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FIG. 7 in Discovery of Oligocene-aged mammals in Glacier National Park (Kishenehn Formation), Montana
FIG. 7. — Size analysis of CM 87801 and other specimens of Paciculus Cope, 1879: A, comparison of the size of m1; B, comparison of the size of m2. GNP denotes the specimen from Glacier National Park (Kishenehn Formation). Note that the data for P. cedrus Korth, 2014 are in fact a species mean. Sample size is indicated for each comparison. See Table 6 for data. Abbreviations: GNP, Glacier National Park specimen; Pce, P. cedrus; Pco, P. copiosus Korth, 2010; PCP, P. cf. P. montanus; Pd, P. dakotensis Korth, 2010; Pg, P. gloveri MacDonald, 1970; Pi, P. insolitus Cope, 1879; Pm, P. montanus Black, 1961; Pmc, P. mcgregori MacDonald, 1970; Pn, P. nebraskensis Alker, 1969; Pwa, P. walshi Lindsay, Whistler, Kalthoff & von Koenigswald, 2016; Pwo, P. woodi MacDonald, 1963.
FIG. 8 in Discovery of Oligocene-aged mammals in Glacier National Park (Kishenehn Formation), Montana
FIG. 8. — Shape analysis of CM 87801 and other specimens of Paciculus Cope, 1879: A, comparison of the shape of m1; B, comparison of the shape of m2. GNP denotes the specimen from the Glacier National Park (Kishenehn Formation). Note that the data for P. cedrus Korth, 2014 are in fact a species mean. Each point otherwise represents a specimen. Disparity is showed for species when three or more specimens were measured. See Table 6 for data. Abbreviations: GNP, Glacier National Park specimen; Pce, P. cedrus Korth, 2014; Pco, P. copiosus Korth, 2010; PCP, P. cf. P. montanus; Pd, P. dakotensis Korth, 2010; Pg, P. gloveri MacDonald, 1970; Pi, P. insolitus Cope, 1879; Pm, P. montanus Black, 1961; Pmc, P. mcgregori MacDonald, 1970; Pn, P. nebraskensis Alker, 1969; Pwa, P. walshi Lindsay, Whistler, Kalthoff & von Koenigswald, 2016; Pwo, P. woodi MacDonald, 1963.
FIG. 3 in Discovery of Oligocene-aged mammals in Glacier National Park (Kishenehn Formation), Montana
FIG. 3. — Size analysis of GLAC 26988 and other leptomerycids from the Arikareean and Hemingfordian: A, comparison of the depth of the dentary at different tooth positions; B, comparison of the size of the premolars; C, comparison of the size of the molars. GNP denotes the specimen from Glacier National Park (Kishenehn Formation); data from the left jaw. Note that the data for Pseudoparablastomeryx Frick, 1937 are in fact a species mean. Sample size is indicated for each comparison. See Tables 1-3 for data. Abbreviations: ant, anterior lophid; post, posterior lophid; Ppf, Pseudoparablastomeryx francescita (Frick, 1937); Pps, P. Pseudoparablastomeryx scotti (Frick, 1937); Ps, Pronodens silberlingi Koerner, 1940; Psp, Pronodens sp.; Pt, P. transmontanus (Douglass, 1903).
Fig. 3 in Molecular and Morphological Snapshot Characterisation of the Protist Communities in Contrasting Alpine Glacier Forefields
Fig. 3. Rarefaction analysis derived from the clone libraries of the vegetated transects of Tiefen forefield and Wildstrubel forefield. Dashed lines correspond to 95% confidence intervals.
Fig. 2 in Molecular and Morphological Snapshot Characterisation of the Protist Communities in Contrasting Alpine Glacier Forefields
Fig. 2. Relative abundances (in percentage) of ciliate-related sequences detected in the 18S rRNA gene clone libraries from the vegetated transects of the (a) Tiefen forefield and (b) Wildstrubel forefield. Species names are based on BLAST comparison of the sequences with the NCBI database (first similarity with a known taxonomic group).
Fig. 1 in Molecular and Morphological Snapshot Characterisation of the Protist Communities in Contrasting Alpine Glacier Forefields
Fig. 1. Location of the two sampled forefields of the (a) Tiefen glacier and (b) Wildstrubel glacier. Dots indicate sampling spots (white: unvegetated transects; black: vegetated transects).
Figure 3 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska
Figure 3. Population trends of harbor seals on the southeastern Kenai Peninsula during pupping (Panel A) and the molt (Panel B) based on aerial surveys conducted from 2004 to 2013.
