  To enable the reusability of massive scientific datasets by humans and
machines, researchers aim to adhere to the principles of findability,
accessibility, interoperability, and reusability (FAIR) for data and artificial
intelligence (AI) models. This article provides a domain-agnostic, step-by-step
assessment guide to evaluate whether or not a given dataset meets these
principles. We demonstrate how to use this guide to evaluate the FAIRness of an
open simulated dataset produced by the CMS Collaboration at the CERN Large
Hadron Collider. This dataset consists of Higgs boson decays and quark and
gluon background, and is available through the CERN Open Data Portal. We use
additional available tools to assess the FAIRness of this dataset, and
incorporate feedback from members of the FAIR community to validate our
results. This article is accompanied by a Jupyter notebook to visualize and
explore this dataset. This study marks the first in a planned series of
articles that will guide scientists in the creation of FAIR AI models and
datasets in high energy particle physics.
