• Energy Research
• ZENODO close
• Technical University of Denmark close

This is the ERA5 dataset that can be used to calculate air density for wind energy purposes using the equations presented in https://doi.org/10.3390/en12112038. The NetCDF file contains mean fields of monthly means from 2010 to 2020 of the variable temperature, surface pressure, specific humidity and the lapse rate. The horizontal grid resolution is 0.25 degrees and covers the whole globe. These fields are used in the WAsP software version 12.6 and above to calculate the air density at specified heights above mean sea level. The WAsP software (www.wasp.dk) is the industry standard method to calculate the annual energy production of wind farms. The ERA5 data are generated using Copernicus Climate Change Service information [2020]

Supporting data for publication 'Performance analysis of a high-efficiency multi-bed active magnetic regenerator device" submitted to the Applied Thermal Engineering (DOI 10.1016/j.applthermaleng.2021.117569)The Excel sheet summarizes the experimental output parameters for the performance data presented in the publication. All data were measured continuously after reaching steady-state conditions, and the data were averaged over a time span of 600 s.

The supporting information of the journal article "Evaluation of sugar feedstocks for bio-based chemicals: A consequential, regionalized life cycle assessment" from Fabbri et al. (2022) includes one file with the following content: S1 Details of consequential modelling: feedstock S1.1 Identification type of changes (demand or supply) S1.2 Identification of constrains in the market S1.3 Identification of product substitutions S1.4 Identification of affected production technology S1.5 Identification of marginal crop and marginal supplier S2 Details of consequential modelling: by-products S3 Model parameters and unit processes S3.1 Sugar beet S3.2 Sugar cane S3.3 Wheat S3.4 Maize S3.5 Wood S3.6 Residual woodchips and sawdust S4 Review of land use change accounting methods S4.1 Direct land use change (dLUC) S4.2. Indirect land use change (iLUC) S5 Additional results S5.1 Influence of spatial differentiation in LCIA S5.2 Influence of indirect land use change (iLUC) S6 References This work was funded by the Innovation Fund Denmark under the Grand Solutions instrument; project ReMEG "Renewable Mono Ethylene Glycol for PET Plastic".

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 723649 Indoor environmental quality (IEQ) measurement and survey data of demonstration buildings in the hybridGEOTABS project

Supporting data for publication 'Impact of hysteresis on caloric cooling performance" submitted in the International Journal of Refrigeration (DOI 10.1016/j.ijrefrig.2020.10.012) The data comprise:1) An Excel file presenting the modeling results from the 1D regenerator model with hysteresis term (Qhys) to predict how modeled materials with a range of hysteresis values affect the cooling performance, including COP and cooling performance (Pcool). The Excel file presents the results for the six model materials, which have different physical (and thermodynamic) properties, i.e., specific heat capacity at zero field (c) and isothermal entropy change (s). For example, the material 'LoCLoS' has both a low specific heat capacitiy and a low isothermal entropy change. 2) An MS Word document with the additional Figs. S1 and S2 showing the modeled COPs for five model materials for a design cooling power of 50 W as a function of cycle frequency for different hysteresis values. The document also comprises a Table S1 presenting the modeled COPs for a design cooling power of 300 W as a function of cycle frequency for different hysteresis values.

Database prepared in Excel including four elements, as background information for RIBuild Deliverable D1.1 about the historic building stock: Historic buildings stock energy consumption (1) Historic building stock description (2) Building construction elements (3) Case studies (4) Element (1)-(3) are referring to the historic building stock in RIBuild partner countries in general, while element (4) contains examples of carried out renovation projects, involving internal insulation of a historic building. If available, the case study sheets contain information about the floor area, present use, the building envelope (thickness, materials), renovation history, pre- and post-energy usage and renovation cost. Further, information about typical defects and the main driving forces for the renovation project, planning or design tools used, whether the goal with the renovation was achieved and the satisfaction of the users. Overview of data files to be found in 'RIBuild data WP1' as part of this dataset.

