Discourse Graphs are an information model that enables researchers and communicators to map their ideas and arguments in a modular, composable graph format.
example from Joel Chan’s Roam Research Discourse Graph extension documentation
Discourse Graphs simplify scientific communication into its constituent parts: questions, claims, & evidence, and allow researchers to exchange data and ideas with more granularity.
Discourse Graphs enable researchers to exchange knowledge in a form that makes it straightforward to construct, update, and view cross-disciplinary syntheses of what is known and unknown for any research problem or area of inquiry.
Like Lego™️ bricks, the modular components of the D/Graph data model make it easy to choose which parts of a scientific project you wish to share and build upon.
Discourse Graphs are a decentralized knowledge exchange protocol designed to be implemented and owned by researchers — rather than publishers — to share results at all stages of the scientific process.
D/Graphs are client-agnostic with decentralized push-pull storage & and can be implemented in any networked notebook software (Roam, Notion, LogSeq, Obsidian, etc.) straight from a repo, allowing researchers to collaborate widely while working in the tool of their choice.
D/Graphs support and incentivize knowledge sharing by making it easy to push to and pull from a shared knowledge graph — and to claim credit for many more types of contributions
Discourse Graphs are like github for scientific communication.
The flexible Discourse Graph framework has been adapted to communicate experimental concepts and practice, helping to bridge the gap between experimentalists & theoreticians and between bench scientists & computational scientists.
Results Graphs — a Discourse Graph dialect — can be used to support
➡️ which leads to better-executed experiments
↪️ and a faster discovery & innovation cycle
📄 *Discourse Graphs and the Future of Science* by **Matt Akamatsu & Evan Miyazono, in conversation with Tom Kalil
📄 Roam Research Discourse Graph extension documentation (contains additional useful links)
📄Knowledge synthesis: A conceptual model and practical guide
📃Joel Chan on Scaling Synthesis
📃Joel Chan, Rob Haisfield, and Brendan Langen on the role of context in synthesis
📃Joel Chan on Sustainable Authorship Models for a Discourse-Based Scholarly Communication Infrastructure
🧰 Cybrarian Michael Gartner’s Discourse Graph template: get cracking building your graphs!
💻 blogpost on Deep Science Ventures’ Outcomes Graph