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1,398 results for “shallow waters”
Fig. 5 in Tanaidaceans from Brunei III. A New Genus and Two New Species of Shallow-water Sphyrapodids (Crustacea: Peracarida: Tanaidacea) from the South China Sea
Fig. 5. Poligarida keriakis sp. nov., A, female holotype; B, male allotype. Scale: 1 mm.
Fig. 1 in Shallow-water Comatulids (Echinodermata: Crinoidea) from Ambon and Lombok Islands, Indonesia
Fig. 1. Sampling localities in Lombok and Ambon islands, Indonesia.
Load, pressure, rubble pile geometry and video data from model-scale tests on shallow water ice-structure interaction
<p>The data is obtained from model-scale experiments on shallow water ice-structure interaction. During the conducted experiments, a ten-meter wide initially intact ice sheet was pushed against a sloping structure of the same width. As the ice failed against the structure, a grounded rubble pile accumulated in front of it. The structure consisted of ten identical one-meter-wide segments and the horizontal load on each of these segments was measured independently with load cells. These measurements are presented as load-time datasets. The horizontal load acting on the false bottom was measured with load cells and are also presented as load-time datasets. Furthermore, the ice pressure on two of the segments was measured with tactile sensors. These pressure measurements are presented as array-based pressure-time datasets. Video footage filmed from two different video angles is included in the data. In addition, the coordinates of the rubble pile geometries at the end of each experiment are published. The data includes the top and side rubble pile geometries. In total, seven experiments were conducted. The data can be used by researchers, engineers and designers who work with ice structure interaction related issues in order to, for instance, optimize the design of offshore structures, improve ice load predictions or develop future experiments and simulations. A full description of the experimental set-up and the published data is submitted to the journal Data in Brief. </p>
Exploring Jupiter's Polar Deformation Lengths with High Resolution Shallow Water Modeling
<p>Movies for simulations of non-dimensional eddy potential vorticity generated using the Pencil Code for the article "Exploring Jupiter's Polar Deformation Lengths with High Resolution Shallow Water Modeling". These movies show the Jovian North polar region in the co-rotating frame using the shallow water approximation with the gamma-plane approximation. Simulations named with a preceding A correspond to Case A (Figure 2 in the article), while those with B correspond to Case B (Figure 5 in the article). Red indicates cyclonic behavior and blue indicates anticyclonic behavior.</p> <p>Long term trends are clear for most cases as early as day 10,000. However, the dynamical behavior of the system continues to evolve well past energy equilibration. For more details regarding these simulations, please read the parent article in the Planetary Science Journal.</p>
Table 1 for Radiocarbon and Stable Carbon Isotope Constraints on the Propagation of Vent CO2 to Fluid in the Acidic Kueishantao Shallow Water Hydrothermal System
<p>This table contains radiocarbon (<sup>14</sup>C) and stable carbon isotope (<sup>13</sup>C) compositions of CO<sub>2</sub> in vent gas, dissolved inorganic carbon and particulates of hydrothermal fluid from Kueishantao shallow water hydrothermal system, offshore northeastern Taiwan.</p>
FIG. 25 in Triphoridae (Gastropoda) from Martinique sampled by the MADIBENTHOS expedition, with notes on shallow-water species from Guadeloupe
FIG. 25. — Depth range of triphorid species in Martinique, sampled by MADIBENTHOS.
FIG. 10 in Triphoridae (Gastropoda) from Martinique sampled by the MADIBENTHOS expedition, with notes on shallow-water species from Guadeloupe
FIG. 10. — Metaxia sp. MNHN, sta. AB181, 2.6 mm. Scale bars: A, B, 1 mm; C, 200 μm; D, 100 μm.
Data for publication 'Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape' (Scientific Reports 2019)
<p>Data on vessel tracks and underwater noise levels presented in the publication Hermannsen, L., Mikkelsen, L., Tougaard, J., Beedholm, K., Johnson, M. and P. T. Madsen, "Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape", Scientific Reports 9:15477 (<a href="https://doi.org/10.1038/s41598-019-51222-9">https://doi.org/10.1038/s41598-019-51222-9</a>).</p>
Fig. 2 in Some New And Poorly Known Benthic Foraminifera From Late Maatrichtian Shallow-Water Carbonates Of The Zagros Zone, Sw Iran
Fig. 2 Location map of the studied area.
Figure. Sampling stations (BS: beach seine, BT: beam trawl). in Length-weight relationships of forty-nine fish species from shallow waters of Gökçeada Island, northern Aegean Sea
Figure. Sampling stations (BS: beach seine, BT: beam trawl).
Figure 1 in Unusual shallow-water boreal gastropod species associations at the Northern part of Arctic archipelago Novaya Zemlya
Figure 1. Map of studied areas off Novaya Zemlya.
