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2,155 results for “Ridging”
List of VLFEs obtained in the paper "Influence of a subducted oceanic ridge on the distribution of shallow VLFEs in the Nankai Trough as revealed by moment tensor inversion and cluster analysis"
<p>List of VLFEs obtained in Toh et al., (2020, GRL).</p> <p>"Influence of a subducted oceanic ridge on the distribution of shallow VLFEs in the Nankai Trough as revealed by moment tensor inversion and cluster analysis" by Akiko Toh, Wan-Jou Chen, Nozomu Takeuchi, Douglas Dreger, Wu-Cheng Chi, and Satoshi Ide. </p> <p> </p>
MFS-M-00001 Air temperature at +2 m, raised bog-ridge, Thermochron (DS1921G-F5)
<p>Air temperature at 2m measured in a raised bog ecosystem (ridge) by Thermochron logger, 2009-present (with several breaks) as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00002 Air temperature at 2m measured in a raised bog-ridge, DS18B20 (APIK)
<p>Air temperature at 2m measured in a raised bog ecosystem by DS18B20 (temperature logger), 2018-2019, 30 min frequency, N60.89494 E68.66999, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
NACLIM - Fluxes: Wyville Thomson Ridge overflow transport
<p><strong>Last update: 14 March 2014</strong></p> <p><strong>Data set: </strong>Wyville Thomson Ridge overflow transport</p> <p><strong>Description: </strong>Daily mean of overflow volume transport </p> <p><strong>Period:</strong> September 2003 – May 2013</p> <p><strong>Location: </strong>60°15′ N 9°0′ W</p> <p><strong>Instruments:</strong> CTD, moored current meters and ADCPs</p> <p><strong>Variables:</strong> Total and undiluted cold water volume transports </p> <p><strong>Source: </strong>Toby Sherwin and Clare Johnson (SAMS)</p>
FIGURE 13. Sinobatis kotlyari n in A new deepwater legskate, Sinobatis kotlyari n. sp. (Rajiformes, Anacanthobatidae) from the southeastern Indian Ocean on Broken Ridge
FIGURE 13. Sinobatis kotlyari n. sp., holotype male 331 mm TL, ZMMU P- 17178, radiograph of claspers in dorsal view, with right glans spread open, and of posterior pelvic lobes.
FIGURE 17. Sinobatis kotlyari n in A new deepwater legskate, Sinobatis kotlyari n. sp. (Rajiformes, Anacanthobatidae) from the southeastern Indian Ocean on Broken Ridge
FIGURE 17. Sinobatis kotlyari n. sp., holotype male 331 mm TL, ZMMU P- 17178, radiograph of head; note short first propterygia inserting at mesocondyles of scapulocoracoids (white arrows).
FIGURE 8. Sinobatis kotlyari n in A new deepwater legskate, Sinobatis kotlyari n. sp. (Rajiformes, Anacanthobatidae) from the southeastern Indian Ocean on Broken Ridge
FIGURE 8. Sinobatis kotlyari n. sp., holotype male 331 mm TL, ZMMU P- 17178, pelvic region with tail origin and pair of claspers in dorsal view.
FIGURE 19. Sinobatis kotlyari n in A new deepwater legskate, Sinobatis kotlyari n. sp. (Rajiformes, Anacanthobatidae) from the southeastern Indian Ocean on Broken Ridge
FIGURE 19. Sinobatis kotlyari n. sp., holotype male 331 mm TL, ZMMU P- 17178, radiographs of shoulder girdle in oblique views. Left scapulocoracoid directed to the bottom of the image in (A) and to the top in (B).
FIGURE 18. Sinobatis kotlyari n in A new deepwater legskate, Sinobatis kotlyari n. sp. (Rajiformes, Anacanthobatidae) from the southeastern Indian Ocean on Broken Ridge
FIGURE 18. Sinobatis kotlyari n. sp., holotype male 331 mm TL, ZMMU P- 17178, radiograph of pelvic girdle in dorsal view.
FIGURE 20. Sinobatis kotlyari n in A new deepwater legskate, Sinobatis kotlyari n. sp. (Rajiformes, Anacanthobatidae) from the southeastern Indian Ocean on Broken Ridge
FIGURE 20. Sinobatis kotlyari n. sp., holotype male 331 mm TL, ZMMU P- 17178, radiograph in dorsal total view.
Udayagiri, Madhya Pradesh. Markings on the platform, central ridge.
