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  • European Marine Science
  • 2014-2023
  • Research data
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  • DK
  • IT
  • Aurora Universities Network

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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: El-Hokayem, Léonard; De Vita, Pantaleone; Usman, Muhammad; Link, Andreas; +1 Authors

    Groundwater dependent vegetation (GDV) is essential for maintaining ecosystem functions and services, providing critical habitat and sustaining human livelihoods. A novel multicriteria framework helps to identify areas where potential groundwater dependent vegetation (pGDV) occurs in the Mediterranean biome. Globally-available datasets targeting 1) groundwater vegetation interaction; 2) soil water holding capacity; 3) topographical landscape wetness potential; 4) land use land cover and 5) hydraulic conductivity of rocks are combined in a weighted, easy-to-use index, composed of eleven thematic layers. Input layers for the index calculation are available in the data collection: 1) pre-processed (rasterised and clipped to the Mediterranean) and 2) harmonised and reclassified. All input data was extracted globally. Either, directly from the respective studies or through the data catalogue in the Google Earth Engine. All datasets were acquired and processed in 2022 and 2023. Time series data for potential inflow dependency and Normalized Difference Vegetation Index (NDVI) were extracted for the period 2003-2021. Finally, the mean value was calculated over this period. All other data sets, however, mark a fixed point in time. Ground truth vegetation data was used to calculate layer weightings with a Random Forest. 10 m * 10 m vegetation plots were collected in July and August 2021 and 2022 in southern Italy (Campania region) inside the 'Cilento, Vallo di Diano and Alburni National Park'. 236 vegetation plots are available, containing general information on the vegetation (habitat, species number, stratification), mean indicator values, plant life forms, leaf anatomy as well as a calculated ecohydrological potential for the presence of GDV. The potential was calculated based on the coverage of phreatophyte species and the moisture value of non-phreatophyte species. The final pGDV maps including different weightings of the eleven thematic layers are compiled at a resolution of 500 m in WGS1984 (EPSG 4326). Finally, five pGDV classes (very low to very high potential) were derived and the share of high pGDV was calculated for level 8 HydroBASINS in the Mediterranean. Results support prioritisation of areas for essential regional high-resolution identification of GDV, to ensure sustainable groundwater management and in turn protect GDV as local biodiversity hotspots.

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    PANGAEA
    Dataset . 2023
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2023
      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: Rasmussen, Sune Olander; Svensson, Anders M; Vinther, Bo Møllesøe;

    Greenland Ice-Core Chronology 2005 (GICC05) annual layer depths for various Greenland ice cores. This is the high-resolution version (full, annual resolution) data file. Previously, 10- and 20-year resolution data files containing the time scale and resampled d18O data have been released for different time intervals together with the papers mentioned below. Ages are reported as years before A.D. 2000 / 2000 CE, abbreviated b2k.The file contains the location of the annual markers in the GICC05 time scale for each core's depth sections where data was available and sufficiently resolved to allow annual dating. Details are given in the papers listed below. The markers are placed in the winter and spring depending on the availability of data (e.g. using the winter d18O minimum, winter Sodium concentration maximum, spring dust/Calcium concentration maximum, or visual stratigraphy grey-scale peaks in the deepest parts). Across data gaps, markers are placed by interpolation or using other impurity species with different seasonality (e.g. using summer Ammonium or Nitrate peaks). Therefore, the criteria for where the annual markers are places vary between sections, and care should be taken when interpreting data on annual scale. The dating of the 0-7.9 ka b2k part is described in the paper Vinther et al., 2006The dating of the 7.9-14.7 ka b2k part is described in the paper Rasmussen et al., 2006The dating of the 14.7-41.8 ka b2k part is described in the paper Andersen et al., 2006The dating of the 41.8-60.0 ka b2k part is described in the paper Svensson et al., 2008When counting layers, uncertainty is introduced when an annual layer is backed up by evidence only in some of the data series, or when a certain well-resolved feature is suspected to contain more than one annual layer. The cases of ambiguity in the annual layer identification process have been marked using so-called uncertain layer markings. These uncertain layer markings were included in the time scale as ½ ± ½ years, with the ± ½ years forming the basis for quantifying the so-called maximum counting error. The concept of maximum counting error is further discussed in Rasmussen et al. (2006). In a standard deviation context, the maximum counting error can be regarded as 2 sigma as discussed in Andersen et al. (2006).In the Holocene, GS-1, and GI-2, the published time scale was derived from annual layer markings by manually determining which half of the uncertain layer markings to count as years, and which to skip. The maximum counting error was estimated from the number of uncertain layer markings as a constant relative uncertainty for each period with similar data availability and characteristics: 21-3,845 a b2k (0.25%), 3,846-6,905 a b2k (0.5%), 6,906-10,276 a b2k (2%), 10,277-11,703 a b2k (0.67%), 11,703-12,896 a b2k (3,3%), 12,896-14,075 a b2k (2.6%), 14,075-14,692 a b2k (2.7%) (see table 2 in Vinther et al, 2006, and table 3 in Rasmussen et al., 2006). From GS-2 and below (Andersen et al., 2006; Svensson et al., 2008) every 2nd uncertain layer was counted as a year and the maximum counting uncertainty increased by one year (giving rise to a variable relative counting error ranging from 4% in the warm interstadial periods to 7% in the cold stadials, and averaging 5.3%). In data set "Greenland NGRIP2 Ice-Core annual layer markings"(https://doi.pangaea.de/10.1594/PANGAEA.943194), the depths of the annual layer markings (including the uncertain ones) are provided with indication of which of these were counted as annual layers. This data set is only available below 10.2 ka. Above this, the locations of the discarded half of the uncertain layer markings have only been recorded on paper.The NGRIP1 core reaches down to a depth of 1372 m. The NGRIP2 core (drilled 20 meters away from the NGRIP1 core) reaches from surface to bedrock, but high-resolution measurements have only been carried out downwards from 1346 m. In the 26 m overlap zone, the cores are offset by 0.43 m on average, probably due to uncertainties in the logging procedure (see Schøtt Hvidberg et al., Ann. Glac. 35, 2002). Thus, the same features appear at larger depths in the NGRIP1 than in the NGRIP2 core. We recommend that NGRIP1 depths are used until 9820 b2k, and NGRIP2 depths are used below this.Note that the GICC05 time scale has later been revised. The first section of the new time scale, named GICC21, is described in the paper "A multi-ice-core, annual-layer-counted Greenland ice-core chronology for the last 3800 years: GICC21", Climate of the Past volume 18, p. 1125-1150, 2022, https://doi.org/10.5194/cp-18-1125-2022. Updated GICC21 annual-layer positions are released in the supplement to the paper. Annual markers forming the GICC05 time scale for NGRIP1, NGRIP2, GRIP, and DYE-3 where data was available and sufficiently resolved to allow annual dating. The markers are placed in the winter and spring depending on the availability of data (e.g. using the winter d18O minimum, winter Sodium concentration maximum, spring dust/Calcium concentration maximum, or visual stratigraphy grey-scale peaks in the deepest parts). Across data gaps, markers are placed by interpolation or using other impurity species with different seasonality (e.g. using summer Ammonium or Nitrate peaks). Therefore, the criteria for where the annual markers are places vary between sections, and care should be taken when interpreting data on annual scale. Ages are reported as years before A.D. 2000 / 2000 CE, abbreviated b2k. Depths (in meter) are true depths below the undisturbed surface the year when drilling started.

