Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
496
datasets available to search
ShareScore release 0.7.1
Dataset results
496 results for “Volcan”
Climatic and societal impacts of a "forgotten" cluster of volcanic eruptions in 1108-1110 CE
<p>This repository contains all the tree-ring and historical archives used by Guillet et al. (2020) to assess the climatic impacts of the 1108-1110 CE volcanic eruptions</p> <p>For more information, we refer the user to the readme file entitled "Guillet_et_al_SciReports2020_Readme.txt"</p> <p>We note that investigations of European historical archives are still carried ongoing. The file entitled "Guillet_et_al_SciReports2020_Supp_Info_Table_S1_S2_Historical_Sources.xlsx" will be updated as new material is discovered.</p> <p>We welcome every addition or contribution that may help to extend the number of historical sources available and better document the climatic and societal response to the 1108-1110 CE cluster of eruptions. Thank you ;-)!</p>
Northeast Pacific deoxygenation and volcanism during the last deglaciation
<p><strong>File structure:</strong></p> <p><strong>Source data</strong></p> <ul> <li>Contains source data to main text and extended data figures. Data sources are identified within and listed below.</li> </ul> <p> </p> <p><strong>Computer codes</strong></p> <ul> <li><strong>Geochemical inversion</strong> –</li> </ul> <ul> <li>“Data for geochemical inversion.xlsx”: contains Gulf of Alaska sediment and volcanic/terrigenous endmember geochemical data used for data inversion.</li> <li>“geochemical inversion.r”: R script used to perform geochemical inversion.</li> <li>“GOA inversion fraction.csv”: Result of the geochemical inversion, including the volcanic and terrigenous fractions in each Gulf of Alaska sediment sample.</li> <li>“GOA inversion residual.csv”: Result of the geochemical inversion, including the residuals of each element.</li> <li><strong>cluster volcanic geochemical data </strong>– <ul> <li> “Database of volcanic geochemistry.xlsx”: compiled database of the geochemistry of volcanic endmember samples.</li> <li> “cluster.r”: R script used to perform cluster analysis on the volcanic samples.</li> <li> “Clustered volcanic data.csv”: the results of cluster analysis.</li> <li> “Dendroplot.r”: R script used to plot the dendrogram for the cluster analysis.</li> <li> “dendro 15 complete euclidean.pdf”: The dendrogram.</li> <li> “Volcanic endmembers.csv”: final geochemical volcanic endmembers based on the cluster analysis.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Global volcanic eruption compilation –</strong></li> </ul> <ul> <li>“eruption.database.intcal20.xlsx”: This file includes the eruption database compiled by this study, as well as the previous compilation of Huybers and Langmuir 2009 EPSL (referred to as HL09).</li> <li>“eruption.ratio.R” and “volc.freq.R”: These are R scripts that compute the eruption frequency of glaciated and unglaciated volcanoes using the eruption database. “eruption.ratio.R”calls the function inside “volc.freq.R”.</li> <li>“Volcanic eruption summary.xlsx”: This file contains the outputs of the R scripts.</li> </ul> <p> </p> <ul> <li><strong>PISM sensitivity experiment – </strong></li> </ul> <ul> <li>“ciscyc.5km.epica.ts.10a.nc” and other netcdf files: The output of PISM sensitivity experiments, from <em>Seguinot. (2020). Cordilleran ice sheet glacial cycle simulations continuous variables [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3606536">https://doi.org/10.5281/zenodo.3606536</a></em>. Click the link to see the documentation of these files.</li> <li>“temperature timeseries.xlsx”: the temperature forcing used in the sensitivity experiments.</li> <li>“PISM sensitivity.r”: R script used to analyse the PISM sensitivity experiments, including data binning, lag correlation and regression between ice sheeting response and temperature forcing.</li> <li>“PISM sensitivity.xlsx”: Output of the R script.</li> <li>“GOA.calibration.csv”: SST record from the Gulf of Alaska site 85JC/U1419, calibrated using bayspline.</li> <li>“GOA.Ensemble.csv”: 1000 ensemble output of the bayspline calibration.</li> <li>“predict ice volume SST.r”: R script used to predict the response of CIS ice volume to GOA SST forcing. The script will call the results of PISM sensitivity experiments in “PISM sensitivity.xlsx” and the GOA SST forcing in “GOA.calibration.csv” and “GOA.calibration.csv”.</li> <li>“PISM ice vol GOA SST.csv”: predicted PISM ice vol based on GOA SST forcing and taking into account all sensitivity experiments and the uncertainty in SST reconstruction.</li> <li>“PISM ice vol GOA SST model.csv”: predicted PISM ice vol based on GOA SST forcing based on each sensitivity experiment and the uncertainty in SST reconstruction.