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966 results for “Snow”
Snow properties measurements (in situ & retrived from satellite) at Dome C, East Antarctica Plateau
<p>The dataset contains the data inputs for the electromagnetic model:</p> <ul> <li>the snow density profile down to 20 m depth (measured in 2010)</li> <li>the snow SSA profile down to 20 m depth (measured in 2010)</li> <li>the snow temperature profile down to 20 m depth (measured from 1 December 2006 to 4 October 2011)</li> </ul> <p>The dataset contains also the data retrieved from satellite:</p> <ul> <li>the retrieved surface snow density from AMSR-E satellite (obtained from 18 June 2002 to 4 October 2011)</li> </ul> <p>The dataset contains finally the data measured in situ to compare with the data retrieved from satellite:</p> <ul> <li>the surface snow density from CALVA program (measured from 3 February 2010 to 4 October 2011)</li> <li>the surface snow density from PNRA program measured in snow pits (measured from 18 December 2007 to 4 October 2011)</li> <li>the surface snow density from PNRA program measured next to stakes (measured from 9 May 2008 to 4 October 2011)</li> </ul>
Eco-data for "An ecosystem-wide reproductive failure with more snow in the Arctic"
<p>Supporting data for "An ecosystem-wide reproductive failure with more snow in the Arctic", PLOS Biology.</p> <p>Time series of abundance and phenology of various plants, arthropods, birds, mammals and snow from Zackenberg, NE Greenland. Time series cover the period 1996 to 2018.</p> <p>Data were collected as part of the Greenland Ecosystem Monitoring Program, and raw data are available at <a href="http://data.g-e-m.dk">http://data.g-e-m.dk</a>.</p>
Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios"
<p>Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios" submitted to Earth's Future in April 2021</p> <p>Contains model output from both the Icepack and CCSM4 experiments from the paper. File descriptions for the Icepack and CCSM4 data are contained in the files README_icepack and README_CCSM respectively.</p>
Global datasets for duration of frozen ground with snow cover and without snow cover
<p>These are the main datasets associated with the submitted manuscript--Climate change causes functionally colder winters for snow cover-dependent organisms. The datasets include duration of frozen ground with snow cover (Dsc) and without snow cover (Dfwos) for the historical (1982-2014) and future (2071-2100) periods, which are available with GeoTIFF format at 5-km resolution. Dsc and Dfwos are defined as the number of days during the frozen season when frozen ground is covered by snow or not, which are calculated using AVHRR/MODIS snow cover product and NASA MEaSUREs Global Record of Daily Landscape Freeze/Thaw Status dataset.</p>
Snow Albedo Measurements in Mountainous Regions Using a Dual-sensor Unmanned Aerial Vehicle (UAV)
<p>We used a commercially available UAV (drone) to measure the albedo of the Earth in snowy, mountainous environments. These data represent four initial flights conducted during the spring of 2019 in SW Montana, USA. These UAV-based measurements of albedo allow us to measure a larger and more varied area than do measurements from a stationary tower. </p>
Datasets for the publication "Simulation of snow management in Alpine ski resorts using three different snow models"
<p>Snow model simulation results used for the paper "Simulation of snow management in Alpine ski resorts using three different snow models".</p> <p>The following results are available for each of the nine ski resorts:</p> <ul> <li><em><resort></em>_swe_<em><date></em>.tif or <em><resort></em>_swe.geojson: Spatially distributed SWE outputs for 2016-12-24 and 2017-12-24 in GeoJSON (for the two French resorts) or GeoTIFF (for all other resorts) format. Unit: kg m<sup>-2</sup></li> <li><em><resort></em>_point_depth.csv, <em><resort></em>_point_swe.csv: Time series of snow depth (in m) and SWE (in kg m<sup>-2</sup>) for the respective points of interest. The column names correspond to the snow management configurations as shown in Fig. 5 of the paper.</li> </ul>
Cairngorm National Park snow cover duration 1960 - 2080
<p>Created for work commissioned by the Cairngorms National Park through ClimateXChange</p> <p>Contains Ordnance Survey data © Crown copyright and database right 2019.</p> <p>Contains Met Office UKCP09 and UKCP18 data licensed under the Open Government Licence v3.0.</p> <p>Downscaling and Correction copyright 2019 The James Hutton Institute.</p>
Fig. 2 in The Distribution of Soil Testate Amoebae under Winter Snow Cover at the Plot-scale Level in Arctic Tundra (Qeqertarsuaq/Disko Island, West Greenland)
Fig. 2. Maps of the spatial distribution of the explanatory environmental variables (A – microtopography; B – snow depth; C – loge(substrate density)) plotted against their spatial coordinates at 57 sampling locations within an 8 × 15 m plot in arctic tundra in Qeqertarsuaq/Disko Island (West Greenland). All data are centred on 0, so square sizes are proportional to the deviations from the mean values at the plot. Open symbols are used for negative values and the filled symbols are used for positive values. Spatial patterns are visualised as aggregations of similar size and colour.
