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The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites

Overview The following dataset presents the energy cycle characteristics for 5G/6G mobile systems supported by Renewable Energy Sources (RES) and/or Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RISs). In addition, within the dataset, the energy gain related to the engagement of RES within the Radio Access Network (RAN) has also been distinguished. Scenario The considered network scenario includes 8 three- (_results_gcas.csv) or one-cell (_results_scas.csv & _results_kras.csv) base stations (BSs) placed within the Poznan city (surroundings of the old market) and supported by Renewable Energy Sources — photovoltaic panels (PVs) and/or wind turbines (WTs). The aforementioned base stations can be treated as stationary towers or mobile access points (e.g., drones/UAVs). Those latter have been additionally equipped with RIS devices, which are able to reflect and manipulate a radio signal to influence occurrences such as interferences, coverage, or human exposure. However, the use of RISs has been taken into account only to evaluate the impact of the engagement of such devices on the energy side of the mobile system, omitting the changes in radio characteristics. The network traffic has been assumed to be fixed (64 mobile users (UEs) with 100 Mbps downlink — DL, and 25 Mbps uplink — UL, per each), however, its density in specific parts of the city is modeled randomly for each simulation run. The simulation runs have been performed for 4 dates (vernal equinox, summer solstice, autumn equinox, winter solstice), each one from a different season of the year. The aim of such an approach was to highlight the impact of the time of the day and the year on the energy gain obtained thanks to enabling RES generators. The weather conditions assumed within the simulation are typical for the climate in Poland. Methodology The energy-cycle calculations (system's power consumption, renewable energy production, and excessive energy storage) have been based on the mathematical formulas from the scientific literature and performed within the digital simulation runs by using the Green Radio Access Network Design (GRAND) tool (developed by teams from the Ghent University & Poznan University of Technology). The UE-BS association process within the mobile system has been done by doing multi-objective optimization using the Gurobi software, which has taken into account parameters like path loss, predicted power consumption of BSs, and guaranteed DL & UL bit rates for UEs. Simulation setup The setup of the input parameters for used mathematical models (power consumption, energy generation, energy storage) has been done in accordance with the values attached within the delivered literature positions (cited within the publications included in the Related works section of the following dataset) and adjusted to the considered study. Furthermore, the data used to model the network environment (building distribution, coverage area, base stations' locations) as well as to predict weather conditions are the real data (for the year 2022) collected by the city hall of Poznan, one of the Polish mobile operators, and weather stations placed in Poznan, respectively. The number of simulation runs performed has been equal to 10 (each run has included energy-cycle calculations for 4 seasons of the year), with the time step of a single run set to 1 hour of the day. Results The results of the aforementioned investigations have been included in the attached files, which can be described as follows: File _results_gcas.csv The first column denotes the date (season of the year), for which the values have been obtained. The columns from second to fifth present observed values of the State of Charge (SoC) of a battery system (in %) for a single network cell on average in a time step. Those columns are the obtained values for the RAN, in which no RES, only PVs, only WTs, and both types of RES generators have been enabled, respectively. Files _results_scas.csv & _results_kras.csv The first column denotes the date (season of the year), for which the values have been obtained. The second and third columns denote the number of drone base station (DBS) exchanges within the wireless system on average in a particular time step, where no RES and only PVs are enabled, respectively. The fourth and fifth columns present the conventional (fossil-fuels-based) energy consumption (in kWh) for the whole system in a specific time step, in which no RES and only PVs are engaged for all the access nodes. The sixth column is the energy savings (in kWh) related to the use of RES generators within the mobile network. Furthermore, the seventh and eighth columns represent the amount of renewable energy harvested from the solar radiation in total and the peak value of this amount observed during the entire day, respectively. Acknowledgment More details about the conducted studies have been described within the attached papers (Related works section). The data has been collected within the COST CA10210 INTERACT. M. Deruyck is a Post-Doctoral Fellow of the FWO-V (Research Foundation – Flanders, ref: 12Z5621N). The work (including the following dataset preparation) by A. Samorzewski and A. Kliks was realized within project no. 2021/43/B/ST7/01365 funded by the National Science Center in Poland.

