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.
91
datasets available to search
ShareScore release 0.9.0
Dataset results
91 results for “creep”
Monitoring creep along the Hayward Fault using structure-from-motion photogrammetry of offset curbs"
<p>Point clouds for each observed offset curbs along the Hayward Fault in Fremont, California between 2016 to 2018. </p>
High temperature properties incuding creep, creep crack growth rate and thermal fatigue linked with chemical composition of alloys derived from 1.4848 refractory stainless steels
<p>The following set of data results from cast alloys modifying chemical composition taking as reference 1.4848 alloy and getting sound samples that have been tested to calculate creep, creep crack growth rate and thermal fatigue data. </p>
Creep and Syrope test of nylon ropes
<h1>Description of Tests</h1> <p>Two different tests were used to determine the creep properties and the dynamic stiffness of nylon ropes. These tests were performed by DNV AS Energy Systems in Bergen, Norway, on one set of ropes from each of three vendors.</p> <h2>Creep Test</h2> <p>The first test is designed to determine the creep properties of the rope. The test is summarized in four steps:</p> <ul> <li>Load to a (low) reference load, measure the gauge length and start the data acquisition. </li> <li>Tension the rope to 20 % of MBS (Minimum Breaking Strength) and maintain this constant load for 30 minutes. Relax to the reference load and measure any remaining elongation.</li> <li>Repeat the second step for 30 %, 40 % and 50 % of MBS.</li> <li>Increase the tension to 50 % of MBS and hold the constant tension for 18 hours. Record the rope elongation.</li> </ul> <h2>Syrope Test</h2> <p>The Syrope test is designed to find the dynamic stiffness of a rope. The test combines quasi static tension loads with irregular dynamic tension loads. The test is summarized in the bullet points below:</p> <ul> <li>The variation of tension level is defined by pairs of ramps and constant tension, both with duration 30 minutes. The constant tensions will vary from 10 % of MBS to 40 % of MBS</li> <li>An irregular tension variation, including both wave-frequency and low-frequency contributions, is superimposed on the ramp/constant tension sequence. The standard deviation is based on the expected, calculated response in the mooring system.</li> </ul>
Supplemental Material for "Slow-to-fast Transition and Shear Localization in Accelerating Creep of Clayey Soil"
<div> <p>Dataset and plotter for "Slow-to-fast Transition and Shear Localization in Accelerating Creep of Clayey Soil" by Chengrui Chang.</p> <p>This dataset contains all 40 fluid-injection shear experiments on clayey soil. Each .mat file includes raw data and constrained parameters for the accelerating creep. A spreadsheet summarizes the experimental protocols and constrained parameters.</p> </div> <div>Article information:</div> <div>Chang, C., Noda, H., Xu, Q., Huang, D., & Yamaguchi, T. (2024). Slow‐to‐fast transition and shear localization in accelerating creep of clayey soil.<br>Geophysical Research Letters, 51, e2024GL111839. <a href="https://doi.org/10.1029/2024GL111839">https://doi.org/10.1029/2024GL111839</a></div>
Comparison of brittle- and viscous creep in quartzites: Implications for semi-brittle flow of rocks
<p>A data compilation of deformation experiments performed on quartzites in .pdf and .xlsx formats.</p>
Data for Aeolian Ripple Migration and Associated Creep Transport Rates
<p><strong>Overview:</strong></p> <p>The attached spreadsheet, "AeolianRippleMigration_ShermanEtAl2019.csv," summarizes the ripple migration and related data acquired from the wind tunnel and field experiment literature and from the field experiments at Jericoacoara, Ceará, Brazil (2008) and Oceano, California, USA (2015), associated with the article "Aeolian Ripple Migration and Associated Creep Transport Rates" by Douglas J. Sherman, Pei Zhang, Raleigh L. Martin, Jean T. Ellis, Jasper F. Kok, Eugene J. Farrell, and Bailiang Li.