Classifying scientific performance on a metric-by-metric basis

Eric Bell, Eric Marshall, Ryan Hull, Keith Fligg, Antonio Sanfilippo, Don Daly, Dave Engel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we outline a system for evaluating the performance of scientific research across a number of outcome metrics (e.g. publications, sales, new hires). Our system is designed to classify research performance into a number of metrics, evaluate each metric's performance using only data on other metrics, and to cast predictions of future performance by metric. This study shows how data mining techniques can be used to provide a predictive analytic approach to the management of resources for scientific research.

Original languageEnglish
Title of host publicationProceedings of the 25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25
Pages400-403
Number of pages4
Publication statusPublished - 20 Aug 2012
Event25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25 - Marco Island, FL, United States
Duration: 23 May 201225 May 2012

Publication series

NameProceedings of the 25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25

Conference

Conference25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25
CountryUnited States
CityMarco Island, FL
Period23/5/1225/5/12

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Bell, E., Marshall, E., Hull, R., Fligg, K., Sanfilippo, A., Daly, D., & Engel, D. (2012). Classifying scientific performance on a metric-by-metric basis. In Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25 (pp. 400-403). (Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25).