Abstract

We have applied the sensitize-train-hack-community model to build awareness of and capacity in bioinformatics in Kenya. Open science is the practice of science openly and collaboratively, with tools, techniques, and data freely shared to facilitate reuse and collaboration. Open science is not a mandatory curriculum course in schools, whereas bioinformatics is relatively new in some African regions. Open science tools can significantly enhance bioinformatics, leading to increased reproducibility. However, open science and bioinformatics skills, especially blended, are still lacking among students and researchers in resource-constrained regions. We note the need to be aware of the power of open science among the bioinformatics community and a clear strategy to learn bioinformatics and open science skills for use in research. Using the OpenScienceKE framework—Sensitize, Train, Hack, Collaborate/Community—the BOSS (Bioinformatics and Open Science Skills) virtual events built awareness and empowered researchers with the skills and tools in open science and bioinformatics. Sensitization was achieved through a symposium, training through a workshop and train-the-trainer program, hack through mini-projects, community through conferences, and continuous meet-ups. In this paper, we discuss how we applied the framework during the BOSS events and highlight lessons learnt in planning and executing the events and their impact on the outcome of each phase. We evaluate the impact of the events through anonymous surveys. We show that sensitizing and empowering researchers with the skills works best when the participants apply the skills to real-world problems: project-based learning. Furthermore, we have demonstrated how to implement virtual events in resource-constrained settings by providing Internet and equipment support to participants, thus improving accessibility and diversity.

Keywords

open science; bioinformatics; BHKi; OpenScienceKE; Africa

Published in

Frontiers in Research Metrics and Analytics
2023, Volume: 8, article number: 1070390

SLU Authors

• Landi, Michael

  • Department of Animal Biosciences, Swedish University of Agricultural Sciences
    
  • International Institute of Tropical Agriculture (IITA)

UKÄ Subject classification

Bioinformatics (Computational Biology)
Learning


Publication identifier

DOI: https://doi.org/10.3389/frma.2023.1070390

Permanent link to this page (URI)

https://res.slu.se/id/publ/127865