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  • Energy Research
  • 13. Climate action
  • 15. Life on land
  • 2. Zero hunger
  • 8. Economic growth
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  • Authors: Rafael Eigenmann; Thomas Foken;

    Project: Convective and Orographically-induced Precipitation Study - Weather forecast models have not been successful in improving the Quantitative Precipitation Forecast during the last 16 years. One reason for this stagnation is the lack of comprehensive, high-quality data sets usable for model validation as well as for data assimilation, thus leading to improved initial fields in numerical models. Theoretical analyses have identified the requirements measured data have to meet in order to close the gaps in process understanding. In field campaigns, it has been shown that the newest generation of remote sensing systems has the potential to yield data sets of the required quality. It is therefore time to combine the most powerful remote sensing instruments with proven ground-based and airborne measurement techniques in an Intensive Observations Period (IOP). Its goal is to serve as a backbone for the Priority Program SPP 1167 by producing the demanded data sets of unachieved accuracy and resolution. This requires a sophisticated scientific preparation and a careful coordination between the efforts of the institutions involved. For the first time, the pre-convective environment, the formation of clouds and the onset and development of precipitation as well as its intensity will be observed in four dimensions simultaneously in a region of sufficient size. This shall be achieved by combining the IOP with international programs and by collaboration between leading scientists in Europe, US, and other countries. Thus, the IOP, which we call Convective and Orographically-induced Precipitation Study (COPS), is a unique opportunity for an international field campaign featuring the newest generation of measurement systems such as scanning radar and lidar and leading to outstanding advances in atmospheric sciences. Please be aware of the common COPS/GOP/D-PHASE data policy, which you please find at http://cops.wdc-climate.de/ Summary: The energy balance stations run by University of Bayreuth continuously measured radiation and soil parameters over different land types with a sampling frequency of 1 Hz averaged to 1 min values within the data logger. After a check for plausibility the 1 min values have been averaged to 30 min intervals, which are provided in this data set. The instrumentation was different on each location. The following was measured depending on the station: - soil heat flux - soil temperature - volumetric soil water content - longwave radiation components - shortwave radiation components - tipping bucket rain gauge measurements The ground heat flux including the heat storage in the upper soil layer was determined from the measured soil heat flux, soil temperatures and volumetric soil water contents according to the 'simple measurement' (SM) method according to Liebethal and Foken (2007).

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    Authors: Srivastava, Amit Kumar;

    The yield gap for maize across the Ethiopia has been estimated using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. The yield gap of a crop grown in a certain location and cropping system is defined as the difference between the yield and biomass under optimum management and the average yield achieved by farmers. Yield under optimum management is labeled as potential yield (Yp) under irrigated conditions or water-limited potential yield (Yw) under rain-fed conditions.Yp is location specific because of the climate, and not dependent on soil properties assuming that the required water and nutrients are non-limiting and can be added through management. Thus, in areas without major soil constraints, Yp is the most relevant benchmark for irrigated systems. Whereas, for rain-fed crops, Yw, equivalent to water-limited potential yield, is the most relevant benchmark. Both Yp and Yw are calculated for optimum planting dates, planting density and region-specific crop variety which is critical in determining the feasible growth duration, particularly in tropical climatic conditions where two or even three crops are produced each year on the same field. Purpose: To increase food production, identifying the regions with untapped production capacity is of prime importance and can be achieved by quantitative and spatially explicit estimates of Yield gaps, thus considering the spatial variation in environment and the production system. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c2bbd5ed-fd4c-4a3f-b0b1-113a5d4f3ddf}. The yield gaps plotted in the map were calculated as the average values of 7 years (the year 2004 -2010). The unit is Megagram per hectare (Mg ha-1) which is equivalent to tons ha-1. The climate data at the national scale was made available from the National Aeronautics and Space Administration (NASA), Goddard Institute of Space Studies(https://data.giss.nasa.gov/impacts/agmipcf/agmerra/), AgMERRA.The dataset is stored at 0.25°×0.25° horizontal resolution (~25km). Soil parameter values were extracted from the soil property maps of Africa at 1 km x 1 km resolution (http://www.isric.org/data/soil-property-maps-africa-1-km). Maize yields (Mg ha-1) and fertilizer application (Nitrogen and Phosphorus) rates over seven years (2004 - 2010) at administrative zone level have been collected from the Central Statistical Agency, Ethiopia.

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    https://dx.doi.org/10.60507/fk...
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
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      https://dx.doi.org/10.60507/fk...
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
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    Authors: Leybourne, Daniel J; Preedy, Katharine F; Valentine, Tracy A; Bos, Jorunn I B; +1 Authors

