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  • SDSN - Greece
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Aparicio-Ruiz, R.; Ortiz-Romero, Clemente; Casadei, Enrico; García-González, Diego L.; +12 Authors

    This data corresponds to an inter-laboratory study (5 labs) applying a harmonized SPME-GC-MS method to analyze volatile compounds on 15 samples. The data shows the min-max concentration and the repeatability and reproducibility results. This work is relevant to know the performance of this method to be applied as a supporting tool for sensory assessment of virgin olive oil. The data are presented for 18 volatile compound that were selected for their contribution to positive attributes (fruitiness) and sensory defect. This work was developed in the context of the project OLEUM “Advanced solutions for assuring authenticity and quality of olive oil at global scale”, funded by the European Commission within the Horizon 2020 Programme (2014–2020, GA no. 635690). The data set consists of: - a numerical quantitative file saved both in .xlsx and .ods formats: “OLEUM_Dataset_SPME-GC-MS_xlsx.xlsx”, “OLEUM_Dataset_SPME-GC-MS_ods.ods”. - a README file: “OLEUM_Dataset SPME-GC-MS_README_rtf.rtf”.-- Content of the file OLEUM_Dataset_SPME-GC-MS_xlsx/ods: SPME-GC-MS: This sheet presents the repeatability and reproducibility values, expressed as relative standard deviation (RSD%), for three quantification methods (QM1, QM2 and QM3). The three quantification methods are described in Casadei et al., 2021 (Food Control, Vol. 123, 2021, 107823, p. 107823. doi: 10.1016/j.foodcont.2020.107823). Additionally, the concentrations (min and max) determined by the 5 labs for each of 15 samples are shown. OLEUM (Advanced solutions for assuring the overall authenticity and quality of olive oil), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 635690; http://www.oleumproject.eu/. Peer reviewed

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    DIGITAL.CSIC
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      DIGITAL.CSIC
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Gómez-Coca, R. B.; Pérez Camino, María del Carmen; Moreda, Wenceslao;

    These data were generated in our lab, with the help of several laboratory assistants and master students, within the OLEUM project (http://www.oleumproject.eu): ‘Advanced solutions for assuring the authenticity and quality of olive oil at a global scale’ . The general objectives of the Project focus on olive oil fraud detection in a way that it improves the existing analytical methods and it has developed new strategies of analysis. It has been organized in seven work packages distributed in a number of tasks. This work is within Work Package 4 (‘Analytical solution addressing olive oil authentication issues’), and it concentrates on the detection of illegal blends, i.e. of illicit processing (deodorization) in extra virgin olive oil (EVOO) In our endeavor to detect soft deodorized olive oil in EVOO we have focused on the convenience of using two new factors (R1 and R2) obtained as a result of combining the total DAG content and the free acidity value of the samples under the microscope. We chose this methodology following our trend of using well-known, widely reputable routine parameters, avoiding in this way more complicated strategies that, although commonly used in the field of olive oil authentication, could require a more specific personnel training and laboratory apparatus. According to this, we demonstrated that since there is a relationship between free acidity and DAG concentration (both of them come from triacylglyceride hydrolysis and/or biosynthesis), and that such relationship breaks once the oil has gone through a refining process (free fatty acids are removed during the deodorization stage), it was possible to detect the presence of soft deodorized oil in EVOO by using a mathematical combination of both measurements at least to a certain extent. We actually proposed two factors to confirm the absence of soft deodorized oils in EVOO: R1 (10 x free acidity/DAGexp) >/= 0.23 and R2 (DAGexp-DAGtheor) < 0, in genuine EVOO, and we established that such approach was useful to detect the presence of soft deodorized olive oil when this was at least at 30 % in the mixture (Food Chemistry, volume 330, 2020, 127226). Samples were provided by Fera (Fera Science Ltd, Sand Hutton, York) and Institut des Corps Gras (ITERG, Canéjan, France). Excel file consisting of 14 sheets in which the calculation of the total diacylglycerol (DAG) content of a number of defective, non-defective, and soft deodorized olive oil samples, and of some of their corresponding mixtures, are given as examples. Data also show free acidity values and the theoretical DAG composition, together with the calculation of the R1 and R2 factors. It also includes the three resulting tables published as part of Food Chemistry, volume 330, 127226. Data must be read and interpreted within the context of such publication. Original chromatograms are not given since they are considered confidential. The Project has received funding from the European Commission within the Horizon 2020 Program (2014–2020), GA no. 635690. Peer reviewed

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    DIGITAL.CSIC
    Dataset . 2021
    Data sources: Datacite
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      DIGITAL.CSIC
      Dataset . 2021
      Data sources: Datacite
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    Authors: Gómez-Coca, R. B.; Pérez Camino, María del Carmen; Moreda, Wenceslao;

