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Seeds of 52 species of herbaceous plants typical from European grassland ecosystems were obtained from a commercial supplier (Planta naturalis). When species germinated in Petri dishes the seedlings were then transplanted to plastic pots (11 x 11 x 12 cm height, 1L volume). Pots were filled with a mixture of a potting substrate (Biolan Murumuld) and sand. Pots were randomly placed in the greenhouse of the University of Tartu, Estonia. Then, we established monocultures with seven individuals of a single species per pot which were grown under well-watered conditions. One month after transplanting the seedlings to the pots, a drought treatment was applied to half of the pots (five pots per species). The experiment was harvested in late July 2020, when the first individuals started flowering, after month-long drought treatment. Plant traits related to drought responses and resource use strategies were selected and measured for each species following established protocols. These included seven above- and belowground traits: Vegetative plant height (H, cm), Leaf Area (LA, mm2), Specific Leaf Area (SLA, mm2 mg-1), Leaf Dry Matter Content (LDMC, mg g-1), Specific Root Length (SRL, cm g-1), Average root Diameter (AvgD, mm), Root Dry Matter Content (RDMC, mg g-1). Before harvesting, we measured the plant height and collected one leaf per individual for three individuals per pot. Afterward, we collected the aboveground biomass and belowground biomass of all the individuals in each pot. Due to the difficulty in untangling the roots of the different individuals in a pot, root traits were estimated at the pot level. Roots were washed and a sample of finest roots (10-50mg) was collected. Leaves and fine roots were scanned at 300dpi and 600dpi, respectively, using an Epson perfection 3200 Photo scanner for leaves and Epson V700 Photo scanner for fine roots. After scanning, leaves and roots were oven-dried at 60°C for 72h. AvgD and root length were determined using WinRHIZO Pro 2015 (Regent Instruments Inc., Canada), and leaf area with ImageJ software. We averaged all traits values at the species level, attaining a single value for each trait in each treatment. The total aboveground biomass and total belowground biomass of each pot were oven-dried at 60°C for 72h and weighed. Drought is expected to increase in future climate scenarios. Although responses to drought of individual functional traits are relatively well-known, simultaneous changes across multiple traits in response to water scarcity remain poorly understood despite its importance to understand alternative strategies to resist drought. We grew 52 herbaceous species in monocultures under drought and control treatments and characterized the functional space using seven measured above- and belowground traits: plant height, leaf area, specific leaf area, leaf dry matter content, specific root length, average root diameter, and root dry matter content. Then, we estimated how each species occupied this space and the amount of functional space occupied in both treatments using trait probability density functions. We also estimated intraspecific trait variability (ITV) for each species as the dissimilarity in trait values between the individuals of each treatment. We then mapped drought resistance and ITV in the functional space using generalized additive models. The response of species to drought strongly depended on their traits, with species that invested more in root tissues and conserved small size being both more resistant to drought and having higher ITV. We also observed a significant trend of trait displacement towards less conservative strategies. However, these changes depended strongly on the trait values of species in the control treatment, with species with different traits having opposing responses to drought. These contrasting responses resulted in lower trait variability in the species pool in drought compared to control conditions. Our results suggest strong trait filtering acting on conservative species as well as the existence of an optimal part in the functional space to which species converge under drought. Our results show that changes in species trait-space occupancy are key to understand plant strategies to withstand drought, highlighting the importance of individual variation in response to environmental changes, and suggest that community-wide functional diversity and biomass productivity could decrease in a drier future. Knowing these shifts will help to anticipate changes in ecosystem functioning facing climate change. The complete dataset is in the file.

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.

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.

The analyses of future climate change over South Africa as described in the Third National Communication, are from the projections of the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Assessment Report (AR) 5 of the Intergovernmental Panel on Climate Change (IPCC). These projections are used to inform on the uncertainty range of the large-scale climate change futures over the southern African region. At the Council for Scientific Industrial Research (CSIR), a dynamic regional climate model CCAM (conformal-cubic atmospheric model) of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) was used to downscale CMIP5 CGCM projections to 50 km resolution over Africa. These downscalings were for both Representative Concentration Pathway (RCP) 8.5 and Representative Concentration Pathway (RCP) 4.5 of AR5 of the IPCC. RCP 4.5 describes a future with relatively ambitious emission reductions whereas RCP 8.5 describes a future with no reductions in emissions. Emissions in RCP 4.5 peak around 2040, then decline and in RCP 8.5 emissions continue to rise throughout the 21st century. The change in temperature is expressed as an anomaly, the difference between the average climate over a period of the last several decades (1971-2000), and the projected climate (short to medium term 2021 to 2050). The simulations were performed on supercomputers of the CSIRO and on the Centre for High Performance Computing (CHPC) of the Meraka Institute of the CSIR in South Africa.

