I'm Karen DePauw and it's my pleasure, I served as
vice president and dean for graduate education here and it is my
pleasure to be one of the sponsors of this event.
I think open access is an extremely critical topic
movement and something that we definitely need to pay attention
to at both the um graduate student level undergrad, faculty
and the whole university. So it's my pleasure to
welcome you here this evening and that ends my comments
because indeed now I'm going to ask Tyler Walters,
he will introduce our speaker. Well, thank you.
Good evening. I'm Tyler Walters, Dean
of the University Libraries here at Virginia Tech. I have a
couple of program notes as we move forward with this
to let you know how the genesis of the talk
and this week and things we're up to right
now. Uh I wanna thank you all for coming.
I definitely want to thank Dean DePauw for
cosponsoring open access week with us in the libraries.
This is the beginning of our of our activities uh
before I really get into introducing our speaker a couple
of things, we're going to have some printouts of
other panel sessions and workshops this week. They're gonna
be appearing soon on the back table there with drinks
and snacks are so please pick up one of those
on your way out so you can find other talks
that are going on like this throughout the week and
we love to see there. So please consider attending.
In addition to Cameron Neylon being our keynote lecturer
speaker today, he's also Virginia Tech's second distinguished
Innovator in Residence which we started last year in the
spring. So with that this program is actually,
we have two things going on. We have Open Access Week.
We're partnering with the graduate school. And the Distinguished
Innovator in Residence program is a partnership that we have
we started up with Learning Technologies. So we're equal
partners and really love doing this and we're really happy to have Cameron consent
to be our second person. So for that program were
very interested in bringing people in who are innovators that
can help us understand through universal creativity, how to
solve global problems that we're looking at. I want thank
Anne Moore in particular for Learning Technologies
for cosponsoring that with us. I want to mention that uh
I saw Cameron speak this summer at the International
conference of Open Repositories in Edinburgh Scotland when we sat
down and talked about open access week here in Virginia
and we thought about who we could bring in that would be
good. I thought immediately about Cameron. I was
hoping he would consent to come. Other people quickly checked his
blog or already knew of his blog Science in the
Open, it is definitely worth checking out and we
put the word out and the Cameron was very quick
and good about wanting to come. So we very
much appreciate his time travels. He's quite the world
travelers, so we're very thrilled that he could uh
squeeze in a few days with us here. He
brings a lot of scientific expertise and technical expertise as
well as expertise around open access. But what you're
going to hear a lot about today as well is
really about open research and network science and how people
are learning and sharing and collaborating network world. So
I think you're really will enjoy the public. It's
quite an interesting. Cameron is a bio scientist who's
always worked in interdisciplinary areas. He's an advocate of
open research practice and improved data management. He currently
works as the Director of Advocacy for PLOS of the Library of
Science, which is a large, open excess science
publisher. Along with his work in structural biology and
biophysics. His research and writings focus on the interface
of web technology with science and the application of generic,
especially designed tools and academic research environments. Cameron
is a coauthor of the panting principles for open
data and science and he writes regularly on social technical
and policy issues on open research and you can see
an awful lot of that law science. And the
after this session today to were immediately after this,
we're going to have a talk about open access at
Virginia Tech and some of the findings that I'm gonna
A group that went through the executive development institutes.
After if you want to stay afterwards, we'd
love to have you attend. And we're going Canon
and scott Farmer talk about their open access findings and
how they see some recommendations rolling out here every day
. So with that we welcome the camera. Yeah
. Yes, hopefully. I'll be all right given
I've got two of these microphones on. Yeah,
so thank you so much for the introduction. Thank
you for the invitation. I've already had a really
interesting day talking to all sorts of people here.
