生物技术.生命科学中数据格式和描述的要求

[中文适用范围]： 本文件规定了生命科学（包括生物技术、生物医学以及非人类生物研究和开发）中数据和相应元数据（即描述数据及其上下文的数据）的一致格式和记录的要求。 它为生命科学中的数据呈现可查找、可访问、可互操作和可重用（公平）提供了指导。 本文件适用于出于其他目的而系统地捕获、记录或集成生命科学中的数据和相应元数据的手动或计算工作流程。 本文件提供了手动获得的主要实验或程序数据和机器导出数据的格式要求。 本文件还描述了生命科学中数据和相应元数据的存储、共享、访问、互操作性和重用的要求。 本文件规定了从生命科学自动化高通量工作流程系统地获取大量数据的要求，以及通过其他生命科学技术和手动数据采集获取的大规模和小规模数据集的要求。 本文件适用于生物技术和生命科学的许多领域，包括但不限于：生命科学所有领域的基础/应用研究，以及工业、医学、农业或环境生物技术（不包括用于诊断或治疗目的）以及方法论驱动的领域，例如基因组学（包括大规模并行测序、宏基因组学、表观基因组学和功能基因组学）、转录组学、翻译组学、蛋白质组学、代谢组学、脂质组学、糖组学、酶学、免疫化学、合成生物学、系统生物学、系统医学及相关领域。 [外文原描述]： This document specifies requirements for the consistent formatting and documentation of data and corresponding metadata (i.e. data describing the data and its context) in the life sciences, including biotechnology, and biomedical, as well as non-human biological research and development. It provides guidance on rendering data in the life sciences findable, accessible, interoperable and reusable (F-A-I-R). This document is applicable to manual or computational workflows that systematically capture, record or integrate data and corresponding metadata in the life sciences for other purposes. This document provides formatting requirements for both primary experimental or procedural data obtained manually and machine derived data. This document also describes requirements for storing, sharing, accessing, interoperability and reuse of data and corresponding metadata in the life sciences. This document specifies requirements for large quantities of data systematically obtained from automated high throughput workflows in the life sciences, as well as requirements for large-scale and small-scale data sets obtained by other life science technologies and manual data capture. This document is applicable to many domains in biotechnology and the life sciences including, but not limited to: basic/applied research in all domains of the life sciences, and industrial, medical, agricultural, or environmental biotechnology (excluding for diagnostic or therapeutic purposes), as well as methodology-driven domains, such as genomics (including massive parallel sequencing, metagenomics, epigenomics and functional genomics), transcriptomics, translatomics, proteomics, metabolomics, lipidomics, glycomics, enzymology, immunochemistry, synthetic biology, systems biology, systems medicine and related fields.