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Dataset of process simulations results of the natural gas sweetening and flue gas treatment (first and second sheet, respectively as indicated by the sheet name in the .xlsx file). The dataset refers to the publication Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies by V. Negri, Vàzquey D., Sales-Pardo, Marta, Guimerà, R. and Guillén-Gosàlbez, G. The training and testing dataset are used to generate the figures in the main manuscript and supplementary information.

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.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' 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 MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

Dataset Information This dataset presents long-term term indoor solar harvesting traces and jointly monitored with the ambient conditions. The data is recorded at 6 indoor positions with diverse characteristics at our institute at ETH Zurich in Zurich, Switzerland. The data is collected with a measurement platform [3] consisting of a solar panel (AM-5412) connected to a bq25505 energy harvesting chip that stores the harvested energy in a virtual battery circuit. Two TSL45315 light sensors placed on opposite sides of the solar panel monitor the illuminance level and a BME280 sensor logs ambient conditions like temperature, humidity and air pressure. The dataset contains the measurement of the energy flow at the input and the output of the bq25505 harvesting circuit, as well as the illuminance, temperature, humidity and air pressure measurements of the ambient sensors. The following timestamped data columns are available in the raw measurement format, as well as preprocessed and filtered HDF5 datasets: V_in - Converter input/solar panel output voltage, in volt I_in - Converter input/solar panel output current, in ampere V_bat - Battery voltage (emulated through circuit), in volt I_bat - Net Battery current, in/out flowing current, in ampere Ev_left - Illuminance left of solar panel, in lux Ev_right - Illuminance left of solar panel, in lux P_amb - Ambient air pressure, in pascal RH_amb - Ambient relative humidity, unit-less between 0 and 1 T_amb - Ambient temperature, in centigrade Celsius The following publication presents and overview of the dataset and more details on the deployment used for data collection. A copy of the abstract is included in this dataset, see the file abstract.pdf. L. Sigrist, A. Gomez, and L. Thiele. "Dataset: Tracing Indoor Solar Harvesting." In Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA '19), 2019. Folder Structure and Files processed/ - This folder holds the imported, merged and filtered datasets of the power and sensor measurements. The datasets are stored in HDF5 format and split by measurement position posXX and and power and ambient sensor measurements. The files belonging to this folder are contained in archives named yyyy_mm_processed.tar, where yyyy and mm represent the year and month the data was published. A separate file lists the exact content of each archive (see below). raw/ - This folder holds the raw measurement files recorded with the RocketLogger [1, 2] and using the measurement platform available at [3]. The files belonging to this folder are contained in archives named yyyy_mm_raw.tar, where yyyy and mmrepresent the year and month the data was published. A separate file lists the exact content of each archive (see below). LICENSE - License information for the dataset. README.md - The README file containing this information. abstract.pdf - A copy of the above mentioned abstract submitted to the DATA '19 Workshop, introducing this dataset and the deployment used to collect it. raw_import.ipynb [open in nbviewer] - Jupyter Python notebook to import, merge, and filter the raw dataset from the raw/ folder. This is the exact code used to generate the processed dataset and store it in the HDF5 format in the processed/folder. raw_preview.ipynb [open in nbviewer] - This Jupyter Python notebook imports the raw dataset directly and plots a preview of the full power trace for all measurement positions. processing_python.ipynb [open in nbviewer] - Jupyter Python notebook demonstrating the import and use of the processed dataset in Python. Calculates column-wise statistics, includes more detailed power plots and the simple energy predictor performance comparison included in the abstract. processing_r.ipynb [open in nbviewer] - Jupyter R notebook demonstrating the import and use of the processed dataset in R. Calculates column-wise statistics and extracts and plots the energy harvesting conversion efficiency included in the abstract. Furthermore, the harvested power is analyzed as a function of the ambient light level. Dataset File Lists Processed Dataset Files The list of the processed datasets included in the yyyy_mm_processed.tar archive is provided in yyyy_mm_processed.files.md. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums. Raw Dataset Files A list of the raw measurement files included in the yyyy_mm_raw.tar archive(s) is provided in yyyy_mm_raw.files.md. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums. Dataset Revisions v1.0 (2019-08-03) Initial release. Includes the data collected from 2017-07-27 to 2019-08-01. The dataset archive files related to this revision are 2019_08_raw.tar and 2019_08_processed.tar. For position pos06, the measurements from 2018-01-06 00:00:00 to 2018-01-10 00:00:00 are filtered (data inconsistency in file indoor1_p27.rld). v1.1 (2019-09-09) Revision of the processed dataset v1.0 and addition of the final dataset abstract. Updated processing scripts reduce the timestamp drift in the processed dataset, the archive 2019_08_processed.tar has been replaced. For position pos06, the measurements from 2018-01-06 16:00:00 to 2018-01-10 00:00:00 are filtered (indoor1_p27.rld data inconsistency). v2.0 (2020-03-20) Addition of new data. Includes the raw data collected from 2019-08-01 to 2019-03-16. The processed data is updated with full coverage from 2017-07-27 to 2019-03-16. The dataset archive files related to this revision are 2020_03_raw.tar and 2020_03_processed.tar. Dataset Authors, Copyright and License Authors: Lukas Sigrist, Andres Gomez, and Lothar Thiele Contact: Lukas Sigrist (lukas.sigrist@tik.ee.ethz.ch) Copyright: (c) 2017-2019, ETH Zurich, Computer Engineering Group License: Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) References [1] L. Sigrist, A. Gomez, R. Lim, S. Lippuner, M. Leubin, and L. Thiele. Measurement and validation of energy harvesting IoT devices. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017. [2] ETH Zurich, Computer Engineering Group. RocketLogger Project Website, https://rocketlogger.ethz.ch/. [3] L. Sigrist. Solar Harvesting and Ambient Tracing Platform, 2019. https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing Appears in the Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA '19)

