A Data Model for Stratigraphic Knowledge with Explicit Provenenace: Toward Reproducible and Reusable Geoscience Data

Stratigraphic knowledge is distributed across a large and heterogeneous body of literature, making it difficult to locate and compare definitions of units, boundaries, and correlations when needed. Although many higher-level chronostratigraphic boundaries are formally defined by Global Boundary Stratotype Sections and Points (GSSPs), access to detailed information—particularly for regional stratigraphy and lower-rank units—remains limited and fragmented. Major syntheses such as GTS2020 provide standardized frameworks and widely accessible reference points, but expose final interpretations rather than the richer body of underlying statements and source literature as reusable data. Here we introduce a data model to address this in which stratigraphic knowledge is represented as explicit, source-linked statements compiled from the literature. Rather than enforcing a single framework, content from multiple sources is organized into comparable profiles, allowing definitions and correlations to be examined side by side. This approach makes stratigraphic data more structured and directly reusable. We implement this model within SCODA (Self-Contained Data Artifact), a framework for packaging structured scientific knowledge as portable, versioned, and citable datasets, together with tools for interactive visualization and cross-source comparison. Its operation is demonstrated using trilobita.scoda, a trilobite taxonomy dataset, and extended here to a SCODA dataset compiling regional stratigraphic frameworks of Korea, illustrating how the structure of ICS/GTS-style information can be extended to regional and lower-rank stratigraphic data within a single reproducible system. SCODA thus offers a practical model for compiling, comparing, visualizing, and distributing stratigraphic knowledge as reusable, machine-readable data in an open-science context, constituting a concrete step toward the open-science data infrastructure envisioned by initiatives such as GTS2030.