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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

Extension to the TOPAS Simulation Toolkit for Cellular and Sub-cellular Radiobiology.

By the 100th download or the time I deem it right, I may no longer share the whole paper for free. It will then be replaced with a view-restricted version with the link to get the full research text. The full paper will be provided upon request at a small donation as fund raising for the LMSEE Project (support via PayPal through this link - https://lnkd.in/gSXKyyiU or scan the provided QR code). Send e-mail at jgparamo.global@gmail.com for the reference number of your donation or other requests and inquiries. So check it out now while still free! Related article for the paper and the LMSEE Project at https://bit.ly/lmseeprimer, https://zenodo.org/communities/thelmseeproject, and https://bit.ly/leo-lmseedevice. THE FUTURE IN RENEWABLE ENERGY - This paper embodies a manifesto on the undoubtedly preeminent nature of an electron as a basic source of energy, how such energy can be practically harnessed from this subatomic particle of matter so can be usable to help address contemporary power needs out of clean, sustainable and environmentally friendly manner, depicting a strong contrast with recent mainstream energy sources and means. Mankind today favors clean energy technology and is showing progress on global warming mitigation, carbon footprint reduction or elimination, net-zero emissions, energy efficiency, and the sustainability or preservation of the world's resources. This undertaking proposes ways of utilizing the electron's characteristics when subjected to forces from sources of energy such as certain electric or electromagnetic fields and other energy possessing entities. The electron, in materials at the nano-particle size or smaller that could mainly exist in semiconductors, can be induced to a behavior that will constitute motion that can be exploited for practical power generation applications and other innovative solutions to address real-world challenges, ushering a new era of renewable energy technologies. An e-book shall follow this work soon, providing more details, explanations and illustrations so stay tuned on this novel energy harnessing technique but using conventional or mainstream resources.

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/"]}

This zenodo archive contains data and code that was used to conduct the master thesis. Overview: Code Demand Data Matching Scripts that were used to match the demand data from the Pathway Explorer data Format the the format that is needed as input by PyPSA EUR demand_data_matching.zip Result analysis Scripts that were used to extract the result data from the PyPSA EUR [1] network result files result_analysis_code.zip Data Demand Input Data Data that was generated with the Pathway Explorer by CLIMACT [2] Files: pwe_files.zip Result data Output Files from the PyPSA EUR Model including the model config files Files: mainResults+network_Sensitivity.zip CC_sensitivity.zip CarbonSeq+fossil_Sensitivity.zip Postprocessing Output Data data files that were generated by the scripts in result_analysis_code Files: post_processing_data_output.zip Licensing The code that was written in this thesis is published under the MIT License, see file license.txt All PyPSA EUR datasets are licensed under CC-BY-4.0, see also [3] CLIMACT's Pathway Explorer is licensed under CC-BY-NC-SA, see also [4] References: [1] Brown, T., Victoria, M., Zeyen, E., Hofmann, F., Neumann, F., Frysztacki, M., Hampp, J., Schlachtberger, D., & Hörsch, J. PyPSA-Eur: An open sector-coupled optimisation model of the European energy system (Version 0.9.0) [Computer software] [2] https://pathwaysexplorer.climact.com/model-resources [3] https://pypsa-eur.readthedocs.io/en/latest/licenses.html [4] https://pathwaysexplorer.climact.com/legal

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.

Foraging opportunities can be unpredictable. When foragers face a choice between resources that vary in predictability, foraging decisions not only depend on the profitability of food but also on their physiological state. This risk-sensitive foraging approach, in which animals take greater foraging risks when starving, remains relatively untested in reptiles compared to other taxa. We tested the risk-sensitive foraging theory in the tropical lizard, Psammophilus dorsalis, by manipulating energy budgets (satiated vs. 48-hour starved) and measuring foraging preferences for options that differed in rewards: constant (2 mealworms) vs. variable (0 or 4 mealworms). We find that satiated lizards were risk averse to variability in reward amounts and chose the constant food option more frequently than the variable option. By contrast, starved lizards were risk prone and chose the variable reward option more often than the constant one. At the end of 28 foraging trials, these strategies resulted in both starved and satiated groups achieving similar net resource gains. As new support for risk-sensitive foraging in a tropical reptile species, these results provide insight on how resource uncertainty influences foraging strategies. For lizards in the tropics, which have high energy requirements year-round, risk-sensitive foraging could be an effective strategy in stochastic environments.