Collaborative & Reproducible Research

Handouts for this lesson need to be saved on your computer. Download and unzip this material into the directory (a.k.a. folder) where you plan to work.

Collaboration & Code

As your research project moves from conception, through data collection, modeling and analysis, to publishing and other forms of dissemination, its components can fracture, lose their development history, and—worst of all—become conflicted or lost. This lesson introduces strategies for structuring your collaboration, focusing on the core principle of “version control” (VC) and the related software and cloud solutions. The main objective is to demonstrate that version control is much more than history, it is a way for distributed teams to work efficiently on a shared codebase. Version control is essential for reproducible research.

A cloud hub stores project files and their history. Researchers are spokes on the wheel, each working on a local repository. Project integrity is maintained by rules for synchronizing commits between the hub and spokes when users execute a push or pull.

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Lesson Objectives

By cultivating new habits for scripting, documenting, and committing, much of the research cycle can be made reproducible.

Credit: Philip Guo

Specific Achievements

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Reproducible Pipeline

The result of reproducible research is more than a published paper. It includes the whole data-to-document pipeline. In a typical socio-environmental synthesis project, a finished pipeline ideally includes the following steps:

  1. Acquire raw data from online repository.
  2. Extract, transform, and load data into storage for analysis.
  3. Perform data analysis (e.g. model inference) and visualization.
  4. Update documentation and reports.
  5. Publish results, including reports, data and code/software.

The pipeline is a direct path through the research cycle, avoiding all its twists and turns, so others may reproduce your final analysis.

Credit: Philip Guo

A UC Berkeley professor who is a strong advocate for open science, Carl Boettiger, has released several reproducible pipelines on GitHub. For example, check out his work leading up to the paper “Pretty Darn Good Control” in the project pdg_control.

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RStudio + git

Login to your RStudio Server account and upload (If you are participating in the Summer Institute, instead login to RStudio on, and will already be uploaded for you.) Click on handouts/handouts.Rproj to open the directory as a project.

RStudio provides a GUI (a point-and-click interface) to the core tools provided by git. Every folder that contains a “*.Rproj” file is an RStudio project (simply a collection of files and configuration settings). We use git to turn our project into a “repository,” or “repo” for short, a term from the software development community that encompasses a project along with its development history. On your local machine, the repository simply appears as a folder with project-associated files. git tracks the history of the changes you and collaborators have made to the project files over time.

Note on terminology and configuration

As of October 1, 2020, all new repositories created on GitHub will have a default branch called main. Previously, the default name was master. The change was made to promote inclusive language in the version control world. SESYNC is planning to update the GitLab server to match this new default. However, the git client will still default to master if you create a repository locally, unless you configure it as described below. You should also be aware that any documentation, tutorial, or StackOverflow post written before 2020 will assume your default branch is called master.

If you are doing this lesson on your local machine, we recommend setting the default branch name for new repositories you create to main. Enter the following into your terminal prompt. You can access the prompt from RStudio’s Terminal tab.

git config --global init.defaultBranch main

This option is available for git version 2.28 or later.

Initialize git

Convert your RStudio project to a git repository by enabling version control, available from the menu bar under “Tools” > “Version Control” > “Project Setup”.

Adding a git repository creates a hidden folder in your project called .git, storing all the data about your project’s current and past states.


Once RStudio refreshes your project, there will be a “Git” tab in the same window as the Environment tab. The window shows files that have content not already committed in the current state of your project. At first, this will be every file in the directory, because you created a blank repository. Choose “Commit” to open a new window for easy staging and committing.

  1. check and handouts.Rproj
  2. write a short and informative (<50 characters) commit message
  3. commit

You will get an error message! Read on to resolve it.

For GitHub to associate commits with your account, configure git with your GitHub username and email address. (If you don’t have a GitHub user account yet, sign up for one now.)

git config --global jdoe
git config --global

Configure your username and email address by entering the git config commands into RStudio’s Terminal window. After configuring your username and email address, return to the Commit window and commit your changes.

Now, author information will be associated with the commit. In the future, configure your and before starting a project. This is a one-time configuration for each computer on which you use RStudio, so you don’t have to repeat this for any subsequent repositories you create on this machine.

Saving, staging, and commiting are each separate steps, none of which imply any of the others. This may seem like a hassle, but is a good thing! As your project grows larger, you will frequently save changes you don’t want to commit: staging lets you choose what changes get packaged into a commit.


The history of your project shows a single commit. Every new commit will be chained on top of a preceding commit.

Create the Hub

Sign in on GitHub, then create an empty repository.

  1. Give your repository the same name as your RStudio project.
  2. Add a short “tag line” about your workshop experience.
  3. Do not check the “Initialize this repository with a README” box.

