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.

Doing scientific research is becoming increasingly complex, both in terms of the tools and technologies used, and in the collaboration across disciplines and locations that is increasingly commonplace. While the way we write up and publish research is of course also very different from 25 years ago, I would argue that our tools and services haven’t quite evolved at the same pace.

The standard local file formats for bibliographic data are probably bibtex and RIS. They have been around for a long time, and are supported by all reference managers and many other tools and services.

After the post last week and the crazy discussion that followed I would understand that you feel you have heard enough about citations in markdown. But I had the feeling last week that something was still missing, and I have done some more thinking.