Better infrastructure for scientific synthesis & communication

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

example from Joel Chan’s Roam Research Discourse Graph extension documentation

Discourse Graphs are a better way to build and communicate ideas

Discourse Graphs simplify scientific communication into its constituent parts: questions, claims, & evidence, and allow researchers to exchange data and ideas with more granularity.



Synthesize and Update

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.



Client-Agnostic & Researcher-aligned

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 natural OS for a Cloud Laboratory

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 Graph Resources

📄 *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