    1. Aphids are abundant in natural and managed vegetation, supporting a diverse community of organisms and causing damage to agricultural crops. Due to a changing climate, periods of drought are anticipated to increase, and the potential consequences of this for aphid-plant interactions are unclear. 2. Using a meta-analysis and synthesis approach, we aimed to advance understanding of how increased drought incidence will affect this ecologically and economically important insect group, and to characterise any potential underlying mechanisms. We used qualitative and quantitative synthesis techniques to determine whether drought stress has a negative, positive, or null effect on aphid fitness and examined these effects in relation to 1) aphid biology, 2) geographical region, 3) host plant biology. 3. Across all studies, aphid fitness is typically reduced under drought. Subgroup analysis detected no difference in relation to aphid biology, geographical region, or the aphid-plant combination, indicating the negative effect of drought on aphids is potentially universal. Furthermore, drought stress had a negative impact on plant vigour and increased plant concentrations of defensive chemicals, suggesting the observed response of aphids is associated with reduced plant vigour and increased chemical defence in drought-stressed plants. 4. We propose a conceptual model to predict drought effects on aphid fitness in relation to plant vigour and defence to stimulate further research. Please check the ReadMe for an explanation of the values included in the dataset. Please note that n/a values are included in the Global_Dataset tab for plant meta-analysis data (_Plant_Vigour, _Plant_Defence, and _Plant_Nutrition), these indicate studies that did not report these parameters. Data was collected and curated using standard systematic literature synthesis approaches. The effect size (Hedges' g) reported in the dataset was calculated from extracted means and standard deviations.

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    ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2021
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2021
      License: CC 0
      Data sources: Datacite
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    Authors: Tian, Ke (Institute of Organic Chemistry, Heidelberg University); Elbert, Sven Michael (Institute of Organic Chemistry, Heidelberg University); Hu, Xinyue (Institute of Organic Chemistry, Heidelberg University); Kirschbaum, Tobias (Institute of Organic Chemistry, Heidelberg University); +4 Authors

    Anthropogenic greenhouse gases contribute to global warming. Among those gases, perfluorocarbons (PFCs) are thousands to tens of thousands times more harmful to the environment than comparable amounts of carbon dioxide. Till date, materials that selectively adsorb perfluorocarbons in favor to other less harmful gases have not been reported. Here a series of porous organic cage compounds with alkyl-, fluoroalkyl, and partially fluorinated alkyl groups are presented. Their isomorphic crystalline states allowed us to study the property-relationship of the degree of fluorination of the alkyl chains on gas sorption properties for PFCs and their selective uptakes in comparison to other, non-fluorinated gases. By this approach, one compound was identified, having superior selectivities of PFCs vs N2 or CO2 under ambient conditions.

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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: Sanchez-Benítez, Antonio; Goessling, Helge; Pithan, Felix; Semmler, Tido; +1 Authors

    We provide the storyline data (in NetCDF format) used in the paper: ���The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging��� published in Journal of Climate. The data is structured in four .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climate. The data from the five ensemble members (E1 to E5) have been included separately in 3-months files. Atmospheric variables (Files are named as: {variable}_E{ensemble member}_{starting month}{year}.nc: Latent heat flux (ahfl) Sensible heat flux (ahfs) Monthly Global Mean 2m Temperature (GMTT2mMonthly) Maximum 2m Temperature (t2max) Mean 2m Temperature (t2mean) Minimum 2m Temperature (t2max) Soil Wetness (ws) Only for present climate: 850 hPa Temperature (T850) Total Cloud Cover (TCC) 500 hPa Geopotential Height (Z500) Five layers soil moisture (Only for present climate, Files are named as: From20172019in2017Climatessp370{ensemble member}_{year}{starting month}.01_jsbid.nc) Oceanic variables (from FESOM, Files are named as: {variable}_E{ensemble member}_{year}{starting month}01.nc: Sea Ice Concentration (SIC) Sea Surface Temperature (SST) Please, note that FESOM uses an unstructured mesh. This work was supported by the Helmholtz-Climate-Initiative through the HI-CAM project (Drivers cluster). Author Goessling acknowledges the financial support by the Federal Ministry of Education and Research of Germany in the framework of SSIP (Grant 01LN1701A). The simulations were performed at the German Climate Computing Center (DKRZ) using the ESM-Tools (Barbi et al. 2021). We thank Sebastian Rast (MPI-M) for support with the spectral nudging in ECHAM, and the ESM-Tools staff for their assistance during the simulation.

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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    Authors: Mirschel, Wilfried; Meier, Kristin; Lemke, Andreas;

    Detailed measurements on soil, plant and atmosphere are required for the development and validation of crop growth and agroecosystem models. These measurements should be available with a high temporal resolution. With the aim of creating a growth model for winter wheat, an experiment with winter wheat under integrated cultivation conditions was carried out at the intensive experimental field of the Müncheberg Research Centre for Soil Fertility, Germany, between 1979 and 1981, both with and without irrigation. Field chambers were used for daily measurements of the CO2 balance of the crop stand. The daily evaporation was measured with two different evaporation pans. The different biomass components of the winter wheat crop stand were measured in weekly intervals from April to harvest in July/August. The different biomass components were analysed in the laboratory concerning their carbon, nitrogen, phosphorus and potassium content. Based on this coherent data set, the growth model TRITSIM for winter wheat was developed at the Müncheberg Research Centre for Soil Fertility in the 1980s. TRITSIM was incorporated into the complex agroecosystem model AGROSIM-WHEAT of the Research Institute of Plant Protection Eberswalde, Germany, for the identification of optimal plant protection measures under practical field conditions. The data set presented here can also be the basis for the verification and validation of further winter wheat growth and/or agroecosystem models.

