For experimental scientists, data is the lifeblood of research. Collecting, organizing, and sharing data both within and across fields drives pivotal discoveries in science. 

Making data open and available, however, only answers part of the question about how different scientists — often with very different training — can draw useful conclusions from the same dataset. To promote and guide the cultivation and exchange of data, researchers have developed a set of principles that could make the data more findable, accessible, interoperable and reusable, or FAIR, for both people and machines.

The primary focus of this project is to advance our understanding of the relationship between data and AI models by exploring relationships among them through the development of FAIR frameworks. Using high-energy physics (HEP) as the science driver, this project is developing a FAIR framework to advance our understanding of AI, provide new insights to apply AI techniques, and provide an environment where novel approaches to AI can be explored.