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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: Christine McKenna;

    This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article: McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. Geophysical Research Letters, 45, 945-954. https://doi.org/10.1002/2017GL076433 Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'. All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are: ua: zonal winds zg: geopotential height ts: surface temperature hfls, hfss, rlds, rlus: surface heatfluxes Flat, Fz, divF: Eliassen-Palm flux vectors and their divergence (only for months November-February) The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc. Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files. 

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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    Authors: Romero-Alvarez, Johana; Lupaşcu, Aurelia; Lowe, Douglas; Badia, Alba; +4 Authors

    Tropospheric ozone (O3) concentrations depend on a combination of hemispheric, regional, and local-scale processes. Estimates of how much O3 is produced locally vs. transported from further afield are essential in air quality management and regulatory policies. Here, a tagged-ozone mechanism within the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) is used to quantify the contributions to surface O3 in the UK from anthropogenic nitrogen oxide (NOx) emissions from inside and outside the UK during May–August 2015. The contribution of the different source regions to three regulatory O3 metrics is also examined. It is shown that model simulations predict the concentration and spatial distribution of surface O3 with a domain-wide mean bias of −3.7 ppbv. Anthropogenic NOx emissions from the UK and Europe account for 13 % and 16 %, respectively, of the monthly mean surface O3 in the UK, as the majority (71 %) of O3 originates from the hemispheric background. Hemispheric O3 contributes the most to concentrations in the north and the west of the UK with peaks in May, whereas European and UK contributions are most significant in the east, south-east, and London, i.e. the UK's most populated areas, intensifying towards June and July. Moreover, O3 from European sources is generally transported to the UK rather than produced in situ. It is demonstrated that more stringent emission controls over continental Europe, particularly in western Europe, would be necessary to improve the health-related metric MDA8 O3 above 50 and 60 ppbv. Emission controls over larger areas, such as the Northern Hemisphere, are instead required to lessen the impacts on ecosystems as quantified by the AOT40 metric.

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    Authors: Lehmann, Moritz K; Gurlin, Daniela; Pahlevan, Nima; Alikas, Krista; +73 Authors

    Projects include:* Estonian Ministry of Education and Research* Estonian Research Council* European Commission, Award: FP7, H2020, FP7-ENV-2007-1-226224* Federal Ministry for Economic Affairs and Energy, Germany, Award: LAKESAT 50EE1340* Federal Ministry of Education and Research Germany, Award: 03G0218A* Helmholtz Infrastructure Initiative, Award: FRAM* NASA ROSES, Award: 80HQTR19C0015, 80NSSC 21K0499, 80NSSC22K1389* New Zealand Ministry for Business, Innovation & Employment, Award: UOWX1503, UOWX1802, KENTR1601* USGS Landsat Science Team Award, Award: 140G0118C0011* Vietnam National Foundation for Science and Technology Development (NAFOSTED), grant number 105.08-2019.329 The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement, chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 53 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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    Authors: Piredda, Roberta; Sarno, Diana; De Luca, Daniele; Kooistra, Wiebe H.C.F.;

    Marine planktonic diatom species can exhibit contrasting distribution patterns, from endemic to cosmopolitan. Endemicity is counter-intuitive for planktonic species given their potentially large population sizes and ample migration opportunities by means of ocean currents. Here, we analyse the biogeography of six species of the diatom genus Bacteriastrum with apparently contrasting distribution patterns. Occurrence data obtained from metabarcode reads in samples from Ocean Sampling Day (OSD) sites and from Tara Oceans stations as well as from their observational records included in biodiversity data repositories of GBIF and OBIS were plotted in occurrence maps. According to metabarcoding data, the six species examined here occur all over the temperate and tropical parts of the oceans. Observational records corroborate this finding for B. elegans, B. furcatum, B. hyalinum and B. mediterraneum. Instead, atypical colony former B. jadranum is encountered at just a few distant sites, and solitary B. parallelum only in the Gulf of Naples. The metabarcoding data reveal that the latter two species are far more widely distributed than their actual sightings according to GBIF and OBIS, and that B. hyalinum also occurs in the Arctic. The most likely reasons for the discrepancies among the different data sources are the relatively recent description of B. jadranum and B. parallelum, their diminutive and inconspicuous habitus, and their possible misidentification in routine plankton counting. HIGHLIGHTS • Metabarcoding data of Tara Oceans and OSD discern Bacteriastrum species.• Pan-oceanic distribution of Bacteriastrum species according to global metabarcoding data.• Distribution patterns of phytoplankton species can be inferred from global metabarcode datasets.

