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10,553 results for “measurements”
Optical tomography measurements and reconstructions of a multiple-scattering 3d-printed microphantom
<p>This dataset contains 2 sets of measurements of a 3d-printed microphantom, carried out with optical diffraction tomography system at Warsaw University of Technology. The measurements are conducted for 2 different wavelengths: 633nm and 835nm. Also, tomographic reconstructions of these datasets are shown. The reconstructions were computed with 3 algorithms: GPSC [1], MSBP-I [2] and MSBP-E [3]. Additionally, model of the 3D-printed microphantom is given.</p> <p>All files are *.mat files.</p> <p>In the reconstruction files there are 4 variables:</p> <ul> <li>REC - reconstruction matrix with information about 3D refractive index values in the microphantom</li> <li>dx - sample size in the reconstruction in x-y direction</li> <li>dz - sample size in the reconstruction in z direction (if not given, dz=dx)</li> <li>niter - number of iterations that were computed to generate the reconstruction</li> </ul> <p>The variables in the sinogram files are:</p> <ul> <li>dx - sample size in tomographic projections</li> <li>lambda - wavelength</li> <li>M - magnification in the optical system</li> <li>n_immersion - refractive index of the immersion medium</li> <li>NA - numerical aperture of the optical system</li> <li>rayXY - x-y coordinates of vectors representing illumination directions from which tomographic projections were acquired</li> <li>SINOamp - amplitude distribution of tomographic projections</li> <li>SINOph - phase distributions of tomographic projections</li> </ul> <p>The variables in the phantom model files are:</p> <ul> <li>dx - sample size</li> <li>n_immersion - refractive index of simulated immersion</li> <li>n_phantom - refractive index of the phantom model</li> </ul> <p>[1] W. Krauze, “Optical diffraction tomography with finite object support for the minimization of missing cone artifacts,”277<br> Biomed. optics express 11, 1919–1926 (2020)<br> [2] S. Chowdhury, M. Chen, R. Eckert, D. Ren, F. Wu, N. Repina, and L. Waller, “High-resolution 3D refractive index292<br> microscopy of multiple-scattering samples from intensity images,” Optica 6, 1211 (2019).<br> [3] U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach288<br> to optical tomography,” Optica 2, 517 (2015).</p>
Canopy solar-induces chlorophyll fluorescence measurements at seven sites
<p>This dataset will accompany the paper "Direct validation of TROPOMI solar-induced chlorophyll fluorescence products using tower-based measurements reveals shortcomings with the current satellite SIF products" in the Remote Sensing of Environment. This dataset includes the canopy SIF measurements at noon timescale for six sites and a public hourly canopy SIF dataset that accompanies the paper "Mechanistic evidence for tracking the seasonality of photosynthesis with solar induced fluorescence" in the Proceedings of the National Academy of Sciences. All relevant methodological information can be found in the paper: Shanshan, D., Xinjie, L., Jidai, C., Weina, D., and Liangyun, L. in press.</p>
High-resolution topography and layer attitude measurements over Juventae Chasma (Valles Marineris, Mars)
<p>Stereo-derived topography over mounds in Juventae Chasma (Valles Marineris, Mars), including derived measurements (ASCII .csv and .xls). File naming in measurements follows the numbering of NASA MRO HiRiSE stereo pairs.</p> <p>Please note for all DTMs</p> <p>Format: GeoTiff<br> Projection: Equirectangular<br> Datum: Mars 2000 Sphere</p> <p>Bit depth: 32bit</p> <p>Spatial resolution: 1m/pixel</p> <p>HiRise images avalable at:https://hirise.lpl.arizona.edu/<br> (use one of the image numbers listed below in the search box)</p> <p>Stereo pairs:</p> <p>HiRISE 1 (PSP_002590_1765_RED-PSP_002946_1765_RED-DEM_cyli.tif)<br> PSP_002590_1765<br> PSP_002946_1765</p> <p><br> HiRISE 2 (PSP_006915_1760_RED-PSP_007060_1760_RED-DEM_cyli.tif)<br> PSP_006915_1760<br> PSP_007060_1760</p> <p>HiRISE 3 (ESP_015934_1760_RED-ESP_016646_1760_RED-DEM_cyli.tif)<br> ESP_016646_1760<br> ESP_015934_1760</p> <p>HiRISE 4 (ESP_020470_1755_RED-ESP_014378_1755_RED-DEM_cyli.tif)<br> ESP_020470_1755<br> ESP_014378_1755</p> <p>HiRISE 