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Exeter Postgraduate Researcher Podcast

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  • Summary

  • This podcast from the Researcher Development and Research Culture Team at University of Exeter covers the skills and knowledge needed for postgraduate researchers’ professional development.
    Copyright 2023 All rights reserved.
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Episodes
  • Episode 3- Open Research (Professor Sabina Leonelli, Professor of Philosophy and History of Science)

    Episode 3- Open Research (Professor Sabina Leonelli, Professor of Philosophy and History of Science)
    Apr 15 2024
    Professor Sabina Leonelli (Professor of Philosophy and History of Science) talks to Dr Chris Tibbs, Research Data Officer at University of Exeter about open research, the use of Artificial Intelligence in research, and the importance of understanding the diversity of research environments when implementing open research practices. Podcast transcript Chris Tibbs: Hello and welcome. I'm Dr Chris Tibbs and I'm the University Research Data Officer, part of the open research team based in the library here at the University of Exeter. My role involves providing support for researchers across the university as they work with and manage their research data, and today I have the pleasure to be joined by Professor Sabina Leonelli, a Professor of Philosophy and History of Science at the University of Exeter. So welcome, Sabina. Just to start, would you like to tell us a little bit about the research area that you work in? Sabina Leonelli: Thank you and hello everyone. So, I'm interested in the dynamics of research and research processes. Why is it that people who work in science use the methods that they use, handle data in particular ways, decide to publish in particular ways? Why do they choose certain research goals, and how does that occur historically but also conceptually? And what are the social implications of those choices? Chris Tibbs: That's very interesting. So you're really looking at these sort of different approaches and the different methodologies that different researchers are taking and that's very interesting because obviously different research areas will have different approaches and methodologies that they use. Now one thing that I noticed that you're very interested in, based on your web profile, is obviously open science and openness in research, and the European Commission and the United Nations, among others, all use this term of open science and just so that everyone listening is clear, open science is the approach to research based on openness and co-operative working, and it really emphasises the sharing of knowledge, results and the tools as widely as possible. But I just wanted to point out also that obviously these approaches can apply to all research disciplines, not just science. And so, for example, we are the open research team. And so, I tend to regard open research and open science as synonymous. So, I just wanted to get your take on this, Sabina. Do you see these as separate terms, or do you use them interchangeably? Sabina Leonelli: I also tend to use them interchangeably, but I think it is very unfortunate that it’s the term open science that has gotten so much mileage in the English language because in the English language we are aware of the fact that it does tend to be taken to refer to the natural sciences, more rarely to the social sciences, and never to the humanities and the arts. And this is different for lots of other languages. I mean, most, I guess famously the term wissenschaft in German tends to encompass all of the research domains, including humanities and the arts. I’m very partial to that, partly because I think that we're in a moment where research is so interdisciplinary and the boundaries between domains are so blurred, that actually making strict distinctions between what counts as a humanist approach, and what counts as a natural science approach, or a mathematical approach is becoming more and more difficult. As of course in history it has been very difficult throughout. So yeah, so I'm very partial to the use of the idea of open research in English, but of course we tend to use a lot the term open science too, because this is, as you were saying, very well recognised by policymakers and by funding bodies and a lot of people working in academia more generally. Chris Tibbs: OK. Well, thank you for explaining that. And again, the reason I just wanted to confirm this is because I want to ensure that everyone listening can be clear that what we mean by the term open science and that they don't feel that this doesn't apply to them, maybe because they don't see themselves as a scientist. So that's what I just wanted to clear up, and so that these practises do apply to all disciplines. Now moving on. Sabina, you hold many different roles and one in particular that I would like to mention is that you are the theme lead for the data governance, openness and ethics strand of the Exeter Institute for Data Science and Artificial Intelligence. So, given this particular role, I'd really be interested to hear your thoughts on how you feel artificial intelligence can play a role in the research process, and particularly around openness and the open research. Sabina Leonelli: Yes, thank you. So, I guess openness lies at the heart of what it means to do research, no matter how you look at it, right. I mean, doing research basically means trying to answer a certain question, trying to solve a problem that you may have encountered in your everyday life and within ...
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    29 mins
  • Episode 2- Open Research- Dr Gavin Buckingham (Associate Professor in Public Health and Sport Sciences)

