 Welcome everyone. Welcome to our webinar for today, meeting the most unmet need. RDM training for researchers, high degree research students and indeed data trainers. So what we have for you today is four different models of data training and the people who have created them. This webinar is part of a series of, we will be a part of a series of webinars and this is the first one. For those of you who are interested in fair data, which has taken the world by storm, over the next four weeks we've got four webinars coming up which explores each of the aspects of fair data, findable, accessible, interoperable and reusable and you can see the short URL down the bottom of the screen. So feel free to have a look at those webinars and see if that's something that you'd be interested in coming along to today. We have a pretty stellar lineup I have to say and people from all around Australia. So we've got Frankie Stevens who's going to be talking about the Intersect Model, Frankie Sin Sidney, Pauline Tau from Edith Cowan in Perth and Pauline is going to be talking to us about top 10 marine science research data things. Roxanne Missingham and Imogen Ingram from ANU talk data to me and Liz Stokes, they're in Sydney from UTS is going to be talking about research data management training for librarians. But what she's talking about is actually applicable to many different groups who do research data training. Now I'm going to hand over to our first speaker Frankie Stevens. So I'll start with a little on Intersect. We're a not-for-profit organisation that provides a variety of e-research services to our members which at present are scattered across New South Wales, Victoria and the ACT. We also edge into Queensland a little. One of our most valued services is that of the training that we provide. This year we're going to deliver over 200 courses which equates to approximately one every working day of the year. So this is one of our most popular member services and it keeps us pretty busy. So historically our training courses have covered rather more advanced research computing challenges. We train in a range of different topics that are organised into compute, software and data elements. And the data elements are ones that perhaps require less technical background and this is where research data management fits in. One of the challenges that we have as a company is that our membership is broad and diverse. So if you recall my first slide you would have seen that geographically our members are in numerous locations across a number of different states and some members even have a number of different campuses. And our members also have different capabilities when it comes to supporting their researchers in data management. So some have e-research departments, some have very active libraries that assist researchers, some have policies and procedures in place and so on. And some even have infrastructure that provides tools and software to assist with data management. But others don't. We're also in a fortunate position in which we're being approached more and more these days to deliver training to external organisations such as government departments etc. So given this landscape we've been building our training capabilities to be scalable and flexible enough to deliver research data and our other training opportunities in a more tailored fashion to our members and potential future members. So traditionally we've had a very hands-on approach to training. We organise group training headed up by an expert trainer with the local institutional Intersect e-research analysts on hand to provide a one-on-one approach where this is required. And this model is hugely popular across our whole membership. It actually enables attendees to get help with practical exercises and it introduces them to the local e-research analysts so that they can call on them for future help. It actually also means that our researchers can use the training courses as an opportunity to present a real-life research challenge that they've been grappling with and they often get a resolution to this then and there. And this model is also good for intersectors. It can lead to further research streamlining activities that we can provide through our consultation and software engineering service. We actually enable all our attendees to access all course materials and exercises after the course so that they can actually continue their technology learning after the course is concluded. In research data management this means that our attendees leave with the beginnings of a research data management plan which they can actually continue to refine as their research goes on. But as you can imagine this model whilst it's really popular it doesn't lend itself all that well to scalability. So we have a number of members who have distributed campuses which means that catering to researchers on different campuses means holding a greater number of courses with all the associated logistical challenges. And so along with new opportunities arising from the Ecole review recommendations around the HDR experience we've actually been able to work with some members to evolve our training model. So we've been developing a digital research program for HDR students that provides a skills development framework which engenders students with broader transferable skills. And this program consists of six different awareness raising and training courses. There's an introductory course, a course on digital footprint, citizen science, big data, database concepts and of course research data management. But what does this look like practically? Well the biggest change is that we've moved to a fully digital format and this enables our members who have distributed campuses to access webinars from one or two or three or perhaps multiple universities even at once. Students can log in from home or wherever they like really. This is good from the university perspective as we're catering for their students at any location which is something that our members