Though the Sloan Foundation has funded several initiatives to make the citation of data a regular, established practice in science, data citation is itself unidirectional. In a properly cited scientific article, the reader will know what datasets are being referenced and used, but the creator or curator of those cited datasets may have no way to know his or her data is being cited. Yet knowing how a dataset is being used and by whom can be a crucial factor in making decisions about its value, how to extend it, and how to increase its usefulness. This grant supports work by Sayeed Choudhury, associate dean for research data management at Johns Hopkins University, to develop a third-party service called “Matchmaker” that would independently map the relationships between articles and data, linking between existing publishing platforms and data repositories. These relationships could be created by a number of different stakeholders in the scholarly communication process: by a publisher, by a data archive, by an individual researcher, or even by a library. When fully developed, these relationships would then form a "graph" that could be queried without having to repeatedly poll every repository and publisher, a complement to more traditional citation services like ISI or Google Scholar.