ResearchFlow: Understanding the Knowledge Flow between Academia and Industry

ABSTRACT: Understanding, monitoring, and predicting the flow of knowledge between academia and industry is of critical importance for a variety of stakeholders, including researchers, policymakers, institutional funding bodies, companies operating in the innovation space and others. To this purpose, we introduce ResearchFlow, an approach for quantifying the evolution of research topics across academia and industry. ResearchFlow integrates data from publication, patents, and organizations in order to characterize each topic according to its frequency in time of i) publications in academia, ii) publications in industry, iii) patents in academia, and iv) patents in industry. This representation is then used to produce several analytics regarding the academia/industry knowledge flow and to forecast the impact of research topics on industry. We applied ResearchFlow to a dataset of 7M papers and 2M patents in Computer Science and report several patterns that suggest a very coherent configuration of the academia/industry knowledge flow. Specifically, we found that the industry sector seems to anticipate academia for the average topic by about two years, and that their research papers anticipate patents of about three years. We evaluated ResearchFlow on the task of forecasting the impact of a topic and found that its granular characterization the topic evolution improves significantly the performance with respect to alternative solutions.


In this page you can download all data regarding the evaluation of ResearchFlow (POE), described in the paper:
Salatino, A.A., Osborne, F., Motta, E.: ResearchFlow: Understanding the Knowledge Flow between Academia and Industry. Submitted to TPDL 2020.

ResearchFlow integrates data from publication, patents, and organizations in order to characterise each topic according to its frequency in time of i) publications in academia, ii) publications in industry, iii) patents in academia, and iv) patents in industry. This representation is then used to produce several analytics regarding the academia/industry knowledge flow and to forecast the impact of research topics on industry.

We evaluated ResearchFlow on the task of forecasting the impact of a topic and found that its granular characterisation the topic evolution improves significantly the performance with respect to alternative solutions.

For any question about ResearchFlow and the evaluation please contact angelo.salatino@open.ac.uk or francesco.osborne@open.ac.uk.