Review Dataset

A dataset is a structured collection of data used for training, testing, evaluation, or analysis.

A dataset is an organized collection of examples used for training, testing, evaluation, or analysis. Its records may contain inputs, labels, metadata, or other measurements.

A model inherits important limits from the data selected, excluded, labeled, and maintained. Dataset provenance and intended use are therefore part of system quality, not clerical detail.

A maintenance dataset contains equipment photos, failure labels, capture dates, locations, and the inspection method used to verify each label.

A dataset is not neutral simply because it is large. Collection choices, missing groups, label quality, and leakage can shape the result.

A dataset is a designed collection, not raw reality.

* [[training-data|Training Data]] * [[data-provenance|Data Provenance]] * [[synthetic-data|Synthetic Data]] * [[data-leakage|Data Leakage]]

* [Google for Developers, “Machine Learning Glossary”](https://developers.google.com/machine-learning/glossary) — provides established boundaries for core machine-learning vocabulary. * [NIST, “The Language of Trustworthy AI”](https://doi.org/10.6028/NIST.AI.100-3) — provides a use-case-neutral vocabulary foundation for AI and machine learning.

* Compare [[training-data|Training Data]], [[data-provenance|Data Provenance]], [[synthetic-data|Synthetic Data]], and [[data-leakage|Data Leakage]] to place Dataset in the surrounding system. * Follow the cited source material above for the method, standard, or research context behind this draft.