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Orbital-radar.py test data repositroy
<p>The data set provided in this data repository contains the data set used to run the python notebook of the orbital-radar tool - <a href="https://github.com/igmk/orbital-radar">See GitHub page of the orbital-radar.py (DOI </a><a href="../doi/10.5281/zenodo.13375013">10.5281/zenodo.13375013</a><a href="https://github.com/igmk/orbital-radar">).</a><br>The data set consists mainly from ground-based w-band radar at JOYCE avilable at the ACTRIS database CLU (JOYCE data 2021-04-06), and the data set for the ground-based w-band radar at Mindelo during the ASKOS campaign, too (Mindelo data 2022-07-15). The GEOMS data filed from the ground-based w-band radars at JOYCE and Mindelo are provided by the University of Cologne and stored in this ZENODO database. In addition the data base contains a day of ground-based ARM radar data from the Cap Verdes, presented in Rémillard and Tselioudis, 2015, <em>J. Climate</em>, <a href="https://doi.org/10.1175/JCLI-D-15-0066.1" target="_blank" rel="noopener">https://doi.org/10.1175/JCLI-D-15-0066.1</a>. The airborne data set from the AFLUX campaign can be found in the PANGAEA database (AFLUX data 2022). The forward-modelled radar data using ICON output and the PAMTRA tool for the NyAlesund is provided by the University of Cologne and stored in the ZENODO database. </p> <ul> <li>JOYCE data 2021-04-06: <a href="https://doi.org/10.60656/e8c4957887854659">https://doi.org/10.60656/e8c4957887854659</a></li> <li>Mindelo data 2022-07-15: <a href="https://doi.org/10.60656/c5e09106ba0246bc">https://doi.org/10.60656/c5e09106ba0246bc</a> </li> <li>AFLUX data 2022: <a href="https://doi.org/10.1594/PANGAEA.944506">https://doi.org/10.1594/PANGAEA.944506</a> </li> <li>ARM data set from Cap Verdes: <a href="https://doi.org/10.1175/JCLI-D-15-0066.1" target="_blank" rel="noopener">https://doi.org/10.1175/JCLI-D-15-0066.1</a></li> </ul> <p>JOYCE and Mindelow data should be sorted in a YYYY/MM/DD. folder structure to be read by the tool. Other data need a simple path to the folders data are placed in.</p>
Kinematics and timed function tests of facioscapulohumeral muscular dystrophy and myotonic dystrophy
<p>This dataset contains <a href="https://www.opencap.ai/">OpenCap</a> kinematics (body position and joint angles), clinician-scored timed function tests (TFTs), and extracted biomechanical metrics for 129 individuals:</p> <ul> <li> <p>28 participants with facioscapulohumeral muscular dystrophy (FSHD)</p> </li> <li> <p>58 participants with myotonic dystrophy (DM)</p> </li> <li> <p>43 participants with typical movement</p> </li> </ul> <p>For more details, please see the accompanying paper (in submission):</p> <blockquote> <p><strong>Video-based biomechanical analysis captures disease-specific movement signatures of different neuromuscular diseases</strong></p> <p>Parker S. Ruth*, Scott D. Uhlrich*, Constance de Monts, Antoine Falisse, Julie Muccini, Sydney Covitz, Shelby Vogt-Domke, John Day, Tina Duong,† Scott Delp†</p> <p>*Contributed equally, †Contributed equally</p> </blockquote>
Test dataset for omero-vitessce
<h1>Test datasets for omero-vitessce</h1> <p>Dataset designed for testing the omero-vitessce <a href="https://github.com/NFDI4BIOIMAGE/omero-vitessce" target="_blank" rel="noopener">(https://github.com/NFDI4BIOIMAGE/omero-vitessce</a>) plugin for OMERO (<a href="https://www.openmicroscopy.org/omero/" target="_blank" rel="noopener">https://www.openmicroscopy.org/omero/</a>). The omero-vitessce repository contains a cropped version of this dataset for automated testing (<a href="https://github.com/NFDI4BIOIMAGE/omero-vitessce/tree/main/test/data/MB266" target="_blank" rel="noopener">https://github.com/NFDI4BIOIMAGE/omero-vitessce/tree/main/test/data/MB266</a>).</p> <h2>Files</h2> <ul> <li><code>MAX_MBEN_ff_Xenium_0018446_MB-266_DAPI_2024-01-23_12.47.34_Fused_405nm_corr_cropped.png</code> = PNG image with the DAPI channel.</li> <li><code><span>MAX_MBEN_ff_Xenium_0018446_MB-266_DAPI_2024-01-23_12.47.34_Fused_405nm_corr_cropped_cp_masks.png</span></code>= Cell segmentation mask pixel values correspond to cell identities, 0 = background).