Every row should have a history.

Working method

The exact workflow varies by source, but the governing principles stay consistent: use authoritative records, keep transformations reproducible, validate the result, and describe uncertainty in plain language.

1. Define the question. Establish the intended scope, users, fields, time period, and appropriate unit of observation.
2. Inventory sources. Identify official record locations, formats, update patterns, access conditions, and likely gaps.
3. Preserve provenance. Retain enough source detail to connect normalized records back to their origin.
4. Extract and normalize. Convert formats, standardize types and units, map changing labels, and record transformation rules.
5. Validate and review. Test completeness, uniqueness, ranges, relationships, and samples against source records.
6. Document and version. Publish definitions, caveats, coverage, methods, changes, and unresolved issues alongside the data.

Quality is contextual

A dataset can be internally consistent and still be incomplete or misleading. Checks therefore include both structural tests and subject-matter review. Outliers are investigated, not automatically removed. Missingness is measured, not silently filled. Material judgment calls are documented.

Provenance and corrections

When practical, records retain source identifiers and references that make audits possible. Corrections should be versioned and described so users can distinguish source changes from DatasetPress processing changes.

Rights and responsible release

Public availability does not automatically settle every question about reuse, redistribution, personal information, or commercial publication. DatasetPress evaluates those issues before release and will narrow, delay, or decline publication when the responsible path is unclear.