The fabric of science is changing, driven by a revolution in digital technologies that facilitate the acquisition and communication of massive amounts of data. This is changing the nature of collaboration and expanding opportunities to participate in science. If digital technologies are the engine of this revolution, digital data are its fuel. But for many scientific disciplines, this fuel is in short supply. The publication of primary data is not a universal or mandatory part of science, and despite policies and proclamations to the contrary, calls to make data publicly available have largely gone unheeded. In this short essay I consider why, and explore some of the challenges that lie ahead, as we work toward a database of everything

Smith, Vincent S

English

PubMed

Abstract The fabric of science is changing, driven by a revolution in digital technologies that facilitate the acquisition and communication of massive amounts of data. This is changing the nature of collaboration and expanding opportunities to participate in science. If digital technologies are the engine of this revolution, digital data are its fuel. But for many scientific disciplines, this fuel is in short supply. The publication of primary data is not a universal or mandatory part of science, and despite policies and proclamations to the contrary, calls to make data publicly available have largely gone unheeded. In this short essay I consider why, and explore some of the challenges that lie ahead, as we work toward a database of everything.</p

Smith Vincent S

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BMC Research Notes

Data publication: towards a database of everything

Vincent S Smith

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BioMed CentralBMC Research Notes
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Data publication: towards a database of everything
Vincent S Smith
Address: Natural History Museum, Cromwell Road, London, SW7 5BD, UK
Email: Vincent S Smith - vince@vsmith.info
Abstract
The fabric of science is changing, driven by a revolution in digital technologies that facilitate the
acquisition and communication of massive amounts of data. This is changing the nature of
collaboration and expanding opportunities to participate in science. If digital technologies are the
engine of this revolution, digital data are its fuel. But for many scientific disciplines, this fuel is in
short supply. The publication of primary data is not a universal or mandatory part of science, and
despite policies and proclamations to the contrary, calls to make data publicly available have largely
gone unheeded. In this short essay I consider why, and explore some of the challenges that lie
ahead, as we work toward a database of everything.
Discussion
Journal articles as immutable, citable, archives of knowl-
edge, have been, and continue to be, the mainstay of
scholarly communication. Viewed by many scientists as
the end product of their engagement in a piece of research,
the "article" contains an argument or statement about an
hypothesis, backed up by supporting data. However, as
new technologies drive research toward larger and more
complex datasets, these two features of the journal article
are becoming increasingly disarticulated [1]. In some sci-
entific disciplines – for example crystallography, astron-
omy and molecular biology – digital repositories have
become important avenues for "publishing" data. This
approach has found common cause with social and polit-
ical forces that are arguing for greater accountability and
transparency of science. The Open Science movement for
the free use (and re-use) of data, results and protocols, is
championed by many as the best way to improve the col-
lective societal return on our investment in scientific
research [2]. Data publication is widely recognised as
being central to delivering this. But in truth, outside a
handful of disciplines, publication of science data is the
exception, not the rule.
Data publication has the potential to deliver significant
benefits from local to global scales. Organisations and
research disciplines can benefit from increased recogni-
tion [3]. There are significant potential cost savings for
funders through greater reuse of data [4], and economic
benefits by stimulating entrepreneurial uses of data by
commercial companies [5]. Data publication can help to
discourage scientific misconduct [6], and in many cases
(e.g. environmental and ecological data) provides the
only outlet for data that are irreplaceable because of the
unique circumstances in which they were collected. So
why, when so much is to be gained from data publication,
do scientists compromise scientific development, and
effectively leave their work unfinished by not publishing
their data? I argue that it is not through lack of money or
policy that scientists behave in this way. Likewise, misun-
derstandings and inertia with the scientific community
are only partly to blame. A more likely cause is that the
benefits to an individual of making their data publicly
available are less evident to the scientist than they are to
the governments, funding agencies and scientific commu-
nity that support them. Only by addressing this imbal-
ance, and making these benefits immediate and
Published: 24 June 2009
BMC Research Notes 2009, 2:113 doi:10.1186/1756-0500-2-113
Received: 26 May 2009
Accepted: 24 June 2009
This article is available from: http://www.biomedcentral.com/1756-0500/2/113
© 2009 Smith, licensee BioMed Central Ltd. 
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 3
(page number not for citation purposes)
BMC Research Notes 2009, 2:113 http://www.biomedcentral.com/1756-0500/2/113transparent to practising scientists, will data publication
become the norm. Here are three suggestions on how this
can be achieved:
Make it easy – developing the cyberinfrastructure
For those scientists for whom data publication is possible,
it is too often considered a chore. Of course it is dangerous
to generalize across a multiplicity of scientific disciplines,
each with their own specialised norms and practices, but
as a taxonomist and systematist generating molecular,
morphological and phylogenetic data in support of my
biodiversity research program, my experience with data
publication systems has always been a painful affair.
Almost without exception they require a substantial time
investment, sometimes involving personal contact with a
remote database manager who massages my data into a
form such that it can be readily parsed. If data publication
is to become a part of normal scientific practice it has to
be easy to achieve. This requires a robust infrastructure
that is quick and simple to use, works with the applica-
tions and data formats currently employed, and gives the
scientist confidence that it will work and still be there
when needed. Data standards are part of this process, but
perhaps more important is the development of robust
applications that hide the complexity of these data stand-
ards through a well designed interface. Funding agencies
need to respond to these infrastructural needs, which have
to be maintained beyond the typical lifecycle of a standard
grant application if they are to have lasting impact.
