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65 results for “Avalanches”
GEODAR data of snow avalanches at Vallée de la Sionne: Seasons 2010/11, 2011/12, 2012/13 & 2014/15 [Data set]
<p>This data repository contains radar data from 77 snow avalanches recorded using the GEODAR (GEOphysical flow dynamics using pulsed Doppler radAR) system at the Swiss full-scale avalanche testsite Vallée de la Sionne. GEODAR is a purpose built, advanced phased-array FMCW system.</p> <p>The data contain range-time plots of intensities gained from moving target identification (MTI) processing (-MTI.h5), an PDF preview image (-MTI.pdf), the trajectory of the front in range and time (-TRAJ-001.h5), the corresponding Thalweg as steepest descent from release area (-Thal-001.h5) and a processing info file in Matlab format (-info.mat).</p> <p>This document covers details about the different versions of the radar setup and raw data processing steps as well as a description of the repository content (see file geodar_repository.pdf).</p> <p> </p>
Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes
<p><strong>Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes</strong><br>The associated publication can be found on https://iopscience.iop.org/article/10.1088/1361-6560/ad3326.<br>All graphs inside the publication can be recreated with this dataset. Similar to the publication, the data for the timewalk and offset correction are only given for one sensor and one channel as they only serve a representative purpose. The raw data for all other channels can be shared upon request. Furthermore, as in the publication, the data for the water-equivalent-thickness (WET) calibration and proton radiography (pRAD) creation are given by the median and the interquartile range of the measured quantities of the individual graphs. Those data are also calibrated. If required, the raw, unprocessed data of each measurement can be shared upon request.<br><br>In the following, a description of the individual files and corresponding figures in the publication is given. If not specified otherwise, the physical units are given in brackets next to the name of the corresponding physical quantity (usually first line in file):<br><br></p> <ul> <li><em><strong>Figure 6:</strong></em> <ul> <li> RawToTspectrumrescaledLGAD3.txt: <ul> <li>Describes the re-scaled time-over-threshold (ToT) spectrum measured inside the third LGAD of the time-of-flight-based ion computed tomography (TOF-iCT) demonstrator using 800 MeV protons (Figure 6a). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence (counts[#]).</li> </ul> </li> <li>ToTspectrumrescaledLocMaxLGAD3.txt <ul> <li>Describes the re-scaled ToT spectrum measured inside the third LGAD of the TOF-iCT demonstrator using only the local ToT maxima inside each 4D-cluster. The spectrum was obtained using 800 MeV protons (Figure 6b). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence (counts[#]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 7:</strong></em> <ul> <li>offsetpraecalib.txt: <ul> <li>Describes the raw, uncalibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7a). The first column represents the detector channel nr in LGAD3, the second column the raw, uncalibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li>offsetpraecalib.txt: <ul> <li>Describes the time walk and offset-calibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7b). The first column represents the detector channel nr in LGAD3, the second column the calibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li> praetwdata.txt: <ul> <li>Describes the ToT dependence of the measured time difference between LGAD1 and LGAD2 using the raw ToT of channel 31 in LGAD1 (figure 7c). The first column represents the raw, unscaled and uncalibrated ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column, the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> <li>posttwdata.txt <ul> <li>Describes the time walk-calibrated ToT vs TDiff spectrum using the measured time difference between LGAD1 and LGAD2 and the ToT of channel 31 in LGAD1 (figure 7d). The first column represents the ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> </ul> </li> <li><em><strong>Figure 8:</strong></em> <ul> <li>tofinaridata.txt: <ul> <li>Describes the measured TOF in air through the scanner w.r.t the TOF measured at 800MeV, i.e. the median TOF value at 800MeV was subtracted from all data points (Figure 8a). The first column describes the beam energy (beamenergy[MeV]), the second column the first quartile of the measured TOF per pixel (TOFperpixelQ1[ps]), the second column the median TOF per pixel (TOFperpixelQ2[ps]) and the last column the third quartile of the measured TOF per pixel (TOFperpixelQ3[ps]).</li> </ul> </li> <li>tofinairtheodata.txt: <ul> <li>Describes the theoretical TOF in air through the scanner w.r.t the theoretical TOF at 800MeV, i.e. the theoretical TOF value at 800MeV was subtracted from all data points (Figure 8a).