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.
26
datasets available to search
ShareScore release 0.9.0
Dataset results
26 results for “Tensile Test”
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>
Supporting data for "In situ Quantitative Tensile Tests on Antigorite in a Transmission Electron Microscope"
<p>Abstract: The determination of the mechanical properties of serpentinites is essential towards the understanding of the mechanics of faulting and subduction. Here, we present the first in situ tensile tests on antigorite in a transmission electron microscope. A push-to-pull deformation device is used to perform quantitative tensile tests, during which force and displacement are measured, while the microstructure is imaged with the microscope. The experiments have been performed at room temperature on beams prepared by focused ion beam. The specimens are not single crystals despite their small sizes. Orientation mapping indicated that some grains were well-oriented for plastic slip. However, no dislocation activity has been observed even though engineering tensile stress went up to 700 MPa. We show also that antigorite does not exhibit an pure elastic-brittle behaviour since, despite the presence of defects, the specimens underwent plastic deformation and did not fail within the elastic regime. Instead, we observe that strain localizes at grain boundaries. All observations concur to show that under our experimental conditions, grain boundary sliding is the dominant deformation mechanism. This study sheds a new light on the mechanical properties of antigorite and calls for further studies on the structure and properties of grain boundaries in antigorite and more generally in phyllosilicates.</p>
KupferDigital mechanical testing datasets: Brinell hardness, Vickers hardness, and tensile tests
<p><span>The Kupfer<em>Digital</em> project aims to develop digital methods, tools, and data space infrastructures for digitalizing the entire life cycle of copper materials. The mechanical testing process is one of the main chains of such life cycles which generates lots of important testing data about the mechanical properties of the materials and their related materials and testing metadata. To train the digitalization of the mechanical testing process, different kinds of copper alloys were provided for this project, and their mechanical properties were measured by typical methods like Brinell and Vickers hardness and tensile testing. The primary raw testing data as well as the secondary datasets of these tests are provided. The detailed materials specifications, the utilized mechanical testing methods, and the provided datasets are described in the content file. The test data files of heterogeneous structures are processed by the Kupfer<em>Digital</em> digital tools to be converted to standardized machine-readable data files.</span></p>
Tensile Test
<p>Simulation data for a tensile test</p> <p> </p> <p>For basics in Finite Element Analysis, see "Introduction to nonlinear finite element analysis" by Nam H. Kim.</p>
Tensile tests results on artificially aged aluminum-magnesium-silicon alloy blanks subjected to different localized heat treatment strategies
<p>Tensile tests results on artificially aged aluminum-magnesium-silicon alloy blanks subjected to different localized heat treatment strategies </p>
Monotonic Tensile Testing of Corroded Reinforcing Steel Bars Database
<p>The database presented here provides a collection of 1,387 monotonic tensile tests performed on corroded reinforcing steel bars from 26 experimental programs available in the literature. The database includes 1,255 corroded reinforcing bars, 132 uncorroded control samples, 19 independent variables, and 17 dependent variables. Nine of the independent variables are categorical, and ten are numerical. 274 samples were naturally corroded, 271 were corroded under environmentally-induced conditions (typically salt-spray exposure), and 707 were corroded artificially under an impressed-current regime. All observations (individual bar tests) are statistically independent, as each data entry represents one independent test. Empty cells indicate non-reported variables.</p> <p>This database was compiled as part of the primary author's Ph.D. research for the purpose of predictive machine learning and empirical modeling. Published articles applying the database can be accessed at:</p> <ul> <li><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.conbuildmat.2024.137023" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.conbuildmat.2024.137023</a></li> <li><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dibe.2024.100527" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.dibe.2024.100527</span></span></a></li> </ul> <p>Please see the accompanying User's Manual PDF for a complete description of the input and response variables, nomenclature, abbreviations, and formulations included in this database. </p>
