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.
2,118
datasets available to search
ShareScore release 0.9.0
Dataset results
2,118 results for “metal”
Hyperspectral X-ray CT datasets of an aluminium phantom containing three metal-based powders
<p><strong>General Data description:</strong></p> <p>This is a set of two hyperspectral (energy-resolved) X-ray CT projection datasets of a multi-phase phantom. It was acquired in a custom-built, laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction, following two hyperspectral scans of a metal, multi-phase phantom. The phantom consists of an external aluminium cylinder, with three holes, each filled with a different metal-based powder (CeO<sub>2</sub>, ZnO, Fe). Each powder provides a unique attenuation signal, with CeO<sub>2</sub> in particular producing a distinct spectral marker which can be measured by an energy-sensitive detector. Two identical scans were acquired, with only the exposure time per projection changed.</p> <p>Note: Zenodo Version 2 of this dataset contains the incorrect version of the 180s, 180 projection phantom dataset, if wishing to analyse the dataset used in the associated hyperspectral paper. This version (Version 3) contains the correct dataset from the paper.</p> <p><strong>File descriptions:</strong></p> <p>Contained is an image (.jpg) of the sample, along with five MATLAB (.mat) data files, as well as a single text (.txt) file. Where necessary, the files have been named to match the dataset they belong to, based on the different exposure times used for each dataset.</p> <p>Phantom_design_measurements.jpg contains a photograph of the physical phantom, combined with a diagram showing full sample measurements.</p> <p>Powder_phantom_scan_geometry.txt gives a breakdown of the full sample and detector geometry used when acquiring the raw projections for both scans.</p> <p>Powder_phantom_30s_30Proj_sinogram.mat contains the 4D sinogram constructed following flatfield normalisation of the raw projection data, where an exposure time of 30 s was used for each projection. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired during scanning. The total number of channels in the file is 200.</p> <p>Powder_phantom_180s_180Proj_sinogram.mat is the 4D sinogram for the dataset, when exposure times of 180 s were used for each projection, following flatfield normalisation. A discontinuity occurs at projection 137 due to an interruption in the scan procedure. The total number of channels in the file is 200.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning. This is the same for both datasets.</p> <p>FF_30s.mat contains the 4D flatfield data acquired when no sample was present, in the case of 30 s exposure times. This data was used to normalise the projection datasets, as the sinogram was constructed. The first 200 channels are included.</p> <p>FF_180s.mat contains the 4D flatfield data for the dataset where 180 s exposure times were used. The first 200 channels are included.</p>
XCT data of metallic feedstock powder with pore size analysis
<p><strong>X-Ray computed tomography (XCT) scan of 11 individual metallic powder particles, made of (Mn,Fe)<sub>2</sub>(P,Si) alloy</strong></p> <p>The data set consists of 4 single XCT scans which have been stitched together [3] after reconstruction.<br> The powder material is an (Mn,Fe)<sub>2</sub>(P,Si) alloy with an average density of 6.4 g/cm³. The particle size range is about 100 - 150 µm with equivalent pore diameters up to 75 µm. The powder and the metallic alloy are described in detail in [1, 2].</p> <p><strong>Data acquisition</strong></p> <p>The data was acquired using a Zeiss Xradia 620 Versa X-ray microscope which provides the opportunity of optical magnification.</p> <table> <caption><strong>Tomographic imaging parameters</strong></caption> <tbody> <tr> <td>XCT system</td> <td>Zeiss Xradia 620 Versa</td> </tr> <tr> <td>Voltage</td> <td>80</td> <td>kV</td> </tr> <tr> <td>Power</td> <td>10</td> <td>W</td> </tr> <tr> <td>Source filtering</td> <td>"<em>LE2</em>" (system specific)</td> <td>-</td> </tr> <tr> <td>Source-object distance</td> <td>10</td> <td>mm</td> </tr> <tr> <td>Object-detector distance</td> <td>10</td> <td>mm</td> </tr> <tr> <td>Geom. magnification</td> <td>2</td> <td>-</td> </tr> <tr> <td>Optical magnification</td> <td>20</td> <td>-</td> </tr> <tr> <td>Native pixel size</td> <td>13.5</td> <td>µm</td> </tr> <tr> <td>Binning</td> <td>2x2</td> <td>px</td> </tr> <tr> <td>Voxel size</td> <td>0.68</td> <td>µm</td> </tr> <tr> <td>No. of