In this module, we have explored open workflows, their importance in reproducibility and replicability in research across disciplines and best practices for organizing files/documentation. An open workflow, generally, has the following elements:
- Publicly accessible documentation of research process and outcomes, including experiment design, data, analysis, code, etc.
- The use of best practices around file naming conventions, project metadata, file formats, tools used, etc. to enable long term preservation and access of files
As you being the process of creating an open workflow, consider the following questions:
- Are there existing open projects that can inspire your workflow?
- For each step in your process, are there relevant/disciplinary-specific open tools and open file formats that you should keep in mind? If your discipline does not have best practice protocols, which ones could you incorporate into your work?
- As your project moves forward and you start creating outputs, how will you keep track of things? Document everything and consider preregistering your study design and analysis information to increase transparency
Key Takeaways
- A research workflow is a documentation of every step taken and decision made as part of a research project. By creating an open workflow, you ensure that enough information is shared about how you got from an idea to a conclusion, an inspiration, or an output, and the tools that you used along the way to allow someone else to replicate or reproduce your findings.
- Open workflows are incredibly important for minimizing the impact of the reproducibility crises happening across many disciplines.
- Open workflows begin with considering ways to increase documentation and sharing research processes and outputs in a manner that is easily identified, collated, and packaged for openly sharing.
- Using open workflow tools (e.g., OSF) provides a central landing place for all research collaborators, project files, and to track versions of project outputs.