Based in Calgary, Canada, Curvenote is a web-first platform built atop a slate of open-source tools that are already widely used by data scientists. Researchers often host their data in GitHub repositories and run analyses in computational notebook environments such as R Markdown or Jupyter Notebook. But to publish a paper, they must distil hundreds of lines of code into a paragraph or two, sacrificing important details and relegating the code itself to supplemental materials. When the paper is converted to a Word document or PDF, it separates text and figures from the code and data used to create them and flattens otherwise dynamic data to static representations. By combining computational notebooks with a user-friendly formatting language called MyST Markdown, Curvenote makes it possible to draft and manage an interactive manuscript from start to finish, including peer review, without the cumbersome translations. And because Curvenote outputs to an XML format called Journal Article Tag Suite, which many publishers use, the tool should allow any publisher to work with the documents directly, says Rowan Cockett, a geophysicist who co-founded the platform in 2019.