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  • Energy Research
  • 2021-2025
  • 7. Clean energy
  • 11. Sustainability
  • 15. Life on land

  • Authors: Chan, Gabriel; Heeter, Jenny; Xu, Kaifeng;

    This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2021. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

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    Authors: Vuorinen, Tommi A.T.; Veikkolainen, Toni; Taylor, George; Gal, Martin; +3 Authors

    In summer 2020 the energy company St1 carried out its second stimulation of deep geothermal wells in Otaniemi, Espoo, in the Helsinki metropolitan area, southern Finland. Institute of Seismology of University of Helsinki (ISUH) monitored the induced seismicity during the stimulation, and also months before and after it. In the second half of 2022 ISUH consulted the Australian company Institute of Mine Seismology (IMS, https://www.imseismology.org) for providing an automatic phase picking on the ISUH 2020 event and waveform data catalogue (doi:10.23729/cdfd937c-37d5-46b0-9c16-f6e0c10bc81f) using an algorithm based on machine learning (doi: 10.1785/0220210068). The dataset provided by IMS was later transferred to formats used by ISUH. The resulting dataset comprises of phase pickings and relevant waveforms of 85 induced earthquakes that occurred between 8 March 2020 to 8 December, 2020, with local magnitudes between -1.1 and 1.4. Note that the event location and other metadata of the resulting dataset are still based on the ISUH 2020 catalogue in order to preserve the consistency within the dataset as some events did not have enough automatic phase picks for reliable relocation. Waveform, location and timing data have been produced at ISUH using seismic stations of the Finnish National Seismic Network (doi: 10.14470/UR044600) including the Helsinki local broadband network, the temporary HEL broadband network in Helsinki and Espoo, the temporary borehole network of St1 (doi: 10.1785/0220190253), and a pool of lightweight mobile seismic instruments operated by ISUH (GIPP data cubes, doi: 10.5880/GIPP.201925.1; SmartSolos and Refteks, doi: 10.1785/0220210195). The deployment is described in Rintamäki et al., 2021, A Seismic Network to Monitor the 2020 EGS Stimulation in the Espoo/Helsinki Area, Southern Finland, doi:10.1785/0220210195. Event data, event metadata, and station metadata are provided in distinct directories, and for event data, each event is assigned a subdirectory. Data formats follow generally accepted seismological standards.

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    Fairdata IDA Research Data Storage Service
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      Fairdata IDA Research Data Storage Service
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Additional file 2. Detailed list of failure modes.

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    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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  • Authors: O’Gorman, E.J.; Warner, E.; Marteinsdóttir, B.; Helmutsdóttir, V.F.; +2 Authors

    Herbivory assessments were made at the plant community and species levels. We focused on three plant species with a widespread occurrence across the temperature gradient: cuckooflower (Cardamine pratensis, Linnaeus), common mouse-ear (Cerastium fontanum, Baumgerten), and marsh violet (Viola palustris, Linnaeus). For assessments of invertebrate herbivory at the species level, thirty individuals per species of C. pratensis, C. fontanum, and V. palustris were marked in each of ten plots, using a stratified random sampling method where individuals were randomly selected, but the full range of within-plot soil temperatures was represented. For assessments of invertebrate herbivory at the community level, five 50 × 50 cm quadrats were marked at random points in eight of the plots that best captured the full temperature gradient. The community-level herbivory assessment was conducted on 19th June. The number of damaged plants was recorded out of 100 random individuals, selected using a 10 × 10 grid within each 50 × 50 cm quadrat. For the species-level herbivory assessment, individual marked plants were surveyed for signs of invertebrate herbivory every two weeks from 30th May to 2nd July, generating three time-points per species. At each survey, all marked individuals for each species were assessed within a 48-hour period. Plants were recorded as damaged or not damaged by invertebrate herbivores at each time-point. Further details of how phenological stage of development, vegetation community composition, soil temperature, moisture, pH, nitrate, ammonium, and phosphate were recorded are provided in the supporting documentation. This is a dataset of environmental data, vegetation cover, and community- and species-level invertebrate herbivory, sampled at 14 experimental soil plots in the Hengill geothermal valley, Iceland, from May to July 2017. The plots span a temperature gradient of 5-35 °C on average over the sampling period, yet they occur within 1 km of each other and have similar soil moisture, pH, nitrate, ammonium, and phosphate.