5 (PSP_002379_1755_RED-PSP_002023_1755_RED-DEM_cyli.tif)<br> PSP_002379_1755<br> PSP_002023_1755</p> <p>HiRISE 6 (ESP_016567_1755_RED-ESP_017279_1755_RED-DEM_cyli.tif)<br> ESP_016567_1755<br> ESP_017279_1755</p> <p>HiRISE 7 (PSP_003790_1755_RED-PSP_004291_1755_RED-DEM_cyli.tif)<br> PSP_003790_1755<br> PSP_004291_1755</p> <p>HiRISE 8 (ESP_016145_1775_RED-ESP_017424_1775_RED-DEM_cyli.tif)<br> ESP_016145_1775<br> ESP_017424_1775</p> <p>HiRISE 9 (PSP_008708_1780_RED-PSP_008998_1780_RED-DEM_cyli.tif)<br> PSP_008708_1780<br> PSP_008998_1780</p> <p>HiRISE 10 (ESP_011688_1760_RED-ESP_019613_1760_RED-DEM_cyli.tif)<br> ESP_019613_1760<br> ESP_011688_1760</p>
High-resolution digital topography and layer attitude measurements over Juventae Chasma (Valles Marineris, Mars)
<p>Stereo-derived topography over mounds in Juventae Chasma (Valles Marineris, Mars), including derived measurements (ASCII .csv and .xls). File naming in measurements follows the numbering of NASA MRO HiRiSE stereo pairs.</p> <p>Please note for all DTMs</p> <p>Format: GeoTiff<br> Projection: Equirectangular<br> Datum: Mars 2000 Sphere</p> <p>Bit depth: 32bit</p> <p>Spatial resolution: 1m/pixel</p> <p>HiRise images avalable at:https://hirise.lpl.arizona.edu/<br> (use one of the image numbers listed below in the search box)</p> <p>Stereo pairs:</p> <p>HiRISE 1 (PSP_002590_1765_RED-PSP_002946_1765_RED-DEM_cyli.tif)<br> PSP_002590_1765<br> PSP_002946_1765</p> <p><br> HiRISE 2 (PSP_006915_1760_RED-PSP_007060_1760_RED-DEM_cyli.tif)<br> PSP_006915_1760<br> PSP_007060_1760</p> <p>HiRISE 3 (ESP_015934_1760_RED-ESP_016646_1760_RED-DEM_cyli.tif)<br> ESP_016646_1760<br> ESP_015934_1760</p> <p>HiRISE 4 (ESP_020470_1755_RED-ESP_014378_1755_RED-DEM_cyli.tif)<br> ESP_020470_1755<br> ESP_014378_1755</p> <p>HiRISE 5 (PSP_002379_1755_RED-PSP_002023_1755_RED-DEM_cyli.tif)<br> PSP_002379_1755<br> PSP_002023_1755</p> <p>HiRISE 6 (ESP_016567_1755_RED-ESP_017279_1755_RED-DEM_cyli.tif)<br> ESP_016567_1755<br> ESP_017279_1755</p> <p>HiRISE 7 (PSP_003790_1755_RED-PSP_004291_1755_RED-DEM_cyli.tif)<br> PSP_003790_1755<br> PSP_004291_1755</p> <p>HiRISE 8 (ESP_016145_1775_RED-ESP_017424_1775_RED-DEM_cyli.tif)<br> ESP_016145_1775<br> ESP_017424_1775</p> <p>HiRISE 9 (PSP_008708_1780_RED-PSP_008998_1780_RED-DEM_cyli.tif)<br> PSP_008708_1780<br> PSP_008998_1780</p> <p>HiRISE 10 (ESP_011688_1760_RED-ESP_019613_1760_RED-DEM_cyli.tif)<br> ESP_019613_1760<br> ESP_011688_1760</p>
Accelerometer and Force/Torque Sensor Measurements for Parameter and State Estimation of an Unknown Robot End Effector
<h1>Introduction</h1> <p>This dataset was created as part of a study on the development of an estimator for the contact wrench (force and torque) of an unknown robot end effector. A conference paper from this study has been submitted and accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR 2024) [1]. </p> <p>A force/torque sensor (FTS) was attached to the robot wrist, and the unknown end effector was attached to the FTS. An inertial measurement unit (IMU) was in turn attached to the end effector. The FTS measurement can be decomposed into the (1) sensor bias, (2) contact wrench, and the effects from (3) gravity, (4) inertia, (5) vibrations, and (6) noise. Estimation of the contact wrench requires that the remaining effects are compensated for. The FTS and IMU sensor biases, as well as mass and mass center of the unknown end effector, were estimated as described by Vougioukas [2]. His method requires FTS and IMU samples from 24 specific orientations of the sensors. See his paper for a description of this calibration method.</p> <p>The hardware used to generate this dataset were:</p> <ul> <li>KUKA LBR Med 14 serial robot (KUKA AG, Germany)</li> <li>ATI Gamma FTS (ATI Industrial Automation, Inc., USA)</li> <li>ATI Netbox (ATI Industrial Automation, Inc., USA)</li> <li>MPU6886 IMU (M5Stack, China) </li> <li>Arduino Mega 2580 with a W5500 Ethernet Shield </li> </ul> <h1>Method</h1> <p>The robot was used to move the end effector, FTS, and IMU such that a trajectory could be replicated with high precision and accuracy. The trajectory was a simple rotation about the FTS y-axis. This trajectory and the resulting measurements were performed three times. The sensor signals were sampled during each iteration when:</p> <ol> <li>The robot moved freely without any kind of disturbance (<strong>basline</strong>).