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.

KOI-net Protocol Specification Draft v1.0

The KOI-net protocol defines the standard communication patterns and coordination norms needed to establish and maintain Knowledge Organization Infrastructure (KOI) networks. KOI networks are heterogeneous compositions of KOI nodes, each of which is capable of autonomously inputting, processing, and outputting knowledge. The behavior of each node and configuration of each network can vary greatly; therefore, the protocol is designed to be a simple, flexible, and interoperable foundation for future projects to build on. The protocol only governs communication between nodes, not how they operate internally. As a result we consider KOI-nets to be fractal-like, in that a network of nodes may act like a single node when viewed from outside.

OSF

The new way we’ll do science

Papers should become human-readable views over a graph of data, tools, results, and certificates.

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

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

The Ground Truth Institute

Could we engineer scientific revolutions, then spin out radical improvements to everyday life?

datasetpapers — a public research experiment

An experimental approach to versioned, forkable, machine-readable analyses. A prototype, not a product or service.

Large language models are not the problem

If a Large Language Model (LLM) can replicate your scientific contribution, the problem is not the LLM. What does it say about our field that so much of the anxiety about AI comes down to the fear that a machine could do what we do? Perhaps it says we should be doing something better.

modular research multi-agent slack-like environment demo

a demo of a slack-like workspace with multiple collaborating agents with access to a lab discourse graph and modular science and open social infrastructure.

Buzz! 🐝

Buzz is a channel-driven workspace where people, agents, repos, and decisions work together in one signed, open-source room.

Buzz! 🐝

Buzz is a channel-driven workspace where people, agents, repos, and decisions work together in one signed, open-source room.

Charting AI’s Role in Scientific Discovery — Renaissance Philanthropy – A brighter future for all through science, technology, and innovation

Renaissance Philanthropy, with support from Google.org , is conducting a landscape study of AI integration in scientific research — and we want your perspective.

An inference cooperative for academic AI – Writings and rehearsals by Nathan Schneider

Universities, like other institutions, are currently being confronted with a dilemma: embrace the AI tools currently available from big-name tech companies, and be part of the future, or reject the miraculous machines and stick your head in the sand. This dilemma is a false one, on several counts. It is far from clear what role generative AI will have in the future of academic life, for one thing. And beyond rewording the choices, surely there are other options that this dilemma fails to consider.

#predictingthefuture #newfutureofwork | Jaime Teevan

🌱 Prediction: Knowledge will outgrow publication. We’re already seeing academic publication start to buckle under AI, sometimes absurdly. I still publish research more or less the way Darwin did. I run a study, write it up, a few other scientists check it over, and the result gets filed away as a document with my name on the front. Faster than Darwin, with better figures, but the same basic shape. I predict that shape won’t last another decade. Academic authors are starting to slip hidden instructions into papers to flatter the AI that might review them. Reviewers are spending time checking whether citations exist or were hallucinated. Researchers asking AI to tell them about a paper instead of reading it directly. These are signs that the creation of new knowledge is outgrowing the articles that used to contain it. An academic paper serves many purposes at once. It makes an argument legible. It lets strangers check one's reasoning. It assigns credit and responsibility. It records who knew what and when. A paper was the only container we had for these different jobs, so it carried all of them together. With AI, they can be separated. My guess is that means the unit of publication will get smaller. Much of my research has focused on microproductivity, developing the idea that large accomplishments can be built from many small contributions. Publication will start to become a form of microproductivity. Instead of holding onto a result until it can be wrapped in a narrative large enough to justify a paper, researchers will publish it the moment it’s solid. Each finding, method, or negative result will be citable and carry its own provenance, so credit and reasoning travel with it. Reviewing will shrink to match, so claims get checked as they’re made instead of in one verdict at the end. But more than changing publication, the deeper change will be to how research itself is done. You may have heard the term “compound engineering,” where every bug fixed, evaluation written, workflow documented, or lesson learned becomes part of the system’s memory. I predict we’re about to see “compound science,” where every experiment, evaluation, insight, artifact, and learned capability becomes a reusable asset for future discovery. Findings will become evidence. Methods will become building blocks. Failed approaches will become constraints. For centuries, science has relied on humans to navigate an ever-growing body of knowledge. Soon that body of knowledge will help navigate itself. Scientists will spend less time searching for hypotheses and more time deciding which opportunities to pursue. AI systems will propose explanations, design experiments, run analyses, and explore many possibilities in parallel. Every discovery will become a part of the machinery that produces the next one. Papers ten years from now will look less like my current papers than my current papers look like Darwin’s. If they exist at all. #PredictingTheFuture #NewFutureOfWork

GainForest — Biodiversity Observations & Nature Projects

Explore field observations, biodiversity records, and nature projects from communities and organizations using GainForest.

Coordination Tech in Science: Letters, Journals, and Whatever Comes Next | shishyko!

To modernize our scientific infrastructure, we need new contextualization and coordination technologies that decouple trust from legacy branding — shifting from gatekeeping on write to algorithmic contextualization on read.

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...

What Scientists Said: Results from Astera's First Essay Competition

Astera recently hosted its first essay competition, focused around metascience.

The Principles of Open Scholarly Infrastructure (v2.0, 2025)

POSI version 2.0 released October 2025 The POSI Adopters reviewed the version 1.1 principles and consulted with the community to create version 2.0, released in October 2025. The new/always-current …