# Data from: Eocene Shark Teeth from Peninsular Antarctica: Windows to Habitat Use and Paleoceanography. [https://doi.org/10.5061/dryad.qz612jmq2](https://doi.org/10.5061/dryad.qz612jmq2) The repository folder includes scripts and spreadsheets for phosphate oxygen stable isotope (δ18Op) analysis measured from shark tooth biogenic apatite collected from the Eocene deposits of the La Meseta and Submeseta formations (West Antarctica, Seymour Island). It also contains Fourier-Transform Infrared Spectroscopy (FTIR) analysis, a Bayesian model for temperature estimates, and model output extraction scripts from the iCESM simulation for the Early Eocene (Zhu et al., 2020). Scripts and data are stored in specific folders on the type of analysis. All scripts are in R or Python language. **Usage notes** **1 "iCESM modeling scripts" directory** The folder includes scripts in Jupiter Notebook format for extracting and plotting iCESM seawater outputs for the Eocene. The folder includes two files: 1) “d18Ow Analysis Script.ipynb” - This is a Python script primarily using the XArray library, to import iCESM output from Zhu et al. (2020), calculating δ18Ow, and reorganizing the output into monthly time intervals along 25 m and 115 m depth slices, while also averaging output down to these depths; 2) “NetCDF Plotting.ipynb” - this is a Python script primarily using the XArray, Matplotlib, and Cartopy libraries. The script writes a single callable function that creates Matplotlib contour plots from iCESM history output. Variables include temperature, salinity, ideal age, oxygen isotopes, and neodymium isotopes, and map projections include Plate Carree, Mollweide, and orthographic (centering on the Drake Passage). Options are built to enable scale normalization or to set maximum and minimum values for data and select colormaps from a predefined selection of Matplotlib’s “Spectral”, “Viridis”, “Coolwarm”, “GNUplot2”, “PiYG”, “RdYlBu”, and “RdYlGn”. For further questions on model output scripts, please email Adam Aleksinski at [aaleksin@purdue.edu](https://datadryad.org/stash/dataset/doi:10.5061/aaleksin@purdue.edu). **2 "d18O data and maps" directory** The folder includes δ18Op of shark tooth bioapatite and other datasets to interpret shark paleoecology. These datasets include: · δ18Op of shark tooth bioapatite (“shark FEST d18Op.csv”). Isotope measurements were run at the Stable Isotope Ecosystem Laboratory of (SIELO) University of California, Merced (California, USA). · Reference silver phosphate material δ18Op for analytical accuracy and precision (“TCEA reference materials.csv"). Isotope measurements were run at the Stable Isotope Ecosystem Laboratory of (SIELO) University of California, Merced (California, USA). · Bulk and serially sampled δ18Oc data of co-occurring bivalves (Ivany et al., 2008; Judd et al., 2019) (“Ivany et al. 2008_bulk.csv” and “Judd et al., 2019_serial sampling.csv"). · iCESM model temperature and δ18Ow outputs at 3x and 6x pre-industrial CO2 levels for the Early Eocene (Zhu et al., 2020) (“SpinupX3_25m_Mean_Monthly.nc”, “SpinupX6_25m_Mean_Monthly.nc.”, and “CA_x3CO2.csv”). Simulations are integrated from the surface to 25 m. · δ18O values of invertebrate species published in Longinelli (1965) and Longinelli & Nuti (1973), used to convert bulk δ18Oc (V-SMOW) data of bivalves into δ18Op (V-SMOW) values after δ18Oc (V-PDB) - δ18Oc (V-SMOW) conversion found in Kim et al. (2015) (“d18O carbonate and phosphate references.csv”). · R script for data analysis ("d18O data and maps.Rmd”). The script provides annotation through libraries, instrumental accuracy and precision tests, tables, statistical analysis, figures, and model output extractions. . ("TELM_diversity.csv") displays diversity trends of chondrichthyans across TELMs in one of the main figures of the manuscript. **2.1 Dataset description** **shark FEST d18Op.csv** · *Sample_ID*: Identification number of tooth specimens. · *Other_ID*: Temporary identification number of tooth specimens. · *Taxon*: Species assigned to shark tooth specimens. · *TELM*: Stratigraphic units of La Meseta (TELM 2-5; ~45 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). · *d18Op*: Mean