 Yeah, so I guess for the past year or so, I've been working on an open source textbook with a group of carpentries instructors. They're on the screen, including Greg Wilson, who some of you might be familiar with, who co-founded Software Carpentry and has written a number of programming books in the past. And we're currently at kind of the review phase of the publication process. And so we're really looking for feedback on the book and what's in it and whether people think it's useful and things like that. So this conference kind of came around at a really good time. So the motivation for the book's really from teaching Software Carpentry over the years. And obviously that's the model, I guess, that's used in that is a two-day workshops where you basically expose researchers to some programming best practices. And the outcome is that you hope that they take on some of those best practices and their personal workflows become more efficient. The nagging question for me, and I think for a lot of carpentries instructors is, what if we have more time than that? What if we had a whole university semester or something like that? What would we teach? How far can we progress people? And there are some university courses that have been popping up that are starting to use carpentries materials in them and things like that. And so we thought, yeah, I guess with that extra time, you could get people to the point where they weren't, you know, they're not only just able to, you know, make their personal workflows more efficient, but get them to the point where they can write code that others can easily understand, install and contribute to, i.e., writing research software, basically. And so once we kind of committed to this idea of essentially writing, you know, software carpentry of the university course, one of the first things we decided for the content is that we wanted it to be a work example, not just a reference book. And so basically in the book, what we do is we actually build a research software package from scratch. The one that we build counts the words in large text and analyzes the word count distribution. So, for instance, in the top right there is the word count distribution from our package for Jane Eyre, the novel. And so, yeah, and so basically along the way we write this package and we verify something called ZIF's law, which says that in big texts, the most frequent word is generally two times more frequent than the second most frequent word, three times more frequent than the third most frequent word and so on. So it's a pretty cool kind of research result. And with this software package, we can explore that and check whether different texts are consistent with ZIF's law. And along the way, basically, as we're doing this in the process of building this package, we cover a whole bunch of topics. So the Unix shell, writing command lines with programming, with Python, version control, working in teams, automatic analyses, program configuration error handling, testing, which is obviously a big one, provenance talking about if we were going to publish these results in a journal paper or something like that, how do we document the computational aspects of that research so it's reproducible. And then Python packaging, how do we package this up so that others can install it, how do we document it so others can contribute to it, use it effectively, all those kinds of things. And I guess it's really, on the right hand side of the slide there is three kind of characters we use throughout the story and their backgrounds there. And so it's really, I guess, thinking about training up research software engineers, if you like, from people who have come from a research background and are moving into writing software, as opposed to, I guess, the other way into research software engineering, which would be you have a computer science degree or something like that and you're getting up to speed on the research. We provide exercises and solutions and things like that. And it is kind of framed as that it could be used hopefully as a university semester course, but also flexible to be used as something for self-study or hopefully something that carpentries and instructors can take bits of it here and there to use in workshops, shorter workshops and things like that. So hopefully got some flexibility and it's not just a university kind of textbook and that's it. Yeah, so the title of it is Research Software Engineering with Python. It'll be published in early 2021 with Taylor and Francis but it's a really cool thing. They let you do it with a Creative Commons license. So the online version will still be up even when the book is published. It will still be on GitHub when the book is published. We can still take contributions, continue developing the book as we go along. And in terms of future plans for those who aren't Pythonistas, there's a small group, I'm not involved in this one, but some of the authors and some new people are kind of coming on board to start writing an equivalent book using R. So if you're kind of wanting, if you might be interested in getting involved with that on the ground for maybe let me know and I can put you in contact with the group of carpentries people that are starting to put that book together. Yeah, that's all from me. We'd love, yeah, really love any feedback at that GitHub repo before we actually publish the thing. Thanks. Thanks, Damian. I think we'll take maybe five minutes for questions. If there are any questions, what questions do attendees have? All right, we don't have any questions yet. Mark, are there questions? Sorry, I can see an unmuted microphone. Okay, I think we'll move to the next talk and then if people have questions, please do feel free to ask them at the end. So next, I will share a video from Titus Tang who is a trainer on the data science and AI platform at Monash. And he's leading the effort to design, develop and deliver data science and AI training courses for Monash researchers and students and also the wider research community in Australia. And he has a background in deep learning and computer vision with a PhD in computer vision engineering, also from Monash. So bear with me as I try to share the video of this presentation and Titus does join us. So there will be opportunity for questions at the end of the video. There we go. I think that's the one I want. And... Hello, my name is Titus Tang. I'm a training manager on the Monash University Data Science and AI platform. I'm here to talk about a collaboration in skills training and workshop organization between Monash University, the University of Queensland and the Queensland Cyber Infrastructure Foundation. I'm here representing a large group of people working on this project. My counterparts are Dr. Nick Hamilton from the University of Queensland and Mark Crow from QCIF. This project that we're working on is funded by the ARDC platform project called Environments to Accelerate Machine Learning Based Discovery. As the name indicates, the purpose of this project is to create the right kinds of environments for hardware, software and people in order to facilitate the adoption and acceleration, accelerate the adoption of machine learning in research. What I'm going to talk about today focuses on the subset of this project that looks at skills training and workshop organization. So one of the first questions we asked ourselves when we embarked on this project was how do we upskill people in machine learning, upskill our researchers, staff and students in machine learning, not just in our home organization, but across partner institutions and across Australia in general. One of our observations that we made was that each institution typically had a set of workshop content that they have created and are running on a regular basis in their own institution. To our surprise, there was a very little overlap in the content developed by one institution relative to that developed by some other institution. And so that was good because everyone had a piece of the puzzle that we could place together to form a better, bigger picture.