Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
27
datasets available to search
ShareScore release 0.9.0
Dataset results
27 results for “4D STEM”
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>
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>
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>
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>
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>
Simulated 4D WSe2 STEM simulation
<p>This file is a simulated 4D STEM data set of defective WSe2 with a slightly aberrated electron probe. It is a demo data for the software pyptychostem</p>
Exploring deep learning models for 4D-STEM-DPC data processing
<p>This repository contains scanning transmission electron microscopy data and processing files used in the journal publication <strong>"Exploring deep learning models for 4D-STEM-DPC data processing"</strong>. DOI: <a href="https://doi.org/10.1016/j.ultramic.2024.114058">10.1016/j.ultramic.2024.114058</a></p> <p><strong>Prerequisites</strong></p> <p>The scripts presented below require certain open-source Python packages to run. Library versions used to run the scripts are:</p> <ul> <li>hyperspy 1.7.1</li> <li>pyxem 0.14.2</li> <li>fpd 0.2.5</li> <li>pytorch 1.12.1 (cudatoolkit 11.6.0)</li> <li>jupyterlab 4.0.7</li> </ul> <p><strong>Data files</strong></p> <p>Three zipped folders are included. Two of them contain the training- and inference data for the neural networks, aptly named <em>training_data.zip</em> and <em>inference_data.zip</em>. PyTorch state dictionaries for trained models are included in the <em>models.zip</em> folder.</p> <p><strong>Processing scripts</strong></p> <p>All scripts are included in an IPython notebook format (.ipynb extension). The notebooks <em>Segmentation.ipynb</em> and <em>Regression.ipynb</em> contain the code for training and inference of the segmentation and regression models, respectively. The <em>Training_data_creation.ipynb<strong> </strong></em>notebook contains the code to preprocess the training data for both neural network models. The <em>Standard_algorithms.ipynb</em> notebook has the code for doing center of mass and edge filtering/disc detection algorithms for STEM-DPC processing.</p>
4D STEM dataset - DECTRIS ARINA detector - SmB6 sample
<p>4D STEM dataset recorded with DECTRIS ARINA detector.</p> <p>Sample is a monocrystalline domain of SmB6 oriented along the <110> zone axis, prepared with FIB by Elisabeth Mueller at PSI.</p> <p>Data collection was with a probe-corrected 200kV TEM microscope, supported by Mingjian Wu at FAU.</p> <p>Further experimental parameters are listed with the included txt file.</p> <p>Data visualization and processing can be done with NOVENA software, freely available at DECTRIS website.</p> <p>Alternatively, the files can be opened using a HDF5 file reader.</p> <p> </p>
Pyxem 4D STEM Demo Data
<p>These are some example data files for doing 4D STEM using pyxem. </p>
Dataset for "Studying GPI zones in Al-Zn-Mg alloys by 4D-STEM"
<p>This dataset contains the data used in the publication titled "Studying GPI zones in Al-Zn-Mg alloys by 4D-STEM" currently in review in Materials Characterization.</p> <p>The data in this dataset are:</p> <ul> <li>Raw SPED data</li> <li>All structural models used in the density functional theory (DFT) calculations</li> </ul> <p>The raw SPED data are given as .mib- and .hdr files and can be opened using e.g. the Python package HyperSpy. The jupyter notebook used to analyse the data is available from <a href="https://doi.org/10.5281/zenodo.5518852">10.5281/zenodo.5518852</a>. A total of five SPED datasets were used in the analysis and are included in the .zip-file.</p> <p>The DFT calculations are given in the OUTCAR files. OUTCAR1 contains all the information about the initial relaxation. OUTCAR2 contains all the information about the final relaxations steps at a higher accuracy than OUTCAR1. OUTCAR3 contains all the information about the accurate energy calculations. </p> <p> </p>
Pre-processed raw data of 4D-SCED 4D-STEM(NBD) datasets
