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966 results for “Snow”
Global MODIS-based snow cover monthly long-term (2000-2012) at 500 m, and aggregated monthly values (2000-2020) at 1 km
<p>The Global monthly snow cover repository contains multiple products (based on the MODIS/Terra MOD10A2):</p> <ol> <li>Global snow cover monthly long-term (2000–2012) P90 and standard deviation derived from the <a href="http://maps.elie.ucl.ac.be/CCI/viewer/index.php">ESA CCI snow cover weekly product</a>;</li> <li>Global snow cover monthly values P05, P50 and P95 for the period 2000–2020 derived using <a href="https://climate.esa.int/en/odp/#/project/snow">ESA snow cover fraction daily 1-km values</a>;</li> <li>Min and max geometric temperatures for the mid-month (dtm_temp.max_geom.*_m_1km_s0..0cm_xxxx_epsg4326_v1.tif);</li> </ol> <p>Quantiles (probability either 0.05, 0.5, 0.9 and/or 0.95) have been derived by matching dates in the filenames (daily or weekly values). After deriving quantiles, gaps were filled using temporal neighbors (e.g. missing values for year 2002 were filled using average of values between year 2001 and 2003). The gaps were especially large for months of November, December, January and February, northern Hemisphere. Important note: maps still contain some artifacts due to high reflections of white-sands e.g. Salar de Uyuni desert in Bolivia and similar. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/snow.cover"><strong>here</strong></a>. Antarctica is not included.</p> <p>To access and visualize global datasets use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> </ul> <p>All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>snow.cover = variable: snow cover fractions,</li> <li>esa.modis = data source ESA snow product,</li> <li>p.90 = upper 90% quantile,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2012 = time reference aggregated: from 2000 to 2012,</li> <li>v1 = version number: 1,</li> </ul>
Data and code for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps"
<p>Data and code used for the manuscript "From white to green: Snow cover loss and increased vegetation productivity in the European Alps" by Rumpf et al., submitted December 2021 to Science</p> <p>See file ReadMe.txt for a description of the content and the original publication for further explanations.</p> <p>You are free to use these data and code for scientific purposes but are obliged to cite the above-mentioned publication.<br> For further questions, contact sabine.rumpf@unibas.ch</p>
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>
Grand Mesa 2017-02-01 snow depth estimate
<p>Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series of very-high-resolution commercial stereo imagery. More formal releases of data products will be available in the coming years.<br> <br> Analysis of this dataset is presented in the following publication:</p> <ul> <li>McGrath, D., Webb, R., Shean, D., Bonnell, R., Marshall, H-P., Painter, T., Molotch, N., Elder, K., Hiemstra, C., Brucker, L., (2019), Spatially Extensive Ground-Penetrating Radar Snow Depth Observations During NASA's 2017 SnowEx Campaign: Comparison With In Situ, Airborne, and Satellite Observations, Water Resources Research, 55 (19), 10026-10036, doi:<a href="https://doi.org/10.1029/2019WR024907">10.1029/2019WR024907</a>.</li> </ul> <p>If you use these data, please cite the above publication.</p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 12 2020 0000 UTC to January 13 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 12 and January 13 2020 with snow cover. The night between January 12 and January 13 2020 featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period. Also available in a different dataset are data from simulations of the night between January 16 and January 17 2020, which initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight, with snow cover (Rauchöcker et al., 2024a) and also without snow cover (Rauchöcker et al., 2024b).</p> <h3><strong>WRF Simulation Output</strong></h3> <h3><strong> </strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 12 2020 and run until 12:00 UTC January 13 2020 and results for the same night but a coarser grid spacing are described in Rauchöcker (2022). Compared to the simulation with 200m grid spacing presented there, this simulation offers a significantly improved resolution. As input, we used ERA5 reanalysis data, 1-arc second SRTM terrain data and Corine 2018 land cover classification. The simulation was performed with modified snow cover as described in Rauchöcker (2022) and the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan12</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan12</em>. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan12</em>.</p>
Water stable isotope, temperature and electrical conductivity dataset (snow, ice, rain, surface water, groundwater) from a high alpine catchment (2019-2021).
