Just as researchers routinely provide a bibliographic reference to sources such as journal articles, reports and conference papers, Data Citation is the practice of providing reference to datasets.
Like traditional bibliographic references, Data Citations acknowledge the original author/creator and help other researchers find the dataset.
Citing data like other sources also increases its validity and significance within the scholarly communications cycle and can be counted and tracked (in a similar manner to journal articles) to measure impact.
Further detailed information and assistance is available from:
Elements of a Data Citation should generally include at least:
By including such a Data Citation, proper acknowledgment is being provided to the originator of the data, verification of the data and results is facilitated, and Data Citation metrics can be compiled and tracked.
DataCite provides more information on suggested citation standards, while recognising that the requirements for citation may vary across disciplines.
Publishing your research data can increase it's visibility, and raise your profile.
There is also some research that suggests making your data openly available leads to an increased citation rate for your literature.
Possingham, Hugh and Watts, Matthew (2013): Marxan software archive. The University of Queensland. Data Collection.
Phinn, Stuart R., Karen Elizabeth Joyce and Roelfsema, Christiaan M. (2012): Airborne hyperspectral image data of Heron Reef, Australia. Pangaea. Dataset. doi:10.1594/PANGAEA.788686. http://dx.doi.org/doi:10.1594/PANGAEA.788686