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738 Research products, page 1 of 74

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  • Open Access English
    Authors: 
    De Mendoza, Francesco Paladini; Schroeder, Katrin; Langone, Leonardo; Chiggiato, Jacopo; Borghini, Mireno; Giordano, Patrizia; Verazzo, Giulio; Miserocchi, Stefano;
    Publisher: Zenodo
    Project: EC | HERMIONE (226354), EC | COCONET (287844)

    This data set includes n.4 files (NetCDF format) containing observational data and related metadata from two mooring sites, sites BB and FF, located in the Southern Adriatic Sea from the period from March 2012 to June 2020. The stand-alone moorings are equipped with an ADCP-RDI system which measures currents along the last 100 meters of the water column and a CTD probe located approximatively 10 m above the bottom. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic submarine observatory of EMSO-ERIC European Consortium. The data are described in data paper Paladini et al., (In prep): Deep water hydrodynamic observations of two moorings sites on the continental slope of the Southern Adriatic Sea (Mediterranean Sea).

  • Other research product . Other ORP type . 2022
    Open Access English
    Authors: 
    Katharina Biely;
    Publisher: Zenodo
    Project: EC | SUFISA (635577)

    This is the English version of the informed consent that has been used for staekholder interactions. Similar forms have been used for focus groups and workshops.

  • Open Access English
    Authors: 
    Berndt, Christian; Canals, Miquel; Urgeles, Roger; Camerlenghi, Angelo; Papenberg, Cord;
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: EC | HERMIONE (226354)

    3D reflection seismic data were acquired using the P-Cable system of the National Oceanographic Centre, Southampton, UK during cruise 178 Leg 2 onboard RRS Charles Darwin between the 5th and 8th of April 2006. The responsible PI's was C. Berndt, Southampton Oceanography Centre, Southampton, UK. The aim of this cruise was to map submarine landslides on the eastern slopes of the Eivissa Channel, western Mediterranean Sea located between the islands of Ibiza-Formentera and the Spanish mainland. Berndt et al. (2012) used the acquired data to study repeated slope failure linked to fluid migration, while Lafuerza et al. (2012) studied geotechnical aspects of slope stability using this as additional data. Acquisition parameters: The source during seismic acquisition consisted of four 40 in3 Bolt 600B air guns spaced 0.75 m apart and tower at a depth of 1.5 m about 20 m behind the stern of the vessel (Berndt et al., 2012). The air guns are fitted with wave shape kits that emit approximately 10 in3 of air prior to the main volume to reduce the bubble pulse. The air pressure is 2000 psi, and the gun controller triggers the guns to figure every 7 seconds. The data were collected with 11 single-channel analogue streamers that were towed 10 m apart. The seismic cube in the Eivissa Channel covers an area of ca. 14 km2 (ca. 6.4 EW x 2.2 NS km) located at 306091.83 4280497.41; 305951.42 4278353.92; 312321.94 4277936.57 in UTM zone 31N. 3D seismic processing: Data were frequency filtered from 45 to 220 Hz and binned at 10 m bin interval before a Stolt time migration with a migration velocity of 1500 ms-1 was carried out. The resolution of the data is approximately 5-6 m vertically and for the 10 m inline and crossline spacing the horizontal resolution is 10-15 m (Berndt et al., 2012). Seismic data acquisition was performed between 10:05 PM on the 5th of April until 08:30 PM on the 7th of April 2006 (CD178 cruise report). The seismic cube is located at water depths of 550 to 825 m from east to west. Raw data is available here:doi:10.1594/PANGAEA.943523.

  • Open Access English
    Authors: 
    Morato, Telmo; Juliano, Manuela; Pham, Christopher Kim; Carreiro-Silva, Marina; Martins, Ines; Colaço, Ana;
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: EC | MERCES (689518), EC | iAtlantic (818123), EC | MIDAS (603418), FCT | IF/01194/2013/CP1199/CT0002 (IF/01194/2013/CP1199/CT0002), EC | ATLAS (678760)

