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203 results for “Seismic data”
Ringlaser and seismic data at Fürstenfeldbruck and Wettzell for time-frequency analysis of microseisms
<p>Ringlaser rotation data and seismic data at Fürstenfeldbruck and Wettzell for the time-frequency analysis of seismic noise. Programs are also attached.</p>
Indexed Data Set From Molisan Regional Seismic Network Events
<p>Abstract:</p> <p><em>After the earthquake occurred in Molise (Central Italy) on 31st October 2002 (Ml 5.4, 29 people dead), the local Servizio Regionale per la Protezione Civile to ensure a better analysis of local seismic data, through a convention with the Istituto Nazionale di Geofisica e Vulcanologia (INGV), promoted the design of the Regional Seismic Network (RMSM) and funded its implementation. The 5 stations of RMSM worked since 2007 to 2013 collecting a large amount of seismic data and giving an important contribution to the study of seismic sources present in the region and the surrounding territory. This work reports about the dataset containing all triggers collected by RMSM since July 2007 to March 2009, including actual seismic events; among them, all earthquakes events recorded in coincidence to Rete Sismica Nazionale Centralizzata (RSNC) of INGV have been marked with S and P arrival timestamps. Every trigger has been associated to a spectrogram defined into a recorded time vs. frequency domain.<br> The dataset has been fully indexed in respect of the recorded spectra: list of all records, list of earthquakes, list of multiple earthquakes records.<br> The main aim of this structured dataset is to be used for further analysis with data mining and machine learning techniques on image patterns associated to the waveforms.</em></p>
Helheim Seismic data August 2014-2015
<p>Seismic traces for the BHE/BHN/BHZ channels (Easting, Northing and Vertical components) from August 2014 - August 2015 at Helheim Glacier (66.4N 38.2W). The locations of the seismometers HEL1-HEL4 and more details about the seismometers are given in a Cryosphere journal article (Mei, M. J., Holland, D. M., Anandakrishnan, S., and Zheng, T.: Calving localization at Helheim Glacier using multiple local seismic stations, The Cryosphere, 11, 609-618, doi:10.5194/tc-11-609-2017, 2017) that uses this data.</p>
Data set to article "Synthetic inversions for density using seismic and gravity data" by Blom, Boehm and Fichtner
<p><strong>Data set to “Synthetic inversions for density using seismic and gravity data” by Nienke Blom, Christian Boehm and Andreas Fichtner</strong></p> <p>This data set relates to our paper <em>“Synthetic inversions for density using seismic and gravity data”</em><em>, </em><em>in which we discuss the imaging of density variations inside the Earth as a separate, independent parameter using seismic waveform tomography and gravity measurements</em>. The research consists of synthetic experiments conducted using a home-written MATLAB wave propagation code. The data set contains the code itself, the input files and output files for each of the experiments described in the manuscript and its supplementary material, all the figures, some extra material (such as a video of Figure 1 in the manuscript) and some scripts.</p> <p>Below I’ll give a description of the contents of this data set and how they are structured, followed by an overview of the experiments conducted for the paper.</p> <p>In this data set, the following things can be found:</p> <ul> <li> <p>There is a directory with all the figures: FIGURES. This contains the figures in *.pdf, *.eps and *.png formats.</p> </li> <li> <p>There is a directory FD2D_ADJOINT_CODE with in it the MATLAB code fd2d-adjoint. If you plan on using our code, it would be awfully kind if you'd make a reference both to the code and to this paper. It was a lot of work to develop the code and the experiments. NOTE: the code supplied here is a snapshot of the code taken in February 2017. A more up-to-date version might be found on github (www.github.com/Phlos/fd2d-adjoint)</p> </li> <li> <p>For each (series of) experiment(s) described in the paper, there is a directory T1, T2, …, Tn. This also holds for the supplementary tests, the folders for which are designated with the suffix .SUPPLEMENTARY.</p> </li> <li> <p>For Figure 1 in the manuscript, there is a directory Fig1.snapshots. In this directory, everything pertaining to the snapshots figure and its corresponding video can be found.</p> </li> <li> <p>There is a separate directory SCRIPTS with a couple of useful scripts that might be used in addition to the ones in the fd2d-adjoint code.