 Hi everyone, a very brief overview of the skills development program at Monash will be the starting point for this but then I'm also just going to focus on some very practical tips for face-to-face sessions which a lot of us are running or planning to run in future. And then I just wanted to spend a little bit of time at the end giving you a very idiotic perspective on what some of the future directions might be as I see them. So in terms of what happened at Monash during my time there, we did build up a fairly significant program over time but I guess what I wanted to say was that it did build up over time. And really by the time it had started there were several years of existing work that had already been put into building governance structures and policy frameworks and putting infrastructure in place. So this maybe wasn't as new to people at Monash as it might be at some other places. We did start small and we built up over time which I think is important to know. You don't have to try to do everything all at once. You'll see from looking at the screen that as well as focusing on HCI students we did also focus on superbazes and that came later and it was partly due to feedback that we were getting from the HCI students. We were saying that it's all very well that you're telling us this stuff while we're in this training course but that's not the message that I'm getting from the superbazes. So we really wanted to make sure that that message was getting out from the superbazes as well. We very deliberately decided to piggyback on existing programs where we could and use the advertising channels and networks that were already available. So that does mean I guess that you have to compromise sometimes and what perhaps you would like to deliver. So I mean it wouldn't for example have been my choice to get the last 15 minutes of a full day long seminar to talk about research data management to a big group of superbazes. But it took about two years of lobbying to get that slide in the first place so I was happy to take it when it did come up. And just to note that all the copies of the presentations from Monash are available on the refreshed Monash Research Data Management website and they're all available under an open license so you can use those as well. In terms of the feedback on the HDR seminars most people found them useful or extremely useful in terms of the feedback and the sorts of qualitative comments that we got back. You can see on the screen there and I guess what I would point out I suppose is the one on the bottom right. Rebecca was talking last week about the importance of really being fairly honest about what you know and what you don't know at the moment. And I guess when I've been delivering this training I've always made it clear that I don't consider myself an expert in this area and I'm still learning all the time and we're all learning together. Of course all the feedback wasn't you know totally 100% positive and we did do a lot of work evaluating this from the library perspective getting information literacy experts in. And the kinds of things that we realised were issues I guess the learning objectives weren't as clear as they might have been. We weren't really addressing discipline specific needs and some people wished that we would. On the other hand other people said they liked the cross-disciplinary nature of it so you're never going to please everyone. People wanted the sessions to be more interactive and hands on and I suspect everyone gets that feedback. And I mentioned the mixed messages that people felt that they weren't getting a consistent message in this course from what they're getting from other places. And there was also other logistical problems from the trainer perspective so in particular just getting low numbers when you've got a five campus university can be had to get everyone together. In the numbers that make it sufficient to run a workshop in the first place and we did have the perennial problem of people booking and they're not turning up. So sometimes 50% of people would be no shows and that's always hard on the presenters. We did get about 150 HDR students through this in that three years which sounds great but then when you look at the fact that there's kind of nearly 4,000 PhD students. When I show you all you do start questioning whether this is really the best way to do it and I'll talk a bit about that later. I just wanted to move on to some really practical things that I have learned in my time from delivering these face-to-face classes. And I'll apologize if they seem really simplistic but they were the things that really I found made a big difference in how effective the classes were. So the thing that I'm showing you now really works best in a longer session. You probably need an hour and a half or two hour session to do this and it has to be a session with not too many people. So what I would do at the start of the class was ask people to introduce themselves, say which department they were from and give a very brief description of the research topic. And then a little bit later, maybe about 5 or 10 minutes into the session, after I'd given them an overview of the different kinds of research data that people might be generating, I asked two or three people in the class to describe their research in a bit more detail and to start drilling into the kinds of data that they thought they might generate as part of their project. And as they were doing that, I would kind of probe a little bit so they said, I'm going to do a survey. I would say, will it be online or printable? And what are you going to do with the results? Will you put it in a spreadsheet or how are you going to analyze it? If they said interviews, I'd ask them if they were recording it. Would it be audio or video? What device were they going to use to record? Would they do transcripts? That kind of thing. And what that meant after I had done that little exercise, which really only took maybe 5 minutes, was that I had these real-life examples up on the board that I could come back to throughout the class. And I just found this really, really helpful. When I got to the ethics part, I could go back to a real example that someone in the class was likely to have a particular kind of problem that I wanted to talk about. Someone was going to be getting data from somewhere else, so I could talk about their particular third-party IP issue and so on. Now, it does mean being able to think on your feet. No, no, that's not for everyone, but I found it really helpful. The second thing I found quite helpful, which I didn't do until very late in that three years, was actually give people a visual overview of the range of things that they were going to have to think about in order to be able to manage their data effectively. And I guess I like that this shows that a lot of the things they need to think about aren't technical things. I think they came to the class thinking it was all going to be about storage, and they left knowing that it wasn't just about storage. And what I liked about having this one slide overview was that you could