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476 results for “Microstructure”
A compilation of experimental data on the mechanical properties and microstructural features of Ti-alloys
<p>A compilation of mechanical properties of 282 distinct multicomponent Ti-based alloys. The majority of the data was published in high-quality journals after 2010 (≈84%) and concerns alloys produced via an ingot metallurgy route, followed by solubilization and water quench (≈58%), considered a standard condition for β-Ti alloys. The dataset includes the chemical composition (in at.%), phase constituents, Young modulus, hardness, yield strength, ultimate strength, and elongation, among other relevant features. The authors established a blind-review procedure for 1/3 of the dataset to mitigate human error during data extraction.</p> <p>Files:</p> <ul> <li><strong>dax-ti-static.csv</strong>: static version of the dataset; can be easily imported into your preferred data processing software.</li> <li><strong>table1-static.md</strong>: detailed description of properties and additional fields included in the database; requires *markdown extra* syntax;</li> <li><strong>utils.py</strong>: a helper script to load and filter desired entries; dependencies are matplotlib (3.4.3+), numpy (1.21.2+), and pymatgen (2022.0.16+).</li> </ul> <p>For more information, please visit <strong>https://gitlab.com/comari/dax-ti</strong>.</p>
Data for: Changes in Processing Characteristics and Microstructural Evolution during Friction Extrusion of Aluminum
<p>This dataset contains measurement data, micrographs and machine logs for the publication "Changes in Processing Characteristics and Microstructural Evolution during Friction Extrusion of Aluminum".</p>
Raw Data - High resolution electrochemical additive manufacturing of microstructured active materials: case study of MoSx as a catalyst for the hydrogen evolution reaction
<p>The dataset contains raw data that complements the article:</p> <p>High resolution electrochemical additive manufacturing of microstructured active materials: Case study of MoSx as a catalyst for the hydrogen evolution reaction, J. Mater. Chem. A, 2021, 9, 22072-22081.</p> <p>C. Iffelsberger and M. Pumera*</p> <p>https://doi.org/10.1039/D1TA05581J</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Al-Ni-Co quasicrystalline melt-spun alloy - microstructure and catalytic properties
<p>This set contains supplementary data for the work: Al-Ni-Co decagonal quasicrystal application as an energy-effective catalyst<br>for phenylacetylene hydrogenation, Sustainable Materials and Technologies 41 (2024) e01055, https://doi.org/10.1016/j.susmat.2024.e01055</p> <p> </p> <p>SEM BSE images present the microstructure of the cross-section of the ribbon.</p> <p>MS_Surf images show the surface of the ribbons acquired using an optical microscope.</p> <p>TEM images were named as follows:</p> <p>ms_ribb - melt-spun ribbon</p> <p>nabh4_ribb - ribbon cleaned with NaBH4 aqueous solution</p> <p>liq_ribb - ribbon recovered after phenylacetylene hydrogenation reaction </p> <p><a href="../api/records/13371995/draft/files/phenylacetylene%20hydrogenation%20reactions.ods/content" target="_blank" rel="noopener noreferrer">phenylacetylene hydrogenation reactions.ods</a> - Reaction course of phenylacetylene hydrogenation reactions with new portions of catalyst. Chemical composition of the reaction mixture was evaluated using the gas chromatography method.</p> <p>XPS spectra were collected for surfaces of ribbons in a melt-spun form and recovered after the phenylacetylene hydrogenation reaction. </p> <p> </p> <p>The material preparation and microstructural analyses were performed at the Institute of Metallurgy and Materials Science of the Polish Academy of Sciences.</p> <p>The experimental procedure for material preparation, instrumentation, data collection and results analysis were described in the work: https://doi.org/10.1016/j.susmat.2024.e01055</p> <p> </p> <p>Preparation of materials: Amelia Zięba</p> <p>TEM images collection (FEI Tecnai G2, ThermoFisher Titan Themis G2 200 Probe Cs-Corrected): Amelia Zięba, Lidia Lityńska-Dobrzyńska</p> <p>SEM images acquisition (FEI E-SEM XL-30): Amelia Zięba</p> <p>Catalytic performance tests: Dorota Duraczyńska</p> <p>XPS study: Mateusz Marzec</p> <p> </p> <p><em><strong>Acknowledgements</strong></em></p> <p><strong><em>The work was financially supported by the National Science Centre (NCN), Poland, project No. 2021/41/N/ST8/02533.</em></strong></p> <p> </p>