report they've always struggled to respond to when providing their teaching resources to their more remote students. It also means that Intersect has less logistics to deal with and we can train more of our members in one go. But what about the hands-on approach that I mentioned before? This is really huge and popular. But our model that we've come up with this digital model does incorporate a secondary online expert that can respond to student questions and provide additional context and perhaps even enable those solutions to real life research challenges just as before. So we're even looking into the possibility of having local research data management resources participate here. So for example we could have some local librarians assisting with the online moderating or information provision. So again this enables students awareness of the local resources available to them which they can refer to in future. And where the university requires it, we've also actually introduced an assessment process into the program which consists of a series of digital comprehension Q&As that a student completes throughout the webinar. This verifies that we're actually providing a quality teaching environment and let's face it it also enforces the required attention span. So the program materials are also being provided to students at completion so that they can refer back to these and investigate items accordingly. For research data management this means further info is available on local policies, procedures, and they also get to initiate that digital research data management plan. And finally we actually have the potential to blend both of these models into one where we might have a large cohort of students assemble in a computer lab on a main campus for the program webinars and the local institutional ERA or research analyst can actually be present in the room to provide that more traditional one-on-one experience where needed. So as before this not only introduces students to the E-research analyst but it also provides avenues for any eventual consultation potential. We've also been working on a continuous improvement initiative where we enable surveys and follow-up aspects and this is helping us to build a view as to the qualitative elements to our training offerings in addition to the more traditional quantitative ones. So I'll leave it there but I'm happy to provide more information to people offline. So thanks Karen. Thank you Frankie. Pauline works with the library at the Edith County University in Perth and she's cleverly adapted the top ten health and medical things developed last year by Kate LeMay into a flexible online activity based on the needs of a particular group and in this case it's the marine science community. So thank you Pauline. I'll pass over to you now. Good afternoon everyone. Thank you for inviting me to talk about the 10 Marine Science Things Lib Guide. I'm currently a librarian with the ECU Library Research Services team promoting awareness on RDM issues to our researchers is one of our team's responsibilities. Today I'll share some information on why we developed the Lib Guide, how we are currently using it at ECU, the benefits of it and a quick demo of the Lib Guide. Earlier this year the ECU Library had the opportunity to work on our third ENDS project. The project focused on the marine science research area. Similar to the previous ENDS project, the ECU Library views this project as a means to help enhance the visibility of the ECU research data sets and to continue to find ways to promote RDM awareness among our researchers. Last year 10 ECU library staff participated in the ENDS 23 Things program and together we learned a lot more on RDM. So the ENDS project was very timely and provided us with the great opportunity to apply the knowledge we have learned from the 23 Things. For those who participated in the 23 Things last year, you may remember that at the end of the program a challenge was thrown to the participants to find ways to repurpose the 23 Things material. One of the great examples of how it could be done was of course the 10 medical and health things. So as one of the project activities we decided to do something similar in the marine science area. But we wanted to do it using an online library guide format. So here's what we did. The 23 Things and 10 medical and health things contents were first reviewed and materials that could be repurposed for the marine science lip guide were identified. The selected materials were then used to create a word version of the lip guide. A copy of the word version is available from the handouts box today. Then using the word version as the basis, the marine science lip guide was created on the library website. What you see on the screen now is the homepage of the lip guide. Each tab leads you to a different thing, and in each thing you will see the different activities. While developing the lip guide, advice was sought from the very kind and experienced data consultants, namely Aggie Gideon, Catherine Tethaso, and Julia Martin. Together they provided some great examples of marine science resources that could be included in the lip guide. We then got our science subject library to help review the first draft of the lip guide. And together with another team member, the existing ECU RDM guide was reviewed and updated to embed the guide. The lip guide went live in mid-May this year. Since it's an open resource, anyone can now use it as a self-paced learning program to learn on RDM. We can also use it as a teaching tool when we run our RDM sessions with the HDR students. The online lip guide software was chosen to create this lip guide because it allows flexible designing of the guide. It also enables inclusions of materials such as embedded videos and images. These resources make the lip guide visually attractive and help to enhance the user's experience in the online environment. The lip guide software also allows tracking of usage statistics. Thus far, since it went live, we have had more