Data citations allow for identification of data used to support a finding and, ideally, the citation itself points to a permanent location where the data is housed. It is important to cite data, similar to journal articles and other publications, to give proper attribution to the owners/authors/producers of datasets.

Data citations typically have six core components:

• Author
• Title
• Date of publication
• Publisher
• Edition or version
• Access information (e.g., URL, DOI, other persistent identifier)

See the Citing Sources guide for general citation guidance. Contact a medical librarian if you have questions.

If you have a digital object identifier (DOI) for a dataset, you can use the DOI Citation Formatter to generate a citation in your desired style. You can use this for a dataset you have found and would like to cite, and you can use it to generate a citation for a dataset you have created as well.

Reuse of data is made easier by overall transparency in the research process. Transparency, in regards to data in this context, refers to the availability of the methods involved in creating, collecting, and analyzing data as well as the accessibility of the findings resulting from the data in question.

This is a sample research workflow with examples of "open" (i.e., free, open source) tools that can be used to facilitate transparency throughout the various stages of a research project. This image was created and made available by Jeroen Bosman and Bianca Kramer as part of the 101 Innovations in Scholarly Communication under a Creative Commons CC-BY license.