Introduction to
Science Commons
John Wilbanks
Executive Director, Science Commons
James Boyle
William Neal Reynolds Professor of Law,
Duke Law School
August 3, 2006

www.sciencecommons.org

Introduction to Science Commons www.sciencecommons.org
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John Wilbanks and James Boyle September 7, 2006
Imagine a Brazilian postdoctoral student driven to cure
malaria. She knew she would not be able to do her work
in Brazil with the same impact she would have in the
United States or Europe (she wouldn't have the resources,
or the level of access to journals, tools, and
collaborations) so she joined the legions of expatriate
scientists in Boston. She is ridiculously talented, and very
lucky. She gets a prestigious grant and finds a position at
Harvard.
She is working on a protein called glycophorin A. It's a
key part of the way malaria infects blood cells. She
checks the major literature repository and finds nearly
2000 papers with a glycophorin A search. Her 50%
overhead from her National Institutes of Health grant,
combined with the grants of the other researchers there, is
enough to pay for an elite library with subscriptions to
all the journals. So at least she can read them. Yet behind
her stand thousands of other scientists and potential
scientists from around the world who cannot get access to
this material and who thus are lost to her and to us as
potential collaborators.
There are many problems other than access to scientific
journals and research to be dealt with, some of them more
fundamental. But even after the inequalities in access to
basic and scientific education, and after eliminating
research problems that require hugely expensive technical
infrastructure, we still effectively "discard" minds we
might need to solve problems because they do not have
full access to the research texts they need. Given the
rising cost of scientific publications and research
services, this group is not confined to the developing
world. It is a global problem.
Stay with our main character. If she reads all the papers at
the rate of one a day, it will take her five years to process
the relevant knowledge about her target, much less the
dozens of related entities in the cell that are involved in
malaria. And this is just the documents. There are
hundreds of databases to access, and thousands of data
sets. The digital knowledge is simply overwhelming. This
too is a global problem, one that is faced by commercial
and academic researchers from every country. It is one
that is actually exacerbated for the "information rich."
© Robert Cudmore; licensed to the public under Attribution-
ShareAlike 2.0.
Of course, in theory, computers could help us mine the
wealth of data that computers have made available to us.
Our researcher could use some advanced technology to
help her. She could use software tools to extract the facts
from the literature, to find new connections in the
existing knowledge, to tie datasets and journals together
and tag the information so that it could be found by
others in the future. Unfortunately, the contracts that
Harvard signed with the publishers often make that
illegal, and digital rights management technologies
enforce those contracts.
©3
rd
Coast Chick; licensed to the public under Attribution-
NonCommercial-ShareAlike 2.0.

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John Wilbanks and James Boyle September 7, 2006
If she builds a collaboration with the inventor of the
World Wide Web to try out his new "semantic web"
technologies on the articles and data she needs, she puts
Harvard at financial risk for breach of contract. [The
semantic web is explained in more detail in the
penultimate section of this paper.] And she's not allowed
to email copies of key papers to her collaborators, either,
so she can only really work with other scientists who
have access to wealthy libraries. There are software
companies that serve the pharmaceutical industry that
might be able to help but their software costs $100,000 a
year, more than twice her salary.
So she uses the services available to her - free text search,
Google, the free digital resources published by the United
States National Institutes of Health, some biology driven
desktop applications, Microsoft Office - and she narrows
down to a few key papers. By necessity she has thrown
away the vast majority of information that might be
relevant but is separated by the accident of an inapposite
or unlikely keyword, or a source in an apparently
unrelated scientific process.
She reads in a paper published by a prestigious journal
that glycophorin A is a key mechanistic part of malaria.
She needs to get some "research tools," actual physical
stuff this time – cell lines with and without glycophorin A
- to verify the published results and start looking at
potential ways to understand the mechanism in the
context of glycophorin A. Her grant covers this, to a
certain extent.
So tools are available, but she needs the actual tools that
were used in the paper she read to reproduce the result
she is interested in. To get access to those is hard. She has
to track down the contact information of the key authors,
call the lab, discover that the tool actually came from the
fourth author in another lab, call him, ask him to assign a
student to create a supply of the cell line and mail it to
her at Harvard. All this takes time. And even after finding
the fourth author, and finding him willing to share the
materials, there are more hurdles.
Sending the cell lines from his institution requires the
execution of a contract called a Materials Transfer
Agreement. And everyone involved in science agrees that
these can be a problem. Wendy Streitz and Alan Bennett,
of the University of California Office of Research
Administration and Technology, capture the problem
eloquently from the scientist's perspective: "One of your
colleagues at BigAg, Inc. (or at BigAg University) says
that she'd be happy to send you her transposon insertion
lines that saturate the right arm of chromosome 9; you'll
just need to have a material transfer agreement (MTA)
signed by your institution. Six months later, the terms of
the agreement are still under negotiation, you've missed
the field season, your grant has expired and there is now a
better resource that's been developed at LittleAg
University--and if you start negotiating an MTA now..."
(2)
Of course there are other reasons things might not go
well. The scientist with the cells simply won't share at all,
perhaps out of fear of being scooped, perhaps out of
competitive spirit, perhaps because it is a diversion of his
laboratory's scarce resources to generate materials for
another researcher. The Journal of the American Medical
Association published a study in 2002 describing a world
where 47% of academic geneticists had been rejected in
their efforts to secure access to data or materials related
to research by other academics.
This represented an increase from 34% who had been
rejected in a previous study in the mid 1990s. There were
multiple causes involved in this pattern but the leading
one was the effort required to produce and transfer the
materials, effort to which the MTA negotiation process
frequently adds. So our scientist is not alone when she

