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Addressing the long tail in empirical research data management

At present, efforts are being made to pick up research data as bibliographic artifacts for re-use, transparency and citation. When approaching research data management solutions, it is imperative to consider carefully how filed data can be retrieved and accessed again on the user side. In the field of economics, a large amount of research is based on empirical data, which is often combined from several sources such as data centers, affiliated institutes or self-conducted surveys. Respecting this practice, we motivate and elaborate on techniques for fine-grained referencing of data fragments as to avoid multiple copies of same data archived over and over again, which may result in questionable transparency and difficult curation tasks. In addition, machines should have a deeper understanding of the given data, so that high-quality services can be installed.
 The paper first discusses the challenges of data management for the management of research data as used in empirical research. We conclude a comparison of referencing and copying strategies and reflect on their implications respectively. As a result from this argumentation, we elaborate on a data representation model, which we further examine in regard to considerable extensions. A Generating Model is subsequently introduced to enable citation, transparency and re-use. Eventually, we close with the demonstration of an explorative prototype for data access and investigate a distance metric for assisting in finding similar data sets and evaluating existing compositions.