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Research data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: YU, Yongqiang;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.CAS.FGOALS-f3-L' 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 FGOALS-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:figshare Authors: Yong Li (15029); Long-Chen Shi (10976866); Nan-Cai Pei (10976869); Samuel A. Cushman (7903859); +1 AuthorsYong Li (15029); Long-Chen Shi (10976866); Nan-Cai Pei (10976869); Samuel A. Cushman (7903859); Yu-Tao Si (10258564);Additional file 1. Summary of sequence data from 24 samples.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:ICPSR - Interuniversity Consortium for Political and Social Research Authors: Saberian, Soodeh;doi: 10.3886/e127263v1 , 10.3886/e127263
This paper evidenced sensitivity of US immigration judge decisions to temperature in the city of arbitration on date of adjudication. This note serves to correct errors noted since publication. Main results from both the main linear specifications are qualitatively unchanged, with estimated treatment effects similar in size to the original and retaining statistical significance at conventional levels. Some secondary results lose significance with erosion of sample size. We also acknowledge the additional finding by Spamann (2020) with respect to external validity. Immigration judges in the United States.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Authors: Gordon McFadzean; Ciaran Gilbert; Jethro Browell;Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}
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visibility 122visibility views 122 download downloads 263 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.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 United KingdomPublisher:University College London Pullinger, Martin; Few, Jessica; McKenna, Eoghan; Elam, Simon; Webborn, Ellen; Oreszczyn, Tadj;This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5522/04/20039816.v1&type=result"></script>'); --> </script>
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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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.8208638&type=result"></script>'); --> </script>
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.8208638&type=result"></script>'); --> </script>
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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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5497632&type=result"></script>'); --> </script>
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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 2022Publisher:Science Data Bank Authors: Shuai ZHANG;Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009. Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Stolar, Jessica; Stralberg, Diana; Naujokaitis-Lewis, Ilona; Nielsen, Scott E.; Kehm, Gregory;Climate-informed conservation priorities in British Columbia (Version 1.0) Territorial acknowledgement: We respectfully acknowledge that we live and work across diverse unceded territories and treaty lands and pay our respects to the First Nations, Inuit and Métis ancestors of these places. We honour our connections to these lands and waters and reaffirm our relationships with one another. Suggested citation: Stolar, J., D. Stralberg, I. Naujokaitis-Lewis, S.E. Nielsen, and G. Kehm. 2023. Spatial priorities for climate-change refugia and connectivity for British Columbia (Version 1.0). Place of publication: University of Alberta, Edmonton, Canada. doi: 10.5281/zenodo.8333303 Corresponding author: stolar@ualberta.ca Summary: The purpose of this project is to identify spatial locations of (a) vulnerabilities within British Columbia’s current network of protected areas and (b) priorities for conservation and management of natural landscapes within British Columbia under a range of future climate-change scenarios. This involved adaptation and implementation of existing continental- and provincial-scale frameworks for identifying areas that have potential to serve as refugia from climate change or corridors for species migration. Outcomes of this work include the provision of practical guidance for protected areas network design and vulnerabilities identification under climate change, with application to other regions and jurisdictions. Project results, in the form of multiple spatial prioritization scenarios, may be used to evaluate the resilience of the existing protected area network and other conservation designations to better understand the risks to British Columbia’s biodiversity in our changing climate. Description: These raster layers represent different scenarios of Zonation rankings of conservation priorities for climate resilience and connectivity between current and 2080s conditions for a provincial-scale analysis. Input conservation features included metrics of macrorefugia (forward and backward climate velocity (km/year), overlapping future and current habitat suitability for ~900 rare species in BC), microrefugia (presence of old growth ecosystems, drought refugia, glaciers/cool slopes/wetlands, and geodiversity), and connectivity. Please see details in the accompanying report. File nomenclature: .zip folder (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.zip): Contains the files listed below. Macrorefugia (2080s_macrorefugia.tif): Scenarios for each taxonomic group (equal weightings for all species) (Core-area Zonation Function) Climate-type velocity + species scenarios from above (Core-area Zonation; equal weightings) Microrefugia (microrefugia.tif): Scenario with old growth