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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: Harris, Eliza; Yu, Longfei; Wang, Ying-Ping; Mohn, Joachim; +15 Authors

    This dataset comprises a compilation of soil bulk delta-15-N nitrogen isotopic composition that has been measured and/or published since the compilation of d15N data by Craine et al. (2015; doi:10.1007/s11104-015-2542-1; doi:10.1038/srep08280). The data was measured by the data owner / contact indicated in the dataset. All data remains the property of the listed owner but may be used for non-commercial purposes. In the case of significant use of this data for scientific research, please cite this dataset as well as the associated publication(s) and consider contacting data owners to offer co-authorship where relevant. Project: Identifying drivers of N2O emissions in a changing climate (https://www.oecd.org/agriculture/crp/fellowships/). Award: OECD Cooperative Research Program for Sustainable Agricultural and Food Systems (OECD-CRP) grant.

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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: Seibt, Ulli; Abadie, Camille; Maignan, Fabienne; Shi, Mingjie; +17 Authors

    Presented here are estimates of soil carbonyl sulfide (OCS) fluxes calculated with two orthogonal approaches. Both datasets present monthly average OCS fluxes from global soils for 2000 to 2019 based on variables generated with the ORCHIDEE land surface model. The mechanistic approach was based on the model in Ogee et al., 2016 (https://doi.org/10.5194/bg-13-2221-2016) and the results were first presented by Abadie et al., 2022 (https://doi.org/10.5194/bg-19-2427-2022). The empirical approach leverages all available field and incubation observations of soil OCS fluxes and was first presented in Whelan et al., 2022 (https://doi.org/10.1029/2022JG006858) We first developed an empirical model of soil OCS exchange based on all available observations. We then applied this model to the soil moisture and temperature reported by the ORCHIDEE land model to create average monthly estimates of OCS exchange globally from 2000 to 2019. Global

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    Authors: Widhalm, Barbara; Bartsch, Annett; Goler, Robert;

    Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in heterogeneous landscapes. Multiple acquisitions from overlapping orbits with sufficient incidence angle range are processed in order to obtain parameters of the location specific normalization function. We propose a simpler method for C-band data, using single scenes only. It requires stable dielectric properties (no variations of liquid water content). This method is therefore applicable for frozen conditions. Winter C-band data have been shown of high value for a number of applications in high latitudes before. In this paper we explore the relationship of incidence angle and Sentinel-1 backscatter across the tundra to boreal transition zone. A linear relationship (coefficient of determination R2 = 0.64) can be found between backscatter and incidence angle dependence (slope of normalization function) as determined by multiple acquisitions on a pixel by pixel basis for typical land cover classes in these regions. This allows a simplified normalization and thus reduced processing effort for applications over larger areas.The following regions are covered in the dataset: Yamal peninsula (Russia), Usa Basin (Russia), Lena Delta (Russia), Mackenzie Delta (Canada), Barrow, Toolik and Teshekpuk Lake region (Alaska). Supplement to: Widhalm, Barbara; Bartsch, Annett; Goler, Robert (2018): Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments. Remote Sensing, 10(4), 551

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    PANGAEA - Data Publisher for Earth and Environmental Science
    Other dataset type . Dataset . 2019
    License: CC BY
    Data sources: Datacite; Sygma
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    PANGAEA
    Dataset . 2019
    Data sources: B2FIND
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      PANGAEA - Data Publisher for Earth and Environmental Science
      Other dataset type . Dataset . 2019
      License: CC BY
      Data sources: Datacite; Sygma
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      PANGAEA
      Dataset . 2019
      Data sources: B2FIND
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    Authors: Högström, Elin; Heim, Birgit; Bartsch, Annett;

