Gammaannotated

Gammaannotated is a term used in data management and digital imaging to describe datasets that carry explicit gamma-related metadata. The idea is to annotate data with gamma parameters that describe how the data should be transformed, interpreted, or modeled, particularly when nonlinear luminance, photon counts, or statistical counts are involved. This labeling aims to improve processing accuracy and reproducibility across tools and workflows.

A gammaannotated data item typically includes fields such as gamma_type (the class of gamma transformation or model), gamma_value (a numeric exponent or correction factor), gamma_uncertainty, and gamma_source (origin of the annotation). These files can be stored as sidecar metadata in JSON or YAML, embedded within file headers (for example, FITS or EXIF), or in dataset schemas used by machine learning pipelines.

Domains of use include imaging and photography, astronomy and astrophysics, and machine learning where gamma corrections, photon-count modeling, or gamma-distribution assumptions are relevant. For example, a raw image may be annotated with gamma_value to indicate the exponent used for display or analysis, or a scientific dataset may record instrument response gamma_type to guide calibration.

Benefits of gammaannotated data include improved reproducibility, consistent preprocessing across tools, and clearer provenance for analyses. Limitations include a lack of universal standards, potential for misinterpretation, and added annotation burden. The term emerged in several research communities in the late 2010s as a practical label for gamma-aware datasets and has since seen adoption in niche data catalogs and processing pipelines.

See also: gamma correction, metadata, data annotation, gamma distribution.