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  • 2020-2024
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  • Aurora Universities Network
  • European Marine Science

  • 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: Jong, Dirk; Bröder, Lisa; Tesi, Tommaso; Tanski, George; +6 Authors

    This study aims to give insight into the processes affecting permafrost organic carbon (OC) during transport from its source to its sink, through a study on three sediment fractions along a land-to-ocean transect. Material was followed from thawing permafrost, through a dynamic 'disturbed zone' and the nearshore zone, to an enclosed basin offshore Herschel Island - Qikiqtaruk, to assess sorting and degradation processes on specific fractions of sediment OC. Sediment, soil and permafrost samples were taken along a transect from the source (undisturbed active layer and permafrost), via two transitional zones (a terrestrial disturbed zone, i.e. the 'scar zone' of the RTS, and the marine nearshore zone up to a water depth of 5 m), to sink (basin sediment, water depth >20 m) at the coast of Herschel Island – Qikiqtaruk and the (semi-enclosed) Herschel Basin in Yukon, Canada, just west of the Mackenzie River delta. Samples were taken in May 2016 and July 2017. Sample material was fractionated with an aqueous (MilliQ) solution of sodium polytungstate (SPT; Na6[H2W12O40]), with a density of 1.8 g cm³, followed by wet-sieving over a 63 µm mesh, thus separating loose OC from mineral-associated OC. Each fraction was analysed for element content (TOC, TN), carbon isotopes (δ¹³C, Δ¹⁴C), molecular biomarkers (n-alkanes, n-alkanoic acids, lignin phenols, cutin acids), and mineral surface area. The relative abundance of specific biomarkers can also be used as indicator for degradation of organic carbon. Furthermore, the OC 'loading' (concentration of OC normalised to mineral surface area; in mgOC/m²) and terrestrial biomarker loading (µgOC/m²) can be used to assess loss of (permafrost) OC from mineral particles. The combination of these methods allows us to disentangle sorting processes from degradation of OC along the land-to-ocean continuum, and provides a detailed insight into the fate of thawed and eroded permafrost OC.

    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/ PANGAEA - Data Publi...arrow_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/
    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/
    PANGAEA
    Dataset . 2024
    Data sources: B2FIND
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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/ PANGAEA - Data Publi...arrow_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/
      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/
      PANGAEA
      Dataset . 2024
      Data sources: B2FIND
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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: de Bar, Marijke W; Weiss, Gabriella M; Yildiz, Caglar; Rampen, Sebastiaan W; +13 Authors

    #0 = coel.; *0 = not available.Note 1: If there was no salinity data (WOA13) available for a certain core-top location, and there was also no LDI or C32 1,15-diol fractional abundance value for this core-top, then we have not adopted data from nearby grids.Note 2: If there was no SST data (WOA13) available for a certain core-top location, and there was also no LDI value for this core-top, then we have not adopted data from nearby grids.Note 3: If there was no seasonal SST or nutrient data available for a certain core-top location from WOA13, and this core-top was not included in the final LDI calibration, then we have not adopted data from nearby grids.Note 4: The fractional abundances of a few core-tops from the Iberian margin do not sum up to exactly 1, this is because in the original paper (de Bar et al., 2016) the core-tops were analyzed two to three times, and the fractional abundances were averaged.Note 4: The fractional abundances of a few core-tops from the "Rampen et al., 2012" reference do not sum up to exactly 1, because the original data was not available, and thus the reported data from Rampen et al. (2012) was used which are rounded to two decimal places.Note 5: Long chain diols marked as "n.a." or "coel." (=co-elutes) were treated as zero for calculating the final fractional abundances. In case one of the LDI or Diol Index diols was marked as "n.a." or "coel.", the LDI or Diol Index was not calculated.Note 6: For certain samples fractional abundances are given but not the LDI (mainly in Arctic and Antarctic), as the relative LDI diol abundances were considered too low for index calculation.Note that the number of decimals shown here is different from the original dataset, i.e. the dataset in the supplements of the article.