Tensile tests on PEEL and LAP hybrid self-pierce riveting joints with and without adhesive
<p>The following repository contains experimental data from tensile tests on PEEL and LAP hybrid self-pierce riveting joints with and without adhesive. The uploaded files are in .xlsx format, and each file reports the time, displacement and force obtained during the experimental tests.</p> <p> </p> <p><strong>NextGenerationEU: National Sustainable Mobility Center CN00000023, Italian Ministry of University and Research Decree n. 1033 - 17/06/2022, Spoke 11 - Innovative Materials & Lightweighting.</strong></p>
Notch Effect in Acrylonitrile Styrene Acrylate (ASA): Tensile and Fracture Tests - FRADDCO project
<p>This dataset contains tensile and fracture tests of specimens made of acrylonitrile–styrene–acrylate (ASA) material, manufactured by fused filament fabrication (FFF). The files include 9 tensile specimens and 72 single-edge-notch bending (SENB) specimens containing U-notches with different nominal notch radii (from 0 mm -crack-like defects- up to 2.0 mm) and fabricated with three different raster orientations: 0/90, 30/−60 and 45/−45.</p>
Cyclic tensile-compressive tests on thin concrete boundary elements with a single layer of reinforcement prone to out-of-plane instability
<p>The growing need for residential housing in Latin American countries has led to the construction of reinforced concrete buildings with wall thicknesses as low as 8-10 cm. Such walls have typically only a single layer of vertical rebars and are therefore particularly susceptible to out-of-plane failure. To investigate the response of the corresponding wall boundary elements, twelve reinforced concrete columns with a single layer of vertical rebars were tested under tension-compression cycles. The objective of this test series was to gain insights into parameters triggering wall instability and out-of-plane failure. The experimental tests investigate the effect of thickness, reinforcement ratio, and eccentricity of the longitudinal rebars with respect to the element axis. This paper summarises the results of the test campaign. The specimen response is analysed at the global and local level, and the influence of the crack pattern on the out-of-plane response of the column and the conditions leading to out-of-plane failure are described. Furthermore, the differences between members with a single layer of vertical rebars to members with two layers are discussed. The influence on the response of the parameters analysed in the experimental campaign is addressed, showing that section with small thickness and large reinforcement content are more prone to out-of-plane failures. Finally, the predictions of existing models are compared to the new experimental data. The entire data set is publically available.</p>
Tensile test results for 3D printed unidirectional composites reinforced with continuous fibres
Open the record for dataset details and reuse information.
Room temperature and elevated temperature tensile test and elastic properties data of Al-alloy EN AW-2618A after different aging times and temperatures
<p><span>The dataset contains two types of data: elastic properties (Young's and shear modulus, Poisson's ratio) between room temperature and 250 °C and a set of tensile tests at different aging times, aging temperatures, and test temperatures. </span></p>
Results of the mechanical characterization by tensile tests and the in vitro cell-biomaterial interaction analyses by WST-1 and Live/Dead of 3D-printed scaffolds generated by PLA, PCL, FF, FD and GelMA
<p>Dataset containing the quantitative results of the mechanical characterization, and the in vitro cell-biomaterial interaction analyses with neural cells after 72 hours and 7 days of cell culture of the following 3D printed scaffolds:</p> <ul> <li>Polylactic acid (PLA)</li> <li>Polycaprolactone (PCL)</li> <li>Conductive Filaflex (FF)</li> <li>Flexdym (FD)</li> <li>GelMA (G)</li> </ul> <p>The Live/Dead results were quantified using ImageJ software to determine area fractions corresponding to live (green) and dead (red) cells.</p>
Dataset of the round-robin programme for longitudinal tensile testing
Open the record for dataset details and reuse information.
