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Research data keyboard_double_arrow_right Dataset 2018Publisher:Zenodo Funded by:EC | REINVENTEC| REINVENTHansen, Teis; Keaney, Monica; Bulkeley, Harriet A.; Cooper, Mark; Mölter, Helena; Nielsen, Hjalti; Pietzner, Katja; Sonesson, Ludwig B.; Stripple, Johannes; S.I. Aan Den Toorn; Tziva, Maria; Tönjes, Annika; Vallentin, Daniel; Van-Veelen, Bregje;This database includes more than 100 decarbonisation innovations in Paper, Plastic, Steel and Meat & Dairy sectors, across their value chains, as well as in Finance. For each innovation there is a description, information about its contribution to decarbonisation, actors and collaborators involved, sources of funding, drivers, (co)benefits and disadvantages. More information on the method for selecting innovations for the database is available here. The database was created as part of REINVENT – a Horizon 2020 research project funded by the European Commission (grant agreement 730053). REINVENT involves five research institutions from four countries: Lund University (Sweden), Durham University (United Kingdom), Wuppertal Institute (Germany), PBL Netherlands Environmental Assessment Agency (the Netherlands) and Utrecht University (the Netherlands). More information can be found on our website: www.reinvent-project.eu.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | RECEIPTEC| RECEIPTAuthors: Kotz, Maximilian; Levermann, Anders; Wenz, Leonie;The zipped file of this repository contains code and data to reproduce the results of the publication: Kotz et al, Nature, The effect of rainfall changes on economic production. (2021). https://doi.org/10.1038/s41586-021-04283-8 Economic data are derived from the DoSE database: https://doi.org/10.5281/zenodo.4681305 Climate data are derived from the ERA-5 reanalysis: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 Please see the README document for detailed: - Descriptions of the code and data provided - Lists of the required dependencies - Naming conventions for variables
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:Zenodo Funded by:EC | sEEnergiesEC| sEEnergiesAuthors: Kermeli, Katerina and Crijns-Graus, Wina;Data set with reference scenarios. As it is not possible to include the entire dataset in this report, we only include two Tables on final energy demand. Table 1 shows the Final Energy Demand projections per industrial subsector and EU28 country in the Reference Scenario and Table 2 the Final Energy Demand projections per industrial subsector and EU28 country in the Frozen Efficiency Scenario. The full dataset, including physical production (in ktonnes) and fuel and electricity demand (in TJ) per industrial sub-sector, per fuel type and per EU 28 country is available upon request to the project coordinator.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:EC | XROTOREC| XROTORGiri Ajay, Adhyanth; Morgan, Laurence; Wu, Yan; Bretos, David; Cascales, Aurelio; Pires, Oscar; Ferreira, Carlos;This repository can be used to reproduce the power, thrust, blade forces, and vertical induction from the journal paper 'Aerodynamic model comparison for an X-shaped vertical-axis wind turbine (https://doi.org/10.5194/wes-2023-115)'. The processing and plotting files are in MATLAB format (*.m). As an alternative to MATLAB, Octave can be used to run these files as well.