Allotrope is used as a semantic framework in context of scientific data. Or is it really? The framework seems to have received awards. But, on deeper inspection, it looks like a hacked out solution, not only does it increase complexity, it also enforces tight coupling of semantics, lacks consistency, laced with bugs, lacks sufficiency in test coverage, and impedes the data lineage steps necessary for integrating with machine learning components for better explainability. The framework looks like a work in progress, lacks clarity, and accessibility of documentation. In fact, the documentation is inaccessible to public unless one becomes a member. It also only supports Java and C#, with no real equivalent Python API. However, an eventual Python API does appear to be in the works but only time will tell when such a solution is available for use. Although, the use of HDF5 as a reference data format is a good choice, the implementation on the whole as a semantic execution platform is not. And, worst of all most of the testing is enforced via SHACL validation using reversed engineering from open-world assumption to a closed-world assumption especially as data can take on multiple dimensions of complexity, not to mention running into the vicious cycle of unmaintainable and unreadable recursive validation cases where there is no clear cut way for requirement elicitation and subsumption testing. Enforcing what and how one validates semantic data is questionable at best. The framework tries to tackle data complexity with more complexity rather than keeping things simple, accessible, and reusable. After all, it is suppose to be an effort in using standards but seems to be applying them in a very opinionated fashion. That is another case in point, the entire framework lacks reusability and a lot of duplication of the work with reinventing the wheel. Data and design patterns are half-hearted and not well baked into the framework. There must be better ways of doing things than to use frameworks that impede productivity and only increase in the frustration of working with semantic data complexity and constraints where inevitably the solution becomes the problem.
