Kai Li

@[email protected]
64 Followers
87 Following
41 Posts
Assistant Professor at School of Information Sciences at the University of Tennessee, Knoxville. #scientometrics and #metadata researcher. Former #librarian.
ORCIDhttps://orcid.org/0000-0002-7264-365X
CVhttp://nalsi.github.io/curriculum_vitae/cv.pdf
Kai LiNov 30, 2023
I am glad to serve as the Chair of
ASIS&T SIG-MET (https://www.asist.org/sig/sigmet/) this year. If you are in the community of #scientometrics, #scisci, and #scholarlycomm, please consider following us! We will have more events coming next year.
SIG-MET

Welcome to SIG Metrics SIG-MET is the Association for Information Science & Technology (ASIS&T) Special Interest Group for the measurement of information production and use. About Our SIG SIG-MET is the Association for Information Science & Technology (ASIS&T) Special Interest Group for the measurement of information production and use. It encourages the development and networking of all those interested in the measurement of…

Association for Information Science and Technology | ASIS&T
Kai LiNov 4, 2023
Personally and professionally, leaving #twitter or #x is harder than I expected. Having a new open, stable, reliable, and popular academic social media is really important.
Kai LiOct 26, 2023
Thrilled to share our new article published in Scientific Data
: "How are exclusively data journals indexed in major scholarly databases? an examination of four databases" collabolared with
@chenyue and Dr. Zhichao Fang. https://rdcu.be/dpq9X
Kai LiOct 19, 2023
Glad to present our preprint entitled "Are research contributions assigned differently under the two contributorship classification systems in PLoS ONE?" (https://arxiv.org/abs/2310.11687) In this article, we examined how author contribution is assigned differently using the two contributorship classification systems used by @PLOS. We did find consistencies in many traditional aspects of contribution, but there are vast differences in writing and resource-related contributions.
Are research contributions assigned differently under the two contributorship classification systems in PLoS ONE?

Contributorship statements have been effective at recording granular author contributions in research articles and have been broadly used to understand how labor is divided across research teams. However, one major limitation in existing empirical studies is that two classification systems have been adopted, especially from its most important data source, journals published by the Public Library of Science (PLoS). This research aims to address this limitation by developing a mapping scheme between the two systems and using it to understand whether there are differences in the assignment of contribution by authors under the two systems. We use all research articles published in PLoS ONE between 2012 to 2020, divided into two five-year publication windows centered by the shift of the classification systems in 2016. Our results show that most tasks (except for writing- and resource-related tasks) are used similarly under the two systems. Moreover, notable differences between how researchers used the two systems are also examined and discussed. This research offers an important foundation for empirical research on division of labor in the future, by enabling a larger dataset that crosses both, and potentially other, classification systems.

arXiv.org
Kai LiAug 9, 2023
Glad to share our new print entitled “Explicit or Implicit Digital Humanities? An Examination of Search Strategies to Retrieve Digital Humanities Publications from Large-Scale Scholarly Databases” (https://osf.io/preprints/socarxiv/k3zfm/). In this manuscript, we examined how different the DH journal publication sample will be if we use keyword search or core journal list. This sheds new light on how to query DH publications from scholarly databases and internal communities within DH.