EN100-web

Special theme: Scientific Data Sharing and Re-use
how, and with whom. Open data is
sometimes viewed simply as releasing
data without payment of fees. In
research contexts, open data may pose
complex issues of licensing, ownership,
responsibility, standards, interoperability,
and legal harmonization. To
scholars, data can be assets, liabilities,
or both. Data have utilitarian value as
evidence, but they also serve social and
symbolic purposes for control, barter,
credit, and prestige. Incentives for scientific
advancement often run counter
to those for sharing data.
To librarians and archivists, data are
scholarly products to curate for future
users. However, data are more difficult
to manage than publications and most
other kinds of evidence. Rarely are data
self-describing, and rarely can they be
interpreted outside their original context
without extensive documentation.
Interpreting scientific data often
requires access to papers, protocols,
analytical tools, instruments, software,
workflows, and other components of
research practice – and access to the
people with whom those data originated.
Sharing data may have little practical
benefit if the associated hardware,
software, protocols, and other technologies
are proprietary, unavailable, or
obsolete and if the people associated
with the origins of the data cannot be
consulted [2, 3].
Claims that data and publications
deserve equal status in scholarly communication
for the purposes of citation
raise a host of theoretical, methodological,
and practical problems for bibliometrics.
For example, what unit should
be cited, how, when, and why? As
argued in depth elsewhere, data are not
publications [1]. The “data publication”
metaphor, commonly used in promoting
open access to data and encouraging
data citation, similarly muddies the
waters. Transferring bibliographic citation
principles to data must be done
carefully and selectively, lest the problems
associated with citation practice be
exacerbated and new ones introduced.
Determining how to cite data is a nontrivial
matter.
Rather than assume that data sharing is
almost always a “good thing” and that
doing so will promote the progress of
science, more critical questions should
be asked: What are the data? What is the
utility of sharing or releasing data, and
to whom? Who invests the resources in
releasing those data and in making them
useful to others? When, how, why, and
how often are those data reused? Who
benefits from what kinds of data
transfer, when, and how? What
resources must potential re-users invest
in discovering, interpreting, processing,
and analyzing data to make them
reusable? Which data are most important
to release, when, by what criteria,
to whom, and why? What investments
must be made in knowledge infrastructures,
including people, institutions,
technologies, and repositories, to sustain
access to data that are released?
Who will make those investments, and
for whose benefit?
Only when these questions are
addressed by scientists, scholars, data
professionals, librarians, archivists,
funding agencies, repositories, publishers,
policy makers, and other stakeholders
in research will satisfactory
answers arise to the problems of data
sharing [1].
References:
[1] C.L. Borgman: "Big Data, Little
Data, No Data: Scholarship in the
Networked World". MIT Press, 2015.
[2] C.L. Borgman et al.: "The Ups and
Downs of Knowledge Infrastructures
in Science: Implications for Data
Management", ACM/IEEE Joint
Conference on Digital Libraries (JCDL
2014) and International Conference on
Theory and Practice in Digital
Libraries (TPDL 2014) (London,
2014), 2014.
[3] J.C. Wallis et al.: "If we share data,
will anyone use them? Data sharing
and reuse in the long tail of science
and technology", PLoS ONE. 8, 7 (Jul.
2013), e67332.
Please contact:
Christine L. Borgman
University of California, Los Angeles,
USA
E-mail: Christine.Borgman@ucla.edu
Enhancing the value of Research Data
in Australia
by Andrew Treloar, Ross Wilkinson, and the ANDS team
Over the last seven years, Australia has had a strong investment in research infrastructure,
and data infrastructure is a core part of that investment.
Much has been achieved already. The
Government understands the importance
of data, our research institutions
are putting in place research data infrastructure,
we can store data, we can
compute over data, and our data providing
partners – research institutions,
public providers, and NCRIS data intensive
investments are ensuring that we
are establishing world best data and
data infrastructure.
The Australian National Data Service
(ANDS) commenced in 2009 to establish
an Australian research data commons.
It has progressively refined its
mission towards making data more
valuable to researchers, research institutions
and the nation. Over the last 5
years ANDS has worked across the
whole sector in partnership with major
research organisations and NCRIS
facilities. It has worked collaboratively
to make data more valuable through
bringing about some critical data transformations:
moving to structured data
collections that are managed, connected,
discoverable and reusable. This
requires both technical infrastructure
and community capability, and can
deliver significant research changes [1].
We have seen many examples where
these transformations have been suc-
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ERCIM NEWS 100 January 2015