• Energy Research
• 2021-2025close
• GB close
• NL close
• UA close

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.ScenarioMIP.MOHC.UKESM1-0-LL.ssp245' 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 UKESM1.0-N96ORCA1 climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 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: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

# Scottish mountain hares do not respond behaviorally to increased camouflage mismatch [https://doi.org/10.5061/dryad.hqbzkh1rc](https://doi.org/10.5061/dryad.hqbzkh1rc) **Abstract** Climate change has resulted in myriad stressors to wild organisms. Phenotypic plasticity, including behavioral plasticity, is hypothesized to play a key role in allowing animals to cope with rapid climate change and mitigate its negative fitness consequences. Camouflage mismatch resulting from decreasing duration of snow cover presents a stressor to species that undergo coat color molts to maintain camouflage against seasonally changing backgrounds. Winter white animals appear highly conspicuous against dark, snowless background and experience increased predation-induced mortality. Here, we evaluate the potential of behavioral plasticity to buffer against camouflage mismatch in mountain hares (*Lepus timidus*) in Scotland. We carried out field surveys in three populations over two years and found no evidence that hares modify their behaviors in response to increasing camouflage mismatch. Hares did not prefer to rest closer to light-colored rocks or farther from conspecifics with increasing color contrast. Furthermore, whiter hares did not seek to rest closer to snowy backgrounds; rather, hares preferred to sit farther from snow. These results suggest that behavioral plasticity might not be a universal, rapid mechanism facilitating adaptation to climate change. ## Description of the data and file structure This dataset contains the following variables: * Date: Date of stationary hare observation. * Area: Area where the observation was taken. * Dist_Rock: The distance a stationary hare was to the closest light-colored rock in meters. Measurements are in 1 m increments between 0 and 20 m. >20 marks occasions when a hare was farther than 20 m from the closest rock. 0 indicates occasions when a resting hare was immediately adjacent to a rock. 'n/a's signify that data was not collected. * Dist_Snow: The distance a stationary hare was to the closest snow field/patch in meters. Measurements are in 1 m increments between 0 and 20 m. >20 marks occasions when a hare was farther than 20 m from the closest snow field/patch. 0 indicates occasions when a hare was resting on snow field/patch. 'n/a's signify that data was not collected. * Dist_Hare: The distance a stationary hare was to the closest conspecific. Measurements are in 1 m increments between 1 and 20 m. >20 marks occasions when a hare was farther than 20 m from another hare. 'n/a's signify that data was not collected. * Snow_5m: The percent snow cover within 5-m radius circle centered at the hare's resting site. Estimated in 25% increments. 'n/a's signify that data was not collected. * Snow_10m: The percent snow cover within 10-m radius circle centered at the hare's resting site. Estimated in 25% increments. 'n/a's signify that data was not collected. * Hare_White: Hare's coat color measured in percent white in four categories ; 0% (completely dark), 25% (mostly dark), 50% (half-dark and half-white), 75% (mostly white) or 100% white (completely white). For detailed description of categories see Zimova et al. 2020 ProcB. 'n/a's signify that data was not collected.  Study Sites Field surveys were carried out at three sites (Lecht [57.193 ̊ N, −3.240 ̊ W], Findhorn High [57.235 ̊ N, −4.136 ̊ W], Findhorn Low [57.206 ̊ N, −4.102 ̊ W]) in the northeast and central highlands of Scotland, UK. All sites were located between 430-730 m a.s.l. and dominated by dwarf heath and subalpine plant communities. The Lecht site included areas of eroded peat, and both sites were scatted with occasional white/pale, sometimes lichen covered, hare-sized rocks. Field Surveys We surveyed mountain hares twice a month in fall (October–January) and spring (March–June) seasons during 2015 and 2016 for a total of 5–11 surveys per season (Zimova et al. 2020b). During each survey, one surveyor walked along a predetermined route (ca 3–6 km long) and observed hares as they were either flushed (moved from their resting site in response to disturbance), or less frequently, detected by the surveyor during the frequent and thorough binoculars scans of the landscape. Hares are largely inactive during the day when they sit at a resting site above ground. We only used observations during which the observer had a clear view that allowed coat color to be assessed and was confident of the hare’s original resting location. For all hares detected within 200 m of the observer, we photographed the hare and recorded coat color following (Watson 1963). Coat color was ranked into four categories ; 0% (completely dark), 25% (mostly dark), 50% (half-dark and half-white), 75% (mostly white) or 100% white (completely white) (for detailed description of categories see Zimova et al. 2020b). For observations accompanied by photographs (> 80% of all observations) field estimates of molt were later verified by one of us (MZ). Finally, for each hare’s original resting site, we visually estimated the minimum distance a hare was to 1) any light-colored rocks of equal or larger size than the size of a resting hare, 2) another hare, and 3) snow. All distances between 0 and 20 m were estimated in 1 m increments; all other distances were recorded as ‘>20 m’ as we were not able to accurately estimate distances beyond 20 m. Climate change has resulted in myriad stressors to wild organisms. Phenotypic plasticity, including behavioral plasticity, is hypothesized to play a key role in allowing animals to cope with rapid climate change and mitigate its negative fitness consequences. Camouflage mismatch resulting from decreasing duration of snow cover presents a stressor to species that undergo coat color molts to maintain camouflage against seasonally changing backgrounds. Winter white animals appear highly conspicuous against dark, snowless background and experience increased predation-induced mortality. Here, we evaluate the potential of behavioral plasticity to buffer against camouflage mismatch in mountain hares (Lepus timidus) in Scotland. We carried out field surveys in three populations over two years and found no evidence that hares modify their behaviors in response to increasing camouflage mismatch. Hares did not prefer to rest closer to light-colored rocks or farther from conspecifics with increasing color contrast. Furthermore, whiter hares did not seek to rest closer to snowy backgrounds; rather, hares preferred to sit farther from snow. These results suggest that behavioral plasticity might not be a universal, rapid mechanism facilitating adaptation to climate change.

