Predicting highly cited papers: A Method for Early Detection of Candidate Breakthroughs
| Autor: | Laurel L. Haak, Charles J. Hackett, Ilya Ponomarev, Duane E. Williams, Joshua D. Schnell |
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| Rok vydání: | 2014 |
| Předmět: |
Computer science
Early detection Scientometrics Bibliometrics computer.software_genre Data science Management of Technology and Innovation Rare events Science policy Data mining Business and International Management Project portfolio management Citation computer Applied Psychology Technology forecasting |
| Zdroj: | Technological Forecasting and Social Change. 81:49-55 |
| ISSN: | 0040-1625 |
| DOI: | 10.1016/j.techfore.2012.09.017 |
| Popis: | Scientific breakthroughs are rare events, and usually recognized retrospectively. We developed methods for early detection of candidate breakthroughs, based on dynamics of publication citations and used a quantitative approach to identify typical citation patterns of known breakthrough papers and a larger group of highly cited papers. Based on these analyses, we proposed two forecasting models that were validated using statistical methods to derive confidence levels. These findings can be used to inform research portfolio management practices. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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