projections per scan</td> <td>801</td> <td>1</td> </tr> <tr> <td>No. of scans</td> <td>4</td> <td>-</td> </tr> <tr> <td>Exposure time per projection</td> <td>5</td> <td>s</td> </tr> </tbody> </table> <p> </p> <p><strong>Projection data</strong> (801 single TIFF-files each):</p> <ul> <li>proj_00</li> <li>proj_01</li> <li>proj_02</li> <li>proj_03</li> </ul> <p><strong>Reconstructed data</strong>:</p> <ul> <li>raw-volume (MnFePSi-Powder_80kV_10W_LE2_20x_5s_801_0p68_BHC=2_Stitch_U16_966x1020x2916.raw + header.txt)</li> <li>analyzed data as Volume Graphics Studio MAX 3.4.5 project</li> </ul> <p><strong>Stitched 2D data</strong> (images stitched with ImageJ-Plugin described in [3]<strong>:</strong></p> <ul> <li>Stitched_0deg_Projections.tif</li> <li>Pores+Particles_Analysis.tif</li> </ul> <p> </p> <p>[1] G.-R. Jaenisch, U. Ewert, A. Waske, and A. Funk, “Radiographic Visibility Limit of Pores in Metal Powder for Additive Manufacturing,” Metals, vol. 10, no. 12, p. 1634, Dec. 2020. https://doi.org/10.3390/met10121634</p> <p>[2] X. Miao et al., “Printing (Mn,Fe)2(P,Si) magnetocaloric alloys for magnetic refrigeration applications,” J. Mater. Sci., vol. 55, no. 15, pp. 6660–6668, May 2020. https://doi.org/10.1007/s10853-020-04488-8</p> <p>[3] S. Preibisch, S. Saalfeld, and P. Tomancak, “Globally optimal stitching of tiled 3D microscopic image acquisitions,” Bioinformatics, vol. 25, no. 11, pp. 1463–1465, Jun. 2009.</p>
Dataset supporting the paper "Electronic decoupling of polyacenes from the underlying metal substrate by sp3 carbon atoms. Communications Physics 3, 159 (2020)"
<p>Dataset corresponding to theoretical calculations of the paper "Electronic decoupling of polyacenes from the underlying metal substrate by sp3 carbon atoms". Communications Physics 3, 159 (2020). <a href="https://doi.org/10.1038/s42005-020-00425-y">https://doi.org/10.1038/s42005-020-00425-y</a> </p> <p>Two folders corresponding to pentacene and dihydroheptacene structures on Ag(001):</p> <ul> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> <li>.siesta files: STM images in WsXM format (<a href="http://www.wsxm.eu/">http://www.wsxm.eu/</a>) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).<br> </li> </ul> <p> </p>
Data from: Land use, season, and parasitism predict metal concentrations in Australian flying fox fur
<p>There are two .csv files in this upload. The "Pteropus_metal_data_wide.csv" file contains metal concentrations (reported in ng/g) measured in fur samples collected from <em>Pteropus </em>flying foxes (<em>P. alecto</em>, <em>P. conspicillatus</em>, <em>P. poliocephalus</em>). Flying foxes were captured from 2015-2018 at multiple sites across Australia. The file also contains capture information (e.g. date, location) and biological information (e.g. species, sex, age class, parasitism) for each flying fox. The "Pteropus_metadata.csv" file provides further details on all column names in the primary data file, including the specific metals that were quantified. Detailed information on the study methods and results can be found in the associated Science of the Total Environment publication, "Land use, season, and parasitism predict metal concentrations in Australian flying fox fur" by Sánchez et al.</p>
Organic micropollutants and heavy metals in stormwater runoff of five different catchment types in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (67), heavy metals (8) and standard parameters (9) for stormwater runoff taken from separated sewers of five catchments between 3 and 37 ha in Berlin (Germany). It also includes rain data of analyzed events as separate file. Samples were taken as part of the OgRe research project of Kompetenzzentrum Wasser Berlin (<a href="https://www.kompetenz-wasser.de/en/project/ogre/">www.kompetenz-wasser.de/en/project/ogre/</a>) in 2014 and 2015. Sampling and analytical methods are detailed in "Concentrations of micropollutants in urban stormwater runoff of different land uses" (<a href="https://doi.org/10.3390/w13091312">https://doi.org/10.3390/w13091312</a>). A dataset with concentrations of the urban stream Panke in Berlin during dry and wet weather (samples were taken as part of the same project) is available separately (<a href="https://zenodo.org/record/4633779">https://zenodo.org/record/4633779</a>).