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289 results for “hydraulics”
An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau
<p>Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 soil moisture monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a soil hydraulic properties and soil moisture dataset for a high and cold mountainous area, Northeastern Qinghai-Tibet Plateau. Specifically, the soil hydraulic properties include clay, silt, sand, soil organic carbon, soil saturated hydraulic conductivity, soil water retention curve parameters (Van Genuchten model) and soil dry bulk density.</p>
Permeability and groundwater flow dynamics in deep-reaching orogenic faults estimated from regional-scale hydraulic simulations
<p>Input file and porosity and permeability datasets to run the case in Figure 3a.</p>
Growth resilience of conifer species decreases with early, long-lasting and intense droughts but cannot be explained by hydraulic traits
<p><span>Drought events may reduce growth and survival of conifer trees. The effects of the intensity and timing of drought on the growth resilience, including growth reductions during drought and recovery of growth after drought, remains however highly uncertain.</span></p> <p><span>Growth resilience of 20 conifer species to 11 dry years was compared in a common garden experiment. We assessed 1) the relationships among growth resistance, recovery and resilience, 2) the impacts of different drought dimensions (intensity, onset and length) on resistance, and 3) the underlying mechanisms in terms of growth potential and hydraulic traits. </span></p> <p><span>Droughts led to 22% reduction in stem growth for 85% of species, but most species (85%) were resilient due to high recovery. Growth resistance decreased with an early onset of drought (significant for 55% of species), and longer lasting (35%) and intense droughts (60%). While </span><span>fast-growing species and slow-growing species were similar in resistance and recovery, fast-growing species were more resilient. </span><span>Unexpectedly, resilience could not be explained by hydraulic traits, possibly because the species grew on poor sandy soils and were acclimated to drought with large hydraulic safety margins.</span></p> <p><span><em>Synthesis </em>Our study shows that in a mild maritime climate almost all conifer species are resilient to drought, and that putative hydraulic traits may be less important here for growth resilience. It also highlights the importance of addressing multiple dimensions of drought, i.e., timing, duration and severity, to predict species responses to climate change.</span></p>
GSHP: Global database of soil hydraulic properties
<p>A total of 15,259 SWCCs from 2,702 sites were assembled from published literature and other sources, standardized, and quality-checked to obtain global database of soil hydraulic properties (GSHP). The GSHP database covers most regions across the globe, with the highest number of curves from North America followed by Africa, Europe, Asia, South America, Australia/Oceania. In addition to SWCCs, other soil variables such as soil texture (12,233 measurements), bulk density (15,125 measurements), and soil organic carbon (2,255 measurements) are also listed in the database.</p> <p>The R code used for this study is available here: https://github.com/ETHZ-repositories/GSHP-database</p> <p>For more details / to cite this dataset please use:</p> <ul> <li>Gupta, S., Papritz, A., Lehmann, P., Hengl, T., Bonetti, S., and Or, D., (2022): Global Soil Hydraulic Properties dataset based on legacy site observations and robust parameterization”. Manuscript accepted to <strong>Scientific Data.</strong></li> </ul> <p>Examples of using the GSHP database to generate van Genuchten parameters maps can be found in <a href="https://doi.org/10.5281/zenodo.6343570">10.5281/zenodo.6343570</a>.</p> <p><strong>Description of the files</strong>:</p> <p>The datasets in this repository include: </p> <p><strong>WRC_dataset_surya_et_al_2021_final </strong>provides a global compilation of soil hydraulic properties and the information described in the<strong> Readme_GSHP file</strong>. <strong>Dataset_notebook </strong>shows the graphical representation of the GSHP database. </p> <p>The study was supported by ETH Zurich (Grant ETH-18 18-1). </p>
