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462 results for “Volcano”
Compiled eruption chronostratigraphy including eruption styles for Santorini volcano, Greece, from ~360 ka to present day
<p>This dataset is a compiled chronostratigraphy for the volcanic island of Santorini, Greece. It comprises information on eruption dates, dating methods and numbers of eruptions of each type (Plinian, interplinian and lava), compiled from existing published sources (all references provided) and some supplementary fieldwork. The record is detailed and quantitative from the present day back to 224 ka, and less detailed and qualitative from 224 ka back to ~360 ka. Two maps are provided to give location context for the dataset, as well as a reference list, and other associated reading. This dataset forms part of the supplementary information for the publication 'Eruptive Activity of the Santorini Volcano Controlled by Sea Level Rise and Fall' by Satow et al. (2021) in Nature Geoscience- <a href="https://doi.org/10.1038/s41561-021-00783-4">https://doi.org/10.1038/s41561-021-00783-4</a></p>
Seismic noise recorded at Solfatara Volcano in April 2007
<p>Seismic noise recorded during a seismic survey carried out at Solfatara Volcano in the period 2-6 April 2007. Five circular seismic arrays were deployed inside the crater; an other seismic station was installed on the eastern rim for a hardrock reference. Details on the experiment, as well as data description and station coordinates are reported in: Petrosino, S., Damiano, N., Cusano, P., Veneruso, M., Zaccarelli, L., Torello, V., & Del Pezzo, E. (2008). Seismic noise at Solfatara Volcano (Campi Flegrei, Italy): acquisition techniques and first results. <em>Quaderni di Geofisica</em>.</p> <p>Shallow crustal structure of Solfatara volcano, inferred from dataset analysis has been published in: Petrosino, S., Damiano, N., Cusano, P., Di Vito, M. A., de Vita, S., & Del Pezzo, E. (2012). Subsurface structure of the Solfatara volcano (Campi Flegrei caldera, Italy) as deduced from joint seismic‐noise array, volcanological and morphostructural analysis. <em>Geochemistry, Geophysics, Geosystems</em>, <em>13</em>(7).</p>
Mechanical properties of Nevado del Ruiz - St. Isabel volcanoes
<p><strong>Description and structure of the data </strong><br> Data of static and dynamic mechanical material properties organized in 6 columns. The first three columns contain the position in UTM coordinates (UTM zone 18N): easting, northing, and depth (positive numbers refer to points above the mean sea level and negative numbers to points below the sea level). The last three columns contain Poisson’s dynamic ratio, Young’s static modulus, and density. The first row of the file is dedicated to the following header: UTM_x (m), UTM_y (m), Z (m), Poisson_dy, Young_st (GPa), density (kg/m^3)</p> <p><strong>Static and dynamic mechanical elastic properties</strong></p> <p>The dynamic elastic properties are calculated from P-wave (Vp) and S-wave (Vs) tomographic velocities, derived from the seismicity recorded by the Colombian Geological Survey - Volcanological and Seismological Observatory of Manizales (OVSM) between January 1st, 2016 and February 19, 2019.</p> <p>Empirical relationships are used for the conversion into static values <a href="https://www.zotero.org/google-docs/?BltUnS">(Hautmann et al., 2013; Wang, 2000)</a>. The relationship between dynamic Young’s modulus (<span class="math-tex">\(E_{dy}\)</span>) and shear wave velocity, Vs, inferred from the tomography <a href="https://www.zotero.org/google-docs/?TLLWSb">(Telford et al., 1976)</a> is<strong> <strong><span class="math-tex">\(E_{dy} = 2\rho\left( 1+\nu_{dy} \right)V_{s}^2\)</span></strong></strong></p> <p>where ρ is density <a href="https://www.zotero.org/google-docs/?HcYYHH">(Jaeger, 2007)</a>, defined through the Nafe-Drake empirical curve <a href="https://www.zotero.org/google-docs/?hTnbN9">(Brocher, 2005)</a> that describes the density (g/cm3) as function of Vp between 1.5 km/sec and 8.5 km/sec:</p> <p><span class="math-tex">\(\varrho=1.6612V_{p}-0.4721{V}_{p}^{2}+0.0671{V}_{p}^{3}-0.0043{V}_{p}^{4}+0.000106{V}_{p}^{5}\)</span></p> <p>and the dynamic Poisson’s modulus, <span class="math-tex">\(\nu_{dy}\)</span>, is calculated from the relationship of Vp and Vs, as in the case of an isotropic medium for lack of better information (i.e. borehole tests), with the following formula <a href="https://www.zotero.org/google-docs/?VD2VWu">(Guéguen and Palciauskas, 1994; Heap et al., 2014)</a>:</p> <p><span class="math-tex">\({\nu}_{dy}=\frac{V_{p}^2-2V_{s}^2}{2(V_{p}^2-V_{s}^2)}\)</span></p> <p>The values of the dynamic Young’s modulus derived, <span class="math-tex">\(E_{dy}\)</span>, increase from 12 GPa to 135 GPa, while the range of dynamic Poisson’s ratio values is between 0.17 and 0.30.