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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.

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.

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.HighResMIP.CNRM-CERFACS.CNRM-CM6-1-HR.control-1950' 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 CNRM-CM6-1-HR climate model, released in 2017, includes the following components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T359; Gaussian Reduced with 181724 grid points in total distributed over 360 latitude circles (with 720 grid points per latitude circle between 32.2degN and 32.2degS reducing to 18 grid points per latitude circle at 89.6degN and 89.6degS); 91 levels; top level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA025, tripolar primarily 1/4deg; 1442 x 1050 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.

This dataset and codebook correspond to the initial round of survey data gathered in Germany in 2022, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.

TECHNICAL INFO No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the Technical documentation of the urbisphere-Paris campaign. This publication may include a number of files that contain the same measurements (duplicate files, "dupes") as multiple raw data backups were performed on the data. The duplicate data can be differentiated by the information found in the timestamp column and file header. ACKNOWLEDGEMENTS Authors thank SIRTA/LMD staff for providing support and facilities; ATMO-TNA-3—0000000125 funding COPYRIGHT NOTICE Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved. CREATOR NOTICE This work is owned by the Principal Investigators (PIs) of the Urbisphere project. ATTRIBUTION NOTICE The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). DISCLAIMER NOTICE The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected. MATERIAL NOTICE The notices cover data in databases, text and images contained in the work. MATERIAL URI Urbisphere project Original logger data files from radiometer intercomparison measurements at SIRTA Atmospheric Observatory in Palaiseau in the Greater Paris region.

Files for the manuscript “Radiocarbon Isotopic Disequilibrium Shows Little Incorporation of New Carbon in Soils and Fast Cycling of a Boreal Forest Ecosystem” 1. “Raw_Data” folder contains the files in .xlsx: - Lab_Atmospheric_Samples: D14C results from ambient air at the sampled heights. - Lab_Soil_Respiration: D14C results with date and integration time for the FFSR sampling campaign. - Lab_Solid_Samples: D14C and TOC results for soil, vegetation, roots, fungi and incubation samples.

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)

The CARBO-ACID research cruise (EUROFLEETS+ SEA02_10) was carried out on the RV Ramón Margalef between August 2nd and August 11st, with departing from Vigo – Spain and ending in Lisbon – Portugal. The main objective of this cruise was to collect data and samples to study the potential effects of ocean acidification on carbonate marine organisms (coccolithophores, pteropods, planktonic and benthic foraminifera, and corals) along the Iberian margin. With this objective, oceanographic data and water samples, plankton, cold-water corals and sediment samples were collected during an upwelling season, along two transects coinciding with the two persistent upwelling filaments off the Iberia Margin: the Cape Finisterra and the Cape Roca. In this dataset is guiven all the acquired data recollected onboad. During the CARBO-ACID cruise we did a total of 7 stations, 4 stations along the Cape Finisterra transect (from W to E: CA3, CA2, CA7, CA8) and 3 stations at the Cape Roca (from W to E: CA6, CA5, CA4) transect (Fig). At each station we usually started with a multibeam survey, a CTD and Rosette cast. These initial operations allowed to identify the different water masses present in this area, characterize their physical properties and to recover seawater samples at specific depth levels. The seawater samples were onboard subsampled, preserved in cold conditions or with chemicals and/ or filtered for several further analysis in the shore-based laboratories: DNA, chlorophyll, fitoplankton, coccolithophores, pH, alkalinity, stable isotopic composition, trace elements concentration and Suspend Particulate Matter. Subsequently to these operations, at each station, two vertical tows with a plankton multinet (with 5 nets) were done on the top 700 m of the water column to sample the planktonic communities of the different water depths. After this, sediment samples were recovered with a box-corer to study the past oceanographic conditions, between the pre-industrial Era and the Present, with multi-proxies used in paleoceanography and sedimentology. A total of 10 box-cores were recollected and each of them was onboard sub-sampled for eDNA, enzymes and benthic foraminifera. Fifteen shipek grab samples were recollected at the Fontanelas seamount (Estremadura Spur), station CA6, to characterize the sedimentary cover and to evaluate the presence of deep cold-water corals. Preliminary results show that the stations CA7, CA8 and CA4, located close to the coast, as expected, are the most influenced by the coastal upwelling, exhibiting colder surface water, higher values of fluorescence, and more zooplankton content reflecting higher phyto-zooplankton concentrations, as typical of the upwelling waters. At station CA4 temperature was higher and fluorescence showed lower values, indicative of less phytoplankton, and interpreted as indicating a different upwelling source water from that upwelled further north. Based on the CTD data, the Cape Roca transect is more influenced by the subtropical East North Atlantic Central Water (ENACWst), while the Cape Finisterra transect is more under the influence of the subpolar branch (ENACWsp). Seafloor sediment samples showed significant differences between the stations. Along the northern transect (Cape Finisterra) the seafloor sediments show an increase in grain size from the offshore to the coast. The offshore stations CA3 and CA2 revealed finer grained sediments, CA8 were composed of coarser sand and the station CA7, the shallowest station 77 m, presented the sediment composed mainly of shell fragments and coarse grain sand. Along the southern transect (Cape Roca), the offshore station CA6 (Fontanelas seamount) has coarser sandy sediments with rock clasts and cold-water coral fragments, and the stations CA5 and CA4 with fine sand to muddy sediments. The detailed CA6 bathymetry allowed to verify the existence of small plateaus on the slope of the Fontanelas seamount, where the fossil cold-water corals fragments were found, suggesting that this area is a very interesting system deserving further study with a ROV, and to characterize the corals fields and verify if there are live corals. These recollected data and samples will allow not only to reconstruct the pH variability under different environmental conditions, but also to estimate the biogeochemical changes along the coastal ocean waters as the anthropogenic influence increases. These results will contribute to better understand and model the effects on the biota under the future expected oceans pH changes.

The data used in this article are related to the rice yield following the use of the RiceAdvice technology recommendation and the rice yield following the recommended level of fertilizer recommendations. The data were collected in 5 sites in Mali. In all sites, rice yield with RiceAdvice was higher than rice yield following the conventional levels of fertilizer. On average, rice yield with RiceAdvice was 0.7 t/ha higher than rice yield with the conventional level of fertilizer recommendations.