Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

Energy transfers and reflexion of infragravity waves at a dissipative beach under storm waves.

%%% Author:     Xavier Bertin (xbertin@univ-lr.fr)                                                                  %%% %%% Date:         15/04/2020                                                                                                  %%%     %%% Purpose:    This repository provides the field observations and XBeach model input  %%% %%%                required to reproduce the results presented in paper referred below.           %%% %%% Reference:    Bertin, X., Martins, K., de Bakker, A., Guérin, T., Chataigner, T.,            %%% %%%                Coulombier, T. et de Viron, O., 2020. Energy transfers and reflexion of        %%% %%%                infragravity waves at a dissipative beach under storm waves. In press        %%% %%%                to Journal of Geophysical Research-Ocean.                                                 %%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% *The directory Obs includes:     -The wave bulk parameters computed as explained in the paper for the 10 sensores used in this     study: the offshore ADCP1, the intertidal PT1, PT2, ADCP2/PT3, PT4, PT5, ADV/PT6, PT7/Altus, PT8     and PT9. Each file has the same format and includes: the date (YYYY MM DD), the time (HH MM SS),      the mean water depth, the spectral significant wave height Hm0, mean wave periods Tm01 and Tm02,      the discrete and continuous peak periods, the energetic wave period Tm0,-2 and the spectral     significant height of IG waves Hm0,IG.      -The spectral significant height Hm0,IG+ and mean wave period Tm02,IG+ of incoming IG waves     separated at the ADCP2 and ADV using the method of Guza et al. (1984). The two files have the same      format and includes the date (YYYY MM DD), the time (HH MM SS), Hm0,IG+ and Tm02,IG+.     -The position of each sensore measured with a geodetic GNSS and provided in the same datum as the      bathymetry used in the model (Lambert93 and mean sea level).       *The directory XBeach includes all the necessary files required to reproduce the simulations presented  in this study     -The bathymetry interpolated over a rectilinear grid, with X and Y given in Lambert93 coordinates (files     X_L93.grd and Y_L93.grd) and Z referred with respect to mean sea level (Z_L93.grd).     -The water level fluctuations measured at ADCP1 (WLevel_ADCP_201702.dat).     -The XBeach input file (params.txt) and a file providing the list of directional wave spectra     provided in the directory "Spectra_WWIII". These spectra were computed from a regional application of      WaveWatchIII over the North Atlantic Ocean and forced with CFSR wind fields but they were converted     in the format of SWAN, readable by XBeach.

opencc-by-4.0Jan 2024View details →

The state of the lower Volta Delta Beaches in Ghana from field observations.

The continuous collection of data is important, particularly for developing models for local applications. It is also important to collect in situ data in this observation scarce region to improve our understanding of coastal evolution. The data presented here were planned and collected along a 90 km coastline. The experiment holds significant scientific value as it marks the first comprehensive data-gathering effort along the Volta Delta within the Bight of Benin, West Africa. Several projects, decision-makers, and scientists will make use of this data to calibrate their models. The study was executed in 2023, and it provides a comprehensive description of the morphodynamical characteristics of the study area and identifies the factors influencing the changes. The data include nearshore and riverine bathymetry, topography, sediment grain size distribution, and waves in the study area. The research experiment provides a foundation for future extensive and long-term investigations along the LVD and promotes localized investigations.

opencc-by-4.0Nov 2021View details →

Figure 9 of paper: It's not only the sea: a history of human intervention in the beach-dune ecosystem of Costa da Caparica (Portugal)

This if the figure 9 of paper with DOI 10.5894/rgci-n432. Dunes of Trafaria and Costa da Caparica. This figure was adapted by Dissanayake M. Ruwan Sampath. The original source can be found at Archive from Instituto para a Conservação da Natureza e Florestas (Portugal). Representation of the works made by the Forestry Services between 1884 and 1910. Reference to an area flooded by the ocean in 1905 [sementeira de 1905 inundada pelo mar] and areas where new sowings had to be done [resementeiras]. Notice the drainage systems [valla] and the fences [sébe] near the coastline to protect the plants.

