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Times, I know it would seem there any biography of her which is in the book eminently Turin's by listeners straight here.

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And it was written during the First World War and it contained long essays about four eminent Victorians.

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Three of them were comprehensively demolished as pretentious and altogether unpleasant.

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Florence Nightingale, of course, got much more favoured treatment.

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How objective it really was.

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I'm not quite sure. But it did emphasise very strongly careful planning and also the very wide ranging nature of her interests.

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Both juries, both in the outskirts of what's known Istanbul in the outskirts of Istanbul.

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Both that, but also very wide ranging interests and activity after issue returned from the crime.

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It raises the question of the nature of the statistical work that she did.

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And I think once. Point is that.

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Computational. Facilities are very limited in those days.

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Log tables, slide rules, I don't know how many people in the audience remember a slide rule is a piece of wood.

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And if you listen very carefully, you could show that two times two with something between three point nine nine and four point one.

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And if you had a very sophisticated slide rule, as I expect to go, did, you might get another digit coming out.

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But a. Otherwise, computationally, she would have been very limited.

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This accounts of the fact, I think in part that that her.

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Writing the little obits, I've read lips very strong emphasis on graphical methods of presentation done in a very skilful and careful way.

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Models of Tyra say that a presentation of quite complicated pieces of data.

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In addition to contact from her confinement house.

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She was in touch with Cantlay in Belgium, who is the leading perhaps the leading biostatistician of the 19th century,

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Kateri go see inventor of cauterise index body mass over height squared,

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which is this is still a primary to choose in contact with Castaway and about various aspects of the more technical side, the statistics.

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I think that. Dr. LaDawn.

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Well, training find their computational point, a computational cereal's would have been limited.

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As I said, he the shooter had French curves.

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I don't know how many people know what a French curve is in the sense that I'm using eight other senses or might think of it, but.

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French curve is a shaped piece of wood, which can be used in effective to fit spine functions to 55 cubic seim shrines to empirical data.

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And it was still in when I first started statistical work just more years ago than I want to think.

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But I did the use of a spy was expected to be one of the things one would have expected to know.

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I was working in in an engineering department.

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And spine's way spy Unfitting was used with these French curves to fit smooth curves to parallel functions.

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So that was one side.

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But the main development. In the development of the more technical side of statistics, Hany is very, very strongly on developments in computation.

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And it wasn't until about 1870 or 1880, which is towards the end of the Florence Nightingale period and my inactivity.

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It wasn't till then that the Hattan Brunswick calculator was invented and perfected in, I think, by collaboration between a Russian and a Swede.

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And the sound of the broughton's, the calculator, the grinding sound it made,

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echoed through the lecture halls of London and the universe is no doubt in others and working in many fields,

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the sound of the Bundesliga calculator was still to be heard as late as 1970 or so.

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I'm going to say some more. About concretisation, what it would do would be to add up a large number.

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It had no memory, but it would it would add up a large number of numbers and that squares and their protest.

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So you could form some square some of products, some scale of two variables.

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Some are products of them in one cumulative operation on the machine.

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But nevertheless, there is no storage. And so when one, for example, inverted a small matrix, I remember inverting a seven by seven matrix.

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But the best debateable numerical methods available should have been about nineteen seventies.

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Nineteen fifties. And it took eight hours to avert a seven by seven matrix because every step had to be checked.

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Before before I tried to do that calculation, I had quite dark brown hair.

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You can see now it suddenly turned grey from the stress of doing that calculation.

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So computation is always. Driven.

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What part of statistics is commonly and widely used?

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It's very exceptionally that one that one can use when in the past use methods that were not very favourably computationally.

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So the history of the development of statistical theory and statistical methods is very, very much tied with the nature of computation.

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And in particular, the nature of computation, which is relatively painlessly available.

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Now, we've heard great lecture. I hope people have written up so we can study in detail.

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Thank you again, Deborah, for it. Thanks, David.