PMMA tensile test until failure, loaded in force
<p>This document present the tensile test results for PMMA (acquired from McMaster). Digital Image Correlation (DIC) was used to measure displacements during the test. Results obtained provide the stress-strain plot, the evolution of Poisson's ratio and Young's Modulus over time. Such results could be compared with simulation results or used as part of an experimental dataset.<br> Raw results and a complete dataset are provided with this data package.</p>
AA5086 tensile tests with Portevin-Le Chatelier (PLC) effect, complete raw dataset: DIC images, raw output and elements of post-processing
<p><em>This dataset is associated with a paper published in Materials Science and Engineering: A.<br> DOI: </em><a href="https://doi.org/10.1016/j.msea.2019.01.009">10.1016/j.msea.2019.01.009</a></p> <p>Hardening and 2D digital image correlation data obtained on AA5086 sheets with Portevin-Le Chatelier (PLC) effect and Piobert-Lüder (PL) bands.</p> <ul> <li>rolled sheets</li> <li>28 tensile tests in rolling direction, room temperature</li> <li>constant imposed velocity with nominal strain rate from 1E-4 to 1E-1 Hz</li> <li>2 geometries: <ul> <li>(mac) large, Lw=60mm</li> <li>(mic or mes) small, Lw=4.2mm</li> </ul> </li> </ul> <p><strong>File contents</strong></p> <ul> <li>summary of experiments <ul> <li>libreoffice database format: experiments-summary.ods</li> <li>specifications, details, measurements and post treated quantities of each test</li> </ul> </li> <li>specimens blueprints (.pdf)</li> <li>video record of a test (.mp4)</li> <li>raw DIC images archives <ul> <li>format: <nominalStrainRate><geometry><index>.zip</li> <li>from 300 to 2300 images per test</li> </ul> </li> <li>time series <ul> <li>archived in timeSeries.zip</li> <li>format: <nominalStrainRate><geometry><index>_timeSeries.dat</li> <li>tabulation separated values, one line per picture (previous item)</li> <li>index, force, cross-head displacement, Stress, axial strain</li> </ul> </li> <li>bands kinetics post-treated quantities <ul> <li>archived in bandKinetics.zip, 2 TSV data files (one per geometry)</li> <li>stress at nucleation, strain magnitude jump and instantaneous working area relative elongation rate.</li> </ul> </li> </ul>
Tensile test data of a 9 microns thick aluminium foil
<p>Material testing data from tensile tests performed on an aluminium AA8079 alloy. The specimens are 100mm long and have a cross section of 10x0.009mm^2. Specimens are cut tested in 0°, 10°, ...40°, 45°, 50°, 60°, ....90° clock wise to the direction of rolling during manufacturing of the foil. Unfortunately the precise meaning of "clock wise" vis á vis the rolling direction and side of the foil is not known but is consistent in this text. The naming of the files reveal the direction of the test and the specimen no. for the respective direction, e.g "45_RawDataySpecimen_2" is to the second specimen tested in a direction 45° clock wise to the rolling direction.</p> <p>The tests are described in detail in B. Käck & C. Malmberg, Master thesis: Aluminium foil at multiple length scales, mechanical tests and numerical simulations in abaqus (2015), div. of Solid Mechanics, Lund Institute of Technology, Sweden.</p> <p>The tests were evaluated in "On the stiffness tensor in AA8079 at small and intermediate strains" by E. Andreasson, W. Reheman, P. Ståhle and S. Kao-Walter, submitted for publication. The major discoveries were that 1) plastic deformation appear almost immediately, i.e., practically at zero load, 2) the compliances increase linearly with strain, from the value of the inverse elastic modulus to almost five times that 3) principal material directions are not along the rolling direction but rather 5° to 15° anti-clock wise from the rolling direction and finally 4) there is a minimum stress in the region of between 35° to 45° clock wise from the rolling direction and for symmetry reasons also at 55° to 65° in the anti-clock wise direction.</p>
Datasets from tensile testing of unidirectional carbon fiber composites using different specimen types
<p>The raw tensile data behind figure 8 in the reference:</p> <p>Rajnish Kumar, Lars P Mikkelsen, Hans Lilholt and Bo Madsen, Experimental Method for Tensile Testing of Unidirectional Carbon Fibre Composites Using Improved Specimen Type and Data Analysis, Materials, 14, 3939, https://doi.org/10.3390/ma14143939, 2021.</p> <p>to where a reference should be given. </p>
Dataset and code from tensile test experiments - Rhamnogalacturonan-II dimerization deficiency impairs the coordination between growth and adhesion maintenance in plants
<p>This data set contains brightfield light microscopy images, extensometer reading and other measurments associated with tensile test experiment performed on <em>Arabidopsis thaliana </em>dark grown hypocotyls of various wildtype and mutant plants, along with the prossessing and quantified data as reported in the study "Rhamnogalacturonan-II dimerization deficiency impairs the coordination between growth and adhesion maintenance in plants" (<a href="https://www.biorxiv.org/content/10.1101/2024.11.26.625362v1">https://www.biorxiv.org/content/10.1101/2024.11.26.625362v1</a>). Data was acquired following the method described in the publication. Processing of the raw data was perfomed using the RRQuant workflow (<a href="https://doi.org/10.5281/zenodo.14173186">10.5281/zenodo.14173186</a>).</p>
Mechanical_Test_Tensile
<p>SmartFan Project public data</p>
Sigle fiber tensile tests on recycled basalt fibers_WP5_T5.4_MOST
Open the record for dataset details and reuse information.
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.