    These data were generated in our lab, with the help of several laboratory assistants and master students, within the OLEUM project (http://www.oleumproject.eu): ‘Advanced solutions for assuring the authenticity and quality of olive oil at a global scale’ . The general objectives of the Project focus on olive oil fraud detection in a way that it improves the existing analytical methods and has developed new strategies of analysis. It has been organized in seven work packages distributed in a number of tasks. This work is within Work Package 4 (‘Analytical solution addressing olive oil authentication issues’), and it concentrates on the study of legal blends between olive oils and other vegetable oils. The notion of legal blends comes from the authorization of the European Commission to market blends of olive oil with other vegetable oils and to emphasize the presence of olive oil in a place other than in the ingredient list, only if it accounts for at least 50 % of the blend (Commission Implementing Regulation (EU) No 29/2012 of 13 January 2012 on marketing standards for olive oil. Official Journal of the European Union L12, 14-21). This evinced a key weakness in the olive oil control chain: The lack of analytical methods to demonstrate the amount of such oil in declared mixtures. In this way, it was the purpose of this work to look for an analytical strategy that helped us to confirm if the amount of olive oil in a label-claimed blend was at least 50 %. In order to do that we used two of the most representative seed oils: normal type and high oleic sunflower oils (NTSO and HOSO, respectively), mixed on the one hand with olive oil (OO), and on the other hand with extra virgin olive oil (EVOO), at 60:40, 50:50, and 40:60 v/v proportions. We demonstrated that there was no need of developing new methods of analysis but that it was enough to combine four of the official purity parameters described in the legislation (International Olive Council (2016). Trade standard applying to olive oils and olive pomace oils. COI/T.15/NC No 3/Rev. 11, 1-17). Those parameters were: triacylglycerols (TAG), acyclic saturated hydrocarbons (SHC), free sterols (FS), and tocopherols (TCPH). They were also organized them in the form of decisional trees in a way that the blend who claimed to be composed of at least 50 % olive oil must comply not just with one but with the four of them (Food Chemistry, volume 315, 15 June 2020, 126235). Samples were provided by Fera (Fera Science Ltd, Sand Hutton, York) Excel file consisting of 12 sheets in which the composition of a number of olive oil and sunflower oil samples and their corresponding mixtures are given. Data show triacylglycerol, sterol, tocopherol, and linear aliphatic hydrocarbon contents. It also includes the five resulting tables published as part of Food Chemistry, volume 315, 15 June 2020, 126235. Data must be read and interpret within the context of such publication. The Project has received funding from the European Commission within the Horizon 2020 Program (2014–2020), GA no. 635690 Peer reviewed

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    DIGITAL.CSIC
    Dataset . 2021
    Data sources: Datacite
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      DIGITAL.CSIC
      Dataset . 2021
      Data sources: Datacite
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    Authors: Casadei, Enrico; Valli, Enrico; Aparicio-Ruiz, R.; Ortiz-Romero, Clemente; +6 Authors

    This data set contains the underlying data of the scientific publication: Casadei, Enrico; Valli, Enrico; Aparicio-Ruiz, Ramón; Ortiz-Romero, Clemente; García-González, Diego L.; Vichi, Stefania; Quintanilla-Casas, Beatriz; Tres, Alba; Bendini, Alessandra; Gallina Toschi, Tullia; 2021; Peer inter-laboratory validation study of a harmonized SPME-GC-FID method for the analysis of selected volatile compounds in virgin olive oils. Food Control 123, 107823. https://doi.org/10.1016/j.foodcont.2020.107823. In the context of supporting the panel test in the classification of virgin olive oils, the qualitative and quantitative analysis of a number of volatile compounds responsible for their aroma is of great importance. Herein, the data obtained from three laboratories that analyzed the same samples are presented with the view to develop an inter-laboratory validation study of a harmonized solid-phase micro-extraction coupled with gas-chromatography with flame ionized detector (SPME-GC-FID) method for determination of selected volatile compounds. The dataset presents the results of the validation study. Three quantification strategies were considered. Repeatability showed a mean relative standard deviation (RSD%) lower than 14% except for ethyl propanoate, 3-methyl-1-butanol, 1-octen-3-ol, and (E)-2-decenal. Linearity was satisfactory (R2 > 0.90) for all compounds when the calibration curves were corrected by the internal standard. The results are showed for 18 volatile compounds that were selected for being for volatile markers of the main sensory defects of virgin olive oil. Peer reviewed EC - H2020.

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    DIGITAL.CSIC
    Dataset . 2021
    Data sources: Datacite
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      DIGITAL.CSIC
      Dataset . 2021
      Data sources: Datacite
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    Authors: Martínez-Sancho, Elisabet; Slámová, Lenka; Morganti, Sandro; Grefen, Claudio; +70 Authors

    This publication is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676876 (GenTree). This work was also supported by the Swiss Secretariat for Education, Research and Innovation (SERI) under contract No. 6.0032. We want to thank to Olivier Ambrosio, Francisco Auñón, Virgilijus Baliuckas, Eduardo Ballesteros, Giorgina Beffa, Sabine Brodbeck, William Brunetto, Jurata Buchovska, Fernando del Caño, Andreas Fera, Rene Graf, Berit Gregorsson, Markus Hartmann, Andreas Helmersson, Enja Hollenbach, Jan Philipp Kappner, Johannes Lambertz, David Lopez-Quiroga, Jérémy Marchon, David Matter, Benjamin Meier, Helge Meischner, Pekka Närhi, Daniel Nievergelt, Juri Nievergelt, Anne Eskild Nilsen, Hans Nyeggen, Geir Østreng, Sebastian Richter, Christoph Rieckmann, Marcus Stefsky, Sergio San Segundo, Ivan Scotti, Jørn Henrik Sønstebø, Arne Steffenrem, Jussi Tiainen, Anne Verstege and Mikael Westerlund for their support during the field campaigns. We are also grateful to all the forest owners and national administrations for providing sampling permissions. The dataset presented here was collected by the GenTree project (EU-Horizon 2020), which aims to improve the use of forest genetic resources across Europe by better understanding how trees adapt to their local environment. This dataset of individual tree-core characteristics including ring-width series and whole-core wood density was collected for seven ecologically and economically important European tree species: silver birch (Betula pendula), European beech (Fagus sylvatica), Norway spruce (Picea abies), European black poplar (Populus nigra), maritime pine (Pinus pinaster), Scots pine (Pinus sylvestris), and sessile oak (Quercus petraea). Tree-ring width measurements were obtained from 3600 trees in 142 populations and whole-core wood density was measured for 3098 trees in 125 populations. This dataset covers most of the geographical and climatic range occupied by the selected species. The potential use of it will be highly valuable for assessing ecological and evolutionary responses to environmental conditions as well as for model development and parameterization, to predict adaptability under climate change scenarios. Update notice Author Correction: The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe (Scientific Data, (2020), 7, 1, (1), 10.1038/s41597-019-0340-y) Scientific Data, Volume 7, Issue 1, 1 December 2020, Article number 114 Peer reviewed 7 Pág.

