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.
13,800
datasets available to search
ShareScore release 0.9.0
Dataset results
13,800 results for “Testing”
Flume Experiment Testing the Impact of Artificial Streambank Roots on Velocity, Reynold's Shear Stress, and Turbulent Kinetic Energy using an Acoustic Doppler Profiler
The data published here is expected to accompany one publicly available dissertation (Chapter 4 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Impact of Flexible and Rigid Artificial Roots on Stream Hydrodynamics
A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures
Open the record for dataset details and reuse information.
A dataset recorded during development of an affective brain-computer music interface: testing session
Open the record for dataset details and reuse information.
CODE-test: An annotated 12-lead ECG dataset
<pre># Annotated 12 lead ECG dataset Contain 827 ECG tracings from different patients, annotated by several cardiologists, residents and medical students. It is used as test set on the paper: "Automatic diagnosis of the 12-lead ECG using a deep neural network". https://www.nature.com/articles/s41467-020-15432-4. It contain annotations about 6 different ECGs abnormalities: - 1st degree AV block (1dAVb); - right bundle branch block (RBBB); - left bundle branch block (LBBB); - sinus bradycardia (SB); - atrial fibrillation (AF); and, - sinus tachycardia (ST). Companion python scripts are available in: https://github.com/antonior92/automatic-ecg-diagnosis -------- Citation ``` Ribeiro, A.H., Ribeiro, M.H., Paixão, G.M.M. et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun 11, 1760 (2020). https://doi.org/10.1038/s41467-020-15432-4 ``` Bibtex: ``` @article{ribeiro_automatic_2020, title = {Automatic Diagnosis of the 12-Lead {{ECG}} Using a Deep Neural Network}, author = {Ribeiro, Ant{\^o}nio H. and Ribeiro, Manoel Horta and Paix{\~a}o, Gabriela M. M. and Oliveira, Derick M. and Gomes, Paulo R. and Canazart, J{\'e}ssica A. and Ferreira, Milton P. S. and Andersson, Carl R. and Macfarlane, Peter W. and Meira Jr., Wagner and Sch{\"o}n, Thomas B. and Ribeiro, Antonio Luiz P.}, year = {2020}, volume = {11}, pages = {1760}, doi = {https://doi.org/10.1038/s41467-020-15432-4}, journal = {Nature Communications}, number = {1} } ``` ----- ## Folder content: - `ecg_tracings.hdf5`: The HDF5 file containing a single dataset named `tracings`. This dataset is a `(827, 4096, 12)` tensor. The first dimension correspond to the 827 different exams from different patients; the second dimension correspond to the 4096 signal samples; the third dimension to the 12 different leads of the ECG exams in the following order: `{DI, DII, DIII, AVR, AVL, AVF, V1, V2, V3, V4, V5, V6}`. The signals are sampled at 400 Hz. Some signals originally have a duration of 10 seconds (10 * 400 = 4000 samples) and others of 7 seconds (7 * 400 = 2800 samples). In order to make them all have the same size (4096 samples) we fill them with zeros on both sizes. For instance, for a 7 seconds ECG signal with 2800 samples we include 648 samples at the beginning and 648 samples at the end, yielding 4096 samples that are them saved in the hdf5 dataset. All signal are represented as floating point numbers at the scale 1e-4V: so it should be multiplied by 1000 in order to obtain the signals in V. In python, one can read this file using the following sequence: ```python import h5py with h5py.File(args.tracings, "r") as f: x = np.array(f['tracings']) ``` - The file `attributes.csv` contain basic patient attributes: sex (M or F) and age. It contain 827 lines (plus the header). The i-th tracing in `ecg_tracings.hdf5` correspond to the i-th line. - `annotations/`: folder containing annotations csv format. Each csv file contain 827 lines (plus the header). The i-th line correspond to the i-th tracing in `ecg_tracings.hdf5` correspond to the in all csv files. The csv files all have 6 columns `1dAVb, RBBB, LBBB, SB, AF, ST` corresponding to weather the annotator have detect the abnormality in the ECG (`=1`) or not (`=0`). 