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17 results for “TEM images”
Herbarium specimen image of Cruciata mixta Ehrend. & Schönb.-Tem., part of the collection of Royal Botanic Gardens, Kew
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
TEM images of Cu decorated carbon nanotubes
<p>Atlas of TEM images for proprietary copper-decorated carbon nanotubes.</p>
Instance Segmentation of Dislocations in TEM Images
<p>This is the dataset and software for the IEEE publication <em>Instance Segmentation of Dislocations in TEM Images </em>to be published in 2023.</p>
Machine learning refinement of in situ images acquired by low electron dose LC-TEM
<p>This is a dataset of images acquired in vacuum condition and corresponding images in a solution listed in the text file list_dataset.txt. </p><p> </p><p>The code is available at <a href="https://github.com/hiroyasukatsuno/Machine-learning-refinement-of-images-acquired-by-LC-TEM">this website</a>:</p><p>https://github.com/hiroyasukatsuno/Machine-learning-refinement-of-images-acquired-by-LC-TEM/</p><p><br>Equipment of TEM:</p><ul><li>field-emission gun (JEM-2100F, JEOL, Tokyo)</li><li>OneView IS (Gatan, Inc., Pleasanton, CA, USA)</li></ul><p> </p>
Supplementary material for the publication: Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"
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>
Understanding the Influence of Receptive Field and Network Complexity in Neural-Network-Guided TEM Image Analysis
<p>TEM images of Au nanoparticles of various sizes on ultra-thin carbon substrates and their corresponding labels for semantic segmentation. The images have a dataset label of "images" and the labels have a dataset label of "labels". </p>
Data related to: Simulated TEM imaging of a heavily irradiated metal
<p>Data set related to article "Simulated TEM imaging of a heavily irradiated metal"</p> <p>The data is the output of a simulation of single-crystal tungsten with periodic boundary conditions evolving under irradiation up to high dose (1 dpa). The simulation was done in LAMMPS, only every 100th snapshot in LAMMPS Dump format is uploaded here. A frame corresponds to a dose increment of 0.000167 dpa, i.e. frame 1000 corresponds to a dose of 0.167 dpa. The files are zipped. A finer resolution can be supplied upon request.</p> <p>The simulations were performed for the article: <a href="https://doi.org/10.1103/PhysRevMaterials.6.063601" target="_blank" rel="noopener">10.1103/PhysRevMaterials.6.063601</a>, where more details on the simulations are found. The data is here made available for the article: <a href="https://doi.org/10.48550/arXiv.2401.14781" target="_blank" rel="noopener">10.48550/arXiv.2401.14781</a>.</p> <p>The TEM simulation software is available at: <a href="https://github.com/mason-daniel/simulated_tem">https://github.com/mason-daniel/simulated_tem</a>.</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>
BIR-MicroED: TEM image series revealing bend contour motion in static microcrystals (biotin, Zn(II)-methionine, Co(II)-porphyrin, AVAAGA) and diffraction patterns acquired from the same crystals at 200 kV
<p>This deposition contains a series zip files each containing TEM image series and electron diffraction images in .mrc file format. Each folder collects data acquired from crystals of a particular compound under the same conditions (electron energy, temperature). Zip files are named according to the format: <em>"CompoundName</em>_bendcontour_imageseries_<em>AcceleratingVoltage</em>_<em>Temperature</em>.zip"</p> <p>Data is further divided into sub-directories according to the particular crystal studied (crystal1, crystal2, crystal3), each containing a TEM image series (name format: "<em>CompoundName</em>_static_imageseries_<em>AcceleratingVoltage</em>_<em>Temperature</em>_crystal#.mrc") and 10 diffraction snapshots (2 frames each, each convering 1 second of electron beam exposure) acquired at equally spaced time intervals throughout the image series. These are named according to the format:</p> <p>"CompoundName_bendcontour_crystal#_diffraction_snap#.mrc"</p>
FIGURES 15–20. Mallomonas voloshkoae and M. pechlaneri. TEM images. FIGURES 15–18 in Mallomonas voloshkoae sp. nov. (Synurales, Chrysophyceae) and distribution of M. pechlaneri in mountain lakes of Siberia
FIGURES 15–20. Mallomonas voloshkoae and M. pechlaneri. TEM images. FIGURES 15–18. Mallomonas voloshkoae sp. nov. Body scales from different habitats. Figure 15. Unnamed lake 1. Figure 16. Unnamed lake 2. Figure 17. Unnamed lake 3. Figure 18. Lake Frolikha. FIGURES 19–20. Mallomonas pechlaneri. Figure 19. Scales with bristles from Teletskoe Lake. Figure 20. Scales with bristles from Ilchir Lake. Scale bars: Figs 15–20: 2 μm.
