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562 results for “faulting”
Cosmogenic Ages from Alluvial Fans offset by Central Walker Lane Faults
<p>Here are a series of cosmogenic 10Be and 36Cl ages and metadata for samples related to a faulting study southeast of Reno, NV, USA. Both 10Be and 36Cl concentrations were measured at the PRIME lab at Purdue. All 10Be samples were processed in the Geochronology Laboratories at the University of Cincinnati. The 36Cl samples were processed and analyzed at the PRIME lab. Boulder sampling focused on the largest boulders (~50–150-cm-diameter) from alluvial fan surfaces. Approximately 500 g samples were taken from the upper 2–5 cm of each of these boulders. 10Be concentrations and laboratory data are listed in these tables. The 10Be boulder exposure ages were calculated using the Cosmic Ray Exposure Program (CREp). The calculator requires input describing the geographic coordinates and elevation of the samples, local shielding of the sample, density of the sample, and estimation of the boulder erosion rates resulting from processes such as boulder grussification and spalling. The age estimates are also dependent on the assumption of particular scaling models designed to estimate the long-term production rate of cosmogenic 10Be. The 10Be ages use a production rate of 4.05 ± 0.30 at/g SiO2/yr determined at Twin Lakes, which is located at a higher elevation than the fan surfaces here, but is within 100 km of all study sites, the ERA40 atmosphere model, the Lifton-VDM2016 geomagnetic database, and the LSD scaling scheme.The 36Cl boulder ages are calculated using the CRONUS calculator for 36Cl.</p>
Fault interaction and its impact on crustal extrusion in southeastern Tibetan Plateau: insights from geodynamical numerical modeling
<p>Files include the data of the numerical model and model results of all cases in the study.</p>
Supplementary Material: Exposing Previously Undetectable Faults in Deep Neural Networks
<p>Supplementary material for ISSTA 2021 submission #58. The supplementary material is under 600MB and uploaded before the submission deadline, but uploading to the submission website did not work. Editing this supplementary material is not possible after upload.</p>
The Altotiberina Low-angle normal fault and Gubbio fault in seismic cluster of 2014-2015 period.
<p>Seismology data fron INGV and ISC catalogue.</p>
Generic, Scalable and Decentralized Fault Detection for Robot Swarms
<p>This raw data archive includes the data on fault detection in a simulated swarm of 20 e-puck robots. The data was used in the paper Generic, Scalable and Decentralized Fault Detection for Robot Swarms by D. Tarapore et al. (2017).</p> <p>See readme.txt for more details.</p>
Supporting data for: "Heritage of Tethyan oceanic transform faults within Alpine orogens: Paleomagnetic evidence from the Shkoder-Peja transverse zone (Northern Albania)"
<p>Paleomagnetic data from the Shkoder-Peja transverse zone, collected in the Krasta-Cukali and Albanian Alps tectonic units (Northern Albania).</p> <p>To open and navigate with Remasoft software (https://www.agico.com/text/software/remasoft/remasoft.php).</p>
Machine-learning-based seismic detection and location around the Tanlu fault zone in eastern China
<p>REAL, HypoInverse, and HypoDD catalog around the Tanlu fault zone in eastern China.</p>
Files for "Fluid-driven fault kinematics in the 2024 Noto earthquake: cascading rupture from ultraslow to supershear and afterslip"
<p>Files containing back-projection results, fluid overpressure data, and coseismic and postseismic deformation derived from GNSS and SAR measurements, along with the preferred coseismic slip and postseismic afterslip models for the 2024 Mw 7.5 Noto earthquake.</p>
Dataset for manuscript : "Weak and shallow secondary frictional faults revealed by large earthquakes in Haiti".
