Scientific workflows automate the analysis of large-scale scientific data, fostering the reuse of data processing operators as well as the reproducibility and traceability of analysis results. In exploratory research, however, workflows are continuously adapted, utilizing a wide range of tools and software libraries, to test scientific hypotheses. Script-based workflow engines cater to the required flexibility through direct integration of programming primitives but lack abstractions for interactive exploration of the workflow design by a user during workflow execution. To derive requirements for such interactive workflows, we conduct an empirical study on the use of Snakemake, a popular Python-based workflow engine. Based on workflows collected from 1602 GitHub repositories, we present insights on common structures of Snakemake workflows, as well as the language features typically adopted in their specification.

中文翻译：

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用户如何设计科学的工作流程？蛇形的案例

科学工作流程自动化大规模科学数据的分析，促进数据处理操作员的重用以及分析结果的可重复性和可追溯性。然而，在探索性研究中，工作流程不断调整，利用各种工具和软件库来测试科学假设。基于脚本的工作流引擎通过直接集成编程原语来满足所需的灵活性，但缺乏用户在工作流执行期间对工作流设计进行交互式探索的抽象。为了得出此类交互式工作流程的要求，我们对 Snakemake（一种流行的基于 Python 的工作流程引擎）的使用进行了实证研究。基于从 1602 个 GitHub 存储库收集的工作流程，我们提出了对 Snakemake 工作流程的常见结构的见解，