</li> </ul> <p> </p> <p><strong>Please cite the following studies when using the data, in addition to citing the present study:</strong></p> <p><strong>GOA age model, IRD and MAR:</strong></p> <p>Walczak, M. H. et al. Phasing of millennial-scale climate variability in the Pacific and Atlantic Oceans. Science 370, 716–720 (2020).</p> <p>Velle, J. H. et al. High resolution inclination records from the Gulf of Alaska, IODP Expedition 341 Sites U1418 and U1419. Geophys. J. Int. 229, 345–358 (2022).</p> <p>Heaton, T. J. et al. Marine20—The Marine Radiocarbon Age Calibration Curve (0–55,000 cal BP). Radiocarbon 62, 779–820 (2020).</p> <p><strong>GOA SST: </strong></p> <p>Praetorius, S. K. et al. North Pacific deglacial hypoxic events linked to abrupt ocean warming. Nature 527, 362–366 (2015).</p> <p>Romero, O. E., LeVay, L. J., McClymont, E. L., Müller, J. & Cowan, E. A. Orbital and Suborbital-Scale Variations of Productivity and Sea Surface Conditions in the Gulf of Alaska During the Past 54,000 Years: Impact of Iron Fertilization by Icebergs and Meltwater. Paleoceanogr. Paleoclimatology 37, e2021PA004385 (2022).</p> <p>Tierney, J. E. & Tingley, M. P. BAYSPLINE: A New Calibration for the Alkenone Paleothermometer. Paleoceanogr. Paleoclimatology 33, 281–301 (2018).</p> <p><strong>GOA benthic foraminifera assemblage:</strong></p> <p>Belanger, C. L., Sharon, Du, J., Payne, C. R. & Mix, A. C. North Pacific deep-sea ecosystem responses reflect post-glacial switch to pulsed export productivity, deoxygenation, and destratification. Deep Sea Res. Part Oceanogr. Res. Pap. 164, 103341 (2020).</p> <p>Sharon, Belanger, C., Du, J. & Mix, A. Reconstructing Paleo-oxygenation for the Last 54,000 Years in the Gulf of Alaska Using Cross-validated Benthic Foraminiferal and Geochemical Records. Paleoceanogr. Paleoclimatology 36, e2020PA003986 (2021).</p> <p><strong>GOA productivity:</strong></p> <p>Romero, O. E., LeVay, L. J., McClymont, E. L., Müller, J. & Cowan, E. A. Orbital and Suborbital-Scale Variations of Productivity and Sea Surface Conditions in the Gulf of Alaska During the Past 54,000 Years: Impact of Iron Fertilization by Icebergs and Meltwater. Paleoceanogr. Paleoclimatology 37, e2021PA004385 (2022).</p> <p>Addison, J. A. et al. Productivity and sedimentary δ15N variability for the last 17,000 years along the northern Gulf of Alaska continental slope. Paleoceanography 27, PA1206 (2012).</p> <p><strong>GOA bulk sediment neodymium isotopes:</strong></p> <p>Du, J., Haley, B. A., Mix, A. C., Walczak, M. H. & Praetorius, S. K. Flushing of the deep Pacific Ocean and the deglacial rise of atmospheric CO 2 concentrations. Nat. Geosci. 11, 749–755 (2018).</p> <p><strong>GOA volcanic endmember data compilation:</strong></p> <p>Cameron, C. E., Snedigar, S. F. & Nye, C. J. Alaska Volcano Observatory geochemical database. DDS 8 http://www.dggs.alaska.gov/pubs/id/29120 (2014) doi:10.14509/29120.</p> <p>Sarbas, B., Jochum, K. P., Nohl, U. & Hofmann, A. W. GEOROC, the MPI geochemical rock database: a new tool for geochemists. Eos Trans. AGU 80, F1184 (1999).</p> <p><strong>Global and regional volcanic eruption data compilation:</strong></p> <p>Huybers, P. & Langmuir, C. Feedback between deglaciation, volcanism, and atmospheric CO2. Earth Planet. Sci. Lett. 286, 479–491 (2009).</p> <p>Global Volcanism Program, 2013. Volcanoes of the World, v. 4.8.7. 10.5479/si.GVP.VOTW4-2013. (2013).</p> <p>Bryson, R. U., Bryson, R. A. & Ruter, A. A calibrated radiocarbon database of late Quaternary volcanic eruptions. EEarth Discuss 1, 123–134 (2006).</p> <p>Watt, S. F. L., Pyle, D. M. & Mather, T. A. The volcanic response to deglaciation: Evidence from glaciated arcs and a reassessment of global eruption records. Earth-Sci. Rev. 122, 77–102 (2013).</p> <p>Crosweller, H. S. et al. Global database on large magnitude explosive volcanic eruptions (LaMEVE). J. Appl. Volcanol. 1, 4 (2012).</p> <p>Cameron, C. E., Snedigar, S. F. & Nye, C. J. Alaska Volcano Observatory geochemical database. DDS 8 http://www.dggs.alaska.gov/pubs/id/29120 (2014) doi:10.14509/29120.</p> <p>Praetorius, S. et al. Interaction between climate, volcanism, and isostatic rebound in Southeast Alaska during the last deglaciation. Earth Planet. Sci. Lett. 452, 79–89 (2016).</p> <p>Wilcox, P. S. et al. A new set of basaltic tephras from Southeast Alaska represent key stratigraphic markers for the late Pleistocene. Quat. Res. 92, 246–256 (2019).</p> <p>Davies, L. J., Jensen, B. J. L., Froese, D. G. & Wallace, K. L. Late Pleistocene and Holocene tephrostratigraphy of interior Alaska and Yukon: Key beds and chronologies over the past 30,000 years. Quat. Sci. Rev. 146, 28–53 (2016).</p> <p><strong>GIA models:</strong></p> <p>Roy, K. & Peltier, W. R. Relative sea level in the Western Mediterranean basin: A regional test of the ICE-7G_NA (VM7) model and a constraint on late Holocene Antarctic deglaciation. Quat. Sci. Rev. 183, 76–87 (2018).