jupyter-guide/ten-rules-jupyter
Ten Simple Rules for Writing and Sharing Computational Analyses in Jupyter Notebooks
This resource provides practical guidelines and examples for creating clear, reproducible, and shareable computational analyses using Jupyter Notebooks. It helps researchers, data scientists, and analysts structure their work, making it easier for others (or their future selves) to understand and reuse. You'll get best practices for organizing your notebooks and the data that goes with them.
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Use this if you are a researcher, data scientist, or analyst who wants to ensure your computational work in Jupyter Notebooks is easily understood and reproducible by your colleagues or the broader scientific community.
Not ideal if you are looking for a software library or tool to automate your analysis, as this project focuses on methodological guidelines rather than direct computational functionality.
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Jupyter Notebook
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MIT
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Jun 21, 2024
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