Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
39
datasets available to search
ShareScore release 0.9.0
Dataset results
39 results for “Rock glacier”
Stream water chemistry data for Green Lake 5 Rock Glacier, 1998 - ongoing.
This is a summary of major ion concentrations for stream water samples collected at the Green Lake 5 Rock Glacier, near Green Lake 5 in the Boulder County Watershed Green Lakes Valley.
UAV-based colour-infrared orthomosaics and digital elevation models of basalts and rock glaciers on Disko Island, West Greenland
<p><span>This data set contains multispectral surveys conducted with an unoccupied aerial vehicle over rock glaciers and steep mafic outcrops (intrusive and flood volcanics) near the coastline of Disko Island.</span></p> <ul> <li><span>Acquisition date: 07.08.2019 – 10.08.2019</span></li> <li><span>Location: Illukunnguaq, Disko Island, Greenland</span></li> <li><span>UAV: SenseFly eBee Plus</span></li> <li><span>Flight altitude above ground level: >100m</span></li> <li><span>Image Overlap forward/side: various</span></li> <li><span>Camera: Parrot Sequoia multispectral</span></li> <li><span>EPSG: 32622</span></li> <li><span>Center coordinates: 69.885277°N, -52.577724°E</span></li> <li><span>Flight mode: automatic flight plan</span></li> </ul> <p><span>Data products: </span></p> <ul> <li><span>Orthomosaic colour-infrared, 10-16 cm pixel resolution</span></li> <li><span>Colour-infrared spectral bands: 790nm, 660nm, 550nm</span></li> <li><span>DEM, 20-30cm pixel resolution</span></li> <li><span>Data coverage: approx. 5500 x 2500 m</span></li> <li><span>Elevation profile: 20-680m </span></li> <li><span>Processing in Agisoft Metashape</span></li> </ul> <p><span>Additional data supplement for article:<br>Barnes, E. (2020). Assessment of Drone-Borne Multispectral Mapping in the Exploration of Magmatic Ni-Cu Sulphides–an Example from Disko Island, West Greenland. <br><em>URN: urn:nbn:se:uu:diva-418858</em></span></p> <p>MULSEDRO field campaign was conducted under scientific survey licence (VU-00158-2019) within mineral exploration licence MEL 2018-16 by Blue Jay Mining PLC. This research has been supported by the project MULSEDRO, funded by HZDR-HIF & EITRawMaterials (project ID 16193) and the European Union.</p>
Rock glaciers in the Himalaya
<p><span>This dataset consists in rock glacier inventoried over the Himalayas, described briefly in Jones et al. (2021) and in more detail in the associated paper Harrison et al. (2024). </span></p> <p> </p> <p><span>Data description:</span></p> <p> </p> <p><span>1) Point-based inventory for the Greater Himalayas (24,968 landforms)</span></p> <p><span> Rock glacier identification for the Greater Himalaya was done in Google Earth Pro based on geomorphic indicator</span></p> <p><span> (surface flow structure, rock glacier body and frontal slope). For consistency, each pin point was digitised at the elevation at which the base of the frontal slope met the slope downstream. </span></p> <p> </p> <p><span>2) Sampled rock glacier points</span></p> <p><span> Randomly selected sample (5%, n= 2070 landforms) from the Western, Central, and Eastern Himalaya regions, provided as point shapefile.</span></p> <p> </p> <p><span>3) Sampled rock glacier polygons </span></p> <p><span><em> </em></span><span>Randomly selected sample (5%, n= 2070 landforms) from the Western, Central, and Eastern Himalaya regions digitised on Google Earth. The extended geomorphological footprint was used, in agreement with RGIK (2022) guidelines. </span><span><em>In the GH rock glacier inventory both intact and relict rock glaciers were digitised. Data are provided as polygon shape file and excel sheet.</em></span></p> <p><span><em>Data are provided in<span> </span>Geographic Coordinate System: GCS_WGS_1984</em></span></p> <p><span><em>Datum: D_WGS_1984</em></span></p> <p> </p>
Fecal glucocorticoid metabolite levels of American pika (Ochotona princeps) and habitat characteristics of their associated territories found in rock glaciers adjacent to Niwot Ridge and within Rocky Mountain National Park, 2018 - 2019.