    It is increasingly recognised that deep-sea mining of seafloor massive sulphides (SMS) could become an important source of mineral resources. These operations will remove the targeted substrate and produce potentially sediment toxic plumes from in situ seabed excavation and from the return water pumped back down to the seafloor. However, the spatial extent of the impacts of deep-sea mining plumes is still uncertain because few field experiments and models of plumes dispersion have been conducted. Morato et al. (2022) used three-dimensional hydrodynamic models of the Azores region together with a theoretical commercial mining operation of polymetallic SMS to simulate the potential dispersal of sediment plumes originating from different phases of mining operations and to assess the magnitude of potential impacts. The areas used in the modelling work were (from North to South): Cavala seamount (38.265, -30.710), Lucky Strike Hole (37.503, -31.955), Menez Hom (37.109, -32.618), Famous (37.001, -33.039), Saldanha (36.658, -33.420), and Rainbow (36.262 -33.824). The datasets published here contain all the model outputs, namely for 1) the in situ excavation sediment plume, 2) the return water discharge plume, and 3) the return sediments discharge plume:1) The concentration of solids and of the discharge water in each horizontal 2-dimensional space cell is calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during each trimester and during a 12-months simulation.1.1) Concentration of sediments produced during the in situ excavation sediment plume calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation. Sediments were composed of six classes of different particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and >2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1.1.2) Concentration of return water discharge plume (shown in dilution folds) in six study areas calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation and assuming a control temperature as the annual minimum temperature of each location (T1). The salinity of discharge was calculated assuming the MOHID salinity of 83.3% surface water and 16.7% of seafloor water.1.3) Concentration of sediments in the return sediment discharge plume, calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation. The average particle diameter was assumed to be 4 µm with an average particle density of 3,780 kg·m-3 and a resultant settling velocity of 0.002 cm·s-1.2) The proportion of simulated time (temporal frequency) that a specific 2-dimensional space contained plume concentrations higher than the adopted thresholds; 1.2 mg·L-1 for sediment solids and 5,000 fold dilution for discharge water. Those cells whose temporal frequency above the thresholds was greater than 50%, i.e. 6 months out of 12 months, were considered as cells with persistent plumes.2.1) Proportion of simulated time (temporal frequency) that a specific a 2-dimensional space cell, in six study areas, contained in situ excavation sediment plume above a 1.2 mg·L-1 concentration threshold, during a 12-months simulation, assuming six classes of particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and >2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1.2.2) Proportion of simulated time (temporal frequency) that a specific 2-dimensional space, in six study areas, contained return water discharge plume concentrations higher than the adopted thresholds (i.e., 5,000 fold dilution), during a 12-months simulation and assuming a control temperature as the annual minimum temperature of each location (T1). The salinity of discharge was calculated assuming the MOHID salinity of 83.3% surface water and 16.7% of seafloor water.2.3) Proportion of simulated time (temporal frequency) that a specific 2-dimensional space cell, in six study areas, contained return sediments discharge plume above a 1.2 mg·L-1 concentration threshold, during a 12-months simulation, assuming an average particle diameter of 4 µm, an average particle density of 3,780 kg·m-3, and a resultant settling velocity of 0.002 cm·s-1.3) In addition to the thresholds and targets described above, the datasets also present the model results for Cavala seamount and Lucky Strike Hole against other thresholds: 5 mg·L-1, 10 mg·L-1 and 25 mg·L-1 for sediments and 1,000, 600, 300 and 200 fold dilution for discharge water.4) Seasonal variations in the model outputs for plumes dispersal are also presented for Cavala seamount and Lucky Strike Hole by computing the probability of concentration above thresholds for four periods of three months (January-March, April-June, July-September, and October-December). In these scenarios, the model run duration was approximately 90 days.5) The sediment thickness of the settled sediments from the discharge sediment and excavation.5.1) Bottom thickness of settled sediments produced during the in situ excavation sediment plume assuming six classes of particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and >2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1. The duration of the simulation is one year.5.2) Bottom thickness of settled sediments from the return sediment discharge plume modelled assuming an average particle diameter of 4 µm, an average particle density of 3,780 kg·m-3, and a resultant settling velocity of 0.002 cm·s-1. The duration of the simulation is one year. The downloadable zip folders contain GeoTIFF files with the model outputs used in the publication: Modelling the dispersion of Seafloor Massive Sulphide mining plumes in the Mid Atlantic Ridge around the Azores. Files are organized by:Study site (Cavala, Famous, Lucky Strike Hole, Menez Home, Rainbow, Saldanha)Plume type (Discharge sediments, Discharge water, Excavation)Parameters settings (4 micra, 4 micro rho, 8 micra; Discharge temperature 1, Discharge temperature 2, Discharge temperature ambient; 2m_group)Variable measured (Footprint in mm, Max tracer in mg·L-1, Probability (i.e. temporal frequency) in %)Temporal periods (Year: 2011-12-31_2012-01-01; trimester: 2011-01-10_2011-04-01, 2011-04-01_2011-07-01, 2011-07-01_2011-10-01, 2011-10-01_2012-01-01)

  • Open Access English
    Authors: 
    Pallacks, Sven; Ziveri, Patrizia; Martrat, Belén; Mortyn, P Graham; Grelaud, Michaël; Schiebel, Ralf; Incarbona, Alessandro; García-Orellana, Jordi; Anglada-Ortiz, Griselda;
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: EC | MEDSEA (265103)
  • Open Access English
    Authors: 
    González-Irusta, José Manuel; Fauconnet, Laurence; Das, Diya; Catarino, Diana; Afonso, Pedro; Viegas, Cláudia Neto; Rodrigues, Luís; Menezes, Gui; Rosa, Alexandra; Pinho, Mário Rui Rilhó; +3 more
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: EC | iAtlantic (818123), EC | DiscardLess (633680), EC | ATLAS (678760)