</li> <li><code>cells.csv</code> = </li> <li><code>embeddings.csv</code> = UMAP embeddings for drawing an interactive scatterplot.</li> <li><code>feature_matrix.csv</code> = Transcript counts in each cell.</li> <li><code>transcripts.csv</code> = Gene name and coordinates (pixel) of each transcript.</li> <li><code>VitessceConfig.json</code> = Example configuration file generated by the omero-vitessce plugin for the Vitessce, an equivalent file can be generated by using the form provided by the plugin in OMERO.web.</li> </ul> <p>See the repository README file for more details on the formats of these files:<a href="https://github.com/NFDI4BIOIMAGE/omero-vitessce?tab=readme-ov-file#config-files"> </a><a href="https://github.com/NFDI4BIOIMAGE/omero-vitessce?tab=readme-ov-file#config-files" target="_blank" rel="noopener">https://github.com/NFDI4BIOIMAGE/omero-vitessce?tab=readme-ov-file#config-files</a></p> <h2>Usage</h2> <ol> <li>Add the omero-web-zarr and omero-vitessce plugins to your OMERO.web installation.</li> <li>Import the images into OMERO in the same dataset.</li> <li>Attach all the .csv data files.</li> <li>Use the form in the "Vitessce" tab of the right-panel to generate a configuration file and open the Vitessce viewer.</li> </ol> <p>See the repository README file for more details on usage (<a href="https://github.com/NFDI4BIOIMAGE/omero-vitessce?tab=readme-ov-file#usage" target="_blank" rel="noopener">https://github.com/NFDI4BIOIMAGE/omero-vitessce?tab=readme-ov-file#usage</a>) and installation (<a href="https://github.com/NFDI4BIOIMAGE/omero-vitessce?tab=readme-ov-file#installation" target="_blank" rel="noopener">https://github.com/NFDI4BIOIMAGE/omero-vitessce?tab=readme-ov-file#installation</a>)</p> <h2>Data Sources</h2> <p>Adapted from the full original data at: <a href="https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1093" target="_blank" rel="noopener">https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1093</a> (<a href="https://doi.org/10.6019/S-BIAD1093">10.6019/S-BIAD1093</a>).</p> <p>The original data were produced and analysed in the course of this study:</p> <p><a href="https://www.biorxiv.org/content/10.1101/2024.04.03.586404v1" target="_blank" rel="noopener">https://www.biorxiv.org/content/10.1101/2024.04.03.586404v1</a></p>
Efficient Detection of Test Interference in C Projects (Artifact)
<p>This record provides research artifacts for the article "Efficient Detection of Test Interference in C Projects", accepted and to be presented at <a href="https://conf.researchr.org/home/ase-2024">ASE 2024</a>. Please refer to the README.md in the tgz file for details about the artifact and how it relates to the manuscript describing our study. Please also see our related Zenodo record with the container images used in the study: <a href="https://doi.org/10.5281/zenodo.7935821" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7935821</a>.</p>
FLOATECH WP3 experimental dataset : wave-tank hybrid testing of a 10 MW turbine based on a spar platform (ECN)
<p>This dataset presents the experimental measurements made in the Hydrodynamic and Ocean Engineering wave tank of Ecole Centrale de Nantes, in France, with the model of a 10 MW turbine supported by a spar platform at a scale 1:40. </p> <p>The tests were performed using a real-time hybrid testing method (or software-in-the-loop) called SoftWind presented and published in Ocean Engineering (the paper is available at this <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). </p> <p> </p> <p><strong>Presentation of the experimental model:</strong></p> <p>The model is presented in details in the provided Excel file "FLOATECH_C3_Project data and model description.xlsx". </p> <p> </p> <p><strong>In the dataset:</strong></p> <p>The measurement files of the tests are gathered in folders by "series", and each test file has a test number. The series and the test conditions of each run are detailed in the provided Excel file "FLOATECH_C3_Database_Matrix.xlsx". </p> <p>Decay tests, pull-out tests and