Technology was supposed to make your job easier. But if you've found yourself spending hours building notebooks, managing dependencies, setting up cloud storage, perplexed by version control, configuring containers, resourcing GPUs, and hunting for software solutions for each new problem, then you need Nextjournal.
Sharing is hard...
... but it doesn't have to be. Share your notebook as a work in progress or a published article. Share it with the world or in private with a select few. Your peers and readers can run your code with no setup or installation required.
Nextjournal was built from the ground up to combat the reproducibility crisis; the platform automatically creates perfectly reproducible notebooks without a second thought from the user. Sharing means your code will run the exact same on every computer and it's as easy as sending a URL.
Walls make collaboration difficult.
Nextjournal doesn't have the same boundaries as traditional code repositories or notebooks. The platform allows you to take work from one notebook and import it into another.
For example, one data scientist might create an article to build and test a computational environment. This work is essential for reproducing the results but tangential to the core research. A collaborator can import an environment built in one notebook and use it to execute code in another. The result is a fully reproducible notebook where the code and commentary focus on the research at hand, not on installing packages and setting defaults.
Collaborate in real time. Collaborate across notebooks. Create groups for an open science enterprise or share a private notebook for one on one feedback.
Stop wasting time on setup...
... and start with a click. Nextjournal offers single-click defaults for Python, R, Julia, Clojure and select machine learning libraries. These defaults offer more than a language runtime, they all include typical data science tools out of the box. There is absolutely no installation required for many types of projects, from data science plots to machine learning experiments.
Don't worry about making changes...
... experiment! Nextjournal makes time travel easy by offering version control across the entire notebook - from your code to your data, from execution results to the dependencies. If you break something, simply travel back to the last relevant point in time. There are no commits or save points. You just focus on moving forward with your work.
No need to learn Git or any other version control system. No need to learn Docker or any other containerization software. It all happens automatically!
Multiple dependency stacks? Multiple language runtimes?
No problem. Create notebooks that support multiple Python stacks or language combinations like R+Python or Clojure+Julia. No special commands, no special configuration - just import a second (or third, or fourth, or more!) environment and hit run.
The notebook is remains fully reproducible with an automatically version controlled dependency stack across all languages.
Some of the most powerful features of Nextjournal are invisible to the user. To learn more about how the promise of full-stack reproducibility enables experimentation and sharing, read the introduction to environments and the introduction to remixing. If you'd like to try it yourself, get in touch for Private Beta access.