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Research data keyboard_double_arrow_right Dataset 2021Embargo end date: 02 Jun 2021 United KingdomPublisher:University of Strathclyde Authors: Roberts, Jen;Results of surveys about shale gas collected at UK conferences and public events in 2014
University of Strath... arrow_drop_down University of Strathclyde KnowledgeBase DatasetsDataset . 2021License: CC BYData sources: Dataciteadd 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.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert University of Strath... arrow_drop_down University of Strathclyde KnowledgeBase DatasetsDataset . 2021License: CC BYData sources: Dataciteadd 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 2018Publisher:Zenodo Authors: Sridharan, Vignesh; Broad, Oliver; Shivakumar, Abhishek; Howells, Mark;This repository consists of the following datasets 1. EAPP_reference scenario_datafile.DD- This dataset is a model file that needs to be used with the code available in this GitHub link. This data file (in concurrence with the OSeMOSYS code) can be used to create a linear programming file (LP file) to be solved using any mathematical optimisation solver like GLPSOL/C-PLEX/GUROBI/CBC. 2. Main article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the main article. 3. Supplementary article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the supplementary article.
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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.
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.1478149&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
more_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.
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.1478149&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:Zenodo Funded by:UKRI | Sustaining Himalayan Wate...UKRI| Sustaining Himalayan Water Resources in a Changing Climate (SusHi-Wat)Authors: Dau, Quan; Adeloye, Adebayo;This dataset contains time series of reservoir releases (including any spills), evaporation loss, and rule curves for the Pong and Bhakra reservoirs, India. {"references": ["https://doi.org/10.3390/w11071413", "https://doi.org/10.1016/j.scitotenv.2019.06.021"]}
ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)DANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)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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visibility 41visibility views 41 download downloads 6 Powered bymore_vert ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)DANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)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:World Data Center for Climate (WDCC) at DKRZ Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim;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.MOHC.HadGEM3-GC31-MM.piControl' 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 HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
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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.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_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.
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=10.26050/wdcc/ar6.c6cmmohgmpc&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Berrang-Ford, Lea; Sietsma, Anne J.; Callaghan, Max; Minx, Jan C.; Scheelbeek, Pauline; Haddaway, Neal R.; Haines, Andy; Dangour, Alan D.;This is a complementary dataset associated with the following publication: Berrang-Ford, Lea, et al. "Systematic Mapping of Global Research on Climate and Health Using Machine Learning." The Lancet Planetary Health. Meta-data are included.
ZENODO arrow_drop_down Smithsonian 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.
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.4972515&type=result"></script>'); --> </script>
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more_vert ZENODO arrow_drop_down Smithsonian 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.
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.4972515&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Clinical Trial 2007 United KingdomPublisher:nct Authors: Professor Margot Umpleby;In obese subjects (BMI 33-38kg/m2) completing 12 months of treatment with the CB1 antagonist rimonabant (SR141716) there was an average weight loss from baseline of approximately 8.5 kg. These studies also showed the weight loss was accompanied by a decrease in plasma triglyceride (TG), an increase in HDL cholesterol and an improvement in insulin sensitivity measured by HOMA-IR. When adjusted for weight loss 50% of the improvements in TG, HDL cholesterol, and insulin sensitivity was not attributable to weight loss. This suggests that rimonabant has direct effects on fat metabolism. This study will investigate the direct effects of rimonabant (ie independent of weight loss) in a 2 group randomised study. One group will receive rimonabant for 12 weeks and the other group will have a dietary intervention to match the weight loss in the rimonabant group. Measurements of energy expenditure (using indirect calorimetry and Actiheart monitors),fatty acid and triglyceride metabolism (using stable isotope techniques) and body fat distribution (by magnetic resonance imaging) will be made before and after the intervention. To determine the possible mechanisms of the changes in metabolism, gene expression of key regulators of fatty acid metabolism in adipose and muscle tissue and circulating levels of adipokines will be measured. This study will determine in obese subjects the direct effects of the weight loss drug rimonabant (ie independent of weight loss) on energy expenditure, fat metabolism and and body fat distribution. We hypothesise that rimonabant will increase energy expenditure. The fuel for the increased energy expenditure will come from fat. As a result of burning more fat there will be a decrease in fat in blood and an improvement in the body's response to insulin.