    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 . 2022
    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 . 2022
      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: Rasmussen, Sune Olander; Svensson, Anders M; Vinther, Bo Møllesøe;

    Greenland Ice-Core Chronology 2005 (GICC05) annual layer depths for various Greenland ice cores. This is the high-resolution version (full, annual resolution) data file. Previously, 10- and 20-year resolution data files containing the time scale and resampled d18O data have been released for different time intervals together with the papers mentioned below. Ages are reported as years before A.D. 2000 / 2000 CE, abbreviated b2k.The file contains the location of the annual markers in the GICC05 time scale for each core's depth sections where data was available and sufficiently resolved to allow annual dating. Details are given in the papers listed below. The markers are placed in the winter and spring depending on the availability of data (e.g. using the winter d18O minimum, winter Sodium concentration maximum, spring dust/Calcium concentration maximum, or visual stratigraphy grey-scale peaks in the deepest parts). Across data gaps, markers are placed by interpolation or using other impurity species with different seasonality (e.g. using summer Ammonium or Nitrate peaks). Therefore, the criteria for where the annual markers are places vary between sections, and care should be taken when interpreting data on annual scale. The dating of the 0-7.9 ka b2k part is described in the paper Vinther et al., 2006The dating of the 7.9-14.7 ka b2k part is described in the paper Rasmussen et al., 2006The dating of the 14.7-41.8 ka b2k part is described in the paper Andersen et al., 2006The dating of the 41.8-60.0 ka b2k part is described in the paper Svensson et al., 2008When counting layers, uncertainty is introduced when an annual layer is backed up by evidence only in some of the data series, or when a certain well-resolved feature is suspected to contain more than one annual layer. The cases of ambiguity in the annual layer identification process have been marked using so-called uncertain layer markings. These uncertain layer markings were included in the time scale as ½ ± ½ years, with the ± ½ years forming the basis for quantifying the so-called maximum counting error. The concept of maximum counting error is further discussed in Rasmussen et al. (2006). In a standard deviation context, the maximum counting error can be regarded as 2 sigma as discussed in Andersen et al. (2006).In the Holocene, GS-1, and GI-2, the published time scale was derived from annual layer markings by manually determining which half of the uncertain layer markings to count as years, and which to skip. The maximum counting error was estimated from the number of uncertain layer markings as a constant relative uncertainty for each period with similar data availability and characteristics: 21-3,845 a b2k (0.25%), 3,846-6,905 a b2k (0.5%), 6,906-10,276 a b2k (2%), 10,277-11,703 a b2k (0.67%), 11,703-12,896 a b2k (3,3%), 12,896-14,075 a b2k (2.6%), 14,075-14,692 a b2k (2.7%) (see table 2 in Vinther et al, 2006, and table 3 in Rasmussen et al., 2006). From GS-2 and below (Andersen et al., 2006; Svensson et al., 2008) every 2nd uncertain layer was counted as a year and the maximum counting uncertainty increased by one year (giving rise to a variable relative counting error ranging from 4% in the warm interstadial periods to 7% in the cold stadials, and averaging 5.3%). In data set "Greenland NGRIP2 Ice-Core annual layer markings"(https://doi.pangaea.de/10.1594/PANGAEA.943194), the depths of the annual layer markings (including the uncertain ones) are provided with indication of which of these were counted as annual layers. This data set is only available below 10.2 ka. Above this, the locations of the discarded half of the uncertain layer markings have only been recorded on paper.The NGRIP1 core reaches down to a depth of 1372 m. The NGRIP2 core (drilled 20 meters away from the NGRIP1 core) reaches from surface to bedrock, but high-resolution measurements have only been carried out downwards from 1346 m. In the 26 m overlap zone, the cores are offset by 0.43 m on average, probably due to uncertainties in the logging procedure (see Schøtt Hvidberg et al., Ann. Glac. 35, 2002). Thus, the same features appear at larger depths in the NGRIP1 than in the NGRIP2 core. We recommend that NGRIP1 depths are used until 9820 b2k, and NGRIP2 depths are used below this.Note that the GICC05 time scale has later been revised. The first section of the new time scale, named GICC21, is described in the paper "A multi-ice-core, annual-layer-counted Greenland ice-core chronology for the last 3800 years: GICC21", Climate of the Past volume 18, p. 1125-1150, 2022, https://doi.org/10.5194/cp-18-1125-2022. Updated GICC21 annual-layer positions are released in the supplement to the paper. Annual markers forming the GICC05 time scale for NGRIP1, NGRIP2, GRIP, and DYE-3 where data was available and sufficiently resolved to allow annual dating. The markers are placed in the winter and spring depending on the availability of data (e.g. using the winter d18O minimum, winter Sodium concentration maximum, spring dust/Calcium concentration maximum, or visual stratigraphy grey-scale peaks in the deepest parts). Across data gaps, markers are placed by interpolation or using other impurity species with different seasonality (e.g. using summer Ammonium or Nitrate peaks). Therefore, the criteria for where the annual markers are places vary between sections, and care should be taken when interpreting data on annual scale. Ages are reported as years before A.D. 2000 / 2000 CE, abbreviated b2k. Depths (in meter) are true depths below the undisturbed surface the year when drilling started.

