number generator settings. This makes every dataset reproducible, using the simulation codes provided in <a href="https://github.com/ThFriedrich/ap_data_generation">https://github.com/ThFriedrich/ap_data_generation</a>.</p> <p><strong>Technical details</strong></p> <p>Every Datapoint consists of a 3x3 set of adjacent Convergent Beam Electron Diffraction pattern (CBEDs), the coherent exit wave phase and amplitude in real and reciprocal space, and the probe functions phase and amplitude in real space. All patterns are 64x64 pixel in 16 bit unsigned integer data format.</p> <p>Every hdf5 file has the following structure:</p> <table> <tbody> <tr> <td>Attributes</td> <td>'Seed': 6108236<br> 'State': 251786606 ...<br> 'Type': 'twister'<br> 'arch': 'glnxa64'<br> 'gpu': 'NVIDIA GeForce RTX 3080'<br> 'matlab_ver': '2021a'</td> </tr> <tr> <td>Dataset 'features'</td> <td> <p>Size: 64x64x9x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'labels_k'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'labels_r'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'probe_r'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'meta'</td> <td> <p>Size: 19x5000<br> Datatype: H5T_IEEE_F32LE (single)</p> </td> </tr> </tbody> </table> <p>The data was written to hdf5 in matlab. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly. The integer arrays were scaled to use the full range of the uint16 datatype. The scaling values are stored under "meta". To restore the original values in floating point numbers, convert the arrays like this:</p> <p>Matlab:</p> <pre><code>hdf_file = ['db_h5_b_5_Training.h5']; n = 128; % load `n` k-space exit waves x = single(h5read(hdf_file, '/labels_k', [1,1,1,1], [64,64,2,n])); % `meta` contains parameters and scaling factors for a given datapoint in following order: [E_0(keV), cond_lens_outer_aper_ang(mrad), collection angle(rA), step_size(A), scale_cbed_1 ... scale_cbed_9, scale_phase_k, scale_amp_k, scale_phase_r, scale_amp_r, scale_probe_phase_r, scale_probe_amp_r] s = h5read(hdf_file, '/meta', [14,1], [2,n]); amplitude = zeros(64,64,n); phase = zeros(64,64,n); for ix = 1:n phase(:,:,n) = (x(:,:,1,n)*s(1,ix) / 65536) - pi; amplitude(:,:,n) = (x(:,:,2,n)*s(2,ix)) / 65536; end % The 9 CBEDs correspond to a 3x3 kernel of patterns. The order in [x,y] is: %[[3, 6, 9]; % [2, 5, 8]; % [1, 4, 7]] </code></pre>
Nanoscale mapping of point defect concentrations with 4D-STEM
<p>The following 4D-STEM data sets were collected on the ThemIS and TitanX scanning transmission electron microscopes located at the National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. </p> <p>1• Au_thermal_beforeHT_17C.dm4, Au_thermal_HT_800C.dm4, Au_thermal_HT_1000C.dm4 and Au_thermal_afterHT_17C.dm4 are datasets from 4D-STEM measurements conducted <em>in situ</em> on an FEI ThemIS image corrected microscope at 300 kV during a thermal cycling experiment. These digital micrograph (.dm4) files were collected at 17 C before heat treatment, 800 C during heat treatment, 1000 C during heat treatment, and 17 C after heat treatment, respectively. Nano-diffraction data was collected using a Gatan K2-IS (2k x 2k) detector at 400 frames per second. Each dataset contains a set of electron diffraction patterns taken at each scan position with a ~ 1 nm probe step size. Approximately 80 x 80 scan positions were recorded from each region with a dwell time of 0.0025 seconds per frame. A custom 40µm patterned “bullseye” circular probe forming aperture was used to enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 3.20 milli-radians, spot size of 8, and diffraction pixel size of 0.16 Å<sup>-1</sup> was used in micro-probe lens configuration. The data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Data processing were performed using strain mapping scripts provided in the open source py4DSTEM software package. Au_thermal_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.