# A FAIR and AI-ready Higgs boson decay dataset

arXiv: https://arxiv.org/abs/2108.02214 · HTML: https://mlsanity.com/paper/2108.02214/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 70 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [Lorentz Group Equivariant Neural Network for Particle Physics](https://mlsanity.com/paper/2006.04780/) (2020)
- [ParticleNet: Jet Tagging via Particle Clouds](https://mlsanity.com/paper/1902.08570/) (2019)
- [Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection](https://mlsanity.com/paper/2012.08545/) (2020)
- [Enabling real-time multi-messenger astrophysics discoveries with deep learning](https://mlsanity.com/paper/1911.11779/) (2019)
- [Jet Flavour Classification Using DeepJet](https://mlsanity.com/paper/2008.10519/) (2020)
- [DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding](https://mlsanity.com/paper/1909.07817/) (2019)
- [The Tracking Machine Learning challenge : Throughput phase](https://mlsanity.com/paper/2105.01160/) (2021)
- [End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data](https://mlsanity.com/paper/1902.08276/) (2019)
- [Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources](https://mlsanity.com/paper/2105.06479/) (2021)
- [HEPData: a repository for high energy physics data](https://mlsanity.com/paper/1704.05473/) (2017)

## Built on by (most cited first, 14 of 14)

- [Particle Transformer for Jet Tagging](https://mlsanity.com/paper/2202.03772/) (2022)
- [Applications of physics informed neural operators](https://mlsanity.com/paper/2203.12634/) (2022)
- [Finetuning Foundation Models for Joint Analysis Optimization](https://mlsanity.com/paper/2401.13536/) (2024)
- [FAIR for AI: An interdisciplinary and international community building perspective](https://mlsanity.com/paper/2210.08973/) (2022)
- [FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy](https://mlsanity.com/paper/2207.00611/) (2022)
- [Building Machine Learning Challenges for Anomaly Detection in Science](https://mlsanity.com/paper/2503.02112/) (2025)
- [FAIR AI Models in High Energy Physics](https://mlsanity.com/paper/2212.05081/) (2022)
- [Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors](https://mlsanity.com/paper/2309.06782/) (2023)
- [Data Science and Machine Learning in Education](https://mlsanity.com/paper/2207.09060/) (2022)
- [Interpreting a Machine Learning Model for Detecting Gravitational Waves](https://mlsanity.com/paper/2202.07399/) (2022)
- [Vision Transformers for End-to-End Quark-Gluon Jet Classification from Calorimeter Images](https://mlsanity.com/paper/2506.14934/) (2025)
- [AI Explainability and Governance in Smart Energy Systems: A Review](https://mlsanity.com/paper/2211.00069/) (2022)
- [Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC](https://mlsanity.com/paper/2303.15291/) (2023)
- [Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels](https://mlsanity.com/paper/2605.17559/) (2026)

## Abstract (arXiv)

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.