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Research data keyboard_double_arrow_right Dataset 2024Publisher:Zenodo Funded by:UKRI | CoccoTrait: Revealing Coc...UKRI| CoccoTrait: Revealing Coccolithophore Trait diversity and its climatic impactsde Vries, Joost; Poulton, Alex J.; Young, Jeremy R.; Monteiro, Fanny M.; Sheward, Rosie M.; Johnson, Roberta; Hagino, Kyoko; Ziveri, Patrizia; Wolf, Levi J.;CASCADE is a global dataset for 139 extant coccolithophore taxonomic units. CASCADE includes a trait database (size and cellular organic and inorganic carbon contents) and taxonomic-specific global spatiotemporal distributions (Lat/Lon/Depth/Month/Year) of coccolithophore abundance and organic and inorganic carbon stocks. CASCADE covers all ocean basins over the upper 275 meters, spans the years 1964-2019 and includes 33,119 taxonomic-specific abundance observations. Within CASCADE, we characterise the underlying uncertainties due to measurement errors by propagating error estimates between the different studies. Full details of the data set are provided in the associated Scientific Data manuscript. The repository contains five main folders: 1) "Classification", which contains YAML files with synonyms, family-level classifications, and life cycle phase associations and definitions; 2) "Concatenated literature", which contains the merged datasets of size, PIC and POC and which were corrected for taxonomic unit synonyms; 3) "Resampled cellular datasets", which contains the resampled datasets of size, PIC and POC in long format as well as a summary table; 4) "Gridded data sets", which contains gridded datasets of abundance, PIC and POC; 5) "Species lists", which contains spreadsheets of the "common" (>20 obs) and "rare" (<20 obs) species and their number of observations. The CASCADE data set can be easily reproduced using the scripts and data provided in the associated github repository: https://github.com/nanophyto/CASCADE/ (zenodo.12797197) Correspondence to: Joost de Vries, joost.devries@bristol.ac.uk v.0.1.2 has some fixes: 1. The wrongly specified S. neapolitana was removed from synonyms.yml (this species is now S. nana)2. Longitudes were corrected for Guerreiro et al., 20233. A double entry for Dimizia et al., 2015 was fixed4. Units in Sal et al., 2013 were correct to cells/L (previously cells/ml)5. Data from Sal et al., 2013 was re-done, as some species were missing6. Duplicate entries from Baumann et al., 2000 were dropped
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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 2022Publisher:Zenodo Funded by:UKRI | Hydrogen Infrastructure U...UKRI| Hydrogen Infrastructure Uncertainty Management for Heat DecarbonisationAuthors: Vassilis M. Charitopoulos; Mathilde Fajardy; Chi Kong Chyong; David M. Reiner;Input data set for OPHELIA optimisation model investigating optimal heat decarbonization pathways through electrification for net zero economy in Great Britain.
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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.6022815&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018Publisher:Zenodo Funded by:UKRI | RootDetect: Remote Detect...UKRI| RootDetect: Remote Detection and Precision Management of Root HealthAuthors: John W. Williams, Karyn Tabor;This dataset contains two metrics for climate change exposure using downscaled climate projections with the SRES A2 emissions scenario (Tabor and Williams, 2007).The metrics represent dissimilarity measurements of the squared Euclidean distance between seasonal (June–August and December–February) temperature and precipitation variables in the 20th century climate and mid-21st century climate. (1) disappearing climate risk - measure of dissimilarity between a pixel’s late 20th century climate and its closest matching pixel in the global set of 21st-century climates (2) novel climate risk - measure of dissimilarity between a pixel’s future climate and its closest matching pixel in the global set of late 20th-century climates. The data are in arcASCII format. All data are in units of standard Euclidean distance and multiplied by 1000. This is the original data. To scale the data similar to Tabor et al. (2018), remove outliers above the 99th percentile distribution before rescaling from 0-1. Unprojected number of columns 2160 number of rows 857 Lower Left X Center -179.917 Lower Left Y Center -59.084 Cell size 0.166667 decimal degrees (10 minutes or ~17 km) {"references": ["Tabor, K. et al. (2018). Tropical Protected Areas Under Increasing Threats from Climate Change and Deforestation: https://doi.org/10.3390/land7030090", "Tabor and Williams (2010). Globally downscaled climate projections for assessing the conservation impacts of climate change. https://doi.org/10.1890/09-0173.1", "Williams, J.W. et al. (2007). Projected distributions of novel and disappearing climates by 20100 AD. https://doi.org/10.1073/pnas.0606292104"]} Support for this project was provided by Conservation International, the Land Tenure Center at the University of Wisconsin, the Center for Climatic Research at the University of Wisconsin, and the Environment Program at the University of Wisconsin–Madison. This research has been funded in part by the Walton Family Foundation, the Gordon and Betty Moore Foundation, and a gift from Betty and Gordon Moore.