</p> <p><strong>Notes for data sources:</strong></p> <p>"a" - indicates that the data from a particular study were included in our final analyses</p> <p>"b" - indicates an estimate of threshold shear velocity (calculated as per Lorenz et al., 2011) with A = 0.1</p> <p>"c" - the value for ripple height in this study is the average of about 200 measurements for ripples in equilibrium or near-equilibrium with the wind field</p> <p>"d" - the data from this study were digitized as depicted in terms of ust/ust_th and u_r/(gd)^0.5 (see "Key to variables" below)</p> <p>"e" - Shear velocity (ust) values are from Martin & Kok, 2017. Median grain diameter (d) and threshold shear velocity (ust_th) values are from Martin & Kok, 2018 (see Table 2: "Date interval")</p> <p><br> <strong>Key to variables [units]:</strong></p> <p>Source - literature origin of previous studies or field location of observations for this study</p> <p>Note - annotation for additional information about study (see above "Notes for data sources")</p> <p>StudyType - classified as "field" or "wind tunnel"</p> <p>Date - date of observation for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>StartTime - start time of observation window (local time) for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>EndTime - end time of observation window (local time) for observations at Jericoacoara and Oceano ( "N/A" for other sites)</p> <p>u_r [mm/s] - calculated ripple migration speed ( "N/A" for Zhu et al, 2011, see "u_r_alt" below)</p> <p>sigma_u_r [mm/s] - uncertainty in ripple migration speed. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p> <p>u_r_alt - dimensionless proxy values for ripple migration speed for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as u_r/(gd)^1/2, where "g" is gravitational acceleration and "d" is median surface grain diameter </p> <p>ust [m/s] - shear velocity ( "N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>d [mm] - median surface grain diameter ("N/A" if not reported for literature studies)</p> <p>ust_th [m/s] - threshold shear velocity ("N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>ust_over_ust_th - dimensionless proxy values for shear velocity for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as ust/ust_th</p> <p>length [m] - ripple wavelength ("N/A" if not reported or measured)</p> <p>height [mm] - ripple amplitude ("N/A" if not reported or measured)</p> <p>sigma_height [mm] - uncertainty in ripple amplitude. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p>
Data for Aeolian Ripple Migration and Associated Creep Transport Rates
<p><strong>Overview:</strong></p> <p>The attached spreadsheet, "AeolianRippleMigration_ShermanEtAl2019.csv," summarizes the ripple migration and related data acquired from the wind tunnel and field experiment literature and from the field experiments at Jericoacoara, Ceará, Brazil (2008) and Oceano, California, USA (2015), associated with the article "Aeolian Ripple Migration and Associated Creep Transport Rates" by Douglas J. Sherman, Pei Zhang, Raleigh L. Martin, Jean T. Ellis, Jasper F. Kok, Eugene J. Farrell, and Bailiang Li.</p> <p><br> <strong>Notes for data sources:</strong></p> <p>"a" - indicates that the data from a particular study were included in our final analyses</p> <p>"b" - indicates an estimate of threshold shear velocity (calculated as per Lorenz et al., 2011) with A = 0.1</p> <p>"c" - the value for ripple height in this study is the average of about 200 measurements for ripples in equilibrium or near-equilibrium with the wind field</p> <p>"d" - the data from this study were digitized as depicted in terms of ust/ust_th and u_r/(gd)^0.5 (see "Key to variables" below)</p> <p>"e" - Shear velocity (ust) values are from Martin & Kok, 2017. Median grain diameter (d) and threshold shear velocity (ust_th) values are from Martin & Kok, 2018 (see Table 2: "Date interval")</p> <p><br> <strong>Key to variables [units]:</strong></p> <p>Source - literature origin of previous studies or field location of observations for this study</p> <p>Note - annotation for additional information about study (see above "Notes for data sources")</p> <p>StudyType - classified as "field" or "wind tunnel"</p> <p>Date - date of observation for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>StartTime - start time of observation window (local time) for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>EndTime - end time of observation window (local time) for observations at Jericoacoara and Oceano ( "N/A" for other sites)</p> <p>u_r [mm/s] - calculated ripple migration speed ( "N/A" for Zhu et al, 2011, see "u_r_alt" below)</p> <p>sigma_u_r [mm/s] - uncertainty in ripple migration speed. