Dissemination Information Packages for Information Reuse (DIPIR)
DIPIR is a joint, IMLS-funded project with the University of Michigan. Together with collaborators at the Inter-university Consortium for Political and Social Research (ICPSR), the University of Michigan Museum of Zoology (UMMZ), and Open Context, the project team is studying how Dissemination Information Packages (DIPS) can better incorporate information from designated communities to facilitate reuse of digital information.
The project investigates data reuse in three academic disciplines (quantitative social sciences, archaeology, and zoology) to identify how contextual information about the data that supports reuse can best be created and preserved. A major aim of the project is to contribute to digital curation practices through a better understanding of significant properties and representation information and provide models for incorporating this information into implementations of the Reference Model for an Open Archival Information System (OAIS).
Our research employs mixed methods (both qualitative and quantitative data collection) to investigate the reuse of digital data in the three disciplinary traditions to identify significant properties. We will then determine how these properties might be expressed as representation information in OAIS. Our central research question is: How does a deeper knowledge of data reuse affect our understanding of significant properties and what does this mean for representation information within OAIS?
Project objectives Include:
- understanding how each designated community reuses data from the site
- identifying the significant properties of the data that each community needs for reuse
- determining how to express the significant properties as representation information in the Reference Model for an Open Archival Information System (OAIS)
There are several areas in which this research can have significant results and impact. These include:
- developing a more precise specification and reevaluation of the significant properties and representation information and how these relate to DIPS
- creating a methodology to identify discipline-based significant property information for data reuse
- generalizing significant properties across disciplines.
Most recent updates: Page content: 2014-06-24
Ixchel Faniel, Ph.D. (Principal Investigator)
Elizabeth Yakel, Ph.D. (Co-Principal Investigator)
Nancy McGovern, Ph.D.
William Fink, Ph.D.
Eric C. Kansa, Ph.D.
Visit the DIPIR site for more about the project team.