Dataset for the study: Closed-boundary reflections of shallow water waves as an open challenge for physics-informed neural networks
<p>This dataset supports a study on using Physics-Informed Neural Networks (PINNs) to solve the 1D-Shallow Water Equations. It focuses on closed boundary reflection test cases, which are crucial for accurately modeling geophysical fluid dynamics in coastal regions, particularly for storm surge and flood modeling. Properly representing reflections is also essential for accurately modeling related phenomena, such as Kelvin waves and amphidromic systems, which influence coastal water levels during such events. The individual sub-datasets in NetCDF format provide the results used for one figure each. The data arrays within the datasets are named with reference to the figures for ease of comparison. <br><br>Cite as: Demir, K.T.; Logemann, K.; Greenberg, D.S. Closed-Boundary Reflections of Shallow Water Waves as an Open Challenge for Physics-Informed Neural Networks. <em>Mathematics</em> 2024, <em>12</em>, 3315. <a href="https://doi.org/10.3390/math12213315">https://doi.org/10.3390/math12213315</a></p>
Salinity variation with water level in shallow lakes
<p>This dataset and associated R code describes water level and salinity data collected by the Department of Biodiversity Conservation and Attractions and its predecessors as part of the South-West Wetland Monitoring Program (SWWMP). SWWMP measured 152 lakes on multiple occassions between 1977 - 2019. The R code presents analyses of this data and associated groundwater quality and streamflow. The dataset consists of the following:</p> <p><strong>Water_Data.csv</strong></p> <p>A comma delimited ascii file containing the following columns of data.</p> <p>Code: A string denoting the lake identifier.</p> <p>Date: dd/mm/yyyy</p> <p>Depth (m): Lake water depth, relative to its deepest point.</p> <p>Salinity (ppt): g/L estimated from electrical conductivity measurments. </p> <p><strong>Salinity_variation_shallow_lakes.R</strong></p> <p>An R script for the analysis and interpreation of salinity vs water level data, utilising the data files included here.</p> <p><strong>Dulbinning_StageDischarge.csv</strong></p> <p>A comma delimited ascii file containing the following columns of data.</p> <p>Date,Depth (m),Salinity (mg/L)</p> <p>Date: dd/mm/yyyy</p> <p>Depth (m): Lake water depth, relative to its deepest point.</p> <p>Salinity (mg/L): Stream salinity.</p> <p>Q (Ml/day): Daily discharge from Department of Water and Environmental Regualtion stream gauge.</p> <p>EC (uS/cm): Stream electrical conductivity.</p> <p>d: Adjusted lake depth (m) relative to threshold depth at which streamflow is initiated.</p> <p>Q: Stream discharge (ML/day)</p> <p><strong>SiteSummary.xlsx</strong></p> <p>A Microsoft excel file with four sheets.</p> <p>Sheet 1: Data, containing the following columns.</p> <p>Code: A string denoting the lake identifier.</p> <p>Name: A string. The full lake name.</p> <p>Reserve_Name: A string. The full name of the nature reserve (if any) containing the lake.</p> <p>Easting: UTM easting in meters.</p> <p>Northing: UTM northing in meters.</p> <p>Zone: UTM zone.</p> <p>Longitude: decimal degrees.</p> <p>Latitude: decimal degrees.</p> <p>Bathy_Beta: Bathymetry exponent.</p> <p>StreamOutflow Level (m): estimate of level (relative to lake bottom) stream outflow is inititiated from bathymetry data.</p> <p>Lake Overflow level (m): estimate of level (relative to lake bottom) lake overtops its banks.</p> <p>model: prior expectation of the pattern of salinity variation with water level as estimated visually from the data.</p> <p>Soil_types: String describing the soil classes adjacent the lake described by Department of Primary Industries and Regional Development</p> <p>Inflow stream: Whether a stream geomorphic feature was seen in aerial imagery flowing into the lake and its type, None if no stream seen, perrenial if observed as wide and full of water, ephemeral if a dry stream feature with riparian vegetation seen.</p> <p>Outflow stream: Whether a stream geomorphic feature was seen in aerial imagery flowing from the lake and its type, None if no stream seen, perrenial if observed as wide and full of water, ephemeral if a dry stream feature with riparian vegetation seen. Outflow DWER gauge: Department of Water and Environmental Regulation stream gauge identifier.</p> <p>Aridity: ratio of annual potentia evaporation to annual rainfall.</p> <p>P_ann: Annual rainfall in mm.</p> <p>E_ann: Annual potential evaporation in mm.</p> <p> </p> <p>Sheet 2: Data Description is a briefer version of the data description for Sheet 1 as detailed above.</p> <p> </p> <p>Sheet 3: BEVE n-Q</p> <p>Lake water level data from Lake Beverly and DWER gauged streamflow at the outlet of Lake Beverly.</p> <p> </p> <p>Sheet 4: STAT n-Q</p> <p>Lake water level data from Station Lake and DWER gauged streamflow at the outlet of Station Lake.</p>
Dataset for PI-DeepOnet to solve the Shallow Water Equations
<p>The dataset supports the paper entitiled “Physics-Informed DeepOnet to rapidly solve the Shallow Water Equations".</p> <p>The Matlab code can be used for visualization.</p>
Field Data associated with study: Investigating the water movements around a shallow shipwreck in Big Tub Harbour of Lake Huron: implications for managing underwater shipwrecks.
