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Transcript of Webinar: Data management plans (DMPs) - audio

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Video and slides available via: http://www.ands.org.au/news-and-events/presentations/2017
Have you implemented a Data Mangement Plan (DMP) tool at your institution or are you currently involved in discussions to implement one? Woudl you like to connect with others who are involved in implementing DMPs? Then this webinar is for you!
This webinar brings together those involved in planning or implementing DMP to exchange information and explore ideas around DMP.

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Transcript of Webinar: Data management plans (DMPs) - audio

  1. 1. Webinar: Data Management Plans (DMP) 15 Feb 2017 Speakers: Katheryn Unsworth (ANDS), Natasha Simons (ANDS), Nick Smale (University of Melbourne) START OF TRANSCRIPT Kathryn Unsworth: Welcome everybody. We're going to, obviously, talk about data management plans today. Well, I best introduce myself, first. Kathryn Unsworth, data librarian at ANDS, out of Melbourne. The next slide is the three circles that ANDS has, in relation to research data assets - making research data assets more valuable for researchers and research institutions in the nation. 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 service, as well. The DOI minting services and also in terms of enhancing capabilities, so building capability within the research space around data management. 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. We've got three presenters today. Oh, well - yes, we do. Myself, in Melbourne, Natasha in Brisbane and we've also got Nick Smale from the University of Melbourne, who is going to re-do his talk that he did at the eResearch DMP BoF. Now, I'm not able to show the slide of the DMP ANDS webpage. On that page, there are a number of tiles with various topics that fall under the topic - within data management plans and you can click out to those and get a lot more information on that there.
  2. 2. Page 2 of 14 Moving on, obviously, today's topic is about research data management and I've spoken initially about how we've 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. We'll have talks - one from Natasha, giving us an overview of DMPs and an intro to second generation DMPs, DMP Birds of a Feather recap that we did at eResearch Australasia. Nick will slot into that particular talk, as well, and he'll talk about DMPs at the University of Melbourne and also highlight some case studies that we've put together - or use cases, actually, not case studies. Then it will be open mike 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. Then, from there, we'll do - we want to talk about the possibility and the interest of actually initiating a DMP community of practice. We'll get to that at the end of our talk. But, again, just a reminder, tweet #andsdata, questions to put in the question pod. I'll just throw over to Natasha for her talk. Natasha Simons: 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 ALIA Information Online in Sydney and a special thanks to Liz Stokes at UTS library and also to the ALIA 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 Kathryn 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.
  3. 3. Page 3 of 14 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 ANDS website, which includes a guide on data management plans. 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 ANDS 23 (research data) Things program, and I've put the link in there. Thing 15 actually 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. The carrot is that well organised and structured data and that's what you have to do when you write a data management plan, is easier to access, analyse, store securely, describe fully and share publically 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. Some funders, particularly international funders such as the 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
  4. 4. Page 4 of 14 reflected in the DMPs. Also, to reduce risk associated with unorganised data collection. So, basically, if someone, a researcher at an institution, is asked to verify the results of their findings, they need to be able to produce the data. 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. The University of Colorado 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 a 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 DMPonline 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 am not attempting to make any sort of list here, but I'm just mentioning QCIF - 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. 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. This picture shows an afternoon tea that was put on at one of the IDW events. Basically, you take your apples and you dip them in the peanut butter and it's surprisingly delicious. By the way, this is the only time you will see apples and peanut butter on an ANDS slide. [Laughs] But, for me, there's an analogy 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. Explaining in a little bit more detail, the apples or first version of DMP
  5. 5. Page 5 of 14 tools are, basically, just a PDF or Word document. Something that's - they are 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 and you've done your DMP now. The outcome 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. 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. There are 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? 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. The idea of this - there's some work being done for a project called EAGER - E-A-G-E-R - which is led by Victoria Stodden and funded by the National Science Foundation. I've put a link to her talk at the bottom of the slide, there. (http://web.stanford.edu/~vcs/talks/RDA-DMP-2016-STODDEN.pdf) In that, she is looking at the next generation of data management planning tools. Some of the characteristics of the 2.0 versions are that they are public documents. There's actually some debate around whether DMPs should be public or not and its 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 are more along the lines of, well then people can simply copy one of the public ones and make that their own. DMPs, version 2.0 are also something that's measurable. Did you do what you said you were going to do in your data management plan?
