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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 dataset CO2_flux_measurements_Acoculco contains data on CO2 fluxes, coordinates (UTM), air temperature, atmospheric pressure measured in selected sites belonging to the Acoculco Geothermal Field: in particular, the areas named Lagunilla, Alcaparrosa, Los Azufres and also the area between them were investigated. CO2 flux measurements were performed using the accumulation chamber method. The dataset Field_meas_Acoculco_waters reports the ID, coordinates (UTM), Altitude (m.a.s.l.), temperature, flow rate, pH, Electrical Conductivity and Dissolved Oxygen for water samples collected in the central sector of the Acoculco geothermal field, but also in other sectors located inside and outside the Acoculco caldera. Total depth is also included for samples collected from water wells. The dataset Chemical_isotopic_data_Acoculco_waters reports major and minor chemical components and stable isotopic composition for hydrogen and oxygen determined in collected water samples in Acoculco geothermal field. Calculated partial pressures (in bars and log10-value) and CO2 concentrations of dissolved CO2 were also included. The dataset Chemical_isotopic_data_Acoculco_gas reports chemical and isotopic data for collected samples from Los Azufres and Alcaparrosa natural gas manifestations.

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.

This dataset contains the underlying data for the following publication: Doukas, H., Spiliotis, E., Jafari, M. A., Giarola, S. & Nikas, A. (2021). Low-cost emissions cuts in container shipping: Thinking inside the box. Transportation Research Part D: Transport and Environment, 94, 102815, https://doi.org/10.1016/j.trd.2021.102815.

Input files for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study. The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at https://ites-fe.ethz.ch/openaccess/. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at https://doi.pangaea.de/10.1594/PANGAEA.924502 while the CHELSA climate data for future scenarios are available at https://www.chelsa-climate.org. If interested in using this dataset for a research study or a project, please contact Marco Mina ----------------------------------------------------------------------- Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) Contrasting impacts of climate change on protection forests of the Italian Alps. Frontiers in Forests and Global Change, 6, 2023 https://doi.org/10.3389/ffgc.2023.1240235 ABSTRACT. Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

This dataset supplements the journal paper: "Short-term load forecasting for non-residential buildings contrasting artificial occupancy attributes". Authors: J. Massana, C. Pous et al. Journal: Energy and Buildings, 2015, vol. 130, p. 519-531. The paper is accessible in the below link: https://doi.org/10.1016/j.enbuild.2016.08.081 Description: Each excel file contains hourly data of one building located at the Campus of Unviersity of Gironal. Data were collected from 2011 to 2014. Column information for the excel files: - Hora: hour of the day (0, 1... 23). - Dia: day of the month (1, 2... 31). - Mes: month (1,2... 12) - Any: year (2011... 2014). - Dia_set: day of the week (1,2... 7). - Temp: Outdoor temperature (oC). - Perfil_dia: daily profile (school day, non-school day, examination day, school-leaving examination day, August day, holiday and weekend day and, finally, Easter and Christmas holiday). - Indicador X.X: occupancy indicators, as described in the paper. - Cons: electrical consumption (kWh)

These are the raw data generated in the 2do Joint Experiments sponsored by IRP Wind EU project

Author: Daniel Vázquez Pombo (dvapo@elektro.dtu.dk) ------------------------------------------------------------------------------- This dataset corresponds to the results of the paper titled: "Multi-Horizon Data-Driven Wind Power Forecast: From Nowcast to 2 Days-Ahead" 4th International Conference on Smart Energy Systems and Technologies (SEST) - 2021 -> https://sites.univaasa.fi/sest2021/ Submmited: Dec 2020 Accepted: Feb 2021 Published: Sep 2021 ------------------------------------------------------------------------------- The folder contains all the results presented in the paper, for clarity. Additional resources might be supplied under request. -------------------------------------------------------------------------------