QDataSet

Nominal, Interval, and Ratiometric Data

There should be a section talking about these three data types handled. Stevens identifies four types of data, of which we use three. Nominal data can be thought of as an array of strings, like ['Chicago', 'Paris', 'Beijing'] or [ 'Time', 'Density', 'B-x', 'B-y', 'B-z' ]. Comparisons for equality can be made, but no other operators are allowed. Interval data is defined with a basis. The Celcuis temperature scale is an example, where the number of degrees above freezing is recorded. Comparisons can be made and differences are meaningful. Time locations like CDF_TT2000 and us2000 (the number of microseconds since 2000-01-01T00:00Z) are another example. Last, we have ratiometric data which have a physical zero, such as the heat energy or density. These can also be divided.

• A "simple bundle" is a rank 2 dataset having nominal data for DEPEND_1. Note there is "bundle data set" which allows for bundling complex data, with metadata attached to each bundled data set.

Schemes

QDataSet uses "Duck Typing", which is to say if it looks like a duck, it's a duck. There's no strong typing which asserts that a QDataSet can only be interpreted in some way. The page QDataSet.Schemes attempts to enumerate different types and the properties they have.