Find research datasets worth reusing

openCC0Aug 2023View details →

Groundwater levels and temperature in coastal tundra adjacent to Simpson Lagoon, Alaska, 2022

The coastal tundra adjacent to Simpson Lagoon, along Alaska's Beaufort Sea coast, was visited in July and September/October 2022 to assess coastal groundwater dynamics along the lagoon. Groundwater levels were monitored in five piezometers aligned in a transect oriented perpendicular to Simpson Lagoon, as well as surface water levels in the lagoon. Loggers were deployed on July 22, 2022, and collected on September 29, 2022. Data include surface water and groundwater elevation data from July 22, 2022, to September 29, 2022, with the elevation relative to a local datum. All levels have been corrected for barometric pressure (see methods). Also included is groundwater temperature data from the well closest to the lagoon (Well 5).

openCustomDec 2022View details →

Satellite-based remote sensing of water clarity in the shallow coastal lagoons of Virginia 2013-2021

This dataset contains raw data, analysis products and code for a study of satellite-based estimation of water clarity. The files are: Match-up.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2. Satellite overpasses occurred +/- 0-1 days within in situ sampling. Valid remote sensing reflectance values (Rrs) from NASA SeaDAS (not masked by quality flags) were recovered at 12 of 17 in situ sampling sites: 6 ocean inlet sites, 2 lagoon site, and 3 mainland tidal creek sites. Therefore, there are 12 in situ sites available for comparison with satellite estimates. compare_L8S2.csv: Satellite data and water clarity estimates from 150 randomly sampled sites across 5 clear day images in the Virginia Coast Reserve, 2021. Satellite data are from Landsat-8 and Sentinel-2 and processed/atmospherically-corrected using NASA SeaDAS 8.2. The Virginia Coast Reserve is a coastal lagoon system located in Virginia, USA, near the southern tip of the Delmarva Peninsula. Due to low nitrogen inputs and frequent exchange with the Atlantic Ocean via inlets between barrier islands, water quality is high relative to many other coastal bays in the United States and worldwide. Spatial_averaging_analysis.csv: Secchi depths at in situ water quality sites at 10 m resolution (Sentinel-2 only), 30 m resolution (Landsat-8 and Sentinel-2), and 90 m resolution (Landsat-8 and Sentinel-2) where there are in situ match-ups. atmocorrect.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2 and ACOLITE Version 2022022.00. L8_ALL.csv: All Landsat-8 Secchi depth data available between 2013-2021 at in situ water quality sites. S2_ALL.csv: All Sentinel-2 Secchi depth data availab

openCustomJul 2024View details →

Abundance and Size of Seagrass-Associated Fishes in the Virginia Coastal Lagoons, 2019-2024

These data comprise annual summer estimates of the abundance (counts) and size (length) of fishes across restored seagrass meadows of the Virginia coastal lagoons. Fish were collected using a 25-ft (7.62-m) wide beach seine hauled by hand over a 25 m linear swath of the seafloor. Seine hauls were collected in June at 31 sites (1 haul per site). All fish caught in the seine were identified to lowest practical taxonomic level, counted, measured (total length), and released. Data collection began in June 2019 and continues annually (sampling was not carried out in 2020 due to logistical interruptions associated with the COVID-19 pandemic). Data on water temperature, salinity, and conductivity were collected while sampling occurred using a YSI 30 probe. Dissolved oxygen measurements were collected using a YSI ProODO probe. In 2019, these data were collected on at the top and bottom of the water column, but in 2021 and subsequent sampling only one observation (mid-water column) was made. To reconcile this difference for the combined data set, top and bottom environmental measurements from 2019 were averaged. Each fish collection site is co-located with a nearby synoptic site where long-term measurements of seagrass, sediments, and fauna are made. The relationship between site names and coordinates are given in Synoptic_fish_sites.csv. The sites where fish sampling occurred are different and are given by the "fish_sites" column, with coordinates for these sites under the "fish_longitude" and "fish_latitude" columns. Importantly, the coordinates of where sampling occurred will differ slightly between years without a change to the name of the site. Site geographic coordinates for individual years are in the PhysicalSamples.csv file. Sites are separated by at least 300 meters. In 2023, three new sites were added to represent unvegetated areas outside of but near the seagrass meadows. These sites are HI29, SPDR-bare, and SS-bare, and are designed to serve as references for se

openCustomNov 2024View details →

Abundance, biomass, and length of seagrass-associated invertebrates in the Virginia coastal lagoons, 2019-2023