    Data layers producedProbPresence: This dataset contains the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function, through the implementation gam in the package mgcv. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.ProbPresence_Error: This dataset contains the standard error associated with the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function, through the implementation gam in the package mgcv. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.BinPresence_Kappa: This dataset contains the binary maps of the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function and a threshold that maximizes Kappa. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.BinPresence_MSS: This dataset contains the binary maps of the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function and the maximization of the sum of sensitivity and specificity (MSS) threshold, which minimizes misclassification likelihoods of false negatives and false positives. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.PredAbundance: This dataset contains the predicted abundance (Pa) of 6 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with negative binomial distributions and a log link, through the implementation of gam in the package mgcv. Etmopterus spinax; Deania profundorum; Raja clavata; Etmopterus pusillus; Deania calcea; Galeorhinus galeus.PredAbundance_Error: This dataset contains the standard error associated with the predicted abundance (Pa) of 6 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) the Azores, using a Generalized Additive Models (GAM) approach with negative binomial distributions and a log link, through the implementation gam in the package mgcv. Etmopterus spinax; Deania profundorum; Raja clavata; Etmopterus pusillus; Deania calcea; Galeorhinus galeus.FinalAbundance: This dataset contains the final predicted abundance (Fpa) of 6 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Delta Generalized Additive Models (GAM) approach recommended for zero-inflated data. This approach involves using the Probability of presence and the presence-only data to predict species abundances (Pa) (as described in other datasets). The final predicted abundance values (Fpa) were computed by multiplying the Pp by the Pa. Etmopterus spinax; Deania profundorum; Raja clavata; Etmopterus pusillus; Deania calcea; Galeorhinus galeus.Extent: West -37.479533; East -18.832939; North 44.355782; South 32.678347Spatial Reference:Type: ProjectedGeographic coordinate reference: GCS_WGS_1984Projection: WGS_1984_UTM_Zone_26NPoint of Contact: Luis Rodrigues; Ocean Sciences Institute - Okeanos, University of the Azores, Rua Professor Doutor Frederico Machado 4, 9901-862 Horta, Portugal. lmcrod@gmail.com Description: We developed predictive distribution models of deep-sea elasmobranchs for up to 2000 m depth in the Azores EEZ and neighboring seamounts, from approximately 33��N to 43��N and 20��W to 36��W. Georeferenced presence, absence, and abundance data were obtained from scientific surveys and commercial operations reporting at least one deep-sea elasmobranch capture. A 20-year 'survey dataset' (1996-2017) was compiled from annual scientific demersal surveys using two types of bottom longlines (types LLA and LLB), and an 'observer dataset' (2004-2018) from observer programs covering commercial fisheries operations using bottom longline (similar to type LLA) and vertical handline ('gorazeira'). We used the most ecologically relevant candidate environmental predictors for explaining the spatial distribution of deep-sea elasmobranch in the Azores: depth, slope, northness, eastness, Bathymetric Position Index (BPI), nitrates, and near bottom currents. We merged existing multibeam data for the Azores EEZ with bathymetry data extracted from EMODNET (EMODnet Bathymetry Consortium 2018) to calculate depth values (down to 2000m). All variables were projected with the Albers equal-area conical projection centered in the middle of the study area and were rescaled using bilinear interpolation to a final grid cell resolution of 1.12 x1.12 km (i.e., 0.012��). Slope, northness, and eastness were computed from the depth raster using the function terrain in the R package raster. BPI was derived from the rescaled depth with an inner radius of 3 and an outer radius of 25 grid cells using the Benthic Terrain Model 3.0 tool in ArcGIS 10.1. Nitrates were extracted from Amorim et al. (2017). Near-bottom current speed (m��s-1) average values were based on a MOHID hydrodynamic model application (Viegas et al., 2018) with an original resolution of 0.054��. Besides the environmental variables, we also included three operational predictors in the analysis: year, fishing effort (number of hooks) and gear type (longline LLA and LLB, and gorazeira).

  • Open Access English
    Authors: 
    Berndt, Christian; Canals, Miquel; Urgeles, Roger; Camerlenghi, Angelo; Papenberg, Cord;
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: EC | HERMIONE (226354)

    3D reflection seismic data were acquired using the P-Cable system of the National Oceanographic Centre, Southampton, UK during cruise 178 Leg 2 onboard RRS Charles Darwin between the 5th and 8th of April 2006. The responsible PI's was C. Berndt, Southampton Oceanography Centre, Southampton, UK. The aim of this cruise was to map submarine landslides on the eastern slopes of the Eivissa Channel, western Mediterranean Sea located between the islands of Ibiza-Formentera and the Spanish mainland. Berndt et al. (2012) used the acquired data to study repeated slope failure linked to fluid migration, while Lafuerza et al. (2012) studied geotechnical aspects of slope stability using this as additional data. Acquisition parameters: The source during seismic acquisition consisted of four 40 in3 Bolt 600B air guns spaced 0.75 m apart and tower at a depth of 1.5 m about 20 m behind the stern of the vessel (Berndt et al., 2012). The air guns are fitted with wave shape kits that emit approximately 10 in3 of air prior to the main volume to reduce the bubble pulse. The air pressure is 2000 psi, and the gun controller triggers the guns to figure every 7 seconds. The data were collected with 11 single-channel analogue streamers that were towed 10 m apart. The seismic cube in the Eivissa Channel covers an area of ca. 14 km2 (ca. 6.4 EW x 2.2 NS km) located at 306091.83 4280497.41; 305951.42 4278353.92; 312321.94 4277936.57 in UTM zone 31N. 2D seismic processing: During seismic processing of the 3D dataset significant ghost-artefacts were identified because some of the streamers were towed too deep. This required de-ghosting. Unfortunately, these attempts did not yield improved quality of the 3D seismic data. This was mainly because the 12.5 m streamers were too short for commonly used de-ghosting technique used in the industry. To increase vertical resolution individual 2D profiles were extracted from the raw dataset. Processing steps included frequency bandpass filtering, burst noise attenuation, binning, NMO-correction, stacked, and a Stolt migration with 1520 ms-1 was applied that resulted in higher resolution 2D profiles for 85 lines and 11 streamers (channels).

  • Open Access English
    Authors: 
    Martins, Ines; Godinho, Antonio; Rakka, Maria; Carreiro-Silva, Marina;
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: FCT | UIDB/05634/2020 (UIDB/05634/2020), EC | MERCES (689518), EC | iAtlantic (818123)

    Herein we report the respiration rates (O2 consumption) of the cold-water coral Viminella flagellum exposed to acute Cu concentrations. In a lab experiment, sixty nubbins of V. flagellum were distributed in six aquaria of 8 L (ten nubbins per aquarium) of each Cu solution (0 (control); 60; 150; 250; 450 and 600 μg/L) for 96 h. After this period, four nubbins from each Cu treatment, selected randomly, were incubated individually for 6 h in glass chambers filled with ca. 110 mL of 0.2 μm pre-filtered seawater, with the respective Cu dilutions (4 chambers per Cu concentration). The incubation period was set to 6 h to record changes in O2 consumption without exposing corals to oxygen levels below 80 % (air saturation, a.s.). During the incubation period, dissolved O2 (μmol/L) depletion rates were recorded every 30 min and corrected by the corresponding rates/variations in chambers without corals. Coral respiration rates were normalized to the coral surface area and time. Results are presented by µmol of O2 consumption per m2 per h. Treatment [Cu] µg/L: [0]: Control, no copper addition[60]: Cu concentration of 60 μg/L[150]: Cu concentration of 150 μg/L[250]: Cu concentration of 250 μg/L[450]: Cu concentration of 450 μg/L[600]: Cu concentration of 600 μg/L