    Episode 2- Open Research- Dr Gavin Buckingham (Associate Professor in Public Health and Sport Sciences)
    Nov 16 2023
    Dr Gavin Buckingham (Associate Professor in Public Health and Sport Sciences) talks to Dr Chris Tibbs, Research Data Officer at University of Exeter about the different types of research data he works with and best practices for managing research data during your project. Podcast transcript Chris Tibbs: Hello and welcome. I'm Dr Chris Tibbs and I'm the University of Research Data Officer, part of the open research team based in the library here at the University of Exeter. My role involves supporting researchers across the university as they work with and manage their research data, and so this episode is going to be all about research data and how best to look after it and manage it during your project. And to discuss all of this, today I have the pleasure to be joined by Dr Gavin Buckingham, an Associate Professor in Public Health and Sport Sciences here at the University of Exeter. So just to start with Gavin, would you like to tell us a little bit about your research and the different types of data that you work with?   Gavin Buckingham: Hi, there, Chris. Yeah, I'm a cognitive psychologist by training, and I'm interested in human perception and human motor control. And I've been looking at this in the context of measuring the movements and forces people apply to pick objects up, and more recently I've been looking at this in the context of immersive virtual reality as well. Now, most of this data takes the form of pretty simple time streams, time series of data, so numbers representing forces or positions of things in multiple dimensions, and their expression over time. So many thousands of lines of data potentially that we then take maybe the largest value or the value at some critical other time points and that reflects some aspect of human behaviour. So that pretty simply is really what it is that we deal with here.   Chris Tibbs: So thinking about all those types of data that that you're working with, I mean you mentioned, like numerical time series data. I just want to point out that, you know, data can also mean a wide variety of other types of data and many people might not think that they work with data. But generally, when I refer to data, you know, I'm thinking about any sort of information, evidence, materials that are being collected and used for that research. So I’d just like to hear your thoughts on, so when you're thinking about your data and why it's important that you look after your data and you manage your data in terms of helping your research and also then potentially making that data available.   Gavin Buckingham: Yeah, it's a really interesting question because the pipeline that goes from the stuff that comes out of the apparatus that I used to capture people's data to the things that are subsequently reported in the paper, that's a pretty lengthy pipeline that has many different steps. And those steps can be fairly clearly articulated, but being able to show the consequences of each of those steps, I think is a really key part in terms of people being able to eventually understand your data and make sense of it and use it in other sorts of ways and I really feel that's the narrative I feel most passionately about in many ways. I'm perhaps, slightly selfishly, I'm not so interested in other people finding mistakes that are present in my data, God forbid, but I'm more interested in this resource that was collected that could potentially be a useful thing for other people in ways that I cannot even really imagine. That for me is the really big value I see in my dataset and I work with clinical populations. I work with children, with older adults, typically developing university aged people, all of whom have interesting ways that they interact with the world around them that you know could feed into hitherto unforeseen mechanisms or rehabilitation or technological advances and, you know, I really see sort of the value of data just sitting there waiting for someone to be able to harvest in that way. Chris Tibbs: Yeah, all of this sort of potential that's in that data, that you know, doing analysis that are just completely irrelevant, that are completely separate from your research. So when did you sort of first start thinking about making, like managing your data, to make it available so that others could have it, and be able to analyze it? Was this sort of something that you had a discussion with, maybe your supervisor as a PhD student? Was this something that, you know, you sort of just picked up on sort of later during your career? Gavin Buckingham: Yeah. When I was a PhD student and postdoc, this wasn't really part of the narrative at all. There was no real sense that this is what you would do, but it was actually more to do with the experimental and analytical code: the MATLAB files in my case that I fairly vividly remember asking someone if I could use the MATLAB files to run an experiment of my own, and they're like, well, these were developed in collaboration with...
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    25 mins
  • Episode 1- Open Research- Dr Eilis Hannon (Senior Research Fellow in the Complex Disease Epigenetics Group at the University of Exeter Medical School)

