 So, thank you to ANS for having me. My presentation is about data management plans of course and how we try to get the researchers as they gather at the water holes. My name, there we go, I'm Matthias Liffis. My title is Coordinated Research Services but I like to call myself a data librarian. If you need to tweet, call or email me, there are my details. Now, I always forget to put in a slide introducing Curtin University itself because I assume everybody knows of us. Curtin University is the largest university in Western Australia, we're a member of the Australian Technology Network. Our research profile is growing, I think it's the best way to put it, so traditionally not a very strong research focus but we are absolutely growing our capability in that area at the moment quite rapidly. So I don't like using the analogy of carrots and sticks anymore, I prefer to talk about water holes after a conversation with Jens Klump at Syro. Carrots and sticks, well they of course carrots are good things, sticks are bad things but really when it comes to research administration and research management, most things are a combination of good and bad depending on what point of view you take. So we like to put some things in place so that when researchers congregate at the water hole to get something that they need, that's where we approach them and involve them in data management planning for example. So data management planning is just one of the larger, one of many services available at Curtin University. We have training which is delivered by the library. There are monthly data management seminars and I just finished a month of very intensive seminars, I delivered about eight over the course of three weeks. We have a library guide on research data management. There is the data management planning tool, storage for data, the R drive and a facility for publishing data including DOI minting and of course we also provide advice to researchers on all sorts of things around data management like ethics, IP, grant application so on and so forth. Now I'd like to quickly talk about what a researcher actually is from, depending on who you talk to in the university, they're mostly concerned with just one kind of researcher, staff or students, I like to think about all researchers, doesn't matter where they are, how early they are or how late they are in their career, I especially like to get them while they're young, train up early career researchers in good data management practices. Now the services that we have developed at the university were very developed under a very, very strong collaboration between the library, the IT department, the office of research development and the records and information management team. So we all came together providing our own expertise in particular areas but it wasn't as though just the library was developing these things in a silo, it was certainly a strong collaboration. Now on to the actual data management planning tool, I tend to be quite skeptical of technology working during presentations like this, so I've elected not to give you an in-depth demonstration of our data management planning tool, if you'd like to see it down the line please get in touch with me and I'll happily organise a screen sharing session or something and walk you through it. So our data management planning tool was developed in-house by our IT department. Development started quite a long time ago, probably about four or five years ago but there was a lengthy hiatus in between and then eventually they picked it up, polished off what was needed and made it available, so it would have been first available almost two years ago to the month in fact. Now at the moment the data management planning tool is based around a series of pages, there are three pages with open-ended questions with text boxes that researchers can answer. Now these questions are things like what does your data cover, who does it belong to, who's going to be accessing the data, how is that data going to be safeguarded from human or machine error, so on and so forth. We have deliberately chosen to not moderate them or mark them or put them through an approval process, although there is an exception, of course there is, because you'll see shortly we have a huge number of data management plans and it would be an incredible workload for somebody to actually have to mark them and provide feedback as they come through. Now the first point, the first waterhole that we went to embed to the data management planning process was to get access to the R-drive. So the R-drive and the data management planning tool were introduced at the same time and you must create a data management plan in order to apply for a folder on the R-drive. Since its inception two years ago we have been performing regular updates roughly yearly to the data management planning tool, we're currently in the process of planning the next phase of enhancements and they are mostly to do with upcoming integration with an ethics management module that we are also implementing. So the R-drive, so that waterhole, it is a very plain and simple network drive, there's no fancy ways of accessing it, but it is effectively unlimited to researchers, they can ask for as much storage as they need, although we might raise an eyebrow if they need more than five terabytes of storage and have a conversation to see if there's another way to provide them with what they need. They don't need to pay a cent to access this storage and the storage access controls are on this per person basis. So you can say I'm collaborating with this researcher from another department, we both need access but nobody else. That's a relatively novel thing for our network drives at Curtin University which are traditionally mostly based around organizational units. Students cannot apply for storage by themselves, they must apply, sorry the supervisor must apply on their behalf. I mentioned R-drive because it was very tightly coupled with the data management plan and of course we we'd like to think research is very interested in having free sort of unlimited storage for their research, I think that's a good way to get them to think about data management planning. So I did mention that we started all of this, the implementation of things quite some time ago. So in April 2014 was when we introduced a new research data and primary materials policy and September that year was a soft launch of the planning tool and the R-drive. There was an even softer launch in April but that was more of a prototype to do some usability testing with early adopters and then we progressively found more watering holes to introduce data management planning tool too. So in January 2015 we introduced stricter research data management for human research ethics. In August we then added it to higher degree by research students. In September we hit a thousand data management plans which was a pretty exciting time. Then in January this year it was introduced for animal research ethics and just the other day I discovered that some honors and fourth year undergraduate student coordinators were planning to introduce stricter research data management for some of their students. I checked this morning and we have we're pushing 2,000 data management plans. I think it'll take us about a month to get to that magic number. And then coming up soon we are implementing a human research ethics management module component of InfoEd and that is necessitating some of the changes to our data management planning tool. So stricter research data management is a few things combined together. So first up we have that creation of maintenance of a data management plan and that is primarily what I would say is responsible for the huge number of plans that we have received. We also want researchers to deposit a copy of their data on suitable institutional storage which might be the R drive but it could also be one of our other network drives at the university. And we also don't want sensitive data to be stored on personal devices at all ever. I mean that was always the case but we've been really highlighting that much more strongly recently. So you might have seen that we revised our data management policy after only a year of it being in place. The reason why we did that was because we had this lovely policy that was based on very little experience of actually doing it. We looked at policies from other universities and organizations and put our own together but then we discovered that when the rubber hit the road that it was lacking in a few places. So we added some much stronger links to the Australian Code for the Responsible Conductive Research. We added a new reference to sensitive data within the policy and we also had stronger references or typo to data sharing and publication. This is a pretty chart of the data management plans over time. Now it is over two years and you can see we've experienced massive growth. Now I can't point out things on my screen to you. I'm used to giving presentations in person but you can see there that in from August to September when the data management planning tool was made publicly available there was a small spike there in growth but really it was January 2015 when it was data management planning was made mandatory for ethics human research ethics approvals that it shot started shooting up. In August 2015 was the introduction for higher degree by research students and another spike again and it's still too early to see if there's been much of an impact by the introduction for animal research ethics but we'll see how we go. We're still experiencing a huge number of new plans every month so I think last month we had about 150 new plans. So data management planning which I haven't necessarily spoken too much about itself but what I feel is important is how we've embedded it in different processes in the research life cycle. So you need access to storage we'd like a data management plan. You need ethics approval please provide a DMP and finally if you want to be a higher degree by research student at Curtin you need to write a data management plan. So what's next? Well we've got 2000 almost 2000 data management plans which in and of itself is an excellent data set that is begging to be analyzed so that's on my list of things to do and also to support the introductions of all these mandates more training more training more training for researchers. So thank you very much that's my presentation.