    Four automatic stations measuring soil temperature and VWC were deployed in the central Lena River Delta, Siberia in August 2013 and retrieved in August 2014. They were installed in a very shallow depth on the islands Kurungnakh and Samoylov.Three stations were placed on Kurungnakh (K1, K2, K3) and one on Samoylov (S1). Each station on Kurungnakh consisted of a) one VWC Campbell Recording Sensors CR625 and one Temperature T109 sensor at the most upper depth one (W1 and T1), b) one VWC CR625 sensor and one T109 sensor at depth two (W2 and T2; Figure 1C). The station on Samoylov had the same setup as those on Kurungnakh, with the exception that only one depth could be instrumented (W1 and T1). The sensors at depth one were placed in the lower end of the uppermost porous moss layer (in average of 5 to 7 cm thickness). The sensors at depth two were placed in the moss fibric layer, a thin layer of ca 2 to 3 cm which is the water storage layer of the moss and mostly water saturated.The field work in the Lena Delta has been supported by two scholarships for transnational access (2013 and 2014) of the International Network for Terrestrial Research and Monitoring in the Arctic (FP7 INTERACT). The data have been analyzed for satellite derived soil moisture within the framework of the FP7 project PAGE21. Supplement to: Högström, Elin; Heim, Birgit; Bartsch, Annett; Bergstedt, Helena; Pointner, Georg (2018): Evaluation of a MetOp ASCAT-derived surface soil moisture product in tundra environments. Journal of Geophysical Research-Earth Surface, 123

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    PANGAEA
    Dataset . 2018
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2018
      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: Reschke, Julia; Bartsch, Annett; Schlaffer, Stefan; Schepaschenko, Dmitry;

    Wetlands store large amounts of carbon, and depending on their status and type, they release specific amounts of methane gas to the atmosphere. The connection between wetland type and methane emission has been investigated in various studies and utilized in climate change monitoring and modelling. For improved estimation of methane emissions, land surface models require information such as the wetland fraction and its dynamics over large areas. Existing datasets of wetland dynamics present the total amount of wetland (fraction) for each model grid cell, but do not discriminate the different wetland types like permanent lakes, periodically inundated areas or peatlands. Wetland types differently influence methane fluxes and thus their contribution to the total wetland fraction should be quantified. Especially wetlands of permafrost regions are expected to have a strong impact on future climate due to soil thawing. In this study ENIVSAT ASAR Wide Swath data was tested for operational monitoring of the distribution of areas with a long-term SW near 1 (hSW) in northern Russia (SW = degree of saturation with water, 1 = saturated), which is a specific characteristic of peatlands. For the whole northern Russia, areas with hSW were delineated and discriminated from dynamic and open water bodies for the years 2007 and 2008. The area identified with this method amounts to approximately 300,000 km**2 in northern Siberia in 2007. It overlaps with zones of high carbon storage. Comparison with a range of related datasets (static and dynamic) showed that hSW represents not only peatlands but also temporary wetlands associated with post-forest fire conditions in permafrost regions. Annual long-term monitoring of change in boreal and tundra environments is possible with the presented approach. Sentinel-1, the successor of ENVISAT ASAR, will provide data that may allow continuous monitoring of these wetland dynamics in the future complementing global observations of wetland fraction. ENIVSAT ASAR Wide Swath data was tested for operational monitoring of the distribution of areas with a long-term high degree of saturation with water (hSW) as well as open water (10 day intervals). For the whole northern Russia, areas with hSW were delineated and discriminated from dynamic and open water bodies for the years 2007 and 2008. The identified areas overlap with zones of high carbon storage. Comparison with a range of related datasets (static and dynamic) showed that hSW represents not only peatlands but also temporary wetlands associated with post-forest fire conditions in permafrost regions. This experimental wetland product has been compiled within the framework of the ESA STSE ALANIS Methane project (www.alanis-methane.info). It covers most lowland areas of Siberia. The maps represent the snow-free season of 2007 and 2008, including open water with 10 day intervals for July and August 2007 over selected regions. Please consult the product guide regarding known issues and documentation. Quality is constrained by data availability, which is documented in the *day (days since last update) and *num (number of acquisitions used for the 10 day period) files. Nominal resolution is ~75m and projection is Universal Polar Stereographic North, WGS84. The the region of interest has been subdivided into 10 parts with varying extent. Supplement to: Reschke, Julia; Bartsch, Annett; Schlaffer, Stefan; Schepaschenko, Dmitry (2012): Capability of C-Band SAR for operational wetland monitoring at high latitudes. Remote Sensing, 4(12), 2923-2943

