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.
342
datasets available to search
ShareScore release 0.7.1
Dataset results
342 results for “electron microscopy”
Supplementary Materials for "Accelerating data sharing and re-use in volume electron microscopy"
<p>The deposition contains supporting materials for "Accelerating data sharing and re-use in volume electron microscopy" Comment</p> <ul> <li>Sample preparation protocol for cell monolayers optimized for serial block face scanning electron microscopy</li> <li>Supporting movies showing models of biological specimens imaged using volume electron microscopy</li> </ul>
Mapping mineralogical heterogeneities at the nm-scale by scanning electron microscopy in modern Sardinian stromatolites: Deciphering the origin of their laminations
<p>These are the raw or processed data used for a paper published in Chemical Geology by Debrie et al. (2022), entitled "Mapping mineralogical heterogeneities at the nm-scale by scanning electron microscopy in modern Sardinian stromatolites: Deciphering the origin of their laminations", <a href="https://doi.org/10.1016/j.chemgeo.2022.121059">https://doi.org/10.1016/j.chemgeo.2022.121059</a></p> <p>The data content is summarized in the List_description_of_data.xlsx file</p>
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>
Synthetic cryo electron microscopy single particle images containing biomolecular complexes with continuous conformational variability used for validating DeepHEMNMA method and validation results
<p>This archive contains a synthetic dataset used for validating DeepHEMNMA method and the validation results. DeepHEMNMA is a deep learning extension of HEMNMA approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron (cryo-EM) microscopy single particle images. We provide a training set of 20,000 images and an inference set of 50,000 images. The training images were used (1) to estimate the conformational and rigid-body parameters with HEMNMA and (2) to train the neural network using the parameters previously estimated with HEMNMA (the file with the HEMNMA-estimated parameters is provided). The inference images were used to infer the parameters with the trained neural network. Also, we provide (1) the input PDB structure, its normal modes, and the conformational and rigid-body parameters used to synthesize the 20,000 training images (ground-truth parameters) and (2) the conformational and rigid-body parameters inferred from the set of 50,000 inference images.</p> <p>The DeepHEMNMA method and the method for synthesizing images have been fully described in the following article: "Hamitouche I and Jonic S (2022), DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images. Front Mol Biosci 9, 965645. <a href="https://doi.org/10.3389/fmolb.2022.965645">https://doi.org/10.3389/fmolb.2022.965645</a> (in press)". Additionally, this article describes a test of DeepHEMNMA using one experimental cryo-EM dataset (available in EMPIAR database under the accession code EMPIAR-10016). </p>
Supplementary data - Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry – a perspective
<p>Analysis files and scripts for <a href="https://doi.org/10.1101/2024.06.21.600107" target="_blank" rel="noopener">associated manuscript</a>. </p> <ul> <li>CR3022.zip: script (in Rust) and necessary data to run said script for CR3022 analysis with the results from running the script.</li> <li>MA-analysis-script.zip: script (in Rust) and necessary data to run said script for automated analysis of MA benchmark results.</li> <li>MA-analysis-data.zip: data from running the MA-analysis-script, containing all MA and Stitch output files.</li> <li>MA-analysis-data-EMPEM.zip: data from running MA and Stitch on the EMPEM benchmark.</li> </ul>
Negative Staining Electron Microscopy Procedure - Video
<p>The Video shows the procedure of negative staining which is used to prepare particles of suspensions for transmission electron microscopy. The procedure is described in a document available at https://zenodo.org/record/1468676.</p>
Electron microscopy of particles collected by different techniques from field measurements in the Moroccan Sahara during FRAGMENT 2019
<p>An intensive field campaign between 4-30 September 2019 was conducted at a major source region on the edge of the Saharan desert in Morocco (29.83 °N 5.87 °W) in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. Samples were collected with three different sampling techniques, namely: flat-plate sampler (FPS), free-wing impactor (FWI), and a micro-orifice uniform deposit impactor (MOUDI). Substrates in the MOUDI and FWI were collected two times a day with a typical sampling duration of a few minutes to avoid overloading the substrate for individual particle analysis. For the flat-plate sampler, the average exposure time was half a day. Here we present dataset of the elemental composition and morphology of more than 300,000 freshly emitted individual particles by performing offline analysis in the laboratory using Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive X-ray Spectrometry (EDX).</p>
Dataset: Correlative Light, Electron Microscopy and Raman Spectroscopy Workflow to Detect and Observe Microplastic Interactions with Whole Jellyfish
<p>ABSTRACT</p> <p>Many researchers have turned their attention to understanding microplastic interaction with marine fauna. Efforts are being made to monitor exposure pathways and concentrations, and to assess the impact such interactions may have. To answer these questions, it is important to select appropriate experimental parameters and analytical protocols. This study focuses on medusae of <em>Cassiopea andromeda</em> jellyfish: a unique benthic jellyfish known to favor (sub-)tropical coastal regions which are potentially exposed to plastic waste from land-based sources. Juvenile medusae were exposed to fluorescent poly(ethylene terephthalate) and polypropylene microplastics (< 300 µm), resin embedded, and sectioned before analysis with confocal laser scanning microscopy as well as transmission electron microscopy and Raman Spectroscopy. Results show the fluorescent microplastics were stable enough to be detected with the optimized analytical protocol presented, and that their observed interaction with medusae occurs in a manner which is likely driven by the microplastic properties (<em>e.g.</em> density, hydrophobicity).</p>
Field Emission Scanning Electron microscopy from Zr-Cu-Ag metallic glass coatings after antibacterial test with E.Coli
<p>Field Emission Scanning Electron Microscopy Figures from metallic glass (Zr-Cu-Ag) antibacterial coatings. Coatings have the name SP in their file name. The non-coated comparison is PBT. This is after the antibacterial test with <em>E.coli</em> after 24 hours. </p>
Diagnostic electron microscopy of viruses with low-voltage electron microscopes. Raw image files with brief description.
