Notes of Interview
Project: IDRC Data Sharing Pilot - https://doi.org/10.3897/rio.2.e8880
Date: 24 September 2015
Location: Paris, France - Research Data Alliance Meeting
Interviewer: Cameron Neylon
Interviewee: Kevin Ashley
Interviewee Affiliation: Digital Curation Centre, UK

Audio: 
../Interview_Audio/Interview-24_09_2015-16_50-Kevin_Ashley[partial].wav
../Interview_Audio/Interview-24_09_2015-16_50-Kevin_Ashley[partial].flac

The beginning of the interview is missing.

Discussion at the beginning which was lost from the audio included experience of Edinburgh University data management implementation and in particular the way in which policy implementation was framed as a set of mutual promises between researchers and support systems. Also noted that the implementation process for RDM worked best when it aligned with the culture of the institution. In Edinburgh case they are decentralised and non-heirachical at least in interactions with academics.

From email of 20th June 2017 confirming details with Kevin:
“What Edinburgh put in place first wasn't infrastructure but policy. There was *some* infrastructure in place prior to the policy, but not much. (Just Edinburgh Datashare, a repository suitable or open data.) The policy was then used to help make the case for
infrastructure whilst at the same time ramping up pressure on academics to comply. As it was framed as a set of mutual promises (the university will do X, the researcher shall do Y) this worked well. So no one expected full compliance with the policy on day 1 - it was aspirational, and that fits with the organisational culture. In other places, policies are enacted with lots of sharp teeth from the outset and if they aren't, they are considered worthless.”

Begin transcript:

...they all at least publish about it. Being able to discover that your data exists and that at least that there is some sort of process by which I can have a dialog about am I allowed to use it, what sort of criteria do I need to satisfy to be able to use it. We seem to be having that argument over and over again. So something has gone wrong there with not the original ideas but how they're being communicated.

And the other that is really brought home to me at the moment, just because there've been so many discussions about this in the last couple of days here at RDA is around Data Management Planning. Where the original concept in my my mind about why people said this was a good idea and it was about improving research practice, and as a consequence you also achieve more reusable data and everything else but right at the beginning what you get is better research. Its no different from teaching any other sort of research method. Take care of your stuff so that you can reuse it and reunderstand it a week later, a month later, a year later. Achieve that first and then other good things come out of it.

And somehow its turned into this beuracratic model in which Datta Management Planning is seen as being all about funders' demands and a beuqacratic exercise in which you produce a static document which researchers feel quite disassociated from. And even when they are working in environments where they are doing genuinely good data management planning I think they often see that as being a completely distinct thing from this document they have to produce to satisfy a funder policy. So that's now, that's sad that we've allowed that to happen and its just a waste of effort. So again, I don't think the concept, the idea behind thinking about planning about data was a bad thing, but the realisation somehow has gone wrong.

You might characterise them as training issues but they're also issues where something becomes focussed down on a single point, rather than an exercise in process.

2:40 

yeah, I think so. And one of the simple things we're trying to do at DCC, we've got this tool to help people do data management planning and I think in some ways we're responsible for some of that. The bad...thinking but I would also like to think that we're trying to help the right sort of practice. So the original version of that tool was all geared at helping an individual probably the PI produce a document that accompanied a bid. And we rapidly realised that was getting away from the idea of in reality data management planning is often a communal process. You often have lots of different people involved, when you have large projects with multiple partners, data is often distributed. Different people are going to be able to contribute different pieces of information at different times, around the planning. So just changing the tools so that they encourage that idea that of multiple contributors of being able to parcel out the ideas and different people commenting on, these are the things I can speak to. None of us can write the whole thing, and encouraging the idea that also you have commenters as well as creators in a plan. That you can invite in other people to say, this is what we're proposing to say and we're not asking you to write these bits of the plan but we are asking you to comment on it and to say yeah yeah, that meets my understanding. 

And I would hope then that thinking of those of those plans as more of a shared responsibility in that sense in a project might also lead to the idea that given that data often, the custodians of research data often change over time, particularly in those disciplines where there are large domain repositories, maybe you should think of the plan as being something that you hand over with the data. That the people responsible for that change over time. So originally its the researchers that produce it and at some point later its a different piece of data and instead of handing over this document that you wrote five years ago when you put this bid in you're handing over something that's a more apt [? document]. And that allows you to track much more the whole lifetime, the provenance, of what did people do to this data at different points in time and why.

