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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. 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 bodies in the EU and US have studied this issue, and are continuing to refine technology solutions and best practices. 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: journals:\cite{joint}  \begin{enumerate}  \item To improve the availability and findability of research data, encourage authors of research Papers to deposit researcher validated data in trustworthy and reliable Data Archives.  \item Encourage Data Archives to enable bi-directional linking between Datasets and publications by using established and community endorsed unique persistent identifiers such as database accession codes and Digital Object Identifiers (DOIs). {DOI was approved as ISO Standard 26324:2012 in May 2012}