1. Victoria Prince
  2. Professor and Dean for graduate affairs
  4. University of Chicago
  1. Stefano Allesina
  2. https://allesinalab.uchicago.edu/
  3. Professor
  5. University of Chicago
  1. Stephanie Palmer
  2. Associate Professor
  4. University of Chicago

Reproducibility and rigor in quantitative biology: a hands-on approach

NSF Awards: 1734818

2019 (see original presentation & discussion)


Current research in biology is producing increasingly large and complex data sets, e.g. DNA sequences, brain images, or species in an ecosystem. Unlocking the information within these data requires sophisticated mathematical and computational approaches. Standard doctoral-level biology curricula were designed before this data deluge, and today’s graduate students are being inadequately trained. Further, there are growing concerns that scientists are unable to reproduce published findings. An inability that often results from poor data analysis strategies. Success of the US research mission hinges on training students to use data analysis approaches that are both rigorous and reproducible. Our project seeks to meet this need by developing a new and effective approach to train early stage graduate students in quantitative analysis of biological data. The overarching goal is to teach students to critically evaluate quantitative analysis methods in the scientific literature, and to acquire good programming habits that support reproducibility and rigor in their own research. The training program involves an intensive residential week-long boot camp that brings together students across diverse sub-fields of biology to promote teamwork and prepare them for interdisciplinary research. The boot camp includes introductory tutorials in computer programming, statistics, and modeling in modern biology, as well as more advanced tutorials in statistical approaches to large data sets and practical lessons in organizing and sharing code and data. The boot camp is capped off with a series of workshops in which students apply what they have learned to real biological data spanning a wide range of fields.

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