</li> </ul> </li> <li>intrinsictimeresolution.txt: <ul> <li>Describes the energy dependence of the intrinsic time resolution per channel measured inside LGAD1 (figure 8b). The first column represents the primary beam energy (beamenergy[MeV), the second column the corresponding energy loss in MIPs (relativeenergylossi[MIP]), the third column the first quartile of the intrinsic time resolution per LGAD channel (timeresperpixelQ1[ps]), the fourth column the median of the intrinsic time resolution per LGAD channel and the last column the third quartile of the intrinsic time resolution per LGAD channel (timeresperpixelmedian[ps],timeresperpixelQ3[ps]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 9:</strong></em> <ul> <li>wetcalib.txt <ul> <li>Describes the measured TOF increase per pixel w.r.t to the TOF in air (i.e. without a phantom) for a given WET and primary beam energy. The first column represents the WET of the irradiated sample (WET[mm]), the second column the used beam energy (beamenergy[MeV]), the third column the first quartile of the measured TOF distribution (TOFperpixelQ1[ps]), the fourth column the median (TOFperpixelQ2[ps]) and the sixth column the third quartile (TOFperpixelQ3[ps]).</li> <li>For each energy, a fifth-order polynomial was used to fit the WET and the TOF increase (Delta TOF(E)~sum_i a_i*(WET_i )^i, with i in [0,5] ). The fit parameters are given in the following for each beam energy:<br> <ul> <li>83 MeV: a_i=[-4.70496227e-02,4.64323118e-01, -2.71391535e-02,4.23655842e-03, -1.13034255e-04,1.23725678e-06]</li> <li>100.4 MeV: a_i=[-3.28976022e-02,-3.68818468e-02,1.96339858e-02,7.31585040e-04, -4.38697681e-05 ,7.52163384e-07]</li> </ul> </li> </ul> </li> </ul> </li> <li><em><strong>Figure 10:</strong></em> <ul> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 83 MeV (Figure 10a). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 100.4 MeV (Figure 10b). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 11:</strong></em> <ul> <li>wetdistrdata83MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 83 MeV protons (Figure 11a). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> <li>wetdistrdata100MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 100.4 MeV protons (Figure 11b). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> </ul> </li> </ul>
The intermittency regions of powder snow avalanches [Data-set]
<p>This data repository contains the data-sets presented in the publication:</p> <p>Sovilla, B., McElwaine, J. N., & Köhler, A. (2018). The intermittency regions of powder snow avalanches. Journal of Geophysical Research: Earth Surface, 123, <a href="https://doi.org/10.1029/2018JF004678">https://doi.org/10.1029/2018JF004678</a>.</p> <p>This data set should be cited, together with the publication, as a:</p> <p>B. Sovilla, J. N. McElwaine, and A. Köhler (2018), The intermittency regions of powder snow avalanches [Data set]. Zenodo. <a href="https://doi.org/10.5281/">https://doi.org/10.5281/</a> zenodo.1415456.</p> <p>Information on the data can be found in the Readme file or can be obtained by writing an e-mail at: avalanche.data@slf.ch.</p>
Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects
<p>The attached folder contains the dataset for the manuscript entitled "Magnon Modes of Microstates and Microwave-Induced Avalanche in Kagome Artificial Spin Ice with Topological Defects".</p>
All-Optical Data Processing with Photon-Avalanching Nanocrystalline Photonic Synapse
<h2>Abstract</h2><p>Data processing and storage in electronic devices are typically performed as a sequence of elementary binary operations. Alternative approaches, such as neuromorphic or reservoir computing, are rapidly gaining interest where data processing is relatively slow, but can be performed in a more comprehensive way or massively in parallel, like in neuronal circuits. Here, time-domain all-optical information processing capabilities of photon-avalanching (PA) nanoparticles at room temperature are discovered. Demonstrated functionality resembles properties found in neuronal synapses, such as: paired-pulse facilitation and short-term internal memory, in situ plasticity, multiple inputs processing, and all-or-nothing threshold response. The PA-memory-like behavior shows capability of machine-learning-algorithm-free feature extraction and further recognition of 2D patterns with simple 2 input artificial neural network. Additionally, high nonlinearity of luminescence intensity in response to photoexcitation mimics and enhances spike-timing-dependent plasticity that is coherent in nature with the way a sound source is localized in animal neuronal circuits. Not only are yet unexplored fundamental properties of photon-avalanche luminescence kinetics studied, but this approach, combined with recent achievements in photonics, light confinement and guiding, promises all-optical data processing, control, adaptive responsivity, and storage on photonic chips.</p>
Data for 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'
