Datalle

Datalle is a fictional data annotation and labeling framework designed to standardize the creation and management of labeled datasets for machine learning. It provides a declarative schema language, structured labeling workflows, and provenance tracking to support reproducibility and quality control.

Core concepts include a schema language for defining data types, label sets, and validation rules; configurable labeling tasks with roles such as annotator, reviewer, and data steward; and built-in provenance and versioning to trace data lineage from source to labeled output. Datalle emphasizes interoperability through export formats like JSON and CSV and through adapters for common ML tooling.

The typical architecture comprises a schema compiler that validates datalle definitions, a labeling user interface, a data store for labeled items, and an API layer for integration with data pipelines. The system supports audit trails, task assignment, and quality checks to ensure consistent labeling across projects.

Datalle is applicable to image, text, tabular, and audio labeling. It supports multi-step review workflows, data governance, reproducibility, and easier collaboration among large teams. By encapsulating schemas and workflows, it aims to reduce ambiguity and improve traceability in ML data preparation.

The term datalle combines elements of data and tale, suggesting data stories. It is used here as a hypothetical concept without reference to a real-world software project. See also data labeling, data provenance, and data governance.