What is data citation?

Data citation is the practice of providing a reference to data, in the same way researchers routinely provide a bibliographic reference to outputs such as journal articles, reports and conference papers. Citing data is increasingly being recognised as one of the key practices leading to recognition of data as a primary research output. This is important because:

• when datasets are routinely cited they will achieve greater validity and significance within the scholarly communications cycle

• citation of data enables recognition of scholarly effort with the potential for reward based on data outputs

• the use of data should be appropriately attributed in scholarly outputs as with other types of publication.

Benefits of data citation

Citation of data brings numerous benefits for researchers and institutions. For example:

• Evidence suggests that including citable data in related publications increases the citation rate of those publications

• Routine citation of data will assist in gaining acknowledgement of data as a first class research output

• Citations for published data can be included in CVs along with journal articles, reports and conference papers

• Only citated data can be counted and tracked (in a similar manner to journal articles) to measure impact

Information supplied by ANDS (Australian National Data Service) http://www.ands.edu.au/cite-data/index.html

Data citation standards are evolving and may vary across disciplines and publishers.  However, ANDS has undertaken some work in this area and recommends using one of the following formats:

Creator (Publication Year): Title. Publisher. Identifier

Abraham, Gad. Kowalczyk, Adam. Loi, Sherene. Haviv, Izhak, Zobel, Justin (2011) Five human breast cancer microarray gene expression datasets.  Computer Science and Software Engineering, The University of Melbourne. doi:10.4225/02/4E9F695934393

Creator (Publication Year): Title. Version. Publisher. Resource Type. Identifier

Version (Edition):

Colley, Sarah (2010) Archaeological Fish Bone Images Archive Tables. 1st edition. Sydney. Sydney eScholarship. http://ses.library.usyd.edu.au/handle/2123/6253, Sydney eScholarship Repsoitory

Resource Type:

Abraham, G; Kowalczyk, A; Loi, S; Haviv, I; Zobel, J. (2011) Computational Model for Gene Set Analysis to predict breast cancer prognosis based on microarray gene expression data.  Computer Science and Software Engineering, The University of Melbourne. Computational Model. doi:10.4225/02/4E9F69C011BC8