Visualizing Protein Data Sets in R through a Student Peer-review Rubric
Document Type
Article
Publication Date
2022
Keywords
big data, open source R, rubric, student peer review
Digital Object Identifier (DOI)
https://doi.org/10.1002/bmb.21648
Abstract
The R programming language and computing environment is a powerful and common platform used by life science researchers and educators for the analysis of big data. One of the benefits of using R in this context is its ability to visualize the results. Using R to generate visualizations has gained in popularity due to the increased number of R packages available to convert data to graphic display. In this paper, I ask the following question: how can student engagement with protein analysis be promoted using R-based visualizations in the classroom? During the 2021 IUBMB/ASBMD workshop “Teaching Science with Big Data”, I presented a teaching strategy that used R for the visualization of protein data. In this report, I provide a teaching procedure and a summary of how students engaged with these data in our Introduction to R for Professional Data Science class. This report is based on a case study methodology by reviewing student peer comments for protein analyses conducted in R. The results indicated that students were active participants in the peer-review process and that they learned to take a critical view of data visualization.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Biochemistry and Molecular Biology Education, v. 50, issue 5, p. 453-456
Scholar Commons Citation
Friedman, Alon, "Visualizing Protein Data Sets in R through a Student Peer-review Rubric" (2022). School of Information Faculty Publications. 672.
https://digitalcommons.usf.edu/si_facpub/672
