Data Interview with Jude Towers

Already our third Data Interview! This time with Dr Jude Towers. Jude is Lecturer in Sociology and Quantitative Methods and the Associate Director Violence and Society UNESCO Centre. She holds Graduate Statistician status from the Royal Statistical Society, is an Accredited Researcher through the ONS Approved Researcher Scheme, and is level 3 vetted by Lancashire Constabulary. Her current research is focused on the measurement of violence. Jude also presented at the first Data Conversations.

Q: Jude, what data do you currently work with?

Jude: The main data I work with is the Crime Survey for England and Wales. That is available on the UK Data Service. The different parts of it have different access requirements. The main questionnaire which I now mostly use is relative straightforward. You can just download it and use it.

Then we comply with the Home Office and ONS [Office for National Statistics] recommendations about the sizes of cells for publication. They say there should be a minimum of 50 respondents in a cell before it’s statistically analysed. You must ensure that you if you’re doing cross tabulations, for example, the numbers are sufficient that you couldn’t identify individual respondents. That is relatively straightforward and I would say that’s general good practice in dealing with that kind of data.

We have also used the Intimate Violence module, which is a self-complete module as part of the Crime Survey. For that there is a special level of access which requires training from what used to be the Administrative Data Liaison Service. That was a one day training course in London, signing of lots of different agreements. Then you access that data through your desktop computer, it has to be a static IP address, and everything is held on their server. You go into their server, you can’t bring anything out, and everything you do has to be done in there.

That means if you want to write a journal article using that data you have to write it inside their server. Anything that you produce using that data, whether it’s a presentation in PowerPoint, a table in a slide, all of that has to have approval from the UK Data Service before it can come off the server into any form of public domain. That has to be done each time you use it. It is quite onerous in some ways but is a very high level of security.

Jude Towers

Q: That data is already in an archive so there is no need to share it again. Is citing that data straightforward in case somebody wants to see the data that you used?

Jude: Yes, it’s straightforward to cite. If people want to have access to the raw data they’d have to be accredited in the same way I got accredited. We got the whole team accredited at the same time so we can share data as we produced the work. There is nobody in our team who isn’t accredited. There is no problem …. we can sit in front of the computer and look at that data as we’re trying to develop the work.

Q: So if I were to look at your screen here to view the data I’d have to have the accreditation.

Jude: Yes! Actually it’s interesting that some of these requirements are similar to the ones for police data.

We are doing a lot of work with Lancashire Constabulary. We as a team have just been vetted to Level 3 which gives us the same access as any serving police officer. We have direct access to raw data at the individual level. This is for two reasons. One is that you can ask for data that the police put together, anonymise and give you but if you don’t know what data there is, it is really difficult to know what to ask for. And the second reason is being able to explore the data at that level means that you can make links that you couldn’t otherwise make. You can find individual people in different datasets that allows you to ask much more complex research questions and then anonymise and take it out as a dataset.

That’s been quite an interesting process. First of all, you have to be vetted. Then you get your police access card. Rather than it being on a secure server what we have now got is police laptops. We access the police server through that police laptop. Again, you can’t take anything out until it is anonymised. The keyboard on the laptop records every keystroke so someone can exactly see who you have looked for and why you have looked for them.

Then the requirements that are similar to some of the Home Office ones which are being in a locked office without public access so someone can’t look at what you’re doing over your shoulder whilst you’re doing it. I couldn’t take my police laptop and work in the Library. You can’t work on it in public spaces.

That’s quite interesting because we just got two ESRC Studentships with Lancashire Constabulary and they will do the same. They go through Level 3 vetting and they’ll have the police laptops. But then we came across the problem where do we put them? They can’t go in an office with other PhD students who are not vetted. They are at different stages in their PhD. So actually, what we’ve had to do were quite specific arrangements so that those students share a room that’s locked. You can’t have someone else in the room who is not vetted!

Q: Is it more difficult in this case to cite data because the data is not in an archive like the UK Data Archive?

Jude: What we haven’t yet done in any official capacity, but we’ve had discussions. The Crime Survey data people can access. What we have done in some of the cases where we have produced new data we’ve done data tables and can release those. So people can see the data we use, completely anonymized, aggregated to a very high level. If people want the raw data they can get accredited or they can go to the UK Data Service. If people just want to re-run our statistical tests then the “semi-raw data” if you like is there.

Jude Towers

Q: Is that what you could do with the police dataset?

Jude: That is the conversation we are currently having with the police: Is there any point at which that data can be released into the public domain. We haven’t yet made agreements about that. I think what we’ll end up doing will be very interesting. There are very few researchers who are doing it in this way. Most people get given anonymised data that the police have anonymised themselves.

So we are doing a series of test cases saying that as we increasingly aggregate and anonymise the data at what level can that data put into the public domain and at what level is it useful? We’ll have to see if we can find a place that matches where it is still useful and it can go public. If we are able to do that then we’ll put it into archives.

Q: That is really interesting!

Jude: Yes, but is very clear that in the ESRC Studentships that the police have the final say on that.

Jude at the first Data Conversations

Q: Do the police have a level of expertise and confidence in providing data and working with you? Does that work well?

Jude: It does work well. The police are in a really interesting position. They [are] systematically, some more quickly than others… [nationally] moving to evidence based policing and significantly improving their research capacity. At the moment they are doing that in two ways. One is by working closely with universities and the other is by more systematically training police officers and associate staff.