To understand whether stress-associated hormones vary with metrics of habitat quality, we measured fecal glucocorticoid metabolite (FGM) levels in the American pika (Ochotona princeps), a small mammal with well-defined habitat (talus), that can vary in quality depending on the presence of rock ice features (RIFs). In 2018, we sampled pika scat from two types of RIFs: “active” rock glaciers thought to harbor subsurface ice recently, and “fossil” rock glaciers considered long devoid of subsurface ice (as classified by Janke 2005, 2007). Specifically, fecal pellets were collected from pika territories located in rock glaciers within eight sites along the Front Range of Colorado: four in Rocky Mountain National Park (2 active, 2 fossil) and four adjacent to Niwot Ridge (2 active, 2 fossil) (pika_fecal_glu_rg.aw.csv). To account for possible seasonal variation in pika FGM, scat samples were collected in the alpine spring and fall. To understand other influences of habitat quality on FGMs, we also measured fine-scale habitat differences between rock glaciers in 2019, including talus depth, clast size, and land cover metrics related to forage (pika_fecal_habitat_rg.aw.csv).
Displacements on selected rock glaciers in the United States
<p>Lateral displacements between two years on selected rock glaciers in the United States (Rocky Mountains and westwards). Displacements were measured using image correlation on repeat orthoimages from the United States Geological Survey archive. Correlation software used: CIAS (http://mn.uio.no/icemass).</p> <p><em><strong>The data are the raw data behind the following publication. This publication contains more details about the measurements. </strong></em></p> <p><a href="https://www.nature.com/articles/s41467-024-52093-z">Kääb, A., Røste, J. Rock glaciers across the United States predominantly accelerate coincident with rise in air temperatures. Nat Commun 15, 7581 (2024). https://doi.org/10.1038/s41467-024-52093-z</a></p> <h3>Detailed rock glacier positions [lat°, lon°]:</h3> <p>Star Peak [44.253,-120.417]<br>Galena creek [44.645,-109.791]<br>Sulphur creek [44.617,-109.756]<br>Crater Mtn [44.023,-109.627]<br>Old Hyndman [43.743, -114.106]<br>Ferguson ranch [39.270, -107.194]<br>Thomas lake [39.270,-107.155]<br>Arapaho [40.020,-105.641]<br>Mt Mears [38.018,-107.871]<br>Mt Sneffels [38.010,-107.781]<br>Teakettle Mtn [38.011,-107.768]<br>Twin Sisters [37.762,-107.803]<br>Pine creek [37.078,-118.450]<br>Birch creek [37.065,-118.431]<br>Cardinal Mtn N [37.011,-118.414]<br>Cardinal Mtn S [37.008,-118.409].</p> <h3>Names of individual displacement files:</h3> <p>Short-name-of-rock-glacier_year1_year2_correlation-window-size_search-window-size_xxx.txt/.csv<br>xxx is either 'helm' indicating that the two orthoimages have been coregistered using Helmert transformation, or 'filt' indicating Helmert transformation and filtering of grid-based measurements for outliers. </p> <h3>Data columns of each file:</h3> <p>X: UTM coordinate, Easting of displacement measurement point <br>Y: UTM coordinate, Northing of displacement measurement point<br>dx: displacement component in east<br>dy: displacement component in north<br>length: pythagoras of dx and dy (vector length)<br>direction: azimuth of vector (from North, clock-wise)<br>max_corrcoeff: correlation coefficient of displacement match<br>avg_corrcoeff: background correlation coefficent at a matching poistion. <br><em><strong> </strong></em></p> <p> </p>
RTK-GPS measurements on 6 rock glaciers in the La Sal and Uinta Mountains, Utah between 2021 and 2023
