A Data Dictionary, or data codebook, defines and describes the elements of a dataset so that it can be understood and used at a later date. The information typically recorded in a data dictionary might be requested in a different form when submitting your research to a data repository. Explore additional Documentation & Metadata practices.

What is a Data Dictionary?

A data dictionary is a document that outlines the structure, content, and variable definitions for a dataset or collection of data. A data dictionary is a critical tool for reproducibility because it allows others to understand your data.

A good resource to learn more is Open Science Framework's How to Make a Data Dictionary.

The purpose of a data dictionary is to explain what all the variable names and values in your data files (e.g., spreadsheets, images) really mean. You might include things like:

  • Variable names
  • Human-readable variable names
  • Measurement units for the variable
  • Allowed values for the variable
  • Definition of the variable

The term codebook is often used interchangeably with data dictionary, though the data dictionary can contain more information about the structure of a database. For example, in REDCap the data dictionary is a CSV file containing information on the variables and the structure of the REDCap database, while the codebook is a human readable document that provides information on each data element. 

It is useful to create a data dictionary to describe your tabular data and accompany deposit in a repository so that others can interpret and reuse your data.

  • If the data you are describing is tabular, the description could be in a tabular form as well. Data dictionaries can also be created as plain text files. 
  • A shared data dictionary provides precise definitions for specific data elements and ensures that the meaning, relevance, and quality of data elements are the same for all users
  • Adopt and implement existing data standards in your definitions. Use naming conventions across datasets. Utilize naming conventions to further ensure standardization.
  • As new data elements are identified, add them to the data dictionary, and maintain an updated dictionary throughout the project lifecycle. 
  • Another type of supplement file that may be necessary for long-term projects is a crosswalk file that maps variable changes over time. The most common crosswalk for dataset version documentation is a table of the variables and/or questions highlighting the changes from an old version to a new version, or changes over multiple iterations. See examples from the Centers for Medicare & Medicaid Services (PDF) and United States Census Bureau.

Data Dictionary Resources