</p> <p>Lambeck, K., Purcell, A. & Zhao, S. The North American Late Wisconsin ice sheet and mantle viscosity from glacial rebound analyses. Quat. Sci. Rev. 158, 172–210 (2017).</p> <p><strong>PISM sensitivity experiments and temperature forcing:</strong></p> <p>Seguinot, J., Rogozhina, I., Stroeven, A. P., Margold, M. & Kleman, J. Numerical simulations of the Cordilleran ice sheet through the last glacial cycle. The Cryosphere 10, 639–664 (2016).</p> <p>Seguinot. (2020). Cordilleran ice sheet glacial cycle simulations continuous variables [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3606536</p> <p>Dansgaard, W. et al. Evidence for general instability of past climate from a 250-kyr ice-core record. Nature 364, 218–220 (1993).</p> <p>Andersen, K. K. et al. High-resolution record of Northern Hemisphere climate extending into the last interglacial period. Nature 431, 147–151 (2004).</p> <p>Jouzel, J. et al. Orbital and Millennial Antarctic Climate Variability over the Past 800,000 Years. Science 317, 793–796 (2007).</p> <p>Petit, J. R. et al. Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399, 429–436 (1999).</p> <p>Herbert, T. D. et al. Collapse of the California Current During Glacial Maxima Linked to Climate Change on Land. Science 293, 71–76 (2001).</p> <p><strong>Be10 data compilation:</strong></p> <p>Lesnek, A. J., Briner, J. P., Baichtal, J. F. & Lyles, A. S. New constraints on the last deglaciation of the Cordilleran Ice Sheet in coastal Southeast Alaska. Quat. Res. 96, 140–160 (2020).</p> <p>Haeussler, P. J. et al. Late Quaternary deglaciation of Prince William Sound, Alaska. Quat. Res. 1–20 (2021) doi:10.1017/qua.2021.33.</p> <p>Walcott, C. K., Briner, J. P., Baichtal, J. F., Lesnek, A. J. & Licciardi, J. M. Cosmogenic ages indicate no MIS 2 refugia in the Alexander Archipelago, Alaska. Geochronology 4, 191–211 (2022).</p> <p>Briner, J. P. et al. The last deglaciation of Alaska. Cuad. Investig. Geográfica 43, 429–448 (2017).</p> <p>Tulenko, J. P., Briner, J. P., Young, N. E. & Schaefer, J. M. Beryllium-10 chronology of early and late Wisconsinan moraines in the Revelation Mountains, Alaska: Insights into the forcing of Wisconsinan glaciation in Beringia. Quat. Sci. Rev. 197, 129–141 (2018).</p> <p>Menounos, B. et al. Cordilleran Ice Sheet mass loss preceded climate reversals near the Pleistocene Termination. Science 358, 781–784 (2017).</p> <p>Dulfer, H. E., Margold, M., Engel, Z., Braucher, R. & Team, A. Using 10Be dating to determine when the Cordilleran Ice Sheet stopped flowing over the Canadian Rocky Mountains. Quat. Res. 102, 222–233 (2021).</p> <p>Lesnek, A. J., Briner, J. P., Lindqvist, C., Baichtal, J. F. & Heaton, T. H. Deglaciation of the Pacific coastal corridor directly preceded the human colonization of the Americas. Sci. Adv. 4, eaar5040 (2018).</p> <p>Tulenko, J. P., Briner, J. P., Young, N. E. & Schaefer, J. M. The last deglaciation of Alaska and a new benchmark 10Be moraine chronology from the western Alaska Range. Quat. Sci. Rev. 287, 107549 (2022).</p>
Data for: Impact of SO2 injection profiles on simulated volcanic forcing for the Sarychev 2009 eruptions - investigating the importance of using high vertical resolution methods when compiling SO2 data
<p>The files are data assosicated with the study High-resolution stratospheric volcanic SO2 injections in WACCM. The files are associated with four differnt simulaions described in the paper: M16, S21-1D, S21-3D and No-Volc. The files with "input" in the name are the SO2 input files used in the WACCM (Whole Atmosphere Community Climate Model) simulations in the paper. The files with "monthly_averages" in the filenames are monthly averages of model output data the variables used in the paper. </p> <p>The CALIOP_monthly_averages.nc file is monthly average of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) satellite data used in the study to evaluate the WACCM simulations. </p> <p> </p>
Data for "Effects of External Water on Volcanic Column Height and Collapse"
<p>Generate data for the publication "Effects of External Water on Volcanic Column Height and Collapse" (in prep). Please cite Carrillo, E.L. (2024) if any data in this repository is used.</p>
East African topography and volcanism explained by a single, migrating plume: supplementary data
<p>These data accompany the following paper:</p> <p>Hassan, R., Williams, S.E., Gurnis, M. and Müller, D., 2020. East African topography and volcanism explained by a single, migrating plume. <em>Geoscience Frontiers</em>, <em>11</em>(5), pp.1669-1680.</p> <p>The data (in simple text form) correspond to the dynamic topography and change in dynamic topography shown in Figure 7.</p>
Database of pyroclastic cover deposit thickness measurements (PT-Cam) in peri-volcanic areas of Campania (Italy)
