Data Services
Open Access Services
Controlled vocabularies and ontology so salmon terms, code lists, and metadata stay aligned across projects.
Salmon Data Standards
Challenge: Salmon data arrives with inconsistent terms and code lists, making cross-project integration and analysis slow and error-prone.
Solution: A maintained set of SKOS controlled vocabularies plus the GC DFO Salmon Ontology that links concepts, code lists, and metadata fields.
Outcome: Biologists and stewards can align columns, codes, and documentation quickly, improving interoperability and trust.
Key Features:
- Published term lists with definitions for salmon entities and attributes
- Ontology that maps relationships between terms and code lists
- Versioned HTML site and GitHub sources for transparency
- Supports ontology-aware data packages and code lists
Terms & Definitions | Ontology
Structured data collection and automated products for the annual Salmon Outlook.
Open data linking SMUs, CUs, and DUs so assessments and reporting stay aligned.
Shiny app to classify and review salmon escapement estimates for stock assessment.
Salmon Escapement Estimates Toolkit
Challenge: Escapement estimates need consistent classification and review to support stock assessments.
Solution: A Shiny-based toolkit that walks users through classification and provides reference visuals.
Outcome: More consistent escapement inputs and transparency for assessment teams.
Key Features:
- Guided classification workflow for escapement estimates
- Hosted Shiny app for easy access
- Screenshots and examples for training
Access the Salmon Escapement Estimates Toolkit
R package for ontology-aware salmon data packages with unified semantic fields and code lists.
metasalmon R package
Challenge: Salmon data arrives as messy spreadsheets without standardized semantics, making it hard for biologists and stewards to align columns, code lists, and metadata to the evolving DFO Salmon Ontology.
Solution: The Metasalmon 0.0.2 update unifies semantic fields (term_iri/term_type), streamlines code-list handling, and ships clear GPT/R workflows plus schemas and pkgdown docs to build frictionless-style CSV data packages.
Outcome: Teams can rapidly produce ontology-aware, validated data packages and code lists that are ready for analysis, sharing, and future knowledge-graph ingestion—without inventing IRIs or guessing schemas.
Key Features:
- Unified semantic fields (term_iri, term_type) across dictionary and codes schemas
- SKOS concept schemes confined to code lists (concept_scheme_iri in codes.csv)
- Updated GPT collaboration prompts and Custom GPT template to avoid invented IRIs
- Validated R workflows (infer/validate/apply/package) with refreshed pkgdown docs and NEWS for 0.0.2
- Clear ontology-gap handling with proposed terms and relationships (broader/narrower or superclass)
Internal DFO Services
Shared PSC–DFO data system to move Qualark sonar counts quickly for in-season run size estimates.
A regional database that centralizes key salmon population index data to support stock assessments and improve transparency.
Salmon Population Summary Repository
Challenge: Core salmon population index data (spawner, catch, recruitment, exploitation, age) sit in many spreadsheets and local systems, slowing FSAR production and reducing transparency.
Solution: A regional SPSR database that centralizes derived indices and metadata without replacing source systems, making data FAIR and consistent.
Outcome: Faster, more transparent assessments with versioned index data, documented methods, and better inter-branch communication.
Key Features:
- Regional compilation of spawner, catch, recruitment, exploitation, and hatchery metrics
- Metadata on methods, assumptions, infilling, and data quality
- Standardized reference data (benchmarks, limit/removal references)
- Feeds for FSAR outputs and monitoring reviews
- Version-controlled releases with changelog
Access the Salmon Population Summary Repository
Self-service Power BI access to commercial salmon catch, openings, and vessel activity.
Fishery Operations System Data Explorer
Challenge: Staff needed FOS summary statistics but access required SQL accounts or manual data pulls from custodians.
Solution: A self-service Power BI app with pre-computed commercial salmon catch, openings, and vessel activity summaries.
Outcome: Faster access to trusted FOS summaries with less load on data custodians.
Key Features:
- Pre-computed catch, openings, and vessel activity summaries
- Self-service Power BI access for authorized staff
- Downloadable outputs for analysis and reporting
A centralized repository for genetic stock identification and parentage-based tagging results to improve consistency and integration across DFO programs.
Collaborative wiki for salmon data sources, methods, and best practices.