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.
562
datasets available to search
ShareScore release 0.9.0
Dataset results
562 results for “faulting”
Permeability Anisotropy Data of Carbonate Fault Rocks
<p>Permeability and porosity data from core plugs of faulted and unfaulted carbonate lithofacies. 2D and 3D image analysis results of samples used in the publication also included.</p>
Thrust-dominated unilateral rupture of a blind listric fault associated with the 2024 Hualien earthquake
<p>Slip model and the interferometric synthetic aperture radar (InSAR) of the 2024 Hualian Mw 7.4 earthquake.</p>
Microseismic catalogue from seismicity on the Jericho Fault (Dead Sea) [Dataset]
<p>This is a database of results linked to the associated manuscript (Klinger et al., 2024) which has been submitted to Geophysical Journal International and is currently in review. </p> <p>We report locations, magnitudes, timing and location errors for microseismic events linked to seismic acitivity on the Jericho Fault.</p> <p>Prior to publication please cite this database using the following two references:</p> <p>Klinger, A.G., Kurzon, I., Sagy, A. (2024). Microseismic and damage-zone characteristics of a fully locked fault segment on the Dead Sea Transform. <em>Manuscript submitted to Geophysical Journal International . </em></p> <p>Klinger, A.G., Werner, M.J. (2021). Microseismic catalogue from seismicity on the Jericho Fault (Dead Sea) [Data set]. Zenodo. https://zenodo.org/uploads/11653666.</p>
MATLAB Codes for: Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation
<p>The following MATLAB codes belong to the paper following paper which has been publication in MDPI Machines. This repository includes all the necessary MATLAB scripts and functions used in the research for diagnosing faults in drones using advanced acoustic emission analysis and optimization techniques. The dataset used in this paper is referenced in the article. Please check the publication for the dataset reference. Download the dataset, decompress it, and place it in the same repository as these codes to ensure proper functionality. For any queries or further information, please refer to this paper.</p> <p>Berghout, Tarek, and Mohamed Benbouzid. 2024. "Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation" <em>Machines</em> 12, no. 8: 504. https://doi.org/10.3390/machines12080504 </p> <p> </p>
Arrival-time data of the Longmenshan Fault Zone in East Tibet
<p>This file is the manually arrival-time data of the Longmenshan Fault Zone in East Tibet, including P, S and PmP arrival times.</p>
High-resolution topography data and fault trace along the Southern Riyueshan fault, NE margin of Tibetan Plateau, China
<p>High-resolution digital elevation models (DEM) topography data extracted from the uncrewed aerial vehicle (UAV) of a DJI (Dajiang Innovations Science and Technology Co., Ltd.) Phantom 4 RTK, and GF-7 satellite stereo imagery. The trace of the Riyueshan fault is interpreted based on these high-resolution topography data.</p>
Robustness assessment through iterative statistical fault injection: LEON3 processor as a case study
<p><strong>Dataset exemplifies an approach of iterative statistical fault injection to assess the robustness of HDL models.</strong></p> <p>Contents:<br> 1. Results of exhaustive fault injection experiments (bit-flip faults) into LEON3 processor model;<br> 2. Interactive querying interface, allowing to obtain custom samples from exhaustive results, and visualize them;<br> 3. Python scripts simulating 3 approaches to statistical fault injection: conservative, error-driven, time-driven.</p> <p> </p> <p><strong>Installation guide:</strong></p> <p> 1. Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise download and install 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.</p> <p><br> 2. Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p> <br> 3. Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> </p> <p> – search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the results looks like:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p> 4. Unpack the contents of *.zip package into the folder on the Web Server. <br> For instance into 'Web-server root folder'/Dataset.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p> 5. In the web-browser navigate to the root directory of extracted package:<br> http://localhost/Dataset/index.html</p>
Comparing developer-provided to user-provided tests for fault localization and automated program repair: Artifacts
<p>Artifacts for the paper <em>Comparing developer-provided to user-provided tests for fault localization and automated program repair.</em></p> <p>Note that the artifacts are maintained in the following repositories:</p> <ul> <li>https://github.com/rjust/defects4j</li> <li>https://bitbucket.org/rjust/tests-tested-data</li> <li>https://bitbucket.org/rjust/fault-localization-data</li> </ul>