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    Authors: Richter, Daniel J.; Watteaux, Romain; Vannier, Thomas; Leconte, Jade; +45 Authors

    Supplementary Table 1. List of Tara Oceans samples sequenced with a metabarcoding (18S V9) approach and with a metagenomic approach, including identifiers for sequencing reads deposited in the DDBJ/ENA/GenBank Short Read Archives (SRA). [This Table is identical in version 2.] Supplementary Table 2. Table of environmental parameters for each sample. [This Table is identical in version 2.] Supplementary Table 3. Matrix of metagenomic dissimilarity for the 0-0.22 μm size fraction. [This Table is identical in version 2.] Supplementary Table 4. Matrix of metagenomic dissimilarity for the 0.22-1.6/3 μm size fraction. [This Table is identical in version 2.] Supplementary Table 5. Matrix of metagenomic dissimilarity for the 0.8-5 μm size fraction. [This Table is identical in version 2.] Supplementary Table 6. Matrix of metagenomic dissimilarity for the 5-20 μm size fraction. [This Table is identical in version 2.] Supplementary Table 7. Matrix of metagenomic dissimilarity for the 20-180 μm size fraction. [This Table is identical in version 2.] Supplementary Table 8. Matrix of metagenomic dissimilarity for the 180-2000 μm size fraction. [This Table is identical in version 2.] Supplementary Table 9. Matrix of OTU dissimilarity for the 0-0.22 μm size fraction. [This Table is identical in version 2.] Supplementary Table 10. Matrix of OTU dissimilarity for the 0.22-1.6/3 μm size fraction. [This Table is identical in version 2.] Supplementary Table 11. Matrix of OTU dissimilarity for the 0.8-5 μm size fraction. [This Table is identical in version 2.] Supplementary Table 12. Matrix of OTU dissimilarity for the 5-20 μm size fraction. [This Table is identical in version 2.] Supplementary Table 13. Matrix of OTU dissimilarity for the 20-180 μm size fraction. [This Table is identical in version 2.] Supplementary Table 14. Matrix of OTU dissimilarity for the 180-2000 μm size fraction. [This Table is identical in version 2.] Supplementary Table 15. Matrix of minimum travel time, in years. [This Table is identical in version 2.] Supplementary Table 16. Matrix of minimum geographic distance (without traversing land), in kilometers. [This Table is identical in version 2.] Supplementary Table 17. Matrix of imaging-based dissimilarity. [This Table is identical in version 2.] Supplementary Table 18. Matrix of metagenome-assembled genome (MAG)-based dissimilarity for the 20-180 μm size fraction. [The filename of this Table was modified from version 2. The contents of the Table are identical.] Supplementary Table 19. The cophenetic correlation coefficient for different methods of clustering metagenomic dissimilarity. [This Table is identical in version 2.] Supplementary Table 20. Baker's Gamma index comparing clustering results within size fractions. [This Table is identical in version 2.] Supplementary Table 21. Rand Index for K-means and spectral clustering, and multivariate ANOVA calculated by the adonis function. [This Table is identical in version 2.] Dataset 1. Reference database (in FASTA format) used to perform taxonomic assignment of metabarcodes. The header line of each reference V9 rDNA barcode (with a > sign) contains a unique identifier derived from GenBank accession number, followed by the taxonomic path associated to the reference barcode. [This Dataset is identical in version 2.] Dataset 2. V9 rDNA abundance at the metabarcode level. md5sum = unique identifier; totab = total abundance across all samples; cid = identifier of the OTU to which the barcode belongs (see Dataset 3); pid = best percentage identity to a barcode in Dataset 1; refs = identifier(s) of the best matching barcode(s) in Dataset 1; lineage = taxononmic lineage of the best match in Dataset 1; taxogroup = high-level taxonomic grouping of the best match in Dataset 1; sequence = V9 rDNA sequence; TV9_XXX = barcode abundance by sample (see Supplementary Table 1 for sample identifiers). [This Dataset is identical in version 2.] Dataset 3. V9 rDNA abundance at the OTU (operational taxonomic unit) level. cid = identifier of the OTU; md5sum = unique identifier of the most abundant barcode in the OTU; pid, refs, lineage, taxogroup, sequence = defined as in Dataset 2; rtotab = total abundance of the most abundant barcode in the OTU; ctotab = total abundance of all barcodes in the OTU; TV9_XXX = abundance by sample of all barcodes in the OTU (see Supplementary Table 1 for sample identifiers). [This Dataset is identical in version 2.] Dataset 4. Relative abundances of metagenome-assembled genomes (MAGs) in metagenomic samples from the 20-180 μm size fraction. [This Dataset is new in version 3.] Biogeographical studies have traditionally focused on readily visible organisms, but recent technological advances are enabling analyses of the large-scale distribution of microscopic organisms, whose biogeographical patterns have long been debated. Here we assessed the global structure of plankton geography and its relation to the biological, chemical and physical context of the ocean (the 'seascape') by analyzing metagenomes of plankton communities sampled across oceans during the Tara Oceans expedition, in light of environmental data and ocean current transport. Using a consistent approach across organismal sizes that provides unprecedented resolution to measure changes in genomic composition between communities, we report a pan-ocean, size-dependent plankton biogeography overlying regional heterogeneity. We found robust evidence for a basin-scale impact of transport by ocean currents on plankton biogeography, and on a characteristic timescale of community dynamics going beyond simple seasonality or life history transitions of plankton. Peer reviewed

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      Dataset . 2020
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    Authors: Aparicio-Ruiz, R.; Barbieri, Sara; Gallina Toschi, Tullia; García-González, Diego L.;