opencc-by-4.0Nov 2021View details →

Figure 11 of paper: It's not only the sea: a history of human intervention in the beach-dune ecosystem of Costa da Caparica (Portugal)

This is the figure 11 of the article with DOI 10.5894/rgci-n432. Forests of Trafaria and Costa da Caparica in the 1930s-1940s. This figure was adapted by Dissanayake M. Ruwan Sampath. Original source can be found at the Archive of Instituto para a Conservação da Natureza e Florestas. In green, the existing forests. In pink, the Forestry Services areas given to other institutions or services for public uses. In yellow, the dunes to be afforested.

opencc-by-3.0-usMar 2022View details →

Model results and observation data in Zhang et al. modeling of wave interference at Ocean Beach, CA

The dataset contains modeling results and observation data supporting the manuscript of Phase-resolved modeling of wave interference and its effects on nearshore circulation in a large ebb shoal-beach system by Yu Zhang, Fengyan Shi, Jim Kirby, Xi Feng.

opencc-by-4.0Nov 2017View details →

Data from: A semi-automated approach to classify and map ecological zones across the dune-beach interface

This is the raw data behind the publication:  A semi-automated approach to classify and map ecological zones across the dune-beach interface Abstract: Habitat classification and mapping underpins most conservation and management tools, because habitats are often used as a surrogate for all biodiversity. Some habitat boundaries are easy to delineate; however, sandy shores are ecotones or ecoclines given their dynamic interface between the marine and the terrestrial realms. Although methods for mapping habitats along shorelines have been broadly applied, we aim to test a semi-automated approach to mapping across-shore “sub-environments” in this transition zone at a finer scale. Using an empirical dataset of photographs covering a small area (three across-shore transects from each of two different areas) with a high resolution, we tested seven machine learning algorithms to determine which one had the best classification accuracy, and to identify which environmental variables are the main determinants of classifications. The randomForest, stochastic gradient boosting, and C5.0 algorithms most accurately classified the photographs as the correct sub-environment. Based on the randomForest algorithm, the variables entropy, drift cover rate, local slope, segmented vegetation cover and number of points with sand or marine litter had the highest influence on the classification. There was no sensitivity to spatial variation alongshore. This approach can be used to map sub-environments at larger scales using drone technology to capture georeferenced digital photographs systematically. Consequently, coastal habitats can be mapped at a finer scale without causing disturbance to this especially sensitive ecotone.

opencc-by-4.0Apr 2019View details →

A Multi-Year Data Set of Beach-Foredune Topography and Environmental Forcing Conditions at Egmond aan Zee, the Netherlands

The data set contains 39 digital elevation models and 11 orthophotos of a beach-foredune system near Egmond aan Zee, the Netherlands, a high-wave storm-dominated site with an approximately 25 m high foredune. The elevation data set combines a long duration (six years; January 2013 - January 2019) with a high temporal resolution (typically 2-4 months) and is spatially extensive (1.4 km alongshore) with a high spatial (1 m) resolution. To facilitate the testing and further development of coastal dune evolution models, the data set is supplemented with high-frequency time series of offshore wave, water level and wind characteristics as well as several subtidal bathymetries.The data set is described in detail in the following open-access, peer-reviewed paper:Ruessink, G.; Schwarz, C.S.; Price, T.D.; Donker, J.J.A. A Multi-Year Data Set of Beach-Foredune Topography and Environmental Forcing Conditions at Egmond aan Zee, The Netherlands. Data 2019, 4, 73. <a href="https://doi.org/10.3390/data4020073">https://doi.org/10.3390/data4020073</a>Update December 7, 2023: The data descriptor paper contains a typo related to the rotation of the RD and local coordinate schemes. On page 4/15 it is said that this rotation angle is 177 degrees, it should be 172.8 degrees. A big thank-you to Haoyang Peng (UNSW, Australia) for pointing out that the 177 degrees is incorrect.

