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734 results for “North Sea”

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zenodo56/100

Daily runoff and nutrient loads for the North Sea and the Baltic Sea based on modelling and observations for the period 1961 to 2019 and adapted to NEMO-SCOBI

This dataset consists of daily values of runoff and reconstructed nutrient loads for the period 1961 to 2019 for the North Sea-Baltic Sea system. Both runoff and nutrient loads were obtained from a model simulation performed with the European application of the Hydrological Predictions for the Environment model v.3.1.8 (E-HYPE). This dataset includes a more realistic number of river outlets than those from observational-based datasets, as not all rivers are monitored, and captures well the interannual variability of all parameters. However, the E-HYPE v.3.1.8 was calibrated to represent 2010 and thus cannot simulate all historical changes related to land management (i.e., the increase of fertilizers in the 1960s). Consequently, the observed rise of nutrients from land due to increased fertilizers and the consequent reduction due to nutrient regulation policy in the 1980s is not captured in the outputs from E-HYPE directly. In the North Sea and the Baltic Sea, this is of primary importance for management policy in eutrophication and deoxygenation. Therefore, we have adapted the E-HYPE nutrient loads based on yearly estimates of historical loads that use riverine concentrations, so that the high tempo-spacial resolution is kept, but with a decadal variability that is closer to reality. This dataset is mainly intended as river forcing for biogeochemical-ocean models (i.e. NEMO-SCOBI), but can also provide information on rivers that are not included in monitoring programs. Information on the dataset and the methods used to create it is given as a downloadable PDF file (E-HYPE DecVar documentation.pdf) together with two datasets and the mesh grid file (area_NEMO-Nordic.nc). The datasets are yearly netCDF files one containing daily runoff and nutrient loads for phosphate, nitrate, ammonium, organic nitrogen and organic phosphorus (zip_ehypeDecVar.zip) and the other one provides monthly silica loads (zip_silica.zip).

opencc-by-4.0Feb 2024View details →

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Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) October 2023 - May 2024

Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from October 2023  up to May 2024 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: TIME in UTC [yyyy-MM-ddThh:mm:ssZ]; Latitude [deg]; Longitude [deg]; nominal depth [m]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature [°C];  Conductivity [mmS/cm]. Missing data are defined as NaN.

opencc-by-4.0Jul 2024View details →

zenodo48/100

North Sea Wave Database (NSWD) 2005-2011

North Sea Wave Database (NSWD) The dataset contains each year of spectral metocean condition for Significant Wave Height (HSIGN) and wave energy period (TMM10), in meters and seconds. Each variable has a year timestap which the data corresponds too i.e 1980. The latitudes and longitudes of the dataset have resolution of 0.025 degrees at each direction. Latitude starting coordinate is 50 degrees and Longitude 0. For more information on the process that developed the dataset, the methodogies followed, calibration, valdiation and sensitivity analysis,  see:  Lavidas, G., & Polinder, H. (2019). North Sea Wave Database (NSWD) and the Need for Reliable Resource Data: A 38 Year Database for Metocean and Wave Energy Assessments. Atmosphere, 10(9), <a href="https://doi.org/10.3390/atmos10090551">https://doi.org/10.3390/atmos10090551 </a> Lavidas, G., & Polinder, H. (2019). Wind effects in the parametrisation of physical characteristics for a nearshore wave model. Proceedings of the 13th European Wave and Tidal Energy Conference 1-6 September 2019, Naples, Italy. The dataset was produced by Dr George Lavidas during the WAVe Resource for Electrical Production (WAVREP, which received funding from the European Union's Horizon 2020 research & innovation programme under the Marie Sklodowska-Curie grant agreement No 787344. The dataset is accompanied by two publication that (i) present the calibration-validation and production (ii) analysis of the dataset. The official CORDIS website is https://cordis.europa.eu/project/id/787344 A list of outcomes for the NSWD and the WAVREP project is found at the researcher's page: <a href="https://www.researchgate.net/project/WAVe-Resource-for-Electrical-Production-WAVREP ">https://www.researchgate.net/project/WAVe-Resource-for-Electrical-Production-WAVREP </a> It can also be found at the official CORDIS website <a href="https://cordis.europa.eu/project/id/787344">https://cordis.europa.eu/project/id/787344</a> Sharing and Access information Creative Commons Attribution (CC BY-SA).  The Creative Commons Attribution license allows others remix, tweak, and build upon your work, as long as they credit you and license their new creations under the identical terms.