</li> <li>The robot moved freely with gentle taps to the robot body, using a rubber hammer (<strong>vibrations</strong>).</li> <li>The robot moved with gentle taps to the body using the hammer, and with a manual force exerted on the end effector (<strong>vibrations and contact</strong>).</li> </ol> <p>The IMU signal was obtained by the Arduino Mega using I2C, and the signal was sent from the Arduino to the external PC using the ethernet shield. This setup resulted in <strong>a phase of the IMU signal by 8416 μs</strong>. This was compensated for in the offline analysis of the study on the contact wrench estimator [1]. The sensor samplig rates were different for each sensor; they were approximately 100 Hz for the robot controller (FTS orientation measurements), 700 Hz for the FTS, and 254 Hz for the IMU. The frequency for each signal can be obtained through the timestamps in the dataset.</p> <h1>Dataset</h1> <p>Each CSV file has a row which serves as the header, which labels the columns of each file. The following nomenclature of the column labels were used:</p> <p><strong>t </strong> - Timestep in microseconds. Epoch time. <br><strong>fx,</strong> <strong>fy, fz</strong> - The force components as measured by the FTS.<br><strong>tx, ty, tz </strong>- The torque components as measured by the FTS.<br><strong>ax, ay, az </strong> - The acceleration components measured by the IMU.<br><strong>gx,gy,gz </strong>- The direction of the gravitational vector in the FTS frame.<br><strong>r11, r12, r13, r21, r22, r23, r31, r32, r33 </strong>- The components of the rotation matrix that represents the FTS orientation in the world frame. (R_wf)</p> <p>The measurements from the FTS and IMU signals from the 24 orientations (as required for the calibration method described by Vougioukas [2]), are stored in <strong>0-calibration_fts-accel.csv</strong>. Additionally, the files <strong>0-steady-state_wrench.csv </strong>and <strong>0-steady-state_accel.csv</strong> contains the continuous sensor signal from the FTS and IMU, respectively, while they were at rest; these two files can be used to calculate the sensor signal variances.</p> <p>After calibration, each sensor signal was recorded independently and stored in a separate file from the other sensors. The raw (biased) values were stored. Each test iteration produced three files:</p> <ol> <li>The end effector/FTS/IMU orientation in <strong>[test_iteration]_orientation.csv</strong></li> <li>The unbiased wrench as measured by the FTS in [<strong>test iteration]_wrench.csv</strong></li> <li>The unbiased acceleration as measured by the IMU in <strong>[test_iteration]_accel.csv</strong></li> </ol> <p>The test iteration prefix for these files are: <strong>1-baseline</strong>, <strong>2-vibrations, </strong>and <strong>3-vibrations-contact, </strong>as described in the previous section "Method". To obtain the relative time between samples across the test iteration files ([]<strong>_orientation</strong>, []<strong>_wrench</strong>, and []<strong>_accel.csv</strong>), load each dataset and determine which has the earliest timestamped sample on the first row. Then, subtract this initial timestamp value from all timestamps across the files for the respective test iteration.</p> <p>Note that the IMU frame does not align with the FTS frame (<strong>_accel.csv</strong> vs <strong>_wrench.csv</strong>), the following table describes the rotation matrix R_fa which can be used to transform the acceleration measurements from the IMU frame {a} to the FTS frame {f}. </p> <p>R_fa = </p> <table> <tbody> <tr> <td>0</td> <td>-1</td> <td>0</td> </tr> <tr> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <td>-1</td> <td>0</td> <td>0</td> </tr> </tbody> </table> <h1>References</h1> <p>[1] A. Skrede, "A Linear Discrete Kalman Filter to Estimate the Contact Wrench of an Unknown Robot End Effector", Accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR), Ålesund, Norway, June 2024 </p> <p>[2] S. Vougioukas, “Bias Estimation and Gravity Compensation For Force-Torque Sensors,” in Recent Advances in Simulation, Computational Methods and Soft Computing. WSEAS Press, 2001, pp. 82–85. </p>
