[Incf-ocns-software-wg] Software Highlight: Kay Robbins: HED (Hierarchical Event Descriptors)

[Ankur Sinha]

Dear all,

Apologies for the cross-posts.

Please join the INCF/OCNS Software Working Group for our next Software
Highlight session:

Kay Robbins[0] will introduce and discuss HED, a practical system for
describing an experiment using an analysis-ready framework.

https://ocns.github.io/SoftwareWG/2023/02/17/software-highlight-kay-robbins-hed.html

- Date: February 28, 2023, 1600 UTC (Click here to see your local time[1]) (Add to calendar[2]).
- Zoom (link): https://ucl.zoom.us/j/99321986413?pwd=OUdFTlJ3NVloUmJ1U0Q3WE9vRERMZz09

The abstract for the talk is below:

In human neuroimaging experiments, a record of what a participant
experiences together with a clear understanding of the participant
(task) intent are key to interpreting recorded brain dynamics. HED
(Hierarchical Event Descriptors, https://www.hedtags.org) annotations
and supporting infrastructure can provide human-understandable
machine-actionable descriptions of events experienced during laboratory
and/or real-world time series recordings. HED, which is well-integrated
into BIDS (Brain Imaging Data Structure) has an ecosystem of tools
supporting researchers at various stages including data acquisition,
annotation, sharing, and analysis. This talk will describe HED
principles, focusing on basic representations of an experiment and its
design. Various tools in the HED ecosystem to support search, summary
and analysis will be introduced and demonstrated. Finally, we’ll discuss
how tool developers can leverage the HED infrastructure to build
advanced analysis tools capable of automated analysis in support of
machine learning. HED is an entirely open-source project, and the HED
Working Group welcomes contributors and contributions.

Papers and resources:

- Capturing the nature of events and event context using Hierarchical Event Descriptors (HED). NeuroImage. https://www.sciencedirect.com/science/article/pii/S1053811921010387
- Building FAIR functionality: Annotating events in time series data using Hierarchical Event Descriptors (HED). Neuroinformatics. https://link.springer.com/article/10.1007/s12021-021-09537-4
- Resources: https://www.hed-resources.org
- GitHub organization: https://github.com/hed-standard

[0] https://www.utsa.edu/sciences/computer-science/faculty/KayRobbins.html
[1] https://www.timeanddate.com/worldclock/fixedtime.html?msg=Software+Highlight%3A+Kay+Robbins%3A+HED&iso=20230228T16&p1=1440
[2] https://ocns.github.io/SoftwareWG/extras/ics/20230228-kay-robbins-hed.ics


On behalf of the Software WG,

-- 
Thanks,
Regards,
Ankur Sinha (He / Him / His) | https://ankursinha.in
Research Fellow at the Silver Lab, University College London | http://silverlab.org/
Free/Open source community volunteer at the NeuroFedora project | https://neuro.fedoraproject.org
Time zone: Europe/London