    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/ PANGAEA - Data Publi...arrow_drop_down
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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/
    PANGAEA
    Dataset . 2024
    Data sources: B2FIND
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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/ PANGAEA - Data Publi...arrow_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/
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      PANGAEA
      Dataset . 2024
      Data sources: B2FIND
  • 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: Pérez-Jorge, Sergi; Oliveira, Cláudia; Prieto, Rui; Cascão, Irma; +2 Authors

    The development of sophisticated multi-sensor tags incorporating high-resolution movement sensors and hydrophones has enabled unprecedented views of the 3D fine-scale movement behaviour of cetaceans, especially for those species that use sound to forage. However, these tags are expensive, making them inaccessible to most researchers. Time-Depth Recorders (TDRs), which have been widely used to study diving and foraging behaviour of marine mammals, offer a more affordable alternative. Unfortunately, data collected by TDRs are bi-dimensional (time and depth only), so quantifying foraging effort from those data is challenging.Pérez-Jorge et al. (2023) developed a predictive model of prey capture attempts (PCAs) for sperm whales from low-resolution time-depth data. To develop this model, high-resolution movement and acoustic data from 12 sperm whales instrumented with digital acoustic recording tags (Dtags; Johnson et al., 2003; Oliveira et al., 2022) between 2017 and 2019 in the Azores archipelago, Portugal. This data was used to extract time-depth values at a sampling frequency of 1 second (typical sampling rate of low-resolution time-depth data) and detect buzzes, considered to represent PCAs. Based on the extracted time-depth values, a suite of dive metrics (ie., average depth, variance of depth) were obtained for different segment durations (30 seconds, 60 s, 180 s and 300 s). The present dataset includes the extracted dive metrics for the four segment durations selected on the final model of the study (Pérez-Jorge et al., 2023).Data provided for each record include the event number, individual identification, dive identification, date of sampling, latitude, longitude, dive phase, segment duration, number of buzzes, average of water depth per segment, variance of water depth per segment and variance of velocity. Additional funding: Marine mammal and Ecosystem: anthropogenic Threat Assessment (META), FA_06_2017_017, Portuguese Republic through Fundo Azul

    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/ PANGAEAarrow_drop_down
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    PANGAEA
    Dataset . 2024
    Data sources: B2FIND
    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/
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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/ PANGAEAarrow_drop_down
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      PANGAEA
      Dataset . 2024
      Data sources: B2FIND
      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/
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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: Moran, Peter A.; Bosse, Mirte; Marien, Janine; Halfwerk, Wouter;

