How can we document software and computational analyses in such a way that others can convince themselves of their validity, and build on them for their own work? The question has been around for many years, and a number of attempts have been made to provide partial answers. This post provides a brief review and describes my own tentative answer, inviting you to play with it. Explainable AI is a hot topic today.

Most regular readers will know about DinoCon, a two-day semi-technical/semi-popular conference being run by SV-POW!’s own Darren Naish. (Darren is very much a silent partner here, and is much better known for his own blog Tetrapod Zoology, and of course for his technical work.) The first ever DinoCon will be this summer — Saturday 16th & Sunday 17th August at the University of Exeter.

What began in the fall of 2024 as a weekly experiment - just me, a blank page, and a question about how technology and humanity are reshaping each other - quickly grew into something more.

Being a researcher implies a long stretch of studies at the university to specialize in a certain field. To me personally, it felt like I just kept studying and studying, year after year, and… here I am.  One day, when I was 18 I went to the university and I am still there. I have come to realize that I never really mentally switched from being a student to being a professional or having a job.

Imagine having a super-powered map that connects every important idea, tool, and discovery in mathematics — that’s what zbMATH, and its incorporated platforms such as swMATH, a specific portal for mathematical research software, do. These platforms are like the ultimate librarians of math research, gathering and organizing knowledge from academic papers, software, datasets, and even citation networks. But they don’t just collect information; they make it smarter.

The post Mapping Mathematics – Integrating zbMATH Open and the PID Graph appeared first on DataCite.

I have an idea for an experiment.

If you are reading this blog on our website, you may have noticed that alongside each post we now list a Crossref DOI link, which was not the case a few months ago (though we have retroactively added DOIs to all older posts too). You can find the persistent link for this post right above this paragraph. Go on, click on it, we’ll wait. Are you back here? Good.

This tutorial shows two ways to accomplish skeletal editing reactions in the RDKit: By using Reaction SMARTS or atom editing. As my collaborator Dr. Phyo Phyo Zin posted in How to Use Skeletal Editing in Drug Discovery: Key Benefits &

My colleague Jim Schombert pointed out a nifty new result published in Nature Astronomy which you probably can’t access so here is a link to what looks to be the preliminary version.

Peter Carruthers reviews Concepts at the Interface, by Nicholas Shea

por Kenia Aubry No hay más ciego y sordo que quien ignora a la literatura. Los horrorismos hallados en los objetos personales, en las huellas de la tortura y la muerte en el rancho Izaguirre, en Teuchitlán, Jalisco, en México, avivan el dolor de la pérdida de cientos de familias mexicanas y, posiblemente, centroamericanas.