</p> <p>In order to convert the dynamic values of the Young modulus into static values, we apply a standard empirical relationship <a href="https://www.zotero.org/google-docs/?Zir7pl">(Wang, 2000)</a>: </p> <p><span class="math-tex">\(E_{st} = 0.415\times E_{dy} (GPa) - 1.056\)</span></p> <p><br> The resulting values for <span class="math-tex">\(E_{st}\)</span> of the upper crust is from 5 GPa to 56 GPa.</p> <p> </p> <p><strong>References</strong></p> <p><strong>Brocher</strong>, T.M., 2005. Empirical relations between elastic wavespeeds and density in the Earth’s crust. Bulletin of the Seismological Society of America 95, 2081–2092. https://doi.org/10.1785/0120050077<br> <strong>Guéguen</strong>, Y., Palciauskas, V., 1994. Introduction to the Physics of Rocks, Princeton University Press. ed. Princeton, New Jersey.<br> <strong>Hautmann</strong>, S., Hidayat, D., Fournier, N., Linde, A.T., Sacks, I.S., Williams, C.P., 2013. Pressure changes in the magmatic system during the December 2008/January 2009 extrusion event at Soufrière Hills Volcano, Montserrat (W.I.), derived from strain data analysis. Journal of Volcanology and Geothermal Research 250, 34–41. https://doi.org/10.1016/j.jvolgeores.2012.10.006<br> <strong>Heap</strong>, M.J., Baud, P., Meredith, P.G., Vinciguerra, S., Reuschlé, T., 2014. The permeability and elastic moduli of tuff from Campi Flegrei, Italy: implications for ground deformation modelling. Solid Earth 5, 25–44. https://doi.org/10.5194/se-5-25-2014<br> <strong>Telford</strong>, W.M., Geldart, L.P., Sheriff, R.E., Keys, D.A., 1976. Applied Geophysics. Cambridge University Press, Cambridge.<br> <strong>Wang</strong>, Z., 2000. Dynamic versus static elastic properties of reservoir rocks, in: Seismic and Acoustic Velocities in Reservoir Rocks. Soc. of Explor. Geophys., Tusla, Oklahoma, pp. 531–539.</p> <p> </p> <p>This dataset is one of the results of PICVOLC project. PICVOLC has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 793811.</p> <p> </p> <p> </p>
Data for: Analytical transfer function for volcano deformation with T-dependent viscoelasticity
<p>Rocks can modulate the triggering, duration, and style of volcanic eruptions. When heated, the host rocks surrounding a magmatic reservoir is typically considered as a viscoelastic material. The viscoelastic rheology (viscosity especially) is temperature dependent; however, the dynamics and consequence on surface deformation resulting from heterogeneous crustal temperature and viscosity around magmatic reservoirs have not been explored systematically. </p> <p>This dataset incorporates the parameters and numerical codes used for generating results in the manuscript 'History-dependent volcanic ground deformation from broad-spectrum viscoelastic rheology around magma reservoirs' submitted to GRL and authord by Yang Liao, Leif Karlstrom, and Brittany Erickson. The dataset consists of a README file detailing the data structure, a matlab .mat file that contains the parameters assumed in the magma chamber model, and several matlab program .m files that can be applied to the .mat file to generate results presented in the manuscript. </p>
Tracking the 2018-2019 lava dome of Merapi volcano, Indonesia, using TanDEM-X and Pléiades