Figure 2 in Diet selection of snow leopard (Panthera uncia) in Chitral, Pakistan
Figure 2. Microphotographs of hair scale pattern of Cape hare (Lepus capensis): a) reference hair (10 × 100×); b) hair found in scat sample (10 × 100×).
Figure 4 in Diet selection of snow leopard (Panthera uncia) in Chitral, Pakistan
Figure 4. Microphotographs of hair scale pattern of palm civet (Paguma larvata): a) reference hair (10 × 100×); b) hair found in scat sample (10 × 100×).
Figure 3 in Diet selection of snow leopard (Panthera uncia) in Chitral, Pakistan
Figure 3. Microphotographs of hair scale pattern of markhor (Capra falconeri): a) reference hair (10 × 40×); b) hair found in scat sample (10 × 40×).
Figure 2 in Stable isotope record from snow pit ITASE_S2
Figure 2 – Distributional records of Oxycheilinus samurai. Circles and stars indicate specimen- and photograph- based records, respectively. Open and closed symbols indicate previously published records and new records, respectively.
Figure 3 in Stable isotope record from snow pit ITASE_S2
Figure 3. – Preserved specimen of Oxycheilinus samurai from Payo Bay, Halmahera, Indonesia (WAM P. 32973-003, 46.3 mm SL).
Figure 1 in Stable isotope record from snow pit ITASE_S2
Figure 1. – Underwater photographs of Oxycheilinus samurai from Prony Bay, New Caledonia, 25-30 m depth. Photo: R. Bajol.
Repository: Potential for Photosynthesis on Mars within snow and ice
<p>This repository contains:</p> <p>1. Modeled Spectral Irradiances (W m-2 micron-1) within Vertically Inhomogeneous Glacier Ice from Khuller, Warren, Christensen & Clow (2024)</p> <p>a) Fig_1_12cm_model: Modeled Spectral Irradiance at 12 cm <br>b) Fig_1_36cm_model: Modeled Spectral Irradiance at 36 cm <br>c) Fig_1_58cm_model: Modeled Spectral Irradiance at 58 cm <br>d) Fig_1_77cm_model: Modeled Spectral Irradiance at 77 cm </p> <p>2. Modeled Spectral Actinic Flux (W m-2 micron-1) from Khuller, Warren, Christensen & Clow (2024)</p> <p>a) Clean Snow/Firn/Ice (without dust) at 33 S latitude<br> i) Fig_2a: Pure snow with 0.5 mm grain size<br> ii) Fig_2b: Pure firn with 2.5 mm grain size<br> iii) Fig_2c: Pure glacier ice with 14 mm grain size</p> <p>b) Clean Snow/Firn/Ice (without dust) at 54 N latitude<br> i) Ext_Fig_2a: Pure snow with 0.5 mm grain size<br> ii) Ext_Fig_2b: Pure firn with 2.5 mm grain size<br> iii) Ext_Fig_2c: Pure glacier ice with 14 mm grain size </p> <p>c) Dusty Snow/Firn/Ice (with 0.01% dust by mass) at 33 S latitude<br> i) Fig_2d: Dusty snow with 0.5 mm grain size<br> ii) Fig_2e: Dusty firn with 2.5 mm grain size<br> iii) Fig_2f: Dusty glacier ice with 14 mm grain size</p> <p>d) Dusty Snow/Firn/Ice (with 0.01% dust by mass) at 54 N latitude<br> i) Ext_Fig_2d: Dusty snow with 0.5 mm grain size<br> ii) Ext_Fig_2e: Dusty firn with 2.5 mm grain size<br> iii) Ext_Fig_2f: Dusty glacier ice with 14 mm grain size</p> <p>e) Dusty Snow/Firn/Ice (with 0.1% dust by mass) at 33 S latitude<br> i) Fig_2g: Dusty snow with 0.5 mm grain size<br> ii) Fig_2h: Dusty firn with 2.5 mm grain size<br> iii) Fig_2i: Dusty glacier ice with 14 mm grain size</p> <p>f) Dusty Snow/Firn/Ice (with 0.1% dust by mass) at 54 N latitude<br> i) Ext_Fig_2g: Dusty snow with 0.5 mm grain size<br> ii) Ext_Fig_2h: Dusty firn with 2.5 mm grain size<br> iii) Ext_Fig_2i: Dusty glacier ice with 14 mm grain size </p> <p>3. Modeled Depths for DNA Damage Limit, PAR Upper Limit, and PAR Lower Limit (meters) from Khuller, Warren, Christensen & Clow (2024)</p> <p>a) Sensitivity to dust content<br>FILE FORMAT: Dust Content (ppmw), DNA Damage Limit Depth (m), PAR Lower Limit Depth (m), PAR Upper Limit Depth (m)<br> i) final_Depths_south_dust_sensitivity: sensitivity to dust content for the martian southern hemisphere<br> ii) final_Depths_north_dust_sensitivity: sensitivity to dust content for the martian northern hemisphere</p> <p>b) Sensitivity to ice grain radius<br>FILE FORMAT: Ice Grain Radius (micron), DNA Damage Limit Depth (m), PAR Lower Limit Depth (m), PAR Upper Limit Depth (m)<br> i) final_Depths_south_radius_sensitivity: sensitivity to ice grain radius for the martian southern hemisphere<br> ii) final_Depths_north_radius_sensitivity: sensitivity to ice grain radius for the martian northern hemisphere</p> <p>c) Sensitivity to latitude<br>FILE FORMAT: Latitude (degrees), DNA Damage Limit Depth (m), PAR Lower Limit Depth (m), PAR Upper Limit Depth (m)<br> i) final_Depths_south_latitude_sensitivity: sensitivity to latitude for the martian southern hemisphere<br> ii) final_Depths_north_latitude_sensitivity: sensitivity to latitude for the martian northern hemisphere</p> <p>d) Sensitivity to solar zenith angle<br>FILE FORMAT: Solar Zenith Angle (degrees), DNA Damage Limit Depth (m), PAR Lower Limit Depth (m), PAR Upper Limit Depth (m)<br> i) final_Depths_south_zenith_sensitivity: sensitivity to solar zenith angle for the martian southern hemisphere<br> ii) final_Depths_north_zenith_sensitivity: sensitivity to solar zenith angle for the martian northern hemisphere</p> <p>4. Wavelengths used for files listed in 1 from Khuller, Warren, Christensen & Clow (2024)<br>wavelengths_greenland: wavelength in microns</p> <p>5. Wavelengths used for files listed in 2 and 3 from Khuller, Warren, Christensen & Clow (2024)<br>wavelengths: wavelength in microns</p> <p>6. Depths used for files listed in 2 from Khuller, Warren, Christensen & Clow (2024)<br>depths: depths in meters</p> <p>7. Normalized DNA spectrum used in Khuller, Warren, Christensen & Clow (2024)<br>norm_dna_spectrum: normalized DNA spectrum</p> <p> </p>
Data from: A snow-dwelling tropical butterfly? An unprecedented discovery of a new genus of the Pedaliodes clade in an extreme, high-altitude Andean environment (Lepidoptera: Nymphalidae, Satyrinae)
<p><span><span>A new genus of satyrine butterflies, </span></span><span><span><em>Nivaliodes </em></span></span><span><span><strong>gen. nov.</strong></span></span><span><span>, is described for three species, all new – </span></span><span><span><em>N. negrobueno </em></span></span><span><span><strong>sp. nov.</strong></span></span><span><span>,</span></span><span><span><em>N. virococha</em></span></span><span> </span><span><span><strong>sp. nov.</strong></span></span><span><span> and </span></span><span><span><em>N. puriq </em></span></span><span><span><strong>sp. nov.</strong></span></span><span><span> (Lepidoptera, Nymphalidae) – with a support of molecular data and adult morphology. Target enrichment-based phylogeny indicates </span></span><span><span><em>Nivaliodes </em></span></span><span><span><strong>gen. nov.</strong></span></span><span><span> is sister to the genus </span></span><span><span><em>Pherepedaliodes</em></span></span><span><span>within an extremely diverse </span></span><span><span><em>Pedaliodes</em></span></span><span><span> clade of the predominantly Andean subtribe Pronophilina</span></span><span><span><em>. </em></span></span><span><span>Whereas an overwhelming majority of species of this group occur in tropical montane forests, </span></span><span><span><em>N. negrobueno </em></span></span><span><span><strong>sp. nov.