datasetter

Datasetter is a term used infrequently in data science to describe a person or system involved in assembling, curating, and maintaining datasets for analysis, training, or repository sharing. In practice, a datasetter may perform data collection, cleaning, labeling, annotation, and quality assurance, as well as documenting provenance and licensing. They work across domains—academia, industry, and government—and coordinate with data stewards and researchers to ensure datasets meet requirements for reliability, reproducibility, and compliance.

Key responsibilities include data sourcing and selection, cleaning and deduplication, normalization and schema design, metadata creation, version control, and access controls. They also manage licensing, data attribution, and privacy considerations, including de-identification and consent management when necessary. Effective datasetters emphasize documentation, reproducible workflows, and transparent provenance to enable reuse.

Common tools and approaches involve data cataloging platforms, version control for data (such as data versioning tools), data labeling and annotation tools, and automated quality checks. They may apply data governance practices, including data quality metrics, lineage tracking, and adherence to standards like metadata schemas and licensing terms.

Challenges include bias and representativeness, privacy and consent restrictions, licensing compatibility, and handling evolving data. The role is often collaborative, intersecting with data scientists, engineers, ethicists, and legal teams to ensure datasets are usable, lawful, and trustworthy.