<p>This repository consists of a series of topographic surfaces of Merapi volcano (Indonesia), presented as a series of Digital Elevation Models (DEMs) differences with respect to a reference DEM of 2013. They are obtained from multiple Pléiades stereoscopic surveys acquired in 2019 and from TanDEM-X acquisitions between 2018-2019. These DEMs differences (5 Pléiades and 15 TandEM-X) are used to calculate thicknesses of a lava dome that appeared in August 2018. We then computed volumes and effusion rates from these DEMs.</p> <p>The resolution of the DEMs is 3*3 m pixel size and DEMs are georeferenced and provided under TIFF format.</p> <p>Pléiades DEMs have been masked (Nans) on areas covered by clouds.</p> <p>TanDEM-X DEMs have been masked (Nans) on areas that have not been unwrapped. They have also been corrected from a vertical offset by susbstracting the mean of non deformed areas to the initial DEM.</p> <p>Files are formatted as followed :</p> <p>-for Pléiades : YYYYMMDD_PL_msk.tiff</p> <p>-for TanDEM-X : YYYYMMDD_TDX_offset.tiff</p> <p>For the methodology to produce these DEMs, and for the applications of these DEMs, please refer to the recentely published paper "Tracking the evolution of the summit lava dome of Merapi volcano between 2018 and 2019 using DEMs derived from TanDEM-X and Pléiades data" with the following doi : https://doi.org/10.1016/j.jvolgeores.2022.107732</p> <p>or the following URL : https://www.sciencedirect.com/science/article/pii/S0377027322002633</p>
Data for the paper "Two small volcanoes, one inside the other: geophysical and drilling investigation of Bažina maar in western Eger Rift"
<p>This dataset consists of all data used in the manuscript of "Two small volcanoes, one inside the other: geophysical and drilling investigation of Bažina maar in western Eger Rift". </p> <p>The repository contains</p> <p>1. Gravity data after topographic correction - for details see README.TXT</p> <p>2. Magnetic data - for details see README.TXT</p> <p>3. Ground resistivity measurement using the multielectrode method - for details see README.TXT</p> <p>4. Lithology and logging data from the two boreholes S4 and S4 - file Stratigraphy_and_Logging_S4.pdf</p>
Drone survey, time-lapse camera, and satellite SAR data covering the 2021 summit craters and late fractures at Tajogaite volcano, Cumbre Vieja, La Palma
<p>A new eruption started on 19 September 2021 at the Tajogaite volcano, which is at the western flank of the Cumbre Vieja, just 1•5km to the north of the vents of the 1949 eruption, and terminated after 85 days on 13 December 2021. The location of the 2021 eruption at the Cumbre Vieja was not foreseen, although a diffuse unrest was identified years before already. At the location of the eruption, the slope of the edifice was gentle, but a number of older vents, mostly open to the west, were evident. Here we present field and satellite data showing (a) the development of the craters at the summit of the evolving Tajogaite volcano, and (b) the formation of a pronounced structural trend interpreted to be related to tensile faulting during the late stage of the eruption.</p> <p>Data contains:</p> <ol> <li>Drone data acquired by DJI drones (Phantom RTK and Mavic2) during two periods showing the summit craters and the tensile fracture set in detail.</li> <li>Time-lapse camera records from the east and the north-northeast showing eruption and morphology changes.</li> <li>Satellite radar amplitude data acquired in three different geometries (2 ascending and 1 descending track) of the Cosmo Skymed Satellite constellation</li> </ol> <p>Use data without restriction but cite our work; for details on acquisition geometries and maps refer to the papers published by:</p> <ul> <li>Walter, T.R.; Zorn, E.Z.; Gonzalez, P.J.; Sansosti, E.; Munoz, V.; Shevchenko, A.V.; Plank, S.; Reale, D.; Richter, N. (in press) Late complex tensile fracturing interacts with topography at Cumbre Vieja, La Palma, VOLCANICA 5(2): 300–316. https://doi.org/10.30909/vol.05.02.300</li> <li>Muñoz, V.; Walter, T.R.; Zorn, E.U.; Shevchenko, A.V.; González, P.J.; Reale, D.; Sansosti, E. Satellite Radar and Camera Time Series Reveal Transition from Aligned to Distributed Crater Arrangement during the 2021 Eruption of Cumbre Vieja, La Palma (Spain). Remote Sens. 2022, 14, 6168. https://doi.org/10.3390/rs14236168</li> </ul> <p> </p>
Watching the Volcano. A Frances Burney d'Arblay Primer
