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Same Salmon Shared Semantics; Cross-community Salmon Data Standards for Data Integration and Decision Support

Johnson, Brett

Publisher
Zenodo
DOI
10.5281/zenodo.17883973
Repository
zenodo.org
Language
en

Abstract

Salmon decisions stall on semantics, not on science. Take “wild salmon”: locally it can mean natural-origin fish, fish spawning naturally this year (including hatchery-origin spawners), or simply adipose-intact fish—definitions that change counts and benchmarks and challenge regional analyses. This fragmentation slows management, obscures accountability, and undermines confidence in otherwise excellent science. What’s needed is a shared vocabulary and an agreed-upon map of salmon terms—clear definitions and relationships that connect local labels to common meanings so people and software interpret data the same: a shared dictionary and rulebook for salmon data, an ontology. The DFO Salmon Ontology provides that map of how terms relate, and the controlled vocabularies that underpin it supply precise definitions—showing where terms differ, how they align, and where they should converge. Together, they standardize key terms across programs and regions. Teams can map local terms once, keep source systems unchanged yet aligned regionally, and link inputs to methods, benchmarks, and policy thresholds for Fisheries Science Reports, the Fish Stock Provisions, and the Wild Salmon Policy. Developed by the Fishery & Assessment Data Section in the Pacific Region Science Branch, this work builds on the International Year of the Salmon Data Mobilization initiative and collaborations with the National Center for Ecological Analysis and Synthesis (U.S.) and the global Research Data Alliance. It is open source and implements community standards from the W3C, OBO Foundry, and Darwin Core. By removing terminology friction, it prepares us for AI-assisted data integration and cross-discipline interoperability while immediately letting biologists spend less time cleaning data. Our goal is straightforward: to provide persistent, web-accessible definitions that help scientists and software combine data efficiently, support reproducible analyses, and strengthen confidence in salmon management.

Full text

Same Salmon, Shared Semantics Cross-community Salmon Data Standards for Data Integration and Decision Support Presented by: Brett Johnson 3 Data Stewardship Unit, Pacific Region Science Branch, DFO Co-authors: Melissa Morrison, Tom Bird Dec 9, 2025 · PSSI Salmon Science Symposium Wild Salmon? Hatchery Operations Adipose-Intact Salmon Genetics Natural-Origin Stock Assessment Naturally Spawning Share Some Confusing Salmon Terms + In the chat P The Confusing Terms What specific salmon related words or definitions have you encountered that caused ambiguity or misunderstanding? The Root of the Confusion Describe why these terms led to confusion. Was it due to conflicting definitions, different contexts, or jargon? The Real-World Impacts What were the consequences of this terminological confusion? Did it affect decisions, communication, or conservation efforts? International Year of the Salmon High Seas Expeditions Canadian Integrated Ocean Observing System North Pacific Marine Science Organization (PICES) Hakai Institute Juvenile Salmon Program Salish Sea Marine Survival Project Brett Johnson Data Stewardship Unit, DFO Vancouver, Canada "Different projects, the same pattern repeated." Data Friction Cause and Effect Cycle 1 2 3 4 5 The same pattern repeats. But the fundamental friction does too: Lack of shared data practices & common vocabulary Lack of shared data practices & common vocabulary Incomparable terms & data formats: costly to reconcile Implicit context in data limits sharing due to fear of misuse Partial, or delayed evidence sets Advice given or decisions made without complete picture How We Cope Today Current workarounds are resourceful but don't scale4they're patches, not infrastructure: Ad Hoc Glossaries & Data Dictionaries Business glossaries and data dictionaries capture definitions, but they're siloed, version-controlled nowhere, and forgotten by the next project. Bespoke Crosswalks Every data integration spawns a new Excel spreadsheet mapping terms. These one-off translations don't accumulate into shared knowledge. Manual Reconciliation Scripts and manual reviews reconcile datasets case-bycase. Each synthesis project starts from scratch, reinventing the wheel. Extra Coordination Meetings Entire meetings dedicated to aligning on definitions before real work begins. Time lost that can't be recovered. These are patches, not infrastructure. We need a foundation that scales, persists, and serves the entire community. The Solution: A "shared data dictionary and thesaurus" made up of: Standard terms define at persistent URLs Tools and templates to support adoption A simple process for community contributions of terms and definitions How? Salmon Data Standards Controlled vocabulary and an ontology for shared terms and definitions Salmon Data Package & Dictionary Template Bundle and transfer data and metadata using standardized data dictionary templates. metasalmon R package To standardize, package, validate, & share FAIR salmon data Custom GPT: Salmon Data Standardizer An AI assistant with deep salmon science knowledge Salmon Data Standards Controlled Vocabulary Terms and Definitions dfo-pacific-science.github.io DFO Salmon Data Standards 3 FADS Open Science Documentation Hub The Data Stewardship Unit (DSU) has created the DFO Salmon Data Controlled Vocabulary4a standardized, community-curated list of key terms and definitions related to salmon data collection, analysis, and policy& Learning Ladder: From Standards Use to Creation We're creating a learning pathway that takes you from using existing data standards to creating your own, when needed. Not everyone needs to create standards. But ideally, we reuse the same terms. Use existing standards Search existing vocabularies, use standard URIs in your data Document your terms Write clear definitions and labels others can understand Align with standards Create mapping tables to reconcile different datasets Publish vocabulary Share your vocab with persistent URIs Propose new standards Submit terms for integration review Build tools Create R packages, ontologies, or other tools that use the standards You keep your existing tools (Excel, R, Access, Postgres, whatever) 3 you just anchor your columns to shared concepts that have permanent URLs.