Dataset for article " Deformation of polycrystalline MgO up to 8.3 GPa and 1270 K: microstructures, dominant slip-systems, and transition to grain boundary sliding"
<p>This archive countains the dataset used to produce the paper "Deformation of polycrystalline MgO up to 8.3 GPa and 1270 K: microstructures, dominant slip-systems, and transition to grain boundary sliding" in press by Frontiers in Earth Science.</p> <p>(c) Estelle Ledoux, Université de Lille, France, 2020</p>
Data for "Microstructural mapping of A. islandica shells reveals environmental and physiological controls on biomineral size"
<p>This data contains the image segmentation workflow, data processing procedure and all data generated for the publication "Microstructural mapping of A. islandica shells reveals environmental and physiological controls on biomineral size" currently under review.</p>
Data for "Shell microstructures (disturbance lines) of Arctica islandica (Bivalvia) – A potential proxy for severe oxygen depletion"
<p>All data used in the publication "Shell microstructures (disturbance lines) of <em>Arctica islandica</em> – A potential proxy for severe oxygen depletion" currently under review. This includes in situ environmental data from the Mecklenburg Bight, Baltic Sea (ODIN 2, Leibnitz Institute for Baltic Sea Research, https://odin2.io-warnemuende.de/), and biomineral unit (BMU) morphology measurements in scanning electron microscopy images of shells of <em>Arctica islandica</em>, as well as the BMU classifier used in Ilastik (Berg et al., 2019).</p> <p>Each BMU measurement represents summary statistics of the 15 % largest BMUs within one image (25, 50 and 75 % percentile of each BMU parameter). Environmental data were measured at 20 m water depth, ca. 5 m above the sediment surface.</p>
Raw Data: Gold Coated ZnO Microstructures by Bragg Coherent X-Ray Diffraction Imaging
<p>Two sets of raw data from gold coated ZnO microstructure (rod) investigated by Bragg coherent X-ray diffraction imaging used in publication: "Visualizing Intrinsic 3D-Strain Distribution in Gold Coated ZnO Microstructures by Bragg Coherent X-Ray Diffraction Imaging and Transmission Electron Microscopy with Respect to Piezotronic Applications" (<a href="https://doi.org/10.1002/aelm.202100546">https://doi.org/10.1002/aelm.202100546</a>)</p> <p>Included is data from two different spatial positions along the c-axis of the ZnO rod. Futher on called position 1 (P1) and position 2 (P2). For each position there is a .nxs file of a rocking scan around the {10-10} Bragg reflection, collected by a 2D detector and other recorded values, e.g. motor positions, counter values. </p>
Microstructure of experimental faults in Pāpaku Fault core samples (IODP site U1518)
<p>Backscattered electron images of experimentally deformed samples from the Pāpaku Fault, Hikurangi Margin, New Zealand (International Ocean Discovery Program, Site U1518). In the experiments, intact mini-cores extracted from drill core samples were deformed in a single-direct shear box at MARUM, University of Bremen. Details of the experiments and the experimental data are available from the Pangea data publisher at: </p> <p>The data set contains original mosaics of whole thin sections, cut parallel to the shear direction and perpendicular to the experimental fault. These are in TIFF format and named SAMPLE#.tif.</p> <p>Annotated images include interpretations of the deformed zone (in red shading) superimposed on images that have been enhanced for better contrast. These are in Adobe Illustrator format and named SAMPLE#_annotated.ai.</p> <p>For image analysis, traces of the inferred deformed zone were extracted and scaled in ImageJ. These traces are available in TIFF format and named SAMPLE#_dz_trace.tif. Our measurements based on these traces are tabulated in Papaku_experiments_microstructure_data.csv.<br> </p>
Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties
<p>This dataset contains the data from the publication</p> <p><strong>"Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties"</strong></p> <p> </p> <p> </p>
Prenatal diet programs the transgenerational inheritance of brain macro and microstructure defects, coding for anxiety-like behavior in male rats.
