One of the major factors underlying the rapid rise of altmetrics is that the web has enabled research outputs to be more diverse. The measure of research is no longer solely the publication – they are becoming research “products” (so much so that NSF now requires products rather than publications to be listed on their grant applications). The data set is one of the strongest examples in that research product category – yet finding, and citing, data is still cumbersome at best.
So when we had a Thomson Reuters customer education product specialist give a webinar to our library (among others), I jumped at the chance to learn more about their Data Citation Index (DCI).
I personally have not used DCI yet – but from what I can tell it is a souped-up federated search. It lets you search for datasets across different repositories (institutional and broader ones such as Figshare) as well as across different disciplines. Another added value feature is that DCI then links out to the actual research publications that are based on these datasets.
The rationale for DCI is quite clear – having data sets live in individual repositories, which have varying search structures – in effect, creating data silos. DCI aims to transcend those barriers.
Being the librarian that I am, I also love the DCI “suggested citation” feature. There is no current set of best practices when it comes to attributing data sets, but attribution is crucial nonetheless. Whenever you look at a record within DCI, each record has a “How to Cite this Resource” button that will generate something like this:
You can also set citation alerts to be notified whenever a publication (within Web of Knowledge) cites a specific data set.
University of Michigan affiliated readers should feel free to explore this resource, and if you have any questions you can always contact us at the Taubman Health Sciences Library.