  6. 6. Page 6 of 14 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. 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 is described in the DMPs is consistent with the FAIR principles - findable, accessible, interoperable and reusable. Also, the concept that DMPs 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, actually rethink some of the things that you thought at the start and therefore you go back to the data management planning tool and say, oh, I've decided to store my data here and not there. This idea of machine readable DMPs - and the EAGER project was actually something raised by Chris Erdmann from North Carolina State University at the eResearch Australasia Birds of a Feather session. I'm going to hand over, now, to Kathryn, who's going to talk to you a bit more about that. Kathryn Unsworth: This is what they called the BoF. DMPs aligning use to motivations and intended outcomes. Part of the abstract was to look at the mechanisms for researchers to state their intentions on how they would manage their data across the lifecycle were. We looked at - we were hoping to have a look at the agents and motivations and how they are different. There was a number of use cases that we came up with, to examine and interrogate, which Nick will talk about a little later on. But we were looking at the multiple agents of funding bodies, to encourage data sharing. 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? Also, more importantly, with intended outcomes, what are the expected outcomes? And enterprise level DMP tools, one
  7. 7. Page 7 of 14 size fits all, what is their place in the landscape? And is best practice for researchers an aim or a hoped for by-product? The first speaker that we had up was - as Natasha mentioned - Chris Erdmann who is the chief strategist for research collaboration at North Carolina State University. He first of all talked about the services at North Carolina. There's an article about this DMP service, written by Chris and David, around what they're doing at North Carolina State University. At the moment - it's probably a really useful article to read, but they're offering a DMP review service. They're like 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 EAGER project, which is basically - this is really allowing funders to identify trends in data and software submission, repository use patterns and carry out other analysis that consist in understanding community use patterns and needs. That's also, 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 spoken about - actually publishing DMPs, so that they are more transparent and accountable. He gives an example here of the DMP for a more investigatory data driven discovery grant. Also, part of his talk was about access plans - public access plans. 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. Our second speaker was Sue Cook from CSIRO I've just put up her goals slide. Helping the research group to reach document and communicate data management decisions. 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.
  8. 8. Page 8 of 14 Sue talked about guided questions, which is basically scaffolding the process of filling out a research data management plan for researchers. It's 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. She also spoke about researcher driven. Their engagement with researchers was quite strong in the work that they're doing with implementing it and - well developing and implementing their DMPs. She was talking about future aspirations. They're not there yet with the full integration into organisation project proposal and planning systems. Also, about metadata cascade, which is a term that came up at UQ, evidently, through all data management ecosystems, so metadata being reused for the data repository, metadata reused for storage provisioning requests and so on. Also, she spoke about machine-actionable, which is obviously a pretty hot topic around DMPs, and persistent URLs. Then we had Libby Blanchard from Central Queensland University who - her - the essential tenet, 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, eResearch and also risk management and ethics, as well. It was quite a broad working party. 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 procedure. They have a policy in place at the moment that actually mandates the completion of DMPs and then - in terms of the policy and procedure, socialising 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. Then, of course, the big ticket item is the systems integration, which is still a way off for them, obviously. They're very much at the beginning of this process.
