WEBVTT
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I'm Karen DePauw and it's my pleasure, I served as
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vice president and dean for graduate education here and it is my
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pleasure to be one of the sponsors of this event.
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I think open access is an extremely critical topic
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movement and something that we definitely need to pay attention
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to at both the um graduate student level undergrad, faculty
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and the whole university. So it's my pleasure to
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welcome you here this evening and that ends my comments
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because indeed now I'm going to ask Tyler Walters,
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he will introduce our speaker. Well, thank you.
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Good evening. I'm Tyler Walters, Dean
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of the University Libraries here at Virginia Tech. I have a
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couple of program notes as we move forward with this
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to let you know how the genesis of the talk
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and this week and things we're up to right
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now. Uh I wanna thank you all for coming.
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I definitely want to thank Dean DePauw for
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cosponsoring open access week with us in the libraries.
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This is the beginning of our of our activities uh
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before I really get into introducing our speaker a couple
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of things, we're going to have some printouts of
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other panel sessions and workshops this week. They're gonna
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be appearing soon on the back table there with drinks
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and snacks are so please pick up one of those
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on your way out so you can find other talks
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that are going on like this throughout the week and
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we love to see there. So please consider attending.
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In addition to Cameron Neylon being our keynote lecturer
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speaker today, he's also Virginia Tech's second distinguished
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Innovator in Residence which we started last year in the
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spring. So with that this program is actually,
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we have two things going on. We have Open Access Week.
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We're partnering with the graduate school. And the Distinguished
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Innovator in Residence program is a partnership that we have
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we started up with Learning Technologies. So we're equal
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partners and really love doing this and we're really happy to have Cameron consent
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to be our second person. So for that program were
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very interested in bringing people in who are innovators that
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can help us understand through universal creativity, how to
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solve global problems that we're looking at. I want thank
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Anne Moore in particular for Learning Technologies
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for cosponsoring that with us. I want to mention that uh
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I saw Cameron speak this summer at the International
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conference of Open Repositories in Edinburgh Scotland when we sat
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down and talked about open access week here in Virginia
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and we thought about who we could bring in that would be
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good. I thought immediately about Cameron. I was
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hoping he would consent to come. Other people quickly checked his
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blog or already knew of his blog Science in the
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Open, it is definitely worth checking out and we
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put the word out and the Cameron was very quick
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and good about wanting to come. So we very
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much appreciate his time travels. He's quite the world
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travelers, so we're very thrilled that he could uh
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squeeze in a few days with us here. He
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brings a lot of scientific expertise and technical expertise as
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well as expertise around open access. But what you're
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going to hear a lot about today as well is
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really about open research and network science and how people
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are learning and sharing and collaborating network world. So
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I think you're really will enjoy the public. It's
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quite an interesting. Cameron is a bio scientist who's
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always worked in interdisciplinary areas. He's an advocate of
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open research practice and improved data management. He currently
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works as the Director of Advocacy for PLOS of the Library of
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Science, which is a large, open excess science
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publisher. Along with his work in structural biology and
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biophysics. His research and writings focus on the interface
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of web technology with science and the application of generic,
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especially designed tools and academic research environments. Cameron
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is a coauthor of the panting principles for open
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data and science and he writes regularly on social technical
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and policy issues on open research and you can see
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an awful lot of that law science. And the
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after this session today to were immediately after this,
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we're going to have a talk about open access at
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Virginia Tech and some of the findings that I'm gonna
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A group that went through the executive development institutes.
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After if you want to stay afterwards, we'd
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love to have you attend. And we're going Canon
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and scott Farmer talk about their open access findings and
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how they see some recommendations rolling out here every day
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. So with that we welcome the camera. Yeah
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. Yes, hopefully. I'll be all right given
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I've got two of these microphones on. Yeah,
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so thank you so much for the introduction. Thank
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you for the invitation. I've already had a really
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interesting day talking to all sorts of people here.
