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.
287
datasets available to search
ShareScore release 0.9.0
Dataset results
287 results for “alloy”
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>
Experimental data related to the publication: "Phase Composition of AlTiNbMoV, AlTiNbTaZr and AlTiNbMoCr Refractory Complex Concentrated Alloys: A Correlation of Predictions and Experiment
<h2>EDS data</h2> <ul> <li>SPC and SPD files: EDAX Genesis raw data for EDS maps of as-sintered condition (Figures 1, 5 and 10).</li> <li><em>EDS_homogenized.h5</em> file: EDAX APEX raw data for maps (Figures 2, 6 and 11) and linescans (Figures 3, 7 and 12) of the homogenized condition</li> </ul> <h2>XRD data</h2> <p>ASCII tab-separated files for XRD diffractograms (Figures 4, 9 and 14). Contains measured intensity and calculated intensities for the displayed phases.</p> <h2>CALPHAD data</h2> <p>ASCII tab-separated files to recreate Figure 15 (resulting phase fractions only).</p> <p> </p>
Supplementary Information and EBSD data for 'Intermetallic phase layers in cold metal transfer aluminium-steel welds with an Al-Si-Mn filler alloy'
<p>Supplementary information and electron backscatter diffraction (EBSD) data for the article entitled 'Intermetallic phase layers in cold metal transfer aluminium-steel joints with an Al-Si-Mn filler alloy'. There are three EBSD datasets, I-III, named "I_EBSD.dat" - "III_EBSD.dat", each with corresponding calibration and background patterns, as well as secondary electron scanning electron microscopy images showing the scanned area and text files containing the acquisition parameters. The data analysis workflow has been published on GitHub, see References.</p>
Process Simulation of Friction Extrusion of Aluminum alloys
<p>The friction extrusion process (FEP) is a solid-state material processing technique in which a translating extrusion die is pressed against a billet/feedstock material in a rotating extrusion container to produce an extruded rod or wire. A key aspect of FEP is the non-equilibrium thermo-mechanical condition created at the contact region between the die and billet, leading to a possibly improved microstructure. Numerical simulations of FEP are highly complex due to contact between the tool and the workpiece, and interplay between thermo-mechanical conditions and the present severe plastic deformation. In the present work, a three-dimensional finite element model is developed to study the material flow behavior for different extrusion ratios for a 60° die angle during friction extrusion. The developed model is numerically validated against experimental data. The spatial temperature, strain and velocity distributions illustrate the effect of extrusion ratio on the deformation characteristics of the extruded aluminum alloys, thereby assisting in experimental microstructure interpretation.</p>
Original data for "Imaging and quantifying the chemical communication between single particles in metal alloy" article
<p>This upload contains original data and supplementary data for the publication titled "Imaging and Quantifying the Chemical Communication Between Single Particles in Metal Alloy" by L. Godeffroy, A. Makogon, S. G. Derouich, F. Kanoufi, V. Shkirskiy in the ACS Analytical Chemistry journal. The preprint of the paper is included in this upload and is also available on ChemRxiv (<a href="https://doi.org/10.26434/chemrxiv-2022-rn77b-v2">https://doi.org/10.26434/chemrxiv-2022-rn77b-v2</a>). </p> <p>The file "OpticalData.zip" contains the original optical images. Each pixel in the images has a size of 180 nm, and the time interval between frames is 1.87 seconds.</p> <p>The file "sem_identical.png" contains a scanning electron microscopy (SEM) image of the same surface area as the optical images, captured using secondary electrons.</p> <p>The file "WorkingWithData.ipynbb" is a Jupyter Lab file that demonstrates a few examples of how data can be loaded and processed in a Python environment. Python 3 was used for this purpose.</p> <p>There are also two supplementary movies:</p> <p>Movie S1: This movie provides an enlarged view (marked with a square in Movie S2) of the surface film transformation on Al6061 during immersion in 10 mM H2SO4. It includes snapshots at 17 s, 28 s, 37 s, and 84 s, along with a related SEM image of the same location.</p> <p>Movie S2: This movie presents a wide-field view of the surface film transformation on Al6061 during immersion in 10 mM H2SO4, along with a related SEM image of the same location. The square highlights the area around which the narrative in the manuscript is focused.</p>
Dataset for "A general purpose potential for glassy and crystalline phases of Cu-Zr alloys based on the ACE formalism"
