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3,295 results for “fractures”
Data of "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework"
<p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework",<br> journal = "Engineering Fracture Mechanics",<br> year = "2022",<br> volume = "275",<br> pages = "108844 ",<br> doi = "https://doi.org/10.1016/j.engfracmech.2022.108844",<br> author = "Antoine Hilhorst, Julien Leclerc, Thomas Pardoen, Pascal J. Jacques, Ludovic Noels, Van-Dung Nguyen"</p> <p>New version following review.</p> <p> </p> <p> </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>
Data from: Evaluating species richness using proteomic fingerprinting and DNA-barcoding – a case study on meiobenthic copepods from the Clarion Clipperton Fracture Zone
<p><span>The Clarion Clipperton Fracture Zone (CCZ) is a vast deep-sea region harboring a highly diverse benthic fauna, which will be affected by potential future deep-sea mining of metal-rich polymetallic nodules. Despite the need for conservation plans and monitoring strategies in this context, the majority of taxonomic groups remains scientifically undescribed. However, molecular rapid assessment methods such as DNA-barcoding and Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) provide the potential to accelerate specimen identification and biodiversity assessment significantly in the deep-sea areas. In this study, we successfully applied both methods to investigate the diversity of meiobenthic copepods in the eastern CCZ, including the first application of MALDI-TOF MS for the identification of these deep-sea organisms. Comparing several different species delimitation tools for both datasets, we found that biodiversity values were very similar, with Pielou's Evenness varying between 0.97 and 0.99 in all datasets. Still, direct comparisons of species clusters revealed differences between all techniques and methods, which are likely caused by the high number of rare species being represented by only one specimen, despite our extensive dataset of more than 2000 specimens. Hence, we regard our study as a first approach toward setting up a reference library for mass spectrometry data of the CCZ in combination with DNA-barcodes. We conclude that proteome fingerprinting, as well as the more established DNA-barcoding, can be seen as a valuable tool for rapid biodiversity assessments in the future, even when no reference information is available.</span></p>
aretu/fracture_energy: Second release of the fracture energy dataset
<p>In this release, were included:</p> <ul> <li>several corrections to the "fracture energy" data (.csv files) performed during the revision process;</li> <li>two descriptive .tex files that describe the data in detail;</li> <li>a jupyter notebook for quick visualization of data;</li> <li>a draft of the github workflow to export the .tex files as .pdf files</li> </ul>
Observed water level from 2015 to 2019 in 4 wells from a porous fractured aquifer in Burgundy, France
<p>This repository hosts the data used for drawing figure 8 from Jeannot et al.(submitted). The dataset is made of observed water levels from 2015 to 2019 in 4 wells from a porous fractured aquifer in Burgundy, France. The sampling time step is 2 hours.</p> <p><strong>Cited bibliography</strong></p> <p>Jeannot, B., Schaper, L., and Habets, F., submitted. Water Level in Observation Wells Simulated from Fracture and Matrix Water Heads Outputed by Dual-continuum Hydrogeological Models : POWeR-FADS. <em>Water Resources research</em></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>
Rock-temperature, fracture displacement and acoustic/micro-seismic data measured at Matterhorn Hörnligrat, Switzerland
<p>This repository contains data, which were acquired in the context of project X-Sense2 (financed by nano-tera.ch, ref. no. 530659) at the Matterhorn Hörnligrat fieldsite on 3500 m a.s.l. from 2015 until 1 April 2018. These data were used in the following publication:</p> <p>Weber, S., Faillettaz, J., Meyer, M., Beutel, J., and Vieli, A.: Acoustic and micro-seismic characterization in steep bedrock permafrost on Matterhorn (CH), Journal of Geophysical Research: Earth Surface, 123(6), 1363-1385, doi: 10.1029/2018JF004615, 2018.</p> <p><strong>AM-DATA</strong> This repository contains selected accelerometer data with SI unit m/s<sup>2</sup> (hourly .miniseed-files, MH40 refers to AM<sub>scarp</sub>). These data were measured continuously using an accelerometer based on a Wilcoxon 728A/T (10 − 10000 Hz, 24 kHz resonance frequency), netADC data acquisition system and netSP+ seismological processor of Institute of Mine Seismology. Data were synchronized to a global time reference using GPS (<1 μs). The data is stored in .miniseed-format and splitted in hourly files.