Key Terms

Acronyms, Abbreviations, and Terms

Term	Meaning
Logging	Automatically recording configuration settings, inputs, and outputs when a workflow runs. Logging helps ensure that analyses can be reproduced and errors traced efficiently.
Maintainability	The ease with which code can be read, understood, and updated by others. Well-documented, modular workflows are more maintainable and easier to build upon.
Modularity	Writing code in reusable, independent components (modules or functions). This makes workflows easier to test, debug, and share between projects.
Notebooks	Interactive documents combining code, text, and results (e.g., Jupyter Notebooks). Ideal for exploration and training, but final reproducible workflows should also be submitted as scripts or packaged code to ensure consistent execution.
Packages	Reusable code collections distributed with documentation (e.g., Python packages or R libraries). Packages promote consistency, reduce duplication, and simplify sharing code used in workflows.
Parameters and Arguments	Configurable inputs that allow workflows to be flexible and reusable. Parameters define expected inputs; arguments are the specific values passed when running the workflow.
Pipeline	A structured sequence of processing steps, where outputs from one stage feed into the next. Workflows should define clear input, processing, and output stages to ensure reproducibility.
Readability	The clarity of your code — making it understandable for collaborators and reviewers. Readable code is consistent, well-documented, and logically structured.
Reproducible Analytical Pipelines (RAP)	Workflows built using open tools, good coding practices, and automation, ensuring that results can be independently reproduced. RAPs embody the FAIR and Open Science principles promoted by EarthCODE.
Scripts	A code file that performs specific analysis steps or orchestrates an entire workflow. All scripts should run without manual intervention to support reproducibility.
Version Control	Tracking and managing changes in your code over time. Tools like Git, combined with platforms such as GitHub, GitLab, or BitBucket, allow collaborative development and open publication of your workflows.
Virtual Environments	Isolated software setups containing the specific versions of dependencies required by a workflow. They help ensure that analyses remain reproducible across systems and time.