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 ...