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I believe that David Spiegelhalter has also got a few comments that he'd like to make, so I'll just pass on to David Spiegelhalter.

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Okay. I hope you can hear me all right. Yeah.

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Are you going to excuse me being a bit nervous?

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I always I'm nervous speaking with David Cox around ever since he was my p h d examiner back in the 14th century,

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sometime when I had dark hair as well. And this is a fascinating lecture.

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There are also some details by Florence that I didn't know about. One could see the window and I really enjoyed it.

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I'd like to focus on one particular aspect, and that's really tied up with the pandemic that we're in at the moment where,

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as Debra mentioned, the scientists and the statisticians have been working there.

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What's itself. And, you know, it's just working so hard.

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I mean, every other slight aside, I do hope that this is appreciated by people, just how hard, you know, everyone has been working during this period.

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And for a lot, you know, either for the same salary or for no money at all.

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It is. And the stress involved in it. So I.

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But I think in a way, I'm sure Florence Nightingale would have relished this opportunity to bring together a data policy,

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and she should have been it right in the middle of it.

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I'm sure, because she as I mentioned to my when I did this lecture last year, she was like, I you know, one woman.

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Pressure groups know collecting data influencing all over the place.

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You knew everybody all from her bed, writing letters, sending presence and, you know, extraordinary energy and effort that she put in.

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But she was concerned with policies. She she wanted to change things.

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She wanted to make things happen. And that's what I.

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That's on your last year I said, which you use Twitter.

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But even now, I really wonder, would she have been content to be a statistician who tries to gather evidence?

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Of course. And to that that can be used by policymakers.

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But would she have been actually advocating for specific policies?

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I would love some people's opinion.

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Look, David's opinion crystals in anyone's opinion on this, because I kind of think it's an it is an important issue.

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It's something I'd been wrestling with all throughout this whole pandemic. I've been dying like other statisticians say.

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They died like Jan and others, beating a lot of media work. And I've been trying all the time to steer this middle way.

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I just don't feel I don't feel happy to say this is what should be done.

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All I want to be able to do is to try to explain what the evidence is and, if necessary,

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appeal for better communication of the evidence and better evidence and to go out there and actually collect the data.

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But at no point where I'm always trying to avoid it, in spite of always being asked, well, what do you think should be done?

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What do you think about locked? Do you agree with it or not? I just I'm not going to say and I've been trying to keep that line the whole way through.

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And I wonder if Florence would have been happy to stick to that line if she was sitting there in her bedroom as as Deborah said,

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you know, being an absolute role model for lockdown. She'd been there quite possibly doing online interviews with News Night,

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which she had been happy just to explain about the evidence or which you've been telling everybody what she thought should be done.

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And I suppose I'd just like to leave that as a slightly open question, my ideas and maybe just my particular feeling.

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One thing I really like about the statistical profession is that they do not tend to be advocates for what should be done.

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They're advocates for better data and evidence and trying to understand things are, you know, to see the world as it is.

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But they do not tend to be people who say, well, this is then what should be done.

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And I think that is a good role sometime to say putting saying my opinion immediately.

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And I might find myself actually. I would hate to disagree with Florence.

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I don't got to have a dad do that. But some I do think that this is an important issue that she brings that.

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Yes, that her model isn't the right one, is the one we should follow or not.

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So. Okay, so just to finish off, I'd like to say thank you so much, Deborah.

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A fantastic lecture. I really learnt a lot. Great slides.

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And I love the tie in with what's going on at the moment, all the work at Imperial and elsewhere and the role of statistician's in all of that.

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Wonderful. And so I had a great arc to it now. Lovely, lovely structure.

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And so with that, I'm going to stop rabbiting on and let Jen take control of whatever we're going to get asked next.

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Thank you very much indeed. Thank you very much, David. Yeah, you make a very good point.

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I'm constantly being asked the same thing. They want to give you an opinion on what should or shouldn't be happening and very much just trying to run.

