一键让数据可视化脚本可复现，无需修改代码
yProv4DV: Reproducible Data Visualization Scripts Out of the Box
- 通过溯源信息追踪代码、数据和输出，实现零修改集成
- 单次调用即可保存完整可视化流程，支持完全复现
- 适合希望提升论文可复现性的科研人员与实践者
尽管结果可视化是学术成果传播的关键环节，但图表常被分享时缺少完整的代码、输入数据、执行环境和输出，导致无法独立复现。现有可复现性解决方案多聚焦于计算流程或工作流管理，未覆盖研究者常用的基于脚本的可视化实践。此外，当前 Python 可视化库的极简特性虽加速绘图，却削弱了用户为脚本集成额外工具的动力。本文提出 yProv4DV，一个轻量级库，通过引入溯源信息，使数据可视化脚本在不修改代码的前提下实现可复现性。仅需一次调用，即可追踪输入、输出和源代码文件，支持完整保存与复现。该方法填补了科研工作流中可视化可复现性的空白，助力科学出版物中的图表复现。
原文摘要 · Abstract (English)
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.
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