Getting Started Creating Data Dictionaries: How to Create a Shareable Dataset

As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand its contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a dataset. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search engine indexing to reach a broader audience of interested parties. This tutorial first explains the terminology and standards surrounding data dictionaries and codebooks. We then present a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared dataset accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we explain how to use freely available web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable (FAIR; Wilkinson et al., 2016).

随着研究人员采纳开放且透明的数据共享理念，他们需提供关于其数据的信息，以有效协助他人理解其内容。缺乏适当的文档，存储于在线存储库如OSF中的数据往往难以被其他研究人员和索引搜索引擎发现与阅读。数据字典与编码簿提供了关于变量、数据收集及其他数据集重要方面的丰富信息。此类信息，即所谓的元数据，为数据在研究中的进一步应用提供了关键洞见，并促进了搜索引擎索引，以触及更广泛的感兴趣群体。本教程首先阐释了围绕数据字典与编码簿的术语与标准。随后，我们展示了一个指导性工作流程，从原始数据（例如Qualtrics上的调查回答）到公开共享的数据集，该数据集伴随一个遵循既定标准的数据字典或编码簿。最后，我们解释了如何利用免费可用的网络应用来协助确保心理学数据可被发现、可访问、可互操作及可重用（FAIR；Wilkinson等，2016年）。