    This data set contains the underlying data of the scientific publication: Aparicio-Ruiz R., Barbieri S., Gallina Toschi T., García-González D.L. (2020). Formulations of Rancid and Winey-Vinegary Artificial Olfactory Reference Materials (AORMs) for Virgin Olive Oil Sensory Evaluation. Foods, 9 (12), 1870, https://doi.org/10.3390/foods9121870. Panel test is the only sensory method included in international regulations of virgin olive oils and its application is compulsory. At present, there is no reference material (RM), in the strict sense of the term, to be used as a validated standard for sensory defects of virgin olive oil with which tasters can be trained. Usually, real samples of virgin olive oils assessed by many panels for the International Olive Council (IOC) ring tests are used as materials of reference in panel training and control. These data correspond to the work carried out to formulate RM that emulate rancid and winey-vinegary defects found in virgin olive oils with the aim of providing reproducible RMs that can be prepared on demand. Under the criteria of representativeness, verified with the advice of the IOC, aroma persistence, and simplicity in formulation, two RMs for winey-vinegary and rancid were obtained by diluting acetic acid and ethanol (winey-vinegary defect) and hexanal (rancid defect) together with other compounds that are used to modify aroma and avoid non-natural sensory notes. Content of the file OLEUM_Portable_ReferenceMaterials_VOO.xlsx/.ods: • Volatile markers: this sheet contains data of volatile markers of the virgin olive oils sensory defects winey-vinegary and rancid, sensory characteristics and corresponding odor threshold relating to an oil matrix. • Relative areas: this sheet contains data of relative areas of volatile compounds selected to emulate winey-vinegary and rancid defects in VOOs in a set of 60 samples and in RMs provided by the International Olive Council for each of the defects (RM IOC). • Formulations RM AV: this sheet contains data of the main formulations (volatile compounds and concentrations in mg/kg) emulating winey-vinegary aroma in virgin olive oil and evaluation by assessors in terms of suitability as possible RM. • Formulation RM R: this sheet contains the data of the main formulations (volatile compounds and concentrations in mg/kg) emulating rancid aroma in virgin olive oil and evaluation by assessors about their eligibility as possible RM. OLEUM (Advanced solutions for assuring the overall authenticity and quality of olive oil), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 635690; http://www.oleumproject.eu/. Aparicio-Ruiz, R.; Barbieri, Sara; Gallina Toschi, Tullia; García-González, Diego L. Peer reviewed

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    DIGITAL.CSIC
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      DIGITAL.CSIC
      Dataset . 2020
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    Authors: Álvarez-Salgado, Xosé Antón; Latorre, G.; Fernández-Reiriz, María José; Labarta, Uxío;

    Number of days per month that the mussel cultivation areas of the Ría de Areas-Betanzos (A Coruña, NW Spain), Arnela and Lorbé, have been closed to mussel extraction. The toxicity causing the closures (ASP, DSP and PSP) is also indicated. Data covers the period from 2000 to 2007. The Technological Institute for the Monitoring of the Marine Environment in Galicia (INTECMAR) announces the closures and their causes (ASP, DSP, PSP) on a daily basis through the web page http://www.intecmar.org. However, previous daily reports are not freely available in the web page. This data has been published in X.A. Alvarez-Salgado, F.G. Figueiras, M.J. Fernandez-Reiriz, U. Labarta, L. Peteiro and S. Piedracoba (2011). Control of lipophilic shellfish poisoning outbreaks by seasonal upwelling and continental runoff. Harmful Algae 10, 121-129. Peer reviewed

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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: ZENODO; Sygma
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    ZENODO
    Dataset . 2019
    License: CC BY
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      ZENODO
      Dataset . 2019
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      Data sources: ZENODO; Sygma
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      ZENODO
      Dataset . 2019
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    Authors: Álvarez-Salgado, Xosé Antón; Latorre, G.; Fernández-Reiriz, María José; Labarta, Uxío;

    Flesh yield of commercial mussels cultured in the Ría de Ares-Betanzos (A Coruña, NW Spain). The flesh yield is calculated as the percentage of the total weight of 1 kg of live mussels > 50 mm collected in a mussel raft that is meat weight after opening the valves with water vapour. Flesh yield data have been aggregated monthly and the seasonal cycle of each year has been adjusted to the following harmonic function: FY (%) = A1 + A2* cosine (2*Pi*t/12 + A3) where A1 is the seasonal average value of FY, A2 is half the amplitude of the seasonal cycle of FY; and A3 is the month of the year when FY is halfway between the seasonal minimum and the seasonal maximum. The values of A1, A2 and A3 for years 2002 to 2012 are reported for the two mussel cultivation areas of the Ría de Ares-Betanzos (Arnela and Lorbé) These data have been published in X.A. Álvarez-Salgado, U. Labarta, V. Vinseiro and M.J. Fernández Reiriz (2017). Environmental drivers of mussels flesh yield in a coastal upwelling system. Ecological Indicators 79, 323-329. Peer reviewed

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    ZENODO
    Dataset . 2019
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    ZENODO
    Dataset . 2019
    License: CC BY
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      ZENODO
      Dataset . 2019
      License: CC BY
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      ZENODO
      Dataset . 2019
      License: CC BY
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    Authors: Álvarez-Salgado, Xosé Antón; Fuentes-Santos, I.; Otero, Jaime;

    File containing time series of daily values of coastal winds, upwelling index (offshore Ekman transport), continental runoff, solar irradiance and sea surface temperature for selected sites in the NW Galician coast (NW Spain). These data can be freely downloaded from the web sites of the Galician Meteorological Agency MeteoGalicia (http://www.meteogalicia.es), the Instituto Español de Oceanografía (http://www.indicedeafloramiento.ieo.es) and ICOADS (http://icoads.noaa.gov). We just put them together in a single file. The file includes metadata indicating units, the position of the selected stations and the origin of the data European Commission: ClimeFish - Co-creating a decision support framework to ensure sustainable fish production in Europe under climate change (677039) Peer reviewed

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    ZENODO
    Dataset . 2019
    License: CC BY
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    ZENODO
    Dataset . 2019
    License: CC BY
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      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: ZENODO; Sygma
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      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Aparicio-Ruiz, R.; Ortiz-Romero, Clemente; Casadei, Enrico; García-González, Diego L.; +12 Authors