1. `cardiologist[1,2].csv` contain annotations from two different cardiologist. 2. `gold_standard.csv` gold standard annotation for this test dataset. When the cardiologist 1 and cardiologist 2 agree, the common diagnosis was considered as gold standard. In cases where there was any disagreement, a third senior specialist, aware of the annotations from the other two, decided the diagnosis. 3. `dnn.csv` prediction from the deep neural network described in the paper. THe threshold is set in such way it maximizes the F1 score. 4. `cardiology_residents.csv` annotations from two 4th year cardiology residents (each annotated half of the dataset). 5. `emergency_residents.csv` annotations from two 3rd year emergency residents (each annotated half of the dataset). 6. `medical_students.csv` annotations from two 5th year medical students (each annotated half of the dataset). </pre>
SM2RAIN test dataset with ASCAT and SMAP satellite soil moisture (plus ERA5 evapotranspiration)
<p>Are you looking for a research contest?</p> <p>Here [SM_RAIN_EVAP_1009points.nc] you can find a 5-year dataset at 1009 points in Italy, the United States, India and Australia of co-located in space and time:</p> <ol> <li>satellite soil moisture (from ASCAT, Wagner et al., 2013, doi:10.1127/0941-2948/2013/0399)</li> <li>evapotranspiration (from ERA5 reanalysis by ECMWF: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview)</li> <li>ground-based rainfall.</li> </ol> <p>and a ~3-year dataset at the same points including soil moisture from SMAP (April-2015 --> December 2017) [SM_SMAP_ASCAT_ETERA5_Pobs_1009opints.nc]</p> <p>The dataset can be used for testing multiple approaches for rainfall estimation from soil moisture, as done in <a href="https://www.linkedin.com/feed/hashtag/?keywords=%23SM2RAIN">#SM2RAIN</a> algorithm (<a href="http://hydrology.irpi.cnr.it/research/sm2rain/">http://hydrology.irpi.cnr.it/research/sm2rain/</a>).</p> <p>The global dataset we have developed is available here: <a href="https://zenodo.org/record/3635932">https://zenodo.org/record/3635932</a></p> <p>The NetCDF file contains all the data, and the figures (PNG files) represent an example of the results we have obtained in the paper and of the new dataset including SMAP.</p> <p><strong>Reference</strong><br> Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. <em>Earth System Science Data</em>, 11, 1583–1601, doi:10.5194/essd-11-1583-2019. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a>.</p> <p>For clarifications and support contact me at <a href="mailto:luca.brocca@irpi.cnr.it?subject=SM2RAIN%20test%20dataset">luca.brocca@irpi.cnr.it</a> </p>
Phindr3D: Test Data Set 1 (primary mouse cortical neurons)
<p>3D confocal image stacks of primary cortical neurons under different treatment conditions to test the functionality of Phindr3D. Explanatory .txt file contained in the ZIP file.</p> <p>Please see the manuscript for details and on how to access the full data set:</p> <p> </p> <p><strong>Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning</strong></p> <p>Philipp Mergenthaler*, Santosh Hariharan*, James M. Pemberton, Corey Lourenco, Linda Z. Penn, David W. Andrews</p> <p><em>PLOS Computational Biology, DOI: <a href="https://dx.doi.org/10.1371/journal.pcbi.1008630">10.1371/journal.pcbi.1008630</a></em></p> <p> </p> <p><strong>Phindr3D is available on GitHub</strong>: <a href="https://github.com/DWALab/Phindr3D">GitHub - DWALab/Phindr3D</a></p> <p> </p>
Phindr3D: Test Data Set 2 (human MCF10A breast cancer organoids)
<p>3D confocal image stacks of human MCF10A breast cancer organoids expressing different oncogenes to test the functionality of Phindr3D. Explanatory .txt file contained in the ZIP files.</p> <p>Please see the manuscript for details and on how to access the full data set:</p> <p> </p> <p><strong>Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning</strong></p> <p>Philipp Mergenthaler*, Santosh Hariharan*, James M. Pemberton, Corey Lourenco, Linda Z. Penn, David W. Andrews</p> <p><em>PLOS Computational Biology, DOI: <a href="https://dx.doi.org/10.1371/journal.pcbi.1008630">10.1371/journal.pcbi.1008630</a></em></p> <p> </p> <p><strong>Phindr3D is available on GitHub</strong>: <a href="https://github.com/DWALab/Phindr3D">GitHub - DWALab/Phindr3D</a></p>