<p>RTK-GPS surveying of points marked on the surfaces of 6 rock glaciers in the La Sal and Uinta Mountains, Utah</p> <p>All<span> </span>measurements made with a pair of Emlid Reach RS2 RTK-GPS receivers connected in FIX mode</p> <p>Comparison of the x/y coordinates for points in subsequent surveys reveals planimetric motion of the rock glacier</p> <p>Error on measurements in the z direction (vertical) is large enough that up/down changes in the rock glaicer surface cannot be quantified from these data alone</p>
Supplementary files: A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau
<p>Supplement of "A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau". The supplementary files provide the outlines and parameters of the rock glaciers inventoried by InSAR-assist kinematic-based approach in the Daxue Shan, Southeast Tibet Plateau. </p> <p>Based on the Sentinel-1A ascending SAR images acquired between 2015 and 2019, we derived a five-year-long LOS mean velocity map of the study area. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images.</p>
Datafiles resulting from work at the RG-2 rock glacier in the Uinta Mountains, 2021-2022
<p>Datafiles resulting from work at the RG-2 rock glacier in the Uinta Mountains, 2021-2022</p> <ul> <li>2023JF007453-Table S1 – list of interferograms processed</li> <li>2023JF007453-Table S2 – cosmogenic surface-exposure dating results</li> <li>RG-2 Surveying – RTK-GPS surveying results for 32 points on the rock glacier surface. Used to calculate displacement between September 2021 and September 2022</li> <li>GPR – exported ground penetrating radar survey transects at the head of the rock glacier.Files are provided in .sgv form and the locations of each transect are provided in .kml form</li> <li>UAV – orthophotomosaic and digital elevation model for the RG-2 rock glacier produced from images collected by a drone mapping mission flown on 9/22/2021</li> </ul>
Subsurface Temperature Data from Seven Rock Glaciers in Rocky Mountain National Park and Niwot Ridge LTER, Colorado, USA, 2018-2019
HOBO temperature-sensing data loggers (hereafter, sensors) were placed under the surface of seven rock glaciers predicted to have the presence (n=4) or absence (n=3) of sub-surface ice in Rocky Mountain National Park and near Niwot Ridge Long-term Ecological Research site in Colorado. Sub-surface temperatures were recorded to evaluate habitat differences among American pika populations. All sensors (model H08 pro, H08, or H02; Onset Computer Corporation, onsetcomp.com) recorded temperature (±0.2° Celsius) every one or two hours for one year starting in September 2018. Each rock glacier received at least one sensor, positioned near the center and near pika sign. Three rock glaciers received an additional sensor positioned near the edge of the rock glacier near pika sign. Sensors were placed at similar depths (~0.75 m) and similar aspects.
Glacier Bay National Park glacial rock avalanche inventory (1984-2020)
<p>An inventory of supraglacially deposited rock avalanches that occurred in Glacier Bay National Park, Alaska, between 1984 and 2020.</p> <p>Reference: Smith, W.D., Dunning, S.A., Ross, N., Telling, J., Jensen, E.K., Shugar, D.H., Coe, J.A. and Geertsema, M. (2023) Revising supraglacial rock avalanche magnitudes and frequencies in Glacier Bay National Park, Alaska. <em>Geomorphology</em>, doi: <a href="https://doi.org/10.1016/j.geomorph.2023.108591">https://doi.org/10.1016/j.geomorph.2023.108591</a></p>
Data from GB-SAR measurements on Lazaun rock glacier (Val Senales, South Tyrol, northern Italy)