I am doing a lot of work with Leeds University on data analytics for the police and we are setting up CPD [Continuing Professional Development] for data analysts in the police to have a more systemic and academic approach to research questions. Now that’s really interesting because the position they are in in their organisation tends to be relatively low but some of the things they are asked are just impossible.

So we are trying to give them the tools to say you can’t ask me for this when you don’t collect it. Or you want me to evaluate something but nobody told me it was happening so there is no data from before. We’re getting them to think through the research process in order to influence how data analytics are used inside the police. It is interesting because there is a bit of a debate about whether they really need data analysts or they can spend their money buying really good algorithms [which] will sort all this stuff out. Our argument is that you need really good data analysts because you need them to explicate the inherent theories that people have, that they’re trying to test, that they can talk people through that research process.

In Lancashire Police those things are coming together. They are much more actively working with academics and they are much more systemically embedding academic research processes inside the institution. They have a Futures team that includes multiple PhDs, M.A.s and now even some undergraduate students. They have a list of research questions that they are interested in as an institution, and they are actively going out looking for people who do that research for them and to sit inside the police while they do it.

Q: That is really fascinating! Is there anything Lancaster University could do to help you or your colleagues with your research? Or does the set up work for you?

Jude: I think it’s OK. The sticky parts are things we are working through for example around contracts. Who owns the Intellectual Property? Who gets final say over publications? We’ve been lucky so far that we’ve negotiated things but I know in other areas these have been problematic: getting clarity and setting up protocols is useful.

There’s been some talk about setting up secure data hubs and I’m in two minds about it. I think in some ways they’d be really useful but I think in other ways they are perhaps a bit inflexible. My colleague across the corridor is doing the same as us with social work data and they’ve done what we have done. They accredited the individuals and have given them a specific laptop to access that data directly, and that works really well.

Thanks very much for the interview Jude!

 You can find out more about Jude and her research here. Her current research papers are: with Walby and Francis, ‘Is violent crime increasing or decreasing?’ (BJC 2016); with Walby, ‘Measuring violence to end violence’ (Journal of Gender-based Violence forthcoming); and with Walby et al, The Concept and Measurement of Violence against Women and Men (Policy Press 2017).

Data Interview with Jo Knight

This is our second Data Interview. This time we were glad to have a chat with Dr Jo Knight.

Jo is a Reader within the CHICAS research group, Research Director in the Lancaster Medical School and theme lead for Health within Lancaster’s Data Science Institute. Jo has experience in developing new methods for analysing genetic data as well as experience in applying known techniques to a large variety of datasets.

The Conversation by Michael Dunne, Flickr, CC BY-NC

Q: Jo, when you talked at our recent Data Management event about a “positive” data management story and a “negative” story there was a lot of interest in that, so we thought we could use this in our next Data Interview. Which story would you like to start with?

Jo: I think it would be good to start with a negative one so I can end on a positive note. And chronologically that is how it occurred.

So the negative story relates to an early time in my career. I had some genetic data on a number of individuals, about 120. I did some statistical analysis of the data. I noticed that some of the patterns that I had in my analysis seemed unusual. They weren’t characteristic of the type of patterns you would expect given that the individuals in this sample were supposed to be siblings. I didn’t have enough genetic information to establish their relationships completely but I did have enough to see that overall patterns didn’t look how I expected them to.

I took the data to someone more experienced and said: “There is something wrong with the patterns here”, and he said “Yep, there is definitely something wrong. Those individuals clearly aren’t related to each other.”

At that time, given the technologies that were available, we couldn’t just get more data to determine the relationships. We had to throw all of that data away!

It was essentially because the data and the samples had not been linked and managed. At some point between labelling the samples, entering the labels into a database and recording the relationships and rest of the information about the individuals something had gone wrong. So the data management had gone wrong and these samples were now completely useless. As well loss of my time we couldn’t use these samples for any other work either. They no longer had the data provenance.

Q: Can you quantify how much time you invested in that project?

Jo: It’s hard to remember but for me it would have been months of work to interrogate the samples! It would also have cost a fair amount in reagents. And for the person that collected the data probably up to a year’s work getting all the DNA samples from the individuals. Furthermore those individuals had given samples for medical research that was not been able to be undertaken.

Q: That is a rather sad story.

Jo: Yes, it is.

Dr Jo Knight

Q: Now the positive story. What happened?

Jo: I’m involved in a Consortium now, the Psychiatric Genomics Consortium, and in this Consortium over 800 researchers from 38 countries have come together and worked really very hard through ethical approvals, data procedures, data collection and data pooling in order to collate samples.

And they have been able to collect data that is now published, actually a couple of years ago in 2014, on more than 35,000 schizophrenic cases and even more control samples than that. And through the good and appropriate management of data it has meant that we were able to identify 108 genetic risk loci for schizophrenia. It has enabled us to move the field forward in terms of beginning to understand the genetic contribution to schizophrenia.

For a long time we knew that schizophrenia has a genetic component but we were unable to pinpoint very many of the risk variants at all, and this study was a real landmark in identifying a large number of the risk variants involved in the disorder. Lots more work needs to be done! What is really exciting about the Consortium is that the original paper is just the tip of the iceberg. That was the paper where the first analysis was done but the data is now held and managed in a manner that researchers who work in psychiatric genetics are able to access that data, analyse that data and answer lots of different questions about the genetic predisposition to schizophrenia.

The Psychiatric Genomics Consortium holds data on lots of other disorders as well. Basically, the appropriate management of that data means we are able to learn a lot more about diseases than we would have if people hadn’t got together and as a large group effectively managed the data.

Q: What is the key step in doing this?

