This dataset is associated with the following publication, where further details are provided and which should be cited for further applications: Huang et al. 2022, Non-linear response of temperature-related mortality risk to global warming in England and Wales, Environ. Res. Lett., https://doi.org/10.1088/1748-9326/ac50d5. The data contain daily temperature-related mortality estimates for ten NUTS 1 regions of England and Wales over the period 1900 to 2099, based on temperatures from the 2018 UK Climate Projections (UKCP18) simulations. Exposure-response relationships are based on present-day observations and extrapolated where necessary. Variables in the csv files are as follows: "tmean": daily mean temperature (in degrees C)
"bAD": backward attributable deaths
"fAD": forward attributable deaths RCP 8.5 and RCP 2.6 scenarios are considered, as indicated in the file names. Model numbers refer to UKCP18 climate models.

This dataset is associated with the following publication, where further details are provided and which should be cited for further applications: Huang et al. 2022, Non-linear response of temperature-related mortality risk to global warming in England and Wales, Environ. Res. Lett., https://doi.org/10.1088/1748-9326/ac50d5. The data contain daily temperature-related mortality estimates for ten NUTS 1 regions of England and Wales over the period 1900 to 2099, based on temperatures from the 2018 UK Climate Projections (UKCP18) simulations. Exposure-response relationships are based on present-day observations and extrapolated where necessary. Variables in the csv files are as follows: "tmean": daily mean temperature (in degrees C)
"bAD": backward attributable deaths
"fAD": forward attributable deaths RCP 8.5 and RCP 2.6 scenarios are considered, as indicated in the file names. Model numbers refer to UKCP18 climate models.

The MERRA2 reanalysis data (1980-2018) has been used to calculate the hourly, country aggregated wind and solar power generation for 28 European countries based on a distribution of wind and solar farms which is considered to be representative of the current situation (2017). In addition a corresponding daily time series of nationally aggregated electricity demand is provided. The data sets have been produced to investigate the inter-annual variability of the three weather-dependent power system components.

The ERA5 reanalysis data (1979-2018) has been used to calculate the three-hourly country aggregated wind and solar power generation for 28 European countries based on a distribution of wind and solar farms which is considered to be representative of the current situation (2017). In addition a corresponding daily time series of nationally aggregated electricity demand is provided. The datasets have been produced to investigate the inter-annual variability of the three weather-dependent power system components. ** This is an update on the previous version of the data where there were issues with the timestamps in the 3-hourly wind and solar power data. **

Sub-seasonal forecasts of daily country-level European electricity demand, wind power and solar power generation, along with the driving meteorological variables, from two sub-seasonal to seasonal prediction systems and lead times extending to 44 days. The matching ERA5-derived variables are also provided to facilitate verification analyses. When citing this dataset please refer to the publication: Sub-seasonal forecasts of demand, wind power and solar power generation for 28 European countries (see Related CentAUR publications).

The ERA5 reanalysis data (1979-2019) has been used to calculate the hourly country aggregated wind and solar power generation for 28 European countries based on a distribution of wind and solar farms which is considered to be representative of the 2017 situation. In addition a corresponding daily time series of nationally aggregated electricity demand is provided. The datasets have been produced to investigate the inter-annual variability of the three weather-dependent power system components. When citing this dataset please refer to the publication: Sub-seasonal forecasts of demand, wind power and solar power generation for 28 European countries (see Related CentAUR publications).

A list of atmospheric gravity wave parameters from the IS17 infrasound station in Ivory Coast, West Africa between the years 2007 and 2012. A data set for the paper: Marlton, G. J., Charlton‐Perez, A. J., Harrison, R. G., Blanc, E., Evers, L., Le‐Pichon, A., & Smets, P. S. M. (2019). Meteorological source variability in atmospheric gravity wave parameters derived from a tropical infrasound station. Journal of Geophysical Research: Atmospheres, 124. https://doi.org/10.1029/2018JD029372.

The ERA5 reanalysis data (1979-2018) has been used to calculate the three-hourly country aggregated wind and solar power generation for 28 European countries based on a distribution of wind and solar farms which is considered to be representative of the current situation (2017). In addition a corresponding daily time series of nationally aggregated electricity demand is provided. The datasets have been produced to investigate the inter-annual variability of the three weather-dependent power system components. ** Issues with the timestamps in the 3-hourly wind and solar power in this dataset were identified, and a corrected version is available at http://dx.doi.org/10.17864/1947.273. **

Survey results from both an online and in-paper survey designed to determine if how uncertainty information was displayed changed users' decisions from and interpretations of the data. Participants for the survey were recruited in different ways to target different audiences.

Eye-tracking and survey results from a survey designed to determine if how uncertainty information was displayed changed users’ decisions from and interpretations of the data. Participants for the survey were 65 students from the University of Reading. Participants were recruited by email. They received £10 for participating in the experiment.