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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, and verify the version, integrity, and provenance of digital datasets.\cite{national2012For} Anyarchiving  data archiving  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}