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662 results for “seismicity”
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>
Integrated Analysis of Seismic Sources and Structures: Understanding Earthquake Clustering during Hydraulic Fracturing
<p>The uploaded files include the 3D velocity model, 2D seismic reflection profiles, and horizontal slice utilized in this study.</p>
Experimental dataset on basal stresses and seismic signals generated by granular flows moving on a 3D-printed bumpy substrate
<p>This dataset provides the data supporting the experimental study of granular flows moving on a 3D-printed bumpy substrate considering the response of basal stresses and seismic signatures.</p> <p>S1_video clips_the side-view of the kinematic behaviors of the granular flows tracked by a high-speed camera.</p> <p>S2_data_stress and seismic signals measured at the instrumented plate</p> <p>S3_data_example of velocity fields downstream and normal to the base<br>S4_data_propagation features of the flow characterized by basal stresses and seismic signals<br>S5_data_variations of the depth-averaged velocity of the granular flows and corresponding nondimensionalized downslope velocity profiles<br>S6_data_profiles of the velocity of the granular flows normal to the base along with their depth-averaged velocities in the basal layers<br>S7_data_ nondimensionalized shear rates of the granular flows for various particle diameters <br>S8_data_depth-averaged shear rates and corresponding inertial numbers for granular flows with different particle sizes<br>S9_data_the mean normal stress and shear stress and stress fluctuations normal and tangential to the base<br>S10_data_characteristics of seismic signals in terms of peak amplitude, mean envelope, seismic deviation factor, and the signal mean frequency<br>S11_data_effective basal friction coefficient and equivalent friction coefficient as functions of particle diameter<br>S12_data_relationships between nondimensionalized normal stress fluctuations and nondimensionalized basal vertical velocity, inertial number, effective basal friction coefficient and equivalent friction coefficient of the flows.<br>S13_data_seismic deviation factor as functions of nondimensionalized normal stress fluctuations, inertial number, effective basal friction coefficient and equivalent friction coefficient</p> <p>S14_data_Comparison of effective basal friction coefficient μ_b and that scaled by the nondimensionalized basal vertical velocity.</p>
Seismic Imaging and Identification of Cold Seep Plumes in the Shenhu Area, Northern Continental Slope of the South China Sea
<p>The upload <a href="https://zenodo.org/api/records/14786925/draft/files/pstm_final.dat/content" target="_blank" rel="noopener noreferrer">pstm_final.dat</a> is the final pre-stacked time migration section which is used in our paper. <a href="https://zenodo.org/uploads/14786925" target="_blank" rel="noopener noreferrer">read_dat_file.m</a> is a MATLAB script, which is used to read dat file and plot the image. </p>
SKS measurements and null measurements for seismic stations in Eritrea and Yemen
<p>These files relate to "Channelized mantle flow through the Afar Triple Junction and around the Arabian plate: evidence from seismic anisotropy" by Gauntlett et al. (2024). Original seismic waveforms are from the Eritrea Seismic Project (<a href="https://doi.org/10.7914/sn/5h_2011">Hammond et al., 2011</a>) and the Young Conjugate Margins Lab in the Gulf of Aden network (<a href="10.7914/SN/XW_2009">Leroy et al., 2007</a>) and are publicly available through<a href="http://service.iris.edu/fdsnws/dataselect/1/">EarthScope Data Services</a>.</p> <p>The repository consists of three files:</p> <ol> <li>SKS_ALL.csv</li> <li>SKS_NULL_ALL.csv</li> <li>stations_all.csv</li> </ol> <p>The first file provides individual shear-wave splitting results reported in the study for the SKS phase. The columns are as follows: </p> <ul> <li>Station longitude</li> <li>Station latitude</li> <li>Orientation of the fast split shear wave (φ)</li> <li>Time delay between the fast and slow shear waves (dt)</li> <li>Error in phi</li> <li>Error in dt</li> <li>Event longitude</li> <li>Event latitude</li> <li>Event backazimuth</li> <li>Distance in degrees from event-station</li> <li>Event code</li> <li>Station name</li> </ul> <p>The second file provides information on null results reported in the study, where no shear-wave splitting is observed for the SKS phase. The columns are as follows: </p> <ul> <li>Station longitude</li> <li>Station latitude</li> <li>Event longitude</li> <li>Event latitude</li> <li>Event backazimuth</li> <li>Distance in degrees from event-station</li> <li>Event code</li> <li>Station name</li> </ul> <p>The station file contains the station name, the station latitude, station longitude and station elevation in km above sea level. </p> <p> </p> <p> </p> <p> </p>
