Efforts to make research data FAIR (Findable, Accessible, Interoperable, and Reusable) have transformed how we share information, yet many datasets still fall short of enabling true interoperability and reuse. In this talk, we introduce the Truth Effect Database (TED), which addresses this gap within research on the illusory truth effect, providing a harmonized, trial-level, open resource that integrates meta-data and raw data from 56 studies, 27 publications, over 12,000 participants, and nearly 780,000 trials.

To promote usability, TED focuses on user-friendly data submission using a custom entry website and data extraction using the R-package acdcquery. These tools guide researchers through both data entry and retrieval, eliminating the need for direct interaction with the database’s internal structure.

In this talk, we will introduce TED’s architecture and tools, demonstrate example analyses using Bayesian multilevel models, and show how the database reveals robust individual differences in the truth effect moderated by temporal delay. We will also discuss potential future applications of TED and the direction of open data in the field.

By combining transparent infrastructure with harmonized design, TED illustrates how psychological science can advance beyond open data toward genuine data interoperability and cumulative, collaborative discovery.