than 700 views on the tabs provided. Apart from the tabs, all the hyperlinks created in the lip guide could also be monitored. This allows easy assessment and helps identify if there are any gaps in the contents and areas for improvement. We can easily update or add new materials as and when necessary. For those who are using the same lip guide software, the software also allows very quick and efficient replication if other librarians from ECU or any other organizations wish to create other subject-based 10 things guide. Overall, I believe the 10 marine science lip guide provides us with an easy way to reach out to the ECU researchers with more relevant marine science RDM materials. Apart from benefiting our ECU users, working on repurposing the materials to develop this lip guide indirectly allowed the librarians to learn much more about RDM itself, especially with regards to the marine science data sets and resources. It was great to have worked with the different colleagues from my own library, as well as with the ENDS team while developing the lip guide. The link to the lip guide and the original Word version was currently available on the ENDS website. Recently, we also found links to the lip guide populated on the website of two other organizations. One is on the Western Australian Marine Science Institute website and the other is on the Agricultural Information Management Standards block. So hopefully, there will be more subject-based 10 things that can be developed in the future for the community. Thank you. Thank you. That was excellent. Okay, I'm now going to pass over to Roxanne, Missingham and Imogen Ingram. Roxanne, Missingham is the ANU University Librarian and Imogen Ingram works for the Information Literacy Program at ANU and Canberra. There, talk data to me and publish and prosper. Online modules are a lively mix of videos, reading, quizzes and case studies. Over to you. Thank you. Hello everybody. Lovely to be here today. So, we started on this journey from various interesting spaces that you do in a university when we developed a lip guide, we developed data management training, we had been doing traditional stuff and we had a community that was seeking to really understand how they should grasp e-resource data management, e-research data management issues and a range of different challenges within a large group of eight universities. So, when we started on the journey that has produced the modules that you will be able to see when you click on it at the end of the show, our researchers felt a little bit like these penguins on this road that they were being equipped in various places with little bits of information and then they would try to jump in and hit their head on a sea of barriers when they knew there were resources and support within the university that was not transparent to them. So, some people knew a little bit about the supercomputer and felt frustrated and some people knew a little bit about the Australian data rather color and we were particularly talking to early career academics and higher degree research students and they felt they were very much at sea. So, in the university we created a number of discussion areas. We'd had three e-research committees set up and complete their terms trying to figure out what the university should do on a large scale and that turned out to be too big a problem to solve in any way but a number of the clear gaps to us were things that we started to fill. So, our first little segue up to the lead guide which had been around for a while was to create a data management single website for the university where we pointed to the code practice, all the university policies and procedures, all the information that was on faculty websites and we tried to bring it together in one cohesive way. That took us 12 months of discussion and investigation and there were probably still things that we didn't find at the end and it was a collaborative effort between the research services staff, the library staff, the repository staff, the IT staff and the research training staff. We had a separate area that supports HDR training. So, everything that we learned from that was what we took along our journey. So, when we really were talking to the audience about what we need and there were various workshops, it became clear that we needed to use some different pedagogies and different tools to be able to really create a very successful solution, particularly focusing on the researchers who were going to be the researchers of the future. So, we looked at MOOCs and in looking at MOOCs, we thought we're going to be the ninjas. We may not be here to identify them but we're going to try this new technology to see if we can use the sorts of learning that's happening about education and knowledge transfer in a way that will help us learn how to communicate about scholarly communications. So, we mapped out six modules and one of them was the research data module. We put out this lovely structure which started with the first one so people would understand the concepts and we very quickly decided what we needed to do was get how to publish and how to manage your data out very quickly because the need was so strong. So, MOOCs were important to us because we were thinking in many ways as previous speakers have about what, how can we support the researcher who at 2 a.m., our hypothetical research, 2 a.m. in Calwill, on a bit of not very good bandwidth necessarily, needs to just do that first bit of data management or the last bit of data management in order to finish their project, their thesis, and they don't necessarily want to go through a whole course end-to-end and they're certainly not going to come to our one-hour courses. How can we reach out to people who are living in this world of Facebook, Instagram, Snapchat, and MOOCs is the way to do it. So, we really tried to bring those concepts together. We talked to a lot of people who had done MOOCs, who said to us authenticity is the most important thing. Not a huge lot