Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
47
datasets available to search
ShareScore release 0.7.1
Dataset results
47 results for “Sediment transport”
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part IV: Post-processing)
<p>This is Part IV of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the post-processing of all model results.</p> <p>To be able to run the scripts as is, the folder structure should be as follows:</p> <p>Runs (includes all model run folders from Part II and Part III)<br>Post/Basic/Channels<br>Post/Basic/Cross_sections<br>Post/Basic/Integrals<br>Post/Basic/Median_neighborhood_analysis (includes all unzipped MNA_TIGER_XX.zip folders)<br>Post/Basic/Skeleton_clean<br>Post/Basic/Skeleton_final<br>Post/Basic/Skeleton_raw<br>Post/Basic/Unchanneled_path_length<br>Post/Basic/Watersheds<br>Post/Basic/Scenarios.txt<br>Post/Basic/TIGER_2km_5m.slf<br>Post/Paper_1/Erosion-deposition<br>Post/Paper_1/Fluxes<br>Post/Paper_1/Profiles<br>Post/Paper_1/Std</p>
Model data and code for "Freeze-thaw effects on daily sediment transport in an Alpine river"
<p>Supporting information for the research article "Freeze-thaw effects on daily sediment transport in an Alpine river" by Skålevåg et al., submitted to Water Resources Research.</p> <p>This data repository contains the processed data, model code, and results presented in the research article. Please refer to the article and its supplementary information for details on primary data.</p> <p> </p> <p><strong>Contents:</strong></p> <ul> <li>processed data: <ul> <li>Standardised target and predictor variables, in addition to non-standardised data used for freeze-thaw state classification <a href="https://zenodo.org/api/records/13928999/draft/files/model_variables.csv/content" target="_blank" rel="noopener noreferrer">model_variables.csv</a></li> <li>Means and standard deviations of standardised variables <a href="https://zenodo.org/api/records/13928999/draft/files/regression_variables_mean_std.csv/content" target="_blank" rel="noopener noreferrer">regression_variables_mean_std.csv</a></li> </ul> </li> <li>model code: <ul> <li>final model presented in research article: <a href="https://zenodo.org/api/records/13928999/draft/files/model.py/content" target="_blank" rel="noopener noreferrer">model.py</a></li> <li>model comparison performed as part of model development: <a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_predictors_and_segmentation.html/content" target="_blank" rel="noopener noreferrer">model_comparison_predictors_and_segmentation.html</a></li> </ul> </li> <li>results: <ul> <li>final model: <ul> <li>Inference trace from the pymc model <a href="https://zenodo.org/api/records/13928999/draft/files/inference.nc/content" target="_blank" rel="noopener noreferrer">inference.nc</a></li> <li>Summary table of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary.csv/content" target="_blank" rel="noopener noreferrer">inference_summary.csv</a></li> <li>Visualisation of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_trace.png/content" target="_blank" rel="noopener noreferrer">inference_trace.png</a></li> </ul> </li> <li>other models: <ul> <li>non-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_SRC.nc/content" target="_blank" rel="noopener noreferrer">inference_SRC.nc</a> and <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_SRC.csv</a></li> <li>non-segmented "pooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_nonsegmented.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_nonsegmented.csv</a></li> <li>freeze-thaw-state-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_segm_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_segm_SRC.csv</a></li> <li>freeze-thaw-state-segmented "unpooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_unpooled.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_unpooled.csv</a></li> </ul> </li> <li>model comparison: <ul> <li><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_waic.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_waic.csv</a></li> <li> <div><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_loo.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_loo.csv</a></div> </li> </ul> </li> </ul> </li> </ul>
Data related to the manuscript "Bayesian Calibration and Validation of a Large-scale and Time-demanding Sediment Transport Model"
