A data dictionary is a collection of names, definitions, and attributes about data elements that are being used or captured in a database, information system, or part of a research project. It describes the meanings and purposes of data elements within the context of a project and provides guidance on interpretation, accepted meanings, and representation. A data dictionary also provides metadata about data elements, which can assist in defining the scope and characteristics of data elements, as well as the rules for their usage and application.
A data dictionary can be used to catalog and communicate the structure and content of data, and provides meaningful descriptions for individually named data objects. It can be used to fill in entity and attribute sections or feature catalogs of formal metadata. The specific components of a data dictionary can vary, but they typically take the form of various types of metadata, such as data object listings, data element properties, entity relationship diagrams, system-level diagrams, reference data, missing data and quality indicator codes, and business rules for validation of data quality and schema objects.
Data dictionaries are useful for a number of reasons. They assist in avoiding data inconsistencies across a project, help define conventions that are to be used across a project, provide consistency in the collection and use of data across multiple members of a research team, make data easier to analyze, and enforce the use of data standards. By using standards, researchers in the same disciplines will know that the way their data are being collected and described will be the same across different projects. Using data standards as part of a well-crafted data dictionary can help increase the usability of research data and ensure that data will be recognizable and usable beyond the immediate research team.
