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8,171 results for “Mountain”
Fig. 12 in A mountain of millipedes VII: The genus Eviulisoma Silvestri, 1910, in the Udzungwa Mountains, Tanzania, and related species from other Eastern Arc Mountains. With notes on Eoseviulisoma Brolemann, 1920, and Suohelisoma Hoffman, 1963 (Diplopoda, Polydesmida, Paradoxosomatidae)
Fig. 12. Eviulisoma akkariae sp. nov., paratypes from Udzungwa Mts National Park, Sanje Kati camp and plot (A, C) and Udzungwa Mts National Park, Milo Mitatu (B, D), right gonopods. A. Mesal view. B. Ridged area on inner surface of solenophore. C. Ventral view. D. Lateral view. Abbreviations: cxl = coxal lobe; iap = intermediate acropodital process; map = mesal acropodital process; prf = prefemur; ra = ridged area of solenophore; slm = solenomere; sph = solenophore; sph-d, sph-i, sph-v = dorsal, intermediate and ventral lobes of solenophore; sr = serrated ridge; tr = transverse ridges. Scales: A, C–D = 0.1 mm; B = 0.05 mm.
Fig. 13 in A mountain of millipedes VII: The genus Eviulisoma Silvestri, 1910, in the Udzungwa Mountains, Tanzania, and related species from other Eastern Arc Mountains. With notes on Eoseviulisoma Brolemann, 1920, and Suohelisoma Hoffman, 1963 (Diplopoda, Polydesmida, Paradoxosomatidae)
Fig. 13. Eviulisoma cetafi sp. nov., paratype, right gonopod. A. Mesal view. B. Meso-dorsal view. C. Latero-ventral view. D. Ventral view. E. Spiny tip of iap. F. Ridged area of solenophore with tip of solenomere sticking out. Abbreviations: cxl = coxal lobe; iap = intermediate acropodital process; map = mesal acropodital process; prf = prefemoral part; ra = ridged area of solenomere; slm = solenomere; sph-d, sph-i, sph-v = dorsal, intermediate and ventral lobes of solenophore. Scale bars: A–D = 0.1 mm; E = 0.01 mm; F = 0.02 mm.
Snow accumulation patterns in a high mountain Andean catchment from optical tri-stereoscopic remote sensing
<p><strong>1) DBSM_Data_RioYeso'</strong> = Automatic weather station (AWS) data from Yeso Embalse and Termas del Plomo meteorological stations (available from Chilean Water Directorate, 'Dirección General de Aguas' or 'DGA' http://www.arcgis.com/apps/OnePane/basicviewer/index.html?appid=d508beb3a88f43d28c17a8ec9fac5ef0), used to force a distributed blowing snow model of Essery et al. (1999) to derive spatial snow depth of the Rio del Yeso catchment, Chile. The format is as follows:</p> <p><em>{'Year','Month','Day','Hour','Incoming shortwave radiation (Wm2)','Incoming longwave radiation (Wm2)','SnowfallRate(mm/hr)','RainfallRate(mm/hr)','Air temperature (celsius)','Relative humidity (%)','Wind speed (m s-1)','Compass wind direction','Air pressure (hPa)'};</em></p> <p><strong>2) 'snowHeightPleiadesREG' </strong>= A snow depth map (horizontal resolution 4m) derived from triplets of high resoution stereo optical satellite images (Pléiades) following the methodology of Marti et al. (2016). The snow depth map is derived for a high mountain catchment (Rio del Yeso) of the central Chilean Andes (see Burger et al., 2018).</p> <p><strong>3) 'L2_LiDAR_4m'</strong> = A LiDAR (Light detection and Ranging) spatial snow depth map at a horizontal resolution of 4 m. The data were captured by a Reigl VZ-6000 LiDAR scanner and generated from the difference of two constructed digital elevation models (DEMs) between the dates 13th September, 2017 (with snow) and 12th December, 2017 (without snow). </p> <p><strong>4) 'L2_Pleiades_SDLidar_NEW' </strong>= The Pléiades snow depth map as described in <strong>2)</strong>, extracted by the areas of LiDAR scan described in <strong>3)</strong>. </p> <p><strong>5) 'SnowDepthResults'</strong> = A folder containing a corrected and gap-filled Pléiades snow depth map (<strong>'SD_PleiadesCORR'</strong>) and for comparison: <strong>'SD_TOPO'</strong>, a statistical estimation of snow depth using topographic parameters and the regression equation of Grünewald et al. (2013) and; The physically based estimates of snow depth using the DBSM model as in <strong>1)</strong> without snow transport for the 4th September, 2017 (<strong>'SD_EXTP_Sep04'</strong>) and 13th September, 2017 ('<strong>SD_EXTP_Sep13'</strong>) and with snow transport for those dates (<strong>'SD_Wind_Sep04','SD_Wind_Sep13'</strong>).</p> <p><strong>6) 'rdyDEM'</strong> = An independent ASTER GDEM (https://asterweb.jpl.nasa.gov/gdem.asp) cut to the area of the study catchment (horizontal resolution = 30 m). </p> <p><strong>7) '</strong><strong>PlanetScope_20170907_TPK' </strong>= An stitched optical PlanetScope image of the catchment (horizontal resolution of 3.25 m) derived from access under the research and teaching iniative (planet.com). </p> <p><strong>Cited work:</strong></p> <p><strong>Burger, F. et al.