<p>In an eruptive event, tephra deposits (i.e. ash and pumice) disperse in the atmosphere and deposit on the ground surface according to the speed and direction of the wind. Because the geotechnical and hydraulic properties of the unconsolidated pyroclastic fall deposits usually differ from the bedrock, their spatial thickness significantly influences geomorphological and hydrogeological processes such as landscape evolution, erosion, landslide, and hillslope hydrogeology.</p> <p>The PT-Cam database presents the thickness of tephra deposits (i.e. the unconsolidated materials over the bedrock) in Campania region (Italy), measured through in-situ investigations of some territories around the Somma-Vesuvius, Campi Flegrei, Roccamonfina, and Ischia volcanoes during the last decades. The measurements were conducted with probing tests, dynamic penetration tests, trenches, man-made pits, seismic surveys, and outcrops.</p> <p>Explanation for database attribute:</p> <ul> <li>CODE: identification code of the measurement;</li> <li>z: measured thickness expressed in cm;</li> <li>type_z: thickness type (i.e. if investigation has reached to the bedrock the type is “total” otherwise it is “partial”);</li> <li>type_investigation: method of in-situ investigation;</li> <li>locality: municipality to which the measurement point belongs;</li> <li>Longitude, Latitude: km coordinates in UTM WGS 84 system.</li> </ul>
Hourly non-gridded volcanic ash properties retrieved from SEVIRI measurements for the Eyjafjallajökull 2010 eruption
<p>- Publishing date:<br> 14.05.2020</p> <p>- Title:<br> Hourly non-gridded volcanic ash properties retrieved from SEVIRI<br> measurements for the Eyjafjallajökull 2010 eruption </p> <p>- Authors of data set:<br> Arve Kylling (aky@nilu.no), NILU - Norwegian Institute for Air Research<br> Espen Sollum, NILU - Norwegian Institute for Air Research</p> <p>- Description:<br> Ash satellite detection and retrievals were made using infrared<br> measurements by SEVIRI on board the MSG-2 satellite. MSG-2 is<br> geostationary, centred at approximately 0N latitude, and has a 70<br> degree view coverage (Schmetz et al., 2002). Pixel resolution is 3 ×<br> 3 km at nadir, while at the edge of the coverage it increases to 10<br> × 10 km. Observations are available every 15 min. Pixels are<br> identified as containing ash if the brightness temperature<br> difference (BTD) between the SEVIRI 10.8 and 12.0 μm channels<br> (Prata, 1989) is below a certain threshold value, here −0.5 K. The<br> BTDs have been adjusted for water vapour absorption using the approach of<br> Yu et al. (2002). Ash clouds give negative BTDs, ice give positive<br> BTDs, and BTDs of water clouds are closer to zero. The ash mass<br> loading and effective ash particle radius are retrieved as described<br> in Kylling et al. (2015). The retrieval is based on a modification<br> of the Bayesian optimal estimation technique used by Francis et<br> al. (2012). We assume andesite ash with refractive index from Pollack<br> et al. (1973), spherical ash particles, and a lognormal size<br> distribution. The lognormal size distribution is described by the<br> geometric mean radius and the geometric standard deviation. The data<br> set includes retrievals for geometric standard deviation of 1.5,<br> 1.75, 2.0, and 2.25, which is a subset of the values used by Francis<br> et al. (2012). The data set has been used by Steensen et al. (2017).</p> <p> Data comes as hourly files broadly covering Iceland, Europe and the<br> surrounding oceans. The files are in bzip2 netcdf-format which<br> should be self-explanatory. </p> <p>- Version:<br> 1.0</p> <p>- Language:<br> English</p> <p>- Keywords<br> Volcanic ash, remote sensing, SEVIRI, Eyjafjallajökull 2010</p> <p>- Additional notes<br> None</p> <p>- Access right:<br> Open access</p> <p>- License:<br> CC BY-SA 4.0 </p> <p>- Funding:<br> Partly funded by the Norwegian ash project financed by the Norwegian<br> Ministry of Transport and Communications and Avinor. </p> <p>- References:<br> Francis, P. N., Cooke, M. C., and Saunders, R.W.: Retrieval of<br> physical properties of volcanic ash using Meteosat: A case study<br> from the 2010 Eyjafjallajokull eruption, J. Geophys. Res. Atmos.,<br> 117, D00U09, https://doi.org/10.1029/2011JD016788, 2012.</p> <p> Kylling, A., Kristiansen, N., Stohl, A., Buras-Schnell, R., Emde,<br> C., and Gasteiger, J.: A model sensitivity study of the impact of<br> clouds on satellite detection and retrieval of volcanic ash, Atmos. <br> Meas. Tech., 8, 1935-1949, https://doi.org/10.5194/amt-8-1935-<br> 2015, 2015.<br> <br> Pollack, J. B., Toon, O. B., and Khare, B. N.: Optical properties of<br> some terrestrial rocks and glasses, Icarus, 19, 372-389,<br> https://doi.org/10.1016/0019-1035(73)90115-2, 1973. </p> <p> Prata, A. J.: Observations of volcanic ash clouds in the 10-12 um<br> window using AVHRR/2 data, Int. J. Remote Sens., 10, 751-761,<br> 1989.