<p>1) Riverbed_Elevation_Measurements.txt<br> Description: Measured riverbed geometry of available years<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2002 [m asl], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation <br> 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 2) Hydro_FT_2D_manual.txt<br> Description: Simulation results of the manually calibrated full model<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>3.1) Hydro_FT_2D_CollocationPointBase.txt<br> Description: Parameter combinations of the collocation point base for each of the 20 simulations conducted with the full model to <br> construct the surrogate<br> Rows: Critical Shields parameter, Grain Roughness, Grain Size distribution</p> <p>3.2) Hydro_FT_2D_CollocationResults.txt<br> Description: Simulation results of the 20 simulations conducted with the full model at the collocation points<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig<br> [m asl], Elevations 2010 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig [m asl], Elevations 2013 [m asl] of<br> simulation 1 through 20<br> ----------------------------------------------------------------------------------------------------------------------------<br> 4.1) aPC_MC_N_Combinations_Weights_prior.txt<br> Description: ID of prior MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.2) aPC_MC_2005_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of MC run 1 through 100,000<br> 4.3) aPC_MC_2010_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of MC run 1 through 100,000<br> 4.4) aPC_MC_2013_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2013 [m asl] of MC run 1 through 100,000<br> <br> 4.5) aPC_MC_N_Combinations_Weights_posterior.txt<br> Description: ID of accepted (posterior) MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of accepted MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.6) aPC_MC_2005_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of accepted MC run 1 through 857<br> 4.7) aPC_MC_2010_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of accepted MC run 1 through 857<br> 4.8) aPC_MC_2013_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2013 [m asl] of accepted MC run 1 through 857<br> ----------------------------------------------------------------------------------------------------------------------------<br> 5) aPC_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated aPC surrogate model using the MAP parameter <br> combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>6) Hydro_FT_2D_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated full model using the MAP parameter combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 7) dz.txt<br> Description: Riverbed Evolution for all nodes in the section of interest (n=1138) obtained with differently calibrated models for all <br> considered time periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</p> <p>8) dz_CalibrationNodes.txt<br> Description: Riverbed Evolution for calibration nodes (n=204) obtained with differently calibrated models for all considered time<br> periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</p> <p> </p>
Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream
<p>Structure from Motion orthoimagery, lidar differencing products, and grain size data to be published with the submission of "Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream" to <em>Journal of Geophysical Research: Earth Surface.</em> </p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Upper Big Creek:</p> <p>J_2016.tif, J_2021.tif, J_2022.tif, K_2016.tif, K_2021.tif, K_2022.tif, L_2016.tif, L_2021.tif, L_2022.tif</p> <p>Files titled K_[year].tif encompass our upstream study reach; files titled J_[year].tif encompass our middle study reach; files titled L_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Devil's Creek:</p> <p>G_2016.tif, G_2021.tif, G_2022.tif, _2016.tif, H_2021.tif, H_2022.tif, I_2016.tif, I_2021.tif, I_2022.tif</p> <p>Files labeled G_[year].tif encompass our upstream study reach; files labeled H_[year].tif encompass our middle study reach; files labeled I_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Upper Big Creek with units in meters:</p> <p>BC_2016.csv, BC_2021.csv, BC_2022.csv</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Devil's Creek with units in meters:</p> <p>DC_2016.csv, DC_2021.csv, DC_2022.csv</p> <p> </p> <p>Differenced lidar digital terrain models for Big Creek and Devil's Creek with units in meters:</p> <p>DoD_11_22.tif (difference between 2011 and 2022 lidar DTMs), DoD_11_15.tif (difference between 2011 and 2022 lidar DTMs)</p> <p> </p> <p>This work was funded by the Geological Society of America, the National Center for Airborne Laser Mapping, the Washington Section of the American Water Resources Association, the Western Washington University Research and Sponsored Programs Office, and the Western Washington University Geology Department.</p>
Experimental data on "Sediment storage and fluvial sediment transport linkages across an experimental flood sequence"
<p>The repository contains data used in manuscript "Sediment storage and fluvial sediment transport linkages across an experimental flood sequence" by Hassan, Pierce, Chartrand. </p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part III: Extra runs)
