Many fields—including psychology, sociology, communications, political science, and computer science—use computational methods to analyze text data. However, existing text analysis methods have a number of shortcomings. Dictionary methods, while easy to use, are often not very accurate when compared to recent methods. Machine learning models, while more accurate, can be difficult to train and use. This research demonstrates that the large-language model GPT is capable of accurately detecting various psychological constructs (as judged by manual annotators) in text across 12 languages, using simple prompts and no additional training data. GPT thus overcomes the limitations present in existing methods. GPT is also effective in several lesser-spoken languages, which could facilitate text analysis research from understudied contexts.

The guide aims to introduce the role of persistent identifiers and metadata in facilitating open and FAIR research and walk the reader through ways to actively engage in implementing PIDs and metadata workflows, by committing resources, enacting congruent policies and guidelines, and providing support around grant application, management, and reporting.

In this guide, we outline steps researchers can take to share all their research outputs with PIDs and metadata to make their research process and practices FAIR.