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Universal Workflow Language and Software Enable Geometric Learning and FAIR Scientific Protocol Reporting: Article No. 102317

  • National Research Council
  • United States Department of the Air Force
  • University of Colorado Boulder

Research output: Contribution to journal › Article

Abstract

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.
Original languageAmerican English
Number of pages13
JournalJoule
Volume10
Issue number4
DOIs
StatePublished - 2026

NLR Publication Number

  • NLR/JA-5K00-90416

Keywords

  • digital twins
  • experimental metadata
  • FAIR data principles
  • geometric learning
  • GNNs
  • graph neural networks
  • graph-based data architecture
  • machine learning
  • photovoltaics
  • protocol reporting
  • scientific reproducibility

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