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SDSN - Greece

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Research datakeyboard_double_arrow_right Bioentity 2016Funded by:EC | BIOSTRUCT-X, EC | INMARE
EC| BIOSTRUCT-X ,
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Authors: M. Ramirez-escudero; J. Sanz-aparicio;
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Bioentity . 2021
License: CC 0
Data sources: Protein Data Bank in Europe
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Bioentity . 2017
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Bioentity
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Bioentity . 2021
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Bioentity . 2017
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Research datakeyboard_double_arrow_right Bioentity 2016Funded by:EC | BIOSTRUCT-X, EC | INMARE
EC| BIOSTRUCT-X ,
EC| INMARE
Authors: M. Ramirez-escudero; J. Sanz-aparicio;
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Bioentity . 2021
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Data sources: Protein Data Bank in Europe
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Bioentity . 2017
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Bioentity . 2021
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Bioentity . 2017
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Data_WP2_1_Future activity-emission factor matrices for the participating cities
Research datakeyboard_double_arrow_right Dataset 2021Publisher:ZenodoFunded by:EC | ICARUS
EC| ICARUS
Authors: Dorothea Schmid (10349421);
doi: 10.5281/zenodo.4593796 , 10.5281/zenodo.4593797
doi: 10.5281/zenodo.4593796 , 10.5281/zenodo.4593797
The dataset includes data on future activity-emission factor matrices for all the ICARUS participating cities and for the classical air pollutants (NH3, NMVOC, NOX, PM10, PM2.5, CO, SO2, BC and OC), GHGs (CO2, CO, N2O, CH4), heavy metals, PAHs and dioxins, relevant to the years 2015, 2020 and 2030. A detailed description of the structure of the DB can be found in deliverable D2.2 freely downloadable from the ICARUS website. Additionally, bottom-up activity data and emission factors are combined to sectoral emission grids (1x1 km) for each city. The sites targeted cover all the ICARUS pilot cities. The data are available in MS-Access format (accdb) to maximise the interoperability for use with a variety of analytical software. The sectoral emission grids are provided in standard GIS vector format (shp).
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Data_WP2_1_Future activity-emission factor matrices for the participating cities
Research datakeyboard_double_arrow_right Dataset 2021Publisher:ZenodoFunded by:EC | ICARUS
EC| ICARUS
Authors: Dorothea Schmid (10349421);
doi: 10.5281/zenodo.4593796 , 10.5281/zenodo.4593797
doi: 10.5281/zenodo.4593796 , 10.5281/zenodo.4593797
The dataset includes data on future activity-emission factor matrices for all the ICARUS participating cities and for the classical air pollutants (NH3, NMVOC, NOX, PM10, PM2.5, CO, SO2, BC and OC), GHGs (CO2, CO, N2O, CH4), heavy metals, PAHs and dioxins, relevant to the years 2015, 2020 and 2030. A detailed description of the structure of the DB can be found in deliverable D2.2 freely downloadable from the ICARUS website. Additionally, bottom-up activity data and emission factors are combined to sectoral emission grids (1x1 km) for each city. The sites targeted cover all the ICARUS pilot cities. The data are available in MS-Access format (accdb) to maximise the interoperability for use with a variety of analytical software. The sectoral emission grids are provided in standard GIS vector format (shp).
ZENODOarrow_drop_down
ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2021
License: CC BY
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ZENODO
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https://doi.org/10.5281/zenodo...
