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This dataset includes input data to estimate learning-by-doing (LbD) and learning-by-researching (LbR) rates for onshore wind and solar PV in the United States. Using different learning curve approaches the simulated technology cost developments are also presented. Coefficient of determination (R square) and Root Mean Square Error (RMSE) were applied for quantification of the agreement between simulated and observed technology costs.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CSIRO-ARCCSS.ACCESS-CM2.historical' 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 Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model, released in 2019, 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), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run by the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS (Australian Research Council Centre of Excellence for Climate System Science). Mailing address: CSIRO, c/o Simon J. Marsland, 107-121 Station Street, Aspendale, Victoria 3195, Australia (CSIRO-ARCCSS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

FROST summary outputs of the area burnt by short interval fires, high intensity fires, and overall area burnt of alpine ash stands in Victoria under various climate scenarios.

An editable communications and analysis tool for presenting the relevance of all UN SDGs and their subsidiary targets and indicators to a particular locality or municipality. Default selections are derived from UN-Habitat classifications of urban relevance across all 17 Goals, with shaded segments representative of relevant targets, and the stars within each segment denoting the indicators aligned with each target (current as of mid-2020). This communications tool has potential relevance and adaptability to other sectors beyond local government that apply the SDGs.

Output data prepared for analysis of 25 diverse global cities by the Global Healthy and Sustainable City-Indicator Collaboration study, published in The Lancet Global Health Series on urban design, transport, and health. 2022. https://www.thelancet.com/series/urban-design-2022 Boeing, G. et al. (2022) ‘Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities’, The Lancet Global Health, 10(6), pp. e907–e918. Available at: https://doi.org/10.1016/S2214-109X(22)00072-9. Data outputs were generated through use of the global-indicators software tool, designed for this study and available from: https://github.com/global-healthy-liveable-cities/global-indicators Further detail on the methods used is provided in the following publication: Liu, S., Higgs, C., Arundel, J., Boeing, G., Cerdera, N., Moctezuma, D., Cerin, E., Adlakha, D., Lowe, M. and Giles-Corti, B. (2021), A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data. Geogr Anal. https://doi.org/10.1111/gean.12290 The study made use of OpenStreetMap, Global Human Settlements and custom data, and is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/

This dataset includes data from the validation of double U-tube borehole and seasonal solar thermal energy storage system TRNSYS models. The simulated transient temperatures at various points of the systems were compared with the measured ones. To quantify the agreement between each simulated and measured temperature of interest, mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (CC) were applied.