    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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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
      Data sources: B2FIND
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    Authors: Skov, Christian;

    Fangstjournalen is a citizen science project for anglers, i.e. recreational fishers using reel and rod. The system collects data when anglers report information from their fishing trips including their catches of fish. When reporting a fishing trip anglers provide information about fishing location, hours fished, target fish species as well as information about catches i.e. species, length or weight, fate (released or harvested), and gear used. We also collect other species-specific data about fish catches, e.g parasites, tags, gender, maturity and much more. Anglers are encouraged to report blank trips, which allow calculations of Catch-Per-Unit-Effort estimates which we use to compare densities of fish between years and fishing sites. Data is being collected via an electronic platform including a browser version and a smartphone app (for android and iPhone). Anglers can also report a range of different observations that they make make during their fishing trip, e.g. presence of large marine mammals, tuna, invasive species and more. Additional entries for observations can be made in the backend of the system, e.g. as part of collaboration projects with other researchers who wish to engage anglers in their citizen science data collection.Upon registration participants are encouraged to fill out entries that support with information about demographics (postal code, gender, age) and angling characteristics (e.g. experience, preferred fishing types, importance of angling as a hobby). This information combined with GPS of fishing sites can provide information about travel patterns. See CSV file for more information about data that is being collected.Data is not shared directly due to content of personal information, but contact Christian Skov, ck@aqua.dtu.dkORCID 0000-0002-8547-6520. He is happy to engage in collaborative projects.See a popular introduction to the Citizen Science project Fangstjournalen here.https://doi.org/10.11581/DTU:00000094

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    DTU Data
    Dataset . 2021
    License: CC BY
    Data sources: DTU Data
    https://doi.org/10.11583/dtu.1...
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    https://doi.org/10.11583/dtu.1...
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      DTU Data
      Dataset . 2021
      License: CC BY
      Data sources: DTU Data
      https://doi.org/10.11583/dtu.1...
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      https://doi.org/10.11583/dtu.1...
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Jensen, Anders Dalhoff Bruhn; Stedmon, Colin;

    The data has been generated from a chemostat experiment performed in June 2019. The data contain bacterial (bacterial abundance, bacterial community composition) spectral (absorbance and fluorescence) and chemical data (nutrients, dissolved organic carbon, total dissolved nitrogen). The data has been used in the published article "Terrestrial dissolved organic matter mobilized from eroding permafrost controls microbial community composition and growth in Arctic coastal waters" by Bruhn et al. 2021."Explanation of filename (e.g. FLU1C01):1. First three letters (FLU, LAC, MOR) relates to the glacial deposit type.2. The following number refers to chemostat replicate within each deposit type (1,2,3,4)3. The letter C or M between the numbers refers to Culture or Medium (the two components of a classic chemostat setup)4. The last number refers to the day of sampling, but from the start the FLU chemostats (FLU was started day0, MOR was started day1, LAC was started day2)The spectral data is saved as .dat files and can be opened with MatLab. I recommend downloading DrEEM toolbox to explore the dataset. DrEEM toolbox can be downloaded from the website http://dreem.openfluor.org/.The rest of the data (bacterial and chemical) is saved in excel files.If anything is missing, do not hestitate to write me on my email: adbj@aqua.dtu.dkGreetings,Anders Dalhoff Bruhn