Electronic Supplement to Structural configuration of the Otates fault (southern Basin-and-Range Province) and its rupture in the 3 May 1887 MW = 7.5 Sonora, Mexico earthquake
<p>Electronic supplement to "Structural configuration of the Otates fault (southern Basin-and-Range Province) and its rupture in the 3 May 1887 MW = 7.5 Sonora, Mexico earthquake" (Seismological Society of America Bulletin, v. 98, no. 6, p. 2879-2893, 2008) with color-coded elevation model, satellite image of major Basin andRange normal faults in the study area, color version of geologic map, and additional photographs.</p> <p>High-resolution files of these figures are also available without restriction from </p> <p>http://www.seismosoc.org/Publications/BSSA_html/bssa_98-6/2008129-esupp/</p>
Seismic interpretation of key stratigraphic and structural surfaces, and crustal faults within the Galicia 3-D reflection survey
<p>This repository contains all the seismic interpretations utilized for the analysis in the article "<em>Origin of serpentinization patterns beneath the S-reflector detachment fault in the Galicia margin, offshore Spain</em>". </p> <p>The "<em>Surfaces</em>" file contains the CPS-3 shape files of the major stratigraphic and structural surfaces (seafloor, base of post-rift sedimentary strata, base of pre/syn-rift sedimentary strata, base of crystalline basement, S-reflector detachment fault, Moho). The "<em>Faults</em>" file contains the interpretations of the major crustal faults overlying the S-reflector detachment. The "<em>Data</em>" file contains the spatial boundary of where the S-reflector is the crust-mantle boundary, the P-wave velocities of Schuba et al. (2019) and calculated degree of serpentinization (Schuba et al., submitted) based on Christensen's (2004) 200 MPa/200<sup>o</sup>C serpentinite compilation study. </p> <p>All seismic interpretations were carried out on Petrel<sup>TM</sup> versions 2015 and 2017. The seismic reflection volume that was interpreted can be found at https://doi.org/10.1594/IEDA/500151.</p> <p> </p> <p>References: </p> <ul> <li>Christensen, N.I. (2004). Serpentinites, Peridotites, and Seismology. <em>International Geology Review</em>, <em>46</em>(9), 795-816. https://doi.org/10.2747/0020-6814.46.9.795</li> <li>Schuba, C.N., Schuba, J.P. Gray, G.G., and Davy, R.G., (2019). Interface targeted velocity estimation using machine learning. <em>Geophysical Journal International</em>, <em>218</em>(1), 45-56. https://doi.org/10.1093/gji/ggz142</li> <li>Schuba, C.N., Gray, G.G., Morgan, J.K., Schuba, J.P., and Sawyer, D.S., (submitted). Interface targeted velocity estimation using machine learning. <em>Geochemistry, Geophysics, Geosystems.</em></li> </ul>
Flights of a Multirotor UAS with Structural Faults: Failures on Composite Propeller(s)
<p>Data acquired from several flights of a custom-fabricated Hexacopter UAS with composite structure (carbon fiber arms and central hub) and composite (carbon fiber) propellers is presented here. The Hexacopter was assembled from a commercially available kit (Tarot 690) and was flown in manual and autonomous mode; take-offs and landings were under manual control and the bulk of the flight tests were conducted with the Hexacopter in a “Position Hold” mode. All flights were flown within the UAS flight cage at Parks College of Engineering, Aviation and Technology at Saint Louis University, for approximately 5 minutes each. Several failure conditions (different types of manually induced) on the composite (carbon fiber) propellers were tested, including failures on up to two propellers. The data set described in this article contains flight data from the onboard flight controller (Pixhawk) as well as three 3-axis accelerometers mounted on the arms of the Hexacopter UAS. The data is included as supplemental material.</p>
A Novel Hybrid Finite Element-Spectral Boundary Integral Scheme for Modeling Earthquake Cycles: Application to Rate and State Faults with Low-Velocity Zones
<p>We present a novel hybrid finite element (FE) - spectral boundary integral (SBI) scheme that enables efficient simulation of earthquake cycles. This combined FE-SBI approach captures the benefits of finite elements in modelling problems with nonlinearities, as well as the computational superiority of SBI. The domain truncation enabled by this scheme allows us to utilize high-resolution finite elements discretization to capture inhomogeneities or complexities that may exist in a narrow region surrounding the fault. Combined with an adaptive time stepping algorithm, this framework opens new opportunities for modeling earthquake cycles with high-resolution fault zone physics. In this initial study, we consider a two dimensional (2-D) anti-plane model with a vertical strike-slip fault