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | HELIXEC| HELIXThiery, Wim; Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; Gudmundsson, Lukas; Seneviratne, Sonia I.; Andrijevic, Marina; Frieler, Katja; Emanuel, Kerry; Geiger, Tobias; Bresch, David N.; Zhao, Fang; Willner, Sven N.; Büchner, Matthias; Volkholz, Jan; Bauer, Nico; Chang, Jinfeng; Ciais, Philippe; Dury, Marie; François, Louis; Grillakis, Manolis; Gosling, Simon N.; Hanasaki, Naota; Hickler, Thomas; Huber, Veronika; Ito, Akihiko; Jägermeyr, Jonas; Khabarov, Nikolay; Koutroulis, Aristeidis; Liu, Wenfeng; Lutz, Wolfgang; Mengel, Matthias; Müller, Christoph; Ostberg, Sebastian; Reyer, Christopher P. O.; Stacke, Tobias; Wada, Yoshihide;This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2015Publisher:Zenodo Funded by:EC | ECLAIRE, EC | ANIMALCHANGEEC| ECLAIRE ,EC| ANIMALCHANGELeip, Adrian; Billen, Gilles; Garnier, Josette; Lassaletta, Luis; Reis, Stefan; Simpson, David; Sutton, Mark A.; de Vries, Wim; Weiss, Franz; Westhoek, Henk;doi: 10.5281/zenodo.58514
Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)-1 yr-1] or [g N (kg product)-1 yr-1] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | GEMexEC| GEMexAuthors: Calcagno, Philippe; Vaessen, Loes; Gutiérrez-Negrín, Luis Carlos; Liotta, Domenico; +1 AuthorsCalcagno, Philippe; Vaessen, Loes; Gutiérrez-Negrín, Luis Carlos; Liotta, Domenico; Trumpy, Eugenio;Construction of this dataset is described in the peer-reviewed publication: Calcagno, P., Trumpy, E., Gutiérrez-Negrín, L.C., Liotta, D. A collection of 3D geomodels of the Los Humeros and Acoculco geothermal systems (Mexico). Sci Data 9, 280 (2022). https://doi.org/10.1038/s41597-022-01327-0 The geomodel is available in the form of the following files and formats: Metadata sheet description pdf format GeoModeller project format PDF3D format TSurf format VTK format {"references": ["Calcagno, P., Trumpy, E., Guti\u00e9rrez-Negr\u00edn, L.C., Liotta, D. A collection of 3D geomodels of the Los Humeros and Acoculco geothermal systems (Mexico). Sci Data 9, 280 (2022). https://doi.org/10.1038/s41597-022-01327-0"]}
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021 Spain, NetherlandsPublisher:Elsevier BV Funded by:EC | VEEPEC| VEEPAbraham T. Gebremariam; Ali Vahidi; Francesco Di Maio; J. Moreno-Juez; I. Vegas-Ramiro; Artur Łagosz; Radosław Mróz; Peter Rem;This study focuses on formulating the most sustainable concrete by incorporating recycled concrete aggregates and other products retrieved from construction and demolition (C&D) activities. Both recycled coarse aggregates (RCA) and recycled fine aggregates (RFA) are firstly used to fully replace the natural coarse and fine aggregates in the concrete mix design. Later, the cement rich ultrafine particles, recycled glass powder and mineral fibres recovered from construction and demolition wastes (CDW) are further incorporated at a smaller rate either as cement substituent or as supplementary additives. Remarkable properties are noticed when the RCA (4–12 mm) and RFA (0.25–4 mm) are fully used to replace the natural aggregates in a new concrete mix. The addition of recycled cement rich ultrafines (RCU), Recycled glass ultrafines (RGU) and recycled mineral fibres (RMF) into recycled concrete improves the modulus of elasticity. The final concrete, which comprises more than 75% (wt.) of recycled components/materials, is believed to be the most sustainable and green concrete mix. Mechanical properties and durability of this concrete have been studied and found to be within acceptable limits, indicating the potential of recycled aggregates and other CDW components in shaping sustainable and circular construction practices. The authors wish to acknowledge the financial support from EU Horizon 2020 Project VEEP ‘‘Cost-Effective Recycling of C&DW in High Added Value Energy Efficient Prefabricated Concrete Compo-nents for Massive Retrofitting of our Built Environment” (No.723582).
