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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: 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: 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: Díaz-Delgado, Ricardo; Afán, Isabel; Aragonés, David; García, Diego; +1 Authors

    Time Series of flooded areas derived from Landsat TM, ETM+ & OLI in the Path 202 Row 34 (Doñana). Also, these products and its metadata are freely available to consult or downloaded in the LAST-EBD Cartography Server: http://mercurio.ebd.csic.es/imgs/ Methodology is described in this paper: Remote Sensing 8(9):775 · September 2016. DOI: 10.3390/rs8090775 European Commission: ECOPOTENTIAL - ECOPOTENTIAL: IMPROVING FUTURE ECOSYSTEM BENEFITS THROUGH EARTH OBSERVATIONS (641762) Peer reviewed

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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2019
    License: CC BY
    Data sources: ZENODO
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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/
      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: ZENODO
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    Authors: Díaz-Delgado, Ricardo; Afán, Isabel; Aragonés, David; García, Diego; +1 Authors

    Time Series of annual Hydroperiods derived from Landsat MSS, TM, ETM+ & OLI in the Path 202 Row 34 (Doñana) cover the period 1974-2018. European Commission: ECOPOTENTIAL - ECOPOTENTIAL: IMPROVING FUTURE ECOSYSTEM BENEFITS THROUGH EARTH OBSERVATIONS (641762) 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
    Data sources: Datacite
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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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    Authors: Langer, Gerald; Probert, Ian; Nehrke, Gernot; Ziveri, Patrizia;

    Four strains of the coccolithophore Emiliania huxleyi (RCC1212, RCC1216, RCC1238, RCC1256) were grown in dilute batch culture at four CO2 levels ranging from ~200 µatm to ~1200 µatm. Coccolith morphology was analyzed based on scanning electron micrographs. Three of the four strains did not exhibit a change in morphology over the CO2 range tested. One strain (RCC1256) displayed an increase in the percentage of malformed coccoliths with increasing CO2 concentration. We conclude that the sensitivity of the coccolith-shaping machinery to carbonate chemistry changes is strain-specific. Although it has been shown before that carbonate chemistry related changes in growth- and calcification rate are strain-specific, there seems to be no consistent correlation between coccolith morphology and growth or calcification rate. We did not observe an increase in the percentage of incomplete coccoliths in RCC1256, indicating that the coccolith-shaping machinery per se is affected by acidification and not the signalling pathway that produces the stop-signal for coccolith growth. 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). The date of carbonate chemistry calculation by seacarb is 2014-01-20. Supplement to: Langer, Gerald; Probert, Ian; Nehrke, Gernot; Ziveri, Patrizia (2011): The morphological response of Emiliania huxleyi to seawater carbonate chemistry changes: an inter-strain comparison. Journal of Nannoplankton Research, 32(1), 29-34

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    PANGAEA
    Dataset . 2011
    Data sources: B2FIND
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    PANGAEA - Data Publisher for Earth and Environmental Science
    Other dataset type . 2011
    License: CC BY
    Data sources: Datacite
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      PANGAEA
      Dataset . 2011
      Data sources: B2FIND
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      PANGAEA - Data Publisher for Earth and Environmental Science
      Other dataset type . 2011
      License: CC BY
      Data sources: Datacite
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    Authors: Alvarez-Salgado, Xosé Antón; Latorre, G.; Fernández-Reiriz, María José; Labarta, U.;

    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
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY
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      ZENODO
      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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    Authors: Quintanilla-Casas, Beatriz; Bertin, Sofia; Leik, Kerstin; Bustamante Alonso, Julen; +6 Authors

    Data base containing SPME-GC-MS raw analytical data obtained and used by Quintanilla-Casas et al. (Food Chemistry, 2020, 125556). Data correspond to 82 authentic and traceable olive oil samples, declared as EVOO by the suppliers obtained in the framework of OLEUM project (EC H2020 Programme 2014–2020) from seven different EU and non-EU countries: Croatia (n=11); Slovenia (SVN) (n=8); Spain (ESP) (n=17); Italy (ITA) (n=15); Greece (GRC) (n=6); Morocco (MAR) (n=15) and Turkey (TUR) (n=10). With the aim of reflecting the real production scenario, EVOO samples in this prospective study were obtained under usual production practices for commercial purposes, and thus consisted of both monovarietal oils as well as market blends of olive cultivars typical of each geographical origin. Briefly, data correspond to SPME-GC-MS scan intensities of the specific sesquiterpene hydrocarbon (SH) ions (m/z 93, 107, 119, 135, 157, 159, 161, 189 and 204) obtained from the Total Ion Current (TIC) between the 18th to the 30th minute. Thus, 2467 scans were obtained for each m/z ion implying 22203 variables per sample. SPME-GC-MS data have been obtained by researchers in the OLEUM project 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 Program (2014–2020, grant agreement no. 635690). The information and views set out in this article are those of the author(s) and do not necessarily reflect the official opinion of the European Union. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein. The study was also supported by the Ministerio de Ciencia, Innovación y Universidades (MICINN) from Spain through the Juan de la Cierva and Ramon y Cajal programs (JCI-2012_13412 and RYC-2017-23601), and by the Ministerio de Educación, Cultura y Deporte (MECD) from Spain through the FPU pre-doctoral program (FPU16/01744). Dades de l'article publicat a la revista Food Chemistry, Volume 307, 1 March 2020, 125556. El podeu consultar a http://hdl.handle.net/2445/143358