δ18Op values of silver phosphate crystals precipitated from shark tooth bioapatite. Specimens were run in triplicates, corrected, and standardized on the V-SMOW scale. · *sd*: Standard deviation of silver phosphate triplicate samples per specimen. · *Protocol*: Silver phosphate protocols used to precipitate crystals from shark tooth bioapatite. We adopted the Rapid UC (“UC_Rapid”) and the SPORA (“SPORA”) protocols after Mine et al. and (2017) Larocca Conte et al. (2024) based on the tooth specimen size and sampling strategy. Descriptions of the methods are included in the main manuscript. · *Environment*: Inferred shark habitat based on taxonomy classified as benthic or pelagic environment. · *Collection*: Institutional abbreviations of museum collections from which shark tooth specimens are housed. NRM-PZ is the abbreviation for the Swedish Natural History Museum (Stockholm, Sweden), PRI is the abbreviation for the Paleontological Research Institute (Ithaca, New York, United States), and UCMP is the University of California Museum of Paleontology (Berkeley, California, United States). **TCEA reference materials.csv** · *Identifier_1*: unique identifier number per sample. · *sample*: reference silver phosphate materials (USGS 80 and USGS 81). · *amount*: weight of samples in mg. · *Area 28*: peak area of mass 28 (12C16O). · *Area 30*: peak area of mass 30 (12C18O). · *d18O_corrected*: corrected δ18Op value of reference materials following drift correction, linearity correction, and 2-point calibration to report values on the V-SMOW scale. **Ivany et al. 2008_bulk.csv** · *Telm*: Stratigraphic units of La Meseta (TELM 2-5; ~45 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). · *Locality*: Locality code from which bivalves were collected. · *Genus*: Genera of bivalves. Specimens are assigned to *Cucullaea* and *Eurhomalea* genera. · *Line*: Sampling areas of specimens. The sampling strategy is described in Ivany et al. (2008). · *d13C*: δ13C values of specimens from sampled lines. Values are reported in the V-PDB scale. · *d18Oc_PDB*: δ18Oc values of specimens from sampled lines. Values are reported in the V-PDB scale. **Judd et al., 2019_serial sampling.csv** · *Horizon:* horizons of the TELM 5 unit (La Meseta Formation) from which bivalves were collected. Horizon 1 is stratigraphically the lowest, while horizon 4 is the highest (Judd et al., 2019). · *ID*: Identification number of specimens. · *Latitude*: Geographic coordinate where bivalve specimens were collected. · *Longitude*: Geographic coordinate where bivalve specimens were collected. · *Surface sampled*: Specific sampling area, indicating whether sampling occurred in the interior or exterior portion of shells. · *distance*: The distance from the umbo in mm from which sampling occurred along a single shell. · *d18Oc_PDB*: δ18Oc values of specimens from sampled areas of shells. Values are reported on the V-PDB scale. **SpinupX3_25m_Mean_Monthly.nc** See section 1 ("iCESM modeling scripts" directory, “d18Ow Analysis Script.ipynb” script) for a full description of the iCESM model output extraction. **SpinupX6_25m_Mean_Monthly.nc** See section 1 ("iCESM modeling scripts" directory, “d18Ow Analysis Script.ipynb” script) for a full description of the iCESM model output extraction. **CA_x3CO2.csv** · *lat*: Geographic coordinate where temperature and δ18Ow model values are extracted from the iCESM simulation scaled at 3x preindustrial CO2 levels (values averaged within a seawater column depth of 25 m). · *long*: Geographic coordinate where temperature and δ18Ow model values are extracted from the iCESM simulation scaled at 3x preindustrial CO2 levels (values averaged within a seawater column depth of 25 m). · *T_mean*: Simulated seawater temperature values in °C. · *d18Ow*: Simulated seawater δ18Ow values (V-SMOW). · *d18Op*: Simulated seawater δ18Op values (V-SMOW). Values were calculated by using seawater temperature and δ18Ow arrays following the paleothermometer equation after Lécuyer et al. (2013). **d18O carbonate and phosphate references.csv** · *species*: Species of invertebrate taxa. · *type*: Specimen type, including barnacles, brachiopods, crabs, and