This dataset contains statistics of the sonnendach.ch dataset at the national level. See README.md for more information.

Laboratory corrosion and scaling testing of metallic materials exposed in high temperature and pressure environments generally involves complex, multi-instrument measurement setups. Here, we present a setup including an autoclave that is instrumented for in-situ electrochemical testing and that contains a ZrO2-based solid-state pH electrode and devices for temperature control and solution stirring. We show results highlighting the importance of adequate pre-calibration of the pH measurement, due to the hysteresis depending on temperature sweep. Additionally, we illustrate how interfacing the autoclave and the electrochemical cell to measuring and controlling instruments, using different data communication interfaces, can create ground loops. These ground loop interferences can introduce significant errors in the measurement, such as a potential shift of >100 mV. In complex, multi-instrument setups, a complete understanding of ground loops may often be difficult. Thus, we recommend systematic checks to identify the ground loops and we propose measures to avoid them. Measurement, 155 ISSN:0263-2241 ISSN:1873-412X

Background On-going global climate change poses a serious threat for natural populations unless they are able to evolutionarily adapt to changing environmental conditions (e.g. increasing average temperatures, occurrence of extreme weather events). A prerequisite for evolutionary change is within-population heritable genetic variation in traits subject to selection. In relation to climate change, mainly phenological traits as well as heat and desiccation resistance have been examined for such variation. Therefore, it is important to investigate adaptive potential under climate change conditions across a broader range of traits. This is especially true for life-history traits and defences against natural enemies (e.g. parasites) since they influence organisms’ fitness both directly and through species interactions. We examined the adaptive potential of fitness-related traits and their responses to heat waves in a population of a freshwater snail, Lymnaea stagnalis. We estimated family-level variation and covariation in life history (size, reproduction) and constitutive immune defence traits [haemocyte concentration, phenoloxidase (PO)-like activity, antibacterial activity of haemolymph] in snails experimentally exposed to typical (15 °C) and heat wave (25 °C) temperatures. We also assessed variation in the reaction norms of these traits between the treatments. Results We found that at the heat wave temperature, snails were larger and reproduced more, while their immune defence was reduced. Snails showed high family-level variation in all examined traits within both temperature treatments. The only negative genetic correlation (between reproduction and antibacterial activity) appeared at the high temperature. However, we found no family-level variation in the responses of most examined traits to the experimental heat wave (i.e. largely parallel reaction norms between the treatments). Only the reduction of PO-like activity when exposed to the high temperature showed family-level variation, suggesting that the cost of heat waves may be lower for some families and could evolve under selection. Conclusion Our results suggest that there is genetic potential for adaptation within both thermal environments and that trait evolution may not be strongly affected by trade-offs between them. However, rare differences in thermal reaction norms across families indicate limited evolutionary potential in the responses of snails to changing temperatures during extreme weather events. BMC Evolutionary Biology, 17 ISSN:1471-2148