GaDGeT: An open-source R-workflow for fast and flexible GDGT index calculations
Understanding recent rapid environmental and climatic changes requires placing them in a long-term context derived from reconstructions from natural archives. Membrane lipids such as glycerol dialkyl glycerol tetraethers (GDGTs) are increasingly recognized as vital proxies for paleoenvironmental and climatic reconstructions. While this field is rapidly evolving, with frequent publications introducing new calibrations, indices, and ratios derived from a growing range of compounds, there remains a notable lack of automated workflows for subsequent data processing. In response, we introduce <em>GaDGeT</em>, a comprehensive software designed to streamline GDGT data analyses, including the calculation of concentrations, fractional abundances, and published GDGT-indices and ratios. Users input raw HPLC peak area datasets, and <em>GaDGeT</em> automatically processes and organizes the results into structured output directories as CSV files. Implemented in the open-source language R, it ensures reproducibility and transparency. <em>GaDGeT</em>’s platform-independent, modular design makes it accessible and adaptable for researchers of varying expertise levels and enables the software to evolve alongside advancements in the field. <em>GaDGeT</em> currently focuses on data processing and output generation rather than visualization. The software is continuously updated with additional calibrations, and its open framework allows users to extend the tool with their own functions and indices. We encourage users to consult the original publications for appropriate interpretation and to share their HPLC peak area files to promote transparency, support peer review, and enhance the scientific utility of GDGT datasets.
Authors
- Tobias Schneider (ORCID: https://orcid.org/0000-0002-1593-0273)
- Isla S. Castañeda (ORCID: https://orcid.org/0000-0002-2524-9326)
Publication Details
- Journal
- SoftwareX
- Published
- 2025-10-21
- DOI
- https://doi.org/10.1016/j.softx.2025.102374
- Primary Topic
- Research Data Management Practices
- Type
- article
- Field-Weighted Citation Impact
- 0.00