</p> <p><strong>SM-DATA</strong> This repository contains selected raw seismometer data in counts (hourly .miniseed-files, MHDL refers to SM<sub>scarp</sub> and MHDT refers to SM<sub>ridge</sub>). These data were measured using a Lennartz electronic low-noise seismometer LE-3Dlite MKIII (1−100 Hz) and Nanometrics Centaur digital recorder, a 24-bit high-resolution seismic data acquisition system disciplined by GPS (<100 μs) with a sampling rate of 1000 sps. The data is stored in .miniseed-format and splitted in hourly files.</p> <p><strong>TIMESERIES</strong> This repository contains 8 timeseries:</p> <ul> <li> <p><em>AS_scarp_high.csv</em> describes the threshold triggeres acoustic emission hits acquired with a piezoelectric sensor Mistras Physical Acoustics Corporation R6α, 35−100 kHz, 55 kHz resonance frequency.</p> </li> <li> <p><em>AS_scarp_low.csv</em> describes the threshold triggeres acoustic emission hits acquired with a piezoelectric sensor Mistras Physical Acoustics Corporation R.45, 5−30 kHz, 20 kHz resonance frequency.</p> </li> <li> <p><em>CR_old.csv</em> described the measured fracture displacement in mm.</p> </li> <li> <p><em>SMridge_nofilter.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm without filtering. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>SMridge_filtered.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm in the frequency band 33-67 Hz. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>SMscarp_filtered.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm without filtering. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>SMscarp_nofilter.csv</em> describes automatically triggered events using a recursive short-term/long-term average (STA/LTA ) algorithm in the frequency band 33-67 Hz. Peak amplitude in µm/s and energy in µm<sup>2</sup>/s<sup>2</sup>.</p> </li> <li> <p><em>temperature.csv</em> describes the rock temperature (in °C) at different depths: 5, 10, 20, 30, 50 and 100 cm.</p> </li> </ul> <p>All time stamps are in UTC.</p>
Dataset - Permeability and viscoelastic fracture of a model tumor under interstitial flow
<p>This includes all the experimental data which are presented in the paper titled "Permeability and viscoelastic fracture of a model tumor under interstitial flow", Soft Matter, 2018 by Quang D. Tran, Marcos and David Gonzalez-Rodriguez.</p> <p>In each data folder, we have attached a Readme file to instruct readers how to analyze or compute our data.</p>
6th International Conference "Fracture Mechanics of Materials and Structural Integrity" (June 3–6, 2019, Lviv, Ukraine)
<p><strong>12.06.2019</strong></p> <p>03–06 червня 2019 року у м. Львові відбулася 6-та Міжнародна конференція з механіки руйнування матеріалів і цілісності конструкцій (“Fracture Mechanics of Materials and Structural Integrity”, FMSI 2019). Співорганізаторами конференції виступили Європейське товариство з цілісності конструкцій (European Structural Integrity Society, ESIS), Українське товариство з механіки руйнування матеріалів, Фізико-механічний інститут імені Г.В. Карпенка НАН України та Національний університет “Львівська політехніка”.</p> <p><em>Матеріали прес-служби НАН України</em></p>
Fracture toughness of mixed-mode anticracks in highly porous materials dataset and data processing
<blockquote> <div>This repository contains the code and datasets used in the data analysis for "Fracture toughness of mixed-mode anticracks in highly porous materials". The analysis is implemented in Python, using Jupyter Notebooks.</div> </blockquote> <h2>Contents</h2> <ul> <li><code>main.ipynb</code>: Jupyter notebook with the main data analysis workflow.</li> <li><code>energy.py</code>: Methods for the calculation of energy release rates.</li> <li><code>regression.py</code>: Methods for the regression analyses.</li> <li><code>visualization.py</code>: Methods for generating visualizations.</li> <li><code>df_mmft.pkl</code>: Pickled DataFrame with experimental data gathered in the present work.</li> <li><code>df_legacy.pkl</code>: Pickled DataFrame with literature data.</li> </ul> <h2>Prerequisites</h2> <ul> <li>To run the scripts and notebooks, you need:</li> <li>Python 3.12 or higher</li> <li>Jupyter Notebook or JupyterLab</li> <li>Libraries: <code>pandas</code>, <code>matplotlib</code>, <code>numpy</code>, <code>scipy</code>, <code>tqdm</code>, <code>uncertainties</code>, <code>weac</code></li> </ul> <h2>Setup</h2> <ol> <li>Download the zip file or clone this repository to your local machine.</li> <li>Ensure that Python and Jupyter are installed.</li> <li>Install required Python libraries using <code>pip install -r requirements.txt</code>.</li> </ol> <h2>Running the Analysis</h2> <ol> <li>Open the <code>main.ipynb</code> notebook in Jupyter Notebook or JupyterLab.</li> <li>Execute the cells in sequence to reproduce the analysis.