</p> <p><strong>Description of fields (concentrations):</strong></p> <ul> <li><strong>SampleID</strong>: unique sample identifier</li> <li><strong>SiteID</strong>: unique site identifier (catchment type) <ul> <li> 1 - OLD: area with typical five-storey perimeter blocks built between 1870 and 1930 (31 ha)</li> <li> 2 - NEW: newer area of 4-8-storey concrete slab buildings built between 1960 and 1980 (16 ha)</li> <li> 3 - STR: 1.3 km of a busy streeat with intersection with traffic lights and bus stops (3 ha)</li> <li> 4 - OFH: a residential area characterized by one-family houses and villas with gardens (17 ha)</li> <li> 5 - COM: a commercial and industrial area of high imperviousness with large flat-roof buildings and yards (37 ha)</li> <li> 6 - PNK: urban stream Panke (characterized by strong stormwater inputs from separate sewer discharges - available in separate dataset)</li> </ul> </li> <li><strong>LocalDateTime</strong>: start time of sampling (local)</li> <li><strong>DateTimeUTC</strong>: start time of sampling (UTC)</li> <li><strong>UTCOffset</strong>: UTC offset to local time in h</li> <li><strong>SampleType</strong>: either "composite" for volume proportional composite sample (all samples from storm sewers) or "single" for grab sample (all stream samples, separate dataset)</li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p><strong>Description of fields (rain data):</strong></p> <ul> <li><strong>SampleID</strong>: sample identifier of matching sample (see above)</li> <li><strong>SiteID and SiteName</strong>: unique site identifier and name (catchment type) (see above)</li> <li><strong>tBeg_rain, tEnd_rain</strong>: begin and end of rain event in local time</li> <li><strong>depth.mm</strong>: rain depth of rain event in mm</li> <li><strong>duration_rain.h</strong>: duration of rain event in h</li> <li><strong>intensity_max_10min.mm_h</strong>: maximum rain intensitity of rain event in 10-min interval in mm/h</li> <li><strong>intensity_mean_event.mm_h</strong>: mean rain intensitity of rain event in mm/h</li> <li><strong>ADD.d</strong>: number of antecedent dry days in days</li> </ul> <p>Rain data was collected by rain gauge network of Berlin waterworks (>40 gauges) — gauge with best correlation between rain depth and event volume in storm sewer was chosen (distances to monitoring sites: 2–6 km).</p> <p>Two data files are provided in comma separated format:</p> <ul> <li>"OgRe_drain.csv" contains concentrations of all stormwater runoff samples taken in separate storm sewers</li> <li>"OgRe_rain.csv" contains rain data for all stormwater runoff samples</li> </ul>
Perhydrobenzyltoluene dehydrogenation using monometallic M/Al2O3 and bimetallic Pt-M/Al2O3 catalysts (M = Co, Ni): Effect of metal content.
<p>Hydrogen production from renewable sources emerges as a key strategy for decarbonizing the energy system. However, the advancement of the hydrogen-based energy economy is delayed by limitations in storage and transportation systems. Over the past decade, H<sub>2 </sub>chemical storage, particularly systems based on liquid organic hydrogen carriers (LOHCs), have emerged as a promising solution, employing reversible catalytic reactions for hydrogen storage within organic compounds [1-3].</p> <p>Platinum-Group-Metal (PGM)-based catalysts have been identified as optimal for LOHC technology [4-7]. However, their high cost and environmental impact set significant barriers for large scale applications. To mitigate this challenge, we investigated the perhydrobenzyltoluene dehydrogenation process to benzyltoluene, focusing on minimizing PGM usage.</p> <p>In this work, we synthesized bimetallic catalysts (Pt-M/Al<sub>2</sub>O<sub>3</sub>) with low Pt-content (0.5 wt.%) and varying second metal loads (M = Co, Ni). Catalysts were prepared using the incipient wetness impregnation method, wincorporating Co and Ni first, followed by a second impregnation of Pt, as described elsewhere [6]. Additionally, monometallic Co/Al<sub>2</sub>O<sub>3</sub> and Ni/Al<sub>2</sub>O<sub>3</sub> catalysts were prepared for comparison. Dehydrogenation tests were conducted in a laboratory-scale batch reactor, with hydrogen release quantified using a flow indicator (Brooks SLA5800).</p> <p>The activity results, summarized in the following figure and attached as a dataset, reveal that monometallic Co/Al<sub>2</sub>O<sub>3</sub> and Ni/Al<sub>2</sub>O<sub>3</sub> exhibited poor dehydrogenation performance, with a Degree of Dehydrogenation (DoD) lower than 10%. Conversely, bimetallic catalysts, particularly those with reduced Ni and Co contents, demonstrated enhanced dehydrogenation activity, achieving optimal rates with a 0.5 wt.% metal load. Upon comparing the activity results, it was observed that Co exhibited greater activity than Ni when considering the same metal content. These findings highlight the superior dehydrogenation promotion capability of Co.</p> <p>In summary, this study underscores the potential of low-metal content and sustainable catalysts as initial steps toward optimizing metal content for LOHC technology. These findings offer promising possibilities for enhancing the efficiency and sustainability of hydrogen storage and release systems, crucial for realizing the full potential of hydrogen as a clean energy carrier.</p>
