For evolvable data exchange, you need to be able to continually add qualified references galore so that participants can reason by analogy – i.e., each new thing resembles something known before. This is FAIR principle I3, which depends on I1 and I2 for robustness. Subscribe to get short notes like this on Machine-Centric Science delivered to your email. M. Minsky, The Society of Mind . New York: Simon and Schuster, 1986, p.

I’ve been recording introductions to each of the 15 FAIR Principles and releasing them as episodes of my Machine-Centric Science podcast (https://podcast.polyneme.xyz/). I just released the 13th one, featuring an overview of various data and code licenses. Listen here. Full transcript below (but also linked to via the episode landing page): ====== Hello, and welcome to Machine-Centric Science.

Sending signal-signs ¹ to steer engines of compute, the wheel does no work. Subscribe to get short notes like this on Machine-Centric Science delivered to your email.

What is that strange possession that stays the same throughout its life? ¹ Can we recollect how things appeared to us before we learned to link new meanings to those things? What is this body of changelessness in spite of change?

Metadata modeling and formatting are separate concerns. It is reasonable that different scientific domains and studies within domains may have widely varying modeling concerns. Controlled vocabulary terms, validity constraints, and other metadata elements will surely vary and evolve over time. What’s not as obvious is why different scientific domains and studies within domains would have different formatting concerns.

What does “reproducible science” encompass? A tug-of-war Here is one decomposition into “repeatability”, “reproducibility”, and “replicability”: ¹ Here is a conflicting account of the relationship between “replicability” and “reproducibility”: ² It seems that ACM’s “replicability” is Edward Raff’s “reproducibility”, and vice versa.

How can it be that complex, dynamic objects can be described by short and simple strings and words? We often seek: ¹ Selectivity – Our images are often falsely clear. We may think of an object’s “personality” in terms of that which we can easily describe. We may set aside the rest for now as though it simply weren’t there.

We often have sound practical reasons for making choices that have no reasons by themselves but have effects on larger scales. Familiar styles make it easier for us to recognize and classify the things we see. For example, we may choose furniture according to systematic styles or fashions. We protect ourselves from distractions by adopting uniform styles.

Regarding a resource – dataset, model, tool, standard, agent, etc. – as a single thing can be helpful: in allocating physical space, in dealing with privacy and responsibility, in de-confusing mental activity. ¹ Are human mental processes actually clean “streams of consciousness”, or is narrative a tool used to “straighten things out”, to simplify the representation of what happened?

Design is taking things apart in order to be able to put them back together. ² You must design the digital resources you archive and disseminate, so that you don’t “need to dockerize and distribute Robert” (overheard in a Slack room), which, of course, you can’t. Subscribe to get short notes like this on Machine-Centric Science delivered to your email.