of very high quality process materials so we worked with a lot of people who we thought would be able to contribute so that we would be able to package everything up in short-shot collections of information under themes with quizzes. The two things I wanted to particularly emphasise before I move on to Imogen is that we used what was called at that stage a spot. So, it's not a MOOC, which is the massive online open courses. It's a sort of special private online course. Everything is fully openly available and it's not like the MOOCs where you have to join. You can join at any time. And the intention was to get lots of three to five-minute slots where people can talk about an important issue. And we had fantastic buy-in from around the world and also to be a bit entertaining and use new technology. So, you will see when you see it, but it doesn't have all of the AMU branding that you see on our slides because we went, as we say, off-peast into an exciting space and used a number of different technologies we hadn't used before. So, that was very useful for us. And the other characteristic for us was we actually asked for the way through, particularly career academics, what they wanted. And I think they would see us walking around the campus in dark sometimes so that we wouldn't ask. Sometimes we offered them, you know, Coca-Cola and a pizza. Very successful. I highly recommend that as a strategy. So, we really wanted to be quite intuitive in building our solution that met their needs and being able to be flexible. So, I'll hand over to you to that. Thanks, Roxanne. Okay. So, our module talk data to me, you can see that we have a great lineup of presenters and it was wonderful working with all these people from NCI, from the ARC, from... Let's have a little look there. And we also scored a presentation from Dr Tony Hay, the Chief Data Scientist from the Science and Technology Facilities Council in the UK. So, this particular module, as Roxanne said, is one that sits amongst a series of modules. We've looked at data management plans, data citation, funder's data requirements. And again, with that idea, as Roxanne said, of assisting researchers navigate the scholarly communications and publishing environment, specific in this case to research data management, but not just researchers now, this is research to the future, getting people into this space, thinking about what's required really early on, even as early as, say, late high school students. Our wonderful presenters listed there. You can see them, as I said. And we have also got more in the pipeline. So, those other modules are being developed. We are really fortunate to have had, again, amazing presenters from Yale, Oxford, and the input and different perspectives from a number of experts in this area. So, watch this space. And as you can see, it's definitely been a team effort. It's been wonderful collaborating both within the library and between other areas of the ANU, as well as students who have definitely played a role in the development of this module and the series. Okay, thanks. Thank you, Roxanne and Imogen. I'll move on now to Liz Stoke. Liz is a data librarian at UTS in Sydney and her presentation is about training the data trainers. And as we said, this one in particular, this is aimed at librarians, but she makes the case for both RDM and data science training needs and how communities frame RDM training for their members. So, in this case, librarians. Thank you, Liz. Over to you. All right. Hi, everyone. Okay. So, research data management training. This question, what are we training librarians for? It's been weighing heavily on my mind, actually. So, today, I want to talk about how we frame, how the library community frames research data management for our own development, as well as meeting our client community needs and what's been happening at UTS. So, when I first got here, I thought that my plan was fairly simple. Research data management is the future of libraries. All I had to do was teach research data management to my colleagues, turn them all into librarians, data librarians, provoke the university into action on research data management by any means necessary, figure out what works, and do more of that. But it wasn't actually that simple. Research data management may be the future, but it's hard to promote that future when it looks like another freaking web form. So, at UTS, our research data management is largely driven by an asset management imperative, which is very important, actually. So, we have policy which mandates data management plans for all projects, including PhD students and higher degree research. We also have stash records for research data, which are mandatory as well for any researchers publishing data, even as a supplementary item to a journal article that's going to be official in January next year, and that will be very interesting for us. In general, the library is responsible for research data management training, and the e-research group in the IT division at UTS develops the infrastructure. But my question is, how you say RDM? Not just how do you say it, but how we say it, okay? I'm increasingly uncomfortable with these ideas around RDM and DMPs. These are terms that we've created by and for librarians. I think that RDM is as much an artificial construct as a DMP, which is more often than not an administrative requirement or a matter of compliance, rather than an actual thing that is supporting the doing of research. I've realised that my anxiety around research data management has come from having the responsibility of persuading my colleagues to make space for a new area of expertise, which is still as relatively abstract as the myriad training resources and online courses that are available to learn it, while still remaining fully committed to business as usual. Moreover, on reflection, I learnt how to be a data librarian by actually doing it, not through doing a course, okay? It was through working on projects, learning how to be a partnering with