Curvenote Overview 2022
Hi, this is Rowan from Curvenote and I'm gonna give a quick overview of the Curvenote platform. So Curvenote is an open source authoring and publishing tool for scientists that directly integrates with Computational Jupyter Notebooks to improve and accelerate science communication. And really our goal in this is to help get science out of PDFs enable rich and beautiful interactive scientific communication on the web and make science communication and sharing of science faster, more interactive, more reproducible, and more collaborative. And so one of the places that we'll start is in Jupyter Notebooks where a lot of computational science is done through data analysis, and sharing of data.
But to actually communicate those results. Often screenshots of the figures, or results or tables of results are, are taken and then shared in common communication tools like Word docs or PDFs. And the problem with this is that there's no interactivity. When there's new results or the data is changed, there's no way to update those results automatically.
And again, there's no interactivity in these static communication assets. And so Curvenote is a modern tool in this space that integrates directly into programmatic environments like Jupyter Notebooks and GitHub, and through APIs. and it is primarily a writing tool that, I'll show in a minute, that can export out to all of the standard communication products as well as directly out into markdown and other forms through our APIs.
And so to give you an idea of what Curvenote looks like, this is the Curvenote editor. And it is a scientific. Designed for scientific communications. So things like reference management is directly included, that you can add references from a DUI or upload a bi tech or directly integrate from your reference management library and insert those citations and these have more metadata like the DOI and the abstract potentially linked directly in there. You can add things like equations directly in line. You can reference those equations by just selecting an equation and hitting enter and that is all cross-linked. That also works for things like figures.
And there's this is a fully collaborative, real time editor that has commenting and collaboration built in and one of the things that sets this apart is the ability to add interactive figures directly in line. And these figures and data are coming from that Jupyter Notebook environment. And so now I'll jump over into our integration into Jupyter Notebooks.
And so if you're familiar with Jupyter, this is Jupyter Lab. And Curvenote offers an extension that adds version control and collaboration to Jupyter Notebooks. So a scientist is often doing their data analysis in Jupyter Notebooks, and then they have an output possibly that is interactive, that they'd like to share, that showcases some result that they have.
And so with the Curvenote extension, you can directly copy a link to this. Come over to the article that you're writing and hit command or control V depending on your platform. And that now brings the interactive figure actually directly in line. And this is actually a reference to that figure. And so things like comments and collaboration go from this article view, which is much friendlier what-you-see-is-what-you-get, editor. All the way into the Jupyter Notebook, and so those comments, we can reply over here or we could even update the results. And so if we change those results and save the new output, you'll see that that takes a second to save to the Curvenote platform and then over in our article view, we can look and pull in the latest version of those results.
And so this is really making that change to be as reproducible and interactive as possible and bringing those results directly into the reports that we're writing. And at the end of the day, once you have an article that you are happy with, you can export that as a PDF document, for example. And so that can go out to any number of different formats.
And so you just export as a PDF document or a LaTeX document, and you can choose any one of about 400 different journal templates and then add extra information about that and then create the, the PDF document if you want to export this out to other formats. We also have a web-based format that allows you to create this for a web-based audience. And that has all of those interactive figures and cross references completely linked together. And if you're adding this in some sort of other publication workflow, every single bit of content in Curvenote has an API that you can get the full structured data out and export that as marked down LaTeX or any other form. And so our goal is really to connect up this full publication workflow. Make it really easy for authors of any type to work in the Curvenote what-you-see-is-what-you-get editor, and then export out to all sorts of other formats to make your science communication as easy as possible.
All right. Thanks so much. Bye.
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