Konrad Hinsen's blog

A Data Utopia for Science-of-Science

Here I want to briefly sketch out a vision for how to solve a key set of problems facing science-of-science researchers, using the relatively new idea of a ‘data trust.’ In my ideal wor…

Konrad Hinsen's blog

Knowledge refinement is the ever ongoing process in science (and beyond it) that shepherds knowledge from lab notebooks into journal articles and then on to review articles, monographs, reference handbooks, university textbooks, and finally professional domain expertise and school education for a wider public. It has been going on for a few centuries, but we hardly talk about it. In fact, I made up the term because I couldn't find an established one. Computational knowledge has not yet found its place in the knowledge refinement process. Why not? And what can we do to make it happen?

The Bazaar of Scientific Knowledge | shishyko!

Is the current form of the scientific paper still optimal in 2025? How do we preserve, and efficiently leverage, the uncut gems of the scientific process?

datasetpapers — a public research experiment

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

datasetpapers — a public research experiment

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

Universal Scientific Protocols, Inc.

Universal Scientific Protocols, Inc. — research publishing, reconsidered. A knowledge management platform for machine learning researchers.

The new way we’ll do science

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

The Engine of Scientific Discovery: How New Methods and Tools Spark Major Breakthroughs

Abstract. How do we spark new scientific discoveries? Why do some breakthroughs seem even accidental? And most importantly, how can we accelerate them and

Governing by dismantling: tech oligarchy and the stifling of public data infrastructure

Published in Science as Culture (Ahead of Print, 2026)

Science is open software

TL;DR I claim that modern science is synonymous with open source software. This post explains why, why it matters, and what you can (and should) do next. Why do you care about (open source) software? - Everyone I spend a lot of my time working on software. I have been asked why software matters more times than I can remember. Software is, people say, not science. It’s a time sink, something to rush past in the pursuit of what really matters: results (and papers if you’re in academia).

Linking the world's research to the code it runs on - OpenAlex blog

Research relies on software. Software written by scientists, for science, runs through the entire modern research stack: NumPy and SciPy, R and ggplot2, Jupyter, BLAST, ImageJ, AlphaFold. Yet in the scholarly record, that software is nearly invisible. Software is not usually cited formally in publications and is usually just mentioned in the text, which means […]

The Bazaar of Scientific Knowledge | shishyko!

What if we didn't collapse all the knowledge from the scientific process into one paper?

The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale

Sir Isaac Newton famously wrote, “If I have seen further, it is by standing on the shoulders of giants”. Scientific contributions are rarely developed in isolation, but build upon prior contributions, such as problem framings, experimental methods, and empirical findings. Understanding these prerequisite relationships is important for studying scientific progress, and for automated scientific discovery systems that must reason about which existing capabilities can be used to develop new ones (e.g. Lu et al., 2024; Jansen et al., 2025b; Baek et al., 2025).

AI for science needs reasoning, not just data

AI agents that can model the human process of research will accelerate discoveries in science.

Artificial intelligence and illusions of understanding in scientific research

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.