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p> <p>u_r_alt - dimensionless proxy values for ripple migration speed for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as u_r/(gd)^1/2, where "g" is gravitational acceleration and "d" is median surface grain diameter </p> <p>ust [m/s] - shear velocity ( "N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>d [mm] - median surface grain diameter ("N/A" if not reported for literature studies)</p> <p>ust_th [m/s] - threshold shear velocity ("N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>ust_over_ust_th - dimensionless proxy values for shear velocity for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as ust/ust_th</p> <p>length [m] - ripple wavelength ("N/A" if not reported or measured)</p> <p>height [mm] - ripple amplitude ("N/A" if not reported or measured)</p> <p>sigma_height [mm] - uncertainty in ripple amplitude. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p> <p><br> <strong>References:</strong></p> <p>Andreotti, B.; Claudin, P.; Pouliquen, O. Aeolian Sand Ripples : Experimental Study of Fully Developed States. 2006, 028001, 1-4.</p> <p>Borsy, Z. A homokfodrok. Fldrajzi rtesito 1973, 22, 109-115.</p> <p>Cheng, H.; Liu, C.; Zou, X.; Li, J.; He, J.; Liu, B.; Wu, Y.; Kang, L.; Fang, Y. Aeolian creeping mass of different grain sizes over sand beds of varying length. Journal of Geophysical Research: Earth Surface 2015, 120, 1404-1417.</p> <p>Cornish, V. On the formation of sand-dunes. The Geographical Journal 1897, 9, 278-302.</p> <p>Kindle, E.M. Recent and fossil ripple-mark; Canada Department of Mines, Geological Survey: 1917; pp 9-29.</p> <p>Ling, Y.-q.; Qu, J.-j.; Li, C.-z. Study on sand ripple movement with close shoot method. Journal of Desert Research 2003, 23, 118-120.</p> <p>Lorenz, R.D. Observations of wind ripple migration on an Egyptian seif dune using an inexpensive digital timelapse camera. Aeolian Research 2011, 3, 229-234.</p> <p>Martin, R.L.; Kok, J.F. Aeolian saltation fieldwork 30-minute wind and saltation values (Dataset). Zenodo, https://doi.org/10.5281/zenodo.291798: 2017.</p> <p>Martin, R.L., Kok, J.F. Distinct Thresholds for the Initiation and Cessation of Aeolian Saltation From Field Measurements. Journal of Geophysical Research - Earth Surface 2018, 123, 1546–1565. https://doi.org/10.1029/2017JF004416</p> <p>Seppälä, M.; Lindé, K. Wind tunnel studies of ripple formation. Geografiska Annaler: Series A, Physical Geography 1978, 60, 29-42.</p> <p>Sharp, R.P. Wind ripples. The Journal of Geology 1963, 71, 617-636.</p> <p>Stone, R.O.; Summers, H.J. Study of Subaqueous and Subaerial Sand Ripples; University of Southern California: Los Angeles, 1972.</p> <p>Zhu, W. Investigations on the formation and evolution of aeolian sand ripples. Lanzhou University, 2011.</p>
Strain partitioning, interseismic coupling, and shallow creep along the Ganzi-Yushu fault from Sentinel-1 InSAR data
<p>The dataset includes the InSAR velocity data and fault coupling model in the article "Strain partitioning, interseismic coupling, and shallow creep along the Ganzi-Yushu fault from Sentinel-1 InSAR data" (<a href="https://doi.org/10.1029/2024GL111469">https://doi.org/10.1029/2024GL111469</a>). The "insardata.zip" file includes original data of 5 tracks export from MintPy software, and the detailed format of the data can be found in the instruction provided by the MintPy software (<a href="https://github.com/insarlab/MintPy">GitHub - insarlab/MintPy: Miami InSAR time-series software in Python</a>). The "couplingmodel.gmt" is the fault coupling distribution along the Ganzi-Yushu fault, formatted for utilization in GMT software (<a href="https://github.com/GenericMappingTools/gmt">GitHub - GenericMappingTools/gmt: The Generic Mapping Tools</a>).</p>
Data related to: Fast low-temperature irradiation creep driven by athermal defect dynamics
<h2>Data set related to article "Fast low-temperature irradiation creep driven by athermal defect dynamics"</h2> <h2>Simulation data</h2> <h3>Molecular dynamics data</h3> <p>The molecular dynamics data is the output of simulations of single-crystal tungsten with periodic boundary conditions evolving under irradiation up to high dose (0.5 dpa). The simulations are performed under a constant externally applied stress, uniaxial to z-direction, and zero stress conditions otherwise. Simulations were performed for stresses of -1.0 GPa, -0.5 GPa, 0 GPa, 0.5 GPa, 1.0 GPa, 1.5 GPa, and 2.0 GPa. Each stress condition was simulated five times independently. Simulations were performed in <a href="https://www.lammps.org/">LAMMPS</a> (see below for the simulation script). Only every 100th frame in LAMMPS Dump format is uploaded here. A frame corresponds to a dose increment of approximately 0.0002 dpa, i.e. frame 1000 corresponds to a dose of approximately 0.2 dpa. The files are zipped. A finer resolution can be supplied upon request (up to every 5th frame).