How should you cite data? A guide to citing data | Author Services

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Citing data

A how-to guide

Are you submitting your article to a Taylor & Francis journal, and do you need to refer to a data set in your article?

In that case, you’ll need to ensure that you cite data appropriately. Taylor & Francis supports the Force 11 Joint Declaration of Citation Principles which recognize data as important, citable products of research, and that data citations should be both understandable by humans and machine-readable (i.e. can be read and processed by a computer).

Why cite data?

Data citation, like the citation of other evidence and sources, is good research practice and is part of the scholarly ecosystem supporting data reuse.

Data are a key output of research and therefore need to be appropriately cited. Just like you’d cite a journal article, figure or conference paper if you used it within your article, you should cite all data.

Citing data is important, because it:

  • ensures you give credit to the individual or group who generated the data
  • helps the reader identify and find the data
  • promotes data reproducibility, enabling others to replicate and verify research results
  • facilitates collaboration for researchers to reuse and build on research findings
  • helps track the impact and reuse of data sets
  • increases the discoverability of your research by enabling anyone reading the article to locate the dataset

All data referenced in articles published by Taylor & Francis should be accompanied with a citation. Many of our journals have data sharing policies which specifically state that authors are, for example, highly encouraged or required to cite data associated with their article.

How should I cite data?

 “Data citation methods should be sufficiently flexible to accommodate the variant practices among communities, but should not differ so much that they compromise interoperability of data citation practices across communities.”

There is no universal referencing style for citing data – this varies across disciplines and journals. However, you need to cite in a clear and consistent way to make the citation useful, enabling the reader to identify and find the data set referenced in your article.

First, check the ‘Instructions for Authors’ page of the journal you’re submitting to for guidance on citing data. For Taylor & Francis journals with data sharing policies, you will find instructions and examples of data citation, all of which adhere to the Force 11 Joint Declaration of Data Citation Principles.

In general, you should always include the following elements in data citations:

  • Author – the individual(s) responsible for the creation of the data
  • Material Designator – the tag “[dataset]”
  • Electronic Retrieval Location – a persistent identifier (e.g. DOI) where this is available
  • Publisher Location – this is often the repository where the author has deposited the data set

This will help the reader identify and find the data set, and ensures you give credit to the individual or group who created the data. You should also include relevent information about your data (such as the DOI and publisher location) in your data availability statement.

Don’t forget: even if you’re referring to your own data set within your article, it’s important that you cite it. Just like citing another person’s data set, you need to acknowledge yourself as the author and tell the reader where the data is located.

See our guidance on data sharing for further information on how to share your data.

Find out more

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