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    https://dx.doi.org/10.4228/zal...
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      https://dx.doi.org/10.4228/zal...
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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    Authors: Robroek, Bjorn J.M.; Jassey, Vincent E.J.; Payne, Richard J.; Martí, Magalí; +10 Authors

    Environmental dataBioclimatic data and environmental data for all 56 European peatland site (geo referenced by longitude [long], latitude [lat] and altitude [ALT]. MAT = Mean annual temperature (°C), TS = Seasonality in temperature, MAP = Mean annual precipitation (mm), PS = Seasonality in precipitation, tot_sox = Total sulphur deposition SOx (mg m-2 yr-1), tot_noy = Total oxidized nitrogen deposition (mg m-2 yr-1), tot_nhx = Total reduced nitrogen deposition (mg m-2), PT warm = Lang’s moisture index. The four bioclimatic variables (MAT, TS, MAP, PS) were extracted from the WorldClim database (Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005)), and averaged over the 2000-2009 period. Atmospheric deposition data were produced using the EMEP (European Monitoring and Evaluation Programme)-based IDEM (Integrated Deposition Model) model (Pieterse, G., Bleeker, A., Vermeulen, A. T., Wu, Y. & Erisman, J. W. High resolution modelling of atmosphere‐canopy exchange of acidifying and eutrophying components and carbon dioxide for European forests. Tellus B 59, 412–424 (2007)) and consisted of grid cell averages of total reduced (NHx) and oxidised (NOy) nitrogen and sulphur (SOx) deposition. The moisture index (PTwarm) was calculated as the ratio between mean precipitation and mean temperature in the warmest quarter (Thornwaite, C. W. & Holzman, B. Measurement of evaporation from land and water surfaces. USDA Technical Bulletin 817, 1–143 (1942))Data 1_environmental data.txtplant community dataAbundance data (% cover) for all vascular plant and bryophyte species from five randomly chosen hummocks and lawns (0.25 m2 quadrats; ten in total) across 56 European Sphagnum-dominated peatlands were collected in two consecutive summers (2010 and 2011). Vascular plants and Sphagnum mosses were identified to the species level. Non-Sphagnum bryophytes were identified to the family level. Lichens were recorded as one group.Data 2_plant community data.txttraits vascular plantsPlant functional traits used to calculate functional indices for the vascular plant communities. Traits were extracted from LEDA (Kleyer, M. et al. The LEDA Traitbase: a database of life‐history traits of the Northwest European flora. J. Ecol. 96, 1266–1274 (2008)). Only trait data available for all species our data-set were extracted.ncomms_Data 3_traits vascular plants.txttraits SphagnumTrait values (means) for Sphagnum spp. C = tissue carbon content (mg g-1), N = tissue nitrogen content (mg g-1), P = tissue phosphorus content (mg g-1), Productivity ( St.w = stem width (mm), l.h.c. = length hyaline cells (µm), w.h.c. = width hyaline cells (µm), l.s.l. = length stem leaves (mm), w.s.l. = width stem leaves. These measured traits were complemented with traits extracted from the literature. These latter traits included plant length (Hill, M. O., Preston, C. D., Bosanquet, S. & Roy, D. B. BRYOATT: attributes of British and Irish mosses, liverworts and hornworts. Centre for Ecology & Hydrology, Huntingdon, UK (2007)), spore diameter and capsule diameter (Sundberg, S., Hansson, J. & Rydin, H. Colonization of Sphagnum on land uplift islands in the Baltic Sea: time, area, distance and life history. Journal of Biogeography 33, 1479–1491 (2006)), productivity (Gunnarsson, U. Global patterns of Sphagnum productivity. J. Bryol. 27, 269–279 (2005))ncomms_Data 4_traits Sphagnum.txt In peatland ecosystems, plant communities mediate a globally significant carbon store. The effects of global environmental change on plant assemblages are expected to be a factor in determining how ecosystem functions such as carbon uptake will respond. Using vegetation data from 56 Sphagnum-dominated peat bogs across Europe, we show that in these ecosystems plant species aggregate into two major clusters that are each defined by shared response to environmental conditions. Across environmental gradients, we find significant taxonomic turnover in both clusters. However, functional identity and functional redundancy of the community as a whole remain unchanged. This strongly suggests that in peat bogs, species turnover across environmental gradients is restricted to functionally similar species. Our results demonstrate that plant taxonomic and functional turnover are decoupled, which may allow these peat bogs to maintain ecosystem functioning when subject to future environmental change.

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    ZENODO
    Dataset . 2018
    License: CC 0
    Data sources: ZENODO
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    B2FIND
    Dataset . 2017
    Data sources: B2FIND
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    Research@WUR
    Dataset . 2017
    Data sources: Research@WUR
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    EASY
    Dataset . 2017
    Data sources: EASY
    DRYAD
    Dataset . 2018
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2018
      License: CC 0
      Data sources: ZENODO
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      B2FIND
      Dataset . 2017
      Data sources: B2FIND
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      Research@WUR
      Dataset . 2017
      Data sources: Research@WUR
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      EASY
      Dataset . 2017
      Data sources: EASY
      DRYAD
      Dataset . 2018
      License: CC 0
      Data sources: Datacite
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    Authors: Aman Malik; Christoph Bertram; Elmar Krigler; Gunnar Luderer;

    The file contains code to create the figures used in main text and supplementary information of the paper Climate policy accelerates structural changes in energy employment. To run the RMD file and see the resulting figures, press Knit on R studio (requires the package knitr), or else see the attached HTML file, already created through such a process. Additional funding source: University of Tokyo's "Global Commons Stewardship Framework" project.