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John Wilbanks and James Boyle September 7, 2006
finds it exceedingly hard to verify the results claimed in
the paper.
She presses on. She spends her grant money on the
commercial tools, or tools that are similar to the tools she
is looking for, and is able to verify some elements of the
research, though it is a second-best approach. She decides
to invest her postdoctoral time on looking at potential
mechanisms for malaria, based on this glycophorin A
work. One year in, the results are promising. Two years
in she finds a paper, published years earlier, related to
glycophorin A's activity in a totally unrelated field -
cancer. It was published in an obscure journal and it
wasn't very well indexed at the time, so it would have
been very hard to find. Even if it had turned up in her
searches she would probably have ignored it in her
attempt to narrow the field. But it contained a nugget of
knowledge that would have saved her a full year of
money and a full year of progress towards the end goal of
curing disease. And it means that her key result has
already been published, though not in the context she was
exploring, which makes her paper much less likely to
help her get tenure, or another grant.
Sometimes, of course, that nugget of information is
necessary for the science to progress and, though it is out
there in the archives, it is never found. Sometimes it
never even gets into the literature, because the materials
necessary to do the experiment cannot be acquired.
Sometimes the experiment is not even attempted because
scientists with talents and good ideas do not have
practicable access to the literature. And this holds true
whether the research is on a drug for a neglected disease,
like malaria, for which the commercial market is in doubt
and which will probably need alternative sources of
funding, or research on a drug for a disease that has a
thriving commercial market, such as diabetes or heart
disease.
We do not know how many cases like that there are. We
do not know how much fuller our faltering drug pipeline
would be if at every stage of the process described, we
had managed to lower even a few of the economic, legal
and technical barriers to scientific journal research, data
mining and linking, materials acquisition and testing. We
do not know what would happen if we could eliminate
some of the legal and technical barriers to building a
"semantic web" for science.
Perhaps the result would be dramatic; some fairly
impressive scientists and computer scientists believe so.
Perhaps it would be more modest. But where it is
practicable to do so, lowering those barriers is clearly a
good idea. It might be a great idea. That is the idea
behind Science Commons and it would be surprisingly
cheap - by the standards of science funding - to make the
idea a reality.

4Introduction to Science Commons www.sciencecommons.org
John Wilbanks and James Boyle September 7, 2006
History of Science Commons
Creative Commons was formed to deal with a problem of
access to materials caused by the conjunction of
technological developments - computers' increasing
capability to store and process data vastly enhanced in
effect by interconnection via the World Wide Web--and
legal change. Creative Commons enables creators to
select among various copyright license options to make
their work available to the public on generous terms. The
licenses are designed so that they can be understood not
merely by lawyers, but also by ordinary people and even
by computers - the license terms are expressed in an easy
to understand "commons deed" complete with icons, but
also in "metadata" so that one can search not only for the
content of the work, but also for its degree of legal
openness. (Give me calculus textbooks that are available
for non commercial use and modification, say.)
Creative Commons' charge initially was entirely in the
cultural and copyright realms - in the world of music,
texts, blogs, pictures, films and so on. Nevertheless, at the
first board meeting, the founding board members
expressed strong interest in the possibilities of developing
the creative commons model in the scientific area.
Several times, in fact, board members expressed the
feeling that the Creative Commons approach might be
more of a "killer app" in science than in culture.
Recognizing that developing open pathways for scientific
research would be complex and contentious, the Creative
Commons board did not feel that at that point we had the
expertise or the technical capability to enter this field.
Creating an open regime of sharing and reuse in the
sciences is a complicated proposition. Though copyrights
guard the final published documents in peer reviewed
journals, patents protect inventions (some more unique
than others) and a web of handshakes and contracts guard
the tools, materials, datasets, databases and informal
knowledge transfer of day-to-day science. What works
for a biologist will likely fail for a physicist, neither of
whose solutions will perfectly solve the legal problems of
the anthropologist.
In some fields, especially the life and health sciences, the
commercial opportunities are as immense as the risks -
hundreds of millions of dollars in research and testing
costs bet against the possibility that a drug will make it
through clinical trials and perhaps become a billion dollar
blockbuster. Thus, any sharing regime for science must