mollusks. · *depth*: Depth of seawater column where specimens were collected, reported in meters below sea level when specified. · *d18Op*: δ18Op values of invertebrate specimens (V-SMOW). · *d18Oc_PDB*: δ18Oc values of invertebrate specimens (V-PDB). · *Reference*: Citations from which data were taken to build the dataset (Longinelli, 1965; Longinelli & Nuti, 1973). **TELM diversity.csv** · *genus:* genera of sharks and rays compiled from literature (Engelbrecht et al., 2016a, 2016b, 2017a, 2017b, 2019; Kriwet, 2005; Kriwet et al., 2016; Long, 1992; Marramá et al., 2018). · *species*: species of sharks and rays compiled from literature (Engelbrecht et al., 2016a, 2016b, 2017a, 2017b, 2019; Kriwet, 2005; Kriwet et al., 2016; Long, 1992; Marramá et al., 2018). · *Environment*: Inferred shark habitat based on taxonomy classified as benthic or pelagic environment. · *TELM*: Stratigraphic units of La Meseta (TELM 1-5; ~44 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). **3 “FTIR data” directory** The folder includes FTIR acquisitions and data analysis scripts on reference materials and shark tooth bioapatite for quality checks to test diagenesis effects on δ18Op of sharks. The folder includes: · The R project file “apatite_ftir.Rproj”. This project file navigates through scripts for raw data processing and data analysis. The background of the raw data was processed following custom R functions from Trayler et al. (2023; [https://github.com/robintrayler/collagen_demineralization](https://github.com/robintrayler/collagen_demineralization)). · The “.Rproj.user” folder includes project-specific temporary files (e.g. auto-saved source documents, window-state, etc.) stored by the R project file “apatite_ftir.Rproj”. The folder may be hidden depending on directory view options. · The “raw data” directory stores spectra acquisitions as .dpt files. Spectra files are stored in the folders “apatite” and “calcite” based on the material type. Spectra were obtained in the 400 – 4000 cm⁻¹ range using a Bruker Vertex 70 Far-Infrared in ATR located at the Nuclear Magnetic Resonance Facility at the University of California Merced (California, USA). · The “processed” directory includes processed spectra stored as .csv files (“apatite_data.csv” and “calcite_data.csv”) following the background correction (Trayler et al., 2023) and processed infrared data from Larocca Conte et al. (2024) (“Larocca Conte et al._SPORA_apatite_data.csv”) from which the NIST SRM 120c spectrum was filtered. Infrared spectra data in “Larocca Conte et al._SPORA_apatite_data.csv” were obtained and corrected following the same methodologies mentioned above. · The “R” directory includes R scripts of customized source functions for background correction (Trayler et al., 2023; inspect the "functions" directory and the R script "0_process_data.R") and data analysis (“data_analysis.R”). The scripts provide annotation through libraries and functions used for data processing and analysis. · Additional datasets. The “data_FTIR_d18O.csv” includes infrared data and δ18Op values of specimens, while the “Grunenwald et al., 2014_CO3.csv” is the dataset after Grunenwald et al. (2014) used to predict carbonate content from the materials featured in this work. **3.1 Dataset description** Spreadsheets included in the “processed” directory The datasets “apatite_data.csv”, “calcite_data.csv”, and “Larocca Conte et al._SPORA_apatite_data.csv” are structured with the following variables: · *wavenumber*: infrared wavenumber in cm-1. · *absorbance*: infrared absorbance value. · *file_name:* .dpt file name from which infrared wavenumber and absorbance values were obtained following the background correction. **data_FTIR_d18O.csv** · *file_name:* .dpt file name from which infrared wavenumber and absorbance values were obtained following the background correction. · *v4PO4_565_wavenumber*: Wavenumber of maximum infrared absorbance around the first νPO4 band, usually at 565 cm-1. · *v4PO4_565*: Peak absorbance value of the first ν4PO4 band (~565 cm-1). · *v4PO4_valley_wavenumber*: Wavenumber of valley between ν4PO4 bands. · *v4PO4_valley*: Absorbance value of the valley between ν4PO4 