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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.

Methane emissions from aquatic and terrestrial ecosystems play a crucial role in global warming, which is particularly affecting high-latitude ecosystems. As major contributors to methane emissions in natural environments, the microbial communities involved in methane production and oxidation deserve a special attention. Microbial diversity and activity are expected to be strongly affected by the already observed (and further predicted) temperature increase in high-latitude ecosystems, eventually resulting in disrupted feedback methane emissions. The METHANOBASE project has been designed to investigate the intricate relations between microbial diversity and methane emissions in Arctic, Subarctic and Subantarctic ecosystems, under natural (baseline) conditions and in response to simulated temperature increments. We report here a small subunit ribosomal RNA (16S rRNA) analysis of lake, peatland and mineral soil ecosystems.

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 The implementation of occupancy-controlled and daylighting-dimmed lighting systems has an impact on the energy consumption of residential buildings. The BAC factor method of EN ISO 52120-1 estimates that 8% of the lighting energy can be saved compared to conventional manual control. However, it is assumed that their ability to potentially lower the lighting energy consumption is strongly related to external factors, such as the extent of daylight entrance and the behaviour of the inhabitants. By means of simulations in EnergyPlus, the performances of automated and manual lighting control are compared for an apartment and semi-detached building located in Brussels (Belgium) with variation in the occupant behaviour and orientation. It appears that an automated lighting control including 0-100% dimmer reduces the lighting energy demand for all investigated cases with savings up to 38.4%, whereas a similar control without dimmer does not necessarily reduce the lighting electricity demand. However, the results show a considerable variation, making prediction methods as the BAC factor method highly inaccurate. The actual relative energy performance depends on the automation system, type of building, orientation and occupant behaviour (i.e. number of inhabitants and occupancy rate). Hereby, the number of inhabitants has the most considerable impact on the relative energy performances with differences up to 50%, while the occupancy rate shows a significant correlation, especially for low numbers of inhabitants.

We show per record the assigned classification of the wear. We classified the condition of wear of the upperwings in 12,425 butterflies according to four categories: (1) immaculate: fresh, or with at most one tiny scratch on the upper wings or one minor dent to the wing edges, (2) slightly worn: some scratches on the upper wing and/or small dents to the edges, (3) moderately worn: many scratches to the upper wing and/or dented edges, and (4) heavily worn: (parts with) colors faded and/or heavily dented edges. The categories were chosen to allow the distinction of recently emerged individuals in particular: there is “more wear” between categories 3 and 4 than between the first two. Persistent wear and abrasions with multiple impacts at several spots was given more weight in the classification than a single major incident that resulted in larger parts of the wing missing. Pictures were randomly sorted before being classified by Jos Van Kerckhoven, who worked from a series of reference examples.

The rich information of electron energy-loss spectroscopy (EELS) comes from the complex inelastic scattering process whereby fast electrons transfer energy and momentum to atoms, exciting bound electrons from their ground states to higher unoccupied states. To quantify EELS, the common practice is to compare the cross-sections integrated within an energy window or fit the observed spectrum with theoretical differential cross-sections calculated from a generalized oscillator strength (GOS) database with experimental parameters [1]. The previous Hartree-Fock-based [2] or DFT-based [3] GOS was calculated from Schrödinger's solution of atomic orbitals, which does not include the full relativistic effects. Here, we attempt to go beyond the limitations of the Schrödinger solution in the GOS tabulation by including the full relativistic effects using the Dirac equation within the local density approximation using FAC [4], which is particularly important for core-shell electrons of heavy elements with strong spin-orbit coupling. This has been done for all elements in the periodic table (up to Z = 118) for all possible excitation edges using modern computing capabilities and parallelization algorithms. The relativistic effects of fast incoming electrons were included to calculate cross-sections that are specific to the acceleration voltage. We make these tabulated GOS available under an open-source license to the benefit of both academic users as well as allowing integration into commercial solutions. If you wish to be notfied by the database updates, please register here. For details, you can find the paper on arxiv. Database Details: Covers all elements (Z: 1-108) and all edges Large energy range: 0.01 - 4000 eV Large momentum range: from minimum momentum transfer to double Bethe ridge for each edge. Adaptive momentum sampling is developed in such a manner to maximize the physical information for a given finite number of sampling points. For example, for C edge this range is 0.14 -67 Å-1 Fine log sampling: 128 points for energy and 256 points for momentum Data format: GOSH [3] Calculation Details: Single atoms only; solid-state effects are not considered Unoccupied states before continuum states of ionization are not considered; no fine structure Plane Wave Born Approximation Frozen Core Approximation is employed; electrostatic potential remains unchanged for orthogonal states when a core-shell electron is excited Self-consistent Dirac–Fock–Slater iteration is used for Dirac calculations; A modified local density approximation is used for the correct asymptotic behavior of the exchange energy; continuum states are normalized against asymptotic form at large distances Both large and small component contributions of Dirac solutions are included in GOS Final state contributions are included until the contribution of the last states falls below 0.1%. A convergence log is provided for reference. Version 1.6.5 release note: Add a compact version of the database which uses (a) single precesion, (b) 80x80 sampling in the energy and momentum space (c) 'gzip' to compress the gos data array. This helps for user with limited bandwidth for downloading. Version 1.6.1 release note: Add missing metadata Version 1.6 release note: Improved convergence for M and N edges for some elements Version 1.5 release note: Adaptive sampling for momentum space (previously it is fixed at 0.05 -50 Å-1, now adaptive for each edge) Improved convergence Version 1.2 release note: Add “File Type / File version” information Version 1.1 release note: Update to be consistent with GOSH data format [3] All the edges are now within a single hdf5 file. A notable change in particular, the sampling in momentum is in 1/m, instead of previously in 1/Å. Great thanks to Gulio Guzzinati for his suggestions and sending conversion script for GOSH format. [1] Verbeeck, J., and S. Van Aert. Ultramicroscopy 101.2-4 (2004): 207-224. [2] Leapman, R. D., P. Rez, and D. F. Mayers. The Journal of Chemical Physics 72.2 (1980): 1232-1243. [3] Segger, L, Guzzinati, G, & Kohl, H. Zenodo (2023). doi:10.5281/zenodo.7645765 [4] Gu, M. F. Canadian Journal of Physics 86(5) (2008): 675-689. The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through Project No.G.0502.18N. This project has also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 770887 PICOMETRICS and No. 823717 ESTEEM3).

For the reconstruction of flight peaks, an abundance index was calculated by relating records to search efforts based on the evidence of field activities left in the database by proficient observers. For this search-effort correction, we selected observers with at least 50 records of at least 10 butterfly species each year. As a measure of their collective search effort, we calculated the sum for each day of all the 100 × 100 m grid cells from which an observer had reported records (of any species, including non-butterflies; also including absence records). This method of ‘proven day-grid-visits’ has become the standard proxy for search effort when analyzing incidental observations of the portal waarnemingen.be. Day-grid-visits do not cover search effort completely, because records are not submitted from every visited hectare grid cell, but strongly correlates with it. We provide the raw data containing day (2009-2020), the X and Y coordinate of the centroid of the 100x100 m grid cell (In Lambert 72, EPSG:31370 Projected coordinate system for Belgium), the number of peacock butterflies reported, and the number of hectare day grid visits.