What parts of the academic knowledge creation & dissemination pipeline can we automate with AI?
Four working prototypes testing which parts of the research pipeline, from writing to review to publishing, can actually be automated.
Topic
Experimentation
Working prototypes and small experiments testing what can genuinely be automated in the research pipeline.
OpenAccess.ai: How Cheap Can We Make Academic Publishing?
Article processing charges run to thousands per paper. OpenAccess.ai tests how much of the editorial stack can genuinely be automated.
Preprints.ai: How Much of Peer Review Can We Automate?
A large share of peer review is mechanical checking. Preprints.ai tests how much of it can be scored automatically, at preprint scale.
OpenScience.ai: Using Open Data to Generate Research That Doesn't Yet Exist
What claims are already latent in ClinVar, GTEx, STRING and Open Targets that nobody has written up yet? OpenScience.ai explores that gap.
FAIRdata.ai: Making Open Data FAIR-er
Most repository data is technically FAIR and practically unusable. FAIRdata.ai is an experiment in closing that gap automatically.
Innovating Academia From The Outside - The Web3 Solution to Academia’s Peer Review Problem
ResearchHub pays for peer review in crypto. That terrifies plenty of researchers, but the rate of innovation on the platform is hard to ignore.
Open Access: Mo money, mo problems
From discovering PLOS ONE in a stem cell lab to today's article processing charges: how Open Access got expensive, and what that cost us.
Making poorly described data FAIR-er using GenAI
A small experiment: can ChatGPT-4 improve the metadata, and therefore the FAIR-ness, of badly described datasets already published openly?