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This paper aims to present a collaborative mashup platform for dynamic integration of heterogeneous data sources. The platform encourages sharing and connects data publishers, integrators, developers and end users.

This approach is based on a visual programming paradigm and follows three fundamental principles: openness, connectedness and reusability. The platform is based on semantic Web technologies and the concept of linked widgets, i.e. semantic modules that allow users to access, integrate and visualize data in a creative and collaborative manner.

The platform can effectively tackle data integration challenges by allowing users to explore relevant data sources for different contexts, tackling the data heterogeneity problem and facilitating automatic data integration, easing data integration via simple operations and fostering reusability of data processing tasks.

This research has focused exclusively on conceptual and technical aspects so far; a comprehensive user study, extensive performance and scalability testing is left for future work.

A key contribution of this paper is the concept of distributed mashups. These ad hoc data integration applications allow users to perform data processing tasks in a collaborative and distributed manner simultaneously on multiple devices. This approach requires no server infrastructure to upload data, but rather allows each user to keep control over their data and expose only relevant subsets. Distributed mashups can run persistently in the background and are hence ideal for real-time data monitoring or data streaming use cases. Furthermore, we introduce automatic mashup composition as an innovative approach based on an explicit semantic widget model.

A previous paper by the present author described the pros and cons of using the three largest cited reference enhanced multidisciplinary databases and discussed and illustrated in general how the theoretically sound idea of the h‐index may become distorted depending on the software and the content of the database(s) used, and the searchers' skill and knowledge of the database features. The aim of this paper is to focus on Google Scholar (GS), from the perspective of calculating the h‐index for individuals and journals.

A desk‐based approach to data collection is used and critical commentary is added.

The paper shows that effective corroboration of the h‐index and its two component indicators can be done only on persons and journals with which a researcher is intimately familiar. Corroborative tests must be done in every database for important research.

The paper highlights the very time‐consuming process of corroborating data, tracing and counting valid citations and points out GS's unscholarly and irresponsible handling of data.