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  • Energy Research
  • 2021-2025
  • 15. Life on land
  • 2. Zero hunger
  • DE
  • AT

  • Authors: orcid bw Mercer, C.;
    Mercer, C.
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Mercer, C. in OpenAIRE
    orcid bw Jump, A.;
    Jump, A.
    ORCID
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    Jump, A. in OpenAIRE
    orcid bw Morley, P.;
    Morley, P.
    ORCID
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    Morley, P. in OpenAIRE
    orcid bw O’Sullivan, K.;
    O’Sullivan, K.
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    O’Sullivan, K. in OpenAIRE
    +2 Authors

    Tree cores were sampled using increment borers. At each site three trees were chosen for coring, with two or three cores taken per tree. Cores were sanded and ring widths measured based on high-resolution images of the sanded cores. Cores were cross-dated and summary statistics used to compare cross-dating accuracy. The dataset contains the resulting dated ring width series. This dataset includes tree ring width data, derived from tree cores, that were sampled from sites across the Rhön Biosphere Reserve (Germany). At each chosen site three trees were cored, with two or three cores taken per cored tree. Data was collected in August 2021.

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    Authors: Opito, Emmanuel A.; Alanko, Timo; orcid bw Kalbitzer, Urs;
    Kalbitzer, Urs
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Kalbitzer, Urs in OpenAIRE
    Nummelin, Matti; +3 Authors

    Data from: 30 Years Brings Changes to the Arthropod Community of Kibale National Park, Uganda by Opito, E.A., T. Alanko, U. Kalbitzer, M. Nummelin, P. Omeja, A. Valtonen, and Colin A. Chapman. 2023, Biotropica, Article DOI: 10.1111/btp.13206

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    https://dx.doi.org/10.17617/3....
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
    B2FIND
    Dataset . 2023
    Data sources: B2FIND
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      https://dx.doi.org/10.17617/3....
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
      B2FIND
      Dataset . 2023
      Data sources: B2FIND
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    Authors: orcid bw Schupfner, Martin;
    Schupfner, Martin
    ORCID
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    Schupfner, Martin in OpenAIRE
    orcid bw Wieners, Karl-Hermann;
    Wieners, Karl-Hermann
    ORCID
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    Wieners, Karl-Hermann in OpenAIRE
    Wachsmann, Fabian; orcid bw Steger, Christian;
    Steger, Christian
    ORCID
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    Steger, Christian in OpenAIRE
    +47 Authors

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DKRZ.MPI-ESM1-2-HR.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutsches Klimarechenzentrum, Hamburg 20146, Germany (DKRZ) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: orcid bw Minx, Jan C.;
    Minx, Jan C.
    ORCID
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    Minx, Jan C. in OpenAIRE
    orcid bw Lamb, William F.;
    Lamb, William F.
    ORCID
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    Lamb, William F. in OpenAIRE
    orcid bw Andrew, Robbie M.;
    Andrew, Robbie M.
    ORCID
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    Andrew, Robbie M. in OpenAIRE
    orcid bw Canadell, Josep G.;
    Canadell, Josep G.
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Canadell, Josep G. in OpenAIRE
    +13 Authors

    Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: orcid bw Neubauer, David;
    Neubauer, David
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Neubauer, David in OpenAIRE
    orcid bw Ferrachat, Sylvaine;
    Ferrachat, Sylvaine
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Ferrachat, Sylvaine in OpenAIRE
    Siegenthaler-Le Drian, Colombe; Stoll, Jens; +18 Authors

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: orcid bw Garner, Gregory;
    Garner, Gregory
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Garner, Gregory in OpenAIRE
    orcid bw Hermans, Tim H.J.;
    Hermans, Tim H.J.
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Hermans, Tim H.J. in OpenAIRE
    orcid bw Kopp, Robert;
    Kopp, Robert
    ORCID
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    Kopp, Robert in OpenAIRE
    orcid bw Slangen, Aimée;
    Slangen, Aimée
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Slangen, Aimée in OpenAIRE
    +22 Authors

    Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Müller, Wolfgang; Ilyina, Tatiana; orcid bw Li, Hongmei;
    Li, Hongmei
    ORCID
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    Li, Hongmei in OpenAIRE
    orcid bw Timmreck, Claudia;
    Timmreck, Claudia
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Timmreck, Claudia in OpenAIRE
    +48 Authors

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.MPI-M.MPI-ESM1-2-LR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: orcid bw Kalt, Gerald;
    Kalt, Gerald
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Kalt, Gerald in OpenAIRE
    orcid bw Mayer, Andreas;
    Mayer, Andreas
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Mayer, Andreas in OpenAIRE
    orcid bw Haberl, Helmut;
    Haberl, Helmut
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Haberl, Helmut in OpenAIRE
    orcid bw Kaufmann, Lisa;
    Kaufmann, Lisa
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Kaufmann, Lisa in OpenAIRE
    +4 Authors

