Capturing the nature of events and event context using Hierarchical Event Descriptors (HED)

Robbins, Kay A.; Truong, Dung; Appelhoff, Stefan; Delorme, Arnaud; Makeig, Scott

doi:10.1101/2021.05.06.442841

Cited by 6 publications

(8 citation statements)

References 32 publications

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“…We have also re-released a HED-3G annotated version of the MEG/EEG components of a publicly-available multiparticipant, multi-modal neuroimaging dataset on Open-Neuro (https:// openn euro. org) under accession number ds003645 along with an extensive case study in HED-3G annotation based on this dataset (Robbins et al, 2021). The data is from an experiment by Daniel Wakeman and Richard Hansen (Wakeman & Henson, 2015), originally shared under accession number ds000117.…”

Section: Hed Now and Future: A Roadmap Forwardmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Hed Now and Future: A Roadmap Forwardmentioning

confidence: 99%

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

et al. 2021

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“…In the neuroimaging domain the Hierarchical Event Descriptor standard (HED 24 , Bigdely-Shamlo et al, 2016;Robbins et al, 2021) is an infrastructure which defines rules for controlled and hierarchically organized vocabularies. Terms from these vocabularies can then be used to describe the nature and time course of an experiment, that was performed while brain data was recorded, by tagging the data with keywords while assuring findability of these tags during downstream analyses.…”

Section: Hed Tags and The Neuroimaging Data Model (Nidm)mentioning

confidence: 99%

FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards

et al. 2023

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Modern AI supported research holds many promises for basic and applied science. However, the application of AI methods is often limited because most labs cannot, on their own, acquire large and diverse datasets, which are best for training these methods. Data sharing and open science initiatives promise some relief to the problem, but only if the data are provided in a usable way. The FAIR principles state very general requirements for useful data sharing: they should be findable, accessible, interoperable, and reusable. This article will focus on two challenges to implement the FAIR framework for human neuroscience data. On the one hand, human data can fall under special legal protection. The legal frameworks regulating how and what data can be openly shared differ greatly across countries which can complicate data sharing or even discourage researchers from doing so. Moreover, openly accessible data require standardization of data and metadata organization and annotation in order to become interpretable and useful. This article briefly introduces open neuroscience initiatives that support the implementation of the FAIR principles. It then reviews legal frameworks, their consequences for accessibility of human neuroscientific data and some ethical implications. We hope this comparison of legal jurisdictions helps to elucidate that some alleged obstacles for data sharing only require an adaptation of procedures but help to protect the privacy of our most generous donors to research … our study participants. Finally, it elaborates on the problem of missing standards for metadata annotation and introduces initiatives that aim at developing tools to make neuroscientific data acquisition and analysis pipelines FAIR by design. While the paper focuses on making human neuroscience data useful for data-intensive AI the general considerations hold for other fields where large amounts of openly available human data would be helpful.

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“…We have also released a HED-3G annotated version of the MEG/EEG components of a publicly-available multi-participant, multi-modal neuroimaging dataset on OpenNeuro (https://openneuro.org) under accession number ds003645 along with an extensive case study in HED-3G annotation based on this dataset (Robbins et al 2021). The data is from an experiment by Daniel Wakeman and Richard Hansen (Wakeman and Henson 2015), originally shared under accession number ds000117.…”

Section: Hed Now and Future: A Roadmap Forwardmentioning

confidence: 99%

Building FAIR functionality: Annotating events in time series data using Hierarchical Event Descriptors (HED)

Robbins¹,

Truong²,

Jones³

et al. 2020

Preprint

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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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Capturing the nature of events and event context using Hierarchical Event Descriptors (HED)

Cited by 6 publications

References 32 publications

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

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

FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards

Building FAIR functionality: Annotating events in time series data using Hierarchical Event Descriptors (HED)

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