</p> <p>The zip archive naming convention is as follows:</p> <blockquote> <p>For example, archive "srim_neg1p0.zip" contains data of the 5 simulations for -1.0 GPa (='negative 1 point 0'), where snapshot "srim_neg1p0_2/srim_neg1p0_2.1300.dump" refers to independent simulation ID 2, frame 1300, i.e. at a dose of 0.26 dpa. </p> <p>"srim_pos0p5.zip" contains the 5 simulations for 0.5 GPa (='positive 0 point 5'), and so on.</p> </blockquote> <p>Archive "logfiles.zip" contains the simulation output information, containing stresses and box dimensions for every cascade iteration of each simulation. These can be used to generate the box eigenstrains. The columns are defined as follows:</p> <blockquote> <p>iteration number, dose (dpa), total potential energy (eV), pressure xx (bar), pressure yy (bar), pressure zz (bar), pressure xy (bar), pressure xz (bar), pressure yz (bar), simulation box width x (Å), simulation box width y (Å), simulation box width z (Å)</p> </blockquote> <p>The files contain a few lines labelled with "# restart", which marks points at which the simulation was terminated and then continued.</p> <p>Archive "md_eigenstrains.zip" contains the eigenstrain tensor components parallel and perpendicular to the uniaxial stress direction obtained directly from the MD simulations. The format is as follows:</p> <blockquote> <p>For example, files "eigenpara_pos0p5_0.dat" and "eigenperp_pos0p5_0" contain the MD eigenstrains parallel and perpendicular to the uniaxial loading direction of simulation "eigenpara_pos0p5_0", respectively. The first column is the NRT dose (dpa), and the second column is the eigenstrain value at this dose. The perpendicular eigenstrain is the average of the eigenstrain components xx and yy.</p> </blockquote> <h3>Molecular dynamics script</h3> <p>The LAMMPS script for performing high-dose collision cascade simulations is available at:</p> <p> <a href="https://github.com/mb4512/ezcascades/">https://github.com/mb4512/ezcascades</a></p> <p>The simulation input files required to replicate the molecular dynamics simulations of this work are supplied here. Archive "md_input_files.zip" contains simulation input files, specifying box dimensions, stress constraints, the interatomic potential, paths to simulation and scratch directories, and so on. For example, "srim_neg1p0_0.json" is the input file for the "srim_neg1p0_0" simulation.</p> <p>The interatomic potential "W_MNB_JPCM17.eam.fs" used here is the embedded atom method potential for tungsten developed by Mason et al: <a href="https://doi.org/10.1088/1361-648X/aa9776">10.1088/1361-648X/aa9776</a>, available at the <a href="https://www.ctcms.nist.gov/potentials/">NIST Interatomic Potentials Repository</a>.</p> <h3>Surrogate model data</h3> <p>Archive "eigenstrain_models.zip" contains the MLE model parameters for the eigenstrain surrogate models. The format is as follows:</p> <blockquote> <p>For example, files "eigenpara_neg0p5.log" and "eigenperp_neg0p5.log" contain the cubic spline knot points for the MLE model of eigenstrains parallel and perpendicular to the uniaxial loading direction at -0.5 GPa, respectively. The file contains columns labelled as x, y, and sigma, which correspond to dose (dpa), eigenstrain mean, and eigenstrain standard deviation, respectively. The spline boundary conditions are f(x = 0) = 0, f''(x = 0) = 0, and f'(x_end) = 0, where x_end is the final dose value in the list. The model is extrapolated for higher doses: f(x > x_end) = f(x_end).</p> <p>The log files also contain the covariance matrix of the eigenstrain mean values, from which model uncertainties can be derived.</p> </blockquote> <p>Archive "doseprofile.zip" contains the irradiation dose profile as generated using <a href="https://www.srim.org/">SRIM</a> data following the procedure described in the Supplemental Information. The content is as follows:</p> <blockquote> <p>Files "vacgrid_x_8micro_HR.dat", "vacgrid_y_8micro_HR.dat" contain the x and y coordinates of the 2 dimensional grid, respectively. File "vacgrid_z_8micro_HR.dat" contains a list of dose values (in arbitrary units). The values are ordered consistently, that is, the n-th values of each file give the matching tuple (x, y, dose(x,y)).</p> </blockquote> <p> </p>