<p>Data collected in the Otemma forefield in Switzerland (45°56’03”N,7°24’42”) from July 2019 to October 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>Description of the dataset</strong></p> <p>This dataset contains water stable isotope (δ<sup>2</sup>H, δ<sup>17</sup>O, δ<sup>18</sup>O), water temperature and water electrical conductivity (EC) measurements collected from the Otemma glacier catchment.</p> <p>All water isotope samples were collected directly from the source and stored in 12 mL amber glass vials with an air-tight caps. River samples were first collected with an automatic ISCO 6712 portable water sampler with 1L open plastic bottles and transferred in 12 mL vials every one to two weeks. All isotope analysis were performed using a Wavelength-Scanned Cavity Ring Down Spectrometer (Picarro 2140-I, Santa Clara, California, USA) and expressed relative to the international Vienna Standard Mean Ocean Water (VSMOW) standards.</p> <p>All EC and water temperature measurements were performed with a WTW Multi 3510 IDS logger with a IDS TetraCon® 925 probe.</p> <p>The dataset contains measurements performed at various locations within the catchment. A total of approximately 1500 measurements are provided. In the dataset each point correspond to a measurement station (column "<strong>Station</strong>") which we classified in specific class of water (column "<strong>Type</strong>") as follows :</p> <ul> <li><strong>Stream </strong>: samples collected at three locations, from the glacier snout, after a small outwash plain and 2km downstream.</li> <li><strong>Tributary </strong>: 5 hillslopes tributaries originating from small seasonal overland flow or small springs at the base of the morainic hillslope. Those tributaries were monitored weekly. In addition, a few other seasonal lateral streams were sampled in various locations (Type: Other tributaries).</li> <li><strong>Bedrock </strong>: A few exfiltrations directly leaking out of the bedrock outcrop were sampled.</li> <li><strong>Ice </strong>: Ice was sampled either as surface ice (small cores 5 cm deep), as deeper cores (5 to 8m deep) or as meltwater from supraglacial gullies. All solid ice samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials.</li> <li><strong>Snow </strong>: The snowpack was sampled either at the surface (0 to 5cm) or at about 20 cm depth. Where possible, meltwater leaking from the snowpack was sampled. At 3 locations in 2021, we dug snowpits from which we sampled snow at different layers with depth. All solid snow samples were melted at ambiant air temperature in air-tight plastic bags before being transferred into 12 mL vials</li> <li><strong>Rain </strong>: Rainwater was mostly sampled at our camp site at 2450 m. asl. Rainwater samples represent single rain events which are identified by dry periods of at least one day long.</li> <li><strong>Groundwater </strong>: shallow (2 to 3 meters) fully-screened groundwater wells were installed in the outwash plain and water sampled monthly in the snow-free season.</li> </ul> <p>- GPS coordinates are provided with each point (Swiss coordinate system CH1903+ / LV95<strong> (EPSG: 2056)).</strong></p> <p>- Dates are provided in local timezone (GMT+1 with daylight saving time) and in UTC date format.</p> <p>- Analyitcal error from the Picarro spectrometer is reported as 1 standard deviation.</p> <p>More information can be accessed in the corresponding publication by Müller et al. (to be published in 2023).</p> <p><strong>Data files</strong></p> <ul> <li><em>Otemma_isotope_EC_T_2019_2021.csv</em> : file containing all data with GPS coordinates</li> <li> <p><em>isotope_locations_Otemma.jpg</em> : an overview of the locations of each measurement point</p> </li> <li> <p><em>Otemma_Isotopes_2019-2020.html </em>: interactive plots of all datasets (δ<sup>2</sup>H, EC, temperature), classified by Type.</p> </li> </ul>
Contamination pattern and risk assessment of polar compounds in snow melt: an integrative proxy of road runoffs
