The often reported reproducibility crisis in the biomedical sciences also applies to enzymology and biocatalysis, and mainly results from incomplete reporting of reaction conditions. In this Concept article, an infrastructure based on EnzymeML is sketched, which enables reporting, exchange, and storage of enzymatic data according to the FAIR data principles. EnzymeML is a novel data exchange format for enzymology and biocatalysis, which facilitates the application of the STRENDA Guidelines and thus makes data on enzyme-catalyzed reactions findable, accessible, interoperable, and reusable. EnzymeML enables the comprehensive documentation of metadata, thus fostering reproducibility and replicability in enzymology and biocatalysis. An EnzymeML Application Programming Interface integrates electronic lab notebooks with modelling platforms and databases on enzymatic reactions, and thus enables the seamless flow of enzymatic data from measurement to modelling to publication, without the need for manual intervention such as reformatting or editing. EnzymeML serves as a valuable tool for the design of biocatalytic experiments and contributes to the vision of a unified research data infrastructure for catalysis research.

中文翻译：

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标准化数据、可扩展文档、可持续存储——EnzymeML 作为生物催化中公平数据管理的基础

生物医学科学中经常报道的重现性危机也适用于酶学和生物催化，主要是由于反应条件报告不完整造成的。在这篇概念文章中，概述了一个基于 EnzymeML 的基础设施，它能够根据 FAIR 数据原则报告、交换和存储酶数据。EnzymeML 是一种用于酶学和生物催化的新型数据交换格式，它促进了 STRENDA 指南的应用，从而使酶催化反应的数据可找到、可访问、可互操作和可重复使用。EnzymeML 能够对元数据进行全面记录，从而促进酶学和生物催化的再现性和可复制性。EnzymeML 应用程序编程接口将电子实验室笔记本与酶促反应建模平台和数据库集成在一起，从而使酶促数据从测量到建模再到发布无缝流动，无需重新格式化或编辑等手动干预。EnzymeML 是设计生物催化实验的宝贵工具，有助于实现催化研究的统一研究数据基础设施的愿景。