Dataset . 2021
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Dataset . 2021
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Koljoefjord cabled observatory RDCP data, Sweden (2012-12)
Research datakeyboard_double_arrow_right Dataset 2012Publisher:PANGAEAFunded by:EC | HYPOX
EC| HYPOX
Authors: Per Hall;
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Koljoefjord cabled observatory RDCP data, Sweden (2012-12)
Research datakeyboard_double_arrow_right Dataset 2012Publisher:PANGAEAFunded by:EC | HYPOX
EC| HYPOX
Authors: Per Hall;
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Additional file 1: of Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data
Research datakeyboard_double_arrow_right Dataset 2019Publisher:figshareFunded by:TARA | Tara Oceans, EC | INMARE
TARA| Tara Oceans ,
EC| INMARE
Authors: Pereira-Flores, Emiliano; Glรถckner, Frank; Fernandez-Guerra, Antonio;
doi: 10.6084/m9.figshare.9776540.v1 , 10.6084/m9.figshare.9776540
doi: 10.6084/m9.figshare.9776540.v1 , 10.6084/m9.figshare.9776540
Single-copy genes universally present in prokaryotes. Table showing the COG number of the 35 single-copy genes identified by Raes et al. (2007), and the number of sequences included in each COG. (XLSX 42 kb)
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Dataset . 2019
License: CC BY
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Dataset . 2019
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Dataset . 2019
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Additional file 1: of Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data
Research datakeyboard_double_arrow_right Dataset 2019Publisher:figshareFunded by:TARA | Tara Oceans, EC | INMARE
TARA| Tara Oceans ,
EC| INMARE
Authors: Pereira-Flores, Emiliano; Glรถckner, Frank; Fernandez-Guerra, Antonio;
doi: 10.6084/m9.figshare.9776540.v1 , 10.6084/m9.figshare.9776540
doi: 10.6084/m9.figshare.9776540.v1 , 10.6084/m9.figshare.9776540
Single-copy genes universally present in prokaryotes. Table showing the COG number of the 35 single-copy genes identified by Raes et al. (2007), and the number of sequences included in each COG. (XLSX 42 kb)
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Dataset . 2019
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Additional file 8: Table S6. of Gene-centromere mapping in meiotic gynogenetic European seabass
Research datakeyboard_double_arrow_right Dataset 2017Publisher:FigshareFunded by:EC | AQUAEXCEL
EC| AQUAEXCEL
Authors: Oral, Münevver; Colléter, Julie; Bekaert, Michaël; Taggart, John; +8 Authors
Oral, Münevver; Colléter, Julie; Bekaert, Michaël; Taggart, John; Palaiokostas, Christos; McAndrew, Brendan; Vandeputte, Marc; Chatain, Béatrice; Kuhl, Heiner; Reinhardt, Richard; Peruzzi, Stefano; Penman, David;
doi: 10.6084/m9.figshare.c.3798103_d8 , 10.6084/m9.figshare.c.3798103_d8.v1
doi: 10.6084/m9.figshare.c.3798103_d8 , 10.6084/m9.figshare.c.3798103_d8.v1
Correlation of marker order in genome assembly with that of genetic linkage map of Palaiokostas et al. [19]; n refers to number of markers in common between genome assembly and map of [19]. *:LGs with “ambiguous” arm structure based on heterozygosity pattern in the present study (see main text). (CSV 484 bytes)
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Dataset . 2017
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Dataset . 2017
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Additional file 8: Table S6. of Gene-centromere mapping in meiotic gynogenetic European seabass
Research datakeyboard_double_arrow_right Dataset 2017Publisher:FigshareFunded by:EC | AQUAEXCEL
EC| AQUAEXCEL
Authors: Oral, Münevver; Colléter, Julie; Bekaert, Michaël; Taggart, John; +8 Authors
Oral, Münevver; Colléter, Julie; Bekaert, Michaël; Taggart, John; Palaiokostas, Christos; McAndrew, Brendan; Vandeputte, Marc; Chatain, Béatrice; Kuhl, Heiner; Reinhardt, Richard; Peruzzi, Stefano; Penman, David;
doi: 10.6084/m9.figshare.c.3798103_d8 , 10.6084/m9.figshare.c.3798103_d8.v1
doi: 10.6084/m9.figshare.c.3798103_d8 , 10.6084/m9.figshare.c.3798103_d8.v1
Correlation of marker order in genome assembly with that of genetic linkage map of Palaiokostas et al. [19]; n refers to number of markers in common between genome assembly and map of [19]. *:LGs with “ambiguous” arm structure based on heterozygosity pattern in the present study (see main text). (CSV 484 bytes)
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Dataset . 2017
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Dataset . 2017