</strong> (2018) ‘Interannual variability in glacier contribution to runoff from a high ‐ elevation Andean catchment : understanding the role of debris cover in glacier hydrology’, Hydrological Processes, pp. 1–16. doi: 10.1002/hyp.13354.</p> <p><strong>Essery, R</strong>., Li, L. and Pomeroy, J. (1999) ‘A distributed model of blowing snow over complex terrain’, Hydrological Processes, 13(14–15), pp. 2423–2438. doi: 10.1002/(SICI)1099-1085(199910)13:14/15<2423::AID-HYP853>3.0.CO;2-U.</p> <p><strong>Grünewald, T. et al.</strong> (2013) ‘Statistical modelling of the snow depth distribution in open alpine terrain’, Hydrology and Earth System Sciences, 17(8), pp. 3005–3021. doi: 10.5194/hess-17-3005-2013.</p> <p><strong>Marti, R. et al</strong>. (2016) ‘Mapping snow depth in open alpine terrain from stereo satellite imagery’, The Cryosphere, pp. 1361–1380. doi: 10.5194/tc-10-1361-2016.</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>
FIG. 24 in Fishes of the Mitaraka Mountains (French Guiana)
FIG. 24. — Picture of the Site 7 torrential stream, colonised by a single species, Ituglanis nebulosus de Pinna & Keith, 2003. Photograph: S. Brosse.
FIG. 2 in Fishes of the Mitaraka Mountains (French Guiana)
FIG. 2. — Principal Component Analysis (PCA) showing species assemblages according to the sites: A, Correlation circle; B, species shown on the two first axes plane. Inertia is indicated on each axis. Species codes are as in Table 2. 28 out of the 37 species were indicated on the figure. The remaining species (ATEM, ATET, BGUY, CALB, CSP, CORI, CTMA, HGUI, HMAR, LGRA) although considered in the PCA analysis were hidden by the group of species made of GMEG, LGOS, CLON, HMAL. Species codes are as in the species list.
FIG. 1 in Fishes of the Mitaraka Mountains (French Guiana)
FIG. 1. — Location of the study site: A, general map of French Guiana showing the location of Mitaraka Mountains; B, detailed map of the study area showing the localities of the 14 sampling sites on the Alama River and its tributaries.
Topographic steering in mountain rivers with thousands of large bed elements
<p>Morphological control of river hydraulics, or ‘topographic steering’, influences the spatial patterning and persistence of aquatic habitats and channel-change processes. While imperative to addressing questions or problems in river engineering, fluvial geomorphology, and aquatic ecology the study of topographic steering in natural rivers is still limited, particularly amidst the landform complexity of mountain rivers. This study presents new theory, methods, and findings for investigating landform structure, hydraulics, and mechanisms by which topographic steering asserts control in mountain rivers. Using a novel combination of point-cloud processing and topographic differencing 52,926 individual boulders and bedrock outcrops were mapped within a 13.2-km segment of the mountainous Yuba River (Northern California). Employing a 15% cover threshold these large bed elements (LBEs) were identified as an important source of landform non-uniformity. Coupled with outputs from two-dimensional (2D) hydrodynamic modeling and an original flow pattern analysis framework, LBEs were also revealed as significantly contributing to highly non-uniform flow patterns classified during discharge simulations ranging from baseflow to a ~3.5-year flood. New metrics on flow pattern hydraulics and spatial persistence outline a spectrum of hydraulic-morphodynamic mechanisms describing stage-dependent spatial stratification of erosional and depositional tendencies. Within this continuum three hydraulic-morphodynamic conceptual models (“chutes-and-traps”, “stage-dependent dynamic mobility”, and “variable-scale topographic steering”) are proposed and tested for their relative roles in the Yuba River study site. Findings show the site is characterized by a mechanism where changing flow patterns provide a template for many combinations for sediment mobilization and deposition important to the sediment balance of the supply-limited study site.</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>
Fig. 5 in The genus Parbatthorax Tanasevitch, 2019 (Araneae, Linyphiidae) new to China, with a new species from the Gaoligong Mountains
Fig. 5. Parbatthorax proiectus sp. nov., one of the paratypes collected with the holotype (CGY107). A. Epigyne, ventral view. B. Internal genitalia, dorsal view. Scale bars: 0.1 mm.
