NSF Award
2318549
Two important issues facing modern empirical research are those of transparency (how well others can figure out how the research was done) and replicability (whether the outcomes will be the same if someone else does the same experiments). Lack of transparency and failures of experiments to replicate stifle scientific progress and lead to a mistrust of the scientific method. This project develops an open-source programming language, SweetPea, that automates experimental design. It will help researchers understand and replicate the results of others, minimize errors and bias, and increase the efficiency and accuracy of the entire experimental process. Consequently, this technology will contribute to advancing scientific discoveries, make them more accessible and reliable, and provide researchers in empirical sciences with a valuable tool to aid their work.<br/><br/>Many replication problems in the behavioral sciences arise because of the challenges encountered in implementing accurate and appropriately balanced experimental designs while avoiding confounding factors. Additionally, the lack of clear and transparent documentation reduces transparency and the ability to replicate experimental results. The SweetPea programming language is designed to facilitate reproducible experimental design. This project extends SweetPea's core functionality to support various design strategies, automating the documentation process, and expanding the community of users and contributors. SweetPea uses an intuitive interface for the declarative expression of experimental designs, and advanced computational algorithms for sampling and analysis. The software ensures that experimental designs are properly implemented without introducing unexpected confounds. In addition, the project leverages large language models for robust documentation of experimental designs. The project includes outreach activities to engage psychologists, neuroscientists, behavioral economists, and machine learning experts. Overall, this project improves the accuracy, transparency, and replicability of experimental designs, offering researchers an accessible and powerful tool for scientific investigation. This project is jointly funded by the Human Networks and Data Science - Infrastructure program and the Established Program to Stimulate Competitive Research (EPSCoR).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