High-frequency wind (u, w, v, Ts) and gas concentration measurements of CO2 and H2O over an agricultural field in Braunschweig, Germany
<p>This dataset contains high-frequency eddy covariance (EC) measurements over a flat agricultural field at the Thünen Institute in Braunschweig, Germany (52.30° N, 10.45° E).</p> <p>The data collection period spanned <strong>77 days </strong>in the year 2020 split into three files</p> <p> </p> <table> <tbody> <tr> <td>BS2020_06.rds</td> <td>June 11 to July 15</td> </tr> <tr> <td>BS2020_10.rds</td> <td>October 1 to November 10</td> </tr> <tr> <td>BS_2020_07_subset.rds</td> <td>July 11 to July 25</td> </tr> </tbody> </table> <p> </p> <p>Included in the dataset are 3D wind velocity data, recorded using a uSonic-3 Class A sonic anemometer from Metek GmbH. Additionally, the dataset provides gas concentration measurements for carbon dioxide (CO2) and water vapor (H2O), captured using an LI-7500A open-path infra-red gas analyzer from LI-COR Biosciences GmbH, Germany.</p> <p>Variables in the dataset</p> <table> <tbody> <tr> <th>Variable Name</th> <th>Description</th> <th>Units</th> </tr> </tbody> <tbody> <tr> <td>time</td> <td>Unique time stamp (POSIXct format)</td> <td>Seconds since Unix epoch</td> </tr> <tr> <td>CO2</td> <td>Wet molar density of carbon dioxide</td> <td>µmol m⁻³</td> </tr> <tr> <td>H2O</td> <td>Wet molar density of water vapor</td> <td>mmol m⁻³</td> </tr> <tr> <td>Ts</td> <td>Sonic temperature</td> <td>Kelvin</td> </tr> <tr> <td>u, v, w</td> <td>3D wind velocity components</td> <td>m s⁻¹</td> </tr> </tbody> </table> <p> </p> <p> </p> <div> <div> <div> <p>The dataset is in RDS format (version 3), compatible with R version 3.5.0 or higher.</p> <p>RDS is a binary file format native to the R programming environment</p> </div> </div> </div>
Monacha claustralis and M. cartusiana measurements
<p>The file is a tab-delimited text file (13 columns, 106 rows of data + 1 header row) detailing the quantitative data used in the following article:</p> <p>Williams, B.M.J., Hutchinson, J.M.C., Reise, H., Zauder, O. & Schlitt, B. (2024) Difficulties in distinguishing <em>Monacha claustralis</em> from <em>M. cartusiana</em> in Germany and Poland. <em>Journal of Molluscan Studies</em>. https://doi.org/10.1093/mollus/eyae030</p> <p><strong> </strong></p> <p><strong>Focal/reference:</strong> focal population (F) or a reference population (ca = <em>M. cartusiana</em>, cl = <em>M. claustralis</em>).</p> <p><strong>Cat. no. or figure:</strong> numbers beginning with p are catalogue numbers of the Senckenberg Museum of Natural History Görlitz; those beginning with DCB refer to the loan from J. Pieńkowska; other entries refer to illustrations in Pieńkowska et al. (2015) or Pieńkowska et al. (2018).</p> <p><strong>Shell:</strong> shell diameter in mm; NA = missing value.</p> <p><strong>The next five columns</strong> are lengths of components of the distal genitalia measured in mm; NA = missing value.</p> <p><strong>Vagina_gm:</strong> length of vagina from origin of penis to mucus glands.</p> <p><strong>Vagina_t</strong>: length of vagina from origin of penis to bursa duct.</p> <p><strong>V sac score:</strong> prominence of vaginal sac scored from 1 (absent) to 5.</p> <p><strong>GenBank:</strong> GenBank number of haplotype.</p> <p><strong>Haplogroup:</strong> ca = <em>M. cartusiana</em>, cl = <em>M. claustralis</em>.</p>
Effective realization of abatement measures can reduce HFC-23 emissions
<p>Atmospheric observations (mole fractions) of halogenated greenhouse gases (HFC-23 (CHF<sub>3</sub>), PFC-318 (c-C<sub>4</sub>F<sub>8</sub>), HCFC-22 (CHClF<sub>2</sub>), HCFC-21 (CHCl<sub>2</sub>F), HFC-4310mee (C<sub>5</sub>H<sub>2</sub>F<sub>10</sub>), HFC-161 (C<sub>2</sub>H<sub>5</sub>F)) at the tall tower site at Cabauw, the Netherlands (51.972 °N, 4.927 °E, altitude -0.7 m a.s.l., 207 m a.g.l.), for the duration of a tracer (HFC-161) release experiment (17.06.2022 – 07.08.2022) within an extended (19.11.2021 – 7.8.2022) measurement campaign of halogenated greenhouse gases (>60 substances) at the Cabauw tall tower site. The measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography and mass spectrometry (GC-MS), as is used within the global AGAGE network (<a href="https://agage.mit.edu/">https://agage.mit.edu/</a>). The HFC-161 tracer was released at 22 km distance from the Cabauw tall tower site, at 4 m a.s.l., 10 m a.g.l, at various flow rates. HFC-161 mole fractions are provided as the measured mole fractions and as the measured mole fractions normalised to the set tracer release flow rates.