Research Data Management

Researchers should cite data when communicating their scholarly or scientific findings in the same way that they cite articles, books, and other sources. Data citation gives credit and attribution to the creator, encourages sharing, collaboration, and re-use, enables verification of research results, and allows for tracking usage and impact. Data takes many forms across academic disciplines. Some of these include:

• Instrument readings
• Spreadsheets
• Data from structured and unstructured interviews
• Survey data
• Genetic sequences
• Textual corpora
• Satellite and geographic data
• Software code
• 3-D Modelling data

Although uniform citation formats have been slow to develop, below are the commonly accepted elements of data citation:

• Author(s) - a person, organization, government agency, or other responsible party
• Title - name given to dataset or the study
• Year of publication - The date when the dataset was made available, either published or released or the last version updated
• Publisher - the data center/repository
• Edition or version
• Access - URL, DOI, or other location information for the data

This guide on data citation from Columbia University offers strategies and resources for citing data.

When citing data for publication, below are a number of places researchers can look for guidance: 

• Journals often have instructions on how to cite data in manuscript submissions.
• Refer to relevant style guides, some of which specifically address data citation. 
• Archives and repositories usually provide a suggested data citation.
• Absent any other guidance, researchers should produce their own data citations using the common elements above. Arrange elements according to the order and punctuation of style being used.