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Dataset . 2017
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Dataset . 2017
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Dissolved oxygen content in the bottom water at HAUSGARTEN during the Polarstern expedition ARK-XVIII/1
Research datakeyboard_double_arrow_right Dataset 2010Publisher:PANGAEAFunded by:EC | HYPOX
EC| HYPOX
Authors: Soltwedel, Thomas;
doi: 10.1594/pangaea.738039 , 10.1594/pangaea.738038 , 10.1594/pangaea.738042
doi: 10.1594/pangaea.738039 , 10.1594/pangaea.738038 , 10.1594/pangaea.738042
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Dataset . 2010
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Dissolved oxygen content in the bottom water at HAUSGARTEN during the Polarstern expedition ARK-XVIII/1
Research datakeyboard_double_arrow_right Dataset 2010Publisher:PANGAEAFunded by:EC | HYPOX
EC| HYPOX
Authors: Soltwedel, Thomas;
doi: 10.1594/pangaea.738039 , 10.1594/pangaea.738038 , 10.1594/pangaea.738042
doi: 10.1594/pangaea.738039 , 10.1594/pangaea.738038 , 10.1594/pangaea.738042
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Dataset . 2010
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Biogeochemical model projections years 1982-2007 in North East Atlantic
Research datakeyboard_double_arrow_right Dataset 2020Publisher:ZenodoFunded by:EC | COMFORT, EC | CERES
EC| COMFORT ,
EC| CERES
Authors: Jose Antonio; Momme Butenschön; Thomas L. Frölicher; Andrew Yool;
doi: 10.5281/zenodo.3693595 , 10.5281/zenodo.3693596
doi: 10.5281/zenodo.3693595 , 10.5281/zenodo.3693596
Marine enviromental data from Biogeochemical models in paper "Can we project changes in fish abundance and distribution in response to climate?" at Global Change Biology journal
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Dataset . 2020
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Biogeochemical model projections years 1982-2007 in North East Atlantic
Research datakeyboard_double_arrow_right Dataset 2020Publisher:ZenodoFunded by:EC | COMFORT, EC | CERES
EC| COMFORT ,
EC| CERES
Authors: Jose Antonio; Momme Butenschön; Thomas L. Frölicher; Andrew Yool;
doi: 10.5281/zenodo.3693595 , 10.5281/zenodo.3693596
doi: 10.5281/zenodo.3693595 , 10.5281/zenodo.3693596
Marine enviromental data from Biogeochemical models in paper "Can we project changes in fish abundance and distribution in response to climate?" at Global Change Biology journal
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Dataset . 2020
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Multibeam bathymetry processed data (EM 120 echosounder working area dataset) of RV HESPERIDES during cruise HERMESIONE, Eivissa Channel, Mediterranean Sea
Research datakeyboard_double_arrow_right Dataset 2024Publisher:PANGAEAFunded by:EC | HERMIONE
EC| HERMIONE
Authors: Canals, Miquel; Amblas, David; Durán, Ruth; Lastras, Galderic; +1 Authors
Canals, Miquel; Amblas, David; Durán, Ruth; Lastras, Galderic; Sager, Thore Falk;
doi: 10.1594/pangaea.964213
doi: 10.1594/pangaea.964213
Multibeam bathymetry data were collected during the HERMESIONE cruise in 2009 with the Spanish research vessel BIO Hespérides using a SIMRAD EM-120 (12 kHz, 191 beams). The data were processed using Caris HIPS&SIPS to a 25x25 m grid. The data is fully processed from raw files, which are available upon request. The data is projected to UTM WGS 1984 31N.
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Dataset . 2024
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Dataset . 2024
License: CC BY
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Dataset . 2024
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Dataset . 2024
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Multibeam bathymetry processed data (EM 120 echosounder working area dataset) of RV HESPERIDES during cruise HERMESIONE, Eivissa Channel, Mediterranean Sea
Research datakeyboard_double_arrow_right Dataset 2024Publisher:PANGAEAFunded by:EC | HERMIONE
EC| HERMIONE
Authors: Canals, Miquel; Amblas, David; Durán, Ruth; Lastras, Galderic; +1 Authors
Canals, Miquel; Amblas, David; Durán, Ruth; Lastras, Galderic; Sager, Thore Falk;
doi: 10.1594/pangaea.964213
doi: 10.1594/pangaea.964213
Multibeam bathymetry data were collected during the HERMESIONE cruise in 2009 with the Spanish research vessel BIO Hespérides using a SIMRAD EM-120 (12 kHz, 191 beams). The data were processed using Caris HIPS&SIPS to a 25x25 m grid. The data is fully processed from raw files, which are available upon request. The data is projected to UTM WGS 1984 31N.