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    figshare
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    figshare
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      figshare
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Guerreiro, Catarina V; Baumann, Karl-Heinz; Brummer, Geert-Jan A; Valente, André; +4 Authors

    Data Pangaea Guerreiro et al. 2021_M1U = Coccolith-CaCO₃ fluxes (mg/m²/d), Coccolith-CaCO₃ %, and Coccolith-% contribution to CaCO₃ flux of Calcidiscus leptoporus, Emilinia huxleyi, Florisphaera profunda, Gladiolithus flabellatus, Gephyrocapsa ericsonii, Gephyrocapsa muellerae, Gephyrocapsa oceanica, Helicosphaera spp., Umbellosphaera spp. Rhabdosphaera spp., Umbilicosphaera spp., Reticulofenestra sessilis, other taxa, total coccolith-CaCO₃. Auxiliary PIC (Particulate Inorganic Carbon) data from NASA's Ocean Biology Processing Group (https://oceancolor.gsfc.nasa.gov) are also provided.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Mohammed Taha, Hiba; Aalizadeh, Reza; Alygizakis, Nikiforos; Antignac, Jean-Philippe; +93 Authors

    Additional file 3: Summary of Zenodo view and download statistics, plus citations (CSV format) as of 28 April 2022 [235].

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    figshare
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    figshare
    Dataset . 2022
    License: CC BY
    Data sources: Datacite; Sygma
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    Authors: Cao, Mengli; Hefter, Jens; Tiedemann, Ralf; Lembke-Jene, Lester; +2 Authors

    We present TEX86 (Tetraether index of tetraethers consisting of 86 carbon atoms) data from sediment cores SO202/1_18-3 and SO202/1_18-6 in the Bering Sea shelf. TEX86 is a sea surface temperature (SST) proxy based on the distribution of archaeal isoprenoid glycerol dialkyl glycerol tetraethers (GDGTs), and reconstructs SST development during the latest two glacial-interglacial cycles. The branched isoprenoid tetraether (BIT) index (Hopmans et al., 2004) is an indicator of the relative contribution of soil-derived GDGT to marine GDGT. Crucially, a global core top calibration of marine sediments reveals a linear correlation between sedimentary TEX86 and in situ annual mean SST where terrestrial inputs are low (Kim et al., 2008). If BIT values are above the critical value of 0.3 (Weijers et al., 2006), where terrigenous GDGTs potentially bias TEX86-derived SST reconstruction. The regional calibration of SST and TEX86 is based on Seki et al. (2014). TEX86= log (GDGT-2/(GDGT-1+GDGT-2+GDGT-3)) SST/℃ = 27.2 × TEX86+ 21.8

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Duncan, Anthony; Barry, Kerrie; Daum, Chris; Eloe-Fadrosh, Emiley; +10 Authors

    Additional file 2. Average Nucleotide Identity plots and data in tab-separated format for related groups of MAGs and reference genomes.

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    figshare
    Dataset . 2022
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    Data sources: Datacite
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    figshare
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      Dataset . 2022
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      figshare
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Jongejans, Loeka Laura; Liebner, Susanne; Knoblauch, Christian; Mangelsdorf, Kai; +1 Authors

    This dataset describes two 17 m long sediment cores taken from beneath two thermokarst lakes in the Yukechi Alas, Central Yakutia, Russia. The first core was taken from below an Alas thermokarst lake (YU-L7; 61.76397°N, 130.46442°E) and the second core below and Yedoma lake (YU-L15; 61.76086°N, 130.47466°E). The dataset presents biogeochemical and biomarker parameters of sediment cores YU-L7 and YU-L15. Biogeochemical analyses include total carbon (TC) content, total organic carbon (TOC) content, total nitrogen (TN) content. Biomarker parameters include the n-alkane concentration, average chain length (ACL), carbon preference index (CPI), brGDGT concentration, archaeol concentration and the isoGDGT-0 concentration. The n-alkanes were measured in the aliphatic fraction by gas chromatography-mass spectromety using a Trace GC Ultra coupled to a DSQ MS. The branched and isoprenoid glycerol dialkyl glycerol tetraethers, as well as the dialkyl glycerol diether lipid (archaeol) were measured in the NSO fraction using a Shimadzu LC-10AD high-performance liquid chromatograph coupled to a Finnigan TSQ 7000 mass spectrometer via an atmospheric pressure chemical ionization interface. The pH soil is the sediment pH which was assessed by adding 6.12 mL of 0.01 M CaCl~2~ to ~2.5 g dried sediment and measuring with a Multilab 540 (WTW) at 20°C.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Guerreiro, Catarina V; Baumann, Karl-Heinz; Brummer, Geert-Jan A; Valente, André; +4 Authors

    Data refer to export fluxes of carbonate produced by calcifying phytoplankton (coccolithophores), and coccolith-CaCO₃ percent contribution to total carbonate flux across the tropical North Atlantic, from upwelling affected NW Africa, via three ocean sites along 12°N to the Caribbean. Sampling was undertaken by means of a spatial array of four time-series sediment traps (i.e., CB at 21°N 20°W; M1U at 12°N 23°W; M2U at 14°N 37°W; M4U at 12°N 49°W; Guerreiro et al., 2021) collecting particle fluxes in two-week intervals, from October 2012 to February 2014, allowing to track temporal changes along the southern margin of the North Atlantic central gyre. Auxiliary PIC (Particulate Inorganic Carbon) data from NASA's Ocean Biology Processing Group (https://oceancolor.gsfc.nasa.gov) are also provided for the sediment sampling period at all four trap sites. Particle flux data (mg/m²/d) of CaCO₃, organic matter, particulate organic carbon (POC), biogenic silica (bSiO₂) and unspecified residual fraction are provided for sediment trap site CB.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      Dataset . 2022
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    Authors: Christine McKenna;

    This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article: McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. Geophysical Research Letters, 45, 945-954. https://doi.org/10.1002/2017GL076433 Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'. All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are: ua: zonal winds zg: geopotential height ts: surface temperature hfls, hfss, rlds, rlus: surface heatfluxes Flat, Fz, divF: Eliassen-Palm flux vectors and their divergence (only for months November-February) The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc. Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files. 