<div>This dataset contains avalanche deposit outlines (as shapefiles) derived for the study 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'</div> <div> </div> <div>They were outlined at three different sites (Mt Blanc, Everest and Hispar regions) for the periods 11/2016-10/2021 (Mt Blanc) and 11/2017-10/2022 (Everest and Hispar). The time period is indicated in the file name.</div> <div> </div> <div>For each dataset we give the raw outlines (Automated_outlines_dates), the manually updated (Automated_outlines_dates_ManualUpd) and the manually updated after accounting for surface elevation change (Automated_outlines_dates_ManualUpd_shifted). </div> <div> </div> <div>In order to know which scenes were used for the mapping (if no avalanche was detected, we did not provide a shapefile, but this doesn't been that there is a gap in the Sentinel-1 time series), we provide a Sentinel1_date file that shows all the Sentinel-1 RGB pairs that we used to detect the avalanches.</div> <div> </div> <div>We also provide as geotiffs the temporally aggregated outlines (Automated_outlines_dates_ManualUpd_shifted_aggregated; over one specific year yn - from 01/11/yn-1 to 01/11/yn - or the full study period):</div> <div>- as heatmaps (where the value of each pixel corresponds to the number of avalanches that occured) </div> <div>- as binary maps of deposits (where 1 is when an avalanche occured over the time period and 0 is where none were detected).</div> <div> </div> <div> </div> <div>Finally we provide a csv file for each region with metrics per glacier:</div> <div> </div> <div>RGI ID</div> <div>Glacier size (in m^2)</div> <div>Catchment size (in m^2)</div> <div>Area of slopes steeper than 30° (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the ascending obits (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the descending obits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the ascending orbits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the descending orbits (in m^2)</div> <div>The area of the glacier visible in the ascending orbits (in m^2)</div> <div>The area of the glacier visible in the descending orbits (in m^2)</div> <div> </div> <div> </div> <div>The main Google Earth Engine and Matlab scripts used to pre-process the Sentinel-1 GRD images and to map the avalanches are available on GitHub: https://github.com/MarinKneib/S1_avalanches</div> <div> </div>
Determination of areas with release potential of snow avalanche in Sharr Mountains in the Republic of Kosovo
<p>Avalanches represent a very high risk in residential areas, road infrastructure, environment, and economy, and can have fatal consequences if the human factors do not take any action. Advances in geospatial technology and access to spatial data have enabled spatial analysis to assist in decision-making regarding spatial planning in avalanche-prone locations. Determining locations with snow avalanche discharge potential is a crucial step in the avalanche zoning process.</p> <p>This research deals with areas with snow avalanche potential disjunction, based mainly on topographic factors followed by meteorological ones. Topographic factors were mainly determined according to morphometric techniques, which are achieved through geographic information systems (GIS), as well as meteorological ones from statistical data and various processing of spatial and non-spatial data. Spatial analysis are also supported by geostatistical methods Fuzzy Logic and AHP, which in interaction with GIS have enabled the achievement of the purpose of this paper. The results from the spatial analysis have been verified based on comparison methods, such as the ROC method which was used during this final phase, in which the analysis has shown that the methods used in this research have given satisfactory results. As the main result, we obtained maps of areas with snow avalanche potential discharge in the study area relating to two geostatistical methods.</p>
Experimental data related to publication: "Dislocation Avalanches: Earthquakes on the MicronScale"
<p>Each .tar file corresponds to a single micropillar compression experiment. The nomenclature of the uploaded dia_<strong>{dia}</strong>_v_<strong>{v}</strong>_p_<strong>{p}</strong>.tar files is the following:</p> <ul> <li>{dia}: The diameter of the micropillar in µm</li> <li>{v}: The compression platen velocity in µm / s</li> <li>{p}: Unique identifier of the micropillar for a given diameter and compression platen velocity</li> </ul> <p>The tar files have the following content:</p> <ul> <li>A tar.gz file which contains the raw Acoustic Emission (AE) data in a compressed file format. Uncompressing it results in one or multiple wav files. In case of multiple ones the covered time range is included at the end of the file name like: 300to800 means the time interval from the compression experiment between 300 s and 800 s. The units of the AE measurement data is 0.1 mV and the sampling rate is 25 MHz.</li> <li>The raw data from the nanoindenter (time, force, displacement, etc.) is located in the file with .dat extension.</li> </ul>
Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation - source data
<p>Full data files for the paper:</p> <p>Duvillier, C., Eckert, N., Evin, G., and Deschâtres, M.: Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation, Nat. Hazards Earth Syst. Sci., 23, 1383–1408, https://doi.org/10.5194/nhess-23-1383-2023, 2023.</p> <p>Can be used to reproduce all the results of the paper and for further benchmarking of snow avalanche potential release area detection methods.</p>
Supplementary Movies and Dataset for Debris Flows, Debris Avalanches and Rock Avalanches Impacting A Flexible Ring Net Barrier.