CO2 refrigeration systems, as used e.g. for supermarket applications, often encounter a drastical decrease of COP when operation moves into a transcritical mode. There are several options to enhance the system efficiency to reduce this penalty. All of them influence the optimum value for discharge and intermediate pressure. This paper discusses these influences for parallel compression and for an additional booster compressor. For comparison a system simulation was performed. The compressor model in the simulation is based on a data map of a currently available compressor. As a result the system’s efficiency improvement potential of both methods is presented under consideration of optimized operation conditions.

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.

This round of Eurobarometer surveys queried respondents on standard Eurobarometer measures, such as how satisfied they were with their present life, whether they attempted to persuade others close to them to share their views on subjects they held strong opinions about, whether they discussed political matters, what their country's goals should be in the next 10 to 15 years, and how they viewed the need for societal change. Additional questions focused on the respondents' knowledge of and opinions on globalization and on the European Union (EU), including how well-informed they were about the EU, what sources of information about the EU they used, whether their country had benefited from being an EU member (or would benefit from being a future member), and the extent of their personal interest in EU matters. Other questions queried respondents about their country's public administration, the transparency of both their own government institutions and those of the EU, and how important they thought transparency was in their functioning. Respondents were asked which countries, specifically Turkey, Croatia, and the Former Yugoslav Republic of Macedonia, they would favor joining the EU. National and European identity is a major focus of the survey. Questions focused on to what extent respondents felt they were a citizen of their region, of their country, of Europe, and of the world, whether they were content with their identity, and their feelings on the importance of being European. The second major focus of the survey was European elections. Respondents were queried about their interest in the elections, whether or not they would vote, the main criteria in making these decisions, and what themes the electoral campaign should focus on. In addition, respondents were asked to name the party they voted for in the European Parliament (EP) elections in June 2004, May 2007, November 2007, and the latest parliamentary elections in their respective countries. For the third major focus, European values and value priorities, respondents were asked to identify their personal values, whether they thought EU member states shared common values, and how close or distant these shared values were. In addition, respondents were asked to select the most important values they associated with the idea of happiness. For the final major focus of the survey, climate change, respondents were queried about their knowledge of and views on climate change, including whether they thought climate change was a serious problem, whether enough is being done to fight it, and the reasons why individuals may or may not take action in fighting climate change. Respondents were also asked to identify the personal actions they have taken regarding climate change and to evaluate the objectives proposed by the EU to limit the impact of climate change. Demographic and other background information includes age, gender, nationality, origin of birth (personal and parental), marital status, left-right political self-placement, strength of party attachment, occupation, age when stopped full-time education, household composition, ownership of a fixed or a mobile telephone and other durable goods, type and size of locality, region of residence, and language of interview (in select countries). face-to-face interview The original data collection was carried out by TNS Opinion and Social on request of the European Commission Between March 25th 2008 and May 4, 2008. Data for this Eurobarometer study are being released in two parts. Part 1 includes original study materials supplied by GESIS: (1) SPSS portable file; (2) SPSS syntax file containing user-defined missing values; and (3) documentation files. These files are being released in a zipped package. A documentation file has been provided by ICPSR to describe the contents of this zipped package. Part 2 includes study materials supplied by ICPSR: (1) SPSS, SAS, and Stata setup files; (2) SPSS and Stata system files, and a SAS transport (CPORT) file; (3) ASCII column-delimited and tab-delimited data files; and (4) documentation files. The data in Part 1 and Part 2 are identical, except for the following: Variable V1 'ARCHIVE STUDY NUMBER - DISTRIBUTOR' in Part 2 has been recoded to the ICPSR study number. Documentation files have been provided both by GESIS and ICPSR; the ICPSR documentation files may contain additional information. Data on voting behavior in European (D53) and national parliamentary elections (D54), and on party attachment (D2) have now been provided by the data producer and are now available. The codebook and setup files for this collection contain characters with diacritical marks used in many European languages. A split ballot was used for one or more questions in this survey. The variable V892 defines the separate groups. The documentation and/or setup files may contain references to Norway, but Norway was not a participant in this wave of Eurobarometer surveys. This collection contains no data for Norway. The fieldwork dates in the data file for Malta and Croatia are not consistent with the fieldwork dates in the "Technical Specifications" section of the ICPSR codebook. No documentation was supplied by the data producer for responses to open specifications of "OTHER" items for questions QC4, QC6, QD7, QE1, QE6, QE7 and QE8. The basic sample design applied in all states is a multistage, random (probability) one. In each country, a number of sampling points were drawn with probability proportional to population size (for a total coverage of the country) and to population density. In order to do so, the sampling points were drawn systematically from each of the "administrative regional units", after stratification by individual unit and type of area. They thus represent the whole territory of the countries surveyed according to the EUROSTAT NUTS II (or equivalent) and according to the distribution of the resident population of the respective nationalities in terms of metropolitan, urban and rural areas. In each of the selected sampling points, a starting address was drawn, at random. Further addresses (every Nth address) were selected by standard "random route" procedures, from the initial address. In each household, the respondent was drawn, at random (following the "closest birthday rule"). All interviews were conducted face-to-face in people's homes and in the appropriate national language. As far as the data capture is concerned, CAPI (Computer Assisted Personal Interview) was used in those countries where this technique was available. Please refer to the GESIS documentation and Technical Specifications within the ICPSR Codebook for additional sampling information. For each country a comparison between the sample and the universe was carried out. The Universe description was derived from Eurostat population data or from national statistics offices. For all countries surveyed, a national weighting procedure, using marginal and intercellular weighting, was carried out based on this Universe description. In all countries, gender, age, region and size of locality were introduced in the iteration procedure. For international weighting (i.e. EU averages), TNS Opinion and Social applies the official population figures as provided by EUROSTAT or national statistic offices. Please refer to the GESIS Documentation and Technical Specifications within the ICPSR Codebook for additional weighting information. Citizens of the EU aged 15 and over residing in the 27 EU member countries: Austria, Belgium, Bulgaria, Republic of Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and the United Kingdom, and the national population of citizens and the population of citizens of all the EU member countries aged 15 and over residing in the three EU candidate countries: Croatia, Turkey, and the Former Yugoslav Republic of Macedonia, and in the Turkish Cypriot Community. Smallest Geographic Unit: country ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Datasets: DS0: Study-Level Files DS1: GESIS DS2: ICPSR Eurobarometer Survey Series

