Datajoukot

Datajoukot are collections of related data items organized for analysis. In statistics and data science, a dataset comprises observations (rows) and variables (columns). Each observation represents a unit of study; each variable captures a measurable attribute. Datasets can be structured (tabular with rows and columns) or unstructured (text, images, audio), and may be stored in formats such as CSV, JSON, Parquet, or in database tables. Metadata describes the dataset, including variables, units, provenance, collection date, and licensing.

Creation and sources: Datasets are produced by experiments, simulations, sensors, surveys, administrative records, or web collection. They may be gathered by organizations or shared publicly through data portals. Licensing ranges from open data to restricted access; privacy-preserving techniques such as anonymization or differential privacy may be applied.

Quality and preprocessing: Datasets vary in size and quality. Common issues include missing values, duplicates, inconsistent coding, sampling bias, and outdated information. Data cleaning, normalization, and validation are typical steps before analysis or model training.

Uses: They underpin statistical analysis, reproducible research, and machine learning, enabling model training, hypothesis testing, data visualization, and benchmarking of methods.

Standards and governance: Datasets are accompanied by metadata and may be cataloged using standards such as DCAT or schema.org to improve discovery and interoperability. Open datasets are often hosted in repositories or data portals with clear licensing and usage terms.

Privacy and ethics: When datasets involve people, privacy considerations and compliance with laws (for example, data protection regulations) are important. Measures such as de-identification, access controls, and ethical review are commonly employed.