Neuroscience research has evolved to generate increasingly large and complex experimental data sets, and advanced data science tools are taking on central roles in neuroscience research. Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss our labs’ efforts to implement NWB data science pipelines. We describe general principles and specific use cases that illustrate successes, challenges, and non-trivial decisions in software engineering. We hope that our experience can provide guidance for the neuroscience community and help bridge the gap between experimental neuroscience and data science. Key takeaways from this article are that (1) standardization with NWB requires non-trivial design choices; (2) the general practice of standardization in the lab promotes data awareness and literacy, and improves transparency, rigor, and reproducibility in our science; (3) we offer several feature suggestions to ease the extensibility, publishing/sharing, and usability for NWB standard and users of NWB data.

神经科学研究已经发展到产生越来越大和复杂的实验数据集，先进的数据科学工具在神经科学研究中发挥着核心作用。 Neurodata Without Borders (NWB) 是一种神经生理学数据的标准语言，最近已成为数据管理、分析和共享的强大解决方案。我们在这里讨论我们的实验室为实施 NWB 数据科学管道所做的努力。我们描述了一般原则和具体用例，以说明软件工程中的成功、挑战和重要决策。我们希望我们的经验能为神经科学界提供指导，并帮助弥合实验神经科学和数据科学之间的差距。本文的主要结论是：(1) NWB 标准化需要重要的设计选择； (2) 实验室标准化的一般做法提高了数据意识和素养，并提高了科学的透明度、严谨性和可重复性； (3) 我们提供了一些功能建议，以简化 NWB 标准和 NWB 数据用户的可扩展性、发布/共享和可用性。