PANGAEAarrow_drop_down
PANGAEA
Dataset . 2024
Data sources: B2FIND
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
License: CC BY
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
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Dataset . 2024
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Dataset . 2024
License: CC BY
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
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Seawater carbonate chemistry and Cladocora caespitosa gross calcification during experiments, 2011
Research datakeyboard_double_arrow_right Dataset 2011Publisher:PANGAEAFunded by:EC | EPOCA, EC | MEDSEA
EC| EPOCA ,
EC| MEDSEA
Authors: Rodolfo-Metalpa, Riccardo; Houlbrèque, Fanny; Tambutté, Eric; Boisson, Florence; +7 Authors
Rodolfo-Metalpa, Riccardo; Houlbrèque, Fanny; Tambutté, Eric; Boisson, Florence; Baggini, Cecilia; Patti, F P; Jeffree, Ross; Fine, M; Foggo, A; Gattuso, Jean-Pierre; Hall-Spencer, Jason M;
doi: 10.1594/pangaea.777721
doi: 10.1594/pangaea.777721
In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Lavigne and Gattuso, 2011) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI).
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PANGAEA
Dataset . 2011
Data sources: B2FIND
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
License: CC BY
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
License: CC BY
Data sources: Datacite
https://doi.pangaea.de/10.1594...
Dataset
Data sources: Microsoft Academic Graph
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Dataset . 2011
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Dataset . 2011
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
License: CC BY
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
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Seawater carbonate chemistry and Cladocora caespitosa gross calcification during experiments, 2011
Research datakeyboard_double_arrow_right Dataset 2011Publisher:PANGAEAFunded by:EC | EPOCA, EC | MEDSEA
EC| EPOCA ,
EC| MEDSEA
Authors: Rodolfo-Metalpa, Riccardo; Houlbrèque, Fanny; Tambutté, Eric; Boisson, Florence; +7 Authors
Rodolfo-Metalpa, Riccardo; Houlbrèque, Fanny; Tambutté, Eric; Boisson, Florence; Baggini, Cecilia; Patti, F P; Jeffree, Ross; Fine, M; Foggo, A; Gattuso, Jean-Pierre; Hall-Spencer, Jason M;
doi: 10.1594/pangaea.777721
doi: 10.1594/pangaea.777721
In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Lavigne and Gattuso, 2011) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI).
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Dataset . 2011
Data sources: B2FIND
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
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https://doi.pangaea.de/10.1594...
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Dataset . 2011
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Dataset . 2011
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Paradoxical risk mitigation behavior in private households
Research datakeyboard_double_arrow_right Dataset 2022Publisher:ZenodoFunded by:EC | SafeConsumE
EC| SafeConsumE
Authors: Kasza, Gyula; Csenki, Eszter Zita; Izsó, Tekla; Scholderer, Joachim;
Abstract More than 40% of registered foodborne outbreaks can be attributed to unsafe food handling practices in private households (World Health Organization (WHO), 2016). Although households represent the final stage of the food chain (where mitigation of food safety risks would be vital), consumers generally do not consider themselves to be a responsible actor in this respect (Taché & Carpentier, 2014). Poor hygiene and food handling practices are among the major risk factors for household-related foodborne illnesses, specifically cross-contamination and inefficient handwashing. In this work, we present two studies in which we analyzed hygiene and safety aspects of the poultry handling habits of consumers. Study 1 was an in-home behavior observation study involving 15 households. Besides inefficient handwashing, washing of poultry meat in the sink was the most problematic behavior. Salmonella and/or Campylobacter spp. were detected in 47% of meat samples taken during the home visits. Study 2 was a nationally representative household survey (N = 1007). 83% of the respondents reported that they washed poultry meat before food preparation. Based on patterns of poultry washing practices, knowledge about and interest in food safety, and worries about the safety of homemade dishes, five clusters of consumers were identified. Only one of these clusters had a significantly reduced tendency to wash poultry before preparation. The results indicate that, paradoxically, the more the conscious consumers are of microbiological hazards, the more likely they are to wash their poultry: most consumers believe that washing poultry before preparation is good hygiene practice and are not aware of cross-contamination risk.