    The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use. The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions. The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)". Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information. This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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    Authors: orcid bw Maus, Victor;
    Maus, Victor
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Maus, Victor in OpenAIRE
    orcid bw da Silva, Dieison M;
    da Silva, Dieison M
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    da Silva, Dieison M in OpenAIRE
    orcid bw Gutschlhofer, Jakob;
    Gutschlhofer, Jakob
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Gutschlhofer, Jakob in OpenAIRE
    da Rosa, Robson; +5 Authors

    This dataset updates the global-scale mining polygons (Version 1) available from https://doi.org/10.1594/PANGAEA.910894. It contains 44,929 polygon features, covering 101,583 km² of land used by the global mining industry, including large-scale and artisanal and small-scale mining. The polygons cover all ground features related to mining, .e.g open cuts, tailing dams, waste rock dumps, water ponds, processing infrastructure, and other land cover types related to the mining activities. The data was derived using a similar methodology as the first version by visual interpretation of satellite images. The study area was limited to a 10 km buffer around the 34,820 mining coordinates reported in the S&P metals and mining database. We digitalized the mining areas using the 2019 Sentinel-2 cloudless mosaic with 10 m spatial resolution (https://s2maps.eu by EOX IT Services GmbH - Contains modified Copernicus Sentinel data 2019). We also consulted Google Satellite and Microsoft Bing Imagery, but only as additional information to help identify land cover types linked to the mining activities. The main data set consists of a GeoPackage (GPKG) file, including the following variables: ISO3_CODE, COUNTRY_NAME, AREA in squared kilometres, FID with the feature ID, and geom in geographical coordinates WGS84. The summary of the mining area per country is available in comma-separated values (CSV) file, including the following variables: ISO3_CODE, COUNTRY_NAME, AREA in squared kilometres, and N_FEATURES number of mapped features. Grid data sets with the mining area per cell were derived from the polygons. The grid data is available at 30 arc-second resolution (approximately 1x1 km at the equator), 5 arc-minute (approximately 10x10 km at the equator), and 30 arc-minute resolution (approximately 55x55 km at the equator). We performed an independent validation of the mining data set using control points. For that, we draw 1,000 random samples stratified between two classes: mine and no-mine. The control points are also available as a GPKG file, including the variables: MAPPED, REFERENCE, FID with the feature ID, and geom in geographical coordinates WGS84. The overall accuracy calculated from the control points was 88.3%, Kappa 0.77, F1 score 0.87, producer's accuracy of class mine 78.9 % and user's accuracy of class mine 97.2 %.

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    B2FIND
    Dataset . 2022
    Data sources: B2FIND
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      B2FIND
      Dataset . 2022
      Data sources: B2FIND
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    Authors: orcid bw Schild, Laura;
    Schild, Laura
    ORCID
    Derived by OpenAIRE algorithms or harvested from 3rd party repositories

    Schild, Laura in OpenAIRE
    orcid Kruse, Stefan;
    Kruse, Stefan
    ORCID
    Harvested from ORCID Public Data File

    Kruse, Stefan in OpenAIRE
    orcid Heim, Birgit;
    Heim, Birgit
    ORCID
    Harvested from ORCID Public Data File

    Heim, Birgit in OpenAIRE
    orcid Stieg, Amelie;
    Stieg, Amelie
    ORCID
    Harvested from ORCID Public Data File

    Stieg, Amelie in OpenAIRE
    +7 Authors

    Vegetation surveys were carried out in four different study areas in the Sakha Republic, Russia: in the mountainous region of the Verkhoyansk Range within the Oymyakonsky and Tomponsky District (Event EN21-201 - EN21-219), and in three lowland regions of Central Yakutia within the Churapchinsky, Tattinsky and the Megino-Kangalassky District (Event EN21220 - EN21264). The study area is located within the boreal forest biome that is underlain by permafrost soils. The aim was to record the projective ground vegetation in different boreal forest types studied during the RU-Land_2021_Yakutia summer field campaign in August and September 2021.Ground vegetation was surveyed for different vegetation types within a circular forest plot of 15m radius. Depending on the heterogeneity of the forest plot, multiple vegetation types (VA, VB, or VC) were chosen for the survey. The assignment of a vegetation type is always unique to a site. Their cover on the circular forest plot was recorded in percent.In total, 84 vegetation types at 58 forest plots were assessed. All data were collected by scientists form the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) Germany, the University of Potsdam Germany, and the North-Easter Federal University of Yakutsk (NEFU) Russia.

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    PANGAEA
    Dataset . 2023
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2023
      Data sources: PANGAEA
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