<p>The *.csv files report the kinematic data extracted from the displacement maps obtained from the processing of ground-based SAR measurements of Lazaun rock glacier (Senales Valley, Northern Italy) and presented in the paper <strong><em>“Unprecedented observation of hourly rock glacier velocity with Ground-Based SAR</em></strong><strong><em>”</em></strong> (Bertone et al., 2023, submitted). The data were used to display the time series reported in the paper. The data refer to two field campaigns named <em>survey1</em> (<em>from 09/08/2018 to 18/08/2018</em>), and <em>survey2 </em>(<em>from 13/09/2018 to 03/10/2018</em>).</p> <ul> <li><em>TS_August_moving.cs</em>v: includes the survey 1 displacement data of moving points on the rock glacier.</li> <li><em>TS_August_stable.csv</em>: includes the survey 1 displacement data of stable points outside the rock glacier.</li> <li><em>TS_September_moving.csv</em>: includes the survey 2 displacement data of moving points on the rock glacier.</li> <li><em>TS_ September_stable.csv</em>: includes the survey 2 displacement data on stable points outside the rock glacier.</li> </ul> <p>Each *.csv file contains the following fields:</p> <ul> <li>“<em>displacement</em>”: cumulative displacement (mm);</li> <li>“<em>pointName</em>”: name of point where the time series are computed, as displayed in figure 3;</li> <li>“<em>time</em>”: time of acquisition, in <em>day</em>/<em>month</em>-<em>hour</em>:<em>minute </em>format;</li> <li>“<em>velocity</em>”: velocity (mm/hour);</li> <li>“<em>X</em>” and “<em>Y</em>”: coordinates of points (EPGS 32632) where the time series were extracted.</li> </ul> <p>The polygon representing the area of the Lazaun rock glacier is provided by the shapefile <em>Polygon_RG_Lazaun.shp.</em></p> <p>The velocity distribution on the Lazaun rock glacier as obtained in suvey1 and survey2 is provided by raster files. The velocities were obtained along the LOS and are in mm/day. The two raster files, named <em>raster_survey_1.tif</em> and <em>raster_survey_2.tif</em>, are completed by two files of the same name with the colored overlay (<em>raster_survey_1.lyr</em> and <em>raster_survey_2.lyr</em>).</p> <p>The two velocity maps obtained from survey1 and survey2 are also provided as complete georeferenced figures in geotiff format and named <em>figure_survey_1.tif</em> and<em> figure_survey_2.tif.</em></p> <p>All the geospatial data are provided in the EPSG 32632 spatial reference.</p> <p> </p> <p><strong>AIR TEMPERATURE</strong></p> <p>The LAZ_AIRT_survey1.csv and LAZ_AIRT_survey2.csv files contain the air temperature measured about 250 m North from the Lazaun rock glacier by a Gemini Tinytag TGP 4020 data logger connected to an external probe installed in a passive radiation shield. The data cover the two GB-SAR field campaigns (survey 1 from 09/08/2018 to 18/08/2018, and survey 2 from 13/09/2018 to 03/10/2018).</p> <p>The csv files contain the following fields:</p> <ul> <li>Date: time of acquisition, in <em>day/month/year</em> <em>hour:minute</em> format;</li> <li>Mean: the mean air temperature recorded by the datalogger during the corresponding hour;</li> <li>Maximum: the maximum air temperature recorded by the datalogger during the corresponding hour;</li> <li>Minimum: the minimum air temperature recorded by the datalogger during the corresponding hour;</li> </ul>
The First Rock Glacier inventory for the Greater Caucasus
<p>In this study we produce the first inventory of rock glaciers from the entire Greater Caucasus region—Russia, Georgia, and Azerbaijan. A remote sensing survey was conducted using Geo-Information System (GIS) and Google Earth Pro software based on high-resolution satellite imagery—SPOT, Worldview, QuickBird, and IKONOS, based on data obtained during the period 2004–2021. Sentinel-2 imagery from the year 2020 was also used as a supplementary source. The ASTER GDEM (2011) was used to determine location, elevation, and slope for all rock glaciers. </p> <p>This inventory provides a database for understanding the extent of permafrost in the Greater Caucasus and is an important basis for further research of geomorphology and palaeoglaciology in this region.</p>
