Sound, reproducible scholarship rests upon a foundation of robust, accessible data. For this to be so in practice as well as theory, data must be accorded due importance in the practice of scholarship and in the enduring scholarly record. In other words, data should be considered legitimate, citable products of research. Data citation, like the citation of other evidence and sources, is good research practice and is part of the scholarly ecosystem supporting data reuse. In support of this assertion, and to encourage good practice, we offer a set of guiding principles for data within scholarly literature, another dataset, or any other research object.

1 Introduction1.1 The DataCite Consortium1.2 DataCite Community Participation1.3 The Metadata Schema1.4 Version 4.1 Update2 DataCite Metadata Properties2.1 Overview2.2 Citation2.3 DataCite Properties3 XML Example4 XML Schema5 Other DataCite ServicesAppendicesAppendix 1: Controlled List DefinitionsAppendix 2: Earlier Version Update NotesAppendix 3: Standard values for unknown informationAppendix 4: Version 4.1 Changes in support of software citationAppendix 5: FORCE11 Software Citation Principles Mapping

I was pleased to present at IDCC16 on a research paper called The location of the citation: Changing practices in how publications cite original data in the Dryad Digital Repository (see preprint: Mayo et al, (2015)). Recently, organizations including...