Hierarchical Event Descriptors (HED) is an open standard and supporting ecosystem for describing experimental events, conditions, and experiment organization in a format that is both human- and machine-readable to enable analysis, re-analysis, and meta/megaanalysis. HED is particularly relevant for neuroimaging and behavioral experiments where events are a central organizing focus for analysis. HED consists of:

1. A specification for how standardized vocabularies should be constructed, how these vocabularies should be used to annotate dataset, and how tools should handle HEDannotated dataset in search, summary, and analysis.
2. A set of community-developed standardized vocabularies and a specified process for how other community groups can develop additional vocabularies.
3. A reference implementation in Python for validation and other operations as described in the specification.

The goals of HED are:

• To enable and support the storing and sharing of recorded data in a fully analysis ready format for efficient and effective within and cross-study data search, summary, and analysis.
• To make the process of annotation accessible and usable for the global neuroimaging and related communities.
• o open opportunities for new types of analysis and automation.

Links:

HED Homepage
HED specification
HED on GitHub (src)

Publications:

• Hierarchical Event Descriptor library schema for clinical EEG data annotation. (arXiv)

• Building FAIR functionality: Annotating events in time series data using  Hierarchical Event Descriptors (HED). Neuroinformatics Special Issue Building the NeuroCommons. Neuroinformatics  (2022). https://doi.org/10.1007/s12021-021-09537-4

• Automated EEG mega-analysis II: Cognitive aspects of event related features. NeuroImage. 2019 Sep 4:116054. doi: 10.1016/j.neuroimage.2019.116054, PMID:  31491523.