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John Wilbanks and James Boyle September 7, 2006
be flexible, adaptable, contemplate copyrights, patents,
and contracts and more. From the beginning, it must be
compatible with commercial innovation as well as the
academy. Ill-conceived intervention that makes
commercial development more difficult will hurt rather
than help.
Adding to the complexity of the pure legal and policy
work is the sheer size and variability of the stakeholders.
Science requires universities, funders, companies,
researchers, publishers, consumers, technicians, librarians
and more. Each stakeholder represents an opportunity to
inject control into the scientific process, especially as
each one moves into the networked culture - and some of
that control is beneficial or necessary. To find which
barriers to sharing are unnecessary is a problem that
demands both interdisciplinary and practical
investigation. To remove the unnecessary barriers
requires an ability to produce consensus among disparate
parties, and even more, a large degree of humility: neither
the problems nor their solutions might be predicted by
reigning academic theory.
Creative Commons returned to science in early 2005 with
the launch of Science Commons. Millions of creative
works were already on the Web under Creative Commons
licenses (the current count is 140,000,000 - ranging from
music, films and political blogs, to textbooks and MIT's
Open Courseware) and we had gained significant
experience in open licensing approaches, complex
negotiations, and community building. We had the
ambition of achieving for the world of science and data,
what Creative Commons had begun to achieve for the
world of culture, art and educational material: to ease
unnecessary legal and technical barriers to sharing, to
promote innovation, to provide easy, high quality tools
that let individuals and organizations specify the terms
under which they wished to share their material.
Scientific American seemed to like the idea.
The first six months of Science Commons revolved
around building the right set of people to run the project
and conducting a broad survey of the various discipline-
specific efforts in open science.
John Wilbanks, an entrepreneur and former
bioinformatics CEO with experience at Harvard Law
School's Berkman Center and the World Wide Web
Consortium, came in to lead the effort as Executive
Director.
We built an advisory board composed of two Nobel
Laureates, Sir John Sulston and Joshua Lederberg, a
Berkeley scientist and leading expert in "open access"
publishing, Michael Eisen, the distinguished innovation
economist Paul David, and the prominent intellectual
property academic Arti Rai.
Four board members of Creative Commons join this
group and act as a steering committee: James Boyle, from
Duke, Mike Carroll an expert on intellectual property and
scholarly publishing from Villanova, Hal Abelson, a
renowned MIT computer scientist, and Eric Saltzman, a
lawyer, filmmaker and former Director of Harvard's
Berkman Center.
Science Commons hosted a series of private meetings
covering research funding, drug patent licensing,
biological materials transfer, and access to scholarly
literature. Wilbanks made a tour of different
communities: biology, chemistry, archaeology,
geospatial, physics, geography and more.
We reached out widely and formed relationships with key
players in discipline-specific efforts in agriculture,
neuroscience, anthropology, information technology, and

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John Wilbanks and James Boyle September 7, 2006
more. We forged working relations with funders of
research, universities, technology managers, software
companies, standards organizations, and libraries. We
were delighted by the reception that we received. From
the beginning we were guided by a set of principles. Like
Creative Commons, our proposals use coordinated
private action, not public fiat, to lower barriers to
research and sharing. This makes these proposals both
much cheaper and faster to implement than solutions
which require Congress or other regulators to act.
Wherever possible our solutions were based on both
empirical and interview-based investigation of the
problems. We tried to discard preconceptions; when we
formed the organization, for example, we expected to
spend more time on patent pooling. While we do not rule
that out, we found Materials Transfer Agreements to be a
more important area on which to focus initially. We tried
to come up with projects where success was not an all-or-
nothing proposition - selecting issues where any
alleviation of the problems we identified was a good
thing. We picked projects that played to our strengths and
to the considerable experience that Creative Commons
had acquired in negotiating standard form agreements
among disparate communities, merging legal and
technical solutions, making deals comprehensible to non
lawyers, and using metadata and the semantic web to
produce "usable openness" and machine-readable
contracts. Finally, we sought places where all sides
agreed there was a problem and where many stakeholders
would benefit from its removal.
Sample metadata from a Creative Commons license
Out of this research, we discovered a mix of legal,
cultural, and technical controls - at least one of which
bore down on the scientific process at each step,
preventing the realization of the promise of new
technologies like the Semantic Web. Some of these
controls were necessary, of course, but many were not.
Some of the problems came from fractured contract
regimes which created high transaction costs and
confusion, preventing the emergence of smooth
electronic transfer systems for knowledge and research
materials. Other problems were technical: digital controls
designed to prevent widespread copying of entire articles,
which also prevented the extraction of key facts from
papers for publication in new web languages. Some were
based on legal mistake; overbroad claims of copyright in
unoriginal databases, for example. Still others were a
matter of institutional policy, practical difficulty or
scientific culture. For example, commercial publishers
can hardly be blamed when even those scientists who
have the right to "self archive" their articles do not make
their work freely available online. Sharing between
laboratories is inhibited more by a complex mixture of
transactional, practical and prestige obstacles, than it is
by overbroad patents. And so on.
We realized that we could and should tackle each class of
problem individually, but that our overall goal should to
bring the projects together so as to enable the true
possibilities of open science in a networked world.

