* [Lisa DeBruine](http://debruine.github.io) * [Scienceverse R package](http://scienceverse.github.io) * [Scienceverse shiny app](http://shiny.ieis.tue.nl/scienceverse/) * [Psych-DS](http://psych-ds.github.io) * [Preprint](http://psyarxiv.com/5xcda) of Lakens, D., & DeBruine, L. M. (in press). Improving Transparency, Falsifiability, and Rigour by Making Hypothesis Tests Machine Readable. Advances in Methods and Practices in Psychological Science. Abstract: 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. I will discuss what problems machine-readable study descriptions might solve, potential use cases, and demonstrate the feasibility of a prototype machine-readable study description in a real-life example using the R package scienceverse and its associated shiny app.