This guide will help you to track and measure the impact of research data, whether your own or that of your department/institution. It provides an overview of the key impact measurement concepts and the services and tools available for measuring impact. After discussing some of the current issues and challenges, it provides some tips on increasing the impact of your own data. This guide should interest researchers and principal investigators working on data-led research, administrators working with research quality assessment submissions, librarians and others helping to track the impact of data within institutions.
See more at: http://www.dcc.ac.uk/resources/how-guides/track-data-impact-metrics#sthash.0iXrTVQQ.dpuf
Received from Martie van Deventer:
Data citation principles:
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.
These principles are the synthesis of work by a number of groups. As we move into the next phase, we welcome your participation and endorsement of these principles.