[sc-announce] Voices from the future of science: Rufus Pollock of the Open Knowledge Foundation -- Science Commons blog

Donna Wentworth dwentworth at creativecommons.org
Mon Aug 18 15:32:44 EDT 2008


 Voices from the future of science: Rufus Pollock of the Open Knowledge
Foundation

August 18th, 2008<http://sciencecommons.org/weblog/archives/2008/08/18/voices-from-the-future-of-science-rufus-pollock-of-the-open-knowledge-foundation/>by
dwentworth

If there's a single quote that best captures the ethos of open science, it
might be the following *bon mot* from Rufus
Pollock<http://www.rufuspollock.org/about/>,
digital rights activist, economist at the University of Cambridge and a
founder of the Open Knowledge Foundation <http://www.okfn.org/>: "The best
thing to do with your data will be thought of by someone else."

It's also a pithy way to convey both the challenge and opportunity for
publishers of scientific research and data. How can we best capitalize on
the lessons from the rise of the Web and open source software to accelerate
scientific research? What's the optimal way to package data so it can be
used in ways no one anticipates?

I talked to Pollock, who's been a driving force behind efforts to improve
sharing and reuse of data, about where we stand in developing a common
legal, technical and policy infrastructure to make open science happen, and
what he thinks the next steps should be.

*What strategies and concepts can we use from open
source<http://en.wikipedia.org/wiki/Open_source>to foster open
science <http://en.wikipedia.org/wiki/Open_research>? Can you give us a big
picture description of the role you see the Open Knowledge Foundation
playing?*

I'd say that in terms of applying lessons from open source, the biggest
thing to look at is data. Code and data have so many similarities — indeed,
in many ways, the distinction between code and data are beginning to blur.
The most important similarity is that both lend themselves naturally to
being broken down into smaller chunks, which can then be reused and
recombined.

This breaking down into smaller, reusable chunks is something we at the Open
Knowledge Foundation
refer<http://blog.okfn.org/2006/05/09/the-four-principles-of-open-knowledge-development/>to
as "componentization." You can break down projects, whether they are
data
sets or software programs, into pieces of a manageable size — after all, the
human brain can only handle so much data — and do it in a way that makes it
easier to put the pieces back together again. You might call this the Humpty
Dumpty principle. And splitting things up means people can work
independently on different pieces of a project, while others can work on
putting the pieces back together — that's where "many
minds<http://m.okfn.org/files/talks/xtech_2007/>"
come in.

What's also crucial here is openness: without openness, you have a real
problem putting things together. Everyone ends up owning a different piece
of Humpty, and it's a nightmare getting permission to put him back together
(to use jargon from economics, you have an anti-commons problem). Similarly,
if a data set starts off closed, it's harder for different people to come
along and begin working on bits of it. It's not impossible to do
componentization under a proprietary regime, but it is a lot harder.

With the ability to recombine information as the goal, it's critical to be
explicit about openness — both about what it is, and about what you intend
when you make your work available. In the world of software, the key to
making open source work is licensing, and I believe the same is true for
science. If you want to enable reuse — whether by humans, or more
importantly, by machines operated by humans — you've got to make it explicit
what can be used, and how. That's why, when we started the Open Knowledge
Foundation back in 2004, one of the first things we focused on was defining
what "open" meant. That kind of work, along with the associated licensing
efforts, can seem rather boring, but it's absolutely crucial for putting
Humpty back together. Implicit openness is not enough.

So, in terms of open science, one of the main things the Open Knowledge
Foundation has been doing is conceptual work — for example, providing an
explicit definition of openness for data and knowledge in the form of the open
knowledge/data definition <http://opendefinition.org/>, and then explaining
to people why it's important to license their data so it conforms to the
definition.

So, to return to the main question, I think one of the strategies we should
be taking from open source is its approach to the Humpty Dumpty problem. We
should be creating and sharing "packages" of data, using the same principles
you see at work in Linux distributions — building a
Debian<http://en.wikipedia.org/wiki/Debian>of data, if you like.
Debian has currently got something like 18,000
software packages, and these are maintained by hundreds, if not thousands,
of people — many of whom have never met. We envision the community being
able to do the same thing with scientific and other types of data. This way,
we can begin to divide and conquer the complexity inherent in the vast
amounts of material being produced — complexity I don't see us being able to
manage any other way.

*Your Comprehensive Knowledge Archive Network (CKAN <http://www.ckan.net/>)
is a registry for open knowledge packages and projects, and people have
added more than 100 in the past year. Can you tell us how the project got
started? What have the recent
updates<http://blog.okfn.org/2008/07/08/ckan-06-released/>achieved?
And what are your future plans — where do you hope to go next?
*

If you've got an ambitious goal like this one [of radically changing data
sharing and production practices], you've got to start with a modest
approach — asking, "what is the simplest thing we can do that would be
useful?" So we began by identifying some of the key things necessary for a
knowledge-sharing infrastructure, to figure out what we could contribute.
Sometimes what's needed is conceptual, like our definitions. Sometimes you
need a guide for applying concepts, like our principles for open knowledge
development<http://blog.okfn.org/2006/05/09/the-four-principles-of-open-knowledge-development/>.
And you need a way to share resources, which is why we started
KnowledgeForge <http://www.knowledgeforge.net/>, which hosts all kinds of
knowledge development projects.

The impetus behind CKAN was to make it easier for people to find open data,
as well as to make their data available to others (especially in a way that
can be automated). If you use Google to search for data, you're much more
likely to find a page about data than you are to find the data itself. As a
scientist, you don't want to find just one bit of information — you want the
whole set. And you don't want shiny front
ends<http://blog.okfn.org/2007/11/07/give-us-the-data-raw-and-give-it-to-us-now/>or
permission barriers at any point in the process. We've been making
updates to CKAN so machines can better interact with the data, which makes
it so people who want data don't have to jump as many hurdles to get it.
Ultimately, we want people to be able to request data sets and have the
software automatically install any additions and updates on their computers.

