This is why

There has been a strong movement across the environmental research community away from commercial statistical software and towards the development of custom code for data analysis. Coding isn’t just for nerds anymore, but popular programming environments like R, Python, Matlab, Julia and others make it relatively easy to develop scripts for a specific purpose. This freedom is a blessing, because our analyses are no longer limited to a set of pre-defined functions in a purchased statistics program. Unlimited creativity! However, there are some drawbacks to a scientific community where everybody is happily coding away: Methods are becoming less transparent and workflows less reproducible, solutions are becoming more diverse to the point where results potentially become inconsistent, and young researchers at the beginning of their careers may be simply overwhelmed with the wealth of example code that they can find on online forums and in tutorials. We therefore see a growing need to make code for common analyses more accessible, standardize workflows to increase transparency, and thereby save time. This is why we developed the DEEP toolbox as a platform that connects researchers and methods, and as a service to the dendroecological and forest ecophysiological research community.

Ceterum censeo: Trees are wonderful things.

This is how

In dendroecology and ecophysiology, many tasks are in one way or another related to timeseries data curation and processing. Yet, although a plethora of freely available analysis techniques exist in the form of R scripts and packages, a persistent gap remains between their availability and implementation. The DEEP toolbox aids in bridging this divide by providing user-friendly guidelines and broadly applicable custom R code for common analyses. The presented functionalities together form a baseline for the processing and analysis of various types of measurements, including tree-ring parameters, dendrometer and sap-flow measurements, eddy-covariance data, or gridded climate and Earth observations. We encourage our peers to use and enrich this baseline, which will increase the comparability between studies, as well as facilitate robust, transparent, and reproducible large-scale syntheses or meta-analyses.