<p>This is Part III of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the extra simulations used in the paper (Figures S3, S8-S10).</p> <p>Each zip file corresponds to a model run. </p> <p>TIGER_XX.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 0-100.<br>TIGER_XX_100.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 100-200.<br>TIGER_XX_HYYY.zip: Scenario XX, hydro-morphodynamics only, year YYY.</p> <p>Main scenarios:<br>- 01: Spartina (Figures 1-5, S3-S10)<br>- 02: Salicornia (Figures 1-5, S3-S10)<br>- 83: No vegetation (Figures 1-5, S3, S8-S10)</p> <p>Additional scenarios:<br>- 146: Spartina, low bulk drag coefficient (Figure S3)<br>- 147: Spartina, very low bulk drag coefficient (Figure S3)<br>- 148: Salicornia, low bulk drag coefficient (Figure S3)<br>- 149: Salicornia, very low bulk drag coefficient (Figure S3)<br>- 122: Spartina, low settling velocity (Figure S8)<br>- 123: Spartina, high settling velocity (Figure S8)<br>- 124: Salicornia, low settling velocity (Figure S8)<br>- 125: Salicornia, high settling velocity (Figure S8)<br>- 126: No vegetation, low settling velocity (Figure S8)<br>- 127: No vegetation, high settling velocity (Figure S8)<br>- 128: Spartina, low critical bed erosion shear stress (Figure S8)<br>- 129: Spartina, high critical bed erosion shear stress (Figure S8)<br>- 130: Salicornia, low critical bed erosion shear stress (Figure S8)<br>- 131: Salicornia, high critical bed erosion shear stress (Figure S8)<br>- 132: No vegetation, low critical bed erosion shear stress (Figure S8)<br>- 133: No vegetation, high critical bed erosion shear stress (Figure S8)<br>- 134: Spartina, low Partheniades constant (Figure S8)<br>- 143: Spartina, high Partheniades constant (Figure S8)<br>- 136: Salicornia, low Partheniades constant (Figure S8)<br>- 144: Salicornia, high Partheniades constant (Figure S8)<br>- 138: No vegetation, low Partheniades constant (Figure S8)<br>- 145: No vegetation, high Partheniades constant (Figure S8)<br>- 150: Spartina, low sediment dry bulk density (Figure S8)<br>- 151: Spartina, high sediment dry bulk density (Figure S8)<br>- 152: Salicornia, low sediment dry bulk density (Figure S8)<br>- 153: Salicornia, high sediment dry bulk density (Figure S8)<br>- 154: No vegetation, low sediment dry bulk density (Figure S8)<br>- 155: No vegetation, high sediment dry bulk density (Figure S8)<br>- 76: Spartina, replicate #1 (Figures S9-S10)<br>- 77: Spartina, replicate #2 (Figures S9-S10)<br>- 78: Spartina, replicate #3 (Figures S9-S10)<br>- 88: Spartina, replicate #4 (Figures S9-S10)<br>- 80: Salicornia, replicate #1 (Figures S9-S10)<br>- 81: Salicornia, replicate #2 (Figures S9-S10)<br>- 82: Salicornia, replicate #3 (Figures S9-S10)<br>- 89: Salicornia, replicate #4 (Figures S9-S10)<br>- 85: No vegetation, replicate #1 (Figures S9-S10)<br>- 86: No vegetation, replicate #2 (Figures S9-S10)<br>- 87: No vegetation, replicate #3 (Figures S9-S10)<br>- 90: No vegetation, replicate #4 (Figures S9-S10)</p> <p> </p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part V: Figures)
<p>This is Part V of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files to generate the figures of the paper.</p> <p>To be able to run the scripts as is, the path (at the beginning of each script) to the following folders must be updated:</p> <p>Runs (includes all model run folders from Part II and Part III)<br>Post (includes all post-processing folders from Part IV)</p>
Influence of storm sequencing and beach recovery on sediment transport and beach resilience data set at CIEM large scale wave flume.
<p>The Influence of storm sequencing and beach recovery on sediment transport and beach resilience (RESIST) experiments project proposes to study experimentally sequences of storm induced erosion and beach recovery, with a particular focus on the poorly known morphodynamic processes under low energy conditions. Series of large scale experimental tests were done to collect data on the cross-shore hydrodynamics, sediment transport and beach evolution. The main aim of this proposal is to investigate the influence of sequences of beach erosion-recovery in the overall beach profile evolution.</p> <p>The tested wave conditions (2 erosive and 3 Accretive bichromatic conditions) were combined to form three sequences of changing high/mild energy conditions. Each condition started from an initial beach 1/15 handmade profile.</p> <p>The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona within the program of Transnational Access of Hydralab+.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p>
Coupled High Frequency Measurements of Swash Sediment Transport and Morphodynamic data set produced at the CIEM flume, Hydralab IV
