Assistant Professor Kai Li is an experienced information scientist and scholar who has worked for libraries and companies worldwide, in addition to higher education institutions. Li obtained a bachelor’s degree in history from Beijing Normal University, a master’s degree in information science from Syracuse University, and a PhD from Drexel University. During his studies, he developed a fascination with data analysis.
Before coming to UT in 2023, Li worked at a public library in Beijing, China, and as a cataloger at Ingram Content Group.
He said, “Through those jobs, I realized the importance of quality data, especially metadata, and what data can help people do.”
Li said there aren’t many differences between working in library sciences in the United States and working in China.
“The nature of the job isn’t that different. Working in a library requires one to have a specific type of task, especially as a cataloger. We follow very similar rules. In China, there are different catalog standards, of course. But the fact that we analyze the book and record metadata and that metadata will be put into a catalog for people to use—all those things are quite similar,” he explained.
Li has focused his research on the publication of data papers, which is a relatively new topic that has only emerged in the past decade. His most recent work examines how research data is published and used to shape scientific progress.
“There is a significant push for scientists from all research fields to pay more attention to research data. They should consider how to publish and curate their data because publishing the data can bring them a lot of benefits. Understanding how our research data is published, cited, and used is a major research interest for me,” he said.
Traditionally, researchers publish research articles that describe their research and the results obtained. These articles undergo a peer-review process before being accepted for publication. The research design—which includes the methodologies and processes used to gather data—is an essential element of the article, and researchers need to interpret the results in their publications. These scientific publications contribute to the body of knowledge that builds on previous research. However, data papers are different.
“It is still peer-reviewed. But in a data paper, one doesn’t need to explain the research design, but explains how a data set is collected and processed,” he said.
One significant limitation that hinders people from analyzing data papers is the lack of knowledge about the comprehensiveness of data papers included in databases. This issue is especially true in the field of scientometrics, where researchers need to rely on metadata to retrieve data on specific topics. Therefore, the importance of Li’s research lies in providing a solution that enables researchers to use and retrieve data about particular topics effectively.
Li has been teaching an information organization and retrieval course along with an introduction to data analytics and visualization course.
“Using data, analyzing data—and especially using quantitative methods—is part of my academic training. The classes are really about the various analytical methods that students can use to acquire knowledge from data,” he stated.
Li said that, by the time students take his courses, they understand the significance of data metrics and analytics in information sciences and the gaps between the library system and the external landscape of how data and metadata are created and shared.
“I understand that the community feels a sense of urgency as we are falling behind the latest advances. Cataloging is a notable example as the existing library standards have been in place for decades. As a result, our data cannot communicate with the larger technological infrastructure. So, it is still important to update our standards as there is more information and data available. We also need to update our technical services to keep up with the demands of users,” he said.
Li has shared his research at two conferences in the past year. The first was at The International Society for Scientometrics and Informetrics (ISSI) conference in Bloomington, Indiana. The second was at the International Conference on the Science of Science and Innovation (ICSSI) in Chicago.