With $21M in funding, Code Ocean aims to help researchers replicate data-heavy science

Every branch of science is increasingly reliant on big data sets and analysis, which means a growing confusion of formats and platforms — more than inconvenient, this can hinder the process of peer review and replication of research. Code Ocean hopes to make it easier for scientists to collaborate by making a flexible, shareable format and platform for any and all datasets and methods, and it has raised a total of $21M to build it out.

Certainly there’s an air of “Too many options? Try this one!” to this (and here’s the requisite relevant XKCD). But Code Ocean isn’t creating a competitor to successful tools like Jupyter or Gitlab or Docker — it’s more of a small-scale container platform that lets you wrap up all the necessary components of your data and analysis in an easily shared format, whatever platform they live on natively.

The trouble appears when you need to share what you’re doing with another researcher, whether they’re on the bench next to you or at a university across the country. It’s important for replication purposes that data analysis — just like any other scientific technique — be done exactly the same way. But there’s no guarantee that your colleague will use the same structures, formats, notation, labels, and so on.

That doesn’t mean it’s impossible to share your work, but it does add a lot of extra steps as would-be replicators or iterators check and double check that all the methods are the same, that the (Read more...)