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John Wilbanks and James Boyle September 7, 2006
Proposals
In 2006, we began to act on our conclusions. We targeted
three areas; scholarly publishing, licensing policies, and
the realization of the "semantic web" for science. In each
we have been running "proof of concept" projects and we
now have early-stage efforts in scholar's copyrights,
biological materials transfer, and the intersection of
semantic web with Open Access content in neuroscience.
Our projects are designed to intersect to yield evidence of
the benefits of the overarching Science Commons vision
of open, networked science, but also to stand on their
own as worthwhile efforts in their own right.
Scholarly Communication
Scholarly communication in the sciences primarily
involves three kinds of information:(1) data generated by
experimental research,(2) peer-reviewed journal articles
explaining and interpreting the data, and(3) metadata that
describes or interprets articles or their underlying data.At
each of these levels, the Internet and associated digital
networks create a range of opportunities and challenges
for changing the nature of what information is gathered,
stored and communicated as well as how and when such
information is shared, identified and located.
The Science Commons Publishing Project promotes
effective use of digital networks to broaden access to all
three types of information. Science publishing is
obviously an area that has attracted a great deal of
attention. There are many stakeholders already engaged
in attempts to make scholarly publishing more open, and
a variety of strongly - some might say "religiously" - held
beliefs about which approaches work best. Science
Commons approach has been extremely pragmatic and
"non denominational." We have identified a series of
places where opportunities were not being fully seized,
where absence of collaboration was preventing
innovation, or simply where our specific expertise could
add value.
i.) Pragmatic Open Access Publishing: Some publishers
of peer reviewed science journals are employing a new,
Open Access business model where the authors grant
generous rights in their articles to the public under
Creative Commons licenses. These licenses make clear to
the public the broad range of uses they may make of the
articles, without further permission or fee.
The goal of open access is to broaden the dissemination
of knowledge about the natural world to researchers and
other readers who can put this knowledge to use. But for
this goal to succeed it is vital that readers easily grasp
what rights they are granted under the license; a
traditional Creative Commons concern. Publishers that
have adopted this approach, and who are using our
licenses to implement it, include the Public Library of
Science, BioMed Central and Springer's OpenChoice
program. (It is notable that this group includes
commercial, non-profit and government-funded
publishing efforts.)
ii.) Enabling Self-Archiving: It is increasingly common
for scholarly authors to be given rights to "self archive"
their work in institutional repositories. Some journals
explicitly give these rights, while others are willing to
give them only if asked. The rights vary as to the versions
of the paper that may be posted and the timing of the
post, leading to confusion among researchers. Worst of
all, perhaps, even where the rights do clearly exist, they
are used only infrequently, at least partly because of the
perceived practical difficulties involved in the process.
Self-archiving could be an incredibly valuable way of
achieving freer access to scholarly materials. Science
Commons has analyzed all the impediments to it and is
working to minimize or remove them.

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John Wilbanks and James Boyle September 7, 2006
We have already developed "Author Addenda" - a range
of short amendments, with varying degrees of openness,
that authors can attach to the copyright transfer form
agreements from publishing companies. The Addenda
ensure, at a minimum, that scholarly authors retain
enough rights to archive their work on the public Internet.
We are spreading the word in the scholarly community
and finding that there is considerable interest from
institutions who have good reasons to want to ensure that
their researchers retain enough rights to enable self-
archiving.
In the Fall of 2006 and Spring of 2007 we plan to release
• A Web-based tool that will enable faculty
authors to generate the Addendum of their
choice with all form fields automatically filled
in.
• Layperson-readable versions of the Addenda
(similar to the Creative Commons "Commons
Deed" copyright documents).
• Machine-readable versions of the Addenda to
enable advanced software usage of the
Addenda, database tracking, and empirical
evidence gathering. This builds on our pre-
existing metadata partnership with SPARC.
• We are also developing an application that will
sit on the scientist's desktop and enable "drag
and drop" self-archiving to an appropriate
repository. The Internet Archive has agreed to
host CC licensed material for free
permanently, and numerous institutional
repositories - using tools such as D-Space -
are also available.
iii.) Facilitating the Use of Metadata:
Within its Publishing Project, Science Commons has
convened a working group comprised of publishers,
librarians, and researchers to explore ways of better
associating research articles with research data and for
standardizing the metadata associated with both.
Licensing
Science Commons' Licensing Project aims to simplify
licensing so as to speed science. We have been working
on the creation of a "research commons" for neglected or
orphan diseases (so that funders can simply specify that
funded research must be available to all researchers in the
field).
We have also been approached by one of the world's
largest pharmaceutical companies with the idea of
forming a "tox commons" that allows all researchers to
pool toxicity data from failed commercial drug attempts,
in a pre-competitive process of sharing. The idea is
simple. While a successful drug application results in
open data - the FDA requires publication and review -
every failed drug results in secrets and obscurity. So a
tempting target, tried and again and again, can mean
repetition of failure. It's as if each company has just a few
pieces of the treasure map, and each company beaches on
a different set of rocks on the way.
Enter Science Commons.
Take a drug target for which compound after compound
has failed in the clinic due to toxicity concerns, a
graveyard of over $10,000,000,000 in sunk costs and
uncounted years of now-hidden research. Extract all the
relevant facts about the target and its toxicity, its
mechanisms, interactions, annotations, and more, from
the literature and databases.
Attach annotations and data from the internal files of
pharmaceutical companies who have tried, and failed, to
get drugs to the market. Integrate the relevant
descriptions of biological materials and public data sets.