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.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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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.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:MDPI AG Authors: Bogna Janik; Katarzyna Maruszewska;doi: 10.3390/su12239855
This paper evaluated the environmental effects of socially responsible investments (SRIs) in European countries and analyzed the differentiation between them in terms of SRIs and selected features in the environmental dimension. The first section of the paper discusses contemporary trends in Europe and in certain European countries, whilst the second compares SR environmental investments and environmental factors in selected European countries from a multidimensional perspective. The aim of the study was to identify and evaluate these trends as well as to find similarities and differences between European countries, and subsequently to indicate groups of countries with similar approaches to pro-ecological investments. In order to solve the problem, descriptive and multidimensional statistical methods were used, namely correspondence analysis (CA). Although the research results clearly revealed upward tendencies in the volume of SR environmental investments in the analyzed period, they nonetheless represent a relatively low share in the total number of socially responsible investments. The overall growth in SRIs in Europe may have resulted from the more intense activities of policymakers in some countries as a consequence of concluding agreements reached during the 21st Conference of the Parties (COP21) in 2015. The results of the study also revealed no significant correlations between SR environmental investments and environmental variables among the European countries analyzed; hence, there is no substantial evidence that investors’ assets contribute to the improvement of the environment.
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For further information contact us at helpdesk@openaire.euAccess Routesgold 5 citations 5 popularity Top 10% influence Average impulse Average Powered by BIP!
more_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.
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=10.3390/su12239855&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV M. Nion-Huang; O. Ali-Brandmeyer; K. Blanckaert; L. Simon; Gabriel Birgand;pmid: 34534602
This study assessed the hand hygiene performance in French nursing homes using the consumption of alcohol-based hand rubs (AHRs) as a surrogate. Nursing homes from the 17 French regions were contacted to collect their AHR consumption and occupancy in 2018 and 2019. A total of 1290 nursing homes from 15 French regions participated in the survey. The estimated median number of hand hygiene actions per resident-day was 1.48 (interquartile range: 1.04-2.03) in 2018 and 1.60 (1.10-2.26) in 2019. A significantly higher AHR consumption was observed in public nursing homes with an infection control team or link nurse.
Journal of Hospital ... arrow_drop_down Journal of Hospital InfectionArticle . 2021 . Peer-reviewedLicense: Elsevier TDMData 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.euAccess Routesbronze 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
more_vert Journal of Hospital ... arrow_drop_down Journal of Hospital InfectionArticle . 2021 . Peer-reviewedLicense: Elsevier TDMData 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.
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=10.1016/j.jhin.2021.09.002&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:MDPI AG Funded by:UKRI | Assessing the feasibility...UKRI| Assessing the feasibility of vertical farming for second generation bioenergy cropsAuthors: Zoe M. Harris; Yiannis Kountouris;doi: 10.3390/su12198193
The Intergovernmental Panel on Climate Change (IPCC) report that to limit warming to 1.5 °C, Bioenergy with Carbon Capture and Storage (BECCS) is required. Integrated assessment models (IAMS) predict that a land area between the size of Argentina and Australia is required for bioenergy crops, a 3–7 time increase in the current bioenergy planting area globally. The authors pose the question of whether vertical farming (VF) technology can enable BECCS deployment, either via land sparing or supply. VF involves indoor controlled environment cultivation, and can increase productivity per unit land area by 5–10 times. VF is predominantly being used to grow small, high value leafy greens with rapid growth cycles. Capital expenditure, operational expenditure, and sustainability are challenges in current VF industries, and will affect the ability to utilise this technology for other crops. The authors argue that, whilst challenging, VF could help reach wider climate goals. Application of VF for bioenergy crops could be a game changer in delivering BECCS technologies and may reduce the land footprint required as well as the subsequent associated negative environmental impacts. VF bioenergy could allow us to cultivate the future demand for bioenergy for BECCS on the same, or less, land area than is currently used globally.
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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=10.3390/su12198193&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 9 citations 9 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_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.
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=10.3390/su12198193&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Funded by:EC | CORALASSISTEC| CORALASSISTAuthors: Lachs, Liam; Humanes, Adriana; Martinez, Helios;Image dataset used for a colour analysis of coral branches throughout a long-term marine heatwave emulation experiment using machine learning. Article: "Within population variability in coral heat tolerance indicates climate adaptation potential" by Humanes and Lachs et al. Code to analyse the dataset is found at 10.5281/zenodo.6256164. LL received funding from Natural Environment Research Council (NERC) ONE Planet Doctoral Training Partnership (NE/S007512/1).