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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: Smith, Linnea C; Orgiazzi, Alberto; Eisenhauer, Nico; Cesarz, Simone; +10 Authors

    The aim of this study was to quantify direct and indirect relationships between soil microbial community properties (potential basal respiration, microbial biomass) and abiotic factors (soil, climate) in three major land-cover types. Location: Europe Time period: 2018 Major taxa studied: Microbial community (fungi and bacteria) We collected 881 soil samples from across Europe in the framework of the Land Use/Land Cover Area Frame Survey (LUCAS). We measured potential soil basal respiration at 20ºC and microbial biomass (substrate-induced respiration) using an O2-microcompensation apparatus. Climate and soil data were obtained from previous LUCAS surveys and online databases. Structural equation modeling (SEM) was used to quantify relationships between variables, and equations extracted from SEMs were used to create predictive maps. Fatty acid methyl esters were measured in a subset of samples to distinguish fungal from bacterial biomass. Soil microbial properties in croplands were more heavily affected by climate variables than those in forests. Potential soil basal respiration and microbial biomass were correlated in forests but decoupled in grasslands and croplands, where microbial biomass depended on soil carbon. Forests had a higher ratio of fungi to bacteria than grasslands or croplands. Soil microbial communities in grasslands and croplands are likely carbon-limited in comparison with those in forests, and forests have a higher dominance of fungi indicating differences in microbial community composition. Notably, the often already-degraded soils of croplands could be more vulnerable to climate change than more natural soils. The provided maps show potentially vulnerable areas that should be explicitly accounted for in coming management plans to protect soil carbon and slow the increasing vulnerability of European soils to climate change. [Methods] Soil samples were collected during the 2018 LUCAS soil sampling campaign. Soil chemical and physical properties were measured at the Joint Research Centre in Ispra, Italy (Orgiazzi et al., 2018). Soil microbial respiration and biomass, as well as water content and water holding capacity, were measured in the Eisenhauer lab of the German Centre for Integrative Biodiversity Research. Fungi/Bacteria was measured by fatty acid analysis by Felipe Bastida at CEBAS CSIC. Climate and geographical data were harvested from various databases, which are listed in Appendix 1 (data sources) of the associated paper. For more details on the soil sampling and physical and chemical properties, see: Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A., & Fernández-Ugalde, O. (2018). LUCAS Soil, the largest expandable soil dataset for Europe: a review. European Journal of Soil Science, 69(1), 140-153. https://doi.org/10.1111/ejss.12499 For more details on the measurements of soil microbial respiration and biomass, fatty acids, and water holding capacity, see the supplementary methods of the associated paper (Appendix 2). [Usage Notes] Fatty acid analysis was performed for a subset of 267 samples. Water holding capacity and associated measurements of basal respiration was analyzed in a subset of 100 samples. The samples that were not in these subsets have NA values for the columns associated with these measurements. In order to protect the precise locations of the LUCAS sampling sites, latitude and longitude values could not be given. The approximate location of each sampling site is instead described by the NUTS3 region. If you wish to replicate the structural equation modeling described in the paper, for which latitude is required, please get in touch. A description of each column is available in the associated metadata file. Deutsche Forschungsgemeinschaft, Award: FZT 118-202548816. European Research Council, Award: 694368. European Commission. Directorate-General for the Environment. Direction Générale Opérationnelle Agriculture, Ressources Naturelles et Environnement du Service Public de Wallonie. Eurostat. Peer reviewed

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    ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2022
    License: CC 0
    Data sources: Datacite
    Digital.CSIC
    Dataset . 2021
    License: CC 0
    Data sources: Datacite
    Digital.CSIC
    Dataset . 2021 . Peer-reviewed
    Data sources: Digital.CSIC
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      ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2022
      License: CC 0
      Data sources: Datacite
      Digital.CSIC
      Dataset . 2021
      License: CC 0
      Data sources: Datacite
      Digital.CSIC
      Dataset . 2021 . Peer-reviewed
      Data sources: Digital.CSIC
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    Authors: Kunz, Friedrich; Weibezahn, Jens; Hauser, Philip; Heidari, Sina; +8 Authors

    This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector). While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references). The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D. {"references": ["Kunz, Friedrich et. al. (2017). Electricity, Heat and Gas Sector Data for Modeling the German System. DIW Data Documentation 92."]}

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    ZENODO
    Dataset . 2017
    Data sources: Datacite
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    ZENODO
    Dataset . 2017
    Data sources: Datacite
    ZENODO
    Dataset . 2017
    Data sources: ZENODO
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      ZENODO
      Dataset . 2017
      Data sources: Datacite
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      ZENODO
      Dataset . 2017
      Data sources: Datacite
      ZENODO
      Dataset . 2017
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  • Authors: Rafael Eigenmann; Thomas Foken;