Test set for geodesics
<p>This is a set of 500000 geodesics for the WGS84 ellipsoid; this is an ellipsoid of revolution with equatorial radius <em>a</em> = 6378137 m and flattening <em>f</em> = 1/298.257223563.</p> <p>Each line of the test set gives 10 space delimited numbers</p> <ul> <li>latitude at point 1, φ<sub>1</sub> (degrees, exact)</li> <li>longitude at point 1, λ<sub>1</sub> (degrees, always 0)</li> <li>azimuth at point 1, α<sub>1</sub> (clockwise from north in degrees, exact)</li> <li>latitude at point 2, φ<sub>2</sub> (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>longitude at point 2, λ<sub>2</sub> (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>azimuth at point 2, α<sub>2</sub> (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>geodesic distance from point 1 to point 2, <em>s</em><sub>12</sub> (meters, exact)</li> <li>arc distance on the auxiliary sphere, σ<sub>12</sub> (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>reduced length of the geodesic, <em>m</em><sub>12</sub> (meters, accurate to 0.1 pm)</li> <li>the area between the geodesic and the equator, <em>S</em><sub>12</sub> (m<sup>2</sup>, accurate to 1 mm<sup>2</sup>)</li> </ul> <p>These are computed using high-precision direct geodesic calculations with the given φ<sub>1</sub>, λ<sub>1</sub>, α<sub>1</sub>, and <em>s</em><sub>12</sub>. The distance <em>s</em><sub>12</sub> always corresponds to an arc length σ<sub>12</sub> ≤ 180°, so the given geodesics give the shortest paths from point 1 to point 2. For simplicity and without loss of generality, φ<sub>1</sub> is chosen in [0°, 90°], λ<sub>1</sub> is taken to be zero, α<sub>1</sub> is chosen in [0°, 180°]. Furthermore, φ<sub>1</sub> and α<sub>1</sub> are taken to be multiples of 10<sup>−12</sup> deg and <em>s</em><sub>12</sub> is a multiple of 0.1 μm in [0 m, 20003931.4586254 m]. This results in λ<sub>2</sub> in [0°, 180°] and α<sub>2</sub> in [0°, 180°].</p> <p>The contents of the file are as follows:</p> <ul> <li>100000 entries randomly distributed</li> <li>50000 entries which are nearly antipodal</li> <li>50000 entries with short distances</li> <li>50000 entries with one end near a pole</li> <li>50000 entries with both ends near opposite poles</li> <li>50000 entries which are nearly meridional</li> <li>50000 entries which are nearly equatorial</li> <li>50000 entries running between vertices (α<sub>1</sub> = α<sub>2</sub> = 90°)</li> <li>50000 entries ending close to vertices</li> </ul> <p>The values for <em>s</em><sub>12</sub> for the geodesics running between vertices are truncated to a multiple of 0.1 pm and this is used to determine point 2.</p>
Test data for the transverse Mercator projection
<p>This is a set of 287000 geographic points together with their coordinates in the transverse Mercator projection. The WGS84 ellipsoid (equatorial radius <em>a</em> = 6378137 m, flattening <em>f</em> = 1/298.257223563) is used, with central meridian 0°, central scale factor 0.9996 (the UTM value), false easting = false northing = 0 m.</p> <p>Each line of the test set gives 6 space delimited numbers</p> <ul> <li>latitude, φ (degrees, exact)</li> <li>longitude, λ (degrees, exact — see below)</li> <li>easting (meters, accurate to 0.1 pm)</li> <li>northing (meters, accurate to 0.1 pm)</li> <li>meridian convergence (degrees, accurate to 10<sup>−18</sup> deg)</li> <li>scale (accurate to 10<sup>−20</sup>)</li> </ul> <p>These are computed using high-precision calculations using the exact formulas for the projection, see Lee (1976). The latitude and longitude are all multiples of 10<sup>−12</sup> deg and should be regarded as exact, except that λ = 82.63627282416406551° should be interpreted as exactly (1 − <em>e</em>) 90°, where <em>e</em> is the eccentricity given by <em>e</em><sup>2</sup> = <em>f</em> (2 − <em>f</em> ).