The past decade has seen tremendous breakthroughs in computation and there is no indication that this will slow any time soon. Machine learning, large-scale computing resources, and increased industry focus have resulted in rising investments in computer-driven solutions for data management, simulations, and model generation. However, with this growth in computation has come an even larger expansion of data and with it, complexity in data storage, sharing, and tracking. In this work, we introduce ZnTrack, a Python-driven data versioning tool. ZnTrack builds upon established version control systems to provide a user-friendly and easy-to-use interface for tracking parameters in experiments, designing workflows, and storing and sharing data. From this ability to reduce large datasets to a simple Python script emerges the concept of Data as Code, a core component of the work presented here and an undoubtedly important concept as the age of computation continues to evolve. ZnTrack offers an open-source, FAIR data compatible Python package to enable users to harness these concepts of the future.

过去十年在计算领域取得了巨大的突破，并且没有任何迹象表明这种发展会减缓。机器学习、大规模计算资源和增加的行业关注导致对数据管理、模拟和模型生成的计算驱动解决方案的投资不断增加。然而，这种计算的增长也带来了数据规模更大的扩展，以及在数据存储、共享和跟踪方面的复杂性。在这项工作中，我们介绍了ZnTrack，一种基于Python的数据版本管理工具。ZnTrack建立在已经建立的版本控制系统上，为实验参数跟踪、工作流设计、数据存储和共享提供了一个用户友好且易于使用的界面。从将大型数据集简化为一个简单的Python脚本的能力中，我们提出了数据即代码的概念，这是本文提出的工作的核心组成部分，也是在计算时代持续发展的一个无疑重要的概念。ZnTrack提供了一个开源的、符合FAIR数据标准的Python包，使用户能够利用未来的这些概念。

ZnTrack -- 数据即代码