<p>Frances Burney d’Arblay, who was born in 1752 and died in 1840, spent 72 years of her long and eventful life writing letters and diaries, published only after her death.</p> <p>Burney experienced lockdown and mental health issues during her life at the Court of the ‘mad’ king George III. She married a French officer during the great European wars when England and France were worst enemies. She lived in England as the wife of a Catholic Frenchman, and in France as a Protestant Englishwoman. She was the breadwinner in her family thanks to her published work, but she also tried unsuccessfully to make a living by writing for the stage.</p> <p>Burney underwent a devastating, anaesthetic-less mastectomy, of which she left a shocking narrative, the earliest first-person account of this type of surgery. Her onomastic odyssey – from the baby-like “Fannikin” of her youth and the informal “Fanny” Burney she is still known by, to the present-day excision of her married surname d’Arblay-- this odyssey is a biographical journey worth recounting in its own right.</p> <p>Frances Burney d’Arblay was a remarkable woman who was also a pioneering writer, destined to confront familial and social apparatuses throughout her life as well as in her afterlife.</p> <p> </p> <p>This research is part of the Horizon 2020 project called "Opening Romanticism: Reimagining Romantic Drama for New Audiences"(OpeRaNew) ID 892230 within the ERC programme Horizon 2020 MSCA-IF-2019. The PI is Francesca Saggini. See CORDIS website at <a href="https://cordis.europa.eu/project/id/892230">https://cordis.europa.eu/project/id/892230</a></p>
A multi-station volcano-tectonic earthquakes monitoring based on Transfer Learning techniques.
<p>A multi-station volcano-tectonic earthquakes monitoring based on Transfer Learning techniques.</p> <p>Manuel Titos (1), Ligdamis Gutiérrez (2,3), Carmen Benítez (1), Pablo Rey Devesa (2,3), Ivan Koulakov (4) and Jesús. M. Ibáñez (2,3)</p> <p><br> <strong>Institutions associated:</strong></p> <p>(1) CITIC, Department of Signal Processing, Telematic and Communications, University of Granada, 18071. Granada. Spain.<br> (2) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain.<br> (3) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain.<br> (4) Laboratory for Seismic Forward and Inverse Problems, Institute of Petroleum Geology and Geophysics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia</p> <p><br> <strong>Acknowledgment:</strong></p> <p>This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.</p> <p>a) This work is part of the research by the Spanish FEMALE project (PID2019-106260GB-I00). <strong>FEMALE </strong>(<em>Forecasting Volcanic Eruptions Using Signal Processing and Machine Learning Techniques on Seismic Signals</em>) https://femalevolcanoes.es/</p> <p><br> b) JMI and LG were partially funded by the Spanish project PROOF-FOREVER (EUR2022.134044).</p> <p><br> <strong>Keywords:</strong></p> <p>Automatic volcanic monitoring, real-time monitoring, Artificial Intelligence, Transfer Learning, Recurrent Neural Networks, Temporal Convolutional Networks.</p> <p> </p> <p><strong>Data availability statement:</strong></p> <p>Seismic data from Bezymianny volcano (2017), Kamchatka, Russia.</p> <p> </p> <p><strong>Contents:</strong></p> <p>Seismic Data from Bezymianny volcano recorded at stations BZ01, BZ02, BZ06 and BZ10.<br> The data represent the vertical component of the seismic signal, associated to the period analyzed in the study:</p>
Geophysical dataset of Stromboli volcano
<p>This dataset is associated with the paper entitled « The thermal plumbing system of Stromboli volcano, Aeolian Islands (Italy) inferred from electrical conductivity and induced polarization tomography” by A. Revil, A. Finizola, T. Johnson, T. Ricci, M. Gresse, E. Delcher, S. Barde-Cabusson, P.A. Duvillard, and M. Ripepe submitted to Journal of Geophysical Research-Solid Earth (one of the leading journals of the American Geophysical Union). The dataset comprised 6 datasets. 1. Remote sensing data acquired in 2013 for the Island of Stromboli. 2. Self-potential data for the Island of Stromboli acquired between 2004 and 2013. 3. Temperature data for the Island of Stromboli acquired between 2004 and 2013. 4. CO2 concentration data for the Island of Stromboli acquired between 2004 and 2013. 5. Induced polarization data along a profile crossing the volcano of Stromboli acquired in 2008. 6. Electrical resistivity data for the 3D electrical resistivity tomography of the volcano of Stromboli acquired between 2003 and 2013</p>
Lightning and volcanic plume data from the climactic eruption of Hunga Volcano, Tonga, in January 2022
