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765 results for “Alps”
Interactive map of distribution of gene fragments indicative of cyanotoxin biosynthesis and cyanotoxins in the European Alps
<p><span>Distribution of cyanotoxins and cyanotoxin biosynthesis genes in Alpine region determined by LC-MS/MS and (q)PCR. Cyanotoxins and cyanotoxin genes are mapped on separate layers, and two basemaps are available (simple and relief). Results can be filtered by location, sample type, water body type, cyanotoxins and cyanotoxin genes. Note that cyanotoxin analyses were not performed on all sampling points.</span></p>
Supplementary material for "Demographic assessment of reintroduced bearded vultures in the Alps: success in the core, challenges in the periphery"
<p><strong>Abstract</strong></p> <ol> <li> Regular assessment of reintroduced populations is essential to guide management and provide lessons for other reintroduction projects. Bearded vulture <em>Gypaetus barbatus</em> reintroduction in the Alps began in 1986 with the release of the first fledglings, the first successful reproduction was recorded in 1997, and the population has grown steadily since. A previous assessment suggested that no further releases would be required to establish a self-sustaining population from a demographic point of view. However, this conclusion was based on a small sample size and spatially homogeneous demographic rates of released individuals, which may differ from and be spatially variable among wild-hatched individuals.</li> <li>Using longitudinal data and breeding site survey data, we constructed an integrated population model to examine the demography of the entire Alpine population, spatially stratified into a core and a periphery. We performed retrospective population analyses to identify demographic reasons of spatial differences in population growth, and conducted population viability analyses to assess the impact of future threats and reintroduction options.</li> <li>In 2021, an estimated 173 (CRI: 149-199) females were present in the Alps, of which 65 (CRI: 63-67) were breeders. Adult survival and productivity were higher in the core than in the periphery, so the population grew more strongly in the core than in the periphery. Differences in adult survival contributed most to the differences in population growth between the two areas.</li> <li>The population viability analysis predicts that the Alpine population will double in 10 years but that an increase in the mortality hazard above 0.055 will lead to a population decline. Unlike the population in the core, the population in the periphery is dependent on further releases at this stage.</li> <li>Bearded vulture reintroductions in the Alps have succeeded in creating a self-sustaining population with higher reproductive success and similar survival probabilities to the autochthonous Pyrenean population. In general, management should focus on preventing further mortality risks. In the periphery, reducing current mortality and increasing reproductive success are essential to make the population independent of releases.</li> </ol> <p> </p> <p><strong>Readme</strong></p> <p>Data files and code for all analyses and figures presented in the paper. The two data files are provided in csv format (SightingData.csv, OccupancyData.csv). There are five code files written for R, but some the main analysis requires software JAGS. The main code (IPM_Code.txt) contains a description of the data, code for loading and managing the data, and for fitting the integrated population model. The other files contain custom written functions (Functions.txt), code for the density dependence test (DensityDependence_Code.txt), code for the retrospective analyses (tLTRE_Code.txt) and code for miscellaneous statistics and figure generation (Figure_Code.txt).</p>
Supplementary Material to "Partial melting of amphibole–clinozoisite eclogite at the pressure maximum (eclogite type locality, Eastern Alps, Austria)"
<p><span>Here we briefly describe the supplementary materials for the publication “Partial melting of amphibole–clinozoisite eclogite at the pressure maximum (eclogite type locality, Eastern Alps, Austria)” in the European Journal of Mineralogy, 35(5), 715-735 Schorn, S., Rogowitz, A., & Hauzenberger, C. A. (2023).</span></p>
Data supporting 'Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model'
