Abstract
The concept of reproducibility is widely considered a cornerstone of scientific methodology. However, recent problems with the reproducibility of empirical results in large-scale systems and in biomedical research have cast doubts on its universal and rigid applicability beyond the so-called basic sciences. Reproducibility is a particularly difficult issue in interdisciplinary work where the results to be reproduced typically refer to different levels of description of the system considered. In such cases, it is mandatory to distinguish between more and less relevant features, attributes or observables of the system, depending on the level at which they are described. For this reason, we propose a scheme for a general 'relation of relevance' between the level of complexity at which a system is considered and the granularity of its description. This relation implies relevance criteria for particular selected aspects of a system and its description, which can be operationally implemented by an interlevel relation called 'contextual emergence'. It yields a formally sound and empirically applicable procedure to translate between descriptive levels and thus construct level-specific criteria for reproducibility in an overall consistent fashion. Relevance relations merged with contextual emergence challenge the old idea of one fundamental ontology from which everything else derives. At the same time, our proposal is specific enough to resist the backlash into a relativist patchwork of unconnected model fragments.