</p> <p>2• Al_irradiated.dm4 is a dataset from 4D-STEM measurement conducted <em>in situ </em>on an FEI TitanX microscope equipped to do high-angle STEM tomography and operating at 300 kV. Nano-diffraction data was acquired using a Gatan Orius 830 (2k x 2k) detector capable of collecting 30 frames per second. Each dataset contains a stack of convergent beam electron diffraction (CBED) patterns taken at each scan position with maximum resolution equivalent to 1.6 nm probe size. Approximately 50 x 50 frame scan regions were recorded with a dwell time of 0.01 seconds per frame. A custom 70 µm patterned “bullseye” circular C2 aperture was used to greatly enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 2.7 milli-radians, spot size 10, and camera length 195 mm was used in micro-probe lens configuration. With a measured screen current of 300 pA in this configuration, the total sum of electrons incident in a region of the sample, commonly known as the fluence (total dose), was determined at 67,100 electronsÅ<sup>-2</sup> per 4D-STEM scan. The 4D-STEM data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Al_irradiated_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.</p>
Free-Breathing Self-Gated 4D-Lung MRI using wave-CAIPI
<p>MRI raw data set of a volunteer examination for the project "Free-Breathing Self-Gated 4D-Lung MRI using wave-CAIPI". The upload includes the measured k-space for 8 different breathing phases, the respective image reconstructions and the coil sensitivity maps required for the Conjugate Gradient SENSE reconstruction.</p> <p>C++ source code for image reconstruction can be downloaded at <a href="https://github.com/expRad/4d_lung">https://github.com/expRad/4d_lung</a>.</p> <p> </p>
Research data supporting "4D Electron Tomography of Dislocations Undergoing Electron Irradiation"
<p>All TEM micrographs and electron diffraction patterns for tomography</p>
Figure 4d. from: A first integrative study of the identity and origins of the British Dwarf Pill Millipede populations, Trachysphaera cf. lobata (Diplopoda, Glomerida, Glomeridae) - Biodiversity Data Journal 3: e5176 (09 June 2015) https://doi.org/10.3897/BDJ.3.e5176
Figure 4d. - Habitus. Preserved Trachysphaera specimens from two locations, multi-layer photographs, showing high variation in colour and/or encrusted dirt. Not to scale; for further specimen information see Checklists.Figure 4a.TW15: T.lobata, male, Isle of WightFigure 4b.TW18: T.lobata, female, Isle of WightFigure 4c.TW20: T.lobata, male, Isle of WightFigure 4d.TW26: T.lobata, female, South WalesFigure 4e.TW31: T.lobata, female, South WalesFigure 4f.TW32: T.lobata, female, South Wales <br> TW32: T.lobata, female, South Wales
Figure 4d. from: Three new species of Ametadoria Townsend (Diptera: Tachinidae) from Area de Conservación Guanacaste, Costa Rica - Biodiversity Data Journal 3: e5039 (10 August 2015) https://doi.org/10.3897/BDJ.3.e5039
Figure 4d. - Ametadoriamauriciogurdianisp. nov. a-c: holotype male; d-f: paratype female (DHJPAR0030011)Figure 4a.Habitus, dorsalFigure 4b.Habitus, lateralFigure 4c.Head, frontalFigure 4d.Habitus, dorsalFigure 4e.Habitus, lateralFigure 4f.Head, frontal <br> Habitus, dorsal
Figure 4d. from: Two new species of Scymnini (Coleoptera: Coccinellidae) from Karnataka, India - Biodiversity Data Journal 3: e5296 (22 June 2015) https://doi.org/10.3897/BDJ.3.e5296