Understanding the deformation creep and role of intermetallic compound-microstructure in Sn-Ag-Cu solders
<p>The data released here is for the paper "Understanding the deformation creep and role of intermetallic compound-microstructure in Sn-Ag-Cu solders". DOI: 10.1016/j.msea.2024.147429.</p> <p>Tianhong Gu*1,2, Yilun Xu*1.3, Christopher M. Gourlay1, Fionn P.E. Dunne1 and T. Ben Britton1,4</p> <p>1Department of Materials, Imperial College London, SW7 2AZ, UK.<br>2Department of Civil Engineering and XJTLU Advanced Materials Research Center<br>(AMRC), Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, China.<br>3 Institute of High Performance Computing (IHPC), Agency for Science, Technology and<br>Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632,<br>Republic of Singapore.<br>4Department of Materials Engineering, University of British Columbia, Vancouver,<br>British Columbia, V6T 1Z4, Canada.<br>*Corresponding author: Tianhong.gu@xjtlu.edu.cn; Xu_yilun@ihpc.a-star.edu.sg </p> <p>The data bundle was prepared by Tianhong Gu</p>
Creep of Basalts Undergoing Carbonation: Effect of Rock-Fluid Interaction
<p>Geological carbon sequestration provides permanent CO<sub>2</sub> storage to mitigate the current high concentration of CO<sub>2</sub> in the atmosphere. CO<sub>2</sub> mineralization in basalts has been proven to be one of the most secure storage options. For successful implementation and future improvements of this technology, the time-dependent deformation behavior of basalts in presence of reactive fluids needs to be studied in detail. We conducted load stepping creep experiments on basalts from the CarbFix site (Iceland) under several pore fluid conditions (dry, H<sub>2</sub>O-saturated and H<sub>2</sub>O+CO<sub>2</sub>-saturated) at temperature, T≈80°C and effective pressure, P<sub>eff</sub> = 50 MPa, during which we collected mechanical, acoustic and pore fluid chemistry data. We observed transient creep at stresses as low as 11% of the ultimate failure strength, well below the stress level at the onset of bulk dilatancy. Acoustic emissions (AEs) correlated strongly with strain accumulation, indicating that the creep deformation was a brittle process in agreement with microstructural observations. The rate and magnitude of AEs were higher in fluid-saturated experiments than in dry conditions. The creep data can be empirically fitted using either a log - time or power law time model with stress dependent fitting parameters. We infer that the predominant mechanism governing creep deformation is time- and stress-dependent sub-critical dilatant cracking. Our results suggest that the presence of aqueous fluids exerts first order control on creep deformation of basaltic rocks, while the composition of the fluids plays only a secondary role under the studied conditions.</p>
The Ubiquitous Creeping Segments on Oceanic Transform Faults (supplementary)
<p>The supplementary material to <em><strong>The ubiquitous creeping segments on oceanic transform faults, Pengcheng Shi, Meng (Matt) Wei, Robert A. Pockalny, 2021</strong></em>.</p> <p>Link to <a href="https://github.com/shipengcheng1230/OTF2021/tree/v1.0.1">GitHub</a>.</p>
Fairway reflectance and clipping yield from: Reduced creeping bentgrass fairway reflectance following synthetic colorant application
<p>Repeated measures using multispectral radiometry resolutely quantify canopy attributes of identical turfgrass cultivars under similar management. Concern regarding multispectral radiometric characterization of turfgrass canopies <24 h following synthetic phthalocyanine colorant application has been affirmed and is accordingly now avoided; yet explicit guidance on subsequent employ, at time(s) >24-h post-application, is lacking. Our objective assessed how petroleum-derived spray oil (PDSO) and synthetic Cu II phthalocyanine colorant (C) combination product influences creeping bentgrass (<em>Agrostis stolonifera</em> L.) reflectance up to 10 d following application. A maintained fairway received semi-monthly 9.76 kg ha<sup>–1</sup> soluble N treatments alone or in combination with 27 L PDSO+C ha<sup>–1</sup>. Treatment by PDSO+C increased mean shoot growth (kg ha<sup>–1</sup>) and dark green color index (DGCI) calculated by visible waveband reflectance. Yet reduced far red and near infrared reflectance from PDSO+C treated plots artificially deflated mean normalized differential vegetative indices (NDVI). Cautious interpretation of vegetative indices relying on 710- to 810-nm canopy reflectance is encouraged when evaluating fairways treated by PDSO and Cu II phthalocyanine combination product(s).</p>
