Skip to Main Content

How to Cite

Data: Citation Elements

Most of the time this information wouldn't be included in the dataset itself, but would be located in the item record of the data repository.

Many data repositories provide information about how to cite their products - look closely to see if you can find anything. This is your best bet for relevant information, as the structure of repositories and how they display different elements varies widely.

The most important elements to include are:

Author/Creator - This could either be the personal name of the researcher, or the institution that collected the data.

Title - Include the full title as it appears in the record for the dataset, including table or catalogue numbers if they are provided. If there is more than one title and you want to cite a part within a whole (such as a series within a table, etc), you can include both titles in the same way that you would include other parts within a whole, such as an article within a journal, or a chapter within a book.

Publication date - Most datasets should include some kind of publication date, even if it is hard to find.

Identifier and/or Link - Most published datasets should have some sort of a unique identifier, most commonly a DOI or a URI. This is the most reliable way to identify a particular resource. Many dataset providers will include a permanent URL in addition to or instead of a unique identifier. Link the DOI to the data source if you are working digitally, or include the URL in print.

Other elements that may be good to include:

Edition or Version - This may help to identify your dataset if it is one that undergoes continuous changes.

Resource Type - Include if the style you are using normally includes a resource type

Publisher - This could be the repository where it's located, or whoever has verified the data.

Consult the style manual for your discipline to see how to correctly cite data.

Statistics Canada has a How to Cite Statistics Canada Products guide with examples using APA format. There is also an archived reference building tool that can help you identify which elements to include for a wide range of data and statistics products.