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It's arriving since arriving last night. Um, it
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was also a great opportunity to not head straight back
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to the UK and deal with the immediate jet lag
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from the trip I was already on. So it
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was it was a great opportunity, very happy to
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be here, extremely honored to be following in the
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innovative program Jon Udell who was really one of my
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heroes. And in a sense, this talk will
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build, I imagine on the kinds of things he
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talked about last year. Much of his writing is
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the inspiration for my thinking in this space. So
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I want to talk about network enabled research and I
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think that's bigger than just open access, but that
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open access is a fundamental enabler of that. But
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I want to start by putting my money where my
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mouth is and making it clear that if while I'm
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advocating that other people should be comfortable about giving other
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people the rights to reuse replay, reintegrate their work
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, that I'm very happy to be doing that as
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well. Um so you can feel free to take
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audio video pictures. You're also free to do this
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sort of traditional things like take notes, think disagree
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. Um, okay, maybe less than the like
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the latter one, but you know, we'll work
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up to that. So, so let me start
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with asking the question, what is the what is
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the challenge we face? What is this space that
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we that we want to occupy and understand? And
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so I'm a recovering academic. I was a researcher
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until very recently. And this is not unusual experience
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of the undergraduate graduate or professional researcher in the modern
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day world. There is just too much to cope
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with, There is too much to do and there
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is certainly not enough time to do it. We
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also have quite a lot of this kind of thing
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going on. I leave it as an exercise to
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you as to which one of those is the student
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in which the researcher which is the university administration in
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which the researcher which is the funder which is the
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researcher. But very often we get into these conversations
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and arguments where we just talk past each other and
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that's born out of these frustrations. This view that
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we could do better. The view that there's something
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missing from where we are today as compared to where
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we could be. But there's an enormous potential if
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we could figure out how to tap it. And
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in particular, I want to make the case that
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there is a sense that we could deliver much more
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effectively for the people who fund the research and scholarship
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. We do. There is something missing in the
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supply chain in terms of getting from the money that
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comes in to the research that goes out to the
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applications in the real world. So the structure this
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talk around 3-1 pattern, I do this very often
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because economic count three um certainly backwards. So it
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would talk about three areas where I think we need
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to deliver. There are two conceptual changes that I
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think we need to tackle to deal with this new
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and modern world and that's wrapped up in one central
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principle around how we can think about information knowledge and
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it's transferring communication in the modern world. But let
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me start by telling you where I am coming from
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, what my agenda and attitude is in this talk
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. So A work for an open access publisher.
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I've been involved in the open access movement for getting
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on for 10 years now, and I've also been
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involved in the open data movement trying to ensure that
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more research data is made available, even if it
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isn't necessarily associated with published papers. So you might
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think therefore that what I was going to do is
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take a sort of hippie position. Wouldn't it be
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nice if we all just got on and everyone did
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the right thing? Everyone went home early and exercised
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and ate well and I could do that. I
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could make the sort of public good argument around open
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access around public access around the taxpayer contribution. But
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I want to make a different argument. What I
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want to make is a fairly hard nosed and pragmatic
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case because we live in pretty hard nosed and pragmatic
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times. The NH is currently operating on continuation money
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. Universities across the globe are struggling financially in a
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wide range of areas and research funding while rocketing in
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some countries, is tending to do so in a
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way that's not exactly comfortable for those of us,
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dependent on that money for our careers, for our
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work and to support people doing work. So I
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want to make a business case and I want to
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make a case. This is something that we cannot
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choose not to do if we want to continue to
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survive in the current economic climate and as a business
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case is built around concerns, the quality of service
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value for money, particularly important at the moment,
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but also sustainability. We can't just jump change and
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ripped the whole system down and we can't just say
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, we're going to do X Wales said if next
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week or next month the whole system will collapse because
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of a lack of infrastructure, a lack of financing
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or lack of support. So all those three things
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are important. But the question is for who?