  • Open Access English
    Authors: 
    Tanhua, Toste; Kazanidis, Georgios; Sá, Sandra; Neves, Caique; Obaton, Dominique; Sylaios, Georgios;
    Publisher: Zenodo
    Project: EC | EurofleetsPlus (824077), EC | NAUTILOS (101000825), EC | iAtlantic (818123), EC | Blue Cloud (862409), EC | JERICO-S3 (871153), EC | AtlantECO (862923), EC | ODYSSEA (727277), EC | EuroSea (862626), EC | ATLAS (678760), EC | MISSION ATLANTIC (862428)

    Ten innovative EU projects to build ocean observation systems that provide input for evidence-based management of the ocean and the Blue Economy, have joined forces in the strong cluster ‘Nourishing Blue Economy and Sharing Ocean Knowledge’. Under the lead of the EuroSea project, the group published a joint policy brief listing recommendations for sustainable ocean observation and management. The cooperation is supported by the EU Horizon Results Booster and enables the group to achieve a higher societal impact. The policy brief will be presented to the European Commission on 15 October 2021. The ocean covers 70% of the Earth’s surface and provides us with a diverse set of ecosystem services that we cannot live without or that significantly improve our quality of life. It is the primary controller of our climate, plays a critical role in providing the air we breathe and the fresh water we drink, supplies us with a large range of exploitable resources (from inorganic resources such as sand and minerals to biotic resources such as seafood), allows us to generate renewable energy, is an important pathway for world transport, an important source of income for tourism, etc. The Organisation for Economic Cooperation and Development (OECD) evaluates the Blue Economy to currently represent 2.5% of the world economic value of goods and services produced, with the potential to further double in size by 2030 (seabed mining, shipping, fishing, tourism, renewable energy systems and aquaculture will intensify). However, the overall consequences of the intensification of human activities on marine ecosystems and their services (such as ocean warming, acidification, deoxygenation, sea level rise, changing distribution and abundance of fish etc.) are still poorly quantified. In addition, on larger geographic and temporal scales, marine data currently appear fragmented, are inhomogeneous, contain data gaps and are difficult to access. This limits our capacity to understand the ocean variability and sustainably manage the ocean and its resources. Consequently, there is a need to develop a framework for more in-depth understanding of marine ecosystems, that links reliable, timely and fit-for-purpose ocean observations to the design and implementation of evidence-based decisions on the management of the ocean. To adequately serve governments, societies, the sustainable Blue Economy and citizens, ocean data need to be collected and delivered in line with the Value Chain of Ocean Information: 1) identification of required data; 2) deployment and maintenance of instruments that collect the data; 3) delivery of data and derived information products; and 4) impact assessment of services to end users. To provide input to the possible future establishment of such a framework, ten innovative EU projects to build user-focused, interdisciplinary, responsive and sustained ocean information systems and increase the sustainability of the Blue Economy, joined forces in a strong cluster to better address key global marine challenges. Under the lead of the EuroSea project, the group translated its common concerns to recommendations and listed these in the joint policy brief ‘Nourishing Blue Economy and Sharing Ocean Knowledge. Ocean Information for Sustainable Management.’. Following up on these recommendations will strengthen the entire Value Chain of Ocean Information and ensure sound sustainable ocean management. In this way, the 10 projects jointly strive to achieve goals set out in the EU Green Deal, the Paris Agreement (United Nations Framework Convention on Climate Change) and the United Nations 2021-2030 Decade of Ocean Science for Sustainable Ocean Development. Toste Tanhua (GEOMAR), EuroSea coordinator: “It was great to collaborate with these other innovative projects and make joint recommendations based on different perspectives and expertise.”

  • Research data . 2021
    Open Access English
    Authors: 
    Green, Caroline;
    Publisher: Zenodo
    Project: EC | Respon-SEA-ble (652643)

    Data and R Scripts necessary to reproduce results of ESD study at NUIG conducted and analysed in 2020-2021. The study aimed to investigate whether Systems Thinking (ST) and/or System Dynamics simulation (Sim) increased the effectiveness of Sustainability Education. In the study, participants were randomly allocated to one of four groups, and interacted with an online learning tool which contained embedded quizzes. Their performance in these quizzes formed the basis for comparison between groups. An open access of the learning tool is available here: https://exchange.iseesystems.com/public/carolineb/sustainability-learning-tool/. The R Scripts folder contains 4 R scripts used for data analysis. Run the script function_qualitative.R first. The data folder contains the anonymised research data. Create a folder called data beneath the folder containing your R scripts. The model folder contains the System Dynamics deer model (.stmx) file, used for the simulation exercises. The surveys folder is for reference only. It contains the surveys and quizzes used to collect the data, together with quiz marking schemes, code books and quiz answers. In the data, group members are identified by a number from 0 to 3: group 0 means control group, group 1 means ST group, group 2 means Sim group and group 3 means ST + Sim group. For more details see README.md and the readme.txt files in each folder. Additional funding from the Higher Education Authority (HEA), Ireland.