Broadband seismic constraints on the nature of the basement of the Junggar Basin, the southwestern Central Asian Orogenic Belt
<p>The cross-correlation function (CCF) of ambient noise recordings between two receivers under the equipartition assumption.</p> <p>Sacdir is a directory that stores cross-correlation functions between all pairs of seismic stations, with the output format in SAC.</p> <p>Sacdir contains the cross-correlation results of seismic data from 96 permanent stations and 59 temporary stations in the Xinjiang region, spanning 13 months from June 2021 to June 2022, totaling 11,935 pairs.</p> <p>The "CCFs-pairs.txt" file shows the table listing the geographic coordinates of the station pairs of all the CCFs files (in SAC-format) in the Sacdir directory. </p> <p>The processing program is modified from: [CC-FJpy: A Software Package for Ambient Noise Cross-Correlation and Frequency-Bessel Transform Method](https://github.com/ColinLii/CC-FJpy)</p>
Dataset of relative seismic velocity variations (dv/v) of R66E3 Raspberry Shake and groundwater level variations of BSS002NNZL borehole from 2022/04/28 to 2025/02/04
<p>Dataset of relative seismic velocity variations (dv/v) of R66E3 Raspberry Shake and groundwater level variations of BSS002NNZL borehole from 2022/04/28 to 2025/02/04.</p> <p>A Raspberry Shake RS3D, a three-components geophone (station R66E3 and network code AM) with a natural frequency of 4.5 Hz (electronically extended to 0.5 Hz), is installed in the technical room located 8 m from the BSS002NNZL borehole (a groundwater monitoring borehole), in the town of La Trinité, Martinique. </p> <p>Seismic recordings for station R66E3 (network AM: <a href="https://urldefense.com/v3/__https:/doi.org/10.7914/SN/AM__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjstW1a3C0w$">https://doi.org/10.7914/SN/AM</a>) are collected from the Raspberry Shake data center (<a href="https://urldefense.com/v3/__https:/data.raspberryshake.org/fdsnws/__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjsumhGvdYA$">https://data.raspberryshake.org/fdsnws/</a>). The raw continuous seismic recordings and relative velocity variations are processed by using MATLAB (<a href="https://urldefense.com/v3/__https:/mathworks.com__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjss0LIo2Pw$">https://mathworks.com</a>).</p> <p>This dataset contains the resulting 2-3 Hz frequency range relative velocity variations data. </p> <p>The dataset also contains daily mean groundwater level data (in altitude and pressure) of BSS002NNZL borehole. The borehole is equipped with a PARATRONIC SNP pressure sensor with data recording every minutes. The probe resolution is ±1 mm.</p> <p>Rainfall and air temperature are available close to the site thanks to the French climatic network operated by Météo-France (<a href="https://meteo.data.gouv.fr/">meteo.data.gouv.fr</a>). Daily rainfall (from Morne des Esses station) and daily air temperature data (from Spoutourne station) are also provided in the dataset.</p>
The Repository for the Manuscript "Temperature and Precipitation Dominate Seasonal Variations in Seismic Velocity and Attenuation in Deserts"
<p><strong><span>Overview</span></strong></p> <p><span>This dataset contains the essential code and data for calculating the Horizontal-to-Vertical Spectral Ratio (HVSR), analyzing vehicle-generated seismic events, retrieving Q-values, and comparing them with meteorological data. It also includes waveform data from 20 seismic events.</span></p> <p><span>The seismic data originate from a temporary broadband seismic array deployed in the Tarim Basin, from July 2017 to October 2019 (Zuo et al., 2022). This dataset focuses on three seismic stations: T12, T52, and T23. Stations T12 and T23 recorded data from July 2017 to October 2019, while station T52 recorded from November 2018 to October 2019.</span></p> <p><span> </span></p> <p><strong><span>Code</span></strong></p> <p><span>The dataset includes Python scripts for calculating HVSR and retrieving Q-values. The HVSR calculation follows Li et al., (2023), while forward modeling is based on Antonio García-Jerez et al., (2016).</span></p> <p><span>The codes for Q-value estimation are stored in ‘Retrieving Q-value’ folder. The Q-value estimation process, demonstrated for station T12 in Jupyter Notebook, involves extracting single vehicle signals from continuous data, time-frequency spectrogram calculations, two-dimensional correlation coefficient of their time-frequency amplitude calculations, using hierarchical clustering algorithm to classify vehicle signals, vehicle speed estimation, and performing Q-value inversion.