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I'm going to make a business case. I'm going
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to focus on this question of services and value for
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money. Then who is the customer? Who is
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the customer? Were serving in the process of delivering
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the research enterprise And your first thought might be these
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people, but its government supporting research, whether it's
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state or federal governments, whether it's direct money through
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funding of grants or whether it's indirect money through other
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sources into the system. But I want to suggest
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that government is just the system through which this money
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comes and that ultimately it's the taxpayer who is funding
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the government, who is funding our research through funds
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through salaries. By whatever means the majority of the
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money that we receive is public funds. So I
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want to suggest that the customer is the global public
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because research is a global enterprise, it doesn't fit
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neatly within any given country anymore. And that the
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product that we're selling to our customer is research outcomes
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and I use outcomes advisedly. I do not mean
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outputs. Yeah. The wider public could not care
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less about the number of papers or where they're published
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or the number of patterns, what they want to
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know is is there air cleaner, do their tools
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run better? Is there a drug for the disease
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that Children have? Those are the concerns that we
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have to deliver on? And that's the world in
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which we live. So let me take one slight
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step sideways. And the last question, why are
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we having this conversation? Why does this matter now
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in a way that it didn't matter 5, 10
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, 15, 20 years ago. It matters in
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part because of the financial situation. But the financial
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situation has always been going on. It's always been
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tough, More or less since the 60s or 70s
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. And why now, what has changed about the
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world we live in that makes this conversation relevant today
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? And the answer is is a rather obvious.
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And the answer is the web and the internet that
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we're living through the largest change in our network communications
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capacities and functionality. Certainly since the invention of the
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printing press. And some would argue since the invention
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of writing the web and the internet are a infrastructure
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capacity and network at a scale that has never existed
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before and its larger by orders of magnitude than anything
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we've ever had before. And that changes things and
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it doesn't just change them quantitatively. It doesn't mean
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we can just do things better or faster or more
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efficiently. It means we can do things that are
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different. So some of you are old enough to
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remember the world before email. Some of you are
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probably old enough to remember the world before mobile phones
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. I suspect some of you aren't, which makes
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it a change from the usual audiences I give this
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talk to. So if I pick on mobile phones
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, there was a time when no one had them
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when one or two people might have had them and
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of course they were useless because whoever you get a
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phone on them, none of the people you're interested
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in talking to have one of these things. But
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then something happens, then You start with one person
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with a phone alexander Graham Bell, if you want
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to think about it that way and then you create
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one more connection and then maybe you create a few
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more connections and a few more and all that's happening
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is that a small proportion of the population here are
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able to connect a little bit more effectively a little
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bit more efficiently. But something happens as you grow
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that network, you go from a point where most
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people were not connected to a point where most people
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are connected. Mhm. For those of you who
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are physical science scientists, this is a phase change
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. This is a crystallization process. This is a
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qualitatively different system because the connectivity has changed to a
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level where the structural characteristics are different. And what
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that means is that any one of you could probably
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walk into anyone. Any city in the United States
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arrive at an airport, phone a couple of people
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and meet up food or a drink. That was
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not possible 10 years ago because people wouldn't have had
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phones, you would have had to set that up
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weeks in advance. What we can do changes.
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Okay, so that's a nice story about mobile phones
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or email or whatever it might be. But let
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me give you an example that actually comes from science
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and actually comes from mathematics. There will be a
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test on the maths at the end of the lecture
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of course. So Tim Gowers professor certain galas is
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one of the world's leading living mathematicians. He also
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happens to be a blogger and he writes a blog
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which I understand none of because it's largely about mathematics
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, but a lot of people find it find it
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very interesting. But he also uses this blog to
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think about the process by which he does his mathematics
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And in January 2009, he was interested in this
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question as a mathematician who had done all of his
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work with a pencil and a piece of paper and
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interested in the possibilities that this new sort of collaborative
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science was emerging. He was interested in the question
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whether mathematics could be done collaboratively and whether that could
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be done at a large scale and so to do
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that, he poses a question a specific problem and
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it's a problem in which is interested it in and
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it's a problem to which he thinks he might have
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solution. And this maths is particularly well posed for
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this kind of situation because he thinks he has a
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way of making a proof. And if the proof
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falls at any given part of the process, if
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any link along the chain breaks, then the proof
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has fallen. And so having lots of people look
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at each of the links is a good way to
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test whether his idea will work or not. So
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of course what he does like any good researcher writing
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any good funding application, he immediately scopes back the
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degree and the amount which claim is going to be
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able to do here. So he says quite specifically
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, the case is not to try and make this
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proof work, but simply to see whether it could
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work to identify whether there are any problems. And
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even in that case, He feels that the chances
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are less significantly less than 100% of making any real
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progress. He said later that if he'd actually tackled
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this problem himself with the proof, had, that
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probably would have taken him somewhere between six and 18
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months to work through the proposed set of ideas that
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he had to see whether they would work or not
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. Remember. Fields medalist, one of the world's
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greatest mathematicians. six weeks later, he declares he
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thinks appropriate. Problem's been solved actually. Not that
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the problem has just been solved, but a more
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general case of the problem has been solved and has
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been solved by a method and a proof. That
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was not the one he originally proposed six weeks.