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The following results are related to European Marine Science. Are you interested to view more results? Visit OpenAIRE - Explore.
738 Research products, page 1 of 74
  • Open Access English
    Authors: 
    De Mendoza, Francesco Paladini; Schroeder, Katrin; Langone, Leonardo; Chiggiato, Jacopo; Borghini, Mireno; Giordano, Patrizia; Verazzo, Giulio; Miserocchi, Stefano;
    Publisher: Zenodo
    Project: EC | HERMIONE (226354), EC | COCONET (287844)

    This data set includes n.4 files (NetCDF format) containing observational data and related metadata from two mooring sites, sites BB and FF, located in the Southern Adriatic Sea from the period from March 2012 to June 2020. The stand-alone moorings are equipped with an ADCP-RDI system which measures currents along the last 100 meters of the water column and a CTD probe located approximatively 10 m above the bottom. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic submarine observatory of EMSO-ERIC European Consortium. The data are described in data paper Paladini et al., (In prep): Deep water hydrodynamic observations of two moorings sites on the continental slope of the Southern Adriatic Sea (Mediterranean Sea).

  • Other research product . Other ORP type . 2022
    Open Access English
    Authors: 
    Katharina Biely;
    Publisher: Zenodo
    Project: EC | SUFISA (635577)

    This is the English version of the informed consent that has been used for staekholder interactions. Similar forms have been used for focus groups and workshops.

  • Open Access English
    Authors: 
    Berndt, Christian; Canals, Miquel; Urgeles, Roger; Camerlenghi, Angelo; Papenberg, Cord;
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: EC | HERMIONE (226354)

    3D reflection seismic data were acquired using the P-Cable system of the National Oceanographic Centre, Southampton, UK during cruise 178 Leg 2 onboard RRS Charles Darwin between the 5th and 8th of April 2006. The responsible PI's was C. Berndt, Southampton Oceanography Centre, Southampton, UK. The aim of this cruise was to map submarine landslides on the eastern slopes of the Eivissa Channel, western Mediterranean Sea located between the islands of Ibiza-Formentera and the Spanish mainland. Berndt et al. (2012) used the acquired data to study repeated slope failure linked to fluid migration, while Lafuerza et al. (2012) studied geotechnical aspects of slope stability using this as additional data. Acquisition parameters: The source during seismic acquisition consisted of four 40 in3 Bolt 600B air guns spaced 0.75 m apart and tower at a depth of 1.5 m about 20 m behind the stern of the vessel (Berndt et al., 2012). The air guns are fitted with wave shape kits that emit approximately 10 in3 of air prior to the main volume to reduce the bubble pulse. The air pressure is 2000 psi, and the gun controller triggers the guns to figure every 7 seconds. The data were collected with 11 single-channel analogue streamers that were towed 10 m apart. The seismic cube in the Eivissa Channel covers an area of ca. 14 km2 (ca. 6.4 EW x 2.2 NS km) located at 306091.83 4280497.41; 305951.42 4278353.92; 312321.94 4277936.57 in UTM zone 31N. 3D seismic processing: Data were frequency filtered from 45 to 220 Hz and binned at 10 m bin interval before a Stolt time migration with a migration velocity of 1500 ms-1 was carried out. The resolution of the data is approximately 5-6 m vertically and for the 10 m inline and crossline spacing the horizontal resolution is 10-15 m (Berndt et al., 2012). Seismic data acquisition was performed between 10:05 PM on the 5th of April until 08:30 PM on the 7th of April 2006 (CD178 cruise report). The seismic cube is located at water depths of 550 to 825 m from east to west. Raw data is available here:doi:10.1594/PANGAEA.943523.

  • Open Access English
    Authors: 
    Morato, Telmo; Juliano, Manuela; Pham, Christopher Kim; Carreiro-Silva, Marina; Martins, Ines; Colaço, Ana;
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: EC | MERCES (689518), EC | iAtlantic (818123), EC | MIDAS (603418), FCT | IF/01194/2013/CP1199/CT0002 (IF/01194/2013/CP1199/CT0002), EC | ATLAS (678760)

