 Welcome everybody, we're going to obviously talk about data management plans today and well I best introduce myself first, Katherine Unsworth, data librarian at ANS out of Melbourne. Okay so the next slide is the three circles that ANS has in relation to research data assets, making research data assets more valuable for researchers, research institutions in the nation and we do that through our trusted partnerships with various communities related to research also in terms of our reliable services such as research data Australia, the research vocab services as well, the DIY minting services and also in terms of enhancing capability, so building capability within the research base around data management and as part of this webinar is part of that building data management capability for our Australian institutions and obviously research data management plans are a key element in this. So we've got three presenters today, oh well yes we do, myself in Melbourne, Natasha in Brisbane and we've also got a mixed mail from the University of Melbourne who is going to redo his talk that he did at the e-research DMP BOF. Now I'm not able to show the slide of the DMP ANS web page and on that page there are a number of tiles with various topics that fall within that data management plans and you can click out to those and get a lot more information on that there. So moving on obviously today's topic is about research data management and I'd sort of spoken initially about how we've sort of organised it previously but due to the numbers which is quite exciting that there's so many people really interested in this topic we've decided to break it up into three parts. So we'll have talks for one from Natasha on giving us an overview of DMPs and an intro to second generation DMPs, DMP birds of a feather recap that we did at e-research Australasia and Nick will sort of slot into that particular talk as well and he'll talk about DMPs at University of Melbourne and also sort of highlight some case studies that we've put together or used cases actually not case studies. And then it'll be open mic time and that's when we'll be expecting all of you guys to come in and provide some comment and talk about the issues that you are having in your own institutions, what you're doing in your institutions in terms of DMPs and the challenges and any exciting news around that space as well would be really welcome and then from there we'll do, we want to talk about the possibility and the interest of actually initiating a DMP community of practice so we'll get to that at the end of our talk. But again, just a reminder, tweet, hashtag and starter questions to put in the question pod so I'll just throw over to Natasha for her talk. Okay, yeah so we are really delighted to have a lot of people attending this webinar and I just wanted to say a special welcome to the people attending from Aaliyah Information Online in Sydney and special thanks to Liz Stokes, ETS Library and also to the Aaliyah Executive for helping to make that happen. They've got a special room there where they're tuning in so that's really exciting. But I think when Catherine and I looked at the registration list we realised that there's a really large variety of backgrounds represented in the people attending this webinar so I'm just going to do a very short overview of DMPs and DMP tools and then look at some of the characteristics of DMP version 2.0. So what's a research data management plan? Well it's a formal document that describes how data will be collected, organised, described, shared and preserved through the course of a research project and beyond. Data management plans are structured to provide needed information about the kinds of data collected, the formats, descriptions, how long the data will be retained, in what manner the data will be disseminated and how data will be preserved over the long term. If you want to learn more about data management plans I've put in the website sorry, the link to the ANN's website which includes a guide on data management plans. And also if you haven't already you can actually undertake more of a look at data management planning tools and so forth through thing 15 of the ANN's 23 Research Data Things Program and I've put the link in there. So thing 15 actually has sparked quite a lot of interesting reflections and discussions both in person and on the online meetup boards and I'm hoping that some of the people who contributed to that discussion will share their thoughts at this webinar today. Why do we have data management plans or why do we need them? There's a carrot and the carrot is that well organised and structured data and that's what you have to do when you write a data management plan. It's easier to access, analyse, store securely, describe fully and share publicly at the end of a project or even during a project. The stick is that data management planning is actually required by the Australian Code for the Responsible Conduct of Research and some founders, particularly international funders such as National Science Foundation, mandate the completion of data management plans. There are also institutional reasons for data management plans. One of them is so that institutions can keep a registry of who at their institution has got funds for collecting data and therefore that will help them if people fill out a data management plan on how they can actually plan their resources at their institution to match the needs as reflected in the DMPs. Also to reduce risk associated with unorganised data collection so basically if someone at an institution is asked to verify the results of their findings they need to be able to produce the data and having a data management plan does help researchers to think about that process and to plan for that eventuality. There are also institutions are thinking of ways to have added incentives to researchers for filling out data management plans and the University of Colorado at Boulder had a DMP competition in 2015 and they put up the winners on the website there and they've actually got a variety of disciplines represented in the winners of that DMP competition so it's worth having a look at that. Also at Curtin University data management plans are mandated for researchers if you're an HDR student if you required human or animal research ethics approval and if you want access to data storage at Curtin. There's a range of DMP tools available but probably DMP online by the Digital Curation Centre in the