These data comprise annual summer estimates of the abundance (counts), biomass (dry mass), and individual lengths of infaunal and epifaunal invertebrates across restored seagrass meadows (eelgrass Zostera marina) of the coastal lagoons of Virginia, USA. Infauna were collected during low tide by hand using cylindrical benthic cores. Epifauna were collected during low tide using cubic weighted throw traps that were sampled with dip nets. Incidentally captured fishes are included in these data. In 2019-2022, 50 sites were sampled, using 3 replicates per sampling method per site. Beginning with 2023 sampling, two additional sites were added that are consistently bare of seagrass (unvegetated seafloor). At sites with patchy areas of seagrass and bare substrate, cores were collected within seagrass only and thus represent seagrass-associated fauna at those sites, rather than a spatially haphazard sample. At the few sites that lack seagrass, cores were collected in bare substrate. All cores were separated by 25 m. Regardless of substrate and seagrass conditions, throw traps were deployed haphazardly and separated by at least 10 m. In the laboratory, invertebrates were first sorted to broad taxonomic groups and later identified to lowest practical taxonomic level and enumerated. Most taxonomic groups were either dried and weighed by taxon or measured as individual length by specimen. Existing data include one table with counts and weights for broad taxonomic groups (2019-2023) and three tables related to lowest practical taxonomic identification (2019-2020), including one for counts and biomass, one for individual lengths, and one for taxonomic information. Data collection began in July 2019 and continues annually in June-July.

zenodo48/100

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at Berre coastal lagoon, BEFR site (France)

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at Etang de Berre in France (BEFR). It is a subset of the complete data record which consists of the best quality BEFR measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as: ρwnosc=π(Lu−ρFLd)/Ed   where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). For the BEFR site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided: ρw=π(Lu−ρFLd)/Ed−ϵ   These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info. The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included. To obtain this dataset, we start from the full BEFR data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation: 1. The coefficient of variation in water reflectance is below 10% in the 500-600 nm range 2. The water reflectance (after correction for the NIR similarity) between 700-900 nm is below 0.01

opencc-by-4.0Jun 2023View details →

zenodo48/100

Time Series of Water Levels in a Coastal Barrier-Lagoon System, NW Spain (2009-2012)

This repository contains the data recorded by water-level loggers (survey-pressure transducers) deployed in a barrier-lagoon coastal system, which were used in the study by R. González-Villanueva, M. Pérez-Arlucea, and S. Costas titled 'Lagoon Water-Level Oscillations Driven by Rainfall and Wave Climate,' published in Coastal Engineering, Volume 130, 2017, Pages 34-45, ISSN 0378-3839, available at <a href="https://doi.org/10.1016/j.coastaleng.2017.09.013">https://doi.org/10.1016/j.coastaleng.2017.09.013</a> The repository consists of three text files: <ol> <li>lagoon_water_level.txt</li> <li>sea_level.txt</li> <li>phreatic_level.txt</li> </ol> Each file includes a header with metadata and information for each column in the data file, as follows: <ul> <li>pt_id: ID of the individual record</li> <li>pt: instrument used</li> <li>lat: Latitude in WGS84</li> <li>long: Longitude in WGS84</li> <li>units: Indicates the measurement unit for the water level recordings</li> <li>temporal resolution: Indicates the time interval between two consecutive measurements</li> <li>column 1: Description of the data contained in column 1</li> <li>column 2: Description of the data contained in column 2</li> <li>column n: Description of the data contained in column n</li> </ul>

