An organizational approach for discipline prediction in research projects

Abstract

The prediction of research disciplines has gained increasing attention in recent years due to its potential implementations in a variety of fields, such as academic advising, career counseling, and academic research funding allocation. Research information systems storing projects (meta) data play a crucial role in managing and evaluating research (meta) data across different disciplines and fields of study. In this context, research projects are manually assigned one or more research disciplines to facilitate this process. This is usually done by research administrators due to the limited time the principal researchers themselves might have. In addition to being rather subjective and time-consuming, this can lead to inconsistencies in discipline assignments and hence impact the quality of data used for monitoring and reporting. In this paper, we propose a novel approach to predict the disciplines of research projects in a research information system. The proposed approach uses machine learning algorithms and extracted disciplines from researchers and their related information such as organizations, projects, co-authors on projects, publications, and co-authors on publications.