    This data corresponds to an inter-laboratory study (5 labs) applying a harmonized SPME-GC-MS method to analyze volatile compounds on 15 samples. The data shows the min-max concentration and the repeatability and reproducibility results. This work is relevant to know the performance of this method to be applied as a supporting tool for sensory assessment of virgin olive oil. The data are presented for 18 volatile compound that were selected for their contribution to positive attributes (fruitiness) and sensory defect. This work was developed in the context of the project OLEUM “Advanced solutions for assuring authenticity and quality of olive oil at global scale”, funded by the European Commission within the Horizon 2020 Programme (2014–2020, GA no. 635690). The data set consists of: - a numerical quantitative file saved both in .xlsx and .ods formats: “OLEUM_Dataset_SPME-GC-MS_xlsx.xlsx”, “OLEUM_Dataset_SPME-GC-MS_ods.ods”. - a README file: “OLEUM_Dataset SPME-GC-MS_README_rtf.rtf”.-- Content of the file OLEUM_Dataset_SPME-GC-MS_xlsx/ods: SPME-GC-MS: This sheet presents the repeatability and reproducibility values, expressed as relative standard deviation (RSD%), for three quantification methods (QM1, QM2 and QM3). The three quantification methods are described in Casadei et al., 2021 (Food Control, Vol. 123, 2021, 107823, p. 107823. doi: 10.1016/j.foodcont.2020.107823). Additionally, the concentrations (min and max) determined by the 5 labs for each of 15 samples are shown. OLEUM (Advanced solutions for assuring the overall authenticity and quality of olive oil), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 635690; http://www.oleumproject.eu/. Peer reviewed

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    DIGITAL.CSIC
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      DIGITAL.CSIC
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Gómez-Coca, R. B.; Pérez Camino, María del Carmen; Moreda, Wenceslao;

    These data were generated in our lab, with the help of several laboratory assistants and master students, within the OLEUM project (http://www.oleumproject.eu): ‘Advanced solutions for assuring the authenticity and quality of olive oil at a global scale’ . The general objectives of the Project focus on olive oil fraud detection in a way that it improves the existing analytical methods and it has developed new strategies of analysis. It has been organized in seven work packages distributed in a number of tasks. This work is within Work Package 4 (‘Analytical solution addressing olive oil authentication issues’), and it concentrates on the detection of illegal blends, i.e. of illicit processing (deodorization) in extra virgin olive oil (EVOO) In our endeavor to detect soft deodorized olive oil in EVOO we have focused on the convenience of using two new factors (R1 and R2) obtained as a result of combining the total DAG content and the free acidity value of the samples under the microscope. We chose this methodology following our trend of using well-known, widely reputable routine parameters, avoiding in this way more complicated strategies that, although commonly used in the field of olive oil authentication, could require a more specific personnel training and laboratory apparatus. According to this, we demonstrated that since there is a relationship between free acidity and DAG concentration (both of them come from triacylglyceride hydrolysis and/or biosynthesis), and that such relationship breaks once the oil has gone through a refining process (free fatty acids are removed during the deodorization stage), it was possible to detect the presence of soft deodorized oil in EVOO by using a mathematical combination of both measurements at least to a certain extent. We actually proposed two factors to confirm the absence of soft deodorized oils in EVOO: R1 (10 x free acidity/DAGexp) >/= 0.23 and R2 (DAGexp-DAGtheor) < 0, in genuine EVOO, and we established that such approach was useful to detect the presence of soft deodorized olive oil when this was at least at 30 % in the mixture (Food Chemistry, volume 330, 2020, 127226). Samples were provided by Fera (Fera Science Ltd, Sand Hutton, York) and Institut des Corps Gras (ITERG, Canéjan, France). Excel file consisting of 14 sheets in which the calculation of the total diacylglycerol (DAG) content of a number of defective, non-defective, and soft deodorized olive oil samples, and of some of their corresponding mixtures, are given as examples. Data also show free acidity values and the theoretical DAG composition, together with the calculation of the R1 and R2 factors. It also includes the three resulting tables published as part of Food Chemistry, volume 330, 127226. Data must be read and interpreted within the context of such publication. Original chromatograms are not given since they are considered confidential. The Project has received funding from the European Commission within the Horizon 2020 Program (2014–2020), GA no. 635690. Peer reviewed

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    DIGITAL.CSIC
    Dataset . 2021
    Data sources: Datacite
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      DIGITAL.CSIC
      Dataset . 2021
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Gómez-Coca, R. B.; Pérez Camino, María del Carmen; Moreda, Wenceslao;

