Data Citation

Why data citation?

Data citation means references to data, in the same way researchers routinely provide a bibliographic reference to other scholarly resources. Although data are often shared, they are not always cited the same way as journal articles or other publications. Data citation is important because it facilitates access, transparency and reproducibility, reuse, credit for researchers and visibility for the repositories that share data.

Data repositories play an important role in data citation by linking their data to the articles that cite them. DataCite metadata schema has specific fields that can be populated in order to generate a link between the data and the publication that cites it. For more information, see Contributing Citation and References.

Repositories and metadata harvesters can also consume data citation information via DataCite’s APIs, including Event Data. For more information, see Consuming Citations and References.

Data citation resources

Make Data Count

Make Data Count is a global, community-led initiative focused on the development of open research data assessment metrics, including data usage and data citation. The initiative is led by project partners at California Digital Library, Crossref, DataCite, DataONE, ScholCommLab, University of Ottawa, and ZBW – Leibniz Information Centre for Economics. For information about the initiative and project partners, see

In early 2022, DataCite and Make Data Count collaborated on a webinar series focused on data citation. The recordings are hosted on YouTube:

SCHOLIX: A Framework for Scholarly Link eXchange

The Scholix initiative is a high level interoperability framework for exchanging information about the links between scholarly literature and data. It aims to build an open information ecosystem to understand systematically what data underpins literature and what literature references data.

DataCite Event Data implements a Scholix-compliant interface. To learn more about Scholix, see the Scholix FAQ.

Further reading

  • Cousijn, H., Feeney, P., Lowenberg, D., Presani, E., & Simons, N. (2019). Bringing Citations and Usage Metrics Together to Make Data Count. Data Science Journal, 18(1), 9.
  • Fenner, M., Crosas, M., Grethe, J. S., Kennedy, D., Hermjakob, H., Rocca-Serra, P., Durand, G., Berjon, R., Karcher, S., Martone, M., & Clark, T. (2019). A data citation roadmap for scholarly data repositories. Scientific Data, 6(1), 28.
    • This article presents a practical roadmap for scholarly data repositories to implement data citation in accordance with the Joint Declaration of Data Citation Principles (Data Citation Synthesis Group, 2014), a synopsis and harmonization of the recommendations of major science policy bodies.