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Let's just talk about the evidence.

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We had one question come in asking about whether or not you feel some of the statistical results have been hidden from the community.

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And I'm guessing it's maybe in response to kind of it that what we think the transparency has been like.

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Do you think we've been seeing all of the data that has been available?

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Shall I start on that? I think there's been a real balance between doing what we normally do and putting things out slowly through peer review papers.

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So our first react data, we didn't get out there because we were doing it through normal channels.

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And actually, the speed of this, you can't. And we see it with the vaccine studies that some data goes out there and then people have it.

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Has that been peer reviewed? But. Companies have to tell their shareholders with the studies that I'm sharing data monitoring committee

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for where we're still having debates about what the right channels of communication would be.

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And there's a real tension between immediacy, scrutiny,

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and you certainly would keep things absolutely hidden that if the others have got better solutions to that.

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I'd love to hear them. Yes.

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Do either of the Davids have any comment on that? Oh, yes, I do.

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Yes, I did. I'll begin by David, divided by bodging, first open my big mouth first hand.

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This is incredibly important and am I so strongly believe as you as we all do in openness and transparency about evidence and data.

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But of course, and many people within the we got to distinguish, I think,

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between government and the organisations that are working within government is absolutely crucial.

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And I distinguish that very strongly. And I spend my time criticising as loudly as possible.

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What I feel are lapses on behalf of what we've got as the political adviser, the number ten comms group,

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et cetera, which essentially are representing what I feel are the politicians, the government.

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And at the same time, I do tend, I think, quite reasonably,

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to defend very strongly the role of the organisations that are collecting the data and analysing data,

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presenting it to the policymakers and to to a really quite extraordinary extent, getting it out to the public.

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I know.

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I'd like to point to you know, I'm sorry, I'm conflict of interest on now non-executive director on the board of the U.K. Statistics Authority.

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So I would say that the AU and are doing a fantastic job at this, but the AU and ESA are doing a fantastic job.

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I think you're getting the data out. Just Bozman is new analysis, use a requested datasets and so on public health thing.

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And that dashboard was pretty crummy to start with, but they've had a fantastic team on that and now it is extraordinary.

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And the masses of hits I was on, I'm on it, you know, cut us for so every day I'm I'm clicking on it and checking every day.

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You know, it's a really fantastic resource. And and to provide an API so everyone could drag the data off that.

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So to go into other Web sites, the BBC one is very good.

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And all these other Web sites that people are constructing to to analyse that data.

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Excellent transparency. And and it's not just transparency. And in terms what you call fishbowl transparency,

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just going black and putting stuff out is massive pdaf that you can't get there you can't do much with.

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And this is transparency in a way that enables people that's I think empowers people.

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Now joined by security centre and which is, you know, keeps a really low profile.

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And I mean, I should say, because the people may not realise that the director of that is Claire Gardner,

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who was might be a student who's a Bayesian statistics and works at Imperial.

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So which I think sets a very good standard. And actually, the data that are used that the basis for the tearing decisions is out there.

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You know, it's being reported very, very quickly now.

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It is. It is. There is a 79 page PDAF, but the data files are underneath.

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It's not easy to find particularly, you know. What's going on in every single area and all the stuff, the masses of it.

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And and and I've been saying all day, as I say, all these billions being spent on tests and trace vast amounts on, you know, management consultants.

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Couldn't they have spent some more money on a decent website, given all the sacrifices we are all making continually?

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And we should be able to know exactly why these decisions are being made about us.

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So I would appeal it. I think there has been great efforts made.

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But more could be done. But it is extraordinary what we have got access to.

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I think I think compared. I'm not talking yet. I'm not talking about the use of graphs in briefings so that we could get onto that later.

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But I have to control my language, of course.

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But but in terms of the transparency, the data, there has been a very good example shown.

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And I think basically within the agency is a very huge amount of goodwill to do this.

