This presentation by Dr Birgit Schmidt was given at the Scholarly Communication and Research Infrastructures Steering Committee Workshop. The workshop title was Libraries and Research Data Management – What Works?

1. Libraries and Research Data Management
– What Works?
Lessons learned from the first LIBER Case Studies
Steering Committee Scholarly Communication and Research Infrastructures
LIBER Annual Conference, Riga, 2 July 2014
Dr Birgit Schmidt, Goettingen State and University Library, SC SCRI Secretary

2. Why LIBER case studies on research data
management?
10 Recommendations on RDM, filling this with life
 Several institutions across Europe are building up capacities for RDM, combining
infrastructures and support services
 There is a lot of knowledge to share with the community
 Engagement starts for different reasons and from various perspectives
 Sharing of the rationale, how to start and proceed, some challenges faced and the way forward,
i.e. ‘lessons learned’
 Where do you feel you just started, where do you make good progress, where do feel more
confident?
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3. 10 Recommendations for libraries to get started with
research data management
Regrouping the recommendations – Areas of engagement
Support services
#1 Offer research data management support, including data management plans for grant applications,
intellectual property rights advice and information materials. Assist faculty with data management plans and the
integration of data management into the curriculum.
#6 Support the lifecycle for research data by providing services for storage, discovery and permanent access.
Infrastructure & standards
#2 Engage in the development of metadata and data standards and provide metadata services for research
data.
#5 Liaise and partner with researchers, research groups, data archives and data centers to foster an
interoperable infrastructure for data access, discovery and data sharing.
#7 Promote research data citation by applying persistent identifiers to research data.
#8 Provide an institutional Data Catalogue or Data Repository, depending on available infrastructure.
#10 Offer or mediate secure storage for dynamic and static research data in co-operation with institutional IT
units and/or seek exploitation of appropriate cloud services.
Policy & disciplinary practices
#4 Actively participate in institutional research data policy development, including resource plans. Encourage
and adopt open data policies where appropriate in the research data life cycle.
#7 ... (with some disciplinary views on data citation)
Skills & staffing
#3 Create Data Librarian posts and develop professional staff skills for data librarianship.
#1 … and the integration of data management into the curriculum.
Pick your level-of-engagement card now
for one area
Red – You have not started yet but would
love to, why? What would you need?
Yellow – First steps have been taken, what
works well, what challenges do you face?
Green – You feel more confident, what
message would you like to share?
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4. Case Studies
http://libereurope.eu/committee/scholarly-research/research-data-management-case-studies/
 What was the starting point?
 What kind of research data is targeted?
 What is the organisational framework?
 Roles and responsibilities
 Policies
 What kind of support services are provided to
researchers?
 What kind of infrastructure is provided?
 What have you learned so far? What’s next?
 Further information
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5. The case studies released so far
Would you like to see your approach & experiences here too?
Institutional perspectives
 Raw data at the Spanish National Research Council and related services by the institutional
repository DIGITAL.CSIC
 Developing research data management services at Humboldt University Berlin
 Piloting research data support at the University of Goettingen
 University of Oxford research data management infrastructure
 Research data services at University College London
 Factors for enabling sharing and reuse of research data – Library and archive services at the
University of Vienna
 Research data management at Radboud University
With emphasis on training the workforce
 Developing research data management training and support at Helsinki University Library
 Essentials 4 Data Support
National e-infrastructure and services
 Promoting data citation by DataCite Netherlands, a DOI registration service of TU Delft Library
 The Lithuanian national open access research data archive (MIDAS)
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6. What did they learn? Support services
Some take-away messages
 There is still a lack of information on what services we offer them for their raw data (CSIC).
 People do not trust new services: Pilot users are generally using the service to ‘back up’ their
data. This is slowly starting to change (UCL).
 In some fields such as physics and geoscience, the practice of somehow managing data seems
more developed than in other areas, but in general the researcher community thinks we
should preserve and facilitate their access and would therefore value enhanced support
(CSIC).
 The library will concentrate on informing and supporting researchers… Services will be mainly
provided by other institutions, national and international players. The library plays a part in
bridging the gap between several services providers and researchers’ needs (UHEL).
 So far, we have been providing advice on a one-to-one basis, responding to questions by single
researchers (CSIC).
 RDM services are best developed by holding in-depth conversations with researchers.
They are tailor-made and therefore time-consuming for both researchers and supporting staff.
Different types of data require differtn advice and support (U Radboud).
 A challenge for embedded RDM services is the imbalance of supply and demand. Institutions
usually do not have the capacity to initiate the foundations of the infrastructure before the project
starts even though basic services are often required early on in the project (UGOE).
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7. What did they learn? Infrastructure and standards
Some take-away messages
 Learning from a national study and institutional practices, and adding provisions for research
data in the operation of the institutional repository. Expansion of knowledge and resources on a
nation-wide basis is the next step (e-Infra Austria) (UVIE).
 Access restrictions depend on the data exchange agreement as developed by the research
group and are facilitated through the system’s technical setting. The agreement clarifies the terms
of data delivery, access and use as well as restrictions and conditions (e.g. metadata must
be provided before the beginning of data collection, deposited as soon as possible but not later
than one year after the field sample / laboratory experiment has been taken/executed) (UGOE).
 Closely work with clients to improve functionality. Provide a stable infrastructure and support
integration of DOI services in other repositories. Learn from other DataCite registration agencies
(DataCite NL).
 Designing and creating data archives is usually very expensive, time-consuming and demanding
work. Sufficient financial and human resources should be gathered and guaranteed in advance.
 Much of the work of setting up an RDM infrastructure involves gaining a clear understanding of
what is required by whom …The difficulty to creating a quantitative business case for such an
infrastructure is also a challenge (UOX).
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8. What did they learn? Policies and disciplinary
practices
Some take-away messages
 Important actors in the process of keeping data accessible and usable: data creators,
repositories and downstream users (UVIE).
 With many stakeholders involved it can be a challenge to ensure buy-in (UOX).
 From a survey: the majority of respondents does not has an implicit or explicit policy for
managing research data in place. Those with a policy in place include the complexity of data,
funder requirements, the structure of the research group, and the need for third-party data
access/analysing/annotation (CSIC).
 University-wide survey to inform the development of a RDM concept and policy, to get an idea
of the types and characteristics of research data, gather requirements and undertake a gap
analysis. In addition, a roadmap to adopt a coordinated approach when developing demand-
oriented RDM services and to serve as a guide for central support and services (HUB).
 Researchers will be encouraged to share. However, the decision on what restrictions should
apply or when the right point in time is to release data will remain their own responsibility. The
biggest challenge can be to find a balance in roles and responsibilities between an individual
researcher and the institution as a whole (HUB).
 RDM policies are best made within research institutes as they are aware of the different
practices and needs (U Radboud).
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9. What did they learn? Skills and staffing
Some take-away messages
 There is a large skills variance… this often requires more one-to-one consultations which are
resource intensive (UCL).
 Training should be provided and aimed at specific groups such as doctoral students, digital
humanities researchers and subject librarians (HUB).
 Professional skills regarding RDM have to be shared more efficiently, especially with younger
researchers (HUB).
 Embedded librarians that include management of raw data are still a minority, with most cases
concentrated on humanities, social sciences and natural resources / agriculture (CSIC).
 For motivating researchers to actually provide data policies are only of limited use for this even if
researchers willingly accept them. This depends on acceptance of data managers within the
project (UGOE).
 Few, if any, staff have a great deal of confidence offering advice to all phases of the research
data lifecycle, although many individuals have some degree of expertise in particular aspects of
RDM (UOX).
 Currently, focus is on awareness for this new RDM task as a shared task for all units within the
library (user, collection, and information services) …(U Radboud).
 Neither technology nor culture change will work independently. There needs a merging of
the two and this represents a new professional space (UCL).
 Skilled and enthusiastic people are extremely hard to find and often attract a premium cost
(UCL).
 Participants urgently needed practical information about setting up a front office for data
management services (Essentials4DataSupport).
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10. Coming back to the 10 recommendations: Have your
say
Let’s vote on the 10 recommendations – your institution’s level
of engagement in one color
What is the message you’d like to share?
What recommendation is missing?
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Your level-of-engagement card
Red – You have not started yet but would
love to, why? What would you need?
Yellow – First steps have been taken, what
works well, what challenges do you face?
Green – You feel more confident, what
message would you like to share? 10