</p> </li> </ul> <p><br> In each of the test directories T1...Tn, there are subdirectories for each experiment conducted within that test framework. Each of the subdirectories has a name Systematic.test-[xxx]. Within those Systematic.. directories, the following can be found:</p> <ul> <li> <p>an input file Systematic….input_parameters.m that can be copied to [fd2d-adjoint]/input/input_parameters.m in order to re-run the experiment. As the code has been under development while the tests were run, it may be that some input parameters are missing from the earlier experiments.</p> </li> <li> <p>A mat-file obs.all-vars.mat. If this file is copied to [fd2d-adjoint]/output/Systematic.test… , this saves the recalculation of the ‘obs’ data when the code is run.</p> </li> <li> <p>A mat-file initial_misfits.mat. If this file is copied to [fd2d-adjoint]/output/Systematic.test… , this saves the recomputation of the initial misfits with respect to the obs data when the code is run.</p> </li> <li> <p>A file lbfgs_output_log.txt which monitors the misfit and gradient development across the iterations. If the inversion was restarted a couple of times, all of this remains in the logfile.</p> </li> <li> <p>For each iteration of the inversion iter[xxx], an iter[xxx].all-vars.mat file, which contains most of the matlab output files for this iteration.</p> </li> <li> <p>For each iteration of the inversion iter[xxx], some figures:</p> <ul> <li> <p>a model plot of the current model anomalies with respect to the background model iter[xxx].model-diff.rhovsvp.png.</p> </li> <li> <p>a gravity plot of the gravity vector difference between the current model and the background model iter[xxx].gravity_difference.png.</p> </li> <li> <p>a kernel plot of the total relative kernels (whether seis only or seis+grav) of the current model in rho-mu-lambda parametrisation: iter[xxx].rho-mu-lambda.png.</p> </li> </ul> </li> </ul> <p><br> </p> <p>Now follows a brief description of each of the (series of) tests conducted for the paper. The test numbers are mostly chronological, and so are the Systematic.test… subdirectories.</p> <ul> <li> <p><strong>Figure 1</strong>: shows snapshots of wave propagation past a density anomaly. The full data for this and the full video are given in the Fig1.snapshots. <em>Discussed in: Figure </em><em>1 of the manuscript.</em></p> </li> <li> <p><strong>T1: </strong><strong>reference.</strong> A reference test in which we assess to which density can be recovered as an independent parameter. <em>Discussed in: Figure </em><em>4</em></p> <ul> <li> <p>Reference experiment: Systematic.test-033</p> </li> </ul> </li> <li> <p><strong>T2: </strong><strong>ignored density.</strong> A test in which the effect is explored if density is ignored, i.e. if it is kept fixed to the starting model. <em>Discussed in: Figure </em><em>4</em></p> <ul> <li> <p>Fixing density: Systematic.test-040</p> </li> </ul> </li> <li> <p><strong>T3: </strong><strong>starting model</strong>. A series of test in which is explored to what extent the starting models of P and S seismic velocity influence the recovery of density. In the different sub-tests, different levels of information on P and S velocity are already present. <em>Discussed in: Figure </em><em>6</em></p> <ul> <li> <p>vs, vp 100% correct: Systematic.test-029</p> </li> <li> <p>vs, vp 75% correct: Systematic.test-037</p> </li> <li> <p>vs,vp 50% correct: Systematic.test-036</p> </li> </ul> </li> <li> <p><strong>T4: </strong><strong>fixed velocities</strong>. A series of tests in which is explored to what extent one can “get away with” only updating density, assuming that the models for P and S velocity are already sufficiently accurate. <em>Discussed in: Figure </em><em>7</em></p> <ul> <li> <p>vs,vp fixed at 50% correct: Systematic.test-038</p> </li> <li> <p>vs, vp fixed at 75% correct: Systematic.test-041</p> </li> <li> <p>vs, vp fixed at 100% correct: Systematic.test-039</p> </li> </ul> </li> <li> <p><strong>T5: </strong><strong>gravity</strong>. A set of tests in which the addition of gravity data to the (up until here purely) seismic inversion. Both the full gravity vector and its potential are used as gravity data. <em>Discussed in: Figure </em><em>8</em></p> <ul> <li> <p>seismic + full gravity vector (x,z) data: Systematic.test-045</p> </li> <li> <p>seismic + gravity potential data (‘geoid’): Systematic.test-046</p> </li> </ul> </li> <li> <p><strong>T6: noise</strong>. A series of tests in which the addition of noise to the seismic data is explored. Both correlated and uncorrelated noise