Scientific publishing keeps borrowing the Spotify metaphor, but most versions focus on access and UX rather than structure. This piece argues that the real shift in music wasn’t streaming albums more efficiently, but changing what moved through the system: tracks became interoperable objects, while albums became one way to assemble them.
The analogy breaks when applied to science because papers are treated as both the song and the album. Preprints removed gatekeeping, but without shared formats and explicit relationships, figures, data, and methods still don’t circulate independently.
Curious where this analogy fails, especially from people building research infrastructure or working on AI systems that rely on scientific content.
Then the switching of templates actually is one line. There is also hope to get a bit further in the publishers system (which ultimately needs JATS XML), because MyST can also export to JATS (which is used for pubmed etc.).
JATS is gaining adoption in publishing, but is difficult for scientists to actually _author_ in. This is one of the main reasons behind tools like MyST (https://myst-tools.org) and Quarto (https://quarto.org/) - both are semantic authoring tools that allow authors to export to JATS but author in Markdown (or in JupyterNotebooks).
That is where we are headed for sure - our version control allows very granular addressing of content (i.e. an equation or figure). We haven't quite got to the hashing stage yet, because we aren't quite sure what all fits in the content, is the author, date, license also hashed in there? Also right now the way we store our content is still evolving pretty fast as it is more than just a text file etc. that we are storing.
Would love other pointers/thoughts if you have them.
I think this is a really powerful feature for sure.
Happy to be in touch to hear more about your workflows. We do have custom deployments of Curvenote (e.g. private cloud) on our roadmap!
Let me know if you want to hear more <[email protected]>.
I have also seen a lot of that sort of file-naming-version-control still in use. Git is really far out of reach for most people, and IMO not always the most appropriate for writing workflows where the version control sort of lives at the paragraph/figure/equation level.
Word export (and hopefully good import as well) is certainly on our roadmap. :)
Thanks! The ability to copy/reference code and figures from a notebook to multiple other articles/notes and have comments and updates between all the places you reference a Block is something that is really handy when researching and collaborating.
A lot of times I have found that a notebook will have a different narrative than when you are referencing and talking about the work in other articles and books, etc. More of a focus on "why" rather than "how". Curvenote allows you to keep things linked together, with pretty minimal overhead.
Not requiring your colleagues to know LaTeX is a plus in a lot of collaborations, but still getting to that professional export at the end of the day! I was using a weird mashup of LaTeX in GoogleDocs for my thesis ... !
I think that the collaboration workflows in academia could use some improvements for sure! A lot of the challenges I saw were around multi-disciplinary work, where the collaboration was necessary - but pretty limited. Curvenote is aiming to improve that with lower friction tools, as well as trying to influence the "size" of what can be/is shared.
Right now a lot of the sharing is done right at the end, with a large scientific paper - and that is when peer-review comes in. I like the analogy of github here - in the open-source software world, there is a lot more iterative sharing of smaller, in-progress work where the feedback from a colleague/peer has a lot of potential to influence and improve future work.
Curvenote can ideally help in tracking of the version-control/attribution around reusing and referencing work, and helping with collaboration along the way - rather than just at the "end".
Lots of similarities between Jupyter and Matlab. Jupyter is open source (jupyter.org) and is great for exploratory data analysis and integrating documentation/thought process with markdown. We are helping link that environment into your other notes, and keep track of reproducibility as you create a document or article.
Our document format/schema is open source, basically opinionated HTML, and the ability to translate between latex/markdown etc. (see https://curvenote.dev/editor for a demo of the editor, the schema is linked there as well).
Thanks - this is the tool I wanted doing research, that could help me out from group meeting notes all the way to the manuscripts, grants and papers I was writing.
Thanks, over time in academia we would like to move our pricing towards departments or institutions paying, so our pricing model will likely evolve with that shift. Right now Curvenote is free for public projects (and one private one!) - and a similar price point to Overleaf (currently $17-$34/mo).
I have been working on components/tools to help with interactive scientific writing and authoring explorable explanations. The collection at curvenote.dev has two main parts (1) a WYSIWYG text editor/schema; and (2) a set of reactive (web-)components to create explorable explanations.
Super inspired by the work that @mathisonian++ have been doing on Idyll (https://hn.algolia.com/?q=idyll), which has similar goals. I am hoping that our WYSIWYG editor will start to talk Idyll-lang soon, we started with MyST (https://myst-parser.readthedocs.io/) which has been getting traction in the Jupyter Notebook community lately.
All open source, MIT. Happy to answer any questions!
The analogy breaks when applied to science because papers are treated as both the song and the album. Preprints removed gatekeeping, but without shared formats and explicit relationships, figures, data, and methods still don’t circulate independently.
Curious where this analogy fails, especially from people building research infrastructure or working on AI systems that rely on scientific content.