Dataset for 'Identifying Key Drivers of Product Formation in Microbial Electrosynthesis with a Mixed Linear Regression Analysis'

DOI:10.4121/5e840d08-55f6-4daa-a639-048cebcd8266.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/5e840d08-55f6-4daa-a639-048cebcd8266

Datacite citation style

Zegers, Marika; Roy, Moumita; Jourdin, Ludovic (2025): Dataset for 'Identifying Key Drivers of Product Formation in Microbial Electrosynthesis with a Mixed Linear Regression Analysis'. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/5e840d08-55f6-4daa-a639-048cebcd8266.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

The analysed data and complete scripts for the permutation tests and mixed linear regression models (MLRMs) used in the paper 'Identifying Key Drivers of Product Formation in Microbial Electrosynthesis with a Mixed Linear Regression Analysis'.

Python version 3.10.13 with packages numpy, pandas, os, scipy.optimize, scipy.stats, sklearn.metrics, matplotlib.pyplot, statsmodels.formula.api, seaborn are required to run the .py files. Ensure all packages are installed before running the scripts. Data files required to run the code (.xlsx and .csv format) are included in the relevant folders.

History

  • 2025-10-06 first online, published, posted

Publisher

4TU.ResearchData

Format

.xlsx, .csv, .py

Funding

  • This project is funded by the Department of Biotechnology of Delft University of Technology as part of the Zero Emission Biotechnology programme. [more info...] Delft University of Technology
  • "e-Heat: Understanding and controlling heat to enable large scale electrolysers” (NWO OTP 19757) (grant code NWO OTP 19757) NWO

Organizations

TU Delft, Faculty of Applied Sciences, Department of Biotechnology

DATA

Files (11)