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65 results for “Avalanches”
Determination of areas with release potential of snow avalanche in Sharr Mountains (Kosovo) - Appendix A
<p>Map of areas with avalanche release potential in Sharr Mountains (Kosovo) - Fuzzy Logic potential determination method</p>
Determination of areas with release potential of snow avalanche in Sharr Mountains (Kosovo) - Appendix B
<p>Map of areas with avalanche release potential in Sharr Mountains (Kosovo) - AHP potential determination method</p>
Glacier Bay National Park glacial rock avalanche inventory (1984-2020)
<p>An inventory of supraglacially deposited rock avalanches that occurred in Glacier Bay National Park, Alaska, between 1984 and 2020.</p> <p>Reference: Smith, W.D., Dunning, S.A., Ross, N., Telling, J., Jensen, E.K., Shugar, D.H., Coe, J.A. and Geertsema, M. (2023) Revising supraglacial rock avalanche magnitudes and frequencies in Glacier Bay National Park, Alaska. <em>Geomorphology</em>, doi: <a href="https://doi.org/10.1016/j.geomorph.2023.108591">https://doi.org/10.1016/j.geomorph.2023.108591</a></p>
The critical avalanche of runaway electrons
<p>This dataset contains simulation results and figures used in Oreshkin. (2018, GRL)<br> “The critical avalanche of runaway electrons”.</p>
Snow cover simulations and avalanche dynamics simulations for the area of Davos, Switzerland (2011-2014)
<p>Alpine3D model simulations of the snow cover at 100m resolution for the area surrounding Davos for the period October 2010 to September 2014.</p> <p>Avalanche dynamics simulations using the RAMMS-Extended model of 169 selected avalanches in the same period, using initial conditions as simulated by the Alpine3D model.</p> <p>This dataset belongs to:</p> <p>Wever, N., Vera Valero, C., and Techel, F. (2018): <em>Coupled snow cover and avalanche dynamics simulations to evaluate wet snow avalanche activity</em>, J. Geophys. Res. Earth Surf., 123, 1772–1796 <a href="https://doi.org/10.1029/2017JF004515">doi: 10.1029/2017JF004515.</a></p>
An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology
<div><strong>Overview</strong>:</div> <div>The dataset contains measurements of Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) collected from Long-Range (LoRa) devices in avalanche Search and Rescue (SAR) scenarios. Data were collected on a plateau located in Col de Mez (Falcade, Italy) at 1870 m in the Italian Dolomites, at two different times of the year: March and April 2024. The depth and conditions of the snow are different: in March, the snow is mostly dry and over one meter deep, while in April, the snow is wetter, with a greater presence of liquid water, and approximately 55 centimeters deep.</div> <div> </div> <div>The dataset includes three test typologies:</div> <div> <ol> <li>Cross test: 1 buried transmitter, at different depths, and 4 receivers on a tripod, positioned at 10 different distances from the burial point along 4 orientations: North, South, East, West. Distances are: 0.6 m, 1.2 m, 1.8 m, 3 m, 5 m, 10 m, 20 m, 30 m, 40 m, 50 m.</li> <li>Maximum Distance test: 1 buried transmitter and 1 receiver, held in hand and moved away from the burial point until the signal is completely lost. The receiver stops periodically, collecting 2 minutes data in specific markers.</li> <li>Drone Flyover test: 1 buried transmitter and 1 receiver mounted on the bottom of a quadcopter professional drone. The drone stands on 121 measurement points, creating a precise grid covering an area of 100 square meters, with the burial location at the center.</li> </ol> </div> <div>All the tests include precise Ground Truth (GT) annotations, indicating the exact positions of the receivers and the burial depth of the transmitter. The dataset is organized in three folders, one for each test: cross, max_dist and drone. In a separate folder, the snow profiles for the two data collection periods, march and april 2024, are also included, according to the AINEVA Model 4.</div> <div> </div> <div>The dataset aims to assess the ability to locate a victim in an avalanche scenario. The collected data allow for the evaluation of the quality of the LoRa signal in various environmental conditions, as well as the snow depth and snowpack profile. By using precise Ground Truth annotations, it is possible to assess the potential performance of a localization system.</div> <div> </div> <div><strong>How to use the dataset</strong>:</div> <div>Please, read the README file detailing the dataset's format and the data collection campaign. In summary, collected data include:</div> <div> </div> <div>1. Cross test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>rx_pos</li> <li>distance</li> <li>depth</li> <li>polarization</li> </ul> </div> <div>2. Maximum Distance test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>depth</li> <li>id_marker</li> <li>longitude</li> <li>latitude</li> </ul> </div> <div>3. Drone Flyover test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>longitude</li> <li>latitude</li> <li>x</li> <li>y</li> <li>depth</li> </ul> </div> <div><strong>How to cite this dataset</strong>:</div> <div>- DOI number of this datsaset: 10.5281/zenodo.12750580</div> <div>- M. Girolami, F. Mavilia, A. Berton, G. Marrocco and G. Maria Bianco, "An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology," in <em>IEEE Access</em>, vol. 12, pp. 171015-171035, 2024, doi: 10.1109/ACCESS.2024.3497654</div>