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    This archive includes a minimal dataset needed to reproduce the analysis as well as a table (CSV) and spatial polygons (ESRI shapefile) of the resulting output from the publication: Hoecker, T.J., S. A. Parks, M. Krosby & S. Z. Dobrowski. 2023. Widespread exposure to altered fire regimes under 2°C warming is projected to transform conifer forests of the Western United States. Communications Earth and Environment. Publication abstract: Changes in wildfire frequency and severity are altering conifer forests and pose threats to biodiversity and natural climate solutions. Where and when feedbacks between vegetation and fire could mediate forest transformation are unresolved. Here, for the western U.S., we used climate analogs to measure exposure to fire-regime change; quantified the direction and spatial distribution of changes in burn severity; and intersected exposure with fire-resistance trait data. We measured exposure as multivariate dissimilarities between contemporary distributions of fire frequency, burn severity, and vegetation productivity and distributions supported by a 2 °C-warmer climate. We project exposure to fire-regime change across 65% of western US conifer forests and mean burn severity to ultimately decline across 63% because of feedbacks with forest productivity and fire frequency. We find that forests occupying disparate portions of climate space are vulnerable to projected fire-regime changes. Forests may adapt to future disturbance regimes, but trajectories remain uncertain.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Tatebe, Hiroaki; Watanabe, Masahiro;

    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.CMIP.MIROC.MIROC6.historical' 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 MIROC6 climate model, released in 2017, includes the following components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 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: Patryk Błaszczak;

    The dataset contains the FTIR spectra collected during the electrical measurements DIR-SOFC with LSCNT layer. The SOFC was reduced at 800degC in H2 prior to mesurements. The spectra were collected every 10 min.

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  • Authors: Buchmann, Nina; Feigenwinter, Iris; Hörtnagl, Lukas;

    The Swiss FluxNet Site Davos is a managed subalpine evergreen forest, located on the Seehorn mountain near Davos in the Swiss Alps. The site is dominated by Norway spruce. The tower is owned by the Federal Office for the Environment (FOEN). Ecosystem flux measurements of CO2, H2O (since 1997) as well as CH4 and N2O (since 2016) are performed with the eddy covariance method. In addition to Swiss FluxNet, the site is part of the National Air Pollution Monitoring Network (NABEL), the Long term Forest Ecosystem Research (LWF), the biological drought and growth indicator network (TreeNet) and of ICOS Switzerland (Integrated Carbon Observation System). Since November 2019, the site is an ICOS Class 1 Ecosystem station.Measurements- Ecosystem flux measurements of CO2, H2O vapour (since 1997) as well a CH4 and N2O (since 2016) are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor) and laser spectrometers (for CH4 and N2O), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site.- Sub-canopy eddy fluxes (CO2, H2O, since 2023 also CH4).- Continuous profile concentration and forest floor flux measurement of CO2, H2O, CH4, N2O.- Auxiliary micrometeorology and soil climate measurements.Data availabilityNear real-time flux and meteo data uploaded daily to the ICOS Carbon Portal. Processed flux and meteo data are also available from the European Fluxes Database Cluster and part of Fluxnet2015 dataset.Data policyICOS data license: [https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license)Detailed site info: [https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/)

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    Authors: Paula Castesana (11024216); Melisa Diaz Resquin (11024219); Sabine Darras (11024222); Darío Gómez (11024225); +6 Authors