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140 150 different mathematicians all engaged in the comments section
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of a blog with tackling different parts of this mathematical
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problem, teasing apart, putting it back together,
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taking the resources that each of them contributed. Nuggets
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of mathematics and taking those thinking about them, modifying
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them and putting them back. Mathematics is a very
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easy way to transfer knowledge and information. The notation
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is precise, it is can be digital and it
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can be easy to push back and forwards even though
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the comments section of a blog is really not the
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right way to do it. And he writes later
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that this is though as though to normal research has
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driving is to pushing the car, pushing a car
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. That realization that you've got those, these things
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in your hand that have a metal thing, they
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go in the car somewhere and you can turn them
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and something happens and you can go places and do
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things that you never even thought about before because you
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never even conceived of them as being possible. Qualitatively
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different capacity for solving a mathematical problem, even though
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the platform they used was actually very poor. Again
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, the comments section of a blog 2nd example,
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many of you have probably come across Galaxies. Galaxy
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Zoo is a place where members of the public can
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come into a website and by classifying Galaxies the structure
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of Galaxies and a certain number of types that can
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contribute to a big database um system. So it's
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actually it's one of the main poster child for the
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citizen science, for the engagement of people in wider
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scientific problems. But I want to take it from
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a slightly different perspective. I want to pull down
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into the detail of why the problem was difficult in
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the first place. And the problem is this,
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if you want to try and understand the evolution of
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Galaxies and build robust theories, you do that by
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proposing models, testing those models and suggesting what sort
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of proportion of Galaxies will be circular versus elliptical,
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left handed versus right handed, blue versus green.
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And that means you got to look at a lot
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of images and classify a lot of Galaxies. So
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if you're a professional researcher, a professor, then
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maybe you can do about 100 of these in a
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day or a week depending on how much other administrative
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stuff you have to do. This is a slow
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, fairly slow and arduous process. You need at
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least, well, a lot more than that to
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get past the statistical requirements to publish a paper.
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But that's okay. We have automated systems for doing
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this and we call them graduate students. And so
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if you've got enough graduate students and you can get
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to the kind of criterion, you need to tackle
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a paper. And here is one of the greatest
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graduate students of all time who over the course of
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his PhD Classified 50,000 of these Galaxies handed his thesis
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in and said, I am never going to do
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that again. And even at this level, it
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was turning out they didn't actually have the statistical power
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to effectively distinguish between different models. Now it wasn't
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a problem with the data because there's plenty of data
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. Sloan Digital Sky Survey had a million of these
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, but there was just simply no way of classifying
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them. The computational systems are just not good enough
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to do this kind of discrimination. We have a
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data resource which was freely available on the web in
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the form of the Sloan Digital Sky survey, we
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have human beings who are capable of distinguishing between these
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different types of Galaxies and the piece of genius is
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to put that data in the hands, to distribute
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the data out to these resources. These people who
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can solve this problem and then enable them to very
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easily push the knowledge that they have created by doing
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the classification back to the center and do this at
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massive scale. Over the course of a couple of
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months, around 300,000 people classify those million Galaxies.