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    https://doi.org/10.11583/dtu.1...
    Dataset . 2021
    License: CC BY SA
    Data sources: Datacite; Sygma
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      https://doi.org/10.11583/dtu.1...
      Dataset . 2021
      License: CC BY SA
      Data sources: Datacite; Sygma
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  • Authors: Goerres, Achim; Spies, Dennis C; Mayer, Sabrina J;

    DE: Dieser Datensatz besteht aus den Transkripten von vier Fokusgruppeninterviews der Migrantenwahlstudie. Ziel des Projektes war es, für die Bundestagswahl 2017 die erste deutsche Wahlstudie unter deutschen Staatsbürger/innen mit Migrationshintergrund durchzuführen, d.h. unter solchen Personen, die entweder selbst nach Deutschland immigriert sind oder die mindestens einen Elternteil mit eigener Migrationserfahrung haben. Die Migrantenwahlstudie umfasst eine qualitative und eine quantitative Phase. Ziel der ersten qualitativen Phase (Oktober 2016 bis Juli 2017) war der explorative Zugang zur Themen- und Kandidatenorientierung von Migrant/innen, um die Ergebnisse für eine Publikation sowie die Fragebogenentwicklung der quantitativen Phase zu nutzen: Welche Themenfelder werden als wichtig erachtet? Welche Vorstellung von Links-Rechts gibt es? Welche Kandidateneigenschaften sind besonders relevant? Wie stark ist die Bindung an das Herkunftsland? Als Methode haben wir dabei auf Gruppendiskussionen mit Russlanddeutschen zurückgegriffen, die in Duisburg und Köln durchgeführt wurden. Dabei haben wir mit etwa 5-6 Teilnehmer/innen jeweils knapp zwei Stunden lang diskutiert. Die Forschungsdaten der quantitativen Phase wurden beim Forschungsdatenzentrum GESIS archiviert.EN: This dataset is composed of the transcripts of four focus group interviews for the Immigrant German Election Study. The project aims to conduct the first Immigrant German Election Study for the federal election in 2017, targeting German citizens with an immigrant background, i.e. people who either migrated to Germany themselves (first generation) or have at least one parent who was born in another country (second generation). The Immigrant German Election Study encompasses a qualitative and a quantitative phase. The first qualitative stage of the project (October 2016 until July 2017) explored the issue and candidate orientations of migrants. The results were used for a publication as well as for the development of the questionnaire for the quantitative stage. The core questions are: Which political issues are classified as important to all Germans/all migrants from the same group? What political issues do Germans of immigrant origin perceive as "left" and "right"? What are the identity contents that Germans of migrant origin associate with being German? We used focus group interviews as the research method in the Duisburg/Cologne area that consisted of 5-6 participants each and lasted for about 90 minutes.The research data originating from the quantitative phase have been archived at GESIS Data Archive. Transcription method: Standardorthographie, geglättetStudy-Materials note: verfügbare Kontexmaterialien sind Stimuli, Leitfaden, standardisierter Fragebogen, Liste der Kodes, StudienberichtDE: Die Forschungsdaten können auf Anfrage beim Forschungsdatenzentrum Qualiservice erhalten werden. Für weitere Informationen besuchen Sie bitte die Qualiservice-Website: https://www.qualiservice.org/de/daten-nutzen.html

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    Authors: Tesi, Tommaso; Geibel, Marc C.; Pearce, Christof; Panova, Elena; +8 Authors

    Recent Arctic studies suggest that sea ice decline and permafrost thawing will affect phytoplankton dynamics and stimulate heterotrophic communities. However, in what way the plankton composition will change as the warming proceeds remains elusive. Here we investigate the chemical signature of the plankton-dominated fraction of particulate organic matter (POM) collected along the Siberian Shelf. POM (> 10 µm) samples were analysed using molecular biomarkers (CuO oxidation and IP25) and dual-carbon isotopes (δ13C and Δ14C). In addition, surface water chemical properties were integrated with the POM (> 10 µm) dataset to understand the link between plankton composition and environmental conditions. δ13C and Δ14C exhibited a large variability in the POM (> 10 µm) distribution while the content of terrestrial biomarkers in the POM was negligible. In the Laptev Sea (LS), δ13C and Δ14C of POM (> 10 µm) suggested a heterotrophic environment in which dissolved organic carbon (DOC) from the Lena River was the primary source of metabolisable carbon. Within the Lena plume, terrestrial DOC probably became part of the food web via bacteria uptake and subsequently transferred to relatively other heterotrophic communities (e.g. dinoflagellates). Moving eastwards toward the sea-ice-dominated East Siberian Sea (ESS), the system became progressively more autotrophic. Comparison between δ13C of POM (> 10 µm) samples and CO2aq concentrations revealed that the carbon isotope fractionation increased moving towards the easternmost and most productive stations. In a warming scenario characterised by enhanced terrestrial DOC release (thawing permafrost) and progressive sea ice decline, heterotrophic conditions might persist in the LS while the nutrient-rich Pacific inflow will likely stimulate greater primary productivity in the ESS. The contrasting trophic conditions will result in a sharp gradient in δ13C between the LS and ESS, similar to what is documented in our semi-synoptic study.

