Dissertation
A team science characterization of the national COVID cohort collaborative (N3C)
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Spring 2024
DOI: 10.25820/etd.007360
Abstract
This thesis presents a general analysis of team science dynamics within the National COVID Cohort Collaborative (N3C), focusing on research behavior, project characteristics, and success factors associated with the use of its data. Utilizing constructs identified within the literature, data regarding N3C projects and members, and both exploratory and targeted analysis, the study seeks to identify trends within N3C projects and teams accounting for the impact of COVID-19. Additionally, by examining the relationship between project characteristics and success metrics, this research attempts to identify predictors of success.
In general, N3C teams are fairly consistent with others examined within the team science literature; more teams than individuals worked within the N3C Enclave, more males than females engaged with the initiative both as lead investigators and as general researchers, although this percentage point difference decreased over time; and evidence suggested that the COVID-19 pandemic impacted general trends and success measures. With relation to research topics, three high-level categories were associated with over 50% of N3C projects: (1) Health Care, (2) Analytical, Diagnostic and Therapeutic Techniques, and Equipment, and (3) Diseases. Finally, it is unlikely that with the currently available metadata, success measures can be predicted for N3C teams.
The overall goal of this study was to provide insights that can help inform the design of tools, resources, and workflows to support researchers within the N3C and similar initiatives. The insights gained can be applied to enhance understanding of team science in the context of such large-scale collaborative research projects, ultimately improving research efficiency.
Details
- Title: Subtitle
- A team science characterization of the national COVID cohort collaborative (N3C)
- Creators
- Alexis S. Graves
- Contributors
- David Eichmann (Advisor)Iulian Vamanu (Advisor)Frederick Boehmke (Committee Member)Warren Kibbe (Committee Member)Luke Tierney (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Informatics (Information Science)
- Date degree season
- Spring 2024
- Publisher
- University of Iowa
- DOI
- 10.25820/etd.007360
- Number of pages
- ix, 105 pages
- Copyright
- Copyright 2024 Alexis S. Graves
- Language
- English
- Date submitted
- 04/22/2024
- Description illustrations
- color illustrations
- Description bibliographic
- Includes bibliographical references.
- Public Abstract (ETD)
To facilitate the generation and dissemination of vital COVID-19 health insights, the National COVID Cohort Collaborative (N3C), along with NCATS and other agencies, overcame barriers associated with healthcare data sharing and created and harmonized one of the largest secure collections of COVID-19 health data. This research seeks to explore the teams working with N3C data to gain insights that can be applied to better support their collaborations and help them more efficiently engage with the tool.
These teams comprise over 1000 individuals from diverse institutions and domains, all seeking to answer clinical questions. Understanding, enabling, and supporting such a community can help aid future team science initiatives. The data created for this study illustrates how team composition characteristics, such as team size, relate to the team's overall success. In addition to understanding the unique characteristics of the teams and individuals working within the N3C, the impact that COVID-19 had on their workflows and overall success is also explored. Gaining insights about these relationships helps contextualize the findings within this analysis.
- Academic Unit
- IDGP in Informatics
- Record Identifier
- 9984647453502771
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