First rock glacier inventory of the Peruvian Andes: distribution, classification and climatic characterization
<p>The contours of the Rock glaciers (RGs) were manually digitized, identifying a total of 2271 (967 active, 507 inactive, 311 intact and 486 relict) covering 109.1±1.5 km2 and distributed in 15 of the 20 cordilleras of Peru. The Minimum Altitude Front (MAF) of RGs is 4339 m a.s.l., while most of them are between 4800 to 5000 m a.s.l. For the Southwest zone (Z-IV) where 94% of the RGs are located, the Binary Logistic Regression Model (BLRM) shows that MAAT and slope are the variables that have the greatest influence on the presence of 76.5% RGs evaluated. While in the North (Z-I), Central (Z-II) and Southeastern (Z-III) zones, the topoclimatic variables analyzed did not show statistical significance. This first inventory represents an important input for modeling the spatial distribution of permafrost in the Peruvian Tropical Andes.</p>
On following pages: 48. Mutable Shrew (Sorex mutabilis); 49. Verapaz Shrew (Sorex veraepacis); 50. Rock Shrew (Sorex Mexican Long-tailed Shrew (Sorex oreopolus); 55. Orizaba Long-tailed Shrew (Sorex orizabae); 56. Chestnut-bellied Shrew (Sorex rohweri): 60. South-eastern Shrew (Sorex longirostris); 61. Masked Shrew (Sorex cinereus); 62. Maryland Shrew Mountain Shrew (Sorex milleri): 66. Preble's Shrew (Sorex preblei); 67. Prairie Shrew (Sorex hayden); 68. Pribilof Island Saint Lawrence Island Shrew (Sorex jackson); 72. Kamchatka Shrew (Sorex camtschaticus); 73. Paramushir Shrew (Sorex (Sorex ornatus); 77. American Water Shrew (Sorex palustris); 78. Western Water Shrew (Sorex navigaton; 79. Glacier Bay Baird's Shrew (Sorex bairdi); 83. Pacific Shrew (Sorex pacificus); 84. Fog Shrew (Sorex sonomae); 85. New Mexico Shrew dispan; 51. Smoky Shrew (Sorex fumeus); 52. Inyo Shrew (Sorex tenellus); 53. Dwarf Shrew (Sorex nanus); 54. (Sorex ventralis); 57. Veracruz Shrew (Sorex veraecrucis); 58. Ixtlan Shrew (Sorex ixtlanensis); 59. Olympic Shrew (Sorex fontinalis); 63. Mount Lyell Shrew (Sorex lyell); 64. Zacatecas Shrew (Sorex emarginatus), 65. Carmen Shrew (Sorex pribilofensis); 69. Barren Ground Shrew (Sorex ugyunak); 70. Portenko's Shrew (Sorex portenkol); 71. leucogasten; 74. American Pygmy Shrew (Sorex hoyi); 75. Vagrant Shrew (Sorex vagrans); 76. Ornate Shrew Water Shrew (Sorex alaskanus); 80. Eastern Water Shrew (Sorex albibarbis); 81. Marsh Shrew (Sorex bendiril); 82. (Sorex neomexicanus); 86. Montane Shrew (Sorex monticolus). in Soricidae
On following pages: 48. Mutable Shrew (Sorex mutabilis); 49. Verapaz Shrew (Sorex veraepacis); 50. Rock Shrew (Sorex Mexican Long-tailed Shrew (Sorex oreopolus); 55. Orizaba Long-tailed Shrew (Sorex orizabae); 56. Chestnut-bellied Shrew (Sorex rohweri): 60. South-eastern Shrew (Sorex longirostris); 61. Masked Shrew (Sorex cinereus); 62. Maryland Shrew Mountain Shrew (Sorex milleri): 66. Preble's Shrew (Sorex preblei); 67. Prairie Shrew (Sorex hayden); 68. Pribilof Island Saint Lawrence Island Shrew (Sorex jackson); 72. Kamchatka Shrew (Sorex camtschaticus); 73. Paramushir Shrew (Sorex (Sorex ornatus); 77. American Water Shrew (Sorex palustris); 78. Western Water Shrew (Sorex navigaton; 79. Glacier Bay Baird's Shrew (Sorex bairdi); 83. Pacific Shrew (Sorex pacificus); 84. Fog Shrew (Sorex sonomae); 85. New Mexico Shrew dispan; 51. Smoky Shrew (Sorex fumeus); 52. Inyo Shrew (Sorex tenellus); 53. Dwarf Shrew (Sorex nanus); 54. (Sorex ventralis); 57. Veracruz Shrew (Sorex