opencc-by-4.0Oct 2021View details →

Beach-face slope dataset for Australia

This repository contains a dataset of beach-face slopes for the Australian coastline. It includes more than 13,200 km of sandy coast with estimates of the beach-face slopes provided every 100 m. The methodology and dataset are described in: Vos, K., Deng, W., Harley, M. D., Turner, I. L., and Splinter, K. D. M.: Beach-face slope dataset for Australia, Earth Syst. Sci. Data, 14, 1345–1357, https://doi.org/10.5194/essd-14-1345-2022, 2022. The beach-face slope data is provided in 2 separate GEOJSON files: Australia_slopes_by_transect.geojson and Australia_slopes_by_beach.geojson. The first one presents the data along each transect (total of 132,132 beach transects) and the second one presents the data for each individual beach/embayment (total of 5,207 beaches).  Additionally, there are three layers that contain polygons for the Australian coastal regions, primary compartments and secondary compartments, respectively. The coordinate system for the geospatial layers is WGS84. 1. Australia_slope_by_transect.geojson: contains a geospatial layer with cross-shore transects along the Australian sandy coastline. Each feature in this layer is a transect (2 point linestring) with the following attributes:   - transect_id: Database id for each transect, e.g., aus0001-0000, aus0001-0001, …   - beach_id: Database id for each beach, e.g., aus0001, aus0002, …, aus5255   - beach_slope: estimate of the beach-face slope between Mean Sea Level (MSL) and Mean High Water Springs (MHWS), value between 0.01 and 0.2   - lower_conf_bound: Lower limit of the confidence band for the slope estimate   - upper_conf_bound: Upper limit of the confidence band for the slope estimate   - width_conf_band: Width of confidence band, value between 0 and 0.19)   - sl_points: Number of datapoints in the shoreline time-series used for beach-face slope estimation (minimum set to 100)   - quality_flag: Quality flag indicating the confidence in the slope estimate at this transect (High, Medium or Low)   - coastal_region: Database id corresponding to the 23 coastal regions as identified by Thom et al. (2018)   - primary_comp_id: Database id corresponding to the 100 primary sediment compartments as identified by Thom et al. (2018)   - secondary_comp_id: Database id corresponding  to the 361 secondary sediment Compartments as identified by Thom et al. (2018) 2. Australia_slope_by_beach.geojson: contains a geospatial layer with each individual beach/embayment (as a linestring) along the Australian sandy coastline. Each feature has the following attributes:   - beach_id: Database id for each beach, e.g., aus0001, aus0002, …, aus5255   - beach_slope_average: Average of the beach-face slope at the site, weighted by the width of the confidence bands, value between 0.01 and 0.2   - width_ci_average: Average width of confidence band over the comprised transects, value between 0 and 0.19   - quality_flag: Quality flag indicating the confidence in the slope estimate at this transect (High, Medium or Low)   - mstr: Mean Spring Tide Range at the beach calculated from the closest grid point in the FES2014 global tide model   - hsig_median: Median Significant Wave Height from the closest grid point in the CAWCR re-analysis dataset   - prc_msrt_obs: percentage of the Mean Spring Tide Range observed by the satellite-derived shorelines   - min_tide_obs: Lowest tide level observed by the satellite-derived shorelines   - max_tide_obs: Highest tide level observed by the satellite-derived shorelines   - sl_points_average: Average number of datapoints in the shoreline time-series over the comprised transects   - beach_length: Length of the beach or embayment, very long beaches (>50km) were split to optimise memory usage when downloading the satellite images   - coastal_region: Database id corresponding to the 23 coastal regions as identified by Thom et al. (2018)   - primary_comp_id: Database id corresponding to the 100 primary sediment compartments as identified by Thom et al. (2018)   - secondary_comp_id: Database id corresponding  to the 361 secondary sediment Compartments as identified by Thom et al. (2018) In addition to these two layers, the 3 different levels of the Sediment Compartments framework (Thom et al.. 2018) with their average beach-face slopes are also included here. 3. coastal_regions.geojson: contains a geospatial layer of each coastal region (as polygon) as defined by Thom et al. 2018. Each feature has the following attributes:   - name: name of the coastal region, e.g., Pilbara, Kimberley, etc   - beach_slope_average_by_beach: Beach-face slope in each coastal region averaged across all the individual beaches inside the polygon   - beach_slope_average_by_transect: Beach-face slope in each coastal region, averaged across all the individual 100-m spaced transects inside the polygon 4. primary_compartments.geojson: contains a geospatial layer of each primary sediment compartment (as polygon) as defined by Thom et al. 2018. Each feature has the following attributes:   - primary_comp_id: Database id corresponding to the 100 primary sediment compartments as identified by Thom et al. (2018)   - name: name of each primary sediment compartment   - beach_slope_average_by_beach: Beach-face slope in each coastal region averaged across all the individual beaches inside the polygon   - beach_slope_average_by_transect: Beach-face slope in each coastal region, averaged across all the individual 100-m spaced transects inside the polygon 5. secondary_compartments.geojson: contains a geospatial layer of each secondary sediment compartment (as polygon) as defined by Thom et al. 2018. Each feature has the following attributes:   - secondary_comp_id: Database id corresponding to the 361 secondary sediment compartments as identified by Thom et al. (2018)   - name: name of each secondary sediment compartment   - beach_slope_average_by_beach: Beach-face slope in each coastal region averaged across all the individual beaches inside the polygon   - beach_slope_average_by_transect: Beach-face slope in each coastal region, averaged across all the individual 100-m spaced transects inside the polygon

