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

[Ankur Sinha]

Dear all,

A reminder of today's software highlight session on HED. We hope to see
you all there.

On Tue, Feb 21, 2023 11:02:13 +0000, Ankur Sinha wrote:
> 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