Towards a Recommender System for Statistical Research Data
Daniel Bahls, Guido Scherp, Klaus Tochtermann, Wilhelm Hasselbring · ZBW Publication Archive (ZBW – Leibniz Information Centre for Economics) · 2012
Abstract. To effectively promote the exchange of scientific data, retrieval services are required to suit the needs of the research community. A large amount of research in the field of economics is based on statistical data, which is often drawn from external sources like data agencies, statistical offices or affiliated institutes. Since producing such data for a particular research question is expensive in time and money—if possible at all — research activities are often influenced by the availability of suitable data. Researchers choose or adjust their questions, so that the empirical foundation to support their results is given. As a consequence, researchers look out and poll for newly available data in all sorts of directions due to a lacking information infrastructure for this domain. This circumstance and a recent report from the High Level Expert Group on Scientific Data motivate recommendation and notification services for research data sets. In this paper, we elaborate on a case-based recommender system for statistical data, which allows for precise query specification. We discuss required similarity measures on the basis of cross-domain code lists and propose a system architecture. To address the problem of continuous polling, we elaborate on a notification service to inform researchers on newly avaible data sets based on their personal request.