<p><strong>Abstract</strong></p> <p>To assess the contamination and potential risk of snow melt with polar compounds, road and background snow was sampled during a melting event at 23 sites at the city of Leipzig and screened for more than 500 chemicals using LC-HRMS. Additionally, six 24 h composite samples were taken from the influent and effluent of the Leipzig WWTP during the snow melt event. 207 compounds were at least detected once (concentrations between 0.80 ng/L and 75 µg/L). A toxic unit-based assessment was performed to investigate the risk of adverse environmental effects in the receiving water.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset contains the list of sampling points, the target compounds, the chemical findings, the results of the toxic unit assessment, the underlying ecotoxicity data, and the estimated compound removal rates in WWTP. The data is provided in xlsx and ods formats.</p>
CESM2 Idealized Experiment Output: Summer atmospheric response to zero May North American snow cover
<p>The National Center for Atmospheric Research’s Community Earth System Model version 2.2 (CESM2) (Danabasoglu et al., 2020) was run in the Atmospheric Model Intercomparison Project (AMIP) configuration. SSTs and sea-ice were prescribed as monthly varying seasonal cycles based on the observed climatology from 2005 to 2015 (i.e., component set: F2010climo) (Hurrell et al., 2008). We employed the Community Atmosphere Model version 6 (CAM6) (Bogenschutz et al., 2018)<span> </span>as the atmospheric component and the Community Land Model version 5 (CLM5) (Lawrence et al., 2019) as the land-surface component. Each model was run with a horizontal resolution of 0.9˚ latitude by 1.25˚ longitude.</p> <p>We ran a control simulation in this configuration for ten consecutive years. We then modified the land-surface restart files for May 1st of each year by reducing the snow cover over North America to zero. Using these modified files, we then completed a reduced snow simulation by rerunning three-month simulations from May through July for each of the ten years. </p>
New Hampshire Soil Sensor Network: Snow depth (2012-2022)
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes data from snow depth sensors. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
GM Snow Study at the University of Michigan Biological Station, Pellston, MI (1982-1987)
Wet and dry deposition were monitored at the University of Michigan Biological Station, which is located near the northern tip of Michigan's lower peninsula, for three winters. Dry deposition was measured by both conventional bucket method and by measuring increases in concentration in snow samples. Average results of the two methods were in reasonable agreement. The cumulative wet and dry deposition quantities are in good agreement with snowpack accumulations until the first thaw period. Dry deposition to snow accounts for less than 15% of the total H+, SO4-2, NO-3, NH4+, and approximately 25% of the Ca2+, Mg2+, Na+, J+, and Cl-, during an average precipitation year. Snowpack measurements were also made under deciduous and red pine canopies. Decreases in H+ and NO-3 were observed under the red pine canopy. Snowmelt and runoff were studied during the 1986-87, 1983-84, and the 1982-83 winters at the University of Michigan Biological Station. For the 1982-83 and 1983-84 winters the first 50% of snowpack acidity was released in melt and rain water equal to 25% of the original snowpack water content. Interaction between the meltwater and the litter layer produced large changes in the concentrations of most species. Runoff to two streams had high SO4-2 and very low NO-3 concentrations. It is concluded that most of the NO-3 is either biologically utilized or retained in the ecosystem, even during the early snowmelt period at this site.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): CiPEHR snow depth manual data 2009-2025
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes manual measurements of snow depth collected in early spring on winter warming and control treatment plots.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): CiPEHR dates snow-free 2010-2025
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes weekly thaw depth measurements collected from winter warming, summer warming, and control treatment plots at CiPEHR. Additional measurements from on-plot gas flux wells, water table monitoring wells, and off-plot locations are also reported. Note that the experimental warming portion of this experiment concluded in 2022. These data are a continuation of measurements taken at previously warmed plots but plots were not actively manipulated in 2023.
Snow depth, soil frost depth and snow water content along an elevation gradient at the Hubbard Brook Experimental Forest.