This realistic ocean simulation was run using the Coastal and Regional Ocean COmmunity model (CROCO), based on the Regional Ocean Modelling System (ROMS), which has 60 terrain-following vertical levels. This output (WOES 0.25) is the largest grid of a triply nested configuration: WOES I, WOES II and WOES III, with horizontal resolutions of ~22.5, 7.5 and 2.5 km respectively. Monthly ouputs of the 0.25 degree GLORYS ocean reanalysis is used to force the boundaries of WOES I. The surface forcing for this model is provided by a bulk formulation using daily ERA-Interim atmospheric reanalysis (with a resolution of ~80 km) and using a relative wind approach. The output is saved as daily averages, in monthly netcdf files spanning January 1993 - December 2014. WOES 0.25 spans 55.7degS to 3.18388 degS and 10degW to 102.25degE and covers most of the Southern Subtropical Indian Ocean and a part of the Southern Atlantic Ocean. Model output includes: averaged free-surface (zeta), averaged vertically integrated u-momentum component (ubar), averaged vertically integrated v-momentum component (vbar), averaged u-momentum component (u), averaged v-momentum component (v), averaged potential temperature (temp), averaged salinity (salt), averaged vertical momentum component (w). Numerical computations were performed on the IDRIS (Institut du Developpement et des Ressources en Informatique Scientifique) IBM "ADA" computer facility (under grant A0020107630)

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.

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/)