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.

In fields such as human electrophysiology, high-precision time series data is often acquired in complex, event-rich environments for interpretation of complex dynamic data features in the context of session events. However, a substantial gap exists between the level of event description information required by current digital research archive standards and the level of annotation required for successful meta-analysis or mega-analysis of event-related data across studies, systems, and laboratories. Manifold challenges, most prominently ontological clarity and extensibility, tool availability, and ease of use must be addressed 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 the near decade-long development of the Hierarchical Event Descriptor (HED) system for addressing these issues. We discuss the evolution of HED, the lessons we have learned, the current status of HED vocabulary and tools, some generally-applicable design principles for annotation framework development, and a roadmap for future development. We believe that without consistent, sufficiently detailed and field-relevant annotations as to the nature of each recorded event, the potential value of data sharing and large-scale analysis in behavioral and brain imaging sciences will not be realized.

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