hammer tests were performed and are given in the dataset. </p> <p> </p> <p><strong>Real-time simulation models</strong></p> <p>The numerical models used in the real-time OpenFAST simulations are also provided in the compressed file "RT Simulations files.zip". </p> <p> </p> <p><strong>Data used in the published paper:</strong></p> <p>Some of the tests were used in the paper (see <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). The corresponding test numbers are given in the table below. </p> <table> <tbody> <tr> <td><strong>Load cases</strong></td> <td><strong>Hs (m)</strong></td> <td><strong>Tp (s)</strong></td> <td><strong>Uhub (m/s)</strong></td> <td><strong>TI (%)</strong></td> <td><strong>Wave dir. (°)</strong></td> <td><strong>Wind dir(°)</strong></td> <td><strong>TestNum 1C</strong></td> <td><strong>TestNum 3C</strong></td> <td><strong>TestNum 5C</strong></td> </tr> <tr> <td>1.2</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>0</td> <td>269</td> <td>268</td> <td>270</td> </tr> <tr> <td>2.1</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>25</td> <td>275</td> <td>307</td> <td>281</td> </tr> </tbody> </table> <p> </p> <p> </p>
Test dataset for "Steam condensation scaled experiment in the presence of non-condensable gases for small modular reactor containment passive safety"
<p>This study presents scaled experiments using steam condensation with non-condensable gas (NCG)—helium (He), simulating hydrogen, and nitrogen (N<sub>2</sub>)—as these experiments are pivotal for water-cooled reactor passive containment cooling system (PCCS) design and analysis. Research into PCCSs for small modular reactors (SMRs) is especially important in light of SMR system design; however, studies in the literature reflect limitations due to test geometry and operational condition variations, without considering SMR prototypic design. To address these challenges, a scaled test facility was developed to accurately replicate SMR PCCSs. This facility includes vertical down-flow condensing test sections with 1-, 2-, and 4-in.-diameter condensing tubes, accompanied by annular water cooling. Experiments were conducted using both superheated and saturated steam, with steam mass flow rates in the presence of NCG varying from: (a) 55 to 66 kg/hr. of steam, and 1.8 to 22 kg/hr. of He (as the NCG); (b) 58 to 63 kg/hr. of steam, and 4.4 to 13.3 kg/hr. of N<sub>2</sub> (as the NCG). Test data were collected on (a) the axial temperatures of the annular cooling water; (b) the outer wall temperature of the condensers; and (c) the mass flow rate, temperature, and pressure at the test section inlets and outlets. These primary test data were used in conjunction with a standard data reduction methodology to estimate essential thermal parameters such as heat fluxes, heat transfer coefficients, and condensation rates. The effects of NCGs on steam condensation within the geometry of the scaled test sections were then presented in regard to various testing conditions.</p>
Testing whole slide image for OpenPhi - Open Pathology Interface
<p>An anonymous whole slide image in Philips iSyntax format for running software tests on OpenPhi - Open Pathology Interface (https://zenodo.org/record/4680748#.YNnBxDqxXJU). See the repository (https://gitlab.com/BioimageInformaticsGroup/openphi/) for up to date information.</p>
Lithium-ion battery charge and discharge testing data - current, voltage, soc, ta - at constant levels of power
<p>This dataset helped in the composition of a battery testing and modelling validation, of a lithium-ion battery. The data has the charge and discharge testing acquisition data - current, voltage, soc, ta - at constant levels of power.</p>
Test input dataset for simulation of supercapacitors
<p>Metalwalls is an application for simulating supercapacitors, developed by CNRS and used in the context of H2020 FET-HPC EXA2PRO project. </p> <p>This dataset contains application test input of various sizes. </p>
UTCI-Test-Data
