The MATLAB-based Toolbox for Environmental Research “TEnvR” (pronounced “ten-ver”) contains 44 open-source codes for automated data analysis from a multitude of techniques, such as ultraviolet-visible, fluorescence, and nuclear magnetic resonance spectroscopies, as well as from ultrahigh resolution mass spectrometry. Provided are codes for processing data (e.g., spectral corrections, formula assignment), visualization of figures, calculation of metrics, multivariate statistics, and automated work-up of large datasets. TEnvR allows for efficient data analysis with minimal by-hand manual work by the user, which allows scientists to do research more efficiently.

TEnvR is free software for non-commercial use: you can redistribute it and/or modify it under the terms of the GNU General Public License (GPL) as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version (GPL-3.0-or-later). The GNU GPL is a copyleft license allowing users to freely use, modify, and redistribute the codes within this toolbox. However, any further codes or software products derived from this product must be open-source. For further information please refer to the License.txt file located in TEnvR 2022\Supplementary files. TEnvR is also registered with the U.S. Copyright Office. We require that the copyright statement (© Old Dominion University Research Foundation) remains in the license notice section of the codes and the toolbox is cited accordingly in any produced work as following:

Goranov, A. I., Sleighter, R. L., Yordanov, D. A., and Hatcher, P. G. (2023): TEnvR: MATLAB-Based Toolbox for Environmental Research, Analytical Methods, doi: 10.1039/d3ay00750b.

   The toolbox is free for non-commercial purposes. Users wishing to use TEnvR commercially must obtain a commercial license from Old Dominion University Research Foundation – for more information please contact the corresponding authors.

   Lastly, while TEnvR provides multiple “turn-key” tools for automated data analysis, we urge the users to avoid viewing TEnvR as a black box. It is important to know and understand the underlying workflow and how the data is processed. Related to this is that environmental samples often deviate from normality, and computational routines may have to be adjusted for particular datasets and/or types of matrices. Thus, users should utilize appropriate caution and ensure they have robust quality assurance/quality control checks in place in order to evaluate results in a reliable fashion.