Jo: It’s a willingness to share data and to see the bigger scientific question that can be answered if you share the data, and not just try to hold onto it and answer your own smaller questions. It is a willingness to put considerable amounts of time into data management. So there are lots of people including myself that have informal unpaid roles in managing that data to make it accessible.

Q: What can we as an institution do to encourage that willingness to share data?

Jo: I think Lancaster University as an institution has a very strong positive view of collaborative research across the Faculties and beyond the University. And that’s the kind of thing that does encourage people to share data and be involved in these projects. I think that is something we need to continue to pursue. And also the support systems that we have in place, the people and systems that help us to deposit data and make it available.

Thanks very much for the interview Jo!

 You can find out more about Jo and her research here. The full reference of the article on schizophrenia mentioned by Jo is:

“Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci.” (2014) Nature. 24 July. 511 (7510): 421-7. doi:10.1038/nature13595

 

Data Interview by Hardy Schwamm (@HardySchwamm), 3 May 2017.

 

2nd Data Conversations 4 May 2017 – Data Security and Confidentiality

The 2nd Data Conversations had the theme of Data Security and Confidentiality. More than 20 Lancaster researcher attended. It was nice to start with a slice of pizza and a brew.

Always nice to start an event with food!

As at the 1st Data Conversations we had five lightning talks. You can see the agenda below.

You can find a short summary of the event, the slides and some photos below.

Denes Csala – The sensor cloud around us: collecting, mining and visualizing the energy and building management data of the campus

Dr Denes Csala is a newly appointed lecturer in Energy Storage Systems Dynamics with Energy Lancaster.

There are 30,000 sensors on campus capturing all sorts of data about energy and energy consumption.  This has the potential for us to understand a huge amount about the way energy is managed and used but at the same time throws up the issue of managing extremely sensitive commercial and personal data.  Access to the data is strictly controlled but Energy Lancaster are very excited about the possibilities of what could be done with the data.

You can see an animated visualization of the campus energy metering system sensor data here:

Kopo Ramokapane – Could computing: When is Deletion Deletion

Kopo Ramokapane is a PhD student in the School of Computing and Communications. Kopo gave an overview about the growing importance of “the cloud”. But do we also see the implications that cloud computing has on security and privacy of our data?

Kopo reported that when you delete data in the cloud there is no way to be sure that all copies or all versions have been deleted from the cloud provider. This issue isn’t new but doesn’t get as much attention as it should be.  Because of the way Cloud storage operates it is almost impossible even for the service providers to be certain that all the data has been deleted.  Avoid storing confidential data in the Cloud and learn more about how the systems work! Lancaster University has a contract with cloud service Box which ensures that compliance issues are dealt with in relation to storage of confidential or sensitive data.

Karen Broadhurst and Stuart Bedston – Better data for better justice: Towards data-driven analyses of Family Court policy and practice

Professor Karen Broadhurst and Stuart Bedston from the Sociology Department reported on concerns about transparency in family court-decision-making.  Greater transparency and “open data” would have a positive impact in many ways but is hard to achieve looking at the security requirements and potential risks.

Karen and Stu presenting on “Better data for better justice”

Karen and Stu highlighted the changes that would be needed in order to strengthen interdisciplinary research using controlled-data here at Lancaster University but also the difficulties that stand in the way.

John Couzins – Security Overview at Lancaster University

Next on was John Couzins, the IT Security Manager of Lancaster University. John who works for the institutional IT service ISS reported on the certifications that are necessary to fulfil requirements of certain providers of confidential data. Current examples are Cyber Essentials Plus and the IG Toolkit (Information Governance Toolkit) which is used by the NHS.

Mateusz Mikusz – Running Research as a Service. Implications for Privacy Policies and Ethics

Mateusz Mikusz is working on his PhD in the School of Computing and Communications. He is working on a project that develops pervasive displays where students can get personalised content on public screens on campus if they use an app or iLancaster.

The issue regarding the data is that is used for two purposes:

  • To make the app and its use cases work
  • To create research data of usage and other properties that can be analysed by the project team

Mateusz explained that he is working hard to bring both things together in an ethical way that still allows innovative research.

Mateusz presenting the project

It was a great showcase for a lot of fantastic research that is taking place at Lancaster University and the way in which handling sensitive data and tackling data security is at the forefront of this.  There were probably as many questions raised as there were answers given but it was a great opportunity to share approaches to handling data securely and ethically.

Want to know more?  Get in touch with the RDM team rdm@lancaster.ac.uk

3rd Data Conversation – 19th September 2017

Join us for our next Data Conversation on 19th September on Software as Data with a special guest speaker Neil Chue Hong from the Software Sustainability Institute.

Sharing Qualitative Data Workshop

On 5 April we invited Libby Bishop to give a workshop on how to share qualitative data. Libby is well known in the Research Data Management (RDM) world as the Manager for Producer Relations at the UK Data Archive (University of Essex) although she introduced herself as a “maverick social science researcher”.

Libby explaining the workshop plan

Why have a workshop on sharing qualitative data?

The short answer is: because it is difficult! If we look at the datasets deposited in our Lancaster Research Directory (currently about 150) you will find very few qualitative datasets. The reason for that is that there are many challenges in sharing this type of data. Which is why we invited expert advice from Libby.

Workshop Highlights

Firstly, you can have a look at Libby’s slides below but I would like to highlight a few things that were especially of interest to me further on.

Qualitative data does get reused! Not just for research.