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John Wilbanks and James Boyle September 7, 2006
Broker a set of contracts for access and recontribution to
the data. Then let the scientists go after the combined
knowledge, free of clickwraps and free to exploit
$10,000,000,000 of previously private, unintegrated,
inaccessible, invaluable knowledge.
In this introduction, though, we will concentrate on one
project that seems to exemplify the Science Commons
approach - the attempt to streamline of the process of
acquisition of research materials.
Biological Materials Transfer
Research materials are essential to the practice of modern
life sciences' experimentation. Cell lines, model animals,
DNA constructs, and screening assays each represent a
tool for testing and validating hypotheses of biological
function and human health. Each offers a perspective into
biology that cannot be replicated without access to the
material.
Research materials are developed in multiple
environments: university laboratories, startup companies,
biotechnology companies, hospitals, and non-profit
research clinics. Some of the materials are patented;
many are not. These tools are frequently licensed out to
other institutions through material transfer agreements"
(MTAs). Thousands of MTAs are signed each year in the
biological sciences, covering such diverse materials as
genes, proteins, chemicals, tissues, model animals,
software, databases, "know-how" and reagents.
Although "standard" material transfer agreements exist
(the Uniform Biological Material Transfer Agreement, or
UBMTA, was developed in 1995) empirical research
confirms that the licensing of materials remains a
problem. A complex set of interlocking licenses covering
dozens of different materials imposes significant
transaction costs simply to gain the opportunity to begin
research.
The long-term impact of this complexity is severe.
University technology transfer offices can become
clogged with requests that ought to be routine. Scientists
must waste time trying to negotiate agreements.
Commercial researchers find it hard to obtain materials.
The end result benefits no one - we get less research, less
innovation, less diffusion of knowledge.
Discussions with stakeholders reveal a number of
recurring problems. Supposedly uniform agreements are
actually "customized" in time-consuming negotiations,
although all players would benefit if they could bind
themselves to restrict choices to a more limited set of
standard options. Even the "short form" version of
agreements are perceived as too long and too complex.
The agreements themselves are hard to interpret and
scientists often find them mystifying, (or ignore them
altogether as a result.) Finally, there is no connection
between efforts to streamline the legal process for
clearing materials, and efforts to streamline the practical
process of actually fabricating and transferring the
materials themselves.
It would be hard to find an area more perfectly suited to a
Creative Commons-type solution. It is Creative
Commons' raison d'être to analyze creative communities
to find out which are the most common terms under
which rightsholders are willing to make their works
available, to generate licenses through a simple and
intuitive radio button interface that allows a range of
those choices to be expressed, (see Figure 1, page 5).
These licenses are expressed on three layers - lawyer
readable contracts, human readable Commons Deeds,
(see Figure 2, page 5) and machine readable meta data.
(See Figure 1, page 6) This is exactly what is needed for
a more rational Materials Transfer system, particularly if
the process of building consensus around such a system
can be led by a trustworthy third party - neither a funder,

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John Wilbanks and James Boyle September 7, 2006
nor a research unit, nor an academic institution nor a for
profit company.
More ambitiously, in the relatively near future the
material that is now covered by some Materials Transfer
Agreements will be capable of being synthesized directly
by DNA synthesizers. One could literally "print out"
one's research material, or more likely order it from a
third party specializing in such work. The cost at the
moment is about $2 a base pair, but it is dropping. As
MIT Professor Drew Endy points out this could
revolutionise the process of hypothesis formation, testing
and experiment. (Science Commons has been working
with Professor Endy on dealing with such issues in the
emerging field of synthetic biology.)
Science Commons is exploring the implications this
could have for the MTA process. The "blue sky" idea
beyond streamlining of the MTA process (itself hugely
valuable) is of a simple procedure by which a researcher
reading of a development in the literature, could merely
"click to get the cell line." Materials, or the information
that allows them to be synthesized, would be
automatically deposited with intermediaries or clearing
houses, accompanied by metadata-expressed licenses that
clearly expressed the uses to which those materials might
be put. Clear licenses with, clear machine readable terms
would allow quick, perhaps even automated, matching of
institutions or activities with the restrictions on a license.
At the very least, simple licenses with iconic
representations of their terms would allow researchers to
know which materials were available. It is hard to
overstate the advantages in streamlining that such a
process could offer. And in some areas at least, one might
be able to click right from the description in the literature
of an experiment using a DNA sequence, to a cheap
"print out" of that sequence ordered online from a low
cost intermediary, applying the terms of the standard
MTA. This sounds like science fiction, but some of the
experts with whom we have talked argue that for some
materials it is scientifically practicable now. (MIT's
repository of standard biological parts is an example.)
The principal obstacles are not scientific, or a matter of
computer science, or metadata expression. They are a
matter of law, social engineering and institutional
commitment.
Of course, the difficulties of procuring MTA's are not the
only, not even the main reason that it can be hard to
procure research materials. Competitiveness, secrecy, and
the sheer hassle of producing and shipping the materials
all play a part. The prevalence of these tendencies also
varies from one scientific area to another. But the overall
problem of obtaining materials is a huge one. The
literature indicates it may be the single largest reason for
the abandonment of promising lines of research.
Even if the legal transaction costs made up only 15% of
the total impediments, it would be well worth reducing
them. If, in doing so, we could make it easier to set up
streamlined systems for obtaining "pre-cleared" materials
from institutional and commercial repositories, the effect
would be remarkable. And for some scientists it might
actually affect the increasing tendency towards
possessiveness and secrecy. Studies on social sharing
networks indicate that one's willingness to help others is
directly related to one's experience of receiving such help
in the near past.
These beneficial spillover effects are possible but, the
licensing effort does not depend for its success on the
achievement of such ambitious goals. If we could simply
streamline the MTA, or make it easier for Foundations
working on orphan or neglected diseases to create a
"research commons" for all such research, we would have
achieved something extraordinarily important.
Beyond that tangible set of metrics for success, the
licensing project does express a larger vision - one central

