This two-year project will advance, integrate, document, and promote the use of the Hierarchical Event Descriptor (HED) system to describe events in human neuroimaging and behavioral data from research experiments and other sources in sufficient detail to support comparative analysis of human brain dynamics across studies. Relating the recorded data dynamics to temporally- specifiable changes in subject experience, action, and cognition is a major goal (and challenge) for both neuroimaging and biomechanical imaging. Standardizing the annotation of recorded (or post hoc identified) events across such data sets — recorded in diverse sensory environments involving different participant tasks and/or task conditions — in a ‘machine-actionable’ way is essential for systematic reproducible comparative analysis of archival data to enable discovery and modeling of systems-level brain function as well as biomarkers of brain/behavioral function. The HED system is to our knowledge the only ontological system addressing the problem of defining experiential and behavioral events in experimental human neuroimaging and other studies recording behavioral data. The release of third-generation HED represents a dramatic advance in HED usability and annotation capabilities, including the capacity to simply encode the experimental design and experimental structure as well to document experimental stimuli and subject responses within the data in a machine-actionable form. This project will improve supporting infrastructure, formalize HED governance and maintenance processes, support a community of users developing library vocabularies for specialized subfields, and develop additional tools for supporting analysis using HED on common analysis platforms. Substantial effort will be expended in developing practical tutorials, case studies, and a body of open-source HED annotated datasets. Further effort will be devoted to exploring and integrating HED into the wider human neuroinformatics ecosystem including the BRAIN Initiative sponsored OpenNeuro archive and the NEMAR data, tools, and compute resource.