<p>Independent data for testing the accuracy of approximations to values of the Universal Thermal Climate Index (UTCI) calculated by the UTCI-Fiala model, cf. Fig. 12 in https://doi.org/10.1007/s00484-011-0454-1</p> <p>File name: UTCI-Test-Data.txt<br> Purpose: Providing independent test data for assessing the accuracy of UTCI approximations <br> This TAB-delimited file tabulates values of UTCI and the Offset (= UTCI - Ta) in °C calculated by the UTCI-Fiala model in comparison to UTCI approximated by the table look-up approach and the regression polynomial, respectively, for 1000 conditions with different input values of:<br> Ta: air temperature in°C (range: -50 °C to +42 °C)<br> Tr-Ta: difference between mean radiant temperature (Tr) and air temperature in °C (-17 °C to +50 °C)<br> va: wind speed in m/s measured 10 m above ground level (1.0 m/s to 30 m/s)<br> rH: relative humidity in % (5% to 100%)<br> pa: water vapour pressure in kPa (0 kPa to 3.4 kPa)<br> Calculated variables:<br> Offset: Offset (= UTCI - Ta) in °C calculated by the UTCI-Fiala model (range -62 to 13 °C)<br> UTCI: UTCI in °C calculated by the UTCI-Fiala model (range -111 to 43 °C)<br> UTCI_Table: UTCI in °C approximated by the look-up approach using the data table ESM4 from the above referred publication<br> UTCI_polynomial: UTCI in °C approximated by the polynomial regression function from ESM3 of the above referred publication</p> <p>Headers in line 34, first data row in line 35</p>
Mobile Service Robots Crash Testing with Pedestrians: Safety Assessment with Child and Adult Dummies
<p>Data published with the manuscript: “<em>Estimating risks posed by personal mobility devices and service robots to pedestrians: comparative crash testing of adult versus child dummies</em>”. 2021 (Paez-Granados & Billard, 2021)<br> <strong>Summary:</strong></p> <p>This dataset contains injury measures during collisions between a mobile service robot - Qolo - (Paez-Granados, et al, 2018) and pedestrian dummies: male adult Hybrid-III (H3) and child model 3-years-old (Q3). We present multiple collision scenarios for the assessment of pedestrian safety, considering possible impacts at the legs for adult pedestrians, and legs, chest and head for children. In these tests, we followed known methods of safety analysis used in car crash testing and used a standing wheelchair robot "Qolo" as a representative system of mobile service robots, such as delivery bots (robot without occupant), person carrier robots, autonomous wheelchairs, standing mobility vehicles, and other transport robots expected to operate in pedestrian and public areas.</p> <p>The robot was equipped with an experimental front structure allowing different bumper heights and measurement of reaction forces. On the other hand, the human dummies were equipped with standard instrumentation calibrated in accordance with SAE J211-1 for impact tests, thus, the child dummy, Q3 provided head accelerations, neck forces and moments, chest deflections, and accelerations; and pelvis accelerations. The dummy H3 provided forces and moments at the tibia and femur, and accelerations at the pelvis, chest, and head. You will find scripts to read and plot the data, as well as, analysis of the injury risk based on standard crash testing metrics: Head Injury Criteria (HIC-15), head acceleration (a_3ms), Neck Injury (Nij), Chest deflection (CD), and tibia injury (TI).</p> <p><strong>Instructions: </strong></p> <p><em>This dataset contains the following main files:</em></p> <ol> <li><strong><em>Data Description.pdf</em>: </strong>Highly recommended to read through this file for understanding the setup of the collected dataset, as well as, the submitted manuscript.</li> <li><em><strong>collision_test_rawdata.zip</strong>: </em>This file contains all the raw data for each sensor as mentioned in table 3, organized in independent subfolders as described in table 2.<em> ‘test_name’/01_values/’testName’_CFC1000.xlsx</em></li> <li><em><strong>collision_test_analysis.zip</strong>: </em>This file contains all the processed data for each sensor in order to apply known injury metrics (Nij, HIC15, acc_3ms, TI, CC, VCI), organized in independent subfolders as described in table 2.