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This repository contains the scripts and own generated data developed for the article: Miranda Xicotencatl, B., Kleijn, R., van Nielen, S., Donati, F., Sprecher, B., & Tukker, A. (2023). Data implementation matters: Effect of software choice and LCI database evolution on a comparative LCA study of permanent magnets. Journal of Industrial Ecology, 27(5), 1252–1265. https://doi.org/10.1111/jiec.13410 The data folder contains the characterized results of three alternatives for the production of rare earth magnets (REMs), calculated with several versions of the ecoinvent database (v2.2 and cut-off v3.1 to v3.6), their life cycle inventories, the foreground data to reproduce their product system models and the data plotted in Figures 3, 4 and 5 of the related article. The file with the Data Underlying the Figures in the Main Article is: `dataPlottedInFig_3_4_and_5.xlsx`. The document BWimport in the repository guides the user through the interaction with the custom scripts necessary to reproduce the results of this research. It is available as a PDF and as an interactive Jupyter notebook (IPYNB). Please refer to the article and the first part of the supplementary information (SI1) for further details about the context and purpose of this repository.Note on foreground dataSprecher et al. (2014) made their foreground data partially available in a human-readable format under a CC BY-NC 4.0 license. The file `processDataCMLCA.xlsx` is based on their complete product system model and consists of an annotated human- and machine-readable version. This modified version also includes the allocation factors used by Sprecher et al. (2014) that were not reported in their original publication.Note on licensesThe data in the repository is based on the work by Sprecher et al. (2014) and licensed under a Creative Commons Attribution-NonCommercial 4.0 International License by Brenda Miranda Xicotencatl. The underlying source code is available in this repository under the MIT license. If you use the scripts, please refer to the Zenodo repository. For other use cases, please cite the research article.

> COMMON LINK WITH (PUB. & NAME DIFFERENT) bio.tools/gddp (RESEARCH.CCHMC.ORG) | > CORRECT NAME OF TOOL COULD ALSO BE CONCLUSION:SEMA | Graphical data mining of cancer mechanisms with SEMA | SEMA is a graphical hypothesis testing platform for multidimensional cancer genomics data | SEMA is a graphical hypothesis testing platform for multidimensinoal cancer genomics data, written in R Shiny and JavaScript. SEMA uses Structural Equation Modeling (SEM) for the analyses of graphical models | Free HTML5 Template by FreeHTML5.CO | Graphical data mining from cancer genomic datasets

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: Tr��ndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.

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).