%0 Generic %1 bales2020bibliometric %A Bales, Michael E. %A Wright, Drew N. %A Oxley, Peter R. %A Wheeler, Terrie R. %D 2020 %K bibliometrie mapping software visualisierung %T Bibliometric Visualization and Analysis Software: State of the Art, Workflows, and Best Practices %U https://ecommons.cornell.edu/handle/1813/69597 %X Despite the demonstrated value of visualization-based modalities for measuring and mapping science, it remains common practice to search and explore the literature via databases that present lists of articles with little, if any, supplementary visual information. Identifying the desired item in a list is a familiar information retrieval paradigm with a low cognitive load. However, given the rapid emergence of the field of visual text analytics, it is time to challenge the notion that article lists should remain the dominant method to search and organize the scientific literature. One reason that visualization methods are applied relatively rarely in information retrieval may be that it is difficult to develop useful and user-friendly science mapping systems. This article summarizes key workflows for bibliometric mapping, a technique for visually representing information from scientific publications, including citation data, bibliographic metadata, and article content. It describes methods and challenges in extracting, processing, and normalizing data, reducing dimensionality, modeling topics, assigning labels, and visualizing data. It also describes software tools available to support bibliometric analysis and science mapping workflows, outlines methods from other domains that have not been widely applied in bibliometric mapping, and considers opportunities for next generation bibliometric analysis and mapping software systems.

@misc{bales2020bibliometric, abstract = {Despite the demonstrated value of visualization-based modalities for measuring and mapping science, it remains common practice to search and explore the literature via databases that present lists of articles with little, if any, supplementary visual information. Identifying the desired item in a list is a familiar information retrieval paradigm with a low cognitive load. However, given the rapid emergence of the field of visual text analytics, it is time to challenge the notion that article lists should remain the dominant method to search and organize the scientific literature. One reason that visualization methods are applied relatively rarely in information retrieval may be that it is difficult to develop useful and user-friendly science mapping systems. This article summarizes key workflows for bibliometric mapping, a technique for visually representing information from scientific publications, including citation data, bibliographic metadata, and article content. It describes methods and challenges in extracting, processing, and normalizing data, reducing dimensionality, modeling topics, assigning labels, and visualizing data. It also describes software tools available to support bibliometric analysis and science mapping workflows, outlines methods from other domains that have not been widely applied in bibliometric mapping, and considers opportunities for next generation bibliometric analysis and mapping software systems.}, added-at = {2020-01-30T12:33:35.000+0100}, author = {Bales, Michael E. and Wright, Drew N. and Oxley, Peter R. and Wheeler, Terrie R.}, biburl = {https://www.bibsonomy.org/bibtex/2ebb1a25462c0057418288c887b6679b2/wdees}, description = {Bibliometric Visualization and Analysis Software: State of the Art, Workflows, and Best Practices}, id = {https://hdl.handle.net/1813/69597}, interhash = {a2143a1d620404afc6d858be45435723}, intrahash = {ebb1a25462c0057418288c887b6679b2}, keywords = {bibliometrie mapping software visualisierung}, timestamp = {2020-01-30T12:33:35.000+0100}, title = {Bibliometric Visualization and Analysis Software: State of the Art, Workflows, and Best Practices}, type = {preprint}, url = {https://ecommons.cornell.edu/handle/1813/69597}, year = 2020 }