are explored. Noise levels vary across frequencies. <em>Discussed in: Figure </em><em>9</em></p> <ul> <li> <p>correlated noise: Systematic.test-050</p> </li> <li> <p>uncorrelated noise: Systematic.test-052</p> </li> </ul> </li> <li> <p><strong>T7: impedance</strong>. A test in which the impedance contrast across anomaly boundaries are set to zero. It is explored to what extent the recovery of density relies on the presence of an impedance contrast. <em>Discussed in: Figure </em><em>10</em></p> <ul> <li> <p>no impedance contrast: Systematic.test-055</p> </li> </ul> </li> <li> <p><strong>T8: parametrisation (</strong><em><strong>supplementary</strong></em><strong>)</strong>. A test in which it is explored to what extent the inversion is affected if an inversion parametrisation using density and the elastic parameters mu and lambda is used, instead of the otherwise used parametrisation density-S velocity-P velocity. <em>Discussed in: </em><em>Supplementary </em><em>Figure </em><em>1,2 @ </em><em>Supplementary_material.pdf</em></p> <ul> <li> <p>inversion parametrisation rho-mu-lambda (reference target model): Systematic.test-032</p> </li> <li> <p>inversion parametrisation rho-mu-lambda with ‘scaling’ target model: Systematic.test-062a</p> </li> </ul> </li> <li> <p><strong>T9: scaling relations</strong>. A set of tests in which it is explored to what extent the recovery of density and seismic velocities is influenced if density is scaled to S velocity using a fixed scaling. <em>Discussed in: Figure </em><em>5</em></p> <ul> <li> <p>target model with density scaled to S velocity in different ways; all parameters free: Systematic.test-063</p> </li> <li> <p>same target model, but now density is scaled to S velocity with a fixed relationship: Systematic.test-067</p> </li> </ul> </li> <li> <p><strong>T10: anomaly strength (</strong><em><strong>supplementary</strong></em><strong>)</strong>. A set of tests in which the effect of the strength of the anomalies on the recovery of density and the other parameters is investigated. <em>Discussed in: </em><em>Supplementary </em><em>Figure </em><em>3-5 @ </em><em>Supplementary_material.pdf</em><em> </em></p> <ul> <li> <p>target model like reference case, but the anomalies 10% of PREM instead of 1%: Systematic.test-065</p> </li> <li> <p>target model like reference case, but the anomalies <em>in the upper mantle only</em> 10% of PREM instead of 1%: Systematic.test-064</p> </li> </ul> </li> </ul> <p><br> </p> <p>If you have any further questions, feel free to contact me.</p> <p>All the best,</p> <p>Nienke Blom, Utrecht University<br> n.a.blom@uu.nl<br> nienke.blom@posteo.net</p> <p> </p>
Seismic data from the HiSEIZE survey
<p>Seismic data from the HiSEIZE acquisition. The data available:</p> <p>Line 1 raw seismic data (.sgy)</p> <p>Line 2 raw seismic data (.sgy)</p> <p>Line 3 raw seismic data (.sgy)</p> <p>Data is unprocessed and fully geometrized.</p> <p>More details about the geometry can me found inside "Data_Description.txt"</p>
Data from: Brittle sedimentary strata focus a multimodal depth distribution of seismicity during hydraulic fracturing in the Sichuan basin, southwest China
<p>The number of background earthquakes (<em>M<sub>L</sub></em> ≥ 0) in the southern Sichuan basin, southwest China, has increased thirtyfold as a result of hydraulic fracturing. Background events are originally deep (4-6 <em>km</em>) within the sedimentary section but build into a multimodal distribution both at depth and in the shallow stimulated reservoir (2-4 <em>km</em>) - representing a counterpoint to the usual triggering of seismicity on deep sub-reservoir basement faults. Surprisingly, the largest events (<em>M<sub>L</sub></em> ≥ 3) evolve in the deep sedimentary strata (4-6 <em>km</em>) that are hydraulically isolated from the injection zone (2-4 <em>km</em>) by low permeability layers. We evaluate the friction-stability rheology of the strata within the full stratigraphic section to define the feasibility of nucleation within these shallow and deep strata. These show velocity-neutral to velocity-weakening behavior in the shallow reservoir transitioning to more strongly velocity-weakening with increase in both depth and temperature. Poroelastic stress calculations confirms that stress transfer, rather than transmitted fluid pressures, are capable of directly reactivating critically-stressed faults at depth, with fluid pressures the triggering source within the shallow reservoir.</p>
SEG-Y Multichannel seismic data collected during RV METEOR expedition M199 and used for publication by Micallef et al., in prep.