opencc-by-4.0Jan 2023View details →

POPs samples analysis in coastal permafrost soils at Komakuk Beach (Yukon, CA)

The concentration of HCB, 54 congeners PCBs and 22 individual PAHs in 89 active layer and permafrost core samples from Komakuk Beach have been determined by using Accelerated Solvent Extraction (Thermo Scientific Dionex ASE 350) and Gas Chromatography - Triple Quadrupole Mass Spectrometry (Trace 1310 GC coupled with TSQ9000 TQMS, Thermo Scientific) at CNR-ISP Venice, Italy.

opencc-by-4.0Jan 2023View details →

Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.

This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA.  All data is from 2015-2021 The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format.  All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022). The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays.  All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays.  All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).

openCC (other)Nov 2021View details →

SBC LTER: BEACH: Talitrid amphipod (Megalorchestia spp.) mesocosm and pitfall trapping for surface activity on spring and neap tides

These data describe the surface activity of talitrid amphipod species (Megalorchestia spp.), and unidentified juveniles, on a spring tide and a neap tide during July and August 2017. The data tables include (1) the mean number and standard deviation of surface active individuals of each species each hour for a 24 hour period on a spring tide and on a neap tide in mesocosms, and (2) the count of each species and unidentified juveniles from pitfall trap samples collected every 2 hours on 4 replicate transects on a spring tide and on a neap tide. This dataset is to support the journal article: Emery, KA, VR Kramer, NK Schooler, KM Michaud, JR Madden, DM Hubbard, RJ Miller, JE Dugan. 2021. Habitat partitioning by mobile intertidal invertebrates of sandy beaches shifts with the tides. Ecosphere.

openCC (other)Nov 2021View details →

SBC LTER: BEACH: Talitrid amphipod (Megalorchestia spp.) mean positions on spring and neap tides

These data describe the position of four burrowed talitrid amphipod species (Megalorchestia spp.) and unidentified juveniles surveyed on a spring tide and a neap tide in August 2016. The data table includes the counts of each species, including unidentified juveniles, from each core sample (1-30) on each transect (A-F). The distance of the sample from the bluff (0 m) is also given. The dataset is to support the journal article: Emery, KA, VR Kramer, NK Schooler, KM Michaud, JR Madden, DM Hubbard, RJ Miller, JE Dugan. 2021. Habitat partitioning by mobile intertidal invertebrates of sandy beaches shifts with the tides. Ecosphere.

openCC (other)Jun 2022View details →

SBC LTER: Beach: Distribution of terrestrial organic material in intertidal and nearshore marine sediment due to debris flow response efforts

These data describe the distribution and processing of terrestrial organic material observed in the Santa Barbara Channel (Goleta Bay) during the spring of 2018. Specifically, beach, slough, and marine sediments were sampled during and after the debris disposal event that took place between January and February 2018 following the Thomas Fire and the subsequent Montecito Debris Flow. Data are contained in one table, including organic carbon, carbon isotope, lignin phenol, and pyrogenic carbon measurements analyzed from sediment cores collected at different depths and locations in the nearshore region.

