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65 results for “Avalanches”
Mountain Rain Or Snow: Enhancing Avalanche Forecasting With Real-Time Precipitation Phase Data
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The dataset for the paper "Long-Lived and Continual Volcanic Eruptions, Tectonic Activity, Pit Chains Formation and Boulder Avalanches in Northern Tharsis Region: Implications for Late Amazonian Geodynamics and Seismo-Tectonic Processes on Mars" JGR: Planets.
<p>This dataset pertains to the following paper in Journal of Geophysical Research: Planets.</p> <p>Krishnan, V., and Kumar, P.S. (2022), Long-Lived and Continual Volcanic Eruptions, Tectonic Activity, Pit Chains Formation and Boulder Avalanches in Northern Tharsis Region: Implications for Late Amazonian Geodynamics and Seismo-Tectonic Processes on Mars, Journal of Geophysical Research: Planets (for full citation, please see the journal). AGU Manuscript No. 2022JE007511.</p> <p> </p>
Supplementary videos for "Transient wave activity in snow avalanches is controlled by entrainment and topography"
<p>Movie 1. Field avalanche happened at the Vallée de la Sionne test site on 3 February 2015.</p> <p>Movie 2. Numerical simulation of the 2015 VdlS avalanche. The color in the video shows the flow velocity, and the colorbar can be found in Fig. 3 of the manuscript.</p> <p>Movie 3. Roll-waves in the simulated avalanche, where the particles ahead of the wave are eroded and the particles at the wave tail are not deposited. The color in the video shows the flow velocity, and the colorbar can be found in Fig. 3 of the manuscript.</p> <p>Movie 4. Erosion-deposition waves in the simulated avalanche, where the particles ahead of the wave are eroded and the particles at the wave tail are deposited. The color in the video shows the flow velocity, and the colorbar can be found in Fig. 3 of the manuscript.</p>
Avalanche Phenomenon During Airways Opening in Acute Respiratory Distress Syndrome
ClinicalTrials.gov study NCT05224323. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Changes in the Carbon Dioxide Content in the Body During a Simulated Avalanche Burial With and Without the Use of a Breathing Tube System.
ClinicalTrials.gov study NCT06802744. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Snow Properties and Its Modeling for Studying Gas Exchange Under the Simulated Avalanche Snow
ClinicalTrials.gov study NCT03413878. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Evaluation of Gas Propagation in Snow During Breathing of Subjects Under Simulated Avalanche Snow
ClinicalTrials.gov study NCT05262894. IPD Sharing: NO. Countries: 1. Publications: 2.
Posttraumatic Stress Disorder and Quality of Life of Avalanche Survivors From 2014 to 2018, Based on the French North Alpine Avalanche Register
ClinicalTrials.gov study NCT03936738. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Hypercapnia and Gas Exchange Under the Avalanche Snow Model (HyperAvaSM)
ClinicalTrials.gov study NCT02521272. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: InGaAs/InAlAs single photon avalanche diode for 1550 nm photons
A single photon avalanche diode (SPAD) with an InGaAs absorption region, and an InAlAs avalanche region was designed and demonstrated to detect 1550 nm wavelength photons. The characterization included leakage current, dark count rate and single photon detection efficiency as functions of temperature from 210 to 294 K. The SPAD exhibited good temperature stability, with breakdown voltage dependence of approximately 45 mV K−1. Operating at 210 K and in a gated mode, the SPAD achieved a photon detection probability of 26% at 1550 nm with a dark count rate of 1 × 108 Hz. The time response of the SPAD showed decreasing timing jitter (full width at half maximum) with increasing overbias voltage, with 70 ps being the smallest timing jitter measured.