    Project: Convective and Orographically-induced Precipitation Study - Weather forecast models have not been successful in improving the Quantitative Precipitation Forecast during the last 16 years. One reason for this stagnation is the lack of comprehensive, high-quality data sets usable for model validation as well as for data assimilation, thus leading to improved initial fields in numerical models. Theoretical analyses have identified the requirements measured data have to meet in order to close the gaps in process understanding. In field campaigns, it has been shown that the newest generation of remote sensing systems has the potential to yield data sets of the required quality. It is therefore time to combine the most powerful remote sensing instruments with proven ground-based and airborne measurement techniques in an Intensive Observations Period (IOP). Its goal is to serve as a backbone for the Priority Program SPP 1167 by producing the demanded data sets of unachieved accuracy and resolution. This requires a sophisticated scientific preparation and a careful coordination between the efforts of the institutions involved. For the first time, the pre-convective environment, the formation of clouds and the onset and development of precipitation as well as its intensity will be observed in four dimensions simultaneously in a region of sufficient size. This shall be achieved by combining the IOP with international programs and by collaboration between leading scientists in Europe, US, and other countries. Thus, the IOP, which we call Convective and Orographically-induced Precipitation Study (COPS), is a unique opportunity for an international field campaign featuring the newest generation of measurement systems such as scanning radar and lidar and leading to outstanding advances in atmospheric sciences. Please be aware of the common COPS/GOP/D-PHASE data policy, which you please find at http://cops.wdc-climate.de/ Summary: The energy balance stations run by University of Bayreuth continuously measured radiation and soil parameters over different land types with a sampling frequency of 1 Hz averaged to 1 min values within the data logger. After a check for plausibility the 1 min values have been averaged to 30 min intervals, which are provided in this data set. The instrumentation was different on each location. The following was measured depending on the station: - soil heat flux - soil temperature - volumetric soil water content - longwave radiation components - shortwave radiation components - tipping bucket rain gauge measurements The ground heat flux including the heat storage in the upper soil layer was determined from the measured soil heat flux, soil temperatures and volumetric soil water contents according to the 'simple measurement' (SM) method according to Liebethal and Foken (2007).

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    Authors: Srivastava, Amit Kumar;

    The yield gap for maize across the Ethiopia has been estimated using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. The yield gap of a crop grown in a certain location and cropping system is defined as the difference between the yield and biomass under optimum management and the average yield achieved by farmers. Yield under optimum management is labeled as potential yield (Yp) under irrigated conditions or water-limited potential yield (Yw) under rain-fed conditions.Yp is location specific because of the climate, and not dependent on soil properties assuming that the required water and nutrients are non-limiting and can be added through management. Thus, in areas without major soil constraints, Yp is the most relevant benchmark for irrigated systems. Whereas, for rain-fed crops, Yw, equivalent to water-limited potential yield, is the most relevant benchmark. Both Yp and Yw are calculated for optimum planting dates, planting density and region-specific crop variety which is critical in determining the feasible growth duration, particularly in tropical climatic conditions where two or even three crops are produced each year on the same field. Purpose: To increase food production, identifying the regions with untapped production capacity is of prime importance and can be achieved by quantitative and spatially explicit estimates of Yield gaps, thus considering the spatial variation in environment and the production system. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c2bbd5ed-fd4c-4a3f-b0b1-113a5d4f3ddf}. The yield gaps plotted in the map were calculated as the average values of 7 years (the year 2004 -2010). The unit is Megagram per hectare (Mg ha-1) which is equivalent to tons ha-1. The climate data at the national scale was made available from the National Aeronautics and Space Administration (NASA), Goddard Institute of Space Studies(https://data.giss.nasa.gov/impacts/agmipcf/agmerra/), AgMERRA.The dataset is stored at 0.25°×0.25° horizontal resolution (~25km). Soil parameter values were extracted from the soil property maps of Africa at 1 km x 1 km resolution (http://www.isric.org/data/soil-property-maps-africa-1-km). Maize yields (Mg ha-1) and fertilizer application (Nitrogen and Phosphorus) rates over seven years (2004 - 2010) at administrative zone level have been collected from the Central Statistical Agency, Ethiopia.

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    https://dx.doi.org/10.60507/fk...
    Dataset . 2023
    License: CC BY SA
    Data sources: Datacite
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      https://dx.doi.org/10.60507/fk...
      Dataset . 2023
      License: CC BY SA
      Data sources: Datacite
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    Authors: Leybourne, Daniel J; Preedy, Katharine F; Valentine, Tracy A; Bos, Jorunn I B; +1 Authors

