Datamaterialer

Datamaterialer is a term used in Norwegian, Danish and Swedish to refer to collections of data that are used for analysis, modeling, evaluation, or documentation. A datamaterial can include raw measurements, processed datasets, or derived variables, and may be structured (for example in tables), semi-structured (such as JSON or XML), or unstructured (for instance text, images, or audio).

Datamaterialer are created or gathered from a variety of sources, including experiments, sensors, surveys, administrative records, simulations, or public archives. Good documentation is essential, with metadata that describes context, variables, units, sampling methods, collection dates, and data quality checks. Clear provenance helps users understand how the data were produced and transformed.

Data quality and ethics are important considerations. Datamaterialer should be evaluated for completeness, accuracy, consistency, and timeliness. Bias, outliers, missing values, and privacy concerns require appropriate handling, such as cleaning, de-identification, or access restrictions. Licensing and usage terms determine how data can be reused, shared, or modified.

Management and storage practices support reuse and reproducibility. Datamaterialer are typically stored in repositories or data catalogs, with common formats including CSV, JSON, Parquet, and various media types for images or video. Metadata schemas and data dictionaries aid discoverability and interpretation.

Uses of datamaterialer span research, statistics, machine learning, and policy making. They support transparent analyses, model training and benchmarking, replication of results, and the evaluation of methodologies.