Provenance and scientific workflows: challenges and opportunities
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Reproducible Research in Computational Harmonic Analysis
Computing in Science and Engineering
Python Tools for Reproducible Research on Hyperbolic Problems
Computing in Science and Engineering
BURRITO: wrapping your lab notebook in computational infrastructure
TaPP'12 Proceedings of the 4th USENIX conference on Theory and Practice of Provenance
Making Computations and Publications Reproducible with VisTrails
Computing in Science and Engineering
CDE: A Tool for Creating Portable Experimental Software Packages
Computing in Science and Engineering
Automated Capture of Experiment Context for Easier Reproducibility in Computational Research
Computing in Science and Engineering
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Reproducibility is a core component of the scientific process. Revisiting and reusing past results allow science to move forward - "standing on the shoulders of giants", as Newton once said. An impediment to the adoption of computational reproducibility is that authors find it difficult to generate a compendium that encompasses all the required components to correctly reproduce their experiments. Even when a compendium is available, reviewers and readers may have difficulties in verifying the results on platforms different from the ones where the experiments were originally run. 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. We will demonstrate real use cases for ReproZip, how packages are created, and how reviewers can validate and explore experiments.