Snow depth, soil frost depth and snow water content have been measured at several locations at the Hubbard Brook Experimental Forest (HBEF). In October 2010, as part of a study of the relationships between snow depth, soil freezing and nutrient cycling (http://www.ecostudies.org/people_sci_groffman_snow_summary.html), we established 6 20 x 20-m plots (intensive plots) and 14 10 x 10-m plots (extensive plots) following an elevation gradient, with eight of the plots facing north and twelve facing south. Snow and frost depth, and snow water equivalent sampling started in December 2010. Measurements on the extensive plots ended at the conclusion of snow coverage in spring, 2012. Measurements at the 6 intensive plots are ongoing and measurement frequency was increased from approximately bimonthly to approximately weekly beginning in the 2019-2020 snow cover season. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Snow, ice, and total glacier mass balance measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes mass balance changes at each stake on six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys region of Antarctica. The values are the result of an analysis of the raw data presented in other data files (glacier stake heights, snow depths, and glacier snow densities). Included here for each stake are the change in ice and snow water equivalent (mass) values, and the total mass change. The standard deviation or the range for each total is also given. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
Glacier snow density measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes snow density measurements determined at each stake with snow present on glaciers in Taylor Valley (Canada, Commonwealth, Howard, and Taylor) and Miers Valley (Adams), all of which are located in the McMurdo Dry Valleys of Antarctica. Included here for each stake are the snow thickness and density for each layer and the average density for each pit. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing for Adams, Commonwealth, and Howard glaciers. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
Average glacier stake height and snow depth measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes stake height and snow depth measurements to the surface of six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys of Antarctica. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
North Temperate Lakes LTER: Snow and Ice Depth 1982 - current
Snow and ice depth are measured during the winter months on the eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout lakes, unnamed lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Fish, Mendota, Monona and Wingra). 10 snow depth measurements are taken in a circle around the sampling location and averaged to single measurement. Sampling Frequency: every 6 weeks during ice-covered season in the north and typically once during the winter in the south. Number of sites: 11.
Lake snow removal experiment phytoplankton community data, under ice, 2019-2021
Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents phytoplankton community samples (pooled epilimnion and hypolimnion samples representative of 7 m water column) both under-ice and during some shoulder-season (open water) dates. Samples were collected into amber bottles and preserved with Lugol's solution before they were sent to Phycotech Inc. (St. Joseph MI, USA) for phytoplankton taxonomic identification and quantification.
Spatial distribution of snow water equivalent for the Niwot Ridge, 1996 - 2019
This dataset provides a daily estimation of snow water equivalent for the Niwot Ridge during snow melting period from 1997 to 2019 at 30-meter spatial resolution. The dataset includes two series of SWE data: 1) 1996-2007 daily SWE dataset is generated by Jepsen et al., (2012); 2) 2008-2019 daily SWE dataset is generated by Dr. Kehan Yang following the same method used by Jepsen et al., (2012). In brief, a physically based reconstruction model is used to calculate daily SWE backward from snow disappearance date to peak snow accumulation. The infilled hourly climate data set for C1, Saddle and D1 (data available at https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-nwt.168.2) is interpolated and used as the meteorological forcing in the snow energy balance calculation of SWE reconstruction. The shortwave radiation is estimated by downscaling hourly product of the Geostationary Operational Environmental Satellite (GOES) using TOPORAD tool. The USGS Landsat Level-3 fractional snow-covered area product is used to proportion potential energy flux for snowmelt at the pixel scale. Please see detailed methods included with this data package for more details and references.
Spectrometer measurements of snow and bare ground targets and simultaneous measurements of snow conditions
<p>The dataset holds measurements of reflected sunlight spectrum from snow and different snow free targets (moss, lichen, rock surface, ground vegetation). The purpose of the measurements has been to create a dataset to better understand the effect of snow cover characteristics and different bare ground targets on reflected sunlight, and to relate this information to observed at satellite reflectances from snow covered and partially snow covered boreal forests. The instrument in the measurements was Field Spec Pro JR from Analytical Spectral Devices Inc. Additionally to the 237 bands measured between 350-2500 nm, the dataset contains the following parameters from the measurement sites: id, location, time of measurement, target type (snow, bare ground etc.), cloudiness (in octas), snow depth (cm), snow temperature at 5 cm (degree C), snow temperature half way through the snow pack (degree C), soil temperature (degree C), air temperature (degree C), average grain size (mm), grain type, snow water content (subjective), snow patchiness (%) in the surrounding area, visible impurities in snow yes/no and land cover.</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.