    PAPILA dataset is a collection of annual emission inventories of reactive gases (CO, NOx, NMVOCs, NH3 and SO2) from anthropogenic sources in South America, for the period 2014–2016. The dataset was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4.1 (developed by joining CEDS trends and EDGARv4.3.2 historical data), enriching it with derived data from locally available emission inventories for Argentina, Chile and Colombia. The inventories are presented as NetCDF4 files, one for each species and year, gridded with a spatial resolution of 0.1° x 0.1° covering the domain 32° W–120° W and 34° N–58° S. Each file contains 12 variables corresponding to the emissions in Tg/y from the following categories, which are organized and denominated using the nomenclature given by CAMS: thermal power plants (ENE); residential and commercial combustion (RES); road transportation (TRO); non-road transportation (TNR); fugitive emissions (FEF); industries (including fuel consumption in manufacturing industries and construction, refineries, industrial processes and solvent and other products use) (IND); agricultural soils (AGS); agriculture livestock (AGL); inland navigation (SHP); international navigation (SHP-INT); waste (including solid waste, wastewater and incineration) (SWD); and the sum of all sectors (SUM).

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    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
    Dataset . 2021
    License: CC BY
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    Mendeley Data
    Dataset . 2021
    License: CC BY
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    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    B2FIND
    Dataset . 2021
    Data sources: B2FIND
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    B2FIND
    Dataset . 2021
    Data sources: B2FIND
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    B2FIND
    Dataset . 2021
    Data sources: B2FIND
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      Mendeley Data
      Dataset . 2021
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      Data sources: Datacite
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      Mendeley Data
      Dataset . 2021
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      Mendeley Data
      Dataset . 2021
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      Mendeley Data
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      B2FIND
      Dataset . 2021
      Data sources: B2FIND
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      B2FIND
      Dataset . 2021
      Data sources: B2FIND
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      B2FIND
      Dataset . 2021
      Data sources: B2FIND
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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    Authors: Lempidakis, Emmanouil; Ross, Andrew; Börger, Luca; Shepard, Emily;

    Variable list for files: SW wind - Section table on Skomer (Standardised).csv / NW wind - Section table on Skomer (Standardised).csv / SE wind - Section table on Skomer (Standardised).csv /NE wind - Section table on Skomer (Standardised).csv and SW wind - Sections on Skokholm (Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanUMedian; MeanUIQR, MeanUSkewness, MeanUCV: Median, interquartile range,skewness and coefficient of variation of mean wind speed per section HorizontalMedian;HorizontalIQR,HorizontalSkewness,HorizontalCV: Median, interquartile range,skewness and coefficient of variation of horizontal wind speed per section PMedian;PIQR,PSkewness,PCV: Median, interquartile range,skewness and coefficient of variation of preessure per section TKEMedian;TKEIQR,TKESkewness,TKECV: Median, interquartile range,skewness and coefficient of variation of turbulent kinetic energy per section TIMedian;TIIQR,TISkewness,TICV: Median, interquartile range,skewness and coefficient of variation of turbulence intensity per section U_2Median;lU_2IQR;U_2Skewness;U_2CV: Median, interquartile range,skewness and coefficient of variation of vertical wind speed per section EpsilonMedian;EpsilonIQR,EpsilonSkewness,EpsilonCV: Median, interquartile range,skewness and coefficient of variation of turbulent dissipation rate per section NutMedian;NutIQR,NutSkewness,NutCV: Median, interquartile range,skewness and coefficient of variation of kinematic viscosity per section GustsMedian;GustsIQR,GustsSkewness,GustsCV: Median, interquartile range,skewness and coefficient of variation of instataneous gusts per section MeanSectorSlope: Mean slope per section ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: Section table on Skomer - with Mean cliff orientation and Slope (NOT-Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section ApsectClass: Factor indicating whether the mean cliff orientation is lee- or windward to the SW wind ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: SW wind - Sections on Skokholm to predict colonies using cliff orientation and slope model from Skomer (NON - Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots (Uria aalge) breeding on Skomer island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/ wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions, and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions. Methods detailed in manuscript: https://doi.org/10.1111/ecog.05733.