Dataset- Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)
<p>This repository collects all the data (Figures and Tables) presented in the review article "Advancements in surface finish for additive manufacturing of metal parts: A comprehensive review of Plasma Electrolytic Polishing (PEP)"</p>
X-Shooting ULLYSES: Massive Stars at low metallicity - II. DR1: Advanced optical data products for the Magellanic Clouds
<p>Xshooter optical spectroscopic data of Magellanic Clouds targets observed by the ESO Large Program X-Shooting ULLYSES: Massive Stars at low metallicity (PI: Vink; Porgram ID: 106.2011Z). <br><br>This version is identical to the previous version but includes the LMC and SMC atlases and the static calibration files with the new flux models (all arms) and spline anchor points (only UVB).</p>
Direct observation of electron density reconstruction at the metal-insulator transition in NaOsO3
<p>Open access data set for manuscript "Direct observation of electron density reconstruction at the metal- insulator transition in NaOsO3" published in Physical Review B, 98, 115116 (2018)</p>
Variability and bias in measurements of metals mass fractions in automobile shredder residue
<p>Measured mass fractions of various metals in individually digested test samples of automobile shredder light fraction (single_digestions_ppm.csv) and the calculated means and standard deviations of these (mean_sd_ppm.csv). For all metadata see accompanying readme file Loevik2019_metal_mass_fractions_in_automobile_SLF_Readme.txt.</p>
Toward a Generalizable Machine-Learned Potential for Metal-Organic Frameworks
<ul> <li>This repository contains the dataset used in the publication<br> `Toward Generalizable Machine Learned Potential for Metal-Organic Frameworks` Yue Yifei, Saad Aldin Mohammed, Loh Duane*, Jiang Jianwen*<br> <br> Please each the README.md within each subfolder. For brevity, the data is organized into three sections<br> <br> 1. The dataset in DATASET<br> - The training and testing dataset, including structures of MOFs in extxyz format<br> <br> 2. The training output files and logs in NEQUIP-TRAIN<br> - The conda environment details, training scripts and logs<br> - Also Training and testing metrics in csv files<br> - This is split into two zip files NEQUIP-TRAIN1 and NEQUIP-TRAIN2 due to their size<br> <br> 3. Examples of using the developed models in MD simulations<br> - Including LAMMPS scripts, data file and environment details used in our scalability tests<br> - The complied Nequip-patched LAMMPS version is also provided<br> - Details on how to use our models - we used a default model that is slower but more accurate in our study but faster models are also developed.</li> </ul>
17O-EPR determination of the structure and dynamics of copper single-metal sites in zeolites
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>spc</strong>, <strong>par</strong>, <strong>m</strong>, <strong>f34</strong>,<strong> xyz</strong>, <strong>out</strong>, <strong>in</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA</strong>,<strong> spc </strong>and<strong> par.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>Periodic DFT computations with(out) filename extensions <strong>out</strong> and <strong>f34</strong> in ASCII format.</li> <li>Molecular cluster DFT computations with filename extensions <strong>in</strong> and <strong>out</strong> in ASCII format.</li> <li>Geometry information of cluster models is stored in <strong>xyz</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>Q-band and X-band Pulsed-EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with SHQ cavity and ER035 M NMR gaussmeter produced by Bruker.</li> <li>Periodic DFT computations were generated using distributed parallel version of CRYSTAL17 code.</li> <li>Molecular cluster DFT computations were generated using the ORCA (v4.2.1) code.