Section 2.5.3. Definition attributes

Every numerical data item has distinct properties. Consider the number 20 as a measure of temperature in degrees. To understand this number it is essential to know its measurement units. If these are degrees Celsius, one knows the item is in the temperature class, degrees Celsius sub-class, and that a lower enumeration boundary of any value can be specified at −273.15. Such a constraint can be used in data validation. More to the point, without any knowledge of both the class and subclass, a numerical value has no meaning. The number 100 is unusable unless one knows what it is a measure of (e.g. temperature or intensity) and, equally, unless one knows what the units are (e.g. degrees Celsius, Kelvin or Fahrenheit; or electrons or volts).

Knowing the inter-dependency of one data item on another plays a major role in the understanding and validation of data. If a triplet of numbers 5, 3, 0 is identified as Miller indices h, k, l, one immediately appreciates the significance of the index triplet as a vector in reciprocal crystal space. This stipulates that the three numbers form a single non-scalar data item in which the indices are non-associative (e.g. 3, 5, 0 is not equivalent to 5, 3, 0) and irreducible (e.g. the index 3 alone has no meaning). As a reciprocal-space vector, the triplet has other properties if is part of a list of other reciprocal-space data items; namely, its value represents a key or list pointer (i.e. a unique key to a row of items in a list table) to other data items in the list associated with this vector. This means that data forming a `reflection' list are inaccessible if these indices are absent, or invalid if there is more than one occurrence of the same triplet in the list. Such interdependency and relational information is very important to the application of data, and needs to be specified in a dictionary to enable unambiguous access and validation. Other types of data dependencies will be described in Section 2.5.6.