Data files for 'Tan et al., (2023). Tomographic evidences for hydraulic fracturing induced seismicity in the Changning shale gas field, southern Sichuan Basin, China'
<p>station.dat : the station coordinates of the local seismic network (including the station ID, longitude, latitude, elevation(negative)/depth(positive), X, Y)</p> <p>catalog.dat : the seismic phase catalog used in double-difference (DD) seismic tomography</p> <p>relocation.dat : the earthquake relocations obtained by DD tomography</p> <p>1-D Vp&Vs.xlsx : the 1-D Vp and Vs models of the shale gas field</p> <p>3-D Vp.dat: the 3-D Vp model obtained by DD tomography</p> <p>3-D Vs.dat: the 3-D Vs model obtained by DD tomography</p> <p>3-D VpVs.dat: the 3-D Vp/Vs model obtained by DD tomography</p> <p>FMS&pore pressure.xlsx: the focal mechanism solutions and excessive fluid pressures of the selected earthquakes</p> <p>waveforms.rar: the waveforms of the earthquakes recorded by our local sparse array</p>
Influence of rotation speed on flow field and hydraulic noise in the conduit of a vertical axial-flow pump under low flow rate condition
<p>The complex flow inside the axial-flow pump device will cause the problem of hydraulic noise, in order to explore the influence law of rotation speed on the internal flow characteristics and hydraulic noise of the axial-flow pump conduit, the combination of Computational Fluid Dynamics (CFD) and Computational Acoustics (CA) is used to numerically solve the flow field and internal sound field in the pump device. The results show that the flow in the elbow inlet conduit is smooth at different rotation speeds, and there is no obvious unstable flow. The higher the rotation speed, the more disordered the flow pattern in the left half of the elbow, which intensifies the unstable flow in the straight outlet conduit. The impeller is the main sound source of the internal hydrodynamic noise of the vertical axial-flow pump device, when the sound source propagates upstream and downstream along the conduit, the Total Sound Source Intensity (TSSI) will gradually decay with the increase of distance, the greater the rotation speed is, the faster the Total Sound Source Intensity (TSSI) will decay. When the rotation speed is increased from 1450 r/min to 2200 r/min, the TSSI in the straight outlet conduit is attenuated by 8.9 dB, 13.9 dB and 16.0 dB respectively, and the TSSI in the elbow inlet conduit is attenuated by 11.0 dB, 13.5 dB and 25.9 dB respectively. The vortex structure in the conduit will induce flow noise and delay the attenuation of TSSI in the propagation process, with the increase of rotation speed, this delay will be more obvious.</p>
Data from: Trading water for carbon in the future: effects of elevated CO2 and warming on leaf hydraulic traits in a semiarid grassland
<p class="MsoNormal"><a name="_Hlk96844723"></a><span>The effects of climate change on plants and ecosystems are mediated by plant hydraulic traits, including interspecific and intraspecific variability of trait phenotypes. Yet, integrative and realistic studies of hydraulic traits and climate change are rare. In a semiarid grassland, we assessed the response of several plant hydraulic traits to elevated CO<sub>2</sub> (+200 ppm) and warming (+1.5</span><span><span> to </span></span><span><span>3</span></span><span><span>℃;</span></span><span><span> day to night). For leaves of five dominant species (three graminoids, two forbs), and in replicated plots exposed to seven years of elevated CO<sub>2</sub>, warming, or ambient climate, we measured: stomatal density and size, xylem vessel size, turgor loss point, and water potential (pre-dawn). Interspecific differences in hydraulic traits were larger than intraspecific shifts induced by elevated CO<sub>2</sub> and/or warming. Effects of elevated