ABSTRACT:

The dataset comprises geochemical and hydrochemical analysis results from sediment and groundwater samples taken from deeper aquifers in the Lower Rhine Embayment (Germany) to study trace metal release induced by chemo-lithotropic denitrification.
The data is used in the manuscript "Trace Metal Release induced by chemo-lithotrophic Denitrification in deeper Aquifers of the Lower Rhine Embayment, Germany" by Felix Fahrenbach, Thomas R. Rüde, Leonard Stoeckl, Georg J. Hoube, Sarah E. Wünsch and Sven Sindern, which currently reviewed by the journal Applied Geochemistry.

The Python scripts depend on the following packages: numpy, pandas, matplotlib, and scipy. The R script depends on the readxl library.

ABSTRACT:

The dataset comprises analysis results (major ions, N2, and Ar concentrations) from groundwater samples taken from a deeper aquifer in the Lower Rhine Embayment (Germany) to trace denitrification.
The data is used in the manuscript "Tracing denitrification in a deeper aquifer of the Lower Rhine Embayment, Western Germany" by Felix Fahrenbach and Thomas R. Rüde, published in Hydrogeology Journal.

Running the Python scripts requires the following packages: numpy, pandas, matplotlib, mpltern, and scipy.

ABSTRACT:

The dataset comprises analysis results (major ions, N2, and Ar concentrations) from groundwater samples taken in the Lower Rhine Embayment (Germany) to assess the impact of sampling methods on N2, Ar, and excess-N2 concentrations.
The data is used in the manuscript "Comparing Groundwater Sampling Devices for Denitrification Assessment using the N2/Ar Method" by Felix Fahrenbach and Thomas R. Rüde, published in Groundwater.

The libraries tidyverse (Wickham et al. 2019), psych (Revelle 2014), car (Fox and Weisberg 2019), rstatix (Kassambara 2023), and PMCMRplus (Pohlert 2024) need to be installed to run the R scripts. Running the Python scripts requires the following packages: numpy (Harris et al. 2020), pandas (McKinney 2010), scipy (Virtanen et al. 2020), statsmodels (Seabold and Perktold 2010), and matplotlib (Hunter 2007).

References
Fox, J., and S. Weisberg. 2019. An R Companion to Applied Regression. 3rd ed. Thousand Oaks CA: Sage, https://www.john-fox.ca/Companion/.
Harris, C. R., K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, et al. 2020. Array programming with NumPy. Nature 585, no. 7825: 357–62, https://doi.org/10.1038/s41586-020-2649-2.
Hunter, J. D. 2007. Matplotlib: A 2D graphics environment. Computing in Science & Engineering 9, no. 3: 90–95, https://doi.org/10.1109/MCSE.2007.55.
Kassambara, A. 2023. rstatix: Pipe-Friendly Framework for Basic Statistical Tests, https://rpkgs.datanovia.com/rstatix/.
McKinney, W. 2010. Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference, edited by S. van der Walt and J. Millman, 56–61, https://doi.org/10.25080/Majora-92bf1922-00a.
Pohlert, T. 2024. PMCMRplus: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended, https://CRAN.R-project.org/package=PMCMRplus.
Revelle, W. 2014. psych: Procedures for Psychological, Psychometric, and Personality Research. Evanston, Illinois: Northwestern University, https://CRAN.R-project.org/package=psych.
Seabold, S., and J. Perktold. 2010. statsmodels: Econometric and statistical modeling with python. In Proceedings of the 9th Python in Science Conference.
Virtanen, P., R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, et al. 2020. SciPy 1.0: Fundamental algorithms for scientific computing in python. Nature Methods 17: 261–72, https://doi.org/10.1038/s41592-019-0686-2.
Wickham, H., M. Averick, J. Bryan, W. Chang, L. McGowan, R. François, G. Grolemund, et al. 2019. Welcome to the Tidyverse. Journal of Open Source Software 4, no. 43: 1686, https://doi.org/10.21105/joss.01686.