It’s very encouraging to see the positive response we’ve received to this webinar.

We have students, early career researchers, senior researchers and professional staff joining us, so clearly this topic resonates with people at different stages of their research career.

We also have people from other faculties - Health, Law – so welcome!

In my talk I’d like to discuss two things.

The first is to challenge the notion that you have to be an IT person to learn new technical skills. I would like to propose that (humanities) researchers have a tradition of using technology in the process of knowledge creation.

Secondly – perhaps on a more practical level – I’d like to highlight some of the trends we’re seeing across our SCIP consultations and share some advice on how to approach learning new digital tools.

What digital knowledges and skills will humanities researchers need in the future?

This was one of the key questions posed in a project being run by The Australian Academy of the Humanities.

And so It comes as no surprise that data and technical literacy was identified as the most important area for skills development for the future humanities workforce.

The ability to work creatively with such things as textual analysis, visualisation tools, data processing and machine learning.

Augmented by data preservation, collection, management and storage practices.

Digital tools are becoming central to the process of knowledge creation.

And while researchers might engage with these tools in different ways, there’s a pressing need for training and infrastructure to support research within an increasingly digital working environment.

The Covid-19 pandemic has clearly impacted on the way researchers and working (and the wider community).

Everyone starts somewhere Prioritise your time. Focus on one thing to learn as a starting point.

It is perfectly fine to use tools (existing software). Tools are great. We all use different tools ever day to carry out a variety of our daily tasks.

Being reliant on tools does present some issues, such as dependency on commercial software or being able to reproduce & automate your data wrangling and analysis.

As we’ve already established there’s a learning curve and commitment to learning to code. But learning some basic coding can save you time and effort in the long run.

Jupyter notebooks or R Markdown let you save your comments and code together into one file. Great if you need to come back 6 months down the track to recreate your analysis or share your work with others.

The payoff is that it’s easy to reproduce your analysis should you get new data, or there are changes to your existing dataset, as you simply run the program again.

There are a few things I’d like to share that I wish I had of known when I began focusing on digital tools and coding in my work.

Dead-ends – be prepared bc there will be lots of them. Be resilient (which I dare say is an attribute for researchers) and don’t give up. If you’re having a bad day step away and come back to it tomorrow.

Project manage your learning – commit to one area/one tool that you wish to learn. Commit to a set amount of time (per day, per week) to work on it, and give yourself a deadline.

Coding - in many cases you may not need to know kow it all when it comes to a programming language or concept (eg: machine learning) but you might want to know about only one particular thing or method.

There are lots and lots of forums, tutorials, videos and examples of code online that are easy to cut, paste and run. The flip side is that the code can be often out of date (broken).

Even many textbooks on digital methods can be based on an older way of doing things.

It’s worth remembering that often tools can come & go, so it can be important to focus on your understanding of the underpinning theoretical concepts and stages of your analysis.

OK that's the main part of my presentation. There's a few things there I hope are useful for you.

How do we define success? What does success look like? A corny meme?

What does success really look like? Messy. Prioritise your time & Take responsibility for your own learning

You're not alone. We all have gaps in our knowledge

We're all learning..

Thanks for listening! We hope we've left you with some ideas you can use now and later.