CO<sub>2</sub> were greater than effects of warming, and interactions between treatments were weak or not detected. The forbs showed little phenotypic plasticity. The graminoids had leaf water potentials and turgor loss points that were 10 to 50% less negative under elevated CO<sub>2</sub>; thus, climate change might cause these species to adjust their drought resistance strategy away from tolerance and toward avoidance. The C4 grass also reduced allocation of leaf area to stomata under elevated CO<sub>2</sub>, which helps explain observations of higher soil moisture. The shifts in hydraulic traits under elevated CO<sub>2</sub> were not, however, simply due to higher soil moisture. Integration of our results with others' indicates that common species in this grassland are more likely to adjust stomatal aperture in response to near-term climate change, rather than anatomical traits; this contrasts with apparent effects of changing CO<sub>2</sub> on plant anatomy over evolutionary time. Future studies should assess how plant responses to drought may be constrained by the apparent shift from tolerance (via low turgor loss point) to avoidance (via stomatal regulation and/or access to deeper soil moisture).</span></span></p>
Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test [DATA]
<p>Data and metadata for the publication "Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test", published in Earth and Space Science.</p>
Supporting Information: Mapping conduits in two-dimensional heterogeneous karst aquifers using hydraulic tomography
<p>This file contains the supplementary data for a article submitted to Journal of Hydrology.</p>
Dataset for manuscript "Laboratory investigation of hydraulic fracture growth in Zimbabwe gabbro"
<p>Dataset for manuscript: "Laboratory investigation of hydraulic fracture growth in Zimbabwe gabbro" -- dataset of GABB-003, GABB-005 and GABB-006 experiments</p>
SurEau database : A database of hydraulic and stomatal traits for modelling drought resistance in plants
<p>This file contains a database of hydraulic and stomatal traits that accompanies the paper untitled "Pant resistance to drought relies on timely stomatal closure" publish in Ecology Letters. This database was used to built the Figure of this manuscript.</p> <p>> The first page ("Stem_VCurves") contains the parameter of vulnerability curve to embolism for 150 species. Family, genus and species names as well as original reference are provided.</p> <p>> The second page ("Pgs90") contains a first proxy for the water potential causing stomatal closure, it is the value of water potential causing 90% stomatal closure computed from gs versus water potential. Family, genus and species names as well as original reference are provided.</p> <p>> The third page (Ptlp) contains a second proxy for the water potential causing stomatal closure, it is the turgor loss point computed from pressure volume curves. Family, genus and species names as well as original reference are provided.</p> <p>> The fourth page (ALL) contains all the three previous pages together, allowing to reconstruct Figure 1.</p> <p>> The fifth page (PitlpAdultSeedlings) contains values of turgor loss point for adults and seedilngs for 15 species.</p> <p>> The sixth page (P50AdultSeedlings) contains values of embolism resistance for adults and seedilngs for 14 species.</p> <p>> The seventh page (Emin) contains values of minimum (i.e. cuticular) conductance and minimum transpiration for 33 species as well as embolism resistance values for theese species.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Hydraulic model (HEC-RAS) of downstream of Tuttle Creek Reservoir at the confluence of the Big Blue River and the Kansas River near Manhattan, KS
