Language Access in Healthcare — Discourse Graph

An open, AI-assisted evidence synthesis of how language concordance — matching patients with providers or interpreters who share their language — affects healthcare outcomes. Every question, claim, evidence item, caveat, and source is its own addressable node.

Reason Commons | Reason Commons — Issue Trees & Logical Thinking Process

Structure claims into trees. Map evidence to them. Grow shared understanding — together.

Issue-based information system

The issue-based information system (IBIS) is an argumentation-based approach to clarifying wicked problems—complex, ill-defined problems that involve multiple stakeholders. Diagrammatic visualization using IBIS notation is often called issue mapping.

Logial Thinking Process (LTP) / Issue Tree App and Exploration - Issue Trees & Logical Thinking Process

Collaboration of Rufus Pollock with David Joseph to build tooling for structured thinking — specifically a Logical Thinking Process (LTP) / Issue Tree app. Both David and Rufus share a long-standing desire to improve public discourse and improve collaboration: aligning efforts to goals, making claims or hypotheses and their logical structure clearer (claim-trees), allowing debate to accumulate, mapping evidence clearly to specific sub-claims.

LinkedClaims — Decentralized Verifiable Claims on ATProto

LinkedClaims: publish and verify decentralized claims about any URI-addressable subject on the AT Protocol. Open standard by DIF Labs.

Where Should Science Go Next

Where Should Science Go Next? Prashant Garg, April 2026 Every researcher has to choose what to work on next. Science has formal procedures for judging answers, but no comparable procedure for comparing questions. Einstein said in 1918 at Max Planck's sixtieth birthday: "There is no logical path t...

RRGI · Discourse Graph — lens switch

Issue Trees, Hypothesis Trees and SCQA - the Minto Pyramid Principles

Flywheel

Resilient Data Futures — Discourse Graph

A living, content-addressed, contributable form of the SciOS Resilient Data Futures whitepaper.

eLife Claim Trees — eLife Claim Trees

Panel-level claim graphs for reproducibility — eLife Claim Trees

Distinct representational properties of cues and contexts shape fear and reversal learning — eLife Claim Trees

Panel-level claim graphs for reproducibility — eLife Claim Trees

Fenc.es · fenc.es

Break an argument into statements. Rate each one for confidence and importance. The shape it takes is the map — and where your view splits from someone else's lies the crux.

Scientific Web Claims: A survey of definitions, tasks, datasets and methods

Scientific web claims are seen as scientific claims as observed on the Web, across social media, online news, and other platforms. The growing prevalence of scientific discussions on the Web has intensified the need to process and assess this specific type of claims. Unlike claims from scientific publications, scientific web claims are expressed in lay terms, are often decontextualized, and typically lack proper citations, which poses unique challenges for their identification, verification, and communication. Nevertheless, the correct processing of scientific web claims is crucial to keeping online science discussions accurate and informed, for instance through fact-checking. This survey provides the first systematic overview dedicated specifically to scientific web claims. We review and compare existing definitions, task formulations, datasets, and methodological approaches across three major perspectives: (1) Scientific fact-checking on the Web, (2) Scientific citations on the Web, and (3) Science communication on the Web. Our interdisciplinary analysis integrates insights from natural language processing, information retrieval, artificial intelligence, social sciences, and science communication. We identify major methodological challenges, including the lack of unified definitions, domain-agnostic corpora, and foundational models tailored to science-related online discourse. We also discuss challenges related to the existing interplay between emotions and distortions of science online. By mapping current research efforts and highlighting open problems, this survey lays the groundwork for developing robust datasets, methods, and evaluation frameworks to advance the automated processing of scientific web claims, a necessary capability for strengthening the reliability of science-related online discourse at scale.

What’s the best technology that doesn’t exist yet?

We analyzed 100+ conversations and essays on positive futures to find out

NLnet; Nanoarguments

Scientific knowledge is currently scattered across papers, repositories, and disconnected platforms, with no structured way to trace how claims connect to evidence or how arguments develop. Nanoarguments builds a framework and tools for creating, browsing, and contributing to a global, federated graph of scientific discourse and evidence. Researchers and their communities can collaboratively structure claims, evidence chains, and discussion as nanopublications, which are small, cryptographically signed Linked Data snippets with precise provenance and authorship, published to a decentralized peer-to-peer network. The project builds upon the Nanodash interface to help users browse, edit, and aggregate discourse and evidence graphs, and integrates with dokieli to enable in-context authoring of nanopublications as inline annotations while reading or writing a document. A bidirectional ActivityPub connector bridges the nanopublication network and the fediverse, allowing discourse threads to start as social exchanges and crystallize into persistent, machine-readable evidence records. The project will be piloted with early adopter research groups in discourse and evidence modeling. All components will be released as open-source modules that other systems can build upon.

Complete Works (1977–2024) — Hirsch Argument Atlas

This atlas maps the complete argument of E.D. Hirsch Jr. across 10 books spanning 1977 to 2024. Every claim is extracted, every piece of evidence tracked, and every counter-argument surfaced. A separate layer of external scholarly research provides independent context.

Entailment