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    PANGAEA
    Dataset . 2012
    Data sources: B2FIND
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    PANGAEA - Data Publisher for Earth and Environmental Science
    Other dataset type . 2012
    License: CC BY
    Data sources: Datacite
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      PANGAEA
      Dataset . 2012
      Data sources: B2FIND
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      PANGAEA - Data Publisher for Earth and Environmental Science
      Other dataset type . 2012
      License: CC BY
      Data sources: Datacite
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    Authors: Bartsch, Annett;

    Data north of 60°N have been analysed for snowmelt patterns. Spring snowmelt timing (start and end) has been extracted based on diurnal thaw and refreeze detection (Bartsch et al. 2007). Mid winter thaw and refreeze which can be caused by rain on snow (ROS, Bartsch et al. 2010, Wilson et al. 2013) or fog (Semmens et al. 2013) has been extracted for November to February. Number of events with strong backscatter increase have been summed up for single years and all available winter periods. Data are provided as dbf or csv files with point measurements (id, coordinates + number of events (VALUE)). Spacing is 10 km. A shape file based on a filtered (close) image is provided as summary/preview of the mid winter data. Overview maps are also included in Bartsch (2010). The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages. Supplement to: Bartsch, Annett (2010): Ten Years of SeaWinds on QuikSCAT for Snow Applications. Remote Sensing, 2(4), 1142-1156

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    PANGAEA
    Dataset . 2010
    Data sources: B2FIND
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    PANGAEA - Data Publisher for Earth and Environmental Science
    Other dataset type . 2010
    License: CC BY
    Data sources: Datacite
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      PANGAEA
      Dataset . 2010
      Data sources: B2FIND
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      PANGAEA - Data Publisher for Earth and Environmental Science
      Other dataset type . 2010
      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: Harris, Eliza; Yu, Longfei; Wang, Ying-Ping; Mohn, Joachim; +15 Authors

    This dataset comprises a compilation of soil bulk delta-15-N nitrogen isotopic composition that has been measured and/or published since the compilation of d15N data by Craine et al. (2015; doi:10.1007/s11104-015-2542-1; doi:10.1038/srep08280). The data was measured by the data owner / contact indicated in the dataset. All data remains the property of the listed owner but may be used for non-commercial purposes. In the case of significant use of this data for scientific research, please cite this dataset as well as the associated publication(s) and consider contacting data owners to offer co-authorship where relevant. Project: Identifying drivers of N2O emissions in a changing climate (https://www.oecd.org/agriculture/crp/fellowships/). Award: OECD Cooperative Research Program for Sustainable Agricultural and Food Systems (OECD-CRP) grant.

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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: Seibt, Ulli; Abadie, Camille; Maignan, Fabienne; Shi, Mingjie; +17 Authors

    Presented here are estimates of soil carbonyl sulfide (OCS) fluxes calculated with two orthogonal approaches. Both datasets present monthly average OCS fluxes from global soils for 2000 to 2019 based on variables generated with the ORCHIDEE land surface model. The mechanistic approach was based on the model in Ogee et al., 2016 (https://doi.org/10.5194/bg-13-2221-2016) and the results were first presented by Abadie et al., 2022 (https://doi.org/10.5194/bg-19-2427-2022). The empirical approach leverages all available field and incubation observations of soil OCS fluxes and was first presented in Whelan et al., 2022 (https://doi.org/10.1029/2022JG006858) We first developed an empirical model of soil OCS exchange based on all available observations. We then applied this model to the soil moisture and temperature reported by the ORCHIDEE land model to create average monthly estimates of OCS exchange globally from 2000 to 2019. Global

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    Authors: Widhalm, Barbara; Bartsch, Annett; Goler, Robert;

    Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in heterogeneous landscapes. Multiple acquisitions from overlapping orbits with sufficient incidence angle range are processed in order to obtain parameters of the location specific normalization function. We propose a simpler method for C-band data, using single scenes only. It requires stable dielectric properties (no variations of liquid water content). This method is therefore applicable for frozen conditions. Winter C-band data have been shown of high value for a number of applications in high latitudes before. In this paper we explore the relationship of incidence angle and Sentinel-1 backscatter across the tundra to boreal transition zone. A linear relationship (coefficient of determination R2 = 0.64) can be found between backscatter and incidence angle dependence (slope of normalization function) as determined by multiple acquisitions on a pixel by pixel basis for typical land cover classes in these regions. This allows a simplified normalization and thus reduced processing effort for applications over larger areas.The following regions are covered in the dataset: Yamal peninsula (Russia), Usa Basin (Russia), Lena Delta (Russia), Mackenzie Delta (Canada), Barrow, Toolik and Teshekpuk Lake region (Alaska). Supplement to: Widhalm, Barbara; Bartsch, Annett; Goler, Robert (2018): Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments. Remote Sensing, 10(4), 551

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    PANGAEA - Data Publisher for Earth and Environmental Science
    Other dataset type . Dataset . 2019
    License: CC BY
    Data sources: Datacite; Sygma
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    PANGAEA
    Dataset . 2019
    Data sources: B2FIND
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      PANGAEA - Data Publisher for Earth and Environmental Science
      Other dataset type . Dataset . 2019
      License: CC BY
      Data sources: Datacite; Sygma
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      PANGAEA
      Dataset . 2019
      Data sources: B2FIND
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    Authors: Högström, Elin; Heim, Birgit; Bartsch, Annett;

    Four automatic stations measuring soil temperature and VWC were deployed in the central Lena River Delta, Siberia in August 2013 and retrieved in August 2014. They were installed in a very shallow depth on the islands Kurungnakh and Samoylov.Three stations were placed on Kurungnakh (K1, K2, K3) and one on Samoylov (S1). Each station on Kurungnakh consisted of a) one VWC Campbell Recording Sensors CR625 and one Temperature T109 sensor at the most upper depth one (W1 and T1), b) one VWC CR625 sensor and one T109 sensor at depth two (W2 and T2; Figure 1C). The station on Samoylov had the same setup as those on Kurungnakh, with the exception that only one depth could be instrumented (W1 and T1). The sensors at depth one were placed in the lower end of the uppermost porous moss layer (in average of 5 to 7 cm thickness). The sensors at depth two were placed in the moss fibric layer, a thin layer of ca 2 to 3 cm which is the water storage layer of the moss and mostly water saturated.The field work in the Lena Delta has been supported by two scholarships for transnational access (2013 and 2014) of the International Network for Terrestrial Research and Monitoring in the Arctic (FP7 INTERACT). The data have been analyzed for satellite derived soil moisture within the framework of the FP7 project PAGE21. Supplement to: Högström, Elin; Heim, Birgit; Bartsch, Annett; Bergstedt, Helena; Pointner, Georg (2018): Evaluation of a MetOp ASCAT-derived surface soil moisture product in tundra environments. Journal of Geophysical Research-Earth Surface, 123

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    PANGAEA
    Dataset . 2018
    Data sources: B2FIND
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      PANGAEA
      Dataset . 2018
      Data sources: B2FIND
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    Authors: Reschke, Julia; Bartsch, Annett; Schlaffer, Stefan; Schepaschenko, Dmitry;

