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662 results for “Seismicity”
Seismic model and thermal age across the Juan de Fuca and Gorda Plate
<p>This dataset includes three file about the seismic structure across the Juan de Fuca and Gorda Plate:</p> <p>RayleighPhv.nc: netcdf file of Rayleigh wave phase speed across the plates, from 10 to 80 s</p> <p>ThermalAge.nc: netcdf file of lithospheric apparent thermal age estimated from seismic inversion</p> <p>Vsv_Model.zip: zip file of Vsv estimated from seismic inversion</p>
Lake Pertusillo reservoir induced seismicity catalog (Southern Italy)
<p>We present a detailed analysis of the small magnitude (M<sub>L</sub><3) Reservoir Induced Seismicity associated with the Pertusillo water reservoir located in the high seismic hazard zone of Val d’Agri (Southern Italy). </p> <p>We apply template-matching detection to a 13-month-long dense passive survey, obtaining a final high-precision double-difference catalog of 5,068 earthquakes (-0.7<M<sub>L</sub><2.6, M<sub>C</sub>=0.2). </p> <p>The original <em>template</em> dataset is composed of 408 hand-picked earthquakes, recorded during a 13-month-long (2005-2006) passive survey, at a dense network of 46 seismic stations (the average receiver spacing is 5 km) composed by: 22 temporary 3C continuously-recording stations of the temporary experiment described in <em>Valoroso et al.</em>, [2009], plus 24 permanent INGV and ENI (the local oil company) stations, and accurately located in a 3D high-resolution V<sub>P</sub> and V<sub>P</sub>/V<sub>S</sub> velocity model [<em>Improta et al.</em>, 2017]. </p> <p>The attached file is a plain text with a space as separator. </p> <p>Here below the header is explained.</p> <p><strong>ID: </strong>the unique event identifier</p> <p><strong>LAT</strong>: hypocenter latitude expressed in degrees </p> <p><strong>LONG</strong>: hypocenter longitude east of Greenwich, expressed in degrees</p> <p><strong>DEPTH</strong>: hypocenter depth expressed in km </p> <p><strong>OTIME: </strong>date of the origin time in the format YYYY-MM-DD[T]hh:mm:ss.msec</p> <p><strong>ML</strong>: magnitude (pure number)</p> <p> </p>
New Zealand Seismic Hazard Z Factors
<p>This dataset presents our interpretation of the <em>Z</em> factor as a continuous surface across New Zealand. The GeoTiFF has been derived through a range of publicly available online resources including the MBIE website, reports, journal publications, and the <a href="https://gazetteer.linz.govt.nz/">New Zealand Gazetter</a> for matching placenames to locations, amongst others. The coordinate system is EPSG:2193 with ~5 km resolution. The raster has a single band and values are rounded to two decimal places.</p> <p>The <em>Z</em> factor is used to scale the 5% damped design seismic response spectrum based on the magnitude of the expected seismic hazard in different regions in New Zealand, as demonstrated through <a href="https://www.standards.govt.nz/shop/nzs-1170-52004/">NZS 1170.5:2004</a> and referred to in the seismic assessment of potentially earthquake prone buildings (EPB). It is underpinned by the 2001 National Seismic Hazard Model and is influenced by a wide range of factors such as proximity to faults and fault rupture mechanisms, geological and soil characteristics, and topography, amongst others. <em>Z</em> ranges from 0.10 (Northland Region) to 0.60 (Otira/Arthur’s Pass surrounds). Generally speaking, low seismic risk is where <em>Z</em> < 0.15; medium seismic risk where 0.15 ≤ <em>Z</em> < 0.30; and high seismic risk where <em>Z</em> ≥ 0.30.</p> <p>This GIS dataset is intended for educational purposes where students can download the dataset, create their own contours, or directly sample the raster. For more information see the numerous online resources and the official standard <a href="https://www.standards.govt.nz/shop/nzs-1170-52004/">NZS 1170.5:2004</a> where it is available for purchase from Standards NZ.</p>
Seismicity catalog of hydraulic-fracturing-induced earthquakes in the Kiskatinaw area, northeast British Columbia