<em>‘test_name’/01_values/’testName’_Analysis_v2.xlsx --> </em>Dataset with filtered sensor data accordingly to SAEJ21.</li> <li><em><strong>collision_data_matlab_structure.zip</strong>:</em><em> Matlab containers with all data - also available as .mat files for easy reading from Code Ocean capsule.</em></li> <li><em><em><strong>scripts-crash-test-service-robots.zip</strong>:</em> processing of the dataset is provided in this file with structure of data in Matlab containers and scripts for visualizing the data (see section III), further analysis scripts in the linked GitHub: <a href="https://github.com/epfl-lasa/crash-tests-service-robots">https://github.com/epfl-lasa/crash-tests-service-robots</a></em></li> </ol>
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>
Blind Prediction Competition - Sera.ta - Seismic Response of Masonry Cross Vaults: Shaking table tests and numerical validations
<p>Masonry vaults play a much relevant role in the seismic response of heritage masonry buildings, ranging from housing to the greatest cathedrals. Acting as both a ceiling and a structural horizontal diaphragm with significant mass, their mechanical behaviour affects the overall seismic response of buildings, in terms of strength, stiffness, and ductility. Moreover, local damage and collapse of vaults may produce significant losses in terms of cultural assets and casualties. In spite of the importance of this topic, the evaluation of the complex three-dimensional behaviour of vaults is still an important challenge for researchers. The main objectives of the present research project are:<br> 1) to better understand the seismic behaviour of masonry cross vaults by means of shaking table tests on both full-scale and small-scale models;<br> 2) to assess the capability of different modelling/analysis approaches to predict the seismic response of these masonry structures.</p> <p>In particular, three sets of shaking table tests are planned:<br> a. Tests on a 1:1 scale model of a brick unreinforced masonry cross vault: to investigate the behaviour of brick masonry cross vaults under different seismic inputs, in terms of damage, displacement capacity and peak acceleration.<br> b. Tests on a 1:1 scale model of a brick reinforced masonry cross vault: to evaluate the effectiveness of reinforcing techniques to repair the vaults tested in a).</p> <p>In addition to the experimental tests, a blind prediction competition is performed to assess the efficacy of different modelling strategies and analysis techniques. The final aims are to improve the safety assessment procedures proposed for historic masonry buildings in Eurocode 8.3 and to provide better seismic assessment techniques and strengthening measures.</p>
Performance Data of an Ice-Melting Probe from Field Tests in two Different Ice Environments
<p>This dataset was acquired at field tests of the steerable ice-melting probe "EnEx-IceMole" (Dachwald et al., 2014). A field test in summer 2014 was used to test the melting probe's system, before the probe was shipped to Antarctica, where, in international cooperation with the MIDGE project, the objective of a sampling mission in the southern hemisphere summer 2014/2015 was to return a clean englacial sample from the subglacial brine reservoir supplying the Blood Falls at Taylor Glacier (Badgeley et al., 2017, German et al., 2021).</p> <p>The standardized log-files generated by the IceMole during melting operation include more than 100 operational parameters, housekeeping information, and error states, which are reported to the base station in intervals of 4 s. Occasional packet loss in data transmission resulted in a sparse number of increased sampling intervals, which where compensated for by linear interpolation during post processing. The presented dataset is based on a subset of this data: The penetration distance is calculated based on the ice screw drive encoder signal, providing the rate of rotation, and the screw's thread pitch. The melting speed is calculated from the same data, assuming the rate of rotation to be constant over one sampling interval. The contact force is calculated from the longitudinal screw force, which es measured by strain gauges. The used heating power is calculated from binary states of all heating elements, which can only be either switched on or off. Temperatures are measured at each heating element and averaged for three zones (melting head, side-wall heaters and back-plate heaters).</p>