<p>The supplementary movies S1, S2, and S3 (presented in Figures 1, S4, and S6) show typical debris flow, debris avalanche, and rock avalanche impacting a flexible ring net barrier with = 6 m/s, respectively.</p> <p>The experimental data used for comparison in Figure 2b from the large-scale flume test V6-B1 with a flexible ring net barrier was published open access in the below article (Vicari <em>et al.,</em> 2021).</p> <p>Vicari, H., Ng, C. W., Nordal, S., Thakur, V., De Silva, W. R. K., Liu, H., & Choi, C. E. (2021). The Effects of Upstream Flexible Barrier on the Debris Flow Entrainment and Impact Dynamics on a Terminal Barrier. <em>Canadian Geotechnical Journal</em>, <em>59</em>(6), 1007-1019. <a href="https://doi.org/10.1139/cgj-2021-0119">https://doi.org/10.1139/cgj-2021-0119</a></p>
What weather variables are important for wet and slab avalanches under a changing climate in low altitude mountain range in Czechia?
<p>datasets and scripts for Avalanche paper figures and<br> avalanche path characteristics: Avalanche_paths_souckova.xlsx<br> </p>
Vallée de la Sionne Snow Avalanche n. 20213009: GEODAR radar, Doppler radar and infrasound data
<p>This repository hosts infrasound, GEODAR radar, and Doppler radar data collected within a large powder snow avalanche (No. 20213009) that occurred naturally at the Vallée de la Sionne test site in Switzerland.</p> <p>These datasets complement and are described in the following publication:</p> <p>B. Sovilla, E. Marchetti, M. Kyburz, A. Köhler, P. Huguenin, I. Calic, M.J. Kohler, E. Surinach, and C. Pérez-Guillén, under review. "The dominant source mechanism of infrasound generation in powder snow avalanches," submitted to Geophysical Research Letters.</p>
Vallée de la Sionne Snow Avalanche n. 20213009: High-speed camera recording and derived variables
<p>This repository hosts data obtained from high-speed camera measurements conducted within a large powder snow avalanche (No. 20213009) that occurred naturally at the Vallée de la Sionne test site in Switzerland. Positioned 14 meters above the ground on a vertical pylon, the high-speed camera captures visualizations of snow particles within the aerial layers. These images reveal diverse particle clusters, identifiable as bright spots due to their higher light reflectance compared to the surrounding air-snow crystal mixture.</p> <p>Contained within this repository is an overview video recording along with corresponding data on the average brightness of each image captured by the high-speed camera. This dataset facilitates the reconstruction of the temporal evolution and frequency of particle clustering, with brightness intensity acting as a proxy for mass transport. The average brightness for each image is computed from the averaging of values from 2048 x 2048 pixels (greyscale 0 to 255). These datasets complement the findings presented in the following publication:</p> <p>B. Sovilla, E. Marchetti, M. Kyburz, A. Koehler, P. Huguenin, I. Calic, M.J. Kohler, E. Surinach, and C. Pérez-Guillén, under review. "The dominant source mechanism of infrasound generation in powder snow avalanches," submitted to Geophysical Research Letters.</p>
Data set used in the Article "Evaluation of Monte Carlo tools for high-energy atmospheric physics II: relativistic runaway electron avalanches"
<p>Data used for the Relativistic Runaway Electron Avalanches (RREA) simulations of the Article : "Evaluation of Monte Carlo tools for high energy atmospheric physics II: relativistic runaway electron avalanches" by D. Sarria et al.</p> <p>Includes two set of results : The Probability of Generating RREAs, and the Characterizations of RREAs.</p> <p>Link to the article : <a href="https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html">https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html</a></p> <p>DOI of the article: 10.5194/gmd-2018-119</p>