UK is probably the most popular and the most used by institutions worldwide but there are others and I'm not attempting to make any sort of list here but I'm just mentioning QSEF which is the Queensland Cyber Infrastructure Foundation has a platform called Redbox which is used by a number of Australian universities and includes a DMP module. So at International Data Week in the USA in September last year there was some interesting discussions about moving to the next generation of DMP tools just nicknamed DMP version 2.0 and this picture shows them an afternoon tea that was put on at one of the IDW events and basically you take your apples and you dip them in the peanut butter and it's surprisingly delicious and by the way this is the only time you will see apples and peanut butter on an end slide but for me there's an allergy here to make and that is that apples represent the first version of data management planning tools and when you dip them in the peanut butter you get version 2.0. So explaining in a little bit more detail, the apples or first version of DMP tools are basically just a PDF or Word document, something that's not connected to any other system at the university. They're sort of just stand alone fill out this form type things. You complete them at the start of a research project and then that's it. You walk away, you've done your DMP now. The outcome of is not measured so we don't know if a researcher did what they said they were going to do in their data management plan. The DMPs are not machine readable mainly because they're just in that PDF or Word document and they're also private so it's only researchers and the institutions who can actually see the data and the DMPs they're not shared. So there's some questions around the effectiveness of that. Do they just prove that researchers can fill out a form or do they prove that researchers are actually thinking about what to do with their data? Is it a way of prompting them to consider things that they wouldn't have considered if they didn't fill out the data management plan and there's no follow-up again on whether you did what you said you would do. Okay so you get the apples and you add the peanut butter and you get DMP version 2.0 and the idea of this is some work being done for a project called EAGA, E-A-G-E-R which is led by Victoria Stodden and funded by the National Science Foundation and I've put a link to her talk at the bottom of the slide there and in that date she is looking at the next generation of data management planning tools and some of the characteristics of the 2.0 versions are that they are public documents. Now there's actually some debate around whether DMP should be public or not and it's sort of well they should be public the arguments for being public are so that people are accountable with what they say they're going to do, the arguments against them are along the lines of all that then people can simply copy one of the public ones and make that their own. DMP's version 2.0 are also something that's measurable so did you do what you said you were going to do in your data management plan. They're ones which are connected to at least one system plans which are also machine readable and the richness in that is that you can mine information from them so institutions will be able to get some information by using the machine readable access to find out what their researchers are going to do with their data and therefore put resources into supporting that end basically. Also that the data that's described in the DMP's is consistent with the fair principles, findable, accessible, interoperable and reusable and also the concept that DMP's are a flexible living document. You don't just create them once at the start, you're going to through the course of your research project and actually re-fix some of the things that you thought at the start and therefore you go back to the data management planning tools, they all have decided to store my data here and not there. So this idea of machine readable DMP's and the EGAR project was actually something raised by Chris Erdman from North Carolina State Universities at the eResearch Australasia Birds of the Feather session. So I'm going to hand over now to Catherine who's going to talk to you a bit more about that. We called the BOF, DMP's Aligning Use to Motivations and Intended Outcomes and part of the abstract was to sort of look at what the mechanisms for research is to state their intentions on how they would manage their data across the life cycle were and we looked at, well we were hoping to have a look at the agents and motivations and how they are different and there was a number of use cases that we came up with to examine in the area which Nick will talk about a little later on. But we were looking at the multiple agents of funding bodies and to encourage data sharing. But the main thing here is to look at the questions here in terms of why implement a DMP tool, does DMP use align with an agent's motivations and more importantly with intended outcomes, what are the expected outcomes and enterprise level DMP tools, one size fits all, what is their place in the landscape and is best practice for research as an aim or a hope for byproduct. The first speaker that we had up was as Natasha mentioned, Chris Erdman who's the Chief Strategist for Research Collaboration at North Carolina State University and he first of all talked about the services at North Carolina and there's an article that this DMP service written by Cross and Davis around what they're doing at North Carolina State University and at the moment it's probably a really useful article to read but they're offering a DMP review service so their librarians actually help researchers review their DMPs. He also went on to talk about the future around machine readable DMPs which Natasha has already talked about and the EGAR project which is basically you know this is really allowing funders to identify trends in data and software submission repository use patterns and carry out other analyses that consist in understanding community use patterns and needs and that's also you know if you take it from an institutional perspective something that's quite interesting for institutions to have that kind of information too. He also as Natasha has also spoke about actually publishing DMPs so that they are more transparent and accountable and he gives an example here of the DMP for more data-driven discovery in its data discovery grant