Last October, Web of Science (WoS) launched a new service known as the Data Citation Index (DCI). It allows users to track and discover data from tons of research projects. The DCI covers info for datasets from multidisciplinary and international repositories, and is housed within the WoS we already know and love. Researchers comfortable looking up papers in WoS can find related datasets, or vice versa. Collocating research data and papers is great step toward making datasets more discoverable.
Data discoverability through the DCI is good for researchers and librarians. Tracking citations of datasets gives researchers better and more consistent context of work relevant to their own. That enlarges their scope of understanding, reach of influence, and reduces the chances of performing redundant research. By tossing tons of data citations in together, the DCI also creates a context of metadata and attribution facets used for datasets. That helps data librarians create better data management plans for their repositories.
Citation tracking is a great thing for data management, but some crinkles still need to be smoothed out. An evaluative report by the University of Minnesota on their trial of the DCI highlighted some pros and cons. The metadata for different but apparently similar datasets contained equally ambiguous terms, for example. That musses up the sense of proper context and standardization the DCI intends to provide. Datasets themselves, however, are eminently discoverable thanks to WoS’s preexisting search and results refinement functions.
The DCI is notable step for data management and exposes some of its major challenges, such as: reining in variation between metadata, aggregating interdisciplinary repositories, and discoverability.