ReproZip

Computational reproducibility with ease

Fernando Chirigati, Rémi Rampin, Dennis Shasha, Juliana Freire

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present ReproZip, the recommended packaging tool for the SIGMOD Reproducibility Review. ReproZip was designed to simplify the process of making an existing computational experiment reproducible across platforms, even when the experiment was put together without reproducibility in mind. The tool creates a self-contained package for an experiment by automatically tracking and identifying all its required dependencies. The researcher can share the package with others, who can then use ReproZip to unpack the experiment, reproduce the findings on their favorite operating system, as well as modify the original experiment for reuse in new research, all with little effort. The demo will consist of examples of non-trivial experiments, showing how these can be packed in a Linux machine and reproduced on different machines and operating systems. Demo visitors will also be able to pack and reproduce their own experiments.

Original languageEnglish (US)
Title of host publicationSIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages2085-2088
Number of pages4
Volume26-June-2016
ISBN (Electronic)9781450335317
DOIs
StatePublished - Jun 26 2016
Event2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016 - San Francisco, United States
Duration: Jun 26 2016Jul 1 2016

Other

Other2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
CountryUnited States
CitySan Francisco
Period6/26/167/1/16

Fingerprint

Experiments
Computer operating systems
Packaging
Linux

Keywords

  • Computational reproducibility
  • Provenance
  • ReproZip

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Chirigati, F., Rampin, R., Shasha, D., & Freire, J. (2016). ReproZip: Computational reproducibility with ease. In SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data (Vol. 26-June-2016, pp. 2085-2088). Association for Computing Machinery. https://doi.org/10.1145/2882903.2899401

ReproZip : Computational reproducibility with ease. / Chirigati, Fernando; Rampin, Rémi; Shasha, Dennis; Freire, Juliana.

SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data. Vol. 26-June-2016 Association for Computing Machinery, 2016. p. 2085-2088.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chirigati, F, Rampin, R, Shasha, D & Freire, J 2016, ReproZip: Computational reproducibility with ease. in SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data. vol. 26-June-2016, Association for Computing Machinery, pp. 2085-2088, 2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016, San Francisco, United States, 6/26/16. https://doi.org/10.1145/2882903.2899401
Chirigati F, Rampin R, Shasha D, Freire J. ReproZip: Computational reproducibility with ease. In SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data. Vol. 26-June-2016. Association for Computing Machinery. 2016. p. 2085-2088 https://doi.org/10.1145/2882903.2899401
Chirigati, Fernando ; Rampin, Rémi ; Shasha, Dennis ; Freire, Juliana. / ReproZip : Computational reproducibility with ease. SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data. Vol. 26-June-2016 Association for Computing Machinery, 2016. pp. 2085-2088
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