This project stands apart from others due to its open-ended nature. We begin with an excerpt from the abstract of Arslan (2019):

Data documentation in psychology lags behind not only many other disciplines, but also basic standards of usefulness. Psychological scientists often prefer to invest the time and effort that would be necessary to document existing data well in other duties, such as writing and collecting more data. Codebooks therefore tend to be unstandardized and stored in proprietary formats, and they are rarely properly indexed in search engines. This means that rich data sets are sometimes used only once—by their creators—and left to disappear into oblivion. Even if they can find an existing data set, researchers are unlikely to publish analyses based on it if they cannot be confident that they understand it well enough. My codebook package makes it easier to generate rich metadata in human- and machine-readable codebooks. It uses metadata from existing sources and automates some tedious tasks, such as documenting psychological scales and reliabilities, summarizing descriptive statistics, and identifying patterns of missingness. The codebook R package and Web app make it possible to generate a rich codebook in a few minutes and just three clicks. Over time, its use could lead to psychological data becoming findable, accessible, interoperable, and reusable, thereby reducing research waste and benefiting both its users and the scientific community as a whole.

Your first objective is to explore different methods of data documentation using R, aiming to enhance current research practices. Arslan’s paper serves as a foundation, highlighting numerous existing solutions that you should investigate further. Your second objective is to create a guide for Cognitive Science students on documenting data, employing a solution of your choice.

Deliverables:

1. A Written Guide: This should cover the fundamental principles of data documentation, its advantages, and potential challenges in integrating this practice into Cognitive Science. The guide should then instruct the reader on how to document data, which may vary based on the solution you select.
2. A Follow-Along Video: Produce a video guide that presents the topic and walks viewers through the data documentation process, adjusted appropriately for the medium. Aim for a production value and informational quality similar to your favorite YouTube tutorials.

Group Work: This project may be undertaken in groups of no more than three individuals.