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Five times over, they changed the expectations of what
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this kind of science was. They have then gone
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on to publish about 25 papers, including several that
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actually have high school students on them as offers because
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they could engage with these people. So again,
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qualitatively different capacity to tackle a research question. And
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the reasons for this are because of the scale In
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the case of Galaxies, of being able to reach
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300,000 people in the case of the Polymath project,
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being able to effectively harness the contributions of a smaller
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number of experts, mathematicians and by experts. I
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include some of the world's best mathematicians and also a
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bunch of high school teachers who just happen to know
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the particular piece of mathematics that is critical for one
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part of the problem connect to the right person because
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when you do this at scale you will find the
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people who have that kind of expertise. So scale
411
00:24:18.079 --> 00:24:22.910 A:middle L:90%
is critical. The second part is connectivity. It's
412
00:24:22.910 --> 00:24:25.319 A:middle L:90%
no good having resources, it's no good having abilities
413
00:24:25.319 --> 00:24:26.740 A:middle L:90%
and capacities. If they're not connected up to a
414
00:24:26.740 --> 00:24:32.569 A:middle L:90%
system, you need to be able to push the
415
00:24:32.569 --> 00:24:33.950 A:middle L:90%
resources, the questions, the knowledge from one place
416
00:24:33.950 --> 00:24:37.509 A:middle L:90%
to another without the connectivity, you can't do that
417
00:24:37.519 --> 00:24:38.119 A:middle L:90%
. And the other thing you need to be able
418
00:24:38.119 --> 00:24:41.880 A:middle L:90%
to do that effectively is the efficient transfer the face
419
00:24:44.440 --> 00:24:48.779 A:middle L:90%
data sets, knowledge, information, ideas, thoughts
420
00:24:48.789 --> 00:24:55.940 A:middle L:90%
, services analysis. Neither of these projects. Indeed
421
00:24:55.950 --> 00:24:57.480 A:middle L:90%
, virtually none of the projects that fall into this
422
00:24:57.480 --> 00:25:06.559 A:middle L:90%
category used existing institutional infrastructure in part because we tend
423
00:25:06.569 --> 00:25:08.920 A:middle L:90%
not within institutions to think of building the kind of
424
00:25:08.920 --> 00:25:14.799 A:middle L:90%
platforms that support this kind of activity. And it
425
00:25:14.799 --> 00:25:18.509 A:middle L:90%
is a different way of thinking. So the question
426
00:25:18.680 --> 00:25:23.210 A:middle L:90%
if I'm a research funder or a taxpayer or a
427
00:25:23.210 --> 00:25:33.450 A:middle L:90%
provost or a person with a sick child is how
428
00:25:33.450 --> 00:25:34.680 A:middle L:90%
do we get more of this for the research that
429
00:25:34.680 --> 00:25:38.410 A:middle L:90%
matters to me? How do we make networks?
430
00:25:38.410 --> 00:25:42.190 A:middle L:90%
How can we expand this, changing capacity in different
431
00:25:42.190 --> 00:25:45.069 A:middle L:90%
areas of research in different spaces? And how do
432
00:25:45.069 --> 00:25:48.109 A:middle L:90%
we make it possible to convert this from solving a
433
00:25:48.109 --> 00:25:56.029 A:middle L:90%
mathematical problem faster to finding a drug faster and as
434
00:25:56.029 --> 00:25:57.299 A:middle L:90%
service providers. And I want to suggest that all
435
00:25:57.299 --> 00:26:02.250 A:middle L:90%
of us involved in the research and scholarship enterprise should
436
00:26:02.250 --> 00:26:06.539 A:middle L:90%
think of us sells more as service providers, how
437
00:26:06.539 --> 00:26:08.859 A:middle L:90%
do we deliver them? How do we deliver networks
438
00:26:11.140 --> 00:26:18.869 A:middle L:90%
to our customers? So in doing that, there
439
00:26:18.869 --> 00:26:22.210 A:middle L:90%
