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Eleven quick tips for running an interdisciplinary short course for new graduate students

Author(s)
Saunders, Timothy E.; He, Cynthia Y.; Koehl, Patrice; Ong, L. L. Sharon; So, Peter T. C.
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Abstract
Quantitative reasoning and techniques are increasingly ubiquitous across the life sciences. However, new graduate researchers with a biology background are often not equipped with the skills that are required to utilize such techniques correctly and efficiently. In parallel, there are increasing numbers of engineers, mathematicians, and physical scientists interested in studying problems in biology with only basic knowledge of this field. Students from such varied backgrounds can struggle to engage proactively together to tackle problems in biology. There is therefore a need to establish bridges between those disciplines. It is our proposal that the beginning of graduate school is the appropriate time to initiate those bridges through an interdisciplinary short course. We have instigated an intensive 10-day course that brought together new graduate students in the life sciences from across departments within the National University of Singapore. The course aimed at introducing biological problems as well as some of the quantitative approaches commonly used when tackling those problems. We have run the course for three years with over 100 students attending. Building on this experience, we share 11 quick tips on how to run such an effective, interdisciplinary short course for new graduate students in the biosciences.
Date issued
2018-03
URI
http://hdl.handle.net/1721.1/119879
Department
Massachusetts Institute of Technology. Department of Biological Engineering; Massachusetts Institute of Technology. Department of Mechanical Engineering
Journal
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Citation
Saunders, Timothy E., Cynthia Y. He, Patrice Koehl, L. L. Sharon Ong, and Peter T. C. So. “Eleven Quick Tips for Running an Interdisciplinary Short Course for New Graduate Students.” Edited by Francis Ouellette. PLOS Computational Biology 14, no. 3 (March 29, 2018): e1006039.
Version: Final published version
ISSN
1553-7358

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