The increasingly digital workflow in science has made it possible to share almost all aspects of the research cycle, from pre-registered analysis plans and study materials to the data and analysis code that produce the reported results. Although the growing availability of research output is a positive development, most of this digital information is in a format that makes it difficult to find, access, and reuse. A major barrier is the lack of a framework to concisely describe every component of research in a machine-readable format: A grammar of science.
Lakens, D., & DeBruine, L. M. (2021). Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests Machine-Readable. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245920970949