  9. 9. Page 9 of 14 Now, I'll pass over to Nick to talk about what's happening at the University of Melbourne. Nick Smale: 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 Niche] is really leading this effort at the university, but I'm just here putting my own views forward. The University of Melbourne developed a DMP in 2011 and, briefly, it contained two forms - two separate forms that researchers had to look at, about 90 separate questions that researched had to fill in. The DMP template alone had 3,500 words in it that you had to read and 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. It also had no definite, stated purpose. Just vague words, data management. Nothing, really, very - all that specific. I 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. It wasn't actually used. Why is it there? Why do we have - why are we spending bandwidth on it? In 2016 there was the idea, let's make a new DMP. I'm not going to tell you too much about that new DMP, it's still sort of in development, but I'm going to tell you two things. Firstly, all of those numbers in the left hand columns - they're much smaller in the right hand columns, now. We're certainly are asking researchers to complete 90 separate questions or read two separate forms. 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? What are the difference - why - and all of these different reasons why you might want
  10. 10. Page 10 of 14 researchers to do DMPs should theoretically produce DMP templates that actually look quite different. 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 research has gone into this. No one has really said, this is what a good DMP template should look like, this is what a bad DMP template looks like. Don't do that. In fact, the problem is a little bit worse than that, and I'll put it this way, and I made the same offer at eResearch - 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. That's a pretty - I mean, I think someone - there must be some evidence out there, somewhere, but I haven't been able to find it, I know Kathryn has not been able to find it. If anyone has the 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 R] and start doing all sorts of stats right now, because that doesn't [count]. There are many different reasons why you might want to have a DMP. I guess we really drilled down and we thought, what's the reason why the University of Melbourne wants to have a DMP for researchers? 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 using it as a - collecting that data and using it to help plan out how much space we need to procure for our systems and all of that sort of thing. There are other benefits, but the real, main driver is that we want to benefit the individual researchers who are doing it. Kathryn and I have thought through, what are the different use cases of why we would want to have DMPs mandated? Then, secondly, how do you - and you should - how do you measure the outcomes of whether those use cases are actually working for you? We've sort of got four together, here, and you might want to add your own or help us or refine these. But the first one - I'll just, briefly, go through these - is that - we think that one of the reasons is that funding bodies, in
  11. 11. Page 11 of 14 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. 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 has really done that. 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 that was quite small. Another one is, institutions might require researchers to complete DMPs to create changes in research behaviour and culture and use it as an educative tool. The measure there would be, researchers who do DMPs are more efficient and productive and produce more papers in a set period of time. That should be a pretty simple analysis to do, it 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 - create acquisition of data and other resources and to look into what data sharing platforms should be invested in. The measurable outcome there would be actual - the use of that information in decision making by the institution. I know there are a few institutions that have started to do that and it would be really great to see how that's going. The final major use case is that - and this is, perhaps, the original use case, that DMPs were invented for in the 1970s, and that's researchers using DMPs as part of their routine project management design and planning. 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. That would really be, projects that use those DMPs would be more efficient and better capitalised. I think that, whenever talking about DMPs and the DMP with the apple and the
  12. 12. Page 12 of 14 peanut butter together, I think what's really important to [unclear] to add there], in my opinion, is to really think about why? Why do we want to make DMPs, perhaps, mandatory? Or why do we want researchers to use them? Really think about, what should that DMP look like, depending on what that use case is. That's all I have to say. Back to you, Kathryn. Kathryn Unsworth: Thanks, Nick. During the BoF we did a live poll, as well, and asked a number of questions of our audience. 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? 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, they'd probably mostly be librarians, data managers, eResearch 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. Then, in second, came the institutions can capture information about the generation of research data, so the business intelligence tool use case. Then, of course, coming in third, not too far behind, the institutions capturing information - it was the educative tool. Funders and institutions wanting researchers' behaviours to align better with best practice and using DMPs in that way. Then, of course, the fourth one was, basically, recognising the benefit - researchers, themselves, recognising the benefit and utilising DMPs as just a routine part of project management. Natasha Simons: Kathryn, I just have a question related to this: how many participants in that survey? Kathryn Unsworth: I think it was around, about 28, but I'm not - some of the actual questions - not everyone answered each of the polls. But it was around, about that size sample, so not a lot. The next question was, are we seeing change [unclear] behaviours in researchers as a result
  13. 13. Page 13 of 14 of DMPs? This was kind of a good one, I think, because 11 per cent of that sample said yes, 17 per cent, 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 per cent said, basically, not sure. 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. Then the next question was, should Australian funders follow the lead of international agencies and mandate a requirement for DMPs? I was so disappointed with the result of this poll, I have to say. [Laughs] Because 82 per cent said yes to compliance and mandating DMPs by funders and institutions. I actually, from a personal perspective, believe that compliance actually changes a person's mindset. With researchers, they will then just do the barest minimum that they have to, because that's what they have to do, rather than looking at it in terms of a benefit to themselves and their own workflows and practices. I was - again, but you need to bear in mind that the audience here are 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. Then the final question we didn't get to actually ask, but, would you be interested in joining a local DMP interest group that could feed into and connect with international initiatives? We did ask it, verbally, but we didn't get a chance to actually poll people, because we ran out of time. A few people came up and said that they would be interested in joining such a group. 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. I will wind up. Thank you, Natasha, for moving me along. Thanks everyone. END OF TRANSCRIPT
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