</span></p> <p><span> </span></p> <p><strong><span>Dataset </span></strong></p> <p><span>HVSR variations over time for three stations are calculated from continuous seismic recordings and are stored in the <em>‘HVSR’</em> folder under each station directory. </span></p> <p><span>Time-frequency spectrograms for Q-value estimation are stored in the <em>‘Spectrogram’</em> folder, with filenames indicating the record time of each vehicle signal. The Q-value is inverted using these signals, and for stability, we stacked every 100 individual results, which are stored in the 'Q-values' folder under the corresponding station name folder. Due to interference from wind and other sources, Q-value inversion using vehicle signals was unreliable for T23, so Q-values are only provided for T12 and T52.</span></p> <p><span>Meteorological data (temperature and soil water content) are stored in the <em>‘temperature’</em> and <em>‘soil water content’</em> folders under each station directory.</span></p> <p><span>Seismic event waveforms for 20 selected strong earthquakes are stored in the <em>‘events’</em> folder, with filenames indicating the start and end times of the events.</span></p> <p><span> </span></p>
Seismicity patterns and multi-scale imaging of Krafla (N-E) Iceland with local earthquake tomography: Raw event waveforms for all events used in the inversion and manual picks for temporary network
<p>This Data and Software were used in the submitted paper "Seismicity patterns and multi-scale imaging at Krafla (N-E Iceland) wih local earthquake tomography" by Glück et al.<br>The data and software provided here are used to compute the velocity models with TomoTV.<br>The raw data (.mseed format) can be visualised with the Python package Pyrocko/Snuffler, which was also used for the arrival time picking.<br>For the temporary network the manual picks are provided along with the code to prepare the manual picks as the input files for a localisation with NonLinLoc by weighting and quality checking the data. This resulting localsitations and the weighted traveltimes are then used for the LET.<br>The same workflow was used for the picks from the permanent network.</p> <p>Data:<br>- Raw data (\WaveformsPermanentStations): 7s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/ for the years 2021 and 2022.<br>- Raw data (\WaveformsNodes): 5s waveform snippets of the events listed in the ISOR catalogue on http://lv.isor.is:8080/events/browse/2022 recorded with the temporary network of 98 temporary nodes in June and July 2022.<br>- Pickfile (ManualPicks_100Nodes_Kafla2022.txt): Manual picks of the events listed in the ISOR catalogue for the evenst recorded with the temporary network.<br>- Station file (Station_file.txt): The station file includes the coordinates (Lat, Lon, Elevation) of the permanent stations (StationID starting with K...) and of the temporary nodes (StationID starting with N...).</p> <p>Software (Hyp_format.py):<br>- Weighting: The picks are weighted according to their Signal-to-Noise ratio (described in more detail in Section 2.3 in the main text of the paper)<br>- Writing the inputfile for NonLinLoc (with the selecting the mode option "PorS" in line 118), including all picks, also for those stations where not both phases were picked. The file "endfile.txt" is needed to write the picks to the NonLinLoc input format.<br>- Quality check of the picks: Computing a modified Wadati diagram from the traveltime differences of P and S phases for all the events available (with the selecting the mode option "PandS" in line 118)<br>- Python packages needed: numpy, scipy, matplotlib, pandas, obspy</p>
Microseismic source parameters from induced seismicity in the Horn river basin (British Columbia) [Dataset]
<p>This is a database of results linked to the associated manuscript (Klinger and Werner, 2021) which has been submitted to Geophysical Journal International and is currently in review. </p> <p>We report magnitudes, corner frequencies and stress drops of microseismic events linked to fault reactivation during hydro-fracturing operations in the Horn river basin (British Columbia), as well as the corresponding uncertainties for these parameters. Stress drops are calculated using a Brune model (Brune, 1970) and uncertainties are calculated using a bootstrapping technique. </p> <p>Prior to publication please cite this database using the following two references:</p> <p>Klinger, A.G., Werner, M.J. (2021). Stress drops of hydraulic fracturing induced microseismicity in the Horn River basin: Challenges at high frequencies recorded by borehole geophones. <em>Manuscript submitted to Geophysical Journal International . </em></p> <p>Klinger, A.G., Werner, M.J. (2021). Microseismic source parameters from induced seismicity in the Horn river basin (British Columbia) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5603835.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Database of liquefaction phenomena triggered by the March 2021 Thessaly, Greece, seismic sequence