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John Wilbanks and James Boyle September 7, 2006
to Science Commons. The point is that the social and
legal engineering of science has largely lagged behind
the technical engineering and investigation that it
seeks to facilitate.
Science Commons' licensing project attempts to use some
of the developments in computers and metadata, together
with more traditional legal and consensus building skills,
to make the process of legal clearance and practical
availability move at a pace closer to that of science itself.
Data
Introduction to the Semantic Web
In the course of this paper, we have several times used
the term "semantic web." The phrase may be unfamiliar
to some, but the idea behind it is quite simple. "The
Semantic Web is about two things. It is about common
formats for interchange of data, where on the original
Web we only had interchange of documents. Also it is
about language for recording how the data relates to real
world objects. That allows a person, or a machine, to start
off in one database, and then move through an unending
set of databases which are connected not by wires but by
being about the same thing.
A different way to put it is that right now we mainly use
network searches that look for words - say the word
"glycophorin." But of course, such a search would pull up
this paper (of little use to our Brazilian researcher) as
well as a paper that was actually talking about the
biochemical process she wished to investigate.
The semantic web allows searches by function, or
meaning. "Show me all the statements in the literature
which deal with X interaction between glycophorin A and
the malaria disease process." This is accomplished by
"tagging" information with metadata. One simple
example that is familiar from another context, is to tag a
bibliographic record with an "author," "title" and "date"
field. When I search for Bronte within the author field,
my time is not wasted with articles or books about the
Bronte's, nor with maps of a place called Bronte. But
metadata tagging can do much more than this. The
semantic web holds extraordinary promise for science.
At its most ambitious. it would allow seamless
integration between scholarly articles, the data those
articles refer to, and to cross references with other articles
dealing with similar processes in different areas of
science. But the process of mining, linking, tagging and
cross-referencing that the semantic web requires faces
extraordinary difficulties. Some of those difficulties are
financial. Tagging takes time and costs money. Some of
the difficulties involve the coordination of standards and
formats for metadata, something that Creative Commons
has considerable experience in.
Perhaps the single greatest obstacle to the semantic web,
however, is that the process of integration it requires is
now impeded by multiple barriers. The journal article is
copyrighted, and sits behind a digital fence. The data to
which the article refers cannot be integrated because it
too, is protected by licensing agreements, assertions of
copyright (some of them unfounded), and technical
controls. These legal and technical restrictions may be
aimed at preventing very different activities than those
necessary for the semantic web. (Stopping wholesale
copying and transmission of the text of journal articles,
say.) But their negative effect is real.
To solve these problems, one needs an organization with
considerable experience in law, publishing, computer
architecture and metadata. And those of course, are the
central focii of Creative Commons, and of the people
who run Science Commons. (John Wilbanks actually
came to us from the World Wide Web Consortium
(W3C) initiative on the semantic web for science, and
MIT computer science professor Hal Abelson serves on

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John Wilbanks and James Boyle September 7, 2006
the Creative Commons board and Science Commons
steering committee.)
Science Commons is pursuing a number of projects
aimed at enabling the semantic web for science. The most
fully developed at the moment is the Neurocommons.
Neurocommons
The Neurocommons project, a collaboration between
Science Commons and the Teranode Corporation, is
building on Open Access scientific knowledge to create a
Semantic web for neurological research. The project has
three distinct goals.
• To demonstrate that scientific impact is
significantly related to the freedom to reuse and
technically transform scientific information
without violating the law. In short, that a large
degree of Open Access is an essential
foundation for innovation.
• To establish a framework that increases the
impact of investment in neurological research in
a public and clearly measurable manner.
• To develop an open community of
neuroscientists, funders of neurological
research, technologists, physicians, and patients
to extend the Neurocommons work in an open,
collaborative, distributed manner.
Today's life scientist faces a dizzying array of knowledge
sources. Peer reviewed journal articles, online
repositories of sequences and pathways, robot-driven data
collection, all must be integrated into experimental design
and analysis. Many scientists spend as much time on
Google and PubMed as they do at the bench; the
difference between success and failure in the lab or clinic
can be the judicious and timely utilization of information.
But this is all local knowledge utilization. As we pointed
out earlier, the logarithmic explosion of information in
science overwhelms any one individual's ability to store
and model all the relevant science in her head.
The result is a "scalability problem" in life sciences:
while methods for generating information have gone
digital, methods for using that information remain
stolidly analog. Technology can help. Bandwidth,
processing and storage are cheap. Machines can
transmute from a string such as "aaattcaggagattacaggta"
to a physical molecule of DNA - and back again, making
genetic information truly fungible, something that can be
shared via the Web. Advances in language processing
and ontology development allow for the construction of
machine-readable and interpretable representations of
scientific information. Logic and reasoning engines can
crawl across massive data sets and come back with
suggestions on causation.
As we suggested in our introduction to the semantic web
it is neither cheap nor easy to seize the moment and use
technological advances to solve these human and
scientific problems. Legal and economic factors have to
date muted the impact of new technologies on the life
sciences: copyrights and contracts intertwine with
software-enforced restrictions on reusing and
republishing knowledge in a more usable format.
The Neurocommons Project rests on the hypothesis that
there is enough information on the Web, in the form of
taxpayer-funded databases and openly licensed scientific
literature, to demonstrate the utility of a legally open,
technically standardized approach to knowledge. In doing
so, we wish to sow the seeds of a massive change in how
scientific knowledge is licensed and reused.
The life sciences represent an ideal test case for the
semantic web. Semantic Web technologies make the most
sense where there is a certain set of conditions.