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    Copernicus Publications
    Other ORP type . 2018
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    Authors: Masson-Delmotte, V.; Steen-Larsen, H.; Ortega, P.; Swingedouw, D.; +19 Authors

    Combined records of snow accumulation rate, δ18O and deuterium excess were produced from several shallow ice cores and snow pits at NEEM (North Greenland Eemian Ice Drilling), covering the period from 1724 to 2007. They are used to investigate recent climate variability and characterise the isotope–temperature relationship. We find that NEEM records are only weakly affected by inter-annual changes in the North Atlantic Oscillation. Decadal δ18O and accumulation variability is related to North Atlantic sea surface temperature and is enhanced at the beginning of the 19th century. No long-term trend is observed in the accumulation record. By contrast, NEEM δ18O shows multidecadal increasing trends in the late 19th century and since the 1980s. The strongest annual positive δ18O values are recorded at NEEM in 1928 and 2010, while maximum accumulation occurs in 1933. The last decade is the most enriched in δ18O (warmest), while the 11-year periods with the strongest depletion (coldest) are depicted at NEEM in 1815–1825 and 1836–1846, which are also the driest 11-year periods. The NEEM accumulation and δ18O records are strongly correlated with outputs from atmospheric models, nudged to atmospheric reanalyses. Best performance is observed for ERA reanalyses. Gridded temperature reconstructions, instrumental data and model outputs at NEEM are used to estimate the multidecadal accumulation–temperature and δ18O–temperature relationships for the strong warming period in 1979–2007. The accumulation sensitivity to temperature is estimated at 11 ± 2 % °C−1 and the δ18O–temperature slope at 1.1 ± 0.2 ‰ °C−1, about twice as large as previously used to estimate last interglacial temperature change from the bottom part of the NEEM deep ice core.

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    Other ORP type . 2018
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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: El-Hokayem, Léonard; De Vita, Pantaleone; Usman, Muhammad; Link, Andreas; +1 Authors

    Groundwater dependent vegetation (GDV) is essential for maintaining ecosystem functions and services, providing critical habitat and sustaining human livelihoods. A novel multicriteria framework helps to identify areas where potential groundwater dependent vegetation (pGDV) occurs in the Mediterranean biome. Globally-available datasets targeting 1) groundwater vegetation interaction; 2) soil water holding capacity; 3) topographical landscape wetness potential; 4) land use land cover and 5) hydraulic conductivity of rocks are combined in a weighted, easy-to-use index, composed of eleven thematic layers. Input layers for the index calculation are available in the data collection: 1) pre-processed (rasterised and clipped to the Mediterranean) and 2) harmonised and reclassified. All input data was extracted globally. Either, directly from the respective studies or through the data catalogue in the Google Earth Engine. All datasets were acquired and processed in 2022 and 2023. Time series data for potential inflow dependency and Normalized Difference Vegetation Index (NDVI) were extracted for the period 2003-2021. Finally, the mean value was calculated over this period. All other data sets, however, mark a fixed point in time. Ground truth vegetation data was used to calculate layer weightings with a Random Forest. 10 m * 10 m vegetation plots were collected in July and August 2021 and 2022 in southern Italy (Campania region) inside the 'Cilento, Vallo di Diano and Alburni National Park'. 236 vegetation plots are available, containing general information on the vegetation (habitat, species number, stratification), mean indicator values, plant life forms, leaf anatomy as well as a calculated ecohydrological potential for the presence of GDV. The potential was calculated based on the coverage of phreatophyte species and the moisture value of non-phreatophyte species. The final pGDV maps including different weightings of the eleven thematic layers are compiled at a resolution of 500 m in WGS1984 (EPSG 4326). Finally, five pGDV classes (very low to very high potential) were derived and the share of high pGDV was calculated for level 8 HydroBASINS in the Mediterranean. Results support prioritisation of areas for essential regional high-resolution identification of GDV, to ensure sustainable groundwater management and in turn protect GDV as local biodiversity hotspots.

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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: Rasmussen, Sune Olander; Svensson, Anders M; Vinther, Bo Møllesøe;