researchers. The skillset required had more in common with the data scientists than traditional librarians. But of course, I think every librarian here probably begs to differ with that straight away. But as a data librarian, a growth mindset and problem-solving skills are potentially more valuable than the myriad pieces of the puzzle that make up the many matrices of research data management, life cycles and modules. So at UTS, we're kind of doing research data management by stealth, okay? Internally, my department is piloting a Tinker Time project using learning analytics expertise from UTS data scientists to develop our growth mindset and data literacies. So for example, I'm in a little affinity group of amateur game developers, and my project will be creating a character called Bogan the Librarian who must curate research data for a death-medal cultural studies researcher. Well, that's just level one. I haven't got beyond that yet. Also, we're developing introductory data management, which is mainly data cleaning, but also including data visualization skills for undergraduate faculty classes. And we're also promoting open data sets for teaching as part of our open educational resources for our academic teaching staff. So these examples are not strictly in the research data management world because that tends to be skewered more towards or aimed at our research communities, but I believe they're gateway drugs toward it. I guess we could call it seeding the commons for a one to the better word. So speaking of other research data management by stealth initiatives, the recent RESBAS that we held in Sydney in July is an excellent example of that. And that, the Research Bazaar, is a series of workshops on coding and software skills for being able to teach non-computational researchers that kind of computational thinking that comes with using R or Python to harness the power of these new next generation research tools. So we discovered that research students and staff want things like data science training, and they want supporting data management tools, applications, and software. So they want to know what will help them how to do research faster, smarter, more competitively. And we found that from our pilot in July, researchers also want to build community and learn about research tools across disciplines and institutional boundaries. So this is where we embed research data management by offering training in these open source tools and software where things like version control are integrated in things like GitHub. Alongside the RESBAS software workshops, we also provided some research data management workshops with the very name, and I fell off my chair when I realized that that was the second most popular item in our expression of interest form that we sent out initially. And I don't know about you, but we don't get more than 40 people registering for our regular research data management workshops. So there's something else that is happening there that enables people to see research data management as something valuable and useful for them rather than just RDM on its own. This year, UTS has also established a joint steering committee in e-research and research data management training, and this is more at the executive level, and that's going to allow us to embed research data management across a broader spectrum of specialized research support and data science services. And finally, we're integrating librarians into research e-research infrastructure projects such as the Provisioner Project that e-research is running and giving librarians roles such as the go-to trainers for research tool training, for example, lab notebooks, and in the future, RedCap, a research data capture application, and also by giving librarians roles in, say, user acceptance testing of our in-house research data management tool, STASH. We can engage them as experts developing those specifically data librarian skills. So to answer the question I started with, this is what we're training our librarians for. Three roles as active participants in providing enhanced research support services. We're training them as instructors in delivering real data management skills from undergraduates through to our researcher communities. We're training them as advisors to deliver research data management by stealth in a way saying, oh, would you like research data management with that as one might offer fries or curly fries or something like that. And finally, as engaged librarians collaborating in e-research infrastructure development, we can knowledgeably refer clients to more specialized data services, for example, statistical instruction or high performance computing. Being able to perform quite a triage role in that way. And that's the end of my presentation. Thanks, Karen. Thank you, Liz. That's a really interesting way that the UTS has integrated all of the various aspects of research data management training. What we're going to be doing now is we're just going to be finishing off. But firstly, I'd like to thank all of our presenters today. I really appreciate the effort that you've gone to. And mostly the fact that the work that you're doing is open and that people can talk to you. They can reuse the work that you're doing and that they can take the ideas and contextualize them for their specific institution or perhaps you may have a group in your university or institution who are particularly keen on data management. Maybe some of them have been caught on the publishers policies desperate to publish and can't linkages isn't available. And so now we've got the opportunity to come together as a big community and really to share all these things. So I'd remind you to look at 23 things in particular because what you've got there is you've got a group of materials. You can use a small activity from one of them. You don't have to use all of them. You can use it online from our site. You can take it. You can use it in PDF documents so you can just take it down, use it in whatever format you like.