Dissemination Information Packages for Information Reuse (DIPIR)
DIPIR is a joint, IMLS-funded project with the University of Michigan. Together with partners at the Inter-university Consortium for Political and Social Research, the University of Michigan Museum of Zoology, and Open Context, the team is studying data reuse in three academic disciplines to identify how contextual information about the data that supports reuse can best be created and preserved. The project focuses on research data produced and used by quantitative social scientists, archaeologists, and zoologists.
The intended audiences for this project are researchers who reuse data and the digital curators, digital repository managers, data center staff, and others who collect, manage, and store digital information. Knowledge gained from the study will help guide current and future international practices for curating and preserving digital research data.
Our research employs mixed methods (both qualitative and quantitative data collection) to investigate the reuse of digital data in the three disciplinary communities to identify significant properties. We will then explore 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
- identifying the significant properties of the data that each community needs for reuse
- considering how to express the significant properties as representation information in the Reference Model for an Open Archival Information System (OAIS)
Tangible products of the research will be presentations, articles, and a methodology to help repository managers’ work with disciplinary communities to identify significant properties and ensure the ability to meaningfully reuse that data over time.
In terms of impact, this research aims to contribute to the development of audit criteria in Trustworthy Repositories Audit & Certification: Criteria and Checklist and to guidelines for the deposit of federally-funded research data. Finally, this research will contribute to the discussion of significant properties and representation information and spur reconsideration of the types of representation information needed to support data reuse.
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.