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Abstract The value of large‐scale collaborations for solving complex problems is widely recognized, but many barriers hinder meaningful authorship for all on the resulting multi‐author publications. Because many professional benefits arise from authorship, much of the literature on this topic has focused on cheating, conflict and effort documentation. However, approaches specifically recognizing and creatively overcoming barriers to meaningful authorship have received little attention.We have developed an inclusive authorship approach arising from 15 years of experience coordinating the publication of over 100 papers arising from a long‐term, international collaboration of hundreds of scientists.This method of sharing a paper initially as a storyboard with clear expectations, assignments and deadlines fosters communication and creates unambiguous opportunities for all authors to contribute intellectually. By documenting contributions through this multi‐step process, this approach ensures meaningful engagement by each author listed on a publication.The perception that co‐authors on large authorship publications have not meaningfully contributed underlies widespread institutional bias against multi‐authored papers, disincentivizing large collaborations despite their widely recognized value for advancing knowledge. Our approach identifies and overcomes key barriers to meaningful contributions, protecting the value of authorship even on massively multi‐authored publications.
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Co-Authorship Maps to Support Leadership Selection
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. Presented at the 2024 Research Analytics Summit in Albuquerque, NM
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- Award ID(s):
- 2324388
- PAR ID:
- 10566916
- Publisher / Repository:
- University of Kentucky Libraries
- Date Published:
- Subject(s) / Keyword(s):
- FOS: Computer and information sciences
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Proceeding:
- https://doi.org/10.13023/VG16-XQ81
