The "replication crisis" is a methodological problem in which many scientific research findings have been difficult or impossible to replicate. Because the reproducibility of empirical results is an essential aspect of the scientific method, such failures endanger the credibility of theories based on them and possibly significant portions of scientific knowledge. An instance of the replication crisis, analytic replication, pertains to reproducing published results through computational reanalysis of the authors' original data. However, direct replications are costly, time-consuming, and unrewarded in today's publishing standards. We propose that bioinformatics and computational biology students replicate recent discoveries as part of their curriculum. Considering the above, we performed a pilot study in one of the graduate-level courses we developed and taught at our University. The course is entitled Intro to R Programming and is meant for students in our Master's and PhD programs who have little to no programming skills. As the course emphasized real-world data analysis, we thought it would be an appropriate setting to carry out this study. The primary objective was to expose the students to real biological data analysis problems. These include locating and downloading the needed datasets, understanding any underlying conventions and annotations, understanding the analytical methods, and regenerating multiple graphs from their assigned article. The secondary goal was to determine whether the assigned articles contained sufficient information for a graduate-level student to replicate its figures. Overall, the students successfully reproduced 39% of the figures. The main obstacles were the need for more advanced programming skills and the incomplete documentation of the applied methods. Students were engaged, enthusiastic, and focused throughout the semester. We believe that this teaching approach will allow students to make fundamental scientific contributions under appropriate supervision. It will teach them about the scientific process, the importance of reporting standards, and the importance of openness.
Karathanasis, Nestoras; Hwang, Daniel; Heng, Vibol; Abhimannyu, Rimal; Slogoff-Sevilla, Phillip; Buchel, Gina; Frisbie, Victoria; Li, Peiyao; Kryoneriti, Dafni; and Rigoutsos, Isidore, "Reproducibility Efforts as a Teaching Tool: A Pilot Study" (2022). Computational Medicine Center Faculty Papers. Paper 43.
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This is the author's final published version in PLoS Computational Biology, Volume 18, Issue 11, November 2022, Article number e1010615.
The published version is available at https://doi.org/10.1371/journal.pcbi.1010615. Copyright © 2022 Karathanasis et al.