    Greenland Ice-Core Chronology 2005 (GICC05) annual layer depths for various Greenland ice cores. This is the high-resolution version (full, annual resolution) data file. Previously, 10- and 20-year resolution data files containing the time scale and resampled d18O data have been released for different time intervals together with the papers mentioned below. Ages are reported as years before A.D. 2000 / 2000 CE, abbreviated b2k.The file contains the location of the annual markers in the GICC05 time scale for each core's depth sections where data was available and sufficiently resolved to allow annual dating. Details are given in the papers listed below. The markers are placed in the winter and spring depending on the availability of data (e.g. using the winter d18O minimum, winter Sodium concentration maximum, spring dust/Calcium concentration maximum, or visual stratigraphy grey-scale peaks in the deepest parts). Across data gaps, markers are placed by interpolation or using other impurity species with different seasonality (e.g. using summer Ammonium or Nitrate peaks). Therefore, the criteria for where the annual markers are places vary between sections, and care should be taken when interpreting data on annual scale. The dating of the 0-7.9 ka b2k part is described in the paper Vinther et al., 2006The dating of the 7.9-14.7 ka b2k part is described in the paper Rasmussen et al., 2006The dating of the 14.7-41.8 ka b2k part is described in the paper Andersen et al., 2006The dating of the 41.8-60.0 ka b2k part is described in the paper Svensson et al., 2008When counting layers, uncertainty is introduced when an annual layer is backed up by evidence only in some of the data series, or when a certain well-resolved feature is suspected to contain more than one annual layer. The cases of ambiguity in the annual layer identification process have been marked using so-called uncertain layer markings. These uncertain layer markings were included in the time scale as ½ ± ½ years, with the ± ½ years forming the basis for quantifying the so-called maximum counting error. The concept of maximum counting error is further discussed in Rasmussen et al. (2006). In a standard deviation context, the maximum counting error can be regarded as 2 sigma as discussed in Andersen et al. (2006).In the Holocene, GS-1, and GI-2, the published time scale was derived from annual layer markings by manually determining which half of the uncertain layer markings to count as years, and which to skip. The maximum counting error was estimated from the number of uncertain layer markings as a constant relative uncertainty for each period with similar data availability and characteristics: 21-3,845 a b2k (0.25%), 3,846-6,905 a b2k (0.5%), 6,906-10,276 a b2k (2%), 10,277-11,703 a b2k (0.67%), 11,703-12,896 a b2k (3,3%), 12,896-14,075 a b2k (2.6%), 14,075-14,692 a b2k (2.7%) (see table 2 in Vinther et al, 2006, and table 3 in Rasmussen et al., 2006). From GS-2 and below (Andersen et al., 2006; Svensson et al., 2008) every 2nd uncertain layer was counted as a year and the maximum counting uncertainty increased by one year (giving rise to a variable relative counting error ranging from 4% in the warm interstadial periods to 7% in the cold stadials, and averaging 5.3%). In data set "Greenland NGRIP2 Ice-Core annual layer markings"(https://doi.pangaea.de/10.1594/PANGAEA.943194), the depths of the annual layer markings (including the uncertain ones) are provided with indication of which of these were counted as annual layers. This data set is only available below 10.2 ka. Above this, the locations of the discarded half of the uncertain layer markings have only been recorded on paper.The NGRIP1 core reaches down to a depth of 1372 m. The NGRIP2 core (drilled 20 meters away from the NGRIP1 core) reaches from surface to bedrock, but high-resolution measurements have only been carried out downwards from 1346 m. In the 26 m overlap zone, the cores are offset by 0.43 m on average, probably due to uncertainties in the logging procedure (see Schøtt Hvidberg et al., Ann. Glac. 35, 2002). Thus, the same features appear at larger depths in the NGRIP1 than in the NGRIP2 core. We recommend that NGRIP1 depths are used until 9820 b2k, and NGRIP2 depths are used below this.Note that the GICC05 time scale has later been revised. The first section of the new time scale, named GICC21, is described in the paper "A multi-ice-core, annual-layer-counted Greenland ice-core chronology for the last 3800 years: GICC21", Climate of the Past volume 18, p. 1125-1150, 2022, https://doi.org/10.5194/cp-18-1125-2022. Updated GICC21 annual-layer positions are released in the supplement to the paper. Annual markers forming the GICC05 time scale for NGRIP1, NGRIP2, GRIP, and DYE-3 where data was available and sufficiently resolved to allow annual dating. The markers are placed in the winter and spring depending on the availability of data (e.g. using the winter d18O minimum, winter Sodium concentration maximum, spring dust/Calcium concentration maximum, or visual stratigraphy grey-scale peaks in the deepest parts). Across data gaps, markers are placed by interpolation or using other impurity species with different seasonality (e.g. using summer Ammonium or Nitrate peaks). Therefore, the criteria for where the annual markers are places vary between sections, and care should be taken when interpreting data on annual scale. Ages are reported as years before A.D. 2000 / 2000 CE, abbreviated b2k. Depths (in meter) are true depths below the undisturbed surface the year when drilling started.

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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: Rasmussen, Sune Olander; Svensson, Anders M; Vinther, Bo Møllesøe;

