Data sharing is generally acknowledged as important for science, but associated credit for researchers is lacking. The Scholix framework allows discoverability of connections between published journal articles and related datasets, including information on the connection type, such as cited by, supplementary material or underlying data, thereby providing a possible way to start measuring data sharing and citation.
In this project, we propose to use Scholix to find connections between datasets and journal articles indexed in the Scopus database, and use Plum metrics as well as Scopus citation network to analyze the impact of sharing data in the context of journal publishing.
As a first step, we will normalize our observations based on how Scholix and Scopus coverage of different disciplines may affect citation patterns. Then, we will look at usage and other Plum metrics for selected datasets, evaluating their potential impact in connection to their related articles. Finally, we are building an algorithm aimed at extracting data citations from Scopus article references and compare those citations with Scholix coverage, evaluating general data citation patterns in Scopus articles. This way, we expect to provide a first framework for evaluation of the impact of data sharing based on data citation and alternative metrics.