<p>This archive contains data related to the paper “Floodplain evolution and its influence on liquefaction clustering: the case study of March 2021 Thessaly, Greece, seismic sequence” by George Papathanassiou, Sotiris Valkaniotis, Athanassios Ganas, Alexandros Stampolidis, Dimitra Rapti, Riccardo Caputo.</p> <p><br> Submitted to <em>Engineering Geology, Elsevier</em></p> <p> </p> <p><strong>Liquefaction_DB_20210303</strong> contains shapefiles and spreadsheet for the mapped liquefaction phenomena<br> <strong>UAS_Liquefaction_Surveys_Larisa_EQ</strong> contains detailed geospatial data for mapped liquefaction phenomena from UAS surveys</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>
Seismic Anisotropy in the Lower Mantle Transition Zone Induced by Lattice Preferred Orientation of Akimotoite-Dataset
<p>The dataset includes the lattice preferred orientation data of akimotoite aggregates obtained through EBSD and transmitted two-dimensional (2D) X-ray diffraction method at BL04B1 of synchrotron facility of SPring-8, Hyogo, Japan. The employed conditions of 2D X-ray diffraction measurements are also available.</p>
Instrument response files for seismic stations in South Korea (accelerometer)
<p><strong>To do</strong></p><p>[1.01] I have found something wrong in poles and zeros for the KG and KN networks. Please do not use them until update. I am sorry for it. (14 November 2023)</p><p> </p><p><strong>Example 1 (Seismic Analysis Code)</strong></p><p>Examples to deconvolve the instrument response from raw data are below, using the Seismic Analysis Code (SAC, version sac-101.6a). </p><p> </p><p>1. A unit of output is 'm/s2'</p><p>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 w acc.sac q</p><p> </p><p>2. A unit of output is 'm/s'</p><p>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 TRANS FROM ACC TO VEL w vel.sac q</p><p> </p><p>3. A unit of output is 'm'</p><p>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 TRANS FROM ACC TO VEL rtr rmean taper TRANS FROM VEL TO NONE w disp.sac q</p><p> </p><p>Note that the unit of the output is incoherent with the SAC header 'IDEP'.</p><p><strong>Example 2 (StationXML with obspy)</strong></p><p>#this is same as the code for the velocity seismometers.</p><p> </p><p><strong>Update notes</strong></p><ul><li>[1.01]<ul><li>Typo at INPUT UNIT for KG is modified (M/S -> M/S**2)</li><li>Stations KS.CE2A, KS.HA2B are added</li><li>StationXML file is added (ksgn.xml)</li></ul></li><li>[1.00] The files for the KG network are made based on the logs until 12 April 2019 (personal communication with the Korea Institute of Geoscience and Mineral Resources).</li></ul><p><strong>Others</strong></p><p>Velocity seismometer <a href="https://doi.org/10.5281/zenodo.3700312">https://doi.org/10.5281/zenodo.3700312</a><br>Accelerometer <a href="https://doi.org/10.5281/zenodo.3872436">https://doi.org/10.5281/zenodo.3872436</a></p><p> </p><p><strong>License</strong></p><p>This distribution follows the Creative Commons Attribution 4.0 International. You are free to modify and redistribute the files.</p><p> </p>
Instrument response files for seismic stations in South Korea
<p> </p> <p><strong>Citation</strong></p> <p>If you use it, please cite the following article in your work: </p> <p>Lim and Kim (2020), A dataset of seismic sensor responses of South Korea seismic stations. Journal of the Geological Society of Korea, 56(4), 515-524, http://dx.doi.org/10.14770/jgsk.2020.56.4.515 (Korean with English abstract)</p> <p> </p> <p><strong>Example 1 (Seismic Analysis Code)</strong></p> <p>Examples to deconvolve the instrument response from raw data are below, using the Seismic Analysis Code (SAC, version sac-101.6a). </p> <p> </p> <p>1. A unit of output is 'm/s'</p> <pre><code>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 w vel.sac q</code></pre> <p> </p> <p>2. A unit of output is 'm'</p> <pre><code>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 TRANS FROM VEL TO NONE w disp.sac q</code></pre> <p> </p> <p>3. A unit of output is 'm/s2'</p> <pre><code>r $input rtr rmean taper TRANS FROM POLEZERO S $pzfile TO none FREQ 0.05 0.1 1.5 3.0 TRANS FROM VEL TO ACC w acc.sac q</code></pre> <p>Note that the unit of the output is incoherent with the SAC header 'IDEP'.