ReproZip is a project led by Prof. Juliana Freire at NYU, to develop 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.

From the creators:

 As a step towards simplifying the process of creating reproducible experiments, we have developed ReproZip, a tool that automatically captures the provenance of experiments and packs all the necessary files, library dependencies, and variables to reproduce the results. Reviewers can then unpack and run the experiments without having to install any additional software. ReproZip also generates a workflow specification for the experiment which reviewers can use to explore the experiment and try different configurations, while the maintaining the provenance of the review process.

Cite as

Fernando Chirigati, Rémi Rampin, Dennis Shasha, Juliana Freire (2016): ReproZip: Computational Reproducibility With Ease, Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data (SIGMOD), pp. 2085-2088. [preprint]