</p> <p>The contents of the file are as follows:</p> <ul> <li>250000 entries randomly distributed in φ ∈ [0°, 90°], λ ∈ [0°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [0°, 90°], λ = 0°</li> <li>1000 entries randomly distributed on φ = 0°, λ ∈ [0°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [0°, 90°], λ = 90°</li> <li>1000 entries close to φ = 90° with λ ∈ [0°, 90°]</li> <li>1000 entries close to φ = 0°, λ = 0° with φ ≥ 0°, λ ≥ 0°</li> <li>1000 entries close to φ = 0°, λ = 90° with φ ≥ 0°, λ ≤ 90°</li> <li>2000 entries close to φ = 0°, λ = (1 − <em>e</em>) 90° with φ ≥ 0°</li> <li>25000 entries randomly distributed in φ ∈ [−89°, 0°], λ ∈ [(1 − <em>e</em>) 90°, 90°]</li> <li>1000 entries randomly distributed on φ ∈ [−89°, 0°], λ = 90°</li> <li>1000 entries randomly distributed on φ ∈ [−89°, 0°], λ = (1 − <em>e</em>) 90°</li> <li>1000 entries close to φ = 0°, λ = 90° (φ < 0°, λ ≤ 90°)</li> <li>1000 entries close to φ = 0°, λ = (1 − <em>e</em>) 90° (φ < 0°, λ ≤ (1 − <em>e</em>) 90°)</li> </ul> <p>The entries for φ < 0° and λ ∈ [(1 − <em>e</em>) 90°, 90°] use the “extended” domain for the transverse Mercator projection explained in Sec. 5 of Karney (2011). The first 258000 entries have φ ≥ 0° and are suitable for testing implementations following the standard convention.</p>
Listening test results for sound field synthesis localization experiment
<p>Result files from the the localization experiments described in section 5.1 of Wierstorf [1].</p> <p>The results are visually summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Listening Test Results
<p>A listening test was conducted to determine how well auralizations of aircraft match with recordings of aircraft. This dataset contains the results of the listening test.</p>
Test dataset for copick
<p>A test dataset for <a href="https://github.com/uermel/copick">copick</a>.</p> <p>In order to use the filesystem-based models, adjust the overlay root path in <code>filesystem_overlay_only.json</code> or <code>filesystem.json</code> after extraction.</p>
Interactive maps for the visualization of ESRIUM automated driving tests with various EGNSS localization solutions
<p>In order to make the test results available to a broader audience in an easy manner, we have generated interactive maps. These maps are attached to this report and can be viewed in a web-browser. </p><p>Due to the large number of datasets, we have color-coded them on the map and in the menu. An arbitrary number of datasets can be selected at a time.</p><p>Due to the high accuracy of the EGNSS receivers, one can clearly identify the lane on which the vehicle was driving, and where the vehicle was performing a lane-change. However, the satellite/areal-images are not perfectly geo-referenced, thus one can notice a slight offset between satellite/areal-images and real-world lanes.</p><p> </p><p><strong>How to use the map?</strong></p><ul><li>The map can be used in a similar manner than other map-applications, such as google maps. By using the mouse, you can set the focus on the area of your interest. By using the +/- buttons (top left), you can zoom in/out.</li><li>By hovering over the layer-symbol (top right), a popup emerges. Here, you can select different background-tiles (such as satellite/areal-images). In addition, you can select different datasets which should be visualized on the map.