Metadata recommendations for light logging and dosimetry datasets

Abstract

Abstract This article introduces a comprehensive metadata descriptor aimed at capturing crucial metadata information within personalized light exposure datasets. This metadata descriptor fills a critical gap in the field of personalized light exposure research by promoting standardized documentation of light exposure metadata. Light exposure profoundly impacts human physiology and behaviour, playing a central role in regulating the circadian system and influencing various physiological processes. As research on the real-world effects of light exposure gains momentum through the development of wearable sensors and light-logging technologies incorporating digital health approaches, there is a need to harmonize and standardize data collection and documentation across diverse studies and settings. The metadata descriptor was collaboratively developed by an international team of experts through a scoping exercise and synchronous discussions. It covers study-level, participant-level, dataset-level, and device-level metadata. The structure of the descriptor was designed to be modular, allowing for future expansions and customizations. The metadata descriptor comprises four main domains: study-level information, participant-level information, dataset-level information, and device-level information. Each domain includes specific metadata fields, ensuring comprehensive documentation of the data collection process. The metadata descriptor is available in JavaScript Object Notation (JSON) format, facilitating both human and machine readability. A user-friendly web interface has been developed for generating compliant JSON files, making it accessible to a wide range of users. The descriptor follows versioning principles to accommodate future updates and improvements. Following a description of the latest version, the article outlines several future directions for the metadata descriptor, including validation in real-world settings, independent evaluation, community-driven development, implementation in multiple software languages, and endorsement by scientific organizations. Integration with data repositories and platforms is also essential for streamlining data management and sharing. The metadata descriptor adheres to FAIR data principles, ensuring data is findable, accessible, interoperable, and reusable. Researchers are encouraged to adopt this descriptor to enhance the quality and utility of their light dosimetry datasets, ultimately advancing our understanding of the non-visual effects of light in real-world contexts.