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Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).

As the European Union embarks on the energy transition, several challenges need to be faced to ensure that this shift is conducted from a holistic perspective that avoids burden-shifting across sustainability dimensions. One of the main concerns refers to the future availability of materials that clean technologies require. Critical rawmaterial assessment serves to guide the management of such mineral resources in a new paradigm of increasing demand. This work delves into the methodological fundamentals of several product-level criticality indicators in order to discuss their implications within the context of the ecodesign of two hydrogen-related products. Overall, criticality is advised to be assessed making use of several indicators. In the case study of a proton exchange membrane fuel cell stack, the combined interpretation of criticality indicators leads to identifying platinum as the main hotspot, while yttrium and lanthanum account for the most relevant criticality contributions in the casestudy of a solid oxide electrolysis cell stack. The authors would like to thank Gonzalo Puig-Samper and Eleonora Bargiacchi for the insights provided. This work has been carried out within the framework of the projects eGHOST and SH2E. These projects have received funding from the Fuel Cells and Hydrogen 2 Joint Undertaking (now Clean Hydrogen Partnership) under Grant Agreements No. 101007166 and 101007163, respectively. This Joint Undertaking receives support from the European Union’s Horizon 2020 Research and Innovation programme, Hydrogen Europe and Hydrogen Europe Research. The contents of this document are provided “AS IS”. It reflects only the authors’ view and the Joint Undertaking is not responsible for any use that may be made of the information it contains.

Excel document that contains the data of the article: ‘Technology validation of photosynthetic biogas upgrading in a semi-industrial scale algal-bacterial photobioreactor’. This dataset shows the values obtained during the experimental period and it complements the corresponding article.

This project aims to validate experimentally the High definition Modular Multilevel Converter (HD-MMC) which was developed by ORE Catapult. The concept can generate a lower THD than Conventional MMC (C-MMC) helping to increase power density and efficiency. You will find data set and final report here

This dataset includes the KPIs of the DEMO Spain of the REMOTE project (GA 779541)

Time series of the annual mean near-surface air temperature of a grid point in the Southern Pacific in 192 members in each of two ensembles simulated by the Planet Simulator (Fraedrich et al., 2005) as described in Drotos et al. (2017) in relation to Fig. 2. One ensemble was initialised in year 0 (but note that the time series are available only from year 500 on) and the other in year 610 (the temperature of the last unperturbed year, year 609, is also included in the first lines of the corresponding files). The latter date is during a simulated climate change. G. Drótos, T. Bódai and T. Tél (2017), "On the importance of the convergence to climate attractors". Eur. Phys. J. Spec. Top. 226, 2031–2038. https://doi.org/10.1140/epjst/e2017-70045-7 K. Fraedrich, H. Jansen, E. Kirk, U. Luksch, F. Lunkeit (2005), "The Planet Simulator: Towards a user friendly model". Meteorol. Z. 14, 299–304. https://doi.org/10.1127/0941-2948/2005/0043 {"references": ["G. Dr\u00f3tos, T. B\u00f3dai and T. T\u00e9l (2017), \"On the importance of the convergence to climate attractors\". Eur. Phys. J. Spec. Top. 226, 2031\u20132038. https://doi.org/10.1140/epjst/e2017-70045-7", "K. Fraedrich, H. Jansen, E. Kirk, U. Luksch, F. Lunkeit (2005), \"The Planet Simulator: Towards a user friendly model\". Meteorol. Z. 14, 299\u2013304. https://doi.org/10.1127/0941-2948/2005/0043"]}

The dataset consists of the building energy simulation data for three case-study buildings (Infrax office building (BE); Ter Potterie elderly home (BE) and Haus M multi-family building (CH)). For these buildings, HVAC-concepts were simulated: the hybridGEOTABS-MPC concept (not for Haus M), hybridGEOTABS-RBC baseline concept and nonGEOTABS-RBC concept. The dataset includes the hourly time series for a one year simulation for the major outputs of the BES-model. The results are reported in hybridGEOTABS deliverable D4.9. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 723649

This dataset contains the underlying data for the following publication Significant implications of permafrost thawing for climate change control, Climatic Change, DOI: 10.1007/s10584-016-1666-5. This data set contains the permafrost emissions used as inputs for the DICE model. These are estimates of the emissions release from permafrost under the RCP 2.6 scenario (GtCO 2 -eq y −1. Three inputs were used: the median, 16th percentile and 84th percentile pathway.