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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.

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.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126' 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 IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).

The response of the single-celled ciliates to increased temperature during global warming is critical for the structure and functioning of freshwater food webs. I conducted a meta-analysis of the literature from field studies and experimental evidence to assess the parameters characterising the thermal response of freshwater ciliates. The shape of the thermal performance curve predicts the ciliates’ survival at supraoptimal temperatures (i.e., the width of the thermal safety margin, TSM). The ciliates’ typical TSM is ~5°C. One-third of the freshwater ciliates dwelling permanently or occasionally in the pelagial cannot survive at temperatures exceeding 30°C. Likewise, cold-stenothermic species, which represent a significant fraction of euplanktonic ciliates, cannot survive by evolutionary adaptation to rapidly warming environments. The statistical analysis revealed that the ciliates’ thermal performance is affected by their planktonic lifestyle (euplanktonic versus tychoplanktonic), ability to form cysts, and nutritional ecology. Bactivorous ciliates have the widest temperature niche, and algivorous ciliates have the narrowest temperature niche. Phenotypic plasticity and genetic variation, favouring the selection of pre-adapted species in a new environment, are widespread among freshwater ciliates. However, the lack of evidence for the temperature optima and imprecisely defined tolerance limits of most species hamper the present analysis. The extent of acclimation and adaptation requires further research with more ciliate species than the few chosen thus far. Recent eco-evolutionary experimental work and modelling approaches demonstrated that the ciliates’ thermal responses follow general trends predicted by the metabolic theory of ecology and mechanistic functions inherent in enzyme kinetics. The present analysis identified current knowledge gaps and avenues for future research that may serve as a model study for other biota. Thermal adaptation may conflict with adaptation to other stressors (predators, food availability, pH), making general predictions on the future role of freshwater ciliates in a warmer environment difficult, if not impossible, at the moment. # Data from: Thermal response of freshwater ciliates: can they survive at elevated lake temperatures? [https://doi.org/10.5061/dryad.jdfn2z3jr](https://doi.org/10.5061/dryad.jdfn2z3jr) The dataset results from a meta-analysis to assess the parameters characterising the thermal response of freshwater ciliates (i.e., minimum and maximum temperature tolerated, temperature niche breadth). Cyst formation, the nutritional type, and the planktonic lifestyle were considered as factors affecting the ciliates’ thermal performance. ## Description of the data and file structure The main dataset reporting ciliate species and synonyms, taxonomic affiliation, minimum and maximum temperature and the temperature range tolerated, cysts formation, mixotrophic nutrition, food type, and planktonic lifestyle are reported in the 'Dataset_v4.xlsx' file. This is the main document. Taxonomic affiliation (i.e., order) following Adl et al. (2019, reference [65]J, the GBIF Backbone Taxonomy, and Lynn (2008; reference [66]). Details on the references - i.e., authors, publication year, title, journal/book, volume, and page/article numbers used to compile this dataset and some comments can be found in 'References.xlsx'. Empty cells mean that information is unavailable. References A-E are the main sources of the dataset, i.e., comprehensive review articles published by W. Foissner and colleagues in the 1990s. References 1-64 are case studies, published mainly after 1999. References 65 and 66 refer to the taxonomic affiliation of the ciliate species. More details about each column of the main document can be found in the 'Units_table.xlsx' file. ## Sharing/Access information Data was derived from the following sources: * ISI Web of Science (All Data Bases) * Google Scholar ## Code/Software R statistical software (v 4.0.5, R Core Team 2021) with the packages lme4, lmtest, multcomp, AICcmodavg. WebPlotDigitizer (Version 4.6) for data extraction from figures ## Version changes **06-aug-2024**: Taxonomic affiliation (order) corrected according to GBIF. Genus *Tintinnidium* is now in the order Oligotrichida. I scrutinised the detailed literature compilations by Foissner and colleagues published in the 1990s; these references are listed as primary sources A-E in the Dataset, see References.xlsx and README.txt) to obtain an overview of the thermal performance, resting cyst formation, and nutritional ecology of planktonic freshwater ciliates. I then searched the ISI Web of Science (All Data Bases) for updates and cross-references of Foissner’s works and further temperature records from (mainly) field studies. Search terms (in all fields) for the latter were ciliate* AND temperature NOT marine NOT ocean NOT soil NOT parasit* (1,339 hits). I followed the PRISMA guidelines in combination with EndNote 20 to filter out the records eligible for screening and analysis. Temperature data for assessing the minimum (Tmin) and maximum temperature (Tmax) of occurrence were eventually extracted from 68 publications. However, because Foissner’s works present extensive reviews, the actual number of publications used for the analysis is much higher. The final dataset obtained from field studies comprised 206 ciliate species. Next, I searched the ISI Web of Science for experimental results, using ciliate* AND temperature AND growth rate* NOT marine as search terms (218 records). Removing results from unsuitable research areas (mainly from medical research) reduced the records to 71 publications, which were screened. The combination of ciliate* AND numerical response NOT marine yielded 40 studies, ciliate* AND thermal performance 21 hits. I checked the selected articles for citations and cross-references using Google Scholar to identify any publications that might have slipped my attention. Eventually, I picked experimental results from 18 studies. If the literature data were only shown in figures, I extracted the data from the plots with WebPlotDigitizer (Version 4.6). I analysed the dataset with the R Statistical Software using the packages lme4, lmerTest, stats, multcomp, AICcmodavg and car.

The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use. The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions. The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)". Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information. This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.