forest habitat, landscape geodiversity, wetlands/cool slopes/glaciers, drought refugia (Core-area Zonation; equal weightings) Overall scenario (2080s_macro_micro_connectivity.tif): Inputs from above (with equal weightings) + connectivity metrics (each weighted at 0.1) (Additive Benefit Function Zonation) Conservation priorities (Conservation_priorities_2080s.tif): Overall scenario from above extracted to regions of low human footprint. Restoration priorities (Restoration_priorities_2080s.tif): Overall scenario from above extracted to regions of high human footprint. Accompanying report (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.pdf): Documentation of rationale, methods and interpretation. READ_ME file (READ_ME_PLEASE.txt): Metadata. Legend interpretation: Ranked Zonation priorities increase from 0 (lowest) to 1 (highest). Raster information: Columns and Rows: 1597, 1368 Number of Bands: 1 Cell Size (X, Y): 1000, 1000 Format: TIFF Pixel Type: floating point Compression: LZW Spatial reference: XY Coordinate System: NAD_1983_Albers Linear Unit: Meter (1.000000) Angular Unit: Degree (0.0174532925199433) false_easting: 1000000 false_northing: 0 central_meridian: -126 standard_parallel_1: 50 standard_parallel_2: 58.5 latitude_of_origin: 45 Datum: D_North_American_1983 Extent: West -139.061502 East -110.430823 North 60.605550 South 47.680823 Disclaimer: The University of Alberta (UofA) is furnishing this deliverable "as is". UofA does not provide any warranty of the contents of the deliverable whatsoever, whether express, implied, or statutory, including, but not limited to, any warranty of merchantability or fitness for a particular purpose or any warranty that the contents of the deliverable will be error-free. Funding: We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation. We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.8333303&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021 United KingdomPublisher:The University of Sheffield Igneczi, Adam; Sole, Andrew; Livingstone, Stephen; Ng, Felix; Clark, Christopher; Vizcaino, Miren;This ORDA repository location contains estimated contemporary and future surface relief, depression volume and surface lake volume maps of the Greenland Ice Sheet. The maps are produced for 3 time slices which each use different greenhouse gas emission scenarios (i.e. historical or Representative Concentration Pathways (RCP)):Two separate versions are produced for future time-slices:1. incorporating the effects of the changing surface mass balance2. incorporating the effects of both the changing surface mass balance and the changing ice sheet thickness and flow velocityThese datasets are the key outputs of our manuscript entitled: "Enhanced formation of surface lakes in the interior of the Greenland Ice Sheet after 2100 due to growing surface relief".The files are presented in GeoTiff (.tif) format.For further information please see "Readme.txt"
ORDA - The Universit... arrow_drop_down ORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2021License: CC BYData sources: DataciteORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2021License: CC BYData sources: DataciteSmithsonian figshareDataset . 2021License: CC BYData 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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more_vert ORDA - The Universit... arrow_drop_down ORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2021License: CC BYData sources: DataciteORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2021License: CC BYData sources: DataciteSmithsonian figshareDataset . 2021License: CC BYData 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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Research data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors: YU, Yongqiang;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.CAS.FGOALS-f3-L' 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 FGOALS-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:figshare Authors: Yong Li (15029); Long-Chen Shi (10976866); Nan-Cai Pei (10976869); Samuel A. Cushman (7903859); +1 AuthorsYong Li (15029); Long-Chen Shi (10976866); Nan-Cai Pei (10976869); Samuel A. Cushman (7903859); Yu-Tao Si (10258564);Additional file 1. Summary of sequence data from 24 samples.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:ICPSR - Interuniversity Consortium for Political and Social Research Authors: Saberian, Soodeh;doi: 10.3886/e127263v1 , 10.3886/e127263
This paper evidenced sensitivity of US immigration judge decisions to temperature in the city of arbitration on date of adjudication. This note serves to correct errors noted since publication. Main results from both the main linear specifications are qualitatively unchanged, with estimated treatment effects similar in size to the original and retaining statistical significance at conventional levels. Some secondary results lose significance with erosion of sample size. We also acknowledge the additional finding by Spamann (2020) with respect to external validity. Immigration judges in the United States.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Authors: Gordon McFadzean; Ciaran Gilbert; Jethro Browell;Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}
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visibility 122visibility views 122 download downloads 263 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.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022 United KingdomPublisher:University College London Pullinger, Martin; Few, Jessica; McKenna, Eoghan; Elam, Simon; Webborn, Ellen; Oreszczyn, Tadj;This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.
