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.

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.

Some questions may be pursued circularly, where for example you cannot find a final cause – you must ask, What caused that cause? Or you cannot find an ultimate goal – Then what purpose does that serve? Such loops can waste our time. ¹ It is a form of self-control to establish ways to bottom out, to employ base cases to stop recursion.

We often seek to “straighten out” a maze-like, loop-containing situation. We try to find a “path” through “causal” explanations that go in only one direction. Why? Any directed acyclic graph (DAG) can be linearized, i.e. topologically sorted. And we can apply the same types of reasoning to everything we can represent in terms of chains of causes and effects. We can proceed from start to end without any need for a novel thought.

Who’s the last ventriloquist when it comes to a dataset? You pull data and accrete it with other data, reshape it, and reduce it, and ultimately make it dance and speak via structured representation, software action, narrative exposition, etc. Can someone further contribute to the life journey of that processed data, repurposing your representation, inserting their arm to make an adjustment with a tool of their choice?