Supplementary data for "Detrainment and braking of snow avalanches interacting with forests"
<p>We are releasing the data as the supplementary information for our manuscript “Detrainment and braking of snow avalanches interacting with forests”.</p> <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 2 flow regimes: Case 1 and Case 2 with respectively a front velocity of12.5 m/s and 10.6 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)) is used for the theoretical model (Eq. 8)</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. Evolution of the detrainment mass per unit of area with the forest density for different front velocities (regular staggered forest, snow properties: case 2, d = 1 m)</p> <p>Figure 10. Data of Fig. 10. Evolution of the detrainment mass predicted with the model (Eq. 11) and with the observation, r<sup>2</sup>=0.9926</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>
Avalanches catalogue associated to the s1222a Marsquake
<p>This is the catalogue of dust avalanches mapped around the s1222a marsquake.</p> <p>This dataset is associated to the paper : <strong>Discussion on seismically triggered avalanches on Mars after the s1222a Marsquake.</strong></p> <p>which is under review for <em>Geophysical Research Letters.</em></p> <p>Usage of these data requires to cite this paper.</p> <p>The repository contains the avalanche catalogue and the avalanches that have been detected on the post-marskquake images. Both data base are delivered into a pickle file. In addition, the repository contains a Jupyter notebook that read the pickle files and the full resolution of the oblique view of the mapping generated with the Generic Mapping Tools (GMT) available from https://www.generic-mapping-tools.org. </p>
Armed conflict avalanches and Voronoi grids for Africa
<p>This description outlines the dataset associated with the research paper "Discovering the Mesoscale for Chains of Conflict," published in PNAS Nexus (click <a href="https://doi.org/10.1093/pnasnexus/pgad228">here</a> to view the article). Please cite this paper if you use our dataset.</p> <p>The dataset is divided into two main folders:</p> <ol> <li> <p>"voronoi_grids": This folder contains 100 realizations of Voronoi tessellations over the African continent. These tessellations were generated during the research process detailed in the aforementioned paper.</p> </li> <li> <p>"avalanches": The "avalanches" folder comprises conflict avalanches derived from each of the realizations of the Voronoi tessellations. These avalanches were generated using the technique described in the research paper.</p> </li> </ol> <p>Reading avalanches from the pickle files:</p> <p>The avalanches are stored in pickle files, and the following Python code can be used to read them:</p> <pre><code class="language-python">import dill as pickle # Temporal scale. Choose one of the following values: 2, 4, 8, 16, 32, 64, 128, 256, 512 time = 32 # Spatial scale. Choose one of the following values: 20, 28, 40, 57, 80, 113, 160, 226, 320, 453, 640, 905, 1280 dx = 453 # Voronoi grid index. Choose from 0 to 99 gridix = 3 file_path = f"avalanches/battles/gridix_{gridix}/te/te_ava_{str(time)}_{str(dx)}.p" with open(file_path, "rb") as f: ava = pickle.load(f) ava_event = ava["ava_event"]</code></pre> <p> </p> <p>The extracted avalanches are in "event form," represented as a list of lists, where each element corresponds to the index of an individual conflict event in the ACLED dataset. Each sublist represents an avalanche. The total number of avalanches at the given temporal and spatial scale can be determined by the length of <code>ava_event</code> (<code>len(ava_event)</code>).</p> <p>ACLED Dataset:</p> <p>Due to copyright issues, we are providing a filtered version of the ACLED dataset that we used in our study. To obtain the full version of the ACLED dataset containing all the information regarding each conflict event, please refer to the final bullet point of the manual installation instructions of our python pacakge <a href="http://github.com/eltrompetero/armed_conflict_avalanche/tree/PNAS_Nexus_2023">arcolanche</a>.</p>
Data from: The retard-boost effect of fragmentation in rock avalanches
Open the record for dataset details and reuse information.