<p>This archive file contains datafiles used in "Weak and shallow secondary frictional faults revealed by large earthquakes in Haiti".<br><br>README.txt files describing the datasets are available within the archive.</p>
Receiver-function imaging of the Moho discontinuity beneath the Tanlu fault zone and its tectonic significance
<p>Moho depth data around the Tanlu fault zone. </p>
Online repository for Paper "AgentFL: Scaling LLM-based Fault Localization to Project-Level Context"
<h3>Summary</h3> <p>This is the online repository for the arXiv paper "AgentFL: Scaling LLM-based Fault Localization to Project-Level Context".</p> <p>We also provide the results for the TSE'25 paper "SOAPFL: A Standard Operating Procedure for LLM-based Method-Level Fault Localization".</p> <h3>Environment</h3> <ul> <li><a href="https://github.com/rjust/defects4j/tree/v1.4.0">Defects4J-V1.4.0</a> (Note that the buggy items in V1.4.0 is identical with V1.2.0, we use V1.4.0 to avoid some problems in V1.2.0)</li> <li><a href="https://github.com/rjust/defects4j/tree/v2.0.0">Defects4J-V2.0.0</a></li> <li>Python version >= 3.8.5</li> </ul> <h3>Defects4J Mod</h3> <p>Before running AgentFL, please apply the files under the <code>AgentFL/Defects4J_mod</code> directory to modify your Defects4J V1.4.0/V2.0.0.</p> <h3>Run AgentFL</h3> <p>Set your own OpenAI API key in <code>AgentFL/camel/model_backend.py</code></p> <p>It's easy to run AgentFL for localizing a bug with the following command:</p> <p><code>python3 run.py --config <CONFIG_DIR> --version <D4J_VERSION> --project <PROJECT> --bugID <BUG_ID> --model <GPT_MODEL_NAME></code></p> <p>For example:</p> <p><code>python3 run.py --config Default --version 1.4.0 --project Closure --bugID 26 --model GPT_3_5_TURBO</code></p> <p>More configs can be seen under the directory <code>AgentFL/Config</code></p> <h3>Results</h3> <p>We release all of the results of AgentFL in the <code>AgentFL/Results</code> directory, including the evaluation results on Defects4J V1.4.0/V2.0.0 and the ablation study result.</p> <p>For each bug, we record all of the prompts, responses, and intermediate outputs.</p> <blockquote> <p>NEW: We have released the newest results for TSE'25 paper "SOAPFL: A Standard Operating Procedure for LLM-based Method-Level Fault Localization". The results can be found in the `<a href="https://zenodo.org/api/records/16938304/draft/files/SoapFL_results.zip/content" target="_blank" rel="noopener noreferrer">SoapFL_results.zip</a>` file!</p> </blockquote> <h3>Human Evaluation Results</h3> <p>The human evaluation results can be found in the file <code>AgentFL/EvaluationResult/DebugResult_d4j140_GPT35_human.xlsx</code></p> <h3>System Messages for Agents</h3> <ul> <li>Test Code Reviewer:</li> </ul> <blockquote> <p>You are a Test Code Reviewer. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. You can examine the test code and the initialized classes to analyze the similar behavior of the failed tests within the test suite. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote> <ul> <li>Source Code Reviewer</li> </ul> <blockquote> <p>You are a Source Code Reviewer. we are both working at DebugDev. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. Your main responsibilities is to generate a comment for each covered method base on the method call relationship. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote> <ul> <li>Software Test Engineer</li> </ul> <blockquote> <p>You are a Software Test Engineer. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. You main responsibilities include examining the information of the failed tests to analyze the possible causes of the test failures, and determining the method that need to be fixed. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote> <ul> <li>Software Architect</li> </ul> <blockquote> <p>You are a Software Architect. We share a common interest in collaborating to successfully locate the buggy code that cause the test suite to fail. You are very familiar with the architecture of the software, the functions of each class and method in the software. You main responsibilities include examining the given information to locate the possible buggy classes and buggy methods. To locate the bug, you must write a response that appropriately solves the requested instruction based on your expertise.</p> </blockquote>
Dataset of CCFs and 3D Vs model beneath the Tangshan Fault zone
<p>This project contains the datasets needed to produce the 3D detailed shallow shear wave velocity model beneath the Tangshan Fault zone.</p><p> </p><p>The zip file has the following files:</p><p>Z-Z_CCFs_SAC: Rayleigh wave cross-correlation functions (CCFs)</p><p>3DVelocityModel: 3D Vs model</p><p>Readme: Readme</p><p>station.txt: Station coordinates</p><p> </p><p>Each of the Z-Z_CCFs_SAC folders contains the final stacked CCFs in SAC format between all available station pairs, e.g., </p><p>COR_TS001_TS002.SAC</p><p>COR_TS004_TS017.SAC</p><p>COR_TS009_TS080.SAC</p><p> </p><p>The CCF file name show the names of station pairs (e.g., TS001, TS004, TS009).</p><p> </p><p>The 3DVelocityModel folders contains the 3D detailed shallow shear wave velocity model with the format of "longitude (deg), latitude (deg), depth (km), Vs (km/s)".</p><p> </p><p>The station.txt has a format of "sta_name, longitude (deg), latitude (deg)".</p>