governed by rate and state friction in the quasi-dynamic limit under the radiation damping approximation. The proposed approach is first verified using the benchmark problem BP-1 from the Southern California Earthquake Center (SCEC) sequence of earthquake and aseismic slip (SEAS) community verification effort. The computational framework is then utilized to model the earthquake sequence and aseismic slip of a fault embedded within a low-velocity fault zone (LVFZ) with different widths and compliance levels. Our results indicate that sufficiently compliant LVFZs contribute to the emergence of sub-surface events that fail to penetrate to the free surface and may experience earthquake clusters with nonuniform inter-seismic time. Furthermore, the LVFZ leads to slip rate amplification relative to the homogeneous elastic case. We discuss the implications of our results for understanding earthquake complexity as an interplay of fault friction and bulk heterogeneities. The complete work consists of all files listed below. </p>
Synthesis of Fault Traces in SE Louisiana Relative to Infrastructure
<p>Corresponding data set for Tran-SET Project No. 17GTLSU12. Abstract of the final report is stated below for reference:</p> <p>"Geological faulting has been implicated as a contributor to subsidence, coastal land-loss and submergence of marshlands in southern Louisiana. This report reviews whether fault motion, either by slow creep or more sudden slip, can cause deformation of engineered structures resulting in increased infrastructure maintenance and repair costs. The impact of surface, or near-surface, geologic faulting on critical infrastructure is insufficiently documented in southeastern Louisiana, but the state has a vast amount of energy-sector subsurface data that to date has been under-utilized for transportation and other near-surface engineering applications. Recent and on-going work by research groups at Tulane University, University of New Orleans, and University of Louisiana at Lafayette use energy industry subsurface data, including well data and 2D and 3D seismic reflection data to map and project deep-seated faults that have been in place for millions of years, to create provisional surface fault trace maps. Accurate characterization of active fault locations and effects using both subsurface and surface methods will aid in the design and placement of infrastructure, as well as in developing appropriate mitigation methods. Descriptive criteria for reliability of fault locations were developed and are based on resources used in the interpretation and map scale: Level 1 suspected faults – described in the literature and included here from georeferenced maps; Level 2 identified faults – those observed on 2D or 3D seismic and mapped in a geographic reference system; Level 3 confirmed faults – mapped on seismic and ground-truthed with field methods including age-dated sediment borings and high-resolution seismic. We have compiled data resources in a GIS-based system for simple retrieval and map-based review so that additional work specific to critical infrastructure projects can be prioritized. The intent is to give the public information, so that they may better assess the importance of faulting in any particular project area and as a resource to identify areas that already have energy industry seismic available."</p>
Inconsistent Permeability Changes along a Fault Zone Caused by the Xingwen M5.7 Earthquake in SW China
<p>Data for manuscript " Inconsistent Permeability Changes along a Fault Zone Caused by the Xingwen <em>M</em>5.7 Earthquake in SW China "</p>
Fault reactivation during fluid pressure oscillations: transition from stable to unstable slip
<p>This is the ReadMe file corresponding to the study entitled: "Fault reactivation during fluid pressure oscillations: transition from stable to unstable slip"</p> <p>By Noël C., Passelègue X.F, Giorgetti C., Violay M.</p> <p>This study has been published in the Journal of Geophysical Research: Solid Earth under Copyright on October 2019. <br> doi:</p> <p>This Read-Me file has been last edited on October 2019</p> <p>This readme file describes the data repository and supplementary files accompanying the above publication. <br> For any further queries please contact corentin.noel@epfl.ch</p>
The complementary distribution of coseismic and slow-moving landslides along the Longmenshan fault zone