*What are the biggest challenges to making open science work? If you had to
lay out a 3-point agenda for the next five years, what would the action
items be?*

I think that, like with nearly everything else, the social and cultural
challenges may be the biggest hurdle. One aspect of making it work is
ensuring that more people understand exactly what they can gain from
sharing. I think it's like a snowball:  you might not get much back,
initially, from sharing, but over time, you'd be able to see your data sets
plugged in with other data sets, and your peers doing the same thing. The
results might encourage you to share more.

As for a 3-point agenda:

1.) Open access <http://www.earlham.edu/%7Epeters/fos/overview.htm> is very
important. In particular, I'd like to see the funders of science mandate not
just open access to publications but also, as part of the process, open
access to the data. They are paying for the research, so they can provide
the incentive to make the results open. Moreover, it should be easier to get
open access to the data; you wouldn't necessarily have the same kind of
struggle with publishers.

2.) I think we need more evangelism/advocacy for open science. We're seeing
big shifts in the way we do science, but we're still on the cusp of a
movement to bring open approaches together in a common infrastructure.

3.) We need to make it easier for people to share and manage large data
sets. Open science is already working in some respects;
arXiv.org<http://arxiv.org/>is an extraordinary resource, for
instance, but we need a better
infrastructure for handling the data itself. I also think that many people
are put off sharing because they think they don't know how to manage data.
That causes people to hesitate or give up completely. We need to make the
process smoother. Sharing your data should be as frictionless as possible.

*What do you see as the most important development in open science over the
last year?*

Without question, the progress we're making with data licensing. We have the
Science Commons Protocol for Implementing Open Access
Data<http://sciencecommons.org/weblog/archives/2008/08/18/projects/publishing/open-access-data-protocol/>,
which conforms to the Open Knowledge Definition <http://opendefinition.org/>,
and the very first open data licenses that comply with the protocol: the
Open Data Commons Public Domain Dedication and License
(ODC-PDDL<http://www.opendatacommons.org/odc-public-domain-dedication-and-licence/>)
and the CC0 <http://wiki.creativecommons.org/CC0> public domain waiver. We
now need to encourage people to start using these waivers — or any other
open license that complies.

* When I talk to people about what the open science movement is trying to
achieve, the most common response I get is, "Well, won't Google take care of
that?" Do you hear that? What's your response?*

I would ask, "Well, what is 'that'?" You find that many people believe that
if you put something online, it's automatically open, and Google will do the
rest. Google is great, but it can't handle things like community standards
or usage rights. And in any case, I'm deeply skeptical of "one ring to rule
them all" solutions. What we need is more along the lines of "small pieces,
loosely joined." Of course organizations like Google could help a lot (or
hurt!), and they're certainly an important part of the ecosystem. But at the
Open Knowledge Foundation, we like to say that the revolution will be
decentralized <http://m.okfn.org/files/talks/xtech_2007/>. No one person,
organization or company is going to do everything. Even Google didn't make
the Web standards or create the web pages and hyperlinks that make search
engines work. As it stands, Google may be good for finding bits of Humpty,
but not for creating or putting him back together.

*Have you read Chris Anderson's piece, The End of Theory: The Data Deluge
Makes the Scientific Method
Obsolete<http://www.wired.com/science/discoveries/magazine/16-07/pb_theory>?
If so, what's your take on it?*

I'll be politic and say that it's provocative but ultimately unconvincing.
There are reasons why we have theory. Imagine a library where you could have
any book you want, but there are no rules for searching, so you have to
search every book. The knowledge space is just too vast. In economics, just
like in science, you need models to isolate the variables you're interested
in. There may be millions of variables, for instance, to explain why you're
a happy person right now. You had a happy childhood, you just listened to a
symphony, etc. And the number of possible explanations (or, more formally,
"regressions") grows exponentially with the variables, so you're creating a
situation that's computationally hard — problems that, using brute force,
would take longer than the lifetime of the universe to solve, even with the
fastest supercomputers around.

I'd argue that with more data, you need more, not less modeling insight. As
the haystack grows, finding the needle by brute force is likely to be a less
attractive, not more attractive option. Of course it's true that more data
and more computational power are a massive help in making progress in
science or any other area. It's just that they have to be used
intelligently.

*On a more personal note, how does being an economist inform your
approach/perspective?*

Economists study information goods a lot, so I'd say my background has been
very influential. Economics 101 tells us that openness is often the most
efficient way to do things, especially when there's the possibility of
up-front funding by, for instance, the government. There are clear, massive
benefits for society in having a healthy, balanced information commons.
Unfortunately, it is often the case that those who benefit from
proprietarization have better-paid advocates, better-oiled PR machines, etc.

My hope is that this work that so many of us are doing *pro bono*, often in
our spare time, will slowly increase in impact — and that, at a minimum, we
can ensure that all publicly funded scientific research will be open.

#
Previous posts in this series:

   - Voices from the future of science: Lorrie LeJeune from
OpenWetWare<http://sciencecommons.org/weblog/archives/2008/06/03/voices-from-the-future-of-science-lorrie-lejeune-from-openwetware/>
   - A Wellcome future for
science<http://sciencecommons.org/weblog/archives/2008/04/28/a-wellcome-future-for-science/>
   - Voices from the future of
science<http://www.sciencecommons.org/weblog/archives/2008/04/02/voices-from-the-future-of-science/>

Donna Wentworth
Communications Specialist
Science Commons
http://sciencecommons.org
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