</p> <p> Schmetz, J., Pili, P., Tjemkes, S., and Just, D.: An introduction to<br> Meteosat second generation (MSG), B. Am. Meteorol. Soc., 83,<br> 977-992, 2002.<br> <br> Steensen, B. M., Kylling, A., Kristiansen, N. I., and Schulz, M.:<br> Uncertainty assessment and applicability of an inversion method for<br> volcanic ash forecasting, Atmos. Chem. Phys., 17, 9205-9222,<br> https://doi.org/10.5194/acp-17-9205-2017, 2017. </p> <p> Yu, T., Rose, W. I., and Prata, A. J.: Atmospheric correction for<br> satellite-based volcanic ash mapping and retrievals using "split<br> window" IR data from GOES and AVHRR, J. Geophys. Res. Atmos., 107,<br> https://doi.org/10.1029/2001JD000706, 2002. </p>
Landslides from Space - Volcan Landslide (10th January 2017)
<p>On 10th January 2017 a landslide hit the village Volcan. Two people died in this event and the famous Dakar Rally was disrupted.<br> <br> The pre-event acquisition is from 17th December 2016 (Sentinel-2) and the post-event acquisition is from 15th February 2017 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2016-2017)</em></p>
Intital simulation of Hunga-Tonga volcanic aerosol cloud with the UM-UKCA composition-climate model
<p>This dataset is from a series of “forward projection” interactive stratospheric aerosol simulations of the Jan 2022 Hunga-Tonga volcanic aerosol cloud with the UM-UKCA composition-climate model. The model experiments predict how the cloud will disperse through 2022, and apply the UM-UKCA model at GA4 (Walters et al., 2014), with GLOMAP v8.2, as applied for the “MajorVolc” datasets for Agung, El Chichon and Pinatubo (Dhomse et al., 2020), those runs aligned with the Historical Eruption SO2 emissions Assessment experiment within ISA-MIP (Timmreck et al., 2018).</p> <p>The “standard” Hunga-Tonga GA4 UM-UKCA experiment emits 0.4Tg of SO2 at 29-31km, within a 24-hour period, matching the detrainment duration specified for the ISA-MIP HErSEA experiment protocol. Following the stronger than expected mid-visible backscatter ratios (BSR) measured by CALIOP satellite-borne lidar, and from ground-based lidar from Reunion Island (very high BSR values > 200), we also ran UM-UKCA simulations with “scaled-up Hunga-Tonga SO2 emission”, at 0.8, 1.2 and 1.6 Tg of SO2 emitted.</p> <p>Unexpectedly strong stratospheric AOD observed from the OMPS satellite months after the eruption further strengthens the motivation for these simulations.</p> <p>Several hypotheses for the high AOD from Hunga-Tonga have been suggested:<br> 1) an unusual amount of (or influence from) co-emitted ultra-fine ash particles<br> 2) “in-plume oxidised sulphate” already converted from SO2 at the time of detrainment<br> (e.g. via aqueous-phase oxidation within water droplets within the eruptive plume).<br> 3) co-emitted marine aerosol (e.g. sea-salt aerosol) from seawater vaporized in the plume<br> </p> <p>There are 4 types of netcdf files, Stratospheric AOD (saod), Effective Radius (reff), Extinction (ext) and sulphate aerosol surface area density (sad).</p> <p><br> <br> For e.g. <br> saod550_HT_0pt4Tg_T2Mz-20220101-20230831.nc contains<br> Stratospheric aerosol optical depth (sAOD) at 550nm (2D-monthly dataset vs latitude and time) with 0.4 Tg SO2 injection Jan2022 to August 2023<br> Whereas other files<br> reff_HT_0pt4Tg_T2Mz_20220101-20230831.nc,<br> sad_HT_0pt4Tg_T2Mz_20220101-20230831.nc<br> ext550_HT_0pt4Tg_T2Mz-20220101-20230831.nc</p> <p>contain particle effective radius (reff), aerosol surface area density, aerosol extinction as 3D-monthly fields (altitude, latitude , time) from the same simulation.<br> Other saod and extinction files are also available at 870 and 1020 nm.</p> <p> </p> <p>Note that these are preliminary simulations, hence we do not expect good match with the observations. We plan to perform additional UM-UKCA simulations, comparing to the satellite and ground-based lidar measurements, and to in-situ balloon observations from Reunion Island rapid response campaign & upcoming high-altitude balloon sampling flights in Brazil.</p> <p> </p> <p>References :<br> Dhomse SS, Mann GW, Antuña Marrero JC, Shallcross SE, Chipperfield MP, Carslaw KS, Marshall L, Abraham NL, Johnson CE. 2020. Evaluating the simulated radiative forcings, aerosol properties, and stratospheric warmings from the 1963 Mt Agung, 1982 El Chichón, and 1991 Mt Pinatubo volcanic aerosol clouds. Atmospheric Chemistry and Physics. 20(21), pp. 13627-13654</p> <p><br> Timmreck, C., Mann, G. W., Aquila, V., Hommel, R., Lee, L. A., Schmidt, A., Brühl, C., Carn, S., Chin, M., Dhomse, S. S., Diehl, T., English, J. M., Mills, M. J., Neely, R., Sheng, J., Toohey, M., and Weisenstein, D.: The Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP): motivation and experimental design, Geosci. Model Dev., 11, 25812608, https://doi.org/10.5194/gmd-11-2581-2018, 2018.</p> <p> </p>
