Cameron Neylon

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Following on from the discussion a few weeks back [kicked off by
Shirley](http://onebiglab.blogspot.com/2008/04/envisioning-scientific-community-as-one.html)
at [One Big Lab](http://onebiglab.blogspot.com) and [continued
here](http://blog.openwetware.org/scienceintheopen/2008/04/16/the-science-exchange/)
I've been thinking about how to actually turn what was a throwaway
comment into reality:

> What is being generated here is new science, and science isnâ€™t paid
> for *per se*. The resources that generate science are supported by
> governments, charities, and industry but the actual production of
> science is not supported. The *truly* radical approach to this would
> be to turn the system on its head. Donâ€™t fund the universities to
> *do* science, fund the journals to *buy* science; then the system
> would reward increased efficiency.

There is a problem at the core of this. For someone to pay for access to
the results, there has to be a monetary benefit to them. This may be
through increased efficiency of their research funding but that's a
rather vague benefit. For a serious charitable or commercial funder
there has to be the potential to either make money, or at least see that
the enterprise could become self sufficient. But surely this means
monetizing the data somehow? Which would require restrictive licences,
which is not at the end what we're about.

The other story of the week has been the, in the end very useful,
kerfuffle caused by
[ChemSpider](http://www.chemspider.com/ "ChemSpider"){rel="homepage"}
moving [to a CC-BY-SA
licence](http://www.chemspider.com/blog/chemspider-adopts-creative-commons-licenses.html),
and the confusion that has been revealed regarding data, licencing, and
the [public
domain](http://en.wikipedia.org/wiki/Public_domain "Public domain"){rel="wikipedia"}.
John Wilbanks, whose [comments on the ChemSpider
licence](http://feeds.feedburner.com/~r/PetermrsBlog/~3/286632057/),
sparked the discussion has written two posts
\[[1](http://network.nature.com/blogs/user/wilbanks/2008/05/10/chemspider-good-intentions-and-the-fog-of-licensing),
[2](http://network.nature.com/blogs/user/wilbanks/2008/05/10/on-the-erosion-of-the-public-domain)\]
which I found illuminating and have made things much clearer for me. His
point is that data naturally belongs in the public domain and that the
public domain and the freedom of the data itself needs to be protected
from erosion, both legal, and conceptual that could be caused by our
obsession with licences. What does this mean for making an effective
data commons, and the [Science
Exchange](http://blog.openwetware.org/scienceintheopen/2008/04/16/the-science-exchange/)
that could arise from it, financially viable?

I start with a couple of premises:

1.  Data belongs in the public domain
2.  An effective and useful data commons requires well structured data
3.  Preparing high quality data costs money, and the tools do not really
    exist to support this in general
4.  Academic career progression depends at core on one thing. How much
    money you bring in.

**Getting the data**

So I started with the notion of paying researchers to make data
available, originally phrased as 'pay the journals to buy papers'. What
I really meant was paying people to put research results somewhere
useful. So let us imagine we can pay people to deposit data (we'll
figure out how later). We don't want to be swamped with rubbish so the
data has to be well structured, tagged up and machine readable. If we're
paying for it, we set the standards. We also want to encourage re-use of
data, perhaps by paying a premium for the deposition of data that
re-uses other data. And in turn, perhaps pay a premium to those whose
data is re-used. The key point is that the presence of capital and cash
flow will drive patterns of behaviour. By being a market maker you
create the market, and to some extent control the behaviour of the
market, whether that be in commodities like coffee, or scientific data.

The data has to be dedicated to the public domain. The value of having a
central organisation, lets call it the Foundation, directing this is
that this dedication can be centrally managed and effectively handled.
In fact, such an organisation would probably outsource the work to an
expert organisation such as [Science
Commons](http://sciencecommons.org/) or [Open Knowledge
Foundation](http://www.openknowledgefoundation.org/). The system could
allow embargoes or delayed release, but this would be a timed release,
not a 'release on publication' clause. The release cannot be
indefinitely delayed. The fundamental principle must remain that
researchers are being paid to support committing their data to the
public domain.

**Providing the systems**

How do we get the big boys interested in this? What will drive Google,
Microsoft, Amazon, IBM to put real money into supporting such a
foundation, activity, or company. I think the answer to this lies in the
commercial opportunities that are created by having an insider knowledge
of the system that is indexing, collating, and crawling the data. The
data is in the public domain, and probably not in a repository as we
understand it, but on the cloud, or federated amongst multiple
repositories. But insider knowledge of the form, structure, location,
and tagging of the data is a commercially priviledged position.