</strong></span></span><span><span> was discovered in a central Peruvian desert puna at some 4600-4800 m asl., the highest elevation reported for any species of the Pronophilina. Individuals were observed overflying rocky slopes and resting directly on snow-covered surfaces, which is an exceptionally unusual behaviour among butterflies. The other two species of the new genus were found at lower elevations, some 3300-4200 m asl. at the timberline and in puna grassland. </span></span></p>
Inputs (forcing, observations and config file) for the experiments included in "Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter".
<p>Inputs or the experiments included in the manuscript <a href="https://doi.org/10.5194/egusphere-2024-1404">Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter</a>. </p> <p>Three experiment's inputs (forcing, observations and config file) for the Multiple Snow data Assimilation system (<a href="https://doi.org/10.5281/zenodo.11147258">MuSA</a>, v2.1) for the experimental catchment of Izas in the Spanish Pyrenees. All the experiments use ERA5 data downscaled to 20 m spatial resolution with the statistical downscaling tool <a href="https://doi.org/10.21105/joss.05059">TopoPySCALE</a>. The experiments assimilate different variables. </p> <p> C) assimilation of fSCA retrieved from Sentinel-2;</p> <p> D) assimilation of snow depth profiles retrieved with ICESat-2;</p> <p> J) joint assimilation of variables in C) and D).</p> <p> </p> <p>All the experiments assimilate the observations with the deterministic ensemble smoother with multiple data assimilation (DES-MDA) scheme.</p>
Linked collectors and determiners for: A revision of New Caledonian Gossia N. Snow & Guymer (Myrtaceae).
Natural history specimen data linked to collectors and determiners held within, "A revision of New Caledonian Gossia N. Snow & Guymer (Myrtaceae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f7482a89-9b90-4604-8d8f-2be9083f2161">https://bionomia.net/dataset/f7482a89-9b90-4604-8d8f-2be9083f2161</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f7482a89-9b90-4604-8d8f-2be9083f2161">https://gbif.org/dataset/f7482a89-9b90-4604-8d8f-2be9083f2161</a>. Formatted as a Frictionless Data package.
Multi-frequency altimetry snow depth product over Arctic sea ice
<p>Satellite altimetry can be used to estimate sea ice thickness, an essential variable to better understand and forecast the dynamic ice cover. Nevertheless, some sources of uncertainty remain, and one of the most important concerns the snow depth, a key parameter to convert the measured ice freeboard into sea ice thickness.</p> <p>Snow depth can be estimated using different altimeter frequencies with different snow penetration capabilities. We have developed a monthly snow depth product based on the differences between CryoSat-2 SAR Ku and IceSat-2 laser altimeters covering the period 2018-2021 with a spatial resolution of 25 km. </p> <p> </p>
Fractional Snow Covered Area at Ny-Ålesund (Svalbard, Norway)
<p>The gridded datasets is a ensembled product obtained since 2020 processing imagery acquired by different time-lapse cameras located at the Zeppelin Observatory, at the Gruvebadet Snow Research Site and at the Amundsen-Nobile Climate Change Tower. </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.