bands. · *v4PO4_603_wavenumber*: Wavenumber of maximum infrared absorbance around the second ν4PO4 band, usually at 603 cm-1. · *v4PO4_603*: Peak absorbance value of the second ν4PO4 band (~603 cm-1). · *CI*: Crystallinity index calculated after equation provided in (Shemesh, 1990) as (*v4PO4_565* + *v4PO4_603* / *v4PO4_valley*) (i.e., the sum of peak absorbance of νPO4 bands divided by the absorbance value of the valley between peaks). · *material*: Material type of samples (i.e., standard material, enameloid, dentin sampled from the crown or root area of shark teeth, and enameloid mixed with dentin). · *AUC_v3PO4*: Area under the curve of the ν3PO4 and ν1PO4 bands where maximum absorbance is at ~1025 cm-1 and ~960 cm-1, respectively. · *AUC_v3CO3*: Area under the curves of Type-A and Type-B carbonate bands having maximum infrared absorbance at ~1410 (Type-B), ~1456 (Type-B), and ~1545 cm-1 (Type-A). · *v3CO3_v3PO4_ratio*: Ratio between area under the curves of carbonate and phosphate bands (i.e., *AUC_v3CO3* / *AUC_v3PO4*). · *CO3_wt*: Estimated mean carbonate content following the equation in Grunenwald et al. (2014) (i.e. *CO3_wt* = 28.4793 (±1.4803) *v3CO3_v3PO4_ratio* + 0.1808(±0.2710); R2 = 0.985). · *CO3_wt_sd*: Standard deviation of estimated carbonate content calculated by propagating the error around coefficients provided in the Grunenwald et al. (2014) equation (see full equation in *CO3_wt*). · *Taxon*: Species assigned to shark tooth specimens. · *TELM*: Stratigraphic units of La Meseta (TELM 2-5; ~45 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). · *d18Op*: Mean δ18Op values of silver phosphate crystals precipitated from shark tooth bioapatite. Specimens were run in triplicates, corrected, and standardized on the V-SMOW scale. · *sd*: Standard deviation of silver phosphate triplicate samples per specimen. · *Collection*: Institutional abbreviations of museum collections where shark tooth specimens are housed. Infrared spectra were obtained from a selected subset of tooth specimens in the care of the Swedish Natural History Museum (NRM-PZ; Stockholm, Sweden). **Grunenwald et al., 2014_CO3.csv** · *sample*: Sample code. · *material*: Material type of samples (i.e., standard material, bone, and enamel). · *v3CO3*: Area under the curves of Type-A and Type-B carbonate bands having maximum infrared absorbance at ~1410 (Type-B), ~1456 (Type-B), and ~1545 cm-1 (Type-A). · *v3PO4*: *AUC_v3PO4*: Area under the curve of the ν3PO4 and ν1PO4 bands where maximum absorbance is at ~1025 cm-1 and ~960 cm-1, respectively. · *v3CO3_v3PO4_ratio*: *v3CO3_v3PO4_ratio*: Ratio between area under the curves of carbonate and phosphate bands (i.e., *v3CO3* /*v3PO4*). · *CO3_wt*: Carbonate content measured via CO2 coulometry. Further details about the analytical measurements are found in Grunenwald et al. (2014). **4 “Bayes_FEST_Temperautre Estimates” directory** The folder includes the Bayesian approach used to estimate posterior seawater temperature, δ18Ow values from δ18Op of sharks bioapatite using a Bayesian approach modified after Griffiths et al. (2023). The original scripts used in Griffiths et al. (2023) are reposited here: [https://github.com/robintrayler/bayesian_phosphate](https://github.com/robintrayler/bayesian_phosphate). The directory includes: · The R project file “Bayes_FEST.Rproj”. This project file navigates through scripts for raw data analysis. · The “.Rproj.user” folder includes project-specific temporary files (e.g. auto-saved source documents, window-state, etc.) stored by the R project file “Bayes_FEST.Rproj”. The folder may be hidden depending on directory view options. · The “data” folder includes the spreadsheets for modeled seawater temperature and δ18Ow values (“CA_x3CO2.csv”) and δ18Op values of shark tooth bioapatite (“shark FEST d18Op.csv”) used as prior information for the Bayesian model. We refer to section 2.1 for the full description of spreadsheets. · The “R” folder includes customized functions for the Bayesian model stored in the “functions” directory and the script for data analysis (“01_model_sharks.R”). The script includes a comparison of paleothermometer equations after Kolodny et al. (1983), Lécuyer