It's arriving since arriving last night. Um, it
was also a great opportunity to not head straight back
to the UK and deal with the immediate jet lag
from the trip I was already on. So it
was it was a great opportunity, very happy to
be here, extremely honored to be following in the
innovative program Jon Udell who was really one of my
heroes. And in a sense, this talk will
build, I imagine on the kinds of things he
talked about last year. Much of his writing is
the inspiration for my thinking in this space. So
I want to talk about network enabled research and I
think that's bigger than just open access, but that
open access is a fundamental enabler of that. But
I want to start by putting my money where my
mouth is and making it clear that if while I'm
advocating that other people should be comfortable about giving other
people the rights to reuse replay, reintegrate their work
, that I'm very happy to be doing that as
well. Um so you can feel free to take
audio video pictures. You're also free to do this
sort of traditional things like take notes, think disagree
. Um, okay, maybe less than the like
the latter one, but you know, we'll work
up to that. So, so let me start
with asking the question, what is the what is
the challenge we face? What is this space that
we that we want to occupy and understand? And
so I'm a recovering academic. I was a researcher
until very recently. And this is not unusual experience
of the undergraduate graduate or professional researcher in the modern
day world. There is just too much to cope
with, There is too much to do and there
is certainly not enough time to do it. We
also have quite a lot of this kind of thing
going on. I leave it as an exercise to
you as to which one of those is the student
in which the researcher which is the university administration in
which the researcher which is the funder which is the
researcher. But very often we get into these conversations
and arguments where we just talk past each other and
that's born out of these frustrations. This view that
we could do better. The view that there's something
missing from where we are today as compared to where
we could be. But there's an enormous potential if
we could figure out how to tap it. And
in particular, I want to make the case that
there is a sense that we could deliver much more
effectively for the people who fund the research and scholarship
. We do. There is something missing in the
supply chain in terms of getting from the money that
comes in to the research that goes out to the
applications in the real world. So the structure this
talk around 3-1 pattern, I do this very often
because economic count three um certainly backwards. So it
would talk about three areas where I think we need
to deliver. There are two conceptual changes that I
think we need to tackle to deal with this new
and modern world and that's wrapped up in one central
principle around how we can think about information knowledge and
it's transferring communication in the modern world. But let
me start by telling you where I am coming from
, what my agenda and attitude is in this talk
. So A work for an open access publisher.
I've been involved in the open access movement for getting
on for 10 years now, and I've also been
involved in the open data movement trying to ensure that
more research data is made available, even if it
isn't necessarily associated with published papers. So you might
think therefore that what I was going to do is
take a sort of hippie position. Wouldn't it be
nice if we all just got on and everyone did
the right thing? Everyone went home early and exercised
and ate well and I could do that. I
could make the sort of public good argument around open
access around public access around the taxpayer contribution. But
I want to make a different argument. What I
want to make is a fairly hard nosed and pragmatic
case because we live in pretty hard nosed and pragmatic
times. The NH is currently operating on continuation money
. Universities across the globe are struggling financially in a
wide range of areas and research funding while rocketing in
some countries, is tending to do so in a
way that's not exactly comfortable for those of us,
dependent on that money for our careers, for our
work and to support people doing work. So I
want to make a business case and I want to
make a case. This is something that we cannot
choose not to do if we want to continue to
survive in the current economic climate and as a business
case is built around concerns, the quality of service
value for money, particularly important at the moment,
but also sustainability. We can't just jump change and
ripped the whole system down and we can't just say
, we're going to do X Wales said if next
week or next month the whole system will collapse because
of a lack of infrastructure, a lack of financing
or lack of support. So all those three things
are important. But the question is for who?
I'm going to make a business case. I'm going
to focus on this question of services and value for
money. Then who is the customer? Who is
the customer? Were serving in the process of delivering
the research enterprise And your first thought might be these
people, but its government supporting research, whether it's
state or federal governments, whether it's direct money through
funding of grants or whether it's indirect money through other
sources into the system. But I want to suggest
that government is just the system through which this money
comes and that ultimately it's the taxpayer who is funding
the government, who is funding our research through funds
through salaries. By whatever means the majority of the
money that we receive is public funds. So I
want to suggest that the customer is the global public
because research is a global enterprise, it doesn't fit
neatly within any given country anymore. And that the
product that we're selling to our customer is research outcomes
and I use outcomes advisedly. I do not mean
outputs. Yeah. The wider public could not care
less about the number of papers or where they're published
or the number of patterns, what they want to
know is is there air cleaner, do their tools
run better? Is there a drug for the disease
that Children have? Those are the concerns that we
have to deliver on? And that's the world in
which we live. So let me take one slight
step sideways. And the last question, why are
we having this conversation? Why does this matter now
in a way that it didn't matter 5, 10
, 15, 20 years ago. It matters in
part because of the financial situation. But the financial
situation has always been going on. It's always been
tough, More or less since the 60s or 70s
. And why now, what has changed about the
world we live in that makes this conversation relevant today
? And the answer is is a rather obvious.