One of the surprises for me personally was that the reuse purpose of qualitative data is mainly for learning purposes (see figure below). According to Libby’s research 64% of downloads of qualitative data are for learning and 15% for research.

Re-use purposes of qualitative data downloaded from UK Data Service, 2002-2016. From Bishop, Libby and Kuula-Lummi, Arja (2017) ‘Revisiting Qualitative Data Reuse.’ SAGE Open, 7 (1). https://doi.org/10.1177/2158244016685136

In our workshop Libby used a dataset created by Lancaster University researchers to illustrate the benefits of archiving data: It will get re-used! The example is the dataset “Health and Social Consequences of the Foot and Mouth Disease Epidemic in North Cumbria, 2001-2003” which is available from the UK Data Service (http://doi.org/10.5255/UKDA-SN-5407-1). It is a rich qualitative study including interviews with people affected by the Foot & Mouth crisis and diaries documenting experiences in Cumbria 2001-2003.

Libby explained how the researchers themselves thought the data could not be archived but with support (and some extra funding) created an important resource that is being reused in different contexts.

Libby Bishop

Get the consent right!

A major hurdle on the way to sharing qualitative data is the right consent from research participants. Workshop participants worked on some real life examples provided by Libby and realised that critiquing consent forms is much easier than writing one yourselves.

For example, any pledge to “totally anonymise” an interview is a promise you are unlikely to keep. Also, vague statements or legalistic terminology were criticised.

Workshop participants discussing consent forms

Libby highlighted that consent statements actually have become more difficult to write as dissemination tools (including data archives) have diversified.

Some conclusions

Here are a few points that stuck on my mind after the Sharing Qualitative Data workshop:

  • Sharing qualitative data offers many benefits. We heard of examples where research participants were more keen on sharing their (anonymised) data than overly careful researchers.
  • The prime responsibility of the researcher is to protect participants but she/he has also a responsibility to science and funders. Both together according to Libby “is not an easy package”.
  • The three tools for sharing qualitative data are:
  1. A well written and explained informed consent form
  2. Protection of identities (through careful anonymisation)
  3. Regulated access (not all data should be open without restrictions)
“Sharing”, Image by Ryan Roberts, Flickr, CC-BY-NC

Full citation of the paper mentioned above: Bishop, Libby and Kuula-Lummi, Arja (2017) ‘Revisiting Qualitative Data Reuse.’ SAGE Open, 7 (1). https://doi.org/10.1177/2158244016685136

Hardy Schwamm, Research Data & Repository Manager, Lancaster University

Data Interview with David Ellis (Part 2)

Part 2 (of two) of a Data Interview with Dr David Ellis (@davidaellis). See here for Part 1. David is a Lecturer in Computational Social Science and holds a 50th Anniversary Lectureship in Psychology at Lancaster University.

Picture from https://commons.wikimedia.org/wiki/File:Opendata.png CC-BY-SA

Q: In support of Open Data what roles do Policies by funders of the University have? Are they helpful? Or is it seen as just another hurdle in the way of doing research?

David: I could be wrong but I don’t think most people just view it as just a hurdle. I think when people have to write a Data Management Plan for a grant that is a bit of a pain. But I don’t think the idea of having the data freely available is something where most people say “I can’t be bothered”. It is an additional step but it is something people should be doing anyway because if you are going to be clear on what your results are the data should be in a form that’s usable and could be easily moved between people. I think most people say that’s a good thing but maybe I’m biased…

Q: I have talked to PhD students asking if they want to share their data and they said I should have asked them three years ago because now it is so much work. I wonder why that is and if we need to change the way we teach them how to manage their data?

David: I wonder if I would have said the same thing. All the data of my PhD is still around but as I was learning my craft I probably wasn’t the most efficient, and my data wasn’t managed as efficiently as I would do now. I don’t remember going to a data management training or anything. And if someone had done that on day one of my PhD? Data should be kept in an ordered fashion etc. I created a lot of extra work for myself because I would do some analysis, close the file and end up re-doing the same things I have done multiple times. And even on that level that is not very efficient.

David Ellis

Q: Is that something we should teach students, you think?

It’s probably something students wouldn’t be too keen [on].

Q: Yes, you don’t want to patronise them.

David: And it is a bit like saying: For god’s sake back up stuff! If you look at all the horror stories [about] who loses data. It’s only when it goes wrong that it becomes a problem. I think some people are automatically super organised. I was probably somewhere in the middle, probably more organised now. I think the issue is in a lot of academia, you just figure it out as you go. And some people develop brilliant habits and some people, including myself, bad and good. And other people develop really bad habits. And that just carries on.

I sometimes look at Retraction Watch to see what’s in, and there is this really interesting example of an American paper, an American guy who posted a paper in Psychological Science whose undergraduate student collected the data and then it turns out the entire paper is wrong when someone re-analysed the data and found so many mistakes in it. Of course it has been retracted. Now the professor has said it is the student’s fault [whole story here]. But whoever taught that student data management? If that is the issue and it looks like it, they have taken the eye off the ball. And now without a doubt his other papers will be scrutinised. Clearly, there are bad habits ingrained that he’s been passed on.

And it is not just students, it is people higher up as well. The students have been informed by their own supervisors. So I say to my students: back stuff up, make sure things are organised and I can usually tell without going into their file system. What usually happens, if I ask them for something, a piece of data, it will appear quickly because they know where it is, and that is good enough for me. But if it takes ages, that’s when we end up having a talk saying “What are you actually doing with your data?” because this seems really all over the place. But not every supervisor does that, as that guy proved. He didn’t even seem to look at the data. I am not saying that can happen here; but is not only the students.

