Open Data – Sharing practices and the role of research contexts

In this short empirical paper, we have argued that more attention needs to be paid to research contexts when considering Open Data practices. We have shown evidence that the coarse disciplinary classifications frequently used, conceal substantial differences in data practices and sharing at a sub-field level. Further, we have shown that these variances have to do with the specific types of data and the specific role of the data in the research process. Here a central characteristic is the relationship of data production and data use. Researchers who are self-sufficient in that they produce the data needed for their own research and do not use data from third parties tend to share their (own) data rather less frequently compared to those who also use data from third parties. Because this association in turn might be entangled with further aspects relating to the type data, it would be worth taking a further look in this relation, which we cannot do here. Despite its increased depth of focus on field differences compared to previous multidisciplinary surveys, this quantitative study is only a first approximation to the concrete research contexts. To advance our understanding of the influence of research conditions on sharing rates, we need to combine quantitative analysis with qualitative studies, and continue to refine survey instruments in order to capture relevant epistemic differences that do not align with formal disciplinary classifications. Our results underline the point made by Gläser, that an important methodological precondition for survey research in order to be relevant for science studies is to move beyond the rigidity of formal discipline-level field classifications and develop instruments that are better suited to capture differences in research conditions. We conclude that in order to develop a deeper understanding of the variations in rates of sharing, future research on Open Data needs to attend to the specific research contexts, their data economies and their underlying systems of epistemic practices