The C-StARR Data Core is an evolving adaptation of data-related support within the Duke Center based on new opportunities and emerging research questions. The Center was initially funded in 2003 as a NIDA P20 center. The Data Core, originally directed by Dr. Hoyle, provided support to research programs in the collection, analysis, and archiving of data. In 2008, the Center was renewed as a P30 cores-only center, shifting the focus of the Core to support of ongoing projects in the research base and new pilot studies. This shift was reflected in the change of the name to Methodology and Statistics Core. Hoyle directs this core, which focuses on the development and dissemination of methods and strategies for addressing the challenges associated with longitudinal, multilevel analyses of self-regulation and substance abuse through consultation and collaboration with Center investigators. In this application to transition to a NIDA P30 Core Center of Excellence, we return to the name Data Core to reflect the addition of a, focus on existing large, complex, longitudinal data sets that include variables that range from genetics to school administrative data. We will offer support for data collection and analysis using the large NC education data base as well as supplementing and integrating existing data sets. This new emphasis is consistent with broader investments in data infrastructure such as the NSF-NIH BIGDATA initiative, which supports the development and evaluation of "tools that take advantage of available collections of large data sets to accelerate progress in science, biomedical research, and engineering." As reflected in our aims, progress, and plans, the Data Core will contribute to this interagency goal while serving the needs of ongoing and pilot projects in the Center.

National Institute of Health (NIH)
Center Core Grants (P30)
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Special Emphasis Panel (ZDA1)
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Duke University
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Hoyle, Rick H; Gottfredson, Nisha C (2015) Sample Size Considerations in Prevention Research Applications of Multilevel Modeling and Structural Equation Modeling. Prev Sci 16:987-96
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