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The research data landscape: an overview - Oya Rieger, Cornell University
1. Vision for Open Research Data
Oya Y. Rieger
Cornell University Library
rieger@cornell.edu
June 2012, Copenhagen
2. Research involves the systematic collection and analyses of information to
increase our understanding of the phenomenon under study
source: flicker.com
3. Scholarly communication involves the creation, exchange,
and dissemination of knowledge within the context of
academic discourse.
Analysis
Interpretation
?
Authoring
Presenting
Research
Data Collection
KNOWLEDGE
CREATION
Sharing
Networking
Archiving
Preservation
Publishing
Dissemination
4. Access to Research Data
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support open scientific inquiry
encourage diversity of hypotheses & data analysis
encourage interdisciplinary research
provide greater returns for public investment in
research
facilitate the education of new researchers
foster downstream commercialization of outputs
support studies on data collection & analysis methods
engage public in science
Arzberger et al., “Promoting Access to Public Research Data for Scientific,
Economic, and Social Development.” Data Science Journal, 3/29, 2004
10. What might prevent you from sharing the data you have produced or
intend to produce for this project?
Steinhart et al., Journal of eScience Librarianship, 2012; 1(2)
21. technical infrastructure
• scalable and flexible systems to store,
discover, access, and archive content
• interoperability standards to link related
information objects and various types of
repositories
• metadata standards to facilitate discovery,
access, archiving, and repurposing
22. sociocultural issues
• community-based standards for deposit, use,
and maintenance of data
• different access provisions in support of
academic, and entrepreneurial requirements
• incentives and rewards for scientists to share
the outputs of their research endeavors
23. information policies
• information policies to support:
– IPR
– privacy
– confidentiality
– institutional ownership
– security
– access limitations
– retention and deaccessioning
25. usability
• data quality standards
• ease of deposit to encourage end-users
• tools to support analytics, mining, integration,
and visualization
• digital identifiers to persistently locate
• citation standards to reference resources
• metrics to track and communicate impact