FSAR Data Standardization Workflow

Use this workflow when preparing salmon datasets for Fisheries Science Advisory Report (FSAR) analysis, review, and operational SPSR intake.

Canonical package specification (Markdown): Salmon data package specification

If you want a concrete walkthrough, use the companion page: FSAR to SPSR: End-to-End Example.

Step 1 — Define scope, classification, and destination

  • Confirm dataset scope (what, where, when).
  • Confirm data classification and sharing constraints.
  • Confirm whether this package is intended for SPSR intake, external publication, or both.
  • Policy guidance: Publishing Data Externally

Step 2 — Map columns to canonical ontology terms

  1. Open GC DFO Salmon Ontology documentation.
  2. Map core variables (CU identifiers, year fields, key measurements).
  3. Record full canonical IRIs for mapped terms.

Step 3 — Build a salmon data package

Use the salmon data package specification.

At minimum:

  • dataset.csv
  • tables.csv
  • column_dictionary.csv
  • data/*.csv
  • codes.csv (when categorical code lists apply)

Starter assets:

Step 4 — Validate package quality

Run structural and semantic checks before submission.

Example R checks:

library(metasalmon)

pkg_path <- "path/to/your/salmon-data-package"

validate_dictionary(file.path(pkg_path, "column_dictionary.csv"))
validate_semantics(file.path(pkg_path, "column_dictionary.csv"))

Step 5 — Align to current SPSR intake direction (CUYear-first)

Based on current SPSR execution direction, prepare uploads as one unified CU-year intake path:

  • Start from current SPSR templates: https://spsr.dfo-mpo.gc.ca/download_sdp_templates
  • Treat WSP CU assessment fields as the canonical status/benchmark layer.
  • Keep FSAR fishery fields (catch, age, exploitation, etc.) as complementary data on the same CU-year pathway.
  • Do not assume equivalence for similarly named fields (for example, FSAR TOTAL_SPAWNERS vs WSP SpnForAbd_Total); document mapping assumptions explicitly.
  • Keep provenance explicit for transformed/derived values (for example, data source, year type, and method notes where applicable).
  • Keep package metadata and upload CSV(s) versioned together.

Useful SPSR references:

Step 6 — Upload and review in SPSR

  • Upload via SPSR wizard.
  • Resolve validation feedback.
  • Re-upload if needed until intake checks are clean.
  • Record accepted upload version, intake date, and any profile-specific notes.

Step 7 — Publish externally (if in scope and approved)

  • Follow policy + approvals for external release.
  • Include citation/version metadata and release notes.

FSAR readiness checklist