Data Identification, Citation and Tracking Best Practices : a white paper from the observatory best practices/lessons learned series.

Abstract

Data identification and citation are critical for maintaining the standard of reproducibility in science as well as furthering scientific discovery by building off of the works of others. With advances in observation technology and cyberinfrastructure there has been a significant increase in data availability, volume and complexity openly available for scientific research. Often scientists are analysing datasets far too large to be curated by traditional means, i.e. within publications as tables and figures. As such, in order to provide sufficient information for another researcher to be able to access data used in a publication, proper citation of the source of the data and a unique identifier are needed to facilitate traceability back to the data.