<p><span>The dataset comprises 3 multichannel seismic profiles, which have been collected during RV METEOR expedition M199 in February 2024 by the University of Hamburg. Data format is SEG-Y. Trace headers follow SEG-Y Revision 1 standard.</span></p> <p><span>The profiles are:</span></p> <p><span>M199_MCS_HH24-03</span></p> <p><span>M199_MCS_HH24-05</span></p> <p><span>M199_MCS_HH24-29</span></p>
Seismic Reflection Data from the Kentland Impact Structure, Indiana from Robitaille MSc (2024)
<p>This repository contains the correlated and stacked shot gathers and the final unmigrated and migrated files (all in SGY format) collected near the Kentland Crater Impact Structure. These data are associated with the MSc thesis of Brian Robitaille at Purdue University (2024). See citation below.</p>
WindSightNet: Catalogue of wind speed and direction data from NASA InSight lander on Mars using seismic data
<p>Dataset associated with the publication "WindSightNet: the inter-annual variability of Martian winds retrieved from InSight's seismic data with machine learning" submitted to JGR: Planets.</p> <p>Authors:</p> <p>A. E. Stott, R. F. Garcia, N. Murdoch, D. Mimoun, M. Drilleau, C. Newman, A. Spiga, D. Banfield, M. Lemmon, S. Navarro, L. Mora-Sotomayor, C. Charalambous, W. T. Pike, P. Lognonné, W. B .Banerdt</p> <p>Files containing catalogue of winds produced from the seismic data on the NASA InSight mission using machine learning algorithm produced in above publication. Please refer to this publication for technical details.</p> <p> </p> <p>Contents:</p> <p>WindSightNet.csv - file containing wind speed and direction produced from the WindSightNet neural network based on seismic data</p> <p>TWINS.csv - comparitive wind speed and direction from TWINS wind sensor when available. </p> <p>TWINS data originally available from:</p> <p>J A Manfredi, Insight Auxiliary Payload Sensor Subsystem (APSS) Temperatures and Wind Sensor for Insight (TWINS) Archive Bundle, (2019), https://doi.org/10.17189/1518950</p> <p> </p> <p>Each file contains values for:</p> <p>Wind Speed</p> <p>Wind dir.</p> <p>Sol - number of sol of InSight mission </p> <p>UTC - Coordinated Universal Time of sample</p> <p>LTST - Local True Solar Time of sample</p> <p>L_s - Solar longitude value of sample</p> <p>Time - seconds since UNIX epoch</p> <p>Data is considered to be sampled at a rate of 0.01 Hz when there are no gaps.</p> <p> </p> <p>Example code for plotting paper figures can be found:</p> <p>https://doi.org/10.5281/zenodo.14267939</p>
Dataset for the article "Can we use seismic reflection data to infer the interconnectivity of fracture networks?"