We are only accepting well structured data, and this means providing
systems that make structuring research data easy. I'm not talking here
about a few webforms, or a slightly improved LIMS system. I'm talking
about fully functional laboratory notebook systems that talk to
instruments, that already have the API to most equipment built in, that
will provide helpful hints on how to describe your data using the
appropriate controlled vocabularies and above all something that looks
and behaves like a combination of Word and Google home page. Something
that is so easy to use that people actually do use it; that they use it
because it is useful to them and the like using it. Something that is
going to cost on order of \$100M to build.

But once it is built, you've got the best research recording tool
around. Something you can sell to commercial research organisation to
run in their own systems. Running these systems in house will enable
them to control their competitive research but will also make sharing of
pre-competitive research more effective for them. ELN implementations in
big pharma are budgeted at around the \$10M mark. There is a market to
recoup investments at the \$100M level. Particularly if this can be used
as the starting point for the next generation of generic enterprise data
management tools.

**Making it pay**

The Foundation at the centre of this has priviledged access to the data
through knowing and controlling the system that runs it. It will also
have the most timely access. In addition through providing the tools
(free at point of use for the basic service) that will enable data
deposition it is providing a service to the community. That service may
come with some conditions. First right of refusal to commercialise
opportunities that the foundation identifies; the right to sell access
to the products of processing the data; the right to process embargoed
data to identify commercial opportunities.

Research funders may also be interested in using the Foundation to drive
the availability of data. It is possible to imagine schemes whereby part
of a research grant is funnelled through the organisation to pay for the
deposition of data. Perhaps a 20% premium over what would be available
through a conventional grant could be paid to the researcher on
deposition of the data with perhaps 5% being paid to the Foundation.
Funders are putting hundreds of millions into data centres that no-one
is too sure what to do with. Maybe that money could be used more
effectively to drive data deposition quality. Some funders may also see
this as a good model for direct funding. Putting money in to drive the
generation of specific data set. Channel funding through the foundation
to pay groups to deposit the results rather than pay them to do the
research. For small foundations or charitable concerns this may be a
much more effective means of driving the outcomes they want.

Then there is the service model. This commons is not just for academic
research. Commercial bodies will be interested as well. And they will
want to exploit the insider knowledge the Foundation has of these
systems. Premium services to search, not just for the right data, or the
right connection, but the right person for an internal position, the
right group to collaborate with on a specific problem. The Foundation
will be able to provide premium services on top of the data and it will
be able to charge for them. Scientific journals could buy access for the
lodgement of supplementary data. Why hold it on their own servers,
costing money, and not necessarily being much use when it could be
provided in a more useful form for re-use and further exploitation? Or
perhaps turn it on its head, could the foundation pay journals for the
right to host supplementary data (including all the raw data associated
with a paper). Could this be another route to funding Open Access
journals?

Targeted advertising should not be rejected either. It works rather well
for Google as a money raising venture so there is no reason why it
shouldn't here. Care is obviously required not to allow advertising to
drive the process but as a complementary revenue stream it could be
useful. Overall there are funding streams here that could be exploited
to generate serious quantities of cash flow, running towards the \$M100s
per year, in markets that are much, much bigger, so incremental
increases in efficiency are worth these sums.

**Does it scale?**

This can work on a small scale with small amounts of money. The amounts
of data that are currently available in a useful form are vanishingly
small restricting the potential for a flood of submissions. Structuring
data is hard work and costs money so if the rewards are small and the
tools are not readily available a flood is unlikely. As it grows there
is a problem though. This is essentially a problem of liquidity, a large
body of capital is required to support both the flow of information in,
and the exploitation of that information. It is unlikely that the
foundation will be able to fund the full costs of the research that
generates data. It will be able to pay a market driven rate to support
the care, structuring, and processing of that data. Essentially the
Foundation is buying futures in the forward value of the data. The
complication is that it is also the market maker, and driver for those
futures. In some markets this is called insider trading. Here I don't
think anyone is necessarily losing out.