</p> <p> </p> <p><strong>Example 2 (StationXML with obspy)</strong></p> <pre><code class="language-python">#!/usr/bin/env python3 from obspy import read_inventory from obspy import read st = read('/path/to/waveforms') inv = read_inventory('ksgn.xml') st.detrend(type="linear") st.detrend(type="demean") pref = [0.1, 0.2, 15, 20] #output = m/s**2 st2 = st.copy() st2.remove_response(inventory=inv,output='ACC',pre_filt=pref,taper=True,zero_mean=False) st2[0].write('acc.sac',format="SAC") #... #output = m/s st2 = st.copy() st2.remove_response(inventory=inv,output='VEL',pre_filt=pref,taper=True,zero_mean=False) st2[0].write('vel.sac',format="SAC") #... #output = m st2 = st.copy() st2.remove_response(inventory=inv,output='DISP',pre_filt=pref,taper=True,zero_mean=False) st2[0].write('disp.sac',format="SAC") #...</code></pre> <p> </p> <p><strong>Update notes</strong></p> <ul> <li>[1.02] Typo in station name of KS.HA2B is modified (KS.HA2B -> HA2B)</li> <li>[1.01] <ul> <li>Files for the sensor JC-V100 are provided from Won-Young Kim (2 Dec 2021)</li> <li>Station KS.HA2B is added</li> <li>StationXML file is added (ksgn.xml)</li> </ul> </li> <li>[1.00] The files for the KG network are made based on the logs until 12 April 2019 (personal communication with the Korea Institute of Geoscience and Mineral Resources). </li> </ul> <p> </p> <p><strong>Pending works</strong></p> <p>Reflecting a change in logger of KS.DGY2</p> <p> </p> <p><strong>Others</strong></p> <p>Velocity seismometer <a href="https://doi.org/10.5281/zenodo.3700312">https://doi.org/10.5281/zenodo.3700312</a><br> Accelerometer <a href="https://doi.org/10.5281/zenodo.3872436">https://doi.org/10.5281/zenodo.3872436</a></p> <p> </p> <p><strong>License</strong></p> <p>This distribution follows the Creative Commons Attribution 4.0 International. You are free to modify and redistribute the files.</p>
Self-Limiting Earthquake Dynamics and Spatio-Temporal Clustering of Seismicity Enabled by Off-Fault Plasticity
<p>Earthquakes are among nature’s deadliest and costliest hazards. Physics-based simulations are essential for overcoming the lack of data and elucidating the complex patterns of earthquakes. Enabled by a novel numerical scheme, this work discovers a new mechanism for regulating earthquake dynamics that emerges due to the co-evolution of fault slip and fault zone plasticity. It enables transition from periodic events to fully irregular sequences of earthquakes. The impact of plasticity on earthquake source characteristics goes beyond its limited contribution to the overall energy budget, emphasized in earlier studies, to underscore its crucial role on the redistribution of stresses that self-limits earthquake growth and leads to clustering of seismicity. This work highlights the need for characterizing the fault zone mechanical response beyond their elastic properties to better inform seismic hazard models.</p>
WYC seismic phases for "Lighting up an 1-km fault near a hydraulic fracturing well using machine-learning based picker"
<p>In this study, we applied a state-of-the-art package on newly collected nodal-array data around a hydraulic-fracturing well. The array consists of up to 85 nodes with an average station spacing of less than a kilometer. Within the hydraulic-fracturing stimulation weeks, we detected ~3000 seismic events with magnitude down to ~-2. </p> <p>The seismic phases to associate the events are included in Zenodo_share.zip. The final catalog and station locations (in relative scale) are included in the excel spreadsheet.</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>
Seismic acquisition parameters to improve imaging beneath mafic igneous units: Case study from Australia's Northwest Shelf; supplementary material
<p>This dataset comprises two supplementary materials. Supplementary Materials A includes seismic processing workflows conducted by industry on the seismic lines used in this study. The seismic processing workflows are not the property of the author but are publicly available on the NOPIMS and WAPIMS databases. Collating these workflows into supplementary materials provides a simple method for readers to access material important for this research paper. Supplementary Materials B is a collection of 2D seismic lines the author conducted stratigraphic horizon mapping on for this study as viewed in 3D. More details on this dataset can be found throughout the research paper "Seismic acquisition parameters to improve imaging beneath mafic igneous units: Case study from Australia’s Northwest Shelf".</p>
ScienceDex guides
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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)
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.