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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.6256189&type=result"></script>'); --> </script>
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visibility 75visibility views 75 download downloads 8 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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Research data keyboard_double_arrow_right Dataset 2021Embargo end date: 02 Jun 2021 United KingdomPublisher:University of Strathclyde Authors: Roberts, Jen;Results of surveys about shale gas collected at UK conferences and public events in 2014
University of Strath... arrow_drop_down University of Strathclyde KnowledgeBase DatasetsDataset . 2021License: CC BYData sources: Dataciteadd 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.
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=10.15129/a7a906c5-a77e-4a1c-b495-a2d441458d1d&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert University of Strath... arrow_drop_down University of Strathclyde KnowledgeBase DatasetsDataset . 2021License: CC BYData sources: Dataciteadd 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.
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=10.15129/a7a906c5-a77e-4a1c-b495-a2d441458d1d&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018Publisher:Zenodo Authors: Sridharan, Vignesh; Broad, Oliver; Shivakumar, Abhishek; Howells, Mark;This repository consists of the following datasets 1. EAPP_reference scenario_datafile.DD- This dataset is a model file that needs to be used with the code available in this GitHub link. This data file (in concurrence with the OSeMOSYS code) can be used to create a linear programming file (LP file) to be solved using any mathematical optimisation solver like GLPSOL/C-PLEX/GUROBI/CBC. 2. Main article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the main article. 3. Supplementary article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the supplementary article.
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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.
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.1478149&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
more_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 2020Publisher:Zenodo Funded by:UKRI | Sustaining Himalayan Wate...UKRI| Sustaining Himalayan Water Resources in a Changing Climate (SusHi-Wat)Authors: Dau, Quan; Adeloye, Adebayo;This dataset contains time series of reservoir releases (including any spills), evaporation loss, and rule curves for the Pong and Bhakra reservoirs, India. {"references": ["https://doi.org/10.3390/w11071413", "https://doi.org/10.1016/j.scitotenv.2019.06.021"]}
ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)DANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)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.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 41visibility views 41 download downloads 6 Powered bymore_vert ZENODO arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)DANS (Data Archiving and Networked Services)DatasetData sources: DANS (Data Archiving and Networked Services)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:World Data Center for Climate (WDCC) at DKRZ Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim;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.MOHC.HadGEM3-GC31-MM.piControl' 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 HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
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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.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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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.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Berrang-Ford, Lea; Sietsma, Anne J.; Callaghan, Max; Minx, Jan C.; Scheelbeek, Pauline; Haddaway, Neal R.; Haines, Andy; Dangour, Alan D.;This is a complementary dataset associated with the following publication: Berrang-Ford, Lea, et al. "Systematic Mapping of Global Research on Climate and Health Using Machine Learning." The Lancet Planetary Health. Meta-data are included.
ZENODO arrow_drop_down Smithsonian 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.
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.4972515&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
more_vert ZENODO arrow_drop_down Smithsonian 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.
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.4972515&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Clinical Trial 2007 United KingdomPublisher:nct Authors: Professor Margot Umpleby;In obese subjects (BMI 33-38kg/m2) completing 12 months of treatment with the CB1 antagonist rimonabant (SR141716) there was an average weight loss from baseline of approximately 8.5 kg. These studies also showed the weight loss was accompanied by a decrease in plasma triglyceride (TG), an increase in HDL cholesterol and an improvement in insulin sensitivity measured by HOMA-IR. When adjusted for weight loss 50% of the improvements in TG, HDL cholesterol, and insulin sensitivity was not attributable to weight loss. This suggests that rimonabant has direct effects on fat metabolism. This study will investigate the direct effects of rimonabant (ie independent of weight loss) in a 2 group randomised study. One group will receive rimonabant for 12 weeks and the other group will have a dietary intervention to match the weight loss in the rimonabant group. Measurements of energy expenditure (using indirect calorimetry and Actiheart monitors),fatty acid and triglyceride metabolism (using stable isotope techniques) and body fat distribution (by magnetic resonance imaging) will be made before and after the intervention. To determine the possible mechanisms of the changes in metabolism, gene expression of key regulators of fatty acid metabolism in adipose and muscle tissue and circulating levels of adipokines will be measured. This study will determine in obese subjects the direct effects of the weight loss drug rimonabant (ie independent of weight loss) on energy expenditure, fat metabolism and and body fat distribution. We hypothesise that rimonabant will increase energy expenditure. The fuel for the increased energy expenditure will come from fat. As a result of burning more fat there will be a decrease in fat in blood and an improvement in the body's response to insulin.