David presenting at Data Conversations

Q: What could the University do more to assist Open Data supporters like yourself?

David: I really like the fact that the Library is pushing the fact that you can upload datasets. I know there are not many people from my Department that are doing it…  I think that is really interesting. It is something that I – not necessarily challenge – but I do mention it. I don’t really get why. It is the sort of thing where you are submitting a paper you don’t even have to do it formally. There are journals that don’t have a data policy but I can still through our Pure system link data and paper together. I don’t see how that is a bad thing and that there is a huge effort needed to do that.

Maybe academics say it is just another thing to do? A colleague of mine would always say if they want the data they can always email me. Now that might be true but there are lots of cases when you email academics they never get back to you. The same colleague gets so many emails that they have someone to manage their mails. I take the point that the counter argument is that nobody actually will want to see the data and maybe they won’t. But given how random stuff is… you don’t know.  For, what you publish today it might not be important and then suddenly it is important.

So my answer to the question is I am not exactly sure. There is more support in this institution than in my other, to my memory, in terms of: “this is a place to put my dataset”. One of the courses I was on here about data management as part of the 50 Programme was really useful in the sense that I left thinking from now on I am going to put my data there [into Pure].

Q: Should there be other incentives for opening up research data rather than “doing a good thing”? Should there be more credit for Open Data?

David: Yes, probably. We are always judged, when we do PDRs every year, on how much I published and got this much money. But actually, the data output does have a DOI now and it is citable and it is a contribution that the University is getting from the academic. It is additional effort. So it would be interesting to see what happened if it went as far as maybe not a promotion thing, but … part of good practice. I think the question I would ask academics is: if your data is not there, where are you keeping it long term? Now I am working at another project where data cannot be made open and that is fair enough, but in general I do wonder where all that data is going. There is a duty where it needs to be kept for a certain length of time. I think it is easier to put in there [Pure] then I don’t need to think about it if nothing else. That gives me more comfort.

Q: Is there anything you’d like to add?

David: I am certainly in support of Open Data but I write more about data visualisation because I like pictures as much as I like data [laughs].

Thanks David for an interesting interview. We hope to do more Data Interviews soon. In the meantime, if you have any questions or comments leave them below or email rdm@lancaster.ac.uk.

Data Interview with David Ellis (Part 1)

Part 1 (of two) of a Data Interview with Dr David Ellis (@davidaellis). David is a Lecturer in Computational Social Science and holds a 50th Anniversary Lectureship in Psychology at Lancaster University. David presented at the first Data Conversations on Data Visualization.

This is the first interview of hopefully a series to come about the impact of Open Data on research. The interview was conducted by Hardy Schwamm.

Q: We define Open Data as data that can be freely used, shared and built-on by anyone, anywhere, for any purpose. Open Data is also a way to remove legal and technical barriers to using digital information.  Does that go with your idea of what Open Data is?

David: Yes, I think so. I might add to that: the data is actually useful and fit for purpose. To me it’s one thing to just uploading all that data, make it available. But a lot of time, how useful that is on its own is not quite clear. As a psychologist you can run an experiment and you have a lot of data coming out of a study. You can just dump that data online but is there enough information there for other scientists to use that data and get the results?

Q: So would you say that the usefulness of data depends on what we as librarians call metadata, data about the data?

David: Yes, exactly. The definition you gave earlier is spot on. I would just add you need to make sure it is useful to other people. That might also depend on the audience but there are lots of datasets that people post for papers that are just the raw data. That is useful but to understand how they get from the raw data to the conclusions is an important step. There isn’t always space in publications to make that clear.

Q: My next question you have probably already answered already. What is your interest in Open Data? Do you support it as a principle or because it is useful for your research?

David: I do support it as a matter of principle! I always find it weird, even as a student, that you could have papers published and it was just a “Take our word for it” process. I still find that weird now. So absolutely, I support it as a matter of principle. I think as a scientist it just seems right. The data is the cornerstone of every publication. So if that is not there it seems like a massive omission, unless there is a reason for it not to be there. There are lots of mainstream psychology journals that don’t have any policy on data.

Q: That leads me to my next question: To what extent do researchers in your field Psychology support or embrace a culture Open Data?

David: Psychology does have a culture of it and it is probably growing. I think it is inevitable that this is going to become the standard practice if you look at the way Open Access publishing is going.

Q: Why do you think this is happening?

David: Because I think what is eventually happening is that journals are going to say… Lots of people who are doing it but it is like everything else, particularly if that data is going to be usable it does require a bit more effort on the author’s part to make sure that things are organised and that they have a Data Management Plan. I am not suggesting that lots of people don’t have Data Management Plans but it’s something that if you look at current problems in Social Psychology really that wasn’t being followed. There have been leaks and there have been other problems.

So if I tell you the story last week from a 3rd year student at Glasgow University had spotted errors in a published paper and it was actually errors in the Degrees of freedom. They didn’t need the raw data but the point is that a lot of that could have been sorted if the raw data had been made available. There are lots of little issues that keep coming up.

There is nevertheless still resistance and there are plenty of journals where there really is no policy, certainly the journals for which I review for. At the end, there is no data provided, I don’t know what the policy is. It would be nice if in the future authors could upload raw data but that depends on the journal’s policy and if the journal has a policy.

Q: Where should the push for Open Data come from? From journals, funders or the science community?

