The Science
The Higgs boson is a fundamental particle responsible for the generation of mass in all other elementary particles. Since it was discovered at the CERN Large Hadron Collider in 2012, researchers have developed strategies to understand how the Higgs boson interacts with the other elementary particles. Scientists are also seeking cues in this experimental data that could indicate physics beyond our current understanding of nature. This science depends on the ability to extract new insights from massive experimental data sets. To help, researchers have defined practical FAIR (findable, accessible, interoperable, reusable) principles for data. FAIR will help humans and computers use large data sets. It will also enable modern computers to process these data sets. This work is critical for developing artificial intelligence (AI) tools that can identify novel patterns and features in experimental data.
The Impact
This work provides a guide that enables researchers to create and evaluate whether data sets adhere to FAIR principles. This will allow both humans and machines to use (and re-use) data sets, bypassing the need for time-consuming manual pre-processing. It also helps researchers prepare FAIR data sets for use in modern computing environments. If this vision is realized, scientific facilities will be able to seamlessly transfer experimental data to modern computing environments such as high performance computers. There, researchers can use the data to produce novel AI algorithms that provide trustworthy predictions and extract new knowledge.