</p> <p>In addition, a subset of the above-described data is provided. This was used to assess the emissions of the above listed halogenated greenhouse gases from an industrial factory, by reference to the released HFC-161 tracer.</p> <p>The data are related to an article in Nature (https://doi.org/10.1038/s41586-024-07833-y).</p>
Measure While Drilling (MWD) dataset with rock type labels for 15 Norwegian hard rock tunnels
<p>The dataset is presented in the paper: </p> <p><em>Building and analysing a labelled Measure While Drilling dataset from 15 hard rock tunnels in Norway</em>, by T.F. Hansen, Z. Liu, J. Torressen</p> <p>The paper has a preprint on SSRN: <a href="http://dx.doi.org/10.2139/ssrn.4729646" target="_blank" rel="noopener">http://dx.doi.org/10.2139/ssrn.4729646</a> and is under review in a peer-reviewed journal.</p> <p>The dataset is utilised in a machine learning analysis in the paper:</p> <p><em>Predicting rock type from MWD tunnel data using a reproducible ML-modelling process</em>, by T.F. Hansen, Z. Liu, J. Torressen</p> <p>The paper is published in the journal <em>Tunnelling and Underground Space Technology</em>: </p> <p><a href="https://doi.org/10.1016/j.tust.2024.105843">https://doi.org/10.1016/j.tust.2024.105843</a></p> <p> </p> <p><strong>Description of the dataset:</strong></p> <p>Measure While Drilling (MWD) is a technique in rock drilling, mainly used in drill and blast tunnelling, where data about the rock mass is registered by sensors while drilling. The extensive and geologically diversified dataset contains corresponding MWD-data and rock mass mappings for 5205 blasting rounds from 15 hard rock tunnels in Norway. MWD-data are presented as tabular data. 10 different rocktypes are the corresponding labels.</p> <p>Four files are given:</p> <ul> <li>A csv-file of the training dataset - with outliers removed</li> <li>A csv-file of the testing dataset (split train/test 0.75/0.25) - with outliers removed</li> <li>A csv-file with the full unsplitted dataset, cleaned and with outliers removed</li> <li>A csv-file with the raw dataset, before cleaning, processing and outlier removal</li> </ul> <p>The author gratefully acknowledge the tunnel software/hardware company Bever Control, which have facilitated data from the clients Bane NOR, Statens Vegvesen, Nye Veier, and the contractor AF-Gruppen.</p> <p> </p> <p><strong>NOTE:</strong> The dataset is only available for research, no commercial use.</p>
Measurement data of a three-phase grid-side converter with a grid-forming synchronverter-based control method with current limitation
<p>The data set was recorded for a publication currently undergoing the submission process. The published data correspond to the data presented in the figures. The first column represents the time vector. All other columns are linked to the corresponding scenario by an identifier in the column name. The column name also contains the name of the recorded signal and the associated unit. The naming convention is <identifier_to_figure>_<recorded_signal>_<unit>.</p>
Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS
<p>A portable fNIRS system Brite MKII (Artinis, NE) was placed on the PFC of the self-experimenting participant (Male, 43 years). Ten sources and eight detectors are combined into 22 long separation channels (30mm) and two short-separation channels (SSC) to cover the PFC (Figure 1A). The experiment lasted 392s where the participant was subject to pornographic video clips (V) and performed genital self-stimulation (M) until orgasm was reached (O).<br>Citation of the article related to this dataset:</p> <div> <div><strong>Guevara, E.</strong> (2024). <em>Prefrontal cortex activation and functional connectivity during human male orgasm measured with fNIRS</em> [Preprint]. OSF. <a href="https://doi.org/10.31219/osf.io/6y2ze">https://doi.org/10.31219/osf.io/6y2ze</a></div> </div>
Model results: Model-based decision support for the choice of active spring frost protection measures in apple production