<p>The raw 4D-SCED and 4D-STEM (NBD) datasets in our paper:</p> <p>Seeing Structural Evolution of Organic Molecular Nano-crystallites Using 4D Scanning Confocal Electron Diffraction</p> <p>https://arxiv.org/abs/2110.02373v1</p> <ul> <li>Dose-damage_RT_and_Cryo_DRCN5T_PCBM.zip<br> - EF-Diff_cryo_0.16e_per_A2_per_frame.dm4<br> - EF-Diff_RT_0.14e_per_A2s_per_frame.dm4</li> <li>Fig_2_SCED-vs-NB_DH6T.zip<br> - 00.43.44 Spectrum image_1.dm4: raw 3D diffraction pattern stack (NBD) of size 512*512*2500, only converted file format, no other pre-processing applied<br> - 00.54.07 Spectrum image_1.dm4: raw 3D diffraction pattern stack (SCED) of size 512*512*2500, only converted file format, no other pre-processing applied<br> - STEM HAADF 0103.emd: Velox raw image of the region of interest acquired at the end of experiment.<br> - STEM HAADF 0103 data.jpg: exported JPG image with databar from the above raw image file</li> <li>Fig_3n4_4D-SCED-vs-4D-NBD_DRCN5T_PCBM.zip<br> - 1201 4D STEM Dataset SCED.dm4: pre-processed 4D-SCED dataset<br> - 1209 4D STEM Dataset NBD.dm4: pre-processed 4D-STEM (NBD) dataset</li> <li>Fig_5_insitu_4D-SCED_datasets.zip<br> - 1431 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ RT<br> - 1431 SI HAADF RT.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1431 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1455 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 100C<br> - 1455 SI HAADF 100C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1455 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1505 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 120C<br> - 1505 SI HAADF 120C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1505 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1516 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 140C<br> - 1516 SI HAADF 140C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1516 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file<br> - 1529 4D STEM Dataset.dm4: pre-processed 4D-SCED dataset @ 160C<br> - 1529 SI HAADF 160C.emd: simultaneously acquired SCEM-ADF image and STEM-EDXS datacube<br> - 1529 SI HAADF.jpg: exported JPG image of the SCEM-ADF image from the above file</li> <li>STEM-EELS_DRCN5T_PCBM_SVA_CS.zip<br> - ADF Image (SI survey).dm4: simultaneously acquired STEM-ADF image<br> - EELS Spectrum Image (high-loss).dm4: high-loss part of the DualEELS STEM-EELS dataset<br> - EELS Spectrum Image (low-loss).dm4: low-loss part of the DualEELS STEM-EELS dataset</li> <li>STEM-EELS_DRCN5T_PCBM_SVA_CHCl3.zip<br> - ADF Image (SI survey).dm4: simultaneously acquired STEM-ADF image<br> - EELS Spectrum Image (high-loss).dm4: high-loss part of the DualEELS STEM-EELS dataset<br> - EELS Spectrum Image (low-loss).dm4: low-loss part of the DualEELS STEM-EELS datase</li> </ul> <p> </p>
Mixed nanoparticle 4D-STEM data
<p>Gold, silica and iron oxide nanoparticle sample studied with 4D-STEM. Data acquired at the NUANCE center on the JEOL ARM300 taken with the Gatan K3-IS camera. </p> <p> </p> <p> </p>
Multiscan 4D-STEM datasets on molecular crystals: biotin and Ni(dppf)Cl2
<p>This repository contains data from scanning nanobeam electron diffraction experiments acquired using the 4D Camera installed on the double-aberration-corrected TEAM 0.5 instrument at NCEM. </p> <p>Before data acquisition, the incident beam current was reduced to the detection threshold of the fluorescent screen ammeter (<40 pA) by using the monochromator focus as a continuously adjustable gun lens. Beam flux was estimated from previous reference measurements using a Faraday cup and an ammeter under similar conditions.</p> <p>A near-parallel probe was formed by using a custom 10 μm C2 aperture (Norcada) to access a range of narrow (<0.1 mrad) semiconvergence angles. Using a custom DigitalMicrograph script, multiscan 4D-STEM data were acquired with the 4D Camera operating at 87000 frames per second. As an illustrative example, each biotin dataset is comprised of 512×512 probe positions and 576×576 detector pixels, resulting in 174 GB of raw data per scan. These raw data were counted and reduced to a sparse, linear-index-encoded electron event representation (EER) in HDF5 format on the NERSC supercomputer, Perlmutter, using the open-source software package <a href="https://github.com/OpenChemistry/stempy">stempy</a>. These <a href="https://stempy.readthedocs.io/en/latest/StempyH5Format.html">sparse HDF5 files</a> can be interactively viewed using our open-source python-based software, <a href="https://github.com/ercius/DuSC_explorer/">DuSC Explorer</a>.</p> <p>Each directory contains a time series of several consecutive 4D-STEM scans acquired on the same molecular crystal (25 for biotin, 300 for Ni(dppf)Cl2), alongside a sequence of simultaneously acquired HAADF images.