    Episode 1- Open Research- Dr Eilis Hannon (Senior Research Fellow in the Complex Disease Epigenetics Group at the University of Exeter Medical School)
    Aug 15 2023
    Dr Chris Tibbs, Research Data Officer at University of Exeter, discusses research data and how best to manage that data during your project with Dr Eilis Hannon, Senior Research Fellow in the Complex Disease Epigenetics Group at the University of Exeter Medical School. Podcast transcript Chris Tibbs:  Hello and welcome. I'm Dr Chris Tibbs and I'm the University of Research Data Officer, part of the  Open Research team based in the library here at the University of Exeter. So my role involves  supporting researchers across the university as they work with and manage their research data, and  so this episode is going to be all about research data and how best to manage that data during your  project. And to discuss all of this today, I have the pleasure to be joined by Dr Eilis Hannon, a senior  research fellow in Clinical and Biomedical Sciences here at the University of Exeter. So Eilis, would  you like to tell us a little bit about your research, what it involves and the different types of data that  you work with? Eilis Hannon: Yes. Well, thank you very much for inviting me along today. So I'm based in the complex disease  epigenetics group and we have a group of mixed modalities. We've got wet lab scientists and dry lab  scientists, like myself. So we generate and analyze quite a lot of genomic data. So we're primarily  interested in the brain and modelling gene regulation in the brain and we're in a really exciting time  where there are so many different technologies and experiments that we can take advantage of, that the quantity of data we've started to generate has just kind of exploded. So from one single  sample, we can have kind of, you know, be 4, 5, 6 different experiments and kind of layers of data.  And so what I'm quite interested in doing is trying to integrate those different layers together. So a  lot of what I'm working with is experimental data, but because a lot of these technologies are quite  new, we're often developing new methods to analyze them in parallel. And so what we also do  sometimes is simulate data where we kind of know what it looks like. We know what the outcome  should be to kind of test and develop methods. So it's quite a broad spectrum of different data type. Chris Tibbs: Yeah. So you mentioned it there, right? So you maybe have simulated data, you've experimental  data, and so I just wanted to pick up on the point here when we're talking about data and this  obviously might mean different things to different people. And so if you're listening to this  discussion and thinking, oh well, I don't work with data or this doesn't apply to me, then I just want  to really make clear that when I refer to data or research data, it really means all of the information  or the evidence or the materials that are generated or collected or being used for the research, and so that we're clear about data and what it refers to. Why is it so important to manage this data  effectively? I mean, you talked about you're producing a large quantity of data, so I'm guessing that's  one of the reasons why it's important to look after it. Eilis Hannon: Yes. So from my point of view, efficiency in terms of processing that data in, I mean you know if it  wasn't organised in a kind of sensible or a kind of pre-planned format, then it would be incredibly  challenging to work with, so from you know, we take advantage of the high performance computing  available at the University and so to do that efficiently, we need to kind of have some pre-described  format for the data. But there's also ethical implications. So, you know, we're working on data  generated ultimately from a piece of human tissue. So we have requirements in terms of how we  look after that data, what we do with it. Who uses it and how? So we need to make sure that you  know our data is organized that such that those requirements can be met. But also, you know, one  of the really nice things about what we do is from one experiment you can answer lots and lots of  different research questions. So different people within the research group will be taking advantage  of the same dataset. And to, you know, to really maximize that utility, we need to, you know,  organize it in a way that we can find it. We know what's what. And we can really reap the benefit of  that initial kind of financial investment. Chris Tibbs: Yeah. So it's obviously clear, especially if multiple people are working, doing different analysis on the  same data. It's obviously important to know what the data are and make sure that they're obviously  described and who's doing what on the data, and version control, I imagine is something that's very  important for you. Like, it's clear that the data are fundamental for the research, right, and it doesn't  matter if you have, you know, the most sophisticated methodology to analyse the data, if the data  are not described or the data are inaccurate then your results are not ...
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    26 mins
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