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Cocalc And Jupyter Notebooks Scientific Computing Workbook

Leo Migdal
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cocalc and jupyter notebooks scientific computing workbook

We will be using an online platform called Cocalc to run and edit notebooks which run Python code. Cocalc is a collaborative computing platform, which means that you can interactively share your code with other users of the platform. After completing this worksheet, you will be able to: ALL students must do Part 1 Step 1 to set up a new Cocalc project for NSCI0007. The remainder was included in last year’s module NSCI0010 so you only need to do this if you didn’t do that course or if you want to refresh your understanding. Follow the the instruction below to create a Cocalc account and run a notebook file.

The course management interface gives you full control over distributing, collecting, grading and returning everyone's assignments. Contact [email protected] or request a live demo! This material was included in last year’s module NSCI0010. Complete this material if you want to refresh your memory or if you didn’t take that module. After completing this worksheet, you will be able to: Open your NSCI0036 Cocalc project, then click on the Before_you_begin folder.

The online platform we will be using, providing online access to virtual computers hosted in the cloud. You access Cocalc via your browser at www.cocalc.com. Every student has a Cocalc account allowing access to a Cocalc project, which is a virtual computer including operating system (Linux) and software libraries. Collaborative Calculation and Data Science Real-time collaboration for Jupyter Notebooks, Linux Terminals, LaTeX, and more, all in one place. CoCalc: Collaborative Calculations and Data Science

Jupyter Notebooks have become indispensable in physics research. They seamlessly combine code, equations (via LaTeX), visualizations, and narrative text, making them ideal for data analysis, simulations, theoretical derivations, and reproducible research. However, collaborating on a single Jupyter Notebook with a colleague—whether for co-authoring a paper, troubleshooting code, or validating results—can be challenging. Emailing files back and forth leads to version chaos, and local notebook edits often conflict with each other. This blog post focuses on free, practical tools and workflows tailored to two-person physics research teams. We’ll break down step-by-step solutions, their pros and cons, and physics-specific considerations (e.g., handling large datasets, LaTeX equations, or GPU-intensive simulations).

By the end, you’ll know exactly which tool to use to streamline collaboration and keep your research on track. Collaborating on such notebooks introduces unique challenges: The solutions below address these pain points—let’s dive in. Overview: GitHub (with Git) is the gold standard for version control. It lets you track changes to your notebook, revert to old versions, and collaborate asynchronously. Ideal if you and your collaborator prefer working on local Jupyter Notebooks (e.g., with custom environments or sensitive data).

This workbook contains all the required material for the module NSCI0036. Before starting work you need to set up your Cocalc account by following the instructions in the section Before You Begin. For the first five weeks you will be working through the material in the ‘Programming with Python’ section. Each week there are three activities to complete: Work through the notes and complete the embedded exercises. Solutions are provided for you to check your understanding.

It is recommended that you do this before attending the workshop. The workshops lead you through some examples of practical programming in Science. Work on these during your timetabled practical classes. Your best choice for teaching remote scientific courses.

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