Data, models, and FAIRness in ML astronomy

Last updated on 2026-03-10 | Edit this page

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Data, models, and FAIR practices in ML astronomy

Machine learning workflows in astronomy produce more than papers. They produce:

  • training datasets
  • labels and annotations
  • trained models
  • derived catalogues
  • complex preprocessing pipelines

If these outputs are not shared in a FAIR way, ML results become difficult or impossible to reuse, validate, or build upon.

This section focuses specifically on FAIR data and model sharing practices, and how they apply to ML‑based astronomy.

What does FAIR mean in astronomy?

FAIR stands for:

  • Findable
  • Accessible
  • Interoperable
  • Reusable

The FAIR principles were introduced by Wilkinson et al. (2016) and are now widely adopted across scientific domains. In astronomy, FAIR has a concrete and well‑established interpretation through:

  • Virtual Observatory infrastructure
  • IVOA standards
  • long‑lived data archives

An overview of how FAIR maps onto astronomical practice is given by O’Toole and Tocknell (2022).

Summary: FAIR is about making research outputs usable by humans and machines.

Astronomy is close to FAIR by default

Astronomy is often described as “world‑leading” in data stewardship. This is largely true because:

  • most survey data are archived
  • metadata standards are well developed
  • access is usually long‑term

IVOA standards were, in practice, implementing FAIR‑like ideas before the FAIR principles were formally articulated. As a result:

  • raw observational data are often FAIR
  • catalogues linked to publications are often FAIR
  • archive‑level metadata is usually strong

However, ML workflows introduce new outputs that are often not FAIR.

Where ML workflows break FAIRness

In ML‑based astronomy, FAIR failures usually occur downstream of the archive. Common examples include:

  • private training sets derived from public data
  • labels created by individuals and never shared
  • preprocessing scripts that are undocumented
  • trained models released without context
  • catalogues published without provenance

These issues are explicitly identified in FAIR guidance for astronomy, which emphasises that provenance, processing history, and metadata are essential for reuse (O’Toole and Tocknell, 2022).

Remember: FAIR does not stop at the telescope. It extends through the full analysis pipeline.

FAIR applies to models, not just data

A common misconception is that FAIR applies only to raw data. In ML astronomy, FAIR should apply to:

  • training datasets
  • labels and annotations
  • feature representations
  • trained models
  • evaluation datasets

If a trained model is shared without:

  • its training data description
  • preprocessing steps
  • intended scope

then it is not reusable, even if the model file itself is available.

This point is emphasised in astronomy‑focused FAIR discussions, which note that machine‑actionable reuse requires rich metadata and provenance, not just file access (Berriman, 2022).

Discussion

Is your ML output FAIR?

Think about one ML artefact you have produced or used. For that artefact, ask:

  • Can someone find it?
  • Can they access it?
  • Can they understand what it expects as input?
  • Can they reuse it without asking you questions?

Write down one missing piece of information.

No sharing required.

FAIR does not mean open at all costs

Another common misconception is that FAIR means “everything must be open”. This is not true. FAIR explicitly allows for:

  • access controls
  • embargo periods
  • restricted data

The requirement is not openness, but clarity:

  • how the data can be accessed
  • under what conditions
  • with what limitations

FAIR guidance for astronomy explicitly separates:

  • openness (a policy choice)
  • FAIRness (a technical and metadata choice)

Data can be FAIR without being fully open.

Why FAIR matters for ML reuse

ML outputs are frequently reused:

  • by collaborators
  • by downstream projects
  • by future surveys

If ML artefacts are not FAIR:

  • reuse requires personal communication
  • validation becomes difficult
  • results quietly decay over time

FAIR practices reduce this fragility by:

  • making assumptions explicit
  • preserving provenance
  • supporting long‑term reuse

This is especially important in astronomy, where datasets often outlive individual projects and researchers.

Discussion

One FAIR improvement

Identify one concrete change you could make to improve FAIRness:

  • a README
  • a data dictionary
  • a provenance note
  • a model description

Write it down.

That is enough for now.

Key takeaways

  • FAIR applies to data, models, and derived products
  • Astronomy infrastructure already supports FAIR principles
  • ML workflows often fall outside existing FAIR practices
  • Small documentation steps dramatically improve reuse

Thought: “If your ML result cannot be reused without emailing you, it is not FAIR yet.”