Related to this is a need for a career path and recognition
structure for those informaticians who develop the soft-
ware and standards associated with these systems [7].
Without this human infrastructure, the data, computa-
tional and communication components of this cyberin-
frastructure cannot be sustained.
Make it citable – motivating data publication through peer 
recognition
A primary motivation for article publication is to demon-
strate the authors' contribution to science [8]. This attracts
peer recognition that influences the authors' reputation,
employment and research opportunities. Article citation
is the most common metric of peer recognition and if a
comparable metric could be brought to bear on data pub-
lication, it follows that value and impact of data publica-
tion could be similarly tracked to motivate authors.
At present data publication where possible, is largely
motivated by enforcement through the editorial practices
of particular journals. These require that authors lodge
data in a suitable repository as a prerequisite to publica-
tion. In this instance the citation of data usually takes the
form of an opaque identifier (e.g. the GenBank accession
number [9] or Web site URL) rather than the data authors
or editors in a manner equivalent to a traditional article
citation. This failure to cite the authors of an original data
source has plenty of precedent: for example, publications
that describe new species are rarely mentioned in subse-
quent studies. If they were, the scientific contributions of
taxonomists would be amongst the most cited articles
worldwide. Opaque identifiers will continue to be
required for data publication for practical reasons, since
large datasets are increasingly collaborative, often involv-
ing many hundreds of authors. Nevertheless, data pub-
lishers should be able to demonstrate the same editorial
standards as article publishers, by making the authors'
and editors' names and addresses readily accessible, pref-
erably in a way that can be read by both humans and
machines for computation of citation metrics. Not only
would this introduce greater transparency and accounta-
bility in science, but through peer recognition, motivate
authors to publish their data.
Make it useful – moving beyond data archival
One reason for publishing data is to archive those data in
a form that it is available to others for reuse. This activity
however, has little value for the contributor who already
holds the data and may have to exert considerable effort
to publish it. Automatically enhancing the value of the
data to the contributor once it has been published can
address this problem. This may be in form of functional
enhancements that facilitate the subsequent manipula-
tion, editing and annotation of the data, or semantic
enrichments that automatically connect data to other
published sources [10]. This fusion of data might take the
form of descriptive metadata that assists in data discovery,
connection to definitions of concepts and terms found
within the data set, and enhanced visualisations of data.
Importantly, these enhancements should be reciprocal
between linked data, enriching the value of old and new
data alike as the knowledgebase grows. Not only does this
enhance the discoverability of published data but,
because these links are machine readable, it can facilitate
the computation and (perhaps eventually) the semantic
reasoning across the links.
How can we achieve all this with a multiplicity of distrib-
uted stakeholders, many of whom have conflicting or
competing interests? To my mind, data stewardship is best
accomplished in systems and repositories where the cus-
todian has trusted status within relevant communities of
practice. Such trust is earned with difficulty and lost with
ease; therefore it makes most sense to place these reposi-
tories with scientific societies, institutions and journals
that have a history of supporting, archiving and enabling
these communities. This is counter to the trend toward
large national data centres that must accommodate the
diverse interests of potential contributors spanning many
broad scientific disciplines. Scientific data exist in many
types and formats, and is subject to varying legal, cultural,Page 2 of 3
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protection and practical constraints. They are often used
in different ways according to their context and have var-
ying life-cycle requirements. Who better to understand
these needs than the communities that are generating and
using the data. This, however, risks the construction of
data silos – walled parochial gardens of disciplinary data
that remain unconnected to the wider world.
Conclusion
The power of published data is amplified by ingenuity
through applications and uses unimagined by the original
authors and distant from the original field. Without con-
necting these disparate datasets the true potential of data
reuse and repurposing is lost. New data integration serv-
ices are already emerging, transforming data discovery on
the web from lists of search results into tools that compute
answers to structured questions. Recent high profile exam-
ples include Google Squared [11] and Wolfram Alpha
[12]. These offer a vision of what a database of everything
might look like, drawing on public data amassed from
parochial datasets, scientific journals, encyclopaedias,
repositories and other sources freely available on the Web.
At present these tools have the predictability and effi-
ciency of a database, but lack many of the fundamental
features relevant to data publication. There is no easy way
of publishing to these tools, no means to correct errors,
little attribution of sources, and no means of data citation.
Crucially the results returned from tools like Google
Squared and Wolfram Alpha lack the social and intellec-
tual context necessary to judge the value of the data. Until
these deficiencies can be addressed our database of every-
thing, with all its shortcomings, is likely to look similar to
the way it does now – scholarly articles, published in
scholarly journals, by researchers too busy and unmoti-
vated to publish all but a minimum of their underlying
data.
About the Author
Vince Smith is a cybertaxonomist at the Natural History
Museum, London and is part of the EU funded European
Distributed Institute of Taxonomy (EDIT). He created and
manages the Scratchpad project http://scratchpads.eu, a
data publication framework and social networking tool
supporting research communities of biodiversity scien-
tists.
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A: Adventures in Semantic Publishing: Exemplar Semantic Enhancements of a Research Article. PLoS Computational Biology

Alpha computational knowledge engine

Borders in cyberspace: conflicting public sector information policies and their economic impacts.

Data publication in the open access initiative.

Motivating Online Publication of Data. BioScience

RIN: To share or not to share: publication and quality assurance of research data outputs.

Science in the open/or/How I learned to stop worrying and love my blog.

Shedding Light on the Dark Data in the Long Tail of Science. Library Trends

The future of biocuration. Nature

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