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

The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

Journal of Economic Inequality, accepted

Tiny molecules (hormones) are released from the bowels and other organs to the bloodstream in response to meals in order to orchestrate the metabolism. Oxyntomodulin, GLP-1 and glucagon are all known to regulate parameters such as appetite, food intake and induce weight loss. Therefore we want to investigate mechanistic relations between the three hormones.

Wheat Biomass for Kherson and Poltava regions in Ukraine The dataset contains Dry Above Ground Biomass (DAM) estimates over the Kherson and Poltava regions in Ukraine for years 2020,2021 and 2022. - Processing:The processing is done using the AgriCarbon-EOv1.5 processing chain, using the TREX processing centre at CNES France.The input remote sensing data are L2A Sentinel-2 surface reflectances provided by the MAJA processing chain based on the Copernicus Sentinel-2 L1C data.The Landcover maps are provided using ML Deep learning based on the Copernicus L2A data.The daily weather data is extracted from ERA5Land products (C3S). -Geophysical variable:Dry Above ground biomass of winter wheat in g/m2. - Extents: * DAM estimates over the Copernicus Sentinel-2 tile 36TWT cover the Kherson region.* DAM estimates over the Copernicus Sentinel-2 tile 36UVA cover the Poltava region. - Spatial resolution:10m resolution estimlates over wheat plots identified in the landcover map. - Temporal coverage:Estimates are provided at the end of the wheat cycle for cycles:* The year 2020 correspond to cycle: 2019-2020* The year 2021 corresponds to cycle : 2020-2021* The year 2022 corresponds to cycle : 2021-2022 - Projection: EPSG:32636 - File content: Each Raster file has 2 bands containing respectively: * band1: mean value of DAM in g/m2. * band2: standard deviation of DAM in g/m2. - List of maps:* Dry_aboveground_biomass_2020_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2020_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2021_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2021_T36UVA_Poltava_Ukraine.tif* Dry_aboveground_biomass_2022_T36TWT_Kherson_Ukraine.tif* Dry_aboveground_biomass_2022_T36UVA_Poltava_Ukraine.tif

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). This datafile holds the results for rainfed rice.

This dataset and codebook correspond to the second round of survey data gathered in Denmark in 2023, 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. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained. This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, 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. New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.

This data publication provides two datasets of turbulent kinetic energy dissipation rates sampled during the MomarSat 2022 cruise. One dataset was gathered with a deep autonomous Vertical Microstructure Profiler (VMP-6000). The second dataset was gathered with the MicroRiYo mooring as described in the reference paper (Ferron et al. 2024). The two datasets, one for each instrument, are available as tar files. Each tar file contains fourteen NetCDF files. Each NetCDF file contains the dissipation rate profile, the time (UTC) of the profile start, the geographical position (deployment of the VMP or mooring position), and the mean pressure for each dissipation rate estimate (two estimates at each pressure level from the two shear sensors). Each dissipation rate comes with a quality control matrix QC (14 x 4) that characterizes how the associated mean shear spectrum fitted the expected theoretical Nasmyth spectrum: QC( 1:10, 1 ) : Value of the 10 criteria used (see reference paper) for the dissipation rates of shear 1. QC( 1:10, 2 ): Criteria met (=1) or not met (=0) for shear 1 dissipation rates. QC(11,1): Same criteria as QC(10,1) expressed in terms of mean absolute deviation (MAD) instead of variance (see Lueck et al. 2022) (shear 1). QC(11,2): state whether criteria QC(11,1) is met (=1) or not met (=0) (shear 1). QC(12,1): Number of shear spectra averaged to compute one dissipation rate estimate (shear 1). QC(12,2): Number of accelerometer used to remove vibrations (Goodman et al. 2006; Lueck et al. 2022; Ferron et al. 2023) (shear 1) QC(13,1): MAD (shear 1) QC(13,2): unused QC(14,1): index of first used spectral component to compute the shear variance used in the dissipation rate estimate (shear 1). QC(14,2): index of last used spectral component to compute the shear variance used in the dissipation rate estimate (shear 1). QC(:,3): same as QC(:,1) for shear 2. QC(:,4): same as QC(:,2) for shear 2. Shear data were processed following the processing flow chart of the Atomix SCOR Working Group 160 (https://wiki.app.uib.no/atomix/index.php?title=Flow_chart_for_shear_probes). References: Ferron, B., S. Leizour, M. Hamon, O. Peden, 2024: MicroRiYo : An observing system for deep repeated profiles of kinetic energy dissipation rates from shear-microstructure turbulence along a mooring line, submitted to J. Atmos. Ocean. Tech. Lueck, R. G., 2022: The Statistics of Oceanic Turbulence Measurements. Part II: Shear Spectra and a New Spectral Model. J. Atmos. Oceanic Technol., 39, 1273–1282, https://doi.org/10.1175/JTECH-D-21-0050.1.

The complete data set that was the basis of the article: Stratulat, C.; Ginghina, R.E.; Bratu, A.E.; Isleyen, A.; Tunc, M.; Hafner-Vuk, K.; Frey, A.M.; Kjeldsen, H.; Vogl, J. Development- and Validation-Improved Metrological Methods for the Determination of Inorganic Impurities and Ash Content from Biofuels. Energies 2023, 16, 5221. https://doi.org/10.3390/en16135221 This work is part of the 19ENG09 BIOFMET project. This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 re-search and innovation programme.