Imagine buying something in a store and having to remain there to use the thing. People want to find relevant data, evaluate whether they can obtain and use it, and then “take it home” to their chosen environment – Excel, MATLAB, R, Python…whatever. They don’t want to have to learn your analytics tool / interface.

Sharing is a way to facilitate concurrency. Collaboration is a way to orchestrate concurrent operations on what is shared. In this sense, the problem of data sharing and collaboration is an in-the-large version of the in-your-programming-language-and-runtime problem of shared memory and concurrent processes/threads.

In my last post, I have discussed the two main types of scientific models: empirical models, also called descriptive models, and explanatory models. I have also emphasized the crucial role of equations and specifications in the formulation of explanatory models. But my description of scientific models in that post left aside a very important aspect: on a more fundamental level, all models are stories.

It is often said that science rests on two pillars, experiment and theory. Which has lead some to propose one or two additional pillars for the computing age: simulation and data analysis. However, the real two pillars of science are observations and models. Observations are the input to science, in the form of numerous but incomplete and imperfect views on reality. Models are the inner state of science.

Many people are asking for my opinion on the recent impressive success of AlphaFold at CASP14, perhaps incorrectly assuming that I am an expert on protein folding. I have actually never done any research in that field, but it's close enough to my research interests that I have closely followed the progress that has been made over the years. Rather than reply to everyone individually, here is a public version of my comments.

There are a number of reasons to punt on power in a design, to go with a worse-is-better style such that you express the design in the least powerful way.

The materials paradigm ¹ may be represented in the form of a tetrahedron ² , illustrating a set of material concerns and how they are interrelated. The materials science tetrahedron, which illustrates how a material's properties, processing, performance, and structure are interrelated (source). What is a “material”? There is no label for it in the diagram above.

Computational reproducibility has become a topic of much debate in recent years. Often that debate is fueled by misunderstandings between scientists from different disciplines, each having different needs and priorities. Moreover, the debate is often framed in terms of specific tools and techniques, in spite of the fact that tools and techniques in computing are often short-lived.

A sequence diagram can serve as a fat-marker sketch of a happy-path checklist for a spike. Let’s unpack that mouthful. First, the fat-marker sketch is an idea well-described in Ryan Singer’s book Shape Up, based on software design practices at Basecamp. It’s a way to roughly shape a two-dimensional layout for a user interface when consideration of those two spatial dimensions is essential to articulate the intended user experience.

This is a brief note on my perception of the ISO 11179 Metadata Registry (MDR) standard, including my perception of its relation to W3C Semantic Web standards. Examples are taken from this Wikipedia article. In ISO 11179, there are concepts . There are relations of concepts to wider and more narrow concepts.