Konrad Hinsen's blog

Konrad Hinsen's blog

Thirty years after my first contact with computational (ir)reproducibility, I am happy to note that many things have improved. Reproducibility, computational and otherwise, is increasingly recognized as an important aspect of scientific quality control, and mostly considered worth striving for. However, I also note that more and more people, including reproducibility activists, have lost contact with the day-to-day reality in which reproducibility matters. Reproducibility is becoming an item on a checklist, and its precise incarnation the subject of political bickering aimed at making it easy to check off that item. So let's take a look at why computational reproducibility matters for researchers.

The Interface of Kai Krause's Software @mprove

Matthias Müller-Prove, Innovative Benutzungsschnittstellen, 1998/99, University of Hamburg

The Case Against Formal Verification, 50 Years Later - Ivan Gavran

Writings on software correctness, AI, formal verification, and other technical topics.

Ten quick tips to SNIFF out sustainable and secure scientific software

Modern computational biology depends heavily on open-source software tools, analysis pipelines, and containerized workflows developed and shared by the research community. While there is extensive guidance (including Quick Tips and Simple Rules articles) on how to build robust and sustainable scientific software, far less has been written for researchers in the role of software users evaluating whether an existing tool is reliable, secure, and sustainable enough for their work. Here we present ten quick tips to help researchers critically assess the tools they adopt. Our tips are organized around a framework that centers on key evaluation features: source, network, interaction, fit, and fragility (SNIFF). These dimensions prompt researchers to consider who maintains a tool and why, whether it is embedded in a broader ecosystem, how actively its developers and users engage, whether it matches the intended use case and licensing requirements, and how robust its dependencies and security practices are. By applying these tips, researchers can make more informed decisions, reduce the risk of relying on abandoned or insecure software, and contribute to a more sustainable scientific software ecosystem.

Mathematician catches AI-generated research error by OpenAI president | Benjamin Golub posted on the topic | LinkedIn

In case you think the problem of science slop is hypothetical, here's something that happened on X yesterday: The president of OpenAI sharing wrong science slop as proud evidence of the power of mathematical AI. Within a day, the mathematician Tony Feng found a gap that undercut the paper's central argument; the author conceded, and (to his credit!) Brockman deleted the tweet as soon as he became aware. Feng was a hero to catch this. The author of the refuted claim is a mathematician but a self-described outsider to this area, with a prior big claim posted and quickly withdrawn. Greg Brockman is not an expert, either. Neither could check what they were amplifying. Heroes catching errors one at a time won't hold against the volume of AI-generated research now coming. We need an automated, independent verification layer for knowledge work, which is what we're building at Refine (refine.ink). Passing such verification should become a necessary step before big claims, especially outside one's own expertise. A link to a bigger-picture post on this is in the comments. | 23 comments on LinkedIn

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 […]

R. S. Doiel, Software Engineer/Analyst — Robert's ramblings

By R. S. Doiel, 2026-02-21 (revised: 2026-03-03, epilogue added 2026-03-27)

Welcome to the Tidyverse

Wickham et al., (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686

How to SNIFF out Good Scientific Software

New PLOS Computational Biology paper: "Ten Quick Tips to SNIFF Out Sustainable and Secure Scientific Software"

Simon on Twitter / X

Can AI help connect theorems humans write in papers to proofs computers can check?We just released TheoremGraph (https://t.co/PQ8FcFQGat), and I made a 3Blue1Brown style video overview of the idea.This project was my first real research experience, and it meant a lot. Start… pic.twitter.com/yMUA0QziXM— Simon (@waskaja) June 29, 2026

Composure

On yet another foray into the list of opinions I have that I’m sure are making me progressively more unemployable by the day, I asked some people on the internet: who is doing interesting work on software composability these days and where do I find them?

Substrates conference series - Substrates 2026

An increasing number of researchers see their work as interactive authoring tools or software substrates for interactive computational media. By talking about “authoring tools”, we remove the divide between programmers and users; “software substrates” let us look beyond conventional programming languages and systems; and “interactive computational media” promises a more malleable and adaptable notion of tools for thought we are striving for. This workshop aims to bring together a wide range of perspectives on these matters.