The digital preservation of research at Colorado State University: A case study of three departments

Research workflows in higher education have converged onto digital formats. While the technology to store data has improved at an increasing pace, personal and organizational behaviors have not adapted as rapidly. The study sought ways to communicate digital preservation skills to researchers to improve the permanency of their research data. This study proposes three temporal contexts digital – short-term, long-term and trans-generational. Study questions asked selected participants about how they manage their digital data. The study used Diffusion of Innovation theory concepts within an Activity Theory construct and the Open Archive Information System to model key areas of transformation. The key areas were determined by analysis of interviews, surveys and institutional data. The model provides a new way to understand the complex set of issues that can inhibit data preservation. The study used descriptive statistics and social network analysis to elaborate ways to transmit new data preservation attitudes and behaviors more effectively. In particular, the data management plan requirement for National Science Foundation grant submissions was found to be a potentially powerful motivator for a limited number of researchers. The study found that there is an opportunity for the institution to create group activities, such as workshops, that specifically include faculty with NSF grants and those who share other grant submission experience with them. The study also found that information technology staffs need to understand research problems from the researcher perspective better to overcome some trust issues. Finally, campus leadership needs to identify their role in addressing the issue for the long-term benefit of the institution. Strategic goals are an important first step. Building a robust digital preservation environment is an iterative process dependent on many perspectives. The goal of this research is to speed the process by developing a systems-level model for exposing problem areas