are three areas, we need to deliver three things
440
00:26:22.210 --> 00:26:22.960 A:middle L:90%
we need to get in place to make this work
441
00:26:25.440 --> 00:26:29.069 A:middle L:90%
. The first isn't scaled and connectivity, it's reaching
442
00:26:29.079 --> 00:26:30.400 A:middle L:90%
as many people as we can, as many resources
443
00:26:30.400 --> 00:26:33.250 A:middle L:90%
as we can and ensuring that they are connected up
444
00:26:33.250 --> 00:26:37.460 A:middle L:90%
effectively, solve the problem. The second I've mentioned
445
00:26:37.470 --> 00:26:40.539 A:middle L:90%
is to lower the friction of transfer to make it
446
00:26:40.549 --> 00:26:42.990 A:middle L:90%
really easy to push resources, data, ideas whatever
447
00:26:44.000 --> 00:26:48.309 A:middle L:90%
around these networks so that they can find The one
448
00:26:48.319 --> 00:26:52.549 A:middle L:90%
or 2 of people who know exactly what to do
449
00:26:52.549 --> 00:26:53.589 A:middle L:90%
with them. You know exactly how to solve the
450
00:26:53.589 --> 00:26:56.099 A:middle L:90%
problem or have the new idea that we've never even
451
00:26:56.099 --> 00:27:00.660 A:middle L:90%
thought of. The third one is a little conceptually
452
00:27:00.660 --> 00:27:03.430 A:middle L:90%
more difficult to come back to it later but it's
453
00:27:03.430 --> 00:27:04.920 A:middle L:90%
all about reconfiguring the way we think about how we
454
00:27:04.920 --> 00:27:07.910 A:middle L:90%
filter information. But the first two of these are
455
00:27:07.910 --> 00:27:12.789 A:middle L:90%
easy, the web makes them a c we have
456
00:27:12.789 --> 00:27:15.910 A:middle L:90%
the connectivity, we have the scale, we have
457
00:27:15.910 --> 00:27:18.240 A:middle L:90%
four billion people connected to the internet in different ways
458
00:27:18.240 --> 00:27:25.349 A:middle L:90%
and in different places we have millions of computers storage
459
00:27:26.140 --> 00:27:30.700 A:middle L:90%
at A. Level which is literally unimaginable. But
460
00:27:30.700 --> 00:27:32.700 A:middle L:90%
the trouble is we have a bit of a problem
461
00:27:32.880 --> 00:27:36.559 A:middle L:90%
. And the problem is that traditionally our approach to
462
00:27:36.559 --> 00:27:40.490 A:middle L:90%
managing and dealing with research to promoting our own research
463
00:27:40.490 --> 00:27:42.359 A:middle L:90%
has always been about controlling access. And if your
464
00:27:42.359 --> 00:27:47.809 A:middle L:90%
approach to research is controlling access is limiting access to
465
00:27:47.809 --> 00:27:51.650 A:middle L:90%
the outputs and outcomes that arise from research. We
466
00:27:51.650 --> 00:27:52.970 A:middle L:90%
have a problem because you're selling a product that no
467
00:27:52.970 --> 00:27:57.390 A:middle L:90%
one wants, you're selling a product which is deficient
468
00:27:57.400 --> 00:28:02.720 A:middle L:90%
in the marketplace compared to the person is making it
469
00:28:02.720 --> 00:28:06.660 A:middle L:90%
more available and ensuring their research reaches more people again
470
00:28:06.660 --> 00:28:07.779 A:middle L:90%
. Let me try and flesh that out with an
471
00:28:07.779 --> 00:28:12.160 A:middle L:90%
example. We're going to use example a sort of
472
00:28:14.240 --> 00:28:15.549 A:middle L:90%
soundbite from a paper to try and illustrate this.
473
00:28:17.440 --> 00:28:19.730 A:middle L:90%
So paper from going to prison, but it's actually
474
00:28:19.730 --> 00:28:22.660 A:middle L:90%
quite controversial paper. There are some issues with the
475
00:28:22.660 --> 00:28:26.400 A:middle L:90%
methodology. Um but the basic point, I think
476
00:28:26.410 --> 00:28:29.940 A:middle L:90%
makes sense. And the paper shows that for the
477
00:28:29.950 --> 00:28:32.420 A:middle L:90%
data set, that he has pretty clearly clearly that
478
00:28:32.420 --> 00:28:33.670 A:middle L:90%
the number of tweets about a paper in the first
479
00:28:33.670 --> 00:28:38.569 A:middle L:90%
couple of days predicts the number of citations that paper
480
00:28:38.569 --> 00:28:41.380 A:middle L:90%
will receive quite strongly at two years, two years
481
00:28:41.380 --> 00:28:44.869 A:middle L:90%
, of course, being the magic point, which
482
00:28:44.880 --> 00:28:49.589 A:middle L:90%
impact factors are calculated. Okay, so twitter is
483
00:28:49.589 --> 00:28:53.359 A:middle L:90%
not just about your breakfast or about someone else's breakfast
484
00:28:53.740 --> 00:28:56.220 A:middle L:90%
, it has other uses. And people do talk
485
00:28:56.220 --> 00:28:59.359 A:middle L:90%
about research. That's the first important point here.