et al. (2013), Longinelli & Nuti (1973), and (Pucéat et al. (2010) using the bulk δ18Op shark tooth bioapatite, simulated seawater temperature and δ18Ow values as prior inputs. While all paleothermometers estimate similar posterior bulk δ18Op close to empirical values, temperature estimates using the Pucéat et al. (2010) method are often the highest, generating estimates ~8°C higher than other equations. We therefore used the Lécuyer et al. (2013) paleothermomether for temperature estimates using δ18Op of shark bioapatite grouped by taxa because it: 1\) Provides consistent posterior temperature estimates relative to other equations (Longinelli & Nuti, 1973, Kolodny et al., 1983). 2\) provides temperature values from fish tooth specimens consistent with estimates of co-existing bivalves or brachiopod carbonate shells. The script provides annotation through libraries, statistical analysis, figures, and tables. **4 Software** **4.1 R** R and R Studio (R Development Core Team, 2024; RStudio Team, 2024) are required to run scripts included in the "d18O data and maps", “FTIR data”, and “Bayes_FEST_Temperautre Estimates” directories, which were created using versions 4.4.1 and 2024.04.02, respectively. Install the following libraries before running scripts: “cowplot” (Wilke, 2024), “colorspace” (Zeileis et al., 2020), “DescTools” (Signorell, 2024), “lattice” (Sarkar, 2008), “flextable” (Gohel & Skintzos, 2024), “ggh4x” (van den Brand, 2024), “ggnewscale” (Campitelli, 2024), “ggpubr” (Kassambara, 2023a), “ggspatial” (Dunnington, 2023), “ggstance” (Henry et al., 2024), “ggstar” (Xu, 2022), “greekLetters” (Kévin Allan Sales Rodrigues, 2023), “gridExtra” (Auguie, 2017), “mapdata” (code by Richard A. Becker & version by Ray Brownrigg., 2022); “mapproj” (for R by Ray Brownrigg et al., 2023), “maps” (code by Richard A. Becker et al., 2023), “ncdf4” (Pierce, 2023), “oce” (Kelley & Richards, 2023), “rasterVis” (Oscar Perpiñán & Robert Hijmans, 2023), “RColorBrewer” (Neuwirth, 2022), “rnaturalearth” (Massicotte & South, 2023), “rnaturalearthhires” (South et al., 2024),”rstatix” (Kassambara, 2023b), “scales” (Wickham et al., 2023), “tidyverse” (Wickham et al., 2019), “viridisLite” (Garnier et al., 2023). **4.2 Python** Python scripts, including “d18O Analysis Script.ipynb” and “NetCDF Plotting.ipynb”, utilize the Jupyter Notebook interactive ‘platform and are executed using Python version 3.9.16. Install the following libraries before running scripts: “xarray” (Hoyer & Joseph, 2017), “matplotlib” (Hunter, 2007), “cartopy” (Met Office, 2015). **5 References** Amenábar, C. R., Montes, M., Nozal, F., & Santillana, S. (2020). Dinoflagellate cysts of the la Meseta Formation (middle to late Eocene), Antarctic Peninsula: Implications for biostratigraphy, palaeoceanography and palaeoenvironment. *Geological Magazine*, *157*(3), 351–366. [https://doi.org/10.1017/S0016756819000591](https://doi.org/10.1017/S0016756819000591) Auguie, B. (2017). gridExtra: Miscellaneous Functions for “Grid” Graphics. Retrieved from [https://cran.r-project.org/package=gridExtra](https://cran.r-project.org/package=gridExtra) van den Brand, T. (2024). ggh4x: Hacks for “ggplot2.” Retrieved from [https://cran.r-project.org/package=ggh4x](https://cran.r-project.org/package=ggh4x) Campitelli, E. (2024). ggnewscale: Multiple Fill and Colour Scales in “ggplot2.” Retrieved from [https://cran.r-project.org/package=ggnewscale](https://cran.r-project.org/package=ggnewscale) code by Richard A. Becker, O. S., & version by Ray Brownrigg., A. R. W. R. (2022). mapdata: Extra Map Databases. Retrieved from [https://cran.r-project.org/package=mapdata](https://cran.r-project.org/package=mapdata) code by Richard A. Becker, O. S., version by Ray Brownrigg. Enhancements by Thomas P Minka, A. R. W. R., & Deckmyn., A. (2023). maps: Draw Geographical Maps. Retrieved from [https://cran.r-project.org/package=maps](https://cran.r-project.org/package=maps) Douglas, P. M. J., Affek, H. P., Ivany, L. C., Houben, A. J. P., Sijp, W. P., Sluijs, A., et al. (2014). Pronounced zonal heterogeneity in Eocene southern high-latitude sea surface temperatures. *Proceedings of the National Academy of Sciences of the United States of America*, *111*(18), 6582–6587. [https://doi.org/10.1073/pnas.1321441111](https://doi.org/10.1073/pnas.1321441111) Dunnington, D. (2023). ggspatial: Spatial Data Framework for ggplot2. Retrieved from [https://cran.r-project.org/package=ggspatial](https://cran.r-project.org/package=ggspatial) Engelbrecht, A., Mörs, T., Reguero, M. A., & Kriwet, J. (2016a). 