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Paradoxical risk mitigation behavior in private households
Research datakeyboard_double_arrow_right Dataset 2022Publisher:ZenodoFunded by:EC | SafeConsumE
EC| SafeConsumE
Authors: Kasza, Gyula; Csenki, Eszter Zita; Izsó, Tekla; Scholderer, Joachim;
Abstract More than 40% of registered foodborne outbreaks can be attributed to unsafe food handling practices in private households (World Health Organization (WHO), 2016). Although households represent the final stage of the food chain (where mitigation of food safety risks would be vital), consumers generally do not consider themselves to be a responsible actor in this respect (Taché & Carpentier, 2014). Poor hygiene and food handling practices are among the major risk factors for household-related foodborne illnesses, specifically cross-contamination and inefficient handwashing. In this work, we present two studies in which we analyzed hygiene and safety aspects of the poultry handling habits of consumers. Study 1 was an in-home behavior observation study involving 15 households. Besides inefficient handwashing, washing of poultry meat in the sink was the most problematic behavior. Salmonella and/or Campylobacter spp. were detected in 47% of meat samples taken during the home visits. Study 2 was a nationally representative household survey (N = 1007). 83% of the respondents reported that they washed poultry meat before food preparation. Based on patterns of poultry washing practices, knowledge about and interest in food safety, and worries about the safety of homemade dishes, five clusters of consumers were identified. Only one of these clusters had a significantly reduced tendency to wash poultry before preparation. The results indicate that, paradoxically, the more the conscious consumers are of microbiological hazards, the more likely they are to wash their poultry: most consumers believe that washing poultry before preparation is good hygiene practice and are not aware of cross-contamination risk.
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ZENODO
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Research datakeyboard_double_arrow_right Bioentity 2016Funded by:EC | BIOSTRUCT-X, EC | INMARE
EC| BIOSTRUCT-X ,
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Authors: M. Ramirez-escudero; J. Sanz-aparicio;
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Bioentity . 2021
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Bioentity . 2017
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Research datakeyboard_double_arrow_right Bioentity 2016Funded by:EC | BIOSTRUCT-X, EC | INMARE
EC| BIOSTRUCT-X ,
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Authors: M. Ramirez-escudero; J. Sanz-aparicio;
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Bioentity . 2021
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Data sources: Protein Data Bank in Europe
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Bioentity . 2017
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Bioentity . 2021
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Data_WP2_1_Future activity-emission factor matrices for the participating cities
Research datakeyboard_double_arrow_right Dataset 2021Publisher:ZenodoFunded by:EC | ICARUS
EC| ICARUS
Authors: Dorothea Schmid (10349421);
doi: 10.5281/zenodo.4593796 , 10.5281/zenodo.4593797
doi: 10.5281/zenodo.4593796 , 10.5281/zenodo.4593797
The dataset includes data on future activity-emission factor matrices for all the ICARUS participating cities and for the classical air pollutants (NH3, NMVOC, NOX, PM10, PM2.5, CO, SO2, BC and OC), GHGs (CO2, CO, N2O, CH4), heavy metals, PAHs and dioxins, relevant to the years 2015, 2020 and 2030. A detailed description of the structure of the DB can be found in deliverable D2.2 freely downloadable from the ICARUS website. Additionally, bottom-up activity data and emission factors are combined to sectoral emission grids (1x1 km) for each city. The sites targeted cover all the ICARUS pilot cities. The data are available in MS-Access format (accdb) to maximise the interoperability for use with a variety of analytical software. The sectoral emission grids are provided in standard GIS vector format (shp).
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Dataset . 2021
License: CC BY
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Data_WP2_1_Future activity-emission factor matrices for the participating cities
Research datakeyboard_double_arrow_right Dataset 2021Publisher:ZenodoFunded by:EC | ICARUS
EC| ICARUS
Authors: Dorothea Schmid (10349421);
doi: 10.5281/zenodo.4593796 , 10.5281/zenodo.4593797
doi: 10.5281/zenodo.4593796 , 10.5281/zenodo.4593797
The dataset includes data on future activity-emission factor matrices for all the ICARUS participating cities and for the classical air pollutants (NH3, NMVOC, NOX, PM10, PM2.5, CO, SO2, BC and OC), GHGs (CO2, CO, N2O, CH4), heavy metals, PAHs and dioxins, relevant to the years 2015, 2020 and 2030. A detailed description of the structure of the DB can be found in deliverable D2.2 freely downloadable from the ICARUS website. Additionally, bottom-up activity data and emission factors are combined to sectoral emission grids (1x1 km) for each city. The sites targeted cover all the ICARUS pilot cities. The data are available in MS-Access format (accdb) to maximise the interoperability for use with a variety of analytical software. The sectoral emission grids are provided in standard GIS vector format (shp).