On a large scale as the dominant (or one of a set of dominant) index I
believe the model is sustainable as long as there is sufficient
liquidity. The problem lies in moving from the small scale to the large
scale, while undertaking the tools development project. Defining the
financial and commercial viability of that trajectory is beyond my
expertise, but I'd be interested to hear what people think. The
foundation can control its capital reserves by not accepting data, but
this is likely to cause the equivalent of a run on a bank. The details
of the business model need be looked at by people with proper business
heads. But it seems to me that this is plausible, if properly planned
and executed.

This will take large sums of money. And it may well not make money. But
it will enable more effective research. And that is worth a lot of
money, even if indirectly.

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Image from Wikipedia via Zemanta  Following on from the discussion a few weeks back kicked off by Shirley at One Big Lab and continued here I’ve been thinking about how to actually turn what was a throwaway comment into reality:  There is a problem at the core of this. For someone to pay for access to the results, there has to be a monetary benefit to them.

More on the science exchance – or building and capitalising a data commons

[![interior detail, stata center, MIT. just outside science commons
offices.](http://upload.wikimedia.org/wikipedia/commons/thumb/f/fd/Science_Commons-20070327.jpg/202px-Science_Commons-20070327.jpg){decoding="async"}](http://commons.wikipedia.org/wiki/Image:Science_Commons-20070327.jpg)

One of the strong messages that came back from the workshop we held at
the BioSysBio meeting was that protocols and standards of behaviour were
something that people would appreciate having available. There are many
potential issues that are raised by the idea of a 'charter' or
'protocol' for open science but these are definitely things that are
worth talking about. I thought I would through a few ideas out and see
where they go. There are some potentially serious contradictions to be
worked through.To begin with it is useful to point at other efforts at
definition and standardisation. Two that are key for Open Science are
the [Open Knowledge Definition](http://opendefinition.org/1.0/)
developed by the [Open Knowledge
Foundation](http://en.wikipedia.org/wiki/Open_Knowledge_Foundation "Open Knowledge Foundation"){rel="wikipedia"}
and the efforts of [Creative
Commons](http://creativecommons.org/ "Creative Commons"){rel="homepage"}
and [Science
Commons](http://sciencecommons.org/ "Science Commons"){rel="homepage"}
and particularly the development of the [cc0
protocol](http://labs.creativecommons.org/license/zero/) for placing
works in the [public
domain](http://en.wikipedia.org/wiki/Public_domain "Public domain"){rel="wikipedia"}.
At a basic level the work of the OKF can be seen as defining what it
means to be 'Open' whereas CC and SC are working on licenses and
protocols that make content, data, and works available in a way that
satisfies the OKF definition of open.

So to a first approximation we can define Open Science as;

*"Science carried out in such a way that all the products of research
are made available in a way that satisfies the terms of the Open
Knowledge Definition"*

This raises the first of many issues. To strictly satify the OKD it is
necessary to publish in [Strong Open Access
journals](http://en.wikipedia.org/wiki/Open_access_journal "Open access journal"){rel="wikipedia"}
(i.e. strictly BBB compliant journals). A paper is a major output from
most research and therefore it should necessarily be available under an
OKD compliant license (such as CC-BY). An alternative is to publish a
preprint but this raises the question of the contribution of referees
and editors to the content of the article. Is this not part of the
process? Or are these additional contributors not necessarily doing Open
Science so their contribution cannot be expected, necessarily to be
licensed? Publication in strictly Open Access journals is not always
possible or practicable (although [PLoS
ONE](http://en.wikipedia.org/wiki/PLoS_ONE "PLoS ONE"){rel="wikipedia"}
goes a long way to making this argument unsustainable) so from a
practical perspective making a pre-print available is probably
acceptable.

However there are many other technical details that have the potential
to raise significant issues. The Open Knowledge Definition explicitly
states there are no restrictions on the re-use of data. One of John
Cumber's motivations in opening the discussion on protocols was the
desire to provide a mechanism for releasing data while at the same time
requiring that people don't run off and analyse the data that someone
has spent several years of their life on. This is an ethical issue as
well as a 'legal' issue. It may be valuable to separate the two. If data
is made available as part of an Open Science project it appears to me
that it should be issues under an OKD compliant license. This is
separate to the issue of the person making that data available making a
clear statement to request that people do not analyse or process the
data for a specified embargo period. In my view, any license that allows
access to data, but prohibits its re-use in any specific fashion cannot
be described as 'Open'. This is the distinction between Strong Open
Access literature and Weak Open Access literature and is an important
distinction to maintain if the word 'Open' is to have any semantic
value. Respect of people's requests to allow them to finish off a piece
of work is simply being decent. Some may not, but with the data in the
open, a strong case can be made to show that someone has behaved
unethically, even if what they have done is not illegal.

