2025-08-26
Science and Technology Literature for the Past, Present, and the Future
科技文獻的過去、現在與未來/Science and Technology Literature for the Past, Present, and the Future
This course is designed to provide practical training in the analysis, retrieval, and interpretation of scientific and technological literature, examining its past, present, and future. In the past, many scholars have employed bibliometric methods to study the academic development of specific research fields. Such approaches make it possible to trace the evolution of knowledge and scholarly communities through publication timelines, offering valuable contextual analyses for policymakers in science and technology. Bibliometric analysis of research activities can be applied in three main areas: describing the current state, evaluation and assessment, and long-term monitoring. By systematically processing large-scale journal publication data and aggregate statistics, bibliometric methods can be applied at multiple levels: the macro level (regions and nations), the meso level (research programs, institutions, and disciplines), and the micro level (individual researchers). In terms of purpose, bibliometric analysis also serves as a complementary tool to peer review. When examining national trends in science and technology development, it enables the handling of vast datasets, statistical analysis, and periodic monitoring of the growth of a country or field of knowledge. To capture fully the dynamics and contexts of technological development, it is essential to establish a structured framework for the classification, tagging, annotation, and summarization of scientific and technological literature. This framework facilitates subsequent retrieval, reading, and tracking, while uncovering valuable information hidden within massive bodies of literature and identifying previously unrecognized research issues.
This course will feature Dr. Yasutomo Takano, a researcher at the University of Tokyo, who will deliver remote lectures on emerging trends in the use of scientific and technological literature. Drawing on his work in artificial intelligence applications, Dr. Takano will demonstrate how technological literature can be transformed into intermediary data, thereby showcasing potential avenues for enhanced utilization and value-added research applications. Dr. Takano is a co-founder of Paper Digest and CEO of JIYU Laboratories, Inc. (https://jiyu-labs.com/), a company specializing in the analysis of scientific literature for the Japanese government. Paper Digest employs artificial intelligence to generate concise and precise summaries of open-access scientific articles, typically around 300 words, covering the key sections: “Introduction and Objective,” “Results,” and “Discussion and Conclusions.” Its mission is to reduce the time required to read each article to approximately three minutes. Currently, the Paper Digest platform offers an online trial service: by entering the DOI of any open-access publication, users can instantly generate a summary. Since literature review is a crucial part of scientific research, Paper Digest helps researchers enhance the efficiency of their literature surveys, thereby fostering greater diversity and innovation in their scientific work.
Dr. Takano received his Ph.D. from the Tokyo Institute of Technology, specializing in bibliometrics, artificial intelligence, and science and technology policy. After graduation, he was invited to join the University of Tokyo’s Institute for Future Initiatives (IFI, https://ifi.u-tokyo.ac.jp/en/), where he worked on projects to align the university’s research achievements with future industrial applications. As a co-founder of Paper Digest (https://www.paper-digest.com/), Dr. Takano has spearheaded efforts to apply AI to the summarization of global scientific literature, creating a convenient platform for researchers to read academic papers efficiently. By enabling summaries that can be read within three minutes, Paper Digest has successfully commercialized rapid literature summarization services. In 2018, the project was awarded the Catalyst Grant by Digital Science, further affirming the value and market recognition of its AI-based approach to summarizing scientific literature in Japan.