You have created a repository that has no history—it will accept the commits made in RStudio without conflict. The quick start information provided by GitHub explains how to finish configuration of your local git repo. Copy the code under “…or push an existing repository from the command line” and paste it into the RStudio Terminal window.

git remote add origin
git push -u origin main

Enter your username and password when prompted. Note that on Unix operating systems, you don’t see asterisks appearing when you type in the password, but don’t worry, it’s being typed in! After the first time, you will no longer need to use the terminal to push changes if you do not want to. You can use the RStudio dropdown menu to both commit and push changes, and you won’t have to enter your password each time.

Go back to your GitHub account and check out your “hub”.

In addition to being the center point for sharing commits with collaborators, GitHub is a rich platform for managing projects and inspecting the history.

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Syncing Repos

Image by Atlassian / CC BY

The origin is the central repository; in this case it lives on GitHub. Every member of the team gets a local copy of the entire project, called a clone.

Image by Atlassian / CC BY

Cloning is the initial pull of the entire project and all its history. In general, a worker pulls the work of other teammates from the origin when ready to incorporate their work, and she pushes updates to the origin when ready to contribute her own work.

A git repository is a network of commits, although the current network is a tree with no splits. After a worker creates a clone, the local repo is in the same state as the origin.

Notice that the local and remote (origin) repos are both on a branch called main in the diagram below. This is the default name given to the primary version of the repository.

Image by Atlassian / CC BY

When the origin has commits that do not exist in the local repo, it has gotten ahead and a pull is required to synchronize state.

Image by Atlassian / CC BY

A pull, or initially a clone, applies commits copied from the origin to your local repo, as if you had created identical commits locally.

Image by Atlassian / CC BY

In the opposite situation, commits created locally are not immediately synchronized to the origin.

Image by Atlassian / CC BY

A push copies local commits to the origin and applies them remotely.

Image by Atlassian / CC BY

An essential component of version control is the ability to merge commit histories that have diverged.

Image by Atlassian / CC BY

The commit graph splits any time different commits are applied to the same “parent” commit. Automatic merging done by git integrates the changes from both into a new “merge commit,” unsplitting the commit graph.

The origin will not accept a push that requires merging. In order to preserve integrity, the contributor is always responsible for overseeing the merge on a local clone.

Image by Atlassian / CC BY

After the merge commit has been created locally, the same situation now exists that was depicted above. The conflict has been resolved and the origin will now accept a push.

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Collaboration that goes beyond commenting on a final report—integrated work on a project from start to finish—raises serious challenges. Distributed repositories, managed by git, help to answer these questions.

Project Integrity

Version control software works well with text files. Large, non-text components of your project (e.g., very big or binary data files) can slow down any cloning, merging or branch switching. For that reason, data rarely live in a repository with code. Keeping data and code separate also facilitates data reuse—it’s not tied to one pipeline.

Add a section where you can list collaborators to the file. Our aim is to let your collaborators update this list with their own name, so only include yourself. You can use any text editor, and RStudio’s is quite handy.

## Collaborators

- J. Doe


Before you can commit changes involving a new file, you have to tell git which modifications you want to commit by staging.

  1. Go to the “Git” tab in RStudio.
  2. Select “Commit” to open the “Review Changes” window.
  3. Select “Staged” to add modifications (hence “M”) by “”.


  1. Enter a brief (<50 characters) descriptive message about the commit.
  2. Commit!
  3. Close the “Review Changes” window.


Look at the “Git” tab again and notice that your branch is “ahead of origin/main”. Push the commit to your GitHub repo.

If you view the “History” tab you can now see that the local repo and the remote repo have an identical state.

GitHub Collaborators

Even on public GitHub repos, only the owner has “push access” by default. The owner can allow any other GitHub user to push by inviting collaborators under the settings tab (Settings > Manage access > Invite a collaborator).

Choose a partner for this exercise and split the two roles below between you. Be sure to watch each other perform the steps assigned to your individual roles, either in person or by screen sharing.

  1. Owner: add your partner as a collaborator.
  2. Collaborator: accept your partner’s emailed invitation.

Create a Second Spoke

The Collaborator now needs to create a new project in RStudio by cloning the Owner’s project. Under the “File” menu item, choose to create a New Project, and then choose “Version Control”.

You cannot use the same name for two project folders! The Collaborator should choose a different name for their copy of the Owner’s project.

Push & Pull

  1. Collaborator: Add your name to the list in the “”.
  2. Collaborator: Stage, commit, and push (up arrow) your modifications.
  3. Owner: Pull (down arrow) to apply your neighbor’s commit.