</li></ul><p><strong>Background-tiles:</strong></p><ul><li>Basemap – Sat - Satellite/Areal images (from Basemap) -Symbolic map with high resolution (from Basemap)</li><li>Basemap – HighDPI Symbolic map with high resolution (from Basemap)</li><li>OpenStreetMap - Symbolic map (from OpenStreetMap)</li><li>OpenTopoMap - Symbolic map including topology information (from OpenTopoMap)</li></ul><p><strong>Datasets:</strong></p><ul><li>GNSS (Vehicle) - Position of vehicle, according to on-board GPS receiver</li><li>EGNSS (AsteRx SB3 Pro+) - Position of vehicle, according to AsteRx SB3 Pro+ receiver</li><li>EGNSS (mosaic-X5) - Position of vehicle, according to mosaic-X5 receiver</li><li>EGNSS (mosaic-H) - Position of vehicle, according to mosaic-H receiver</li><li>PVT Mode: EGNSS (AsteRx SB3 Pro+) - PVT Mode of AsteRx SB3 Pro+ receiver</li><li>PVT Mode: EGNSS (mosaic-X5) - PVT Mode of mosaic-X5 receiver</li><li>PVT Mode: EGNSS (mosaic-H) - PVT Mode of mosaic-H receiver</li><li>in-lane Offset Change-Request - Position, at which an in-lane offset change (relative to middle of the current lane) was requested via C-ITS</li><li>Lane Change to left - Position, at which a lane-change towards left was performed </li><li>Lane Change to right - Position, at which a lane-change towards right was performed</li></ul><p>Interactive maps are attached are two precision levels one with 4 and the other in 7 digits. The list files and the corresponding test conditions are listed below. </p><p>Test velocities [km/h]: 90, 110, 130 </p><p>interactive map files: </p><p>speed: 90 km/h</p><ul><li>Testrun_01.html</li><li>Testrun_03.html</li><li>Testrun_04.html</li></ul><p>speed: 110 km/h</p><ul><li>Testrun_05.html</li><li>Testrun_06.html</li><li>Testrun_07.html</li></ul><p>speed: 130 km/h </p><ul><li>Testrun_08.html</li><li>Testrun_09.html</li><li>Testrun_10.html</li></ul>
Experimental data of dissipative embedded column base connections tested under cyclic lateral loading
<p>This experimental dataset is comprised of the following items:</p> <p>(a) the deduced experimental data of conventional/dissipative embedded column base connection specimens, which contains base moment, column drift ratio, and axial shortening responses (TestData.xlsx);</p> <p>(b) photos of each specimen taken during cyclic loading (C-N-0_Test_Photos.7z, D-M1-1_Test_Photos.7z, D-M1-3_Test_Photos.7z, D-M1-5_Test_Photos.7z, D-M2-2_Test_Photos.7z);</p> <p>(c) characteristic videos for each specimen that demonstrate the cyclic behavior (Test_Video.7z);</p> <p>(d) Digital image correlation (DIC) images taken during cyclic loading to obtain strain fields near the steel column/reinforced concrete foundation interface (C-N-0_DIC_Photos.7z, D-M1-1_DIC_Photos.7z, D-M1-3_DIC_Photos.7z, D-M1-5_DIC_Photos.7z, D-M2-2_DIC_Photos.7z); </p> <p>(e) Videos that demonstrate strain fields of column flanges of both conventional and dissipative embedded column base connection specimens (DIC_Video.7z) </p> <p>Please read the "README" file contained in each folder for more detailed information regarding each data.</p> <p> </p>
test data for gliderad2cp
<p>Data files used for the demonstration and testing of the gliderad2cp library https://github.com/bastienqueste/gliderad2cp</p><p>Includes hydrographic and ADCP observations in the Gulf of Oman. Subset from https://zenodo.org/doi/10.5281/zenodo.10075773</p><p> </p>
Test-bed PV system performance data
<p>The data is generated from the on-site data acquisition devices installed at the outdoor testing facilities of the Smart Energy Infrastructure | PHAETHON CoE. </p>
Pseudodynamic testing of a substandard infilled steel structure
<p>Data from the pseudodynamic testing of a substandard, infilled steel building, is provided. The structure and related testing activities are described in:</p> <div> <div> <div> <p><em>"Assessment of existing steel frames: Numerical study, pseudo-dynamic testing and influence of masonry infills" (2021) Luigi Di Sarno, Fabio Freddi, Mario D’Aniello, Oh-Sung Kwon, Jing-Ren Wu, Fernando Guti ́errez-Urzúa, Raffaele Landolfo, Jamin Park, Xenofon Palios, Elias Strepelias,Journal of Constructional Steel Research, 185, https://doi.org/10.1016/j.jcsr.2021.106873</em></p> <p> </p> </div> </div> </div>
Wave basin tests of multi-body floating photovoltaics system and an external floating breakwater.(SUREWAVE project)