    These data were generated in our lab, with the help of several laboratory assistants and master students, within the OLEUM project (http://www.oleumproject.eu): ‘Advanced solutions for assuring the authenticity and quality of olive oil at a global scale’ . The general objectives of the Project focus on olive oil fraud detection in a way that it improves the existing analytical methods and has developed new strategies of analysis. It has been organized in seven work packages distributed in a number of tasks. This work is within Work Package 4 (‘Analytical solution addressing olive oil authentication issues’), and it concentrates on the study of legal blends between olive oils and other vegetable oils. The notion of legal blends comes from the authorization of the European Commission to market blends of olive oil with other vegetable oils and to emphasize the presence of olive oil in a place other than in the ingredient list, only if it accounts for at least 50 % of the blend (Commission Implementing Regulation (EU) No 29/2012 of 13 January 2012 on marketing standards for olive oil. Official Journal of the European Union L12, 14-21). This evinced a key weakness in the olive oil control chain: The lack of analytical methods to demonstrate the amount of such oil in declared mixtures. In this way, it was the purpose of this work to look for an analytical strategy that helped us to confirm if the amount of olive oil in a label-claimed blend was at least 50 %. In order to do that we used two of the most representative seed oils: normal type and high oleic sunflower oils (NTSO and HOSO, respectively), mixed on the one hand with olive oil (OO), and on the other hand with extra virgin olive oil (EVOO), at 60:40, 50:50, and 40:60 v/v proportions. We demonstrated that there was no need of developing new methods of analysis but that it was enough to combine four of the official purity parameters described in the legislation (International Olive Council (2016). Trade standard applying to olive oils and olive pomace oils. COI/T.15/NC No 3/Rev. 11, 1-17). Those parameters were: triacylglycerols (TAG), acyclic saturated hydrocarbons (SHC), free sterols (FS), and tocopherols (TCPH). They were also organized them in the form of decisional trees in a way that the blend who claimed to be composed of at least 50 % olive oil must comply not just with one but with the four of them (Food Chemistry, volume 315, 15 June 2020, 126235). Samples were provided by Fera (Fera Science Ltd, Sand Hutton, York) Excel file consisting of 12 sheets in which the composition of a number of olive oil and sunflower oil samples and their corresponding mixtures are given. Data show triacylglycerol, sterol, tocopherol, and linear aliphatic hydrocarbon contents. It also includes the five resulting tables published as part of Food Chemistry, volume 315, 15 June 2020, 126235. Data must be read and interpret within the context of such publication. The Project has received funding from the European Commission within the Horizon 2020 Program (2014–2020), GA no. 635690 Peer reviewed

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    DIGITAL.CSIC
    Dataset . 2021
    Data sources: Datacite
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      DIGITAL.CSIC
      Dataset . 2021
      Data sources: Datacite
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    Authors: Casadei, Enrico; Valli, Enrico; Aparicio-Ruiz, R.; Ortiz-Romero, Clemente; +6 Authors

    This data set contains the underlying data of the scientific publication: Casadei, Enrico; Valli, Enrico; Aparicio-Ruiz, Ramón; Ortiz-Romero, Clemente; García-González, Diego L.; Vichi, Stefania; Quintanilla-Casas, Beatriz; Tres, Alba; Bendini, Alessandra; Gallina Toschi, Tullia; 2021; Peer inter-laboratory validation study of a harmonized SPME-GC-FID method for the analysis of selected volatile compounds in virgin olive oils. Food Control 123, 107823. https://doi.org/10.1016/j.foodcont.2020.107823. In the context of supporting the panel test in the classification of virgin olive oils, the qualitative and quantitative analysis of a number of volatile compounds responsible for their aroma is of great importance. Herein, the data obtained from three laboratories that analyzed the same samples are presented with the view to develop an inter-laboratory validation study of a harmonized solid-phase micro-extraction coupled with gas-chromatography with flame ionized detector (SPME-GC-FID) method for determination of selected volatile compounds. The dataset presents the results of the validation study. Three quantification strategies were considered. Repeatability showed a mean relative standard deviation (RSD%) lower than 14% except for ethyl propanoate, 3-methyl-1-butanol, 1-octen-3-ol, and (E)-2-decenal. Linearity was satisfactory (R2 > 0.90) for all compounds when the calibration curves were corrected by the internal standard. The results are showed for 18 volatile compounds that were selected for being for volatile markers of the main sensory defects of virgin olive oil. Peer reviewed EC - H2020.

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    DIGITAL.CSIC
    Dataset . 2021
    Data sources: Datacite
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      DIGITAL.CSIC
      Dataset . 2021
      Data sources: Datacite
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    Authors: Martínez-Sancho, Elisabet; Slámová, Lenka; Morganti, Sandro; Grefen, Claudio; +70 Authors

    This publication is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676876 (GenTree). This work was also supported by the Swiss Secretariat for Education, Research and Innovation (SERI) under contract No. 6.0032. We want to thank to Olivier Ambrosio, Francisco Auñón, Virgilijus Baliuckas, Eduardo Ballesteros, Giorgina Beffa, Sabine Brodbeck, William Brunetto, Jurata Buchovska, Fernando del Caño, Andreas Fera, Rene Graf, Berit Gregorsson, Markus Hartmann, Andreas Helmersson, Enja Hollenbach, Jan Philipp Kappner, Johannes Lambertz, David Lopez-Quiroga, Jérémy Marchon, David Matter, Benjamin Meier, Helge Meischner, Pekka Närhi, Daniel Nievergelt, Juri Nievergelt, Anne Eskild Nilsen, Hans Nyeggen, Geir Østreng, Sebastian Richter, Christoph Rieckmann, Marcus Stefsky, Sergio San Segundo, Ivan Scotti, Jørn Henrik Sønstebø, Arne Steffenrem, Jussi Tiainen, Anne Verstege and Mikael Westerlund for their support during the field campaigns. We are also grateful to all the forest owners and national administrations for providing sampling permissions. The dataset presented here was collected by the GenTree project (EU-Horizon 2020), which aims to improve the use of forest genetic resources across Europe by better understanding how trees adapt to their local environment. This dataset of individual tree-core characteristics including ring-width series and whole-core wood density was collected for seven ecologically and economically important European tree species: silver birch (Betula pendula), European beech (Fagus sylvatica), Norway spruce (Picea abies), European black poplar (Populus nigra), maritime pine (Pinus pinaster), Scots pine (Pinus sylvestris), and sessile oak (Quercus petraea). Tree-ring width measurements were obtained from 3600 trees in 142 populations and whole-core wood density was measured for 3098 trees in 125 populations. This dataset covers most of the geographical and climatic range occupied by the selected species. The potential use of it will be highly valuable for assessing ecological and evolutionary responses to environmental conditions as well as for model development and parameterization, to predict adaptability under climate change scenarios. Update notice Author Correction: The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe (Scientific Data, (2020), 7, 1, (1), 10.1038/s41597-019-0340-y) Scientific Data, Volume 7, Issue 1, 1 December 2020, Article number 114 Peer reviewed 7 Pág.

