Will This Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora
This work addresses the challenge of understanding and encouraging translational research for researchers, practitioners, and government agencies, though it is incremental as it builds on existing knowledge transfer research by shifting to concept-level analysis.
The study tackled the problem of predicting which scientific concepts spread from academia to practice by analyzing over 450,000 concepts from 1995-2014 across paper abstracts, patents, and clinical trials, identifying factors that distinguish adopted concepts and achieving high accuracy in prediction.
What kind of basic research ideas are more likely to get applied in practice? There is a long line of research investigating patterns of knowledge transfer, but it generally focuses on documents as the unit of analysis and follow their transfer into practice for a specific scientific domain. Here we study translational research at the level of scientific concepts for all scientific fields. We do this through text mining and predictive modeling using three corpora: 38.6 million paper abstracts, 4 million patent documents, and 0.28 million clinical trials. We extract scientific concepts (i.e., phrases) from corpora as instantiations of "research ideas", create concept-level features as motivated by literature, and then follow the trajectories of over 450,000 new concepts (emerged from 1995-2014) to identify factors that lead only a small proportion of these ideas to be used in inventions and drug trials. Results from our analysis suggest several mechanisms that distinguish which scientific concept will be adopted in practice, and which will not. We also demonstrate that our derived features can be used to explain and predict knowledge transfer with high accuracy. Our work provides greater understanding of knowledge transfer for researchers, practitioners, and government agencies interested in encouraging translational research.