<p><a href="../api/records/13143195/draft/files/JZG.zip/content" target="_blank" rel="noopener noreferrer">JZG.zip</a>, The inventory of coseismic landlide for Jiuzhaigou earthquake. </p> <p><a href="../api/records/13143195/draft/files/Surface%20rupture.zip/content" target="_blank" rel="noopener noreferrer">Surface rupture.zip</a> The surface rupture of Wenchuan erthquake</p> <p><a href="../api/records/13143195/draft/files/detected%20slow-moving%20landslide_6660.txt/content" target="_blank" rel="noopener noreferrer">detected slow-moving landslide_6660.txt</a> The detected slow-moving landslide using Sentinel-1 images by deployed phase gradient stacking technique.</p> <p><a href="../api/records/13143195/draft/files/Snow_polygon.zip/content" target="_blank" rel="noopener noreferrer">Snow_polygon.zip</a> The snow-covered polygon in the study area</p> <p><a href="../api/records/13143195/draft/files/ROI-1_ALOS_phase%20gradient.zip/content" target="_blank" rel="noopener noreferrer">ROI-1_ALOS_phase gradient.zip</a> the stacked phase gradient files generated from ALOS-1 images and flagged slow-moving landslide manually for ROI-1</p> <p><a href="../api/records/13143195/draft/files/ROI-1_ALOS_phase%20gradient.zip/content" target="_blank" rel="noopener noreferrer">ROI-2_ALOS_phase gradient.zip</a> the stacked phase gradient files generated from ALOS-1 images and flagged slow-moving landslide manually for ROI-2</p> <p><a href="../api/records/13143195/draft/files/Lushan_ALOS_phase%20gradient.zip/content" target="_blank" rel="noopener noreferrer">Lushan_ALOS_phase gradient.zip</a> the stacked phase gradient files generated from ALOS-1 images and flagged slow-moving landslide manually in the focused region for affected area of Lushan earthquake.</p> <p><a href="../api/records/13143195/draft/files/rivers.zip/content" target="_blank" rel="noopener noreferrer">rivers.zip</a> the rivers in the study area</p> <p><a href="../api/records/13143195/draft/files/rough1.tif/content" target="_blank" rel="noopener noreferrer">rough1.tif</a> the terrain roughness for the study area</p> <p><a href="../api/records/13143195/draft/files/rough1.tif/content" target="_blank" rel="noopener noreferrer">slope1.tif</a> the terrain slope for the study area</p> <p><a href="../api/records/13143195/draft/files/A30m_5km_topo1.tif/content" target="_blank" rel="noopener noreferrer">A30m_5km_topo1.tif</a> the terrain relief for the study area</p> <p><a href="../api/records/13143195/draft/files/grd%20S1_DT62.zip/content" target="_blank" rel="noopener noreferrer">grd S1_DT62.zip</a> part of .grd files of stacked phase gradient for descending track 62 of Sentinel-1 images which covering the plotted regions in the supplementary material</p> <p><a href="../api/records/13143195/draft/files/grd%20S1_AT128.zip/content" target="_blank" rel="noopener noreferrer">grd S1_AT128.zip</a> part of .grd files of stacked phase gradient for ascending track 128 of Sentinel-1 images which covering the plotted regions in the supplementary material</p> <p><a href="../api/records/13143195/draft/files/grd%20S1_AT55.zip/content" target="_blank" rel="noopener noreferrer">grd S1_AT55.zip</a> part of .grd files of stacked phase gradient for ascending track 55 of Sentinel-1 images which covering the northeast section of surface rupture that generated in the 2008 wenchuan earthquake.</p> <p><a href="../api/records/13143195/draft/files/grd_Jiuzhaigou_before_after%20earthquake.zip/content" target="_blank" rel="noopener noreferrer">grd_Jiuzhaigou_before_after earthquake.zip</a> the .grd files of stacked phase gradient for ascending track 128 of Sentinel-1 images which covering the affected regions before and after Jiuzhaigou earthquake.</p> <p> </p> <p> </p> <p> </p>
Active fault surface traces of the southern Alpine Fault Zone, New Zealand
<p>This repository contains detailed, lidar-enabled geomorphic mapping of active fault surface traces assoicated with the southern Alpine Fault Zone in New Zealand.</p> <p> </p> <p> </p>
High-resolution chain transform fault bathymetry from the PI-LAB experiment
<p>Bathymetry data from PI-LAB, MGL1602 (<a href="https://doi.org/10.1029/2018JB015982">https://doi.org/10.1029/2018JB015982</a>).</p> <p>Format: Lat/Lon/Depth [m]</p> <p>For reference, please cite: Harmon, N., Rychert, C., Agius, M., Tharimena, S., Le Bas, T., Kendall, J. M., & Constable, S. (2018). Marine geophysical investigation of the Chain Fracture Zone in the equatorial Atlantic from the PI‐LAB experiment. <em>Journal of Geophysical Research: Solid Earth</em>, <em>123</em>(12), 11-016</p>
Data in support of : Volumetric Deformation Feedback on Fault Stability and Strain Localization Precursor to Stick-Slip Behavior in Laboratory Earthquakes.
<p>This folder contains 3 .txt files:</p> <p> </p> <p>LP1 — low pressure data set.</p> <p>HP1 — High confining pressure experiement.</p> <p>HP2 — High cinfining pressure repeat experiment.</p> <p> </p> <p>The .txt files contain the following columns: Time (s), Pc (bar), VerticalLoad (kN), Horizontalload (kN), DisplacementOE (mm), StrainSG_xx, StrainSG_yy, StrainSG_xy.</p> <p>Strain gauge data are pre-filtered. All other data are raw.</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>
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.