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David coughs Do you have any comments to add to that? No, I feel, sadly totally on the fringe.

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Well, outside the fringe of this, I just look at the telly.

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I'm not a bad thing with the sanity. But like what I say, you know, I think it's.

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Often trying to put too much on one side or top or slide, take it away again.

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And it's an old trick. The older used to be very common in medical issues.

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If I went to any 30 or 40 years ago, people would put up very complicated slides and take them away again before anyone has a chance to read them.

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I see far too much in that. I don.

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So I really criticised the work that's underneath. Yeah, that that's a that's a separate issue.

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That's the actual presentation to the public. Seems to me often astonishingly, very poor.

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David, I'm very. I think you're absolutely right.

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I think it's a great shame because they're very good analysts and people within government, within agencies.

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But then when it comes this public presentation, it's some of the worst examples,

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I think the sort of stuff that if it was done by first year, each student at a test lecture, we'd be there.

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Apsley bellowing out loud and saying this is unacceptable as part of a presentation.

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And then, you know, lots of coloured lines, which I would within it with a legend somewhere.

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So, you know, some awful stuff. And you know, this one recently on the.

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Which I've been ranting on about on the main page, which is explaining the winter plans terribly important.

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And it's some ghastly thing knocked up in Excel when you can't even tell whether whether

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the scale is linear or logarithmic or that somebody is used to make it look science.

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And to put some colour rate and, you know, rather than actually to inform anybody.

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And it's not under any standards whatsoever.

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Which given there are so many good people, you know, you know, in you know, in government agencies who know how to do good visualisations.

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I really I find it very upsetting. This is stuff that comes from there and is much better that.

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OK. So I went there are a couple of people with hands up,

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but I'm going to try and do this and I've been trying to keep an eye on the order in which things have come in.

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I think it's quite interesting. As with a Florence Nightingale lecture, we've had a couple of questions come in on this idea.

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I tend to balance one of which is saying that daughter attended a mass open day and there were no women speakers.

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And when they asked why there were no women, because the comment was how many famous women mathematicians can you name?

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I'm going to ask you to go through them right now. But there's a question of do we always need to make sure that there's a woman on a panel?

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And we've also had a question from Denise Leavesley to David Cox, a festival.

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She says it's a fantastic book. Thank you very much, Deborah.

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But, David, your celebrated in the community for your research collaborations with large number of women.

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And Denise is interested as to whether or not this has been a deliberate decision on your

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part and how you feel it has contributed to your thinking or not a deliberate decision.

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Just a great, great, great fortune to work with.

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Very able people. Both genders, genders. Incidentally, college.

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The appointment of sequestration's certainly had nothing to do with gender at all,

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and we ended up with not far short of them, mixture of men, women on marriage.

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Not because of the new policy. So I tried.

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Right. Anybody who's got some.

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I'm sure I can just work with them and that we can together get productive results better then make good on her.

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But conscience, gender bias. No, I don't have one.

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I think it's a really interesting point at this sort of positive discrimination and unconscious gender bias,

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and should we always make sure there's a woman on the panel? Deborah, do you have any comments on that?

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I do. I think the point at which people apply for a job, you want to appoint the best people.

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The the thing beneath that is making sure that the level playing field so that women get a fair crack

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at doing the things that might get them to being appointed to a lecturer or a chair and so on.

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Where I would take a rather different line is when you're looking at particularly committees or public facing things,

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then absolutely you want balance diversity and whether that's RSS council for which we need nomine.

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And so we we've just elected one batch. We need nominations by the end of this month for the next batch.

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The about the last thing I do as outgoing president is to chair the present nominating committee for Cylvia successor.

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We need diverse nominations for that, because if you haven't got those nominations, you're not drawing on the best.

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And so wherever you haven't got diverse nominations,

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you need to look and ask why on a panel we are sitting here and in this day and age, it would be wrong if this was for men.

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And it's, I think, particularly helpful in that, except for things like celebration, women's mistakes.