    Greenland Ice-Core Chronology 2005 (GICC05) annual layer depths for various Greenland ice cores. This is the high-resolution version (full, annual resolution) data file. Previously, 10- and 20-year resolution data files containing the time scale and resampled d18O data have been released for different time intervals together with the papers mentioned below. Ages are reported as years before A.D. 2000 / 2000 CE, abbreviated b2k.The file contains the location of the annual markers in the GICC05 time scale for each core's depth sections where data was available and sufficiently resolved to allow annual dating. Details are given in the papers listed below. The markers are placed in the winter and spring depending on the availability of data (e.g. using the winter d18O minimum, winter Sodium concentration maximum, spring dust/Calcium concentration maximum, or visual stratigraphy grey-scale peaks in the deepest parts). Across data gaps, markers are placed by interpolation or using other impurity species with different seasonality (e.g. using summer Ammonium or Nitrate peaks). Therefore, the criteria for where the annual markers are places vary between sections, and care should be taken when interpreting data on annual scale. The dating of the 0-7.9 ka b2k part is described in the paper Vinther et al., 2006The dating of the 7.9-14.7 ka b2k part is described in the paper Rasmussen et al., 2006The dating of the 14.7-41.8 ka b2k part is described in the paper Andersen et al., 2006The dating of the 41.8-60.0 ka b2k part is described in the paper Svensson et al., 2008When counting layers, uncertainty is introduced when an annual layer is backed up by evidence only in some of the data series, or when a certain well-resolved feature is suspected to contain more than one annual layer. The cases of ambiguity in the annual layer identification process have been marked using so-called uncertain layer markings. These uncertain layer markings were included in the time scale as ½ ± ½ years, with the ± ½ years forming the basis for quantifying the so-called maximum counting error. The concept of maximum counting error is further discussed in Rasmussen et al. (2006). In a standard deviation context, the maximum counting error can be regarded as 2 sigma as discussed in Andersen et al. (2006).In the Holocene, GS-1, and GI-2, the published time scale was derived from annual layer markings by manually determining which half of the uncertain layer markings to count as years, and which to skip. The maximum counting error was estimated from the number of uncertain layer markings as a constant relative uncertainty for each period with similar data availability and characteristics: 21-3,845 a b2k (0.25%), 3,846-6,905 a b2k (0.5%), 6,906-10,276 a b2k (2%), 10,277-11,703 a b2k (0.67%), 11,703-12,896 a b2k (3,3%), 12,896-14,075 a b2k (2.6%), 14,075-14,692 a b2k (2.7%) (see table 2 in Vinther et al, 2006, and table 3 in Rasmussen et al., 2006). From GS-2 and below (Andersen et al., 2006; Svensson et al., 2008) every 2nd uncertain layer was counted as a year and the maximum counting uncertainty increased by one year (giving rise to a variable relative counting error ranging from 4% in the warm interstadial periods to 7% in the cold stadials, and averaging 5.3%). In data set "Greenland NGRIP2 Ice-Core annual layer markings"(https://doi.pangaea.de/10.1594/PANGAEA.943194), the depths of the annual layer markings (including the uncertain ones) are provided with indication of which of these were counted as annual layers. This data set is only available below 10.2 ka. Above this, the locations of the discarded half of the uncertain layer markings have only been recorded on paper.The NGRIP1 core reaches down to a depth of 1372 m. The NGRIP2 core (drilled 20 meters away from the NGRIP1 core) reaches from surface to bedrock, but high-resolution measurements have only been carried out downwards from 1346 m. In the 26 m overlap zone, the cores are offset by 0.43 m on average, probably due to uncertainties in the logging procedure (see Schøtt Hvidberg et al., Ann. Glac. 35, 2002). Thus, the same features appear at larger depths in the NGRIP1 than in the NGRIP2 core. We recommend that NGRIP1 depths are used until 9820 b2k, and NGRIP2 depths are used below this.Note that the GICC05 time scale has later been revised. The first section of the new time scale, named GICC21, is described in the paper "A multi-ice-core, annual-layer-counted Greenland ice-core chronology for the last 3800 years: GICC21", Climate of the Past volume 18, p. 1125-1150, 2022, https://doi.org/10.5194/cp-18-1125-2022. Updated GICC21 annual-layer positions are released in the supplement to the paper. Annual markers forming the GICC05 time scale for NGRIP1, NGRIP2, GRIP, and DYE-3 where data was available and sufficiently resolved to allow annual dating. The markers are placed in the winter and spring depending on the availability of data (e.g. using the winter d18O minimum, winter Sodium concentration maximum, spring dust/Calcium concentration maximum, or visual stratigraphy grey-scale peaks in the deepest parts). Across data gaps, markers are placed by interpolation or using other impurity species with different seasonality (e.g. using summer Ammonium or Nitrate peaks). Therefore, the criteria for where the annual markers are places vary between sections, and care should be taken when interpreting data on annual scale. Ages are reported as years before A.D. 2000 / 2000 CE, abbreviated b2k. Depths (in meter) are true depths below the undisturbed surface the year when drilling started.

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    PANGAEA
    Dataset . 2022
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2022
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    Authors: Skov, Christian;

    Fangstjournalen is a citizen science project for anglers, i.e. recreational fishers using reel and rod. The system collects data when anglers report information from their fishing trips including their catches of fish. When reporting a fishing trip anglers provide information about fishing location, hours fished, target fish species as well as information about catches i.e. species, length or weight, fate (released or harvested), and gear used. We also collect other species-specific data about fish catches, e.g parasites, tags, gender, maturity and much more. Anglers are encouraged to report blank trips, which allow calculations of Catch-Per-Unit-Effort estimates which we use to compare densities of fish between years and fishing sites. Data is being collected via an electronic platform including a browser version and a smartphone app (for android and iPhone). Anglers can also report a range of different observations that they make make during their fishing trip, e.g. presence of large marine mammals, tuna, invasive species and more. Additional entries for observations can be made in the backend of the system, e.g. as part of collaboration projects with other researchers who wish to engage anglers in their citizen science data collection.Upon registration participants are encouraged to fill out entries that support with information about demographics (postal code, gender, age) and angling characteristics (e.g. experience, preferred fishing types, importance of angling as a hobby). This information combined with GPS of fishing sites can provide information about travel patterns. See CSV file for more information about data that is being collected.Data is not shared directly due to content of personal information, but contact Christian Skov, ck@aqua.dtu.dkORCID 0000-0002-8547-6520. He is happy to engage in collaborative projects.See a popular introduction to the Citizen Science project Fangstjournalen here.https://doi.org/10.11581/DTU:00000094

