International
Tables for
Crystallography
Volume G
Definition and exchange of crystallographic data
Edited by S. R. Hall and B. McMahon

International Tables for Crystallography (2006). Vol. G, ch. 3.1, pp. 84-85

Section 3.1.7.4. Parsable data values versus separate data names

B. McMahona*

aInternational Union of Crystallography, 5 Abbey Square, Chester CH1 2HU, England
Correspondence e-mail: bm@iucr.org

3.1.7.4. Parsable data values versus separate data names

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An advantage of defining multiple data names for the individual components of a complicated quantity is that there is no ambiguity in resolving the separate components. Hence the Miller indices of a reflection in the list of diffraction measurements are specified in the core dictionary by the group of three data names _diffrn_refln_index_h, _diffrn_refln_index_k and _diffrn_refln_index_l. In principle, a single data name associated with the group of three values in some well defined format (e.g. comma separated, as h, k, l) could have been defined instead. However, this would require a parser to understand the internal structure of the value so that it could parse out the separate values for h, k and l.

On the other hand, there are many examples of data values that are stored as string values parsable into distinct components. An extreme example is the reference list mentioned in Section 3.1.7.1[link]. More common are dates ( _audit_creation_date), chemical formulae (e.g. _chemical_formula_moiety), symmetry operations ( _symmetry_equiv_pos_as_xyz) or symmetry transformation codes ( _geom_bond_site_symmetry_1). There is no definitive answer as to which approach is preferred in a specific case. In general, the separation of the components of a compound value is preferred when a known application will make use of the separate components individually. For instance, applications may list structure factors according to a number of ordering conventions on individual Miller indices. As an extreme example of separating the components of a compound value, the mmCIF dictionary defines data names for the standard uncertainty values of most of the measurable quantities it describes, while the core dictionary just uses the convention that a standard uncertainty is specified by appending an integer in parentheses to a numeric value.

When compound values are left as parsable strings, the parsing rules for individual data items need to be made known to applications. The DDL1 attribute _type_construct was envisaged as a mechanism for representing the components of a data value with a combination of regular expressions and reference to primitive data items, but this has not been implemented in existing CIF dictionaries (or in dictionary utility software). An alternative approach used in DDL2-based dictionaries defines within the dictionaries a number of extended data types (expressed in regular-expression notation through the attribute _item_type_list.code).

A related problem is how to handle data names that describe an indeterminate number of parameters. For example, in the modulated structures dictionary an extra eight Miller indices are defined to span a reciprocal space of dimension up to 11. In principle, the dimensionality could be extended without limit. According to the practice of defining a unique data name for each modulation dimension, new data names would need to be defined as required to describe higher-dimensional systems. Beyond a certain point this will become unwieldy, as will the set of data names required to describe the n2 components of the W matrix for a modulated structure of dimensionality n ( _cell_subsystem_matrix_W_1_1 etc.).

The modulated structures dictionary was constrained to define extended Miller indices in this way for compatibility with the core dictionary. Data names describing new quantities that are subject to similar unbounded extensibility should perhaps refer to values that are parsable into vector or matrix components of arbitrary dimension.








































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