A Bottom-up Approach to Data Annotation in Neurophysiology.
A Bottom-up Approach to Data Annotation in Neurophysiology.
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DOI：
10.3389/fninf.2011.00016
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发表时间：
2011
影响因子：
3.5
通讯作者：
Benda J
中科院分区：
文献类型：
--
作者：
Grewe J;Wachtler T;Benda J
元数据提供了有关刺激、数据采集和实验条件的信息，对于实验室内实验数据的分析和管理是必不可少的。但是，很少以结构化、全面和机器可读的形式提供元数据。这给在实验室和各种新兴的公共数据库中查找和检索数据带来了严重的问题。在这里，我们提出一种简单的格式，即“开放元数据标记语言”（open metaData Markup Language, odML），用于以自动化的、基于计算机的方式收集和交换元数据。在odML中，任意元数据信息存储为层次结构中的扩展键值对。odML的核心是格式和内容的清晰分离，也就是说，键和值都不是由格式定义的。这使得odML足够灵活，可以立即存储所有可用的元数据，而无需向本体或受控术语提交新键。可以用odml术语定义公共标准键，以保证互操作性。我们开始为神经生理学数据定义这些术语，但目标是社区驱动的扩展和改进所提出的定义。通过将自定义术语映射到这些标准术语，可以根据需要或偏好对元数据进行命名和组织，而不会软化标准。odML格式可以与为通用编程语言提供的相应库一起集成到实验室工作流程中，从而促进元数据信息的自动收集。odML的灵活性还鼓励社区驱动的术语收集和定义，这些术语用于神经科学中的数据注释。
Metadata providing information about the stimulus, data acquisition, and experimental conditions are indispensable for the analysis and management of experimental data within a lab. However, only rarely are metadata available in a structured, comprehensive, and machine-readable form. This poses a severe problem for finding and retrieving data, both in the laboratory and on the various emerging public data bases. Here, we propose a simple format, the “open metaData Markup Language” (odML), for collecting and exchanging metadata in an automated, computer-based fashion. In odML arbitrary metadata information is stored as extended key–value pairs in a hierarchical structure. Central to odML is a clear separation of format and content, i.e., neither keys nor values are defined by the format. This makes odML flexible enough for storing all available metadata instantly without the necessity to submit new keys to an ontology or controlled terminology. Common standard keys can be defined in odML-terminologies for guaranteeing interoperability. We started to define such terminologies for neurophysiological data, but aim at a community driven extension and refinement of the proposed definitions. By customized terminologies that map to these standard terminologies, metadata can be named and organized as required or preferred without softening the standard. Together with the respective libraries provided for common programming languages, the odML format can be integrated into the laboratory workflow, facilitating automated collection of metadata information where it becomes available. The flexibility of odML also encourages a community driven collection and definition of terms used for annotating data in the neurosciences.