    1. Aphids are abundant in natural and managed vegetation, supporting a diverse community of organisms and causing damage to agricultural crops. Due to a changing climate, periods of drought are anticipated to increase, and the potential consequences of this for aphid-plant interactions are unclear. 2. Using a meta-analysis and synthesis approach, we aimed to advance understanding of how increased drought incidence will affect this ecologically and economically important insect group, and to characterise any potential underlying mechanisms. We used qualitative and quantitative synthesis techniques to determine whether drought stress has a negative, positive, or null effect on aphid fitness and examined these effects in relation to 1) aphid biology, 2) geographical region, 3) host plant biology. 3. Across all studies, aphid fitness is typically reduced under drought. Subgroup analysis detected no difference in relation to aphid biology, geographical region, or the aphid-plant combination, indicating the negative effect of drought on aphids is potentially universal. Furthermore, drought stress had a negative impact on plant vigour and increased plant concentrations of defensive chemicals, suggesting the observed response of aphids is associated with reduced plant vigour and increased chemical defence in drought-stressed plants. 4. We propose a conceptual model to predict drought effects on aphid fitness in relation to plant vigour and defence to stimulate further research. Please check the ReadMe for an explanation of the values included in the dataset. Please note that n/a values are included in the Global_Dataset tab for plant meta-analysis data (_Plant_Vigour, _Plant_Defence, and _Plant_Nutrition), these indicate studies that did not report these parameters. Data was collected and curated using standard systematic literature synthesis approaches. The effect size (Hedges' g) reported in the dataset was calculated from extracted means and standard deviations.

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    ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2021
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2021
      License: CC 0
      Data sources: Datacite
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    Authors: Tian, Ke (Institute of Organic Chemistry, Heidelberg University); Elbert, Sven Michael (Institute of Organic Chemistry, Heidelberg University); Hu, Xinyue (Institute of Organic Chemistry, Heidelberg University); Kirschbaum, Tobias (Institute of Organic Chemistry, Heidelberg University); +4 Authors

    Anthropogenic greenhouse gases contribute to global warming. Among those gases, perfluorocarbons (PFCs) are thousands to tens of thousands times more harmful to the environment than comparable amounts of carbon dioxide. Till date, materials that selectively adsorb perfluorocarbons in favor to other less harmful gases have not been reported. Here a series of porous organic cage compounds with alkyl-, fluoroalkyl, and partially fluorinated alkyl groups are presented. Their isomorphic crystalline states allowed us to study the property-relationship of the degree of fluorination of the alkyl chains on gas sorption properties for PFCs and their selective uptakes in comparison to other, non-fluorinated gases. By this approach, one compound was identified, having superior selectivities of PFCs vs N2 or CO2 under ambient conditions.

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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: Sanchez-Benítez, Antonio; Goessling, Helge; Pithan, Felix; Semmler, Tido; +1 Authors

    We provide the storyline data (in NetCDF format) used in the paper: ���The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging��� published in Journal of Climate. The data is structured in four .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climate. The data from the five ensemble members (E1 to E5) have been included separately in 3-months files. Atmospheric variables (Files are named as: {variable}_E{ensemble member}_{starting month}{year}.nc: Latent heat flux (ahfl) Sensible heat flux (ahfs) Monthly Global Mean 2m Temperature (GMTT2mMonthly) Maximum 2m Temperature (t2max) Mean 2m Temperature (t2mean) Minimum 2m Temperature (t2max) Soil Wetness (ws) Only for present climate: 850 hPa Temperature (T850) Total Cloud Cover (TCC) 500 hPa Geopotential Height (Z500) Five layers soil moisture (Only for present climate, Files are named as: From20172019in2017Climatessp370{ensemble member}_{year}{starting month}.01_jsbid.nc) Oceanic variables (from FESOM, Files are named as: {variable}_E{ensemble member}_{year}{starting month}01.nc: Sea Ice Concentration (SIC) Sea Surface Temperature (SST) Please, note that FESOM uses an unstructured mesh. This work was supported by the Helmholtz-Climate-Initiative through the HI-CAM project (Drivers cluster). Author Goessling acknowledges the financial support by the Federal Ministry of Education and Research of Germany in the framework of SSIP (Grant 01LN1701A). The simulations were performed at the German Climate Computing Center (DKRZ) using the ESM-Tools (Barbi et al. 2021). We thank Sebastian Rast (MPI-M) for support with the spectral nudging in ECHAM, and the ESM-Tools staff for their assistance during the simulation.

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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    Authors: Mirschel, Wilfried; Meier, Kristin; Lemke, Andreas;

    Detailed measurements on soil, plant and atmosphere are required for the development and validation of crop growth and agroecosystem models. These measurements should be available with a high temporal resolution. With the aim of creating a growth model for winter wheat, an experiment with winter wheat under integrated cultivation conditions was carried out at the intensive experimental field of the Müncheberg Research Centre for Soil Fertility, Germany, between 1979 and 1981, both with and without irrigation. Field chambers were used for daily measurements of the CO2 balance of the crop stand. The daily evaporation was measured with two different evaporation pans. The different biomass components of the winter wheat crop stand were measured in weekly intervals from April to harvest in July/August. The different biomass components were analysed in the laboratory concerning their carbon, nitrogen, phosphorus and potassium content. Based on this coherent data set, the growth model TRITSIM for winter wheat was developed at the Müncheberg Research Centre for Soil Fertility in the 1980s. TRITSIM was incorporated into the complex agroecosystem model AGROSIM-WHEAT of the Research Institute of Plant Protection Eberswalde, Germany, for the identification of optimal plant protection measures under practical field conditions. The data set presented here can also be the basis for the verification and validation of further winter wheat growth and/or agroecosystem models.