486
00:29:00.130 --> 00:29:02.799 A:middle L:90%
The second point is to consider what this means.
487
00:29:02.799 --> 00:29:04.549 A:middle L:90%
In a sense, it's obvious that if you imagine
488
00:29:04.549 --> 00:29:08.160 A:middle L:90%
that these blue circles are people who've read a piece
489
00:29:08.160 --> 00:29:11.559 A:middle L:90%
of research, have discovered a piece of research,
490
00:29:11.240 --> 00:29:15.680 A:middle L:90%
but the yellow dot in the top right is the
491
00:29:15.680 --> 00:29:18.259 A:middle L:90%
person who could actually make use of it. That
492
00:29:18.259 --> 00:29:21.470 A:middle L:90%
it makes sense that if you connect those dots up
493
00:29:21.480 --> 00:29:22.420 A:middle L:90%
, the more people that talk, the more potential
494
00:29:22.420 --> 00:29:25.910 A:middle L:90%
to create this kind of connection, the greater the
495
00:29:25.910 --> 00:29:26.990 A:middle L:90%
chance that this piece of research is going to find
496
00:29:26.990 --> 00:29:30.759 A:middle L:90%
the person who can use it. And that makes
497
00:29:30.759 --> 00:29:34.549 A:middle L:90%
sense. And that's true. If you can connect
498
00:29:34.549 --> 00:29:37.710 A:middle L:90%
those specific networks up, you'll do better. This
499
00:29:37.710 --> 00:29:41.059 A:middle L:90%
is why we have lots of collaborative funding programs to
500
00:29:41.059 --> 00:29:45.369 A:middle L:90%
try and build these networks more effectively. But thinking
501
00:29:45.369 --> 00:29:47.660 A:middle L:90%
about it this way is missing the critical point.
502
00:29:48.240 --> 00:29:52.140 A:middle L:90%
Because you're just thinking about the research community, You've
503
00:29:52.140 --> 00:29:56.109 A:middle L:90%
forgotten the 400 million people on Twitter or the billion
504
00:29:56.119 --> 00:30:00.910 A:middle L:90%
people on Facebook at this kind of scale, it's
505
00:30:00.910 --> 00:30:04.829 A:middle L:90%
more likely That the connections will pass through people that
506
00:30:04.829 --> 00:30:08.289 A:middle L:90%
are not associated with the research enterprise. It doesn't
507
00:30:08.289 --> 00:30:14.769 A:middle L:90%
matter that 99.9% of people don't care about the research
508
00:30:15.339 --> 00:30:18.849 A:middle L:90%
When you're talking about 400 million people that's still thousands
509
00:30:18.809 --> 00:30:22.400 A:middle L:90%
. And those thousands might be more loosely connected into
510
00:30:22.400 --> 00:30:26.509 A:middle L:90%
some of these networks and professional researchers. But it
511
00:30:26.509 --> 00:30:30.359 A:middle L:90%
doesn't matter because there are more of them at the
512
00:30:30.359 --> 00:30:33.279 A:middle L:90%
scale that you connect these kinds of things up.
513
00:30:33.279 --> 00:30:37.460 A:middle L:90%
You can find entirely new outcomes. Some of those
514
00:30:37.460 --> 00:30:42.849 A:middle L:90%
outcomes probably aren't even within the traditional research enterprise.
