Reproducible Research Using Jupyter Notebooks

Topic outline

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  General

  • Target audience:

    The course is aimed at graduate students, postdocs, and other researchers who perform computational analysis or work. The material uses basic Python for teaching and illustrating the key concepts. Advanced knowledge of Python is not needed, but some familiarity with Python will aid in absorbing the material.

    
    

    Objectives:

    After completing this course, participants will be able to:
    

    • Understand the value of reproducible research practices for more effective research for current and future you.
    • Understand the value of reproducible research practices for advancing research as a whole.
    • Understand what is meant by making your research more reproducible.
    • Know practices to make your research more reproducible, in particular by using Jupyter Notebooks, and have the skills to do so.
    • Have the confidence and foundation to continue improving reproducibility of your research.
    • Understand what’s possible and they still can learn to be more effective with reproducible research.

    
    

    
    

    Based on https://reproducible-science-curriculum.github.io/workshop-RR-Jupyter/

    
    

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  Data & Project Organization

  • Goals: Students will learn recognizing common data file formats and how to import them into a Jupyter notebook; be able to design and justify a directory structure and file naming convention for a project; be able to move from an empty notebook through exploratory analysis into a more refined script or set of notebooks that communicates results reproducibly.

  • Introduction URL
  • Project Structure URL
  • Metadata URL
  • Modifying Data URL
  • Concluding Thoughts URL
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  Introduction to the Jupyter notebook

  • Goals: Students will understand the concept, importance, and components of reproducible research; understand the strengths of Jupyter Notebooks as a tool for reproducible research; be able tp create and navigate through a Jupyter Notebook containing Markdown and Code cells; and be able to know and access the broader Jupyter and Python ecosystems and communities.

  • Introduction to and Motivation for Reproducibile Research URL
  • Introduction to Jupyter Notebooks URL
  • Navigating the Notebook URL

    Targeted at instructors, but applicable to end-users as well.

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  Data Exploration

  • Goals: Students will be able to assess the structure and cleanliness of their dataset; be able to describe their findings, translate results, and summarize their thought process in a narrative comprised of Markdown text and Python code in a Jupyter Notebook; learn practices for modifying raw data to prepare a clean data set in a reproducible and documented way; and be able to assess whether their data is “Tidy”, and how to arrange it into a tidy format.

  • Data Exploration with the Jupyter Notebook URL
  • Dataset 1 URL
  • Dataset 2 URL
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  Automation

  • Goals: Students will learn how to programmatically assemble a manuscript using elements generated by a notebook, including text, headings and figures generated from code and data.

  • Variable Naming, Defensive Programming, DRY Principle, Functions & Documentation URL
  • Cleaned Dataset URL
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  Publication

  • Goals: Students will learn how to export their notebooks in a variety of formats for publication; be able to describe the utility of documentation to themselves and others; be able to describe and compose appropriate and descriptive keywords for a given record; be able to define and describe the importance of unique identifiers for data, publication and software; and learn how to select an appropriate license for their research artifacts.

  • Documentation URL
  • Exporting the Jupyter Notebook URL
  • Online archiving of research inputs and outputs URL
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  Sharing

  • Goals: Students will learn how to share their Jupyter notebooks online, both static (using GitHub) and interactive (using Binder).

  • Sharing Jupyter Notebooks using GitHub URL
  • Introduction to Binder URL