D
Darrell Donakowski
Researcher at University of Michigan
Publications - 7
Citations - 132
Darrell Donakowski is an academic researcher from University of Michigan. The author has contributed to research in topics: Data transformation & Digital preservation. The author has an hindex of 5, co-authored 7 publications receiving 125 citations. Previous affiliations of Darrell Donakowski include University of Western Ontario & Inter-university Consortium for Political and Social Research.
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Journal ArticleDOI
Digital Preservation through Archival Collaboration: The Data Preservation Alliance for the Social Sciences
Micah Altman,Margaret O'Neill Adams,Jonathan Crabtree,Darrell Donakowski,Marc Maynard,Amy Pienta,Copeland H. Young +6 more
TL;DR: The agreements, processes, and infrastructure that provide a foundation for the collaboration to acquire and preserve data from opinion polls, voting records, large-scale surveys, and other social science studies at risk of being lost to the research community are discussed.
Journal ArticleDOI
Native Canadians, First Nations, or Aboriginals: The effect of labels on attitudes toward native peoples.
Journal ArticleDOI
The selection, appraisal, and retention of social science data
TL;DR: The processes used by two data archives are reviewed to illustrate how data are selected for archiving, how they are appraised, and what steps are required to retain the usefulness of the data for future use.
Journal ArticleDOI
From Preserving the Past to Preserving the Future: The Data-PASS Project and the Challenges of Preserving Digital Social Science Data
Myron P. Gutmann,Mark Abrahamson,Margaret O'Neill Adams,Micah Altman,Caroline Arms,Kenneth A. Bollen,Michael Carlson,Jonathan Crabtree,Darrell Donakowski,Gary King,Jared Lyle,Marc Maynard,Amy Pienta,Richard Rockwell,Lois Timms-Ferrara,Copeland H. Young +15 more
TL;DR: The Data Preservation Alliance for the Social Sciences (Data-PASS), a project supported by the National Digital Information Infrastructure and Preservation Program (NDIIPP), has increasingly turned its attention from identifying and acquiring legacy and at-risk social science data to identifying ongoing and future research projects that will produce data.
The selection, appraisal, and retention of digital social science data
TL;DR: The processes used by two data archives are reviewed to illustrate how data are selected for archiving, how they are appraised, and what steps are required to retain the usefulness of the data for future use.