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Citing Sources

Referencing, avoiding plagiarism, and presentation of the Chicago Style

Why is Data Citation Important?

Making research data open and accessible to other researchers, so that they can reuse the data and verify the results has become an important issue. Dataset citation allows data creators to receive credit and recognition for their work and rewards them for sharing their data. It also allows readers to know where they can access the data on which a claim depends, so that they can evaluate it for themselves.

What Belongs in a Data Citation?

More or less the same information as in the citation of other sources:

  • Name of the author
  • Title of the dataset
  • Year of publication
  • Data repository or archive
  • A persistent identifier, such as a DOI
  • If not clear from the rest of the citation, an indication of the type of source (e.g. "dataset").

Many dataset repositories offer suggested citations.

Principles and Examples

Example (Chicago Style, bibliography):

Hellmüller, Sara, Chiara Lanfranchi, Margaux Pinaud and Xiang-Yun Rosalind Tan, "United Nations Peace Mission Mandates (UNPMM) dataset", version 2.1, 2023, https://peacemissions.info.

Example (Dataverse suggestion):

Badache, Fanny; Hellmüller, Sara; Salayme, Bilal, 2022, "United Nations Security Council peace-related speeches (UNSCPeaS)", https://doi.org/10.7910/DVN/0OROVZ, Harvard Dataverse, V1