Research data producers need to be rewarded and acknowledged for their work. The readers of your research also need to be able to track the sources you used, including the data. This is why data must be cited properly, just like books or papers. Our resident citations expert Catherine Brendow explains how.
Why should you cite data?
“Data should be considered legitimate, citable products of research. Data citations should be accorded the same importance in the scholarly record as citations of other research objects, such as publications”.
(Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. Martone M. (ed.) San Diego CA: FORCE11; 2014 https://doi.org/10.25490/a97f-egyk)
It is essential to cite data in the same way as other references, with the same level of formalization, in order to encourage authors to make their data reusable by other researchers.
So how should it be done?
Most citation styles, including the Chicago Style or the APA, provide guidance on data citation. It is actually very easy to find guidance, including models and examples. Many dataset repositories, such as on the Harvard Dataverse repository, suggest citation formats, as well as some journals.
When citing data, you should provide the following information:
- Name of the author(s)
- Title of the data set
- Year
- Data repository or archive
- Persistent access information, such as a DOI
That is to say, the usual information you need to give when citing other types of sources. It must give readers easy access to the data so that they can evaluate it for themselves. It must answer the questions: What is it? Where can I find it?
Dataset citation has become so mainstream that a dataset item type has been added to citation managers such as Zotero.
Illustration (cropped and colour-enhanced): “Citation needed” stickers, by Tomasz Finc, published under Creative Commons licence CC BY-SA 3.0
https://commons.wikimedia.org/wiki/File:Citation_needed_stickers.jpeg
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