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Dataset . 2021
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Koljoefjord cabled observatory RDCP data, Sweden (2012-12)
Research datakeyboard_double_arrow_right Dataset 2012Publisher:PANGAEAFunded by:EC | HYPOX
EC| HYPOX
Authors: Per Hall;
PANGAEA - Data Publi...arrow_drop_down
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2012
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PANGAEA
Dataset . 2024
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Dataset . 2024
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Koljoefjord cabled observatory RDCP data, Sweden (2012-12)
Research datakeyboard_double_arrow_right Dataset 2012Publisher:PANGAEAFunded by:EC | HYPOX
EC| HYPOX
Authors: Per Hall;
PANGAEA - Data Publi...arrow_drop_down
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2012
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Dataset . 2024
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Additional file 1: of Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data
Research datakeyboard_double_arrow_right Dataset 2019Publisher:figshareFunded by:TARA | Tara Oceans, EC | INMARE
TARA| Tara Oceans ,
EC| INMARE
Authors: Pereira-Flores, Emiliano; Glรถckner, Frank; Fernandez-Guerra, Antonio;
doi: 10.6084/m9.figshare.9776540.v1 , 10.6084/m9.figshare.9776540
doi: 10.6084/m9.figshare.9776540.v1 , 10.6084/m9.figshare.9776540
Single-copy genes universally present in prokaryotes. Table showing the COG number of the 35 single-copy genes identified by Raes et al. (2007), and the number of sequences included in each COG. (XLSX 42 kb)
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Additional file 1: of Fast and accurate average genome size and 16S rRNA gene average copy number computation in metagenomic data
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Single-copy genes universally present in prokaryotes. Table showing the COG number of the 35 single-copy genes identified by Raes et al. (2007), and the number of sequences included in each COG. (XLSX 42 kb)
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Additional file 8: Table S6. of Gene-centromere mapping in meiotic gynogenetic European seabass
Research datakeyboard_double_arrow_right Dataset 2017Publisher:FigshareFunded by:EC | AQUAEXCEL
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Authors: Oral, Münevver; Colléter, Julie; Bekaert, Michaël; Taggart, John; +8 Authors
Oral, Münevver; Colléter, Julie; Bekaert, Michaël; Taggart, John; Palaiokostas, Christos; McAndrew, Brendan; Vandeputte, Marc; Chatain, Béatrice; Kuhl, Heiner; Reinhardt, Richard; Peruzzi, Stefano; Penman, David;
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Correlation of marker order in genome assembly with that of genetic linkage map of Palaiokostas et al. [19]; n refers to number of markers in common between genome assembly and map of [19]. *:LGs with “ambiguous” arm structure based on heterozygosity pattern in the present study (see main text). (CSV 484 bytes)
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Additional file 8: Table S6. of Gene-centromere mapping in meiotic gynogenetic European seabass
Research datakeyboard_double_arrow_right Dataset 2017Publisher:FigshareFunded by:EC | AQUAEXCEL
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Authors: Oral, Münevver; Colléter, Julie; Bekaert, Michaël; Taggart, John; +8 Authors
Oral, Münevver; Colléter, Julie; Bekaert, Michaël; Taggart, John; Palaiokostas, Christos; McAndrew, Brendan; Vandeputte, Marc; Chatain, Béatrice; Kuhl, Heiner; Reinhardt, Richard; Peruzzi, Stefano; Penman, David;
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Correlation of marker order in genome assembly with that of genetic linkage map of Palaiokostas et al. [19]; n refers to number of markers in common between genome assembly and map of [19]. *:LGs with “ambiguous” arm structure based on heterozygosity pattern in the present study (see main text). (CSV 484 bytes)
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Dissolved oxygen content in the bottom water at HAUSGARTEN during the Polarstern expedition ARK-XVIII/1
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doi: 10.1594/pangaea.738039 , 10.1594/pangaea.738038 , 10.1594/pangaea.738042
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Dissolved oxygen content in the bottom water at HAUSGARTEN during the Polarstern expedition ARK-XVIII/1
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Biogeochemical model projections years 1982-2007 in North East Atlantic
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Authors: Jose Antonio; Momme Butenschön; Thomas L. Frölicher; Andrew Yool;
doi: 10.5281/zenodo.3693595 , 10.5281/zenodo.3693596
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Multibeam bathymetry processed data (EM 120 echosounder working area dataset) of RV HESPERIDES during cruise HERMESIONE, Eivissa Channel, Mediterranean Sea
Research datakeyboard_double_arrow_right Dataset 2024Publisher:PANGAEAFunded by:EC | HERMIONE
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Authors: Canals, Miquel; Amblas, David; Durán, Ruth; Lastras, Galderic; +1 Authors
Canals, Miquel; Amblas, David; Durán, Ruth; Lastras, Galderic; Sager, Thore Falk;
doi: 10.1594/pangaea.964213
doi: 10.1594/pangaea.964213
Multibeam bathymetry data were collected during the HERMESIONE cruise in 2009 with the Spanish research vessel BIO Hespérides using a SIMRAD EM-120 (12 kHz, 191 beams). The data were processed using Caris HIPS&SIPS to a 25x25 m grid. The data is fully processed from raw files, which are available upon request. The data is projected to UTM WGS 1984 31N.
