"PML’s scientists are working tirelessly to explore and unlock the ocean’s secrets. My role is to help access the data that drives this work while maximising the impact of emerging data to provide insights, accelerate collaboration and improve decision making."
Tom is our Data Systems Architect, responsible for enabling the exchange of findable, accessible, interoperable and reusable (FAIR) data across PML’s science groups and among community partners.
Building upon a broad range of experience from industrial, academic and research settings, Tom places an emphasis on developing the foundations of data management best practice while researching innovative techniques in model-based design, incremental development and open innovation to maximise the impact of PML’s scientific datasets.
Education:
- BEng (Hons) - Aerospace Systems. Coventry University (2005)
- MSc - Data Communications Networks. Aston University (2011)
- PhD - Data Communication Signals of Opportunity for Navigation. University of Plymouth (2016)
T. Mansfield, C. Szostek, B. O’Driscoll and S. Watson, “Observations towards a modular digital twin architecture to support multi-community research and decision making”, International Conference on Marine Data and Information Systems, 2024. https://doi.org/10.13127/MISC/80
Mansfield, T; Caamaño, P; Carrera, A; Maglione, G; Tremori, A and DaDeppo, S (2022), “A Synthetic War-Game Environment to Assess Emerging and Disruptive Maritime Technologies in NATO Exercises”, NATO Science and Technology Modelling and Simulation Group Report, STO-MP-MSG-197, Vol. 18.
Mansfield, T; Caamaño, P; Godfrey, SB; Carrera, A; Tremori, A; Nandakumar, G; Moberly, K; Cronin, J and Da Deppo, S (2022), “Building Trust in Autonomous Systems: Opportunities for Modelling and Simulation”, Lecture Notes in Computer Science, Vol. 13207, Page 1-16, 2022. http://dx.doi.org/10.1007/978-3-030-98260-7_27
De Rosa, F, Mansfield, T, Jousselme, AL and Tremori, A, (2021) “Modelling Key Performance Indicators for Improved Performance Assessment in Persistent Maritime Surveillance Projects”, Advances in Artificial Intelligence, Software and Systems Engineering, Vol. 271, Page 295-303. https://doi.org/10.1007/978-3-030-80624-8_37