Figure 4d. - Diagnostic characters of Horniolussororius sp. n.Figure 4a.Abdominal postcoxal lineFigure 4b.Male genitalia: Tegmen, ventral viewFigure 4c.Male genitalia: Tegmen, ventral viewFigure 4d.Male genitalia: PenisFigure 4e.Male genitalia: Penis apexFigure 4f.Female genitalia: Spermatheca <br> Male genitalia: Penis
Cryo-4D-STEM datasets on cells and cellular organelles for demonstrating a dose-Efficient cryo-EM technique: tilt-Corrected Scanning Transmission Electron Microscopy
<p>This upload contains three 4D-STEM datasets in .raw format for demonstrating a dose-efficient cryo-EM technique for thick samples: tilt-corrected Scanning Transmission Electron Microscopy (tcBF-STEM). The dataset dimension is 128130256*256. Data were acquired on vitrified intact E.coli cells and isolated human cell organelles. This upload also contains the EFTEM images in .mrc acqired in the same ROI as the 4D-STEM dataset. </p> <p>It also contains analysis of the manuscript's Fig 3 and Ext. data fig 8. </p>
The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"
<p>The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"</p><p>A video file (minimum play resolution is HD to see the mesh) showing the movement of the LCA throughout the heart cycle and .STL files for 10--100% (increment of 10\%) of the heart cycle phase.</p>
MerlinEM quad 4D-STEM dataset
<p>4D-STEM data collected from au-xgrating sample using nano-beam probe on <a href="https://diamondlightsource.atlassian.net/wiki/spaces/EPSICWEB/pages/1511758/ePSIC+Instruments">E02 microscope</a> at ePSIC, Diamond Light Source</p> <p>Grand ARM300F - 300 kV</p> <p>10 um CL aperture</p> <p>~3 msec dwell time. Triggering by AZTEC scan engine with some variation in exposure times.</p> <p>Sample: 500 nm pitch gold cross-grating</p>
Nanobeam 4D-STEM raw data of monolayer WS2-WSe2 lateral heterojunctions
<p>The sample under investigation comprises a monolayer WS2-WSe2 lateral heterojunction featuring in-plane epitaxial interfaces. The datasets were acquired using an electron microscope pixel array detector (EMPAD) at Cornell University in 2016, with the following specifications: Magnification: 27.5 kx, Convergence angle: 1.2 mrad, C2 aperture size: 70, Spot size: 9. This dataset is from the same batch of datasets referenced in the paper by Han et al., Nano Letters 18, 3746-3751 (2018). Additionally, data from the same batch are also cited in the publication by Shi et al., npj Computational Materials 8, 114 (2022). Further details regarding the materials synthesis can be found in the paper by Xie et al., Science 359, 1131-1136 (2018). </p>
Mechanochemical induced swelling-activation of a gastric-deployable 4D printed polypill inspired by natural hygromorphic actuators - Underlying data
<p>Underloying microfocus CT data of <em><strong>Mechanochemical induced swelling-activation of a gastric-deployable 4D printed polypill inspired by natural hygromorphic actuators</strong></em> by Konstantina Chachlioutaki, Nikolaos Papas, Zisis Chatzis, Orestis L. Katsamenis, Stephanie K. Robinson, Konstantinos Tsongas, Nikolaos Bouropoulos, Dimitrios G. Fatouros, Dimitrios Tzetzis, Christina Karavasili</p> <ul> <li>K. Chachlioutaki, Z. Chatzis, D.G. Fatouros, C. Karavasili<br>Laboratory of Pharmaceutical Technology, Department of Pharmacy, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece</li> <li>N. Papas, D. Tzetzis, C. Karavasili<br>Digital Manufacturing and Materials Characterization Laboratory, School of Science and Technology, International Hellenic University, 57001 Thermi, Greece</li> <li>O.L. Katsamenis, S.K. Robinson<br>μ-VIS X-Ray Imaging Centre, Faculty of Engineering and Physical Sciences, University road, Highfield campus, Southampton, SO17 1BJ, UK<br>Institute for Life Sciences, University of Southampton, University road, Highfield campus, Southampton, SO17 1BJ, UK</li> <li>K. Tsongas<br>Department of Industrial Engineering and Management, School of Engineering, International Hellenic University, 57001 Thessaloniki, Greece</li> <li>N. Bouropoulos<br>Department of Materials Science, University of Patras, 26504 Patras, Greece</li> </ul> <p> </p>