PANGAEAarrow_drop_down
PANGAEA
Dataset . 2024
Data sources: B2FIND
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
License: CC BY
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
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PANGAEA - Data Publisher for Earth and Environmental Science
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
License: CC BY
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
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Multibeam bathymetry processed data (EM 120 echosounder working area dataset) of RV HESPERIDES during cruise HERMESIONE, Eivissa Channel, Mediterranean Sea
Research datakeyboard_double_arrow_right Dataset 2024Publisher:PANGAEAFunded by:EC | HERMIONE
EC| HERMIONE
Authors: Canals, Miquel; Amblas, David; Durán, Ruth; Lastras, Galderic; +1 Authors
Canals, Miquel; Amblas, David; Durán, Ruth; Lastras, Galderic; Sager, Thore Falk;
doi: 10.1594/pangaea.964213
doi: 10.1594/pangaea.964213
Multibeam bathymetry data were collected during the HERMESIONE cruise in 2009 with the Spanish research vessel BIO Hespérides using a SIMRAD EM-120 (12 kHz, 191 beams). The data were processed using Caris HIPS&SIPS to a 25x25 m grid. The data is fully processed from raw files, which are available upon request. The data is projected to UTM WGS 1984 31N.
PANGAEAarrow_drop_down
PANGAEA
Dataset . 2024
Data sources: B2FIND
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
License: CC BY
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
License: CC BY
Data sources: Datacite
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Dataset . 2024
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
License: CC BY
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2024
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Seawater carbonate chemistry and Cladocora caespitosa gross calcification during experiments, 2011
Research datakeyboard_double_arrow_right Dataset 2011Publisher:PANGAEAFunded by:EC | EPOCA, EC | MEDSEA
EC| EPOCA ,
EC| MEDSEA
Authors: Rodolfo-Metalpa, Riccardo; Houlbrèque, Fanny; Tambutté, Eric; Boisson, Florence; +7 Authors
Rodolfo-Metalpa, Riccardo; Houlbrèque, Fanny; Tambutté, Eric; Boisson, Florence; Baggini, Cecilia; Patti, F P; Jeffree, Ross; Fine, M; Foggo, A; Gattuso, Jean-Pierre; Hall-Spencer, Jason M;
doi: 10.1594/pangaea.777721
doi: 10.1594/pangaea.777721
In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Lavigne and Gattuso, 2011) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI).
PANGAEAarrow_drop_down
PANGAEA
Dataset . 2011
Data sources: B2FIND
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
License: CC BY
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
License: CC BY
Data sources: Datacite
https://doi.pangaea.de/10.1594...
Dataset
Data sources: Microsoft Academic Graph
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PANGAEA
Dataset . 2011
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PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
License: CC BY
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
License: CC BY
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Seawater carbonate chemistry and Cladocora caespitosa gross calcification during experiments, 2011
Research datakeyboard_double_arrow_right Dataset 2011Publisher:PANGAEAFunded by:EC | EPOCA, EC | MEDSEA
EC| EPOCA ,
EC| MEDSEA
Authors: Rodolfo-Metalpa, Riccardo; Houlbrèque, Fanny; Tambutté, Eric; Boisson, Florence; +7 Authors
Rodolfo-Metalpa, Riccardo; Houlbrèque, Fanny; Tambutté, Eric; Boisson, Florence; Baggini, Cecilia; Patti, F P; Jeffree, Ross; Fine, M; Foggo, A; Gattuso, Jean-Pierre; Hall-Spencer, Jason M;
doi: 10.1594/pangaea.777721
doi: 10.1594/pangaea.777721
In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Lavigne and Gattuso, 2011) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI).