start to draw out the interconnectedness between the different decision points. And so the one that I quite often used was to talk about the kinds of consent processes that they would describe in their ethics application as being really important in terms of the effect that it could have on how they could publish and disseminate that data later. Tip three, stories are always good. So I increasingly used a case study kind of approach during my termination. These were a couple that we used in that supervisor accreditation program that I mentioned earlier. This probably goes back to what Anna was talking about, having your kind of champions that you can get nice quotes from or preferably will them out in person, but you're not always going to be able to get the associate dean of research of your faculty to come along to your training courses. But these can be really powerful. And just before I left Monash, I did a really great training session for the subject librarians and I used scenarios in this. And I think this is a really important point for those of you who haven't built up those case studies from real life because you don't have to have real life examples. You can actually cobble together scenarios or personas that are based on what you know the common kinds of issues are going to be. And this session went really well and I think partly that was because the librarians that attended this training session really realised that you could actually identify that something was problematic without necessarily knowing what infrastructure or services or expertise might be available to address the problem. Even if you don't know all those things, it's actually pretty easy and in a lot of cases just common sense to be able to spot the kinds of potential issues that might come up. And I think that really built their confidence. We're all in the data management space and I think that we forget that most people don't get involved in research because they're passionate about data. They're actually passionate about their research topic. And we often, you know, we love life cycles and we often work through data management in a fairly chronological kind of way. And I guess one of the things that I've found really helpful and this works not just with students but also with researchers. And that's to talk to people about what's going to happen when your project is finished. Who do you want to get your work out to? Who will be interested? What kinds of channels have they got available? Often those channels are not going to be, you know, expensive subscription journals and databases. Once you've got them thinking about their audience, then you can go back and ask them to think about what they need to do in terms of decision making and research practice to achieve those goals later and what role can their research data play in that approach to dissemination. So that was the top tips. I'm going to very briefly move on to talk about some future directions. I guess I currently see data management falling down a bit of a structural gap between research and education. Our skills programs have largely come out of funding agency requirements like the CODE and best practice guidelines coming from ANZ. And I think we really need to be aligning our data skills programs much more closely with teaching and learning drivers. So the Australian qualifications framework, whatever the graduate attributes for your institution are, and government strategies like the one that Belinda mentioned. I mean, I don't know that many of us are really framing our research data management classes in the context of the future needs of the research workforce and I think would have a better chance of getting resourced if we could do that. The second thing I guess, I mean, most of us have probably been fairly focused on delivering sessions face-to-face in the classroom. It's been talking heads and slide shows. I think we know that there's more varied ways of developing and delivering content that could be more effective. I guess we've had feedback that suggests that a more discipline-specific and embedded approach might be good and that has definitely been behind some GISC training projects and I mentioned the work in their arts faculty which would fit into that discipline focus model. I really wonder whether that is sustainable and I guess I've been wondering how more methods or topic-based approach would work. So if you think of the silos and this diagram as being the disciplines, I'm trying to think of what topics there are that would cut across disciplines. So for example, something on managing the data that's generated by surveys could be applicable across a wide range of faculties while still meeting the need of something that's more targeted to specific kinds of research. I've been observing for a while that despite the fact that we're all evangelists for sharing and reuse, we actually don't make it very easy for other people to share our work and I think one of the ways in which that would be easier would be to more clearly distinguish between the content that's specific to our institution. For example, how do you apply to get data storage at Monash and content that covers generic concepts that could be really easily repurposed. And I think we might be better off spending time developing smaller and more targeted chunks of content that maybe address just one or two learning objectives and that could be combined in different contexts. And the UK data archive is one organisation that's gone down this path and so if you look at the creation managed data training resources you see not just that they've put a slide show up but they also have various quizzes separated out into questions and answers so you could imagine using that in a face-to-face class or repurposing it online. And finally, I guess I just really am a bit surprised that given the collaborative nature of most of us at work in this space we really don't yet have a collaborative approach to developing and delivering this material. I would personally like to see us move beyond sharing the final products of what we're doing to actually sharing the work required to build those products in the first place. And so I've just popped a few ideas up there, just my thoughts but certainly the one on the top left, sharing learning objects in a central repository with open licensing is something that came up at a recent meeting of the Australian e-research organisations and I think that's a great idea but they need to know that there would be support for that and that people would use such a repository in ways that would make it worth building it. And that's it from me, just wanted to finish with a thank you to the Folkup Monash. Obviously a lot of these materials have come from my work at Monash and I'm not there anymore but they're always very happy to share materials with the ANS community. Thanks, Sam. That was terrific, some really practical tips and tricks as well as some nice ideas to think about for the future. So that's the last of our three presentations for today. There are a couple of questions in the question pod so I will...