    It is increasingly recognised that deep-sea mining of seafloor massive sulphides (SMS) could become an important source of mineral resources. These operations will remove the targeted substrate and produce potentially sediment toxic plumes from in situ seabed excavation and from the return water pumped back down to the seafloor. However, the spatial extent of the impacts of deep-sea mining plumes is still uncertain because few field experiments and models of plumes dispersion have been conducted. Morato et al. (2022) used three-dimensional hydrodynamic models of the Azores region together with a theoretical commercial mining operation of polymetallic SMS to simulate the potential dispersal of sediment plumes originating from different phases of mining operations and to assess the magnitude of potential impacts. The areas used in the modelling work were (from North to South): Cavala seamount (38.265, -30.710), Lucky Strike Hole (37.503, -31.955), Menez Hom (37.109, -32.618), Famous (37.001, -33.039), Saldanha (36.658, -33.420), and Rainbow (36.262 -33.824). The datasets published here contain all the model outputs, namely for 1) the in situ excavation sediment plume, 2) the return water discharge plume, and 3) the return sediments discharge plume:1) The concentration of solids and of the discharge water in each horizontal 2-dimensional space cell is calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during each trimester and during a 12-months simulation.1.1) Concentration of sediments produced during the in situ excavation sediment plume calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation. Sediments were composed of six classes of different particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and >2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1.1.2) Concentration of return water discharge plume (shown in dilution folds) in six study areas calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation and assuming a control temperature as the annual minimum temperature of each location (T1). The salinity of discharge was calculated assuming the MOHID salinity of 83.3% surface water and 16.7% of seafloor water.1.3) Concentration of sediments in the return sediment discharge plume, calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation. The average particle diameter was assumed to be 4 µm with an average particle density of 3,780 kg·m-3 and a resultant settling velocity of 0.002 cm·s-1.2) The proportion of simulated time (temporal frequency) that a specific 2-dimensional space contained plume concentrations higher than the adopted thresholds; 1.2 mg·L-1 for sediment solids and 5,000 fold dilution for discharge water. Those cells whose temporal frequency above the thresholds was greater than 50%, i.e. 6 months out of 12 months, were considered as cells with persistent plumes.2.1) Proportion of simulated time (temporal frequency) that a specific a 2-dimensional space cell, in six study areas, contained in situ excavation sediment plume above a 1.2 mg·L-1 concentration threshold, during a 12-months simulation, assuming six classes of particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and >2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1.2.2) Proportion of simulated time (temporal frequency) that a specific 2-dimensional space, in six study areas, contained return water discharge plume concentrations higher than the adopted thresholds (i.e., 5,000 fold dilution), during a 12-months simulation and assuming a control temperature as the annual minimum temperature of each location (T1). The salinity of discharge was calculated assuming the MOHID salinity of 83.3% surface water and 16.7% of seafloor water.2.3) Proportion of simulated time (temporal frequency) that a specific 2-dimensional space cell, in six study areas, contained return sediments discharge plume above a 1.2 mg·L-1 concentration threshold, during a 12-months simulation, assuming an average particle diameter of 4 µm, an average particle density of 3,780 kg·m-3, and a resultant settling velocity of 0.002 cm·s-1.3) In addition to the thresholds and targets described above, the datasets also present the model results for Cavala seamount and Lucky Strike Hole against other thresholds: 5 mg·L-1, 10 mg·L-1 and 25 mg·L-1 for sediments and 1,000, 600, 300 and 200 fold dilution for discharge water.4) Seasonal variations in the model outputs for plumes dispersal are also presented for Cavala seamount and Lucky Strike Hole by computing the probability of concentration above thresholds for four periods of three months (January-March, April-June, July-September, and October-December). In these scenarios, the model run duration was approximately 90 days.5) The sediment thickness of the settled sediments from the discharge sediment and excavation.5.1) Bottom thickness of settled sediments produced during the in situ excavation sediment plume assuming six classes of particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and >2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1. The duration of the simulation is one year.5.2) Bottom thickness of settled sediments from the return sediment discharge plume modelled assuming an average particle diameter of 4 µm, an average particle density of 3,780 kg·m-3, and a resultant settling velocity of 0.002 cm·s-1. The duration of the simulation is one year. The downloadable zip folders contain GeoTIFF files with the model outputs used in the publication: Modelling the dispersion of Seafloor Massive Sulphide mining plumes in the Mid Atlantic Ridge around the Azores. Files are organized by:Study site (Cavala, Famous, Lucky Strike Hole, Menez Home, Rainbow, Saldanha)Plume type (Discharge sediments, Discharge water, Excavation)Parameters settings (4 micra, 4 micro rho, 8 micra; Discharge temperature 1, Discharge temperature 2, Discharge temperature ambient; 2m_group)Variable measured (Footprint in mm, Max tracer in mg·L-1, Probability (i.e. temporal frequency) in %)Temporal periods (Year: 2011-12-31_2012-01-01; trimester: 2011-01-10_2011-04-01, 2011-04-01_2011-07-01, 2011-07-01_2011-10-01, 2011-10-01_2012-01-01)

  • Open Access English
    Authors: 
    Pallacks, Sven; Ziveri, Patrizia; Martrat, Belén; Mortyn, P Graham; Grelaud, Michaël; Schiebel, Ralf; Incarbona, Alessandro; García-Orellana, Jordi; Anglada-Ortiz, Griselda;
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: EC | MEDSEA (265103)
  • Open Access English
    Authors: 
    González-Irusta, José Manuel; Fauconnet, Laurence; Das, Diya; Catarino, Diana; Afonso, Pedro; Viegas, Cláudia Neto; Rodrigues, Luís; Menezes, Gui; Rosa, Alexandra; Pinho, Mário Rui Rilhó; +3 more
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: EC | iAtlantic (818123), EC | DiscardLess (633680), EC | ATLAS (678760)

