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The data from a set of experiments investigating the effects of acute LSD administration on binge-like ethanol drinking, binge-like sucrose consumption, homeostatic eating and drinking, and discrete-trial current-intensity intracranial self-stimulation in male C57Bl/6J mice. The data have been published as an article in Journal of Psychopharmacology in June 2022: doi.org/10.1177/02698811221104641

Over the last decade, the science of climate change has evolved rapidly. Nevertheless, there is considerable uncertainty about the evolution of climate over 50 or 100 year time-scales, and while confidence in global circulation models is growing there is greater appreciation of the uncertainties involved, especially in ‘downscaling' the global models to produce climate projections at the regional and local scales. Acknowledging this unavoidable uncertainty, we have developed a statistical approach to incorporate a wide range of possible climate scenarios in impacts models that uses median, and 90th and 10th percentile changes in temperature and rainfall from a number of general circulation models, from which future scenarios are developed. These scenarios were compiled by SANBI (Guy Midgley and Danni Guo). Highest temperature increases and largest rainfall decreases were combined to generate a ‘high-risk' or ‘worst case' scenario, and vice-versa for a ‘low-risk' or ‘best case' scenario. Likewise the median projected changes in rainfall and temperature were combined to generate an intermediate scenario. Based on outputs from 15 global circulation models that were spatially interpolated, we developed three climate scenarios for South Africa for approximately 2050 as follows:1 - Best case scenario: smallest predicted increases in temperature and changes in rainfall, - Intermediate scenario: middle of the range (median) predicted increases in temperature increases and changes in rainfall, - Worst case scenario: greatest predicted increases in temperature and changes in rainfall. This means that the results presented are not dependent on any particular global circulation model but hold under a very wide range of possible climate futures, enhancing the robustness of conclusions as climate changes and as climate science revises outputs and projections. Note that the study was based on medium term data (for 2050) as this represented a compromise between the uncertainty associated with the very long time horizon data (2100) and the very small changes predicted by the shorter duration data (e.g. 2020). This 50 year time horizon also represents a reasonable long term planning horizon as it is within the lifetimes of most people currently living. When this study is updated based on new climate data, if possible (we are dependent on work done by a third party), we will include both the medium term and long term predictions. In the interim, as climate change occurs gradually over time, it is useful to conceptualize the worst case scenarios for 2050 as being likely to represent the intermediate case scenarios in 2100. Available documentation: Driver A., Sink, K.J., Nel, J.N., Holness, S., Van Niekerk, L., Daniels, F., Madjiet, P.A., Jonas, Z. and Maze, K. 2012. National Biodiversity Assessment 2011: An assessment of South Africa's biodiversity and ecosystems. Synthesis Report. South African National Biodiversity Institute and Department of Environmental Affairs, Pretoria.

This dataset contains the data displayed in the figures or the article "High-resolution projections of ambient heat for major European cities using different heat metrics". The different files contain: Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc: Change of yearly maximum temperature in Europe between 1981-2010 and 3 °C European warming relative to 1981-2010. Data_Fig2_timeseries-GSAT-ESAT_EURO-CORDEX_CMIP5_CMIP6_1971-2100_RCP85_SSP585.xlsx: Time series of global mean surface air temperature (GSAT) for CMIP5 and CMIP6 models, and for European mean surface air temperature (ESAT) for EURO-CORDEX, CMIP5, and CMIP6 models for the period 1971-2100. Data_Fig3_TX-distribution_distance-from-city-centre_E-OBS_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for E-OBS for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig3_TX-distribution_distance-from-city-centre_ERA5-Land_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for ERA5-Land for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig3_TX-distribution_distance-from-city-centre_EURO-CORDEX_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for the EURO-CORDEX models for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig3_TX-distribution_distance-from-city-centre_weather-stations_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for GSOD and ECA&D stations for all investigated cities. Temperature data are indicated as a function of the distance to the city centre. Data_Fig4_TX-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models. Data_Fig5_Contribution-of-explanatory-variables-to-total-explained-variance.xlsx: Contribution of different explanatory variables (climate and location factors) to the total explained variance of spatial patterns of heat metrics. Data_Fig6_TN-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx: Nighttime heat metrics for the investigated cities: HWMId-TN at 3 °C European warming relative to 1981-2010, TN exceedances above 20 °C at 3 °C European warming relative to 1981-2010, and TNx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models. Data_Fig7_TX-ambient-heat_CMIP5_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP5 models. Data_Fig7_TX-ambient-heat_CMIP6_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP6 models. Data_Fig8_GCM-RCM-matrix_ambient-heat_3K-European-warming.xlsx: GCM-RCM matrices for the three heat metrics.

This file contains the data and the STATA estimation code to replicate the results in the article entitled: "Information Communication Technology development and energy demand in African countries".

Abstract The metropolitan area of La Plata, Argentina, represents 1.6% of households in Argentina. Based on the analysis of the energy retrofit of a house, we provide indicators in order to propose a methodology that facilitates the extrapolation, at regional level, of the impact of a general energy retrofit in homes. The results obtained are presented from micro to macro level, in context of energy shortage and recurrent crisis in the supply of fuels and electricity in Argentina. The improved thermal resistance of walls and ceilings throughout the residential park of La Plata lead to a reduction of 12% of the energy consumed in heating, and a saving of 30,000 TEP / year. We conclude that the incidence of additional insulation on the cost of building justifies its use, partially solving the lack of fuel in our country, reducing fixed costs in housing, providing thermal comfort to users and generating economic reactivation of the construction sector.

This data presents an experimental investigation carried out in a Boundary Layer Wind Tunnel located at Ruhr University Bochum, Germany. The flow field is measured above the rooftop of a high-rise building with a square cross-section considering two different roof shapes, flat roof and deck roof. The height-to-width ratio of the building was 1:3. The main purpose is to be used for estimation of urban wind energy potential, and besides for future validation of computational fluid dynamic simulations. Besides velocity field, measured at mainly three positions above the roof, the roof surface pressure was also measured. The flow above the roof was measured for different wind angles: 0°, 15°, 30°, and 45°.

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

This dataset provides the data applied in the case studies of the manuscript "Backcasting the Techno-economic Targets For Constructing Low-carbon Power Systems". Both the modified Garver’s 6-bus and realistic Northwest China power system are presented here, in two excel files respectively. The datasets include detailed information about buses, units, existing corridors, and candidate corridors.Average cost variations and load growth rate over the planning period are also provided.