Ensuring social data's reliability is essential in accurately evaluating social and economic impacts across geographical locations, economic sectors and stakeholder categories. Yet, the MRIO model utilized in our research (EXIOBASE) was hindered by out-of-date or significantly proxy fatality statistics, causing potential inaccuracies in our findings. We have comprehensively revised EXIOBASE fatality data to address this shortcoming, incorporating detailed, nation-specific, and up-to-date data. The update includes work-related fatal occupational injuries as well as fatalities associated with occupational exposure to a variety of 17 hazardous substances and conditions such as asbestos, arsenic, benzene, beryllium, cadmium, chromium, diesel engine exhaust, formaldehyde, nickel, polycyclic aromatic hydrocarbons, silica, sulfuric acid, trichloroethylene, asthmagens, particulate matter, gases and fumes, noise and ergonomic factors. Our methodological process is built on three pillars: data acquisition, raw data processing, and computation of fatal injuries by country, gender, year, and EXIOBASE economic sector. Data were sourced from the World Health Organization (WHO) (Pega et al., 2021) and Eurostat databases (Publications Office of the European Union, 2013). The WHO data was carefully screened based on specific criteria such as age above 15 years, gender, and fatal injuries only. Eurostat data provided granular information on work-related fatalities, classified by economic activities in the European Community (or NACE Rev.2 (Eurostat, 2008)). The WHO provided aggregate fatality data for 2010 and 2016. The strategy for allocating these deaths across Eurostat categories depended on the countries' geographical location, with different methods applied to European and non-European nations. For European nations, fluctuations in fatality numbers within a NACE Rev.2 sector mirrored the changes registered by Eurostat. For non-European countries, fatality figures were proportionally allocated across economic sectors split according to the NACE Rev.2 classification, reflecting the workforce size associated with each economic sector. Due to the scarcity of data for nations within Asia, America, or Africa, we adopted a regional approach, computing fatality ratios over each NACE Rev.2 category for each region by integrating data for available countries over a reference year. For 2010 and 2016, the aggregate fatality figures for nations within these three zones were established. Due to the temporal proximity of both reference years, we postulated a linear trend in the fatality count between these two years. The number of fatalities for a specific country, year, and per NACE Rev.2 activity was then calculated by applying the previously mentioned fatality ratio to the total number of deaths for that nation. Last, we applied the European annual ratios to their total mortality figures for the few countries that could not be classified as European or belonging to one of the aforementioned zones. The result is a comprehensive database that includes the number of fatalities (expressed in the number of deaths for work-related fatal occupational injuries and in Disability-adjusted life years (DALYs), for fatalities associated with occupational exposure to a specific risk factor), detailed at the country, gender, and NACE Rev.2 sector levels from 2008 to 2019, providing insights into work-related fatal injuries across different health effects and geographical regions. Nomenclature Archives: Concordance_ISIC_Exiobase.xlsx : Concordance between the International Standard Industrial Classification (ISIC) and the exiobase sectors Concordance_ISO3_EXIO3.xlsx - Concordance between the ISO3 code and the Exiobase regions Workforce_by_ISO3.csv - Number of active persons per Country (ISO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021) Workforce_by_EXIO3.csv - Number of active persons per Exiobase region (EXIO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021) Death_ISO3.csv - Number of death per Country (ISO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Year (from 2009 to 2019) Death_EXIO3.csv - Number of death per Exiobase Region (EXIO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Year (from 2009 to 2019) Injuries_ISO3.zip - Archive of DALY per Country (ISO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019) Injuries_EXIO3.zip - Archive of DALY per Exiobase Region (EXIO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019) Content of Injuries_*.zip: arsenic_*.csv asbestos_*.csv asthmagens_*.csv benzene_*.csv beryllium_*.csv cadmium_*.csv chromium_*.csv diesel_*.csv ergonomic_*.csv formaldehyde_*.csv gases_*.csv nickel_*.csv noise_*.csv polycyclic_*.csv silica_*.csv sulfuric_*.csv trichloro_*.csv

This presentation was part of the EERA JP Wind SP8 WORKSHOP on Future market arrangements for large-scale offshore wind energy development in the European Seas (June 10).