<p>This dataset was used to fit a general purpose machine learning interatomic potential for the Cu-Zr system. It supports the paper "A general purpose potential for glassy and crystalline phases of Cu-Zr alloys based on the ACE formalism".</p>
Dataset for study "Band gap engineering by cationic substitution in Sn(Zr1-xTix)Se3 alloy for bottom sub-cell application in solar cells"
<p>This dataset contains raw and processed data that were used to for the study entilted "Band gap engineering by cationic substitution in Sn(Zr1-xTix)Se3 alloy for bottom sub-cell application in solar cells". </p>
2D EBSD Dataset of the Alpha and Beta Phase Orientations for a Hot-Rolled Model Zircaloy-4 with 7 wt.% Nb Alloy
<p>A set of electron backscatter diffraction (EBSD) data files for a model Zircaloy-4 with 7 wt.% Nb addition alloy following hot-rolling. </p> <p>The 2D data set contains EBSD maps recording the microstructure and texture evolution, from a beta-processed (and annealed) starting condition, following rolling at a temperature of 725C to 50% and 75% reduction. Data for annealing of the rolled materials (750C for 2 hours) is also included. <em>Note, phases in the ctf files marked as 'Titanium Cubic' refer to measurement of the 'Zirconium Cubic' phase.</em></p> <p>Please see our accompanying paper for analysis of the 2D measurements - as well as analysis of a 3D reconstruction from <a href="https://doi.org/10.5281/zenodo.3785084">10.5281/zenodo.3785084</a> - and for interpretation of the coupled crystallographic texture evolution;</p> <p>C.S. Daniel, A. Garner, P.D. Honniball, L. Bradley, M. Preuss, P.B. Prangnell, J. Quinta da Fonseca, Co-deformation and dynamic annealing effects on the texture development during alpha–beta processing of a model Zr-Nb alloy, Acta Materialia 205 (2021) 116538. <a href="https://doi.org/10.1016/j.actamat.2020.116538">10.1016/j.actamat.2020.116538</a></p>
Data for "First Principle Calculation on Pressure Dependent Yielding in Solute Strengthened Aluminium Alloys"
<p>The dataset contains the DFT results which is the basis for the results and discussions in the related article, "First principle calculations of pressure dependent yielding in solute strengthened aluminium alloys". The details of the DFT calculations are written in the article.</p> <p>The two different file-name conventions are explained below.</p> <p>OUTCAR_Al_R1_HSP<br> OUTCAR_X_HSP_POS</p> <p>Al-files represent the pure aluminium models, showing the dislocation energies at various hydrostatic pressures. X (Cu, Si, Mg) represents the solute specie in the full model showing the configurational energy. R1 is the radius of the relaxed region. HSP represents the superimposed hydrostatic pressure, which is given by (-560+HSP*80) MPa. POS is the atomic index in the OUTCAR files, where the solute is substituted.</p>
Using coupled micropillar compression and micro-Laue diffraction to investigate deformation mechanisms in a complex metallic alloy Al13Co4
<p>In this investigation, we have used <em>in-situ</em> micro-Laue diffraction combined with micropillar compression of focused ion beam milled Al<sub>13</sub>Co<sub>4</sub> complex metallic alloy to study the evolution of deformation in Al<sub>13</sub>Co<sub>4</sub>. Streaking of the Laue spots showed that the onset of plastic flow occured at stresses as low as 0.8 GPa, although macroscopic yield only becomes apparent at 2 GPa. The measured misorientations, obtained from peak splitting, enabled the geometrically necessary dislocation density to be estimated as 1.1 x 10<sup>13</sup> m<sup>-2</sup>.</p>
Refractive index obtained from thin films of Au-Ag alloys
<p>This is the optical dataset for Au-Ag alloys, obtained in the paper "Optical properties of Au-Ag alloys: An ellipsometric study" [Opt. Mater. Express 4 (2014) 403-410].</p>
Brinell-Hardness (HBW 2.5/62.5) of Al-alloy EN AW-2618A after different aging times and temperatures
<p>The dataset contains data from Brinell hardness measurements of Al-alloy EN AW-2618A after aging for different times and temperatures. Aging was either load free or with applied tensile load (creep). The investigated material and the applied methods were described in detail in two publications.</p> <p>Version 2.0 has been extended with additional data for further ageing temperatures.</p> <p>Further information is provided in the file content.pdf.</p>
Novel biodegradable Zn alloys obtained by rapid solidification and high pressure torsion - NCN Poland Project - Dataset Feb.2024
<p>Dataset contains results obtained during the first year of the project No. 2021/40/C/ST5/00071 "Novel biodegradable Zn alloys obtained by rapid solidification and high pressure torsion" funded by NCN Poland. </p> <p>The dataset contains: SEM, TEM, nanoindentation, hardness and uniaxial tensile testing results. </p>