</p> <p>Experimental parameters for biotin:</p> <table> <tbody> <tr> <td>Accelerating voltage</td> <td>300 kV</td> </tr> <tr> <td>Semiconvergence angle</td> <td>0.07 mrad</td> </tr> <tr> <td>Scan dimensions</td> <td>512×512</td> </tr> <tr> <td>Real-space step size</td> <td>9.22 nm</td> </tr> <tr> <td>Estimated probe size (FWHM)</td> <td>14.4 nm</td> </tr> <tr> <td>Total illuminated area</td> <td>11.2 μm^2</td> </tr> <tr> <td>Estimated fluence per probe position</td> <td>0.15 e- Å^(-2)</td> </tr> <tr> <td>Total size of raw data</td> <td>4350 GB</td> </tr> <tr> <td>Total size of sparsified data</td> <td>2.18 GB</td> </tr> </tbody> </table> <p> </p> <p>Experimental parameters for Ni(dppf)Cl2:</p> <table> <tbody> <tr> <td>Accelerating voltage</td> <td>200 kV</td> </tr> <tr> <td>Semiconvergence angle</td> <td>0.07 mrad</td> </tr> <tr> <td>Scan dimensions</td> <td>256×256</td> </tr> <tr> <td>Real-space step size</td> <td>15.1 nm</td> </tr> <tr> <td>Estimated probe size (FWHM)</td> <td>18.4 nm</td> </tr> <tr> <td>Total illuminated area</td> <td>7.55 μm^2</td> </tr> <tr> <td>Estimated fluence per probe position</td> <td>1.5 e- Å^(-2)</td> </tr> <tr> <td>Total size of raw data</td> <td>13200 GB</td> </tr> <tr> <td>Total size of sparsified data</td> <td>9.93 GB</td> </tr> </tbody> </table> <p> </p>
Digital vs analog DPC and 4D-STEM detector frame rates
<p>These are raw data sets (.dm3 in zipped folders) from University of Tokyo, and overview of the detector frame rates from datasheets of pixelated detectors (.csv). Used for the paper titled "Ultra-fast Digital DPC Yielding High Spatio-Temporal Resolution for Low-Dose Phase Characterisation" (preprint available on arXiv: <a title="Ultra-fast Digital DPC Yielding High Spatio-Temporal Resolution for Low-Dose Phase Characterisation" href="https://arxiv.org/abs/2405.06367" target="_blank" rel="noopener">https://arxiv.org/abs/2405.06367</a>).</p>
4D STEM acquisition on an atom probe specimen of an ultrafine grain Fe-51.4at% Cr alloy
<p>Material: Fe-51.4at% Cr alloy processed by high pressure torsion </p> <p>Microscope: JEOL F 200<br>Accelration voltage: 200 kV<br>Aperture: 10 µm<br>Camera length: 200 mm<br>Detector size in pixel: 512 x 512<br>Scanned area size in pixel: 250 x 150<br>Scanned area size in nm2: 625 x 375</p>
Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwelltime and faster scans for high precision and low dose applications
<p>The data sets and the processing scripts are a part of the publication: <strong>Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwelltime and faster scans for high precision and low dose applications (</strong><a href="https://arxiv.org/abs/2107.02864">arXiv:2107.02864</a><strong>). </strong>The programming language used to process the data is written with python 3. </p> <p>In this work, a Timepix3 detector is used to perform 4D STEM experiments where instead of a frame based detection, a event driven approach is used. The experiments where performed on a probe-corrected FEI Themis Z with an acceleration voltage of 60 and 200 kV. The files provided show how to get the figures from the publication starting from the raw .t3p data. All intermediate data is added to reduce the computing time and give the ability to retrieve the images without the need to perform all the calculations.</p> <p>The .yml file contains the necessary package needed to perform the data analysis. For the single-sideband ptychographic reconstruction, the gitlab repository https://gitlab.com/pyptychostem/pyptychostem is used.</p> <p> </p> <p> </p> <p> </p>
Synthetic 4D STEM dataset based on a SrTiO3 supercell with two additional artificial spatial frequencies
<p>This dataset allows to investigate phase contrast methods for 4D scanning transmission electron microscopy, such as ptychography.</p> <p>A synthetic dataset has been simulated, based on an SrTiO<sub>3</sub> unit cell as a starting point. Then, a five by five super cell was created by repetition. Two artificial spatial frequencies were added to the phase grating, one with a wavelength of a single unit cell and one with a wavelength of the super cell. To eliminate dynamical scattering, a 4D-STEM simulation with 20 × 20 scan points per unit cell was performed using only one slice with a thickness of one unit cell along electron beam direction [001].</p> <p><strong>Files</strong></p> <ul> <li><em>conf_01.mat</em>: HDF5 file with the phase grating.</li> <li><em>Data extraction and plot of the phase grating.ipynb</em>: Jupyter notebook showing how to access the phase grating file and plot the data.</li> <li><em>slice_00001_thick_1.9525_nm_blocksz100.raw</em>: Simulated 4D STEM dataset as a raw binary file. Shape 100 x 100 x 596 x 596, dtype float32.</li> <li><em>ssb-example.ipynb</em>: Jupyter notebook showing first moment analysis and ptychography with the dataset.</li> </ul> <p><strong>Simulation parameters</strong></p> <ul> <li>Scan points: 100x100</li> <li>Field of view: 1.9525nm</li> <li>Convergence angle: 23mrad, 136 px</li> <li>Acceleration voltage: 300 kV</li> <li>Center: (297, 297)</li> <li>Rotation angle: 0°</li> </ul>
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.