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1. A massive amount of data: We certainly have
that: Clinical images, robot-arrayed "gene
chips", machines that can sort materials cell-by-
cell, gene sequencers and massively high
throughput chemical screens. There are
hundreds of public databases, from flies to
humans to plants, each potentially able to
inform a decision or experimental design.
2. Rapidly changing knowledge: Every journal
article, every paper, every experiment in the lab
creates new knowledge about our bodies and the
world we live in. This makes it very hard to
apply traditional computational approaches or
even integrate the data. We know what goes
into a car - engine, tires, wheels, axles, fenders -
and thus we can create a fairly fixed
representation of a car for a computer, for
model building and more. But we don't have
anything resembling consensus to items as
fundamental as "what is the role of the non-
coding DNA in the human genome?"
3. Distributed knowledge and expertise: the nature
of modern life science is specialization. One
scientist is an expert on the genetics of
Huntington's Disease (a rare neurodegenerative
disease) another an expert on the impact of
protein folding on Alzheimer's Disease. The two
both work on the brain, on many of the same
genes and proteins. But they attend different
conferences and are pressed for time to study
the refereed literature outside their own disease.
Possible synchronicities between the researchers
are at a minimum because their knowledge can't
interoperate without distracting them from the
lab.
For problems which have these features, the semantic
web is a natural fit. Like the Web itself, the semantic web
is intended to "scale" - to be capable of dramatic
expansion in size, mission, and reach - through a process
of decentralization rather than centralization, and an
emphasis on information reuse, not recreation. It is a
means to capture and network the relationships implicit in
high volume data sets, or the outputs of sophisticated
analytic software. It can relate anything to anything, as
long as that anything has a unique name. Data-driven
relationships can attach to the descriptions of related
genes and proteins, and to the knowledge about those
genes and proteins as described in the scientific literature.
The semantic web does not require that the picture be
complete. If the relationships between one gene and
another change as our knowledge changes, the technical
burden is no lower than adding another hyperlink
between web pages. And the concept of integration
around unique names makes it easy to create serendipity
between researchers: instead of bumping into a colleague
in the hall at the right time, a scientist can see the
ecosystem of knowledge around a particular gene
expression in the brain. Whether that knowledge comes
from her work on Alzheimer's or a distant colleague's
work on Huntington's makes no difference. It all gets
published to the semantic web.
The legal and economic problem
If these potential advantages are real, why do we not
already have a vast semantic web for life sciences? The
technological and standards problems are being solved.
The National Institutes of Health has invested in the
national centers for biomedical ontologies, language
processing technologies are evolving in leaps and bounds,
and public databases are investing in machine readability
and open licensing. The problem is simpler. Despite what

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appears to be an information overload, the sparse
availability of truly machine-readable scientific
knowledge has prevented robust testing of the Semantic
Web. The barriers we described earlier - legal, technical
and digital - have prevented easy aggregation of data, and
thus have denied us the ability to test rigorously whether
this approach will indeed be as productive as it promises
to be.
The Strategy
Rather than complain about the problem that machine
readable knowledge is sparse, the Neurocommons Project
is taking as its focus those areas where we do have truly
open access information and thus can build a test case.
We have formed an initial community of
neuroinformaticists, practicing neuroscientists, Semantic
Web experts and language experts to ensure our work is
accurate and scientifically valid. The first stage is
underway:
• Using automated technologies, we are
extracting machine-readable representations of
neuroscience-related knowledge as contained in
full-text Open Access literature, free text such
as the PubMed abstracts, and legally open
databases
• We then assemble those representations into a
semantic web for neuroscience publish the
resulting "graph" freely and
• Assemble a standard software implementation
to store, update, and manage the changes to
the graph as knowledge evolves.
Plans for stage two of the project involve the deployment
of additional software infrastructure, the development of
operational manuals so that interested parties can "port"
the entire Neurocommons approach into new scientific
domains without involving Science Commons, new
publishing techniques to automatically add knowledge to
the Neurocommons graph, and active community
development.
Again, Science Commons has sought partners who can
provide credibility and competence. Teranode, a for
profit company, provides direct financial support to the
Neurocommons project as well as in-kind donations of
software and services.
Jonathan Rees, formerly in charge of the curated protein-
protein interactions database at Millenium
Pharmaceuticals and a veteran of MIT's project MAC,
leads the project on a day to day basis as a Science
Commons Fellow.
The project is deeply involved with the World Wide Web
Consortium's Health Care and Life Sciences Interest
Group as well as MIT's Computer Science and Artificial
Intelligence Laboratory (which hosts Science Commons).
Conclusion
We have tried to pick projects where "even if you fail a
bit, you still succeed." In our most heady and optimistic
moments, we imagine a very different landscape for
science. No longer would the price of access to scientific
literature act as such an impediment to research. Our
Brazilian researcher would be joined by potential
collaborators from poorer countries and institutions, and
could share information freely with them.
Whether the material was obtained from an invigorated
practice of self-archiving, from commercial or non profit
open access journals, of from journals which make their
work openly available after a fixed period of time, the
world of scholarly literature would be more open - and
more open on standard and interoperable terms, easily
understood by all participants.
What's more, literature searches would be transformed, as
the technology of the semantic web cut through the