and also part of his talk was about public access plans as not opposed to DMP plans but just a different approach to how we would accumulate the sort of information that we need from what researchers are doing within their projects and the creation of data. So our second specter was Sue Cook from Syro and I've just put up her goals slide helping the research group to reach, document and communicate data management decisions and so obviously whenever we're talking to researchers we talk about it being a live document we're also very interested in the interoperability between systems so being able to push metadata from existing, well pull metadata from existing systems and then pull that metadata to other systems as well and Sue talked about guided questions which you know is basically sort of scaffolding the process of filling out a research data management plan for researchers providing them with some guidance as they go. Also minimum mandatory questions and also conditional questions where if you answer this question then you need to answer the next five questions or you don't have to answer the next five questions and she also spoke about researcher driven and so their engagement with researchers was quite strong in the work that they're doing with implementing and developing their DMPs and she was talking about future aspirations so they're not there yet with the full integration into organisation project proposal and planning systems and also about metadata cascade which is a term that came up at UQ evidently through all data management ecosystems and so metadata being reused for the data repository, metadata reused for storage, provisioning requests and so on and also she spoke about machine actionable which is obviously a pretty hot topic around DMPs and persistent URLs and then we had Libby Blanchard from Central Queensland University who, the central tenant I think of her talk was around the working party and the fact that the working party had representatives from the library, from IT, from the research office e-research and also risk management and ethics as well so it was quite a broad working party and they're basically looking through all of the issues around in terms of implementation which tool do they choose to start with and how they actually then link that to policy and procedures so they have a policy in place at the moment that actually mandates the completion of DMPs and then in terms of policy and procedures socializing that across the university the complexity that that involves and the work that that involves as well then looking at the actual way they would present the DMP in terms of user experience and all of that sort of stuff and then of course the big ticket item is the systems integration which is still a ways off for them obviously and they're very much at the beginnings of this process. So now I'll pass over to Nick to talk about what's happening at the university of Melbourne. Fantastic, great to be here. I was just going to talk a little bit about the University of Melbourne DMP and the process we went through in making the new DMP. I should just start off by saying that Peter Neesha is really leading this effort at the university but I'm just here putting my own views to get forward. So the University of Melbourne developed DMP in 2011 and briefly it contained two forms, two separate forms that researchers had to look at. About 90 separate questions that researchers had to fill in. The DMP template alone had three and a half thousand words in it that you had to read. There was a 12,000 word 40 page guidance document called Procedures and Guidelines for the management of research data and records that you were supposed to read to complete this document. It was also according to policy mandatory although there's very little evidence of any researchers actually doing it of their own free will. And it's also had no definite stated purpose. Just vague words data management. Nothing really very all that specific. So I sort of think of this and it's a word that's been used a little bit as being a monster DMP. It's just huge and it made no inroads into the research community at all. Wasn't actually used. Why is it there? Why do we have why are we spending bandwidth on it? So 2016 there was the idea let's make a new DMP. I'm not going to tell you too much about that new DMP. Still sort of in development. I'm going to tell you two things and firstly all of those numbers in the left hand columns they're much smaller in the right hand columns now. We certainly are asking researchers to complete 90 separate questions or read two separate forms. And the other thing I'm going to say is that when we first started working on this we really thought about what are the reasons why you'd want a DMP. What is the purpose of this DMP? And all these different reasons why you might want researchers to do DMPs should theoretically produce DMP templates that actually look quite different. So we thought well we want to make a good DMP template. What is a good DMP template versus a bad DMP template? But there's just very little researchers gone into this. No one's really said this is what a good DMP template should look like. This is what a bad DMP template looks like. And in fact the problems are a little bit worse than that. And I'll put it this way and I made the same offer at a research some of you might remember. I'll give $50 to anyone who can show me any non anecdotal and systematic evidence that DMPs have any benefits for anybody. And that's a pretty, I think someone, there must be some evidence out there somewhere but I haven't been able to find it. I know Catherine's not been able to find it. So if anyone has that evidence please come forward and I'll happily give you $50. It's a one day only offer though. So don't go out and get on our and start doing all sorts of stats right now because that doesn't count. So there are many different reasons why you might want to have a DMP. And I guess we really sort of drilled down and we thought what's the reason why the University of Melbourne wants to have a DMP for researchers. And I guess the reason we came up with is that we want to help them with their own project management to do a good job. There are also some secondary benefits around collecting that data and using it to help plan out how much space we need to procure for our systems and all that sort of thing. There are other benefits but the real main driver is that we want to benefit individual