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    Authors: Quintanilla-Casas, Beatriz; Bustamante Alonso, Julen; Guardiola Ibarz, Francesc; García-González, Diego Luis; +5 Authors

    SPME-GC-MS data have been obtained by researchers in the OLEUM project 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 Program (2014–2020, grant agreement no. 635690). The information and views set out in this article are those of the author(s) and do not necessarily reflect the official opinion of the European Union. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein. The study was also supported by the Ministerio de Ciencia, Innovación y Universidades (MICINN) from Spain through the Juan de la Cierva and Ramon y Cajal programs (JCI-2012_13412 and RYC-2017-23601), and by the Ministerio de Educación, Cultura y Deporte (MECD) from Spain through the FPU pre-doctoral program (FPU16/01744). Dataset containing SPME-GC-MS raw analytical data (total ion chromatograms, not aligned) obtained and used by Quintanilla-Casas et al. (LWT - Food Science and Technology 121: 108936). Data correspond to the volatile fingerprint of 174 authentic and traceable virgin olive oil samples previously graded by six official sensory panels (data from 2 outlier samples are not included) in the framework of OLEUM project (EC H2020 Programme 2014–2020). Briefly, data correspond to SPME-GC-MS scan intensities of the total ion chromatogram at each retention time from 5.5 to 61.96 min. These data were aligned and used under a fingerprinting approach by Quintanilla-Casas et al. to develop a classification model (PLS-DA approach) to verify the sensory quality of virgin olive oils, and it was suggested as an instrumental method to support sensory panels. Dades primàries de l'article publicat a la revista Food Science and Technology 121: 108936

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    Authors: Queirós, João; Alves, Paulo C.; Vicente, Joaquín; Gortázar, Christian; +1 Authors

    Tuberculosis (TB) affects a wide range of host species worldwide. Understanding host-pathogen co-evolution remains a global challenge owing to complex interactions among host genetic factors, pathogen traits and environmental conditions. We used an endemic wild boar population that had undergone a huge increase in Mycobacterium bovis infection prevalence, from 45% in 2002/06 to 83% in 2009/12, to understand the effects of host genetics on host TB outcomes and disease dynamics. Host genomic variation was characterized using a high-density single nucleotide polymorphism (SNP) array, while host TB phenotype was assessed using both gross pathology and mycobacterial culture. Two complementary genome-wide association (GWAS) analyses were conducted: (i) infected-uninfected; and (ii) 2002/06–2009/12. The SNPs with the highest allelic frequency differences between time-periods and TB outcomes were identified and validated in a large dataset. In addition, we quantified the expression levels of some of their closest genes. These analyses highlighted various SNPs (i.e. rs81465339, rs81394585, rs81423166) and some of the closest genes (i.e. LOC102164072, BDNF/NT-3, NTRK2, CDH8, IGSF21) as candidates for host genetic susceptibility. In addition to TB-driven selection, our findings outline the putative role of demographic events in shaping genomic variation in natural populations and how population crashes and drift may impact host genetic susceptibility to TB over time. Raw_SNP_data Structure_SNP_data 64_ind_29504_SNPs Dataset used for calculating genome diversity parameters 44_ind_29504_SNPs Dataset applied in the genome-wide association studies 76_ind_8_SNPs Dataset used in the validation of genome-wide association studies GWAS_TB_standard_44 GWAS summary statistics for infected vs infected MTC individuals GWAS_TB_stratified_44 GWAS summary statistics for stratified analysis of infected vs. uninfected MTC individuals GWAS_TB_standard_76 GWAS summary statistics for the validation test between infected vs. uninfected MTC individuals GWAS_TB_stratified_76 GWAS summary statistics for the validation test, stratified analysis, between infected vs. uninfected MTC individuals GWAS_Season_standard_44 GWAS summary statistics of 2002/06 vs. 2009/12 seasons GWAS_Season_stratified_44 GWAS summary statistics of stratified analysis for 2002/06 vs. 2009/12 seasons. GWAS_Season_standard_76 GWAS summary statistics for the validation test between 2002/06 vs. 2009/12 seasons. GWAS_Season_stratified_76 GWAS summary statistics for the validation test, stratified analysis, between 2002/06 vs. 2009/12 seasons. Peer reviewed

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    DRYAD; NARCIS
    Dataset . 2018
    License: CC 0
    Data sources: Datacite; NARCIS
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    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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    ZENODO
    Dataset . 2018
    License: CC 0
    Data sources: ZENODO
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      DRYAD; NARCIS
      Dataset . 2018
      License: CC 0
      Data sources: Datacite; NARCIS
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      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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      ZENODO
      Dataset . 2018
      License: CC 0
      Data sources: ZENODO
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    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: 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: 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
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    Authors: Díaz-Delgado, Ricardo; Afán, Isabel; Aragonés, David; García, Diego; +1 Authors

    Time Series of flooded areas derived from Landsat TM, ETM+ & OLI in the Path 202 Row 34 (Doñana). Also, these products and its metadata are freely available to consult or downloaded in the LAST-EBD Cartography Server: http://mercurio.ebd.csic.es/imgs/ Methodology is described in this paper: Remote Sensing 8(9):775 · September 2016. DOI: 10.3390/rs8090775 European Commission: ECOPOTENTIAL - ECOPOTENTIAL: IMPROVING FUTURE ECOSYSTEM BENEFITS THROUGH EARTH OBSERVATIONS (641762) Peer reviewed

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    ZENODO
    Dataset . 2019
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    ZENODO
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      ZENODO
      Dataset . 2019
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