</li> <li><strong>If t</strong> <ul> <li>Files in <strong>PARACAT_WP3_20210625_01_CW</strong> folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and spc/par formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_02_HYSCORE</strong> folder includes HYSCORE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_03_ESE</strong> folder includes ESE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_04_ENDOR</strong> folder includes ENDOR spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_05_MATLAB</strong> folder includes computer simulations/analyses of the EPR measurements; data are in m formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_06_DFT </strong>folder includes periodic and cluster DFT computation inputs, outputs and geometries in ASCII format.</li> </ul> </li> </ul> <ul> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>ESE</strong> – Electron Spin Echo detected EPR, <strong>HYSCORE</strong> – HYperfine Sublevel CORrelation spectroscopy, <strong>ENDOR</strong> – Electron Nuclear DOuble Resonance spectroscopy, <strong>DFT </strong>– Density Functional Theory, <strong>CHA </strong>– Chabazite, zeolite topology.</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> <li>abbreviations: <strong>6MR, 8MR </strong>are the Cu docking sites; <strong>2Al-3NN</strong>, <strong>2Al-2NN</strong>, <strong>1Al</strong> are the different aluminium distributions analysed; <strong>1w</strong>, <strong>2w</strong>, <strong>3w, 4w</strong> indicates the number of water ligands considered in the models; <strong>eq</strong> and <strong>ax</strong> indicates equatorial and axial ligands. Periodic DFT computations with filename extension <strong>.f34</strong> include structural/symmetry information of optimized structure. Molecular cluster DFT computations with filename extension <strong>.in</strong>/<strong>.out</strong>/<strong>.xyz</strong> are inputs, outputs, and structure of cluster models.</li> </ul> </li> </ul>
Database of Uniaxial Cyclic and Tensile Coupon Tests for Structural Metallic Materials
<p><strong>Database of Uniaxial Cyclic and Tensile Coupon Tests for Structural Metallic Materials</strong></p> <p> </p> <p><strong>Background</strong></p> <p>This dataset contains data from monotonic and cyclic loading experiments on structural metallic materials. The materials are primarily structural steels and one iron-based shape memory alloy is also included. Summary files are included that provide an overview of the database and data from the individual experiments is also included.</p> <p>The files included in the database are outlined below and the format of the files is briefly described. Additional information regarding the formatting can be found through the post-processing library (https://github.com/ahartloper/rlmtp/tree/master/protocols).</p> <p><strong>Usage</strong></p> <ul> <li>The data is licensed through the Creative Commons Attribution 4.0 International.</li> <li>If you have used our data and are publishing your work, we ask that you please reference both: <ol> <li>this database through its DOI, and</li> <li>any publication that is associated with the experiments. See the Overall_Summary and Database_References files for the associated publication references.</li> </ol> </li> </ul> <p><strong>Included Files</strong></p> <ul> <li>Overall_Summary_2022-08-25_v1-0-0.csv: summarises the specimen information for all experiments in the database.</li> <li>Summarized_Mechanical_Props_Campaign_2022-08-25_v1-0-0.csv: summarises the average initial yield stress and average initial elastic modulus per campaign.</li> <li>Unreduced_Data-#_v1-0-0.zip: contain the original (not downsampled) data <ul> <li>Where # is one of: 1, 2, 3, 4, 5, 6. The unreduced data is broken into separate archives because of upload limitations to Zenodo. Together they provide all the experimental data.</li> <li>We recommend you un-zip all the folders and place them in one "Unreduced_Data" directory similar to the "Clean_Data"</li> <li>The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.</li> <li>There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the unreduced data.</li> <li>The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.</li> </ul> </li> <li>Clean_Data_v1-0-0.zip: contains all the downsampled data <ul> <li>The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.</li> <li>There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the clean data.</li> <li>The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.