<p><span>The aim of the EU Horizon Europe project SUREWAVE (2022-2025) is to develop a floating PV solution for offshore environments. A concrete floating breakwater (FBW) configuration will be designed to provide shelter for the floating PV (FPV) against harsh environmental conditions. MARIN’s scope is to support the hydrodynamic design of the system through numerical simulations and wave basin tests. Basin tests are scheduled at two stages of the project: (1) at early design stage (for a global understanding of the preliminary design); (2) at final design stage (for verification and demonstration). The present dataset contains the reuslts of the early stage design stage wave basin testing.</span></p>
Dataset on the experimental investigation of the seismic response of moment-resisting steel frames using shaking table tests
<h2>Description:</h2> <p>This dataset contains data from experimental shake table tests conducted on a two-storey steel-frame structure, involving linear sweep, white noise, impulse, and seismic excitations (see 'Load_Protocols_v1.0.0.pdf'). The experiments were carried out using the uniaxial shaking table at the <a href="https://www.lbb.rwth-aachen.de/cms/lbb/der-lehrstuhl/~bjlfvq/geraetezentzrum/?lidx=1">RWTHDynLab</a> of the <a href="https://www.lbb.rwth-aachen.de/go/id/eaxh/">Chair of Structural Analysis and Dynamics (LBB) - RWTH Aachen University</a>, in cooperation with the <a href="https://www.stb.rwth-aachen.de/cms/~iozv/STB/">Institute of Structural Steel (STB) - RWTH Aachen</a> and the <a href="https://www.cwe.rwth-aachen.de/home-2/">Center for Wind and Earthquake Engineering (CWE) – RWTH Aachen</a>.</p> <p>The experimental campaign was developed to gain a better understanding of the interaction between the main structure and non-structural components, to investigate the reliability and accuracy of analytical methods to predict response floor spectra and non-structural component acceleration described in various guidelines and seismic codes. Regarding applications of Structural Health Monitoring the necessity for additional sensors on non-structural components was studied. Three single-degree-of-freedom oscillators (SDOFs) were connected to the upper floor representing non-structural components. The test structure was subjected to a total of twelve earthquake excitations with different spectral properties. The main objectives of the test campaign were:</p> <ul> <li>Identification of the modal properties of the test structure.</li> <li>Measurement of the floor response in terms of acceleration and displacement.</li> <li>Determination of the real floor response spectra based on the measurements of the acceleration sensors installed on the first and second floors.</li> <li>Comparison of the expected peak accelerations from the floor response spectra with the peak accelerations measured by the accelerometers attached to the three SDOFs.</li> </ul> <h3>Test structure:</h3> <p>The test structure consisted of a two-storey steel structure which was stabilised in the direction of excitation by moment resisting frames (MRFs). In the transverse direction the global stability was ensured by concentrically braced frames (braces QRo 50x5). The structure was designed in accordance with provisions of prEN-1998-1 for energy dissipation and ductile seismic behaviour. As ductile members were considered the frame beams so that the columns and connections remain undamaged. An illustration of the test structure is depicted in 'Test_Structure_Sketch_v1.0.0.pdf'. The dimensions of the test structure are: 2.40 m in length, 2.40 m in width and 3.78 m in total height (1st storey: 2.02 m; 2nd storey: 1.76 m). Four large steel I-sections, each with a dead weight of 1700 kg, were attached to the main structure as masses and secured by U-Profiles. A tank with a dead load of approx. 