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visibility 105visibility views 105 download downloads 30 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 2020 United KingdomPublisher:Zenodo Funded by:UKRI | EPSRC Centre for Doctoral..., UKRI | Strategic Partnership in ..., UKRI | EPSRC Centre for Doctoral...UKRI| EPSRC Centre for Doctoral Training in Nuclear Energy: Building UK Civil Nuclear Skills for Global Markets. ,UKRI| Strategic Partnership in Structural Metallic Systems for Gas Turbines ,UKRI| EPSRC Centre for Doctoral Training in the Advanced Characterisation of MaterialsAuthors: McAuliffe, Thomas P; Foden, Alexander; Bilsland, Chris; Daskalaki-Mountanou, Dafni; +2 AuthorsMcAuliffe, Thomas P; Foden, Alexander; Bilsland, Chris; Daskalaki-Mountanou, Dafni; Dye, David; Britton, T Ben;handle: 10044/1/81527
Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk) This repository is a release of the raw data and analysis results for: 'Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope' https://doi.org/10.1016/j.ultramic.2020.112944 The raw data is given as 'RawData.h5' - this contains patterns, spectra, and metadata in the Bruker-exported format. Outputs of our analysis code (which will be made available via AstroEBSD) are contained in 'PCA_Outputs' subfolders. Exported plots and .mat results files are contained within. These are organised by Figure number in the paper. The provided results are divided into two major sections: (1) Variation in the variance tolerance limit (and corresponding numbers of retained components), and the weighting of the PCA in favour of EBSD or EDS information. RCCs are validated by cross-correlation with the corresponding raw data point pattern and/or spectrum. (2) Full outputs of PCA analysis having varied the weighting parameter. This contains IPF maps, quantified chemical maps, PC scores, and label maps.
ZENODO arrow_drop_down Imperial College London: SpiralDataset . 2020License: 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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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
visibility 286visibility views 286 download downloads 48 Powered bymore_vert ZENODO arrow_drop_down Imperial College London: SpiralDataset . 2020License: 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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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Funded by:UKRI | Fashion Fictions: imagini...UKRI| Fashion Fictions: imagining sustainable fashion worldsAuthors: Twigger Holroyd, Amy;This dataset was generated via a focused piece of research conducted by Dr Matilda Aspinall and Dr Amy Twigger Holroyd which investigated the experiences of students and staff involved in Fashion Fictions projects in 2022 at two institutions: LASALLE College of the Arts in Singapore and Nottingham Trent University. This focused research was situated within the broader Fashion Fictions project. Fashion Fictions, founded by Amy Twigger Holroyd in 2020, brings people together to generate, experience and reflect on engaging fictional visions of alternative fashion cultures and systems. Through these activities, we gain new perspectives on challenges, possibilities and pathways for change in the real world. The project is structured in three stages. Stage 1 prompts contributors to create brief written outlines of fictional fashion cultures and systems, known as Worlds; at Stage 2, participants put flesh on these outlines and create visual or material prototypes to represent their cultures, known as Explorations; at Stage 3, they performatively enact practices or events from the fictional worlds. To find out more about Fashion Fictions, visit the project website. To see other data linked to the project, visit the Fashion Fictions Zenodo community. -------------------------------- In both institutions, Fashion Fictions was initially introduced to the curriculum in the 2020/21 academic year; the activities discussed here took place in the following year, with a second cohort of students. At NTU, first-year undergraduate students from BA (Hons) courses in Fashion Design, Textile Design and Fashion Knitwear Design & Knitted Textiles undertook a short Stage 2 Fashion Fictions project. Spanning three two-hour workshops, the project was part of a Future Thinking toolkit within a module that aims to develop students’ intellectual curiosity and appreciation of the future as something that can be shaped and questioned. Working in small cross-course groups, students were given a specified Stage 1 fiction and asked to create a visual or material prototype to represent everyday life in that world, presented via a selection of images and a short explanatory text. At LASALLE, Fashion Fictions was set up as a major project extending across a 14-week semester for second-year students on two BA (Hons) programmes: Fashion Media and Industries and Fashion Design and Textiles. Also working in cross-course collaborative groups, the students first created their own Stage 1 world and then progressed to create a collection of Stage 2 prototypes