<p>The zipped data container contains the raw (unprocessed) images that we have used for the preparation of our manuscript entiteled:</p> <p>"Diagnostic electron microscopy of viruses with low-voltage electron microscopes" <a href="https://doi.org/10.1369%2F0022155420929438">https://doi.org/10.1369/0022155420929438</a></p> <p>Lars Möller, Gudrun Holland, Michael Laue</p> <p>Advanced Light and Electron Microscopy (ZBS 4), Centre for Biological Threats and Special Pathogens, Robert Koch Institute, D-13353 Berlin, Germany</p> <p>The brief description of the data set comprises the abstract of the manuscript, the figures (including captions) and a description of the materials and methods used for their generation.</p>
Tilted fluctuation electron microscopy data from simulated and deposited amorphous Ta
<p>These datasets were used to compare fluctuation electron microscopy analysis methods on simulated and sputter deposited amorphous tantalum. The Ta is 8 nm thick in both the simulated and deposited samples. The deposited Ta is sandwiched between two layers of amorphous 10 nm-thick SiN<sub>x</sub>. Data are also provided for SiN<sub>x</sub> deposited on SiN<sub>x</sub>.</p> <p>Deposited Ta FEM patterns were collected on a TitanX at 200 kV with a convergence angle of 0.51 mrad and a camera length of 300 mm. Simulated Ta FEM patterns were generated using the Prismatic STEM simulation software (see references).</p> <p>The samples were tilted between 0<sup>o</sup> and 45<sup>o</sup> in 15<sup>o</sup> increments. </p> <p><strong>Deposited Ta: </strong></p> <p>.dm4 (Gatan DigitalMicrograph) files are provided with the raw scanning nanodiffraction data for each tilt angle</p> <p>.png images of the mean CBED pattern for each tilt angle are provided</p> <p><strong>Simulated Ta:</strong></p> <p>.h5, .xyz (atomic coordinates), and .txt (Prismatic input parameters defined) files are provided with the raw scanning nanodiffraction data for each tilt angle</p> <p>.png images of the mean CBED pattern for each tilt angle are provided</p> <p> </p> <p>The atomic coordinates for the simulated Ta were provided by Jun Ding. Simulated FEM patterns were produced by Luis Rangel DaCosta using Prismatic STEM simulation software. Neal Reynolds grew the experimental Ta and SiN<sub>x</sub> thin films.</p>
Electron microscopy of SARS-CoV-2 particles - Dataset 05
<p>The dataset contains transmission electron microscopy image stacks (tomograms) of ultrathin sections through extracellular SARS-CoV-2 particles in Vero cell cultures. The dataset contains 17 image stacks of slightly variable pixel dimensions, which were recorded at either 1.17 or 0.96 nm pixel size (12 bit). Image stacks were size calibrated and stored in 16 bit TIF format. Visualization can be done using ImageJ or Fiji. Each image stack in TIF format is supplemented by a file containing the corresponding raw image tilt series (MRC format; plus meta data files) generated by the tomography acquisition software and by a file with the aligned tiltseries. A PDF document describes the methods used for generation of the image files. The dataset was generated as dataset 05 for a comparative morphometric analysis of SARS-CoV and SARS-CoV-2. Further datasets which were used for the analysis are available in this repository (see dataset description document).</p> <p>Related publication: Laue M, Kauter A, Hoffmann T, Möller L, Michel J, Nitsche A. Morphometry of SARS-CoV and SARS-CoV-2 particles in ultrathin plastic sections of infected Vero cell cultures. Sci Rep. 2021 Feb 10;11(1):3515. doi: 10.1038/s41598-021-82852-7. PMID: 33568700; PMCID: PMC7876034.</p> <p> </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>
ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy
<p>Data for submission to Wellcome Open Research entitled "ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy".</p> <p>Data_ultraLM.tif is an image stack from the fluorescence microscope mounted on the ultramicrotome.</p> <p>Data_miniLM.tif is an image stack from the fluorescence microscope mounted in the SBF-SEM.</p> <p>Data_miniLM_EM.tif is an image stack from the SBF-SEM while the miniLM was in-situ.</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>
Figs 16–21 in Redescription of Strombidium coronatum (Leegaard, 1915) Kahl, 1932 (Ciliophora, Spirotricha) based on live observation, protargol impregnation, and scanning electron microscopy
Figs 16–21. Strombidium coronatum, Irish Sea specimens (16–18, scanning electron micrographs; 19–21, protargol impregnation, micrographs of several focal planes were stacked, using the computer program CombineZP from Alan Hadley). 16 – ventrolateral view; 17 – left lateral view showing uniquely shaped peristome, which is roughly triangular in outline and almost flat, extending in the sagittal plane. The extrusomes insert in short oblique rows anteriorly to the girdle kinety; note that some of them are just ejected (arrowhead); 18 – posterior cell portion showing the sharp, longitudinal ridges that have already been illustrated in the original description by Leegaard (1915); 19 – left lateral view of an early divider; 20 – dorsolateral view of an early divider; 21 – ventrolateral view. AP – apical protrusion, BM – buccal membranelles, CM – collar membranelles, DC – distended cell surface, EX – extrusome attachment sites, GK – girdle kinety, MA – macronucleus, OP – oral primordium, VK – ventral kinety. Scale bars: 40 µm (16), 20 µm (17, 19–21), and 10 µm (18).
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.