datasetfocused

Datasetfocused is a term used to describe an approach to data science and machine learning that treats datasets as first-class artifacts and central drivers of development and evaluation. In a datasetfocused paradigm, the quality, documentation, provenance, and governance of data are prioritized alongside models and algorithms.

Core ideas include rigorous dataset versioning, provenance tracking, and standardized metadata; emphasis on data quality and representativeness; reproducible dataset creation, labeling, and augmentation; and explicit evaluation protocols that account for dataset-specific biases.

Practices include maintaining dataset registries or catalogs, applying data version control, using datasheets for datasets and dataset cards, and data contracts; employing data provenance schemas to record lineage; and integrating dataset considerations into MLOps pipelines.

Advantages of a datasetfocused approach include improved reproducibility, auditability, governance, and accountability; easier collaboration and reuse; and clearer evaluation by isolating model performance from data variation.

Challenges include data privacy and licensing, scale and governance overhead, and the need for standardized metadata and interoperability across tools and teams.

In practice, organizations adopting dataset-focused workflows begin with rigorous data collection and labeling, maintain clear metadata, and use tooling that tracks dataset versions and evaluation results to support reproducible research and product development.

See also: Datasheets for Datasets, Model Cards, data catalogs, and data versioning.