ReproZip: using provenance to support computational reproducibility

Fernando Chirigati · Dennis E. Shasha · Juliana Freire

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Scientific Computing and Data Management Research Data Management Practices Distributed and Parallel Computing Systems

Abstract

We describe ReproZip, a tool that makes it easier for authors to publish reproducible results and for reviewers to validate these results. By tracking operating system calls, ReproZip systematically captures detailed provenance of existing experiments, including data dependencies, libraries used, and configuration parameters. This information is combined into a package that can be installed and run on a different environment. An important goal that we have for ReproZip is usability. Besides simplifying the creation of reproducible results, the system also helps reviewers. Because the package is selfcontained, reviewers need not install any additional software to run the experiments. In addition, ReproZip generates a workflow specification for the experiment. This not only enables reviewers to execute this specification within a workflow system to explore the experiment and try different configurations, but also the provenance kept by the workflow system can facilitate communication between reviewers and authors. 1

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2008Guest Editors' Introduction: Reproducible Research 2012Automated Capture of Experiment Context for Easier Reproducibility in Computational Research 2012Computational reproducibility

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2017noWorkflow 2017A Templating System to Generate Provenance 2024Computational reproducibility of Jupyter notebooks from biomedical publications 2018Towards computational reproducibility: researcher perspectives on the use and sharing of software 2019A Survey on Collecting, Managing, and Analyzing Provenance from Scripts 2014CARE, the comprehensive archiver for reproducible execution 2015Towards Reproducibility in Scientific Workflows: An Infrastructure-Based Approach 2015Collecting and Analyzing Provenance on Interactive Notebooks: when IPython meets noWorkflow

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