Stressful crystal histories recorded around melt inclusions in volcanic quartz
<p>Magma ascent and eruption are driven by a set of internally and externally generated stresses that act upon the magma. We present microstructural maps around melt inclusions in quartz crystals from six large rhyolitic eruptions using synchrotron Laue X-ray microdiffraction to quantify elastic residual strain and stress. We measure plastic strain using average diffraction peak width and lattice misorientation, highlighting dislocations and subgrain boundaries. Quartz crystals preserve similar and relatively small magnitudes of elastic residual stress (mean 53-135 MPa, median 46-116 MPa) in comparison to the strength of quartz (~10 GPa). However, the distribution of strain in the lattice around inclusions varies between samples. We hypothesize that dislocation and twin systems may be established during compaction of crystal-rich magma, which affects the magnitude and distribution of preserved elastic strains. Given the lack of stress-free haloes around faceted inclusions, we conclude that most residual strain and stress was imparted after inclusion faceting. Fragmentation may be one of the final strain events that superimposes stresses of ~100 MPa across all studied crystals. Overall, volcanic quartz crystals preserve complex, overprinted deformation textures indicating that quartz crystals have prolonged deformation histories throughout storage, fragmentation, and eruption. The data collected using Laue microdiffraction at Lawrence Berkeley National Laboratory Advanced Light Source beamline 12.3.2 are included below as .xlsx files. Data was processed and analyzed using XMAS (Tamura, 2014) and XtalCAMP (Li et al., 2020).</p>
Data Set for "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs" I: Lesser Antilles Volcanic Arc
<p>Data set for the 48 friction experiments performed for gouge samples (altered andesitic rocks) from the Lesser Antilles used in the manuscript, "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs". This data set can be used in combination with the data set for the Cascades used in the same manuscript (doi:10.5281/zenodo.10964936). This large combined data set (of 108 frictional experiments) represents a unique opportunity to systematically study frictional behaviour in the framework of rate and state. All samples are tested in wet and dry conditions at 10, 30, and 50 MPa with velocity steps and slide-hold-slides. These two data sets have the further advantage of being performed with exactly the same protocol (same run in, same initial gouge thickness, same velocity steps, same hold periods), in the same machine, by the same operator (or by an operator who was trained and supervised by the original operator). </p>
Exploiting the Greenland volcanic ash repository to date caldera-forming eruptions and widespread isochrons during the Holocene
<p>Polar ice-cores have long been recognised as unrivalled repositories of past volcanic events. Although tephra products from local eruptions tend to dominate these records, improvements in micro-sampling and analytical techniques are uncovering a growing number of cryptotephras erupted from exceptionally distant volcanoes. We present a series of nine Middle Holocene cryptotephra deposits detected within the NGRIP ice-core that originate from five different volcanic regions across the Northern Hemisphere (Alaska, Cascades, Iceland, Japan, Kamchatka). Unique compositional signatures are employed to identify ash from three large caldera-forming events in Kamchatka (KS<sub>2 </sub>from Ksudach), the Cascades (Mazama) and North East Japan (Mashu), along with ash from the Hekla 4 eruption in Iceland. High-precision ice-core ages (adopting a 1950 datum for the GICC05 timescale assigned to the Greenland ice cores) are derived for each eruption: Hekla 4 (4325 ± 8 a b1.95k), KS<sub>2</sub> (7089 ± 26 a b1.95k), Mashu (i-f) (7473 ± 33 a b1.95k) and Mazama (7562 ± 35 a b1.95k), all of which can be employed as chronological fix-points in other proxy records where these deposits are also preserved. Four further cryptotephra deposits and one macro-deposit (in the GRIP ice core) are also identified and traced to sources in Iceland and Alaska. The cryptotephra originating from Alaska is correlated to a deposit identified in lake records from the Kenai Peninsula, thought to originate from Redoubt Volcano. The remaining four deposits are typical of the products of Katla, Grímsvötn and Veiðivötn in Iceland. This ensemble of mid-Holocene tephra deposits highlights the pivotal position of the Greenland ice-sheet and its ice-cores to capture deposition from the convergence of several far-travelled ash clouds. Precise age estimates derived from the annually resolved ice-core record greatly enhances the value of these tephra isochrons.</p> <p> </p>
