A FAIR and AI-ready Higgs boson decay dataset-Reference-Cited by-同舟云学术

A FAIR and AI-ready Higgs boson decay dataset

Published:2022-02-14 Issue:1 Volume:9 Page:
ISSN:2052-4463
Container-title:Scientific Data
language:en
Short-container-title:Sci Data

Author:

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

Abstract

AbstractTo 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.

Funder

U.S. Department of Energy

Haliciouglu Data Science Fellowship.

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

Link

https://www.nature.com/articles/s41597-021-01109-0.pdf

Reference60 articles.

1. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nat 521, 436, https://doi.org/10.1038/nature14539 (2015).

2. Huerta, E. A. et al. Enabling real-time multi-messenger astrophysics discoveries with deep learning. Nat Rev. Phys. 1, 600, https://doi.org/10.1038/s42254-019-0097-4 (2019).

3. Deng, J. et al. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248, https://doi.org/10.1109/CVPR.2009.5206848 (2009).

4. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770, https://doi.org/10.1109/CVPR.2016.90 (2016).

5. van den Oord, A. et al. WaveNet: A generative model for raw audio. In 9th ISCA Speech Synthesis Workshop, 125 (2016).

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Chronic disease outcome metadata from German observational studies – public availability and FAIR principles;Scientific Data;2023-12-05

2. FAIR AI models in high energy physics;Machine Learning: Science and Technology;2023-12-01

3. JetNet: A Python package for accessing open datasets and benchmarking machine learning methods in high energy physics;Journal of Open Source Software;2023-10-30

4. Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements;International Journal of Intelligent Systems;2023-10-26

5. FAIR for AI: An interdisciplinary and international community building perspective;Scientific Data;2023-07-26