<p>A 2D Hydraulic model (HEC-RAS) for below Tuttle Creek Reservoir at the confluence of the Kansas River and the Big Blue River near Manhattan, KS is presented. Model geometry is based on United States Geological Survey (USGS) 3DEP data (2015), with underwater bathymetry "burned" in using cross-sections sampled in the field in April of 2023. The model was calibrated based on water surface measured during data collection. The hydraulic simulations correspond to streamflows during which fish monitoring data were collected by researchers at Kansas State University (L. Rowley and K. Gido, to be published). Results from the hydraulic model, coupled with a sediment transport model, will be used to study fish and macroinvertabrate ecological response to streamflow.</p>
Codes, Catalogues and Data for "Deep Learning Phase Pickers: How Well Can Existing Models Detect Hydraulic-Fracturing Induced Seismicity from a Downhole Array"
<p><strong>Codes, Catalogues and Data available for:</strong> <br>"Deep Learning Phase Pickers: How Well Can Existing Models Detect Hydraulic-Fracturing Induced Seismicity from a Downhole Array"</p> <p><strong>Catalog</strong> folder: Contains the CMM (beam-forming based) event catalogue as well as event and station information for the PNR-1z site.</p> <p><strong>Classification Test</strong> folder: Jupyter notebooks that run the classification tests and mseed input data of isolated phases (P, S, Noise).</p> <p><strong>DL_model_catalogues</strong> folder: Contains full catalogues for each DL phase picker (GPD, U-GPD, EQT and PhaseNet) and the LinMEF-filtered catalogues.</p> <p><strong>Model_run_docs</strong> folder: Util/core files for PhaseNet and EQTransformer to read data with different sampling frequencies (i.e., not 100 Hz)</p> <p><strong>Data</strong> folder: Contains one hour of continuous downhole data (11th December 2018, 9am-10am) from the PNR-1z dataset.</p>
Condition monitoring of hydraulic systems Data Set at ZeMA
<p><strong>Abstract:</strong></p> <p>The data set addresses the condition assessment of a hydraulic test rig based on multi sensor data. Four fault types are superimposed with several severity grades impeding selective quantification.</p> <p> </p> <p><strong>Source:</strong></p> <p>Creator: ZeMA gGmbH, Eschberger Weg 46, 66121 Saarbrücken<br> Contact: t.schneider <strong>'@'</strong> zema.de, s.klein <strong>'@'</strong> zema.de, m.bastuck <strong>'@'</strong> lmt.uni-saarland.de, info <strong>'@'</strong> lmt.uni-saarland.de</p> <p> </p> <p><strong>Data Set Information:</strong></p> <p>The data set was experimentally obtained with a hydraulic test rig. This test rig consists of a primary working and a secondary cooling-filtration circuit which are connected via the oil tank [1], [2]. The system cyclically repeats constant load cycles (duration 60 seconds) and measures process values such as pressures, volume flows and temperatures while the condition of four hydraulic components (cooler, valve, pump and accumulator) is quantitatively varied.</p> <p> </p> <p><strong>Attribute Information:</strong></p> <p>The data set contains raw process sensor data (i.e. without feature extraction) which are structured as matrices (tab-delimited) with the rows representing the cycles and the columns the data points within a cycle. The sensors involved are:<br> Sensor Physical quantity Unit Sampling rate<br> PS1 Pressure bar 100 Hz<br> PS2 Pressure bar 100 Hz<br> PS3 Pressure bar 100 Hz<br> PS4 Pressure bar 100 Hz<br> PS5 Pressure bar 100 Hz<br> PS6 Pressure bar 100 Hz<br> EPS1 Motor power W 100 Hz<br> FS1 Volume flow l/min 10 Hz<br> FS2 Volume flow l/min 10 Hz<br> TS1 Temperature °C 1 Hz<br> TS2 Temperature °C 1 Hz<br> TS3 Temperature °C 1 Hz<br> TS4 Temperature °C 1 Hz<br> VS1 Vibration mm/s 1 Hz<br> CE Cooling efficiency (virtual) % 1 Hz<br> CP Cooling power (virtual) kW 1 Hz<br> SE Efficiency factor % 1 Hz<br> <br> The target condition values are cycle-wise annotated in ‘profile.txt’ (tab-delimited). As before, the row number represents the cycle number. The columns are<br> <br> 1: Cooler condition / %:<br> 3: close to total failure<br> 20: reduced effifiency<br> 100: full efficiency<br> <br> 2: Valve condition / %:<br> 100: optimal switching behavior<br> 90: small lag<br> 80: severe lag<br> 73: close to total failure<br> <br> 3: Internal pump leakage:<br> 0: no leakage<br> 1: weak leakage<br> 2: severe leakage<br> <br> 4: Hydraulic accumulator / bar:<br> 130: optimal pressure<br> 115: slightly reduced pressure<br> 100: severely reduced pressure<br> 90: close to total failure<br> <br> 5: stable flag:<br> 0: conditions were stable<br> 1: static conditions might not have been reached yet</p> <p> </p> <p><strong>Relevant Papers:</strong></p> <p>[1] Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.