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If it's all women, there's at the moment, this is a rebalancing role for that.

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But, you know, I'm very conscious that that is by no means the other diversity.

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We're sitting here for white people. So clearly, there are some things need doing to change that.

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So, yeah, so that's where I'd have to say that them certainly if you want to attract your best students

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to university and you put an all male panel and then you wonder why they go somewhere else.

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Yeah, I thought partitions bear to think it through a little bit better than that.

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But Baker. But I mean, it's one of the reasons I do feel very proud to be part of the statistical profession in that.

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In my experience, there isn't a need to make a big effort to get to a gender balance on research teams or organisation.

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It just it just happens because there's just really as of now and there's a big day there,

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especially with the younger generation coming up who are majority females in the majority.

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I think the younger fellows for the old you know, the old males like her.

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Like me. It's Dave flips around. But that's going to be changing.

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So I really like the fact that with the way not a great effort is needed to get that that sort of that balance in statistics at the moment,

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which is I find I think it's something hugely to celebrate. Would be different in maths, I think.

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But it just shows, I think, what a wonderful subject we're in that we don't have to make this effort.

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But as Deborah is so rightly said, this is only in terms of gender balance, as many other characteristics as well.

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Concerned about. Yeah, I actually did a little bit of an analysis looking up membership and the gender split.

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And, you know, you very much see that in the older generations it's very male dominated.

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But as you filter through, it's becoming more 50/50.

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And I do think that naturally, as time goes on, there will be more women taking president's roles and things like that.

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So, yeah, I'm quite encouraged by it. So we've got a couple of hands up.

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So I'm going to go to Len, first of all, who had a hand up for a little while now.

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So I believe Leon will meet you. See, you can talk. Len, thank you.

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I'd like to respond to David Spiegelhalter question about would she and then present a problem and ask you have any of you know,

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any means of doing anything about her or any answer to it? And it would she have given specific direct advice to policymakers like cabinet ministers?

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Yes, and she did. And her brief to the pull of to the what's called Cubic Space Committee, which was part of the poor law bill of 1867.

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She certainly gave very specific advice on that. And that can be seen as the first step towards getting to a national health service,

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that is of establishing quality care for the poorest, not those who can pay for it.

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So that was that was very important.

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But the problem I want to re raise and we're getting away from the issues you've been talking about now is going back to the Crimea and war.

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We don't have to my knowledge, correct me if you know something, a really decent count of the war dead.

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The Nightingale's analysis and the royal commission analysis were both based on data from the Army Medical Department,

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a nightingale often said how they were conflicting reports of up dead.

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And of course, Alexander Tullock collected data. It wasn't just the official people.

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And she did know in places that the number of people buried was greater than the number of people who died.

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And and was it suggesting anything went wrong. But clearly there were false in the record keeping.

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But you see, her data for those wonderful polar area charts is based on what got to the Army Medical Department.

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And that's only people who got admitted to Hospital B at a regimental hospital or a general hospital.

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But men and officers who died on the battlefield, they're not counted there.

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Now, when I was in the UK last February, March and was planning to stay longer,

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I intended to go to the National Archives because they have the reports of the adjutant general,

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which the British Library doesn't have, and that might help to fill in those gaps.

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But I have never seen a, you know, a table which gives you deaths in regimental hospitals, in General Hospital, blah, blah, blah.

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And deaths on the battle ground and our deaths of doctors and nurses and other people who were there doing other kinds of things.

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So we've never had a really full count on on on that subject.

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Anyone got any ideas or is willing to undertake to do it?

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So I can I bodging Len? Lovely to hear from you. I'm sorry I haven't got any ideas.

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I don't know about these data sources, but I would like to draw a modern parallel because the crucial thing about this is

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that you can't interpret data unless you know the process by which it was collected.

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And the modern parallels that, you know, the daily deaths reported on Kofod off.

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The people who have had a positive test then died within 28 days of any cause.