    Urbanisation is rapidly altering ecosystems, leading to profound biodiversity loss. To mitigate these effects, we need a better understanding of how urbanisation impacts dispersal and reproduction. Two contrasting population demographic models have been proposed that predict that urbanisation either promotes (facilitation model) or constrains (fragmentation model) gene flow and genetic diversity. Which of these models prevails likely depends on the strength of selection on specific phenotypic traits that influence dispersal, survival, or reproduction. Here, we a priori examined the genomic impact of urbanisation on the Neotropical túngara frog (Engystomops pustulosus), a species known to adapt its reproductive traits to urban selective pressures. Using whole-genome resequencing for multiple urban and forest populations we examined genomic diversity, population connectivity and demographic history. Contrary to both the fragmentation and facilitation models, urban populations did not exhibit substantial changes in genomic diversity or differentiation compared to forest populations, and genomic variation was best explained by geographic distance rather than environmental factors. Adopting an a posteriori approach, we additionally found both urban and forest populations to have undergone population declines. The timing of these declines appears to coincide with extensive human activity around the Panama Canal during the last few centuries rather than recent urbanisation. Our study highlights the long-lasting legacy of past anthropogenic disturbances in the genome and the importance of considering the historical context in urban evolution studies as anthropogenic effects may be extensive and impact non-urban areas on both recent and older timescales.  # Genomic footprints of (pre) colonialism: population declines in urban and forest túngara frogs coincident with historical human activity ## Description of the data and file structure The metadata file encompasses sampling location, corresponding population code (pop), and environmental data for forest (F) and urban (U) sites. The collection of light (in Lux), noise (in dB SPL, fast, max, A-weighted) and canopy cover data (percentage canopy cover estimated from pictures) was previously described and published in Halfwerk et al., 2019. The level of urbanisation (Urban_score) was calculated based on the type of landscape-cover for each sampling location using ‘Urbanisation Score’ software (Lipovits et al., 2015; Seress et al., 2014). This program accesses satellite images via GoogleMaps and applies a semi-automated approach to quantify the relative abundance of vegetation and impervious surfaces within a 1 km2 area around each sampling location. These values were then combined using principal component analysis (PCA) and an urbanisation score retained (PC1) for each location. ## Sharing/Access information Related data: Whole genome resequencing data used in this study is available from the European Nucleotide Archive (ENA) (PRJEB60348). ## Code/Software Main bash scripts for running software and R code used for analyses. Additional custom scripts are available from the corresponding authors upon request. Full Methods description provided in manuscript: Moran et al., 2023 Genomic data: Whole genome resequencing data used in this study is available from the European Nucleotide Archive (ENA) (PRJEB60348). Environmental data: The collection of light (in Lux), noise (in dB SPL, fast, max, A-weighted) and canopy cover data (percentage canopy cover estimated from pictures) data was previously described and published in Halfwerk et al., 2019. The level of urbanisation (Urban_score) was calculated based on the type of landscape-cover for each sampling location using ‘Urbanisation Score’ software (Lipovits et al., 2015; Seress et al., 2014). This program accesses satellite images via GoogleMaps and applies a semi-automated approach to quantify the relative abundance of vegetation and impervious surfaces within a 1 km2 area around each sampling location. These values were then combined using principal component analysis (PCA) and an urbanisation score retained (PC1) for each location.

    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/ DRYAD; ZENODOarrow_drop_down
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    DRYAD; ZENODO
    Dataset . 2023
    License: CC 0
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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/ DRYAD; ZENODOarrow_drop_down
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      DRYAD; ZENODO
      Dataset . 2023
      License: CC 0
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    Authors: Hauck, Judith; Landschützer, Peter; Mayot, Nicolas; Jersild, Annika;

    Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. Note: These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2023 (https://doi.org/10.5194/essd-15-5301-2023), are available in the Global Carbon Budget 2023 spreadsheet. The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2023 paper (https://doi.org/10.5194/essd-15-5301-2023), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2023 paper, section 2.5.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes. What is in the files? (1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, southfgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitudesfco2: Surface ocean fCO2, dimensions: time, latitude, longitudearea: Area per pixel, dimensions: latitude, longitudearea_reg: Total surface ocean area covered by native grid, for global, north, tropics, south (2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitudesfco2: Surface ocean fCO2, dimensions: time, latitude, longitudearea: Area per pixel, dimensions: latitude, longitude(3) One file 'GCB-2023_OceanModel_RegionalBreakdown_1959-2022.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual. Fair data use statement:The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.Citation: Please cite the Global Carbon Budget 2023 (Friedlingstein et al., 2023, ESSD, https://doi.org/10.5194/essd-15-5301-2023) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2023 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).Acknowledgement: Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."Co-authorship: An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication. Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make additional 3D output from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Karstensen, Johannes; Krahmann, Gerd;