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    https://dx.doi.org/10.4228/zal...
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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      https://dx.doi.org/10.4228/zal...
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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    Authors: Robroek, Bjorn J.M.; Jassey, Vincent E.J.; Payne, Richard J.; Martí, Magalí; +10 Authors

    Environmental dataBioclimatic data and environmental data for all 56 European peatland site (geo referenced by longitude [long], latitude [lat] and altitude [ALT]. MAT = Mean annual temperature (°C), TS = Seasonality in temperature, MAP = Mean annual precipitation (mm), PS = Seasonality in precipitation, tot_sox = Total sulphur deposition SOx (mg m-2 yr-1), tot_noy = Total oxidized nitrogen deposition (mg m-2 yr-1), tot_nhx = Total reduced nitrogen deposition (mg m-2), PT warm = Lang’s moisture index. The four bioclimatic variables (MAT, TS, MAP, PS) were extracted from the WorldClim database (Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005)), and averaged over the 2000-2009 period. Atmospheric deposition data were produced using the EMEP (European Monitoring and Evaluation Programme)-based IDEM (Integrated Deposition Model) model (Pieterse, G., Bleeker, A., Vermeulen, A. T., Wu, Y. & Erisman, J. W. High resolution modelling of atmosphere‐canopy exchange of acidifying and eutrophying components and carbon dioxide for European forests. Tellus B 59, 412–424 (2007)) and consisted of grid cell averages of total reduced (NHx) and oxidised (NOy) nitrogen and sulphur (SOx) deposition. The moisture index (PTwarm) was calculated as the ratio between mean precipitation and mean temperature in the warmest quarter (Thornwaite, C. W. & Holzman, B. Measurement of evaporation from land and water surfaces. USDA Technical Bulletin 817, 1–143 (1942))Data 1_environmental data.txtplant community dataAbundance data (% cover) for all vascular plant and bryophyte species from five randomly chosen hummocks and lawns (0.25 m2 quadrats; ten in total) across 56 European Sphagnum-dominated peatlands were collected in two consecutive summers (2010 and 2011). Vascular plants and Sphagnum mosses were identified to the species level. Non-Sphagnum bryophytes were identified to the family level. Lichens were recorded as one group.Data 2_plant community data.txttraits vascular plantsPlant functional traits used to calculate functional indices for the vascular plant communities. Traits were extracted from LEDA (Kleyer, M. et al. The LEDA Traitbase: a database of life‐history traits of the Northwest European flora. J. Ecol. 96, 1266–1274 (2008)). Only trait data available for all species our data-set were extracted.ncomms_Data 3_traits vascular plants.txttraits SphagnumTrait values (means) for Sphagnum spp. C = tissue carbon content (mg g-1), N = tissue nitrogen content (mg g-1), P = tissue phosphorus content (mg g-1), Productivity ( St.w = stem width (mm), l.h.c. = length hyaline cells (µm), w.h.c. = width hyaline cells (µm), l.s.l. = length stem leaves (mm), w.s.l. = width stem leaves. These measured traits were complemented with traits extracted from the literature. These latter traits included plant length (Hill, M. O., Preston, C. D., Bosanquet, S. & Roy, D. B. BRYOATT: attributes of British and Irish mosses, liverworts and hornworts. Centre for Ecology & Hydrology, Huntingdon, UK (2007)), spore diameter and capsule diameter (Sundberg, S., Hansson, J. & Rydin, H. Colonization of Sphagnum on land uplift islands in the Baltic Sea: time, area, distance and life history. Journal of Biogeography 33, 1479–1491 (2006)), productivity (Gunnarsson, U. Global patterns of Sphagnum productivity. J. Bryol. 27, 269–279 (2005))ncomms_Data 4_traits Sphagnum.txt In peatland ecosystems, plant communities mediate a globally significant carbon store. The effects of global environmental change on plant assemblages are expected to be a factor in determining how ecosystem functions such as carbon uptake will respond. Using vegetation data from 56 Sphagnum-dominated peat bogs across Europe, we show that in these ecosystems plant species aggregate into two major clusters that are each defined by shared response to environmental conditions. Across environmental gradients, we find significant taxonomic turnover in both clusters. However, functional identity and functional redundancy of the community as a whole remain unchanged. This strongly suggests that in peat bogs, species turnover across environmental gradients is restricted to functionally similar species. Our results demonstrate that plant taxonomic and functional turnover are decoupled, which may allow these peat bogs to maintain ecosystem functioning when subject to future environmental change.

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    ZENODO
    Dataset . 2018
    License: CC 0
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    B2FIND
    Dataset . 2017
    Data sources: B2FIND
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    Research@WUR
    Dataset . 2017
    Data sources: Research@WUR
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    EASY
    Dataset . 2017
    Data sources: EASY
    DRYAD
    Dataset . 2018
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2018
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      B2FIND
      Dataset . 2017
      Data sources: B2FIND
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      Research@WUR
      Dataset . 2017
      Data sources: Research@WUR
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      EASY
      Dataset . 2017
      Data sources: EASY
      DRYAD
      Dataset . 2018
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    Authors: Aman Malik; Christoph Bertram; Elmar Krigler; Gunnar Luderer;

    The file contains code to create the figures used in main text and supplementary information of the paper Climate policy accelerates structural changes in energy employment. To run the RMD file and see the resulting figures, press Knit on R studio (requires the package knitr), or else see the attached HTML file, already created through such a process. Additional funding source: University of Tokyo's "Global Commons Stewardship Framework" project.

