This document summarizes the findings of the SCARP synthesis study, which analyzed disciplinary studies on sharing, curation, reuse, and preservation of research data. The synthesis found that a generic "library-style" approach to data curation will not meet the needs of different research disciplines. Each discipline has unique factors that affect data practices, and institutional data repositories will need domain-specific strategies to work with varying disciplinary requirements. The document concludes by proposing recommendations for funders, institutions, researchers and other stakeholders to better support curation of research data according to disciplinary needs and practices.

1. Because good research needs good data
Disciplinary Dimensions of Digital
Curation:
Introduction and Synthesis
IDCC09 pre-conference workshop
Chris Rusbridge
Funded by:
© Digital Curation Centre, 2009. Licensed under Creative
Commons BY-NC-SA 2.5 Scotland:
http://creativecommons.org/licenses/by-nc-sa/2.5/scotland/
12/2009

2. Because good research needs good data
Contents
• Welcome, and aims of the day
• Introduction to SCARP
• Synthesis study
• Draft recommendations
SCARP synthesis & recommendations 12/2009

3. Because good research needs good data
Welcome and aims
• Overview of the project(s)
• Discussion of the disciplinary studies
• Consultation on recommendations
• Where next (especially for non-discipline-
based data repositories)?
SCARP synthesis & recommendations 12/2009

4. Because good research needs good data
Introduction to SCARP
• “Disciplinary Approaches to Sharing, Curation, Re-
use and Preservation”
• Origin: Warwick Workshop November 2005
• “Digital Curation and Preservation: Defining the research
agenda for the next decade”
• Disciplinary factors in caring for research data
• Perspectives
• Practices
• Methods
• Tools
• Immersive approach
SCARP synthesis & recommendations 12/2009

5. Because good research needs good data
Disciplinary issues still critical
• More demanding data policies
• Curation has to start well before ingest
• Still need better tools and infrastructure for
researchers
• Emerging institutional data repositories and
services
SCARP synthesis & recommendations 12/2009

6. Because good research needs good data
Synthesis study
• Carried out by Key Perspectives
• SCARP studies
• Other studies with disciplinary relevance
• Much more difficult than expected
• High variability in the studies used
• Collected & assembled key themes
• Synthesis rather than commentary or critical
review
SCARP synthesis & recommendations 12/2009

7. Because good research needs good data
Synthesis findings
• A generic “library-style” approach to curation
will be insufficient
• Institutional data repositories (IDRs) will
require domain-specific strategies
SCARP synthesis & recommendations 12/2009

8. Because good research needs good data
Discipline factors
• Community history
• Types & quantities of data
• Uniqueness & re-use value of data
• Common data format standardisation & acceptance
• Sharing vs competition for funding
• Funding body policies
• Available curation infrastructure
• Size of research teams
• Diversity of field
SCARP synthesis & recommendations 12/2009

9. Because good research needs good data
Commonalities
• Protectiveness towards data
• Data re-use as aid for current research
• Bargaining?
• Early to mid-term researchers as change
agents
SCARP synthesis & recommendations 12/2009

10. Because good research needs good data
Recommendations?
• JISC • Learned Societies
• Continue Managing
Research Data • Support discipline req’ts
• Map discipline req’ts • Institutions
• Funders • Identify how curation will
• Mandates be funded & supported
• Recognise value of data
• Support national-scale data • Researchers/scholars
support service • Curate your data!
• Publishers • Develop selection/
• Policy on data appraisal criteria
• Make supp data useful
• Contribute to standards
• Promote data citations
consensus
SCARP synthesis & recommendations 12/2009

11. Because good research needs good data
Questions?
• Doctoral training etc?
• Data scientists, data librarians…
• Develop tools?
• What tools?
• How can Institutional data repositories best
deal with varying disciplinary req’ts?
• Specialist subject librarians?
• Liaison with researchers?
SCARP synthesis & recommendations 12/2009