Testing and Demonstration Data for DataRig Software
<p>This repository holds the testing and demonstration data for <a href="https://github.com/mscaudill/datarig">DataRig</a>, an opensource software program for downloading datasets from data repositories utilizing RESTful APIs. This repository contains 5 sample datasets.</p> <p> </p> <p><strong>annotations_001.txt</strong></p> <p>This data set is a tab-separated text file containing 6 columns that start on line number 7. The column headers are; </p> <p> 'Number' 'Start Time' 'End Time' 'Time From Start' 'Channel' 'Annotation'</p> <p>There are 13 rows of data under each of these column headers representing the start and end times of annotated events from an eeg recording file in this repository called recording_001.edf. The events describe the behavior of a mouse in 5 sec increments with each behavior being one of 'exploring', 'grooming' or 'rest'.</p> <p> </p> <p><strong>recording_001.edf</strong></p> <p>A European Data Format file consisting of 4 channels of EEG data lasting approximately 1 hour. The times in the annotations_001.txt file are referenced against this file.</p> <p> </p> <p><strong>sample_arr.npy</strong></p> <p>A numpy array of shape (4, 250) with values sequentially running from 0 to 1000.</p> <p> </p> <p><strong>sample_excel.xls</strong></p> <p>An excel file with a single column of 10 numbers from 0-9 sequentially.</p> <p> </p> <p><strong>sample_text.txt</strong></p> <p>A text file with 4 rows containing 250 values per row. The values in the file run from 0 to 1000 sequentially.</p>
Test experiments with distributed acoustic sensing and hydrophone arrays for locating underwater sounds.
<p>Whales and dolphins rely on sound for navigation and communication, making them an intriguing subject for studying language evolution. Traditional hydrophone arrays have been used to record their acoustic behavior, but optical fibers have emerged as a promising alternative. This study explores the use of distributed acoustic sensing (DAS), a technique that detects local stress in optical fibers, for underwater sound recording. An experiment was conducted in Lake Zurich, where a fiber-optic cable and a self-made hydrophone array were deployed. A test signal was broadcasted at various locations, and the resulting data was synchronized and consolidated into files. Analysis revealed distinct frequency responses in the DAS channels and provided insights into sound propagation in the lake. Challenges related to cable sensitivity, sample rate, and broadcast fidelity were identified. This dataset serves as a valuable resource for advancing acoustic sensing techniques in underwater environments, especially for studying marine mammal vocal behavior.</p>
Monotonic Flexural Testing of Corroded Reinforced Concrete Beams Database
<p>The database presented here provides a collection of 804 corroded reinforced concrete beams from 54 experimental programs available in the literature. All beam specimens were tested under simply-supported monotonic three/four-point bending conditions and failed in flexure-dominated modes. The database includes 45 independent variables, 11 dependent variables, 649 corroded members, and 155 uncorroded control beams, tested across 14 countries. Of the corroded beams, 11 were naturally corroded, 30 were corroded via long-term environmental exposure (typically in the form of salt spray or fog), and 608 were corroded artificially through the impressed-current method. All observations (individual beam tests) are statistically independent, as each data entry represents one independent test. Highlighted cells indicate non-reported variables.</p> <p>This database was compiled as part of the author's Ph.D. research for the purpose of predictive machine learning. Published articles applying the database can be accessed at:<br><br><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dibe.2024.100527" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dibe.2024.100527</a></p> <p>Please see the accompanying User's Manual PDF for a complete description of all nomenclature, abbreviations, assumptions, and calculations used to derive the input and response parameters.</p> <p>Please feel free to reach out to the authors if you have any queries or concerns. </p>
