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774 results for “glacier”
FIGURE 6 in Tardigrada and Rotifera from moss microhabitats on a disappearing Ugandan glacier, with the description of a new species of water bear
FIGURE 6. Rotifera: A, B—trophi of bdelloid rotifers, C—adult bdelloid rotifer. All scale bars in micrometres.
FIGURE 2 in Tardigrada and Rotifera from moss microhabitats on a disappearing Ugandan glacier, with the description of a new species of water bear
FIGURE 2. Adropion afroglacialis sp. nov.: A—habitus, holotype, dorso-ventral projection (PCM), B—granulation on the dorsal side in the middle section of holotype indicated by arrows (PCM), C—granulation on the dorsal side of paratype (PCM), D—granulation on the dorsal side in the anterior section of holotype indicated by arrows (DIC), E—granulation on the III leg of paratype (PCM). All scale bars in micrometres.
Data presented in: Glacier recession and the response of summer streamflow in the Pacific Northwest United States, 1960‐2099
<p>This is an archive of data that is presented and described in:</p> <p>Glacier recession and the response of summer streamflow in the Pacific Northwest United States, 1960‐2099</p> <p><a href="https://doi.org/10.1029/2017WR021764">https://doi.org/10.1029/2017WR021764</a></p>
Earth's Future 2019 - Bosson et al. - World Heritage Glaciers data
<p>This file gathers the list, the shapefiles and the modeling of future evolution (according to GloGEM model, see Bosson et al., 2019 Earth's Future and Huss & Hock, Frontiers, 2015) of glaciers in natural World Heritage sites in 2018 according to Randolph Glacier Inventory 6.0.</p>
Datasets on multiparameter glacier change dynamics for the Jankar Chhu Watershed, Lahaul Himalaya, India
<p>Characterization of glacier changes in the surface area, terminus, equilibrium line altitude (ELA), elevation, and velocity was worked out for the Jankar Chhu Watershed (JCW) of Lahaul Himalaya using freely available satellite remote sensing data and the limited number of field observations. We studied changes using Corona (1971), Landsat (1993‒2017), Sentinel 2A (2016), the SRTM Digital Elevation Model (DEM; 2000), and the global TanDEM‒X DEM (2014). Our results showed that changes in glacier area (‒14.7 ± 4.3 km²), terminus (‒4.7 ± 0.4 m a¯¹), and ELA (~ 20 m rise) between 1971 and 2016 are smaller than previously reported. Glacier lake area increased by ~0.3 km² during 1971‒2016. An intricate pattern of mass changes across the JCW was observed, with surface lowering on an average of ‒0.7 ± 0.4 m a¯¹ which equates to a geodetic mass balance of ‒0.6 ± 0.4 m w.e. a¯¹ during 2000‒14. The computed glacier surface velocities (1971‒2017) reveal nearly stagnant debris-covered ablation zone but the dynamically active main trunk. The present study provides valuable insights into the recent multiparameter glacier variations, which are of critical importance to assess the future glacier dynamics on a regional scale in areas like the present one.</p>
FIGURE 6 in A new Andiperla Aubert (Plecoptera, Gripopterygidae) species from the Perito Moreno Glacier, Argentina
FIGURE 6. Andiperla willinki, holotype, male. 6a, labels of Holotype. 6b, habitus. 6c, abdominal segment IX–X, lateral view. 6d, abdominal segment X, posterior view.
FIGURE 3. Andiperla morenensis n in A new Andiperla Aubert (Plecoptera, Gripopterygidae) species from the Perito Moreno Glacier, Argentina
FIGURE 3. Andiperla morenensis n. sp., male. 4a–b, detail of branched sensilla (chloride cells) on abdomen membrane, scanning microscope image.
FIGURE 2. Andiperla morenensis n in A new Andiperla Aubert (Plecoptera, Gripopterygidae) species from the Perito Moreno Glacier, Argentina
FIGURE 2. Andiperla morenensis n. sp., female. 2a, abdominal segment IX–X, dorsal view. 2b, abdominal segment IX–X, lateral view. 2a, abdominal segment IX–X, ventral view.