Frying Pan avalanche data
Open the record for dataset details and reuse information.
Long-term individual-based records of mountain goat mortality and terrain use in relation to avalanches in coastal Alaska during 2005-2022
Open the record for dataset details and reuse information.
Experiments on granular flow behavior and deposit characteristics: implications for rock avalanche kinematics
<p>Seven excel files which includes the data supporting Figure 5, Figure 6, Figure 7, Figure 8, Figure 10, Figure 12 and Figure 14 is uploaded saparately.</p> <p>Data Set S1. Data supporting the relationship between flow height and time in Figure 5.</p> <p>Data Set S2. Data supporting normalized velocity profiles and normalized shear rate profiles in Figures 6(a)-6(d).</p> <p>Data Set S3(a). Data supporting the relationships between mean grain size and global shear rate in Figure 7(a).</p> <p>Data Set S3(b). Data supporting the relationships between mean grain size and Savage number in Figure 7(a).</p> <p>Data Set S4(a). Data supporting the relationships between depth averaged velocity and time in Figure 8(a).</p> <p>Data Set S4(b). Data supporting the relationships between depth averaged velocity and time in Figure 8(b).</p> <p>Data Set S4(c). Data supporting the relationships between mean grain size and equivalent friction coefficient in Figure 8(c).</p> <p>Data Set S5(a). Data supporting Figures 10(a) and 10(b).</p> <p>Data Set S5(b). Data supporting the relationships between relative flow height and λ in Figure 10(c).</p> <p>Data Set S5(c). Data supporting the relationships between relative flow height and λ in Figure 10(d).</p> <p>Data Set S6(a). Data supporting the velocity profiles in Figure 12(a).</p> <p>Data Set S6(b). Data supporting the relationships between global shear rate and equivalent friction coefficient in Figure 12(b).</p> <p>Data Set S7. Data supporting the relationships between normalized flow height and Savage number in Figure 14.</p>
Supplementary videos for "The mechanical origin of snow avalanche dynamics and flow regime transitions"
<p>We are releasing the videos as the supplementary information for our submitted manuscript to The Cryosphere. </p> <p>Movie 1. Velocity of the flows in the four typical flow regimes in Fig. 2(a). Frame rate is 24 FPS.</p> <p>Movie 2. Volumetric plastic strain of the flows in the four typical flow regimes in Fig. 2(a). Frame rate is 24 FPS.</p> <p>Movie 3. Velocity of the flow in transition from cold dense to warm shear regimes in Fig. 2(b). Frame rate is 24 FPS.</p> <p>Movie 4. Volumetric plastic strain of the flow in transition from cold dense to warm shear regimes in Fig. 2(b). Frame rate is 24 FPS.</p>
The datasets for the paper "The long-lived and recent seismicity at the lunar Orientale basin: Evidence from morphology and formation ages of boulder avalanches, tectonics and seismic ground motion" JGR: Planets (e2020JE006553)
<p>The datasets contain supporting files for the paper:</p> <p>Mohanty, R., Kumar, P.S., Raghukanth, S.T.G., & Lakshmi, K.J.P., (2020). The long-lived and recent seismicity at the lunar Orientale basin: Evidence from morphology and formation ages of boulder avalanches, tectonics and seismic ground motion, JGR: Planets, e2020JE006553.</p>
Supplementary data for "Transient wave activity in snow avalanches is controlled by entrainment and topography"
<p>We are releasing the data of Figs. 2,4&5 in our manuscript submitted to Communications Earth & Environment.</p>
Supplementary data to: Large rock and ice avalanches frequently produce cascading processes in High Mountain Asia
<p>This data, focusing on large rock and/or ice avalanche events with severe consequences in High Mountain Asia (HMA), <span>provide a valuable first step toward improved understanding of the frequency, scope, and societal impact of such hazards across HMA</span> (linked to a journal article: Large rock and ice avalanches frequently produce cascading processes in High Mountain Asia, published in Geomorphology in 2024).</p>