    Seabird 911plus systems equipped with dual temperature-conductivity-oxygen sensors were employed. All systems had a 24-bottle water sampling rosette with 10 l Niskin bottles. Water sampling, processing, and calibration followed GO-SHIP recommendations (Swift, 2010; McTaggart et al., 2010; Uchida et al., 2010) and included the recommended steps Data Conversion, Sensor Time-Alignment, Creation of Bottle Files, Outlier Removal, Pressure Sensor Filtering, Conductivity Cell Thermal Mass Correction, Ship Roll Correction and Deck Offset Correction by Loop Editing, as well as Derivation of Calculated Properties. After these steps, conductivity and oxygen readings were calibrated against values determined with salinometry and Winkler titration , respectively. Finally, the downcast data was averaged over 1 dbar wide intervals. An independent upcast calibration was used to obtain calibrated CTDO values coincident with the discrete water samples. PO-GLOBAL-SVN = 689 used on 10-Jun-2020 15:15:01Matlab = 9.4.0.813654 (R2018a)Release = 3nc_uncertainty_p = 2.000000nc_uncertainty_t = 0.002000nc_uncertainty_s = 0.003000nc_uncertainty_o = 1.000000

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    PANGAEA
    Dataset . 2023
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2023
      Data sources: B2FIND
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    Authors: ISONET Project Members; Schleser, Gerhard Hans; Andreu-Hayles, Laia; Bednarz, Zdzislaw; +31 Authors

    The ISONET project has been striving to improve greatly our understanding of European climate systems providing independent quantitative data for model verification and policy making. A network of 24 sites provides dendrochronological coverage from Iberia to Fennoscandia, Caledonia and the Tyrol. The stable isotope (C, H, O) ratios of these annually resolved time series shall be analysed within this project, to reconstruct past climate regimes (temperature, relative humidity and precipitation characteristics) for the last 400 years. Climate variability shall be addressed on three timescales; decade-century (source water/air mass dominance); inter-annual (quantifying baseline variability, extreme events and recent trends); and intra-annual (high resolution exploration of seasonality signals within tree-rings). ISONET goes far beyond existing tree-ring analyses in its spatial based investigation and interpretation (see also https://cordis.europa.eu/project/id/EVK2-CT-2002-00147). 24 European annually resolved stable isotope chronologies have been constructed from tree ring cellulose for the last 400 years (1600CE – 2003CE) for carbon and oxygen and for the last 100 years for hydrogen. Data was produced within the ISONET project (400 Years of Annual Reconstructions of European Climate Variability Using a Highly Resolved Isotopic Network,) to initiate an extensive spatiotemporal tree-ring stable isotope network across Europe funded as part of the fifth EC Framework Programme “Energy, Environment and Sustainable Development”. This data set comprises the ISONET δ13C records. Wood increment cores of 15 or more Pinus sylvestris, Quercus robur/petraea or Cedrus atlantica tree individuals were taken. Dendro-dated tree-ring material of 4-5 individuals per site was dissected and pooled year by year. After cellulose extraction and homogenization, 18O/16O-ratios of annually resolved samples were determined by Isotope Ratio Mass Spectrometry (IRMS). Time series of 13C/12C are given as d-values versus PDB. Details can be found in the downloadable “data description” file.

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    GFZ Data Services
    Dataset . 2023
    Data sources: B2FIND
    GFZ Data Services
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      GFZ Data Services
      Dataset . 2023
      Data sources: B2FIND
      GFZ Data Services
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Droste, Elise Sayana; Bakker, Dorothee C E; Hoppema, Mario; Ossebaar, Sharyn; +4 Authors

    Discrete seawater samples for the determination of dissolved inorganic carbon (DIC) and total alkalinity (TA) were collected from CTD stations during RV POLARSTERN expedition PS117, between 15 December 2018 and 7 February 2019. Seawater samples were collected from stations that used the AWI-operated CTD, as well as the Ultra-Clean-CTD, operated by NIOZ. DIC and TA were measured using coulometric titration and potentiometric titration, respectively, on a VINDTA 3C system. Nutrients were measured with UV-Vis spectrophotometry and a continuous gas-segmented flow auto-analyser. Data for station 41 have previously been published on Pangaea and are excluded from this dataset (https://doi.org/10.1594/PANGAEA.946363). Bottle data (including nutrients) from the AWI CTD and Ultra-Clean-CTD stations have already been published and are stored on Pangaea under https://doi.org/10.1594/PANGAEA.910673 and https://doi.org/10.1594/PANGAEA.940209, respectively. Data quality flags follow the WOCE quality code definitions for water sample measurements. Details on sample collection and analysis methods for DIC and TA can be found in Droste et al. (2022).