<p><strong>Background: </strong></p> <p>Apple producers are dealing with weather related risks affecting their production. One important risk, is the damage of buds or young fruits by late spring frosts. Fruit growers can protect their apple orchards against this risk in various ways. With a probabilistic model (available on Git Hub: <a href="https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection">https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection</a>, <a href="https://doi.org/10.5281/zenodo.11473204">https://doi.org/10.5281/zenodo.11473204</a>), we want to support the decision between several active frost protection measures. The measures considered in the model are: overhead irrigation, below-canopy irrigation, stationary wind machines, mobile wind machines, tractor-mounted gas heaters, portable gas heaters, candles and pellet heaters.</p> <p>As case studies, we parameterized the model for two German apple production regions (Rhineland and Lake Constance region).</p> <p><strong>Repository content:</strong></p> <p>This repository contains the simulation results of 100,000 Monte Carlo runs with the model.</p> <p>The results are provided as .RDS and .csv files. The .RDS files are suitable to be uses with the Code on Git Hub to follow the Post-Hoc analysis and figure plotting.</p>
Raw Data for Evaluation of Measurement Uncertainty in Structural Health Monitoring Systems Under Temperature Influence
<p>The documentation on these laboraty tests is titled "Documentation.pdf"</p> <p> </p> <p>Raw data from distance measurements using laser triangulation sensors acquired under different temperatures are provided. Six sensors were tested per experiment (CSV file), and in each experiment the boundary conditions are varied as follows:<br><br>00RawData_LTS_1m: The entire measurement system is subject to temperature change, with initial distances chosen as LTS1/LTS2=17 mm, LTS3/LTS4=21 mm nd LTS5/LTS6=25 mm.<br><br>01RawData_LTS_1m_SwitchedDistances: The entire measurement system is subject to temperature change, with the selected initial distances of LTS1/LTS2=25 mm, LTS3/LTS4=17 mm nd LTS5/LTS6=21 mm.<br><br>02RawData_LTS_1m_SwitchedDistances2: The entire measurement system is subject to temperature change, with initial distances selected as LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>03RawData_LTS_1m_OnlySensor: Only the sensors of the measuring system are subject to temperature change, where the selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>04RawData_LTS_1m_OnlyMeasuringAmplifier: Only the measuring amplifiers of the measuring system are subject to temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>05RawData_LTS_1m_OnlyCable: Only the cables of the measurement system are subject to the temperature change. The selected initial distances are LTS1/LTS2=21 mm, LTS3/LTS4=25 mm nd LTS5/LTS6=17 mm.<br><br>Tested temperature range: -10°C to 50°C<br>Measuring frequency: 1 Hz<br>Measuring amplifier: Q.bloxx.XL A107 Gantner Instruments<br>Cable: 4-pole, 1.00 m length<br>Sensor: OM20-P0026.HH.YIN laser triangulation sensor from Baumer</p>
TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric COF2 profile data set
<p>TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric TCOM-COF2 profile data set </p> <p>Sandip S. Dhomse </p> <p>School of Earth and Enviro, University of Leeds, Leeds, UK</p> <p>National Centre for Earth Observations, University of Leeds, Leeds, UK</p> <p> email: s.s.dhomse@leeds.ac.uk</p> <p> </p> <p>Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2023 time period. Collocated COF2 profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) COF2 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation.</p> <p>Dataset also includes two files containing daily mean zonal mean COF2 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (8766 days/64 latitudes):</p> <p>zmcof2_TCOM_hlev_T2Dz_2000_2023.nc – height level data (10 to 60 km)</p> <p>zmcof2_TCOM_plev_T2Dz_2020_2023.nc – pressure level data (300 to 0.1 hPa)</p> <p>Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.</p>
GEOLAB-PEBSTER-Deltares: Small-scale experiments on Piled Embankments with Basal Steel Mesh Reinforcement - Measurements.