<p>Seismicity catalog of 8,731 hydraulic-fracturing-induced earthquakes in the Kiskatinaw area, northeast British Columbia between 1 July 2017 to 31 Dec 2020. The Study area covers the Kiskatinaw area, wich extends over parts of the Montney Formation, a major shale gas play within the Western Canada Sedimentary Basin. Catalog corresponds to:</p> <p>Roth, M. P., A. Verdecchia, R. M. Harrington, and Y. Liu (2020). High-Resolution Imaging of Hydraulic-Fracturing-Induced Earthquake Clusters in the Dawson-Septimus Area, Northeast British Columbia, Canada, Seismol. Res. Lett. 91, 2744–2756, doi: 10.1785/0220200086.</p>
Seismicity datasets of the 2022 seismic unrest at the North Mid-Atlantic Ridge
<p>These datasets contain information on the seismicity during the 2022 seismic unrest at the North Mid Atlantic Ridge. The seismic catalogs have been produced using seismological techniques dedicated to earthquake location and moment tensor inversion.</p> <p>Data Set 1 is the catalog of relocated earthquake, containing 61 events.<br> Note, that event nar_2022_09_26_14_49_21 is not been associated with one cluster and is therefore not shown in the final plot of relocated seismicity.</p> <p>Data Set 2 is a full moment tensor catalog, containing 77 events, with absolute centroid locations (i.e. out of the moment tensor inversion and not relocated).</p> <p>Data Set 3 is a deviatoric moment tensor catalog, containing 77 events, with absolute centroid locations (i.e. out of the moment tensor inversion and not relocated).</p>
E-Supplement to the Relocation of the seismicity of the Caucasus region
<p>The e-Supplement contains the Ground Truth event list in the Caucasus region, the input and output files for the Bayesloc multiple event location algorithm, as well as the event catalog and event bulletin in ISF2.1 format constructed from the Bayesloc results.</p> <p>This work was produced under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-855583</p>
Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints
<p>This dataset consists of a collection of self-consistent radial seismic Earth models. The models are derived by inverting a large set of normal-mode centre-frequencies, quality factors, and geodetic data, including mass, moment of inertia, and tidal response.</p><p>The dataset is accompanied by a research paper titled "Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints" (DOI: <a href="https://doi.org/10.1093/gji/ggad254">10.1093/gji/ggad254</a>). The paper presents the methodology and findings related to the development of the models.</p><p>This version (V0.2) of the dataset replaces the previous version (V0.1). </p><p><strong>Dataset Details</strong></p><p>The dataset includes confidence intervals (CIs) for these parameters at 25%, 50%, and 75% levels that are representative of the uncertainty of the sampled model parameters. For example, the files <a href="https://zenodo.org/api/files/fa9f19f0-bf6a-41a8-8ca7-489a5b1b9c49/screm-25p-high.dat?versionId=b3b349c2-0a0b-48cc-b1f3-90137b8d1dcb">screm-25p-high.dat</a> and <a href="https://zenodo.org/api/files/fa9f19f0-bf6a-41a8-8ca7-489a5b1b9c49/screm-25p-low.dat?versionId=81db34f2-29f4-48aa-95c4-bb73b050a47f">screm-25p-low.dat</a> contain the upper and lower bound of the 25% CI sampled model range. A python plotting script is available, which plots the CIs.</p><p>The dataset files are provided in comma-separated values (CSV) format, containing the following columns:</p><ol><li><strong>Radius (km)</strong>: Radial distance from the center of the Earth.</li><li><strong>Depths (km)</strong>: Depth from the surface of the Earth.</li><li><strong>Density (g/cm³)</strong>: Radial density structure.</li><li><strong>P-wave velocity (vp) (km/s)</strong>: Radial compressional (P) wave velocity structure</li><li><strong>S-wave velocity (vs) (km/s)</strong>: Radial shear (S) wave velocity structure.</li><li><strong>Qkappa</strong>: Radial bulk attenuation structure.</li><li><strong>Qmu</strong>: Radial shear wave attenuation structure.</li><li><strong>Bulk Modulus (K) (GPa)</strong>: Radial bulk modulus structure.</li><li><strong>Shear Modulus (Mu) (GPa)</strong>: Radial shear modulus structure.</li><li><strong>Pressure (GPa)</strong>: Radial pressure profile.</li><li><strong>Temperature (K)</strong>: Radial geothermal profile.</li></ol><p><strong>Citation</strong></p><p>If you use this dataset in your research or refer to the models, please cite the following paper:</p><p><strong>Title:</strong> Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints<br><strong>Authors:</strong> J. Kemper, A. Khan, G. Helffrich, M. van Driel, D. Giardini<br><strong>Journal:</strong> Geophysical Journal International<br><strong>Year: </strong>2023<br><strong>DOI:</strong> <a href="https://doi.org/10.1093/gji/ggad254">10.1093/gji/ggad254</a></p><p>Please also acknowledge the dataset by providing a link to the Zenodo repository and its DOI.