Scientific data is fundamentally distributed: physically, conceptually, and temporally. You can’t situate all the data you’ll ever need, once and for all, in one place. There will always be another source of data. And another. Centralization doesn’t work for large, global enterprises like modern science.

I have written a bit about benefits of FAIR; however, a reader rightly pointed out to me that there is no such thing as a free lunch – what are some of the costs of FAIR? Below is my first stab at a diagram that I hope distinguishes some costs and benefits of FAIR, and how they are related, in the case of building an “inside track” for an existing research-information lifecycle.

Findability is making reuse possible. If no one can discover your data, if even basic metadata is hidden in a silo, then reuse is simply not possible. Accessibility is making reuse plausible. People and their designated software agents can not only identify relevant resources via metadata, but they can actually retrieve full data for inspection and evaluation. Interoperability is making reuse probable.

A format describes what your content “is”; a model describes what your content is “about”. Is-ness issues will affect people’s ability to use your data (it’s not informative if it’s not “in formation”), but about-ness is at the core of analysis. CSV and JSON are formats. SQL and RDF describe models.

The seminal article on FAIR defines interoperability as “the ability of data or tools from non-cooperating resources to integrate or work together with minimal effort.” Is the “non-cooperating” part important here?

A “knowledge line”, aka “K-line”, is a representation of knowledge that connects what we know with how it’s used – we keep each thing we learn close to the agents that learn it in the first place. ¹ When a K-line is re-activated, the agents attached to it are re-activated, putting a system in a “mental state” similar to when this thing we know was last generated, used, and/or persisted.

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.

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.

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.