Research Analytics Summit 2024
Archived
This content is available here strictly for research, reference, and/or recordkeeping and as such it may not be fully accessible. If you work or study at University of Kentucky and would like to request an accessible version, please use the SensusAccess Document Converter.
Document Type
Presentation
Abstract
VOSViewer co-authorship mapping is a powerful tool typically used for analyzing research collaboration. Users provide publication data and VOSViewer produces a map where authors are plotted on a 2-dimensional map based on how often they are in the author lists of the same publication.
In this presentation, I propose a series of tweaks to the input data that can leverage co-authorship maps to support leadership selection based on how often candidates co-author papers with their institutional peers and some of the attributes of these papers. I will suggest how best to interpret the resulting maps and address the major assumptions that must be kept in mind when using these maps for this purpose. Lastly, I will discuss the lessons learned when we offered such maps to support a series of internal leadership selections for Canada’s largest research hospital.
DOI
https://doi.org/10.13023/VG16-XQ81
Publication Date
2024
Recommended Citation
Chen, Robert HC, "Co-Authorship Maps to Support Leadership Selection" (2024). Research Analytics Summit 2024. 14.
https://uknowledge.uky.edu/research_events2/14
