Learn about the decisions you need to make before creating a data dictionary and the tools that might help. Explore examples of data dictionaries published by other government organisations.
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Before you go about making a data dictionary for each specific dataset, you have a few things to think about:
The answers to those questions will help you decide on the level of detail that you will need to include in your data dictionary.
We have divided our examples into three levels: no data dictionary, basic, and comprehensive. These levels have been made up by us for the purpose of showing you how different aims, audience needs, and data complexities can require different levels of detail in your data dictionary.
Some data doesn’t need detailed information to make it findable and useable. In these cases, there is no need for a data dictionary. For instance, columns or content may obvious to those that want to use the data.
The data about DOC huts published by the Department of Conservation is a good example. Their answers to the planning questions mentioned above might be:
The columns or values in your data could be hard to understand, but the data could be easy for your audience to find.
In these situations, you may only need a basic data dictionary. In that dictionary, you might include a description of the data, a definition of the column headers, and the codes used as values in the columns.
The motor vehicle registry open data dictionary, published by Waka Kotahi - NZTA, is a good example of a basic data dictionary. Their answers to the planning questions mentioned above might be:
The New Zealand Vehicle Fleet Open Data
Motor vehicle registry open data dictionary [CSV 9 KB]
Basic data dictionaries are good in many situations. There are datasets or situations in which basic data dictionaries just aren't enough, for instance:
In these situations, you may need to publish comprehensive data dictionaries that describe every detail of the data.
The data dictionary, published by Stats NZ, on the Consumers Price Index is a good example. In the context of this nationally significant dataset, the answers to the planning questions mentioned above might be:
Stats NZ Consumers Price Index data dictionary
You can make a data dictionary using many software products. Some are free, while others cost. Some are simple, easy to begin with, and will work well for basic dictionaries. Others are comprehensive, hard to master, but provide powerful benefits for those who have complex needs.
Data.govt.nz does not endorse any one product above the other. But, we wish to give you a good idea of what is out there and suited to your needs.
If your data is easy to understand and only needs a simple data dictionary, Microsoft Office products might suit you. You can make a data dictionary in Microsoft Excel or Microsoft Word. The following two links provide good basic templates.
Data dictionary template for Microsoft Excel [CSV, 1 KB]
Data dictionary basic template from the USDA
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The next option is to use the Colectica plug-in for Excel. This option might suit those producing basic data dictionaries for some but not all of their datasets.
Colectica for Excel
Using Colectica in Excel
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For more detailed data dictionaries, you can try Dataverse or the full version of Colectica. These tools are powerful because they will standardise the metadata you include, link the concepts or variables across datasets, populate a data dictionary, improve findability, and producing data dictionaries.
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If you’d like more information, have a question, or want to provide feedback, email datalead@stats.govt.nz.
Content last reviewed 11 January 2021.