Dataverse standardizes the citation of datasets to make it easier for researchers to publish their data and get credit as well as recognition for their work. When you create a dataset in Dataverse, the citation is generated and presented automatically. As an open source framework and research data repository, Dataverse is committed to helping researchers, journals, and organizations make scientific data accessible, reusable, and open (when possible), which includes implementing community accepted standards for data publication (Altman & Crosas 2013). For nearly 20 years, members of IQSS and its Data Science team, who work on Dataverse, have played an active part in the the work to standardize data citation (King 1995, Altman & King 2007, Altman & Crosas 2013). Illustrated in the figure below, is an example of how the data citation is formulated in Dataverse, using the Joint Declaration of Data Citation Principles (2014) : a synthesis of all previously existing principles and initiatives on data citation.
Fig. 1 Example of a Data Citation based on the the Joint Declaration of Data Citation Principles (2014).
In addition to getting recognition with a citation you can also make your particular Dataverse recognizable by setting up your own branding in Dataverse Theme + Widgets.
The citation standard defined here offers proper recognition to authors as well as permanent identification through the use of global, persistent identifiers in place of URLs, which can change frequently. Use of universal numerical fingerprints (UNFs) guarantees to the scholarly community that future researchers will be able to verify that data retrieved is identical to that used in a publication decades earlier, even if it has changed storage media, operating systems, hardware, and statistical program format.
Following are two authentic examples of replication data citations:
From International Studies Quarterly, King and Zeng, 2006, p. 209:
Gary King; Langche Zeng, 2006, “Replication data for: When Can History be Our Guide? The Pitfalls of Counterfactual Inference”, Harvard Dataverse, V2, http://hdl.handle.net/1902.1/DXRXCFAWPK UNF:3:DaYlT6QSX9r0D50ye+tXpA==
From Political Analysis, Hanmer, Banks, and White, 2013:
Hanmer, Michael J.; Banks, Antoine J., White, Ismail K., 2013, “Replication data for: Experiments to Reduce the Over-reporting of Voting: A Pipeline to the Truth”, Harvard Dataverse, V1, http://dx.doi.org/10.7910/DVN/22893 UNF:5:eJOVAjDU0E0jzSQ2bRCg9g==
This citation has seven components. Five are human readable: the author(s), title, year, data repository (or distributor), and version number. Two components are machine-readable:
For information on how to implement the Universal Numerical Fingerprint (UNF), see: