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#First Journal Entry

Open, reproducible science

The first lecture we had went into the definition and meaning of open, reproducible science. The way I understand it, open reproducible science means researchers making their methods and data available for the public and the methodologies should be able to reach the same results when applied by someone else. Open science is accessible, transparent, accessible and collaborative. Within the confines of data science, open reproducible science means that codes are made accessible in published papers, to allow anyone to have access to, and replicate the work that has been done.

In my experience, open science is productive. When I joined my lab, I joined in the middle of a project that had lasted for three years prior to my arrival. These was a large dataset and I came in with very minimal knowledge of data science. To make things easier for me, my advisor made me create a GitHub account and then provided me with R codes that have been used for other parts of the data analysis. These thousands of lines of codes saved me so much time and effort and I was able to change the analyses and run the codes, coming up with results months earlier than I could have done it myself. In summary, open science is helps to save time in the long run and allow lab groups like mine stay organized.

However, on the flip side, open science is hard to implement. As a PhD student, taking the time to ensure that my codes are reproducible, clear and easy to understand takes too much time and effort. Currently, I do not think my codes are reproducible as most of them are just trial and error, and stuff I googled and that worked. However, this session has enabled me to reflect on how open science has helped me in my career and I am more determined to do better going forward. Another issue I thought about was convincing other PIs in my department to do open science, since we do not change things that are not broken. However, I was reminded that things become norm when people do them, and so I am convinced to continue to seek ways to introduce open reproducible science to my lab members, especially new students, and to teach by doing. Hopefully more people will join us especially with the new federal grants stipulations.

The conversation about data ethics and sovereignty helped me to put my current research and the research we do in my lab into perspective. The idea of collaborating with indigenous communities, co-creating knowledge and incorporating indigenous ways-of-knowing will enable us have well rounded scientific results that helps to serve communities. The concept of FAIR (Findability, Accessibility, Interoperability, and Reusability) and CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) principles are important factors that I need to always remember and hope to share with my lab group.