</li> </ul> </li> <li>Database_References_v1-0-0.bib <ul> <li>Contains a bibtex reference for many of the experiments in the database. Corresponds to the "citekey" entry in the summary files. </li> </ul> </li> </ul> <p> </p> <p><strong>File Format: Downsampled Data</strong></p> <p>These are the "LP_<N>_Specimen_<M>_processed_data.csv" files in the "Clean_Data" directory. The <N> is the load protocol designation and the <M> is the specimen number for that load protocol and material source. Each file contains the following columns:</p> <ul> <li>The header of the first column is empty: the first column corresponds to the index of the sample point in the original (unreduced) data</li> <li>Time[s]: time in seconds since the start of the test</li> <li>e_true: true strain</li> <li>Sigma_true: true stress in MPa</li> <li>(optional) Temperature[C]: the surface temperature in degC</li> </ul> <p>These data files can be easily loaded using the pandas library in Python through:</p> <pre><code class="language-python">import pandas data = pandas.read_csv(data_file, index_col=0)</code></pre> <p>The data is formatted so it can be used directly in RESSPyLab (https://github.com/AlbanoCastroSousa/RESSPyLab). Note that the column names "e_true" and "Sigma_true" were kept for backwards compatibility reasons with RESSPyLab.</p> <p> </p> <p><strong>File Format: Unreduced Data</strong></p> <p>These are the "LP_<N>_Specimen_<M>_processed_data.csv" files in the "Unreduced_Data" directory. The <N> is the load protocol designation and the <M> is the specimen number for that load protocol and material source. Each file contains the following columns:</p> <ul> <li>The first column is the index of each data point</li> <li>S/No: sample number recorded by the DAQ</li> <li>System Date: Date and time of sample</li> <li>Time[s]: time in seconds since the start of the test</li> <li>C_1_Force[kN]: load cell force</li> <li>C_1_Déform1[mm]: extensometer displacement</li> <li>C_1_Déplacement[mm]: cross-head displacement</li> <li>Eng_Stress[MPa]: engineering stress</li> <li>Eng_Strain[]: engineering strain</li> <li>e_true: true strain</li> <li>Sigma_true: true stress in MPa</li> <li>(optional) Temperature[C]: specimen surface temperature in degC</li> </ul> <p>The data can be loaded and used similarly to the downsampled data.</p> <p> </p> <p><strong>File Format: Overall_Summary</strong></p> <p>The overall summary file provides data on all the test specimens in the database. The columns include:</p> <ul> <li>hidden_index: internal reference ID</li> <li>grade: material grade</li> <li>spec: specifications for the material</li> <li>source: base material for the test specimen</li> <li>id: internal name for the specimen</li> <li>lp: load protocol</li> <li>size: type of specimen (M8, M12, M20)</li> <li>gage_length__mm_: unreduced section length in mm</li> <li>avg_reduced_dia__mm_: average measured diameter for the reduced section in mm</li> <li>avg_fractured_dia_top__mm_: average measured diameter of the top fracture surface in mm</li> <li>avg_fractured_dia_bot__mm_: average measured diameter of the bottom fracture surface in mm</li> <li>fy_n__mpa_: nominal yield stress</li> <li>fu_n__mpa_: nominal ultimate stress</li> <li>t_a__deg_c_: ambient temperature in degC</li> <li>date: date of test</li> <li>investigator: person(s) who conducted the test</li> <li>location: laboratory where test was conducted</li> <li>machine: setup used to conduct test</li> <li>pid_force_k_p, pid_force_t_i, pid_force_t_d: PID parameters for force control</li> <li>pid_disp_k_p, pid_disp_t_i, pid_disp_t_d: PID parameters for displacement control</li> <li>pid_extenso_k_p, pid_extenso_t_i, pid_extenso_t_d: PID parameters for extensometer control</li> <li>citekey: reference corresponding to the Database_References.bib file</li> <li>yield_stress__mpa_: computed yield stress in MPa</li> <li>elastic_modulus__mpa_: computed elastic modulus in MPa</li> <li>fracture_strain: computed average true strain across the fracture surface</li> <li>c,si,mn,p,s,n,cu,mo,ni,cr,v,nb,ti,al,b,zr,sn,ca,h,fe: chemical compositions in units of %mass</li> <li>file: file name of corresponding clean (downsampled) stress-strain data</li> </ul> <p> </p> <p><strong>File Format: </strong><strong>Summarized_Mechanical_Props_Campaign</strong></p> <p>Meant to be loaded in Python as a pandas DataFrame with multi-indexing, e.g.,</p> <pre><code class="language-python">tab1 = pd.read_csv('Summarized_Mechanical_Props_Campaign_' + date + version + '.csv', index_col=[0, 1, 2, 3], skipinitialspace=True, header=[0, 1], keep_default_na=False, na_values='')</code></pre> <ul> <li>citekey: reference in "Campaign_References.bib".