Libraries and data centres must support data publishing as a prerequisite for data availability, including persistent identification/citation of datasets, and solutions for data description and retrieval, which together facilitate findability. They must also ensure that data is properly documented as a condition for data interpretability and re-usability and prepare for long-term data archiving including data curation and preservation.
“Where do we start?”: opportunities for libraries to support research data management
“Where do we start?”: opportunities for libraries
to support research data management
LIBER: Ligue des Bibliothèques Européennes de Recherche
UCL, 21 Oct 2013
Opportunties for libraries: the researcher perspective
Opportunities for libraries: the policy perspective
LIBER: reinventing the library of the future
Largest network of European reseach libraries: 450 in over 40
To provide an information infrastructure to enable research
in LIBER institutions to be world class
LIBER & EU Projects
Looking at data sharing from the
researcher‘s point of view
“Without the infrastructure
that helps scientists manage
their data in a convenient
and efficient way, no
culture of data sharing will
(German Research Foundation, DFG)
(2) Further data
any kind of
files to articles
the article and
held in data
(5) Data in
drawers and on
disks at the
Library support for the researcher
Libraries and data centres must support…
data as first class research object:
publishing, persistent identification/citation
data description, metadata, standards
documentation and retrieval
proper documentation of data
long-term data archiving including data
curation and preservation
Libraries and data centres opportunities (Chapter 4):
Lower barriers to researchers to make their data available.
Integrate data sets into retrieval services.
Support of persistent identifiers.
Engage in developing common metadescription schemas and common citation practices.
Promote use of common standards and tools among researchers
Support crosslinks between publications and datasets.
Provide and help researchers understand metadescriptions of datasets.
Establish and maintain knowledge base about data and their context.
Curate and preserve datasets.
Archive software needed for re-analysis of data.
Be transparent about conditions under which data sets can be re-used (expert knowledge needed, software needed).
Engage in establishing uniform data citation standards.
Support and promote persistent identifiers.
Transparency about curation of submitted data.
Promote good data management practice.
Collaborate with data creators
Instruct researchers on discipline specific best practices in data creation (preservation formats, documentation of
By Ken Lund (Flickr: Why, Arizona (2)) [CC-BY-SA-2.0
Barriers to success of open data policies
Articulate values for
disciplines that you
Help to define
work with but first work for
Develop and embed
on changing yourcommunities
Definition of research data
Lack of skills/education
Engage in policy
Poorly defined roles and responsibilities
Lack of infrastructure
Altmetrics and citation
Lack of career incentives
What should our priorities be?
LIBER ten recommendations:
“Many researchers do not appear to see the value and
benefits of data citation. There is a gap, which could be
filled by libraries, in advocacy for data sharing, the use of
subject specific repositories, and best practice in data
citation. These, if filled, would increase the number of
researchers sharing and reusing data.”