<p>T1-w 3DFLASH Preprocessed MRI images used for wistar rat used for the deformation-based morphological (DBM) model are released.</p> <p>Nifti files are duplicated. DBM used only mnc format.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data for: Microstructure evolution and texture development during production of homogeneous fine-grained aluminum wire by friction extrusion
<p>This dataset contains the data for paper “Microstructure evolution and texture development during production of homogeneous fine-grained aluminum wire by friction extrusion” published in Materials Characterization.</p> <p>Abstract: This study aims to understand the microstructure evolution and texture development during friction extrusion of aluminium alloys, focusing on AA7075 as exemplary alloy system. Electron backscatter diffraction technique has been employed to obtain crystallographic data from various regions in front of the die and in the wire. It can be deduced that the combination of continuous dynamic recrystallization and geometric dynamic recrystallization mainly govern the formation of a fine-grained structure, however discontinuous dynamic recrystallization may also play a role at high temperature. The global shear deformation during the process was characterized as a simple shear deformation with dominant <span class="math-tex">\(B/\overline{B}\)</span> simple shear texture components. The material flow is mainly driven by the in-plane shear strain and the extrusion-induced shear strain that are determined by die rotational speed and extrusion force, respectively. The in-plane shear strain strongly affects the formation of a homogeneous fine-grained microstructure in the aluminum wire. In this regard, a novel material flow model for friction extrusion has been proposed.</p>
Dataset related to the publication "Transformation Optics: Large Multiphysics Simulation of Nonlinear Optomechanical Coupling in Microstructured Resonant Cavities", DOI: 10.1109/MMM.2018.2821086
<p>This folder contains the raw data from which the graphs in paper "Transformation Optics: Large Multiphysics Simulation of Nonlinear Optomechanical Coupling in Microstructured Resonant Cavities", DOI: 10.1109/MMM.2018.2821086, have been obtained.</p>
Supplementary material for the publication: "Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties"
<p><span><span><span>This dataset contains supplementary code, images and models for the publication „Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties“.</span></span></span></p> <p> </p> <p><span><span><span>The content will be updated and additionally linked to the corresponding git repositories.</span></span></span></p>
Cerebral microstructural alterations in Post-COVID-condition are related to cognitive impairment, olfactory dysfunction, and fatigue
<p>After contracting COVID-19, a substantial number of individuals develop a Post-COVID-Condition (PCC), marked by neurologic symptoms such as cognitive deficits, olfactory dysfunction, and fatigue, which can have detrimental socioeconomic consequences. Despite this, biomarkers and pathophysiological understandings of this condition remain limited. Employing magnetic resonance imaging, we conduct a comparative analysis of cerebral microstructure among patients with post-COVID condition, healthy controls, and individuals who contracted COVID-19 without long-term symptoms. This reveals widespread alterations in cerebral microstructure, attributed to a shift in volume from neuronal compartments to free fluid, associated with the severity of the initial infection. Correlating these alterations with cognition, olfaction, and fatigue unveils distinct affected networks, which are in a close anatomical-functional relationship with the respective symptoms. This plausibility of symptom-specific networks not only provides insights into the disease's pathophysiological foundations, which align well with an accelerated aging process but also underscores the significance of microstructure as an imaging biomarker.</p>
Figures 64–72. Lattice microstructure comparison among Alayotityus sierramaestrae Armas, 1973 in A new monotypic genus and species from China Langxie feti gen et sp. n. (Scorpiones: Buthidae)
Figures 64–72. Lattice microstructure comparison among Alayotityus sierramaestrae Armas, 1973 (female, 64; photograph by G. Lowe), Langxie feti gen. et sp. n. (female, paratype, 65), Janalychas tricarinatus (Simon, 1884) (female, 66), Lychas scutilus C. L. Koch, 1845 (juvenile, 67), Lychas mucronatus (Fabricius, 1798) (female, 68, and male, 69), Tityus footei Chamberlin, 1916 (male, 70), Tityus stigmurus (Thorell, 1876) (female, 71) and Tityus smithii Pocock, 1893 (female, 72).