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And so people who caught it in a sense and dropped dead before they got tested won't be included in the daily count.

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They won't be in the dating statistics. They'll be in the death registration's if if they decide that they, you know, put it on the test.

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That's difficult without being tested. So there's deaths that are not occurring there.

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Again, people who got Koban, they got run over by a bus three weeks later. And what also will be included as a Kovar death.

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And so it is rather important ways to know what is being counted.

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And I think this this whole pandemic, although obviously there have been examples of wonderfully sophisticated modelling and computation,

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hugely elaborate analysis, so much of it has come down to the absolute basics with which night and go would have been so familiar.

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Which is kind of tabulating data and understanding whether you can believe it or not.

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And at what question you're trying to answer,

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because if you're trying to say Deskovic covered directly caused death, that's different to monitoring deaths.

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So if you're clear what the question is, then you can be clear with the data you've got is fit for purpose.

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Don't. David. Yet. David Cox, if you've got any comments.

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I don't know. OK. So I think that there's a really interesting sort because we're only about five minutes left aside now.

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So there's quite an interesting comment here on sort of practical statistics.

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Well, we've also had a comment that saying that Florence Nightingale would definitely be telling us to wash our hands.

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But then there's a question talking about there's a tendency for statistical training

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to become increasingly mathematical to the exclusion of practical problem solving.

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Have you got. What practical experience do the panel members consider essential for the next generation

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of statistical consultants and how best we can communicate that to the public?

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And there's a sort of similar question saying how there's a lot of mathematical

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statistics where they may never have performed an experiment or survey in their life.

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And then we've got investigators on the other side who require statistical training.

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And what's happened to Nightingale's recommendation that practitioners do the education?

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Just take your comments on that. Well, could I make a comment?

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Yes. That's the general principle, is you teach people things by developing out of what they already know or interest.

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So if you are interested, if you have students who've come to study at university,

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study of that and are deeply interested in pure mathematics and nothing much else, then you have to stop it.

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Probably Luti and gradually steer them towards the notion that he's not beyond all of this.

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Mathematics are very, very interesting and important scientific problems, which you can which they may be having to do something useful in that.

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The mathematics and develop into into application.

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For most people, that would be an absolute disaster. You have to start with applications.

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Those are to some extent reached.

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Appreciate. Appreciated. But the student in question.

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You have out of that into some general ideas, out of the out of the narrowing of interest into the unknown.

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And so it depends very much way you present your audience is snarky and it's not a mix.

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You're in trouble because you might have to try and appeal to the different categories of people before you do the more details with mystical ideas.

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And of course, it depends enormously also whether you've a connexions to explain the Hollow's states or they have that very,

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very small and that that makes a big difference. Can I button and they won't.

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I've been working this air for nearly 50 years and I've finally actually learnt the wisdom that David has just expressed.

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It's taken me a long time. I knew I should have listened to him a lot earlier.

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But I know absolutely that I grew up in the mathematical route.

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You know, Jar's joined the pure maths until he got too difficult and went through the

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probability and then mathematical statistics and and then that's the route I took.

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And that's what I taught for years. And that's how I worked with papers I wrote.

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And it's only recently, I think that I really flipped around and produced the book.

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Anyway, the point about this book, The Office Statistics,

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is just my like myself attempts at redemption, at completely flipping the whole thing around.

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And as David said, it depends where people are coming from.

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I was going to call it starts with data scientists because I wanted people to not come up through the mathematical route,

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and that's why probability doesn't come until two thirds of the way through. So the island's statistical inference putting them together is, you know,

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near the end of the book, because there's so much you can do through problem solving.

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Actually, problem driven all the time. Question driven. Problem driven.

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You can do such a vast amount. I'm so amazed me how much you could do with all this mathematical stuff.

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And so I'm I'm you know, I've had my Damascene moment, you know, a few years ago.