<p>The dataset supporting our publication '<strong>Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model</strong>' in <em>Geophysical Research Letters.</em></p> <p>The main .zip archive contains a set of NetCDF files detailing:</p> <ul> <li>Initial optimised glacier states (geology-optimized...)</li> <li>Simulation results (Prog20...)</li> </ul> <p>Initial states and results are given by cluster (see Figure 1 in the paper), as shown in all filenames (C1 through to C12). Prognostic simulation filenames additionally distinguish between runs between 1999 and 2019 (Prog2020) and between 2020 and 2050 (Prog2050). 'NV'/'NoVel' and 'NT'/'NoThk' refer to simulations using the partial optimisation (optimisation without including velocity/thickness observations) as detailed in the paper. 'AV' at the end of the filename denotes the integrated area/volume results file, as opposed to the 2D raster results file. A 'V' before the cluster designation shows that the simulation used the variable SMB as opposed to the fixed SMB (see the paper for details). 'ID' before the cluster designation shows that the simulation was using extrapolated SMB based on the trend in SMB since 2000, instead of assuming the continuation of the current SMB. 'ID' on its own denotes linear extrapolation and 'IDQ' denotes quadratic extrapolation (not used in the published paper). 'SMBF' in the filename shows that the simulation used the SMB-elevation feedback.</p> <p>The additional .zip archive contains the code of IGM v1.0 used to produce the model results. For details on installing and using IGM, please see the Github page at <a href="https://github.com/jouvetg/igm.The">https://github.com/jouvetg/igm</a>.</p> <p>A further .zip archive (in version 3 - Sims2010-2022.zip) contains the simulations based on linear extrapolation of the observed trend in SMB between 2010 and 2022, following the same nomenclature as in the principal archive (see above).</p> <p>Version 4 contains an additional mosaicked DEM of the results for the whole Alps with the ice removed to give the complete basal topography (kindly processed by T. Léger at UNIL) using the Japan Aerospace Exploration Agency (2021) ALOS World 3D 30 meter DEM. V3.2, Jan 2021. Distributed by OpenTopography. <a title="https://doi.org/10.5069/G94M92HB" href="https://doi.org/10.5069/G94M92HB" target="_blank" rel="noreferrer noopener">https://doi.org/10.5069/G94M92HB</a>. Accessed: 2024-09-09.</p>
Eleven years of training data for south foehn for two valleys in the Eastern Alps
<p>This south foehn training data is suited for machine learning purposes. </p> <p>It was created by applying objective foehn classification (OFC, Vergeiner 2004) on hourly data of various stations in the Eastern Alps in Austria. The two valleys Rhine and Inn and two intensities are available on a daily basis, where</p> <ul> <li>0.0 means no foehn on that day,</li> <li>0.5 means localised foehn on that day (at least one third and up to half the stations in the region responded to OFC),</li> <li>1.0 means widespread foehn on that day (more than half the stations in the region responded to OFC),</li> </ul> <p>provided for each valley individually.</p> <p>A paper, where the process of creation is described, is in preperation and will be linked as soon as it is reviewed. </p>
Revised direct band gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment
<p>Raw data and plotting scripts associated with the paper:<br><br>Cónal Murphy, Eoin P. O'Reilly and Christopher A. Broderick, "Revised direct band-gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment", <em>APL Mater.</em> (2024) (undergoing revision)<br><br>Tyndall National Institute, Lee Maltings, Dyke Parade, University College Cork, Cork T12 R5CP, Ireland<br>School of Physics, University College Cork, Cork T12 YN60, Ireland</p>
Snow cover in the European Alps: Station observations of snow depth and depth of snowfall
<p>Auxiliary files, code, and data for paper published in The Cryosphere:</p> <p>Observed snow depth trends in the European Alps 1971 to 2019</p> <p> <a href="https://doi.org/10.5194/tc-15-1343-2021">https://doi.org/10.5194/tc-15-1343-2021</a></p> <p> </p> <p><strong>Auxiliary files:</strong></p> <ul> <li>aux_paper.zip: Auxiliary figures to the paper (time series showing the consistency of averaging monthly mean snow depth of stations within 500 m elevation bins; times of seasonal snow depth and snow cover duration indices).</li> <li>aux_paper_crocus_comparison.zip: Time series comparing spatial statistical gap filling from paper to gap filling using snow depth assimilation into Crocus snow model (only for subset of stations in the French Alps)</li> <li>aux_paper_monthly_time_series.zip: Plots of monthly time series of snow depth, for each station.