Cargando…

A FAIR and AI-ready Higgs boson decay dataset

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...

Descripción completa

Detalles Bibliográficos
Autores principales:	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
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844008/ https://www.ncbi.nlm.nih.gov/pubmed/35165298 http://dx.doi.org/10.1038/s41597-021-01109-0

Ejemplares similares

FAIR for AI: An interdisciplinary and international community building perspective
por: Huerta, E. A., et al.
Publicado: (2023)

AI Fairness
por: Mahoney, Trisha
Publicado: (2020)

Differential Fairness: An Intersectional Framework for Fair AI †
por: Islam, Rashidul, et al.
Publicado: (2023)

Numerical computations with GPUs
por: Kindratenko, Volodymyr
Publicado: (2014)

Explainable AI as evidence of fair decisions
por: Leben, Derek
Publicado: (2023)

A translational perspective towards clinical AI fairness
por: Liu, Mingxuan, et al.
Publicado: (2023)

Big Data in Laboratory Medicine—FAIR Quality for AI?
por: Blatter, Tobias Ueli, et al.
Publicado: (2022)

FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
por: Ravi, Nikil, et al.
Publicado: (2022)

A framework for FAIR robotic datasets
por: Motta, Corrado, et al.
Publicado: (2023)

Explaining machine-learned particle-flow reconstruction
por: Mokhtar, Farouk, et al.
Publicado: (2021)

Fairness and accountability of AI in disaster risk management: Opportunities and challenges
por: Gevaert, Caroline M., et al.
Publicado: (2021)

Detecting shortcut learning for fair medical AI using shortcut testing
por: Brown, Alexander, et al.
Publicado: (2023)

AI chatbots not yet ready for clinical use
por: Au Yeung, Joshua, et al.
Publicado: (2023)

Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
por: Tsan, Steven, et al.
Publicado: (2021)

A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future
por: Logan, Joe, et al.
Publicado: (2023)

Taking a fresh look at FAIR for research software
por: Katz, Daniel S., et al.
Publicado: (2021)

Taking a fresh look at FAIR for research software
por: Katz, Daniel S., et al.
Publicado: (2021)

A catalogue with semantic annotations makes multilabel datasets FAIR
por: Kostovska, Ana, et al.
Publicado: (2022)

An introduction to machine learning interpretability: an applied perspective on fairness, accountability, transparency, and explainable AI
por: Hall, Patrick, et al.
Publicado: (2019)

An introduction to machine learning interpretability: an applied perspective on fairness, accountability, transparency, and explainable AI
por: Hall, Patrick, et al.
Publicado: (2018)

“Just” accuracy? Procedural fairness demands explainability in AI-based medical resource allocations
por: Rueda, Jon, et al.
Publicado: (2022)

Radiotherapy Treatment Planning in the Age of AI: Are We Ready Yet?
por: Zheng, Dandan, et al.
Publicado: (2019)

Artificial intelligence in the medical profession: ready or not, here AI comes
por: Pucchio, Aidan, et al.
Publicado: (2022)

Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset
por: Meng, Chuizheng, et al.
Publicado: (2022)

A dataset for assessing phytolith data for implementation of the FAIR data principles
por: Kerfant, Céline, et al.
Publicado: (2023)

Operationalising fairness in medical AI adoption: detection of early Alzheimer’s disease with 2D CNN
por: Heising, Luca, et al.
Publicado: (2022)

Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms
por: Giovanola, Benedetta, et al.
Publicado: (2022)

You Can’t Have AI Both Ways: Balancing Health Data Privacy and Access Fairly
por: Bak, Marieke, et al.
Publicado: (2022)

A Roadmap to Artificial Intelligence (AI): Methods for Designing and Building AI ready Data for Women’s Health Studies
por: Kidwai-Khan, Farah, et al.
Publicado: (2023)

Introducing the FAIR Principles for research software
por: Barker, Michelle, et al.
Publicado: (2022)

Intention-to-Treat: Is That Fair?
por: Dettori, Joseph R., et al.
Publicado: (2020)

A dataset of skin lesion images collected in Argentina for the evaluation of AI tools in this population
por: Ricci Lara, María Agustina, et al.
Publicado: (2023)

Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation?
por: Jungmann, Florian, et al.
Publicado: (2021)

Curating Cyberbullying Datasets: a Human-AI Collaborative Approach
por: Gomez, Christopher E., et al.
Publicado: (2021)

The Piraeus AIS dataset for large-scale maritime data analytics
por: Tritsarolis, Andreas, et al.
Publicado: (2022)

FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards
por: Reer, Aaron, et al.
Publicado: (2023)

Unmanned aerial image dataset: Ready for 3D reconstruction
por: Shahbazi, Mozhdeh, et al.
Publicado: (2019)

The FAIR Cookbook - the essential resource for and by FAIR doers
por: Rocca-Serra, Philippe, et al.
Publicado: (2023)

Low Mass Higgs Boson Searches from ATLAS and CMS
por: Neubauer, Mark
Publicado: (2018)

COVID-19, AI enthusiasts, and toy datasets: radiology without radiologists
por: Tizhoosh, H. R., et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_oa9teb8ldfaofjubi48nrpce42