Figure 6 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska
Figure 6. Proportion of harbor seal pups counted during pupping relative to total seals (including pups) during pupping (Panel A) and during the subsequent molt (Panel B) at glacial ice (blue) and terrestrial (brown) haul-outs among different locations and time periods. The box (bounded by 13%–23%) represents range of the percent of pups to total seals counted at terrestrial haul-outs outside of Alaska (Venables and Venables 1955, Boulva 1975, Boulva and McLaren 1979, Brown and Mate 1983, Calambokidis et al. 1987). The vertical bar within the box represents the proportion of pups born into an increasing population in British Columbia (Bigg 1969). * designates proportions derived from mean values adjusted for standardized environmental conditions. Sources include (Hoover 1983; Calambokidis et al. 1987; Pitcher 1990; Mathews 1995; Jemison et al. 2006, 2016; Mathews and Pendleton 2006; Womble et al. 2010; Mathews et al. 2016; Kenai Fjords National Park, unpublished).
Figure 5 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska
Figure 5. Comparison of numbers of mother-pup harbor seal pairs (MP) counted in Aialik Bay and Northwestern Fjord from 2004 to 2013 using multiple survey methods. Area charts indicate maximum annual counts of MP in Aialik Bay and Northwestern Fjord (light blue) and in Aialik Bay (darker blue). Maximum annual MP counts in Northwestern Fjord are indicated by the blue green line. White dots with 95% CI error bars show generalized linear model (GLM) mean estimate MP based on remote video observations in Aialik Bay during standardized favorable haul-out conditions.
Figure 4 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska
Figure 4. Comparison of regional changes in numbers of harbor seal pups (Panel A), seals without pups present during pupping (Panel C), and molting seals (Panel D) in four regions of the coastline extending from McCarty Fjord through Johnstone Bay. Pup counts at individual fjords summarized as RB-JB are compared in Panel B. Bars represent maximum counts obtained during aerial surveys. Total counts for all regions (black dots) are measured on the secondary axis. Histogram bars of molt counts represent maximum counts for each year obtained from aerial surveys; triangles indicate counts in Northwestern Fjord based on KFT vessel surveys.
Figure 1 in Harbor seal use of glacier ice and terrestrial haul-outs in the Kenai Fjords, Alaska
Figure 1. Maps showing locations of the study area (Panel A) and of study area place names (Panel B). Red shading along the coastline, in the Panel A inset, illustrates the geographic extent of surveys (portions of flights flown at altitudes <335 m).
Proglacial sediments in High Arctic glacier foreland: A case study of Werenskioldbreen, Svalbard
<p><span>The glacier environment exhibit a high sensitivity to the global climate change leading to progressive deglaciation and the exposure of previously ice-covered land. The newly exposed <a name="_Hlk165289159"></a>terrain provides a valuable opportunity to observe rapid ecosystem changes, such as the accumulation of glacial sediments, the development of soil-forming and progressive alterations in water and biogeochemical cycles. While developing hydrological and hydrogeological models for the Werenskioldbreen proglacial expanding zone, we encountered a significant problem due to insufficient data for parameterizing glacial sediments, constituting the environment for water flow and storage. These data provide detail insight into the physicochemical parameters of glacial sediments and classify them in terms of grain size distribution, hydraulic conductivity, pH, and C<sub>org</sub>, N<sub>t </sub>and P<sub>t</sub> contents. </span><span>Samples for macroscopic examination and further laboratory analysis were collected from each different proglacial sediment in the profile. Macroscopic characterisation in the field was carried out in accordance with standards <span><span>PN-EN ISO 14688-1 and PN-EN ISO 14688-2 introduced into the catalogue of Polish Standards in 2006 and are cited in PN-EN 1997-2:2009, known as Eurocode 7: Geotechnical engineering design - Part 2: Identification and investigation of soils.<br></span></span></span><span><span><span>More information on the data acquisition methodology is included in the publication (same title) or can be obtained through the contact provided.</span></span></span></p>
Dataset for five lake-terminating glaciers in Iceland 2008-2020
