Patrick Widener

8 papers 50 citations h-index 3 2022–2026

Data Readiness for Scientific AI at Scale

This paper examines how Data Readiness for AI (DRAI) principles apply to leadership-scale scientific datasets used to train foundation models. We analyze archetypal workflows across four...

5 citations 2025-07 ICPP Workshops

Workflows Community Summit 2022: A Roadmap Revolution

Rafael Ferreira da Silva, Rosa M. Badia, Venkat Bala, Debbie Bard, Peer-Timo Bremer, Ian Buckley
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Silvina Caino-Lores, Kyle Chard, Carole Goble, Shantenu Jha, Daniel S. Katz, Daniel Laney, Manish Parashar, Frederic Suter, Nick Tyler, Thomas Uram, Ilkay Altintas, Stefan Andersson, William Arndt, Juan Aznar, Jonathan Bader, Bartosz Balis, Chris Blanton, Kelly Rosa Braghetto, Aharon Brodutch, Paul Brunk, Henri Casanova, Alba Cervera Lierta, Justin Chigu, Taina Coleman, Nick Collier, Iacopo Colonnelli, Frederik Coppens, Michael Crusoe, Will Cunningham, Bruno de Paula Kinoshita, Paolo Di Tommaso, Charles Doutriaux, Matthew Downton, Wael Elwasif, Bjoern Enders, Chris Erdmann, Thomas Fahringer, Ludmilla Figueiredo, Rosa Filgueira, Martin Foltin, Anne Fouilloux, Luiz Gadelha, Andy Gallo, Artur Garcia Saez, Daniel Garijo, Roman Gerlach, Ryan Grant, Samuel Grayson, Patricia Grubel, Johan Gustafsson, Valerie Hayot-Sasson, Oscar Hernandez, Marcus Hilbrich, AnnMary Justine, Ian Laflotte, Fabian Lehmann, Andre Luckow, Jakob Luettgau, Ketan Maheshwari, Motohiko Matsuda, Doriana Medic, Pete Mendygral, Marek Michalewicz, Jorji Nonaka, Maciej Pawlik, Loic Pottier, Line Pouchard, Mathias Putz, Santosh Kumar Radha, Lavanya Ramakrishnan, Sashko Ristov, Paul Romano, Daniel Rosendo, Martin Ruefenacht, Katarzyna Rycerz, Nishant Saurabh, Volodymyr Savchenko, Martin Schulz, Christine Simpson, Raul Sirvent, Tyler Skluzacek, Stian Soiland-Reyes, Renan Souza, Sreenivas Rangan Sukumar, Ziheng Sun, Alan Sussman, Douglas Thain, Mikhail Titov, Benjamin Tovar, Aalap Tripathy, Matteo Turilli, Bartosz Tuznik, Hubertus van Dam, Aurelio Vivas, Logan Ward, Patrick Widener, Sean Wilkinson, Justyna Zawalska, Mahnoor Zulfiqar

Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific...

34 citations 2023-03 arXiv.org

F*** workflows: when parts of FAIR are missing

The FAIR principles for scientific data (Findable, Accessible, Interoperable, Reusable) are also relevant to other digital objects such as research software and scientific workflows that operate...

11 citations 2022-09 e-Science

Domain Bounds as a Silent-Fault Detector for AI-Ready Scientific Data

We address an incipient type of data-based faults, driven by increasing amounts of data reuse in AI-enhanced computational science workflows. A dataset is created under specific conditions; its...

2026-09

Automated Data Readiness for Scientific AI

Leadership computing facilities steward large-scale scientific datasets that routinely require substantial transformation before serving as AI training data. However, no existing framework fully...

2026-07

Designing FAIR Workflows at OLCF: Building Scalable and Reusable Ecosystems for HPC Science

High Performance Computing (HPC) centers provide advanced infrastructure that enables scientific research at extreme scale. These centers operate with hardware configurations, software...

2025-12

FAIR Ecosystems for Science at Scale

High Performance Computing (HPC) centers provide resources to users who require greater scale to "get science done". They deploy infrastructure with singular hardware architectures, cutting-edge...

0 citations 2025-05 PEARC

Enabling Seamless Transitions from Experimental to Production HPC for Interactive Workflows

The evolving landscape of scientific computing requires seamless transitions from experimental to production HPC environments for interactive workflows. This paper presents a structured transition...

2025-06