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    Authors: Richter, Daniel J.; Watteaux, Romain; Vannier, Thomas; Leconte, Jade; +45 Authors

    Supplementary Table 1. List of Tara Oceans samples sequenced with a metabarcoding (18S V9) approach and with a metagenomic approach, including identifiers for sequencing reads deposited in the DDBJ/ENA/GenBank Short Read Archives (SRA). [This Table is identical in version 2.] Supplementary Table 2. Table of environmental parameters for each sample. [This Table is identical in version 2.] Supplementary Table 3. Matrix of metagenomic dissimilarity for the 0-0.22 μm size fraction. [This Table is identical in version 2.] Supplementary Table 4. Matrix of metagenomic dissimilarity for the 0.22-1.6/3 μm size fraction. [This Table is identical in version 2.] Supplementary Table 5. Matrix of metagenomic dissimilarity for the 0.8-5 μm size fraction. [This Table is identical in version 2.] Supplementary Table 6. Matrix of metagenomic dissimilarity for the 5-20 μm size fraction. [This Table is identical in version 2.] Supplementary Table 7. Matrix of metagenomic dissimilarity for the 20-180 μm size fraction. [This Table is identical in version 2.] Supplementary Table 8. Matrix of metagenomic dissimilarity for the 180-2000 μm size fraction. [This Table is identical in version 2.] Supplementary Table 9. Matrix of OTU dissimilarity for the 0-0.22 μm size fraction. [This Table is identical in version 2.] Supplementary Table 10. Matrix of OTU dissimilarity for the 0.22-1.6/3 μm size fraction. [This Table is identical in version 2.] Supplementary Table 11. Matrix of OTU dissimilarity for the 0.8-5 μm size fraction. [This Table is identical in version 2.] Supplementary Table 12. Matrix of OTU dissimilarity for the 5-20 μm size fraction. [This Table is identical in version 2.] Supplementary Table 13. Matrix of OTU dissimilarity for the 20-180 μm size fraction. [This Table is identical in version 2.] Supplementary Table 14. Matrix of OTU dissimilarity for the 180-2000 μm size fraction. [This Table is identical in version 2.] Supplementary Table 15. Matrix of minimum travel time, in years. [This Table is identical in version 2.] Supplementary Table 16. Matrix of minimum geographic distance (without traversing land), in kilometers. [This Table is identical in version 2.] Supplementary Table 17. Matrix of imaging-based dissimilarity. [This Table is identical in version 2.] Supplementary Table 18. Matrix of metagenome-assembled genome (MAG)-based dissimilarity for the 20-180 μm size fraction. [The filename of this Table was modified from version 2. The contents of the Table are identical.] Supplementary Table 19. The cophenetic correlation coefficient for different methods of clustering metagenomic dissimilarity. [This Table is identical in version 2.] Supplementary Table 20. Baker's Gamma index comparing clustering results within size fractions. [This Table is identical in version 2.] Supplementary Table 21. Rand Index for K-means and spectral clustering, and multivariate ANOVA calculated by the adonis function. [This Table is identical in version 2.] Dataset 1. Reference database (in FASTA format) used to perform taxonomic assignment of metabarcodes. The header line of each reference V9 rDNA barcode (with a > sign) contains a unique identifier derived from GenBank accession number, followed by the taxonomic path associated to the reference barcode. [This Dataset is identical in version 2.] Dataset 2. V9 rDNA abundance at the metabarcode level. md5sum = unique identifier; totab = total abundance across all samples; cid = identifier of the OTU to which the barcode belongs (see Dataset 3); pid = best percentage identity to a barcode in Dataset 1; refs = identifier(s) of the best matching barcode(s) in Dataset 1; lineage = taxononmic lineage of the best match in Dataset 1; taxogroup = high-level taxonomic grouping of the best match in Dataset 1; sequence = V9 rDNA sequence; TV9_XXX = barcode abundance by sample (see Supplementary Table 1 for sample identifiers). [This Dataset is identical in version 2.] Dataset 3. V9 rDNA abundance at the OTU (operational taxonomic unit) level. cid = identifier of the OTU; md5sum = unique identifier of the most abundant barcode in the OTU; pid, refs, lineage, taxogroup, sequence = defined as in Dataset 2; rtotab = total abundance of the most abundant barcode in the OTU; ctotab = total abundance of all barcodes in the OTU; TV9_XXX = abundance by sample of all barcodes in the OTU (see Supplementary Table 1 for sample identifiers). [This Dataset is identical in version 2.] Dataset 4. Relative abundances of metagenome-assembled genomes (MAGs) in metagenomic samples from the 20-180 μm size fraction. [This Dataset is new in version 3.] Biogeographical studies have traditionally focused on readily visible organisms, but recent technological advances are enabling analyses of the large-scale distribution of microscopic organisms, whose biogeographical patterns have long been debated. Here we assessed the global structure of plankton geography and its relation to the biological, chemical and physical context of the ocean (the 'seascape') by analyzing metagenomes of plankton communities sampled across oceans during the Tara Oceans expedition, in light of environmental data and ocean current transport. Using a consistent approach across organismal sizes that provides unprecedented resolution to measure changes in genomic composition between communities, we report a pan-ocean, size-dependent plankton biogeography overlying regional heterogeneity. We found robust evidence for a basin-scale impact of transport by ocean currents on plankton biogeography, and on a characteristic timescale of community dynamics going beyond simple seasonality or life history transitions of plankton. Peer reviewed

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    Authors: Aparicio-Ruiz, R.; Barbieri, Sara; Gallina Toschi, Tullia; García-González, Diego L.;