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John Wilbanks and James Boyle September 7, 2006
information glut that overwhelms scientists, allowing the
kind of cross-discipline, and cross-disease insights we
can only imagine right now. When hypotheses were
formed, the researcher would be able to click to obtain
research tools and materials, according to truly standard,
machine and human readable Materials Transfer
Agreements. In many cases, those materials could be
obtained automatically and at low cost from depository
institutions.
The results of this research, in turn, would be fed back
into the web of scientific knowledge. The universe of
science would be enlarged, participation rendered more
egalitarian, commercial exploration of drug targets easier,
the drug pipeline fuller and so on.
Nature editorial, November 2005
That is the utopian vision, and we genuinely believe it
has real chance of succeeding, at least in part. But assume
that we fall short of such lofty aspirations, what happens?
At worst, scientific publications are made more
accessible, on more easily comprehensible terms, more
researchers self-archive, and finding the result of that
self-archived material is easy. We form examples of
Semantic Webs for science and test their validity. We
help researchers on orphan and neglected diseases build
research commons, and help companies to pool their
knowledge of discarded drug candidates. We simplify the
process of Materials Transfer, and cut down on the truly
crushing burden that it imposes on all participants. And,
in the process, we learn from our mistakes and come back
with a better plan to try again.
A Final Note on Funding
Science Commons has achieved a remarkable amount
despite the relatively modest amount of start-up funding
it has received. In part that is because it has been able to
draw on Creative Commons' resources and on massive
amounts of highly skilled volunteer labor from its Board,
Advisory Board and pro bono lawyers. In part it is
because the problems are simply ripe for solution and we
have found many partners willing to work with us and
leverage our efforts. But now the first stage of our plan is
almost complete and we need significant new funding to
realize the promise of the projects we describe here, all of
which are already under way.
In each of these three areas we believe we
have a high probability of success. The
communities around the projects are the best
indicator. The major stakeholders agree
there is a problem and that we are a good
vehicle for discussing - and, we hope, creating - the
solution. Even in publishing, where the tension between
corporate publishers, universities and open access
advocates can break through the surface, we maintain
strong relations with major publishers such as Nature and
Springer. Indeed, Springer even uses Creative Commons
licenses as part of the Open Choice alternative for
authors. Also, as you can tell from this document, we are
particularly excited about the prospects for the licensing
and data areas.
In the short term, (4-8 months) we need $500,000 to build
the staffing and institutional framework necessary to
continue the projects described here, to fund the meetings
and conferences that will attract new partners, and to pay
for the expensive legal advice that will be necessary even
beyond the generous pro bono contributions we already
receive.

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In the longer term, we estimate that we need $5 million to
$6 million over the 3 years beyond that to bring all of
these projects to fruition, and to build on the benign
feedback they will generate for each other. (We would be
happy to provide a more detailed budget, of course,
explaining precisely where the money would go.)
The amount of work necessary is staggering, but the
goals are concrete, achievable and worthwhile.
We have tried in this document to describe accessibly and
for a general audience our goals, techniques, strategies
and institutional resources. We hope you found it of
interest and would be delighted to outline our projects
more precisely, and rigorously, as well as to go into
details about the projects not covered here. We ask for
your feedback and, in particular, your suggestions as to
possible funding sources.
1. John Wilbanks is Executive Director, Science
Commons.
James Boyle is William Neal Reynolds Professor of Law
at Duke Law School and faculty co-director, the Center
for the Study of the Public Domain.
This is a draft introduction aimed at readers with a wide
range of backgrounds prepared for the Science Commons
Funders Meeting, Duke Law School Aug 3rd , 2006.
Please do not circulate without permission.
The projects described in these pages were made possible
by seed funding from the HighQ Foundation and Creative
Commons. Science Commons receives additional funding
from the Omidyar Network and the Teranode
Corporation; Edwards Angell Palmer & Dodge LLP
provides pro bono legal services.
2. Material Transfer Agreements: A University
Perspective
Plant Physiology 133:10-13 (2003)
http://www.plantphysiol.org/cgi/content/full/133/1/10
3. Campbell et al,
Data Withholding in Academic Genetics, JAMA Vol.
287 No. 4, January 23, 2002.
4. W3C statement on Semantic Web activity.
http://www.w3.org/2001/sw/