Data citation supports attribution, provenance, discovery, provenance, and persistence. It is not (and should not be) sufficient for all of these things, but its an important component. In the last 2 years, there have been several major efforts to standardize data citation practices, build citation infrastructure, and analyze data citation practices.
This session presented as part of the the Program on Information Science seminar series, examines data citation from an information lifecycle approach: what are the use cases, requirements and research opportunities. And the session will also discuss emerging infrastructure and standardization efforts around data citation.
A number of principles have emerged for citation -- the most central is that data citations should be treated consistently with citations to other objects:Data citations should at least provide the minimal core elements expected in other modern citations; should be included in the references section along with citations to other elements; and indexed in the same way.
Adoption of data citation by journals can provide positive and sustainable incentives for more reproducible science and more complete attribution. This would act to brighten the dark matter of science -- revealing connections among evidence bases that are not now visible through citations of articles.