报告题目：Using Global Overlay Maps and OpenAlex for Responsible Research Assessment

时间：2026年5月27日（星期三）下午14:30-16:45

地点：北京理工大学中关村校区主楼418会议室

报告人：Robin Haunschild 

报告人国籍：德国

报告人职务：Head of Scientific Facility

报告人职称：教授

报告人工作单位：Information Retrieval Service CPT (IVS-CPT), Max Planck Institute for Solid State Research

报告人简介：

Robin Haunschild is a chemist by education and a scientometrician by training. He joined the Central Information Service for the institutes of the chemical, physical, and technical section of the Max Planck Society (IVS-CPT) in 2014 which he is now leading. In 2024, he became Visiting Professor at Peking University. He has published more than 140 papers indexed in Clarivate’s Web of Science. He is an Associate Editor for the journals Scientometrics and Helion, an Academic Editor for PloS ONE, and he serves on the Editorial Boards of Journal of Informetrics, Journal of Data Information and Science, Information, and Metrics.

报告内容简介：

The rapid expansion of scholarly communication demands scalable, interoperable tools that can translate massive bibliometric data into intuitive visual narratives. This presentation introduces a novel suite of global overlay maps built on OpenAlex, designed to illuminate three complementary dimensions of the world’s research ecosystem: (i) amount of research activity across concepts, (ii) research impact across concepts, and (iii) alignment of research output with the United Nations Sustainable Development Goals. Global overlay maps of science are hard to construct. Resulting nodes and clusters are problematic to name when clustering is performed on the individual paper level. Recently, we have proposed an ansatz based on OpenAlex. Thus, the resulting base maps can be freely used for global overlay maps. Six different base maps are provided. Five of them use different time periods. One of them uses a different citation window. Different overlay maps are discussed in two different versions. One version uses raw overlay data. A method is proposed to construct the second version using normalized overlay data. Different focal units are used to present the different maps in order to show the versatility of the approach. The presented maps are discussed with their advantages and shortcomings.

（承办：知识管理与数据分析实验室、科研与学术交流中心）