TECHNICAL INFO No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the Technical documentation of the urbisphere-Paris campaign. This publication may include a number of files that contain the same measurements (duplicate files, "dupes") as multiple raw data backups were performed on the data. The duplicate data can be differentiated by the information found in the timestamp column and file header. ACKNOWLEDGEMENTS Authors thank SIRTA/LMD staff for providing support and facilities; ATMO-TNA-3—0000000125 funding COPYRIGHT NOTICE Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved. CREATOR NOTICE This work is owned by the Principal Investigators (PIs) of the Urbisphere project. ATTRIBUTION NOTICE The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). DISCLAIMER NOTICE The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected. MATERIAL NOTICE The notices cover data in databases, text and images contained in the work. MATERIAL URI Urbisphere project Original logger data files from radiometer intercomparison measurements at SIRTA Atmospheric Observatory in Palaiseau in the Greater Paris region.

This repository consists of two databases- CASE-ONSHORE and CASE-OFFSHORE, generated using OpenFAST v2.4 on NREL's 10-MW reference wind turbine for training data-driven probabilistic load surrogate models. The data is to be used for mapping 10-minute average environmental conditions to the corresponding 10-minute load statistics such as load average, fatigue and range at various locations on the tower and blades.

The repository contains the underlining data for "On the wake deflection of vertical axis wind turbines by pitched blades" This repository consists of experimental (PIV and force measurements) data in the wake of VAWTs with different blade pitch angles. The measured cases include an isolated VAWT with -10, 0, 10 degree pitch, respectively. Author contribution: Ming Huang: Conceptualization; Methodology; Validation; Experimental design; Carrying out the experiment; postprocessing Yugandhar Vijaykumar Patil: Design assistant; Carrying out the experiment; postprocessing Andrea Sciacchitano: Conceptualization; Experimental design; Methodology; Validation; Delphine De Tavernier: Conceptualization Carlos Simao Ferreira: Conceptualization; Methodology; Validation; Experimental design

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.

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.HighResMIP.MOHC.HadGEM3-GC31-HH' 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-N512ORCA12 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N512; 1024 x 768 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA12 tripolar primarily 1/12 deg; 4320 x 3604 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA12 tripolar primarily 1/12 deg; 4320 x 3604 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: 50 km, atmos: 50 km, land: 50 km, ocean: 10 km, seaIce: 10 km.

This repository contains additional data associated to Tedersoo et al. (2022) (https://doi.org/10.1111/gcb.16398).This repository contains those data necessary to rerun the superecoregion design and the endemicity analyses as well as vector maps on the endemicity and OTU richness of individual superecoregions. Further data associated with the same publication, regarding the vulnerability analysis, can be found here: 10.5281/zenodo.6983158. input.zip - Input data needed to run the analysis scripts provided [here](https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability/superecoregions_and_endemicity). The entire folder is to be copied in the working directory to run the pipeline. output.zip - Further data needed to run the analysis scripts provided [here](https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability/superecoregions_and_endemicity). The entire folder is to be copied in the working directory to run the pipeline. maps_otu_numbers_and_se_characteristics - vector maps of the superecoregions and the number of OTUs and endemic OTUs per region for different functional groups maps_traits_endemism - vector maps of endemicity indices per region f different functional groups 01_super_Ecoregions_numbers.pdf - vector map of the superecoregions used in the study numbered from 1 - 174. legend_01_super_ecoregions_numbers.csv - legend with superecoregion names for 01_super_Ecoregions_numbers.pdf. super_eoregions_results - Superecoregions with number of OTUs, number of endemic OTUs and endemicity indices in R data format, for further analyses and visualization super_ecoregions.shp - Superecoregions with number of OTUs, number of endemic OTUs and endemicity indices as shape file, for further analyses and visualization This repository contains additional data associated to Tedersoo et al. (2022) (https://doi.org/10.1111/gcb.16398).This repository contains those data necessary to rerun the superecoregion design and the endemicity analyses as well as vector maps on the endemicity and OTU richness of individual superecoregions. Further data associated with the same publication, regarding the vulnerability analysis, can be found here: 10.5281/zenodo.6983158.