</li> <li>Grade: material grade.</li> <li>Spec.: specifications (e.g., J2+N).</li> <li>Yield Stress [MPa]: initial yield stress in MPa <ul> <li>size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign</li> </ul> </li> <li>Elastic Modulus [MPa]: initial elastic modulus in MPa <ul> <li>size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign</li> </ul> </li> </ul> <p> </p> <p><strong>Caveats</strong></p> <ul> <li>The files in the following directories were tested before the protocol was established. Therefore, only the true stress-strain is available for each: <ul> <li>A500</li> <li>A992_Gr50</li> <li>BCP325</li> <li>BCR295</li> <li>HYP400</li> <li>S460NL</li> <li>S690QL/25mm</li> <li>S355J2_Plates/S355J2_N_25mm and S355J2_N_50mm</li> </ul> </li> </ul>
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>
Research data for Investigation of coatings and metallic materials for icephobic properties, dataset
<p>This dataset is used in deliverable 3.5, 'Investigation of coatings and metallic materials for icephobic properties', where you can get more information.</p> <p>The Dataset includes:</p> <table> <tbody> <tr> <td>Coating Data</td> </tr> <tr> <td>Metalic materials data</td> </tr> <tr> <td>Coating Freezing spike</td> </tr> <tr> <td>Coating Atmospheric freezing</td> </tr> <tr> <td>Coating contact angle</td> </tr> <tr> <td>Coating Ice adhesion</td> </tr> <tr> <td>Submerged freeze depression</td> </tr> <tr> <td>Metalic materials droplet freezing</td> </tr> <tr> <td>Metalic materials Droplet contact angle</td> </tr> <tr> <td>Coating freeze depression brine test</td> </tr> <tr> <td>Coating freeze depression CFT</td> </tr> <tr> <td>Metalic amorphous materials freeze depression</td> </tr> <tr> <td>Metalic pure materials freeze depression</td> </tr> </tbody> </table>
Medieval Metal
<p>Dataset related to an inventorization project of medieval metalwork artefacts excavated in historic towns in Flanders (Belgium). Refer to project page for more information and references: <a href="https://www.projectcest.be/wiki/Publicatie:Middeleeuws_Metaal">https://www.projectcest.be/wiki/Publicatie:Middeleeuws_Metaal</a></p> <p> </p> <p>Het eindverslag werd gepubliceerd als SYNTAR 20: <a href="https://doi.org/10.55465/UNEW5909">https://doi.org/10.55465/UNEW5909</a></p> <p>The final report of the project was published as SYNTAR 20: <a href="https://doi.org/10.55465/UNEW5909">https://doi.org/10.55465/UNEW5909</a></p>
Metal Intrusion Model Results
<p>Model results showing the percentage of geochem preserved and rounding facilitated in the pallasite formation region; all parameters used are included in csv results files.</p>
Data set for the journal article: Colloidal-ALD Grown Metal Oxide Shells Enable the Synthesis of Photoactive Ligand/ Nanocrystal Composite Materials
<p>The data for each figure of the main manuscript is included in this folder.</p> <p>Figure 1 is not included as it contains no data.</p> <p>The folder for Figure 2 contains a sub-folder for the EDX and NMR data of 9-ACA/PbS@AlOx. The NMR data was processed by Mestrenova.</p> <p>The folder for Figure 3 contains optical absorption spectrum data of 9-ACA/PbS@AlOx.</p> <p>The folder for Figure 4 contains NMR data which was processed by Mestrenova. It contains the data for 9-ACA/CuInS2@AlOx, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx.</p> <p>The folder for Figure 5 is made of three sub-folders for figure 5A, 5B and 5C. 5A and 5B contain optical absorption for the CuInS2 and CsPbBr3 datasets while 5C contain time resolved data for CsPbBr3.</p> <p>The folder for Figure 6 contains time resolved PL for the as synthesized CsPbBr3, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx. The 9-PTA/CsPbBr3@AlOx data contain two decays that span 200 ns (short) or 13.5 us (long).</p> <p>The folder for Figure 7 contains time resolved PL for the as synthesized 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx. For both samples the data contain two decays that span 200 ns (short) or 13.5 us (long). Also an NMR folder is present with the 1H spectrum for 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx.</p> <p> </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>
Dataset for 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination'
<p>Associated data for the manuscript 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination' (doi://10.26434/chemrxiv-2023-djhp2)</p> <p> </p> <p> </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.