opencc-by-4.0Apr 2020View details →

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North Sea Wave Database (NSWD) 1989-1997

opencc-by-4.0Apr 2020View details →

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North Sea Wave Database (NSWD) 2012-2017

opencc-by-4.0Apr 2020View details →

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Modelled distributions of fish and epibenthic invertebrates in the southern North Sea

These data include distribution maps of fish and invertabrate species in the southern North Sea from 2014 until 2023. The maps are modelled using point data of presence/absence and biomass (per trawled km²) from scientific fisheries surveys to estimate the distribution of the probability of occurrence (POC) or biomass (kg per km²), respectively. Also included are forecasts of species' distributions assuming increasing water temperatures in the southern North Sea according to the ICCP scenario RCP8.5. Each files contains a raster stack with layers for each species. The data can be read into the R using the 'stack'-command from the 'raster'-package. The raster stacks contain layers with headers, which code the species and size group. For some species of relevance to fisheries managment, Numbers behind the latin names of the species give information on the included size classes in cm with 'no' indicating no size class information was available. The file names are composed of the follwing elements: 'bio' = biomass 'poc' = probability of occurrence 'emp' = observed occurrence/abundance data from fisheries surveys with employed spatial smoother 'sdm' = modelled distributin data from random forests 'fc' = forecast distributions based on temperature predictors according to RCP8.5 'rel.ca2' = core areas (CA) of distribution representing values > then the mid-point of modelled POC value range Year numbers give the time frame of empirical data or model predictions.    You can access the .tiff-files with the following R-commands using the directory path where you have stored the files: library(raster) poc<-stack("your_path/poc.sdm.2014_2023.tiff") poc$gadus.morhua_5_113 # Plots distribution of Atlantic cod as probability of occurrence observed at a size range from 5 - 113 cm tail length

opencc-by-4.0Nov 2023View details →

zenodo48/100

Data on 'Gelatinous macrozooplankton diversity and distribution in the North Sea and Skagerrak/Kattegat during January-February 2021'

This dataset includes raw and analysed data from 'Gelatinous macrozooplankton diversity and distribution in the North Sea and Skagerrak/Kattegat during January-February 2021' Louise G. Køhler1, Bastian Huwer2, José Martín Pujolar1, Malin Werner3, Karolina Wikström3, Anders Wernbo3, Maria Ovegård3, Cornelia Jaspers1*   1Centre for Gelatinous Zooplankton Ecology and Evolution, National Institute of Aquatic Resources, Technical University of Denmark, Kemitorvet 202, 2800 Kgs. Lyngby, Denmark 2National Institute of Aquatic Resources, Technical University of Denmark, Kemitorvet 201, 2800 Kgs. Lyngby, Denmark 3Institute of Marine Research, Department of Aquatic Resources (SLU Aqua), Swedish University of Agricultural Sciences, Turistgatan 5, S- 453 30 Lysekil, Sweden * Corresponding author: <a href="mailto:coja@aqua.dtu.dk">coja@aqua.dtu.dk</a> This dataset includes data on the qualitative and quantitative description of the gelatinous macrozooplankton community of the North Sea during January-February 2021. Sampling was conducted during the 1st quarter International Bottom Trawl Survey (IBTS) on board the Danish R/V DANA (DTU Aqua Denmark) and the Swedish R/V Svea (SLU Sweden), as part of the ichthyoplankton investigation during night-time. A total of 147 stations were investigated in the western, central and eastern North Sea as well as the Skagerrak and Kattegat. Sampling was conducted with a 13 m long  Midwater Ring Net (MIK net, Ø 2 m, mesh size 1.6 mm, cod end with smaller mesh size of 500 µm), equipped with a flow meter. The MIK net was deployed in double oblique hauls from the surface to c. 5 m above the sea floor. Samples were visually analysed unpreserved on a light table and/or with a stereomicroscope or magnifying lamp within 2 hours after catch. A total of 13,610 individuals were counted/sized. Twelve gelatinous macrozooplankton species or genera were encountered, namely the hydrozoan Aequorea vitrina, Aglantha digitale, Clytia spp., Leuckartiara octona, Tima bairdii, Muggiaea atlantica; the scyphozoans Cyanea capillata and Cyanea lamarckii and the ctenophores Beroe spp., Bolinopsis infundibulum, Mnemiopsis leidyi, Pleurobrachia pileus. Abundance data are presented on a volume specific (m-3) and area specific (m-2) basis. Size data have been used to estimate wet weights based on published length-weight regressions (see reference column in the dataset). This dataset contributes baseline information about the gelatinous macrozooplankton diversity and its specific distribution pattern in the extended North Sea area during winter (January-February) 2021. These data can be an important contribution to address global change impacts on marine systems, especially considering gelatinous macrozooplankton abundance changes in relation to anthropogenic stressors.

