CASCONet: A Conference dataset

TL;DR

This paper introduces CASCONet, a comprehensive dataset from 25 years of CASCON conference data, enabling analysis of knowledge mobilization and topic evolution in academia-industry collaborations.

Contribution

The paper presents CASCONet, a novel dataset capturing 25 years of conference data, facilitating research on knowledge mobilization mechanisms and their evolution.

Findings

Knowledge domains at CASCON evolved over time.

The dataset enables analysis of R&D topic trends.

CASCONet supports further research in knowledge transfer.

Abstract

Knowledge mobilization and translation describes the process of moving knowledge from research and development (R&D) labs into environments where it can be put to use. There is increasing interest in understanding mechanisms for knowledge mobilization, specifically with respect to academia and industry collaborations. These mechanisms include funding programs, research centers, and conferences, among others. In this paper, we focus on one specific knowledge mobilization mechanism, the CASCON conference, the annual conference of the IBM Centre for Advanced Studies (CAS). The mandate of CAS when it was established in 1990 was to foster collaborative work between the IBM Toronto Lab and university researchers from around the world. The first CAS Conference (CASCON) was held one year after CAS was formed in 1991. The focus of this annual conference was, and continues to be, bringing together academic researchers, industry practitioners, and technology users in a forum for sharing ideas and showcasing the results of the CAS collaborative work. We collected data about CASCON for the past 25 years including information about papers, technology showcase demos, workshops, and keynote presentations. The resulting dataset, called "CASCONet" is available for analysis and integration with related datasets. Using CASCONet, we analyzed interactions between R&D topics and changes in those topics over time. Results of our analysis show how the domain of knowledge being mobilized through CAS had evolved over time. By making CASCONet available to others, we hope that the data can be used in additional ways to understand knowledge mobilization and translation in this unique context.