Abstract
Reproducibility is a confused terminology. In this paper, I take a fundamental view on reproducibility rooted in the scientific method. The scientific method is analysed and characterized in order to develop the terminology required to define reproducibility. Furthermore, the literature on reproducibility and replication is surveyed, and experiments are modelled as tasks and problem solving methods. Machine learning is used to exemplify the described approach. Based on the analysis, reproducibility is defined and three different degrees of reproducibility as well as four types of reproducibility are specified. This article is part of the theme issue `Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.
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