<p>Data for five lake-terminating glaciers in Iceland for the period 2008-2020, incuding:</p> <p>(1) Surface velocity rasters at different time periods.</p> <p>(2) Shapefiles of glacier terminus position at different time periods.</p> <p>(3) Shapefiles of proglacial lake area at different time periods.</p> <p>Velocity data were generated using select TerraSAR-X scenes and the offset tracking toolbox in SNAP (Sentinel Application Platform). Glacier teminus position and proglacial lake area were digitised from select Landsat 7, Landsat 8 and Sentinel-2 scenes. Full methodology is given in the accompanying paper that is currently in review (preprint DOI to follow). </p> <p>Glaciers of study are Skaftafellsjökull, Svínafellsjökull, Kvíárjökull, Fjallsjökull and Breiðamerkurjökull, all of which are found on the southern side of the Vatnajökull Ice Cap in southeast Iceland.</p> <p>Data for Fjallsjökull and Breiðamerkurjökull are for the period 2008-2020, whereas data for Skaftafellsjökull, Svínafellsjökull, Kvíárjökull are for the period 2010-2020 (there was no TerraSAR-X data from 2008 for these three glaciers). </p>
Datasets associated with "Quantifying debris thickness of debris-covered glaciers in the Everest region of Nepal through inversion of a sub-debris melt model"
<p>Datasets that accompany "Quantifying debris thickness of debris-covered glaciers in the Everest region of Nepal through inversion of a sub-debris melt model". These datasets include the debris thickness estimates derived including and excluding ponds (denoted as wponds and noponds, respectively), flux divergences, the shapefile of the 600 m boxes, the master 10 m DEM, the median x and y velocities, and the change in elevation for each pair of DEMs used in the study for Ngozumpa, Khumbu, and Imja-Lhotse Shar Glaciers. Debris thickness is in units of meters. Flux divergence is in units of meters per year. A negative flux divergence means the box is gaining mass and is called the emergence velocity; while positive flux divergence means the box is losing mass and is called the submergence velocity.</p>
Simulated length of 71 Alpine glaciers over the last millennium using OGGM
<p>This dataset contains the simulated length of 71 Alpine glaciers over the last millennium using the <a href="https://github.com/OGGM/oggm">version 1.0 of the Open Global Glacier Model</a> (OGGM) forced by global climate models (GCM) simulation outputs. For a description of the experimental design, see the associated publication:<br> <br> <a href="https://www.clim-past-discuss.net/cp-2018-48/">Goosse, H., Barriat, P.-Y., Dalaiden, Q., Klein, F., Marzeion, B., Maussion, F., Pelucchi, P. and Vlug, A.: Testing the consistency between changes in simulated climate and Alpine glacier length over the past millennium, Climate of the Past, 2018.</a><br> <br> Each NetCDF file corresponds to OGGM driven by one climate model over the period 1000-2004 CE. The file names are based on the acronyms given in the Table 1 of the associated publication. The variables included in the NetCDF files are:<br> <br> - g_length: Annual mean length of the glaciers, in meters<br> <br> - ID_glacier: an identifier for each glacier, allowing to make the link to the names of the glaciers given in glacier_names.txt.<br> <br> - time_year: the time in years CE<br> <br> In order to remove high frequency variability associated with the presence of snow that may remain in summer at altitudes lower than the glacier front, a filter with a 5-year window has been applied on the OGGM outputs to obtain the results stored in g_length.</p> <p>Please contact <a href="mailto:hugues.goosse@uclouvain.be">Hugues Goosse</a> for more information.</p>
Seismic and meteorological records from Trakarding-Trambau Glacier system, Nepal Himalaya (October 21 - November 9, 2017)
<p>The following geophysical field data (collected between October 21 and November 9, 2017) at Trakarding-Trambau Glacier system in Nepal Himalaya is provided in this dataset: </p> <p> </p> <p>(1) Hourly air-temperature measured at four sites (AWS, T1, T2 and T3). Units are degrees Celcius. Local time.</p> <p>[file name] Hourly_Air_temperature_AWS_T1_T2_T3_degC.csv</p> <p> </p> <p>(2) Hourly wind speed measured at the AWS site. Units are meters per second. Local time.</p> <p>[file name] Hourly_Wind_speed_AWS_MperS.csv</p> <p> </p> <p>The aforementioned stations had the following dGPS-derived coordinates:</p> <p>{Station, lat [deg], lon [deg], elevation [m] }</p> <p>AWS, 27.84356265, 86.4867231, 4805.582206</p> <p>T1, 27.84575824, 86.49189978, 4591.4429</p> <p>T2, 27.82976456, 86.51989785, 4768.8465</p> <p>T3, 27.855872, 86.531197, 5390.48</p> <p> </p> <p>(3) Raw seismic data (vertical component) recorded at five locations (DAM, C1, C2, C3, and C4) by four data-loggers (AKX, AKS, AKT, AKU) with a sampling frequency of 400 Hz. The file format (*.pri0) corresponds to a standard [.mseed]-format. Units are counts representing velocity. UTC time.