Construction and Bui... arrow_drop_down Construction and Building MaterialsArticle . 2021 . Peer-reviewedLicense: CC BYData sources: CrossrefRecolector de Ciencia Abierta, RECOLECTAArticle . 2021Data sources: Recolector de Ciencia Abierta, RECOLECTADelft University of Technology: Institutional RepositoryArticle . 2021Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 46 citations 46 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
visibility 77visibility views 77 download downloads 74 Powered bymore_vert Construction and Bui... arrow_drop_down Construction and Building MaterialsArticle . 2021 . Peer-reviewedLicense: CC BYData sources: CrossrefRecolector de Ciencia Abierta, RECOLECTAArticle . 2021Data sources: Recolector de Ciencia Abierta, RECOLECTADelft University of Technology: Institutional RepositoryArticle . 2021Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2020Publisher:Copernicus GmbH Funded by:EC | METLAKE, EC | VERIFY, EC | IMBALANCE-P +4 projectsEC| METLAKE ,EC| VERIFY ,EC| IMBALANCE-P ,EC| CHE ,RCN| Integrated Carbon Observation System (ICOS)-Norway and Ocean Thematic Centre (OTC) ,EC| VISUALMEDIA ,AKA| Novel soil management practices - key for sustainable bioeconomy and climate change mitigation -SOMPA / Consortium: SOMPAAna Maria Roxana Petrescu; Chunjing Qiu; Philippe Ciais; Rona L. Thompson; Philippe Peylin; Matthew J. McGrath; Efisio Solazzo; Greet Janssens‐Maenhout; Francesco N. Tubiello; P. Bergamaschi; D. Brunner; Glen P. Peters; L. Höglund-Isaksson; Pierre Regnier; Ronny Lauerwald; David Bastviken; Aki Tsuruta; Wilfried Winiwarter; Prabir K. Patra; Matthias Kuhnert; Gabriel D. Orregioni; Monica Crippa; Marielle Saunois; Lucia Perugini; Tiina Markkanen; Tuula Aalto; Christine Groot Zwaaftink; Yuanzhi Yao; Chris Wilson; Giulia Conchedda; Dirk Günther; Adrian Leip; Pete Smith; Jean‐Matthieu Haussaire; Antti Leppänen; Alistair J. Manning; Joe McNorton; Patrick Brockmann; A.J. Dolman;Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model specific uncertainties when reported. In comparing NGHGI with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions and geological sources together account for the gap between NGHGI and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).
https://doi.org/10.5... arrow_drop_down https://doi.org/10.5194/essd-2...Article . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert https://doi.org/10.5... arrow_drop_down https://doi.org/10.5194/essd-2...Article . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2017Publisher:Schweizerbart Funded by:EC | MACC II, EC | MACC-IIIEC| MACC II ,EC| MACC-IIIAuthors: Angela Benedetti; Marion Schroedter-Homscheidt; Niels Killius;The successful electricity grid integration of solar energy into day-ahead markets requires at least hourly resolved 48 h forecasts. Technologies as photovoltaics and non-concentrating solar thermal technologies make use of global horizontal irradiance (GHI) forecasts, while all concentrating technologies both from the photovoltaic and the thermal sector require direct normal irradiances (DNI). The European Centre for Medium-Range Weather Forecasts (ECMWF) has recently changed towards providing direct as well as global irradiances. Additionally, the MACC (Monitoring Atmospheric Composition & Climate) near-real time services provide daily analysis and forecasts of aerosol properties in preparation of the upcoming European Copernicus programme. The operational ECMWF/IFS (Integrated Forecast System) forecast system will in the medium term profit from the Copernicus service aerosol forecasts. Therefore, within the MACC‑II project specific experiment runs were performed allowing for the assessment of the performance gain of these potential future capabilities. Also the potential impact of providing forecasts with hourly output resolution compared to three-hourly resolved forecasts is investigated. The inclusion of the new aerosol climatology in October 2003 improved both the GHI and DNI forecasts remarkably, while the change towards a new radiation scheme in 2007 only had minor and partly even unfavourable impacts on the performance indicators. For GHI, larger RMSE (root mean square error) values are found for broken/overcast conditions than for scattered cloud fields. For DNI, the findings are opposite with larger RMSE values for scattered clouds compared to overcast/broken cloud situations. The introduction of direct irradiances as an output parameter in the operational IFS version has not resulted in a general performance improvement with respect to biases and RMSE compared to the widely used Skartveit et al. (1998) global to direct irradiance conversion scheme. Cloudy situations and especially thin ice cloud cases are forecasted much better with respect to biases and RMSE, but large biases are introduced in clear sky cases. When applying the MACC aerosol scheme to include aerosol direct effects, an improvement especially in DNI biases is found for cloud free cases as expected. However, a performance decrease is found for water cloud cases. It is assumed that this is caused by the lack of an explicit modelling of cloud-aerosol interactions, while other meteorological forcings for cloud processes like the temperature field are modified by the aerosols.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 22 citations 22 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
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Research data keyboard_double_arrow_right Dataset 2018Publisher:Zenodo Funded by:EC | REINVENTEC| REINVENTHansen, Teis; Keaney, Monica; Bulkeley, Harriet A.; Cooper, Mark; Mölter, Helena; Nielsen, Hjalti; Pietzner, Katja; Sonesson, Ludwig B.; Stripple, Johannes; S.I. Aan Den Toorn; Tziva, Maria; Tönjes, Annika; Vallentin, Daniel; Van-Veelen, Bregje;This database includes more than 100 decarbonisation innovations in Paper, Plastic, Steel and Meat & Dairy sectors, across their value chains, as well as in Finance. For each innovation there is a description, information about its contribution to decarbonisation, actors and collaborators involved, sources of funding, drivers, (co)benefits and disadvantages. More information on the method for selecting innovations for the database is available here. The database was created as part of REINVENT – a Horizon 2020 research project funded by the European Commission (grant agreement 730053). REINVENT involves five research institutions from four countries: Lund University (Sweden), Durham University (United Kingdom), Wuppertal Institute (Germany), PBL Netherlands Environmental Assessment Agency (the Netherlands) and Utrecht University (the Netherlands). More information can be found on our website: www.reinvent-project.eu.