opencc-by-4.0Jul 2022View details →

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Supplementary files for Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach

Model input and output files associated with the manuscript entitled "Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach" that will be submitted to Journal of Geophysical Research.

opencc-by-4.0Nov 2023View details →

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Mesoscale Low-Level Jet Climatology for the North and Baltic Seas

Mesoscale Low-Level Jet Climatology for the North and Baltic Seas This dataset contains a mesoscale low-level jet (LLJ) climatology for the Baltic and North Seas. The dataset consists of many individual raster layers of LLJ characteristics zipped in the "llj_climatology.zip" file. Each layer is a netCDF4 file, which can be read directly by QGIS, Python, and many other tools. In the "figures" folder, plots showing most of the layers can be found. A more comprehensive description of the layers is given below. Some examples of layers contained: <ul> <li>LLJ rate-of-occurrence</li> <li>LLJ height</li> <li>LLJ duration</li> <li>Wind speed and direction for at LLJ peak</li> <li>Max shear above and below the LLJ peak</li> <li>Wind speed and direction at 100, 150, 200 m</li> <li>Rotor-equivalent wind speed (REWS) for IEA 15 MW reference turbine</li> </ul> Because of strong seasonality in offshore LLJ occurrences, most layers come as long-term means, including the full five years and seasonality-averaged layers. Several aggregate statistics are available for each layer, such as mean, median, and standard deviation.  The data was created using the Weather Research and Forecasting model v4.2.1 running a five-year hindcast from 2019-06-26 to 2024-06-26. A two-domain setup was used to downscale ERA5 boundary data to 3 km horizontal grid spacing. See the associated paper for a full data generation process and validation description. Based on user feedback, future versions could be expanded to hold additional layers/variables, such as sector-wise Weibull parameters or time-series samples for representative points.  Contact btol@dtu.dk for feedback and requests for future versions.  Full list of variables <ul> <li>ws100, ws150, ws200: wind speed at 100, 150, and 200 meters</li> <li>wd100, wd150, wd200: wind direction at 100, 150, 200 meters</li> <li>rews_iea15mw: rotor-equivalent wind speed for the IEA 15 MW reference turbine</li> <li>cf_iea15mw: capacity factor for the IEA 15 MW reference turbine</li> <li>llj_rate: LLJ detection rate </li> <li>height_of_llj_max: height of LLJ peak in meters</li> <li>llj_ws_max: wind speed of LLJ peak in meters per second</li> <li>llj_wind_direction: wind direction of LLJ peak in degree</li> <li>llj_duration: LLJ duration in hours</li> <li>llj_most_prevalent_hour: most prevalent hour-of-day during LLJ events as hour integers (0-23)</li> <li>llj_most_prevalent_hour_freq: relative frequency of most prevalent hour-of-day during LLJ events</li> <li>llj_most_prevalent_season: most prevalent month-of-year during LLJ events as 0-based month integers (0-11 JAN-DEC)</li> <li>llj_most_prevalent_season_freq: relative frequency of most prevalent month-of-year during LLJ events </li> <li>llj_max_shear_below: maximum shear between the LLJ peak and the minimum below</li> <li>llj_min_shear_above: minimum (maximum negative) shear between the LLJ peak and the minimum above</li> <li>height_of_max_shear_below_llj: height of maximum shear detected below the LLJ peak in meters</li> <li>height_of_min_shear_above_llj: height of minimum shear detected above the LLJ peak in meters</li> <li>llj_depth: the depth of the LLJ measured from "height_of_max_shear_below_llj" to "height_of_min_shear_above_llj" in meters</li> <li>llj_rews_iea15mw: rotor-equivalent wind speed for the IEA 15 MW reference turbine during LLJ events</li> <li>llj_cf_iea15mw: capacity factor for the IEA 15 MW reference turbine during LLJ events</li> <li>llj_abs_falloff_above: absolute wind speed fall-off above the LLJ peak in meters per second</li> <li>llj_rel_falloff_above: relative wind speed fall-off above the LLJ peak </li> <li>llj_abs_falloff_below: absolute wind speed fall-off below the LLJ peak in meters per second</li> <li>llj_rel_falloff_below: relative wind speed fall-off below the LLJ peak </li> </ul> Several layers exist for different aggregation and seasons for each variable. Suffixes describe the aggregation (_mean, _median, _std) and season (_DJF, _MAM, _JJA, _SON) This work is part of the FLOW project and was supported by the European Union Horizon Europe Framework Programme (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205.

