Improving reproducibility of geoscience models with Sciunit

For science to reliably support new discoveries, its results must be reproducible. Assessing reproducibility is a challenge in many fields-including the geosciences- that rely on computational methods to support these discoveries. Reproducibility in these studies is particularly difficult; the researchers conducting studies must agree to openly share research artifacts, provide documentation of underlying hardware and software dependencies, ensure that computational procedures executed by the original researcher are portable and execute in different environments, and, finally, verify if the results produced are consistent. Often these tasks prove to be tedious and challenging for researchers.Sciunit (https://sciunit.run) is a system for easily containerizing, sharing, and tracking deterministic computational applications across environments. Geoscience applications in the fields of hydrology, solid Earth, and space science have actively used Sciunit to encapsulate, port, and repeat workflows across computational environments. In this chapter, we provide a comprehensive survey of geoscience applications that have used Sciunit to improve sharing and reproducibility. We classify the applications based on their reproducibility requirements and show how Sciunit accommodates relevant interfaces and architectural components to support reproducibility requirements within each application. We aim to provide these applications as a Sciunit compendium of use cases for replicability, benchmarking, and improving the conduct of reproducible science in other fields.