Geodetic datasets for analysis of southern San Andreas fault geometry from 2017-2021 shallow creep
<p>Line-of-sight (LOS) Sentinel-1 InSAR velocities, residual fault-parallel velocities, GNSS vectors, and fault nodes used a study of the shallow structure of the Southern San Andreas Fault in the Coachella Valley.</p>
Rheology of mobile sediment beds in laminar shear flow: effects of creep and polydispersity
<p>Simulation data to accompany the journal article "Rheology of mobile sediment beds in laminar shear flow: effects of creep and polydispersity" by Rettinger et al. (2022) in Journal of Fluid Mechanics.</p> <p>The data set features the four simulation setups with varying degrees of polydispersity, denoted as mono, poly10, poly50, and poly100.</p> <p>Each setup has two subfolders: 'particle' and 'fluid'.</p> <p>The 'particle' folder contains files with particle-related quantities. The name of the file indicates the simulation time step. Each file contains seven columns of data: the x-, y-, and z-coordinate of the particle's center of mass, followed by its radius and the three translational velocity components <strong>u</strong><sub>p</sub> = (u<sub>x</sub>, u<sub>y</sub>, u<sub>z</sub>). Each row thus contains the instantaneous properties of a single particle. Additionally, the row-ordering between time steps remains the same, i.e., the x-th row always denotes the same particle.</p> <p>The 'fluid' folder contains files with fluid-related quantities. Again, the name denotes the simulation time step. Each file contains three columns of data: the three velocity components <strong>u</strong><sub>f</sub> = (u<sub>x</sub>,u<sub>y</sub>,u<sub>z</sub>) of the horizontally averaged flow field. Each row corresponds to a particular vertical (z-) coordinate, denoting the height above the bottom plane. This coordinate is given in the additional file 'height.txt'.</p> <p>All data is given in simulation units where the lattice Boltzmann unit system is used.</p> <p> </p> <p>Updated version 1:</p> <ul> <li>Added a jupyter notebook (in python) that exemplifies how vertical profiles, e.g., of solid volume fraction and fluid velocity, can be extracted from the provided data.</li> <li>Fix in fluid height profiles (height.txt).</li> </ul> <p>Updated version 2:</p> <ul> <li>Added missing last column (z-velocity) to the particle data.</li> <li>Maintained consistent ordering of entries in particle data throughout all time steps.</li> </ul>
Creep and Shrinkage Database of Laboratory Data
<p>Creep and shrinkage database of laboratory tests, covering mostly concrete and also mortars and pastes. The database was revamped from Northwestern University database and RILEM database, originating back to 1978. Published in MySQL and xlsx formats with a brief manual.</p>
Fairway reflectance and clipping yield from: Reduced creeping bentgrass fairway reflectance following synthetic colorant application
Open the record for dataset details and reuse information.
Sevilleta site, station Five Points, study of plant cover of Boerhavia spicata (creeping spiderling) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Sevilleta (SEV) contains plant cover of Boerhavia spicata (creeping spiderling) measurements in percent units and were aggregated to a yearly timescale.
Sevilleta site, station Five Points, study of plant cover of Sanvitalia abertii (Abert's creeping zinnia) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Sevilleta (SEV) contains plant cover of Sanvitalia abertii (Abert's creeping zinnia) measurements in percent units and were aggregated to a yearly timescale.
FIGURE19–25 in The creeping water bugs (Hemiptera: Heteroptera: Naucoridae) of China, with description of a new species
FIGURE19–25. Cheirochela grossa sp. nov. 19–21: right paramere in different views; 22–23: aedeagus in different views; 24: dorsomedial process of male genital capsule; 25: Ventral view of posterior abdominal segments of female. Scale bar = 0.5 mm.
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.