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ShareScore release 0.9.0
Dataset results
11 results for “river erosion”
Results from a National Survey of Canadian Perspectives on Predicting River Channel Migration and River Bank Erosion
<p>This dataset contains findings from a national survey of Canadian perspectives on predicting river channel migration and river bank erosion. The online survey was conducted by researchers from the University of Guelph from November 2, 2021 through January 31, 2022 and received more than 40 responses from regions across Canada.</p>
Numerical model of the Messinian Mediterranean combining hydrological water balance, river erosion, and flexural isostasy: TISC code and input dataset for the Lago-Mare
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Dataset of bank soil parameters and stochastic modelling of bank erosion processes in the Middle Yangtze River
<p><span>A probabilistic process-based model of bank erosion has proposed, </span><span>embedding the probability distributions of</span><span> different bank soil parameters. <span><span> The dataset includes the spatial distribution characteristics of critical shear stress, friction angle, and cohesion. The prediction results analyzed the effects of soil erosion resistance capacity and the variability of shear strength parameters in the simulation of bank erosion processes, obtaining the probability of mass failure and the distributions of bank erosion width. Additionally, the study investigated the effect of varying water content on bank erosion modeling and further analyzed how considering more influencing factors in the model affects prediction uncertainty and accuracy. Moreover, the relationship between the variability of these factors and river morphology was discussed. The dataset provides the aforementioned prediction results, and relevant plots were generated using MATLAB or Python, with the associated plotting code also uploaded.</span></span></span></p>
Relative roles of sediment transport and localized erosion on phosphorus load in the lower Susquehanna River and its mouth in the Chesapeake Bay, USA
<p>Data set includes average wind and wave conditions for the mouth of the Susquehanna River that were used to calculate normal and storm conditions; turbidity measurements at each site; mass of particulate matter from water column and erosion experiments; inorganic phosphate from sequential extraction of particulate matter in water column, sediment, and erosion experiments; and δ<sup>15</sup>N, δ<sup>13</sup>C, and C:N ratios of particulate matter from the water column and from erosion experiments.</p>
Tutorial to explore riverbank erosion along Jamuna River, Bangladesh, with the Google Earth Engine
<p>This short tutorial shows the different features and data that are available in a tool that allows you to explore riverbank erosion in Bangladesh. The tool has been developed by: Freihardt & Frey (under submission): Assessing riverbank erosion in Bangladesh using time series of Sentinel-1 radar imagery in the Google Earth Engine.</p> <p>The tool can be accessed via this link: <a href="https://code.earthengine.google.com/3ea8f1fd5d771accc621550d744a914e?hideCode=true">https://code.earthengine.google.com/3ea8f1fd5d771accc621550d744a914e?hideCode=true</a></p> <p>To access the video tutorial without downloading it: <a href="https://youtu.be/_b9AAPDw7Wk">youtu.be/_b9AAPDw7Wk</a></p>
Electronic supplemental material for Mann et al. "Multi-decadal records of shoreline erosion reveal the roles of river delta dynamics, hard protection measures, and other interventions in Eastern Ghana"
<p>Electronic supplemental material for Mann et al. "Multi-decadal records of shoreline erosion reveal the roles of river delta dynamics, hard protection measures, and other interventions in Eastern Ghana"</p> <p><br> The supplemental material comprises the database "Geodatabase Ghana.mdb" and the MSc thesis by Aleksandra Serwa (Serwa 2017), the results of which built the foundation for the paper.</p> <p>The database contains one layer with all digitized shorelines (HWL_END) as indicated in table 1 of the manuscript, and additional baseline and transect layers that are necessary for the shoreline change analyses with DSAS (Thieler & Danforth 1994). Further information can be found in Serwa (2017).</p> <p>References:</p> <p>Serwa, A., 2017. Shoreline changes in the Keta Municipal District (Ghana): Evidence from multi-decadal records. Master Thesis, Department of Geosciences, University of Bremen, 88 p.</p> <p><br> Thieler, E.R. & Danforth, W.W., 1994. Historical Shoreline Mapping ( II ): Application of the Digital Shoreline Mapping and Analysis Systems ( DSMS / DSAS ) to Shoreline Change Mapping in Puerto Rico. Journal of Coastal Research, 10(3), pp.600–620.</p>
Data and code for: River planforms originate from (im)balance between riverbank erosion and bar accretion
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Repeated erosion of sediments with biofilms from Rowley River mud flats, year 2012, Rowley, MA
This study aims to explore the interplay between biofilms and erodability of cohesive sediments. Erosion experiments were run in four laboratory annular flumes with natural sediments. Mud from the Rowley River mudflats was taken at low tide and then placed in flumes. Two of the flumes were bleach to prevent biota, while the other two were allowed to grow; nutrients were also added to the flumes. On different intervals, the flumes were eroded and the amount of erosion and velocity of the flow were measured. Additionally, each day biofilm growth was measured using Pulse-Amplitude Modulation (PAM) Underwater Fluorometry. Reported here are 10 minute averages of shear stress (derived from velocity) and suspended sediment (uncalibrated, in NTUs) along with PAM measurements. The complete dataset is available upon request via .mat files, but the dataset is quite large (kendallv@bu.edu or pie_im@mbl.edu).
Soil Erosion and Sediment Yield Modeling - Southern Caspian Sea River Basins
<p>Soil Erosion and Sediment Yield Modeling - Southern Caspian Sea River Basins</p>
Quantifying channel bank erosion of a small mountain river in Russian wet subtropics using erosion pins (pins measurements dataset)
<p>A set of erosion pins measurements was conducted during 3 years of channel bank monitoring at the Black Sea coast. This is a dataset of the field measurements of the pins. </p>
ABoVE: Riverbank Erosion and Vegetation Changes, Yukon River Basin, Alaska, 1984-2017
This dataset provides a time series of riverbank erosion and vegetation colonization along reaches of the Yukon River (3 study areas), Tanana and Nenana Rivers (1 area), and Chandalar River (1 area) in interior Alaska over the period 1984-2017. The change data were derived from selected 30-m images from Landsat TM, Landsat ETM+, and Landsat Operational Land Imager (OLI) surface reflectance products. Image classification used the Normalized Differenced Vegetation Index (NDVI) with an NDVI threshold of 0.2 to differentiate vegetated from non-vegetated pixels. Images were assigned to one of seven or eight multiyear intervals, within the 1984-2017 overall range, for each study area. Time intervals vary by study site. Change detection identified shifts from one time interval to the next: changes from vegetated to non-vegetated classes were considered riverbank erosion and changes from non-vegetated to vegetated classes were considered vegetation colonization.
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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.
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.