100 kg and a volume of 400 litres was mounted onto the first floor. The tank remained empty during this test series. HEA200 profiles (S355-J2) were selected as column profiles, whereas IPE160 profiles (S235-JR) as frame beams. All main and secondary beams were realised by HEA140 profiles (S235-JR). Four L60x6 bars were arranged in a rhombus shape in the floor plane to ensure a diaphragm action. In the area of the MRF connections, the columns were reinforced with an additional double web plate (t = 10 mm) and with three ribs (t = 10 mm) at the level of the beam top flange, the beam bottom flange and the haunch flange. The beam-to-column connections were classified as full strength and semi-rigid in terms of capacity and stiffness. The critical welds connecting the frame transom to the top plate (t = 15 mm) were designed as full penetration groove welds in accordance with the specifications of Annex E of prEN1998-1 for seismically standardised connections. Twelve M16-10.9 bolts were used to ensure the force transfer between the beam and column. All connections of the secondary beams to the main beams were realised as end plate connections to prevent premature failure due to combined loading by normal and shear forces. The arrangement of the secondary beams and the corresponding force transmission was conceptualized in such a way that the frame beams could be replaced after a series of tests without having to remove the masses and the tank. The columns were hinged to the shaking table (see 'Column_Base_Anchorage_v1.0.0.pdf'). Slots in the anchor plates allow the rotation of the support base around the strong axis of the columns. The anchoring to the shaking table was realised using four M24-8.8 threaded rods. To simulate non-structural components, three SDOFs were attached to the centre of the secondary beams that run across the frame transom on the second floor (see 'Test_Structure_v1.0.0.pdf'). The SDOFs consisted of a flat steel and a mass. Depending on the thickness of the flat steel and the position of the mass, the three SDOFs were calibrated so that the natural frequency of the first SDOF matches the natural frequency of the second modal shape of the test structure in the frame direction, and the natural frequency of the third SDOF corresponds the first natural frequency of the structure. The natural frequency of the second SDOF was set so that it lies between those of the other SDOFs, creating a staggered range of dynamic responses.</p> <h3>Test setup:</h3> <p>The shaking table specifications are:</p> <ul> <li>Table size: 3.0x3.0 m</li> <li>Max. specimen mass: 10 t</li> <li>Max. overturning moment: 30 m t</li> <li>Max. actuator stroke: +/- 250 mm</li> <li>Max. table velocity: +/- 1 m/s at rated load</li> <li>Max. table acceleration: +/- 1g at rated load</li> <li>Test frequency: 0 to 50 Hz</li> </ul> <p>The instrumentation scheme of the test setup consisted of accelerometers and displacement tranducers, measuring the excitation provided by the shaking table and the response of the structure. Regarding the global response of the test structure, the recordings of the accelerometers and displacement tranducers indicated in the uploaded file 'Instrumentation_Scheme_v1.0.0.pdf' are provided. </p> <p>The properties of the accelerometers are:</p> <ul> <li>Type: M3701-series</li> <li>Manufacturer: PCB Piezotronics, Inc.</li> <li>Measurement range: +/- 3g</li> <li>Frequency range: 0-500 Hz</li> <li>Sensitivity: 900 mV/g</li> <li>Resolution: 2.2e-5g</li> <li>Noise: 1 µg/Hz<sup>-0.5</sup></li> </ul> <p>The properties of the displacement tranducers are:</p> <ul> <li>Type: LZW-M-500</li> <li>Manufacturer: WayCon Positionsmesstechnik GmbH</li> <li>Measurement range: +/- 250 mm</li> <li>Linearity: +/- 0.05%</li> <li>Repeatability: 0.01 mm</li> <li>Displacement force: ≤15 N</li> <li>Displacement speed: ≤5 m/s</li> </ul> <h2>Files:</h2> <ul> <li>Column_Base_Anchorage_v1.0.0.pdf <ul> <li>Photo of the column-base anchorage.</li> </ul> </li> <li>Data_v1.0.0.zip <ul> <li>Contains all data files according to the load protocols.</li> <li>The experimental data is provided as .csv files for each load protocol. </li> </ul> </li> <li>Instrumentation_Scheme_v1.0.0.pdf <ul> <li>.pdf file illustrating the sensor placements on the test structure.</li> </ul> </li> <li>Load_Protocols_v1.0.0.pdf <ul> <li>.pdf file listing all load protocols applied to the structure.