Towards more reproducibility in vegetation research

Abstract

Computational reproducibility is the ability to obtain consistent results using the same input data, computational steps, methods, and code from a previous study (Goodman et al., 2016;Ziemann et al., 2023). It plays an important role in science because it: (1) ensures the credibility of scientific results; (2) improves the understanding of complex analytical workflows; (3) promotes knowledge sharing; and (4) allows the saving of research funds (Alston & Rick, 2021). In vegetation science, as in other natural sciences, standards for the computational reproducibility of analyses (hereafter referred to as "reproducibility") have increased recently, driven in part The percentage of articles (standardized by the total number of articles published per year) with available data ranged from 1.5% in 2014 (AVS) to 82.3% in 2022 (JVS). The percentage of articles with available code ranged from 0% in 2013 (AVS) to 26.3% in 2023 (JVS). Both journals showed a steep increase in data availability from 2019 onwards. In comparison, the percentage of code available has increased only moderately since 2019. The differences between the journals showed that JVS had a slightly higher