Testing of Medtronic Percept PC with MEG phantom
<p>The combination of subcortical Local Field Potential (LFP) recordings and stimulation with Magnetoencephalography (MEG) in Deep Brain Stimulation (DBS) patients enables the investigation of cortico-subcortical communication patterns and provides insights into DBS mechanisms. Until now, these recordings have been carried out in post-surgical patients with externalised leads. However, a new generation of telemetric stimulators makes it possible to record and stream LFP data in chronically implanted patients. Nevertheless, whether such streaming can be combined with MEG has not been tested.</p> <p>In the present study, we tested the most commonly implanted telemetric stimulator – Medtronic Percept PC with a phantom in three different MEG systems: two cryogenic scanners (CTF and MEGIN) and an experimental Optically Pumped Magnetometry (OPM)-based system.</p> <p>The dataset and code herein make it possible to reproduce most of the figures in the paper and examine additional conditions not described in detail in the paper. The data can be useful for developing, testing and benchmarking MEG artefact removal methods.</p>
KSR inhibitor APS-2-79 sensitivity test in JURKAT and ALL-SIL T-cell acute lymphoblastic leukemia cell lines measured by Cell Counting Kit 8
<p>APS-2-79 compound was purchased from MedChemExpress (Monmoutyh Junction, NJ, USA). The 20 mg/ml stock solution was prepared in DMSO. To calculate the IC50, JURKAT and ALL-SIL cells were cultured for 72h with a range of APS-2-79 concentrations (5-15 µM) added as equal volumes. Cells treated with 0.5% DMSO (vehicle) were used as negative control. Cells treated with 10% DMSO were used as positive control. The viability of cells was measured using Cell Counting Kit 8 (Sigma Aldrich) and GloMax Microplate Reader system (Promega) with 450 nm wavelength and 600 nm as reference wavelength. The relevant reads are made from following wells: 2A-2D (15 µM APS-2-79), 3A-3D (12.5 µM APS-2-79), 4A-4D (10 µM APS-2-79), 5A-5D (7.5 µM APS-2-79), 6A-6D (5 µM APS-2-79), 7A-7D (vehicle), 8A-8D (positive control).</p>
Flume Erosion Testing of Unamended and Organic Matter Amended Soil Samples Using an Acoustic Doppler Profiler, 2021
This data accompanies a publication titled "Soil Amended with Organic Matter Increases Fluvial Erosion Resistance of Cohesive Streambank Soil". Briefly, fluvial erosion testing was conducted on soil samples using an indoor flume channel. Soil samples were previously collected from the riparian zone of a river near Virginia Tech's campus in Blacksburg, VA, USA. The soil was subsequently air-dried and stored until use. Prior to erosion testing, soil samples were amended with varying amounts of organic matter (0%, 1%, and 4% OM by mass), compacted to a bulk density of 0.95 KilogramsPerCubicCentiMeters in growth containers, and allowed to mature in a greenhouse setting for 50 days prior to flume erosion testing. An Acoustic Doppler Profiler (ADP) was used to measure soil erosion and collect three-dimensional velocity data during erosion tests; raw velocity and soil depth data for each sample tested were stored in MATLAB files. Follow testing, the soil remaining from each sample was collected, stored, and analyzed for aggregate stability, soil organic matter (SOM), and extracellular polymeric substances (EPS). Additionally, soil temperature, water temperature, and volumetric water content were also measured prior to or during erosion testing. Data collected from this study, and the accompanying ADP MATLAB files, are presented here.
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.