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    DTU Data
    Dataset . 2021
    License: CC BY
    Data sources: DTU Data
    https://doi.org/10.11583/dtu.1...
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    https://doi.org/10.11583/dtu.1...
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      DTU Data
      Dataset . 2021
      License: CC BY
      Data sources: DTU Data
      https://doi.org/10.11583/dtu.1...
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      https://doi.org/10.11583/dtu.1...
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Jensen, Anders Dalhoff Bruhn; Stedmon, Colin;

    The data has been generated from a chemostat experiment performed in June 2019. The data contain bacterial (bacterial abundance, bacterial community composition) spectral (absorbance and fluorescence) and chemical data (nutrients, dissolved organic carbon, total dissolved nitrogen). The data has been used in the published article "Terrestrial dissolved organic matter mobilized from eroding permafrost controls microbial community composition and growth in Arctic coastal waters" by Bruhn et al. 2021."Explanation of filename (e.g. FLU1C01):1. First three letters (FLU, LAC, MOR) relates to the glacial deposit type.2. The following number refers to chemostat replicate within each deposit type (1,2,3,4)3. The letter C or M between the numbers refers to Culture or Medium (the two components of a classic chemostat setup)4. The last number refers to the day of sampling, but from the start the FLU chemostats (FLU was started day0, MOR was started day1, LAC was started day2)The spectral data is saved as .dat files and can be opened with MatLab. I recommend downloading DrEEM toolbox to explore the dataset. DrEEM toolbox can be downloaded from the website http://dreem.openfluor.org/.The rest of the data (bacterial and chemical) is saved in excel files.If anything is missing, do not hestitate to write me on my email: adbj@aqua.dtu.dkGreetings,Anders Dalhoff Bruhn

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    https://doi.org/10.11583/dtu.1...
    Dataset . 2021
    License: CC BY SA
    Data sources: Datacite; Sygma
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      https://doi.org/10.11583/dtu.1...
      Dataset . 2021
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  • Authors: Goerres, Achim; Spies, Dennis C; Mayer, Sabrina J;

    DE: Dieser Datensatz besteht aus den Transkripten von vier Fokusgruppeninterviews der Migrantenwahlstudie. Ziel des Projektes war es, für die Bundestagswahl 2017 die erste deutsche Wahlstudie unter deutschen Staatsbürger/innen mit Migrationshintergrund durchzuführen, d.h. unter solchen Personen, die entweder selbst nach Deutschland immigriert sind oder die mindestens einen Elternteil mit eigener Migrationserfahrung haben. Die Migrantenwahlstudie umfasst eine qualitative und eine quantitative Phase. Ziel der ersten qualitativen Phase (Oktober 2016 bis Juli 2017) war der explorative Zugang zur Themen- und Kandidatenorientierung von Migrant/innen, um die Ergebnisse für eine Publikation sowie die Fragebogenentwicklung der quantitativen Phase zu nutzen: Welche Themenfelder werden als wichtig erachtet? Welche Vorstellung von Links-Rechts gibt es? Welche Kandidateneigenschaften sind besonders relevant? Wie stark ist die Bindung an das Herkunftsland? Als Methode haben wir dabei auf Gruppendiskussionen mit Russlanddeutschen zurückgegriffen, die in Duisburg und Köln durchgeführt wurden. Dabei haben wir mit etwa 5-6 Teilnehmer/innen jeweils knapp zwei Stunden lang diskutiert. Die Forschungsdaten der quantitativen Phase wurden beim Forschungsdatenzentrum GESIS archiviert.EN: This dataset is composed of the transcripts of four focus group interviews for the Immigrant German Election Study. The project aims to conduct the first Immigrant German Election Study for the federal election in 2017, targeting German citizens with an immigrant background, i.e. people who either migrated to Germany themselves (first generation) or have at least one parent who was born in another country (second generation). The Immigrant German Election Study encompasses a qualitative and a quantitative phase. The first qualitative stage of the project (October 2016 until July 2017) explored the issue and candidate orientations of migrants. The results were used for a publication as well as for the development of the questionnaire for the quantitative stage. The core questions are: Which political issues are classified as important to all Germans/all migrants from the same group? What political issues do Germans of immigrant origin perceive as "left" and "right"? What are the identity contents that Germans of migrant origin associate with being German? We used focus group interviews as the research method in the Duisburg/Cologne area that consisted of 5-6 participants each and lasted for about 90 minutes.The research data originating from the quantitative phase have been archived at GESIS Data Archive. Transcription method: Standardorthographie, geglättetStudy-Materials note: verfügbare Kontexmaterialien sind Stimuli, Leitfaden, standardisierter Fragebogen, Liste der Kodes, StudienberichtDE: Die Forschungsdaten können auf Anfrage beim Forschungsdatenzentrum Qualiservice erhalten werden. Für weitere Informationen besuchen Sie bitte die Qualiservice-Website: https://www.qualiservice.org/de/daten-nutzen.html

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