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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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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.8208638&type=result"></script>'); --> </script>
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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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.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Science Data Bank Authors: Shuai ZHANG;Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009. Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Stolar, Jessica; Stralberg, Diana; Naujokaitis-Lewis, Ilona; Nielsen, Scott E.; Kehm, Gregory;Climate-informed conservation priorities in British Columbia (Version 1.0) Territorial acknowledgement: We respectfully acknowledge that we live and work across diverse unceded territories and treaty lands and pay our respects to the First Nations, Inuit and Métis ancestors of these places. We honour our connections to these lands and waters and reaffirm our relationships with one another. Suggested citation: Stolar, J., D. Stralberg, I. Naujokaitis-Lewis, S.E. Nielsen, and G. Kehm. 2023. Spatial priorities for climate-change refugia and connectivity for British Columbia (Version 1.0). Place of publication: University of Alberta, Edmonton, Canada. doi: 10.5281/zenodo.8333303 Corresponding author: stolar@ualberta.ca Summary: The purpose of this project is to identify spatial locations of (a) vulnerabilities within British Columbia’s current network of protected areas and (b) priorities for conservation and management of natural landscapes within British Columbia under a range of future climate-change scenarios. This involved adaptation and implementation of existing continental- and provincial-scale frameworks for identifying areas that have potential to serve as refugia from climate change or corridors for species migration. Outcomes of this work include the provision of practical guidance for protected areas network design and vulnerabilities identification under climate change, with application to other regions and jurisdictions. Project results, in the form of multiple spatial prioritization scenarios, may be used to evaluate the resilience of the existing protected area network and other conservation designations to better understand the risks to British Columbia’s biodiversity in our changing climate. Description: These raster layers represent different scenarios of Zonation rankings of conservation priorities for climate resilience and connectivity between current and 2080s conditions for a provincial-scale analysis. Input conservation features included metrics of macrorefugia (forward and backward climate velocity (km/year), overlapping future and current habitat suitability for ~900 rare species in BC), microrefugia (presence of old growth ecosystems, drought refugia, glaciers/cool slopes/wetlands, and geodiversity), and connectivity. Please see details in the accompanying report. File nomenclature: .zip folder (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.zip): Contains the files listed below. Macrorefugia (2080s_macrorefugia.tif): Scenarios for each taxonomic group (equal weightings for all species) (Core-area Zonation Function) Climate-type velocity + species scenarios from above (Core-area Zonation; equal weightings) Microrefugia (microrefugia.tif): Scenario with old growth forest habitat, landscape geodiversity, wetlands/cool slopes/glaciers, drought refugia (Core-area Zonation; equal weightings) Overall scenario (2080s_macro_micro_connectivity.tif): Inputs from above (with equal weightings) + connectivity metrics (each weighted at 0.1) (Additive Benefit Function Zonation) Conservation priorities (Conservation_priorities_2080s.tif): Overall scenario from above extracted to regions of low human footprint. Restoration priorities (Restoration_priorities_2080s.tif): Overall scenario from above extracted to regions of high human footprint. Accompanying report (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.pdf): Documentation of rationale, methods and interpretation. READ_ME file (READ_ME_PLEASE.txt): Metadata. Legend interpretation: Ranked Zonation priorities increase from 0 (lowest) to 1 (highest). Raster information: Columns and Rows: 1597, 1368 Number of Bands: 1 Cell Size (X, Y): 1000, 1000 Format: TIFF Pixel Type: floating point Compression: LZW Spatial reference: XY Coordinate System: NAD_1983_Albers Linear Unit: Meter (1.000000) Angular Unit: Degree (0.0174532925199433) false_easting: 1000000 false_northing: 0 central_meridian: -126 standard_parallel_1: 50 standard_parallel_2: 58.5 latitude_of_origin: 45 Datum: D_North_American_1983 Extent: West -139.061502 East -110.430823 North 60.605550 South 47.680823 Disclaimer: The University of Alberta (UofA) is furnishing this deliverable "as is". UofA does not provide any warranty of the contents of the deliverable whatsoever, whether express, implied, or statutory, including, but not limited to, any warranty of merchantability or fitness for a particular purpose or any warranty that the contents of the deliverable will be error-free. Funding: We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation. We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021 United KingdomPublisher:The University of Sheffield Igneczi, Adam; Sole, Andrew; Livingstone, Stephen; Ng, Felix; Clark, Christopher; Vizcaino, Miren;This ORDA repository location contains estimated contemporary and future surface relief, depression volume and surface lake volume maps of the Greenland Ice Sheet. The maps are produced for 3 time slices which each use different greenhouse gas emission scenarios (i.e. historical or Representative Concentration Pathways (RCP)):Two separate versions are produced for future time-slices:1. incorporating the effects of the changing surface mass balance2. incorporating the effects of both the changing surface mass balance and the changing ice sheet thickness and flow velocityThese datasets are the key outputs of our manuscript entitled: "Enhanced formation of surface lakes in the interior of the Greenland Ice Sheet after 2100 due to growing surface relief".The files are presented in GeoTiff (.tif) format.For further information please see "Readme.txt"
ORDA - The Universit... arrow_drop_down ORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2021License: CC BYData sources: DataciteORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2021License: CC BYData sources: DataciteSmithsonian figshareDataset . 2021License: CC BYData 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.
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 ORDA - The Universit... arrow_drop_down ORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2021License: CC BYData sources: DataciteORDA - The University of Sheffield Research Data Catalogue and RepositoryDataset . 2021License: CC BYData sources: DataciteSmithsonian figshareDataset . 2021License: CC BYData 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.
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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