opencc-by-4.0Aug 2024View details →

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Jensen et al. 2024 - Biodiversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022 - raw dataset

The diversity and distribution of gelatinous macrozooplankton is described by presenting qualitative and quantitative data of the jellyfish and comb jelly community encountered in the North Sea and Skagerrak/Kattegat during January/February 2022. Data were generated as part of the North Sea Midwater Ring Net survey (MIK), an ichthyoplankton survey conducted at night-time during the quarter 1 (Q1) International Bottom Trawl Survey (IBTS), aboard the Danish R/V DANA (DTU Aqua) and the Swedish R/V Svea (SLU) at a total of 100 stations. This dataset accompanies the Data in Brief Article below and should be cited when using this dataset.  Jensen, C.J.D., Køhler, L.G., Huwer, B., Werner, M., Cieters, L., Jaspers, C. (submitted) Biodiversity and distribution of gelatinous macrozooplankton in the North Sea and adjacent waters dataset from winter 2022. Data in Brief.

opencc-by-4.0Oct 2024View details →

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Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) January 2014 - December 2014

Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from January 2014 to December 2014 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 20 m [°C]; Salinity @ 20 m [psu].

opencc-by-4.0Oct 2023View details →

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Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) March 2015 - December 2015

Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from March 2015 to December 2015 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C];  Salinity @ 6 m [psu]; Salinity @ 20 m [psu],  Salinity @ 36 m [psu].

opencc-by-4.0Oct 2023View details →

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Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) July 2016 - May 2017

Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from July 2016 to May 2017 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C];  Salinity @ 6 m [psu]; Salinity @ 20 m [psu],  Salinity @ 36 m [psu]

opencc-by-4.0Oct 2023View details →

edi48/100

Catalog of GenBank sequence read archive (SRA) entries of 16S and 18S rRNA genes from bacterial and protistan planktonic communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2011-2013

Microbial communities in the coastal Arctic Ocean experience extreme variability in organic matter and inorganic nutrients driven by seasonal shifts in sea ice extent and freshwater inputs. Lagoons border more than half of the Beaufort Sea coast and provide important habitats for migratory fish and seabirds; yet, little is known about the planktonic food webs supporting these higher trophic levels. To investigate seasonal changes in bacterial and protistan planktonic communities, amplicon sequences of 16S and 18S rRNA genes were generated from samples collected during periods of ice-cover (April), ice break-up (June), and open water (August) from shallow lagoons along the eastern Alaska Beaufort Sea coast from 2011 through 2013. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA530074 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA530074. This data package is associated with the following publication: Kellogg CTE, McClelland JW, Dunton KH and Crump BC (2019) Strong Seasonality in Arctic Estuarine Microbial Food Webs. Front. Microbiol. 10:2628. doi: 10.3389/fmicb.2019.02628 Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provided site codes (column "site_name" here) and collection dates (column "collection_date" here) in each dataset. Note that the site codes in this package are without hyphens (e.g. JAA) while site codes in the above environmental data package have hyphens (e.g. JA-A). Instead of citing this package which is jus

openCC0Jan 2020View details →

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Catalog of GenBank sequence read archive (SRA) entries of metagenomic DNA sequence analyses of bacterial and archaeal water column communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2012