<br> [2] N. Helwig, A. Schütze, ‘Detecting and compensating sensor faults in a hydraulic condition monitoring system’, in Proc. SENSOR 2015 - 17th International Conference on Sensors and Measurement Technology, oral presentation D8.1, Nuremberg, Germany, May 19-21, 2015, doi: 10.5162/sensor2015/D8.1.<br> [3] Tizian Schneider, Nikolai Helwig, Andreas Schütze, ‘Automatic feature extraction and selection for classification of cyclical time series data’, tm - Technisches Messen (2017), 84(3), 198 – 206, doi: 10.1515/teme-2016-0072.</p> <p><br> </p> <p><strong>Citation Request:</strong></p> <p>Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.</p>
Raw datasets for paper "Multi-scale hydraulic graph neural networks for flood modelling"
<p>The repository contains two zip folders for the synthetic and case study datasets (raw_datasets_mesh.zip, raw_datasets_dk15.zip). </p> <p>Each zip folder comprises 4 subfolders (DEM, Geometry, Hydrograph, Simulations), containing the elevation, boundary polygon, discharge hydrograph, and full hydrodynamic results for all simulations.</p> <p>The overview.csv file provides the seeds used for experiment replicability and the runtime of the numerical model on each simulation.</p>
Experiment and analysis data of Study on the Effect of Pore-Clogging Caused by Fenton's Reagent on the Hydraulic Conductivity Using Time-lapsed Hydraulic Tomography
<p>The Microsoft Office Excel data file ('HT data.xlsx') contains two sheets: the first sheet includes the raw data from the first and second HT experiments, labeled as '1st' and '2nd' respectively; the second sheet contains their processed data.</p> <p>The compressed file ('Models.rar') includes all the models, such as the forward and inverse models. Each model contains its own input files and an executable file.</p>
At-a-station hydraulic geometry exponent b derived from Landsat river width and discharge observation
<h2>Description</h2> <p>Global at-a-station hydraulic geometry exponent b dataset derived from Landsat river width (Feng et al., 2022) and discharge observation (Lin et al., 2019).</p> <p> </p> <p>For more details, please refer to:</p> <div> </div> <p><span>Zimin Yuan, Peirong Lin, Xiwei Guo, Kai Zhang, Hylke E. Beck. Revisiting At-a-Station Hydraulic Geometry Using Discharge Observations and Satellite-Derived River Widths. <em>J Remote Sens.</em> 2024;4:0271. DOI:<a href="https://doi.org/10.34133/remotesensing.0271">10.34133/remotesensing.0271</a></span></p> <p> </p> <h2>Contacts</h2> <ul> <li>Zimin Yuan, <a href="mailto:ziminyuan@pku.edu.cn">ziminyuan@pku.edu.cn</a></li> <li>Peirong Lin, <a href="mailto:peironglinlin@pku.edu.cn" target="_blank" rel="noopener">peironglinlin@pku.edu.cn</a></li> </ul> <p> </p>
Permeable pavement hydraulic performance and clogging experiments using a full-scale urban drainage physical model
<p>This dataset contains the results from 15 tests conducted used a physical model in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain) as part of the POREDRAIN project.</p> <p><br>The objective of the tests is to analyse the hydraulic performance of a porous asphalt layer of the PA-16 type and the impact of clogging on the hydrological behaviour and water quality of the effluent. The porous asphalt was used to retrofit an impervious concrete surface of a 36 m² full-scale street section physical model, which consist of a rainfall simulator placed over the street surface. The behaviour of the porous asphalt layer was assessed by adding surface sediment loads between simulated rainfall events. Stormwater flow discharges were collected from two gully pots and an outlet lateral channel. </p>
