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Conventional analyses suggest the metabolism of heterotrophs is thermally more sensitive than that of autotrophs, implying that warming leads to pronounced trophodynamic imbalances. However, these analyses inappropriately combine within- and across-taxa trends. We present a novel mathematic framework to separate these, revealing that the higher temperature sensitivity of heterotrophs is mainly caused by within-taxa responses which account for 92% of the difference between autotrophic and heterotrophic protists. This dataset contains both the datasets and R codes of per capita growth rates of autotrophic and heterotrophic protists as well as heterotrophic bacteria and insects. The datasets of per capita growth rates against temperature were compiled from the literature. Experimental data were included if they met the following criteria: at least 3 data points with positive growth rate (µ) and at least 2 unique temperatures at which positive µ were measured. To calculate apparent activation energy, we also removed data points with nonpositive µ and those with temperatures above the optimal growth temperature (defined as the temperature corresponding to the maximal µ). We use the free software R (version 4.2.0) with R packages (foreach, nlme, plyr, dplyr) to analyse these datasets. R codes are also provided.

Survey used and data gathered for research into adoption of carbon management strategies amongst universities 2022, which forms part of a PhD thesis and will be submitted for publication in a journal.

Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.

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.

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.

Archive files containing the input files and (where relevant) source code to reproduce data on the dynamics and energy landscape of supercooled silica. The main components of the repository are as follows: source code and input files for producing MD trajectories for binary Lennard-Jones and BKS silica systems as used in the code, python scripts for analysing these trajectories, source code for identifying cage-breaking transitions in quenched trajectories, input files for the Cambridge Energy Landscape Software used to generate the landscape databases, and scripts for analysing those databases.

# 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.