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Origin of the data: Experimental spectroscopic measurements Data Type: experimental measurements, open access supporting information The data are in CSV, DSW and FBSW format. Supporting information are supplied in PDF format. Data generated by instruments: Varian Cary 5E-UV-Vis-NIR spectrophotometer for UV-Vis measurements, Varian Cary Eclipse fluorescence spectrophotomer for fluorescence quenching measurements. Analytical and procedural information: Stern-Volmer fluorescence quenching experiments, UV-Vis measurements and Fluorescent Quantum Yield determination via ferrioxalate actinometry. Definition of variables: Wavelength, Absorbance, Concentration Units of measurement: nanometers (nm), moles-per-litre (mol/l) Abbreviations: File names and data headers use the following abbreviations: FQY refers to Fluorescence Quantum Yield determination experiments Light refers to irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. Dark refers to non-irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. SVQuench refers to Stern-Volmer quenching experiments RAxx refer to measurements related to allylbenzene. Xx is the amount of quencher in mol/l (05 should be intended as 0.5 mol/l and so on). RTxx refer to measurements related to S-(4-methylphenyl) 4-methylbenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. RExx refer to measurements related to 1,2-dimethoxy-4-(prop-2-en-1-yl)benzene. Xx is the amount of quencher in mol/l as above. RSxx refer to measurements related to styrene. Xx is the amount of quencher in mol/l. RTFxx refer to measurements related to S-(4-fluorophenyl) 4-fluorobenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. MesAcrMe Xx refers to data related to catalyst 9-mesityl-10-methylacridinium. Xx is the amount of catalyst in mol/l as above. DMC for measurements employing dimethylcarbonate as solvent. ACN for measurements employing acetonitrile as solvent. FBSW and DSW data are used by the proprietary software of the Varian spectrometers (CARY WinUV and Cary Eclipse). Information can be found at https://www.agilent.com/en/product/molecular-spectroscopy/uv-vis-uv-vis-nir-spectroscopy/uv-vis-uv-vis-nir-software/cary-winuv-software and https://www.agilent.com/en/product/molecular-spectroscopy/fluorescence-spectroscopy/fluorescence-software/cary-eclipse-software

Dataset for TotalControl reference windfarm database simulation of a pressure-driven high Reynolds number boundary layer flow with 0 degree inflow wind direction angle (Casename PDk 0) Included Python files for loading and visualizing the data. Use the plot_*.py files. Further information, including description of the case and dataset can be found in the deliverable report at: https://cordis.europa.eu/project/id/727680/results "Database for reference wind farms part 2: windfarm simulations"

This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

The dataset consists of the building energy simulation data for three case-study buildings (Infrax office building (BE); Ter Potterie elderly home (BE) and Haus M multi-family building (CH)). For these buildings, HVAC-concepts were simulated: the hybridGEOTABS-MPC concept (not for Haus M), hybridGEOTABS-RBC baseline concept and nonGEOTABS-RBC concept. The dataset includes the hourly time series for a one year simulation for the major outputs of the BES-model. The results are reported in hybridGEOTABS deliverable D4.9. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 723649

Supplementary material for the associated publication.

Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model specific uncertainties when reported. In comparing NGHGI with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions and geological sources together account for the gap between NGHGI and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).

The datasets present collected data through resident surveys on the perceptions on tourism development and the state of cultural heritage, as part of the Horizon 2020 funded project SmartCulTour (www.smartcultour.eu). The data is collected on individual respondent level for Local Administrative Units (LAUs) for the following municipalities/cities: Spain: Huesca, Graus, Benasque, Barbastro, Ainsa, Jaca, Sariñena the Netherlands: Rotterdam, Dordrecht, Molenlanden, Ridderkerk, Zwijndrecht, Barendrecht, Delft Belgium: Dendermonde, Puurs-Sint-Amands, Bornem, Berlare, Aalst, Denderleeuw, Willebroek Croatia: Split, Trogir, Kaštela, Solin, Sinj, Dugopolje, Klis Finland: Utsjoki Italy: Vicenza, Caldogno, Grumolo, Pojana Maggiore, Lonigo, Montagnana The data is presented as cross-sectional data and available for the following year: 2020. Please consult the metadata on each dataset for an overview of collected indicators and units of measurement.

AWESCO Wind Field Datasets The present datasets contain time-resolved three-dimensional wind field data computed by means of large-eddy simulations. The atmospheric boundary layer is modelled as pressure driven boundary layer (PDBL) and the computations are performed using the software SPWind developed at KU Leuven. Wind field data is provided for three different roughness classes corresponding to offshore and onshore conditions. For each roughness class, 45 minutes of wind data, sampled every second, is available and stored in HDF5 format for time series of 15 minutes. The file size is approximately 10GB. The data can be accessed using processing scripts provided for both Python and MATLAB. For more information, please read the provided documentation.