<p>This package contains the effective stiffness coefficients of the fractured rock samples explored in the paper of Rubino et al. "Can we use seismic reflection data to infer the interconnectivity of fracture networks?".</p>
Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan by a new analysis method for distributed acoustic sensing data using a seafloor cable and seismic interferometry"
<p>Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan, derived from distributed acoustic sensing data collected using a seafloor cable with seismic interferometry", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
The seismic data of the 2020 Nima earthquake obatined by USGS, CENC and GCMT
<p>The three files contain the seismic data of the 2020 Nima earthquake obtaind by United States Geological Survey, China Earthquake Data Center and Global Centroid-Moment-Tensor (CMT) Project.</p> <p>If you have any questions about this data, please contact me via <a href="mailto:gaohuastudent@163.com">gaohuastudent@163.com</a>.</p>
Marsquake locations and 1-D seismic models for Mars from InSight data
<p>Data used to draw the figures in the paper 'Marsquake locations and 1-D seismic models for Mars from InSight data'.</p>
Mechanical data of rotary shear experiments for the manuscript: "Determination of parameters characteristic of dynamic weakening mechanisms during seismic faulting in cohesive rocks".
<p>Mechanical data of rotary shear experiments and temperature measurements</p> <p>Each experiment is presented in a file with the experiment name (mechanical data of rotary shear experiment) and a file with the experiment name and _Temp (temperature measurement with the optical fiber).</p> <p>Mechanical data are presented in a tab-delimited file with calibrated measurements of:</p> <ul> <li>Time (milliseconds)</li> <li>Normal stress: Normal (MPa) </li> <li>Fault displacement: Slip (mm)</li> <li>Fault velocity: Velocity (mm/s)</li> <li>Shear stress: Shearstress (MPa)</li> <li>Axial shortening: Shortening (mm).</li> </ul>
Strateole-2 data set associated to the publication "A seismic network in the stratosphere"
<p>NetCDF files of the pressure and temperature data of TSEN sensors, and GPS coordinates, on board EUROS gondolas of Strateole-2 project (stratospheric balloons deployed during fall 2021).</p> <p>One file per gondola, associated to the 4 balloons detecting the Flores quake (2021/12/14 3:20:35.8 GMT) and to a single balloon detecting the Northern Peru quake (2021/11/28 10:52:25.8 GMT).</p> <p>These data cover one hour before and 2 hours after the quake. The rest of the Strateole-2 data will be released by the project. This SUbset is associated to the publication "A seismic network in the stratosphere.</p>
Data and program codes to reproduce the results of seismic tomography for Okmok
<p>This file contains the files to reproduce the results presented in the article: Kasatkina, E., Koulakov I., Grapenthin, R., Izbekov, P., Larsen, J., Al Alifi, N., and Qaysi, S.I. (2022). Multiple shallow magma sources beneath the Okmok caldera as inferred from local earthquake tomography, <em>Journal of Geophysical Research, Solid Earth</em>.</p> <p>This file includes:</p> <p>1. The full folder with the LOTOS code for the passive-source seismic tomography (Koulakov, 2009, BSSA). </p> <p>2. Folder with the dataset including arrival times of the P and S waves from local seismicity in the area of the Okmok Caldera in Aleutian Islands.</p> <p>3. README_OKMOK.PDF file with the description of the workflow on how to reproduce the tomography models based on experimental and synthetic data presented in the article. </p> <p>Koulakov, I., 2009, LOTOS code for local earthquake tomographic inversion: Benchmarks for testing tomographic algorithms: Bulletin of the Seismological Society of America, v. 99, p. 194–214, https://doi.org/10.1785/0120080013.</p>
Event Data used in Seismic anisotropy along the Haida Gwaii margin from receiver function analysis
<p>This CSV file contains metadata for earthquake events used in the study: Seismic anisotropy along the Haida Gwaii margin from receiver function analysis</p> <p>Event start time (UTC), latitude, longitude, depth, magnitude and the seismic station at which the event is recorded are included.</p>
Seismic data Krysuvik Iceland