Volcanic Lightning and Continual Radio Frequency Impulses at Sakurajima Volcano: A Multiparametric Dataset
<p>This is a multiparametric data set of volcanic activity at Sakurajima volcano in Japan. The data set was collected in May and June 2015. The data set includes the following types of data: Lightning Mapping Array data, slow and fast electric field waveforms, log-RF VHF data, infrasound data, plume height, velocity, and temperature data.</p>
Supplemental to: Physical and mechanical depth relationships of rocks from the Rotokawa Geothermal Reservoir, Taupō Volcanic Zone, New Zealand
<p>This contains supplementary informations for the Manuscript </p> <p>Physical and mechanical depth relationships of rocks from the Rotokawa Geothermal Reservoir, TaupōVolcanic Zone, New Zealand</p> <p>submitted for review at the New Zealand Journal of Geology and Geophysics</p>
InSAR Time Series Analysis (2018-2021) for Volcanic Monitoring in Northern Chile
<p>This dataset is for the paper "First onset of unrest captured at Socompa: A Recent Geodetic Survey at Central Andean volcanoes in Northern Chile" which is published in GRL: <a href="https://doi.org/10.1029/2022GL102480">https://doi.org/10.1029/2022GL102480</a>.</p> <p><strong>InSAR Data:</strong></p> <p>The folder of InSAR_149A.rar stores the InSAR time series analysis dataset on ascending track 149.</p> <ol> <li>The 'Imagedate' folder stores the empty *.rslc files to indicate the date of each SLCs.</li> <li>Data_Asc.mat stores the main InSAR time series data, which includes the UTC time of the acquisition (for accurate time calculation), the length of perpendicular baselines (unit is meter), the number of days counting from the first epoch, the unwrapped time series data (ifg), the unwrapped time series data with GACOS correction (ifg_aps), the look angles (la, unit is rad), and lat&lon.</li> <li>parms.mat stores the parameters used during the data processing by StaMPS.</li> <li>semi_fit.mat stores the results of the semi-variogram fitting of each interferogram on time series. It provides two versions for the original dataset (semi) and the GACOS-corrected dataset (semi_aps). This file is mainly used to weight the data during the time series fitting.</li> <li>runTSA.m, the main function to run the InSAR time series fitting. See more details in the Code part.</li> </ol> <p>The folder of InSAR_156D.rar stores the same content as the InSAR_149A.rar but for descending track 156.</p> <p><strong>Code:</strong></p> <p>This folder contains the codes of the InSAR time series fitting for this dataset, and the GBIS software.</p> <ol> <li>TSA_findref.m, this function is used to search the reference point of the InSAR data.</li> <li>TSA_EQ_fit.m, is the main function to perform InSAR time series fitting.</li> <li>rb_pixel_fit.m, is the robust way to fit the linear model.</li> <li>TSA_EQ_pixel.m, is the function used to plot the results.</li> </ol> <p>To perform the InSAR time series fitting, you need to put these four functions under your Matlab path, and then run the runTSA.m function in the data folder.</p> <p>The GBIS folder stores the updated version of the GBIS software, which allows you to perform the pCDM, CDM, and pECM. The core functions of these models are provided by Dr. Mehdi Nikkhoo, and you could find them here: https://www.volcanodeformation.com/software</p> <p><strong>GBIS_Modelling_Results:</strong></p> <p>This folder stores the data of InSAR and GPS joint inversion for Socompa Uplift.</p> <ol> <li>The folder Socompa stores the modelling results using the models of Okada(D), pECM(E), Mogi(M), pCDM(N), and Yang(Y), respectively. </li> <li>GPS_data.txt stores the cumulative displacements and the uncertainties of the SOCM station in three directions.</li> <li>Socompa.inp is the configuration file for GBIS running.</li> <li>Vol_asc.mat and Vol_asc_ds.mat stores the original and the downsampled ascending data, while Vol_dsc.mat and Vol_dsc_ds.mat store those of descending.</li> </ol> <p>Many thanks for using our dataset and please let me know if you have any further questions!</p>
OMCF questionnaires - Surveying volcanic crises exercises: From open-question questionnaires to a prototype checklist
