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.
25
datasets available to search
ShareScore release 0.9.0
Dataset results
25 results for “Metallic Glass”
Data supporting: Microscopic observation of two-level systems in a metallic glass model
<p>Dataset of double well potentials sampled from energy landscape exploration of a ternary Lennard-Jones model supporting: "Microscopic observation of two-level systems in a metallic glass model"</p> <p>Thermalised configurations of the ternary Lennard-Jones model are given in the archive (configs.zip) of 1200 atoms at <span class="math-tex">\(T_f\)</span> 0.488, 0.509, 0.558 and 0.617 in the lammps (https://www.lammps.org/) data file format (https://docs.lammps.org/read_data.html).</p> <p>The two datasets each provided as (.zip) archives named dataset1.zip and dataset2.zip</p> <p>Datafiles (.csv) are named nebdf_{:3.3f}_{:05d}.csv where the float is <span class="math-tex">\(T_f\)</span> and the integer is <span class="math-tex">\(\tilde{m}\)</span>. </p> <p>columns of each .csv file are:</p> <p>'transitions', 'forward barriers', 'reverse barriers', 'asymmetry', 'barrier', 'euclidean distance', 'distance along string', 'n_intermediates', 'deltas', 'splittings', 'delta_zeroes', 'gammas', 'PR', 'glass', 'omegas1', 'omegas2', 'omegasts', 'Index 1', 'Index 2', 'Frequency 1>2', 'Frequency 2>1', 'e_1', 'e_2', 'dc', 'Tprep'</p> <p>'glass' is the index of the glassy metabasin sampled</p> <p>omegas1', 'omegas2', 'omegasts' are the curvatures of the minimum energy oaths near the first minimum, second minimum and transition state</p> <p>'e_1', 'e_2' are the energy per atom of the two glass minima </p> <p>'dc' is the typical particle displacement corresponding to <span class="math-tex">\(\sqrt{\dfrac{d^2}{PR}}\)</span></p> <p> </p>
nanoindentation data associated with the publication "On the elastic microstructure of bulk metallic glasses" in Materials&Design 2023
<p>This dataset consists of indentation data measured with a conospherical tip in a Hysitron-Bruker TI980 Nanoindenter on the surface of a <100> Silicon wafer and a polished cross-sectional cut of a Zr65Cu25Al10 bulk metallic glass.</p> <p>It is associated with the following publication: <br>Birte Riechers, Catherine Ott, Saurabh Mohan Das, Christian H. Liebscher, Konrad Samwer, Peter M. Derlet and Robert Maass "On the elastic microstructure of bulk metallic glasses" Materials and Design 229, (2023) 111929. https://doi.org/10.1016/j.matdes.2023.111929</p> <p>All experimental information can be found in this paper and in the accompanying supplementary information.</p> <p>This electronic version of the data was published on the "Zenodo Data repository" found at http://zenodo.org/deposit in the community "Bundesanstalt fuer Materialforschung und -pruefung (BAM)".</p> <p>The authors have copyright to these data. You are welcome to use the data for further analysis, but are requested to cite the original publication whenever use is made of the data in publications, presentations, etc. </p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data format is defined as described below:</p> <p>In total, Fifteen text files exist that result in five different data sets.</p> <p>Two data sets represent measurements on Silicon, these are specifically the topography (mapped height profile) and indentation modulus (Si-topography.txt and Si-modulus.txt). The connected lateral information of these mapped quantities (i.e. Si-X_topography.txt and Si-Y_topography.txt; Si-X_modulus.txt and Si-Y_modulus.txt). This amounts to six .txt files connected to measurements on Silicon.</p> <p>Three data sets represent measurements on the Zr65Cu25Al10 bulk metallic glass. These are specifically the topography (MG-topography.txt), the indentation modulus (MG-modulus.txt), and the curvature-corrected indentation modulus (MG-curvcorr_modulus.txt). The connected lateral information of these mapped quantities (i.e. MG-X_topography.txt and MG-Y_topography.txt; MG-X_modulus.txt and MG-Y_modulus.txt; MG-X_curvcorr-modulus.txt and MG-Y_curvcorr-modulus.txt). This amounts to nine .txt files connected to measurements on the metallic glass.</p> <p>The files are plain text files with the data points separated by commata. Topography data is stated in units of Nanometer, modulus data is stated relative to its mean as unit-less values.</p> <p>Beside the .txt files, one figure (.pdf) with a plot of each data set is provided for reference, and the python code (Riechers_OnTheElasticMicrostructureOfBulkMetallicGlasses_zenodo.ipynb) generating these figures from the data sets is uploaded to this repository as well.</p>