<p>The data set here presented aims to increase the understanding of the nearshore sediment dynamics. The experiments aimed at obtaining high quality data of hydrodynamics, sediment concentration and beach-face evolution with an intra-wave time scale. The specific objectives of CoSSedM access project were i) to obtain information of the effect of the wave group periods on the beach morphological evolution; ii) To obtain detailed sediment transport information at the inner surf and swash zones with different bi-chromatic wave conditions and iii) To obtain intra-wave measurements of beach-face evolution.</p> <p>The present work was developed in the framework of the HYDRALAB IV Transnational Access projects. The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona. This is a wave flume 100 m long, 3 m wide, and 4.5 m deep. The working water depth was at around 2.5 m over the horizontal flume section and was varied slightly depending on the wave test. A beach was installed made of commercial well-sorted sand (d50 = 0.25 mm) with an overall mean beach gradient of approximately 1:15.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p> <p>More information can be found on the published papers:</p> <p>Alsina, J.M., Padilla, E.M. and Cáceres, I., 2016. Sediment transport and beach profile evolution induced by bi-chromatic wave groups with different group periods. Coastal Engineering, Vol. 114, 325-340.</p> <p>Van der Zanden, J.; Alsina, J.; Caceres, I.; Buijsrogge, R. H.; Ribberink, J. , 2015. Bed level motions and sheet flow processes in the swash zone : observations with a new conductivity-based concentration measuring technique (CCM+) . Coastal Engineering, Vol. 105, 47-65.</p>
Mechanisms of unbalanced sediment transport linked to anthropogenic disturbance in world's largest tidal power plant
<p>Analysis_code.zip and Mooring_toolbox.zip contain matlab m-files. The code was designed to analysis the changes from the mooring data.</p> <p>Data.zip contains a mooring observation data in Lake Sihwa. The velocity profiles and backscatter intensities were measured with a downward-looking acoustic Doppler current profiler (ADCP) (RDI, 600 kHz WorkHorse Sentinel). The mooring system was designed to measure the near-bed temperature, salinity, and turbidity using conductivity-temperature-depth (CTD) sensors (RBR, XR-420, and Concerto) and an optical backscatter sensor (OBS) (Seapoint Turbidity Meter).</p> <p>Waterlevel.zip has water level data in the ocean and Lake Sihwa.</p> <p> </p> <p> </p>
Bead tracking experimental ground truth for studying size segregation in bedload sediment transport
<p>Video sequences to study size segregation in bedload transport were recorded. Experiments consisted in mixtures of two-size spherical glass beads entrained by a turbulent supercritical free surface water flow over a mobile bed. The aim is to track all beads over time to obtain trajectories, particle velocities and concentrations, for studying bedload granular rheology, size segregation and associated morphology.</p> <p>This upload consists in :</p> <ul> <li>a 1000-frame experimental image sequence recorded at 130 fps with approximately 400 beads per frame (about 300 coarse and 100 small beads). The image resolution is 1280x320;</li> <li>the ground truth in the directory \result . It was obtained based on a tracking algorithm with subsequent expert modification. The tracking algorithm was developed by H. Lafaye de Micheaux et al. The code implementing the tracking algorithm is available on <a href="https://github.com/hugolafaye/BeadTracking">https://github.com/hugolafaye/BeadTracking</a>. The ground truth is a '.mat' file containing in particular the variable 'trackData' being a cell array of tracking matrices. There is one tracking matrix for each image of the sequence. Complete information on data format is given in the file readme.txt in the github BeadTracking package.</li> <li>In addition it contains three files allowing the user to run the BeadTracking package specifically on the experimental sequence : <ul> <li>sequence_param.txt : parameter file</li> <li>sequence_base_mask.tif : to remove the base</li> <li>template_transparent_bead_rOut10_rIn6.mat : a template for bead detection</li> </ul> </li> </ul>
Data accompanying the paper "The electron flow in marine sediments experiencing microbial long-distance electron transport"
<p>Data accompanying the paper "The electron flow in marine sediments experiencing microbial long-distance electron transport"</p>
Longitudinal transport of suspended sediment in the Modaomen Estuary of the Pearl River: effects of river, tide, and mouth bar: Datasets