So it becomes a living part of the data

5:03 

Yeah, yeah and anyone who has ever had experience trying to reuse data knows that although its great if you have that expressed in a very formal way, sometimes just having access to someones working notes at the time they were producing stuff or cleaning it or whatever helps you understand. Otherwise you just look at this stuff and you know that there's an anomaly there but you don't understand why. And you find some extra...oh that's why they got rid of those records. That's why there's something bizarre about those particular measurements. Now I understand it, I know I can do something about it. 

Thinking globally, is there something that's emerged that wasn't obvious five years ago thats really critically important in the design of policy or implementation processes. Particularly thinking of things that are not obvious that are starting to emerge, surprises.

6:18 

The problem is that the things that surprised you a year ago by now you've forgotten that you were surprised. They've become a part of normal life. In terms of whats changed I think, so I think our organisation is caring more now about the lack of joined up thinking between funders about how they approach this and I don't think, although in the back of our minds you knew that that was important some years ago and perhaps thats why we tried to produce standard check lists for this that we're trying to approach the questions of data management in non-domain specific terms. We hadn't realised just again how much extra burden it creates for everybody that funders...ended up producing requirements that differ for no good reason. And I'm distinguishing that from the cases where the requirements differ for very good reasons. Or where I think in other cases all you're looking at is a difference of emphasis. Its right that a funder of medical research is going to put a lot more emphasis on issues to do with consent and privacy than somebody funding astronomy, where those questions are nearly almost empty questions. Are there any ethical concerns. No. Is the usual answer.

8:01 

What's unhelpful is when two funders who have the same concerns choose to approach it in different ways and ask for compliance in very very different ways. The extent to which that causes issues for researchers that deal with multiple funders, organisations that deal with multiple funders I think has taken us a bit by surprise. 

And I guess something thats taken us by surprise in a different way is how few have adopted the idea that...I think in the UK I'm only aware of the NERC doing this...of saying...of note expecting people to do fully worked up thoughts around data management until they know they've been funded. That's again...reduces the regulatory burden on everyone and also I think fits with the way you do research. There are all sorts of things about a project you don't really give thought to until you know its going to happen. And you only work it up so much at proposal stage. And there's a bunch of stuff...that you leave deliberately open there because you haven't got the intellectual space to think about it. 

And I suppose that also fits with something we didn't give a lot of consideration to and in lots of context is in important is...everyone is focussed on research with external funders and there are many fields of research where its common for research to go on without external funding...where its something thats done with infrastructure funding. And many of the same principles ought to apply but its been harder to translate the work we've done for...sometimes because...in some areas of research the very word data is alien. In other cases in the social science data is not an alien word but its still a case of...people see it so much as a compliance issue...trying to turn it back into...this is about research methods and good practice and about things that ultimately benefit you as a researcher.

thinking forward five years, how we design future policies. What are the new things or things we need to focus on, haven't focussed enough on.

11:04 

I'm sure there'll be something that I haven't thought about and perhaps somebody is better at doing the futurology but I'm not going to come up with. But I'd hope that broadly at least in a five or ten year role that we've got the categories sorted out but perhaps some of those...earlier answers...the emphasis is sometimes wrong. 

In terms of actually making all this work one area that...again I've been reminded of in discussions quite recently is the extent to which we've known for some time to get change it can be important to get a new generation of researchers to think...whether its in terms of open science or open working...to think right from the outset and one way you do this is to design education that fits in with doctoral training but others have pointed out to me that in reality so many early career researchers basically learn their habits from whoever their direct supervisor is. 

And its not just in this area but in all sorts of things to do with their research career. The problem is that they need skills that their immediate superiors don't have and it seems that's quite a generic problem with researchers of how do you ensure that you don't just simply replicate...have a new generation of researchers that look just like the ones you had before. Obviously the things that you do want to hand along...the whole idea that you hand on knowledge and yet we all expect the people to come after us to do things we couldn't of thought of and couldn't do. And with methods and tools that we didn't have access to and therefore don't know how to use.And that's a much bigger problem, than research data management. How you give those skills to the new generation of researchers.