C1labeled

C1labeled is a data annotation standard designed to structure and standardize labeled items in multi-class datasets. It centers on a primary class code, denoted as C1, and supports additional sublabels and structured metadata to enable consistent quality control, interoperability among labeling tools, and easier downstream processing for machine learning workflows.

The standard defines a lightweight data model that can be serialized in common formats such as JSON Lines or CSV. Each labeled item typically includes a unique identifier, a primary label field (c1_label) representing the C1 code, an optional list of sublabels for finer granularity, and a metadata block. Metadata commonly records the annotator identifier, the timestamp, the labeling tool, and a confidence score reflecting annotator certainty. Versioning fields allow tracking of schema changes over time.

C1labeled emphasizes interoperability by providing mappings to widely used labeling schemas and dataset formats. It is designed to be compatible with image, audio, and text datasets, and can be exported or converted to formats used in established benchmarks, enabling integration with existing data pipelines and evaluation tools.

Applications of C1labeled include supervised learning data creation, quality assurance, auditing of labeling processes, and cross-team collaboration where consistent labeling vocabulary is critical. Its structured approach supports reproducibility and accountability in annotation efforts.

Limitations include reliance on a shared understanding of the C1 coding scheme and sublabel taxonomy. Adoption outside communities that standardize C1 codes may require vocabulary governance and ongoing maintenance to prevent label drift.

See also: data annotation, labeling standard, dataset formats, interoperability in labeling.