</p><p>Bibtex:<br>@article{Kemper_etal23,<br>author = {Kemper, J and Khan, A and Helffrich, G and van Driel, M and Giardini, D},<br>title = "{Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints}",<br>journal = {Geophysical Journal International},<br>pages = {ggad254},<br>year = {2023},<br>month = {06},<br>issn = {0956-540X},<br>doi = {10.1093/gji/ggad254},<br>url = {https://doi.org/10.1093/gji/ggad254},<br>} </p><p> </p>
Numerical modeling of the seismic cycle for normal and reverse faulting earthquakes in Italy
<p>Results of the numerical models expressed in terms of nodal stresses, strains and displacements.</p> <p>Data Set S1. Nodal values of the modelled displacements for the L’Aquila 2009 earthquake.</p> <p>Data Set S2. Nodal values of the modelled strain tensor for the L’Aquila 2009 earthquake.</p> <p>Data Set S3. Nodal values of the modelled stress tensor for the L’Aquila 2009 earthquake.</p> <p>Data Set S4. Nodal values of the modelled displacements for the Norcia 2016 earthquake.</p> <p>Data Set S5. Nodal values of the modelled strain tensor for the Norcia 2016 earthquake.</p> <p>Data Set S6. Nodal values of the modelled stress tensor for the Norcia 2016 earthquake.</p> <p>Data Set S7. Nodal values of the modelled displacements for the Emilia 2012 earthquake.</p> <p>Data Set S8. Nodal values of the modelled strain tensor for the Emilia 2012 earthquake.</p> <p>Data Set S9. Nodal values of the modelled stress tensor for the Emilia 2012 earthquake.</p>
Data repository for the paper "Tectonics and seismicity in the Northern Apennines driven by slab retreat and lithospheric delamination"
<p>Output data from a numerical modeling study analyzing the Tectonics and seismicity of the Northern Apennines in relation to the geodynamic mechanism (slab retreat and crustal delamination) suggested to be driving the orogenic system.</p> <p>Understanding how long-term subduction dynamics relates to short-term seismicity and crustal tectonics is a challenging but crucial topic in seismotectonics. We attempt to address this issue in the context of the Northern Apennines orogenic belt, which displays characteristic tectonic and seismogenic behaviors on a wide range of spatiotemporal scales. We use a visco-elasto-plastic seismo-thermo-mechanical (STM) modeling approach with a realistic 2D setup based on available geological and geophysical data. In accordance with regional geodynamics, subduction dynamics and seismicity are simulated together, driven solely by slab pull. Our numerical experiments suggest that lower crustal rheology and lithospheric mantle temperatures modulate the crustal tectonics of the Northern Apennines. Results indicate that the observed spatial distribution of the upper crustal tectonic regimes requires buoyant and highly ductile material beneath the suture zone. This allows protrusion of the asthenosphere in the lower crust, lithospheric delamination, and slab retreat. The resulting horizontal velocities and principal stress axis orientations agree with observations, suggesting that slab delamination and retreat are compatible with regional deformation. Our simulations successfully reproduce the presence of seismicity in the thrust front and on normal faults in the interior of the range. Slab temperatures and lithospheric mantle stiffness distinctly affect the cumulative seismic moment release and the spatial distribution of upper crustal earthquakes. The properties of deep, sub-crustal material are thus shown to influence model shallow seismicity, even though the upper crust is largely mechanically decoupled from the lithospheric mantle. Our simulations therefore highlight the important effect of deep crustal rheologies and self-driven subduction dynamics in controlling the shallow, brittle deformation and related seismicity during an ongoing orogeny.