Building capacity through open approaches: Lessons from developing undergraduate electrophysiology practicals
- PMID: 34868552
- PMCID: PMC8600483
- DOI: 10.12688/f1000research.51049.1
Building capacity through open approaches: Lessons from developing undergraduate electrophysiology practicals
Abstract
Background: Electrophysiology has a wide range of biomedical research and clinical applications. As such, education in the theoretical basis and hands-on practice of electrophysiological techniques is essential for biomedical students, including at the undergraduate level. However, offering hands-on learning experiences is particularly difficult in environments with limited resources and infrastructure. Methods: In 2017, we began a project to design and incorporate electrophysiology laboratory practicals into our Biomedical Physics undergraduate curriculum at the Universidad Nacional Autónoma de México. We describe some of the challenges we faced, how we maximized resources to overcome some of these challenges, and in particular, how we used open scholarship approaches to build both educational and research capacity. Results: We succeeded in developing a number of experimental and data analysis practicals in electrophysiology, including electrocardiogram, electromyogram, and electrooculogram techniques. The use of open tools, open platforms, and open licenses was key to the success and broader impact of our project. We share examples of our practicals and explain how we use these activities to strengthen interdisciplinary learning, namely the application of concepts in physics to understanding functions of the human body. Conclusions: Open scholarship provides multiple opportunities for universities to build capacity. Our goal is to provide ideas, materials, and strategies for educators working in similar resource-limited environments.
Keywords: biomedical sciences; electrophysiology; higher education; open education; open research; open scholarship; open science; undergraduate education.
Copyright: © 2021 McKiernan EC and Medina Gómez L.
Conflict of interest statement
Competing interests: ECM is (or has previously served as) an advisor or editorial board member for several open tools and platforms, including Figshare, Overleaf, Journal of Open Source Education, and Research Ideas and Outcomes. However, these are volunteer positions. The authors do not receive any financial gains for promoting the tools and platforms described herein. The authors declare they have no other competing interests.
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- Acharya UR, Fujita H, Oh SL, et al. : Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci. 2017;415–416:190–198. 10.1016/j.ins.2017.06.027 - DOI
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