In contrast to temperate systems, Arctic lagoons that span the Alaska Beaufort Sea coast face extreme seasonality. Nine months of ice cover up to ∼1.7 m thick is followed by a spring thaw that introduces an enormous pulse of freshwater, nutrients, and organic matter into these lagoons over a relatively brief 2–3 week period. Prokaryotic communities link these subsidies to lagoon food webs through nutrient uptake, heterotrophic production, and other biogeochemical processes, but little is known about how the genomic capabilities of these communities respond to seasonal variability. This study characterizes the metabolic capabilities of microbial communities across three seasons in two lagoons and one open coastal site along the eastern Alaska Beaufort Sea coast. We used metagenomic DNA sequence data of bacterial and archaeal water column communities to identify genes of relevant biogeochemical pathways. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA642637 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA642637. This data package is associated with the following publication: Baker, Kristina D., Colleen T. E. Kellogg, James W. McClelland, Kenneth H. Dunton, and Byron C. Crump. “The Genomic Capabilities of Microbial Communities Track Seasonal Variation in Environmental Conditions of Arctic Lagoons.” Frontiers in Microbiology 12 (2021). https://doi.org/10.3389/fmicb.2021.601901. Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provi

openCC0Apr 2021View details →

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ESA-WOC North Atlantic Sea Surface Salinity maps from a multivariate combination of satellite and in situ surface measurements (2010-2018)

We deliver here the daily sea surface salinity level 4 (SSS L4) product developed in the framework of the  European Space Agency World Ocean Circulation project (ESA-WOC), covering the period 2010-2018. This product was obtained by adapting to a 1/10° North Atlantic grid the multidimensional optimal interpolation algorithm used within the Copernicus Marine Environment Monitoring Service to retrieve the global SSS multi-year dataset (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: MULTIOBS_GLO_PHY_REP_015_002, dataset_id: dataset-sss-ssd-rep-weekly). This algorithm interpolates SMOS observations and in situ SSS observations considering a space-time-thermal decorrelation function, estimated by including information from high-pass filtered daily SST data (Droghei et al., 2016; Buongiorno Nardelli, 2012). Here, we ingested the L3OS 2Q debiased daily valid ocean salinity values product from SMOS satellite, produced and disseminated by the Centre Aval de Traitement des Données SMOS (CATDS, 2017), OSTIA SST data (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) and CORA5.2 surface salinity values (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) as input data, and used CMEMS weekly SSS dataset to build our background field (linearly interpolating it in time between the two closest analysis dates, and upsizing to the 1/10° grid through a cubic spline). All other interpolation parameters were set as in Droghei et al. (2018).               References: Buongiorno Nardelli, B.: A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity, J. Atmos. Ocean. Technol., 29(6), 867–879, doi:10.1175/JTECH-D-11-00099.1, 2012. CATDS (2017). CATDS-PDC L3OS 2Q - Debiased daily valid ocean salinity values product from SMOS satellite. CATDS (CNES, IFREMER, LOCEAN, ACRI). http://dx.doi.org/10.12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0 Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface, J. Atmos. Ocean. Technol., 33, 1211–1223, doi:10.1175/JTECH-D-15-0194.1, 2016. Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084, 2018. Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.

opencc-by-4.0Jul 2020View details →

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Nodal Modulation of Tidal Constituents in the North Sea

Harmonic analysis of sea surface elevation is commonly performed using the f-u correction procedure, which alters the analyzed tidal constituents, based on the longitude of the lunar ascending node at a 18.61 year frequency. Measurement analysis has led us to conclude, that common nodal modulation factors are inaccurate in the shallow regions of the North Sea, due to the friction-induced generation of overtides. For this reason we applied a numerical model to quantify the nodal modulation spatially. The hereby presented data set is therefore derived from model results and includes the nodal amplitude modulation and phase lag for the North Sea on a 7,500m raster as supplemental material to Hagen, et al. (2021, to appear). This data set may be used for the manual correction of nodal modulation in modeling applications, MSL analysis and any other application regarding harmonic analysis in the North Sea region.

opencc-by-4.0Dec 2020View details →

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Modelled time series of CO2 in the vicinity of a seep in the North Sea.