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Sharks in high-latitude oceans, sensitive to these changes, offer insights into both environmental shifts and biological responses, yet few paleoecological studies exist. The Middle-to-Late Eocene units on Seymour Island, Antarctica, provide a rich, diverse fossil record, including sharks. We analyzed the oxygen isotope composition of phosphate from shark tooth bioapatite (δ18Op) and compared our results to co-occurring bivalves and predictions from an isotope-enabled global climate model to investigate habitat use and environmental conditions. Bulk δ18Op values (mean 22.0 ± 1.3‰) show no significant changes through the Eocene. Furthermore, the variation in bulk δ18Op values often exceeds that in simulated seasonal and regional values. Pelagic and benthic sharks exhibit similar δ18Op values across units but are offset relative to bivalve and modeled values. Some taxa suggest movements into warmer or more brackish waters (e.g., Striatolamia, Carcharias) or deeper, colder waters (e.g., Pristiophorus). Taxa like Raja and Squalus display no shift, tracking local conditions in Seymour Island. The lack of difference in δ18Op values between pelagic and benthic sharks in the Late Eocene could suggest a poorly stratified water column, inconsistent with a fully opened Drake Passage. Our findings demonstrate that shark tooth bioapatite tracks the preferred habitat conditions for individual taxa rather than recording environmental conditions where they are found. A lack of secular variation in δ18Op values says more about species ecology than the absence of regional or global environmental changes. See methods in Larocca Conte, G., Aleksinski, A., Liao, A., Kriwet, J., Mörs, T., Trayler, R. B., Ivany, L. C., Huber, M., Kim, S. L. (2024). Eocene Shark Teeth From Peninsular Antarctica: Windows to Habitat Use and Paleoceanography. Paleoceanography and Paleoclimatology, 39, e2024PA004965.

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.

This data set describes the population dynamics of Wilson's Storm Petrels (Oceanites oceanicus) at King George Island (Isla 25 de Mayo, Antarctica) over a forty year period (1978 – 2020). It includes all available data on Wilson's Storm Petrels from two colonies: around the Argentinian Base Carlini (62°14′S, 58°40′W; CA, formerly called Base Jubany) and the Henryk Arctowski Polish Antarctic Station (62°09′S, 58°27′W; HA). Data on population productivity (number of nests, eggs, chicks and fledglings) was collected by regular visits to the colonies and searching for nest burrows, or monitoring of the egg or chick if found. Data on adult abundance and estimated age categories (i.e., presence of foot spots; Quillfeldt et al. (2000, doi:10.1007/s003000000167) were collected at CA by using the same size mistnet every study year in the same location within the breeding colony. Chicks were measured regularly (varying intervals depending on the study) at both CA and HA. Chick tarsus was measured using callipers (vernier or digital depending on the study year) to the nearest 0.1 mm, chick wing length was measured using wing rulers to the nearest 1 mm, and chick body mass was measured using mechanical or digital scales depending on the study year to the nearest 0.1 g. Chick growth rates were calculated based on the linear growth period following Ausems et al. (2020, doi:10.1016/j.scitotenv.2020.138768). Chick food loads (g) were recorded at CA and determined based on changes in chick body mass on consecutive days (Gladbach et al. (2009, doi:10.1007/s00300-009-0628-z); Kuepper et al. (2018, doi:10.1016/j.cbpa.2018.06.018). This study was further supported by the Erasmus+ programm and thee German Academic Exchange Service (DAAD)

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 %.