Dataset for "Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank's Solution by Multisine Impedimetric Monitoring"
<p>This dataset contains raw data of the publication “Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank’s Solution by Multisine Impedimetric Monitoring”. Corrosion Science, Volume 231, 2024, 111979, ISSN 0010-938X, <a href="https://doi.org/10.1016/j.corsci.2024.111979">https://doi.org/10.1016/j.corsci.2024.111979</a>. The data was collected at Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences (Poland) and Gdansk University of Technology (Poland). All data are provided under CC0 license. The dataset includes Scanning electron and optical microscopy images (.tiff and ,jpg format); X-ray photoelectron spectroscopy spectra (.vms format); Electrochemical impedance spectra (.txt format). Dzmitry Kharytonau gratefully acknowledges the financial support of this study by the National Science Centre (Poland) under research Grant Sonatina no. 2021/40/C/ST5/00266. This work was also supported by Gdansk University of Technology under the Aurum Supporting International Research Team Building – ‘Excellence Initiative – Research University’ [grant number: 2/2021/IDUB/II.1.3].<br><br></p>
Effect of Nd on high temperatures deformation and corrosion behavior of AZE Mg alloy
<p>An extruded AZE Mg alloy (Mg-3Al-1Zn-0.6Nd) has been developed and prepared to examine the effect of the precipitated phase on corrosion and hot deformation behavior. High-temperature compression experiments show that dynamic recrystallization (DRX) occurs at various experimental parameters (473 to 673 K and 1×10<sup>-4</sup> s<sup>-1</sup> to 1×10<sup>-3</sup> s<sup>-1</sup>). The deformation mechanism is dominated by dislocation slip and/or twinning. Al<sub>11</sub>Nd<sub>3</sub> precipitates with two different sizes could impede nucleation and the growth of DRXed grains. Serrated flow occurred owing to the interactions between dislocations and precipitates. The corrosion current density decreases with the addition of Nd elements. Moreover, the continuous precipitation of the Al<sub>11</sub>Nd<sub>3</sub> phase at grain boundaries reduces the corrosion rate in NaCl solution, thereby enhancing the corrosion resistance of AZE alloys.</p>
Radii of S-phase Al2CuMg in Al-alloy EN AW-2618A after different aging times at 190°C
<p>The dataset contains data from quantitative microstructural analysis of transmission electron microscopy (TEM) studies of the S-phase (Al<sub>2</sub>CuMg) radii in Al-alloy EN AW 2618A. The investigated material and the applied methods were described in detail in two publications. Further information is provided in the file content.pdf.</p> <p>The conversion factor between pixel and length/area has been corrected/clarified in version 1.2 of the content file.</p>
Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting
<p>Atomic structure data used in the research article entitled "Small Dataset Machine-Learning Approaches to Explore the Design Space of High-Entropy Alloys: Engineering ZnTe-based Multicomponent Alloys for the Photo-Splitting of Water"</p>
Dataset for "From CO2 to Solid Carbon: Reaction Mechanism, Active Species, and Conditioning the Ce-Alloyed GaInSn Catalyst"
<p>Experimental raw data for the article "<em>From CO<sub>2</sub> to Solid Carbon: Reaction Mechanism, Active Species, and Conditioning the Ce-alloyed GaInSn Catalyst</em>", published in <em>Journal of Physical Chemistry C </em>(2024). DOI:10.1021/acs.jpcc.4c05482.</p> <p>The data set is organized according to the publication's figures. XPS data given here is the raw data without binding energy calibration. For the manuscript, binding energies in a series of samples were calibrated by taking the strongest peak, where no chemical shift was to be expected, as a reference.</p>
Phase & Property Data for the NbCrVWZr Alloy System
<h4><strong>General Information</strong></h4> <p>This dataset contains a comprehensive collection of properties, compositions, and phase predictions for alloys from a simplicial grid sampling of the NbCrVWZr quinary alloy system with a spacing of 0.05 mole fraction. The dataset is structured to facilitate analysis of predicted material properties and phase stability for corresponding alloy compositions. Each row corresponds to a unique alloy composition.</p> <h4><strong>Column Descriptions</strong></h4> <ol> <li> <p><strong>umap0, umap1</strong>:<br>2D coordinates obtained from Uniform Manifold Approximation and Projection (UMAP), used for dimensionality reduction and visualizing the high-dimensional alloy composition space.