The Bergen Ocean Model (BOM) is used to simulate dispersion of CO2 leaking from nine different locations in the North Sea, focusing on temporal and spatial variability of the CO2 concentration. For details see: Ali, A., Frøysa, H. G., Avlesen, H., & Alendal, G. (2016). Simulating spatial and temporal varying CO 2signals from sources at the seafloor to help designing risk-based monitoring programs. Journal of Geophysical Research-Oceans, 121(1), 745–757. http://doi.org/10.1002/2015JC011198. The data files contains time series of excess CO2 concentration, velocity components and time, and position in longitude and latitude. One file for each grid position, a total of 53x51 grid points with the seep in location (I,j)=(28,27).

opencc-by-4.0Jun 2017View details →

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LifeWatch observatory data: phytoplankton annotated trainingset by FlowCam imaging in the Belgian Part of the North Sea

<h1>Training dataset </h1> The images were collected in the framework of the Belgian Lifewatch Research Infrastructure. During multidisciplinary campaigns, a number of fixed stations in the Belgian Part of the North Sea (BPNS) are visited on a monthly (onshore stations) or seasonal (offshore stations) basis. Samples are taken using a 55µm mesh size Apstein net and fixed in Lugol's iodine solution. In the lab, the samples are processed using a VS-4 FlowCAM model at 4X magnification targeting a particle size range of 55-300µm. The identification of the image data is done with the use of a CNN and followed by a manual validation step. Since May 2017, this dataset has provided micro- and phytoplankton observations, mainly covering diatoms, dinoflagellates and cilliates, for the Belgian Part of the North Sea (BPNS).   This dataset comprises a trainings datasplit of 337,514 images distributed across 95 classes, with each class containing a minimum of 100 and a maximum of 10,000 images. The goal of this dataset is to be able to facilitate model training, here we have organized the data into a standard split, with 80% allocated for training, 10% for validation, and another 10% for testing purposes. This dataset structure ensures a balanced representation and supports scientific rigor in subsequent analyses. <h1>Technical details </h1> <h2>Data preprocessing </h2> Raw FlowCam output data is fully processed using in-house datapipelines, the VisualSpreadsheet software is only used for data acquisition during the lab run of the sample. Raw images and binary images are never saved during the FlowCam run, we only work on the image collages saved at the end of the run. Single images are cut from these collages using each image coordinates width and height pulled from the .lst file using in-house python code. The background of the images is not removed. These images are then predicted and annotated in-house at VLIZ. <h2>Data splitting </h2> The training dataset is 80% used for training, 10% for validation and 10% for prediction.  <h2>Classes, labels and annotations</h2> The dataset comprises 337,514 images distributed across 95 classes, with each class containing a minimum of 100 and a maximum of 10,000 images. Taxonomic coverage of the dataset comprises mainly of diatoms, dinoflagellates and cilliates, but to a lesser extent also zooplankton and other protists. <h2>Parameters </h2> The images are read using cv2.imread and the values are used as parameters. Metadata Parameter Descriptions: <ul> <li> image_path: The relative path to the image file showing the plankton or particle, usually structured by taxon or project folder. </li> <li> sample_datetime: The exact date and time (in YYYY-MM-DD HH:MM:SS.sss format) when the sample was acquired using the imaging instrument. </li> <li> flowcam_version: The software or hardware version of the FlowCAM instrument used to capture the image and process the sample. </li> <li> station: The sampling location code where the image was taken. These station codes typically refer to predefined geographic or monitoring points in the field. </li> <li> accepted_label: The final validated taxonomic label (e.g., genus or species) assigned to the organism or particle in the image, often based on expert review. </li> <li> accepted_aphia_id: The unique identifier (AphiaID) corresponding to the accepted taxonomic label in the World Register of Marine Species (WoRMS) database, which ensures standardized taxonomic reference. </li> <li> original_reference_id: A unique identifier (often a UUID) assigned to the image or sample in the original classification system (e.g., EcoTaxa), useful for traceability and linking back to the source record. </li> </ul> <h2>Data sources </h2> Images are collected during the monthly monitoring of phytoplankton communities in the Belgian Part of the North Sea during the LifeWatch multidisciplinary campaigns by FlowCam VS-4 benchmodel (Fluid Imaging Technologies, Yarmouth, Maine, U.S.A.). <h2>Data quality </h2> All images are predicted and subsequently manually validated to ensure the quality of the trainingset. <h2>Image resolution </h2> The size range imaged is 55-300µm. Images are acquired using a Sony XCD SC90 digital gray-scale camera. Images are during training of CNN resized to 100px by 100px. <h2>Spatial coverage </h2> The data comes from a number of fixed stations in the Belgian Part of the North Sea (BPNS).  Nine stations onshore are visited monthly: <table> <tbody> <tr> <td>Station</td> <td>Longitude</td> <td>Latitude</td> </tr> <tr> <td>130</td> <td>2.90535</td> <td>51.27055</td> </tr> <tr> <td>780</td> <td>3.057283</td> <td>51.471367</td> </tr> <tr> <td>330</td> <td>2.809083</td> <td>51.434117</td> </tr> <tr> <td>230</td> <td>2.85035</td> <td>51.308683</td> </tr> <tr> <td>710</td> <td>3.138283</td> <td>51.441217</td> </tr> <tr> <td>215</td> <td>2.61075</td> <td>51.274867</td> </tr> <tr> <td>ZG02</td> <td>2.500717</td> <td>51.33515</td> </tr> <tr> <td>120</td> <td>2.702483</td> <td>51.186083</td> </tr> <tr> <td>700</td> <td>3.221017</td> <td>51.377</td> </tr> </tbody> </table> Eight additional offshore stations are visited seasonally: <table> <tbody> <tr> <td>Station</td> <td>Longitude</td> <td>Latitude</td> </tr> <tr> <td>LW01</td> <td>2.256</td> <td>51.568667</td> </tr> <tr> <td>LW02</td> <td>2.556</td> <td>51.8</td> </tr> <tr> <td>435</td> <td>2.790333</td> <td>51.580667</td> </tr> <tr> <td>W07bis</td> <td>3.012517</td> <td>51.588033</td> </tr> <tr> <td>W08</td> <td>2.35</td> <td>51.458333</td> </tr> <tr> <td>W09</td> <td>2.7</td> <td>51.75</td> </tr> <tr> <td>W10</td> <td>2.416667</td> <td>51.683333</td> </tr> <tr> <td>421</td> <td>2.45</td> <td>51.4805</td> </tr> </tbody> </table> <h2> </h2> <h2>Temporal coverage </h2> The monitoring was initiated in May 2017 and has been running continuously every month. <h2>Contact information </h2> For technical questions about training dataset, you can contact wout.decrop@vliz.be.