Text-fig. 3. Textures of main volcaniclastic deposits exposed in abandoned Ludvíkovice quarry. a: radial cracks surrounding some boulders (see arrows) in hot lahar deposit. b: jig-saw fit of fractures (see arrows) within a mega-block of debris-avalanche deposit. c: pseudo-fiamme texture of compacted argillized pumice-fall deposit. d: trachybasaltic lapilli-stone of phreato-magmatic eruption. e: palaeo-relief developed and buried within the pyroclastic unit. f: diagonal bedding in fluvial volcanigenic sandstones. g: diluted and fine-grained lahars embedded in volcanigenic sandstones. in A New Oligocene Flora From Ludvíkovice Near Děčín (České Středohoří Mts., The Czech Republic)
Text-fig. 3. Textures of main volcaniclastic deposits exposed in abandoned Ludvíkovice quarry. a: radial cracks surrounding some boulders (see arrows) in hot lahar deposit. b: jig-saw fit of fractures (see arrows) within a mega-block of debris-avalanche deposit. c: pseudo-fiamme texture of compacted argillized pumice-fall deposit. d: trachybasaltic lapilli-stone of phreato-magmatic eruption. e: palaeo-relief developed and buried within the pyroclastic unit. f: diagonal bedding in fluvial volcanigenic sandstones. g: diluted and fine-grained lahars embedded in volcanigenic sandstones.
Upslope migration of snow avalanches in a warming climate: data and model source files
<p>Complete data and model source files corresponding to:</p> <p>Giacona, F., Eckert, N., Corona, C., Mainieri, R., Morin, S., Stoffel, M., Martin, B., Naaim, M. (2021). Upslope migration of snow avalanches in a warming climate. Proceedings of the National Academy of Sciences America, Nov 2021, 118 (44) e2107306118; DOI: 10.1073/pnas.2107306118</p>
Sensitized photon avalanche nanothermometry in Pr3+ and Yb3+ co-doped NaYF4 colloidal nanoparticles
<p>ABSTRACT</p> <p>Photon avalanche (PA) is a highly nonlinear luminescence phenomenon that occurs in lanthanide doped materials. PA exhibits a very steep power law relationship between luminescence intensity and the optical pump power. Due to the mechanism of PA emission, even weak perturbations to the energy looping and energy distribution within excited levels of lanthanide emitters are expected to significantly modify luminescent properties. Therefore, in this work, we experimentally study the impact of temperature (from – 175 to 175 °C, with 25 °C steps) on the sensitized PA emission in NaYF<sub>4</sub> nanoparticles co-doped with 15% of Yb<sup>3+</sup> and 0.5% of Pr<sup>3+</sup> ions under 852 nm pumping wavelength. Significant variations of the PA nonlinearity (<em>S</em> =&thinsp;4.5–9), PA gain (from 50 up to 175), and PA threshold (from 100 up to 700 kW/cm<sup>2</sup>) were observed under temperature rise from – 175 to 175 °C, respectively. The relative temperature sensitivities based on luminescence intensity changes were larger than 1.5% °C<sup>–1</sup> in the whole temperature range, reaching the maximal value of 7.5% °C<sup>–1</sup> at 0 °C. Moreover, a new thermometric parameter was proposed, namely, the PA pump power threshold, which exhibited over 0.5% °C<sup>–1</sup> relative sensitivities in the same wide temperature range. Owing to PA properties, the temperature sensitivity range and the corresponding relative sensitivities may be intentionally tuned by selecting the appropriate pump intensity in respect to the power dependence relationship. These studies not only provide a better understanding of fundamental processes and susceptibility of the sensitized photon avalanche emission to temperature variation, but also show the possibility of using PA materials as sensitive (nano)thermometers.</p>
Artifacts supplementing the MEMICS 2016 paper "Avalanche effect in improperly initialized CAESAR candidates"