<p>This dataset contains lightning and volcanic plume data for the eruption of Hunga Volcano in Tonga from 13–15 January 2022. The dataset consists of two files. The first is a spreadsheet containing four tabs: (1) Ground-based flashes, which include lightning flashes from combined ground-based networks from 13–15 January 2022; (2) Ground-based rates, which include flash rates and pulse rates in one-minute bins from 13–15 January 2022 using the combined networks; (3) Optical GLM flashes & rates, which include GLM flashes and per-minute rates from 15 January 2022; and (4) Volcanic plume dimensions, which include maximum plume heights and umbrella radii through time on 15 January 2022. The second file is a Google Earth KMZ file of umbrella cloud areas outlined from stereoscopic cloud height retrievals from 04:17–07:07 UTC on 15 January 2022. Refer to journal article "Lightning rings and gravity waves: Insights into the giant eruption plume from Tonga’s Hunga Volcano on 15 January 2022" published in Geophysical Research Letters for further details about data processing. </p>
Datasets associated with a study of extensional faulting around Kolumbo Volcano, Aegean Sea
<p>This contribution of data includes 56 different data files of various formats, plus one excel file ("Data_Inventory_and_Descriptions_Kolumbo_Faulting.xlsx") which provides all of the necessary metadata for each of the 56 data files. The excel file should be used as a readme file, detailing key information for each file including format, georeferencing information, and relevant software for loading and viewing the data. The excel file also shows how each file is related to an associated figure in a manuscript currently being submitted for peer review. Once that paper is published, this description will be updated with the doi of the paper.</p>
Seismic dataset for Ruapehu and Whakaari volcanoes in New Zealand
<p>RSAM, MF, HF and DSAR time series for Ruapehu stations FWVZ over the 14 years explored, and for Whakaari stations WIZ over 9 years.</p> <p>Computing datastreams: we harnessed seismic data from a vertical component station for each individual volcano. We applied data processing techniques that resulted in the generation of four distinct time series, with a sampling interval of 10 minutes. Various measures were employed to capture different aspects of the seismic signal. The first measure, known as the Real-time Seismic Amplitude Measurement (RSAM), was obtained by calculating the 10-minute moving average of the velocity recorded by the vertical station This signal was then subjected to bandpass filtering within the frequency range of 2 to 5 Hz, which focuses on tremor signal of frequent volcanic origin while excluding ocean noise at lower frequencies. Similarly, the Median Frequency (MF) and High Frequency (HF) measures were derived using a comparable approach to RSAM, but with specific bandpass filtering applied. MF was obtained by filtering the signal within the frequency range of 4.5 to 8 Hz, while HF was obtained by filtering within the frequency range of 8 to 16 Hz. The 4.5 Hz threshold between RSAM and MF reflects an assumption that tremor mostly radiates energy below 4.5 Hz. To exclude this effect and explore attenuation related to permeability change (such as sealing), this frequency value is used as a threshold. Lastly, the Displacement Seismic Amplitude Ratio (DSAR) was calculated as the ratio of the integrals of the MF and HF signals. High values of DSAR have been inferred to correlate with high gas levels in the edifice, suggesting either reduced fluid motion and/or trapping that has led to a gas-accumulation.</p>
Supplementary data to 'The origin and differentiation of CO2-rich primary melts in Ocean Island volcanoes: Integrating 3D X-ray tomography with chemical microanalysis of olivine-hosted melt inclusions from Pico (Azores).'
<p>Supplementary data to:</p><blockquote><p>The origin and differentiation of CO2-rich primary melts in Ocean Island volcanoes: Integrating 3D X-ray tomography with chemical microanalysis of olivine-hosted melt inclusions from Pico (Azores).</p></blockquote>
Data for: Analytical transfer function for volcano deformation with T-dependent viscoelasticity
Open the record for dataset details and reuse information.
