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.
40
datasets available to search
ShareScore release 0.9.0
Dataset results
40 results for “transmission electron microscopy”
Short-Exposure Transmission Electron Microscopy of Cilia
<p>Noisy and pseudo ground-truth short-exposure transmission electron microscopy (TEM) images of Cilia used to obtain the results depicted in Fig. 3 in the paper "Zero-Shot Denoising of Microscopy Images Recorded at High-Resolution Limits" (Salwig & Drefs et al., 2024). The images were derived based on a dataset provided upon personal communication with the authors of the paper "Denoising of Short Exposure Transmission Electron Microscopy Images For Ultrastructural Enhancement" (Bajíc et al., 2018). </p> <p>The original dataset consisted of a sequence of 100 noisy short-exposure TEM images of a scene showing a cilium. The images had a resolution of 2048 × 2048 pixels, and each image depicted a slightly shifted version of the scene. The file pseudo-ground-truth.tif was obtained by first aligning all images of the sequence using rigid registration (Marstal et al., 2016) and subsequently computing the pixel-wise median (following a procedure discussed in Bajíc et al., 2018). The file noisy.tif was obtained by randomly selecting one image from the sequence (the 91st image).</p> <p>The images are stored in 16 bit TIF format. For visualization, use an image viewer capable of reading 16 bit images (e.g. ImageJ).</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>
Dataset for Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy
<p>Raw and processed image data resulting from the paper "Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy", by S. Mitchell, F. Parés, D. Faust Akl, S. M. Collins, D. M. Kepaptsoglou, Q. M. Ramasse, D. Garcia-Gasulla, J. Pérez-Ramírez, and N. López (JACS, 2021). </p> <p>The corresponding code can be found under: <a href="https://github.com/HPAI-BSC/AtomDetection_ACSTEM">GitHub - HPAI-BSC/AtomDetection_ACSTEM</a></p>
Raw datasets for work on aberration-corrected transmission electron microscopy with Zernike phase plates
<p>Raw data (electron microscopy images and spectra) obtained for work on aberration-corrected transmission electron microscopy with Zernike phase plates as published in Ultramicroscopy.</p>
Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation
<p>Scanning transmission electron microscopy data related to paper "Scanning transmission electron microscopy data related to paper "Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation", <a href="https://doi.org/10.1017/S1431927620024307">https://doi.org/10.1017/S1431927620024307</a>.</p>
Transmission Electron Microscopy Dataset for Image Deblurring
<p>The dataset consists of images corrupted by motion blur together with corresponding high-quality images from two different samples, one of thin sectioned kidney tissue and one of a calibration grid. The data was collected using a MiniTEM microscope (Vironova AB). The motion corrupted images are created by moving the sample under the microscope. Each low-quality (motion blurry) imaging sequence has corresponding high-quality images (captured by stopping the microscope at each position in the sequence). The high-quality frames have a size of 2048 x 2048 pixels with an overlap of 50% between adjacent frames. The low-quality (motion blurry) frames are captured with a size of 1024x1024 with the same motion direction (approximately vertically upwards). All images were captured at a field of view of 32μm, and with a per image exposure time of 15ms and stored as 16 bit tiff files. Both samples are imaged with the same settings and have four imaging sequences each.</p> <p>The dataset contains the raw image files as well as a partitioning into training, validation and testing. For these images, five low-quality images have been registered to each high-quality image. For the five registered images the intersection of all is cropped and stored. 1 of the 4 imaging sequences are chosen as the test set and the last part of another of the imaging sequences as a validation set. The rest is put in the training set.</p> <p><em><strong>Folder Structures:</strong></em></p> <ul> <li><strong>Raw data:</strong> <ul> <li>Raw data is the unprocessed data and each sample folder contains 4 image sequences. In each of these folders low-quality (motion blurry) images are stored in folder “Low” and corresponding high-quality images are stored in “GT”</li> </ul> </li> <li><strong>TrainValTest:</strong> <ul> <li>TrainValTest consist of data where the low-quality frames have been registered to the high-quality frames and divided into a training, validation and test set.</li> <li>Each of the Train, Val, Test folders contains 3 subfolders. “Low” contains folders names the same as the files in “GT” where each folder contains five low-quality (motion blurry) images, registered the that corresponding high-quality image. “GT” contains the corresponding high-quality images down sampled to the same spatial size as the low-quality images. “GT_hr” contains the same images as “GT” but not down sampled.</li> </ul> </li> </ul>
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>
Nanoparticle Size Estimation by Scanning Transmission Electron Microscopy and Generative AI
<p>The "raw" directories contain unaltered simulated and experimental data. The train and val directories contain normalized data used to train the models of the manuscript. The dataframes directory contains all information about the atomic models. Exp info contains info about the raw experimental data (excluding the gas-cell data). </p>
Dataset of "Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network"
<p>Dataset containing the jupyter notebook used to construct the database of image, to model and train ANN and to analyze the experimental data. Furthermore there are also a reduced database of 100 images that can be utilized to test the ANN, the h5 file containing the ANN weigths and other supporting files.</p>
Dataset of "Theoretical and practical aspects of the design and production of synthetic holograms for transmission electron microscopy"
<p>Dataset with script and article images published in https://doi.org/10.1063/5.0067528</p>
Dominance of Auger excitation in beam heating in transmission electron microscopy: Irradiation experiments and quantitative thermal analysis of α-Al2O3
<p>The collection of uploaded files constitutes a dataset supporting our findings, titled Dominance of Auger excitation in beam heating in transmission electron microscopy: Irradiation experiments and quantitative thermal analysis of α-Al<sub>2</sub>O<sub>3</sub>, to be submitted to a scientific journal.</p> <p>The co-authors are Jihye Kwon and Hyoung Seop Kim, both at Pohang University of Science and Technology (POSTECH), Republic of Korea</p>
Segmented high-resolution transmission electron microscopy images of nanoparticles
Open the record for dataset details and reuse information.