The other key aspect of licensing for Open Science is attribution.
Strong attribution is at the core of good science anyway. I am
unconvinced that explicitly requiring strong attribution as part of an
Open Science license is required although it can do no harm.
Particularly in the case of data, which cannot generally be copyrighted,
it is challenging to enforce a requirement for attribution. Attribution
and its necessity needs to be strong supported by social pressure.
Licensing can help but we need much stronger social protocols, including
the strengthening of journal editorial practise, and if necessary
punishment in place to protect the rights of scientists to be
recognised.

The flip side of this is that we need to recognise that where many smart
people are thinking about the same things it is entirely plausible for
them to independently come to the same conclusion. Proving theft will
always be challenging although technology is making this easier. But the
principle of innocence until proven guilty *must* be maintained.
Witchhunts are a real potential issue, as is decision making and
punishment behind closed doors. If there are concerns then they must be
raised in an open fashion with the evidence (which is after all raw
data) made fully available.

The Open Knowledge Definition makes no comment on timeframes, or
timeliness. One of the aims of Open Science is to make data available as
soon as possible (or practicable). But the definition I've given above
doesn't mention this at all. I think this may be a good thing though. It
allows us a route out of the embargo problem because it implies it is
possible to be practicing Open Science regardless of when the results
are made available. Thus publication in an Open Access journal with
accompaning release of all the raw data is Open Science. Making all the
data available as it is generated is also Open Science, but what is more
it is [Open Notebook
Science](http://drexel-coas-elearning.blogspot.com/2006/09/open-notebook-science.html).

By maintaining this distinction we offer a route both for those who are
making the effort but can't go the whole way as well as making it
possible for people to release data immediately but under a limited or
embargoed license that automatically releases the full rights at a later
date. There are completely legitimate reasons for placing data or
portions of data under an embargo. Some may be under a very long embargo
(e.g. where personal data or medical records are involved) but this need
not in and of itself prevent people practicing and promoting Open
Science.

I therefore propose the definition I have given above with the following
examples of how this definition could be satisified. The following
practices could therefore be labelled as 'Open Science'. However a
specific project could not be said to be open until the complete
products of that research are available under an OKD compliant license:

1.  Making the full body of all research material available as near to
    when it is generated as is practicable. Also known as Open Notebook
    Science.
2.  Making a proportion (which may be zero) of material immediately
    available under and OKD compliant license with the remaining
    material available under a limited license for a specified period of
    time, after which it reverts to an OKD compliant license.
3.  Making a proportion (which may be zero) of material immediately
    available under an OKD compliant license with the remaining material
    unavailable for a specified period, after which it is made available
    under an OKD compliant license.
4.  Any combination of 2 and 3
5.  Publication in a Strong Open Access journal associated with release
    of all relevant material under an OKD compliant licence
6.  Making available a pre-print version of a paper published in a
    non-Open Access journal associated with release of all relevant
    material under an OKD compliant license

The distinction is made here between practicing Open Science, where
there is a committment made to release material at some point in the
future, and whether a given project or set of material is open.
Protocols 5 and 6 are only Open once the material is made available,
whereas Protocols 2, 3, and 4 are Open once the commitment has been made
to make the full set of material available. This makes it possible for
those people who want to make an effort but for whatever reason are not
able to make the material directly available to still consider their
contribution to fall under the banner of Open Science. We don't need to
exclude people who are genuinely philosophically with us. After all we
need all the help we can get.

Image from Wikipedia. Prize for the first person who figures out the
relevance (those who work in the building are excluded)

Edits: Following Peter Suber and Stevan Harnad's [adoption of the terms
'Strong OA' and 'Weak
OA'](http://www.earlham.edu/~peters/fos/2008/04/strong-and-weak-oa.html)
I have edited this post to use those terms.

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One of the strong messages that came back from the workshop we held at the BioSysBio meeting was that protocols and standards of behaviour were something that people would appreciate having available. There are many potential issues that are raised by the idea of a ‘charter’ or ‘protocol’ for open science but these are definitely things that are worth talking about.

Postagens de Rogue Scholar

More on the science exchance – or building and capitalising a data commons

Protocols for Open Science