opencc-by-4.0Jan 2024View details →

zenodo44/100

HG_OOSTENDE - Herring gulls (Larus argentatus, Laridae) breeding at the southern North Sea coast (Belgium)

HG_OOSTENDE - Herring gulls (Larus argentatus, Laridae) breeding at the southern North Sea coast (Belgium) is a bird tracking dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected by the LifeWatch GPS tracking network for large birds (<a href="http://lifewatch.be/en/gps-tracking-network-large-birds">http://lifewatch.be/en/gps-tracking-network-large-birds</a>) for the project/study HG_OOSTENDE, using trackers developed by the University of Amsterdam Bird Tracking System (UvA-BiTS, <a href="http://www.uva-bits.nl">http://www.uva-bits.nl</a>). The study was operational from 2013 until 2022.  In total 60 individuals of European herring gull (Larus argentatus) have been tagged in or near their breeding area at the southern North Sea coast (Ostend and Zeebrugge in Belgium), mainly to study their habitat use. Data are periodically uploaded from the UvA-BiTS database to Movebank and from there archived on Zenodo (see <a href="https://github.com/inbo/bird-tracking">https://github.com/inbo/bird-tracking</a>). No new data are expected. <h2>Files</h2> Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study986040562">986040562</a>. Fields in the data follow the <a href="http://vocab.nerc.ac.uk/collection/MVB">Movebank Attribute Dictionary</a> and are described in <code>datapackage.json</code>. Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/records/10054230/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>. <ul> <li>datapackage.json: technical description of the data files.</li> <li>HG_OOSTENDE-reference-data.csv: reference data about the animals, tags and deployments.</li> <li>HG_OOSTENDE-gps-yyyy.csv.gz: GPS data recorded by the tags, grouped by year.</li> <li>HG_OOSTENDE-acceleration-yyyy.csv.gz: acceleration data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> This dataset was collected using infrastructure provided by VLIZ and INBO funded by Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch.

opencc-zeroDec 2019View details →

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