Universal Workflow Language and Software Enable Geometric Learning and FAIR Scientific Protocol Reporting

Journal Article · Fri Mar 06 00:00:00 EST 2026 · Joule

DOI:https://doi.org/10.1016/j.joule.2026.102317· OSTI ID:3025204

Epps, Robert ^[1]; Volk, Amanda ^[2]; White, Robert ^[1]; Tirawat, Robert ^[1]; Bramante, Rosemary ^[1]; Berry, Joseph ^[1]

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1. National Laboratory of the Rockies, Golden, CO (United States)
2. National Research Council; United States Department of the Air Force

Written language and conventional data structures for representing scientific procedures suffer from low process detail, often fail to accurately represent protocols, and lack universality. New strategies for the handling of experimental data are needed to provide viable process information for both humans and machines. In this work, we present the universal workflow language (UWL) and interface (UWLi). UWL is a findable, accessible, interoperable, and reusable (FAIR)-compatible, graph-based data architecture that can capture arbitrary scientific procedures through workflow representation, and UWLi is an accompanying software package for building, manipulating, and interpreting UWL entries. The UWL format was found to be highly effective in identifying deficiencies in the reported process details of high-impact, peer-reviewed scientific journals, and in simulated scenarios, the graph format was shown to be more effective than conventional methods in predictively modeling the outcome of diverse scientific protocols. Implementation of UWL could enable more accurate scientific communication and more impactful process datasets.

• Research Organization: National Laboratory of the Rockies (NLR), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 3025204
• Report Number(s): NLR/JA-5K00-90416
• Journal Information: Joule, Journal Name: Joule
• Country of Publication: United States
• Language: English

Related Subjects

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experimental metadata
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geometric learning
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graph neural networks
graph-based data architecture
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photovoltaics
protocol reporting
scientific reproducibility