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    PANGAEA
    Dataset . 2023
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2023
      Data sources: B2FIND
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    Authors: Haase, Peter; Bowler, Diana E.; Baker, Nathan J.; Bonada, Núria; +92 Authors

    Site level data

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    Research@WUR
    Dataset . 2023
    Data sources: Research@WUR
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    figshare
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    figshare
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      Research@WUR
      Dataset . 2023
      Data sources: Research@WUR
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      figshare
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Mayot, N.; Le Quéré, C.; Rödenbeck, C.; Bernardello, R.; +18 Authors

    The Southern Ocean is a major sink of atmospheric CO2, but the nature and magnitude of its variability remains uncertain and debated. Estimates based on observations suggest substantial variability which is not reproduced by process-based ocean models, with increasingly divergent estimates over the past decade. We examine potential constraints on the nature and magnitude of climate-driven variability of the Southern Ocean CO2 sink from observation-based air–sea O2 fluxes. On interannual timescales, the variability in the air–sea fluxes of CO2 and O2 estimated from observations is consistent across the two species and positively correlated with the variability simulated by ocean models. Our analysis suggests that variations in ocean ventilation related to the Southern Annular Mode are responsible for this interannual variability. On decadal timescales, the existence of significant variability in the air–sea CO2 flux estimated from observations also tends to be supported by observation-based estimates of O2 flux variability. However, the large decadal variability in air–sea CO2 flux is absent from ocean models. Our analysis suggests that issues in representing the balance between the thermal and non-thermal components of the CO2 sink and/or insufficient variability in mode water formation might contribute to the lack of decadal variability in the current generation of ocean models.This article is part of the discussion meeting issue 'Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities'.

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    Collection . 2023
    License: CC BY
    Data sources: Datacite
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    Collection . 2023
    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: Jong, Dirk; Bröder, Lisa; Tesi, Tommaso; Tanski, George; +6 Authors

    This study aims to give insight into the processes affecting permafrost organic carbon (OC) during transport from its source to its sink, through a study on three sediment fractions along a land-to-ocean transect. Material was followed from thawing permafrost, through a dynamic 'disturbed zone' and the nearshore zone, to an enclosed basin offshore Herschel Island - Qikiqtaruk, to assess sorting and degradation processes on specific fractions of sediment OC. Sediment, soil and permafrost samples were taken along a transect from the source (undisturbed active layer and permafrost), via two transitional zones (a terrestrial disturbed zone, i.e. the 'scar zone' of the RTS, and the marine nearshore zone up to a water depth of 5 m), to sink (basin sediment, water depth >20 m) at the coast of Herschel Island – Qikiqtaruk and the (semi-enclosed) Herschel Basin in Yukon, Canada, just west of the Mackenzie River delta. Samples were taken in May 2016 and July 2017. Sample material was fractionated with an aqueous (MilliQ) solution of sodium polytungstate (SPT; Na6[H2W12O40]), with a density of 1.8 g cm³, followed by wet-sieving over a 63 µm mesh, thus separating loose OC from mineral-associated OC. Each fraction was analysed for element content (TOC, TN), carbon isotopes (δ¹³C, Δ¹⁴C), molecular biomarkers (n-alkanes, n-alkanoic acids, lignin phenols, cutin acids), and mineral surface area. The relative abundance of specific biomarkers can also be used as indicator for degradation of organic carbon. Furthermore, the OC 'loading' (concentration of OC normalised to mineral surface area; in mgOC/m²) and terrestrial biomarker loading (µgOC/m²) can be used to assess loss of (permafrost) OC from mineral particles. The combination of these methods allows us to disentangle sorting processes from degradation of OC along the land-to-ocean continuum, and provides a detailed insight into the fate of thawed and eroded permafrost OC.