opencc-by-4.0Sep 2023View details →

edi48/100

Chemistry of coastal stream and lagoon water from Puerto Rico - 2021-2024

Water samples were collected from coastal streams and lagoons in Puerto Rico from February 2021 to March 2024 as part of ongoing coastal ecosystem monitoring. These samples were analyzed at the University of New Hampshire Water Quality Analysis Laboratory for comprehensive water chemistry including field parameters, major ions, nutrients, dissolved gases, and trace metals. Sampling sites included Quebrada Fajardo (QFJO) at multiple depths and distances upstream, Quebrada Pitahaya (Qpaya), Laguna Pitahaya (LPYHA), Luquillo streams (LUQA, LUQB), and Laguna Cartagena (LCART). Field measurements included pH, conductivity, dissolved oxygen, temperature, turbidity, and atmospheric pressure. Laboratory analyses encompassed dissolved organic carbon (DOC), total dissolved nitrogen (TDN), nutrients (NH4-N, PO4-P, NO3-N), major ions (Cl, SO4, Na, K, Mg, Ca), dissolved gases (CH4, CO2, N2O), and trace metals by Inductively Coupled Plasma (ICP) analysis. The ICP analysis provided enhanced detection capabilities for cations and metals including calcium, iron, manganese, silicon, strontium, sulfur, sodium, magnesium and potassium. Samples were collected as grab samples from the water surface. All samples were filtered through pre-combusted Whatman GF/F for nutrients and organic matter and Whatman WCN Cellulose Nitrate Membranes for metals. Values below detection limits are recorded as 1/2 the detection limit. This dataset provides comprehensive water quality data for Puerto Rican coastal watersheds, with particular focus on stratified sampling in Quebrada Fajardo to understand vertical water column structure and biogeochemical processes in coastal environments influenced by both terrestrial and marine inputs. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecol

openCC (other)Dec 2025View details →

zenodo44/100

Disentangling the effects of eutrophication and natural variability on macrobenthic communities across French coastal lagoons

We present here the raw data and scripts to reproduce the results presented in the preprint "Disentangling the effects of eutrophication and natural variability on macrobenthic communities across French coastal lagoons" available on BioRxiv. Before using the scripts and associated data, we recommend reading the "readme" word document also available, which details the information available in the different data sheets.  Preprint abstract :  Coastal lagoons are transitional ecosystems that host a unique diversity of species and support many ecosystem services. Owing to their position at the interface between land and sea, they are also subject to increasing human impacts, which alter their ecological functioning. Because coastal lagoons are naturally highly variable in their environmental conditions, disentangling the effects of anthropogenic disturbances like eutrophication from those of natural variability is a challenging, yet necessary issue to address. Here, we analyze a dataset composed of macrobenthic invertebrate abundances and environmental variables (hydro-morphology, water, sediment and macrophytes) gathered across 29 Mediterranean coastal lagoons located in France, to characterize the main drivers of community composition and structure. Using correlograms, linear models and variance partitioning, we found that lagoon hydro-morphology (connection to the sea and lagoon surface), which affects the level of environmental variability (salinity and temperature), as well as lagoon-scale benthic habitat diversity (using macrophyte morphotypes) seemed to regulate macrofauna distribution, while eutrophication and associated stressors like low dissolved oxygen, acted upon the existing communities, mainly by reducing species richness and diversity. Furthermore, M-AMBI, a multivariate index composed of species richness, Shannon diversity and AMBI (AZTI's Marine Biotic Index) and currently used to evaluate the ecological state of French coastal lagoons, was more sensitive to eutrophication (18%) than to natural variability (9%), with nonetheless 49% of its variability explained jointly by both. To improve the robustness of benthic indicators like M-AMBI and increase the effectiveness of lagoon benthic habitat management, we call for a revision of the ecological groups at the base of the AMBI index and of the current lagoon typology which could be inspired by the lagoon-sea connection levels used in this study.

opencc-by-4.0Sep 2023View details →

zenodo44/100

Data set for the article "Tides, topography, and seagrass cover controls on the spatial distribution of Pinna nobilis on a coastal lagoon tidal flat"

Data set includes: coordinates of the GNSS points (reference system WGS84 UTM33N); density of P. nobilis and cover of C.nodosa detected in the orthophoto in the 25m2 cells; tidal levels measured (and, for comparison, simulated with the hydrodynamic model) corrected with respect to the IGM datum; number of emersions and flood duration for different levels of the tidal flat; statistics. The first Excel sheet includes a detailed description of the data.

opencc-by-4.0May 2021View details →

zenodo44/100

Phytoplankton list of taxa and cell biovolume from a subtropical coastal lagoon, South America

