Abstract
In my Capstone project, I have read about prior work as well as experimentally collected and
analyzed my own data. Writing my STS thesis on data feminism in the realm of healthcare
information has allowed me to think differently about how other people represent their data and
how I can more effectively represent my own data.
My technical project centers on designing an image analysis workflow for accurately and
efficiently analyzing the results of parasite invasion assays. The purpose of performing the
invasion assays is to discover more about the molecular mechanisms of Toxoplasma gondii entry
into host cells. It is known that cell cytoskeletal morphology and stiffness are influenced by the
stiffness of their substrate and it was hypothesized that the parasites use the force generated by
the host cell cytoskeleton to pull themselves into the cell. Therefore, I performed invasion assays
using host cells plated on substrates of different stiffnesses and observed differences in invasion
rates. It was found that the amount of parasite invasion does differ when host cells are on
substrates of different stiffnesses, and this discovery confirms the hypothesis. In order to quickly
obtain accurate results from the invasion assays, I designed an image analysis workflow that can
count objects in fluorescent images. Using both test and real images, it was found that there was
no significant difference between the mean number of objects counted manually versus
automatically.
For my STS thesis, I look at healthcare infographics about COVID-19 through the lens of
data feminism. Data feminism lays out seven principles that those who create, synthesize, and
interpret data should use to create a more equitable society. With these seven principles in mind,
I look at several examples of infographics about COVID-19 and specifically focus on whether
context was considered and the accessibility and intended audience. I also examine the teaching
of infographics production using the principles of “examine power” and “make labor visible”.
Through this analysis, I find that data feminism is a useful framework for assessing the
effectiveness of healthcare infographics and pointing out ways that infographics can be
improved.
Overall, I am proud of the work I have done this year for both my STS and technical
projects. I experienced several obstacles that I had to troubleshoot for my technical project, but
was able to accomplish what I set out to do. I gained a new perspective on healthcare
infographics through my STS project and learned skills that will be valuable to me in my career.
I would like to thank my STS advisor, Sean Ferguson, and my technical advisor, Brian
Helmke, for guiding me through this year and supporting my work. I would also like to thank
Radhika Pande, my Capstone partner, and other members of the Helmke Lab who assisted my
research.
