An essential aspect of scientific reproducibility is a coherent and complete acquisition of metadata along with the actual data of an experiment. The high degree of complexity and heterogeneity of neuroscience experiments requires a rigorous management of the associated metadata. The odML framework represents a solution to organize and store complex metadata digitally in a hierarchical format that is both human and machine readable. However, this hierarchical representation of metadata is difficult to handle when metadata entries need to be collected and edited manually during the daily routines of a laboratory. With odMLtables, we present an open-source software solution that enables users to collect, manipulate, visualize, and store metadata in tabular representations (in xls or csv format) by providing functionality to convert these tabular collections to the hierarchically structured metadata format odML, and to either extract or merge subsets of a complex metadata collection. With this, odMLtables bridges the gap between handling metadata in an intuitive way that integrates well with daily lab routines and commonly used software products on the one hand, and the implementation of a complete, well-defined metadata collection for the experiment in a standardized format on the other hand. We demonstrate usage scenarios of the odMLtables tools in common lab routines in the context of metadata acquisition and management, and show how the tool can assist in exploring published datasets that provide metadata in the odML format.

中文翻译：

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odMLtables：一种用户友好的神经生理学实验元数据管理方法

科学再现性的一个重要方面是连贯且完整地获取元数据以及实验的实际数据。神经科学实验的高度复杂性和异质性需要对相关元数据进行严格管理。odML 框架代表了一种以人类和机器可读的分层格式以数字方式组织和存储复杂元数据的解决方案。然而，当在实验室的日常工作中需要手动收集和编辑元数据条目时，这种元数据的分层表示很难处理。通过 odMLtables，我们提供了一个开源软件解决方案，通过提供将这些表格集合转换为层次结构元数据格式 odML 的功能，使用户能够以表格表示形式（xls 或 csv 格式）收集、操作、可视化和存储元数据，以及提取或合并复杂元数据集合的子集。由此，odMLtables 一方面以直观的方式处理元数据（与日常实验室例程和常用软件产品完美集成），另一方面以标准化格式为实验实施完整、定义明确的元数据收集，从而弥补了两者之间的差距另一方面。我们在元数据获取和管理的背景下演示了 odMLtables 工具在常见实验室例程中的使用场景，并展示了该工具如何帮助探索以 odML 格式提供元数据的已发布数据集。