Quality Assurance

Quality Assurance

Methodology documentation: write it so someone could disagree with it

Methodology that cannot be examined cannot be challenged — and data nobody can challenge cannot be trusted.

19/08/2026 4 min read

Quality Assurance

Error reports: the ones you receive are a fraction of the ones that exist

Most people who notice an error do not report it. So each report received should be treated as a sample, not an incident.

19/08/2026 4 min read

Quality Assurance

Auditing a pipeline: sample by hand, because automation checks format not truth

Automated checks confirm that data is well-formed. They cannot confirm that it is right.

19/08/2026 3 min read

Quality Assurance

Uncertainty: say what you do not know rather than presenting a clean number

Academic data is uncertain in ways that do not show in the output. Presenting it cleanly is a choice, and usually the wrong one.

19/08/2026 4 min read

Quality Assurance

Institutional records: the institution is the authority on itself

Institutional data drifts constantly through renames, mergers and restructuring — and only the institution knows what actually happened.

19/08/2026 4 min read

Quality Assurance

Trustworthy data: provenance on every field, or it is just a list

What separates a registry from a scraped list is not accuracy. It is being able to show where every value came from.

19/08/2026 4 min read