In this capstone, students will learn how to work with existing scientific models whose code is open source. Students will be exposed to a range of languages, platforms and tools. The first half of the course, involving several computer-based laboratory exercises, will guide students on how to replicate a model-based scientific experiment, how to attempt verification and validation of model code in the absence of a known solution against which computed results can be compared, how to assess the precision and accuracy of a model, how to perform sensitivity analyses of a model, how to conduct software maintenance on model code, how best to visualize model output, and how to approach making changes to a particular code component. Students will also learn about ways of generating scientific models from curated databases in the public domain (the inverse modelling problem).

In the second half of the capstone, students will identify an existing scientific model (or curated database) to work with. The capstone experience will involve applying one or more of the techniques learned about in the first half of the course. For example, a recipe-improving experimental replication might be attempted. Or, data generated from an existing model could be used in inverse modelling and the models compared. Models may address problems in a variety of disciplines including astronomy, biomedical science, biology (ecology), cognitive science, neuroscience, computer science, physics, chemistry, mathematics, earth science, life science and economics. The capstone experience constitutes undergraduate research. Working with external organizations will be restricted to the making use of languages, platforms and tools that are made available by them. A particular capstone experience could involve two or more scientific disciplines.

Having your own laptop will greatly facilitate the laboratory and project work required on this course.