in the form of garments and related media such as photographs and films, accompanied by an extensive body of supporting work. -------------------------------- in April and May 2022 we conducted semi-structured interviews with tutors involved in the projects – Lorraine Warde (Principal Lecturer in Fashion Design) at NTU and Martin Bonney and Kathryn Shannon Sim Yen Ping (Lecturers in Fashion, interviewed together) at LASALLE – and with three student groups, selected by the tutors, from each institution. The interview schedules for students and tutors each comprised four reflective questions, designed to gain an insight into the students’ experiences and the tutors’ observations. Each recorded interview lasted between twenty and sixty minutes. -------------------------------- The dataset is organised in nine folders: 1 Project context Project website About page from February 2022 (explaining the wider project at the time of this research). Project website Education projects page from January 2022 (giving context to the education projects taking place at the time of this research). 2 Activity guidance Project website Stage 1 (World) online guide from January 2022 (as available for use by LASALLE students). NTU virtual workspace Stage 2 (Exploration) guidance (as used by NTU students and providing an indication of the type of guidance that would have been offered to LASALLE students for their Stage 2 work - although their project was much longer in duration). 3 Interview documentation Information sheet and consent form given to research participants. Interview questions for staff and students, shared with all participants in advance. 4 LASALLE staff interview Transcript of interview with Martin Bonney and Kathryn Shannon Sim Yen Ping (Lecturers in Fashion). 5 LASALLE student interviews Transcripts of interviews with three student groups, each identified by the number/letter of the Stage 1 World and Stage 2 Exploration they created (as listed on the project website Worlds and Explorations pages). 6 LASALLE student work Project work (Stage 1 Worlds and Stage 2 Explorations) created by the three student groups interviewed, as displayed on the project website. One group (World 154) did not submit their Exploration for the website. 7 NTU staff interview Transcript of interview with Lorraine Warde (Principal Lecturer in Fashion Design). 8 NTU student interviews Transcripts of interviews with three student groups, each identified by the number/letter of the Stage 2 Exploration they created (as listed on the project website Explorations page). 9 NTU student work Project work (Stage 2 Explorations) created by the three student groups interviewed. Two groups' work is as displayed on the project website. One group (World 95, Exploration X) did not submit their Exploration for the website and so their internal presentation has been included instead.
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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 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.
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.4626241&type=result"></script>'); --> </script>
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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.
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.4626241&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.
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.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.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019Publisher:MDPI AG Funded by:UKRI | RootDetect: Remote Detect...UKRI| RootDetect: Remote Detection and Precision Management of Root HealthKamalakanta Sahoo; Richard Bergman; Sevda Alanya-Rosenbaum; Hongmei Gu; Shaobo Liang;doi: 10.3390/su11174722
Climate change, environmental degradation, and limited resources are motivations for sustainable forest management. Forests, the most abundant renewable resource on earth, used to make a wide variety of forest-based products for human consumption. To provide a scientific measure of a product’s sustainability and environmental performance, the life cycle assessment (LCA) method is used. This article provides a comprehensive review of environmental performances of forest-based products including traditional building products, emerging (mass-timber) building products and nanomaterials using attributional LCA. Across the supply chain, the product manufacturing life-cycle stage tends to have the largest environmental impacts. However, forest management activities and logistics tend to have the greatest economic impact. In addition, environmental trade-offs exist when regulating emissions as indicated by the latest traditional wood building product LCAs. Interpretation of these LCA results can guide new product development using biomaterials, future (mass) building systems and policy-making on mitigating climate change. Key challenges include handling of uncertainties in the supply chain and complex interactions of environment, material conversion, resource use for product production and quantifying the emissions released.
