Thursday, March 8, 2007

Specifications versus Standards

In a prior blog I suggested 16 ways that SDOs (Standards Development Organizations) can protect their standards from embedded patents. Suggestion 12 was "Make specifications, not standards."

This suggestion, I'm sure, is cryptic to most people. A major theme of this blog site is that specifications are different from standards and have a number of features that make them more suitable than standards for describing and exchanging many types of biomedical information.

Though informaticians often use the terms "specification" and "standard" interchangeably, a specification is just a formal way (usually employing RDF) of describing any data object. A data standard is a set of requirements, created by an SDO, that comprise a pre-determined content and format for a set of data related to a very specific kind of data object.

Features of a "specified" object:

1. Anyone can understand the composition and construction of the object

2. If the object is unique, anyone can distinguish the object from all other objects.

3. If the object falls into a known class of objects, anyone can determine, from the specification, the class of the object.

A specification serves most of the purposes of a standard, and much more (data description, data exchange, data merging, data interoperability, semantic logic). Data specifications spare us most of the heavy baggage that comes with a standard (limited flexibility to include changing data objects, locked-in data descriptors, licensing and other intellectual property issues, competing standards for the same domain resulting in limited interoperability, bureaucratic overhead, etc.).

Readers of this blog might want to read an introduction to RDF data specifications written by myself and Dr. G. William Moore. I believe that standards are important, but that specifications are even more important. There are instances in the field of biomedical informatics where specifications could serve better than standards. This was a developed theme in my book, Biomedical Informatics.I hope to provide many examples of specifications (how they are created and used) in future blogs here.

-Jules Berman
Science is not a collection of facts. Science is what facts teach us; what we can learn about our universe, and ourselves, by deductive thinking. From observations of the night sky, made without the aid of telescopes, we can deduce that the universe is expanding, that the universe is not infinitely old, and why black holes exist. Without resorting to experimentation or mathematical analysis, we can deduce that gravity is a curvature in space-time, that the particles that compose light have no mass, that there is a theoretical limit to the number of different elements in the universe, and that the earth is billions of years old. Likewise, simple observations on animals tell us much about the migration of continents, the evolutionary relationships among classes of animals, why the nuclei of cells contain our genetic material, why certain animals are long-lived, why the gestation period of humans is 9 months, and why some diseases are rare and other diseases are common. In “Armchair Science”, the reader is confronted with 129 scientific mysteries, in cosmology, particle physics, chemistry, biology, and medicine. Beginning with simple observations, step-by-step analyses guide the reader toward solutions that are sometimes startling, and always entertaining. “Armchair Science” is written for general readers who are curious about science, and who want to sharpen their deductive skills.