<p>Udayagiri, Madhya Pradesh. Markings on the platform, central ridge, possibly part of a sundial.</p>
Single-point CLM simulations with hillslope hydrology at Niwot Ridge, CO
In this study, we ran ecosystem-scale Community Land Model (CLM) simulations with a novel hillslope hydrology configuration to represent topographically heterogeneous alpine tundra vegetation across a moisture gradient at Niwot Ridge, Colorado, USA. We used local observations to evaluate our simulations and investigated the role of topography and aspect in mediating patterns of snow, productivity, soil moisture and temperature, as well as the potential exposure to climate change across an alpine tundra hillslope. This dataset contains output files from single-point CLM simulations with the hillslope hydrology for the manuscript titled 'Topographic Heterogeneity and Aspect Moderate Exposure to Climate Change Across an Alpine Tundra Hillslope'. Local observations from Niwot Ridge, CO were used to force and evaluate these simulations to represent alpine tundra vegetation across a moisture gradient. Our control simulations were modified to represent a site at Niwot Ridge referred to as the 'Saddle', with an east and west facing knoll and a lowland area between them. Three columns represent distinct moist, wet, and dry meadow vegetation communities. We used the same model setup to run additional experiments on north- and south-facing slopes. Simulations were run using input data from 2008-2021 (historical) and then extended to year 2100 (future) using an anomaly forcing protocol.
- Sternite 1 without a pair of submedian tubercles, tubercles present on sternites 2–4 only (a); general coloration pale to striking yellow with isolated black markings (b) …………………………………7 7. Apex of subtegular ridge concave, laterally flanged (A) ……………………………………………8 - Apex of subtegular ridge convex, without lateral flange (a) ……………………………………9 in A review of the Afrotropical Rhyssinae (Hymenoptera: Ichneumonidae) with the descriptions of five new species
- Sternite 1 without a pair of submedian tubercles, tubercles present on sternites 2–4 only (a); general coloration pale to striking yellow with isolated black markings (b) …………………………………7 7. Apex of subtegular ridge concave, laterally flanged (A) ……………………………………………8 - Apex of subtegular ridge convex, without lateral flange (a) ……………………………………9
Duferco forecast results using Kernel Ridge
<p>The data uploaded represent thirteen months of Duferco forecast (June 2021 - June 2022) using Kernel Ridge regression with hourly granularity for the Calabria wind farm.</p> <p>The three csv files represents the three different methods developed and tested during the project:</p> <ol> <li><strong>BL:</strong> Kernel Ridge regression with gaussian kernel (Baseline).</li> <li><strong>PC:</strong> The Baseline method with the pre-processing of the data using the power curve.</li> <li><strong>PC+K: </strong>The Baseline method with the pre-processing of the data using the power curve, and the post-processing of the forecast using Kalman smoothing filter.</li> </ol> <p> </p>
Preservation of wetting ridges using field-induced plasticity of magnetoactive elastomers
<p>Data for "Preservation of wetting ridges using field-induced plasticity of magnetoactive elastomers".</p> <h3>How to access data:</h3> <p><strong>html</strong>: Open in browser to view interactive figure</p> <p><strong>pdf/png</strong>: Static figure, open with desired program</p> <p><strong>json</strong>: Contains data for figures</p> <p>Open using Python pandas:</p> <pre><code>import pandas as pd data = pd.read_json("filename")</code></pre> <p><code>data</code> then contains a pandas table. <br>The cells in columns describing x,y data might contain entire arrays.</p> <h3>Naming:</h3> <p>Figure files are named after their respective ordering (i.e. figure1_*). <br>Data and figure files are always named correspondingly.</p> <p>Zip archives (indiv_3D_*.zip) contain multiple additional figures, one for each surface measurement.<br>There is a zip archive with the static renders and one with the interactive plots.</p> <p> </p>
Model data repository of "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins"
<p>This dataset contains the data used in Wu et al. (2022): "Styles of Trench-parallel Mid-ocean Ridge Subduction Affect Cenozoic Geological Evolution in circum-Pacific Continental Margins".</p>
Molecular data from "Between a rock and a dry place: phylogenomics, biogeography, and systematics of ridge-tailed monitors (Squamata: Varanidae: Varanus acanthurus complex)"
<p><strong>Phylogenetic_dataset.csv</strong>: Unfiltered DArTseq data used in phylogenetic analyses. Readable by 'dartR' (Gruber et al. 2018).</p> <p><strong>Population_dataset.csv</strong>: Unfiltered DArTseq data used in population-level analyses. Readable by 'dartR' (Gruber et al. 2018).</p> <p><strong>Reference.csv</strong>: Spreadsheet listing individuals included in molecular analyses. Includes vouchers, species, name of each sample in DArTseq data sets, and GenBank accession numbers (GB) for mitochondrial data. ABTC stands for Australian Biological Tissue Collection; AA and CCM for field numbers of uncatalogued specimens. Other collection acronyms follow Sabaj (2019). We refrain from assigning individuals that were not included in the molecular analyses to any given species.</p>
Рис. 5. Passage height preferences (M, ± SD) of birds migrating over Polonyna Borzhava mountain ridge in autumn 2018. in Autumn Migration Of Birds Over Polonyna Borzhava (Ukrainian Carpathians)
Рис. 5. Passage height preferences (M, ± SD) of birds migrating over Polonyna Borzhava mountain ridge in autumn 2018.
Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"
<p><strong>General information:</strong></p> <p>Please contact Claire Newman (claire@aeolisresearch.com) if you have questions about this dataset.</p> <p><em><strong>Title of Dataset: </strong></em>Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"</p> <p><em><strong>Author Information:</strong></em><br> Name: Claire E. Newman<br> Institution: Aeolis Research<br> Email: claire@aeolisresearch.com</p> <p>Recommended citation for this dataset: Newman C.E. (2022) "Modeling output for Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars", Dataset.</p> <p><strong>Summary taken from the paper "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars" by Kathryn M. Stack et al., accepted by JGR Planets for publication in 2022:</strong></p> <p>The orientation of Glen Torridon ridges was compared against outputs from the Mars Weather Research and Forecasting (MarsWRF) model. The MarsWRF model has been used to simulate the atmospheric circulation inside Gale crater to compare with MSL Rover Environmental Monitoring Station (REMS) wind measurements (Newman et al., 2017) and to assist in interpreting observations of dust devils (Newman et al., 2019) and aeolian changes (Baker et al., 2018, 2022). The output used in this work comes from the ‘vertical grid B’ simulations described in Newman et al. (2017), which provide the best match to observed winds and aeolian features. For Gale crater modeling, MarsWRF is run as a global model with nested higher-resolution domains that gradually increase the model’s horizontal resolution over smaller and smaller areas, finally providing output at ~400m grid spacing over the NW quadrant of the crater. The version of MarsWRF used here includes the treatment of radiative transfer in Mars’s dusty CO2 atmosphere, the seasonal CO2 cycle, subsurface-surface-atmosphere exchange of heat and momentum, and vertical mixing of heat and momentum, with surface properties (topography, roughness, albedo, etc.) based on orbital datasets and the seasonally-evolving, non-dust-storm “Mars Climate Database” dust distribution imposed (see Richardson et al., 2007 for more details).</p> <p>The model outputs minute-by-minute predictions of surface friction velocity, u*, and atmospheric density at 1.5m, ρ, for 7 sols at each of 12 periods, which are equally spaced in planetocentric solar longitude (Ls) through the martian year. For each output, the surface wind stress, τ, is found from τ=ρu_*^2. We then adjusted the contribution of each period to account for the varying number of sols Mars spends around each period over its orbit, before producing wind stress roses, which thus represent the total wind stress and direction over a non-dust-storm Mars year.</p> <p><strong>Specifically, contained in the "outputsfullcorr.nc" netCDF data file are:</strong></p> <p><strong><em>Variables:</em></strong></p> <p>PSFC: surface pressure (Pa)</p> <p>T1_5: temperature at 1.5m height above the surface (K)</p> <p>U1_5: zonal (west-to-east) wind at 1.5m height above the surface (m/s)</p> <p>V1_5: meridional (south-to-north) wind at 1.5m height above the surface (m/s)</p> <p>UST: surface friction speed (m/s)</p> <p><strong><em>Output times:</em></strong></p> <p>These variables are output from MarsWRF every minute starting at 15:10 Local True Solar Time in the first sol, with 1440 outputs per martian sol. There are 68 sols of data in total, with between 5 and 7 sols of data from each 30° Ls range, with the relative number chosen to reflect the fraction of a Mars year spent in that Ls range. In detail, the sols of the martian year used (where Ls=0 would be at the start of sol 1 and Ls=360 at the end of sol 669) are:</p> <p>Ls~0°: sols 668-669 & 01-4</p> <p>Ls~30°: sols 60-65</p> <p>Ls~60°: sols 124-130</p> <p>Ls~90°: sols 194-200</p> <p>Ls~120°: sols 254-259</p> <p>Ls~150°: sols 314-319</p> <p>Ls~180°: sols 374-378</p> <p>Ls~210°: sols 424-428</p> <p>Ls~240°: sols 474-478</p> <p>Ls~270°: sols 514-518</p> <p>Ls~300°: sols 564-568</p> <p>Ls~330°: sols 614-618</p> <p><strong><em>Output longitudes and latitudes are in file "static.nc":</em></strong></p> <p>The output is provided for a uniform grid of 19 longitudes by 15 latitudes. The