</p> </li> <li> <p><strong>Nb, Cr, V, W, Zr</strong>:<br>Elemental mole fractions for Niobium (Nb), Chromium (Cr), Vanadium (V), Tungsten (W), and Zirconium (Zr) in the alloy</p> </li> <li> <p><strong>PROP LT (K)</strong>:<br>Liquidus temperature (LT) in Kelvin computed by ThermoCalc</p> </li> <li> <p><strong>PROP ST (K)</strong>:<br>Solidus temperature (ST) in Kelvin computed by ThermoCalc</p> </li> <li> <p><strong>PROP 500C CTE (1/K)</strong>, <strong>PROP 1000C CTE (1/K)</strong>, <strong>PROP 1500C CTE (1/K)</strong>:<br>Coefficients of thermal expansion (CTE) at 500°C, 1000°C, and 1500°C, reported in reciprocal Kelvin</p> </li> <li> <p><strong>EQ 1273K THCD (W/mK)</strong>, <strong>EQ 1523K THCD (W/mK)</strong>, etc.:<br>Thermal conductivity (THCD) in W/(m·K) computed during equilibrium calculations by ThermoCalc at various temperatures (e.g., 1273K, 1523K).</p> </li> <li> <p><strong>EQ 1273K Density (g/cc)</strong>, <strong>EQ 1523K Density (g/cc)</strong>, etc.:<br>Density in g/cm³ computed during equilibrium calculations by ThermoCalc at the respective temperatures.</p> </li> <li> <p><strong>EQ 1273K MAX BCC</strong>, <strong>EQ 1273K SUM BCC</strong>, etc.:</p> <ul> <li><strong>MAX BCC</strong>: Mole fraction of the most abundant body-centered cubic (BCC) phase predicted for the alloy at the specified temperature, predicted with ThermoCalc</li> <li><strong>SUM BCC</strong>: Total mole fraction of all BCC phases predicted at the specified temperature, predicted with ThermoCalc</li> </ul> </li> <li> <p><strong>EQ 2/3*ST THCD (W/mK)</strong>, <strong>EQ 2/3*ST Density (g/cc)</strong>:<br>Thermal conductivity and density computed at two-thirds of the ThermoCalc-predicted solidus temperature.</p> </li> <li> <p><strong>Pugh_Ratio_PRIOR</strong>:<br>Rule of mixture estimate of Pugh's ratio (ratio of bulk modulus to shear modulus)</p> </li> <li> <p><strong>YS 1000C PRIOR</strong>, <strong>YS 1500C PRIOR</strong>:<br>Yield strength (YS) at 1000°C and 1500°C, predicted using the strength model by Maresca and Curtin</p> </li> <li><strong>Single BCC</strong>:<br>Indicates whether a single BCC phase is predicted (1 for yes, 0 for no) from below Scheil ST down to 1000°C</li> <li> <p><strong>SCHEIL ST, SCHEIL LT:</strong></p> <ul> <li><strong>SCHEIL ST</strong>: Solidus temperature derived from Scheil-Gulliver solidification simulations</li> <li><strong>SCHEIL LT</strong>: Liquidus temperature derived from Scheil-Gulliver solidification simulations</li> </ul> </li> <li> <p><strong>Kou Criteria</strong>, <strong>Kou Criteria Normalized</strong>:<br>Values related to the Kou criteria, which predict susceptibility to solidification cracking. Predicted with ThermoCalc Scheil simulations.</p> </li> <li> <p><strong>SCHEIL BCC_B2</strong>, <strong>SCHEIL BCC_B2#2</strong>, <strong>SCHEIL MAX BCC</strong>:</p> <ul> <li><strong>MAX BCC</strong>: Fraction of the most abundant BCC phase predicted from Scheil simulations</li> <li><strong>BCC_B2</strong>: Prediction of the ordered BCC (B2) phase fraction from Scheil simulations</li> <li><strong>SCHEIL BCC_B2#2</strong>: <ul> <li>This refers to a <strong>secondary BCC </strong>structure predicted by ThermoCalc during the solidification process using Scheil simulation</li> <li>The #2 suffix indicates that this is not the primary BCC phase but another distinct composition set, potentially representing A miscibility gap within the BCC phase</li> </ul> </li> </ul> </li> <li> <p><strong>composition_string</strong>:<br>A string representation of the alloy composition</p> </li> <li> <p><strong>Creep Merit</strong>:</p> <ul> <li>Creep merit index, computed at 2000 degrees Celcius, with units of second per square meter [s m^-2]</li> </ul> </li> <li> <p><strong>25/500/1000/1300/1500/2000 Min Creep [VG/NH/CB/PL5] [1/s]</strong>:</p> <ul> <li>Minimum creep rates at various temperatures (in Celcius), measured in strain per second [1/s]</li> <li><strong>VG</strong>: Viscous Glide (Jaswon-Cottrell) creep model</li> <li><strong>NH</strong>: Nabarro-Herring creep model</li> <li><strong>CB</strong>: Coble creep model</li> <li><strong>PL5</strong>: Power-law creep with n=5</li> </ul> </li> </ol>
Dataset for publication:Dataset for publication: "Application of shot peening to improve fatigue properties via enhancement of precipitation response in high-strength Al-Cu-Li alloys"
<p>The dataset contains a set of experimental data used in preparation for the manuscript "Application of shot peening to improve fatigue properties via enhancement of precipitation response in high-strength Al-Cu-Li alloys." The dataset contains results of fatigue tests, residual stress measurements, nanoindentation measurements, and surface roughness data.</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.