</p> <p>The repository consists of the following: 1) the executable code for running the model (i2_istm and in2_istm, the latter of which is used to initialise the model); 2) the setting files for which timesteps to output (mode.t3c and mode_istm.t3c, the latter of which is for the short-term phase of the model), the model setting files (init_istm.t3c), rock type and temperature setup images (prf_app.tif and tfin.tif, respectively); and 3) the output quantities in the model for the last timestep in HDF5 format (app400.gzip.h5), the list of ruptured markers (pick_events_app.txt) and GPS-station-like markers at the surface (eachdt_gpsmarker_app.txt), and the time limits used for computing average velocities from the GPS marker positions (timelims.mat).</p> <p>The files used for the figures in the paper relate to the reference model and 9 other models: 2 models with different rheology for the Adriatic lower crust, 2 models with different temperatures in the mantle, and 5 models with different shear modulus in the Adriatic lithospheric mantle. The two models with different lower crust rheology (granulite and plagioclase) were not run in short-term mode and therefore no GPS-like or ruptured markers logs are available for them. Descriptive prefixes are used to identify which model each file refers to. The rock type setup is common to all models included here. The reference temperature setup is also used for the models with different shear modulus in the slab and the model with granulite lower crust rheology. The model with plagioclase lower crust rheology has a different temperature setup with a hotter lower crust, as mentioned in the paper; it is not a simple exploration of the effect of rheology, but an attempt to get the lower crust to be very ductile through a combination of a ductile rheology (but less so than in the reference model) and high temperatures.</p> <p>For information about the modeling code, setup, results, and interpretation, please refer to the paper. This repository will be updated with the final paper information after publication.</p>
Waveforms, relocated earthquake and matched filter catalog of seismic swarm preceding the 2017 Mount Agung eruption
<p>Datasets for the manuscript:</p> <p>Sianipar, D., Ulfiana, E., and Sipayung, R. (2020), Seismic swarm preceding the 2017 Mount Agung eruption in Bali (Indonesia) enhanced by the matched filter approach (submitted) (preprint is available at EarthArxiv: <a href="https://eartharxiv.org/a7yx2/">https://eartharxiv.org/a7yx2/</a>)</p> <p>by Dimas Sianipar, Emi Ulfiana, and Renhard Sipayung (STMKG, BMKG, Indonesia).</p> <p>Files including:</p> <p>1) List of continuous waveforms</p> <p>2) HypoDD files: dt.cc, dt.ct, event.dat, hypoDD.reloc, phase.dat</p> <p>3) Processed (filtered) 407 template waveforms</p> <p>4) BMKG catalog</p> <p>5) MFT catalog in ZMAP format</p> <p>6) Table S1: template candidates</p> <p>7) Table S2: MFT catalog</p> <p>The compressed file (*.rar) has been successfully extracted in Ms. Windows OS using WinRAR.</p>
DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations."
<p>The data set contains all data presented in the paper: <strong>Observing the subglacial hydrology network and its dynamics with a dense seismic array</strong> published in PNAS ( <a href="https://doi.org/10.1073/pnas.2023757118">https://doi.org/10.1073/pnas.2023757118</a> )</p> <p>See our online presentation of this dataset: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-10710.html.</p> <p>The present data and code concerns the source location obtained with matched-field-processing analysis and the hydraulic potential calculation (Shreve, R. L. Movement of Water in Glaciers. <em>J. Glaciol.</em> <strong>11</strong>, 205–214 (1972)).</p> <p>We perform source location over 1-sec long signal segment of the vertical component only. We filter the signal within the [3-7] Hz frequency range and coherently apply the MFP each 0.1 Hz within this range. To maximize our algorithm efficiency and minimize computational costs we use a gradient-based minimization algorithm (Nelder-Mead optimization) to converge to the best match between the trial and the observed phase delays rather than an exhaustive grid-search exploration. The convergence criterion is reached when the variance of values obtained over the last 5 iterations of the optimization is smaller than 1e<sup>-2</sup> with a maximum of 3000 iterations. Our 29 different starting points used for optimization are located 250 m below the glacier surface and they uniformly cover an area of 800 x 800 m<sup>2</sup> centered on the array. We set the initial velocity to 1800 m.sec <sup>-1</sup>. The 29 punctual locations found per signal segment (1 sec) after convergence are located all in the same place if a clear global convergence exists (i.e. high MFP output) or at up to 29 different locations if up to 29 local minima exist (i.e. low MFP output).