For science to reliably support new discoveries, its results must be reproducible. This has proven to be a challenge in many fields including fields that rely on computational methods as a means for supporting new discoveries. Reproducibility in these studies is particularly difficult because they require open, documented sharing of data and models and careful control of underlying hardware and software dependencies so that computational procedures executed by the original researcher are portable and can be run on different hardware or software and produce consistent results. Despite recent advances in making scientific work more findable, accessible, interoperable and reusable (FAIR), fundamental questions in the conduct of reproducible computational studies remain: Can published results be repeated in different computing environments? If yes, how similar are they to previous results? Can we further verify and build on the results by using additional data or changing computational methods? Can these changes be automatically and systematically tracked? This presentation will describe our EarthCube project to advance computational reproducibility and make it easier and more efficient for geoscientists to preserve, share, repeat and replicate scientific computations. Our approach is based on Sciunit software developed by prior EarthCube projects which encapsulates application dependencies composed of system binaries, code, data, environment and application provenance so that the resulting computational research object can be shared and re-executed on different platforms. We have deployed Sciunit within the HydroShare JupyterHub platform operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) for the hydrology research community and will present use cases that demonstrate how to preserve, share, repeat and replicate scientific results from the field of hydrologic modeling. While illustrated in the context of hydrology, the methods and tools developed as part of this project have the potential to be extended to other geoscience domains. They also have the potential to inform the reproducibility evaluation process as currently undertaken by journals and publishers.

The conduct of reproducible science improves when computations are portable and verifiable. A container provides an isolated environment for running computations and thus is useful for porting applications on new machines. Current container engines, such as Linux Containers (LXC) and Docker, however, have a high learning curve, are resource-intensive, and do not address the entire reproducibility spectrum consisting of portability, repeatability, and replicability. As part of EarthCube, we have developed Sciunit (https://sciunit.run) which encapsulates application dependencies i.e, system binaries, code, data, environment, along with application provenance. The resulting research object can be easily shared and reused amongst collaborators. Sciunit can be used with HydroShare’s JupyterHub CUAHSI notebook environment, and available to the entire community for use. In this poster, we will present three new features in Sciunit which have emerged based on community-provided use cases and discussion. Sciunit is available as a command-line utility. We will: (1) showcase the new Sciunit API. This will allow data facilities to integrate Sciunit as a reproducible environment on portals, (2) show how a Sciunit container can transition to a Docker container and vice versa, and finally, (3) demonstrate the ability to contrast two containers in terms of content and metadata. We will show these capabilities with the Hydrology use case of pySUMMA, a Python API for the Structure for Unifying Multiple Modeling Alternative (SUMMA) hydrologic model.