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).