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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
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      DRYAD
      Dataset . 2021
      License: CC 0
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  • Authors: Chan, Gabriel; Heeter, Jenny; Xu, Kaifeng;

    This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2021. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

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    Authors: Vuorinen, Tommi A.T.; Veikkolainen, Toni; Taylor, George; Gal, Martin; +3 Authors

    In summer 2020 the energy company St1 carried out its second stimulation of deep geothermal wells in Otaniemi, Espoo, in the Helsinki metropolitan area, southern Finland. Institute of Seismology of University of Helsinki (ISUH) monitored the induced seismicity during the stimulation, and also months before and after it. In the second half of 2022 ISUH consulted the Australian company Institute of Mine Seismology (IMS, https://www.imseismology.org) for providing an automatic phase picking on the ISUH 2020 event and waveform data catalogue (doi:10.23729/cdfd937c-37d5-46b0-9c16-f6e0c10bc81f) using an algorithm based on machine learning (doi: 10.1785/0220210068). The dataset provided by IMS was later transferred to formats used by ISUH. The resulting dataset comprises of phase pickings and relevant waveforms of 85 induced earthquakes that occurred between 8 March 2020 to 8 December, 2020, with local magnitudes between -1.1 and 1.4. Note that the event location and other metadata of the resulting dataset are still based on the ISUH 2020 catalogue in order to preserve the consistency within the dataset as some events did not have enough automatic phase picks for reliable relocation. Waveform, location and timing data have been produced at ISUH using seismic stations of the Finnish National Seismic Network (doi: 10.14470/UR044600) including the Helsinki local broadband network, the temporary HEL broadband network in Helsinki and Espoo, the temporary borehole network of St1 (doi: 10.1785/0220190253), and a pool of lightweight mobile seismic instruments operated by ISUH (GIPP data cubes, doi: 10.5880/GIPP.201925.1; SmartSolos and Refteks, doi: 10.1785/0220210195). The deployment is described in Rintamäki et al., 2021, A Seismic Network to Monitor the 2020 EGS Stimulation in the Espoo/Helsinki Area, Southern Finland, doi:10.1785/0220210195. Event data, event metadata, and station metadata are provided in distinct directories, and for event data, each event is assigned a subdirectory. Data formats follow generally accepted seismological standards.

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    Fairdata IDA Research Data Storage Service
    Dataset . 2023
    License: CC BY
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      Dataset . 2023
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    Additional file 2. Detailed list of failure modes.

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    figshare
    Dataset . 2021
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    Dataset . 2021
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      Dataset . 2021
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      Dataset . 2021
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  • Authors: O’Gorman, E.J.; Warner, E.; Marteinsdóttir, B.; Helmutsdóttir, V.F.; +2 Authors

    Herbivory assessments were made at the plant community and species levels. We focused on three plant species with a widespread occurrence across the temperature gradient: cuckooflower (Cardamine pratensis, Linnaeus), common mouse-ear (Cerastium fontanum, Baumgerten), and marsh violet (Viola palustris, Linnaeus). For assessments of invertebrate herbivory at the species level, thirty individuals per species of C. pratensis, C. fontanum, and V. palustris were marked in each of ten plots, using a stratified random sampling method where individuals were randomly selected, but the full range of within-plot soil temperatures was represented. For assessments of invertebrate herbivory at the community level, five 50 × 50 cm quadrats were marked at random points in eight of the plots that best captured the full temperature gradient. The community-level herbivory assessment was conducted on 19th June. The number of damaged plants was recorded out of 100 random individuals, selected using a 10 × 10 grid within each 50 × 50 cm quadrat. For the species-level herbivory assessment, individual marked plants were surveyed for signs of invertebrate herbivory every two weeks from 30th May to 2nd July, generating three time-points per species. At each survey, all marked individuals for each species were assessed within a 48-hour period. Plants were recorded as damaged or not damaged by invertebrate herbivores at each time-point. Further details of how phenological stage of development, vegetation community composition, soil temperature, moisture, pH, nitrate, ammonium, and phosphate were recorded are provided in the supporting documentation. This is a dataset of environmental data, vegetation cover, and community- and species-level invertebrate herbivory, sampled at 14 experimental soil plots in the Hengill geothermal valley, Iceland, from May to July 2017. The plots span a temperature gradient of 5-35 °C on average over the sampling period, yet they occur within 1 km of each other and have similar soil moisture, pH, nitrate, ammonium, and phosphate.