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/su11174722&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 55 citations 55 popularity Top 1% influence Top 10% 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/su11174722&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2024Publisher:Springer Science and Business Media LLC Funded by:UKRI | BBSRC IAA University of G..., WT | Socio-ecological dynamics...UKRI| BBSRC IAA University of Glasgow ,WT| Socio-ecological dynamics of zoonotic and vector-borne diseases in changing landscapes: implications for surveillance and controlFedra Trujillano; G. Jiménez; Luis Edgar Tarazona-Manrique; Najat F. Kahamba; Fredros O. Okumu; Nombre Apollinaire; Gabriel Carrasco-Escobar; Brian Barrett; Kimberly Fornace;Abstract Background In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. Results We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. Conclusions Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.
International Journa... arrow_drop_down International Journal of Health GeographicsArticle . 2024 . 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.
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=10.1186/s12942-024-00371-w&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 International Journa... arrow_drop_down International Journal of Health GeographicsArticle . 2024 . 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 2023Publisher:Frontiers Media SA Funded by:UKRI | Pollutants in the Urban E...UKRI| Pollutants in the Urban Environment: An Integrated Framework for Improving Sustainability of the Indoor Environment (PUrE Intrawise)Authors: Benjamin Greening; Tim Braunholtz-Speight; Ruth Wood; Muir Freer;With the 2015 Paris Agreement pursuing efforts to limit global temperature increase to below 2°C above pre-industrial levels and the “energy trilemma” goals of energy security, energy equity and environmental sustainability, decarbonisation remains a priority across all of the United Kingdom (United Kingdom) energy system, not just electricity. Electricity and thermal energy storage technologies can offer a host of benefits across the energy value chain through the abilityS to capture, store and then release electricity or thermal energy over a period of time. These benefits include helping capture the full potential of renewable generation and providing services such as frequency response and reserve to Great Britain’s (GB) electricity system. In addition, with the aforementioned climate targets in mind, energy storage can also play a role in facilitating the decarbonisation of other activities and sectors. Here we delve deeper into how energy storage technologies can contribute to both energy sector transformation and more broadly, decarbonisation. Furthermore, we discuss the importance of ensuring a technology-agnostic approach to the development of policy and regulation with relevance to energy storage. This ensures that storage technologies with significant potential to contribute to the ‘energy trilemma’ goals are not precluded from entering the market due to unfavourable policy and regulatory frameworks.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!
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Research data keyboard_double_arrow_right Dataset 2024Publisher:Zenodo Funded by:UKRI | CoccoTrait: Revealing Coc...UKRI| CoccoTrait: Revealing Coccolithophore Trait diversity and its climatic impactsde Vries, Joost; Poulton, Alex J.; Young, Jeremy R.; Monteiro, Fanny M.; Sheward, Rosie M.; Johnson, Roberta; Hagino, Kyoko; Ziveri, Patrizia; Wolf, Levi J.;CASCADE is a global dataset for 139 extant coccolithophore taxonomic units. CASCADE includes a trait database (size and cellular organic and inorganic carbon contents) and taxonomic-specific global spatiotemporal distributions (Lat/Lon/Depth/Month/Year) of coccolithophore abundance and organic and inorganic carbon stocks. CASCADE covers all ocean basins over the upper 275 meters, spans the years 1964-2019 and includes 33,119 taxonomic-specific abundance observations. Within CASCADE, we characterise the underlying uncertainties due to measurement errors by propagating error estimates between the different studies. Full details of the data set are provided in the associated Scientific Data manuscript. The repository contains five main folders: 1) "Classification", which contains YAML files with synonyms, family-level classifications, and life cycle phase associations and definitions; 2) "Concatenated literature", which contains the merged datasets of size, PIC and POC and which were corrected for taxonomic unit synonyms; 3) "Resampled cellular datasets", which contains the resampled datasets of size, PIC and POC in long format as well as a summary table; 4) "Gridded data sets", which contains gridded datasets of abundance, PIC and POC; 5) "Species lists", which contains spreadsheets of the "common" (>20 obs) and "rare" (<20 obs) species and their number of observations. The CASCADE data set can be easily reproduced using the scripts and data provided in the associated github repository: https://github.com/nanophyto/CASCADE/ (zenodo.12797197) Correspondence to: Joost de Vries, joost.devries@bristol.ac.uk v.0.1.2 has some fixes: 1. The wrongly specified S. neapolitana was removed from synonyms.yml (this species is now S. nana)2. Longitudes were corrected for Guerreiro et al., 20233. A double entry for Dimizia et al., 2015 was fixed4. Units in Sal et al., 2013 were correct to cells/L (previously cells/ml)5. Data from Sal et al., 2013 was re-done, as some species were missing6. Duplicate entries from Baumann et al., 2000 were dropped
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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 2022Publisher:Zenodo Funded by:UKRI | Hydrogen Infrastructure U...UKRI| Hydrogen Infrastructure Uncertainty Management for Heat DecarbonisationAuthors: Vassilis M. Charitopoulos; Mathilde Fajardy; Chi Kong Chyong; David M. Reiner;Input data set for OPHELIA optimisation model investigating optimal heat decarbonization pathways through electrification for net zero economy in Great Britain.