<p><strong>DOI: 10.5281/zenodo.12627309</strong></p> <h1><strong>Small-scale experiments on basal steel-mesh reinforced piled embankments (PEBSTER, a transnational access project of GEOLAB)</strong></h1> <p>Welcome to the GEOLAB-PEBSTER-Deltares project on Zenodo! This repository contains the measurement data from four small-scale experiments on Piled Embankments with Basal Steel Reinforcement (PEBSTER). Pile-supported (PS) embankments with basal geosynthetic reinforcement (GR) are commonly built in soft soil areas (van Eekelen and Han, 2020). Steel mesh reinforcement (SR) is particularly appealing for high embankments (Topolnicki et al., 2019). Its high axial stiffness, compared to geosynthetics, reduces horizontal deformation at the embankment base, minimizes bending moments in the piles, and ultimately enhances embankment stability.</p> <h2>PEBSTER</h2> <p>This dataset includes measurements from four small-scale tests on steel-reinforced piled embankments, conducted in the Deltares laboratory (van Eekelen et al., 2024a,b). These tests are part of a broader research initiative called Piled Embankments with Basal Steel Reinforcement (PEBSTER), which also includes a large-scale test (Schneider et al., 2024a,b). The small-scale tests were conducted at Deltares, Delft, Netherlands, while the large-scale test was conducted in Darmstadt, Germany, at the Institute of Geotechnics, Technical University of Darmstadt.</p> <h2>GEOLAB</h2> <p>GEOLAB is a project of the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 101006512, addressing Europe's Critical Infrastructure (CI) challenges in the water, energy, urban, and transport sectors.</p> <p>The GEOLAB Research Infrastructure (RI) consists of 11 unique installations across Europe to study subsurface behavior and its interaction with structural CI elements and the environment. During the GEOLAB Transnational Access (TA), users outside the consortium gained access to the GEOLAB installations to perform research and innovation.</p> <h2>Dataset of four small-scale experiments</h2> <p>Here, we share the measurement data from the small-scale experiments that were part of one of the GEOLAB TA projects: PEBSTER. Four piles (diameter 0.1 m, centre to centre 0.55m) passed through a steel plate, that supported a foam cushion, that was sealed and soaked. A tap allowed for drainage of the foam cushion, simulating the consolidation of the subsoil between the piles. The 0.55 m high embankment consisted of medium coarse sand, and was reinforced at its base with a steel mesh reinforcement. A surcharge load up to 100 kPa was applied with a water cushion.<br><br><span>The load distribution is measured by pressure cells and load transducers. </span>Soil strains and displacements were monitored at five elevations within the fill, utilizing distributed fibre optic sensing (DFOS) technology from the Nerve-Sensors family, as depicted in Figure 2. Additionally, the steel mesh reinforcement was extensively instrumented with optic fibres.</p> <h2>PEBSTER Research group</h2> <p>The project was conducted by a research group that includes Deltares, Netherlands, the Institute of Geotechnics of the Technical University of Darmstadt, Germany, Keller (Germany, France, Poland), SHM System, Poland, and FOLAB, Germany.</p> <p> </p>
Acunex intraocular lens Zernike coefficients measured with a NIMO device for several optical zone diameters
<p>Zernike coefficient values obtained in vitro with a NIMO device for the Acunex family of intraocular lenses, both monofocal and multifocal, for three nominal powers (+10, +20, +30) for several optical zone diameters</p>
Monthly TM5-4DVar CO2 fluxes based on GOSAT and in situ measurements for the South American Temperate region from 2009 to 2018
<p>The data set contains monthly CO2 land-atmosphere exchange fluxes (Net Biome Productivity, NBP) for the South American Temperate (SAT) region, as defined by TRANSCOM, from 2009 to 2018. The fluxes are calculated using the atmospheric inversion TM5-4DVar (Basu et al., 2013), as described in Metz et al. (2023), assimilating in situ and/or Greenhouse Gases Observing Satellite (GOSAT) measurements.</p> <p><strong>If the data is used for publications, please contact sanam.vardag@uni-heidelberg.de to discuss potential co-authorship and technical details.</strong></p> <p>The following data sets are included:</p> <p><strong>TM5-4DVar_ACOS_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/ACOSv9 XCO2 data and in situ CO2 concentration measurements together.