David: I think from all! If peer reviewers started asking for data, which I think more are, and I think if more scientists start uploading data as supplementary material as a matter of course then I think journals will start to do that. I guess the other option is that journals will start to be favoured that do provide additional resources. So particularly given how much money places like Elsevier make, what do they actually offer? If they want to sell themselves they could offer lots of things but they don’t seem to be pushing it.

And I appreciate it is very discipline specific, and that came up after my talk at the Data Conversations [on 30 January 2017] some disciplines don’t share data. It has improved massively since I started as a postgrad student. Then it just wasn’t a thing and it has slowly become more of an issue.

Q: Do you think this has to do with skills and knowledge of researchers and PhD students? Do they know how to prepare and share data? Do they know how to use other researchers’ data? Is there something missing?

David: A lot of psychologists are in a kind of hybrid area. They are obviously not statisticians and I do wonder if there is a bit of a concern because what if I upload everything, what if somebody finds a mistake? My view is always: I’d rather know that there is a mistake. But I do wonder if people are sometimes sceptical about. Not because they’ve got anything to hide but because they are not a 100 per cent sure sometimes. They understand the result and they know what the numbers mean but we are not mathematicians.

I am just curious that given the numbers of statistical mistakes being flagged up in psychology papers… I am sure I made mistakes myself. I’d just rather know about them. And having the data there means someone can check if they really want to. My view is that I am quite flattered if someone that bothered to go and re-run my analysis. They are obviously reading it!

The interview with David will be continued in Part 2 which you can find here.

Impressions from IDCC17 in Edinburgh (12th International Digital Curation Conference)

The below is a very quick summary of things that I found interesting, remarkable or funny at IDCC17. But before I start, a big thank you to Kevin Ashley and his team for organising such an interesting event with a varied programme! And thanks for all the conference pictures on Flickr!

Surgeons Hall Edinburgh, IDCC17 venue

Monday, 20 February (Workshops)

Actually a nice idea to have the conference proper sandwiched between two days of workshops which gives attendees the chance to be quite flexible with their time commitment (you need to visit Edinburgh as well while you’re there)! The location was the Surgeons’ Hall which is conveniently located for attractions in the Old Town of Edinburgh.

I went to the “Technical Appraisal of Complex Digital Objects in Evolving Environments” workshop run by the PERICLES project. PERICLES is a four-year Project  funded by the European Union which will be finished in March 2017.

Simon Waddington (King’s College)

The project has the ambitious aim not just to preserve data files, but also the surrounding environment including software, and associated hardware requirements. I enjoyed the discussions about authenticity of objects (how much can you change or convert before you “lose” the original) and identification of videos. But I have to admit that the demo of the Ecosystem tool (using a complex ontology) was a bit too much for my limited understanding. But sometimes it is good to see your limits, so thanks to the presenters from King’s College and the University of Göttingen.

Monday finished in style with a drinks reception in the wonderful Playfair Library!

Drinks reception in Playfair Library

Tuesday, 21 February (Conference)

Tuesday started with the keynote “A Process View of Missing Data” from Maria Wolters who is a Reader in Design Informatics at School of Informatics at the University of Edinburgh.

Maria’s point is that Missing Data can improve overall data quality if we understand why data are missing!

Next up was a Parallel Session on “Curation Communities”. Marta Teperek and Rosie Higman reported on a topic that is close to my heart: engaging researchers in RDM and creating an RDM Community. The challenge our colleagues at Cambridge have is that the University “is a maze” with 150 Departments! Marta reported that the RDM approach in the past was led by the “stick approach” (e.g. pointing out compliance with data policies). This clearly has its limitations (which we also experience at Lancaster University). Instead, the support team in Cambridge is working on a more “democratic” and researcher-led process.

Marta contemplating Democracy

In the same spirit are Cambridge’s Data Champions who “are local experts on research data management and sharing who can provide advice and training within their departments.” Rosie organises training for the Data Champions so that they can in return train their peers in RDM. A great idea and I am curious to hear about the success. This is similar to the idea of Lancaster Data Conversations but more ambitious.

Rosie presenting the idea of Data champions

In the afternoon I went to the Parallel Session on Sensitive Data. Debra Hiom from the University of Bristol who gave a really interesting presentation on safe access (presentation available for download here as .pptx). Debra reported that Bristol have agreed on four standard data access level (Open, Restricted, Controlled and Closed) and have tasked an Expert Advisory Group on Data Access with handling the more sensitive cases.

Bristol data access levels

Tuesday finished with a very enjoyable Conference Dinner in The Caves which felt a bit like dining in underground club (which is exactly what The Caves are often used for).

IDCC17 Dinner at The Caves

Wednesday, 22 February

Wednesday offered more parallel sessions. I became a bit nostalgic at the talk of Alex Ball (Bath University) “Choose your own research data management guidance”. Alex and colleagues from GW4 universities are developing RDM guidance using interactive fiction software Squiffy. This is a very interesting take on RDM guidance which of course reminds of playing interactive games like The Hobbit back in the days. Really curious to see a demo hopefully soon!

Alex Ball, Bath University

Food for thought came from Jez Cope (Sheffield University) who advertised Library Carpentry (slides), a software skills training for library professionals. We have been thinking about digital skills here at Lancaster University, so a programme like Library Carpentry is very timely. Jez’ talk explained the concept of the training and we might well take part soon, so thanks for that.

Thursday, 23 February

Finally, on Thursday I participated in the workshop “Essentials 4 Data Support, the Train the Trainer”, delivered by Ellen Verbakel  (4TU.Centre for Research Data) and Marjan Grootveld (DANS). Ellen and Marjan presented the thinking behind their course (freely available here) which is a combination of face-to-face training with online modules and assignments. The training is aimed at “data supporters” (librarians, IT staff and researchers with duties involving data management).

