The motivation to cite datasets arises from a recognition that data generated in the course of research are just as valuable to the ongoing academic discourse as papers and monographs. Scientific journals have traditionally supported research by disseminating knowledge in such detail that first, peer scientists could judge the strength of the conclusions based on the quality of the premises and research methods employed, and second, further investigations could be based upon it. In many disciplines, though, the paper alone is no longer sufficient for these purposes: the underlying data also need to be shared. As a medium, the journal paper owes its success in part to the control systems put in place around it: mechanisms allowing authors to be open about their research while still receiving due credit; metrics used to translate such attributions into rewards for authors and their institutions; and archives ensuring that the work is permanently available for reference and reuse. If datasets are to be regarded as first-class records of research, as they need to be, a similar set of control systems needs to be constructed around them. A major part of this work can be achieved using a robust citation mechanism for referencing datasets from within traditional publications. Provided the citation contains the name of a responsible agent, it can be used to assign due credit. By providing a globally unique identifier, it can be used to track the impact of a particular dataset. A citation is also an ideal place to provide the information needed to locate and access the dataset. In this way, datasets can take advantage of the infrastructure already in place to manage journal papers. The rise of electronic journals has led to new and valuable services being layered over the top of papers, among them the provision of forward links to papers citing the current one. Such links help the reader to gauge the impact of the paper, place it within the literature and in some cases gain awareness of flaws or issues discovered by others. Forward links from datasets to the papers that cite them provide all the same benefits, as well as ensuring that documentation for the dataset can be found. Ultimately, bibliographic links between datasets and papers are a necessary step if the culture of the scientific and research community as a whole is to shift towards data sharing, increasing the rapidity and transparency with which science advances.