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    PANGAEA
    Dataset . 2024
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2024
      Data sources: B2FIND
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    Authors: de Bar, Marijke W; Weiss, Gabriella M; Yildiz, Caglar; Rampen, Sebastiaan W; +13 Authors

    #0 = coel.; *0 = not available.Note 1: If there was no salinity data (WOA13) available for a certain core-top location, and there was also no LDI or C32 1,15-diol fractional abundance value for this core-top, then we have not adopted data from nearby grids.Note 2: If there was no SST data (WOA13) available for a certain core-top location, and there was also no LDI value for this core-top, then we have not adopted data from nearby grids.Note 3: If there was no seasonal SST or nutrient data available for a certain core-top location from WOA13, and this core-top was not included in the final LDI calibration, then we have not adopted data from nearby grids.Note 4: The fractional abundances of a few core-tops from the Iberian margin do not sum up to exactly 1, this is because in the original paper (de Bar et al., 2016) the core-tops were analyzed two to three times, and the fractional abundances were averaged.Note 4: The fractional abundances of a few core-tops from the "Rampen et al., 2012" reference do not sum up to exactly 1, because the original data was not available, and thus the reported data from Rampen et al. (2012) was used which are rounded to two decimal places.Note 5: Long chain diols marked as "n.a." or "coel." (=co-elutes) were treated as zero for calculating the final fractional abundances. In case one of the LDI or Diol Index diols was marked as "n.a." or "coel.", the LDI or Diol Index was not calculated.Note 6: For certain samples fractional abundances are given but not the LDI (mainly in Arctic and Antarctic), as the relative LDI diol abundances were considered too low for index calculation.Note that the number of decimals shown here is different from the original dataset, i.e. the dataset in the supplements of the article.

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    PANGAEA
    Dataset . 2024
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2024
      Data sources: B2FIND
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    Authors: Pérez-Jorge, Sergi; Oliveira, Cláudia; Prieto, Rui; Cascão, Irma; +2 Authors

    The development of sophisticated multi-sensor tags incorporating high-resolution movement sensors and hydrophones has enabled unprecedented views of the 3D fine-scale movement behaviour of cetaceans, especially for those species that use sound to forage. However, these tags are expensive, making them inaccessible to most researchers. Time-Depth Recorders (TDRs), which have been widely used to study diving and foraging behaviour of marine mammals, offer a more affordable alternative. Unfortunately, data collected by TDRs are bi-dimensional (time and depth only), so quantifying foraging effort from those data is challenging.Pérez-Jorge et al. (2023) developed a predictive model of prey capture attempts (PCAs) for sperm whales from low-resolution time-depth data. To develop this model, high-resolution movement and acoustic data from 12 sperm whales instrumented with digital acoustic recording tags (Dtags; Johnson et al., 2003; Oliveira et al., 2022) between 2017 and 2019 in the Azores archipelago, Portugal. This data was used to extract time-depth values at a sampling frequency of 1 second (typical sampling rate of low-resolution time-depth data) and detect buzzes, considered to represent PCAs. Based on the extracted time-depth values, a suite of dive metrics (ie., average depth, variance of depth) were obtained for different segment durations (30 seconds, 60 s, 180 s and 300 s). The present dataset includes the extracted dive metrics for the four segment durations selected on the final model of the study (Pérez-Jorge et al., 2023).Data provided for each record include the event number, individual identification, dive identification, date of sampling, latitude, longitude, dive phase, segment duration, number of buzzes, average of water depth per segment, variance of water depth per segment and variance of velocity. Additional funding: Marine mammal and Ecosystem: anthropogenic Threat Assessment (META), FA_06_2017_017, Portuguese Republic through Fundo Azul