    Wetlands store large amounts of carbon, and depending on their status and type, they release specific amounts of methane gas to the atmosphere. The connection between wetland type and methane emission has been investigated in various studies and utilized in climate change monitoring and modelling. For improved estimation of methane emissions, land surface models require information such as the wetland fraction and its dynamics over large areas. Existing datasets of wetland dynamics present the total amount of wetland (fraction) for each model grid cell, but do not discriminate the different wetland types like permanent lakes, periodically inundated areas or peatlands. Wetland types differently influence methane fluxes and thus their contribution to the total wetland fraction should be quantified. Especially wetlands of permafrost regions are expected to have a strong impact on future climate due to soil thawing. In this study ENIVSAT ASAR Wide Swath data was tested for operational monitoring of the distribution of areas with a long-term SW near 1 (hSW) in northern Russia (SW = degree of saturation with water, 1 = saturated), which is a specific characteristic of peatlands. For the whole northern Russia, areas with hSW were delineated and discriminated from dynamic and open water bodies for the years 2007 and 2008. The area identified with this method amounts to approximately 300,000 km**2 in northern Siberia in 2007. It overlaps with zones of high carbon storage. Comparison with a range of related datasets (static and dynamic) showed that hSW represents not only peatlands but also temporary wetlands associated with post-forest fire conditions in permafrost regions. Annual long-term monitoring of change in boreal and tundra environments is possible with the presented approach. Sentinel-1, the successor of ENVISAT ASAR, will provide data that may allow continuous monitoring of these wetland dynamics in the future complementing global observations of wetland fraction. ENIVSAT ASAR Wide Swath data was tested for operational monitoring of the distribution of areas with a long-term high degree of saturation with water (hSW) as well as open water (10 day intervals). For the whole northern Russia, areas with hSW were delineated and discriminated from dynamic and open water bodies for the years 2007 and 2008. The identified areas overlap with zones of high carbon storage. Comparison with a range of related datasets (static and dynamic) showed that hSW represents not only peatlands but also temporary wetlands associated with post-forest fire conditions in permafrost regions. This experimental wetland product has been compiled within the framework of the ESA STSE ALANIS Methane project (www.alanis-methane.info). It covers most lowland areas of Siberia. The maps represent the snow-free season of 2007 and 2008, including open water with 10 day intervals for July and August 2007 over selected regions. Please consult the product guide regarding known issues and documentation. Quality is constrained by data availability, which is documented in the *day (days since last update) and *num (number of acquisitions used for the 10 day period) files. Nominal resolution is ~75m and projection is Universal Polar Stereographic North, WGS84. The the region of interest has been subdivided into 10 parts with varying extent. Supplement to: Reschke, Julia; Bartsch, Annett; Schlaffer, Stefan; Schepaschenko, Dmitry (2012): Capability of C-Band SAR for operational wetland monitoring at high latitudes. Remote Sensing, 4(12), 2923-2943

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    PANGAEA
    Dataset . 2012
    Data sources: B2FIND
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    PANGAEA - Data Publisher for Earth and Environmental Science
    Other dataset type . 2012
    License: CC BY
    Data sources: Datacite
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      PANGAEA
      Dataset . 2012
      Data sources: B2FIND
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      PANGAEA - Data Publisher for Earth and Environmental Science
      Other dataset type . 2012
      License: CC BY
      Data sources: Datacite
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    Authors: Bartsch, Annett;

    Data north of 60°N have been analysed for snowmelt patterns. Spring snowmelt timing (start and end) has been extracted based on diurnal thaw and refreeze detection (Bartsch et al. 2007). Mid winter thaw and refreeze which can be caused by rain on snow (ROS, Bartsch et al. 2010, Wilson et al. 2013) or fog (Semmens et al. 2013) has been extracted for November to February. Number of events with strong backscatter increase have been summed up for single years and all available winter periods. Data are provided as dbf or csv files with point measurements (id, coordinates + number of events (VALUE)). Spacing is 10 km. A shape file based on a filtered (close) image is provided as summary/preview of the mid winter data. Overview maps are also included in Bartsch (2010). The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages. Supplement to: Bartsch, Annett (2010): Ten Years of SeaWinds on QuikSCAT for Snow Applications. Remote Sensing, 2(4), 1142-1156

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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 . 2010
    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/
    PANGAEA - Data Publisher for Earth and Environmental Science
    Other dataset type . 2010
    License: CC BY
    Data sources: Datacite
    addClaim

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

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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 . 2010
      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/
      PANGAEA - Data Publisher for Earth and Environmental Science
      Other dataset type . 2010
      License: CC BY
      Data sources: Datacite
      addClaim

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

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