Towards an Ontology Network for the reproducibility of scientific studies

Reproducibility is one of the fundamental characteristics of science. To reproduce scientific results, scientists need to manage and describe the provenance of end-to-end experimental pipelines. To understand, query, and reason how the results are derived, the provenance of the entire study needs to be described in an interoperable manner. Ontologies play an essential role in representing and interchanging provenance information generated in different systems, applications, and domains using a set of classes, properties, and restrictions. However, ontologies on describing provenance for scientific studies for different domains have been developed and used in isolation. They should be related to each other, aligned, and validated to form a network of interlinked ontologies, i.e., an ontology network. To this end, in this paper, we introduce ReproduceMeON, an ontology network for the reproducibility of scientific studies. The ontology network, which includes the foundational and core ontologies, attempts to bring together different aspects of the provenance of scientific studies from various applications to support their reproducibility. We present the development process of ReproduceMeON and the design methodology of developing core ontologies for the provenance of scientific experiments and machine learning using a semi-automated approach. We extend our scope to evolve ReproduceMeON to include ontologies for representing provenance for different subdomains like computational science, bioimaging, and microscopy.