Data Archiving and Networked Services
The Front Office-Back Office
model: supporting research data
management in the Netherlands
Ingrid Dillo, DANS (Data Archiving and
Networked Services)
Conference Strengthening the Baltic-Nordic
research collaboration, Tartu 23 May 2014
DANS is een instituut van KNAW en NWO
The Dutch case as best practice?
What is DANS?
Institute of the
Royal
Netherlands
Academy and
Research Funding
Organisation
(KNAW & NWO)
since 2005
Mission: promote
and provide
permanent access
to digital research
information
First predecessor
dates back to
1964 (Steinmetz
Foundation),
Historical Data
Archive 1989
DANS services
EASY: Electronic Archiving System
Data Seal of Approval
Dutch Dataverse Network
NARCIS: Gateway to scholarly information In the Netherlands
‘Who’ is DANS?
About 50 employees: IT-specialists, archivists, information
experts, policy makers and communication advisors
Data is hot!
•
Riding the Wave report: critical
importance of sharing and preserving
reliable data produced during the
scientific process
•
Executive Order President Obama: Making
Open and Machine Readable the New
Default for Government Information
(data)
•
EU Commissioner Neelie Kroes: “Data is
the new gold”
Proliferation of data
Trend of data sharing/open data policies;
recognition of the value of data; funder
requirements
Advantages:
• Transparency and replication
of research
• Re-use of data
Challenges:
• data management
• preservation and access
RDM: research data life cycle
RDM: what to address in a DMP?
Metadata +
documentation
to be delivered
File
formats
Confidentiality
/ Ethics
Rights
Version
control
Interoperability
Costs
Data
management
approach
Storage
+ archiving
Preservation and access
• Local storage facilities during the research
• Network of trusted digital repositories for long-term
preservation after the research is finished
• Certification of TDRs in order to establish trust
The federated data infrastructure:
a collaborative framework
Data Generators
Data Users
Data Curation
Trust
Front offices:
• Local Data Facilities (University Libraries)
• Domain-Specific Research Infrastructures
Back Offices:
DANS, 3TU.Datacentrum, …
Basic Technical Infrastructure:
SURFsara, Target, …
User functions:
data capture
and transfer
Community
Support
Services
Common Data
Services:
Archiving,
Access, …
Common Data
Services:
Storage,
Backups, …
FO-BO Institutions
• Front offices
– Universities (libraries, local data centers)
– Disciplinary research infrastructures (ESFRI/NL-National
Roadmap)
• Back offices
– DANS (humanities, social sciences)
– 3TU.Datacentrum (technical sciences)
– …SURFsara (big data)
-> Trusted digital repositories!
Data Archiving and Networked Services
DANS is an institute of KNAW en NWO
Services in the model
• Information and
awareness raising
• Training (data
librarians and
researchers)
• Storage (during and
after the research)
Roles and responsibilities: the Front Office
• Focus on information and awareness
raising:
– Information portal research
community
– Awareness raising, support and
training research community
– Supporting VREs (research tools, data
storage during research; Sharepoint,
Dataverse, etc.; transfer for long-term
archiving in trusted digital repository)
– Liaising with back office
– Data acquisition
Roles and responsibilities: the Back Office
• Focus on expertise and long term
storage:
• Expertise and innovation in the area of
permanent storage, data management
and re-use of data
– Providing expertise to the research
community: training courses
– Providing expertise to the front office:
training courses for data experts,
consultancy, contact persons
– Long term preservation of data in a
trusted digital repository
Research Data Netherlands
• BO collaboration in order to serve the FO better and more
efficient
• Existing collaboration DANS-3TU.Datacentrum (training data
experts, Dutch Dataprize)
• Expanding areas of collaboration
• Open to other trusted digital repositories
Future challenges
•Expanding the model over all universities (institutional
agreements)
•Developing a business model to cover the costs
•Creating one single back office “desk” (RDNL)
•Creating a technical infrastructure for automatic data ingest
Thank you for your attention!
ingrid.dillo@dans.knaw.nl
www.dans.knaw.nl
http://www.researchdata.nl/
5 minute video: The what, why and how of data management
planning https://www.youtube.com/watch?v=gYDbGP1CA4