FIGURE 1. Andiperla morenensis n in A new Andiperla Aubert (Plecoptera, Gripopterygidae) species from the Perito Moreno Glacier, Argentina
FIGURE 1. Andiperla morenensis n. sp., male holotype. 1a, abdominal segment X, dorsal view. 1b, abdominal segment X, lateral view. 1c, abdominal segment X, posterior-lateral view.
FIGURE 5. Andiperla willinki, male. 5a, abdominal segments IX–X, lateral view. 5b, abdominal segment X in A new Andiperla Aubert (Plecoptera, Gripopterygidae) species from the Perito Moreno Glacier, Argentina
FIGURE 5. Andiperla willinki, male. 5a, abdominal segments IX–X, lateral view. 5b, abdominal segment X, posterior view.
FIGURE 4. Andiperla morenensis n in A new Andiperla Aubert (Plecoptera, Gripopterygidae) species from the Perito Moreno Glacier, Argentina
FIGURE 4. Andiperla morenensis n. sp., male. 4a, right mandible, ventral view. 4b, left mandible, ventral view. 4c, right mandible, ventral view and detail of mola, scanning microscope image. 4d, left mandible, ventral view and detail of mola, scanning microscope image. 4e, maxilla, ventral view. 4f, maxilla, detail of palpus, lascinia and galea, ventral view. 4g, labium, ventral view.
Supplementary data: Hazard from Himalyan glacier lake outburst floods
<p>We provide supplementary data to the manuscript by <strong>Veh, G., Korup, O., and Walz A.: "Hazard from Himalayan glacier lake outburst floods".</strong></p> <p>These datasets (file ending .rds) are ready to use within the R software for statistical programming. Please copy all files into one single directory and follow the instructions given in the scripts. You can find the following scripts on the associated Github page: <strong>https://github.com/geveh/GLOFhazard</strong></p> <p>bayes_lm_piecewise_regression_stan.R</p> <p>lm_piecewise_const.stan</p> <p>R_Script_lake_area_vs_max_depth.R</p> <p>R_Script_Hazard_from_GLOFs_PNAS_supp.R</p> <p>HDIofMCMC.R</p> <p>Outputs within the scripts are written to disk, again as R-objects, if not suppressed by the user. Make sure you have at least 100 GB of free disk space.</p> <p> </p>
Glacier Area in Alaska and Europe
<p>This contains multispectral imagery of the Sentinel2 satellite. The bands contained are in order: 1,2,3,4,5,6,7,8,8A,9,11,12. Additionally corresponding Glacier Area maps from the RGI are given as a True Mask.</p>
Projections of Greenland periphery glaciers and ice caps's change
<p>Scripts and data for Projections of Greenland periphery glaciers and ice caps’s change by using Open Global Glacier Model.</p>
FESOM2.1 model data used in the paper 'Atmospheric blocking slows ocean-driven melting of Greenland's largest glacier tongue'
<p>This data set includes the data necessary to reproduce the findings of McPherson et al., in revision, and recreate the figures in the manuscript.</p> <p>The output of model simulations with the global ocean sea ice model FESOM2.1 is provided. </p> <p>In particular, the data set includes:</p> <ol> <li>long term means of potential temperature, salinity, velocity and basal melt of the 79N Glacier averaged over 1970-2021 (<a target="_blank" rel="noopener noreferrer">fesom.mean.1970_2021.t.s.u.v.mat</a>)</li> <li>air-sea heat flux anomaly and anomalous wind field at 10m, taking mean winter (DJF) conditions between 2014 - 2016 from the mean of the years 2017 - 2020 (fesom.anom.fh.wind.mat)</li> <li>Atlantic Water temperature and velocity anomalies, taking mean winter (DJF) conditions between 2014 - 2016 from the mean of the years 2017 - 2020 (fesom.anom.t.u.v.mat)</li> </ol> <p>Each file includes information on the model grid (longitude and latitude of nodes, depths of the vertical layers, elements, nodal areas) needed for plotting the data.</p>
Dataset: Meltwater discharge, suspended sediment and bedload flux, air temperature and precipitation at the Otemma Glacier terminus (2022 melt season)