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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.6022815&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018Publisher:Zenodo Funded by:UKRI | RootDetect: Remote Detect...UKRI| RootDetect: Remote Detection and Precision Management of Root HealthAuthors: John W. Williams, Karyn Tabor;This dataset contains two metrics for climate change exposure using downscaled climate projections with the SRES A2 emissions scenario (Tabor and Williams, 2007).The metrics represent dissimilarity measurements of the squared Euclidean distance between seasonal (June–August and December–February) temperature and precipitation variables in the 20th century climate and mid-21st century climate. (1) disappearing climate risk - measure of dissimilarity between a pixel’s late 20th century climate and its closest matching pixel in the global set of 21st-century climates (2) novel climate risk - measure of dissimilarity between a pixel’s future climate and its closest matching pixel in the global set of late 20th-century climates. The data are in arcASCII format. All data are in units of standard Euclidean distance and multiplied by 1000. This is the original data. To scale the data similar to Tabor et al. (2018), remove outliers above the 99th percentile distribution before rescaling from 0-1. Unprojected number of columns 2160 number of rows 857 Lower Left X Center -179.917 Lower Left Y Center -59.084 Cell size 0.166667 decimal degrees (10 minutes or ~17 km) {"references": ["Tabor, K. et al. (2018). Tropical Protected Areas Under Increasing Threats from Climate Change and Deforestation: https://doi.org/10.3390/land7030090", "Tabor and Williams (2010). Globally downscaled climate projections for assessing the conservation impacts of climate change. https://doi.org/10.1890/09-0173.1", "Williams, J.W. et al. (2007). Projected distributions of novel and disappearing climates by 20100 AD. https://doi.org/10.1073/pnas.0606292104"]} Support for this project was provided by Conservation International, the Land Tenure Center at the University of Wisconsin, the Center for Climatic Research at the University of Wisconsin, and the Environment Program at the University of Wisconsin–Madison. This research has been funded in part by the Walton Family Foundation, the Gordon and Betty Moore Foundation, and a gift from Betty and Gordon Moore.
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visibility 105visibility views 105 download downloads 30 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 2020 United KingdomPublisher:Zenodo Funded by:UKRI | EPSRC Centre for Doctoral..., UKRI | Strategic Partnership in ..., UKRI | EPSRC Centre for Doctoral...UKRI| EPSRC Centre for Doctoral Training in Nuclear Energy: Building UK Civil Nuclear Skills for Global Markets. ,UKRI| Strategic Partnership in Structural Metallic Systems for Gas Turbines ,UKRI| EPSRC Centre for Doctoral Training in the Advanced Characterisation of MaterialsAuthors: McAuliffe, Thomas P; Foden, Alexander; Bilsland, Chris; Daskalaki-Mountanou, Dafni; +2 AuthorsMcAuliffe, Thomas P; Foden, Alexander; Bilsland, Chris; Daskalaki-Mountanou, Dafni; Dye, David; Britton, T Ben;handle: 10044/1/81527
Prepared by Tom McAuliffe (t.mcauliffe17@imperial.ac.uk) This repository is a release of the raw data and analysis results for: 'Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope' https://doi.org/10.1016/j.ultramic.2020.112944 The raw data is given as 'RawData.h5' - this contains patterns, spectra, and metadata in the Bruker-exported format. Outputs of our analysis code (which will be made available via AstroEBSD) are contained in 'PCA_Outputs' subfolders. Exported plots and .mat results files are contained within. These are organised by Figure number in the paper. The provided results are divided into two major sections: (1) Variation in the variance tolerance limit (and corresponding numbers of retained components), and the weighting of the PCA in favour of EBSD or EDS information. RCCs are validated by cross-correlation with the corresponding raw data point pattern and/or spectrum. (2) Full outputs of PCA analysis having varied the weighting parameter. This contains IPF maps, quantified chemical maps, PC scores, and label maps.