<p>Field data (Water temperature, pressure, currents, summary of boat presence data) used in analysis of the study (and paper with the same name) titled: Investigating the water movements around a shallow shipwreck in Big Tub Harbour of Lake Huron: implications for managing underwater shipwrecks.</p>
Performance and limits of a shallow-water model for landslide-generated tsunamis: from laboratory experiments to simulations of flank collapses at Montagne Pelée (Martinique) - DATASETS
<p>Datasets of the 6 presented experiments with 4mm beads presented in the paper "Performance and limits of a shallow-water model for landslide-generated tsunamis: from laboratory experiments to simulations of flank collapses at Montagne Pelée (Martinique)".</p> <p>Each datasets (csv file) corresponds to the hand picked profile of either the water free surface or the granular material at 0.1 second of interval. </p> <p>The third dataset for each experiment corespond of the gauges records.</p>
Fig. 16 in Sea spiders (Arthropoda: Pycnogonida) collected during the Madibenthos Expedition from Martinique shallow waters
Fig. 16. Nymphon timons sp. nov., paratype, ♂, MNHN-IU-2021-6623. Oviger.
Fig. 13 in Sea spiders (Arthropoda: Pycnogonida) collected during the Madibenthos Expedition from Martinique shallow waters
Fig. 13. Nymphon ludovici sp. nov., paratype, ♀, MNHN-IU-2017-219. Chelifore.
Data for: Improvements in the Land and Crop Modeling over Flooded Rice Fields by Incorporating the Shallow Paddy Water (Submitting to the Journal of Advances in Modeling Earth Systems)
<p>We incorporated the shallow paddy surface water layer into the Noah-MP land surface model to improve its performance of surface heat fluxes over flooded rice paddies. Field measurements from two crop sites, i.e., SAITO (early rice) and SAGA (late rice), were used to initialize and evaluate the modified Noah-MP model (Maruyama, 2021). Additionally, we investigated the roles of some key parameters in the land and crop modeling. </p> <p>Note that, all numerical experiments in this study were conducted at the field scale using the offline version of Noah-MP (Niu et al., 2011) running within the High-Resolution Land Data Assimilation System (HRLDAS v3.9; Chen et al., 2007). Please refer to the official HRLDAS/Noah-MP unified Github repository (<a href="https://github.com/NCAR/hrldas">https://github.com/NCAR/hrldas-release</a>) for the original model codes.</p> <p>The related model code modifications and model outputs were included in this dataset. Surface observations for the nearest AMeDAS or meteorological observatory stations were obtained from the Japan Meteorological Agency website (<a href="https://www.jma.go.jp/jma/indexe.html">https://www.jma.go.jp/jma/indexe.html</a>), and were also provided in this dataset.</p> <p> </p> <p>References</p> <p>Chen, F., Manning, K. W., LeMone, M. A., Trier, S. B., Alfieri, J. G., Roberts, R. D., et al. (2007). Description and evaluation of the characteristics of the NCAR high‐resolution land data assimilation system. <em>Journal of Applied Meteorology and Climatology</em>, 46(6), 694-713. <a href="https://doi.org/10.1175/JAM2463.1">https://doi.org/10.1175/JAM2463.1</a></p> <p>Maruyama, A. (2021). Data for: Coupling land surface and crop models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies (version 2) [Data set]. Mendeley Data. <a href="https://doi.org/10.17632/tv23z95r5g.2">https://doi.org/10.17632/tv23z95r5g.2</a></p> <p>Niu, G., Yang, Z., Mitchell, K., Chen, F., Ek, M., Barlage, M., et al. (2011). The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. <em>Journal of Geophysical Research, </em>116, D12109. <a href="https://doi.org/10.1029/2010JD015139">https://doi.org/10.1029/2010JD015139</a></p>
Data and Supplementary Plots for A Shallow Water Model Exploration of Atmospheric Circulation on Sub-Neptunes
<p>This repository contains geopotential maps, zonal wind plots, gifs, and data for the ensemble of possible sub-Neptunes presented in the main manuscript. The data, individual plots, and plot grids are based on the averages of the last 100 simulated days. The data are in the pickle format (see https://docs.python.org/3/library/pickle.html)<br> The gifs are based on the last 1000 simulated hours. These figures and gifs support the analysis presented in the main manuscript.</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.