Collaborating to Automate Big Data Cleaning: An Example Using Local Bibliometric Data

Abstract

This session will showcase a unique local collaboration between the Library, the Office of Research, and Institutional Analysis and Planning to support a project involving large amounts of complex data. The highly collaborative approach of this partnership made it possible to automate key data processes of an internal project which ultimately built valuable relationships between key campus units. In the academic environment, one common way to measure research productivity is by using counts of publications and their citations; often called bibliometric data. The University of Waterloo recognizes bibliometric data as an important piece of evidence-based research assessment, and recommends it as one measure, among many, for capturing research productivity trends, and elements of research impact. Centered on an example involving local bibliometric data, this session will introduce the relevance of this type of data to the University, and how leveraging the expertise and knowledge of others created a better final product, saved ~200 hours of manual work, and created a strong foundation for supporting similar projects. This collaborative framework has made it possible to support the integrity of local big data—a key step in supporting this and similar in-demand analyses at the University.