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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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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 2020Publisher:MDPI AG Authors: Bogna Janik; Katarzyna Maruszewska;doi: 10.3390/su12239855
This paper evaluated the environmental effects of socially responsible investments (SRIs) in European countries and analyzed the differentiation between them in terms of SRIs and selected features in the environmental dimension. The first section of the paper discusses contemporary trends in Europe and in certain European countries, whilst the second compares SR environmental investments and environmental factors in selected European countries from a multidimensional perspective. The aim of the study was to identify and evaluate these trends as well as to find similarities and differences between European countries, and subsequently to indicate groups of countries with similar approaches to pro-ecological investments. In order to solve the problem, descriptive and multidimensional statistical methods were used, namely correspondence analysis (CA). Although the research results clearly revealed upward tendencies in the volume of SR environmental investments in the analyzed period, they nonetheless represent a relatively low share in the total number of socially responsible investments. The overall growth in SRIs in Europe may have resulted from the more intense activities of policymakers in some countries as a consequence of concluding agreements reached during the 21st Conference of the Parties (COP21) in 2015. The results of the study also revealed no significant correlations between SR environmental investments and environmental variables among the European countries analyzed; hence, there is no substantial evidence that investors’ assets contribute to the improvement of the environment.
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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 Routesgold 5 citations 5 popularity Top 10% influence Average impulse Average Powered by BIP!
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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.
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=10.3390/su12239855&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2021Publisher:Elsevier BV M. Nion-Huang; O. Ali-Brandmeyer; K. Blanckaert; L. Simon; Gabriel Birgand;pmid: 34534602
This study assessed the hand hygiene performance in French nursing homes using the consumption of alcohol-based hand rubs (AHRs) as a surrogate. Nursing homes from the 17 French regions were contacted to collect their AHR consumption and occupancy in 2018 and 2019. A total of 1290 nursing homes from 15 French regions participated in the survey. The estimated median number of hand hygiene actions per resident-day was 1.48 (interquartile range: 1.04-2.03) in 2018 and 1.60 (1.10-2.26) in 2019. A significantly higher AHR consumption was observed in public nursing homes with an infection control team or link nurse.
Journal of Hospital ... arrow_drop_down Journal of Hospital InfectionArticle . 2021 . Peer-reviewedLicense: Elsevier TDMData 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.euAccess Routesbronze 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
more_vert Journal of Hospital ... arrow_drop_down Journal of Hospital InfectionArticle . 2021 . Peer-reviewedLicense: Elsevier TDMData 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.
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=10.1016/j.jhin.2021.09.002&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020Publisher:MDPI AG Funded by:UKRI | Assessing the feasibility...UKRI| Assessing the feasibility of vertical farming for second generation bioenergy cropsAuthors: Zoe M. Harris; Yiannis Kountouris;doi: 10.3390/su12198193
The Intergovernmental Panel on Climate Change (IPCC) report that to limit warming to 1.5 °C, Bioenergy with Carbon Capture and Storage (BECCS) is required. Integrated assessment models (IAMS) predict that a land area between the size of Argentina and Australia is required for bioenergy crops, a 3–7 time increase in the current bioenergy planting area globally. The authors pose the question of whether vertical farming (VF) technology can enable BECCS deployment, either via land sparing or supply. VF involves indoor controlled environment cultivation, and can increase productivity per unit land area by 5–10 times. VF is predominantly being used to grow small, high value leafy greens with rapid growth cycles. Capital expenditure, operational expenditure, and sustainability are challenges in current VF industries, and will affect the ability to utilise this technology for other crops. The authors argue that, whilst challenging, VF could help reach wider climate goals. Application of VF for bioenergy crops could be a game changer in delivering BECCS technologies and may reduce the land footprint required as well as the subsequent associated negative environmental impacts. VF bioenergy could allow us to cultivate the future demand for bioenergy for BECCS on the same, or less, land area than is currently used globally.
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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.
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=10.3390/su12198193&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 9 citations 9 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_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.
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=10.3390/su12198193&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Funded by:EC | CORALASSISTEC| CORALASSISTAuthors: Lachs, Liam; Humanes, Adriana; Martinez, Helios;Image dataset used for a colour analysis of coral branches throughout a long-term marine heatwave emulation experiment using machine learning. Article: "Within population variability in coral heat tolerance indicates climate adaptation potential" by Humanes and Lachs et al. Code to analyse the dataset is found at 10.5281/zenodo.6256164. LL received funding from Natural Environment Research Council (NERC) ONE Planet Doctoral Training Partnership (NE/S007512/1).
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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.
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.6256189&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
visibility 75visibility views 75 download downloads 8 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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