</p> <p> </p> <p>The aforementioned stations had the following dGPS-derived coordinates:</p> <p>{StationName-Logger, lat [deg], lon [deg], elevation [m] }</p> <p> </p> <p>DAM-AKX, 27.870954, 86.463243, 4375.030762</p> <p>C1-AKS, 27.84546885, 86.49247316, 4594.0462</p> <p>C2-AKT, 27.82939476, 86.52001441, 4777.5211</p> <p>C3-AKU, 27.83593425, 86.53248327, 5288.3311</p> <p>C4-AKX, 27.87962877, 86.542076, 5555.2085</p>
Model results for `Surface pond energy absorption across four Himalayan glaciers accounts for 1/8 of total catchment ice loss'
<p>Model setup (setup.mat) and outputs (allkeyres.mat, postproc.mat) for 5000 runs of Monte Carlo supraglacial pond energy-balance modelling in the Langtang catchment of Nepal. The full set of results are included for the median model run (run_..._n1645.zip).</p> <p>Also included are flux gate results for calculation of emergence velocity (fgates...zip).</p>
Brewster Glacier AWS data 2007-2008
<p>Meteorological data collected over Brewster Glacier, New Zealand for the period December 2007-March 2008.</p> <p>The following paper should be cited for this dataset:</p> <p>Gillett, S. and Cullen, N. J. (2011), Atmospheric controls on summer ablation over Brewster Glacier, New Zealand. Int. J. Climatol., 31: 2033-2048. doi:<a href="https://doi.org/10.1002/joc.2216">10.1002/joc.2216</a></p> <p>The data sets have also undergone quality control consistent with later Brewster Glacier AWS datasets as described in:</p> <p>Cullen, N. J. and Conway, J. P.:(2015) A 22 month record of surface meteorology and energy balance from the ablation zone of Brewster Glacier, New Zealand, Journal of Glaciology, 61, 931-946. doi:10.3189/2015JoG15J004</p> <p>The readme file gives a brief description of the data in each column.</p> <p>Please get in touch if you would like more details - jono.conway@niwa.co.nz or nicolas.cullen@otago.ac.nz</p>
High Mountain Asia glacier velocities 2013-2015 (Landsat 8)
<p>This dataset contains the median glacier surface velocity for the Pamir-Karakoram-Himalaya for the years 2013-2015 (Landsat 8 only). The velocities has been obtained by feature-tracking of Landsat images spaced 1 year apart.</p> <p>The folder contains the following fields at 120 m resolution in GeoTiff format:</p> <p>- the velocity magnitude 'vel' (meters per year)</p> <p>- the x/y velocity components x_vel/y_vel (meters per year)</p> <p>- the associated errors err, x_err, y_err (meters per year)</p> <p>- the standard deviation of all the merged velocities 'std' (meters per year)</p> <p>- the number of image pairs that have been merged in the median</p> <p> </p> <p>I recommend filtering data with error larger than 10 m/yr.</p> <p>For more information and any use of the data, please refer to Dehecq, A., Gourmelen, N., Trouve, E., 2015. Deriving large-scale glacier velocities from a complete satellite archive: Application to the Pamir–Karakoram–Himalaya. Remote Sensing of Environment 162, 55–66. <a href="https://doi.org/10.1016/j.rse.2015.01.031">https://doi.org/10.1016/j.rse.2015.01.031 </a></p>
High Mountain Asia glacier velocities 1999-2003 (Landsat 7)
<p>This dataset contains the median glacier surface velocity for the Pamir-Karakoram-Himalaya for the years 1999-2003 (Landsat 7 SLC-ON only). The velocities has been obtained by feature-tracking of Landsat images spaced 1 year apart.</p> <p>The folder contains the following fields at 120 m resolution in GeoTiff format:</p> <p>- the velocity magnitude 'vel' (meters per year)</p> <p>- the x/y velocity components x_vel/y_vel (meters per year)</p> <p>- the associated errors err, x_err, y_err (meters per year)</p> <p>- the standard deviation of all the merged velocities 'std' (meters per year)</p> <p>- the number of image pairs that have been merged in the median</p> <p> </p> <p>I recommend filtering data with error larger than 10 m/yr.</p> <p>For more information and any use of the data, please refer to Dehecq, A., Gourmelen, N., Trouve, E., 2015. Deriving large-scale glacier velocities from a complete satellite archive: Application to the Pamir–Karakoram–Himalaya. Remote Sensing of Environment 162, 55–66. <a href="https://doi.org/10.1016/j.rse.2015.01.031">https://doi.org/10.1016/j.rse.2015.01.031 </a></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.