</li> <li>aux_paper_spatial_consistency.zip: Aggregate results from spatial consistency (statistical simulation using neighboring stations), and time series of observed versus simulated monthly snow depths.</li> </ul> <p> </p> <p><strong>Code </strong>(working copy, not cleaned, all written in R statistical software): code.zip</p> <ul> <li>to read in the different data sources</li> <li>to do quality checks and data processing</li> <li>to perform statistical analyses as in paper</li> <li>to produce figures and tables as in paper</li> </ul> <p> </p> <p><strong>Data</strong>:</p> <ul> <li>> 2000 stations from Austria, Germany, France, Italy, Switzerland, and Slovenia</li> <li>Daily stations snow depth and depth of snowfall, as .zips, grouped by data provider. Information on column content is provided in "data_daily_00_column_names_content.txt".</li> <li>Monthly stations mean snow depth, sum of depth of snowfall, maximum snow depth, days with snow cover (1-100cm thresholds), as .zips, grouped by data provider. Information on column content is provided in "data_monthly_00_column_names_content.txt".</li> <li>Meta data (name, latitude, longitude, elevation) in "meta_all.csv", along with an interactive map "meta_interactive_map.html", and column information in "meta_00_column_names_content.txt".</li> <li>If you <strong>use the data you agree to adhere to the respective data provider's terms</strong> as listed in "00_DATA_LICENSE_AND_TERMS.PDF"</li> <li>The license terms especially (and additionally to any other terms of the single data providers) include: <strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>] </li> </ul> <p> </p> <p> </p> <p><strong>Version history:</strong></p> <p>v1.3: added maxHS and SCD (with various 1-100cm thresholds) to monthly data</p> <p>v1.2: uploaded data</p> <p>v1.1: changes to aux-paper.zip and code.zip as consequence from submitting a revised manuscript</p> <p>v1.0: initial upload</p>
Microearthquake sequence recorded close to the Montello thrust system (Southeastern Alps)
<p>Seismic catalogues (QuakeML format) of a microearthquake sequence occurred on August 2021 in the Montello thrust area and recorded by the Collalto seismic network (<a href="https://doi.org/10.7914/SN/EV">10.7914/SN/EV</a>); the seismic catalogues are provided, separately, for different earthquake location codes (h71=Hypo71; hel=Hypoellipse; hypoDD=HypoDD; migraloc=Migraloc). DATA_tgz_SAC.tar contains earthquake waveforms (SAC format) with P and S arrival times used for the locations. Other details in Peruzza et al. (2022, doi: 10.3389/feart.2022.1044296). </p>
Data: Recent waning snowpack in the Alps is unprecedented in the last six centuries
<p><strong>Data_Dendro.txt</strong> contains the indexed ring-width chronology derived from living and relict common juniper (<em>Juniperus communis</em> L.) collected within or above the treeline in Ventina valley (46° 18’N, 9° 46’E). This data was used to reconstruct snowpack duration from 1400 to 2018 after calibration/validation with snow cover duration modelled from daily temperature and precipitation series.</p> <p><strong>Data_SALP_Reconstruction.txt</strong> contains the snow cover duration ring-width reconstruction (SALP) spanning from 1400 to 2018.</p> <p><strong>Data_Snow_cover_duration_south_Alps_1951_2018.txt</strong> contains the gridded 30’’ resolution snow cover duration over southern Alps for the period 1951-2018 as derived from a degree-day model applied to daily temperature and precipitation observations and calibrated on 312 snow depth observational series. This data was used to analyse the spatial signature of the indexed ring-width chronology.</p> <p><strong>Data_Time_serie_snow_cover_duration_Ventina.txt</strong> contains the snow cover duration series (period 1834-2018) derived for the specific location of the site chronology, obtained with a degree-day model applied to daily temperature and precipitation observations and calibrated on snow depth observational series. These data were used to calibrate/validate the snow cover reconstruction based on the indexed ring-width chronology.</p> <p>They are documented in the following article:</p> <p>Carrer, M., Dibona, R., Prendin, A. L., Brunetti,M., 2022. Recent waning snowpack in the Alps is unprecedented in the last six centuries. Nature Climate Change.</p>
16-year WRF simulation for the Southern Alps of New Zealand (monthly output)