    This data set contains the underlying data of the scientific publication: Aparicio-Ruiz R., Barbieri S., Gallina Toschi T., García-González D.L. (2020). Formulations of Rancid and Winey-Vinegary Artificial Olfactory Reference Materials (AORMs) for Virgin Olive Oil Sensory Evaluation. Foods, 9 (12), 1870, https://doi.org/10.3390/foods9121870. Panel test is the only sensory method included in international regulations of virgin olive oils and its application is compulsory. At present, there is no reference material (RM), in the strict sense of the term, to be used as a validated standard for sensory defects of virgin olive oil with which tasters can be trained. Usually, real samples of virgin olive oils assessed by many panels for the International Olive Council (IOC) ring tests are used as materials of reference in panel training and control. These data correspond to the work carried out to formulate RM that emulate rancid and winey-vinegary defects found in virgin olive oils with the aim of providing reproducible RMs that can be prepared on demand. Under the criteria of representativeness, verified with the advice of the IOC, aroma persistence, and simplicity in formulation, two RMs for winey-vinegary and rancid were obtained by diluting acetic acid and ethanol (winey-vinegary defect) and hexanal (rancid defect) together with other compounds that are used to modify aroma and avoid non-natural sensory notes. Content of the file OLEUM_Portable_ReferenceMaterials_VOO.xlsx/.ods: • Volatile markers: this sheet contains data of volatile markers of the virgin olive oils sensory defects winey-vinegary and rancid, sensory characteristics and corresponding odor threshold relating to an oil matrix. • Relative areas: this sheet contains data of relative areas of volatile compounds selected to emulate winey-vinegary and rancid defects in VOOs in a set of 60 samples and in RMs provided by the International Olive Council for each of the defects (RM IOC). • Formulations RM AV: this sheet contains data of the main formulations (volatile compounds and concentrations in mg/kg) emulating winey-vinegary aroma in virgin olive oil and evaluation by assessors in terms of suitability as possible RM. • Formulation RM R: this sheet contains the data of the main formulations (volatile compounds and concentrations in mg/kg) emulating rancid aroma in virgin olive oil and evaluation by assessors about their eligibility as possible RM. OLEUM (Advanced solutions for assuring the overall authenticity and quality of olive oil), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 635690; http://www.oleumproject.eu/. Aparicio-Ruiz, R.; Barbieri, Sara; Gallina Toschi, Tullia; García-González, Diego L. Peer reviewed

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    DIGITAL.CSIC
    Dataset . 2020
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      DIGITAL.CSIC
      Dataset . 2020
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    Authors: Álvarez-Salgado, Xosé Antón; Latorre, G.; Fernández-Reiriz, María José; Labarta, Uxío;

    Number of days per month that the mussel cultivation areas of the Ría de Areas-Betanzos (A Coruña, NW Spain), Arnela and Lorbé, have been closed to mussel extraction. The toxicity causing the closures (ASP, DSP and PSP) is also indicated. Data covers the period from 2000 to 2007. The Technological Institute for the Monitoring of the Marine Environment in Galicia (INTECMAR) announces the closures and their causes (ASP, DSP, PSP) on a daily basis through the web page http://www.intecmar.org. However, previous daily reports are not freely available in the web page. This data has been published in X.A. Alvarez-Salgado, F.G. Figueiras, M.J. Fernandez-Reiriz, U. Labarta, L. Peteiro and S. Piedracoba (2011). Control of lipophilic shellfish poisoning outbreaks by seasonal upwelling and continental runoff. Harmful Algae 10, 121-129. Peer reviewed

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    ZENODO
    Dataset . 2019
    License: CC BY
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    ZENODO
    Dataset . 2019
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      ZENODO
      Dataset . 2019
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      ZENODO
      Dataset . 2019
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    Authors: Álvarez-Salgado, Xosé Antón; Latorre, G.; Fernández-Reiriz, María José; Labarta, Uxío;

    Flesh yield of commercial mussels cultured in the Ría de Ares-Betanzos (A Coruña, NW Spain). The flesh yield is calculated as the percentage of the total weight of 1 kg of live mussels > 50 mm collected in a mussel raft that is meat weight after opening the valves with water vapour. Flesh yield data have been aggregated monthly and the seasonal cycle of each year has been adjusted to the following harmonic function: FY (%) = A1 + A2* cosine (2*Pi*t/12 + A3) where A1 is the seasonal average value of FY, A2 is half the amplitude of the seasonal cycle of FY; and A3 is the month of the year when FY is halfway between the seasonal minimum and the seasonal maximum. The values of A1, A2 and A3 for years 2002 to 2012 are reported for the two mussel cultivation areas of the Ría de Ares-Betanzos (Arnela and Lorbé) These data have been published in X.A. Álvarez-Salgado, U. Labarta, V. Vinseiro and M.J. Fernández Reiriz (2017). Environmental drivers of mussels flesh yield in a coastal upwelling system. Ecological Indicators 79, 323-329. Peer reviewed

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    ZENODO
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    ZENODO
    Dataset . 2019
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      ZENODO
      Dataset . 2019
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      ZENODO
      Dataset . 2019
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    Authors: Álvarez-Salgado, Xosé Antón; Fuentes-Santos, I.; Otero, Jaime;

    File containing time series of daily values of coastal winds, upwelling index (offshore Ekman transport), continental runoff, solar irradiance and sea surface temperature for selected sites in the NW Galician coast (NW Spain). These data can be freely downloaded from the web sites of the Galician Meteorological Agency MeteoGalicia (http://www.meteogalicia.es), the Instituto Español de Oceanografía (http://www.indicedeafloramiento.ieo.es) and ICOADS (http://icoads.noaa.gov). We just put them together in a single file. The file includes metadata indicating units, the position of the selected stations and the origin of the data European Commission: ClimeFish - Co-creating a decision support framework to ensure sustainable fish production in Europe under climate change (677039) Peer reviewed

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    ZENODO
    Dataset . 2019
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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    addClaim

    This Research product is the result of merged Research products in OpenAIRE.

    You have already added works in your ORCID record related to the merged Research product.
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: ZENODO; Sygma
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      addClaim

      This Research product is the result of merged Research products in OpenAIRE.

      You have already added works in your ORCID record related to the merged Research product.