Researchmap: a Platform for Science of Science Policy
Researchmap: a Platform for Science of Science Policy

Abstract: 

Science of science policy (SoSP) is an emerging interdisciplinary research area that seeks to develop theoretical and empirical models of the scientific enterprise. This scientific basis can be used to help not only policy makers such as governments, but the all stakeholders of science to make better R&D management decisions and understand the importance of investing on science by establishing a scientifically rigorous, quantitative basis from which they may assess the impacts of the nations scientific and engineering enterprise, improve their understanding of its dynamics, and assess the likely outcomes.

No one would doubt that journal papers and top conference papers are important predicators of science activities. According to reliable sources, the accumulated number of journal papers is soon exceeding 100 million. It is becoming more and more difficult to run basic O(nlog n) algorithms such as collation and identification of papers and authors. We need to keep in mind that even the data from well-known data providers and journals cannot be fully trusted: we often witness wrong publication dates or wrong author names even in Scopus.

Researchmap, Japanese SoSP platform, started in 2009, and more than 0.3 million researchers are now using it to manage their research inputs and outputs. It covers most of active researchers and their activities in Japan. We introduced AI algorithms on researchmap to identify researchers’ names, inputs and outputs, and link them to researchers; IDs. We designed AI so that they learn not from the training data built by crowd workers, but from daily researchers; feedbacks. Yes, it is my paper; or ;No, it is not my paper;.

In my talk, I will mention to other global researchers’ identification platforms (i.e. ORCID, Web of Science ResearcherID/ Publons, Google Scholar), and explain what makes researchmap unique.

Bio: 

Noriko Arai is the program director of an AI challenge, Todai Robot Project, which asks the question: Can AI get into the University of Tokyo? The project aims to visualize both the possibilities and the limitation of current AI by setting a concrete goal: a software system that can pass university entrance exams. In 2016, Todai Robot achieved top 20 percent in the exams, and passed more than 70 percent of the universities in Japan. She is also the program director of Researchmap Project to build a platform for researchers to manage their research activities. The inventor of Reading Skill Test, in 2017 Arai conducted a large-scale survey on reading skills of high and junior high school students with Japan's Ministry of Education. The results revealed that more than half of junior high school students fail to comprehend sentences sampled from their textbooks. Arai founded the Research Institute of Science for Education to elucidate why so many students fail to read and how she can support them.