</p> <p><strong>TM5-4DVar_RT_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating GOSAT/RemoTeCv2.4.0 XCO2 data and in situ CO2 concentration measurements together.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_SAT</strong>: Mean of the monthly NBP fluxes of TM5-4DVar_ACOS_SAT and TM5-4DVar_RT_SAT.</p> <p><strong>TM5-4DVar_IS_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region estimated by assimilating only in situ CO2 concentration measurements.</p> <p><strong>TM5-4DVar_prior_SAT</strong>: Monthly NBP fluxes for the whole South American Temperate region used as prior in the atmospheric inversion TM5-4DVar.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_arideast</strong>: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the eastern SAT region.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_aridwest</strong>: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the arid regions in the western SAT region.</p> <p><strong>TM5-4DVar_GOSAT_MeanAcosRt_humid</strong>: Like TM5-4DVar_GOSAT_MeanAcosRt_SAT but only for the humid regions in the SAT region.</p> <p>All data sets have the following <strong>variables</strong>:</p> <p>MonthDate: date (YYYY-MM-DD) of the middle of the individual month</p> <p>Month: MM</p> <p>Year: YYYY</p> <p>NBP_flux_monthly_TgC_per_subregion: NBP flux as total monthly flux over the whole individual region (SAT, SAT humid, SAT arid east, west) in TgC/month.</p> <p>NBP fluxes are calculated as Net Ecosystem Exchange fluxes + fire emissions. For more details about the atmospheric inversion and the used measurement data, please see Metz et al., 2023.</p> <p> </p> <p>Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., et al. (2013). Global CO 2 fluxes estimated from GOSAT retrievals of total column CO 2. Atmospheric Chemistry and Physics, 13(17), 8695–8717, 2013. </p> <p>Metz, E.-M., Vardag, S.N., Basu, S., Jung, M., Ahrens, B., El-Madany, T., Sitch, S., Arora, V. K., Briggs, P. R. , Friedlingstein, P., Goll, D.S., Jain, A.K., Kato, E., Lombardozzi, D., Nabel,J .E. M. S., Poulter, B., Séférian, R., Tian, H., Wiltshire, A., Yuan, W., Yue, X., Zaehle, S., Deutscher, N.M., Griffith, D.W.T., Butz, A. Soil respiration–driven CO2 pulses dominate Australia’s flux variability. Science, 379, 1332-1335, https://doi.org/10.1126/science.add7833, 2023.</p>
[Dataset] Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy
<p>Research data for the purpose of reproducing the results presented in the journal publication titled "Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy"</p>
ENTICE download speed measurements
<p>A sample of a measurements dataset. Monitoring of a general purpose Cloud storages (e.g. such as AWS S3) is a part of ENTICE Pareto-SLA component. This dataset has been obtained by performing downloads of objects with random data of various sizes, ranging from 1 kB to 1 GB. Each file object has been generated by dd tool: dd if=/dev/urandom of=rand-1M.bin bs=1M count=1. On the server side a Minio S3 server has been setup with a Nginx gateway. The client who performed downloads was residing in the same local 1 Gbps ethernet network as the server. On the server side, network QoS has been controlled through a Linux tc netem tool, emulating various QoS conditions for the packet delay, jitter and packet loss.</p> <p>The dataset contains the following headers:</p> <p>Timestamp - the timestamp of a measurement performed.</p> <p>Client IP - the IP address of a client - anonymised.</p> <p>Server IP - the IP address of a server - anonymised.</p> <p>QoS - min|avg|max|stdev|ploss denoting minimum, average and maximum packet round-trip-time (RTT) between the server and the client, respectively, followed by the standard deviation of the RTT and emulated packet loss with the tc tool, expressed as a percentage.</p> <p>Object size [B] - the size of the file object in bytes.</p> <p>Download time [s] - the download time of the file object from the client perspective.</p> <p>Download speed [B/s] - the ratio (Object size[B]) / (Download time[s]).</p>
Data sets used for: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry
<p>Original videos and reference bulk velocity and water depth data sets used to develop the study: <em>Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry.</em></p> <p>The reference bulk velocity and water depth data sets were obtained with the Nivus OFR Radar and Nivus NivuCompact sensors, respectively.</p>
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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)
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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.