annotations

Annotations are notes added to a primary source or data set to provide metadata, clarify meaning, explain context, or identify features. They can appear in texts, images, datasets, or software artifacts and may range from brief clarifications to structured metadata.

In literature and linguistics, annotations include marginalia, glosses, footnotes, and editor’s notes that aid interpretation, translation, or scholarly commentary. They can highlight ambiguities, provide references, or annotate textual variants in critical editions.

In computer science, annotations are metadata attached to code, data structures, or documents. They may influence compilation, validation, or runtime behavior through frameworks and tools. Examples include Java annotations, C# attributes, and Python type hints; annotation processing and reflection enable code generation, documentation, or runtime checks without changing core semantics.

In data science and machine learning, annotation involves labeling data to create supervised learning datasets. Annotations specify categories, bounding boxes, segmentations, or attribute values, typically produced with specialized tools and subject to quality control to ensure model performance and reproducibility.

In biology and bioinformatics, gene and protein annotations assign functional information to genomic elements. Annotations derive from predictive algorithms and experimental evidence, are stored in public databases, and support interpretation of genomes, pathways, and interactions.

The practice relies on standardized schemes and provenance to ensure consistency and reuse across tools and communities. Challenges include selecting appropriate scope, managing bias, and tracking versioning as data, standards, and software evolve.