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.
Chen, Yifan, Huerta, E. A., Duarte, Javier, Harris, Philip, Katz, Daniel S., Neubauer, Mark S., Diaz, Daniel, Mokhtar, Farouk, Kansal, Raghav, Park, Sang Eon, Kindratenko, Volodymyr V., Zhao, Zhizhen, & Rusack, Roger (2022). A FAIR and AI-ready Higgs boson decay dataset. Scientific Data, 9(1). https://doi.org/10.1038/s41597-021-01109-0
Chen, Yifan, Huerta, E. A., Duarte, Javier, et al., "A FAIR and AI-ready Higgs boson decay dataset," Scientific Data 9, no. 1 (2022), https://doi.org/10.1038/s41597-021-01109-0
@article{osti_1845043,
author = {Chen, Yifan and Huerta, E. A. and Duarte, Javier and Harris, Philip and Katz, Daniel S. and Neubauer, Mark S. and Diaz, Daniel and Mokhtar, Farouk and Kansal, Raghav and Park, Sang Eon and others},
title = {A FAIR and AI-ready Higgs boson decay dataset},
annote = {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.},
doi = {10.1038/s41597-021-01109-0},
url = {https://www.osti.gov/biblio/1845043},
journal = {Scientific Data},
issn = {ISSN 2052-4463},
number = {1},
volume = {9},
place = {United Kingdom},
publisher = {Nature Publishing Group},
year = {2022},
month = {02}}
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 506, Issue 3https://doi.org/10.1016/S0168-9002(03)01368-8
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 977https://doi.org/10.1016/j.nima.2020.164304
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), 2009 IEEE Conference on Computer Vision and Pattern Recognitionhttps://doi.org/10.1109/CVPR.2009.5206848