Modeling Principles Almost all, if not all, applied science is based on the idea of a model. There are problems in the real world, and we would like to create a theory to solve such problems. One of the pillars or the main foundation of quantitative modeling is its effectiveness. But how can a model be effective? By demonstrating that it is simple, reproducible, and applicable in other areas of knowledge.

If we view biodiversity data as part of the "biodiversity knowledge graph" then specimens are a fairly central feature of that graph. I'm looking at ways to link specimens to sequences, taxa, publications, etc., and doing this across multiple data providers. Here are some rough notes on trying to model this in a simple way.

To (mis)quote Oscar Wilde again, ““To lose one methyl group may be regarded as a misfortune; to lose both looks like carelessness.” Here, I refer to the (past) tendency of molecular modellers to simplify molecular structures. Thus in 1977, quantum molecular modelling, even at the semi-empirical level, was beset by lost groups. One of my early efforts (DOI: 10.1021/ja00465a005) was selected for study because it had nothing left to lose;