<p>The climatological dataset was produced using the Weather and Research Forecasting (WRF) model configured with two nested domains at 10 km (D1) and 2 km (D2) horizontal grid spacing. It covers the bulk of the South Island of New Zealand and is centered over Brewster Glacier in the Southern Alps. The model was forced every three hours by ERA5 reanalysis data at its outer lateral boundaries. The dataset covers the period of 1 January 2005 to 31 December 2020, providing daily output in the outer domain (D1) and 3-hourly output in the innermost domain (D2). </p> <p>The dataset was generated as part of a DFG-funded project aimed at investigating the atmospheric processes causing impacts of sea surface temperature changes around New Zealand on variations of glacier surface climate and mass balance in the Southern Alps (using Brewster Glacier as a benchmark glacier).</p> <p>The data provided here are a selection of monthly averages from the finest WRF domain (D2; 2-km grid spacing). They are distributed among three different file types containing 4-dimensional, 3-dimensional and invariant output variables, respectively. For the 4-dimensional variables, the data were cropped to below ~200 hPa. In addition, perturbation and base-state atmospheric pressure (WRF variables P and PB) and geopotential (PH and PHB) were combined to produce full model fields, and perturbation potential temperature (T) was converted to total potential temperature.</p>
Data from air, englacial and permafrost temperature measurements on Mt. Ortles (Eastern European Alps)
<p>The *.csv files report the temperature data recorded between 2010 and 2016 and presented in the paper “Modern air, englacial and permafrost temperatures at high altitude on Mt. Ortles, (3905 m a.s.l.) in the Eastern European Alps” (Carturan et al., 2023, submitted). The data were used to display the time series reported in the paper, which details variable names, data quality flags, maintenance logs of field operations, and characteristics of measurement sites.</p> <p>The data files contain measurements of air temperature, englacial temperature, soil surface temperature and rockwall temperature.</p> <p>The file named ‘Ortles_Temperature_Metadata.pdf’ contains information regarding variable names, structure of data files, quality codes, geolocation, and topographic and geomorphological characteristics of sites instrumented for temperature measurements.</p> <p>Reference:</p> <p>Carturan, L., De Blasi, F., Dinale, R., Dragà, G., Gabrielli, P., Mair, V., Seppi, R., Tonidandel, D., Zanoner, T., Zendrini, T. L., and Dalla Fontana, G.: Modern air, englacial and permafrost temperatures at high altitude on Mt. Ortles, (3905 m a.s.l.) in the Eastern European Alps, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-164, in review, 2023.</p>
Supplementary files for the manuscript "Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand", submitted to JGR Earth Surface
<p>This repository contains supplementary files to the manuscript ""Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand" submitted to JGR: Earth Surface. It contains: </p> <p>- The Matlab script used to find the optimal distance-from-fault and elevation windows ("elevation_distance_window_optimization"), and 3 text files used for input in this script ("data_erates" contains the erosion rates, "data_elev" the number of pixels in each elevation bin, "data_distAF" the number of pixels in each distance-from-fault bin). </p> <p>- An Excel spreadsheet with the same information that the input text files contain, but specifiying the elevation or distance from fault bin values ("elevation and distance from fault with bins")</p> <p>- A shapefile of catchment outlines ("WSAcatch") for the catchments sampled for CRN denudation rates</p> <p>- Raw CRN data ("Table 2_new_CRN_data")</p> <p>- Excel spreadsheet with the compilation of themochronometric cooling ages used in the age2exhume code (van der Beek & Schildgen, 2023; <a href="https://doi.org/10.5281/zenodo.7341603">https://doi.org/10.5281/zenodo.7341603</a>).</p> <p>CRN data and catchment outlines will also be uploaded to the OCTOPUS database (<a href="https://octopusdata.org/">https://octopusdata.org/</a>) after manuscript acceptance.</p>
Online supplementary data linked to the publication "Aubenas-les-Alpes (S-E France). Part III – Last and final part of the mammalian assemblage with some comments on the palaeoenvironment and palaeobiogeography" doi:10.1016/j.annpal.2019.03.001
<p>Online supplementary appendix including the list of Oligocene localities and associated faunal lists compared to Aubenas-les-Alpes, and the size estimation of the non-predatory species for the construction of Fig.10.</p>
Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version
<p><strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong></p> <p> </p> <p>This is the data to the same-titled Data paper, which can be found at <a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>.</p> <p>Along with auxilary files for the <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">cloudremoval package</a>, and example scripts on how to access chunks of the data.</p> <p>The files contain:</p> <ol> <li><strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal</a></li> <li><strong>python-example-data-access.html </strong>: Example script how to access parts of the data using python</li> <li><strong>R-example-data-access.html</strong> : Example script how to access parts of the data using R</li> <li><strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (<a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>), and for algorithm at <a href="https://doi.org/10.3390/rs5010110">https://doi.org/10.3390/rs5010110</a>.</li> <li><strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper.</li> </ol> <p> </p> <p>The maps are GeoTIFF with integer based values:</p> <p>0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata</p> <p> </p> <p>Version history:</p> <p>1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper<br> 1.0.2 : added example scripts</p> <p> </p> <p> </p>
5-day WRF case study for the Southern Alps of New Zealand (hourly output)
<p>Full output file from the Weather and Research Forecasting (WRF) model for a 2 km resolution domain centered over the Southern Alps of New Zealand. Modelling period is 02-Feb-2011 00:00:00 UTC to 07-Feb-2011 00:00:00 UTC (5 days) with hourly output frequency. The data-set was generated in the framework of a case study to investigate the mesoscale influence of an atmospheric river on the mass balance of Brewster Glacier (Southern Alps) during a summertime melt event. For the related publication refer to <a href="https://doi.org/10.1029/2020JD034217">https://doi.org/10.1029/2020JD034217</a>.</p>
First motion data and focal mechanism solutions of 108 earthquakes occurred between 1928 and 2019 in the Southeastern Alps
<p>This dataset contains the P-wave polarities readings (FPS_polarities_input.zip) and the focal mechanisms (FPFIT_solution.pdf, FPFIT_solution.csv) obtained by the FPFIT algorithm (Reasenberger and Oppenheimer, 1985) of 108 earthquakes with 1.9 ≤ M<sub> </sub>≤ 4.8 occurring between 1928 and 2019 in the Southeastern Alps area (latitude 45°N-47.5°N and longitude 10°E-15°E). The preferred solution for each earthquake has been reported in the focal mechanism catalogue of Saraò et al. (2020).</p> <p>The first polarities used to compute the focal mechanisms were manually picked from seismograms of the National Institute of Oceanography and Applied Geophysics (OGS) northeastern Italy seismic and deformation network (Priolo et al., 2005; Bragato et al., 2011, Bragato et al., 2020)or extracted from the Bulletin of the International Seismological Centre the Seismological Bulletin of Slovenia. The polarities were also read from the seismograms archived in various Italian and European seismological observatories, many of which are no longer operating (Osservatorio meteorico-sismico nel Seminario - Chiavari; ENEL, Osservatorio Ximeniano - Florence, Osservatorio Astronomico "Brera" -Milan, &nbspDipartimento di Fisica dell’Università di Padova - Padua,;Osservatorio S. Domenico – Prato, Osservatorio meteoro-sismico nel Santuario di N.S. - Oropa, Osservatorio Bina - Perugia, Osservatorio "Valerio"- Pesaro, Osservatorio meteorico-sismico nel Collegio Alberoni - Piacenza, Osservatorio Meteorico Istituto Fisica - University of Siena,Sismografi Lungo Periodo di Mantovani (Bologna, Bolzano, Grosseto, Naples, Olbia, Palermo, Turin), Osservatorio meteorico-sismico nel Seminario Maggiore - Treviso, Osservatorio meteorico-sismico nel Seminario Patriarcale – Venice, Ljubljana, Munich, Stuttgart, Vienna).</p> <p>For more details</p> <p>Saraò, A., Sugan, M., Bressan, G., Renner, G., and Restivo, A.: A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928–2019, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2020-369, in review, 2021.</p> <p> </p> <p>References:</p> <p>Bragato, P.L., Di Bartolomeo, P., Pesaresi, D., Plasencia Linares, M., and Saraò A.: Acquiring, archiving, analyzing and exchanging seismic data in real time at the Seismological Research Center of the OGS in Italy, Ann. Geophys. 54, 67–75, https://doi.org/10.4401/ag-4958, 2011.