Using acknowledgement data to characterize funding organizations by the types of research sponsored: the case of robotics research
Funded research has been linked to academic production and performance. While the presence of funding acknowledgements may serve as an indicator of quality to some extent, we still lack tools to evaluate whether funding agencies allocate resources to novel and innovative research rather than mature fields. We address this issue in the present study by using bibliometrics. In particular, we exploit the citation network properties of academic articles to classify specific research fields into four categories: change maker, breakthrough, incremental, and matured. We then use funding acknowledgement information to identify the sponsors involved in each research type to characterize funding agencies. We focus our analysis on the robotics field in order to reveal international trends of financial acknowledgements. We find that the incremental and matured research areas show the highest counts of funding acknowledgements. Moreover, although research funded by some agencies is mostly recognized as incremental-type research, those in other categories may perform better in terms of the number of citations. Additionally, we analyze the interest of selected funding agencies in granular subject categories. The characterization of funding agencies in this study may help policymakers and funding organizations assess or adjust their strategies, benchmark with other key players, and obtain an overview of local and global acknowledgement trends.
KeywordsAcknowledgement analysis Funding analysis Citation network Emerging technology Robotics
Mathematics Subject Classification62H25 91B82
JEL ClassificationC38 C81 D02 O32
The authors thank Tiecheng Jin for collaborating in the name disambiguation task. Part of this research was supported by a scholarship from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
- Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94–111. https://doi.org/10.1016/j.biosystemseng.2016.06.014.CrossRefGoogle Scholar
- Boyack, K. W., & Börner, K. (2003). Indicator-assisted evaluation and funding of research: Visualizing the influence of grants on the number and citation counts of research papers. Journal of the American Society for Information Science and Technology, 54(5), 447–461. https://doi.org/10.1002/asi.10230.CrossRefGoogle Scholar
- Caluwaerts, K., Despraz, J., Işçen, A., Sabelhaus, A. P., Bruce, J., Schrauwen, B., et al. (2014). Design and control of compliant tensegrity robots through simulation and hardware validation. Journal of the Royal Society, Interface, 11(8), 20140520. https://doi.org/10.1098/rsif.2014.0520.CrossRefGoogle Scholar
- Giles, C. L., & Councill, I. G. (2004). Who gets acknowledged: Measuring scientific contributions through automatic acknowledgment indexing. Proceedings of the National Academy of Sciences of the United States of America, 101(51), 17599–17604. https://doi.org/10.1073/pnas.0407743101.CrossRefGoogle Scholar
- Joshi, V. A., Banavar, R. N., & Hippalgaonkar, R. (2010). Design and analysis of a spherical mobile robot. Mechanism and Machine Theory, 45(2), 130–136. https://doi.org/10.1016/j.mechmachtheory.2009.04.003.CrossRefMATHGoogle Scholar
- Kang, S. W., Lee, S. C., Lee, S. H., Lee, K. Y., Jeong, J. J., Lee, Y. S., et al. (2009). Robotic thyroid surgery using a gasless, transaxillary approach and the da Vinci S system: The operative outcomes of 338 consecutive patients. Surgery, 146(6), 1048–1055. https://doi.org/10.1016/j.surg.2009.09.007.CrossRefGoogle Scholar
- Ministry of Economy Trade and Industry of Japan. (2015). New robot strategy. http://www.meti.go.jp/english/press/2015/pdf/0123_01b.pdf.
- National Science Foundation. (2016). A roadmap for US robotics. http://jacobsschool.ucsd.edu/contextualrobotics/docs/rm3-final-rs.pdf.
- Nelson, B. J., Kaliakatsos, I. K., & Abbott, J. J. (2010). Microrobots for minimally invasive medicine. Annual Review of Biomedical Engineering, 12, 55–85. https://doi.org/10.1146/annurev-bioeng-010510-103409.CrossRefGoogle Scholar
- SPARC. (2016). Robotics 2020 multi-annual roadmap for robotics in europe. SPARK the partnership for robotics in Europe and the European commission. https://eu-robotics.net/sparc//wp-content/uploads/2014/05/H2020-Robotics-Multi-Annual-Roadmap-ICT-2016.pdf. Accessed March 30, 2017.
- Takano, Y., Mejia, C., & Kajikawa, Y. (2016). Dynamics of the research classification schema across technologies: Case study of IoT-related technologies. In Y. Fei (Ed.), The first international conference of innovation studies (p. 15). Beijing: Tsinghua University.Google Scholar
- Web of Science. (2008). Funding acknowledgements (online). Clarivate analytics. http://wokinfo.com/products_tools/multidisciplinary/webofscience/fundingsearch/. Accessed March 30, 2017.
- Wolcott, H. N., Fouch, M. J., Hsu, E. R., DiJoseph, L. G., Bernaciak, C. A., Corrigan, J. G., et al. (2016). Modeling time-dependent and -independent indicators to facilitate identification of breakthrough research papers. Scientometrics, 107(2), 807–817. https://doi.org/10.1007/s11192-016-1861-1.CrossRefGoogle Scholar
- Yegros-Yegros, A., & Costas, R. (2013). Analysis of the web of science funding acknowledgement information for the design of indicators on “external funding attraction.” In J. Gorraiz (Ed.), The 14th international society of scientometrics and informetrics conference (Vol. 1, pp. 84–95). Viena, Austria. http://www.scopus.com/inward/record.url?eid=2-s2.0-84896874684&partnerID=40&md5=4f327d10e423a71fa0688fc1e04b6788.