Launch HN: Curvenote (YC W21) – Collaborative writing tools for science

Hi HN! I’m Rowan and with my co-founder Steve we are building Curvenote (https://curvenote.com https://curvenote.com) — a technical writing tool for sharing data analysis and research from Jupyter with a wider audience. We are building Curvenote to get science communication out of PDFs and help researchers and data-scientists communicate interactive, reproducible results (graphs, figures, maps, etc.) that are linked to the actual data and computation. There are currently two parts to Curvenote: 1) a WYSIWYG collaborative writing environment for interactive, technical documents; and 2) a Jupyter integration that adds version control and commenting and can link interactive plots and outputs directly into Curvenote documents (including any new versions or comments on those outputs). Steve and I met in the open-source/science community and are coming at this from different angles: Steve has led data science teams, and keeping stakeholders and team members in the loop with up-to-date figures/reports took a lot of time (via emails, screenshots, PPT presentations, customer reports, etc.) — leading to what he calls communication chaos. A lot of my experience is coming from writing a PhD thesis, writing papers, presenting early research to colleagues/supervisors, and developing educational/training material around open-source projects. In both our experiences, there is a collaboration gap between working on data science (for us in Jupyter) and getting feedback or enabling other people on our teams to remix the work, add context or ask questions. We each had a lot of hacked-together solutions, that mostly cut out anyone who wasn’t comfortable in git or Jupyter. Curvenote aims to span this gap by providing tools that enable less technical (or busier) collaborators as well as integrations into anywhere Jupyter lives (e.g. AWS Sagemaker, JupyterHub, locally). We are aiming for the collaboration experience of Google Docs, the precise presentation of LaTeX, and first class integrations into computational notebooks - without changing data science tools. The weaving of computational results into documents and keeping all the links pointing back to your Jupyter notebook cells starts to build an interconnected knowledge graph (similar to Notion or what Roam are doing for personal knowledge databases) — with a heavy focus on research, where ideas, equations, figures, code can be browsed, filtered and discovered. This starts to become a “web of science” — with very granular ways to address and remix content across projects. I get really excited about this. A lot of content I was producing during my PhD was shared between various presentations/reports as I developed ideas over many years; I wanted to see how the ideas were linked together and allow other people (and myself!) to reuse parts of the work with the same ease as importing a software library. We are seeing people producing their lab-group meeting notes [1], writing reports that can be shared inside their companies [2], reproducing research papers [3], writing computational textbooks [4], and cross-importing data-science visualizations across projects. Curvenote has a free tier for public projects and we charge $15/user/month for teams. Our other inspiration is coming from distill.pub [5] and explorable explanations [6]. We are trying to make it really easy to create and share these types of interactive documents and connect them to computational environments. A lot of the components underlying our platform are open-source (see https://curvenote.dev https://curvenote.dev), including our editor which you can try without signing up [7]. We also have an active Slack community [8], with a broad user base: teachers, scientists, data scientists, data journalists. You're welcome to join! Really excited to get some feedback from the HN community - happy to talk more on version control of Jupyter Notebooks, about our open-source article editor, about explorable explanations, and would love to hear if some of the challenges we have faced around collaboration in data science/research resonate with you? [1] https://curvenote.com/@simpeg/meeting-notes/2021-02-24 https://curvenote.com/@simpeg/meeting-notes/2021-02-24 [2] https://curvenote.com/@stevejpurves/computational-finance/modern-portfolio-theory https://curvenote.com/@stevejpurves/computational-finance/mo... [3] https://curvenote.com/@lheagy/pixels-and-their-neighbours/pixels-and-their-neighbours https://curvenote.com/@lheagy/pixels-and-their-neighbours/pi... [4] https://curvenote.com/@geosci/inversion-module/inverse-theory-overview https://curvenote.com/@geosci/inversion-module/inverse-theor... [5] https://hn.algolia.com/?dateRange=all&page=0&prefix=false&query=distill.pub&sort=byPopularity&type=story https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu... [6] https://hn.algolia.com/?dateRange=all&page=0&prefix=false&query=explorable%20explanations https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu... [7] Editor demo here without signing up: https://curvenote.github.io/editor/ https://curvenote.github.io/editor/ [8] https://slack.curvenote.dev https://slack.curvenote.dev