<p>English / French / Italian / Spanish versions</p> <p>Volcanic crisis exercises are usually run to test response capabilities, communication protocols, and decision-making procedures by those agencies, such as volcano observatories and/or Civil Protection authorities, with responsibilities to cope with scenarios of volcanic unrest with inherent uncertainty. During the last decades, the use of questionnaires has been increased to evaluate people’s knowledge on volcanic hazards and their perception of risk, which could affect their preparedness to respond to emergency measures plans. In this paper, we show the study carried out within the EUROVOLC project in extracting information on the experience gained during volcanic-crisis exercises by the project’s participants and beyond. In particular, we first distributed an open-question questionnaire survey within the EUROVOLC-project community. Based on the results obtained, we developed a more user-friendly online multi-choice questionnaire that we submitted to different volcanological communities within and outside the project. From the answers to the on-line questionnaire, we extracted a prototype checklist for guiding those who design such exercises in the future. Here, we give details on the lessons learnt from this study, in particular about the need to increase training activities, to improve external (between players and general public/media) communication tools, equipment and protocols and to better define decision-makers needs. Our preliminary results confirm that this type of survey is a very useful tool for gathering information on participants' experience and knowledge, and to understand which data and information may be useful when designing exercises for scientists, emergency managers and others involved in a volcanic crisis.</p> <p> </p>
Combined exposure to CO2 and H2S significantly reduces the performance of the Mediterranean seagrass Posidonia oceanica: evidence from a volcanic CO2 vent
<p>The dataset is an excel file consisting of six sheets. The associated metadata file contains the description and other information about the dataset</p>
Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" paper
<p>Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo<br>Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" by A. Ukhov, <br>G. Stenchikov, S.Osipov, N. Krotkov, N. Gorkavyi, C. Li, O. Dubovik, and A. Lopatin.</p> <p>Corresponding author: Alexander Ukhov, alexander.ukhov@kaust.edu.sa</p> <p>Contents<br>0. This 'README' file</p> <p>1. Emission profiles for ash and SO2<br> 1.1 In pickle and txt format, when radiative feedback is accounted for:<br> 1.1.1 Files 'ash_2d_emission_profiles_rad_on' [Mt/sec] and 'ash_2d_emission_profiles.txt' [Mt/(m sec)]<br> 1.1.2 Files 'so2_2d_emission_profiles_rad_on' [Mt/sec] and 'so2_2d_emission_profiles.txt' [Mt/(m sec)]</p> <p> 1.2 In pickle format, when radiative feedback is not accounted for:<br> 1.2.1 Files 'ash_2d_emission_profiles_rad_off' [Mt/sec]<br> 1.2.2 Files 'so2_2d_emission_profiles_rad_off' [Mt/sec]</p> <p>2. python script 'draw_supplementary_profiles.py' plots inverted emission profiles <br> (in pickle format) and their time integrated variants.</p> <p>3. WRF-Chem output file 'wrfout_d01_1991-06-16_00:00:00' in netcdf format contains <br> 3-D fields of ash, sulfate, and SO2 concentrations at 0000 UTC on 16 of June. <br> Instructions on how to process WRF-Chem output are available at the Appendix of [1].</p> <p>4. WRF-Chem domain grid description in the file 'wrf_small_grid.txt'. This file can be<br> used for conservative interpolation of 3-D fields to another grid, for example <br> using 'cdo remapcon'.</p> <p>There are two options: <br>1. Use inverted ash and SO2 emission profiles (see p.1 and p.2)<br>2. Use ash, sulfate, and SO2 concentrations from WRF-Chem output file (see p.3 and p.4)<br> as initial conditions for another run.</p> <p><br>References:<br>1. Ukhov, A., Ahmadov, R., Grell, G., and Stenchikov, G.: Improving dust simulations<br> in WRF-Chem v4.1.3 coupled with the GOCART aerosol module, <br> Geosci. Model Dev., 14, 473–493, https://doi.org/10.5194/gmd-14-473-2021, 2021.</p> <p>2. Ukhov et. al, Enhancing Volcanic Eruption Simulations with the WRF-Chem v4.7.x</p>
Three-hourly gridded volcanic ash emissions for the Eyjafjallajökull 2010 eruption
<p>Forward simulations of the Eyjafjälla 2010 eruption with unit emissions. These files are used to create an emission estimate of a volcanic eruption.</p> <p>Each file corresponds to an individual emission time point, and contains 19 individual emission simulations. Each emission simulation emits 1 teragram of ash into a unique vertical level of the model. The levels are labeled L01 .. L19, and designate level number from the top of the atmosphere (top of level 1 is close to 130 hPa or around 14 km ASL). The hybrid sigma levels are defined in Vertical_levels_22_650m.txt. </p> <p>The files were created using eEMEP Unimod_ASH compiled by Alvaro Valdebenito (module cams50/201809) on the Nebula supercomputer. </p>
Figure 2 in New Cenozoic dragonflies from the Most Basin and Středohoří Complex volcanic area (Czech Republic, Germany)
Figure 2. Aeshna zlatkokvaceki sp. nov. (Aeshnidae) (A) Photograph of holotype specimen SMMG CsT 1091 (Senckenberg Naturhistorische Sammlungen Dresden coll., Germany), imprint only; (B) line drawing of fore wing. Scale bars represent 5 mm.
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.