This dataset updates the global-scale mining polygons (Version 1) available from https://doi.org/10.1594/PANGAEA.910894. It contains 44,929 polygon features, covering 101,583 km² of land used by the global mining industry, including large-scale and artisanal and small-scale mining. The polygons cover all ground features related to mining, .e.g open cuts, tailing dams, waste rock dumps, water ponds, processing infrastructure, and other land cover types related to the mining activities. The data was derived using a similar methodology as the first version by visual interpretation of satellite images. The study area was limited to a 10 km buffer around the 34,820 mining coordinates reported in the S&P metals and mining database. We digitalized the mining areas using the 2019 Sentinel-2 cloudless mosaic with 10 m spatial resolution (https://s2maps.eu by EOX IT Services GmbH - Contains modified Copernicus Sentinel data 2019). We also consulted Google Satellite and Microsoft Bing Imagery, but only as additional information to help identify land cover types linked to the mining activities. The main data set consists of a GeoPackage (GPKG) file, including the following variables: ISO3_CODE, COUNTRY_NAME, AREA in squared kilometres, FID with the feature ID, and geom in geographical coordinates WGS84. The summary of the mining area per country is available in comma-separated values (CSV) file, including the following variables: ISO3_CODE, COUNTRY_NAME, AREA in squared kilometres, and N_FEATURES number of mapped features. Grid data sets with the mining area per cell were derived from the polygons. The grid data is available at 30 arc-second resolution (approximately 1x1 km at the equator), 5 arc-minute (approximately 10x10 km at the equator), and 30 arc-minute resolution (approximately 55x55 km at the equator). We performed an independent validation of the mining data set using control points. For that, we draw 1,000 random samples stratified between two classes: mine and no-mine. The control points are also available as a GPKG file, including the variables: MAPPED, REFERENCE, FID with the feature ID, and geom in geographical coordinates WGS84. The overall accuracy calculated from the control points was 88.3%, Kappa 0.77, F1 score 0.87, producer's accuracy of class mine 78.9 % and user's accuracy of class mine 97.2 %.

Contrasting physiological mortality with predator-induced mortality is of tremendous importance for the population dynamics of many organisms but is difficult to assess. I performed a meta-analysis using planktonic ciliates as model organisms to estimate the maximum physiological mortality rates (δmax) across pelagic ecosystems in relation to environmental and biotic factors. Data were compiled from published numerical response (NR) experiments and experimentally determined rates of decline (ROD). Variables reported are ciliate species and order, ciliate specific growth rates (rmax), prey species, temperature, habitat (marine vs freshwater), the coefficients of the numerical response experiments, and reported or calculated ciliate mortality rates. The median δmax of planktonic ciliates was 0.62 d−1 and did not differ between marine and freshwater species. Maximum ciliate mortality rates were species-specific and affected by their rmax, cell volume, and ability to encyst. Cyst-forming species had, on average, higher δmax than species unable to encyst. Maximum mortality rates of ciliates were positively related to rmax but appeared unaffected by temperature. I conclude that (i) in the ocean, physiological mortality is more critical for controlling ciliate population size than ciliate losses imposed by microcrustacean predation, but (ii) in many lakes, the opposite holds; (iii) cyst-formation is an effective ciliate trait to cope with the high mortality of motile cells upon starvation. The lack of a temperature effect on δmax deserves further study; if correct, planktonic ciliates may take advantage of rising ocean and lake temperatures, with important implications for the pelagic food web. I used ISI Web of Science and Google Scholar to search for experiments that measured growth and mortality rates of ciliates as a function of prey concentration (i.e. numerical responses). The search terms were “growth (rate)” or “numerical response” in combination with “ciliate*” to search for numerical response experiments and “starvation” or “starved” in combination with “ciliate*” to search for mortality experiments. In addition, I searched the literature cited in these publications for further datasets. I considered only planktonic ciliates. When studies did not report all parameters of the NR curve, the data were extracted from figures with DataThief III or WebPlotDigitizer (Version 4.6) and fitted with a modified Michaelis-Menten equation that included the threshold prey concentration (P’) as an additional parameter. Mortality rates obtained by ROD experiments used the δmax reported in the respective study or calculated δmax from the maximum rate of decline after digitizing the data from the original curves, as described above. The literature search yielded δmax reported from 41 studies investigating 56 species or strains in 81 NR experiments and 19 ROD experiments. The final dataset (n = 77) included 37 studies and 48 species. I analyzed the dataset using the R Statistical Software using the packages lme4, lmerTest, AICcmodavg, and MuMIn. # Physiological mortality rates of planktonic ciliates ## Description of the Data and file structure I used ISI Web of Science and Google Scholar to search for experiments that measured growth and mortality rates of ciliates as a function of prey concentration (i.e. numerical responses). The main dataset containing available experimental studies reporting ciliate species, experimental temperature, prey species, ciliate maximum growth rates, ciliate cell volumes, habitat of ciliate isolation, method of study and reported or calculated ciliate mortality rates are reported in the 'Dataset_v2.xlsx' file. This is the main document. Missing data codes: N.A. = not available; n/a = not applicable. More details about each column of the main document can be found in the 'Units_table.xlsx' file. Details on the references - i.e. authors, publication year, title, journal/book, volume and page/article numbers - used to compile this dataset can be found in 'References.xlsx'. ## Sharing/access Information The individual data were derived mainly from the ISI Web of Science. The data compilation is novel. Excel, R

Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extent This subdataset covers the West Coast CONUS, i.e. CA OR WA For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. t at 10m x 10m kt at 100m x 100m Mt at 1km x 1km Gt at 10km x 10km For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337. Further information For further information, please see the publication. A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.