Workshop participants

We did a number of exercises including mapping RDM stakeholders and the review of Data Management Plans.

RDM stakeholder map

It was very interesting exchanging views and experiences with international colleagues to how different legal frameworks, cultures and policies inform our work.

Then, finally IDCC was over and attendees faced storm Doris on their way home. Thanks, DCC team for an engaging, intersting and fun event!

First Data Conversations 30 January 2017 – Summary of event & slides

The first Data Conversations happened on Monday, 31st of January 2017. Below is a quick overview of the action. You can find slides of four talks below.

Data Conversations Opening

Adrian Friday opening Data Conversations

The event was opened by Professor Adrian Friday from the Data Science Institute (DSI) who emphasised that the DSI is all about collaboration between disciplines which is also the spirit of Data Conversations. In fact the 25 attendees came from  a range of Departments: Biological and Life Sciences, Chemistry, Computing, Educational Research, History, Law, Lancaster Environment Centre, Politics, Psychology and others.

Data Conversations Talks

Unfortunately, Dr Chris Jewell from the Medical School had to cancel his talk. You can see an overview of the agenda below.

Leif Isaksen – Does Linked Data Have to be Open?

Leif Isaksen from the History Department (Leif is also involved in the Data Science Institute) presented the Pelagios Commons project which provides online resources for using open data methods to link and explore historical places.

Leif Isaksen

Leif stressed that linking data is a social process which is built on open partnerships.

You can see Leif’s presentation below:

Jude Towers – Is Violent Crime Increasing or Decreasing?

Dr Jude Towers from Lancaster’s Sociology Department discussed crime rates, especially the rate of domestic violence over time through the Crime Survey for England and Wales. A current ESRC project is looking at how changing survey methodologies alter the underlying data of crime statistics.

Alison Scott-Baumann – Protecting participants and their data on a sensitive topic

Next up was Alison Scott-Baumann who is a Professor of Society and Belief in the Centre of Islamic Studies in the Near and Middle East Department at SOAS. Alison is the Project lead on (Re)presenting Islam on Campus. Lancaster is a project partner and Dr Shuruq Naguib added to Alison’s presentation.

Alison Scott Baumann

Alison and Shuruq explained how difficult it is to get the balance right between confidentiality and data security required to manage often highly sensitive data, and to meet the expectations of data sharing. They stressed how much effort they spend on explaining the terms of the consent forms to project participants.

David Ellis – Building interactive data visualisations to support publications

Dr David Ellis showed the audience an example of dynamic data visualisation using a dataset he published on Lancaster University’s Research Registry. (http://dx.doi.org/10.17635/lancaster/researchdata/58). David explained how he used the R package Shiny Apps to achieve this.

David explained that the visualisation helps not only other researchers but also enables the interested public to query his data. One example was interest from journalists into his research into predicting smartphone operating system from personality and individual differences.

Chris Donaldson & James Butler – Mining and mapping places with multiple names

Finally, Dr Christopher Donaldson and Dr James Butler talked about their research using a 1.5 million word corpus of Lake District 18th and 19th century literature. Christopher and James use the Edinburgh Geoparser System to automatically recognise place names in text and disambiguate them with respect to a gazetteer.

James demonstrated how he can deal with name variations (secondary names), it is a lot of work. For example, the lake “Coniston” appears in the corpus as:  Thurstan, Coniston Lake, Coniston Water, Thurston, Conistone, Conistone Lake, Cunnistone Lake, Thurston Lake, Coniston Mere, Lake of Coniston, Conis- ton, Conyngs Tun, Conyngeston, Thorstane’s watter, Turstinus.

Chris Donaldson
James Butler

Feedback so far

The feedback from attendees and presenters so far so far is encouraging.

Enjoyed the presentations. I hope these data conversations will become a nice community for those interested in data. Relaxed and nicely themed but not too prescribed. The venue was good and the cakes and biscuits were very good!

We got some comments on the length of the presentations and question time.

Really enjoyable – perhaps a bit more time for each speaker / questions and discussions.

We will look into amending the format. We do like to keep a balance between time for data stories and discussions and giving a number of Lancaster researchers a forum to talk about their experiences. Thanks for the comments and suggestions so far!

Upcoming: 2nd Data Conversations 4th of May

We hope to report on some of the data presentations in more detail in future blog posts. Meanwhile, we are already preparing for the next Data Conversations event on 4th of May (1.45-4 pm). The theme of the event will be “Data Security and Confidentiality”, and registrations are open: http://bit.ly/ludatacon2. Please come along and if you have any questions get in touch with the RDM Support Team: rdm@lancaster.ac.uk.

First Data Conversations – Speakers confirmed!

Data Conversations on 30 January “Sharing Data – Benefits and Boundaries

We are very excited about the first Data Conversations event at Lancaster University coming up on 30 January 2017, 1.45-4pm. There will be 6 short talks from academics talking about aspects of their research data. We can now publish the agenda.

