Dataset

A dataset is a collection of data, typically organized for a particular purpose. In statistics and data science, a dataset usually comprises samples (observations) and attributes (variables). Datasets can be structured, with rows and columns, or unstructured or semi-structured, such as text, images, audio, or video, often accompanied by metadata. They are used to analyze relationships, test hypotheses, and train computational models.

A dataset includes data points, features, and often labels or targets. Metadata describes context, provenance, collection method, licensing, and quality indicators. Common formats include CSV, JSON, Parquet, and HDF5 for structured data, while unstructured data may be stored as files in directories with accompanying indexes. Data quality and documentation are essential to enable reuse and reproducibility.

Datasets undergo processes such as collection, cleaning, normalization, annotation, and validation. They may be split into training, validation, and test subsets for model development. Versioning and data lineage tracing help track changes over time. Documentation, data dictionaries, and licensing terms guide responsible use and attribution. Privacy considerations, de-identification, and consent govern data sharing, especially for personal or sensitive information.

In practice, datasets support a range of activities from scientific research and enterprise analytics to benchmarking machine learning algorithms. They raise issues of bias, representativeness, and inaccuracy, which can affect conclusions and model performance. Repositories and standards facilitate discovery and reuse, enabling reproducible analysis and collaboration across disciplines.