</li> </ul> </li> <li>References_v1.0.0.bib <ul> <li>Contains a bibtex reference with the associated publications.</li> </ul> </li> <li>Shake_Table.jpg <ul> <li>Photo of the shaking table without any specimen.</li> </ul> </li> <li>Test_Structure_v1.0.0.pdf <ul> <li>Photo of the shaking table including the test structure.</li> </ul> </li> <li>Test_Structure_Sketch_v1.0.0.pdf <ul> <li>.pdf file illustrating the test structure.</li> </ul> </li> <li>Time_Histories_v1.0.0.pdf <ul> <li>.pdf file including plots of the measurement data.</li> </ul> </li> </ul> <h2>File format of the datasets:</h2> <p>The data is stored in .csv files, where each file contains the following columns (see also 'Instrumentation_Scheme_v1.0.0.pdf'):</p> <ul> <li>Time (s): Time in seconds since the start of the test (time step equals 0.0025 s).</li> <li>Acc_0 (m/s2): Acceleration signal measured in m/s<sup>2</sup> on the shaking table in the direction of excitation (Axis A-A).</li> <li>Acc_1 (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the first floor in the direction of excitation (Axis A-A).</li> <li>Acc_2 (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the second floor in the direction of excitation (Axis A-A).</li> <li>Acc_L (m/s2): Acceleration response of SDOF I measured in m/s<sup>2</sup> in the direction of excitation.</li> <li>Acc_F (m/s2): Acceleration response of SDOF II measured in m/s<sup>2</sup> in the direction of excitation.</li> <li>Acc_H (m/s2): Acceleration response of SDOF III measured in m/s<sup>2</sup> in the direction of excitation.</li> <li>Acc_G (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the second floor in the direction of excitation (Axis B-B).</li> <li>Acc_J (m/s2): Acceleration response of the structure measured in m/s<sup>2</sup> on the second floor in the transverse direction of excitation (Axis B-B).</li> <li>Dis_0 (mm): Displacement signal measured mm on the shaking table in the direction of excitation (Axis A-A).</li> <li>Dis_1 (mm): Displacement response of the structure measured in mm on the first floor in the direction of excitation (Axis A-A).</li> <li>Dis_2 (mm): Displacement response of the structure measured in mm on the second floor in the direction of excitation (Axis A-A).</li> </ul> <p>These data files can easily be uploaded using the pandas library in Python. For example by:</p> <pre><code>import pandas as pd df = pd.read_csv('1_IM_4mm.csv') time = df["Time (s)"] acc_0 = df["Acc_0 (m/s2)"] dis_0 = df["Dis_0 (mm)"]</code></pre> <h2>Contact:</h2> <p>Please send your enquiries regarding the shaking table to <a href="dynamics@lbb.rwth-aachen.de">dynamics@lbb.rwth-aachen.de</a>. Further information can be found on our <a href="https://www.lbb.rwth-aachen.de/cms/lbb/der-lehrstuhl/~bjlfvq/geraetezentzrum/?lidx=1">website</a>.</p> <h2>Usage/License:</h2> <ul> <li>The data is licensed under CC BY-SA 4.0.</li> <li>If you have used our data and are publishing your work, we ask you to please reference both <ul> <li>this database by its DOI, and</li> <li>any publication that is associated with the experiments. See the "References_v1.0.0.bib" for the associated publication references.</li> </ul> </li> </ul> <h2>Fundings:</h2> <ul> <li>Deutsche Forschungsgemeinschaft - <em>Grant number: INST 222/1161-1 FUGG</em>. Einaxialer Schwingtisch für dynamische Modell- und Bauteilversuche.</li> <li>Bundesministerium für Bildung und Forschung - <em>Grant number: 03G0892A</em>. ROBUST – Nutzerorientiertes Erdbebenfrühwarnsystem mit intelligenten Sensorsystemen und digitalen Bauwerksmodellen – Entwicklung Installation und Anwendung von sensorbasierten Monitoringsystemen mit BIM-Integration zur Echtzeit-Schadenerkennung in kritischen Infrastrukturen.</li> </ul>
Training and test dataset of STED images of microtubules in fixed cells
<p>Training and test dataset of microtubule used in the manuscript "Denoising diffusion models for high-resolution microscopy image restoration".</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.