</p> <p>Timeseries of physical quantities can be found here <a href="https://doi.org/10.5281/zenodo.3701520">https://doi.org/10.5281/zenodo.3701520</a></p> <p>Spatial observations acquired during the same period can be found here <a href="https://doi.org/10.5281/zenodo.3971815">https://doi.org/10.5281/zenodo.3971815</a></p> <p> </p> <p>The RESOLVE project has been supported by a grant from LabEx OSUG@2020 (Investissement d’avenir – ANR10LABX56) and by the IDEX Université Grenoble Alpes. Most of the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad.univ-grenoble-alpes.fr), which is supported by Grenoble research communities, and with the CiGri tool (https://github.com/oar-team/cigri) developed by Gricad, Grid5000 (https://www.grid5000.fr) and LIG (<a href="https://www.liglab.fr/">https://www.liglab.fr/</a>).</p> <p> </p> <p>You can find more information on the method and seismic dataset used in this paper here: <a href="https://zenodo.org/deposit/5645545">https://zenodo.org/deposit/5645545</a></p>
cigKast: A data of 3D synthetic seismic volumes with labeled paleokarsts for deep-learning-based paleokarst interpretation
<p>cigKarst is a dataset created by the <a href="http://cig.ustc.edu.cn/">Computational Interpretation Group (CIG)</a> for the deep-learning-based peleokarst interpretation in 3D seismic images, <a href="http://cig.ustc.edu.cn/xinming/list.htm" target="_blank" rel="noopener">Xinming Wu</a> is the main contributor to the dataset.</p> <p>This dataset contains 120 pairs of synthetic 3D seismic images and the corresponding label images with the ground truth of the paleokarst systems simulated in the seismic images. More detail of building this dataset is discussed in the paper published at the journal of JGR Solid Earth:</p> <p><strong>Wu, X.</strong>, S. Yan, J. Qi, and H. Zeng, 2020, Deep learning for characterizing paleokarst collapse features in 3D seismic images. <strong>JGR, Solid Earth</strong>, Vol. 125(9), 1-23, e2020JB019685. <a href="http://cig.ustc.edu.cn/_upload/tpl/05/cd/1485/template1485/papers/wu2020karst.pdf">[PDF]</a>. doi: 10.1029/2020JB019685</p> <p>Below are some brief description of the dataset:</p> <p>1) The "seismic.zip" contains 120 3D seismic images, each image is with the dimension of 256X256X256;</p> <p> 2) The "karst.zip" contains 120 3D label images of the karsts. Each label image is with the same dimension of 256X256X256. The values in a label image are set with ones in the karst areas while zeros elsewhere, which is why the compressed label images in the karst.zip is much smaller than the seismic images compressed in the seismic.zip</p>
KaKiOS-16: a probabilistic, non-linear, absolute location catalog of the 1981-2011 Southern California seismicity
<p>This is the KaKiOS-16 earthquake catalog for southern California. We locate the southern California seismicity using the state-of-the-art probabilistic and nonlinear method NonLinLoc. We use only the P wavepicks to avoid introducing the velocity-model and picking-time errors of the S phase, which is harder to detect and thus less constrained. Using a subset of the best locatable earthquakes, we conduct a joint inversion using the VELEST software to obtain a minimum 1D velocity model and station corrections. We use the NonLinLoc method with this 1D velocity model and the inferred model uncertainties to obtain realistic location distributions for each event.<br> </p>
Piburgersee core meta data repository for the publication "Seismic control of large prehistoric rockslides in the Eastern Alps"
<p>This dataset comprises the core meta data of Plansee, which is the basis for the publication Oswald et al. "Seismic control of large prehistoric rockslides in the Eastern Alps".</p> <p>The core meta data belongs to a 8m long sediment core composed of 12 individual core sections (see Plansee_core_data.xlsx). For each individual core section the core image (_coreimage.jpg), the CT data (_CT.rar), XRF data, (_XRF.txt) and multi-sensor core logging data (_MSCL.csv) are provided.</p>
