🔎 My Deep Dive into Open Science: A Three-Day Crash Course
- 2 mins🔎 A Three-Day Journey into the Heart of Open Science
With the ongoing reproducibility crisis in science and the increasing demand for transparency, it is clear that it is time for a paradigm shift. Open Science - a movement to make scientific research more accessible and transparent - is heralding this shift. However, understanding and adopting Open Science practices is a journey, one that I decided to embark on through a three-day crash course offered by the LMU Open Science Centre.
📚 The Crash Course Experience
The crash course, which took place over three days from 27 to 29 September 2022, offered a mix of insightful talks and hands-on workshops. The day began with an introduction to the reproducibility crisis and open science, setting the stage for the complex topics we would delve into. Other lectures explored the concepts of credible research through pre-registration, reproducible workflows, data transparency, meta-analysis and bias, among others.
Workshops throughout the course allowed us to apply what we had learned, focusing on topics such as the Open Science Framework, data simulation, common statistical mistakes, and creating reproducible workflows using Git/GitHub and RStudio. These hands-on experiences were invaluable in cementing our understanding and providing practical skills.
🔎 Key Takeaways
The Open Science Crash Course was an enriching experience. Here are some key takeaways:
1. Navigating the reproducibility crisis: The reproducibility crisis has serious implications for the scientific community. Open Science offers strategies to mitigate these concerns by promoting transparency and reproducibility.
2. Pre-registration and reproducible workflows: Preregistration, reproducible workflows and data transparency can help to improve the credibility of research. Tools such as the Open Science Framework and Git/GitHub can help facilitate this.
3. Importance of understanding bias: Understanding potential biases, especially against the null hypothesis, and knowing best practices in statistical design can help produce reliable results.
4. Practical skills: The hands-on workshops allowed me to develop practical skills in areas such as data simulation, identifying common statistical errors and creating reproducible workflows.
🎈 Wrapping Up
The crash course provided a comprehensive introduction to Open Science, demonstrating its importance and providing practical skills to implement it in our research practices. As we move further into the Open Science era, this course has equipped me with the tools to navigate this changing landscape. I look forward to taking these learnings forward and applying them to my future research endeavours. Stay tuned for more of my experiences with Open Science! �
Medina Bajramovic
Data Scientist and Biostatistician