Data set to ''Volcano growth versus deformation by strike-slip faults: morphometric characterization through analogue modelling'
<p>This data set is the supplementary material to Grosse et al. (2020) 'Volcano growth versus deformation by strike-slip faults: morphometric characterization through analogue modelling', published in Tectonophysics (https://doi.org/10.1016/j.tecto.2020.228411). The data set consists of (1) 249 digital elevation models (DEMs) of each step of the the analogue experiments carried out, in standard ENVI format, zipped; and (2) an Excel file containing the DEM-derived morphometric parameters for each of the analogue models.</p> <p>Experiments were carried out at the analogue modelling lab of the Department of Geography at the Vrije Universiteit Brussel (Belgium). A granular mixture of fine-grained quartz sand and kaolin clay was used as analogue material. Experiments were conducted on a fixed table, on which a basal layer of granular material was placed. A basal plate attached to a step-motor was used to simulate pure strike-slip displacements of the basal layer. Volcano growth was simulated by depositing loads of granular material on top of the basal layer from a point source. The analogue models were photographed at regular time intervals during the experiments using four digital cameras. The photographs were used to generate synthetic digital elevation models (DEMs) with 0.2 mm spatial resolution of each step of the analogue models by applying the MICMAC digital stereo-photogrammetry software. The ENVI software was used to re-sample the DEMs to a 0.5 mm spatial resolution and apply the noise-reduction Lee filter. Morphometric data were then extracted from the DEMs by applying two IDL-language algorithms: NETVOLC, used to automatically calculate the volcano edifice basal outline, and MORVOLC, used to extract a set of morphometric parameters.</p>
InSAR stack of Kuju volcano in Kyushu, Japan from ALOS ascending track 422 processed with ROI_PAC
<p>A stack of unwrapped interferograms on Kuju volcano, Kyushu, Japan</p> <p>Sensor: ALOS PALSAR ascending track 422 frame 650</p> <p>Time: 2007.01.06 - 2011.01.17, 24 acquisitions, 167 interferograms</p> <p>Processor: ROI_PAC</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p>
InSAR time series analysis results of ALOS-2/PALSAR-2 data for the post-eruptive displacement of the 2015 phreatic eruption of Hakone volcano, Japan
<p>This repository contains the InSAR products used in Doke et al., GRL (submitted).</p> <p> </p> <p><strong>Dataset 1</strong>: Surface velocity data estimated by InSAR time series analysis with NetCDF grid format.</p> <ol> <li>surface_velocity_p126.nc</li> <li>surface_velocity_p18.nc</li> </ol> <p> </p> <p><strong>Dataset 2</strong>: Time-series of LOS displacements in selected locations with text format.</p> <ol> <li>time_series_p126.txt</li> <li>time_series_p18.txt</li> </ol> <p> </p> <p><strong>Dataset 3</strong>: Inputs and results of model inversion with shapefile.</p> <p>Subsampled observation data, modeled (simulated) displacements, and other parameters are shown in attribute tables in shapefiles. Shapefiles that show the location of the estimated models are also included in ZIP files.</p> <ol> <li>point_source_deflation.zip</li> <li>sill_deflation.zip</li> </ol>
Piparo Mud Volcano
Eruption on 23rd February, 1997 ; The Piparo mud volcano erupted in 1997, a large part of the town was declared a disaster area after the mud volcano erupted. The Volcano caused widespread damage that left 108 people homeless. Three houses, more than a dozen vehicles and scores of farm animals, poultry and houses pets were buried under tons of gaseous grey mud which spewed some 200 feet into the air when the volcano blew. Eleven other houses were partially submerged under the mud. And several others badly damaged by the tremors which accompanied the eruption. More than 300 people within a one-mile radius of the volcano had to be evacuated. Text by: https://www.uwi.edu/ekacdm/node/173 Drone Footage provided by EssenTech Solution https://www.youtube.com/watch?v=gODFBPCLJY8 Source: Objaverse 1.0 / Sketchfab
Maungataketake volcano 1960
Maungataketake or Elletts Mount, a volcanic scoria cone and Maori hillfort, in 1960 before it was quarried away. Auckland, New Zealand. Aerial photos from retrolens.nz . My 3D scene generated with photogrammetry software 3DF Zephyr v4.351 processing 3 images Source: Objaverse 1.0 / Sketchfab
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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
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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.