# Hotter deserts and the impending challenges for the spiny-tailed lizard in India [https://doi.org/10.5061/dryad.x95x69psd](https://doi.org/10.5061/dryad.x95x69psd) This dataset encompasses all the data utilized for analysis within the manuscript. ## Description of the data and file structure \################################################################################################################################################## Field body temperature This datafile contains the data collected from thermal loggers on lizards from 15 March 2021 to 23 May 2021. \* Lizard_ID: Identifies individual lizards \* Date: It refers to the date on which the data was collected using the logger \* Time: It refers to the time on which the data was collected using the logger \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* Temp.body: Calibrated skin temperature of the lizard in °C \################################################################################################################################################### Operative temperature habitat This datafile contains the data collected from copper models in different microhabitats. \* Date: Date on which the data was collected using the logger \* Time: Time on which the data was collected using the logger \* Burrow: Operative temperature in burrow in °C \* Open: Operative temperature in open in °C \**Cells with "null" suggest missing data as we removed copper models from the field on rainy/overcast days. \################################################################################################################################################### Operative temperature site This datafile contains the data collected from copper models in open microhabitat from two sampling sites. \* Date: Date on which the data was collected using the logger \* Time: Time on which the data was collected using the logger \* Open_S: Operative temperature in open collected from Sam sampling site in °C \* Open_B: Operative temperature in open collected from Bedhiya sampling site in °C \################################################################################################################################################### Operative temperature size This datafile contains the data collected from copper models of two different sizes in the three microhabitats. \* Date: Date on which the data was collected using the logger \* Time: Time on which the data was collected using the logger \* Open_ad: Operative temperature in open collected using an average adult STL sized copper model in °C \* Open_juv: Operative temperature in open collected using an average juvenile STL sized copper model in °C \* Bur_ad: Operative temperature in burrow collected using an average adult STL sized copper model in °C \* Bur_juv: Operative temperature in burrow collected using an average juvenile STL sized copper model in °C \################################################################################################################################################### TBAE This datafile contains the data collected from focal sampling and the data derived from operative temperatures using the TBAE algorithm. \* Date: It refers to the date on which the data was collected using the logger \* Time: It refers to the time on which the data was collected using the logger \* Activity_test: Data from focal sampling indicating whether the lizards were active (denoted as 1) or inactive (denoted as 0) \* Activity_algorithm: Data from TBAE algorithm indicating whether the lizards were active (denoted as 1) or inactive (denoted as 0) \################################################################################################################################################### Locomotor performace This datafile contains data collected by analysing performance run videos. \* Lizard_ID: Identifies individual lizards \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* body.temp: Cloacal temperatutre of the lizard in °C \* Speed(m/s): Speed of the lizard in m/s \################################################################################################################################################### Critical thermal limits This datafile contains the data collected in the laboratory about the critical thermal limits of STL. \* Lizard_ID: Identifies individual lizards \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* CTmax: Critical Thermal Maxima of the lizard in °C \* CTmin: Critical Thermal Minima of the lizard in °C \################################################################################################################################################### Preferred Temperature This datafile contains the data collected in the laboratory about the preferred temperature of STL. \* Lizard_ID: Identifies individual lizards \* SVL: Snout-Vent length of the lizard in cms \* Mass: Mass of the lizard in grams \* Sex: Male or Female \* Site: The name of the either of the two sites used for data collection \* BCI: Body Condition Index of the lizard \* Q25: First quartile lower quartile 25th percentile of the preferrence data for the lizard in °C \* Median: Second quartile median 50th percentile of the preferrence data for the lizard in °C \* Q75: Third quartile upper quartile 75th percentile of the preferrence data for the lizard in °C \################################################################################################################################################### Scan Survey This datafile contains the data collected in the field field using scan sampling. \* Open.temp: Operative temperature in open in °C \* Burrow: Proportion of individuals inside burrows \* Basking: Proportion of individuals basking \* Foraging: Proportion of individuals foraging \* Mating: Proportion of individuals engaged in courtship/mating \* Territorial: Proportion of individuals engaged in territorial behavior (fight/intimidation) \* Total: Proportion of individuals active \################################################################################################################################################## Calibration This datafile contains the data collected to calibrate skin and copper model temperatures to cloacal temperatures. \* Lizard_ID: Identifies individual lizards \* Cloacal: Cloacal temperature of the lizard in °C \* Skin: Skin temperature of the lizard in °C \* Copper_model: Copper_model temperature in °C \################################################################################################################################################## ## Code/Software All data was analysed using R 4.1.0. Ectotherms are particularly vulnerable to climate change, especially those living in extreme areas, such as deserts, where species are already thermally constrained. Using the vulnerable herbivorous lizard, Saara hardwickii, as a model system, we used a multi-pronged approach to understand the thermal ecology of a desert agamid and the potential impacts of rising temperatures. Our data included field-based measures of operative temperatures, body temperatures, and activity, as well as lab-based measures of thermal limits, preferences, and sprint speed. As expected, the temperature dependence of locomotor performance and foraging activity was different, and in the worst-case global warming scenario (SSP5-8.5), potential sprint speed may decrease by up to 14.5% and foraging activity may decrease by up to 43.5% by 2099. Burrows are essential thermal refuges, and global warming projections suggest that S. hardwickii may be restricted to burrows for up to 9 hours per day by 2099, which would greatly limit critical activities, like foraging and seeking mating opportunities. Overall, we show that key information on thermal ecology, including temperature-sensitive behaviours in the wild, is necessary to understand the multiple ways in which increasing temperatures may influence ectothermic vertebrates, especially for species like S. hardwickii which are already vulnerable to environmental change. Detailed data collection methodology has been provided in the manuscript.

{"references": ["IPCC (2022). Summary for Policymakers. In P. R. Shukla, J. Skea, R. Slade, A. A. Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, & J. Malley (Eds.), Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://doi.org/10.1017/9781009157926.001"]} Repository reproducing plots and processing used in AR6 WG3 made by Zebedee Nicholls, Malte Meinshausen and Jared Lewis. For questions and comments, please contact Zebedee Nicholls (zebedee.nicholls@climate-energy-college.org), Jared Lewis (jared.lewis@climate-resource.com) and Malte Meinshausen (malte.meinshausen@unimelb.edu.au). For full details, please see https://gitlab.com/magicc/ar6-wg3-plots-and-processing.