Fast Improvement of TEM Images with Low-Dose Electrons by Deep Learning
<p>This is a dataset of High-Dose-Electron (HDE) images and Low-Dose-Electron (LDE) images taken with a transmission electron microscopy used in <a href="https://doi.org/10.1017/S1431927621013799">H. Katsuno, Y. Kimura, T. Yamazaki and I Takigawa, Microsc. Microanal. <strong>28</strong> (2022), pp 138--144</a> (<a href="https://arxiv.org/abs/2106.01718">arXiv:2106.01718</a>).</p> <p>There are two LDE images for each HDE image.</p> <p>cf) HDE image is 0001.tif and corresponding LDE images are 0002.tif and 0003.tif.</p> <p> </p> <p>Equipment of TEM:</p> <p>field-emission gun (JEM-2100F, JEOL, Tokyo)</p> <p>OneView IS (Gatan, Inc., Pleasanton, CA, USA)</p> <p> </p> <p>Typical magnification was 25,000x and 30,000x.</p> <p> </p> <p>HDE image</p> <p>The resolution was 4096 x 4096 pixels and its exposure time was 5 s.</p> <p>The typical total doses was 10<sup>10</sup> e<sup>-.</sup></p> <p> </p> <p>LDE image</p> <p>The resolution was 512 x 512 pixels and its exposure time was 3.3 ms.</p> <p>The typical total doses was 10<sup>6</sup> e<sup>-</sup>.</p> <p> </p> <table> <tbody> <tr> <td>Filename</td> <td>Material</td> <td>Total number of images</td> <td>Total number of a pair of HDE and LDE</td> </tr> <tr> <td>train1_Ni.zip</td> <td>Ni</td> <td>336</td> <td>224</td> </tr> <tr> <td>train2_FeNi.zip</td> <td>FeNi</td> <td>390</td> <td>260</td> </tr> <tr> <td>train3_SiC.zip</td> <td>SiC</td> <td>210</td> <td>140</td> </tr> <tr> <td>train4_Silicate</td> <td>Silicate</td> <td>264</td> <td>176</td> </tr> <tr> <td>train5_Alumina</td> <td>Alumina</td> <td>300</td> <td>200</td> </tr> <tr> <td>val1_Ni.zip</td> <td>Ni</td> <td>54</td> <td>36</td> </tr> <tr> <td>val2_FeNi.zip</td> <td>FeNi</td> <td>60</td> <td>40</td> </tr> <tr> <td>val3_SiC.zip</td> <td>SiC</td> <td>30</td> <td>20</td> </tr> <tr> <td>val4_Silicate</td> <td>Silicate</td> <td>36</td> <td>24</td> </tr> <tr> <td>val5_Alumina</td> <td>Alumina</td> <td>60</td> <td>40</td> </tr> </tbody> </table> <p> </p> <p> </p> <p>The ipynb file and model parameters for machine learning are located in <a href="https://github.com/hiroyasukatsuno/Fast-Improvement-Low-Dose-TEMimages">the GitHub page</a>.</p> <p> </p>
Data and code for "Symmetries in TEM imaging of crystals with strain"
<p>Code and data related to manuscript "On Symmetries in TEM imaging of crystals with strain",<br> arXiv preprint arXiv:2206.01689. To be published in Proc. Royal. Soc. A</p> <p> </p>
FIGURES 17–19. Hassallia littoralis TEM images. Fig. 17. Hormogonia formation through a necridic cell. Fig. 18. Young isopolar hormogonium with a wide stratified sheath. Fig. 19 in Hassallia littoralis sp. nov. (Cyanobacteria, Microchaetaceae) from Mexico's marine supralittoral based on morphological and molecular evidence
FIGURES 17–19. Hassallia littoralis TEM images. Fig. 17. Hormogonia formation through a necridic cell. Fig. 18. Young isopolar hormogonium with a wide stratified sheath. Fig. 19. Evident polar nodule in a basal heterocyte. Scale bars: Fig. 17: 3 µm, Fig. 18: 5 µm, Fig. 19: 1 µm.
