Data curation is the process of selecting, organizing, cleaning, and maintaining data so it stays accurate, well described, and usable over time. It covers deciding which data to keep, adding context and metadata, removing errors and duplicates, and keeping the collection current as sources change.
Good curation makes data easier to find and trust. For example, a research team might curate a labeled image set by removing mislabeled samples, documenting how each was collected, and versioning the set so results can be traced later.
Curation prepares data to be usable in general. Whether a curated dataset is actually ready for a specific AI run, and whether that run can be reproduced, is a separate readiness question.