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\subsection{Data Citation and Attribution}  Well developed and uniform data citation standards are required to ensure linkages between publications and datasets are enduring and that creators of digital datasets receive appropriate credit when their data are used by others. Standards for data citation practices and implementation provide the mechanism by which digital datasets can be reliably discovered and retrieved. Closely related to data citation, other challenges include the ability to reliably identify, locate, access, interpret interpret,  and verify the version, integrity, and provenance of digital datasets.\cite{national2012For} Any archiving data policy must concern itself not only with how manuscripts should appropriately cite the datasets used, but must also require attribution to authors of datasets outside the document. Numerous organizations in the EU and US have studied this issue, and are continuing to refine technology solutions and best practices. These transnational initiatives have recently coalesced to produce a unified Joint Declaration of Data Citation Principles that is appropriate for any type of technical publication.\cite{FORCE11} The eight principles define the purpose, function, and attributes of data citations and address the need for citations to be both understood by humans and processed by machines. With a slightly different perspective focused more on the mechanics of linking published articles with data repositories, DataCite and the International Association of Scientific, Technical and Medical Publishers have issued a joint statement recommending best practices for citation of technical datasets in journals:\cite{joint}   \begin{enumerate} 
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\item Support the principle of data reuse and for this purpose actively participate in initiatives for best practice recommendations for the citation of datasets.  \item Invite other organizations involved in research data management to join and support this statement.  \end{enumerate}  An outstanding technical issue yet to be resolved is the granularity of dataset the datasets  used in a publication, both spatially and temporally. Spatial granularity refers to a subset of the dataset used in the research. Temporal granularity can refers refer  to either the version of the dataset used, or the temporal state of the dataset used if the dataset itself is dynamic. CODATA and the National Academy of Sciences have released a new international study and recommendations on citation of technical data.\cite{CODATA} While further recommendations are provided in this report, further work and coordination amongst organizations is yet required.