Fig. 3 in Bone microstructure of the sphenodont rhynchocephalian Priosphenodon avelasi and its paleobiological implications
Fig. 3. Postcranial histology of the sphenodont rhynchocephalian Priosphenodon avelasi Apesteguía, 2008 (MPCA-Pv 308) from the Upper Cretaceous Candeleros Formation, Argentina. Black arrowheads signal the position of LAGs. A. Bone histology of the humeri. A1, A2, complete cross sections; A3–A5, detailed views of the cortex showing predominance of poorly vascularized parallel fibered bone and distribution of LAGs; A4, detail of the cortex showing primary vascular canals and LAGs; A5, regions of the cortex marked with double arrows where the tissue presents a better organization and LAGs. B. Bone histology of the radius. B1, general view; B2, B3, details. Note the absence of vascular canals and the mass birefringence of the primary bone tissue. C. General view of the ulna. C1, general view; C2, C3, detailed views showing abundant Sharpey's fibers (C2) and distribution of LAGs (C3). D. Detailed view of the cortex of the fibula showing canaliculi and osteocyte lacunae. E. Detailed view of the cortical bone in the phalange "A" showing abundant Sharpey's fibers. F. General view of the presacral vertebra section. Note the predominance cancellous bone tissue. A1, A2, A4, A5, B1, B3, C, D, normal transmitted light; A3, B2, E, F, cross polarized light with lambda filter. Abbreviations: cn, canaliculi; ic, intertrabecular cavities; ICL, inner circumferential layer; lc, longitudinal canals; nc, nutrient canal; ol, osteocyte lacunae; PFB, parallel fibered bone; rc, radial canals; Sf, Sharpey's fibers.
Fig. 2 in Bone microstructure of the sphenodont rhynchocephalian Priosphenodon avelasi and its paleobiological implications
Fig. 2. Appendicular and axial bones of the sphenodont rhynchocephalian Priosphenodon avelasi Apesteguía, 2008 (MPCA-Pv 308) from northern Patagonia, late Cretaceous, sampled for histological analysis. A. Right humerus in anterior view (an image of the left humerus was not processed due to its fragmentary state). B. Left radius in anterior view. C. Left ulna in anterior view. D. Left femur in lateral view. E. Fragmented right femur. F. Tibia in anterior view. G. Fragment of fibula in anterior view. H, I. Phalanges in dorsal view. J. Articulated presacral vertebrae in right lateral view. Scale bars 10 mm.
Fig. 1 in Bone microstructure of the sphenodont rhynchocephalian Priosphenodon avelasi and its paleobiological implications
Fig. 1. Skeletal digital reconstruction of the sphenodont rhynchocephalian Priosphenodon avelasi Apesteguía, 2008, from northern Patagonia, late Cretaceous; by Jorge A. González showing the elements sampled for histological analysis (in red).
Fig. 4 in Bone microstructure of the sphenodont rhynchocephalian Priosphenodon avelasi and its paleobiological implications
Fig. 4. Compaction index (CI) of several of the histological sections analyzed here from Priosphenodon avelasi (northern Patagonia, late Cretaceous). Note the high degree of compaction that both humerus present in comparison with the rest of the bone elements. A. Right humerus. B. Left humerus. C. Left radius. D. Left ulna. E. Right femur. F. Tibia. G. Fibula. H, I. Phalanges. J. Articulated presacral vertebrae. Scale bars 1 mm.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.