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How can you document your research workflow with multiple collaborators?

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Documenting your research workflow is essential for ensuring the reproducibility, transparency, and quality of your statistical analysis. However, when you work with multiple collaborators, the task can become more challenging and complex. How can you coordinate and communicate your workflow effectively with your team members? Here are some tips and tools that can help you document your research workflow with multiple collaborators.

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Brunella D'Anzi

postdoc @ CERN, UCSD | High Energy Physics

1 Choose a common platform

The first step is to choose a common platform where you and your collaborators can store, share, and edit your data, code, and outputs. Ideally, the platform should allow version control, real-time collaboration, and easy access. Some examples of platforms that can support collaborative research workflows are GitHub, Google Drive, Dropbox, or RStudio Cloud. Depending on your preferences and needs, you can also use a combination of platforms, such as GitHub for code and Google Drive for data.

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Anthony Clemons

Senior Data Analyst @ General Dynamics | Statistician | Experience = Analytics, R, SAS | Certified Scrum Master (CSM) | Veteran
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If you're coding, I highly recommend a code repository tool like GitHub. It is an excellent option for collaboration, offering features such as code checkout from repositories, committing changes with comments, and visualizing changes side by side. Its functionality enhances collaboration and makes it easier to work together effectively.

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2 Establish a naming convention

The next step is to establish a clear and consistent naming convention for your files and folders. This will help you and your collaborators to find, organize, and update your files easily. A good naming convention should include relevant information, such as the project name, the date, the version, the author, and the file type. For example, you can name your data file as "project_data_2021_09_15.csv" and your code file as "project_analysis_v2_john.R". You should also avoid using spaces, special characters, or ambiguous terms in your file names.

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Brunella D'Anzi

postdoc @ CERN, UCSD | High Energy Physics
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The naming convention could be explained also in the description of your project. In this README file you can also tell briefly which is the purpose of each code and dataset file. This could also help you to retrieve this basic information after a while you didn't touch this project.

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3 Write a README file

A README file is a document that provides an overview of your project and explains how to run and reproduce your analysis. It should contain information such as the project title, the authors, the objectives, the data sources, the methods, the dependencies, the instructions, and the results. You can write your README file in plain text, markdown, or HTML format, and save it in the root folder of your project. You should also update your README file whenever you make significant changes to your workflow.

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Brunella D'Anzi

postdoc @ CERN, UCSD | High Energy Physics
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The README is an extremely useful description of your project. It should also contain setup requirements (operating system, authorisation certificates) to run your code . You can also cite some improvement goals you want to achieve in the near future, maybe you will attract experts that would like to give you support in reaching them.

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4 Use comments and documentation tools

Comments and documentation tools are useful for adding explanations, notes, and references to your code and outputs. They can help you and your collaborators to understand, debug, and modify your code and outputs more easily. You can use comments to describe the purpose, logic, and steps of your code, and to highlight any issues or questions. You can use documentation tools, such as roxygen2 for R or docstrings for Python, to generate documentation for your functions, variables, and packages. You can also use tools such as knitr or Jupyter Notebook to create dynamic documents that combine code and outputs.

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Brunella D'Anzi

postdoc @ CERN, UCSD | High Energy Physics
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I usually put in my GitHub repositories also Jupiter notebook containing outputs of each runnable cell. In this way people have an idea of what could be the output and decide to run the code by themselves downloading the online material.

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Anthony Clemons

Senior Data Analyst @ General Dynamics | Statistician | Experience = Analytics, R, SAS | Certified Scrum Master (CSM) | Veteran
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Including ample comments in your code and commits is crucial for enabling others to comprehend and grasp the precise nature of your modifications and integrations, as well as the rationale behind them. Such an approach aids in fostering transparency and understanding among collaborators.

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5 Share feedback and suggestions

The final step is to share feedback and suggestions with your collaborators regularly and constructively. You can use tools such as pull requests, issues, or comments on GitHub, or comments or suggestions on Google Docs, to communicate your feedback and suggestions. You should also follow some best practices, such as being specific, respectful, and positive, when giving or receiving feedback and suggestions. By sharing feedback and suggestions, you can improve the quality and efficiency of your research workflow and foster a collaborative culture.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you document your research workflow with multiple collaborators?

1

2

3

4

5

6

1 Choose a common platform

2 Establish a naming convention

3 Write a README file

4 Use comments and documentation tools

5 Share feedback and suggestions

6 Here’s what else to consider

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How can you document your research workflow with multiple collaborators?

1

2

3

4

5

6

1 Choose a common platform

2 Establish a naming convention

3 Write a README file

4 Use comments and documentation tools

5 Share feedback and suggestions

6 Here’s what else to consider

Statistics

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