longitudes and latitudes (variables XLONG and XLAT) in static.nc match those in outputsfullcorr.nc, but below are listed the longitudes and latitudes for convenience:</p> <p>Longitudes (19): 137.321, 137.3292, 137.3374, 137.3457, 137.3539, 137.3621, 137.3704, <br> 137.3786, 137.3868, 137.3951, 137.4033, 137.4115, 137.4198, 137.428, <br> 137.4362, 137.4444, 137.4527, 137.4609, 137.4691 </p> <p>Latitudes (15): -4.786012, -4.777781, -4.769551, -4.761321, -4.75309, -4.74486, -4.736629, -4.728399, -4.720168, -4.711937, -4.703707, -4.695477, -4.687246, -4.679016, -4.670785</p> <p>static.nc also provides the local topographic height used by the model at each location (variable name HGT).</p> <p><strong><em>Output data format:</em></strong></p> <p>NetCDF (Network Common Data Form) is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. It is also a community standard for sharing scientific data. The Unidata Program Center supports and maintains netCDF programming interfaces for <a href="https://docs.unidata.ucar.edu/netcdf-c/current/">C</a>, <a href="https://docs.unidata.ucar.edu/netcdf-cxx/current/">C++</a>, <a href="https://www.unidata.ucar.edu/software/netcdf-java/">Java</a>, and <a href="https://docs.unidata.ucar.edu/netcdf-fortran/current/">Fortran</a>. Programming interfaces are also available for Python, IDL, MATLAB, R, Ruby, and Perl.</p> <p>See: <a href="https://www.unidata.ucar.edu/software/netcdf/">https://www.unidata.ucar.edu/software/netcdf/</a></p> <p><strong>References:</strong></p> <p>Baker, M.M., Lapotre, M.G.A., Minitti, M.E., Newman, C.E., Sullivan, R., Weitz, C.M., Rubin, D.R., Vasavada, A.R., Bridges, N.T., & Lewis, K.W. (2018). The Bagnold Dunes in Southern Summer: Active Sediment Transport on Mars Observed by the Curiosity Rover. <em>Geophysical Research Letters, 45</em>(17), 8853-8863. <a href="https://doi.org/10.1029/2018GL079040">https://doi.org/10.1029/2018GL079040</a>.</p> <p>Baker, M.M., Newman, C.E., Lapotre, M.G.A., Sullivan, R., Bridges, N.T., & Lewis, K.W. (2018). Coarse Sediment Transport in the Modern Martian Environment. <em>Journal of Geophysical Research- Planets, 123</em>(6), 1380-1394. <a href="https://doi.org/10.1002/2017JE005513">https://doi.org/10.1002/2017JE005513</a>.</p> <p>Baker, M.M., Newman, C.E., Sullivan, R., Minitti, M.E., Edgett, K.S., Fey, D., Ellison, D., & Lewis, K.W. (2022). Diurnal variability in aeolian sediment transport at Gale crater, Mars, J. Geophys. Res. (Plan.), 127, e2020JE006734, https://doi.org/10.1029/2020JE006734.</p> <p>Newman, C., Gómez‐Elvira, J. G., Marín, M., Navarro, S., Torres, J., Richardson, M. I., et al. (2017). Winds measured by the Rover Environmental Monitoring Station (REMS) during the Mars Science Laboratory (MSL) rover's Bagnold Dunes Campaign and com- parison with numerical modeling using MarsWRF. <em>Icarus, 291</em>, 203–231. https://doi.org/10.1016/j.icarus.2016.12.016</p> <p>Newman, C. E., Kahanpää, H., Richardson, M. I., Martinez, G. M., Vicente‐Retortillo, A., & Lemmon, M. (2019). Convective vortex and dust devil predictions for Gale crater over 3 mars years and comparison with MSL‐REMS observations, <em>J. Geophys. Res. (Plan.</em>), 124, 3442– 3468, https://doi.org/10.1029/2019JE006082.</p> <p>Richardson, M.I., Toigo, A.D., & Newman, C.E. (2007). PlanetWRF: A general purpose, local to global numerical model for planetary atmospheric and climate dynamics<em>, J. Geophys. Res. (Plan.)</em>, 112, E09001, <a href="https://doi.org/10.1029/2006JE002825">https://doi.org/10.1029/2006JE002825</a>.</p>
New observations of recently active wrinkle ridges in the lunar mare: Implications for the timing and origin of lunar tectonics
<p>Enclosed are the for the tectonically deformed lunar impact crater and recently active wrinkle ridge datasets produced in Nypaver and Thomson, 2022 (GRL). These data are presented in Figure 2 of that Manuscript and are readable in a GIS-based software (shapefile) and the LROC Quickmap interface (json). Lat/Long coordinates for all tectonically deformed craters are also presented in CSV format.</p>
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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.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.