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    This archive includes a minimal dataset needed to reproduce the analysis as well as a table (CSV) and spatial polygons (ESRI shapefile) of the resulting output from the publication: Hoecker, T.J., S. A. Parks, M. Krosby & S. Z. Dobrowski. 2023. Widespread exposure to altered fire regimes under 2°C warming is projected to transform conifer forests of the Western United States. Communications Earth and Environment. Publication abstract: Changes in wildfire frequency and severity are altering conifer forests and pose threats to biodiversity and natural climate solutions. Where and when feedbacks between vegetation and fire could mediate forest transformation are unresolved. Here, for the western U.S., we used climate analogs to measure exposure to fire-regime change; quantified the direction and spatial distribution of changes in burn severity; and intersected exposure with fire-resistance trait data. We measured exposure as multivariate dissimilarities between contemporary distributions of fire frequency, burn severity, and vegetation productivity and distributions supported by a 2 °C-warmer climate. We project exposure to fire-regime change across 65% of western US conifer forests and mean burn severity to ultimately decline across 63% because of feedbacks with forest productivity and fire frequency. We find that forests occupying disparate portions of climate space are vulnerable to projected fire-regime changes. Forests may adapt to future disturbance regimes, but trajectories remain uncertain.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
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      Dataset . 2023
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      Dataset . 2023
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      ZENODO
      Dataset . 2023
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    Authors: Tatebe, Hiroaki; Watanabe, Masahiro;

    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.CMIP.MIROC.MIROC6.historical' 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 MIROC6 climate model, released in 2017, includes the following components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 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
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  • Authors: Patryk Błaszczak;

    The dataset contains the FTIR spectra collected during the electrical measurements DIR-SOFC with LSCNT layer. The SOFC was reduced at 800degC in H2 prior to mesurements. The spectra were collected every 10 min.

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  • Authors: Buchmann, Nina; Feigenwinter, Iris; Hörtnagl, Lukas;

    The Swiss FluxNet Site Davos is a managed subalpine evergreen forest, located on the Seehorn mountain near Davos in the Swiss Alps. The site is dominated by Norway spruce. The tower is owned by the Federal Office for the Environment (FOEN). Ecosystem flux measurements of CO2, H2O (since 1997) as well as CH4 and N2O (since 2016) are performed with the eddy covariance method. In addition to Swiss FluxNet, the site is part of the National Air Pollution Monitoring Network (NABEL), the Long term Forest Ecosystem Research (LWF), the biological drought and growth indicator network (TreeNet) and of ICOS Switzerland (Integrated Carbon Observation System). Since November 2019, the site is an ICOS Class 1 Ecosystem station.Measurements- Ecosystem flux measurements of CO2, H2O vapour (since 1997) as well a CH4 and N2O (since 2016) are performed with the eddy-covariance method. This method is based on measurements of trace gas mixing ratios, using infrared gas analyzers (for CO2, H2O vapor) and laser spectrometers (for CH4 and N2O), combined with wind speed and wind direction measurements, using 3D sonic anemometers. To resolve the short-term turbulent fluctuations in the atmosphere, very fast measurements are needed: we measure at 10-20 Hz, i.e., 10-20 times per second. To assess the energy budget of each ecosystem, also radiation sensors and soil climate profiles are installed at the site.- Sub-canopy eddy fluxes (CO2, H2O, since 2023 also CH4).- Continuous profile concentration and forest floor flux measurement of CO2, H2O, CH4, N2O.- Auxiliary micrometeorology and soil climate measurements.Data availabilityNear real-time flux and meteo data uploaded daily to the ICOS Carbon Portal. Processed flux and meteo data are also available from the European Fluxes Database Cluster and part of Fluxnet2015 dataset.Data policyICOS data license: [https://www.icos-cp.eu/data-services/about-data-portal/data-license](https://www.icos-cp.eu/data-services/about-data-portal/data-license)Detailed site info: [https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/)

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    Authors: Paula Castesana (11024216); Melisa Diaz Resquin (11024219); Sabine Darras (11024222); Darío Gómez (11024225); +6 Authors

