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

Kay Robbins1, Dung Truong2, Alexander Jones1

  • 1Department of Computer Science, University of Texas At San Antonio, San Antonio, USA.

Neuroinformatics
|December 31, 2021
PubMed
Summary
This summary is machine-generated.

The Hierarchical Event Descriptor (HED) system enhances human electrophysiological data annotation. HED-3G facilitates standardized event description for improved data sharing and analysis in neuroscience.

Keywords:
BIDSEEGEvent annotationFAIRHEDHED-3GHierarchical Event DescriptorsNeuroimaging

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Data Science

Background:

  • Human electrophysiological data is often collected in complex, event-rich settings.
  • A gap exists between current data archiving standards and the annotation detail needed for effective analysis of event-related data across studies.
  • Challenges include ontological clarity, vocabulary extensibility, annotation tool availability, and usability for data sharing.

Purpose of the Study:

  • To describe new developments in the Hierarchical Event Descriptor (HED) system.
  • To address the challenges in annotating human electrophysiological and time series data.
  • To promote the sharing of data with effective descriptive detail for labeled events.

Main Methods:

  • Recap the evolution of the HED system and its adoption by the Brain Imaging Data Structure (BIDS) movement.
  • Describe the recent release of HED-3G, a third-generation HED tools and design framework.
  • Discuss future development directions for the HED system.

Main Results:

  • The HED system, particularly HED-3G, offers a framework for standardized event annotation.
  • Improvements in HED address ontological clarity, vocabulary extensibility, tool availability, and usability.
  • The developments aim to motivate data authors to adequately annotate their data.

Conclusions:

  • Consistent, detailed, and tool-enabled annotation using HED is crucial for advancing large-scale analysis and modeling of aggregated time series data.
  • The HED system shows promise for improving data sharing and analysis in behavioral and brain imaging sciences.
  • Future development of HED will further enhance its capabilities for complex data analysis.