One of the essential requirements of scientific experiments is that they are reproducible. For the reproducibility of results, it is important that the experiments are described in a structured way which contains information about the experimental setup and procedure. In recent years, a large number of domain ontologies have been created in numerous application disciplines, which formally model sections of the respective field (see, for example, the NCBI BioPortal with almost 700 ontologies). The expectation is that with the help of these ontologies, finding and linking data can be significantly improved (Walls et al. 2014, Klan et al. 2017). Analogous to the development of domain ontologies for the formal description of data, different approaches to the formal process description have emerged. This includes the provenance ontology PROV-O (Lebo et al. 2013). An extension, REPRODUCE-ME ontology (Samuel et al. 2018) is intended to describe end-to-end provenance of scientific experiments. Independent of these approaches for modeling scientific workflows, first suggestions were published on how such a description for experimental steps using machine learning could look like (Schelter et al. 2018). However, there is no integration of the descriptions of different types of experiment steps, as well as procedures for the further automation of provenance recording and for the use of these descriptions. This gap should be addressed here. The goal of this project is to develop an integrated semi-automatic approach for the management of provenance with human and machine-understandable descriptions which can be used for different purposes.


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