A FAIR and AI-ready Higgs boson decay dataset

Yifan Chen,Philip Harris,Daniel Diaz,Raghav Kansal,Volodymyr V Kindratenko,Roger Rusack,Farouk Mokhtar,Zhizhen Zhao,Mark S Neubauer,Sang Eon Park,Daniel S Katz,Javier Duarte,E A Huerta

doi:10.1038/s41597-021-01109-0

Abstract

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.

Highlights

Much of the success of applications of artificial intelligence (AI) to a broad range of scientific problems[1,2] has been due to the availability of well-documented, high-quality datasets[3]; open source, state-of-the-art neural network models[4,5]; highly efficient and parallelizable numerical optimization methods[6]; and the advent of innovative hardware architectures[7].Across science and engineering disciplines, the rate of adoption of AI and modern computing methods has been varied[2]
Throughout the process of harnessing AI and advanced computing, researchers have realized that the lack of an agreed upon set of best practices to produce, collect, and curate datasets has limited the combination of disparate datasets that with AI may reveal new correlations or patterns[8,9]
The following subsections summarize the results for each principle, and discuss steps that were, or will be, take to increase the FAIRness of the dataset

Summary

Introduction

Across science and engineering disciplines, the rate of adoption of AI and modern computing methods has been varied[2]. Throughout the process of harnessing AI and advanced computing, researchers have realized that the lack of an agreed upon set of best practices to produce, collect, and curate datasets has limited the combination of disparate datasets that with AI may reveal new correlations or patterns[8,9]. From 2014 to 2016, a set of data principles, or best practices, based on findability, accessibility, interoperability, and reusability (FAIR) were defined so that scientific datasets could be readily reused by both humans and machines. The FAIR principles can be applied to address these limitations and increase the potential of AI for discovery in science and engineering. Using high energy physics (HEP) as an example, this article provides a domain-agnostic, step-by-step set of checks to guide in the process of making a dataset FAIR (“FAIRification”)

Objectives

Methods

Results

Conclusion