researchers who are doing it. So Catherine and I have sort of thought through what are the different use cases of why we would want to have DMPs mandated. And secondly how do you and you should, how do you measure the outcomes of whether those use cases are actually working for you. And so we've sort of got four together here to add your own or help us or refine these but the first one I'll just briefly go through these. We think that one of the reasons is that funding bodies in particular really want researchers to complete DMPs because they think that that will encourage researchers to share their managed, share their data and that increases the return on that public investment in that research data. And if that's the case then we should be measuring that. We should be saying researchers who do DMPs are sharing more data. Someone should have done that analysis and as far as I can tell no one's really done that and maybe one person has and they really found that actually researchers aren't more likely to share their data and that was a US study and it was quite small. Another one is institutions might require researchers to complete DMPs to create changes in research behavior and culture and use it as an educative tool. So the measure there would be researchers who do DMPs are more efficient and productive, produce more papers in a set period of time. That should be a pretty simple analysis to do, still hasn't been done all that much. Another one is institutions require researchers to complete DMPs to basically use it as a business intelligence tool. Use it to plan out the acquisition of data and other resources and look at what data sharing platforms should be invested in and the measurable outcome there would be actual the use of that information in decision making by the institution. And I know there are a few institutions that have sort of started to do that and it would be really great to see how that's going. And the final major use case is that and this is perhaps the original use case that really that DMPs were invented for in the 1970s and that some researchers using DMPs as part of their routine project management design and planning. So it's researchers going out creating a DMP and using that to share with fellow researchers and share with others to help them understand what everyone's roles and responsibilities in collection and management of data are. So that would really be our projects that use those DMPs would be more efficient and better capitalized. So I think that whenever talking about DMPs and the DMP with the apple and the peanut butter together I think what's really important and an extra idea to add there in my opinion is to really think about why do we want to make DMPs perhaps mandatory or why do we want researchers to use them and really think about what should that DMP look like depending on what that use case is. So that's all I have to say. Back to you Catherine. Thanks Nick. So during the BoF we did a live poll as well and asked a number of questions of our audience and it was probably only a small sample really in the end if we really thought about it but the first question was in the Australian context what do you see as the main motivations for institutions implementing DMPs and so we asked people to rank those and the first one not surprising and I think if you bear in mind the sorts of people that were in the audience would probably mostly be librarians, data managers e-research folk and not too many researchers that the funders and institutions demonstrating to government return on investment through requiring best practice in data management was going to be the top of the list and then in second came that the institutions can capture information about the generation of research data so the business intelligence tool use case and then of course coming in third not too far behind the institutions capturing information was the educative tool so you know funders and institutions wanting researchers behaviours to align better with best practice and using DMPs in that way and of course the fourth one was actually recognising the benefit researchers themselves recognising the benefit utilising DMPs as just a routine part of project management. Catherine I just have a question related to this from Gareth Denier who's asked how many participants in that survey I think it was around about 28 Gareth but some of the actual questions not everyone answered each of the polls but it was around about that size sample so not a lot and the next question was are we seeing changed RDI and behaviours in researchers as a result of DMPs and this is kind of a good one I think because 11% of that sample said yes, 17% no but as Nick was saying we just don't have any evidence to support whether DMPs are in fact translating into changed behaviours by researchers so 72% said basically not sure and I think really if we're going to get serious about DMPs and the benefits that they have for researchers in terms of efficiency and that translation into best practice then we really need to do some research in this area and find out just what's happening and then the next question was should Australian funders follow the lead of international agencies and mandate a requirement for DMPs as pointed with the result of this poll I have to say because 82% said yes to compliance and mandating DMPs by funders and institutions and I actually from a personal perspective believe that compliance actually changes a person's mindset and with researchers they will then just do the barest minimum that they have to because that's what they have to do and rather than looking at it in terms of a benefit to themselves and their own workflows and practices so I was and again but you need to bear in mind that the audience here basically from that administration point of view so it would make it easier for them as administrators if funders did follow the lead of international agencies and then the final question we didn't get to actually ask but would you be interested in your local DMP interest group that could fit into and connect with international initiatives we did ask it verbally that we didn't get a chance to actually poll people because we ran out of time and a few people came up and said that they would be interested in joining such a group and so that's one of the questions that we're going to have for you guys a little later on so just bear that in mind so I will wind up. Thank you Natasha for moving me along. Thanks everyone.