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This map shows all emissions of CO and CO2 in Europe in selected industrial categories that contain processes with Carbon4PUR compatible emissions according to the E-PRTR. The information contained in this document has been prepared solely for the purpose of providing information about the Carbon4PUR consortium and its project. The document reflects only the Carbon4PUR consortium’s view and the European Commission is not responsible for any use that may be made of the information it contains. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 768919. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768919 {"references": ["https://carbonnext-eu.github.io/"]}

A model of the European power system built using Calliope. This repository contains the workflow routines that automatically build the model from source data. Alternatively to building models yourself, you can use pre-built models that run out-of-the-box. See README.md for further information. If you use euro-calliope in an academic publication, please cite the following article: Tröndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.

Data and simulation files of the paper ���A Global Optimal Solution to the Eco-Driving Problem��� by G.P. Padilla, S. Weiland andd M.C.F. Donkers. Simulation files can be used using Matlab 2017a.

X-Entropy is a parallelized kernel density estimator with automated bandwidth selection to calculate entropy. This library is primarily meant to calculate dihedral entropies from MD simulation data. For this, we use a KDE with automatic bandwidth selection as suggested by Z. Botev et al. We tried to keep the package as generalized as possible, therefore, the package can be used to calculate the entropy of any data, or also to simply calculate the KDE.

The dataset contains results of experiments for the study of our research https://arxiv.org/abs/2011.12770. Data is used to generate results with this reproducible notebook: https://github.com/RafalKucharskiPK/ExMAS/blob/master/ExMAS/spinoffs/corona/02_plots.ipynbFiles:- png with maps- .csv with epidemic modelling results (For any given day, the model outputs information about the number of travellers in each state (S-I-Q-R) and newly infected travellers, based on which we can reproduce epidemic spreading profiles.)Abstract of the study:Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the personal and public health risks considerations associated with the COVID-19 pandemic is hitherto unknown. To answer this, we combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Without intervention, ride-pooling system may substantially contribute to virus spreading. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks (at 50 instead of 800 infections) without sacrificing the efficiency achieved by pooling. Fixed matches among co-travellers disconnect the otherwise dense contact network, encapsulating the virus in small communities and preventing the outbreaks.

This report is part of the data documentation for the Global Gas Model (GGM) as prepared for analyses in the H2020 SET-Nav project1 and subsequent studies. Since some data are proprietary we cannot make available all input data we have used. By describing input data, the processing steps, and the resulting input files we aim for maximum transparency and to allow anyone to reproduce the data sets from scratch if desired. GGM was developed by Egging (2010, 2013), also based on expertise gained in the development and application of the European Gas Model (Egging et al., 2008) and the World Gas Model (Egging et al., 2010, 2009). GGM was applied with various data sets and versions in studies of the future global (Holz et al., 2015) and European gas markets (Holz et al., 2016, 2017). In addition to the impact of climate policy, supply security has been a recurring concern (e.g. Richter and Holz, 2015). Moreover, a stochastic version that used a scenario tree was applied in Egging and Holz (2016). In the SET-Nav project, GGM contributed to the analyses of projects of common interest (PCI), their profitability and public support requirements (Kotek et al., 2018). This is a deterministic model version (i.e. no probabilistic scenario tree).

Web portal for determining pathways for conformational transitions in macromolecules based on the use of discrete molecular dynamics and biasing techniques based on a combination of essential dynamics and Maxwell-Demon sampling techniques.