Dr. Frédérique Bone

Frédérique Bone (born Lang) is a senior researcher at the emerging technologies department of the Fraunhofer Institute for Systems and Innovation Research ISI in Karlsruhe, Germany, since 2022. She worked on understanding the development of innovative biotech research projects through academic entrepreneurship during her PhD at BETA in the University of Strasbourg, which she completed in 2014. From 2014 to 2022, she worked as a researcher in the Science Policy research Unit (SPRU), in the University of Sussex (UK). There she worked on several projects, looking at research dynamics, technological change in the biomedical sector, and more recently in Artificial Intelligence. In these projects she used both quantitative methods, such as bibliometrics, text mining analysis as well as qualitative methods  (independently or as a mix). In 2021-2022, she did a secondment at a research funder, the Medical Research Council (UK) in order to reflect on the research evaluation practice in their research institutes. At Sussex she also taught courses on research methods and data science for innovation. Since joining Fraunhofer ISI, she is working on projects relating to the transformations of the healthcare system (digital and sustainability transformation) using the lens of transition theory, as well evaluation of research organisations, or technology transfer processes in the bioeconomy. She is currently a associate researcher at SPRU, University of Sussex (UK) and BETA, University of Strasbourg (France). 

    • Bibliometrics
    • Research evaluation
    • Research on Research
    • Science and technology indicator
    • Science and innovation policy
    • Knowledge and technology transfer
    • Bone F.; Sherbon, B. (2024): The role of funders in shaping the UK research landscape. Chapters. In: Alis Oancea; Gemma E. Derrick; Nuzha Nuseibeh; & Xin Xu (eds.): Handbook of Meta-Research, chapter 11, pp. 116-132, Cheltenham, UK: Edward Elgar Publishing.
    • Kanger, L.; Bone, F.; Rotolo, D.; Steinmueller, W. E.; Schot, J. (2022): Deep transitions: A mixed methods study of the historical evolution of mass production”. In: Technological Forecasting and Social Change 177, https://doi.org/10.1016/j.techfore.2022.121491.
    • Coburn, J.; Bone, F.; Hopkins, M. M.; Stirling, A. C.; Mestre-Ferrandiz, J.;  Arapostathis, S.; Llewelyn, M. (2021): Appraising research policy instrument mixes: a multicriteria mapping study in six European countries of diagnostic innovation to manage antimicrobial resistance. In: Research Policy 50 (4), https://doi.org/10.1016/j.respol.2020.104140.
    • Bone, F.; Hopkins, M.; Rafols, I.; Molas-Gallart, J.; Tang, P.; Davey, G.; Carr, T. (2020): DARE to be different? A novel approach for analysing diversity in collaborative research projects. In: Research Evaluation 29 (3), pp. 300-315. DOI: 10.1093/reseval/rvaa006.
    • Bone, F.; Rotolo, D. (2019): Text-Mining Historical Sources to Trace Technical Change: The Case of Mass Production. In: Proceedings of the 17th conference of the international society for scientometrics and informetrics, Book I, pp.437-447.
    • Grassano, N.; Rotolo, D.; Hutton, J.; Lang, F.; Hopkins, M. (2017): Funding Data from Publication Acknowledgements: Coverage, Uses and Limitations. In: Journal of the Association for Information Science and Technology 68 (4), pp. 999-1017. DOI: 10.1002/asi.23737.
    • Lang, F.; Chavarro, D.; Liu, Y.  (2016): Can Automatic Classification Help to Increase Accuracy in Data Collection? In: Journal of Data and Information Science 1 (3), pp. 42-58, DOI: 10.20309/jdis.201619.