The reuse of research data is predicated on it being discoverable and accessible to a public audience. One way of achieving this is by citing data in journal articles. The infrastructure that supports the citation of data as a first class research output includes many of the topics that we have already covered in this institute: data publication, persistent identifiers, machine readable metadata, etc.
This session will address the emergence of data citation initiatives in the humanities, arts, and sciences. This includes the motivation for citing data, some principles for encouraging the citation of data in a scholarly communications context, and the practical anatomy of a data citation based on publishers' standards.
Our case study will focus on two institutions that have been at the forefront of data citation initiatives, the National Center for Atmospheric Research, and the Institute for Qualitative Social Science at Harvard University. Both have developed initiatives around citing data, and have partnered with repository developers and scholarly journals to comprehensively provide an infrastructure to facilitate data curation.
Uhlir, P. E. (Ed.). (2012). For Attribution--Developing Data Attribution and Citation Practices and Standards:: Summary of an International Workshop. National Academies Press. PDF
Ball, A. & Duke, M. (2012). ‘How to Cite Datasets and Link to Publications’. DCC How-to Guides. Edinburgh: Digital Curation Centre. Available online: http://www.dcc.ac.uk/resources/how-guides - See more at: http://www.dcc.ac.uk/resources/how-guides/cite-datasets#x1-13000
Task Group on Data Citation Standards and Practices, C. I. (2013). Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data. Data Science Journal, 12(0), CIDCR1-CIDCR75: http://openscholar.mit.edu/sites/default/files/dept/files/outofcite.pdf
Proll, S., & Rauber, A. (2013, October). Scalable data citation in dynamic, large databases: Model and reference implementation. In Big Data, 2013 IEEE International Conference on (pp. 307-312). IEEE.
Mooney, H., & Newton, M. P. (2012). The anatomy of a data citation: discovery, reuse, and credit. Journal of Librarianship and Scholarly Communication, 1(1), 6.
Mayernik, M. (2012). Data citation initiatives and issues. Bulletin of the American Society for Information Science and Technology, 38(5), 23-28. http://www.asis.org/Bulletin/Jun-12/JunJul12_MayernikDataCitation.html
Parsons, M. A., Duerr, R., & Minster, J. B. (2010). Data citation and peer review. Eos, Transactions American Geophysical Union, 91(34), 297-298.
NCAR
Mayernik, M. (2013) NCAR Data Citation Initiative: Updates and Progress. Proceedings of the 2013 AMS annual meeting. https://ams.confex.com/ams/93Annual/webprogram/Paper224528.html
Mayernik, M. (2013) Bridging Data Lifecycles: Tracking Data Use via Data Citations Workshop Report. NCAR: Tech Notes. available here
IQSS - Harvard
Micah Altman and Gary King. (2007). "A Proposed Standard for the Scholarly Citation of Quantitative Data," D-Lib Magazine, Vol. 13, No. 3/4 (March).
Paul E. Uhlir, R., Board on Research Data, Information, Policy, Global Affairs, & National Research Council. (2012). For attribution -- developing data attribution and citation practices and standards: Summary of an international workshop. The National Academies Press.