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In the social sciences, researchers search for information on the Web, but this is most often distributed on different websites, search portals, digital libraries, data archives, and databases. In this work, we present an integrated search system for social science information that allows finding information around research data in a single digital library. Users can search for research data sets, publications, survey variables, questions from questionnaires, survey instruments, and tools. Information items are linked to each other so that users can see, for example, which publications contain data citations to research data. The integration and linking of different kinds of information increase their visibility so that it is easier for researchers to find information for re-use. In a log-based usage study, we found that users search across different information types, that search sessions contain a high rate of positive signals and that link information is often explored.
We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary re
The complexity of emergent wicked problems, such as climate change, culminates in a reformulation of how we think about society and mobilize scientists from various disciplines to seek solutions and perspectives on the problem. From an epistemologica
By combining data from the text, citation, and reference databases with data from the ADS readership logs we have been able to create Second Order Bibliometric Operators, a customizable class of collaborative filters which permits substantially impro
Ocean modelling requires the production of high-fidelity computational meshes upon which to solve the equations of motion. The production of such meshes by hand is often infeasible, considering the complexity of the bathymetry and coastlines. The use
Position bias describes the tendency of users to interact with items on top of a list with higher probability than with items at a lower position in the list, regardless of the items actual relevance. In the domain of recommender systems, particularl