openCustomDec 2009View details →

Beach Morphology of the Virginia Barrier Islands 1998, 2005 and 2009

Beach features (dune crest, dune toe and shoreline) extracted from LiDAR datasets and used in Dana Oster's 2012 M.S. Thesis at the University of Virginia. Also included are overwash probablities associated with a hypothetical storm similar to Hurricane Bonnie.Â

opencc-by-4.0Apr 2020View details →

Beach-face slopes from satellite-derived shorelines along SE Australia and California

This repository contains the data described in Vos, K., Harley, M. D., Splinter, K. D., Walker, A., & Turner, I. L. (2020). Beach Slopes From Satellite‐Derived Shorelines. Geophysical Research Letters, 47(14), e2020GL088365. There are 2 GEOJSON files in this repository. The coordinate system for both geospatial layers is WGS84 (epsg:4326). 1. slopes_along_transects.geojson: contains a geospatial layer with cross-shore transects for sandy coastlines along SE Australia and California. Each feature in this layer is a transect (2 point linestring) with the following attributes:   - site_id: id of the beach in which the transect is located   - id: id of the individual transect   - orientation: orientation of the transect in degrees from North (positive clockwise)   - beach slope: beach-face slope from Mean Sea Level (MSL) to Mean High Water Springs (MHWS) 2.  slopes_along_beaches.geojson: this layer contains sandy beaches instead of transects. For each beach the median slope has been calculated from all the available transects. The following attributes are available:     - id: id of the beach     - name: name of the beach in the OpenStreetMap database (if not available 'noname')     - beach_length: length of the beach in metres     - median_orientation: beach orientation calculated as the median of the orientations of each transect     - Tide range: average tidal range (mean high water - mean low water) at each beach based on FES2014 global tide model     - median_slope: median beach-face slope along the beach based on the estimated beach-face slope along the transects

opencc-by-4.0Jul 2019View details →

Macrofauna, granulometry, n-alkanes and PAH's in sediments of the sandy beaches of the state of Yucatan: November 2018.

This collection corresponds to the species registered and pollutants on sandy beaches of the State of Yucatan, Mexico, using the MBON P2P sampling protocol for sandy beaches, with funding from the LANRESC UNAM-CONACYT (Laboratorio Nacional de Resiliencia Costera, Universidad NAcional Autonoma de Mexico - CONACyT)

opencc-by-4.0Jul 2016View details →

Figure 18 in The biology and functional morphology of the high-energy beach dwelling Paphies elongata (Bivalvia: Mactroidea: Mesodesmatidae). Convergence with the surf clams (Donax: Tellinoidea: Donacidae)

Figure 18. The anatomy of Paphies elongata (a and b) compared with that of Donax hanleyanus (c and d), both drawn to the same relative scale (for actual scales see the earlier illustrations). (a) and (c) are views of the apertures of the inhalant siphons; (b) and (d) are the internal organs of the mantle cavity showing the visceral mass and foot, the musculature, the orientation of the ctenidia and labial palps and the simplified intestine. (c) is re-drawn after Luzzatto and Penchaszadeh (2001, fig. 1); (d) is a compendium of re-drawn figures from Narchi (1978). (See previous illustrations for interpretations of structure).

opencc-by-4.0Jul 2016View details →

Figure 15 in The biology and functional morphology of the high-energy beach dwelling Paphies elongata (Bivalvia: Mactroidea: Mesodesmatidae). Convergence with the surf clams (Donax: Tellinoidea: Donacidae)

Figure 15. Paphies elongata. The course of the intestine in the visceral mass as seen from the right side. AA(1), anterior adductor muscle(1); AN, anus; APR, anterior pedal retractor muscle; CSS, crystalline style sac; DD, digestive diverticulae; DEF, dorsal extension of the foot; EMG, expanded region of the mid gut; F, foot; G, gonad; H, heart; HF, heel of the foot; HG, hind gut; M, mouth; MG, mid gut; PA, posterior adductor muscle; PPR, posterior pedal retractor muscle; R, rectum.