<p class="ListParagraph1">Microearthquake hypocenters were analysed in the Krýsuvík geothermal area in SW-Iceland with data taken from two consecutive passive seismic surveys, 2005 and 2009. Five years prior to the 2005 survey, this area was struck by an earthquake initiating a major top-to-bottom fluid migration in the upper crust. We observe from our surveys a complex bottom-to-top migration of seismicity with time following this fluid penetration, suggesting the migration of a pore pressure front controlled by the upper-crust fracture system. We interpret these data as the time and space development of high-temperature hydrothermal cells from a deep upper crustal fluid reservoir in the supercritical field. These results provide an insight into the coupling mechanisms between active tectonics and fluid flow in upper-crustal extensional systems with high thermal flux.</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>
Supporting Information for "Data analysis of the unsteadily accelerating GPS and seismic records at Campi Flegrei caldera from 2000 to 2021". Data Set S1. Extended dataset of all the analyses
<p>This compressed folder contains supporting information related to the Figures in the manuscript: "Data analysis of the unsteadily accelerating GPS and seismic records at Campi Flegrei caldera from 2000 to 2021".</p> <p>Files and folders labeled with G1…n are related to the GPS data, those labeled with H1…n are related to the seismic data.</p> <p>In particular: <br> Subfolder 1_DATA supports Figure 3 – the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations; the logarithmic plots of all seismic events and of their energy. It also shows the complete plot leveling data from 1905 to 2010 (modified from del Gaudio et al., 2010). It also includes Figure 2 and Figure 6a-c.</p> <p>Subfolder 2_AnnualRate supports Figure 4 - the annual rate of the vertical and horizontal moduli of ground displacement at all analyzed GPS stations; the annual rate of all seismic events and of their energy. These detail the 2-year, the 6-month, and the 30-day average results. It also supports Figure 5 with similar data concerning 2018-2020.</p> <p>Subfolder 3_InverseRate supports Figure S3 - the inverse rate of the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations; the inverse rate of all seismic events and of their energy. These detail the 2-year, 6-month, and 30-day average results, including detailed plots of 2018-2020.</p> <p>Subfolder 4_RateChange supports Figure S2 - the daily rate change of the vertical and horizontal moduli of ground displacement at all analyzed GPS stations; the daily rate change of all seismic events and of their energy. These detail the 2-year, the 6-month, and the 30-day average results, including detailed plots of 2018-2020.</p> <p>Subfolder 5_FourierCoef supports Figure 6 - the Fourier spectrum of the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations. These detail the 2-year, 6-month, and 30-day average results obtained in 2000-2020, 2011-2020, 2018-2020. Also, additional plots that detail other combinations of time domain and part of the Fourier spectrum, thus testing the sensitivity of the main harmonics on the time domain selected.</p> <p>Subfolder 6_ FFM_WaitTime supports Figure 11 – waiting time examples based on vertical and horizontal moduli of ground displacement at all analyzed GPS stations; all seismic events, and their energy. These detail the 2-year, 6-month, and 30-day average rate results, and the 10-year, 5-year and 3-year regressions.</p> <p>Subfolder 7_FFM_FailTime also supports Figure 11 – all the results expressed in terms of the failure time t<sub>f</sub> instead of in terms of the waiting time [t<sub>f</sub>(t) - t].</p> <p>Subfolder 8_pFFM_Regression supports Figure 9 - the pFFM examples based on the vertical and the horizontal moduli of ground displacement at all analyzed GPS stations; all seismic events and of their energy. These detail the 2-year, 6-month, and 30-day average rate results, and the 10-year, 5-year and 3-year regression.</p> <p>Subfolder 9_pFFM_Probability supports Figure S4 - pFFM examples based on vertical and horizontal moduli of ground displacement at all analyzed GPS stations; all seismic events, and their energy. These detail the 2-year, 6-month, and 30-day average rates, and the 10-year, 5-year and 3-year regressions.</p> <p>Subfolder 10_BarplotProb supports Figure S5 - results expressed in terms of the mean failure time probability at 2, 5, 10, and 25 years.It also supports Figure S6 - examples based on 6-month, and 30-day average rate results.</p> <p>Subfolder 11_BarplotWaitTime supports Figure S6 - all the results expressed in terms of the waiting time (t<sub>f</sub> – t) barplot. It also includes Figure S5.</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.