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And now I have completely flipped around how I think statistics should be taught except for the people,

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you know, who have come up through that mathematical route and they have to be brought in in a different way.

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So I think implicit in what David Cox was saying was that you kind of need both.

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Know even saying wherever they start and you need to sort of edge them in the other direction.

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And this may be the time to say that 40 years ago, I was doing a call since Disclaim France, the.

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It was a joint project as a region calls. We had Coxon. Hinckley has our course text.

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And what John France said to us, who is my lecturer, was this is a book you won't get everything out of the first time.

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You need to reread it next year and again in five years or so,

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because the more you have practical experience, the more you'll realise what he's trying to say.

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And equally, I would say those doing applied work from time to time need to get to see the wood for the trees.

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So we wouldn't be upset with covered if we hadn't had people dealing with other infectious diseases.

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I keep the details different, the modelling design.

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So you've got to keep both in tension, which certainly are both today if it's a done throughout their career.

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But I've still got a copy of Hickox and Hinckley, and when I get back into my office, I shall be rescuing and bringing it home.

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Got mine to. OK.

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So we're pretty much at time and I'm aware that I need to give Crystal a few moments to say thanks to everybody.

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And the final goodbyes. But are there any final comments from any of you that you'd like to make?

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That's around at the panel discussion. I just want to know what a pleasure it's been.

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Yes, and thank you again for the lecture. Fantastic. And thank you to Oxford for putting this together, because it's been the most wonderful event.

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And though I'm very sorry that we're not going for tea and cake and water and Oxford and a dinner for some of us,

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I think we've probably had a very different and in many ways richer discussion

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than we might have done if we've done it when it was originally planned. So thank you for reinventing it.

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I'm one of the last things I will say is one of the advantages of doing it online is that everybody

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writes their questions in the box and we have got questions that we haven't been able to cover.

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I don't know if there are going to be any arrangements, but it may be possible.

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So I apologise for any questions that couldn't come up.

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But it may be possible to put those to our panellists after the event and perhaps write it up somewhere.

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And I will have to leave that to Oxford to see if it's possible.

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Sorry if I've given you a task that it's simple, but there are some really interesting questions that we didn't get.

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And I apologise for that. But thank you very much. A really interesting panel discussion.

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Three of you have been incredible. And I will handle far final goodbye.

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OK. Thanks to all of you for taking part and for spending this time being so extra special, thanks to Deborah.

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She did first the lecture, then the panel. But it's been really great to get your views on all of this.

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Both looking back at what Florence did and what she might have done if she were alive now, as well as bringing all these contemporary issues,

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because I think I really genuinely think that mathematics and statistics specifically is just the ultimate transferable skill.

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You can move or work on one thing and to another and and thereby meet such interesting people, as well as helping to answer interesting questions.

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It's been a tremendous pleasure for us to host this sit in a different way than we originally planned, but nonetheless bring it to everybody.

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And hopefully we were able to open this up to a wider audience that wouldn't have been able to to come had it been specifically in Oxford.

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So, you know, there are some silver linings in this. Deborah has said that very clearly.

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She is willing to make her slides available. They are currently in a form that is too big to easily distribute.

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So we will have to do a little bit of I.T. compression of that before people end up hanging, trying to download them.

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But we we have your contact details. And so we will make those available.

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You saw the link already in the chat. So do visit the virtual exhibition.

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If you haven't had a chance to already, there's lots of interesting stuff there.

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It's like you get to stand in a virtual room and and choose what you focus on off the wall.

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It was a tremendous honour for Beverly Layne who helped organise this and to go along and actually see and choose amongst the pieces of her

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original correspondence and huge thanks to Paleo for sharing those with us to Beverly for having organised stage one and then stage two.

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And to everybody who's been involved to bring this to. So thank you all very much.

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It seems like it's the middle of the night, even though it's only five p.m., but really best wishes to everybody.

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Thank you for taking part. And we look forward to next year. When I would.

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Thanks very much.