    PAPILA dataset is a collection of annual emission inventories of reactive gases (CO, NOx, NMVOCs, NH3 and SO2) from anthropogenic sources in South America, for the period 2014–2016. The dataset was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4.1 (developed by joining CEDS trends and EDGARv4.3.2 historical data), enriching it with derived data from locally available emission inventories for Argentina, Chile and Colombia. The inventories are presented as NetCDF4 files, one for each species and year, gridded with a spatial resolution of 0.1° x 0.1° covering the domain 32° W–120° W and 34° N–58° S. Each file contains 12 variables corresponding to the emissions in Tg/y from the following categories, which are organized and denominated using the nomenclature given by CAMS: thermal power plants (ENE); residential and commercial combustion (RES); road transportation (TRO); non-road transportation (TNR); fugitive emissions (FEF); industries (including fuel consumption in manufacturing industries and construction, refineries, industrial processes and solvent and other products use) (IND); agricultural soils (AGS); agriculture livestock (AGL); inland navigation (SHP); international navigation (SHP-INT); waste (including solid waste, wastewater and incineration) (SWD); and the sum of all sectors (SUM).

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    Mendeley Data
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    Dataset . 2021
    License: CC BY
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    Mendeley Data
    Dataset . 2021
    License: CC BY
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    Mendeley Data
    Dataset . 2021
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    B2FIND
    Dataset . 2021
    Data sources: B2FIND
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    B2FIND
    Dataset . 2021
    Data sources: B2FIND
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    B2FIND
    Dataset . 2021
    Data sources: B2FIND
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    Smithsonian figshare
    Dataset . 2021
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    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      Dataset . 2021
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      Dataset . 2021
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      B2FIND
      Dataset . 2021
      Data sources: B2FIND
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      B2FIND
      Dataset . 2021
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      B2FIND
      Dataset . 2021
      Data sources: B2FIND
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      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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      Dataset . 2021
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    Authors: Lempidakis, Emmanouil; Ross, Andrew; Börger, Luca; Shepard, Emily;

    Variable list for files: SW wind - Section table on Skomer (Standardised).csv / NW wind - Section table on Skomer (Standardised).csv / SE wind - Section table on Skomer (Standardised).csv /NE wind - Section table on Skomer (Standardised).csv and SW wind - Sections on Skokholm (Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanUMedian; MeanUIQR, MeanUSkewness, MeanUCV: Median, interquartile range,skewness and coefficient of variation of mean wind speed per section HorizontalMedian;HorizontalIQR,HorizontalSkewness,HorizontalCV: Median, interquartile range,skewness and coefficient of variation of horizontal wind speed per section PMedian;PIQR,PSkewness,PCV: Median, interquartile range,skewness and coefficient of variation of preessure per section TKEMedian;TKEIQR,TKESkewness,TKECV: Median, interquartile range,skewness and coefficient of variation of turbulent kinetic energy per section TIMedian;TIIQR,TISkewness,TICV: Median, interquartile range,skewness and coefficient of variation of turbulence intensity per section U_2Median;lU_2IQR;U_2Skewness;U_2CV: Median, interquartile range,skewness and coefficient of variation of vertical wind speed per section EpsilonMedian;EpsilonIQR,EpsilonSkewness,EpsilonCV: Median, interquartile range,skewness and coefficient of variation of turbulent dissipation rate per section NutMedian;NutIQR,NutSkewness,NutCV: Median, interquartile range,skewness and coefficient of variation of kinematic viscosity per section GustsMedian;GustsIQR,GustsSkewness,GustsCV: Median, interquartile range,skewness and coefficient of variation of instataneous gusts per section MeanSectorSlope: Mean slope per section ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: Section table on Skomer - with Mean cliff orientation and Slope (NOT-Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section ApsectClass: Factor indicating whether the mean cliff orientation is lee- or windward to the SW wind ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: SW wind - Sections on Skokholm to predict colonies using cliff orientation and slope model from Skomer (NON - Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots (Uria aalge) breeding on Skomer island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/ wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions, and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions. Methods detailed in manuscript: https://doi.org/10.1111/ecog.05733.

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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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      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/ ZENODOarrow_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/
      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2021
      License: CC 0
      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.