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Datasets evolve — you add examples, edit annotations, refine tags. If simulation results are pinned to specific dataset states, results stay comparable over time even as the dataset grows.

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How versioning works

Every “significant” change to a dataset creates a new version:
  • Add examples.
  • Delete examples.
  • Edit an annotation.
  • Split into a holdout.
Metadata edits (renaming, tagging) don’t create versions. Versions are numbered (v1, v2, …) and stamped with actor, timestamp, and change summary.

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Pinning simulations to versions

Every simulation run records the dataset version it used. When you compare two runs:
  • If they used the same dataset version, results are directly comparable.
  • If they used different versions, Nora shows the diff (added/removed/changed examples) so you understand the drift.
You can force a simulation to a specific dataset version (default: latest).

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The version history

Datasets → Versions tab. Shows:
  • Each version’s number, timestamp, author.
  • Change summary (X examples added, Y annotations edited).
  • Which simulations used this version.
  • Which Improvements are gated on this version.
Click any version to see the state at that point. Roll back to a prior version if a bad edit was applied.

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When to bump vs. edit in place

Bumping happens automatically on significant changes. But for finer-grained control:
  • Force new version — before a period of many changes, snapshot the current state so you always have a reference point.
  • Edit in place (patch) — for typo fixes and minor annotation clarifications that shouldn’t invalidate prior simulations. Use sparingly.

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Cross-referencing with Flow versions

The Improvement flow shows a matrix: Flow versions × Dataset versions. Each cell has the simulation result. Useful for questions like:
  • “Which Flow version scored best on the current dataset?”
  • “Has the same Flow version’s score changed as the dataset grew?”
Both dimensions matter — a Flow that scores well on a small dataset might struggle on a bigger, more diverse one.

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Immutability

Simulation results are immutable and tied to specific dataset + Flow versions. This means:
  • Old results stay valid even if you retire the dataset version.
  • You can trust historical comparisons — the numbers weren’t recomputed with new data.

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Deleting a version

Versions are permanent. You can archive a version (hides it from the default view) but you cannot delete — this would invalidate any simulation that used it. If storage is a concern, versions can be compacted — the full state is stored only for milestone versions; intermediate versions are stored as diffs. Runs still work; older intermediate versions just take slightly longer to load.

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Export / import

Any version can be exported as JSONL (nora datasets export <slug>@v3 > snapshot.jsonl) and re-imported into another workspace. Useful for sharing datasets across environments (staging → prod, or between teams).