And the answer is the web and the internet that
we're living through the largest change in our network communications
capacities and functionality. Certainly since the invention of the
printing press. And some would argue since the invention
of writing the web and the internet are a infrastructure
capacity and network at a scale that has never existed
before and its larger by orders of magnitude than anything
we've ever had before. And that changes things and
it doesn't just change them quantitatively. It doesn't mean
we can just do things better or faster or more
efficiently. It means we can do things that are
different. So some of you are old enough to
remember the world before email. Some of you are
probably old enough to remember the world before mobile phones
. I suspect some of you aren't, which makes
it a change from the usual audiences I give this
talk to. So if I pick on mobile phones
, there was a time when no one had them
when one or two people might have had them and
of course they were useless because whoever you get a
phone on them, none of the people you're interested
in talking to have one of these things. But
then something happens, then You start with one person
with a phone alexander Graham Bell, if you want
to think about it that way and then you create
one more connection and then maybe you create a few
more connections and a few more and all that's happening
is that a small proportion of the population here are
able to connect a little bit more effectively a little
bit more efficiently. But something happens as you grow
that network, you go from a point where most
people were not connected to a point where most people
are connected. Mhm. For those of you who
are physical science scientists, this is a phase change
. This is a crystallization process. This is a
qualitatively different system because the connectivity has changed to a
level where the structural characteristics are different. And what
that means is that any one of you could probably
walk into anyone. Any city in the United States
arrive at an airport, phone a couple of people
and meet up food or a drink. That was
not possible 10 years ago because people wouldn't have had
phones, you would have had to set that up
weeks in advance. What we can do changes.
Okay, so that's a nice story about mobile phones
or email or whatever it might be. But let
me give you an example that actually comes from science
and actually comes from mathematics. There will be a
test on the maths at the end of the lecture
of course. So Tim Gowers professor certain galas is
one of the world's leading living mathematicians. He also
happens to be a blogger and he writes a blog
which I understand none of because it's largely about mathematics
, but a lot of people find it find it
very interesting. But he also uses this blog to
think about the process by which he does his mathematics
And in January 2009, he was interested in this
question as a mathematician who had done all of his
work with a pencil and a piece of paper and
interested in the possibilities that this new sort of collaborative
science was emerging. He was interested in the question
whether mathematics could be done collaboratively and whether that could
be done at a large scale and so to do
that, he poses a question a specific problem and
it's a problem in which is interested it in and
it's a problem to which he thinks he might have
solution. And this maths is particularly well posed for
this kind of situation because he thinks he has a
way of making a proof. And if the proof
falls at any given part of the process, if
any link along the chain breaks, then the proof
has fallen. And so having lots of people look
at each of the links is a good way to
test whether his idea will work or not. So
of course what he does like any good researcher writing
any good funding application, he immediately scopes back the
degree and the amount which claim is going to be
able to do here. So he says quite specifically
, the case is not to try and make this
proof work, but simply to see whether it could
work to identify whether there are any problems. And
even in that case, He feels that the chances
are less significantly less than 100% of making any real
progress. He said later that if he'd actually tackled
this problem himself with the proof, had, that
probably would have taken him somewhere between six and 18
months to work through the proposed set of ideas that
he had to see whether they would work or not
. Remember. Fields medalist, one of the world's
greatest mathematicians. six weeks later, he declares he
thinks appropriate. Problem's been solved actually. Not that
the problem has just been solved, but a more
general case of the problem has been solved and has
been solved by a method and a proof. That
was not the one he originally proposed six weeks.