Transmission electron microscopy (TEM) image datasets of peptide / protein nanowire morphologies
<p>TEM image dataset containing four nanowire morphologies of bio-derived protein nanowires and synthetic peptide nanowires.</p> <p>The peptide / protein nanowires used in this study were synthesized and imaged by Brian Montz in Prof. Todd Emrick's research group at the Department of Polymer Science and Engineering Department, University of Massachusetts Amherst. </p> <p>We acknowledge financial support from the U.S. National Science Foundation, Grant NSF DMREF #1921839 and DMREF #1921871.</p> <p>Nanowires were classified into either of the four morphologies: bundle, singular, dispersed or network. Each morphology contains 100 images (jpg files).</p> <p>For the dispersed and network morphologies, because these two morphologies are harder to visually distinguish, we have created manual segmentation labels of the nanowires (included in these two morphology folders as png files). Percolation analysis was done on these manually segmented nanowires to provide quantitative metric on whether the nanowires form a network in the image. </p> <p>seg_mask_5_resolutions.zip contains ground truth 2D binary encoding of segmented nanowires at 5 resolutions.</p> <p>encoders_trained_with_optimized_hyperparameter.zip contains 4 sets of encoders trained with either SimCLR or Barlow-Twins self-supervised methods on either generic TEM images, or generic everyday photographic images (each with 5 replicates with different random seed) with optimized hyperparameters.</p> <p>Open-access datasets that have been used during self-supervised training.</p> <ul> <li>2021-CEM500K.zip contains 10,000 images that was used as "generic TEM images" to train the encoders with self-supervised methods, these are a random selection from the CEM500k open-access dataset. DOI: <a href="https://doi.org/10.7554/eLife.65894">10.7554/eLife.65894</a></li> <li>2022-1000-ImageNet.zip contains 1,000 images from the ImageNet1k dataset, each come from a different category. DOI: <a href="http://doi.org/10.1007/s11263-015-0816-y">10.1007/s11263-015-0816-y</a></li> </ul> <p>Open-access datasets that our machine learning workflow have been applied to:</p> <ul> <li>2022-AutoDetect-mNP-morphology.zip contains a selected TEM images of nanoparticles categorized in 3 morphologies from the AutoDetect-mNP datasets: DOI: <a href="http://doi.org/10.6078/D1WT44">10.6078/D1WT44</a> and DOI: <a href="http://doi.org/10.6078/D1S12H">10.6078/D1S12H</a></li> <li>2021-TEM virus.zip contains TEM images of 9 types of viruses from the TEM virus dataset. Matuszewski, Damian; Sintorn, Ida-Maria (2021), “TEM virus dataset”, Mendeley Data, V3, DOI: <a href="http://doi.org/10.17632/x4dwwfwtw3.3">10.17632/x4dwwfwtw3.3</a></li> </ul> <p>The official github page of the implementation of the machine learning models is <a href="https://github.com/arthijayaraman-lab/semi-supervised_learning_microscopy_images">semi-supervised_learning_microscopy_images</a>.</p> <p>If you use the dataset or the codes in the repository linked above, please cite the following <a href="https://doi.org/10.1039/D2DD00066K">manuscript</a>:</p> <p>S. Lu, B. Montz, T. Emrick and A. Jayaraman, <em>Digital Discovery</em>, 2022, <strong>1</strong>, 816-833 , <strong>DOI: </strong>10.1039/D2DD00066K</p>
TEM image of HRP-AuNCs
<p>Transmission electron microscopy (TEM) was performed on a Hitachi H-7500 transmission electron microscope at 80 kV.</p>
Experimental HR-TEM Images of GO samples at 5nm scale
<p>Experimental HR-TEM micrograph in the GO sample, prepared at the highest carbonization temperature (TCA) of 973 K, with 5.3% of oxides coverage estimated experimentally via XPS [42] and taken at a 5-nm scale.</p>
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