2020 ESA Annual Meeting (August 3 - 6)

PS 48 Abstract - Data citation: Getting credit for the data you publish!

Corinna Gries, Center for Limnology, University of Wisconsin, Madison, WI, Kristin Vanderbilt, University of New Mexico, Albuquerque, NM, Margaret O'Brien, Marine Science Institute, University of California, Santa Barbara, Santa Barbara, CA and Mark S. Servilla, Biology, University of New Mexico, Albuquerque, NM
Background/Question/Methods

The Environmental Data Initiative (EDI) - a data repository for ecological data - promotes and supports well formed data citations. All data in EDI are publicly accessible and may be reused, combined and analyzed to answer many different research questions. The original data author, however, will only receive credit for their contribution if any resulting publication makes the effort to cite the original dataset. EDI supports citing data by providing a digital object identifier (DOI) for each dataset that is registered with DataCite. This data DOI is comparable to the DOIs assigned to research papers and a suggested data citation for every dataset uses the style recommended by the Earth Science Information Partners community. This style follows international guidelines, but may be modified according to specific journal requirements.

EDI data DOIs are centrally managed and may be traced back to both the dataset and its authors. Thanks to the underlying EDI citation infrastructure and the efforts of many independent groups, data citations may now be automatically added to personal profiles in applications such as Google Scholar.

Results/Conclusions

Properly citing data is highly recommended starting with the original paper publication to increase visibility. Papers with publicly available data have been found to be cited more frequently. Although far from general practice, EDI can trace a general uptick of the use of its data DOIs in publications. If the practice of citing data with a unique identifier becomes the norm, in addition to crediting the creators, it will be possible to address many emerging questions in harnessing the data revolution in ecology. For instance, how much does data reuse contribute to developing new knowledge in ecology? How much do long-term observations contribute to understanding and managing the changing environment?