URIs are great for namespacing terms. However, the local names — after the last / — could be a free-for-all. One common convention — but not typically a contract — is to use upper camel case for rdfs:Class instances and lower camel case for rdf:Property instances. Is it useful to systematize the construction of local names? Emily Riederer argues yes.

What is the “killer use case” for Reasoning, i.e., inferencing over a semantic graph database? Data in a relational database “knows nothing.” Data in a graph database “knows how it is connected” – you can ask an entity to tell you who its neighbors are 3 hops out, or if two pieces are connected up to 20 hops. Data with Reasoning “knows how to think.” But what does that mean exactly? What do you get?

I recently came across a procedure for achieving a flow state: One activity at a time. The activity is meaningful to you. You work at the edge of your abilities. I can’t recall the source of this. So, dear reader, please send me something I can cite if you have encountered this 3-step procedure elsewhere. In this post, I relate the above procedure to my recent post on mean-ing.

It is better to have a large variety of firm rulers that feed into selection pressure for utility in some environment, than to have a few flexible ones for which your measurements aren’t consistent. Generate and test. Conjecture and refute. May there be a flowering of crisp attribute and entity specs. A flexible schema is like a rubber ruler.

A traditional postdoc filler seeks to transition to a different role, that of professor or staff. What if someone were to specialize in this kind of role, e.g. for a certain kind of project in a certain kind (e.g. group size) of research environment? Such a position would be akin to the modern \(\approx\) 1-3 yr startup position specialized to a certain growth stage of a startup.

With linked data, then, Consider that in RDF, even literals are objects.

A translucent overlay journal could publish FAIR assets based on existing open-access publications. Typically, an overlay journal is transparent – it is a window to selected already-available content. The value added to the existing body of content is that of curation. However, scholarly digital assets – data, algorithms, tools, workflows, etc. – that support the narratives expressed in many publications are often insufficiently FAIR.

A mean is a central tendency or location. Examples include arithmetic and geometric means. A distribution can have a strong or weak central tendency, that is, a small or large dispersion, respectively.

The DIKW ¹ decomposition expresses a layering of context on top of raw observations. Data points are attributes – components of facts. They are actuals (e.g., measured numbers) that can be connected to contexts. Information is data tied to context, i.e. processed data (“history”) that reduces uncertainty wrt what, when, where, and who.

As the new Omicron variant of concern of COVID-19 emerged over Thanksgiving week (of all times!), attention has been turning to Paxlovid (PF-07321332). If, like me, you torment yourself by perusing conspiracy theories, you might have heard it referred to as Pfizermectin.