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    PANGAEA
    Dataset . 2024
    Data sources: B2FIND
    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/
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      PANGAEA
      Dataset . 2024
      Data sources: B2FIND
      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/
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    Authors: Moran, Peter A.; Bosse, Mirte; Marien, Janine; Halfwerk, Wouter;

    Urbanisation is rapidly altering ecosystems, leading to profound biodiversity loss. To mitigate these effects, we need a better understanding of how urbanisation impacts dispersal and reproduction. Two contrasting population demographic models have been proposed that predict that urbanisation either promotes (facilitation model) or constrains (fragmentation model) gene flow and genetic diversity. Which of these models prevails likely depends on the strength of selection on specific phenotypic traits that influence dispersal, survival, or reproduction. Here, we a priori examined the genomic impact of urbanisation on the Neotropical túngara frog (Engystomops pustulosus), a species known to adapt its reproductive traits to urban selective pressures. Using whole-genome resequencing for multiple urban and forest populations we examined genomic diversity, population connectivity and demographic history. Contrary to both the fragmentation and facilitation models, urban populations did not exhibit substantial changes in genomic diversity or differentiation compared to forest populations, and genomic variation was best explained by geographic distance rather than environmental factors. Adopting an a posteriori approach, we additionally found both urban and forest populations to have undergone population declines. The timing of these declines appears to coincide with extensive human activity around the Panama Canal during the last few centuries rather than recent urbanisation. Our study highlights the long-lasting legacy of past anthropogenic disturbances in the genome and the importance of considering the historical context in urban evolution studies as anthropogenic effects may be extensive and impact non-urban areas on both recent and older timescales.  # Genomic footprints of (pre) colonialism: population declines in urban and forest túngara frogs coincident with historical human activity ## Description of the data and file structure The metadata file encompasses sampling location, corresponding population code (pop), and environmental data for forest (F) and urban (U) sites. The collection of light (in Lux), noise (in dB SPL, fast, max, A-weighted) and canopy cover data (percentage canopy cover estimated from pictures) was previously described and published in Halfwerk et al., 2019. The level of urbanisation (Urban_score) was calculated based on the type of landscape-cover for each sampling location using ‘Urbanisation Score’ software (Lipovits et al., 2015; Seress et al., 2014). This program accesses satellite images via GoogleMaps and applies a semi-automated approach to quantify the relative abundance of vegetation and impervious surfaces within a 1 km2 area around each sampling location. These values were then combined using principal component analysis (PCA) and an urbanisation score retained (PC1) for each location. ## Sharing/Access information Related data: Whole genome resequencing data used in this study is available from the European Nucleotide Archive (ENA) (PRJEB60348). ## Code/Software Main bash scripts for running software and R code used for analyses. Additional custom scripts are available from the corresponding authors upon request. Full Methods description provided in manuscript: Moran et al., 2023 Genomic data: Whole genome resequencing data used in this study is available from the European Nucleotide Archive (ENA) (PRJEB60348). Environmental data: The collection of light (in Lux), noise (in dB SPL, fast, max, A-weighted) and canopy cover data (percentage canopy cover estimated from pictures) data was previously described and published in Halfwerk et al., 2019. The level of urbanisation (Urban_score) was calculated based on the type of landscape-cover for each sampling location using ‘Urbanisation Score’ software (Lipovits et al., 2015; Seress et al., 2014). This program accesses satellite images via GoogleMaps and applies a semi-automated approach to quantify the relative abundance of vegetation and impervious surfaces within a 1 km2 area around each sampling location. These values were then combined using principal component analysis (PCA) and an urbanisation score retained (PC1) for each location.

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    DRYAD; ZENODO
    Dataset . 2023
    License: CC 0
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      DRYAD; ZENODO
      Dataset . 2023
      License: CC 0