Radial Hydraulic Fracturing Experiment: 1 Cycle of Fracture Propagation, Arrest, and Closure in Molasse de Villarlod Sandstone - Sample M03
<h4><strong>Overview</strong></h4> <p>This dataset encompasses detailed measurements from a lab-scale radial hydraulic fracturing experiment conducted on a cubic sample of Molasse de Villarlod sandstone, designated as Sample M03. The sandstone, sourced from a quarry in Fribourg, Switzerland, is known for its porosity (18.1%) and permeability, making it an ideal material for studying hydraulic fracture processes. The primary focus of the experiment was to observe and analyze the propagation, arrest, and closure of hydraulic fractures under controlled triaxial stress conditions.</p> <h4><strong>Experimental Setup</strong></h4> <p>The experiment was conducted on a cubic sandstone sample with dimensions of 25 × 25 × 25 cm. The sample was placed in a truetriaxial frame that applied confining stresses in all three principal directions:</p> <ul> <li><strong>Vertical Confining Stress:</strong> 7 MPa</li> <li><strong>Horizontal Confining Stress:</strong> 14 MPa</li> </ul> <p>A <strong>viscous glucose fluid containing a UV additive</strong> was used as the fracturing fluid. This fluid was injected through a 1/8'' high-pressure tube cemented with epoxy into a centrally drilled hole within the sample. An axisymmetric notch was created at the injection point to facilitate fracture initiation and promote the planarity of the fracture.</p> <p>The experiment was designed to simulate one cycle of fracture initiation, propagation, arrest, and closure. The closure of the fracture was occured by the leakoff of the fracturing fluid into the surrounding porous medium.</p> <h4><strong>Acoustic Monitoring</strong></h4> <p>To capture the dynamics of fracture propagation and closure, the experiment employed both passive and active acoustic monitoring systems:</p> <ol> <li> <p><strong>Passive Acoustic Monitoring:</strong></p> <ul> <li><strong>Sensors:</strong> 16 Vallen VS150-M passive piezoelectric sensors were used to capture Acoustic Emissions (AEs) within the frequency range of 50 kHz to 600 kHz.</li> <li><strong>Signal Processing:</strong> Continuous signal analysis and denoising were performed on the captured AE data. The STA/LTA algorithm was applied to the denoised signal to identify potential p-wave arrivals, providing insights into the fracture mechanics.</li> </ul> </li> <li> <p><strong>Active Acoustic Monitoring:</strong></p> <ul> <li><strong>Transducers:</strong> A total of 64 piezoelectric transducers were integrated into the loading platens, with 32 acting as sources and 32 as receivers. The array included 10 shear-wave and 54 longitudinal-wave transducers.</li> <li><strong>Signal Generation and Acquisition:</strong> A Ricker excitation signal with a central frequency adjustable between 300 and 750 kHz was generated and amplified using a high-power amplifier. The signal was routed to one of the 32 source transducers via a multiplexer, and the resulting signals were recorded simultaneously by the 32 receiver transducers at a sampling frequency of 50 MHz. Each source was excited 50 times to improve the signal-to-noise ratio, with the complete acquisition sequence taking approximately 2.5 seconds.</li> </ul> </li> </ol> <h4><strong>Additional Measurements</strong></h4> <p>In addition to acoustic monitoring, several other key measurements were recorded during the experiment:</p> <ul> <li><strong>Fluid Injection Parameters:</strong> The pressure and rate of fluid injection were continuously monitored.</li> <li><strong>Flat-Jack and Piston Parameters:</strong> The pressures and volumes exerted by each pair of flat-jacks were recorded at a frequency of 1 Hz.</li> <li><strong>Fracture Opening Measurement:</strong> An eddy current sensor, an electromagnetic inductive device, was placed inside the wellbore at the notch/inlet to directly measure the fracture opening.</li> </ul> <p>All measurements were synchronized using a dedicated LabView application to ensure consistency across the dataset.