</p> <p>Bragato P.L., P. Comelli, A. Saraò, D. Zuliani, L. Moratto, V. Poggi, G. Rossi, C. Scaini, M. Sugan, C. Barnaba, P. Bernardi, M. Bertoni, G. Bressan, A. Compagno, P. Di Bartolomeo, E. Del Negro, P. Fabris, M. Garbin, M. Grossi, A. Magrin, E. Magrin, D. Pesaresi, B. Petrovic, M.P. Plasencia Linares, M. Romanelli, A. Snidarcig, L. Tunini, S. Urban, E. Venturini and S. Parolai (2020). The OGS- North-Eastern Italy Seismic and Deformation Network: current status and outlook. Submitted to Seism. Res. Lett. </p> <p>Priolo, E., Barnaba, C., Bernardi, P., Bernardis, G., Bragato, P.L., Bressan, G., Candido, M., Cazzador, E., Di Bartolomeo, P., Durì, G., Gentili, S., Govoni, A., Klinc, P., Kravanja, S., Laurenzano, G., Lovisa, L., Marotta, P., Michelini, A., Ponton F., Restivo, A., Romanelli, A., Snidarcig, A., Urban, S., Vuan, A., Zuliani, D.: Seismic monitoring in northeastern Italy: A ten-year experience, Seismol. Res. Lett., 76, 446–454, https://doi.org/10.1785/gssrl.76.4.446, 2005.</p> <p>Reasenberg, P., Oppenheimer, D.: FPFIT, FPPLOT and FPPAGE: Fortran computer programs for calculating and displaying earthquake fault-plane solutions, Open-File Rep., 85-739, USGS, Menlo Park, 109 pp., 1985.</p> <p>Saraò A., Sugan M., Bressan G., Renner G., Restivo A., 2020: Focal mechanisms of Southeastern Alps and surroundings, doi: 10.5281/zenodo.4284971 .</p>
Debris thickness measurements from various glaciers in the Western Alps
<p>Debris thickness measurements collected from seven debris-covered glaciers in Switzerland and Italy in 2019 and 2020. Only data from Miage Glacier was measured in September 2020 all others were measured in August and September in 2019. The RGI Glacier ID is the first cell in each sheet. Location data is reported in decimal degrees. All debris thickness data is in centimeters and elevation data is in meters.</p> <p>Debris was measured by digging through the debris and then directly measuring the debris thickness with a measuring stick. The mean thickness is the most likely average thickness based on 3 measurements within a ~4x4m area. The 'min' measurement is the minimum debris thickness. The 'max' measurement is the absolute maximum estimate of the mean debris thickness, meaning that this estimate is that absolute maximum of the mean debris thickness. This value is determined based on the thickness of the largest non-outlier debris thickness in the area. Three measurements were taken for the mean value and 1 each for the max/min bounds. To save time, sites with mean thicknesses larger than 20 cm the extreme bounds are based on the minimum and maximum measurements within the single hole dug. Elevation data are based on a hand held GPS and this value was only recorded for some debris thickness measurements.</p>
Piburgersee core meta data repository for the publication "Seismic control of large prehistoric rockslides in the Eastern Alps"
<p>This dataset comprises the core meta data of Plansee, which is the basis for the publication Oswald et al. "Seismic control of large prehistoric rockslides in the Eastern Alps".</p> <p>The core meta data belongs to a 8m long sediment core composed of 12 individual core sections (see Plansee_core_data.xlsx). For each individual core section the core image (_coreimage.jpg), the CT data (_CT.rar), XRF data, (_XRF.txt) and multi-sensor core logging data (_MSCL.csv) are provided.</p>
Plansee seismic and core meta data repository for the publication "Seismic control of large prehistoric rockslides in the Eastern Alps"
<p>This dataset comprises the raw seismic data and core meta data of Plansee, which is the basis for the publication Oswald et al. "Seismic control of large prehistoric rockslides in the Eastern Alps".</p> <p>Seismic profiles are provided as .SGY files (Plansee_seismics_SGYfiles.rar)</p> <p>The core meta data belongs to a 7m long sediment core composed of 10 individual core sections (see Plansee_core_data.xlsx). For each individual core section the core image (_coreimage.jpg), the CT data (_CT.rar) and multi-sensor core logging data (_MSCL.csv) are provided.</p>
FIGURE 5. A in The species of the genus Diamesa (Diptera, Chironomidae) known to occur in Italian Alps and Apennines
FIGURE 5. A, Diamesa zernyi Edwards, male genitalia (basimedial setal cluster in green); B I, IX tergite, B II, anal point, B III, basimedial setal cluster, B IV, sternapodeme and phallapodeme, B V, aedeagal lobe, B VI, pars ventralis, B VII, inferior volsella, B VIII, gonostylus.
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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.