11. 10 Recommendations for libraries to get started with
research data management
Regrouping the recommendations – Areas of engagement
Support services
#1 Offer research data management support, including data management plans for grant applications,
intellectual property rights advice and information materials. Assist faculty with data management plans and the
integration of data management into the curriculum.
#6 Support the lifecycle for research data by providing services for storage, discovery and permanent access.
Infrastructure & standards
#2 Engage in the development of metadata and data standards and provide metadata services for research
data.
#5 Liaise and partner with researchers, research groups, data archives and data centers to foster an
interoperable infrastructure for data access, discovery and data sharing.
#7 Promote research data citation by applying persistent identifiers to research data.
#8 Provide an institutional Data Catalogue or Data Repository, depending on available infrastructure.
#10 Offer or mediate secure storage for dynamic and static research data in co-operation with institutional IT
units and/or seek exploitation of appropriate cloud services.
Policy & disciplinary practices
#4 Actively participate in institutional research data policy development, including resource plans. Encourage
and adopt open data policies where appropriate in the research data life cycle.
#7 ... (with some disciplinary views on data citation)
Skills & staffing
#3 Create Data Librarian posts and develop professional staff skills for data librarianship.
#1 … and the integration of data management into the curriculum.
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12. Your expectations towards the SC?
Organize a LIBER-wide survey on RDM?
Workshop on practical issues – writing DMPs and more
advanced topics
A pool of materials with recommendations?
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