140 150 different mathematicians all engaged in the comments section
of a blog with tackling different parts of this mathematical
problem, teasing apart, putting it back together,
taking the resources that each of them contributed. Nuggets
of mathematics and taking those thinking about them, modifying
them and putting them back. Mathematics is a very
easy way to transfer knowledge and information. The notation
is precise, it is can be digital and it
can be easy to push back and forwards even though
the comments section of a blog is really not the
right way to do it. And he writes later
that this is though as though to normal research has
driving is to pushing the car, pushing a car
. That realization that you've got those, these things
in your hand that have a metal thing, they
go in the car somewhere and you can turn them
and something happens and you can go places and do
things that you never even thought about before because you
never even conceived of them as being possible. Qualitatively
different capacity for solving a mathematical problem, even though
the platform they used was actually very poor. Again
, the comments section of a blog 2nd example,
many of you have probably come across Galaxies. Galaxy
Zoo is a place where members of the public can
come into a website and by classifying Galaxies the structure
of Galaxies and a certain number of types that can
contribute to a big database um system. So it's
actually it's one of the main poster child for the
citizen science, for the engagement of people in wider
scientific problems. But I want to take it from
a slightly different perspective. I want to pull down
into the detail of why the problem was difficult in
the first place. And the problem is this,
if you want to try and understand the evolution of
Galaxies and build robust theories, you do that by
proposing models, testing those models and suggesting what sort
of proportion of Galaxies will be circular versus elliptical,
left handed versus right handed, blue versus green.
And that means you got to look at a lot
of images and classify a lot of Galaxies. So
if you're a professional researcher, a professor, then
maybe you can do about 100 of these in a
day or a week depending on how much other administrative
stuff you have to do. This is a slow
, fairly slow and arduous process. You need at
least, well, a lot more than that to
get past the statistical requirements to publish a paper.
But that's okay. We have automated systems for doing
this and we call them graduate students. And so
if you've got enough graduate students and you can get
to the kind of criterion, you need to tackle
a paper. And here is one of the greatest
graduate students of all time who over the course of
his PhD Classified 50,000 of these Galaxies handed his thesis
in and said, I am never going to do
that again. And even at this level, it
was turning out they didn't actually have the statistical power
to effectively distinguish between different models. Now it wasn't
a problem with the data because there's plenty of data
. Sloan Digital Sky Survey had a million of these
, but there was just simply no way of classifying
them. The computational systems are just not good enough
to do this kind of discrimination. We have a
data resource which was freely available on the web in
the form of the Sloan Digital Sky survey, we
have human beings who are capable of distinguishing between these
different types of Galaxies and the piece of genius is
to put that data in the hands, to distribute
the data out to these resources. These people who
can solve this problem and then enable them to very
easily push the knowledge that they have created by doing
the classification back to the center and do this at
massive scale. Over the course of a couple of
months, around 300,000 people classify those million Galaxies.
Five times over, they changed the expectations of what
this kind of science was. They have then gone
on to publish about 25 papers, including several that
actually have high school students on them as offers because
they could engage with these people. So again,
qualitatively different capacity to tackle a research question. And
the reasons for this are because of the scale In
the case of Galaxies, of being able to reach
300,000 people in the case of the Polymath project,
being able to effectively harness the contributions of a smaller
number of experts, mathematicians and by experts. I
include some of the world's best mathematicians and also a
bunch of high school teachers who just happen to know
the particular piece of mathematics that is critical for one
part of the problem connect to the right person because
when you do this at scale you will find the
people who have that kind of expertise. So scale
is critical. The second part is connectivity. It's
no good having resources, it's no good having abilities
and capacities. If they're not connected up to a
system, you need to be able to push the
resources, the questions, the knowledge from one place
to another without the connectivity, you can't do that
. And the other thing you need to be able
to do that effectively is the efficient transfer the face
data sets, knowledge, information, ideas, thoughts
, services analysis. Neither of these projects. Indeed
, virtually none of the projects that fall into this
category used existing institutional infrastructure in part because we tend
not within institutions to think of building the kind of
platforms that support this kind of activity. And it
is a different way of thinking. So the question
if I'm a research funder or a taxpayer or a
provost or a person with a sick child is how
do we get more of this for the research that
matters to me? How do we make networks?