PANGAEAarrow_drop_down
PANGAEA
Dataset . 2011
Data sources: B2FIND
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
License: CC BY
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
License: CC BY
Data sources: Datacite
https://doi.pangaea.de/10.1594...
Dataset
Data sources: Microsoft Academic Graph
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PANGAEA
Dataset . 2011
Data sources: B2FIND
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
License: CC BY
Data sources: PANGAEA - Data Publisher for Earth and Environmental Science
PANGAEA - Data Publisher for Earth and Environmental Science
Dataset . 2011
License: CC BY
Data sources: Datacite
https://doi.pangaea.de/10.1594...
Dataset
Data sources: Microsoft Academic Graph
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Paradoxical risk mitigation behavior in private households
Research datakeyboard_double_arrow_right Dataset 2022Publisher:ZenodoFunded by:EC | SafeConsumE
EC| SafeConsumE
Authors: Kasza, Gyula; Csenki, Eszter Zita; Izsó, Tekla; Scholderer, Joachim;
Abstract More than 40% of registered foodborne outbreaks can be attributed to unsafe food handling practices in private households (World Health Organization (WHO), 2016). Although households represent the final stage of the food chain (where mitigation of food safety risks would be vital), consumers generally do not consider themselves to be a responsible actor in this respect (Taché & Carpentier, 2014). Poor hygiene and food handling practices are among the major risk factors for household-related foodborne illnesses, specifically cross-contamination and inefficient handwashing. In this work, we present two studies in which we analyzed hygiene and safety aspects of the poultry handling habits of consumers. Study 1 was an in-home behavior observation study involving 15 households. Besides inefficient handwashing, washing of poultry meat in the sink was the most problematic behavior. Salmonella and/or Campylobacter spp. were detected in 47% of meat samples taken during the home visits. Study 2 was a nationally representative household survey (N = 1007). 83% of the respondents reported that they washed poultry meat before food preparation. Based on patterns of poultry washing practices, knowledge about and interest in food safety, and worries about the safety of homemade dishes, five clusters of consumers were identified. Only one of these clusters had a significantly reduced tendency to wash poultry before preparation. The results indicate that, paradoxically, the more the conscious consumers are of microbiological hazards, the more likely they are to wash their poultry: most consumers believe that washing poultry before preparation is good hygiene practice and are not aware of cross-contamination risk.
ZENODOarrow_drop_down
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2022
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ZENODOarrow_drop_down
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Dataset . 2022
License: CC BY
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ZENODO
Dataset . 2022
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Paradoxical risk mitigation behavior in private households
Research datakeyboard_double_arrow_right Dataset 2022Publisher:ZenodoFunded by:EC | SafeConsumE
EC| SafeConsumE
Authors: Kasza, Gyula; Csenki, Eszter Zita; Izsó, Tekla; Scholderer, Joachim;
Abstract More than 40% of registered foodborne outbreaks can be attributed to unsafe food handling practices in private households (World Health Organization (WHO), 2016). Although households represent the final stage of the food chain (where mitigation of food safety risks would be vital), consumers generally do not consider themselves to be a responsible actor in this respect (Taché & Carpentier, 2014). Poor hygiene and food handling practices are among the major risk factors for household-related foodborne illnesses, specifically cross-contamination and inefficient handwashing. In this work, we present two studies in which we analyzed hygiene and safety aspects of the poultry handling habits of consumers. Study 1 was an in-home behavior observation study involving 15 households. Besides inefficient handwashing, washing of poultry meat in the sink was the most problematic behavior. Salmonella and/or Campylobacter spp. were detected in 47% of meat samples taken during the home visits. Study 2 was a nationally representative household survey (N = 1007). 83% of the respondents reported that they washed poultry meat before food preparation. Based on patterns of poultry washing practices, knowledge about and interest in food safety, and worries about the safety of homemade dishes, five clusters of consumers were identified. Only one of these clusters had a significantly reduced tendency to wash poultry before preparation. The results indicate that, paradoxically, the more the conscious consumers are of microbiological hazards, the more likely they are to wash their poultry: most consumers believe that washing poultry before preparation is good hygiene practice and are not aware of cross-contamination risk.
ZENODOarrow_drop_down
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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ZENODOarrow_drop_down
ZENODO
Dataset . 2022
License: CC BY
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ZENODO
Dataset . 2022
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