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    ZENODO
    Dataset . 2021
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
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      ZENODO
      Dataset . 2021
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      ZENODO
      Dataset . 2021
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      ZENODO
      Dataset . 2021
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    Authors: Smith, Linnea C; Orgiazzi, Alberto; Eisenhauer, Nico; Cesarz, Simone; +10 Authors

    The aim of this study was to quantify direct and indirect relationships between soil microbial community properties (potential basal respiration, microbial biomass) and abiotic factors (soil, climate) in three major land-cover types. Location: Europe Time period: 2018 Major taxa studied: Microbial community (fungi and bacteria) We collected 881 soil samples from across Europe in the framework of the Land Use/Land Cover Area Frame Survey (LUCAS). We measured potential soil basal respiration at 20ºC and microbial biomass (substrate-induced respiration) using an O2-microcompensation apparatus. Climate and soil data were obtained from previous LUCAS surveys and online databases. Structural equation modeling (SEM) was used to quantify relationships between variables, and equations extracted from SEMs were used to create predictive maps. Fatty acid methyl esters were measured in a subset of samples to distinguish fungal from bacterial biomass. Soil microbial properties in croplands were more heavily affected by climate variables than those in forests. Potential soil basal respiration and microbial biomass were correlated in forests but decoupled in grasslands and croplands, where microbial biomass depended on soil carbon. Forests had a higher ratio of fungi to bacteria than grasslands or croplands. Soil microbial communities in grasslands and croplands are likely carbon-limited in comparison with those in forests, and forests have a higher dominance of fungi indicating differences in microbial community composition. Notably, the often already-degraded soils of croplands could be more vulnerable to climate change than more natural soils. The provided maps show potentially vulnerable areas that should be explicitly accounted for in coming management plans to protect soil carbon and slow the increasing vulnerability of European soils to climate change. [Methods] Soil samples were collected during the 2018 LUCAS soil sampling campaign. Soil chemical and physical properties were measured at the Joint Research Centre in Ispra, Italy (Orgiazzi et al., 2018). Soil microbial respiration and biomass, as well as water content and water holding capacity, were measured in the Eisenhauer lab of the German Centre for Integrative Biodiversity Research. Fungi/Bacteria was measured by fatty acid analysis by Felipe Bastida at CEBAS CSIC. Climate and geographical data were harvested from various databases, which are listed in Appendix 1 (data sources) of the associated paper. For more details on the soil sampling and physical and chemical properties, see: Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A., & Fernández-Ugalde, O. (2018). LUCAS Soil, the largest expandable soil dataset for Europe: a review. European Journal of Soil Science, 69(1), 140-153. https://doi.org/10.1111/ejss.12499 For more details on the measurements of soil microbial respiration and biomass, fatty acids, and water holding capacity, see the supplementary methods of the associated paper (Appendix 2). [Usage Notes] Fatty acid analysis was performed for a subset of 267 samples. Water holding capacity and associated measurements of basal respiration was analyzed in a subset of 100 samples. The samples that were not in these subsets have NA values for the columns associated with these measurements. In order to protect the precise locations of the LUCAS sampling sites, latitude and longitude values could not be given. The approximate location of each sampling site is instead described by the NUTS3 region. If you wish to replicate the structural equation modeling described in the paper, for which latitude is required, please get in touch. A description of each column is available in the associated metadata file. Deutsche Forschungsgemeinschaft, Award: FZT 118-202548816. European Research Council, Award: 694368. European Commission. Directorate-General for the Environment. Direction Générale Opérationnelle Agriculture, Ressources Naturelles et Environnement du Service Public de Wallonie. Eurostat. Peer reviewed

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    ZENODO
    Dataset . 2022
    License: CC 0
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    DRYAD
    Dataset . 2022
    License: CC 0
    Data sources: Datacite
    Digital.CSIC
    Dataset . 2021
    License: CC 0
    Data sources: Datacite
    Digital.CSIC
    Dataset . 2021 . Peer-reviewed
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      ZENODO
      Dataset . 2022
      License: CC 0
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      Dataset . 2022
      License: CC 0
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      Dataset . 2021
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      Dataset . 2021 . Peer-reviewed
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    Authors: Kunz, Friedrich; Weibezahn, Jens; Hauser, Philip; Heidari, Sina; +8 Authors

    This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector). While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references). The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D. {"references": ["Kunz, Friedrich et. al. (2017). Electricity, Heat and Gas Sector Data for Modeling the German System. DIW Data Documentation 92."]}

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    ZENODO
    Dataset . 2017
    Data sources: Datacite
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    ZENODO
    Dataset . 2017
    Data sources: Datacite
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    Dataset . 2017
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      ZENODO
      Dataset . 2017
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      ZENODO
      Dataset . 2017
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