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | RECEIPTEC| RECEIPTAuthors: Kotz, Maximilian; Levermann, Anders; Wenz, Leonie;The zipped file of this repository contains code and data to reproduce the results of the publication: Kotz et al, Nature, The effect of rainfall changes on economic production. (2021). https://doi.org/10.1038/s41586-021-04283-8 Economic data are derived from the DoSE database: https://doi.org/10.5281/zenodo.4681305 Climate data are derived from the ERA-5 reanalysis: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 Please see the README document for detailed: - Descriptions of the code and data provided - Lists of the required dependencies - Naming conventions for variables
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:Zenodo Funded by:EC | sEEnergiesEC| sEEnergiesAuthors: Kermeli, Katerina and Crijns-Graus, Wina;Data set with reference scenarios. As it is not possible to include the entire dataset in this report, we only include two Tables on final energy demand. Table 1 shows the Final Energy Demand projections per industrial subsector and EU28 country in the Reference Scenario and Table 2 the Final Energy Demand projections per industrial subsector and EU28 country in the Frozen Efficiency Scenario. The full dataset, including physical production (in ktonnes) and fuel and electricity demand (in TJ) per industrial sub-sector, per fuel type and per EU 28 country is available upon request to the project coordinator.
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visibility 167visibility views 167 download downloads 128 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.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.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:EC | XROTOREC| XROTORGiri Ajay, Adhyanth; Morgan, Laurence; Wu, Yan; Bretos, David; Cascales, Aurelio; Pires, Oscar; Ferreira, Carlos;This repository can be used to reproduce the power, thrust, blade forces, and vertical induction from the journal paper 'Aerodynamic model comparison for an X-shaped vertical-axis wind turbine (https://doi.org/10.5194/wes-2023-115)'. The processing and plotting files are in MATLAB format (*.m). As an alternative to MATLAB, Octave can be used to run these files as well.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | HELIXEC| HELIXThiery, Wim; Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; Gudmundsson, Lukas; Seneviratne, Sonia I.; Andrijevic, Marina; Frieler, Katja; Emanuel, Kerry; Geiger, Tobias; Bresch, David N.; Zhao, Fang; Willner, Sven N.; Büchner, Matthias; Volkholz, Jan; Bauer, Nico; Chang, Jinfeng; Ciais, Philippe; Dury, Marie; François, Louis; Grillakis, Manolis; Gosling, Simon N.; Hanasaki, Naota; Hickler, Thomas; Huber, Veronika; Ito, Akihiko; Jägermeyr, Jonas; Khabarov, Nikolay; Koutroulis, Aristeidis; Liu, Wenfeng; Lutz, Wolfgang; Mengel, Matthias; Müller, Christoph; Ostberg, Sebastian; Reyer, Christopher P. O.; Stacke, Tobias; Wada, Yoshihide;This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 317visibility views 317 download downloads 197 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.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.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2015Publisher:Zenodo Funded by:EC | ECLAIRE, EC | ANIMALCHANGEEC| ECLAIRE ,EC| ANIMALCHANGELeip, Adrian; Billen, Gilles; Garnier, Josette; Lassaletta, Luis; Reis, Stefan; Simpson, David; Sutton, Mark A.; de Vries, Wim; Weiss, Franz; Westhoek, Henk;doi: 10.5281/zenodo.58514
Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)-1 yr-1] or [g N (kg product)-1 yr-1] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Funded by:EC | GEMexEC| GEMexAuthors: Calcagno, Philippe; Vaessen, Loes; Gutiérrez-Negrín, Luis Carlos; Liotta, Domenico; +1 AuthorsCalcagno, Philippe; Vaessen, Loes; Gutiérrez-Negrín, Luis Carlos; Liotta, Domenico; Trumpy, Eugenio;Construction of this dataset is described in the peer-reviewed publication: Calcagno, P., Trumpy, E., Gutiérrez-Negrín, L.C., Liotta, D. A collection of 3D geomodels of the Los Humeros and Acoculco geothermal systems (Mexico). Sci Data 9, 280 (2022). https://doi.org/10.1038/s41597-022-01327-0 The geomodel is available in the form of the following files and formats: Metadata sheet description pdf format GeoModeller project format PDF3D format TSurf format VTK format {"references": ["Calcagno, P., Trumpy, E., Guti\u00e9rrez-Negr\u00edn, L.C., Liotta, D. A collection of 3D geomodels of the Los Humeros and Acoculco geothermal systems (Mexico). Sci Data 9, 280 (2022). https://doi.org/10.1038/s41597-022-01327-0"]}
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021 Spain, NetherlandsPublisher:Elsevier BV Funded by:EC | VEEPEC| VEEPAbraham T. Gebremariam; Ali Vahidi; Francesco Di Maio; J. Moreno-Juez; I. Vegas-Ramiro; Artur Łagosz; Radosław Mróz; Peter Rem;This study focuses on formulating the most sustainable concrete by incorporating recycled concrete aggregates and other products retrieved from construction and demolition (C&D) activities. Both recycled coarse aggregates (RCA) and recycled fine aggregates (RFA) are firstly used to fully replace the natural coarse and fine aggregates in the concrete mix design. Later, the cement rich ultrafine particles, recycled glass powder and mineral fibres recovered from construction and demolition wastes (CDW) are further incorporated at a smaller rate either as cement substituent or as supplementary additives. Remarkable properties are noticed when the RCA (4–12 mm) and RFA (0.25–4 mm) are fully used to replace the natural aggregates in a new concrete mix. The addition of recycled cement rich ultrafines (RCU), Recycled glass ultrafines (RGU) and recycled mineral fibres (RMF) into recycled concrete improves the modulus of elasticity. The final concrete, which comprises more than 75% (wt.) of recycled components/materials, is believed to be the most sustainable and green concrete mix. Mechanical properties and durability of this concrete have been studied and found to be within acceptable limits, indicating the potential of recycled aggregates and other CDW components in shaping sustainable and circular construction practices. The authors wish to acknowledge the financial support from EU Horizon 2020 Project VEEP ‘‘Cost-Effective Recycling of C&DW in High Added Value Energy Efficient Prefabricated Concrete Compo-nents for Massive Retrofitting of our Built Environment” (No.723582).