<p><strong>This dataset supplements the article: Longitudinal transport of suspended sediment in the Modaomen </strong><strong>E</strong><strong>stuary of the Pearl River: effects of river, tide, and mouth bar, submitted to Marine Geology</strong></p> <p>Contact information: Dr. Liu, F., School of Ocean Engineering and Technology, Sun Yat-sen University, Guangzhou, 510275, China.</p> <p>Email: <a href="mailto:liuf53@mail.sysu.edu.cn">liuf53@mail.sysu.edu.cn</a> (Liu, Feng)</p> <p><strong><em>Brief view of the dataset</em></strong></p> <p>Hydrodynamics, suspended sediment concentration and salinity distribution were measured at three fixed stations along the longitudinal direction of the estuary. The stations were located at Guadingjiao (M1), inside the bar (M2), and outside the bar (M3), and sampling was simultaneously conducted at neap tide (NT) from July 31 to August 1 and spring tide (ST) from August 8 to August 9 in 2017. All observation data has been compiled to include data from the surface to the bottom six layers within 26 hours. This directory contains the following datasets.</p> <p><strong>M10731.xlsx: </strong>Current velocity, current direction, salinity and SSC during neap tide at Station M1 </p> <p><strong>M10808.xlsx: </strong>Current velocity, current direction, salinity and SSC during spring tide at Station M1 </p> <p><strong>M20731.xlsx: </strong>Current velocity, current direction, salinity and SSC during neap tide at Station M2</p> <p><strong>M20808.xlsx: </strong>Current velocity, current direction, salinity and SSC during spring tide at Station M2</p> <p><strong>M30731.xlsx: </strong>Current velocity, current direction, salinity and SSC during neap tide at Station M3</p> <p><strong>M30808.xlsx: </strong>Current velocity, current direction, salinity and SSC during spring tide at Station M3</p>
Dataset accompanying the publication "Transport and retention of micro-Polystyrene in coarse riverbed sediments: Effects of flow velocity, particle and sediment sizes"
<p>The dataset in this repository is accompanying the publication "Transport and retention of micro-Polystyrene in coarse riverbed sediments: Effects of flow velocity, particle and sediment sizes" (in Microplastics and Nanoplastics, 2023, submitted 09.06.2023)</p> <p>The repository contains the raw image files of all sample filters which were scanned using the fluorescence imaging system ChemiDoc and used to analyse the infiltration behaviour of microplastic polystyrene in the manuscript. In addition, we provide the resulting data from the particle identification and geometric analysis which were derived from the raw data using ImageJ in tabular excel format. The data is structured in folders following the naming of the columns from the manuscript.</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part I: Pre-processing)
<p>This is Part I of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files to generate the simulation grids.</p>
UNO experimental dataset for: Multiscale bedform reorganization at the onset of substantial suspended sediment transport
Open the record for dataset details and reuse information.
Data for: Multiscale bedform reorganization at the onset of substantial suspended sediment transport
Open the record for dataset details and reuse information.
Code and datasets for "Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes"
<p>Code and datasets for:</p> <p>Delaney I., M. A. Werder, D. Felix, I. Albayrak, R. M. Boes, D. Farinotti, 2024, Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes. Water Resources Research. </p> <p>For more information, contact Ian Delaney (ianarburua.delaney@unil.ch).</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>
Quasi-Armored Mud Clasts as Indicators of Transport Processes of Subaqueous Sediment Gravity-Flow
<p>Fig. S1 Quasi-armored mud clasts in different case studying. (A) Quasi-armored mud clasts in the H1 division of Hybrid event bed, Eocene Liushagang Formation; (B) Quasi-armored mud clasts in massive sandstone, Early Cretaceous Lingshandao Formation; (C) Quasi-armored mud clasts in massive sandstone, Early Cretaceous, North Falkland Basin (Dodd et al., 2018); (D) Quasi-armored mud clasts in the upper part of massive sandstone, Middle Eocene, Ainsa System (Pickering et al., 2015); (E) and (F) Quasi-armored mud clasts in massive sandstone, Upper Jurassic, North Sea rift system (Jackson et al., 2011); (G) Quasi-armored mud clasts in the H3 division, Late Jurassic, in the northern North Sea (Haughton et al., 2003) ; (H) and (I) Quasi-armored mud clasts in massive sandstone, Late Jurassic, in the northern North Sea (Williams, 2015).</p> <p>Fig. S2 (A) Location of Beibuwan Basin in China. (B) Location of Weixi’nan depression in the Beibuwan Basin. (C) Sedimentary facies distribution of the upper member of the Liushagang Formation in the Weixi’nan depression. (D) Sedimentary sequence and stratigraphic framework of the Weixinan Depression, Beibuwan Basin (Huang et al., 2013).</p> <p>Fig. S3 The lateral distribution of gravity-flow deposits in the Upper of the Liushagang Formation in the research area from proximal to distal. For the location of the section, see Fig. S2 C.</p> <p>Fig. S4 Facies analysis of Well WZ11-7-1 (in relatively distal), dominated by hybrid event beds with common quasi-armored mud clasts in different units.</p> <p>Fig. S5 Facies analysis of Well WZ11-7-4 (in relatively proximal), dominated by high-density turbidities beds with occasional quasi-armored mud clasts. For the legend of facies analysis, see Fig. S4.</p>
ScienceDex guides
Understand access before you commit
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.