Transmission electron microscopy (TEM) images of Silisyum dioxide nanoparticles.
<p>Transmission electron microscopy (TEM) images of Silisyum dioxide nanoparticles.</p>
Soot particle images obtained by Environmental Transmission Electron Microscopy
<p>Transmission electron microscopy images used to generate frame sequences and calculate particle convexities, along with the associated script. The fractal soot particles were produced by combustion, lightly coated by sulfuric acid, deposited on SiN substrate, and then their response to humidification and drying was studied in a controlled microenvironment of a closed cell, using environmental transmission electron microscopy. </p>
Supplementary material for: Sparse Arrays for Four-Dimensional Scanning Transmission Electron Microscopy
<p>Supplementary material for: Sparse Arrays for Four-Dimensional Scanning Transmission Electron Microscopy</p> <p>The link to the main publication with detailed information will be added later.</p> <p>The three videos show liv eand offline processing with the CheeTah T3, ASI Serval, ASI Accos, LiberTEM and CEOS Panta Rhei. They are also available at https://www.youtube.com/playlist?list=PLZCH_qD2RkB5oFdBc12xcYUcbmWZpJQHh</p> <p>benchmark.ipynb is a Jupyter notebook that was used to test the performance, as reported in the paper.</p> <p>raw_csr.zip is a test dataset recorded on a gold grid in the CSR format that can be opened by LiberTEM</p> <p>benchmark.ipynb is a Jupyter notebook that shows how the test dataset can be opened and processed with common Python packages and with LiberTEM.</p>
Transmission electron microscopy (TEM) images of multiwalled carbon nanotubes (MWCNT) detached from polycarbonate (PC) composites
<p>Transmission electron microscopy (TEM) images of multiwalled carbon nanotubes (MWCNT) detached from polycarbonate (PC) composites to determine the MWCNT length distribution. Two sample series of TEM images are included. One based on PC type Makrolon® 2600 and one based on PC type Lexan 141R. The TEM images were taken by Mrs Manuela Heber and the measurement of the MWCNT lengths was carried out by Mrs Manuela Heber and <a href="https://www.ipfdd.de/en/organization/organization-chart/personal-homepages/dr-beate-krause/">Mrs. Dr. Beate Krause</a> (both members of Leibniz-Institut für Polymerforschung Dresden e.V. (<a href="https://www.ipfdd.de/en/home/">IPF</a>)).</p> <p><br>The results of these measurements are presented in the following publication: </p> <p>Petra Pötschke, Tobias Villmow, Beate Krause and Bernd Kretzschmar,<sup> </sup>Influence of Twin-screw Extrusion Conditions on MWCNT Length and Dispersion and Resulting Electrical and Mechanical Properties of Polycarbonate Composites, <strong>polymers </strong>2024, 16(19), 2694. <a href="https://doi.org/10.3390/polym16192694">https://doi.org/10.3390/polym16192694</a></p>
Spectrocopic coincidence experiment in transmission electron microscopy
<p>This dataset contains individual EEL and EDX events where for every event (electron or X-ray), their energy and time of arrival is stored. The experiment was performed in a transmission electron microscope (Tecnai Osiris) at 200 keV. The material investigated is an Al-Mg-Si-Cu alloy. The 'full_dataset.mat' contains the full dataset and the 'subset.mat' has the first five frames of the full dataset. The attached 'EELS-EDX.ipynb' is a jupyter notebook file. This file describes the data processing in order to observe the temporal correlation between the electrons and X-rays. </p> <p>The data is part of the following publication 'Spectroscopic coincidence experiments in transmission electron microscopy' See the full article and supplementary (<a href="https://doi.org/10.1063/1.5092945">https://doi.org/10.1063/1.5092945</a>) for experimental details and results derived from this data. Feel free to process this data in alternative ways but please refer to Zenodo doi and the published paper.</p>
Dataset for Interlacing in atomic resolution scanning transmission electron microscopy
<p>Dataset for the publication: Interlacing in atomic resolution scanning transmission electron microscopy</p>
Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part I: Data Acquisition, Live Processing and Storage
<p>Scanning transmission electron microscopy data related to paper "Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part I: Data Acquisition, Live Processing, and Storage": <a href="https://doi.org/10.1017/S1431927620001713">https://doi.org/10.1017/S1431927620001713</a></p>
Understanding the High Temperature Hydrogen Attack of Steels Utilizing Novel In situ Analytical Transmission Electron Microscopy Techniques
<p>A series of <em>in situ</em> transmission electron microscopy videos detailing the chemical reaction of Fe<sub>3</sub>C carbides in Eutectoid steel with high-temperature hydrogen, demonstrating nano-scale high-temperature hydrogen attack (HTHA). From the Thesis of Thomas Woodward.</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.