Raw data for article "In Situ Synchrotron X-Ray Diffraction Characterization of Corrosion Products of a Ti-Based Metallic Glass for Implant Applications" Gostin et al 2018
<p>This repository contains raw data for the article "In Situ Synchrotron X-Ray Diffraction Characterization of Corrosion Products of a Ti-Based Metallic Glass for Implant Applications" by Gostin et al. 2018 in Advanced Healthcare Materials, 7, 1800338 (https://doi.org/10.1002/adhm.201800338).</p> <p>Most data comes from one beamtime at the Diamond synchrotron in the UK in May 2016. It consists of X-ray diffraction images taken in situ in artificial corrosion pits on a Ti-based metallic glass.</p> <p>Please see the README file for more details.</p>
Two-time correlation function based on speckle patterns from x-ray photon correlation spectroscopy associated with "Intermittent cluster dynamics and temporal fractional diffusion in a bulk metallic glass" (scientific article published in Nature Communications, 2024)
<p>This dataset consists of contrast data, i.e., the two-time correlation function, based on speckle patterns measured at the at the 8ID-E beamline of the Advanced Photon Source at Argonne National Laboratory.</p> <p>Experimental details are stated in the paper specified under "related work" and in the accompanying supplementary information.</p> <p>You are welcome to use this dataset in compliance with the CC BY 4.0 licence assigned to this dataset.</p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data consists of 32 text files in total, which correspond to the main and lower panel Figure 2 of the main publication. </p> <p>30 of these text files are contrast data, which are named "contrast_DT250s_nn.text" wiith "nn" as the identifier of consecutive data sets going from 1 to 30. Each data set consists of p rows and q columns, DT250s denotes the time resolution of data points, which is 250 s along both row and column values.</p> <p>The data set called "Time_Contrast_1to30s.txt" states the start time in seconds of the first data point of each of the thirty contrast data set.</p> <p>The data set called "ScatteredIntensity.txt" states the scattered intensity at full time resolution, i.e. 2.5 s.</p> <p>The files are plain text files with the data points separated by "space" along rows and "new line" along columns.</p>
FESEM after antibacterial test on Ti- bulk metallic glass compared with Ti6Al4V
<p>Field Emission scanning electron microscopy of Ti40Zr10Cu36Pd14 and Ti6Al4V after 24 h of antibacterial test with Aggregatibacter</p>
dynamic water contact angle measurements of metallic glass coatings
<p>dynamic water contact angle measurements of Zr-Cu-Ag metallic glass coatings. The file named PBT is the non-coated sample. The other three samples were metallic glass coatings.</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>
small angle x-ray scattering from Zr-Cu-Ag metallic glass coatings
<p>small angle x-ray scattering from Zr-Cu-ag metallic glass coating to confirm whether they became amorphous or not. The coating is on PBT substrate. </p>
SI data: A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al
<p>Journal: ACS Combinatorial Science<br> Title: A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al<br> Author(s): Joress, Howie; DeCost, Brian; sarker, suchismita; Braun, Trevor; Jilani, Sidra; Smith, Ryan; Ward, Logan; Laws, Kevin; Mehta, Apurva; Hattrick-Simpers, Jason</p>
FESEM images after cultivating dental plaque on Ti-based metallic glass and Ti6Al4V
<p>Field Emission Scanning Electron Microscopy after culturing dental plaque on Ti<sub>40</sub>Zr<sub>10</sub>Cu<sub>36</sub>Pd<sub>14 </sub>bulk metallic glass compared with Ti6Al4V. </p>
Indirect cytocompatibility and antibacterial properties of Zr-Cu-Ag metallic glass coatings
<p>In order to evaluate a possible antibacterial effect of the released ions, specimens (4x4 mm<sup>2</sup> square metallic glass coatings) were submerged with 7 ml of LB broth and maintained at 37°C inside a shaker (120 rpm) for 1, 5 days, following the same procedure exploited for the ions release evaluation. At each time point the supernatants were collected and used to cultivate bacteria at a defined concentration (1x10<sup>5</sup> cells/ml); bacteria cultivated with supernatants obtained from ions-free PBT were considered as control. The released ions killing activity was evaluated in terms of metabolic activity that was measured by the alamar blue assay as previously detailed. The same procedure with LB medium for hMSC was repeated. </p>
Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks
<p>String-like motions (SLMs) cooperative, "snake"-like movements of particles—are crucial for dynamics in diverse glass formers. Despite their ubiquity, questions persist: do SLMs prefer specific paths? If so, can we predict these paths? Here, in Al-Sm glasses, our iso-configurational ensemble simulations reveal that SLMs indeed follow certain paths. By designing a graph neural network (GNN) to featurize the environment around directional paths, we achieve a high-fidelity prediction of likely SLM pathways solely based on the static structure. GNN gauges a structural measure to assess each path's propensity to engage in SLMs, akin to a "softness" metric, but for paths rather than for atoms. Our GNN interpretation reveals the critical role of the bottleneck zone along paths in steering SLMs. By monitoring "path-softness", we elucidate SLM-favored paths transit from fragmented to interconnected upon glass transition. Our findings reveal that, beyond atoms or clusters, glasses have another dimension of structural heterogeneity: "paths".</p>
Inductively coupled plasma mass spectrometry of ions released from Ti40Zr10Cu36Pd14 bulk metallic glass
<p>Inductively coupled plasma mass spectrometry (ICP-MS) of ions released from Ti40Zr10Cu36Pd14 bulk metallic glass after incubation in artificial saliva for 1 day, 3 days and 7 day to see how much it releases its ion into the medium. </p>
static water contact angle measurements of Ti40Zr10Cu36Pd14 bulk metallic glass and Ti6Al4V surfaces
<p>static water contact angle measurements of Ti40Zr10Cu36Pd14 bulk metallic glass and Ti6Al4V surfaces: the measurments were first taken place on their etched surface and was repeated after they were incubated in orbital shaker in distilled water at 37˚C and 120 rpm for 48 hours. </p> <p> </p>
cytocompatibility analysis of Ti6Al4V and Ti40Zr10Cu36Pd14 bulk metallic glass
<p>using Alamar blue to quantitatively measure the metabolic activity of fibroblast after 24 h and 48 h of cell culture on Ti6Al4V and Ti40Zr10Cu36Pd14 bulk metallic glass. </p>
High though-put proteomics analysis of dental plaque cultured on Ti40Zr10Cu36Pd14 bulk metallic glass and Ti6Al4V surface
<p>High though-put proteomics analysis of dental plaque cultured on Ti40Zr10Cu36Pd14 bulk metallic glass and Ti6Al4V surface</p>
Metabolic activity of aggregatibacter on Ti-based bulk metallic glass compared with Ti6Al4V
<p>Metabolic activity of aggregatibacter on Ti40Zr10Cu36Pd14 bulk metallic glass compared with T6Al4V (used as a control and therefore all values are considered as 100). </p>
Predicting the pathways of string-like motions in metallic glasses via path featurizing graph neural networks
Open the record for dataset details and reuse information.
glassDef dataset: metallic glass deformation
<p>The glassDef dataset contains a set of text-based LAMMPS dump files corresponding to shear deformation tests on different bulk metallic glasses. This includes FeNi, CoNiFe, CoNiCrFe, CoCrFeMn, CoNiCrFeMn, and Co5Cr2Fe40Mn27Ni26 amorphous alloys with data files that exist in relevant subdirectories. Each dump file corresponds to multiple realizations and includes the dimensions of the simulation box as well as atom coordinates, the atom ID, and associated type of nearly 50,000 atoms.</p> <p><strong>Load glassDef Dataset in Python</strong></p> <p>The glassDef dataset may be loaded in Python into Pandas DataFrame. To go into the relevant subdirectory, run cd glass{glass_name}/Run[0-3]/ where “glass_name” denotes the chemical composition. Each subdirectory contains at least three glass realizations within subfolders that are labeled as “Run[0-3]”.</p> <p> </p> <p>> cd glassFeNi/Run0; python</p> <p>> import pandas</p> <p>> df = pandas.read_csv("FeNi_glass.dump",skiprows=9)</p> <p> </p> <p>One may display an assigned DataFrame in the form of a table:</p> <p> </p> <p>> df.head()</p> <p> </p> <p>To learn more about further analyses performed on the loaded data, please refer to the paper cited below.</p> <p> </p> <p><strong>glassDef Dataset Structure</strong></p> <p><strong>glassDef Data Fields</strong></p> <ul> <li>Dump files: “id”, “type”, “x”, “y”, “z”.</li> </ul> <p> </p> <p>glassDef Dataset Description</p> <ul> <li>Paper: Karimi, Kamran, Amin Esfandiarpour, René Alvarez-Donado, Mikko J. Alava, and Stefanos Papanikolaou. "Shear banding instability in multicomponent metallic glasses: Interplay of composition and short-range order." Physical Review B 105, no. 9 (2022): 094117.</li> </ul> <p> </p> <ul> <li>Contact: kamran.karimi@ncbj.gov.pl</li> </ul>
Field Emission Scanning Electron Microscopy Figures from metallic glass antibacterial coatings
<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>S.Aureus</em> after 24 hours. </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.