ZENODO arrow_drop_down Imperial College London: SpiralDataset . 2020License: 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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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
visibility 286visibility views 286 download downloads 48 Powered bymore_vert ZENODO arrow_drop_down Imperial College London: SpiralDataset . 2020License: 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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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Publisher:Zenodo Funded by:UKRI | Fashion Fictions: imagini...UKRI| Fashion Fictions: imagining sustainable fashion worldsAuthors: Twigger Holroyd, Amy;This dataset was generated via a focused piece of research conducted by Dr Matilda Aspinall and Dr Amy Twigger Holroyd which investigated the experiences of students and staff involved in Fashion Fictions projects in 2022 at two institutions: LASALLE College of the Arts in Singapore and Nottingham Trent University. This focused research was situated within the broader Fashion Fictions project. Fashion Fictions, founded by Amy Twigger Holroyd in 2020, brings people together to generate, experience and reflect on engaging fictional visions of alternative fashion cultures and systems. Through these activities, we gain new perspectives on challenges, possibilities and pathways for change in the real world. The project is structured in three stages. Stage 1 prompts contributors to create brief written outlines of fictional fashion cultures and systems, known as Worlds; at Stage 2, participants put flesh on these outlines and create visual or material prototypes to represent their cultures, known as Explorations; at Stage 3, they performatively enact practices or events from the fictional worlds. To find out more about Fashion Fictions, visit the project website. To see other data linked to the project, visit the Fashion Fictions Zenodo community. -------------------------------- In both institutions, Fashion Fictions was initially introduced to the curriculum in the 2020/21 academic year; the activities discussed here took place in the following year, with a second cohort of students. At NTU, first-year undergraduate students from BA (Hons) courses in Fashion Design, Textile Design and Fashion Knitwear Design & Knitted Textiles undertook a short Stage 2 Fashion Fictions project. Spanning three two-hour workshops, the project was part of a Future Thinking toolkit within a module that aims to develop students’ intellectual curiosity and appreciation of the future as something that can be shaped and questioned. Working in small cross-course groups, students were given a specified Stage 1 fiction and asked to create a visual or material prototype to represent everyday life in that world, presented via a selection of images and a short explanatory text. At LASALLE, Fashion Fictions was set up as a major project extending across a 14-week semester for second-year students on two BA (Hons) programmes: Fashion Media and Industries and Fashion Design and Textiles. Also working in cross-course collaborative groups, the students first created their own Stage 1 world and then progressed to create a collection of Stage 2 prototypes in the form of garments and related media such as photographs and films, accompanied by an extensive body of supporting work. -------------------------------- in April and May 2022 we conducted semi-structured interviews with tutors involved in the projects – Lorraine Warde (Principal Lecturer in Fashion Design) at NTU and Martin Bonney and Kathryn Shannon Sim Yen Ping (Lecturers in Fashion, interviewed together) at LASALLE – and with three student groups, selected by the tutors, from each institution. The interview schedules for students and tutors each comprised four reflective questions, designed to gain an insight into the students’ experiences and the tutors’ observations. Each recorded interview lasted between twenty and sixty minutes. -------------------------------- The dataset is organised in nine folders: 1 Project context Project website About page from February 2022 (explaining the wider project at the time of this research). Project website Education projects page from January 2022 (giving context to the education projects taking place at the time of this research). 2 Activity guidance Project website Stage 1 (World) online guide from January 2022 (as available for use by LASALLE students). NTU virtual workspace Stage 2 (Exploration) guidance (as used by NTU students and providing an indication of the type of guidance that would have been offered to LASALLE students for their Stage 2 work - although their project was much longer in duration). 3 Interview documentation Information sheet and consent form given to research participants. Interview questions for staff and students, shared with all participants in advance. 4 LASALLE staff interview Transcript of interview with Martin Bonney and Kathryn Shannon Sim Yen Ping (Lecturers in Fashion). 5 LASALLE student interviews Transcripts of interviews with three student groups, each identified by the number/letter of the Stage 1 World and Stage 2 Exploration they created (as listed on the project website Worlds and Explorations pages). 6 LASALLE student work Project work (Stage 1 Worlds and Stage 2 Explorations) created by the three student groups interviewed, as displayed on the project website. One group (World 154) did not submit their Exploration for the website. 7 NTU staff interview Transcript of interview with Lorraine Warde (Principal Lecturer in Fashion Design). 8 NTU student interviews Transcripts of interviews with three student groups, each identified by the number/letter of the Stage 2 Exploration they created (as listed on the project website Explorations page). 9 NTU student work Project work (Stage 2 Explorations) created by the three student groups interviewed. Two groups' work is as displayed on the project website. One group (World 95, Exploration X) did not submit their Exploration for the website and so their internal presentation has been included instead.