Development of the CMA-GFS-AERO 4D-Var assimilation system v1.0- Part 1: System description and preliminary experimental results
<table> <tbody> <tr> <td> <p><span>DA_BC</span></p> </td> <td> <p><span>Assimilating only BC surface observations while excluding operational meteorological observations</span></p> </td> </tr> <tr> <td> <p><span>DA_MET</span></p> </td> <td> <p><span>Assimilating only operational meteorological observations while excluding BC surface observations</span></p> </td> </tr> <tr> <td> <p><span>DA_MET_then_BC</span></p> </td> <td> <p><span>First assimilating operational meteorological observations, then assimilating BC surface observations</span></p> </td> </tr> <tr> <td> <p><span>DA_MET_BC_simult</span></p> </td> <td> <p><span>Assimilating both operational meteorological and BC surface observations </span><span>simultaneously</span></p> </td> </tr> <tr> <td>code.zip</td> <td>Including black carbon observation preprocessing codes, black carbon observation operator codes, black carbon module tangent and adjoint interface codes, the CMA-GFS-AERO 4D-Var main program,and drawing script.</td> </tr> </tbody> </table>
Real Time Integration Center of Mass (riCOM) Reconstruction for 4D-STEM
<p>The datasets are a part of the publication: <strong>Real Time Integration Center of Mass (riCOM) Reconstruction for 4D-STEM</strong></p>
Cryo 4D-STEM Data Set: isotactic Polypropylene/ Ethylene-Octene Copolymer Interface
<p><strong>Sample:</strong> Data is taken of a iPP/EO (DOW ENGAGE<sup>TM</sup> 8540) blend interface. The blend was cryo-microtomed to a nominal slice thickness of 50nm. Transmission electron microscopy was performed using the TEAM I microscope at the Lawrence Berkeley National Laboratory using a Gatan K3 detector and Continuum spectrometer.</p> <p><strong>Data Set 12: </strong>This work was performed at -185°C under liquid nitrogen cooling with a 300kV accelerating voltage and a semi-convergence angle of 0.5mrad which yielded a diffraction-limited probe with a full-width half-max of 2nm. The beam was rastered with a step size of 5nm over a 505 × 500nm<sup>2</sup> field of view. The electron dose per sample area over the entire scan is 20 e<sup>-</sup>/Å<sup>2</sup>. However, 4D-STEM is a converged probe technique in which ~80% of the beam fluence is contained within 1.74 × the FWHM of the probe. Using 1.74 × the FWHM of the probe as the diameter to calculate the irradiated sample area yields a dose of 60 e<sup>-</sup>/Å<sup>2</sup> for this data set. Gold nanoparticles were used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.</p> <p><strong>Data Set 18: </strong>This data was taken at -185°C under liquid nitrogen cooling with a 300kV accelerating voltage and a semi-convergence angle of 0.14mrad which yielded a diffraction-limited probe with a full-width half-max of 10nm. The beam was rastered with a step size of 10nm over a 1.4 × 1.4μm<sup>2</sup> field of view. The electron dose per sample area over the entire scan was 0.50 e-/Å<sup>2</sup> while the dose per probe area was 0.64 e-/Å<sup>2</sup>. Gold nanoparticles were used to calibrate the reciprocal space pixel size.</p> <p><strong>Data Set 19: </strong>This data was taken over the exact same area as Data Set 18 to give a comparison under larger dose accumulation. The data was obtained with the exact same parameters and under the same experimental conditions as Data Set 18.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.