yProv4DV: Reproducible Data Visualization Scripts Out of the Box
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911532788482048 |
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| author | Padovani, Gabriele Fiore, Sandro |
| author_facet | Padovani, Gabriele Fiore, Sandro |
| contents | While results visualization is a critical phase to the communication of new academic results, plots are frequently shared without the complete combination of code, input data, execution context and outputs required to independently reproduce the resulting figures. Existing reproducibility solutions tend to focus on computational pipelines or workflow management systems, not covering script-based visualization practices commonly used by researchers and practitioners. Additionally, the minimalist nature of current Python data visualization libraries tend to speed up the creation of images, disincentivizing users from spending time integrating additional tools into these short scripts. This paper proposes yProv4DV, a library lightweight designed to enable reproducible data visualization scripts through the use of provenance information, minimizing the necessity for code modifications. Through a single call, users can track inputs, outputs and source code files, enabling saving and full reproducibility of their data visualization software. As a result, this library fills a gap in reproducible research workflows by addressing the reproducibility of plots in scientific publications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20437 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | yProv4DV: Reproducible Data Visualization Scripts Out of the Box Padovani, Gabriele Fiore, Sandro Software Engineering Information Retrieval While results visualization is a critical phase to the communication of new academic results, plots are frequently shared without the complete combination of code, input data, execution context and outputs required to independently reproduce the resulting figures. Existing reproducibility solutions tend to focus on computational pipelines or workflow management systems, not covering script-based visualization practices commonly used by researchers and practitioners. Additionally, the minimalist nature of current Python data visualization libraries tend to speed up the creation of images, disincentivizing users from spending time integrating additional tools into these short scripts. This paper proposes yProv4DV, a library lightweight designed to enable reproducible data visualization scripts through the use of provenance information, minimizing the necessity for code modifications. Through a single call, users can track inputs, outputs and source code files, enabling saving and full reproducibility of their data visualization software. As a result, this library fills a gap in reproducible research workflows by addressing the reproducibility of plots in scientific publications. |
| title | yProv4DV: Reproducible Data Visualization Scripts Out of the Box |
| topic | Software Engineering Information Retrieval |
| url | https://arxiv.org/abs/2603.20437 |