Construction and Bui... arrow_drop_down Construction and Building MaterialsArticle . 2021 . Peer-reviewedLicense: CC BYData sources: CrossrefRecolector de Ciencia Abierta, RECOLECTAArticle . 2021Data sources: Recolector de Ciencia Abierta, RECOLECTADelft University of Technology: Institutional RepositoryArticle . 2021Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 46 citations 46 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
visibility 77visibility views 77 download downloads 74 Powered bymore_vert Construction and Bui... arrow_drop_down Construction and Building MaterialsArticle . 2021 . Peer-reviewedLicense: CC BYData sources: CrossrefRecolector de Ciencia Abierta, RECOLECTAArticle . 2021Data sources: Recolector de Ciencia Abierta, RECOLECTADelft University of Technology: Institutional RepositoryArticle . 2021Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2020Publisher:Copernicus GmbH Funded by:EC | METLAKE, EC | VERIFY, EC | IMBALANCE-P +4 projectsEC| METLAKE ,EC| VERIFY ,EC| IMBALANCE-P ,EC| CHE ,RCN| Integrated Carbon Observation System (ICOS)-Norway and Ocean Thematic Centre (OTC) ,EC| VISUALMEDIA ,AKA| Novel soil management practices - key for sustainable bioeconomy and climate change mitigation -SOMPA / Consortium: SOMPAAna Maria Roxana Petrescu; Chunjing Qiu; Philippe Ciais; Rona L. Thompson; Philippe Peylin; Matthew J. McGrath; Efisio Solazzo; Greet Janssens‐Maenhout; Francesco N. Tubiello; P. Bergamaschi; D. Brunner; Glen P. Peters; L. Höglund-Isaksson; Pierre Regnier; Ronny Lauerwald; David Bastviken; Aki Tsuruta; Wilfried Winiwarter; Prabir K. Patra; Matthias Kuhnert; Gabriel D. Orregioni; Monica Crippa; Marielle Saunois; Lucia Perugini; Tiina Markkanen; Tuula Aalto; Christine Groot Zwaaftink; Yuanzhi Yao; Chris Wilson; Giulia Conchedda; Dirk Günther; Adrian Leip; Pete Smith; Jean‐Matthieu Haussaire; Antti Leppänen; Alistair J. Manning; Joe McNorton; Patrick Brockmann; A.J. Dolman;Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model specific uncertainties when reported. In comparing NGHGI with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions and geological sources together account for the gap between NGHGI and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).
https://doi.org/10.5... arrow_drop_down https://doi.org/10.5194/essd-2...Article . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.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.This Research product is the result of merged Research products in OpenAIRE.
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more_vert https://doi.org/10.5... arrow_drop_down https://doi.org/10.5194/essd-2...Article . 2020 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.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.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2017Publisher:Schweizerbart Funded by:EC | MACC II, EC | MACC-IIIEC| MACC II ,EC| MACC-IIIAuthors: Angela Benedetti; Marion Schroedter-Homscheidt; Niels Killius;The successful electricity grid integration of solar energy into day-ahead markets requires at least hourly resolved 48 h forecasts. Technologies as photovoltaics and non-concentrating solar thermal technologies make use of global horizontal irradiance (GHI) forecasts, while all concentrating technologies both from the photovoltaic and the thermal sector require direct normal irradiances (DNI). The European Centre for Medium-Range Weather Forecasts (ECMWF) has recently changed towards providing direct as well as global irradiances. Additionally, the MACC (Monitoring Atmospheric Composition & Climate) near-real time services provide daily analysis and forecasts of aerosol properties in preparation of the upcoming European Copernicus programme. The operational ECMWF/IFS (Integrated Forecast System) forecast system will in the medium term profit from the Copernicus service aerosol forecasts. Therefore, within the MACC‑II project specific experiment runs were performed allowing for the assessment of the performance gain of these potential future capabilities. Also the potential impact of providing forecasts with hourly output resolution compared to three-hourly resolved forecasts is investigated. The inclusion of the new aerosol climatology in October 2003 improved both the GHI and DNI forecasts remarkably, while the change towards a new radiation scheme in 2007 only had minor and partly even unfavourable impacts on the performance indicators. For GHI, larger RMSE (root mean square error) values are found for broken/overcast conditions than for scattered cloud fields. For DNI, the findings are opposite with larger RMSE values for scattered clouds compared to overcast/broken cloud situations. The introduction of direct irradiances as an output parameter in the operational IFS version has not resulted in a general performance improvement with respect to biases and RMSE compared to the widely used Skartveit et al. (1998) global to direct irradiance conversion scheme. Cloudy situations and especially thin ice cloud cases are forecasted much better with respect to biases and RMSE, but large biases are introduced in clear sky cases. When applying the MACC aerosol scheme to include aerosol direct effects, an improvement especially in DNI biases is found for cloud free cases as expected. However, a performance decrease is found for water cloud cases. It is assumed that this is caused by the lack of an explicit modelling of cloud-aerosol interactions, while other meteorological forcings for cloud processes like the temperature field are modified by the aerosols.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 22 citations 22 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!
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