</li> </ol> <h2>Data Description</h2> <div>The data included in this repository is encapsulated in two pickled DataFrame files, <code>df_mmft.pkl</code> and <code>df_legacy.pkl</code>, which contain experimental measurements and corresponding parameters. Below are the descriptions for each column in these DataFrames:</div> <h3><code>df_mmft.pkl</code></h3> <div>Includes data such as experiment identifiers, datetime, and physical measurements like slope inclination and critical cut lengths.</div> <ul> <li><code>exp_id</code>: Unique identifier for each experiment.</li> <li><code>datestring</code>: Date of the experiment as a string.</li> <li><code>datetime</code>: Timestamp of the experiment.</li> <li><code>bunker</code>: Field site of the experiment. Bunker IDs 1 and 2 correspond to field sites A and B, respectively.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h_sledge_top</code>: Distance from sample top surface to the sled in mm.</li> <li><code>h_wl_top</code>: Distance from sample top surface to weak layer in mm.</li> <li><code>h_wl_notch</code>: Distance from the notch root to the weak layer in mm.</li> <li><code>rc_right</code>: Critical cut length in mm, measured on the front side of the sample.</li> <li><code>rc_left</code>: Critical cut length in mm, measured on the back side of the sample.</li> <li><code>rc</code>: Mean of <code>rc_right</code> and <code>rc_left</code>.</li> <li><code>densities</code>: List of density measurements in kg/m^3 for each distinct slab layer of each sample.</li> <li><code>densities_mean</code>: Daily mean of <code>densities</code>.</li> <li><code>layers</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>layers_mean</code>: Daily mean of <code>layers</code>.</li> <li><code>surface_lineload</code>: Surface line load of added surface weights in N/mm.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>notes</code>: Additional notes regarding the experiment or observations.</li> <li><code>L</code>: Length of the slab–weak-layer assembly in mm.</li> </ul> <h3><code>df_legacy.pkl</code></h3> <div>Contains robustness data such as radii of curvature, slope inclination, and various geometrical measurements.</div> <ul> <li><code>#</code>: Record number.</li> <li><code>rc</code>: Critical cut length in mm.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h</code>: Slab height in mm.</li> <li><code>density</code>: Mean slab density in kg/m^3.</li> <li><code>L</code>: Lenght of the slab–weak-layer assembly in mm.</li> <li><code>collapse_height</code>: Weak-layer height reduction through collapse.</li> <li><code>layers_mean</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>surface_lineload</code>: Surface line load from added weights in N/mm.</li> </ul> <p>For more detailed information on the datasets, refer to the paper or the documentation provided within the Jupyter notebook.</p> <h2>License</h2> <div>This work is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</div> <p> </p> <div>You are free to:</div> <ul> <li><strong>Share</strong> — copy and redistribute the material in any medium or format</li> <li><strong>Adapt</strong> — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <div>Under the following terms:</div> <div> <ul> <li><strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</li> </ul> </div> <h2>Citation</h2> <div>Please cite the following paper if you use this analysis or the accompanying datasets:</div> <div> <ul> <li>Adam, V., Bergfeld, B., Weißgraeber, P. van Herwijnen, A., Rosendahl, P.L., Fracture toughness of mixed-mode anticracks in highly porous materials. <em>Nature Communincations</em> <strong>15</strong>, 7379 (2024). https://doi.org/10.1038/s41467-024-51491-7</li> </ul> </div>
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>
Fracture Caging in a Porous Lab Fault: Experiment Dataset (sensor coordinates added)
<p>Update: Sensor coordinates and make that were missing from the previous version were added. </p> <p>We conducted shear fracture caging experiments at the Fractured Earth Laboratory to investigate the potential of fracture caging in limiting induced seismicity in the fault. The experiments involved an injection of fluid into a repeatably constructed assembly of two aluminum wedges bonded by plaster, which is critically loaded for shear-slip. Meanwhile, fluid production is enabled using a five-spot pattern of pre-drilled boundary wells (i.e., a fracture cage). Injected fluid was a gear oil with 8.81 cP viscosity at room temperature . Syringe pumps were used to control and measure the injection rate, volume, and pressure. Microseismic activity was recorded using 16 acoustic emission (AE) sensors that were equally split into high-magnitude and high-sensitivity acquisition systems. Over the course of experiments, raw data collected includes AE waveforms from high-magnitude and high-sensitivity systems, post-mortem shear fracture photos, and synchronized timeseries data for 8 tests, which are presented here. </p>
Quantification of preferential flow in single fracture using electrical monitoring
Open the record for dataset details and reuse information.