    Data layers producedProbPresence: This dataset contains the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function, through the implementation gam in the package mgcv. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.ProbPresence_Error: This dataset contains the standard error associated with the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function, through the implementation gam in the package mgcv. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.BinPresence_Kappa: This dataset contains the binary maps of the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function and a threshold that maximizes Kappa. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.BinPresence_MSS: This dataset contains the binary maps of the predicted probability of presence (Pp) of 15 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with binomial distribution and logit link function and the maximization of the sum of sensitivity and specificity (MSS) threshold, which minimizes misclassification likelihoods of false negatives and false positives. Raja clavata; Galeorhinus galeus; Dipturus batis; Leucoraja fullonica; Dalatias licha; Etmopterus spinax; Squaliolus laticaudus; Etmopterus pusillus; Deania profundorum; Deania calcea; Centrophorus squamosus; Centroscymnus owstonii; Centroscymnus crepidater; Centroscymnus coelolepis; Etmopterus princess.PredAbundance: This dataset contains the predicted abundance (Pa) of 6 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Generalized Additive Models (GAM) approach with negative binomial distributions and a log link, through the implementation of gam in the package mgcv. Etmopterus spinax; Deania profundorum; Raja clavata; Etmopterus pusillus; Deania calcea; Galeorhinus galeus.PredAbundance_Error: This dataset contains the standard error associated with the predicted abundance (Pa) of 6 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) the Azores, using a Generalized Additive Models (GAM) approach with negative binomial distributions and a log link, through the implementation gam in the package mgcv. Etmopterus spinax; Deania profundorum; Raja clavata; Etmopterus pusillus; Deania calcea; Galeorhinus galeus.FinalAbundance: This dataset contains the final predicted abundance (Fpa) of 6 deep-water shark and rays species in a 1000-hook bottom longline fishing set (type LLA) in the Azores, using a Delta Generalized Additive Models (GAM) approach recommended for zero-inflated data. This approach involves using the Probability of presence and the presence-only data to predict species abundances (Pa) (as described in other datasets). The final predicted abundance values (Fpa) were computed by multiplying the Pp by the Pa. Etmopterus spinax; Deania profundorum; Raja clavata; Etmopterus pusillus; Deania calcea; Galeorhinus galeus.Extent: West -37.479533; East -18.832939; North 44.355782; South 32.678347Spatial Reference:Type: ProjectedGeographic coordinate reference: GCS_WGS_1984Projection: WGS_1984_UTM_Zone_26NPoint of Contact: Luis Rodrigues; Ocean Sciences Institute - Okeanos, University of the Azores, Rua Professor Doutor Frederico Machado 4, 9901-862 Horta, Portugal. lmcrod@gmail.com Description: We developed predictive distribution models of deep-sea elasmobranchs for up to 2000 m depth in the Azores EEZ and neighboring seamounts, from approximately 33��N to 43��N and 20��W to 36��W. Georeferenced presence, absence, and abundance data were obtained from scientific surveys and commercial operations reporting at least one deep-sea elasmobranch capture. A 20-year 'survey dataset' (1996-2017) was compiled from annual scientific demersal surveys using two types of bottom longlines (types LLA and LLB), and an 'observer dataset' (2004-2018) from observer programs covering commercial fisheries operations using bottom longline (similar to type LLA) and vertical handline ('gorazeira'). We used the most ecologically relevant candidate environmental predictors for explaining the spatial distribution of deep-sea elasmobranch in the Azores: depth, slope, northness, eastness, Bathymetric Position Index (BPI), nitrates, and near bottom currents. We merged existing multibeam data for the Azores EEZ with bathymetry data extracted from EMODNET (EMODnet Bathymetry Consortium 2018) to calculate depth values (down to 2000m). All variables were projected with the Albers equal-area conical projection centered in the middle of the study area and were rescaled using bilinear interpolation to a final grid cell resolution of 1.12 x1.12 km (i.e., 0.012��). Slope, northness, and eastness were computed from the depth raster using the function terrain in the R package raster. BPI was derived from the rescaled depth with an inner radius of 3 and an outer radius of 25 grid cells using the Benthic Terrain Model 3.0 tool in ArcGIS 10.1. Nitrates were extracted from Amorim et al. (2017). Near-bottom current speed (m��s-1) average values were based on a MOHID hydrodynamic model application (Viegas et al., 2018) with an original resolution of 0.054��. Besides the environmental variables, we also included three operational predictors in the analysis: year, fishing effort (number of hooks) and gear type (longline LLA and LLB, and gorazeira).

  • Open Access English
    Authors: 
    Berndt, Christian; Canals, Miquel; Urgeles, Roger; Camerlenghi, Angelo; Papenberg, Cord;
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: EC | HERMIONE (226354)

    3D reflection seismic data were acquired using the P-Cable system of the National Oceanographic Centre, Southampton, UK during cruise 178 Leg 2 onboard RRS Charles Darwin between the 5th and 8th of April 2006. The responsible PI's was C. Berndt, Southampton Oceanography Centre, Southampton, UK. The aim of this cruise was to map submarine landslides on the eastern slopes of the Eivissa Channel, western Mediterranean Sea located between the islands of Ibiza-Formentera and the Spanish mainland. Berndt et al. (2012) used the acquired data to study repeated slope failure linked to fluid migration, while Lafuerza et al. (2012) studied geotechnical aspects of slope stability using this as additional data. Acquisition parameters: The source during seismic acquisition consisted of four 40 in3 Bolt 600B air guns spaced 0.75 m apart and tower at a depth of 1.5 m about 20 m behind the stern of the vessel (Berndt et al., 2012). The air guns are fitted with wave shape kits that emit approximately 10 in3 of air prior to the main volume to reduce the bubble pulse. The air pressure is 2000 psi, and the gun controller triggers the guns to figure every 7 seconds. The data were collected with 11 single-channel analogue streamers that were towed 10 m apart. The seismic cube in the Eivissa Channel covers an area of ca. 14 km2 (ca. 6.4 EW x 2.2 NS km) located at 306091.83 4280497.41; 305951.42 4278353.92; 312321.94 4277936.57 in UTM zone 31N. 2D seismic processing: During seismic processing of the 3D dataset significant ghost-artefacts were identified because some of the streamers were towed too deep. This required de-ghosting. Unfortunately, these attempts did not yield improved quality of the 3D seismic data. This was mainly because the 12.5 m streamers were too short for commonly used de-ghosting technique used in the industry. To increase vertical resolution individual 2D profiles were extracted from the raw dataset. Processing steps included frequency bandpass filtering, burst noise attenuation, binning, NMO-correction, stacked, and a Stolt migration with 1520 ms-1 was applied that resulted in higher resolution 2D profiles for 85 lines and 11 streamers (channels).

  • Open Access English
    Authors: 
    Martins, Ines; Godinho, Antonio; Rakka, Maria; Carreiro-Silva, Marina;
    Publisher: PANGAEA - Data Publisher for Earth & Environmental Science
    Project: FCT | UIDB/05634/2020 (UIDB/05634/2020), EC | MERCES (689518), EC | iAtlantic (818123)

