mlsanityData to 7 Oct 2026

A FAIR and AI-ready Higgs boson decay dataset

arXiv 2108.02214 ↗First posted 4 Aug 2021hep-ex cs.AI cs.DBcited by 14

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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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 13 of its 70 references that are in the map. First posted on arXiv 4 Aug 2021.

Builds on

13 of 70 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  3. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
  4. 2020Lorentz Group Equivariant Neural Network for Particle Physicscited by 120
  5. 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
  6. 2020Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detectioncited by 20
  7. 2019Enabling real-time multi-messenger astrophysics discoveries with deep learningcited by 18
  8. 2020Jet Flavour Classification Using DeepJetcited by 16
  9. 2019DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Foldingcited by 15
  10. 2021The Tracking Machine Learning challenge : Throughput phasecited by 12
  11. 2019End-to-End Jet Classification of Quarks and Gluons with the CMS Open Datacited by 11
  12. 2021Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sourcescited by 10
1 more
  1. 2017HEPData: a repository for high energy physics datacited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 8; 2023: 2; 2024: 1; 2025: 2; 2026: 1.

14 papers.

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  1. 2022Particle Transformer for Jet Taggingcited by 95
  2. 2022Applications of physics informed neural operatorscited by 33
  3. 2024Finetuning Foundation Models for Joint Analysis Optimizationcited by 18
  4. 2022FAIR for AI: An interdisciplinary and international community building perspectivecited by 13
  5. 2022FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopycited by 10
  6. 2025Building Machine Learning Challenges for Anomaly Detection in Sciencecited by 5
  7. 2022FAIR AI Models in High Energy Physicscited by 4
  8. 2023Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectorscited by 4
  9. 2022Data Science and Machine Learning in Educationcited by 3
  10. 2022Interpreting a Machine Learning Model for Detecting Gravitational Wavescited by 2
  11. 2025Vision Transformers for End-to-End Quark-Gluon Jet Classification from Calorimeter Imagescited by 2
  12. 2022AI Explainability and Governance in Smart Energy Systems: A Reviewcited by 1
2 more
  1. 2023Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHCcited by 0
  2. 2026Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernelscited by 0