Plansee seismic and core meta data repository for the publication "Seismic control of large prehistoric rockslides in the Eastern Alps"
<p>This dataset comprises the raw seismic data and core meta data of Plansee, which is the basis for the publication Oswald et al. "Seismic control of large prehistoric rockslides in the Eastern Alps".</p> <p>Seismic profiles are provided as .SGY files (Plansee_seismics_SGYfiles.rar)</p> <p>The core meta data belongs to a 7m long sediment core composed of 10 individual core sections (see Plansee_core_data.xlsx). For each individual core section the core image (_coreimage.jpg), the CT data (_CT.rar) and multi-sensor core logging data (_MSCL.csv) are provided.</p>
Dataset from "Constraining Martian regolith and vortex parameters from combined seismic and meteorological measurements"
<p>The table provided below (in CSV format) includes derived data obtained from the raw data of the InSight SEIS and APSS experiments. For the raw data, we acknowledge:</p> <p>InSight Mars SEIS Data Service. (2019). SEIS raw data, Insight Mission. IPGP, JPL, CNES, ETHZ, ICL, MPS, ISAE-Supaero, LPG, MFSC. https://doi.org/10.18715/SEIS.INSIGHT.XB_2016</p> <p>The dataset in this table was used to produce Figures 6, 7, 11 and 12 of the following paper:</p> <p>N. Murdoch, A. Spiga, R. Lorenz, R.F. Garcia, C. Perrin, R. Widmer-Schnidrig, S. Rodriguez, N. Compaire, N. H. Warner, D. Mimoun, D. Banfield, P. Lognonné and W.B. Banerdt. Constraining Martian regolith and vortex parameters from combined seismic and meteorological measurements. Journal of Geophysical Research: Planets.</p> <p>The table provides derived vortex parameters of all vortices studied in this paper. The columns of the table contain the following properties of every vortex event: Sol, UTC date and time, Local Mean Solar Time (LMST), Observed pressure deficit <span class="math-tex">\(\Delta P_{obs}\)</span> (determined from the fit to the Ellehoj et al. (2010) model after filtering in the 0.02 - 0.3 Hz frequency band), Observed pressure drop encounter duration <span class="math-tex">\(\tau\)</span> (FWHM determined from the fit to the Ellehoj et al. (2010) model after filtering in the 0.02 - 0.3 Hz frequency band), the Ellehoj et al. (2010) model goodness of fit to the vortex pressure data (after filtering in the 0.02 - 0.3 Hz frequency band), Mean background wind speed <span class="math-tex">\(v\)</span>, Standard deviation of background wind speed <span class="math-tex">\(\sigma_v\)</span>, Maximum radial tilt <span class="math-tex">\(\theta_{obs}\)</span>, Azimuth at maximum radial tilt (i.e. at closest approach) <span class="math-tex">\(\alpha_{obs}\)</span>, Mean miss distance <span class="math-tex">\(x\)</span> (i.e. when <span class="math-tex">\(S = v\)</span>), <span class="math-tex">\(\eta \)</span> (defined as <span class="math-tex">\(E/(1-\nu^2) \)</span>), Mean <span class="math-tex">\(\zeta\)</span> (i.e. when <span class="math-tex">\(S = v\)</span>). For further details about these parameters please see the paper cited above.</p>
Seismic release ratio of 2012 Emilia seismic source during the Earthquake sequances
<p>Seismology data of 2012 seismic sequence for calculate the seismir release ratio model along main seismic source. Data are download from INGV and DISS INGV catalogs. </p>
The "Castelluccio-Amatrice" low-angle normal fault seismic dataset
<p>Seismology data (from INGV database) for idetification the geometry of “Castelluccio-Amatrice” low-angle normal fault. The seismic data are the same of central Italy sesmic sequence in 2014-2017 period. </p>
Uplift and Seismicity driven by Magmatic Inflation at Sierra Negra Volcano, Galápagos Islands
<p>Catalogue of detected earthquakes and cGPS uplift timeseries for Sierra Negra Volcano, Galapagos Islands</p>
The imprint of crustal density heterogeneities on regional seismic wave propagation - dataset
<p>This dataset should provide complete synthetic seismograms and software</p> <p>(python tools for random media generation, signal comparison and histogram stacking)</p> <p>that were used in the publication:</p> <p>Płonka, A., Blom, N., and Fichtner, A.: The imprint of crustal density heterogeneities on regional seismic wave propagation, Solid Earth, 7, 1591-1608, doi:10.5194/se-7-1591-2016, 2016.</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.