Mapping Researchers with PeopleMap

TL;DR

PeopleMap is an open-source tool that visualizes researchers' expertise using NLP and Google Scholar profiles, helping to discover research interests and foster collaborations efficiently.

Contribution

This paper introduces PeopleMap, a novel NLP-based visualization tool that simplifies mapping researchers' expertise from Google Scholar data, enhancing research discovery and collaboration.

Findings

Successfully visualizes researcher interests with minimal manual input.

Open-sourced and well-received at Georgia Tech.

Facilitates discovery of research connections and diversity.

Abstract

Discovering research expertise at universities can be a difficult task. Directories routinely become outdated, and few help in visually summarizing researchers' work or supporting the exploration of shared interests among researchers. This results in lost opportunities for both internal and external entities to discover new connections, nurture research collaboration, and explore the diversity of research. To address this problem, at Georgia Tech, we have been developing PeopleMap, an open-source interactive web-based tool that uses natural language processing (NLP) to create visual maps for researchers based on their research interests and publications. Requiring only the researchers' Google Scholar profiles as input, PeopleMap generates and visualizes embeddings for the researchers, significantly reducing the need for manual curation of publication information. To encourage and facilitate easy adoption and extension of PeopleMap, we have open-sourced it under the permissive MIT license at https://github.com/poloclub/people-map. PeopleMap has received positive feedback and enthusiasm for expanding its adoption across Georgia Tech.