Original waveform of TanluArray portable seismic network in the Tanlu fault zone and surrounding areas
<p>The waveform in this folder was recorded by the National Institute of Natural Hazards (NINH) (cut by catalog from 2019 and 2021). Waveform data were intercepted from the 10s before and 10s after the theoretical arrival-time of Pn. It is only used for scientific research.</p>
MATLAB Codes for: A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults
<p><strong>Description:</strong></p> <p>This repository contains the MATLAB codes used in our paper [1] on fault diagnosis of inter-shaft aircraft bearings, published by MDPI Machines. The codes encompass all the necessary materials to reproduce the findings outlined in the paper. </p> <p><strong>Dataset Access:</strong></p> <p>The dataset utilized in this study is available under request from the authors of reference [8] in our paper. To obtain the dataset, please follow the instructions provided by the respective authors.</p> <p><strong>Data Format:</strong></p> <p>The dataset is saved in '*.npy' 3D variable format. To reproduce this study, it is necessary to transform these variables to '.mat' format since the codes are implemented in MATLAB. You can find the codes for transferring the 3D '*.npy' files to '*.mat' files here [<a href="../records/10184606">here</a>]</p> <p>We appreciate your interest in our work.</p> <p>[1] Berghout, Tarek, Toufik Bentrcia, Wei Hong Lim, and Mohamed Benbouzid. 2023. "A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults" <em>Machines</em> 11, no. 12: 1089. https://doi.org/10.3390/machines11121089</p>
The 2023 Kahramanmaraş, Türkiye earthquake doublet: Cascading-like triggered ruptures on bifurcating faults
<p>Synthetic aperture radar (SAR) data acquired from the ALOS-2 and Sentinel-1 satellites for the 2023 Kahramanmaraş, Türkiye earthquake doublet are included in this repository.</p>
Demonstration of fault-tolerant Steane quantum error correction
<p>Source data underlying the graphical representations used in the figures and corresponding executed quantum circuits.</p>
Dataset for Active shortening simultaneous to normal faulting based on GNSS, geophysical and geological data: The seismogenic Ventas de Zafarraya Fault (Betic Cordillera). Tectonics
<p>Dataset for "Active shortening simultaneous to normal faulting based on GNSS, geophysical and geological data: The seismogenic Ventas de Zafarraya Fault (Betic Cordillera)" Tectonics<br><br>File "hypoDD_10_300.reloc.txt" compile all the relocated seismicity in the study area.<br>Files "810_.NEU, 811_.NEU, 812_.NEU, 813_.NEU, 814_.NEU, 815_.NEU, and 816_.NEU" presents the time series data of GNSS sites of the Zafarraya network.</p>
mzhangrocks/KarakoramFault: Data files for hydrothermal degassing from the Karakoram fault
<p>Tables S1–S2 and Data Sets S1–S2 for geochemical study on hydrothermal degassing from the Karakoram fault in western Tibetan Plateau</p>
TDMT solutions from catalog of "A Large Fault Partially Reactivated During Two Contiguous Seismic Sequences in Central Italy: The Role of Geometrical and Frictional Heterogeneities"
<p>Some new TDMT solutions from catalog at the link <a href="https://doi.org/10.5281/zenodo.10801577">https://doi.org/10.5281/zenodo.10801577</a>. The catalog contains events with M > 3.0, that occurred between January 2009 and April 2021, in Campotosto area, Italy. Moment tensor were calculated by applying the Time Domain Moment Tensor technique, originally proposed by Dreger and Helmberger (1993) and Pasyanos et al. (1996) and successively implemented at INGV by Scognamiglio et al. (2009).</p> <p>For every moment tensor the PDF file contains the event location, waveform fits, nodal planes, magnitude, double couple (DC) and compensated linear vector dipole (CLVD) values, variance reduction, six components of moment tensor and station coverage.</p>
Dataset for the article entitled: "Revealing the strengthening contribution of stacking faults, dislocations and grain boundaries in severely deformed LPBF AlSi10Mg alloy"
<p>Dataset includes:</p> <p>EBSD data:</p> <p>AlSi10Mg_HT320.ang - Heat treated sample</p> <p>HT320E100.ang - Heat treated ECAP processed sample</p> <p>TKD data:</p> <p>HT320E100.ang - Heat treated ECAP processed sample</p> <p>XRD data:</p> <p>AlSi10Mg_HT320.ASC - Heat treated sample</p> <p>AlSi10Mg_HT320_ECAP100.ASC - Heat treated ECAP processed sample</p>
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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)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.