veraecrucis); 58. Ixtlan Shrew (Sorex ixtlanensis); 59. Olympic Shrew (Sorex fontinalis); 63. Mount Lyell Shrew (Sorex lyell); 64. Zacatecas Shrew (Sorex emarginatus), 65. Carmen Shrew (Sorex pribilofensis); 69. Barren Ground Shrew (Sorex ugyunak); 70. Portenko's Shrew (Sorex portenkol); 71. leucogasten; 74. American Pygmy Shrew (Sorex hoyi); 75. Vagrant Shrew (Sorex vagrans); 76. Ornate Shrew Water Shrew (Sorex alaskanus); 80. Eastern Water Shrew (Sorex albibarbis); 81. Marsh Shrew (Sorex bendiril); 82. (Sorex neomexicanus); 86. Montane Shrew (Sorex monticolus).
Multisensor monitoring data of Hochebenkar Rock Glacier
<p>HRG_DSM_stack.tif: Area-wide monitoring time series of Hochebenkar Rock Glacier (Oetz valley, Tyrol, Austria) based on multitemporal digital surface models (DSMs) derived from</p> <ul> <li>photogrammetry using historical arerial imagery (1953, 1971, 1977, 1990, 1997)</li> <li>airborne laser scanning (2006, 2009, 2010, 2011, 2017)</li> <li>and unmanned aerial vehicle-based laser scanning (2018, 2019, 2020, 2021).</li> </ul> <p>The airborne laser scanning data from 2006 and 2017 were provided by the Federal Government of Tyrol and are licenced under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.de) and must be used according to the following conditions of use: https://www.tirol.gv.at/data/nutzungsbedingungen/</p> <p>HRG_HS_stack.tif: Shaded reliefs computed from the multitemporal DSMs, which were used for applying the image correlation technique to derive displacement vectors.</p> <p>HRG_DDSM_stack.tif: Differential digital surface models computed by subtracting the subsequent DSMs. The uncertainty of the individual DDSMs are masked.</p> <p>Shapefiles in HRG_velocity_vectors.zip: Mean velocity vectors (m/yr) computed from the displacement vectors by dividing the vector length by the time period between the acquisition campaigns.</p> <p>All datasets are provided in the Austrian GK West projection (EPSG 31254). The spatial resolution of the raster datasets is 1 metre.</p>
Ground Freezing Index (GFI) and the relative change of the minimum seasonal velocity compared to the mean velocity between 2005 and 2017 at Becs-de-Bosson rock glacier in the Swiss Alps
<p>This dataset contains the Ground Freezing Index (GFI) and the relative change of the minimum seasonal velocity compared to the mean velocity between 2005 and 2017 at Becs-de-Bosson rock glacier in the Swiss Alps. Velocity data is measured by GNSS surveys. GFI is the sum of the daily negative GSTs during the entire freezing season, indicating the coldness of the winter temperature.</p>
Rock glacier inventory from multi-temporal InSAR measurement and optical image interpretation in southern Daxue Shan
<p>Based on the Sentinel-1A ascending SAR images acquired between June 2019 and June 2022, we derived a five-year-long annual average velocity map in southern Daxue Shan. Kinematic characteristics from InSAR measurement and geomorphic characteristics from optical image interpretation are then considered comprehensively for rock glaciers inventorying. A total of 860 rock glaciers are compiled and their geomorphic and kinematic parameters are calculated.</p>
Rock glaciers Gruben, Muragl and Murtel, Switzerland: Area-wide flow fields, Version 1
Besides their thermal and mechanical properties, rock glaciers are essentially defined by their kinematics. Knowledge of the permafrost flow field provides important information about the origin, morphology, development, dynamics and internal structure of creeping mountain permafrost. Monitoring permafrost behavior helps detecting climate signals. Aerial photogrammetry was used for determining digital terrain models (DTM) with high resolution.