515
00:30:48.000 --> 00:30:52.759 A:middle L:90%
Some of them may not even be connected in any
516
00:30:52.759 --> 00:31:00.849 A:middle L:90%
way to our traditional research enterprise. And what happens
517
00:31:00.849 --> 00:31:03.690 A:middle L:90%
when you start to break connections? We start to
518
00:31:03.690 --> 00:31:06.480 A:middle L:90%
restrict access. When you start to try and control
519
00:31:06.480 --> 00:31:14.759 A:middle L:90%
the process of information dissemination is that you start breaking
520
00:31:14.759 --> 00:31:18.410 A:middle L:90%
those connections, you start creating friction, You start
521
00:31:18.420 --> 00:31:22.130 A:middle L:90%
reducing the potential for new connections to be made new
522
00:31:22.130 --> 00:31:27.410 A:middle L:90%
start to reduce the outcomes the impact that your research
523
00:31:27.589 --> 00:31:32.690 A:middle L:90%
has. Every time you break the opportunity for someone
524
00:31:32.690 --> 00:31:34.140 A:middle L:90%
to interact with a piece of research, whether it's
525
00:31:34.140 --> 00:31:37.440 A:middle L:90%
data or a paper or an idea, every time
526
00:31:37.440 --> 00:31:42.930 A:middle L:90%
that research isn't online is only in print, you're
527
00:31:42.930 --> 00:31:47.720 A:middle L:90%
losing those connections and in a world where researchers as
528
00:31:47.720 --> 00:31:51.450 A:middle L:90%
a whole are trying to optimize the impact of their
529
00:31:51.450 --> 00:31:56.160 A:middle L:90%
research, those yellow dots are our value proposition,
530
00:31:57.140 --> 00:32:00.849 A:middle L:90%
then what we're selling to the customer. And every
531
00:32:00.849 --> 00:32:05.619 A:middle L:90%
time we exceed two that impulse to try and control
532
00:32:05.619 --> 00:32:07.240 A:middle L:90%
the process, every time we think we know exactly
533
00:32:07.240 --> 00:32:10.759 A:middle L:90%
what our research is good for. We're blocking one
534
00:32:10.759 --> 00:32:14.980 A:middle L:90%
of those out. So access to control, sorry
535
00:32:14.980 --> 00:32:19.079 A:middle L:90%
, attempts to control access just damage our product.
536
00:32:19.180 --> 00:32:21.549 A:middle L:90%
So you have to ask the question is your research
537
00:32:22.009 --> 00:32:24.509 A:middle L:90%
in a place where when it's made available it can
538
00:32:24.509 --> 00:32:29.279 A:middle L:90%
be spread via twitter. Is it ready for the
539
00:32:29.279 --> 00:32:31.509 A:middle L:90%
network? Is it ready to be spread along those
540
00:32:31.509 --> 00:32:34.400 A:middle L:90%
networks? To wherever it might find its use?
541
00:32:35.240 --> 00:32:38.680 A:middle L:90%
Are the platforms you have as a community, a
542
00:32:38.680 --> 00:32:45.140 A:middle L:90%
disciplinary community and institutional community, a scholarly community ready
543
00:32:45.150 --> 00:32:49.750 A:middle L:90%
to cope with the networks? Do they have scale
544
00:32:49.750 --> 00:32:52.849 A:middle L:90%
and connectivity? Imagine something you've done today and ask
545
00:32:52.849 --> 00:32:55.150 A:middle L:90%
the question if you had to do it 100 times
546
00:32:55.150 --> 00:33:00.769 A:middle L:90%
more with 100 times more people Or 100 times faster
547
00:33:00.769 --> 00:33:05.660 A:middle L:90%
? Could you do it? Consider all the people
548
00:33:05.660 --> 00:33:08.559 A:middle L:90%
you're cutting out of your systems? Have you dropped
549
00:33:09.240 --> 00:33:12.660 A:middle L:90%
the friction in your systems as far as you possibly
550
00:33:12.660 --> 00:33:14.759 A:middle L:90%
can? And again, I want to emphasize this
551
00:33:14.759 --> 00:33:19.619 A:middle L:90%
thing about service thinking are the services we are providing
552
00:33:19.630 --> 00:33:23.680 A:middle L:90%
internally to ourselves and externally to our customers, providing
553
00:33:23.680 --> 00:33:29.569 A:middle L:90%
those systems, solving those problems. And so I
554
00:33:29.569 --> 00:33:31.210 A:middle L:90%
get to this other question. This one of filtering
555
00:33:31.210 --> 00:33:36.849 A:middle L:90%
of quality of the issues of what we put online
556
--> A:middle L:90%
.