We share information about the phytoplankton community from a subtropical coastal lagoon, Laguna de Rocha, a Biosphere Reserve, located in Uruguay, South America (34°37'60" S, 54°18'0" W). The lagoon is ~ 72 km2 with a mean depth of 0.5 m, and is characterized by a strong South-North salinity gradient that reflects its intermittent connection to the Atlantic Ocean (in the South). The lagoon has been subject to eutrophication in the last several decades. The dataset presented here belongs to the publication of Bonilla et al., 2005 (doi.org/10.1007/BF02696017). We present the complete list of phytoplankton taxa identified in a survey (1996-2000) for two sites, South and North, locations described in figure 1 (Bonilla et al., 2005). We also presented the cellular linear measurements and the cell biovolume of the most representative taxa. We believe these data are useful for the scientific community interested in phytoplankton dynamics, diversity and calculation of biomass (biovolume).

opencc-by-4.0Dec 2022View details →

opencc-by-4.0Apr 2022View details →

Figure 5 in Presence of the Pink shrimp Farfantepenaeus brasiliensis (Latreille, 1817) in the coastal lagoons of Uruguay (Crustacea: Decapoda: Penaeoidea)

Figure 5. Box plot of percentage of rostral length (mm) in relation to cephalothorax length (mm) (%RL) for Farfantepenaeus brasiliensis and F. paulensis caught in the Rocha lagoon in April 2019.

opencc-by-4.0Dec 2020View details →

FIGURE 3 in Comparative analysis of the diet composition and its relation to morphological characteristics in Achirus mazatlanus and Syacium ovale (Pleuronectiformes: Osteichthyes) from a Mexican Pacific coastal lagoon

FIGURE 3 | Representation of selected food categories in groups defined by combinations of species and size groups. Diameters of circles proportional to square roots of percentage in weight. Am: Achirus mazatlanus; So: Syacium ovale; Numbers indicate size classes: 1 for LT <10 cm; 2 for LT ≥ 10 cm and LT ≤ 15 cm; 3 for LT> 15 cm.

opencc-by-4.0Dec 2020View details →

FIGURE 2 in Comparative analysis of the diet composition and its relation to morphological characteristics in Achirus mazatlanus and Syacium ovale (Pleuronectiformes: Osteichthyes) from a Mexican Pacific coastal lagoon

FIGURE 2 | A. Dendrogram showing the result of numerical classification of stomach contents. B. Principal coordinate analysis plot. Vector overlay shows food categories with Spearman's correlation values of 0.5 or higher with ordination axes. Data pooled by species (Am: Achirus mazatlanus; So: Syacium ovale) and size classes (1 for LT <10 cm; 2 for LT ≥ 10 cm and LT ≤ 15 cm; 3 for LT> 15 cm).

Fig. 6. A and C in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina

Fig. 6. A and C: Batch fecundity as a function of total length and total weight (without ovary), respectively. B and D: relative fecundity as a function of total length and total weight (without ovary), respectively.

Fig. 8 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina

Fig. 8. Proportion of mature individuals observed for each length classes of Anchoa marinii. A. Females, B. Males.

Fig. 7 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina

Fig. 7. Monthly variation of the gonadosomatic index (GSI), based on an annual cycle. Boxplots with median, 75th percentile and 25th percentile. Bars denote standard deviation. Open circles= outlier values; asterisk= extreme outliers.

Fig. 4 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina

Fig. 4. Stages of oocyte development of Anchoa marinii. A, oogonias (o) and primary growth (p) oocytes; B, cortical alveoli stage oocyte (arrow); C, yolked oocytes (arrow); D, details of a yolked oocyte (r: radiata zone; g: granulosa cells; t: teca cells); E, migration of the nucleus (n); F, hydrated oocytes; G, atretic follicle; H, post- ovulatory follicle (arrow). Scale bars: A, B, D, 20 µm; C, E, F, G, H, 70 µm.

Fig. 3 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina

Fig. 3. Captures per unite effort (CPUE), temperature and salinity values obtained for Anchoa marinii. Black triangles: CPUE; open squares: temperature; circles with dotted line: salinity.