How can we expand this, changing capacity in different
areas of research in different spaces? And how do
we make it possible to convert this from solving a
mathematical problem faster to finding a drug faster and as
service providers. And I want to suggest that all
of us involved in the research and scholarship enterprise should
think of us sells more as service providers, how
do we deliver them? How do we deliver networks
to our customers? So in doing that, there
are three areas, we need to deliver three things
we need to get in place to make this work
. The first isn't scaled and connectivity, it's reaching
as many people as we can, as many resources
as we can and ensuring that they are connected up
effectively, solve the problem. The second I've mentioned
is to lower the friction of transfer to make it
really easy to push resources, data, ideas whatever
around these networks so that they can find The one
or 2 of people who know exactly what to do
with them. You know exactly how to solve the
problem or have the new idea that we've never even
thought of. The third one is a little conceptually
more difficult to come back to it later but it's
all about reconfiguring the way we think about how we
filter information. But the first two of these are
easy, the web makes them a c we have
the connectivity, we have the scale, we have
four billion people connected to the internet in different ways
and in different places we have millions of computers storage
at A. Level which is literally unimaginable. But
the trouble is we have a bit of a problem
. And the problem is that traditionally our approach to
managing and dealing with research to promoting our own research
has always been about controlling access. And if your
approach to research is controlling access is limiting access to
the outputs and outcomes that arise from research. We
have a problem because you're selling a product that no
one wants, you're selling a product which is deficient
in the marketplace compared to the person is making it
more available and ensuring their research reaches more people again
. Let me try and flesh that out with an
example. We're going to use example a sort of
soundbite from a paper to try and illustrate this.
So paper from going to prison, but it's actually
quite controversial paper. There are some issues with the
methodology. Um but the basic point, I think
makes sense. And the paper shows that for the
data set, that he has pretty clearly clearly that
the number of tweets about a paper in the first
couple of days predicts the number of citations that paper
will receive quite strongly at two years, two years
, of course, being the magic point, which
impact factors are calculated. Okay, so twitter is
not just about your breakfast or about someone else's breakfast
, it has other uses. And people do talk
about research. That's the first important point here.
The second point is to consider what this means.
In a sense, it's obvious that if you imagine
that these blue circles are people who've read a piece
of research, have discovered a piece of research,
but the yellow dot in the top right is the
person who could actually make use of it. That
it makes sense that if you connect those dots up
, the more people that talk, the more potential
to create this kind of connection, the greater the
chance that this piece of research is going to find
the person who can use it. And that makes
sense. And that's true. If you can connect
those specific networks up, you'll do better. This
is why we have lots of collaborative funding programs to
try and build these networks more effectively. But thinking
about it this way is missing the critical point.
Because you're just thinking about the research community, You've
forgotten the 400 million people on Twitter or the billion
people on Facebook at this kind of scale, it's
more likely That the connections will pass through people that
are not associated with the research enterprise. It doesn't
matter that 99.9% of people don't care about the research
When you're talking about 400 million people that's still thousands
. And those thousands might be more loosely connected into
some of these networks and professional researchers. But it
doesn't matter because there are more of them at the
scale that you connect these kinds of things up.
You can find entirely new outcomes. Some of those
outcomes probably aren't even within the traditional research enterprise.
Some of them may not even be connected in any
way to our traditional research enterprise. And what happens
when you start to break connections? We start to
restrict access. When you start to try and control
the process of information dissemination is that you start breaking
those connections, you start creating friction, You start
reducing the potential for new connections to be made new
start to reduce the outcomes the impact that your research
has. Every time you break the opportunity for someone
to interact with a piece of research, whether it's
data or a paper or an idea, every time
that research isn't online is only in print, you're
losing those connections and in a world where researchers as
a whole are trying to optimize the impact of their
research, those yellow dots are our value proposition,
then what we're selling to the customer. And every
time we exceed two that impulse to try and control
the process, every time we think we know exactly
what our research is good for. We're blocking one
of those out. So access to control, sorry
, attempts to control access just damage our product.
So you have to ask the question is your research
in a place where when it's made available it can
be spread via twitter. Is it ready for the
network? Is it ready to be spread along those
networks? To wherever it might find its use?
Are the platforms you have as a community, a
disciplinary community and institutional community, a scholarly community ready
to cope with the networks? Do they have scale
and connectivity? Imagine something you've done today and ask
the question if you had to do it 100 times
more with 100 times more people Or 100 times faster
? Could you do it? Consider all the people
you're cutting out of your systems? Have you dropped
the friction in your systems as far as you possibly
can? And again, I want to emphasize this
thing about service thinking are the services we are providing
internally to ourselves and externally to our customers, providing
those systems, solving those problems. And so I
get to this other question. This one of filtering
of quality of the issues of what we put online 556 --> A:middle L:90% .