Building FAIR Functionality: Annotating Events in Time Series Data Using Hierarchical Event Descriptors (HED)

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 20; no. 2; pp. 463 - 481
• Main Authors: Robbins, Kay, Truong, Dung, Jones, Alexander, Callanan, Ian, Makeig, Scott
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2022; Springer Nature B.V
• Subjects: Bioinformatics; Biomedical and Life Sciences; Biomedicine; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Data Curation - standards; Electronic Data Processing; Humans; Medical imaging; Neuroimaging; Neurology; Neurosciences; Original; Time Factors; Time series; Hierarchical Event Descriptors; Neuroimaging; HED-3G; Event annotation; FAIR; HED; EEG; BIDS
• ISSN: 1539-2791; 1559-0089
• DOI: 10.1007/s12021-021-09537-4

Human electrophysiological and related time series data are often acquired in complex, event-rich environments. However, the resulting recorded brain or other dynamics are often interpreted in relation to more sparsely recorded or subsequently-noted events. Currently a substantial gap exists between the level of event description required by current digital data archiving standards and the level of annotation required for successful analysis of event-related data across studies, environments, and laboratories. Manifold challenges must be addressed, most prominently ontological clarity, vocabulary extensibility, annotation tool availability, and overall usability, to allow and promote sharing of data with an effective level of descriptive detail for labeled events. Motivating data authors to perform the work needed to adequately annotate their data is a key challenge. This paper describes new developments in the Hierarchical Event Descriptor (HED) system for addressing these issues. We recap the evolution of HED and its acceptance by the Brain Imaging Data Structure (BIDS) movement, describe the recent release of HED-3G, a third generation HED tools and design framework, and discuss directions for future development. Given consistent, sufficiently detailed, tool-enabled, field-relevant annotation of the nature of recorded events, prospects are bright for large-scale analysis and modeling of aggregated time series data, both in behavioral and brain imaging sciences and beyond.