Integrating Data Science into T32 Training Programs at IUPUI

dc.contributor.authorDixon, Brian E.
dc.contributor.authorStumpff, Julia C.
dc.contributor.authorKasthurirathne, Suranga N.
dc.contributor.authorLourens, Spencer
dc.contributor.authorJanga, Sarath
dc.contributor.authorLiu, Yunlong
dc.contributor.authorHuang, Kun
dc.date.accessioned2020-11-12T13:51:00Z
dc.date.available2020-11-12T13:51:00Z
dc.date.issued2019-06-30
dc.description.abstractData science is critically important to the biomedical research enterprise. Many research efforts currently and in the future will employ advanced computational techniques to analyze extremely large datasets in order to discover insights relevant to human health. Therefore the next generation of biomedical scientists requires knowledge of and proficiency in data science. With support from the U.S. National Library of Medicine, a team of faculty from Indiana University-Purdue University Indianapolis (IUPUI) facilitated curricula enhancement for National Institutes of Health (NIH) T32 research training programs with respect to data science. In collaboration with the existing NIH T32 Program Directors at IUPUI and the IU School of Medicine, the interdisciplinary team of faculty drawn from multiple schools and departments examined the existing landscape of data science offerings on campus in parallel with an assessment of the competencies that future biomedical and clinician scientists will require to be comfortable using data science methods to advance their research. The IUPUI campus possesses a rich tapestry of data science education programs across multiple schools and departments. Furthermore, the campus is home to more than a dozen world-class T32 programs funded by the NIH to train biomedical and clinician scientists. However, existing training programs do not currently emphasize data science or provide specific curriculum designed to ensure T32 graduates possess basic competencies in data science. To position the campus for the future, robust T32 programs need to connect with the rapidly growing data science programs. This report summarizes the rationale for the importance of connection and the competencies that future biomedical and clinical scientists will require to be successful. The report further describes the curriculum mapping efforts to link competencies with available degree programs, courses and workshops on campus. The report further recommends next steps for campus leadership, including but not limited to T32 Program Directors, the Office of the Vice Chancellor for Research, the Executive Associate Dean for Research Affairs at the IU School of Medicine, and the President and CEO of the Regenstrief Institute. Together we can strengthen the IUPUI campus and help ensure its T32 graduates are successful in their research careers.en_US
dc.description.sponsorshipNational Library of Medicineen_US
dc.identifier.urihttps://hdl.handle.net/1805/24375
dc.language.isoen_USen_US
dc.subjectinformaticsen_US
dc.subjectdata scienceen_US
dc.subjectT32 training programsen_US
dc.titleIntegrating Data Science into T32 Training Programs at IUPUIen_US
dc.typeOtheren_US
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