<p><strong>Raw experiment data</strong></p> <ul> <li>Paper: Avalanche effect in improperly initialized CAESAR candidates</li> <li>Auhors: Martin Ukrop and Petr Švenda</li> <li>Conference: Doctoral Workshop on Mathematical and Engineering Methods in Computer Science (MEMICS) 2016</li> <li>Paper details website: http://crcs.cz/wiki/public/papers/memics2016</li> </ul> <p><strong>Files and folders</strong></p> <ul> <li>data-statistical-batteries.zip -- numerical results from NIST STS, Dieharder and TestU01</li> <li>reference-eacirc.7z -- EACirc outputs from reference experiments (both streams random)</li> <li>pmn-zero-eacirc.7z -- EACirc outputs in scenario with zero-initialized public message numbers - see one example run results in folder pmn-zero_2015-12-12_EACcuda_CAESAR_a001_00001</li> <li>pmn-counter-eacirc.7z -- EACirc outputs in scenario with couter-initialized public message numbers - see one example run results in folder pmn-counter_2015-12-12_EACcuda_CAESAR_a001_00001</li> <li>pmn-random-eacirc.7z -- EACirc outputs in scenario with random-initialized public message numbers - see one example run results in folder pmn-random_2015-12-12_EACcuda_CAESAR_a001_00001</li> </ul> <p><strong>File formats</strong></p> <p>NIST STS, Dieharder and TestU01 provide standard output files with resulting p-values of individual tests. For interpretation, see the documentation of the test suites.</p> <p>EACirc's output files adhere to syntax described in the developer wiki at https://github.com/crocs-muni/eacirc Note, however, that the tool evolves and the syntax might have changed. The format tries to be as self-explanatory as possible, but if in doubt, feel free to email the developers.</p>
Frying Pan avalanche data
<p>The data here are large wood data collected in the Summer of 2022 in the Frying Pan River Basin in the Sawatch Mountains of Central Colorado. There are six datasets included or referenced here. The first is general geomorphic and watershed characteristics of the stream reaches surveyed. The second is data from field reports to the CAIC. The third is topographic data for the studied avalanche pathways. The fourth is summary data of the wood volumes within each surveyed reach. The fifth is the unprocessed raw data for all wood jams and individual pieces surveyed. The sixth is a table of literature-derived annual recruitment rates for mechanisms common to mountain streams. Data may also be accessed via the Dryad data repository as linked in the data accessibility statement.</p> <p>Raw data relate several wood jam and individual piece properties, including length, width, and depth of the former, and length and diameter of the latter. Data also indicate several other wood characteristics, such as piece orientation, stability and decay class, and the presence of a rootwad. Finally, data include information about the geomorphic impact of each surveyed piece and jam. Data were collected to examine research questions related to in-stream wood load volumes supplied by snow avalanches and the resultant geomorphic impacts. </p>
Long-term individual-based records of mountain goat mortality and terrain use in relation to avalanches in coastal Alaska during 2005-2022
<p>Snow is a major, climate-sensitive feature of the Earth's surface and a catalyst of fundamentally important ecosystem processes. Understanding how snow influences sentinel species in rapidly changing mountain ecosystems is particularly critical. Whereas the effects of snow on food availability, energy expenditure, and predation are well documented, we report how avalanches exert major impacts on an ecologically significant mountain ungulate - the coastal Alaskan mountain goat (<em>Oreamnos americanus</em>). Using long-term GPS data and field observations across four populations (421 individuals over 17 years), we show that avalanches caused 23-65% of all mortality, depending on area. Deaths varied seasonally and were directly linked to spatial movement patterns and avalanche terrain use. Population-level avalanche mortality, 61% of which comprised reproductively important prime-aged individuals, averaged 8% annually and exceeded 22% when avalanche conditions were severe. Our findings reveal a widespread but previously undescribed pathway by which snow can elicit major population-level impacts and shape demographic characteristics of slow-growing populations of mountain-adapted animals.</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.