ReproZip: Computational Reproducibility With Ease
Open accessAbstract
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.
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All updates are logged with provenance to ensure the evolution process remains auditable.
“All updates are logged with provenance to ensure the evolution process remains auditable [2, 26, 9].”
From SEDM: Scalable Self-Evolving Distributed Memory for Agents (2025) · cites this paper as [9]
Cites
Raise standards for preclinical cancer research
Begley, C. G., & Ellis, L. M. (2012) · Nature · doi:10.1038/483531a · Cited by 2,979 · see citing works
How scientists fool themselves – and how they can stop
Nuzzo, R. (2015) · Nature · doi:10.1038/526182a · Cited by 296 · see citing works
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Liu, D. M., & Salganik, M. J. (2019) · Socius Sociological Research for a Dynamic World · doi:10.1177/2378023119849803 · Cited by 47 · see citing works
SEDM: Scalable Self-Evolving Distributed Memory for Agents
Xu, H., Hu, J., Zhang, K., Yu, L., Tang, Y., Song, X., Duan, Y., Ai, L., & Shi, B. (2025) · arXiv · doi:10.48550/arxiv.2509.09498
“To minimize orchestration cost, abstraction is generated alongside the SCEC-based A/B assessment within the distributed pipeline [7].”
- Authors
- Chirigati, F., Rampin, R., Shasha, D., & Freire, J.
- Affiliations
- New York University (US)
- Venue
- Proceedings of the 2016 International Conference on Management of Data
- Publisher
- ACM
- Published
- 2016-06-26
- Pages
- 2085-2088
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2431100065
- Cited by
- 128
- References
- 2
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 11 · 2023: 8 · 2022: 7 · 2021: 17 · 2020: 12 · 2019: 21 · 2018: 12 · 2017: 13 · 2016: 3
- FWCI
- 32.30
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGMOD International Conference on Management of Data (SIGMOD) (publishedVersion)
- Funders
- National Science Foundation (CNS-1229185, CI-EN 1405927)
- Alfred P. Sloan Foundation
- Gordon and Betty Moore Foundation
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- ReproZip, reproducibility, numerical experiments, software packaging, dependency tracking, cross-platform reproducibility
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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