Supplementary data for: "Transition from sub-Rayleigh anticrack to supershear crack propagation in snow avalanches"
<p>This Folder contains supplementary data for the paper "Transition from sub-Rayleigh anticrack to supershear crack propagation in snow avalanches". Please see ReadMe.txt for more details.</p> <p> </p>
Avalanches and Sample Recording for 'Existence of multiple transitions of the critical state due to anesthetics'
<p>This repository contains data for the manuscript titled 'Existence of multiple transitions of the critical state due to anesthetics'.</p> <p>Additional code related to this manuscript can be found in the following GitHub repository: DOI: 10.5281/zenodo.12690413</p> <p>File description:</p> <p>'SampleRecording_128x128_movementFramesRemoved.mat' - sample quietwakefulness calcium recording. Recording is downsampled from 256x256 to 128x128 and has been pre-alligned to the Allen atlas. Frames of excessive movement have also been removed. Avalanches can be generated using this file as-is using the code 'generateAvalanches.m' from the github repository. </p> <p>'avalancheStatistics_thresh=1.mat' - all avalanche sizes and durations obtained with a threshold of 1 standarddeviation from all recordings. Loads in matlab as a cell with the first cell containing avalanche sizes seperated on a per-recording basis. Second cell is same but for avalanche durations. Third cell has the condition index for each recording (e.g., 1,2,3...), and fourth cell has the corresponding name of the condition (e.g., 'baseline' corresponds to 1, 'iso. 1%' to 2. etc.). Codes in the GitHub repository can be used to perform avalanche analysis.</p>
The mechanisms behind the extreme susceptibility of photon avalanche emission to quenching
<p>Abstract</p> <p>The photon avalanche (PA) process that emerges in lanthanide-doped crystals yields a threshold and highly nonlinear (of the power law order >5) optical response to photoexcitation. PA emission is the outcome of the excited-state absorption combined with a cross-relaxation process, which creates positive and efficient energy looping. In consequence, this combination of processes should be highly susceptible to small perturbations in energy distribution and can thus be hindered by other competitive “parasitic” processes such as energy transfer (ET) to quenching sites. Although luminescence quenching is a well-known phenomenon, exact mechanisms of the susceptibility of PA to resonant energy transfer (RET) remain poorly understood, limiting its practical applications. A deeper understanding of these mechanisms may pave the way to new areas of PA exploitation. This study focuses on the investigation of the LiYF<sub>4</sub>:3%Tm<sup>3+</sup> PA system co-doped with Nd<sup>3+</sup> acceptor ions, which are found to impact both the looping and emitting levels. This effectively disrupts the PA emission, causing an increase in the PA threshold (<em>I</em><sub>th</sub>) and a decrease in the PA nonlinearity (<em>S</em><sub>max</sub>). Our complementary modelling results reveal that ET from the looping level increases <em>I</em><sub>th</sub> and <em>S</em><sub>max</sub>, whereas ET from the emitting level diminishes <em>S</em><sub>max</sub> and the final emission intensity. Ultimately, significant PA emission quenching demonstrates a high relative sensitivity (<em>S</em><sub>R</sub>) to infinitesimal amounts of Nd<sup>3+</sup> acceptors, highlighting the potential for PA to be utilized as an ultra-sensitive, fluorescence-based reporting mechanism that is suitable for the detection and quantification of physical and biological phenomena or reactions.</p>
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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
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.