Detailed agenda

13:45 Registration and Coffee  
14:00 Welcome to Data Conversations Nigel Davies

(Data Science Institute)

14:10 – 14:50 First round of short talks
14:10 – 14:20 1. Does Linked Data have to be Open? Reflections from the Pelagios Commons Leif Isaksen

(History)

14:25 – 14:35 2. The Politics of Counting: Is Violent Crime Increasing or Decreasing? Jude Towers

(Sociology)

14:40 – 14:50 3. Protecting participants and their data on a sensitive topic Alison Scott-Baumann

(PPR)

14:55 – 15:10 Tea and coffee
15:10 – 15:55 Second round of short talks  
15:10 – 15:20 4. Building interactive data visualizations to support publications David Ellis

(Psychology)

15:15 – 15:30 5. Efficient sharing of numerical output Chris Jewell

(Medical School / CHICAS)

15:35 – 15:45 6. Mining and mapping places with multiple names’ Chris Donaldson & James Butler (History)
15:55 Close Hardy Schwamm (Library)

We are very happy that we get speaker from a range of disciplines! We are looking forward to the first Data Conversations and will report on how it went. Watch this space!

 

RDMF16 – Creating a Research Data Community

 

 Creating a Research Data Community

Are research institutions engaging their researchers with Research Data Management (RDM)? And if so, how are they doing it? In this post Hardy Schwamm (@hardyschwamm),  Research Data Manager, Lancaster University, and Rosie Higman (@RosieHLib), Research Data Advisor, University of Cambridge, and explore the work they are doing in their respective institutions.

Whilst funder policies were the initial catalyst for many RDM services at UK universities there are many reasons to engage with RDM, from increased impact to moving towards Open Research as the new normal. And a growing number of researchers are keen to get involved! These reasons also highlight the need for a democratic, researcher-led approach if the behavioural change necessary for RDM is to be achieved. Following initial discussions online and at the Research Data Network event in Cambridge on 6 September, we wanted to find out whether and how others are engaging researchers beyond iterating funder policies.

At both Cambridge and Lancaster we are starting initiatives focused on this, respectively Data Champions and Data Conversations. The Data Champions at Cambridge will act as local experts in RDM, advocating at a departmental level and helping the RDM team to communicate across a fragmented institution. We also hope they will form a community of practice, sharing their expertise in areas such as big data and software preservation. The Lancaster University Data Conversations will provide a forum to researchers from all disciplines to share their data experiences and knowledge. The first event will be on 30 January 2017.

Having presented our respective plans to the RDM Forum (RDMF16) in Edinburgh on 22nd November we ran breakout sessions where small groups discussed the approaches our and other universities were taking, the results summarised below highlighting different forms that engagement with researchers will take.

 

Discussing RDM Community
RDMF16 Working Group discussing RDM Communities

Targeting our training

RDM workshops seem to be the most common way research data teams are engaging with researchers, typically targeting postgraduate research students and postdoctoral researchers. A recurrent theme was the need to target workshops for specific disciplinary groups, including several workshops run jointly between institutions where this meant it was possible to get sufficient participants for smaller disciplines. Alongside targeting disciplines some have found inviting academics who have experience of sharing their data to speak at workshops greatly increases engagement.

As well as focusing workshops so they are directly applicable to particular disciplines, several institutions have had success in linking their workshop to a particular tangible output, recognising that researchers are busy and are not interested in a general introduction. Examples of this include workshops around Data Management Plans, and embedding RDM into teaching students how to use databases.

An issue many institutions are having is getting the timing right for their workshops: too early and research students won’t have any data to manage or even be thinking about it; too late and students may have got into bad data management habits. Finding the goldilocks time which is ‘just right’ can be tricky. Two solutions to this problem were proposed: having short online training available before a more in-depth training later on, and having a 1 hour session as part of an induction followed by a 2 hour session 9-18 months into the PhD.

Tailored support

Alongside workshops, the most popular way to get researchers interested in RDM was through individual appointments, so that the conversation can be tailored to their needs, although this obviously presents a problem of scalability when most institutions only have one individual staff member dedicated to RDM.

IMG_20161122_121401There are two solutions to this problem which were mentioned during the breakout session. Firstly, some people are using a ‘train the trainer’ approach to involve other research support staff who are based in departments and already have regular contact with researchers. These people can act as intermediaries and are likely to have a good awareness of the discipline-specific issues which the researchers they support will be interested in.

The other option discussed was holding drop-in sessions within departments, where researchers know the RDM team will be on a regular basis. These have had mixed success at many institutions but seem to work better when paired with a more established service such as the Open Access or Impact team.

What RDM services should we offer?

We started the discussion at the RDM Forum thinking about extending our services beyond sheer compliance in order to create an “RDM community” where data management is part of good research practice and contributes to the Open Research agenda. This is the thinking behind the new initiatives at Cambridge and Lancaster.

However, there were also some critical or sceptical voices at our RDMF16 discussions. How can we promote an RDM community when we struggle to persuade researchers being compliant with institutional and funder policies? All RDM support teams are small and have many other tasks aside from advocacy and training. Some expressed concern that they lack the skills to market our services beyond the traditional methods used by libraries. We need to address and consider these concerns about capacity and skill sets as we attempt to engage researchers beyond compliance.

RDMF16 at work
RDMF16 at work

Summary

It is clear from our discussions that there is a wide variety of RDM-related activities at UK universities which stretch beyond enforcing compliance, but engaging large numbers of researchers is an ongoing concern. We also realised that many RDM professionals are not very good at practising what we preach and sharing our materials, so it’s worth highlighting that training materials can be shared on the RDM training community on Zenodo as long as they have an open license.

Many thanks to the participants at our breakout session at the RDMForum 16, and Angus Whyte for taking notes which allowed us to write this piece. You can follow previous discussions on this topic on Gitter.

Published on 30 November
Written by Rosie Higman and Hardy Schwamm
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