Date of Award

Summer 7-1-2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

School of Information Studies

Advisor(s)

Qin, Jian

Keywords

Cyberinfrastructure, Data Repositories, eScience, Open Science, Research Data Management, Scholarly Communication

Subject Categories

Library and Information Science | Social and Behavioral Sciences

Abstract

With the 'data deluge' leading to an institutionalized research environment for data management, U.S. academic faculty have increasingly faced pressure to deposit research data into open online data repositories, which, in turn, is engendering a new set of practices to adapt formal mandates to local circumstances. When these practices involve reorganizing workflows to align the goals of local and institutional stakeholders, we might call them 'data articulations.' This dissertation uses interviews to establish a grounded understanding of the data articulations behind deposit in 3 studies: (1) a phenomenological study of genomics faculty data management practices; (2) a grounded theory study developing a theory of data deposit as "articulation" work in genomics; and (3) a comparative case study of genomics and social science researchers to identify factors associated with the institutionalization of research data management (RDM). The findings of this research offer an in-depth understanding of the data management and deposit practices of academic research faculty, and surfaced institutional factors associated with data deposit. Additionally, the studies led to a theoretical framework of data deposit to open research data repositories. The empirical insights into the impacts of institutionalization of RDM and data deposit on long-term data sustainability update our knowledge of the impacts of increasing guidelines for RDM. The work also contributes to the body of data management literature through the development of the "data articulation" framework which can be applied and further validated by future work. In terms of practice, the studies offer recommendations for data policymakers, data repositories, and researchers on defining strategies and initiatives to leverage data reuse and employ computational approaches to support data management and deposit.

Access

Open Access

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