<p>This dataset extends existing timeseries from the Otemma Glacier terminus on meltwater discharge (Müller and Miesen, 2022), suspended sediment and bedload flux (Mancini et al., 2023a), and air temperature and precipitation (Müller, 2022) into the 2022 melt season. The methodologies used for data collection were consistent with those used in previous years (see included technical report). Water and sediment flux measurements were performed at gauging station GS1 (aka Station 1) located in the main proglacial river, approx. 0.35 downstream of the glacier terminus (Müller and Miesen, 2022; Mancini et al., 2023a). Air temperature and precipitation data were collected at the "Glacier snout station" adjacent to GS1 (Müller, 2022). Data collection specifics for the 2022 melt season are provided in the <code>report2022.pdf</code> file.</p> <p><strong>DATA:</strong><br><code>river.csv</code> - water and sediment flux timeseries from GS1 with columns:<br>year: yyyy<br>frac_day: fractional day of the year<br>dt: measurement interval [mins]<br>Qw: meltwater discharge [m3/s]<br>Qss: suspended sediment flux [kg/s]<br>Qb: bedload flux [kg/s]<br>Q<em>xx</em>_lo or Q<em>xx</em>_hi: the lower and upper uncertainty bounds for Qw, Qss, Qb [kg/s]<br><em>Extended data for 2020 and 2021 melt seasons also included*</em></p> <p><code>weather.csv</code> - air temperature and precipitation timeseries spanning 18 Nov 2021 to 16 Aug 2022 from 'Glacier snout station' with columns:<br>datetime: local time (UTC+01 with DST) [yyyy-mm-dd hh:mm:ss] <br>temperature: air temperature [°C]<br>precipitation: liquid or solid precipitation [mm w.eq.]</p> <p><em>*Extended flux datasets are also provided for 2020 and 2021 given the difference in scope between Mancini et al. (2023b) and Jenkin et al. (2024, submitted for review). The datasets in Mancini et al. (2023b) have been post-processed for comparative analysis between two different gauging stations (upstream and downstream), while in this work, the most temporally complete timeseries of subglacial sediment export at the upstream gauging station was required. Therefore, the data provided here for 2020 and 2021 include: (i) a short extension of the timeseries, (ii) the removal of a suspended sediment clipping threshold, and (iii) filling of gaps in the 2020 suspended sediment timeseries with real measured data.</em></p>
2023 Mt. Hood Glacier Margins for "Unprecedented Twenty-First Century Glacier Loss on Mt. Hood, Oregon, U.S.A."
Open the record for dataset details and reuse information.
Thwaites Glacier thins and retreats fastest where ice-shelf channels intersect its grounding zone, dataset+code
<p>Thwaites Glacier thins and retreats fastest where ice-shelf channels intersect its grounding zone, updated dataset+code submitted for publication in The Cryosphere. Dataset includes all data produced in this study, including velocity maps derived from speckle tracking of Sentinel 1 images, maps of rates of ice shelf change and the annual mosaic digital surface models from which they were derived, maps of the basal conditions at Thwaites glacier, shapefiles and masks of the annual hydrostatic boundary (grounding line proxy position), and shapefiles of all hydrostatic boundary features, ice shelf basal channels and surface depressions, intermediate polygons used to filter the features, and digital surface model strips with registration data included as attributes. </p>
Additional code and data for running the simulations presented in the article "Using observations of surface fracture to address ill-posed ice softness estimation over Pine Island Glacier"
<p>The data required to run the inverse problems described in the article "Using observations of surface fracture to address ill-posed ice softness estimation over Pine Island Glacier" along with code specific to the modified inverse problem described therein, additional to that downloadable from BISICLES is downloadable from https://commons.lbl.gov/display/bisicles/BISICLES.</p> <p>These data are restricted to those involved in reviewing the aforementioned article.</p>
Support Materials for Manuscript "Climate change induces rapid growth of dead ice in Asian glaciers" Review
<div>Description of support materials of paper "Climate change induces rapid growth of dead ice in Asian glaciers".</div> <div> </div> <div>The detailed description of files is below:</div> <div> </div> <div>· Total.csv</div> <div>The subregional statistics of HMA dead ice area and mass in 2100 under different SSPs.</div> <div> </div> <div>· Folder ./Inventory</div> <div>Interdecadal potential dead ice inventory in HMA.</div> <div> </div> <div>· Folder ./Individual</div> <div>Interdecadal statistics of individual glaciers' dead ice in HMA for their ablation (unit: Gt), area (unit: km2) and mass (unit: Gt).</div>
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.