Chapter 10 Be a champion for open science
in development…
10.1 Objectives and Resources
To provide resources for you to promote good practices for open and reproducible science in your communities and institutions.
10.2 Three messages
If there are 3 things to communicate to others after this workshop, I think they would be:
1. Data science is a discipline that can improve your analyses
- There are concepts, theory, and tools for thinking about and working with data.
- Your study system is not unique when it comes to data, and accepting this will speed up your analyses.
This helps your science:
- Think deliberately about data: when you distinguish data questions from research questions, you’ll learn how and who to ask for help
- Save heartache: you don’t have to reinvent the wheel
- Save time: when you expect there’s a better way to do what you are doing, you’ll find the solution faster. Focus on the science.
2. Open data science tools exist
- Data science tools that enable open science are game-changing for analysis, collaboration and communication.
- Open science is “the concept of transparency at all stages of the research process, coupled with free and open access to data, code, and papers” (Hampton et al. 2015))
This helps your science:
- Have confidence in your analyses from this traceable, reusable record
- Save time through automation, thinking ahead of your immediate task, reduced bookkeeping, and collaboration
- Take advantage of convenient access: working openly online is like having an extended memory
3. Learn these tools with collaborators and community (redefined):
- Your most important collaborator is Future You.
- Community should also be beyond the colleagues in your field.
- Learn from, with, and for others.
This helps your science:
- If you learn to talk about your data, you’ll find solutions faster.
- Build confidence: these skills are transferable beyond your science.
- Be empathetic and inclusive and build a network of allies
10.3 Build community
Join existing communities locally and online, and start local chapters with friends!
Some ideas:
- Mozilla Study Groups Example: Eco-data-science. Also see (Steven et al. 2018)
- RLadies. Example: RLadies Santa Barbara
These meetups can be for skill-sharing, showcasing how people work, or building community so you can troubleshoot together. They can be an informal “hacky hour” at a cafe or pub!