Fig. 2 in The genus Parbatthorax Tanasevitch, 2019 (Araneae, Linyphiidae) new to China, with a new species from the Gaoligong Mountains
Fig. 2. Parbatthorax proiectus sp. nov., holotype, male palp (CGY107). A. Prolateral view. B. Retrolateral view. C. Ventral view. D. Dorsal view. Scale bars: 0.1 mm.
Fig. 4 in The genus Parbatthorax Tanasevitch, 2019 (Araneae, Linyphiidae) new to China, with a new species from the Gaoligong Mountains
Fig. 4. Parbatthorax proiectus sp. nov., holotype, male palp (CGY107). A. Prolateral view. B. Retrolateral view. Scale bars: 0.1 mm.
Fig. 6 in The genus Parbatthorax Tanasevitch, 2019 (Araneae, Linyphiidae) new to China, with a new species from the Gaoligong Mountains
Fig. 6. Parbatthorax proiectus sp. nov., one of the paratypes collected with the holotype (CGY107). A. Epigyne, ventral view. B. Epigyne cleared, ventral view. C. Internal genitalia, dorsal view. Scale bars: 0.1 mm.
Fig. 1 in The genus Parbatthorax Tanasevitch, 2019 (Araneae, Linyphiidae) new to China, with a new species from the Gaoligong Mountains
Fig. 1. Parbatthorax proiectus sp. nov., ♂, holotype (CGY107) (A–B) and ♀, one of the paratypes collected with the holotype (CGY107) (C–D). A, C. Habitus, dorsal view. B, D. Habitus, ventral view. Scale bars: 0.5 mm.
Fig. 3 in The genus Parbatthorax Tanasevitch, 2019 (Araneae, Linyphiidae) new to China, with a new species from the Gaoligong Mountains
Fig. 3. Parbatthorax proiectus sp. nov., ♂, one of the paratypes collected with the holotype (CGY107). A. Embolic division, ventral view. B. Embolic division, retrolateral view. C. Cephalothorax, anterodorsal view. D. Prosoma, lateral view. Scale bars: A–B = 0.1 mm; C–D = 0.2 mm.
Datasets for "The hydro-climate and freshwater supply of High-Mountain Asia under warming climate conditions"
<p>This repository contains ensemble-mean fields and corresponding standard deviations of post-processed outputs of CMIP5 models for three RCP scenarios (RCP2.6, RCP4.5 and RCP8.5) described in a manuscript "The hydro-climate and freshwater supply of High-Mountain Asia under warming climate conditions".</p> <p>Each netcdf file contains one field (either an ensemble mean of a standard deviation). The fields are the following: Positive Degree Days (PDD), Total Precipitation (Ptotal), Liquid Precipitation (PLiquiq), Frozen Precipitation (Pfrozen) and Glaciers Mass Balance (MB).</p>
Figure 2 in Residential green ZOnes as additiOnal habitats FOr mammals in a mOuntainOus area arOund Beijing, China
Figure 2. Mean counts of detected trace records, mammal species, specialist species, and generalist species in disturbed near-community (grey) and non-disturbed off-community (black) area between July and August 2019 in the mountainous suburb area around Beijing. Significant differences (p <0.05) are denoted by *. The bars represent standard error.
Figure 1 in Residential green ZOnes as additiOnal habitats FOr mammals in a mOuntainOus area arOund Beijing, China
Figure 1. Map of investigation locations within the suburb area surrounding Beijing between July and August 2019; (A) geographical location of the study area in China is shown in the sketch at the top left. The star represents the capital city of China, Beijing, and the grey shadow is the study area. Filled dots represent disturbed plots near human communities (≤1500 m), and blank dots are non-disturbed plots distant from human communities (>1500 m). 115 near-communities and 114 off-community plots were investigated. The elevation gradient change from high to low is represented by red-green colorbar; (B) a sample of the near-community plots; (C) a sample of the off-community plots; (D) a footprint of wild boar (Sus scrofa) was detected in a cornfield of (B); (E) faeces of leopard cat (Prionailurus bengalensis) were detected in a deciduous forest of (C).
Fig. 18 in Frasnian gastropod synecology and bio-events in the Dyminy reef complex of the Holy Cross Mountains, Poland
Fig. 18. Frasnian taxonomic composition changes of gastropod associations from the Holy Cross Mountains (after Krawczyński 1998).
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.