A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
VisualRPI: Visualizing Research Productivity and Impact
Research productivity and impact (RPI) is commonly measured through citation analysis, such as the h-index. Despite the popularity and objectivity of this type of method, it is still difficult to effectively compare a number of related researchers in terms of various citation-related statistics at the same time, such as average cites per year/paper, the number of papers/citations, h-index, etc. In this work, we develop a method that employs information visualization technology, and examine its applicability for the assessment of researchers’ RPI. Specifically, our prototype, a visualizing research productivity and impact (VisualRPI) system, is introduced, which is composed of clustering and visualization components. The clustering component hierarchically clusters similar research statistics into the same groups, and the visualization component is used to display the RPI in a clear manner. A case example using information for 85 information systems researchers is used to demonstrate the usefulness of VisualRPI. The results show that this method easily measures the RPI for various performance indicators, such as cites/paper and h-index.
VisualRPI: Visualizing Research Productivity and Impact
Research productivity and impact (RPI) is commonly measured through citation analysis, such as the h-index. Despite the popularity and objectivity of this type of method, it is still difficult to effectively compare a number of related researchers in terms of various citation-related statistics at the same time, such as average cites per year/paper, the number of papers/citations, h-index, etc. In this work, we develop a method that employs information visualization technology, and examine its applicability for the assessment of researchers’ RPI. Specifically, our prototype, a visualizing research productivity and impact (VisualRPI) system, is introduced, which is composed of clustering and visualization components. The clustering component hierarchically clusters similar research statistics into the same groups, and the visualization component is used to display the RPI in a clear manner. A case example using information for 85 information systems researchers is used to demonstrate the usefulness of VisualRPI. The results show that this method easily measures the RPI for various performance indicators, such as cites/paper and h-index.
VisualRPI: Visualizing Research Productivity and Impact
Chihli Hung (author) / Wei-Chao Lin (author)
2022
Article (Journal)
Electronic Resource
Unknown
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