Open rowlandm opened 6 years ago
and another example:
Why would I use your 'very complex ' thingo? I can just install the software on my laptop! The answer to this should be about reproducible science and Provence, not technical merit. Users tend to only hear and understand the technical merit answer
Another example: Engaging undergrad and post-grads. The undergrad students do not typically leverage eResearch technologies. Progressing this community and engaging at the “course-level” will be beneficial in seeing a change
Another example: Capturing the diversity of learner backgrounds (beginners => more advanced). Maintaining materials over the next 5 years (keep up to date with platforms, tool versions, method changes)
Another example:
how can we integrate the training that we develop into undergraduate curriculum? At the moment we mostly train our users, but it would be great if we can upskill the next generation researchers during their degree and thus integrate the use of Australia's digital infrastructure in undergraduate courses.
how can we make our training program sustainable; or better said, who will fund training and skill development in the future? User support and training is still underfunded and not often recognised as an integral part of our programs (it's getting better though, but I am not sure how this will be in the future).
Another example: Some domains covers vast and varying fields of research with different level of maturity. One size will definitely not fit all. Need to prioritise to maximise the returns on efforts.
I don’t see much discussions around skills development involving consideration of ethics and research integrity issues.
My own comments: How can I trust your training materials?
I saw a training document recently that said to use Tool X with Tool Y. Now Tool Y uses Tool Z. But I know that I can't use Tool X with Tool Z because the creator of Tool Z has said publicly that you shouldn't use Tool X with Tool Z.
So what if we start giving people the wrong advice?
Here's another example: https://twitter.com/lpachter/status/937055346987712512
eg. I got some feedback that said:
What is a compute cluster? How do I process my huge data on my desktop? The skills needed are understanding of virtual machines and centralised systems including repositories.