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    ZENODO
    Dataset . 2022
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    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      Dataset . 2022
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      ZENODO
      Dataset . 2022
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    Authors: Romero-Alvarez, Johana; Lupaşcu, Aurelia; Lowe, Douglas; Badia, Alba; +4 Authors

    Tropospheric ozone (O3) concentrations depend on a combination of hemispheric, regional, and local-scale processes. Estimates of how much O3 is produced locally vs. transported from further afield are essential in air quality management and regulatory policies. Here, a tagged-ozone mechanism within the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) is used to quantify the contributions to surface O3 in the UK from anthropogenic nitrogen oxide (NOx) emissions from inside and outside the UK during May–August 2015. The contribution of the different source regions to three regulatory O3 metrics is also examined. It is shown that model simulations predict the concentration and spatial distribution of surface O3 with a domain-wide mean bias of −3.7 ppbv. Anthropogenic NOx emissions from the UK and Europe account for 13 % and 16 %, respectively, of the monthly mean surface O3 in the UK, as the majority (71 %) of O3 originates from the hemispheric background. Hemispheric O3 contributes the most to concentrations in the north and the west of the UK with peaks in May, whereas European and UK contributions are most significant in the east, south-east, and London, i.e. the UK's most populated areas, intensifying towards June and July. Moreover, O3 from European sources is generally transported to the UK rather than produced in situ. It is demonstrated that more stringent emission controls over continental Europe, particularly in western Europe, would be necessary to improve the health-related metric MDA8 O3 above 50 and 60 ppbv. Emission controls over larger areas, such as the Northern Hemisphere, are instead required to lessen the impacts on ecosystems as quantified by the AOT40 metric.

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    Authors: Lehmann, Moritz K; Gurlin, Daniela; Pahlevan, Nima; Alikas, Krista; +73 Authors

    Projects include:* Estonian Ministry of Education and Research* Estonian Research Council* European Commission, Award: FP7, H2020, FP7-ENV-2007-1-226224* Federal Ministry for Economic Affairs and Energy, Germany, Award: LAKESAT 50EE1340* Federal Ministry of Education and Research Germany, Award: 03G0218A* Helmholtz Infrastructure Initiative, Award: FRAM* NASA ROSES, Award: 80HQTR19C0015, 80NSSC 21K0499, 80NSSC22K1389* New Zealand Ministry for Business, Innovation & Employment, Award: UOWX1503, UOWX1802, KENTR1601* USGS Landsat Science Team Award, Award: 140G0118C0011* Vietnam National Foundation for Science and Technology Development (NAFOSTED), grant number 105.08-2019.329 The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement, chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 53 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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    Authors: Piredda, Roberta; Sarno, Diana; De Luca, Daniele; Kooistra, Wiebe H.C.F.;

    Marine planktonic diatom species can exhibit contrasting distribution patterns, from endemic to cosmopolitan. Endemicity is counter-intuitive for planktonic species given their potentially large population sizes and ample migration opportunities by means of ocean currents. Here, we analyse the biogeography of six species of the diatom genus Bacteriastrum with apparently contrasting distribution patterns. Occurrence data obtained from metabarcode reads in samples from Ocean Sampling Day (OSD) sites and from Tara Oceans stations as well as from their observational records included in biodiversity data repositories of GBIF and OBIS were plotted in occurrence maps. According to metabarcoding data, the six species examined here occur all over the temperate and tropical parts of the oceans. Observational records corroborate this finding for B. elegans, B. furcatum, B. hyalinum and B. mediterraneum. Instead, atypical colony former B. jadranum is encountered at just a few distant sites, and solitary B. parallelum only in the Gulf of Naples. The metabarcoding data reveal that the latter two species are far more widely distributed than their actual sightings according to GBIF and OBIS, and that B. hyalinum also occurs in the Arctic. The most likely reasons for the discrepancies among the different data sources are the relatively recent description of B. jadranum and B. parallelum, their diminutive and inconspicuous habitus, and their possible misidentification in routine plankton counting. HIGHLIGHTS • Metabarcoding data of Tara Oceans and OSD discern Bacteriastrum species.• Pan-oceanic distribution of Bacteriastrum species according to global metabarcoding data.• Distribution patterns of phytoplankton species can be inferred from global metabarcode datasets.

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    figshare
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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