Supplementary data to: Unravelling driving conditions of rock and ice avalanches and resulting cascading processes in High Mountain Asia
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Supplementary data for "Detrainment and braking of snow avalanches interacting with forests"
<p>Figure 5. Data of Fig. 5. Evolution of the detrainment mass per unit of area of snow with the velocity (regular staggered forest, e= 8m).</p> <p>Figure 6. Data of Fig. 6. Evolution of the detrainment mass observed for the one tree arrangement and the fits with the tree diameter for 3 flow regimes: Case 1, Case 2, and Case 3 with respectively a front velocity of 12.5 m/s,10.9 m/s and 10.75 m/s, ‘maximum’ refer to the maximum mass stored behind the tree and ‘final’ refer to the final mass stored. A slope of 30°, and a top wedge angle of 60° (from measurements Feistl et al. (2014)) are used for the theoretical model (Eq. 8). The removed point denotes a special case not considered in the proposed square relation in Eq. 9, since the entire avalanche in this case is stopped due to the low flow velocity and the high tree diameter. Please note that it is a coincidence that Case 1 maximum and Case 2 final agree well.</p> <p>Figure 8. Data of Fig. 8. Evolution of the detrainment mass per unit of area with the tree diameter for different types of snow and front velocity (regular staggered forest, e = 8 m)</p> <p>Figure 9. Data of Fig. 9. (a) Evolution of the detrainment mass per unit of area with the forest density for different front velocities (regular staggered forest, snow properties: Case 2, m). (b) Evolution of the detrainment mass per unit of area with the tree diameter for a constant Stand Density Index, SDI = 925 trees/ha (regular staggered forest, m/s, snow properties: Case 2).</p> <p>Figure 10. Data of Fig. 10. Evolution of the detrainment mass predicted with the model (Eq. 11) and with the observation, the coefficient of determination r² for the model prediction on the detrainment mass is 0.9912.</p> <p>Figure 11. Data of Fig. 11. Temporal evolution of the kinetic and potential energy without forest and with a regular staggered forest (Case 2, v<sub>0 </sub>= 6 m/s).</p> <p>Figure 12. Data of Fig. 12. Temporal evolution of the detrainment energy and dissipation due to the forest (Case 2, v<sub>0 </sub>= 6 m/s).</p> <p>Figure 13. Data of Fig. 13. Energy detrainment and dissipation for different snow properties (1: Case 1, 2: Case 2, 3: Case 3 with M=1) and for 3 types of forest structure (regular aligned, regular staggered and random).</p>
An ice-snow avalanche triggered small glacial lake outburst flood in Birendra Lake, Nepal Himalaya
<p>The Video shows the rapid ice-snow avalanche from the Manaslu glacier into the Birendra lake, flooded with large chuncks of ice that displaced water from the lake producing outburst floods as seen in downstream river. The video is collected and merged from different sources by Nitesh Khadka.</p> <p>The paper linked to this video can be accessed through: https://link.springer.com/article/10.1007/s11069-024-07014-0#article-info</p> <p>Please Cite: Khadka, N., Zheng, G., Chen, X. <em>et al.</em> An ice-snow avalanche triggered small glacial lake outburst flood in Birendra Lake, Nepal Himalaya. <em>Nat Hazards</em> (2024). https://doi.org/10.1007/s11069-024-07014-0</p>
Data from: Thin Al1-xGaxAs0.56Sb0.44 diodes with extremely weak temperature dependence of avalanche breakdown
When using avalanche photodiodes (APDs) in applications, temperature dependence of avalanche breakdown voltage is one of the performance parameters to be considered. Hence, novel materials developed for APDs require dedicated experimental studies. We have carried out such a study on thin Al1–xGaxAs0.56Sb0.44 p–i–n diode wafers (Ga composition from 0 to 0.15), plus measurements of avalanche gain and dark current. Based on data obtained from 77 to 297 K, the alloys Al1−xGaxAs0.56Sb0.44 exhibited weak temperature dependence of avalanche gain and breakdown voltage, with temperature coefficient approximately 0.86–1.08 mV K−1, among the lowest values reported for a number of semiconductor materials. Considering no significant tunnelling current was observed at room temperature at typical operating conditions, the alloys Al1−xGaxAs0.56Sb0.44 (Ga from 0 to 0.15) are suitable for InP substrates-based APDs that require excellent temperature stability without high tunnelling current.
Supplementary videos for "Detrainment and braking of snow avalanches interacting with forests"
<p>We are releasing the videos as the supplementary information for our manuscript “Detrainment and braking of snow avalanches interacting with forests”.</p> <p>Movie 1. Side view of the snow profile for different values of M in Fig. 2.</p> <p>Movie 2. Perspective view of the snow profile for different values of M in Fig. 2.</p> <p>Movie 3. Flow profile for 3 different flow regimes in Fig. 3.</p> <p>Movie 4. Profile of accumulated snow for different flow regimes in Fig. 4.</p> <p>Movie 5. Flow height of the avalanche in a forest with different forest arrangements in Fig. 14.</p>
Avandia™ + Amaryl™ or Avandamet™ Compared With Metformin (AVALANCHE™ Study)
ClinicalTrials.gov study NCT00131664. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Thin Al1-xGaxAs0.56Sb0.44 diodes with extremely weak temperature dependence of avalanche breakdown
Open the record for dataset details and reuse information.
Data from: InGaAs/InAlAs single photon avalanche diode for 1550 nm photons
Open the record for dataset details and reuse information.