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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 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.
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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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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.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2019Publisher:MDPI AG Funded by:UKRI | RootDetect: Remote Detect...UKRI| RootDetect: Remote Detection and Precision Management of Root HealthKamalakanta Sahoo; Richard Bergman; Sevda Alanya-Rosenbaum; Hongmei Gu; Shaobo Liang;doi: 10.3390/su11174722
Climate change, environmental degradation, and limited resources are motivations for sustainable forest management. Forests, the most abundant renewable resource on earth, used to make a wide variety of forest-based products for human consumption. To provide a scientific measure of a product’s sustainability and environmental performance, the life cycle assessment (LCA) method is used. This article provides a comprehensive review of environmental performances of forest-based products including traditional building products, emerging (mass-timber) building products and nanomaterials using attributional LCA. Across the supply chain, the product manufacturing life-cycle stage tends to have the largest environmental impacts. However, forest management activities and logistics tend to have the greatest economic impact. In addition, environmental trade-offs exist when regulating emissions as indicated by the latest traditional wood building product LCAs. Interpretation of these LCA results can guide new product development using biomaterials, future (mass) building systems and policy-making on mitigating climate change. Key challenges include handling of uncertainties in the supply chain and complex interactions of environment, material conversion, resource use for product production and quantifying the emissions released.
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For further information contact us at helpdesk@openaire.euAccess Routesgold 55 citations 55 popularity Top 1% influence Top 10% impulse Top 10% Powered by BIP!
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2024Publisher:Springer Science and Business Media LLC Funded by:UKRI | BBSRC IAA University of G..., WT | Socio-ecological dynamics...UKRI| BBSRC IAA University of Glasgow ,WT| Socio-ecological dynamics of zoonotic and vector-borne diseases in changing landscapes: implications for surveillance and controlFedra Trujillano; G. Jiménez; Luis Edgar Tarazona-Manrique; Najat F. Kahamba; Fredros O. Okumu; Nombre Apollinaire; Gabriel Carrasco-Escobar; Brian Barrett; Kimberly Fornace;Abstract Background In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. Results We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. Conclusions Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.
International Journa... arrow_drop_down International Journal of Health GeographicsArticle . 2024 . 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.
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=10.1186/s12942-024-00371-w&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 International Journa... arrow_drop_down International Journal of Health GeographicsArticle . 2024 . 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.
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=10.1186/s12942-024-00371-w&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:Frontiers Media SA Funded by:UKRI | Pollutants in the Urban E...UKRI| Pollutants in the Urban Environment: An Integrated Framework for Improving Sustainability of the Indoor Environment (PUrE Intrawise)Authors: Benjamin Greening; Tim Braunholtz-Speight; Ruth Wood; Muir Freer;With the 2015 Paris Agreement pursuing efforts to limit global temperature increase to below 2°C above pre-industrial levels and the “energy trilemma” goals of energy security, energy equity and environmental sustainability, decarbonisation remains a priority across all of the United Kingdom (United Kingdom) energy system, not just electricity. Electricity and thermal energy storage technologies can offer a host of benefits across the energy value chain through the abilityS to capture, store and then release electricity or thermal energy over a period of time. These benefits include helping capture the full potential of renewable generation and providing services such as frequency response and reserve to Great Britain’s (GB) electricity system. In addition, with the aforementioned climate targets in mind, energy storage can also play a role in facilitating the decarbonisation of other activities and sectors. Here we delve deeper into how energy storage technologies can contribute to both energy sector transformation and more broadly, decarbonisation. Furthermore, we discuss the importance of ensuring a technology-agnostic approach to the development of policy and regulation with relevance to energy storage. This ensures that storage technologies with significant potential to contribute to the ‘energy trilemma’ goals are not precluded from entering the market due to unfavourable policy and regulatory frameworks.
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=10.3389/fenrg.2022.1109997&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 4 citations 4 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=10.3389/fenrg.2022.1109997&type=result"></script>'); --> </script>
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