If you view the “History” tab, you can see that both the remote and local repo are both updated with a record of the changes made by both collaborators!


In the previous example, everything happened in sequence. First the owner committed and pushed, then the collaborator pulled, committed, and pushed, then the owner pulled again. Each time, changes were made to the most recent “official” version of the repo, so there were no conflicts. However, if two collaborators make changes to the same line of the same file at the same time, git does not know automatically which version to use. However, you can use git to help resolve the conflict because it identifies exactly where the conflicts are so that you can fix them.

You both realize it would have been good to include your affiliation along with your name. Do you need to circulate “” to each collaborator in sequence for an update? No!

  1. Ower AND Collaborator: edit your entry in
  2. Ower AND Collaborator: stage, commit, & push.
  3. Owner OR Collaborator: if you receive an error message, it tells you exactly what to do.

If you receive the merge conflict message, first pull your collaborator’s change from the remote repo. Then, open and you will see that git has put some markers into the file to indicate where the conflict is. Each of the two possible versions appear in the file, one after the other.

<<<<<<< HEAD
- John Doe (SESYNC)
- Jane Roe
- John Doe
- Jane Roe (State U.)
>>>>>>> 17a88283234fe8cda436b

You can also see that the file is conflicted in the Commit window because it now has an orange “U” next to it, for “unresolved.”

Resolve the conflict by deleting the parts of the conflicted section that you do not want to keep. Then delete all the conflict markers. Now, if you click on the checkbox for in the Commit window, the “U” will change to an “M” and you should be able to commit and push without error.

After you successfully complete a merge, move back to your own “handouts” project in RStudio to access your own worksheets.

Important note: If you find resolving merge conflicts confusing, the best way to avoid them is to pull before you push! That means always pull the most recent version of the repo from the remote before making changes. That way, merge conflicts will only occur if you and your collaborator(s) are working on the code at the exact same time.

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What about Data?

The scripts executed in your pipeline are plain text files, but the project may need other assets, such as data, figures, or private configurations.

External Data

The most common pipeline integration is shared data storage.

One good practice is creating “symbolic links” (a.k.a. shortcuts) to data files that live outside a project repo; these let your code use paths that point inside the repo for data.

  from = ...,
  to = 'data'

If you replace the ... above with the path to your data, such as the location of your Dropbox folder or network drive, the shortcut data works like a normal path to your data, as if it were inside your repo. This usually works seamlessly but could be risky on certain operating systems or early versions (before 1.6) of git. It is sometimes possible to add all your data to a commit by accident with git add .. To avoid this, it is a good idea to either update git or “ignore” all files and folders below data/ by adding the line /data/** to the “.gitignore” file in your repo. The leading / refers to the root of the git repository, not to the root of your filesystem.

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A Plug for Reproducibility

Reproducibility is a core tenet of the scientific method. Experiments should be reported in sufficient detail for a skilled practitioner to duplicate the result.

The principle equally applies to modeling and data analysis.

Hallmarks of Reproducibility

Reviewable All details of the method used are easily accessible for peer review and community review.
Auditable [Private] records exist to document how the methods and conclusions evolved.
Replicable Given sufficient resources, a skilled practitioner could duplicate the research without any guesswork.
Open The orginator grants permissions for reuse and extension of the research products.

This table is adapted from “Best practices for computational science: Software infrastructure and environments for reproducible and extensible research” by V. Stodden and S. Miguez (2013).

Habits to Cultivate

Reviewable Thoroughly comment scripts and share continuously with collaborators
Auditable Maintain project history to correct mistakes when necessary
Replicable Provide “one-click” file & data sharing of a streamlined analysis “pipeline”
Open Publically release on GitHub (or similar) with (implied) open licensing

Learning to use git will take care of much of the work needed to make your research reproducible. Once you become efficient at staging, commiting, pushing and pulling, it will become the natural way to communicate with your collleagues through collaborative development of an analysis pipeline.

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Future Directions

The repository you created is an example of the heart of a distributed workflow. Putting the origin of your project on GitHub (or similar) will make it accessible not only by your collaborators, but also available for review and extension by your research community.

Using advanced git to manage contributions to the project as a branching and merging “tree” of commits accomplishes two objectives. First, work can safely proceed in parallel, on separate branches if necessary. Second, a recoverable (and auditable) trail of changes is immediately available in the project history.

The latest software for modeling and analysis in your research field may already be on git. Build better pipelines by contributing bug reports or even pull requests to projects integral to your own work.

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Exercise 1

Make the remaining worksheets show up in your “handouts” repository on GitHub. Remember the three steps: stage, commit, and push.

Exercise 2

Repeat the steps you walked through in pairs, as an Owner and Collaborator, but reverse the roles.

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