Dataset for article "Prediction of thermal shock induced cracking in multi-material ceramics using a stress-energy criterion" published in Engineering Fracture Mechanics
<p>Dataset contains graphical outputs of the numerical analysis performed using Finite element method in finite elements software Ansys Mechanical. It contains also photoes of the tested specimens. Further, material data used for the numerical analysis and measured by authors are included in the csv file and all necessary input codes for the FE system Ansys creating data for graphs in the publication are provided in the subfolder "Models-Ansys" within "Data" directory.</p> <p> </p>
Dataset for fracture topology in mafic formations
<p>This dataset is part of the GEOMIMIC project, funded by the European Union’s Horizon Europe Research and Innovation program, which aims to improve carbon storage and mineralization efficiency in fractured mafic reservoirs. It combines fracture property data from previous studies with new data gathered in this project, along with results from fracture network simulations exploring reactive fluid flow near injection wells in mafic formations.</p>
Investigating Fracture Network Deformation Using Noble Gas Release: Dataset
<p><strong>Data Description</strong></p> <p>Data used in journal article:</p> <p>Investigating Fracture Network Deformation Using Noble Gas Release, 2021, W. Payton Gardner, Stephen J. Bauer, Scott Broome, <em>Geofluids</em></p> <p> </p> <ol> <li><em>Noble gas quadrupole dynamic data</em> are text files with time and logged current intensity for measured masses. File suffix is .xlh – File names are descriptive. Time stamps can be used to correlate with article figures</li> <li><em>He leak rate data</em> – .xlsx (Microsoft Excel) format. Figure in text can be found on its own worksheet.</li> <li><em>Air flow data</em> -Microsoft Excel format. Figures in text are in file.</li> </ol>
Hydraulic fracturing block test experiments in Gabbro & Marble - experiments # GABB-002 & MARB-007
<p>This dataset contains raw, processed and inverted data for 2 hydraulic fracturing tests performed in a Zimbabwe gabbro (GABB-002) and a Carrara Marble (MARB-007) at the Geo-Energy lab @ EPFL.</p> <p>[IMPORTANT NOTE: DUE TO file size-constraint, the raw binary filed containing the acoustic data is not part of this zenodo data set - Please contact Prof. B. Lecampion directly if you are interested in playing with the raw acoustic data file]</p> <p>TEST ID - GABB-002 : Lag/viscosity dominated test in a gabbro</p> <p>TEST ID - MARB-007 : Lag/viscosity dominated test in a carrara Marble</p> <p>The details of these two tests and its analysis is described in details in the following publication:</p> <p><strong>Measurements of the evolution of the fluid lag in laboratory hydraulic fracture experiments in rocks</strong></p> <p>Dong Liu, Brice Lecampion</p> <p>Geo-Energy Laboratory, Gaznat Chair on Geo-Energy, Ecole Polytechique Fédérale de Lausanne, EPFL-ENAC-IIC-GEL, Lausanne, Switzerland</p>
Fracture resistance dataset of composites under mixed-mode non-proportional loading
<p>Fracture resistance dataset of composites under mixed-mode non-proportional loading</p>
Self-propping exists in coal seam hydraulic fractures
<p>This repository contains data used in "Self-propping exists in coal seam hydraulic fractures" submitted by R Li, CZ Qin, SW Wang, and JC Wang.</p>
Supporting Data - Shallow Fracture Buffers High Elevation Runoff in Northwest Greenland
<p>This dataset contains supporting data accompanying Culberg, Chu, & Schroeder, "Shallow Fracture Buffers High Elevation Runoff in Northwest Greenland", <em>Geophysical Research Letters</em>, 2022. It includes the following:</p> <ul> <li>Ice-penetrating radar-derived mappings of transient firn aquifers and buried refrozen ice complexes (ice blobs) beneath ice slabs in Northwest Greenland.</li> <li>Porosity estimates derived from the inversion of ice-penetrating radar reflectivity for the 20150510_01 Ultrawideband MCoRDS transect flow in Northwest Greenland as part of NASA OIB.</li> <li>An NDWI stack image showing the maximum NDWI on a per pixel basis from all Landsat images between 2000 and 2016 for the region.</li> <li>Data for a time series of NDWI within the upslope catchment of each aquifer or blob.</li> <li>Locations of moulins, supraglacial lakes, and drained supraglacial lakes from high resolution optical imagery.</li> <li>Linear features extracted from the high resolution optical imagery.</li> <li>Surface crevasse densities and surface water feature densities inferred from the extracted linear features for the northern and southern portions of the ice slab region.</li> <li>Resistive stress and fracture toughness data for each location where fractures are inferred from the high resolution optical imagery.</li> <li>Full resolution image files for WorldView imagery used in the Supporting Information Figure S6.</li> </ul>
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.