Contextual metadata is the unsung hero of research data. When done right, standardized and structured vocabularies make your data findable, shareable, and reusable. When done wrong, they turn a well intended effort into data cleanup and curation nightmares. In this paper we tackle the surprisingly tricky process of vocabulary standardization with a mix of practical advice and grounded examples. Drawing from real-world experience in contextual data harmonization, we highlight common challenges (e.g., semantic noise and concept bombs) and provide actionable strategies to address them. Our rules emphasize alignment with Findability, Accessibility, Interoperability, and Reusability (FAIR) principles while remaining adaptable to evolving user and research needs. Whether you are curating datasets, designing a schema, or contributing to a standards body, these rules aim to help you create metadata that is not only technically sound but also meaningful to users.

翻译：情境化元数据是研究数据中默默无闻的英雄。当标准化与结构化词汇表处理得当时，它们能使您的数据可发现、可共享且可重用；若处理不当，则会使原本善意的努力转变为数据清理与管理的噩梦。本文结合实用建议与具体实例，探讨词汇标准化这一异常棘手的流程。基于情境数据协调的实际经验，我们重点分析了常见挑战（如语义噪声与概念爆炸），并提供了可操作的应对策略。我们的准则强调与可发现性、可访问性、互操作性和可重用性（FAIR）原则保持一致，同时保持对不断变化的用户与研究需求的适应性。无论您是管理数据集、设计数据模式，还是参与标准制定机构的工作，这些准则旨在帮助您创建不仅在技术上可靠、同时对用户具有实际意义的元数据。