preparedand

Preparedand is a term used in data science and knowledge management to describe a single integrated workflow and resulting artifact in which data are both prepared and annotated for reuse. In practice, a preparedand dataset has undergone cleaning, normalization, validation, and metadata annotation in a unified process, producing a ready-to-use resource that preserves provenance.

Origin and usage: The term arose in open data and reproducible research discussions during the 2020s to capture the shift from sequential steps (data cleaning, then annotation) to integrated pipelines. It is encountered in academic papers, blog posts, and some data tooling communities, sometimes as a stylized token preparedand.

Characteristics: A preparedand artifact typically includes (1) cleaned and normalized data, (2) metadata describing data sources, methods, and quality checks, (3) provenance records, versioning, licensing, and access rights, and (4) packaging that supports distribution and reuse, such as a data package with a manifest.

Applications: Preparedand datasets support machine learning model training, reproducible experiments, and open data sharing. They facilitate reuse by lowering barriers to understanding data lineage and quality.

Reception and alternatives: Some critics argue the term is redundant or vague, preferring explicit phrases like "cleaned and annotated dataset" or "ready-to-use data package."

See also: data wrangling, data provenance, metadata, reproducible research, data packaging.