</p> <h4><strong>Conclusion</strong></h4> <p>This dataset provides a comprehensive view of the hydraulic fracturing behavior of Molasse de Villarlod sandstone under controlled laboratory conditions, with a focus on the processes of fracture propagation, arrest, and closure. The dataset includes raw and processed acoustic data, fluid injection metrics, and direct observations of fracture opening. It is an invaluable resource for researchers and engineers studying hydraulic fracturing, rock mechanics, and related fields. The data is suitable for detailed analysis and modeling of fracture mechanics in porous, permeable sandstones.</p> <h4><strong>Note</strong></h4> <p>Since the fracture did not extend to the boundaries of the sample, the sample was subsequently cut, and a core was extracted. This core was then sent for CT-scan analysis, which was used to reconstruct the residual fracture surfaces and assess their roughness. The dataset from this analysis is available in the Related Work section via the provided URL (Talebkeikhah, M. (2024). CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M03 Sample [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13358916" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13358916</a>).</p> <h4><strong>Processing code</strong></h4> <p>Follow the <strong>URL repositories</strong> below to access to the codes for processing these dataset.</p> <p><a href="https://github.com/GeoEnergyLab-EPFL/ActiveAcoustiX">https://github.com/GeoEnergyLab-EPFL/ActiveAcoustiX</a></p> <p><a href="https://github.com/GeoEnergyLab-EPFL/FracLowRate">https://github.com/GeoEnergyLab-EPFL/FracLowRate</a></p> <p><strong>Contact and Support</strong></p> <p>Email:</p> <p>Brice Lecampion: brice.lecampion@epfl.ch</p> <p>Mohsen Talebkeikhah: m.talebkeikhah@gmail.com</p>
Observed soil water retention and soil hydraulic conductivity data and fits to those data by RIAfitter and KRIAfitter
<p>Weber soils SWRC.7z: zipped file with the Windows folders that hold all input and output data related to fitting the RIA parameterization (de Rooij, 2022, 2024a) to soil water retention data of 13 soils.</p> <p>Weber soils UHCC.7z: the zipped folder structure with fits of several models (de Rooij, 2024b, c) for the unsaturated hydraulic conductivity for the same 13 soils as the other file. The fitting code (KRIAfitter) used the fitted parameters of the other file plus soil hydraulic conductivity data to produce the fits.</p> <p>Both files contain Excel files that process the input and output of all fits.</p> <p>The soils are taken from Weber et al. (2019). The support of T.K.D. Weber in providing the data is gratefully acknowledged,</p> <p>N.B. The output format may differ somewhat from that reported in the User Manual of de Rooij (2024c) because the fits were used to improve details of the code and the way information is provided in the output files. These variations do not affect the parameter fitting process.</p> <p>References</p> <p>de Rooij, G. H.: Technical note: A sigmoidal soil water retention curve without asymptote that is robust when dry-range data are unreliable, Hydrol. Earth Syst. Sci., 26, 5849–5858, doi: 10.5194/hess-26-5849-2022, 2022.</p> <p>de Rooij, G.: Fitting the parameters of the RIA parameterization of the soil water retention curve (2.0), Zenodo [code], doi: 10.5281/zenodo.6491978, 2024a.</p> <p><br>de Rooij, G. H.: Averaging or adding domain conductivities to calculate the unsaturated soil hydraulic conductivity, Vadose Zone J., e20329, doi: 10.1002/vzj2.20329, 2024b.</p> <p><br>de Rooij, G.: Fitting the junction model and other models for the unsaturated hydraulic conductivity curve: KRIAfitter, Zenodo [code], doi: 10.5281/zenodo.14047942, 2024c.</p> <p><br>Weber, T. K. D., Durner, W., Streck, T., and Diamantopoulos, E.: A modular framework for modeling unsaturated soil hydraulic properties over the full moisture range, Water Resour. Res., 55, 4994-5011, doi: 10.1029/2018WR024584, 2019.</p> <p> </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)
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.