Rock glaciers, Bernese Alps, western Switzerland, Version 1
This inventory has been established from the results of the interpretation of aerial photographs and field work between 1992 and 1994.The area investigated covers the entire Bernese Alps (western Switzerland), about 4200 km2, and is situated between 46 deg 10' and 46 deg 50' N and 7 deg 10' and 8 deg 30' E. The Bernese Alps are one of the main European watersheds, separating the catchment area of the Aare (draining into the North Sea via the Rhine) from that of the Rhone (which flows into the Mediterranean Sea). The lowest points of the study area are situated in the valley floors of the two rivers at about 500 m asl, whereas several summits exceed 4000 m asl (highest point-- Finsteraarhorn, 4273 m asl). The main structure of the Bernese Alps is the crystalline Aar massif striking WSW-ENE and culminating in the western part of the study area. To the north and the west, the massif is covered by thrust nappes consisting of sedimentary rocks mainly of Mesozoic and early Tertiary age (chiefly marine limestones, shales, and sandstones). Their summits are considerably lower (mostly between 2000 and 3500 m asl).According to their geographic situation between 46 deg and 47 deg N, the climate of the Bernese Alps is of temperate character typical for the zone of the westerlies. Because of the their horizontal and vertical extension, the Alps themselves have considerable influence on the climate. Based on climatic criteria, two main realms can be distinguished: the moist and quite oceanic part in the north of the main watershed and the slightly more continental part sloping south to the Rhone. The northern part, exposed to the westerlies, shows maximum precipitation during summer, with quite low variability, whereas the south is somewhat dryer, showing no distinct maximum but higher variability. Accordingly, mean cloudiness is higher in the north. This results in an increase in the height of the mean glacier elevation from about 2500 m asl in the northern part of the Bernese Alps to 2900 m asl in the south. Because of the high precipitation (locally exceeding 4000 mm per year) and their external situation, the Bernese Alps show a lower equilibrium line of the glaciers and are the mountain group showing the heaviest glacierization of the Alps, which leaves little space for periglacial phenomena (both the glacier showing the lowermost front and the largest glacier of the Alps are situated within the study site). However, this does not mean that permafrost is rare in the Bernese Alps; above 3300 m asl, permafrost can be considered as continuous. In fact, the bedrock of many of the higher mountains in the Bernese Alps is perennially frozen. The number of rock glaciers found in the Bernese Alps is relatively small compared with other Alpine regions. Furthermore, the objects are quite small. This can be explained with the stronger glacierization. Modelling shows that permafrost ground amounts to roughly half as much as the glacierized area (subglacial permafrost not included). (Imhof, 1996) These data are presented on the CAPS Version 1.0 CD-ROM, June 1998.
Inventory of Rock Glaciers along the Ghunsa Valley, Kanchanjunga Himal, Eastern Nepal, Version 1
This dataset provides information on a number of Nepali rock glaciers distributed above 4250 m ASL along the Ghunsa valley and the Kanchanjunga glacier (originating from the summit of Mt. Kanchanjunga, 8586 m ASL). The rock glaciers were identified on the basis of air photo analysis and were visited in October and November 1999. The longitude, latitude, and elevation of each rock glacier are given. Geographic coordinates were determined using a 1:50,000 topographic map of Nepal. Two categories of rock glaciers are described: glacier-derived and talus-derived rock glaciers. The internal structure of two rock glaciers (KR4 and KR23) were investigated by DC resistivity imaging.
ScienceDex guides
Understand access before you commit
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.