Configurations and scripts to reproduce the numerical simulations of "A two-fluid model for immersed granular avalanches with dilatancy effects" article
<p>This directory contains the main data to reproduce the results presented in the article "A two-fluid model for immersed granular avalanches with dilatancy effects" by Eduard Puig Montellà, Julien Chauchat, Bruno Chareyre, Cyrille Bonamy and Tian-Jian Hsu.<br> <br> The numerical results and experimental data extracted from Pailha et ad (2008) can be found inside "NumericalData" and "ExperimentalData" folders respectively. The script "PressureVelocityPlot.py" displays the evolution of the surface particle velocity and the excess of pore pressure with time for cases ranging from loose to dense granular avalanches. </p> <p>The input files needed to reproduce a dense granular avalanche (phi=0.592) in 1D and 2D are found in the following folders: "1D_DenseCase" and "2D_DenseCase". To accelerate the simulation, the files are given after 200 seconds of sedimentation in order to reach an equilibrium state. Please read the corresponding README.txt files to launch a 1D and/or a 2D simulation. Additionally, python scripts in each configuration are provided to evaluate the evolution of the main parameters during the avalanche. </p>
Data from: Subalpine vegetation changes in the Eastern Sudetes (1973–2021): Effects of abandonment, conservation management and avalanches
<p>This dataset contains the original data used in the article:</p> <p>Klinkovská K., Kučerová A., Pustková Š., Rohel J., Slachová K., Sobotka V., Szokala D., Danihelka J., Kočí M., Šmerdová E. & Chytrý M. (2023) Subalpine vegetation changes in the Eastern Sudetes (1973–2021): effects of abandonment, conservation management and avalanches. <em>Applied Vegetation Science</em>, 26, e12711. <a href="https://doi.org/10.1111/avsc.12711">https://doi.org/10.1111/avsc.12711</a></p> <p>The data contain plant species composition from vegetation plots repeatedly surveyed in the Hrubý Jeseník Mountains (Eastern Sudetes, Czech Republic). Vegetation plots surveyed by Leoš Bureš and Zuzana Burešová in 1973–1978 were resurveyed in 2004–2010 by Martin Kočí and Leo Bureš and resurveyed again in 2021 by the authors of this dataset. Several new plots were also surveyed in 2004–2010 and resurveyed in 2021. In the 1970s, plot locations were related to patches of a detailed vegetation map. In the 2000s, plot locations were measured using GPS with an uncertainty of less than 10 m. In 2021, plots were localized using differential GPS with ~5 cm accuracy (Topcon HiPer SR with Getac PS336 Data Collector) in each corner of the plot. These coordinates are stored in the fields Longitude, Longitude2, Longitude3, Longitude4, Latitude, Latitude2, Latitude3 and Latitude4 in the dataset.</p> <p>The dataset contains records of 148 vegetation plots, of which 66 were sampled three times (1970s, 2000s, 2021) and 82 were sampled twice (2000s, 2021), i.e. 362 vegetation-plot records in total. Their locations are shown at <a href="https://arcg.is/0uSrz9">https://arcg.is/0uSrz9</a>.</p> <p>Plot size and shape varied by vegetation type. Repeated sampling always used the same plot size as the previous sampling. Most plots were squares of 100 m<sup>2</sup> in woodlands and 16 m<sup>2</sup> in grasslands or rectangles of 10 m<sup>2</sup> in springs. All species of vascular plants were recorded in each plot, and their cover was estimated using the nine-grade Braun-Blanquet scale (Westhoff & van der Maarel, 1978). Bryophytes and lichens were recorded in only some plots, focusing on the dominant species.</p> <p>In 2021, soil samples were collected from the mineral soil horizon at 5–10 cm depth from four places within each plot. We mixed these samples for each plot and measured the soil pH and electrical conductivity of a mixed sample in a soil-water suspension (weight ratio 1:2.5) using the HQ40D digital multimeter. These data are found in the fields Soil_ph and Conduct.</p> <p>Plots were divided into managed and unmanaged based on the overlay of plot coordinates with GIS layers indicating the areas managed for conservation purposes in the last ten years provided by the Administration of the Landscape Protected Area Jeseníky (field Mown). In addition, plots were divided into affected and unaffected by the 2019 avalanche based on the positions of damaged trees observed in the field (field Avalanch).</p> <p>The structure of header data follows the structure of the ReSurveyEurope Database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>). In addition, the vegetation type of each plot record in the 1970s, 2000s and 2021 is given in the fields Class_2021, Class_2000 and Class_1970, respectively</p> <p>The data on species composition and environmental variables are provided in two formats:</p> <ul> <li>Turboveg 2 database (see <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) – file <strong>TurbovegDbBackup_Jeseniky_resurvey.zip</strong>. To use this dataset in Turboveg, the database dictionary (TurbovegDdBackup_Default dictionary.zip) and species list (TurbovegSlBackup_Czechia_slovakia_2015.zip) have to be installed.</li> <li>Two TXT files with columns separated by tabs: <ul> <li><strong>Jeseniky_resurvey_species.txt</strong> contains the percentage covers of plant species in plots. Plant nomenclature harmonized according to Kaplan et al. (2019). Bryophytes, vernal species <em>Anemone nemorosa </em>and <em>Cardamine pratensis</em>, hybrids and species that could not be accurately identified were excluded. Woody species occurring in different layers were merged within each plot.</li> <li><strong>Jeseniky_resurvey_head.txt</strong> includes information about the number of species in each plot (spe.nr), the number and proportion of threatened species (IUCN categories CR, EN, VU, columns end.nr and perc.end) and unweighted means of Ellenberg-type indicator values for light (Light_IV), temperature (Temp_IV), moisture (Moist_IV), soil reaction (React_IV) and nutrients (Nutr_IV) used to test changes in these variables through time.</li> </ul> </li> </ul> <p>These data are also stored in the Czech National Phytosociological Database (Chytrý & Rafajová 2003; <a href="https://botzool.cz/vegsci/phytosociologicalDb">https://botzool.cz/vegsci/phytosociologicalDb</a>) and the ReSurveyEurope database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.