    Herein we report the respiration rates (O2 consumption) of the cold-water coral Viminella flagellum exposed to acute Cu concentrations. In a lab experiment, sixty nubbins of V. flagellum were distributed in six aquaria of 8 L (ten nubbins per aquarium) of each Cu solution (0 (control); 60; 150; 250; 450 and 600 μg/L) for 96 h. After this period, four nubbins from each Cu treatment, selected randomly, were incubated individually for 6 h in glass chambers filled with ca. 110 mL of 0.2 μm pre-filtered seawater, with the respective Cu dilutions (4 chambers per Cu concentration). The incubation period was set to 6 h to record changes in O2 consumption without exposing corals to oxygen levels below 80 % (air saturation, a.s.). During the incubation period, dissolved O2 (μmol/L) depletion rates were recorded every 30 min and corrected by the corresponding rates/variations in chambers without corals. Coral respiration rates were normalized to the coral surface area and time. Results are presented by µmol of O2 consumption per m2 per h. Treatment [Cu] µg/L: [0]: Control, no copper addition[60]: Cu concentration of 60 μg/L[150]: Cu concentration of 150 μg/L[250]: Cu concentration of 250 μg/L[450]: Cu concentration of 450 μg/L[600]: Cu concentration of 600 μg/L

  • Open Access English
    Authors: 
    Tanhua, Toste; Kazanidis, Georgios; Sá, Sandra; Neves, Caique; Obaton, Dominique; Sylaios, Georgios;
    Publisher: Zenodo
    Project: EC | EurofleetsPlus (824077), EC | NAUTILOS (101000825), EC | iAtlantic (818123), EC | Blue Cloud (862409), EC | JERICO-S3 (871153), EC | AtlantECO (862923), EC | ODYSSEA (727277), EC | EuroSea (862626), EC | ATLAS (678760), EC | MISSION ATLANTIC (862428)

    Ten innovative EU projects to build ocean observation systems that provide input for evidence-based management of the ocean and the Blue Economy, have joined forces in the strong cluster ‘Nourishing Blue Economy and Sharing Ocean Knowledge’. Under the lead of the EuroSea project, the group published a joint policy brief listing recommendations for sustainable ocean observation and management. The cooperation is supported by the EU Horizon Results Booster and enables the group to achieve a higher societal impact. The policy brief will be presented to the European Commission on 15 October 2021. The ocean covers 70% of the Earth’s surface and provides us with a diverse set of ecosystem services that we cannot live without or that significantly improve our quality of life. It is the primary controller of our climate, plays a critical role in providing the air we breathe and the fresh water we drink, supplies us with a large range of exploitable resources (from inorganic resources such as sand and minerals to biotic resources such as seafood), allows us to generate renewable energy, is an important pathway for world transport, an important source of income for tourism, etc. The Organisation for Economic Cooperation and Development (OECD) evaluates the Blue Economy to currently represent 2.5% of the world economic value of goods and services produced, with the potential to further double in size by 2030 (seabed mining, shipping, fishing, tourism, renewable energy systems and aquaculture will intensify). However, the overall consequences of the intensification of human activities on marine ecosystems and their services (such as ocean warming, acidification, deoxygenation, sea level rise, changing distribution and abundance of fish etc.) are still poorly quantified. In addition, on larger geographic and temporal scales, marine data currently appear fragmented, are inhomogeneous, contain data gaps and are difficult to access. This limits our capacity to understand the ocean variability and sustainably manage the ocean and its resources. Consequently, there is a need to develop a framework for more in-depth understanding of marine ecosystems, that links reliable, timely and fit-for-purpose ocean observations to the design and implementation of evidence-based decisions on the management of the ocean. To adequately serve governments, societies, the sustainable Blue Economy and citizens, ocean data need to be collected and delivered in line with the Value Chain of Ocean Information: 1) identification of required data; 2) deployment and maintenance of instruments that collect the data; 3) delivery of data and derived information products; and 4) impact assessment of services to end users. To provide input to the possible future establishment of such a framework, ten innovative EU projects to build user-focused, interdisciplinary, responsive and sustained ocean information systems and increase the sustainability of the Blue Economy, joined forces in a strong cluster to better address key global marine challenges. Under the lead of the EuroSea project, the group translated its common concerns to recommendations and listed these in the joint policy brief ‘Nourishing Blue Economy and Sharing Ocean Knowledge. Ocean Information for Sustainable Management.’. Following up on these recommendations will strengthen the entire Value Chain of Ocean Information and ensure sound sustainable ocean management. In this way, the 10 projects jointly strive to achieve goals set out in the EU Green Deal, the Paris Agreement (United Nations Framework Convention on Climate Change) and the United Nations 2021-2030 Decade of Ocean Science for Sustainable Ocean Development. Toste Tanhua (GEOMAR), EuroSea coordinator: “It was great to collaborate with these other innovative projects and make joint recommendations based on different perspectives and expertise.”

  • Research data . 2021
    Open Access English
    Authors: 
    Green, Caroline;
    Publisher: Zenodo
    Project: EC | Respon-SEA-ble (652643)

    Data and R Scripts necessary to reproduce results of ESD study at NUIG conducted and analysed in 2020-2021. The study aimed to investigate whether Systems Thinking (ST) and/or System Dynamics simulation (Sim) increased the effectiveness of Sustainability Education. In the study, participants were randomly allocated to one of four groups, and interacted with an online learning tool which contained embedded quizzes. Their performance in these quizzes formed the basis for comparison between groups. An open access of the learning tool is available here: https://exchange.iseesystems.com/public/carolineb/sustainability-learning-tool/. The R Scripts folder contains 4 R scripts used for data analysis. Run the script function_qualitative.R first. The data folder contains the anonymised research data. Create a folder called data beneath the folder containing your R scripts. The model folder contains the System Dynamics deer model (.stmx) file, used for the simulation exercises. The surveys folder is for reference only. It contains the surveys and quizzes used to collect the data, together with quiz marking schemes, code books and quiz answers. In the data, group members are identified by a number from 0 to 3: group 0 means control group, group 1 means ST group, group 2 means Sim group and group 3 means ST + Sim group. For more details see README.md and the readme.txt files in each folder. Additional funding from the Higher Education Authority (HEA), Ireland.